Kenya - Demographic and Health Survey - 1994

Publication date: 1994

Demographic and Health Survey 1993 ~ ~. Republic of Kenya National Council for Population and Development Central Bureau of Statistics Office of the Vice President and Ministry of Planning at~d National Development ~DHS Demographic and Health Surveys Macro International Inc. i Kenya Demographic and Health Survey 1993 National Council for Population and Development Central Bureau of Statistics Office of the Vice President and Ministry of Planning and National Development Nairobi, Kenya Macro International Inc. Calverton, Maryland USA May 1994 This report summarises the findings of the 1993 Kenya Demographic and Health Survey (KDHS) conducted by the National Council for Population and Development and the Central B areau of Statistics. Macro International Inc. provided technical assistance. Funding was provided by the U.S. Agency for International Development (USAID) and the Government of Kenya. The KDHS is part of the worldwide Demographic and Health Surveys (DHS) program, which is designed to collect data on fertility, family planning, and maternal and child health. Additional information about the Kenya survey may be obtained from the National Council for Population and Development, P.O. Box 30478, Nairobi. Kenya (Telephone: 228-411; Fax: 213-642). Additional information about the DHS program may be obtained by writing to: DHS, Macro International Inc., 11785 Beltsville Drive, Calve~on, MD 20705 (Telephone: 301-572-0200; Fax: 301-572- 0999). Recommended citation: National Council for Population and Development (NCPD), Central Bureau of Statistics (CBS) (Office of the Vice President and Ministry of Planning and National Development [Kenya]), and Macro International Inc. (MI). 1994. Kenya Demographic and Health Survey 1993. Calverton, Maryland: NCPD, CBS, and MI. CONTENTS Page List of tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi i List of f igures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii Foreword . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvii Executive summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xix Map of Kenya . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxiv CHAPTER 1 1.1 1.2 1.4 1.5 1.6 INTRODUCTION Geography, History, and Economy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Population . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Health Priorities and Programmes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Objectives of the 1993 Kenya Demographic and Health Survey . . . . . . . . . . . . . . 4 Survey Organisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 CHAPTER 2 2.1 2.2 2.3 CHARACTERIST ICS OF HOUSEHOLDS AND RESPONDENTS Characteristics of the Household Population . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Housing Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 Background Characteristics of Women Respondents . . . . . . . . . . . . . . . . . . . . . 16 CHAPTER 3 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 FERT IL ITY Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 Fertility Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 Fertility Differentials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 Fertility Trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 Children Ever Bom . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Birth Intervals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 Age at First Birth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 Teenage Fertility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 CHAPTER 4 4.1 4.2 4.3 4.4 4.5 4.6 4.7 FERT IL ITY REGULAT ION Knowledge of Contraception . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 Ever Use of Contraception . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 Current Use of Contraception . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 Number of Children at First Use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 Knowledge of Fertile Period . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 T iming of Sterilisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 Source of Supply . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 i i i 4.8 4.9 4.10 4.11 4.12 4.13 Page Future Use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 Nonuse of Family Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 Exposure to Media Programmes on Family Planning . . . . . . . . . . . . . . . . . . . . 53 Attitudes towards Family Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 Source of Family Planning Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 Community-Based Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 CHAPTER 5 5.1 5.2 5.3 5.5 5.6 5.7 5.8 OTHER PROXIMATE DETERMINANTS OF FERTILITY Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 Marital Status . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 Polygyny . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 Age ~t First Sexual Intercourse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 Recent Sexual Activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 Postpartum Amenorrhoea and Insusceptibility . . . . . . . . . . . . . . . . . . . . . . . . . 69 Termination of Exposure to Pregnancy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 CHAPTER 6 6.1 6.2 6.3 FERT IHTY PREFERENCES Desire for More Children . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 Demand for Family Planning Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 Ideal Family Size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 CHAPTER 7 7.1 7.2 7.3 7A 7.5 INFANT AND CHILD MORTAL ITY Assessment of Data Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 Levels and Trends in Infant and Child Mortality . . . . . . . . . . . . . . . . . . . . . . . 84 Socioeconomic Differentials in Infant and Child Mortality . . . . . . . . . . . . . . . . 85 Demographic Differentials in Infant and Child Mortality . . . . . . . . . . . . . . . . . . 87 High-Risk Fertility Behaviour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 CHAPTER 8 8.1 8.2 8.3 8.4 MATERNAL AND CHILD HEALTH Antenatal Cam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 Delivery Care . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 Childhood Vaccination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 Childhood Illness and Treatment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 CHAPTER 9 9.1 9.2 9.3 INFANT FEEDING AND CHILDHOOD AND MATERNAL NUTRITION Breastfeeding and Supplementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 Nutritional Status of Children . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 Nutritional Status of Mothers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 iv CHAPTER 10 10.1 10.2 10.3 10.4 10.5 10.6 Page KNOWLEDGE OF AIDS AIDS Awareness and Knowledge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 Sources of Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 Misconceptions About AIDS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 AIDS Prevention . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 Personal Acquaintance With AIDS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 Number of Sexual Partners and Condom Use . . . . . . . . . . . . . . . . . . . . . . . . . 133 CHAPTER 11 11.1 11.2 11.3 11.4 11.5 RESULTS OF THE MALE SURVEY Background Characteristics of the Male Survey Respondents . . . . . . . . . . . . . . 139 Fertility Regulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142 Nuptiality and Sexual Intercourse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154 Fertility Preferences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 Couples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 CHAPTER 12 12.1 12.2 12.3 LOCAL AVAILABIL ITY OF FAMILY PLANNING AND HEALTH SERVICES Service Availability Questionnaire . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 Availability of Family Planning Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166 Availability of Maternal and Child Health (MCH) Services . . . . . . . . . . . . . . . 170 REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175 APPENDIX A A.I A.2 A.3 APPENDIX B APPENDIX C APPENDIX D APPENDIX E SURVEY DES IGN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 Questionnaires . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179 Sample Design and Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180 Training and Fieldwork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 EST IMATES OF SAMPL ING ERRORS . . . . . . . . . . . . . . . . . . . . . . . . . . 185 DATA QUAL ITY TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207 PERSONS INVOLVED IN THE 1993 KENYA DHS . . . . . . . . . . . . . . . . . 215 QUEST IONNAIRES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219 LIST OF TABLES Table 1.2 Table 2.1 Table 2.2 Table 2.3 Table 2.4.1 Table 2.4.2 Table 2.5 Table 2.6 Table 2.7 Table 2.8 Table 2.9 Table 2.10 Table 3.1 Table 3.2 Table 3.3 Table 3.4 Table 3.5 Table 3.6 Table 3.7 Table 3.8 Table 3.10 Table 3.11 Table 3.12 Table 3.13 Table 3.14 Table 4.1 Table 42 Table 4.3 Table 4A Table 4.5 Page Results of the household and individual interviews . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Household population by age, residence and sex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Population by age from selected sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Household composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Educational level of the male household population . . . . . . . . . . . . . . . . . . . . . . . . . 11 Educational level of the female household population . . . . . . . . . . . . . . . . . . . . . . . . 12 School enrolment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Housing characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 Household durable goods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 Background characteristics of respondents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 Level of education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 Access to mass media . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 Current fertility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 Fertility by background characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 Trends in current fertility rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 Trends in fertility by province . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 Percent pregnant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Age-specif ic fertility rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Fertility by marital duration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Children ever born and living . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 Birth intervals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 Age ~t fi~st birth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 Median age at first birth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 Teenage pregnancy and motherhood . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 Children bom to teenagers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 Knowledge of contraceptive methods and source for methods . . . . . . . . . . . . . . . . . . 36 Trends in knowledge of family planning methods and sources . . . . . . . . . . . . . . . . . 37 Knowledge of modem contraceptive methods and source for methods . . . . . . . . . . . 38 Ever use of contraception . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 Trends in ever use of family planning methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 vii Table 4,6 Table 4,7 Table 4.8 Table 4.9 Table 4.10 Table 4,11 Table 4.12 Table 4.13 Table 4.14 Table 4.15 Table 4.16 Table 4.17 Table 4,18 Table 4.19 Table 4.20 Table 4,21 Table 4.22 Table 4,23 Table 4.24 Table 5, I Table 5.2 Table 5.3 Table 5.4 Table 5.5 Table 5.6 Table 5.7 Table 5.8 Table 5.9 Table 5.10 Table 5.11 Table 5.12 Table 6.1 Page Current use of contraception . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 Trends in current use of family planning methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 Current use of family planning by method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 Current use of contraception by district . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 Number of children at first use of contraception . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 Knowledge of fertile period . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 T iming of sterilisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 Source of supply for modem contraceptive methods . . . . . . . . . . . . . . . . . . . . . . . . . 49 T ime to source of supply for modem contraceptive methods . . . . . . . . . . . . . . . . . . . 50 Future use of contraception . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 Reasons for not using contraception . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 Preferred method of contraception for future use . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 Heard family planning on radio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 Attitudes about family planning for youth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 Discussion of family planning by couples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 Attitudes of couples toward family planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 First source of family planning information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 Main source of family planning information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 Community-based distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 Current marital ~tatus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 Trends in proportion never married . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 Polygyny . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 Number of co-wives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 Age at first marriage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 Median age at first marriage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 Age at first sexual intercourse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 Median age at first sexual intercourse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 Recent sexual activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 Postpartum amenorrhoea, abstinence and insusceptibility . . . . . . . . . . . . . . . . . . . . . 69 Median duration of postpartum insusceptibility by background characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 Termination of exposure to the risk of pregnancy . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 Fertility preference by number of l iving children . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 viii Table 6.2 Table 6.3 Table 6.4 Table 6.5 Table 6.6 Table 6.7 Table 6.8 Table 7.1 Table 7.2 Table 7.3 Table 7.4 Table 8.1 Table 8.2 Table 8.3 Table 8.4 Table 8.5 Table 8.6 Table 8.7 Table 8.8 Table 8.9 Table 8.10 Table 8.11 Table 8.12 Table 8.13 Table 8.14 Table 8.15 Table 9.1 Table 9.2 Table 9.3 Table 9.4 Table 9.5 Table 9.6 Table 9.7 Page Fertility preferences by age . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 Desire to limit (stop) childbearing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 Need for family planning services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 Ideal number of children . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 Mean ideal number of children by background characteristics . . . . . . . . . . . . . . . . . . 80 Fertility planning status . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 Wanted fertility rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 Infant and child mortality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 Infant and child mortality by background characteristics . . . . . . . . . . . . . . . . . . . . . . 86 Infant and child mortality by demographic characteristics . . . . . . . . . . . . . . . . . . . . . 88 High-risk fertility behaviour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 Antenatal care . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 Number of antenatal care visits and stage of pregnancy . . . . . . . . . . . . . . . . . . . . . . . 95 Tetanus toxoid vaccination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 Place of delivery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 Assistance during delivery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 Characteristics of delivery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 Vaccinations by source of information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 Vaccinations by background characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 Vaccinations in the first year of life . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 Prevalence and treatment of acute respiratory infection . . . . . . . . . . . . . . . . . . . . . . 108 Prevalence and treatment of fever . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 Prevalence of diarrhoea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 Knowledge and use of ORS packets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 Treatment of diarrhoea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 Feeding practices during diarrhoea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 Initial breastfeeding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 Breastfceding status . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 Breastfeeding and supplementation by age . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 Median duration and frequency of breastfeeding . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 Nutritional status by demographic and background characteristics . . . . . . . . . . . . . 122 Anthropometric indicators of maternal nutritional status . . . . . . . . . . . . . . . . . . . . . 124 Differentials in matcmal anthropometric indicators . . . . . . . . . . . . . . . . . . . . . . . . . 125 ix Table 10.1 Table 10.2 Table 10.3 Table 10.4 Table 10.5 Table 10.6 Table 10.7.1 Table 10.7.2 Table 10.8.1 Table 10.8.2 Table 10.9 Table 11.1 Table 11.2 Table 11.3 Table 11.4 Table 11.5 Table 11.6 Table 11.7 Table 11.8 Table 11.9 Table 11.10 Table 11.11 Table 11.12 Table 11.13 Table 11.14 Table 11.15 Table 11.16 Table 11.17 Table 11.18 Table 11.19 Table 11.20 Table 11.21 Table 11.22 Page Knowledge of AIDS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128 Sources of AIDS information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 AIDS transmission . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 Protection against AIDS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 Personal knowledge of AIDS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 Personal risk of acquiring AIDS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 Number of recent sexual partners - male respondents . . . . . . . . . . . . . . . . . . . . . . . 134 Number of recent sexual partners - female respondents . . . . . . . . . . . . . . . . . . . . . . 135 Number of l ifetime sexual partners - male respondents . . . . . . . . . . . . . . . . . . . . . . 136 Number of l ifetime sexual partners - female respondents . . . . . . . . . . . . . . . . . . . . . 137 Condom use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138 Background characteristics of respondents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 Level of education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 Access to mass media . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142 Knowledge of contraceptive methods and source for methods . . . . . . . . . . . . . . . . . 143 Knowledge of modem contraceptive methods and source for methods . . . . . . . . . . 145 Ever use of contraception . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146 Current use of contraception . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 Current use of family planning by background characteristics . . . . . . . . . . . . . . . . . 148 Source of supply for modem contraceptive methods . . . . . . . . . . . . . . . . . . . . . . . . 149 Future use of contraception . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150 Reasons for not using contraception . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 Preferred method of contraception for future use . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 Heard family planning on radio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152 Discussion of family planning with wife . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152 Attitudes of couples towards family planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 Current marital status by age . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154 Polygyny . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 Age at first marriage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 Age at first sexual intercourse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156 Fertility preference by number of l iving children . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 Fertility preferences by age . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158 Ideal number of children . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158 X Table 11.23 Table 11.24 Table 11.25 Table 11.26 Table 11.27 Table 11.28 Table 11.29 Table 12.1 Table 12.2 Table 12.3 Table 12.4 Table 12.5 Table 12.6 Table 12.7 Table A. 1.1 Table A. 1.1 Table B. 1 Table B.2 Table B.3 Table B.4 Table B.5 Table B.6 Table B.7 Table B.8 Table B.9 Table B.10 Table B.11 Table B. 12 Page Background characteristics of husbands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 Age difference between spouses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160 Knowledge of methods among married couples . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161 Attitudes of couples towards family planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161 Spouse's perception of other spouse's approval of family planning . . . . . . . . . . . . 162 Desire for more children among couples by number of living children . . . . . . . . . . 163 Spouse's agreement on ideal number of children . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 Distance and time to nearest facility providing family planning services according to type of facility and residence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 Distance to nearest family planning method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168 Distance to nearest facility providing family planning services for users/nonusers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 Distance and time to nearest facility providing antenatal care according to type of facility and province . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170 Distance and time to nearest facility providing delivery care according to type of facility and province . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 Distance and time to nearest facility providing child immunisation services according to type of facility and province . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172 Distance to nearest maternal and child health services for children . . . . . . . . . . . . . 173 Sample implementation: Women . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181 Sample implementation: Men . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182 List of selected variables for sampling errors, Kenya 1993 . . . . . . . . . . . . . . . . . . . 189 Sampling errors - National sample, Kenya 1993 . . . . . . . . . . . . . . . . . . . . . . . . . . . 190 Sampling errors - Urban sample, Kenya 1993 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191 Sampling errors - Rural sample, Kenya 1993 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192 Sampling errors - Nairobi, Kenya 1993 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193 Sampling errors - Central Province, Kenya 1993 . . . . . . . . . . . . . . . . . . . . . . . . . . . 194 Sampling errors - Coast Province, Kenya 1993 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195 Sampling errors - Eastem Province, Kenya 1993 . . . . . . . . . . . . . . . . . . . . . . . . . . . 196 Sampling errors - Nyanza Province, Kenya 1993 . . . . . . . . . . . . . . . . . . . . . . . . . . . 197 Sampling errors - Rift Valley Province, Kenya 1993 . . . . . . . . . . . . . . . . . . . . . . . . 198 Sampling errors - Western Province, Kenya 1993 . . . . . . . . . . . . . . . . . . . . . . . . . . 199 Sampling errors - Mombasa City, Murangja rural and Nyeri rural, Kenya 1993 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200 xi Table B. 13 Table B. 14 Table B.15 Table B.16 Table B.17 Table C.1 Table C.2 Table C.3 Table C.4 Table C.5 Table C.6 Page Sampling errors - Kilifi rural, Taita Taveta rural and Machakos rural, Kenya 1993 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201 Sampling errors - Meru rural, Kisi i /Nyamira rural and Siaya rural, Kenya 1993 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202 Sampling errors - South Nyanza rural, Kericho rural and Nakuru rural, Kenya 1993 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 Sampling errors - Nandi rural, Uasin Gishu rural and Bungoma rural, Kenya 1993 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204 Sampling errors - Kakamegea rural, Kenya 1993 . . . . . . . . . . . . . . . . . . . . . . . . . . . 205 Household age distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 Age distribution of eligible and interviewed women . . . . . . . . . . . . . . . . . . . . . . . . 210 Completeness of reporting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211 Births by calendar year since birth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212 Reporting of age at death in days . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213 Reporting of age at death in months . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214 xii LIST OF FIGURES Figure 2.1 Figure2.2 Figure2.3 Figure 3.1 Figure 3.2 Figure 3.3 Figure 4.1 Figure 4.2 Figure 4.3 Figure 4.4 Figure 6.1 Figure 6.2 Figure 6.3 Figure7.1 Figure7.2 Figure 7.3 Figure8.1 Figure8.2 Figure 8.3 Figure 8.4 Figure 9.1 Figure 11.1 Figure 11.2 Figure 11.3 Page Population pyramid, Kenya 1993 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Distribution of de facto household population by single year of age and sex . . . . . . . . . . . 9 Percentage of males and females who have completed primary education by age group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Age-specific fertility rates by urban-rural residence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 Total fertility rate by selected background characteristics . . . . . . . . . . . . . . . . . . . . . . . . 24 Age-specific fertility rates, 1977/78 KFS, 1989 KDHS and 1993 KDHS . . . . . . . . . . . . . 25 Trends in contraceptive use, currently married women 15-49, KDHS 1989 and KDHS 1993 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 Current use of contraception by selected background characteristics . . . . . . . . . . . . . . . . 44 Current use of modem contraceptive methods by district, KDHS 1989 and KDHS 1993 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 Percent distribution of current users of modern methods by most recent source of supply . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 Fertility preferences of currently married women 15-49 . . . . . . . . . . . . . . . . . . . . . . . . . . 74 Fertility preferences of married women by number of living children . . . . . . . . . . . . . . . 75 Percentage of currently married women who want no more children by background characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 Trends in infant and child mortaltiy, Kenya, 1978-1993 . . . . . . . . . . . . . . . . . . . . . . . . . . 85 Under-five mortality by selected background characteristics . . . . . . . . . . . . . . . . . . . . . . 87 Under-five mortaltiy by selected demographic characteristics . . . . . . . . . . . . . . . . . . . . . 89 Percent distribution of births by number of antenatal care visits and timing of first visit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 Percent distribution of births in the five years preceding the survey by place of delivery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 Vaccination coverage among children age 12-23 months . . . . . . . . . . . . . . . . . . . . . . . . 103 Percentage of children under 5 who received various treatments for diarrhoea in the two weeks preceding the survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 Nutritional status of children under five years, mean Z-scores by age in months . . . . . . 123 Level of education attained by men age 20-54 and women age 15-49 . . . . . . . . . . . . . . 140 Knowledge of contraceptive methods among currently married men 20-54 and women 15-49 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144 Current use of contraceptive methods among currently married men 20-54 and women 15-49 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 xiii FOREWORD The 1993 Kenya Demographic and Health Survey (KDHS) is the second survey of its kind to be carried out in the country; the first one was conducted in 1989. Information on fertility, infant and child mortality, knowledge and practice of family planning, maternal and child health and AIDS awareness was collected. The survey therefore provides a complete set of relevant data for evaluation of population, health and family planning programmes, and necessary information for assessing the overall demographic situation in the country since 1989 and for development of future strategies. We hope that policymakers, health project implementers, social scientists and researchers will make optimal use of the survey data. The 1993 KDHS reinforces evidence of a major decline in fertility which was first revealed by the findings of the 1989 KDHS. Fertility continues to decline and family planning use has increased. However, the disparity between knowledge and use of family planning remains quite wide. There are indications that infant and under five child mortality rates are increasing, which in part might be attributed to the increase in AIDS prevalence. These are some of the critical issues that need to be addressed without delay. Finally, I would like to acknowledge assistance by both the Washington, D.C. and Kenya offices of the United States Agency for International Development (USAID) for financial support, Macro International Inc. of Calverton, Maryland, USA for technical support, and the Central Bureau of Statistics and the National Council for Population and Development for making the 1993 KDHS a success. S.B.A. Bullut, Director, National Council for Population and Development XV ACKNOWLEDGMENTS This report was written by: Anne Cross, Peter Thumbi, Timothy Takona, P.N. Kagwe, Jane Kariuki, John Kekovole, K. Kioga, P. M. L. Kizito, Jenipher Liku, M. K. Mbayah, Muchira Muraguri, Vane Nyonga, Helen Odido, John Owuor, and Tulshi Saha. The authors would like to acknowledge the contribution of Han Raggers for producing the tabulations and to thank the following reviewers: George Bicego, Kate Colson, Cara Egan, John Kekovole, Estelle Mallinoff, Mickey Marckwardt, Beatrice Michoma, Sidney Moore, Han Raggers, and Tulshi Saha. xvii EXECUTIVE SUMMARY The 1993 Kenya Demographic and Health Survey (KDHS) was a nationally representative survey of 7,540 women age 15-49 and 2,336 men age 20-54. The KDHS was designed to provide information on levels and trends of fertility, infant and child mortality, family planning knowledge and use, maternal and child health, and knowledge of AIDS. In addition, the male survey obtained data on men's knowledge and attitudes towards family planning and awareness of AIDS. The data are intended for use by programme managers and policymakers to evaluate and improve family planning and matemal and child health programmes. Fieldwork for the KDHS took place from mid-February until mid-August 1993. All areas of Kenya were covered by the survey, except for seven northem districts which together contain less than four percent of the country's population. The KDHS was conducted by the National Council for Population and Development (NCPD) and the Central Bureau of Statistics of the Government of Kenya. Macro International Inc. provided financial and technical assistance to the project through the intemational Demographic and Health Surveys (DHS) contract with the U.S. Agency for International Development. Fertility. Survey results show that fertility has declined precipitously in Kenya over the last 5-10 years. At current fertility levels, a Kenyan woman will give birth to an average of 5.4 children during her reproductive years. Although this is stin high, it is far lower than the level of 6.7 births per women reported for the late 1980s. A rural woman can expect to have an average of 5.8 children, over two children more than an urban women (3.4 children). Fertility rates are much higher in Western Province (6.4 children per wom an) than in Nairobi and Central Province (3.4 and 3.9, respectively). Childbearing begins earlyin Kenya. Oneinf iveteenagewomen(age 15-19) hasbegunchildbearing (either given birth or is pregnant with her first child). By the time they reach age 19, over 40 percent of women have begun childbearing. Births that occur too soon after a previous birth face higher risks of illness and early death. The KDHS shows that one-quarter of births in Kenya take place less than two years after a prior birth. Marr iage. There has been a steady increase over the past two decades in the age at which Kenyan women first marry. The median age at marriage among women age 25-29 is 19.5, compared to 18.1 among women age 45-49. Women with secondary education generally marry three years later (21.5) than women with no education (17.0). Women in Coast and Nyanza Provinces have the lowest median age at first marriage (17.4). Twenty percent of currently married women are in polygynous unions. Polygyny occurs in all provinces and age groups. It is most common among uneducated women (33 percent). The median age at first sexual intercourse is about 17 years for women. Fertility Preferences. Over half of married women in Kenya either do not want to have any more children or have been sterilised. Another one quarter of women want to wait two years or longer before having their next child. Thus, 78 percent of all married women in Kenya either want to space or to limit their births. xix When asked how many children they would like to have if they could live their lives over and choose exactly, women report an average ideal family size of 3,7 children. There has been a large decline in ideal family size over the past decade, from a mean of 5.8 children reported in a 1984 survey to 4.4 reported in the 1989 KDHS, to 3.7 in 1993. Results from the survey indicate that if unwanted births were eliminated, the fertility rate in Kenya would be 3.4 births per woman or 2 children fewer than the actual fertility rate of 5.4. Men want slightly more children than women. Regardless of the number of children they already have, a higher percentage of men than women say they want to have another child. The average ideal number of children among is 3.8 among men and 3.7 for women. Family Planning. Knowledge of some family planning method is virtually universal among both men and women. Among currently married respondents, 97 percent of women and men know at least one modem contraceptive method. The pill, injection, female sterilisation and condom are the most widely known methods. Moreover, almost all women and men who know a method, also know of a place to obtain it. One third of married women are currently using a contraceptive method. The level of use has almost doubled in the past decade, from 17 percent of married women in 1984 to 33 percent in 1993. Use of modern methods has increased even faster--from 10 to 27 percent of married women. Over 80 percent of women users employ modem methods, principally the pill (10 percent o f married women), injection (7 percent) and female sterilisation (6 percent). Use of the pill and injection has risen particularly rapidly over the last five years. Among both men and women, contraceptive use is higher in urban than in rural areas. The differential in use by education level is particularly striking: 20 percent of married women with no education are using family planning, compared to 52 percent of those with some secondary education. Contraceptive use also varies greatly by province. Women in Central Province have the highest prevalence rate (56 percent), compared to Coast Province with the lowest (20 percent). The government is the most important provider of family planning services, supplying over two- thirds of the women who use modem methods. Over one third of currently married women in Kenya have an unmet need for family planning. This group comprises women who are not using any family planning methods but either want to wait two years or more before their next birth (22 percent) or do not want any more children (15 percent). Combined with the 33 percent of married women who are currently using a contraceptive method, the total potential demand for family planning comprises almost 70 percent of married women in Kenya. Child Mortal ity. KDHS findings indicate that one in ten Kenyan children dies before reaching his/her fifth birthday. For the most recent five-year period (1988-93), under-five mortality was 96 per 1,000 live births and infant mortality 62 per 1,000 live births. There has apparently been no change in childhood mortality over the past decade, according to the birth histories recorded from women interviewed in the survey. Further evidence that the previous rapid decline in childhood mortality has stagnated comes from a comparison of the childhood mortality data from the 1989 and 1993 KDHSs, which also show no real change. XX Differences in mortality by province are quite marked. Childhood mortality is exceptionally high in Nyanza Province, where almost one in five children do not live to see their fifth birthdays. The infant mortality rate in Nyanza Province (128) is almost twice that of the second highest rate (Coast Province--68). KDHS data indicate that spacing births can potentially reduce childhood mortality levels; a child born less than 24 months after a preceding child is almost twice as likely to die before his first birthday than a child born after an interval of four or more years. Risks are also greater for children whose birth order is greater than seven and those born to mothers under age 20. Maternal Health. Utilisation of antenatal services is high. In the five years prior to the survey, mothers received antenatal care for 95 percent of births. The median number of antenatal care visits is 4.7. Mothers reported receiving at least one tetanus toxoid injection for about 90 percent of births in the five years preceding the survey. Over half (55 percent) of births take place at home. Forty-five percent of deliveries are assisted by medically trained personnel, while almost one quarter are assisted by relatives; ten percent of women deliver without assistance. Child Health. The KDHS found that 79 percent of children aged 12-23 months were fully vacci- nated and only 3 percent had not received any vaccinations at all. Seventy-one percent of children had received all the recommended vaccinations during the first year of life. Children of lower birth orders (1-3) are more likely to be fully vaccinated than children of higher birth orders. Coverage levels are higher for children in Central Province and Nairobi and lower for children in Nyanza and Westem Provinces. During the two weeks before the survey, 18 percent of children under age five experienced symptoms of acute respiratory infection (ARl)--cough with short, rapid breathing. Half of these children were taken to a health facility or doctor for treatment. Four in ten children under five (42 percent) were reported to have had fever in the two weeks preceding the survey; half of these children were taken to a health facility for treatment. Many of the children with fever who were taken to a health facility received antimalarial medicine. Fourteen percent of children under five had diarrhoea during the two weeks preceding the survey. About 40 percent of these children were taken to a health facility for treatment. Among children with diarrhoea, one-third were given a solution prepared from ORS packets and almost half received increased fluids to drink. Nutrition. Almost all children born in the five years before the survey (97 percent) were breastfed for someperiod of time. Themediandurationofbreastfeedingis21 months. InKenya, the introduction of supplementary liquids and foods in addition to breast milk occurs far too early in life; over half of children under the age of two months are given some form of supplemental feeding. Use of infant formula is not widespread in Kenya. Bottlefeeding, however, is more common; one in six infants under the age of 4 months is fed with a bottle. One in three children under the age of five is short for his/her age (stunted), which reflects chronic undemutrition. This proportion is 14 times the level expected in a healthy, well-nourished population. Twelve percent of the children were severely stunted. x×i Six percent of children under five are wasted (i.e., low weight low in relation to height). Wasting generally indicates acute undemutrition in recent months and may be related to illness or shortage of food. Women whose height is ! 50 centimetres or less and whose mean body mass index (BMI) falls below 18.5 are considered to be at greater risk of being undernourished than other women. Height and weight measurements were obtained in the KDHS for mothers of children under age five. These data show that less than six percent of mothers are shorter than 150 centimetres. The mean weight was 55.8 kilogrammes; 9 percent of mothers have a BMI below 18.5. Knowledge of AIDS. All but a tiny fraction of respondents reported they had heard about AIDS. Almost all (96 percent of men and 90 percent of women mentioned sexual intercourse as a mode of trans- mission of the AIDS virus. About 90 percent of respondents say it is possible for a mother with the AIDS virus to pass it along to her child at birth. A large majority of men and women said it is possible to protect against getting AIDS. About 40 percent of men and women know someone who either has AIDS or who died of AIDS. And two-thirds of men and almost half of women say they themselves can get AIDS. Misconceptions regarding modes of transmission of the AIDS virus are common. About one-quarter of men and women interviewed said they believed it was possible to get AIDS from sharing clothes or eating utensils with someone who has AIDS; one-third of respondents said it is possible to get AIDS from kissing somone who has AIDS, and over half say it is possible to get AIDS from insect bites. One-third of men and less than 5 percent of women said they had had two or more sexual panners in the six months prior to the survey. Twenty percent of men and 6 percent of women said they had used a condom in the six months before the survey. Condom use was much higher among those who reported having more sexual partners. Availability of Health and Family Planning Services. KDHS data indicate that about half of women (48 percen0 live in communities served by community-based distributors (CBDs) of family planning methods. Of these, half (23 percent of all women) are covered by government-sponsored CBDs and half by CBDs sponsored by non-governmental organisations. At least some method of family planning is readily available in Kenya. Two-thirds of married women live within 5 kilometres of a source of family planning services. Health services are somewhat less proximate. Half of women live within 5 kilometres of a facility that provides antenatal care and only one- third live within 5 kilometres of a facility that provides delivery services. Conclusions. Fertility and family planning behaviour in Kenya have changed dramatically over the past decade. Fertility levels have fallen sharply and use of family planning has "almost doubled. Use of modem contraceptive methods has almost tripled since 1984. Today, virtually all married women and men have heard of at least one family planning method, well over half have used a method at some time, and one- third of married women are currently using a method. The KDHS data also indicate that family planning methods are easily accessible to the vast majority of women, although, of course, not all methods are equally available. Moreover, attitudes towards contraceptive use are generally favourable. Despite these successes of the family planning programme in Kenya, there are a number of continuing challenges. One is that the level of unwanted fertility remains high; one in six recent births was xxii unwanted and one in three was mistimed. KDHS data indicate that the fertility rate in Kenya would be substantially lower if all unwanted births could be avoided. Another challenge is to reduce regional disparities in fertility, fertility preferences and family planning use. For example, fertility in Coast Province has hardly declined at all, no doubt because women there have the highest mean ideal family size, the lowest proportion who want no more children, and the lowest proportions who approve of and use family planning. Thus efforts in Coast Province should concentrate on education and motivation activities. In Western Province, both fertility and unmet need are highest. The results from the KDHS indicate that Kenya has made remarkable progress in the delivery of key child survival interventions: use of antenatal care is high; tetanus toxoid coverage among pregnant women is high; almost half of women deliver with the assistance of medical professionals; childhood immunisation coverage is high; there is a fairly high level of utilisation of curative services for diarrhoea and acute respiratory infections; one-third of children with diarrhoea are given oral rehydration salts. Yet, one in ten Kenyan children dies before reaching his/her fifth birthday. Moreover, declines in childhood mortality have stagnated recently. Poor nutrition may play a role; one-third of children under five are stunted. Spacing births at longer intervals can also reduce the level of childhood mortality. KDHS data show substantially lower infant mortality among children bom four years or more after a prior birth compared to those born two years or less after a sibling. Mortality among children under five is particularly high in Nyanza Province. The AIDS epidemic poses a major threat for the health of adults and children in Kenya. Data on knowledge of AIDS among adult men and women show that AIDS awareness is high, but that the quality of knowledge on AIDS can still be improved. More importantly, the survey results on sexual bebaviour indicate that having multiple partners is common and condom use is not widespread. xxiii KENYA RIF~ UGI _IA RN WE E: MAP KEY 21 Nairobi EASTERN PROVINCE RIFTVALLEY PROVINCE 27 Embu 15 Baringo CENTRAL PROVINCE 37 Isiolo 3 Elgeyo Marakwet 22 Kiambu 29 Kitui 20 Kajiado 26 Kidnyaga 28 Machakos* 14 Kericho* 23 Murang'a 39 Marsabit 16 Laikipia 17 Nyandarua 25 Meru* 18 Nakuru 24 Nyed NORTHEASTERN PROVINCE 9 Nandi 19 Narok COAST PROVINCE 36 Garissa 38 Samburu 33 Kllffl 41 Mandera 4 Trans Nzola 31 Kwale 40 Wajir 1 Turkana 35 Lamu NYANZA PROVINCE 10 Uasin Gishu 32 Mombasa 30 Taita 13 Kisii* 2 West Pokot 34 Tana River 11 Kisumu WESTERN PROVINCE 7 Siaya 5 Bungoma 12 South Nyanza* 6 Susie 8 Kakamega* • Note: Each of the six districts marked with an astedsk was recently subdivided into two or more districts The former boundaries are shown here since they were used in this survey, xxiv CHAPTER 1 INTRODUCTION 1.1 Geography, History, and Economy Geography Kenya covers an area of 582,000 square kilometres. It borders Ethiopia in the north, Sudan in the northwest, Uganda on the west, Tanzania in the south and Somalia in the east. It has 400 kilometres of Indian ocean shoreline. Lying between 3 degrees north and 5 degrees south latitude and between 34 and 41 degrees east longitude, it is entirely within the equatorial zone. The country is almost bisected by the equator. The country falls into two distinct regions, i.e., lowland and highland (upland) Kenya. This distinction affects the climate, patterns of human settlement and agricultural activities. Kenya has an unusually diversified physical environment--savannah, tropical, equatorial volcanic and tectonic. Approximately 80 percent of Kenya's land is arid and semi-arid and only 20 percent is arable. A large part of the arid and semi-arid zones have been set aside for wildlife conservation. The main climatic feature is the long rainy season from March to May. This is followed by a long dry spell from May to October. Short rains come between October and December. In the area around Lake Victoria in the west, rains are well distributed throughout the year. Kenya is divided into 8 provinces, which are sub-divided into districts. In all there are 48 districts, seven of which were recently delineated. History Kenya became a nation independent from British rule on December 12, 1963. It was a multi-party state until 1982, when the constitution was amended to make it a one-party state. In November 1991, in line with political changes taking place the world over, Parliament repealed the section of the constitution which made Kenya a one-party state. A multi-party election was held in December 1992. There are 43 ethno-linguistic groups in Kenya. The major groups are Kikuyu, Luo, Luhya, Kamba, Kalenjin, Mijikenda, Meru, Embu, and Kisii. Kikuyus primarily live in Central Province, Luos inhabit the westem part of Nyanza Province, Luhyas live in Western Province, Kambas in the soutbem part of Eastem Province, Kalenjins in Rift Valley Province, Mijikendas in Coast Province, Merus in the northern part of Eastern Province and Kisiis in the eastern part of Nyanza Province. Christianity and Islam are the major religions. Economy Agriculture is the mainstay of Kenya's economy, accounting for about 25 percent of the gross domestic product (GDP); manufacturing accounts lbr about 13 percent of the GDP. Coffee, tea and tourism are the main foreign exchange earners. Since its independence in 1963, the country has gone through several economic phases. In the first 10 years of independence, the country enjoyed low inllation, high employment creation, and a relatively stable balance of payments position. GDP growth rates averaged 6.5 percent per annum. The second phase (1973-1980) saw the record growth upset by three major shocks. The first was the sharp rise in oil prices in 1973, which created considerable internal and external economic imbalances. In 1977-78, the prices of coffee and tea rose significantly, which immediately improved the balance of payments position, but subsequently created internal imbalances. The third shock was experienced when oil prices rose again in 1979. Despite these setbacks, Kenya enjoyed an average growth rate in GDP of 5.2 percent per annum, reflecting a moderate reduction in the high growth rates achieved in the first 10 years of independence. The third phase (1980-1985) was characterised by slow growth in GDP (2.5 percent). This economic decline resulted from several confounding factors, including high cost ofoil, a global recession in 1980-1982, as well as a drought in 1984. Phase Four started at the end of 1986, when the government implemented adjustment programmes in agriculture, trade and industry. Supported with an adequate external resources flow, principally from the World Bank and the International Monetary Fund, the adjustment program accelerated the growth in GDP to an average of 5.8 percent per annum. Phase Five of the Kenyan economy began in 1990 when GDP growth fell to 4.3 percent. It fell further to 2.2 percent in 1991 and 0.4 percent in 1992. This declining growth is attributed to a decline in external resources and poor performance of Kenya's main exports. 1.2 Population On the basis of census statistics, Kenya's population increased from 5.4 million in 1948 to 15.3 million in 1979 and to 21.4 million in 1989 (CBS, 1994) (see Table 1.1). Results of the 1989 census indicate that the intercensal population growth rate for Kenya is 3.4 percent per annum. This represents a modest decline from the growth rate of 3.8 percent per annum estimated from the 1979 population census. If the population contin- ues to grow at this rate the population of Kenya will increase to 30 million by the year 2000 (Re- public of Kenya, 1994). The crude birth rate increased from 50 per thousand in 1948 to 52 per thousand in 1979, whereas the crude death rate decreased from 17 to 14 per thousand in the same period. The infant mortality rate decreased from 119 deaths per thou- sand births in 1969 to 104 in 1979 and further to about 69 deaths per thousand births in 1989. As a result of high fertility and declining mortality, Kenya is cbaracterised by a young population. Over 50 percent of Kenya's population is less than 15 years of age. Table 1.1 Demographic indicators r Kenya~ 1969 r 1979 r and 1989 Population census Indicator 1969 1979 1989 Population (millions) 10.9 15.3 21.4 Density (pop./sq.km.) 19 27 37 Percent urban 9.9 15.1 17.5" Crude birth rate 50 52 NA Crude death rate 17 14 NA Total fertility rate 7.6 7.9 6.7 b Infant mortality rate (per 1000) 119 104 69 ° Life expectancy at birth 50 54 59 c NA = Not available "Provisional figure bProm 1989 KDHS CFrom World Bank Sources: 1969--CBS. 1970, p.12, 16, 38, 43, 56. 1979--CBS. 1981b, p.4, 5, 87, 88, 103. 1989--CBS. 1994; CBS. 1991a, p.31; NCPD. 1989, p.18; and World Bank. 1991, p.35l. The 1984 Kenya Contraceptive Prevalence Survey (KCPS) showed some evidence of a possible decline in fertility, from a total fertility rate of 8.1 children per woman in 1977/78 (CBS, 1980) to 7.7 in 1984 (CBS, 1984). This evidence was confirmed by the findings of the 1989 KDHS which showed that the total fertility rate had actually dropped to 6.7 children per woman (NCPD, 1989). According to results of the 1989 census, 19 percent of Kenya's population lived in urban areas (CBS, 1991a) and the intercensal growth rate of the urban population was 4.8 percent per annum. This rate is above the national growth rate. The population of the capital city, Nairobi, has increased from 827,775 in 1979 to an estimated 1,324,570 in 1989 (CBS, 1994). This increase can be attributed in large extent to rural-urban migration. 1.3 Population and Family Planning Policies and Programmes The Government of Kenya became concerned about the high rate of population growth after the 1962 population census which showed that population was growing at the rate of 3.3 percent. The Family Planning Association of Kenya (FPAK) was established by private individuals in 1957, but it was not until 1967 that the official national family planning programme was launched. Family planning was integrated into the matemal and child health division of the Ministry of Health. At first, due to lack of an effective health infrastructure and adequate skilled manpower, the Ministry of Health relied mainly on FPAK and expatriate staff for service delivery. After the 1969 census provided evidence of a high level of fertility, the government decided to launch a five-year (1974-1978) family planning programme. The specific goals of the programme were to reduce the high annual rate of natural population increase from 3.3 percent (in 1975) to 3.0 percent (in 1979) and to improve the health of mothers and their children under the age of five. Initially, however, the family planning component of the Matemal and Child Health Programme had limited success. The 1979 census results indicated a population growth rate of 3.8 percent per armum, which was higher than the projected growth rate of 3.0 percent. This failure to achieve the targetted population growth rate could be attributed to shortfalls in the assumptions used to arrive at the target. The plan to reduce the growth rate concentrated on the supply side of family planning services instead of putting emphasis on programmes aimed at changing family size norms. It was with the realisation of the need to improve on the earlier weaknesses of the family planning programme that the Government of Kenya approved the establishment of the National Council for Population and Development (NCPD) in 1982. The Council's mandate is to formulate population policies and strategies and to co-ordinate the activities of govemment ministries, non-govemmental organisations, and donors involved in population, integrated rural health, and family planning programmes. 1.4 Health Priorities and Programmes The 1994-1996 Kenya Development Plan underscores the achievement of "Health For All by the Year 2000" as stipulated in the "Alma Ata Declaration" to be the long-term objective of the health sector. This long-term objective will be achieved through the following policies: • Increasing coverage and accessibility of health services with active community participation; Consolidating maternal and child health and family planning services in order to reduce morbidity and fertility; 3 • Increasing inter-sectoral collaboration with other ministries involved in the improvement of health status; and Encouraging the non-governmental organisations to take a greater role in the delivery and financing of health care services. Use of Community-Based Health Workers (CBHWs) and Community-Based Distributors (CBDs) to provide services is being emphasised. It is estimated that there are slightly over 10,000 CBDs employed by government and non-governmental agencies to provide non-clinical family planning methods (Lewis et al., 1992). In 1981, the Ministry of Health started a major programme in preventive health, the Kenya Expanded Programme on Immunisation (KEPI). Several other government programmes aimed at the reduction of diseases, improvement of nutrition, and provision of maternal and child health services have also been launched. However, budgetary constraints have adversely affected provision of health services in the country. 1.5 Objectives of the 1993 Kenya Demographic and Health Survey The KDHS is intended to serve as a source of population and health data for policymakers and the research community. It was designed as a follow-on to the 1989 KDHS, a national-level survey of similar size that was implemented by the same organisations. In general, the objectives of KDHS are to: assess the overall demographic situation in Kenya, assist in the evaluation of the population and health programmes in Kenya, advance survey methodology, and assist the NCPD to strengthen and improve its technical skills to conduct demographic and health surveys. The KDHS was specifically designed to: provide data on the family planning and fertility behaviour of the Kenyan population to enable the NCPD to evaluate and enhance the National Family Planning Programme, measure changes in fertility and contraceptive prevalence and at the same time study the factors which affect these changes, such as marriage patterns, urban/rural residence, availability of contraception, breastfeeding habits and other socioeconomic factors, and • examine the basic indicators of maternal and child health in Kenya. 1.6 Survey Organisation The 1993 KDHS is a national survey that was carded out by the NCPD in collaboration with the Central Bureau of Statistics (CBS). Macro International Inc. of Calverton, Maryland (USA) provided technical and financial assistance through its contract with the U.S. Agency for International Development (USAID). Sample Design The 1993 KDHS sample is national in scope, with the exclusion of all three districts in North Eastern Province and four other northern districts (Samburu and Turkana in Rift Valley Province and Isiolo and 4 Marsabit in Eastern Province). Together the excluded areas account for less than 4 percent of Kenya's population. The KDHS utilised a two-stage, stratified sample consisting of 536 sample units (clusters). Details of the sample design appear in Appendix A. Despite the emphasis on obtaining district-level data for planning purposes, it was decided that reliable estimates could not be produced from the KDHS for all 48 districts, unless the sample were expanded to an unmanageable size. However, it was felt that reliable estimates of certain variables could be produced for the rural areas in 15 districts: Bungoma, Kakamega, Kericho, Kilifi, Kisii, Macbakos, Meru, Murang'a, Nakuru, Nandi, Nyeri, Siaya, South Nyanza, Taita-Taveta, and Uasin Gishu; in addition, Nairobi and Mombasa were also targetted. These areas were targetted because they are generally the larger districts in their provinces, most were districts in which NCPD had posted District Population Officers, and most were also targetted in the 1989 KDHS. Although six of these districts were subdivided shortly before the sample design was finalised, the previous boundaries of these districts were used for the KDHS in order to maintain comparability with the 1989 survey. Due to this oversampling, the KDHS sample is not self-weighting at the national level. Sample weights were used to compensate for the unequal probability of selection between strata, and weighted figures are used throughout the remainder of this report. Questionnaires The survey utilised four types of questionnaires. A Household Schedule was used to list the names and certain characteristics of all usual members and visitors to a selected household. The Woman's Questionnaire was used to collect information from women age 15-49. In addition, interviewing teams measured the height and weight of mothers and of all her children under age five. Information from a subsample of men age 20-54 was collected using a Man 's Questionnaire. The Serviees Availability Questionnaire was used to collect information on the health and family planning services near the sample areas. One services availability questionnaire was to be completed in each sample point. The questionnaires were developed in English by task forces established in Kenya. All except the Services Availability Questionnaire were translated into and printed in Kiswahili and eight of the most widely spoken local languages in Kenya (for more information, see Appendix A). Fieldwork Fieldwork for the KDHS was carried out by 12 interviewing teams. In total, there were 12 supervisors, 11 field editors, 60 female interviewers, 12 male interviewers and 12 drivers. Each team was also coordinated by one NCPD officer. District Population Officers and some District Statistical Officers coordinated some teams and assisted in field logistics. Before launching the survey on 17th February 1993, interviewers, supervisors and field editors had previously been trained. Fieldwork was completed on 15th August 1993. The proportion of households interviewed by month was approximately: February (6 percent); March (17 percent); April (15 percent); May (18 percent); June (20 percent); July (21 percent); August (3 percent). Table 1.2 shows a summary of response rates from the household and individual interviews. A total of 8,805 households was selected for the survey, of which 7,950 were successfully interviewed. The shortfall is primarily due to dwellings being vacant or in which the inhabitants had left for an extended period at the time they were visited by the interviewing teams (for details, see Appendix Table A.1). Of the 8,185 households that were found, 97 percent were interviewed. Within these households, 7,952 women were identified as eligible for an individual interview and of these, 7,540, or 95 percent, were interviewed. In the one half of the households that were selected for inclusion in the male survey, 2,762 eligible men were identified, of which 2,336, or 85 percent, were interviewed. Response rates were higher in rural than in urban areas. 5 Table 1.2 Results of the household and individual interviews Number of households, number of interviews and response rates, Kenya 1993 Residence Result Urban Rural Total Household Interviews Households sampled 1654 7151 8805 Households found 1483 6702 8185 Households interviewed 1379 6571 7950 Household response rate 93.0 Individual Interviews Number of eligible women Number of eligible women interviewed Eligible woman response rate Number of eligible men Number of eligible men interviewed Eligible man response rate 98.0 97.1 1266 6686 7952 1161 6379 7540 91.7 95.4 94.8 616 2146 2762 480 1856 2336 77.9 86.5 84.6 6 CHAPTER 2 CHARACTERISTICS OF HOUSEHOLDS AND RESPONDENTS The purpose of this chapter is to provide a short descriptive summary of some socioeconomic characteristics of the household population and the individual survey respondents, such as: age, sex, residence and educational level. It also examines the environmental conditions such as household facilities and household characteristics. This information on the characteristics of the households and the individual women interviewed is essential for the interpretation of survey findings and can provide an approximate indication of the representativeness of the survey. 2.1 Characteristics of the Household Population In the KDHS, information was collected about all usual residents and visitors who had spent the previous night in the selected household. A household was defined as a person or group of people who live together and share food. Age and Sex The distribution of the household population in the KDHS is shown in Table 2.1 by five year age groups, according to sex and urban-rural residence. The age distribution is typical of high fertility regimes in which a larger proportion of the population is to be found in the younger age groups than in the older age groups (see Figure 2.1 ). However, it is encouraging that the number of children under five is slightly less than the number age 5-9, which is possible evidence of a recent decline in fertility. Table 2.1 Household population by ag% residence and sex Percent distribution of the de facto household population by residence and sex, Kenya 1993 five-year age groups, according to urban-rural Urban Rural Total Age group Male Female Total Male Female Total Male Female Total 0-4 13.6 13.5 13.6 17.2 15.4 16.3 16.7 15.2 15.9 5-9 10.8 14,0 12.4 18.3 17.9 18.1 17.2 17.4 17.3 10-14 9.6 l l . l 10.3 17,1 16.5 16.8 16.0 15.8 15,9 15-19 8.4 10.9 9.6 11.5 9.1 10.2 11.0 9.4 10.2 20-24 10.2 16.2 13.1 6.5 7.6 7.1 7.0 8.7 7.9 25-29 11.9 11.4 11.7 4.5 6.1 5.3 5.6 6.8 6.2 30-34 11.4 8.1 9.8 4.6 5.2 5.0 5.6 5.6 5.6 35-39 7.6 4.7 6.2 3.3 4.0 3.7 3.9 4.1 4.0 40-44 5.2 3.2 4.2 3.3 3.4 3.4 3,6 3.4 3.5 45-49 4,2 1.7 3,0 2.7 2.3 2.5 2.9 2,2 2.5 50-54 2.4 2.4 2.4 2.1 3.7 3.0 2.2 3.6 2.9 55-59 2.2 0.9 1,6 2.1 2.3 2.2 2.1 2.1 2.1 60-64 1.2 0.6 0.9 2,1 2.4 2.2 1.9 2.2 2.1 65-69 0.5 0,4 0.4 1.5 1.5 1.5 1.3 1.3 1.3 70-74 0.4 0.5 0.4 1.3 1.0 1.1 1.2 0.9 1.0 75-79 0.1 0.2 0.1 0.6 0.6 0.6 0.5 0.6 0.5 80 + 0.2 0.2 0.2 0.8 0.7 0.8 0.7 0.6 0.7 Missing/Don't know 0.3 0.0 0.2 0.4 0.2 0.3 0.4 0.2 0.3 Total I00.0 100.0 100.0 100,0 100.0 100.0 100.0 100.0 100.0 Number 2673 2529 5202 15616 17406 33022 18289 19935 38224 7 Age 80+ 75-79 70-74 Figure 2.1 Population Pyramid, Kenya 1993 55-5~ Male ~ Female 50-54 40-44 2s-~ 20-24 15-19 I 10-140-4 s-9 10 5 0 5 Percent 10 KDHS 1993 There seems to be an excess of females over males at ages 5-24, especially in urban areas and especially at ages 20-24. The irregular bulge of women at age 50-54 is indicative of women from ages 45-49 being pushed to 50-54 age group, perhaps to reduce the workload of the interviewer. This pattern has been observed in other DHS surveys (Rutstein and Bicego, 1990). This pattern is more pronounced among women in rural than in urban areas. However, the impact of these irregularities on the quality of the data is probably small. Figure 2.2 shows the distribution of the male and female household population by single year of age (see also Appendix Table C.1). The data show evidence of a preference to report ages that end in zeros and to a lesser extent, fives (age "heaping" or digit preference) that is commonly found in countries where ages are not known well. There is also a relative dearth of women age 15 and an excess of women age 13 and 14, relative to men. This pattem is almost certainly due to interviewers intentionally pushing women outside of the age range established for the individual interview, thus reducing their workloads. This same phenomenon probably caused the excess of women at ages 51-54 relative to men. It is difficult to know the reason for the somewhat odd excess of boys at ages 0-2 and age 4, or the excess of women at ages 20-28. Table 2.2 compares the broad age structure of the population from the 1977/78 Kenya Fertility Survey (KFS), the 1984 Kenya Contraceptive Prevalence Survey (KCPS), the 1989 KDHS, and the 1993 KDHS. It emerges that the proportion of the population less than 15 years had remained stable through the 1989 KDHS and has only recently declined from 53 percent in 1989 to 49 percent in 1993. Similarly, the proportion of population age 15-64 years has risen from 44 to 47 percent. The most likely explanation for this change is a recent decline in fertility. 8 Figure 2.2 Distribution of De Facto Household Population by Single Year of Age and Sex Percent [ I I I I I I I I I I I I 5 10 15 20 25 30 35 40 45 50 55 60 65 70+ Single Year of Age - 'Ma le - -Female / KDHS 1993 Table 2.2 Population by age from selected sources Percent distribution of the de facto population by age group, selected sources 1977/78 1984 1989 1993 Age group KFS KCPS KDHS KDHS Less than 15 52.5 52.0 52.5 49.1 15-64 43.9 44.9 44.0 47.0 65+ 3.5 2.8 3.5 3.6 Missing/don't know 0.0 0.0 0.0 0.3 Total 100.0 100.0 100.0 100.0 Median age NA NA NA 15.3 Note: Totals may not add due to rounding NA = Not available Sources: KFS--CBS. 1980, p. 45; KCPS--CBS. 1986, p. 22; 1989 KDHS-- NCPD. 1989, p. l l3. Household Composition Table 2.3 shows that a large majority of households in Kenya are headed by males (67 percent), with only one-third (33 percent) headed by women. Female-headed households are more common in rural than in urban areas (35 vs. 22 percent). Among the provinces, Nairobi has the lowest proportion of female-headed 9 Table 2.3 Household composition Percent distribution of households by sex of head of household, household size, kinship structure, and presence of foster childre~rt, according to urban-rural residence and region, Kenya 1993 Residence Province Rift Chenaeterislic Urban Rural Nairobi Centxal Coast Eastern Nyanza Valley Western Total Household headship Male 78.5 64.7 80.3 62.8 73.2 61.6 59.7 74.3 65.9 67.3 Female 21.5 35.3 19.7 37.2 26.8 38.4 40.3 25.7 34.1 32.7 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Number of usual membe~ 1 ~.1 11.1 33.0 19.7 21.0 9.0 11.2 12.7 7.0 14.6 2 18.0 9.8 ~.5 12.3 12.4 6.7 13.1 11.5 7.4 11.3 3 13.6 11.2 13.3 11.9 9.2 11.8 12.4 11.3 11.3 11.7 4 11.8 12.2 13.7 13.2 8.9 10.6 12.8 12.1 12.8 12.1 5 10.2 13.8 8.6 12.9 9.5 12.8 14.6 12.8 17.6 13.1 6 6.1 12.4 4.5 12.5 6.5 14.0 11.4 12.6 10.8 11.2 7 4.4 10.0 3.1 7.5 7.7 12.0 10.0 8.6 10.4 8.9 8 2.8 7.4 1.4 4.1 8.1 9.8 7.2 6.4 6.6 6.5 9+ 3.9 12.1 1.6 5.9 16.5 13.4 7.2 12.0 16.0 10.5 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 I00.0 Meana l~ 3.4 5.1 2.9 4.2 5.2 5.5 4.7 4.9 5.5 4.8 Relationship structure One adult 34.9 23.0 37.1 31.2 28.6 20.6 23.7 22.7 20.8 25.3 Two related adults: Of opposite sex 27.2 32.3 29.3 31.7 21,2 29.8 33.1 36.6 29.5 31.3 Of same sex 8.6 5.2 8.2 5.3 9,4 5.9 6.6 4.6 3.6 5.8 Three or more related adults 20.2 35.1 17.0 27.3 36,8 36.7 34.5 30.6 39.2 32.2 Other 9.0 4,3 8.2 4.5 4,0 6.8 2,1 5.2 6.9 5.2 Total 100.0 100.0 100.0 100.0 100,0 100.0 100.0 100.0 100.0 100.0 With foster children a 10.8 18.9 6.8 11.7 16,8 17.9 21.0 18.0 25.3 17.4 Note: Table is b~ed on de jure members; i.e. usual residents. aFoster children are those under age 15 living in households with neither their mother nor their father present. households (20 percent), followed by Rift Valley Province (26 percent). Nyanza Province has the highest proportion of female-headed households (40 pereen0. The average household size in Kenya is 4.8 people. Households in rural areas are substantially larger, on average, than those in urban areas (5.1 vs. 3.4 persons). Single-person households are more common in urban than in the rural areas. They are more common in Nairobi and in Coast and Central Provinces than in the other provinces. One quarter of Kenyan households consist of only one adult, either with or without children. Roughly one-third of households contain two related adults of opposite sexes (presumably most of which are married couples); another one-third consist of three or more related adults. Households of three or more related adults 10 are more common in Western Province (39 percent) and least common in Nairobi (17 percent) and Central Province (27 percent). The pattern of household structures has been influenced by the high rates of rural- urban migration. Children who live with neither their natural mother nor father have been shown to be socioeconomically disadvantaged. In Kenya, almost one in five households includes one or more of these foster children. Households with foster children are more common in rural (19 percent) than in urban areas (11 percent). Westem Province has the highest proportion of households with foster children (25 percent), while Nairobi (mainly urban) has the lowest (7 percent). Less than one percent of children under age 15 arc orphaned, that is, both their parents have died (data not shown); however, 2 percent have lost their mothers and 5 percent have lost their fathers. Education Kenya's formal education is based on a three-tier system, known as the 8-4-4 system. In this system primary education consists of 8 years and secondary education 4 years. Graduates of secondary school may then further their education by enrolling at any of the five national universities or in any of several private universities, or by joining colleges or technical institutes to acquire certain skills necessary for national development. The 8-4-4 system was adopted in 1985, replacing a four-tier system (7-4-2-3) consisting of seven years of primary school, four of secondary, two of higher secondary, and three of university. KDHS results show that there is still a strong differential in education between males and females in Kenya (Tables 2.4.1 and 2.4.2). The data indicate that 17 percent of men and 27 percent of women age six Table 2.4.1 Edueati~aal level of the male household population Percent disla'ibution of the de facto male household population age six and over by highest level of education attended, according to selected background characteristics, Kenya 1993 Background Primary Primary Second- characteristic None incomplete complete ary+ Missing Total Number Age 1 6-9 35.5 63.4 0.1 0.1 0.9 100.0 2590 10-14 5.0 89.4 4.6 0.6 0,5 100.0 2921 15-19 3.4 44.4 35.5 16.5 0.3 100.0 2016 20-24 4.3 18.3 35.1 41.7 0.6 100.0 1286 25-29 3.7 16,5 32.7 46.7 0.4 1O0.0 1028 30-34 5.5 19.1 30.0 44.7 0.8 100.0 1027 35-39 9.2 23.8 25.3 41.2 0.5 100.0 717 40-44 16.8 26.6 21.4 33.4 l.S 100.0 659 45-49 14.6 28.7 32.7 22.6 1.4 100.0 528 50-54 25.8 36.3 24.8 10.8 2.2 100.0 398 55-59 40.6 34.3 17.0 6.6 1.5 100.0 392 60-64 45.4 41.1 7.1 4.6 1.7 100.0 357 65+ 65. I 26.9 3.5 3.0 1.6 1GO.0 684 Residence Urban 7.0 27.8 19,9 43.9 1.3 100.0 2253 Rural 18.2 50,3 17.8 12.8 0.9 100.0 12420 Province Nairobi 6.3 23.4 21,3 47.6 1.4 100.0 880 Central 11.2 47,0 23.3 17,5 1.0 100.0 2118 Coast 21.3 40.6 19.4 16.7 2.1 100,0 1390 Eastern 17.7 52,4 17.1 12.4 0,4 100,0 2787 Nyanza 16,0 52.2 17.8 13,1 0.9 100.0 2137 Rift Valley 20.3 46.5 16.1 15.8 1.3 100.0 3325 Western 15.9 48.5 15.6 19.8 0.2 100.0 2036 Total 16.5 46.8 18.2 17.5 1.0 100,0 14672 1Excludes 69 men for whom an age was not reported. 11 Table 2,4.2 Educaticctal level of the female hotlsehold pOpulatice Percent distribution of the de facto female bouf, ehold population age six end over by highest level of educallo~ attended, according to selected background characteristics, Kenya 1993 Background Primary Primary Second- characteristic None incomplete complete ary+ Missing Total Number Age I 6-9 35.7 62.9 0.0 0.0 1.4 1(30.0 2865 10-14 5.0 86.8 7.1 0.6 0.5 100.0 3158 15-19 4.3 38.8 37.5 18.8 0.6 100.0 1867 20-24 6.2 25.5 36.4 31.6 0.3 100.0 1736 25-29 12.5 25.6 27.5 33.9 0.5 100.0 1346 30-34 21.4 32.0 18.5 27.6 0.5 100.0 1118 35-39 36.5 29.6 16.2 17.2 0.4 100.0 811 40-44 42.4 30,4 17,9 8.1 1.2 100.0 675 45-49 54.7 26.5 11.9 6.0 0.9 100.0 446 50-54 70.3 21.7 4.2 2.1 1.7 100.0 712 55-59 77.3 17.5 2.5 1.2 1.4 100.0 423 60-64 84.6 11.6 1.2 0.1 2.5 100.0 430 65+ 87.5 8.6 0.6 0.6 2.8 100.0 687 Residence Urban 13.5 31.8 21.1 32.8 0.7 1OO.0 2108 Rural 29.1 46.8 14.4 8.7 1.0 100.0 14207 province Nairobi 12,7 22.1 24.1 40.6 0.6 100.0 707 Central 21.0 45.1 19.7 13.8 0.5 100.0 2409 Coast 41.0 34.1 13.6 8.9 2.4 100.0 1525 Eastern 27.2 47.0 15.3 9,8 0.8 100.0 3200 Nyanza 28.5 49.2 13.0 8.4 0.9 100.0 2583 Rift Valley 28.4 47.0 13.3 9.9 1.4 1(30.0 3552 Western 24.8 47.7 14.5 12.5 0.4 100.0 2339 Total 27.1 44.9 15.3 11.8 1.0 100.0 16315 tExcludes 40 women for whom an age was not reported. and above have not received any formal education. At almost every age group there are smaller proportions of men than women with no education and more men than women with secondary education (Figure 2.3). However, the sex differential is narrowing over time; differences in educational attainment between school- age boys and girls are insignificant. The proportion of both men and women with no education is higher in rural than in urban areas. Rural residents are more than twice as likely to have never attended school compared to urban dwel lers~l 8 vs. 7 percent for males and 29 vs. 14 percent for females. Residents of Nairobi and Central Province are more educated than residents of other provinces. Table 2.5 presents enrolment rates by age, sex and residence of children age 6-24 years. Almost nine in ten children (86 percent) age 6-15 years are enrolled in school. Enrolment drops substantially after age 15; only 44 percent of the older teenagers are still in school and only 9 percent of those in their early twenties are still in school. It is somewhat surprising that at all age groups, enrolment is higher in rural areas than in urban areas. At ages 6-15 boys are slightly more likely to be enrolled than girls (87 percent of boys compared to 85 percent of girls). By ages 16-20, men are much more likely to be enrolled than women (52 percent compared to 36 percent), presumably because of early marriage and childbearing which cause young women to drop out of school. 12 Table 2.5 School enrolment Percentage of the de facto household population age 6-24 years enrolled in school, by age group, sex, and urban- rural residence, Kenya 1993 Male Female Total Age group Urban Rural Total Urb~m Rural Total Urban Rural Total 6-I0 84.6 85.3 85.3 81.9 82.9 82.8 83.2 84.1 84.0 11-15 83,7 89.9 89.4 74.0 88.8 87.4 78.4 89.4 88,4 6-15 84.2 87.4 87.1 78.3 85.5 84.8 81,0 86.4 85.9 16-20 42.6 53.6 52.2 22.7 38.2 35.6 31.0 45.8 43.6 21-24 I0.3 13.1 12.4 6.4 5.7 5.9 8.1 8.9 8.7 100 8O 4O 2O Figure 2.3 Percentage of Males and Females Who Have Completed Primary Education by Age Group Percent 0 [ ] I I I I I " r .,. -,, 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65+ Age Group KDHS 1993 13 2.2 Hous ing Character i s t i cs Soc ioeconomic cond i t ions were assessed by ask ing respondents quest ions about the i r househo ld env i ronment . Th is in fo rmat ion is summar ised in Tab le 2.6. Table 2.6 Housing characteristics Percent distribution of households by housing characteristics, according to urban-rural residence and region, Kenya 1993 Residence Province Rift Characteristic Urban Rural Nalrobi Central Coast Eastern Nyanza Valley Western Total Electricity Yes 42.5 3.4 50.8 9.8 16.9 4.1 3.4 7.1 7.6 10.9 No 57,0 96.2 48.8 89,6 82.7 95.7 95.8 92.5 92.1 88.7 Missing 0.5 0.4 0.4 0.6 0.4 0.2 0,8 0,4 0.3 0.4 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Source of dr inking water Piped into residence 55.8 10.7 65.0 27.4 15.8 14.8 3.5 15.9 14.4 19.4 Public tap 31.4 8.9 27.3 10.7 37.4 11.6 8.4 9.8 5.3 13.2 Well with hand pump 0.9 10.8 0.0 5.5 13.6 3,3 8.2 5.0 31.2 8.9 Well without hand pump 2.1 14.6 0.2 13,6 5.4 13.5 7.3 15.2 21.7 12.2 Lake/pond 0.1 8.7 0.0 1.9 9.4 7.1 21.8 4.6 2.5 7.1 River/stream 1.6 41.2 0.0 33.2 12.1 45.8 43.7 44.0 23.2 33.6 Rainwater 0.6 2.6 0.0 5.2 0.5 2.5 1.3 2.8 1.1 2.2 Other 6.6 1,9 6.8 1.5 5.4 1.3 5.0 2.1 0.7 2.8 Missing/Don't know 0.8 0.5 0.8 1.0 0.4 0.2 0.9 0.8 0.0 0.6 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Sanitation facility Own flush toilet 23.5 1.1 19.3 2.0 5.9 2.1 1.5 6.9 7.2 5.4 Shared flush toilet 21.4 0.5 34,0 1,2 3,8 1.0 1.6 3.5 0.7 4.5 Trad. pit toilet 42.3 71.4 36.5 83.3 58.1 73.4 61.7 55.6 80.2 65.8 Vent. imp. pit latrine 6.9 5.9 3.3 10.3 7.9 5.4 4.3 6.2 4.1 6,1 No facility/bush 2.1 20.3 3.1 0.8 23.7 17.8 29.9 26.0 7.6 16.8 Other 3.0 0.3 3.1 1.2 0.1 0.0 0.2 1.5 0.0 0.8 Missing/Don't know 0.9 0.5 0.8 1.3 0.5 0.4 0.8 0.3 0,3 0.6 Total 100.0 I00.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100,0 Flooring Earth/dung floor 20.6 79.9 24.4 63.4 58.7 72.8 82.8 70.8 82.4 68.5 Wood planks (rudim.) 0.3 0.2 0.4 0.4 0.1 0.0 0.0 0,6 0.0 0.2 Parquet/polished wood 0.7 0.1 1.2 0.1 0.0 0.0 0.4 0.0 0.1 0.2 Vinyl/linoleum/asphalt 0.3 0.0 0.4 0.0 0.0 0.0 0.0 0.1 0.0 0.1 Ceramic files 3,0 0.3 4.9 0.3 0.7 0.3 1.0 0.2 0.1 0.8 Cement 74.0 19.2 67.4 35.1 39.9 26.6 14.9 28.0 17.3 29.7 Other 0.2 0.0 0.4 0.0 0.1 0.0 0.0 0.0 0.0 0.0 Missing/Don't know 0.9 0.3 0.8 0.7 0.5 0.3 0.8 0.2 0.0 0.4 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Persons per sleeping room 1-2 65.1 54.9 66.6 74.2 67.2 55.2 50.2 48.0 47.4 56.9 34 23.5 29.0 21.9 18.8 23.5 28.8 31.8 32.5 32.5 27.9 5-6 7.9 10.2 7.8 5.5 5.4 9.4 10.9 12.3 14.3 9.8 7 + 2.4 4.9 2.9 0.7 3.0 5.7 5.8 5.8 5.4 4.5 Missing/Don't know 1.1 0.9 0.8 0.7 0.8 0.8 1.3 1.4 0.4 1.0 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Mean persons per room 2.4 2,9 2.3 2.i 2.4 2.9 3.0 3.1 3.2 2.8 Number of households 1527 6423 645 1282 678 1348 1245 1745 1007 7950 14 The table shows that electricity is available to only 11 percent of households in Kenya. Electricity is more available in urban households (43 percent) than rural households (3 percent). Electricity is much more common in Nairobi (51 percent of households) and Coast Province (17 percent). Nyanza Province has the lowest proportion of households with electricity (3 percent). Sources of drinking water differ widely by area of residence. In the urban areas, piped water is the major source; 56 percent of households have water piped into the residence and another 31 percent obtain water from a public tap. In rural areas, only 11 percent of households have water piped into the residence. The major sources of water in the rural areas are rivers and streams (41 percent) and wells (25 percent). Water sources differ widely by province of residence. Sixty-five percent of households in Nairobi have water piped into the residence, compared to 27 percent of households in Central Province, about 15 percent of households in Rift Valley, Coast, Eastern, and Western Provinces, and only 4 percent of households in Nyanza Province. Households in Coast Province rely more heavily on public taps as a source of drinking water (37 percent) than households in other provinces, while households in Western Province use wells more frequently as a source of drinking water (53 percent). A majority of households in Kenya (66 percen0 have traditional pit toilets. Seventeen percent have no facility at all, while only 5 percent have their own flush toilets. Households in both urban and rural areas commonly use traditional pit latrines (42 percent and 71 percent, respectively). In urban areas, 24 percent of households have their own flush toilets and 21 percent have shared flush toilets. There are large differences among provinces in types of sanitary facilities; the traditional pit latrine is most common in Central Province (83 percent of households) and least common in Rift Valley Province (56 percent) and Nairobi (37 percent). Thirty percent of households in Nyanza Province have no toilet facilities, compared to less than one percent of households in Central Province. Almost all Kenyan households live in residences with floors made out of either earth (69 percent) or cement (30 percent). Cement flooring is most common in urban areas (74 percent), while earthen floors are most common in rural areas (80 percent). Cement flooring is most common for households in Nairobi (67 percent). Earthen floors are the most common in all the other provinces. As a way of estimating the extent of crowding, information was gathered on the number of rooms households use for sleeping. The majority of households (57 percent) have one or two persons per sleeping room, while about one quarter (28 percent) have three or four persons per sleeping room. The mean is 2.8 persons per sleeping room. There is only a small difference in the number of persons per sleeping room between households in urban and rural areas. Among the provinces, crowding seems to be less common in households in Central Province than in the other provinces. Household Durable Goods Respondents were asked about ownership of particular household goods such as radios and televisions (to assess access to media), refrigerators (to assess access to food storage), bicycles (to assess modes of transportation), and cattle, goats, sheep and cash crops (to assess levels of wealth). The results presented in Table 2.7 indicate that 52 percent of Kenyan households own a radio (68 percent of urban households and 48 percent of rural households) and 6 percent own a television (22 percent in urban areas and 2 percent in rural areas). Only 3 percent of Kenyan households own refrigerators. The relative lack of televisions and refrigerators in rural areas is presumably because of lack of electricity and greater financial constraints. Overall, one in five households (22 percent) owns a bicycle. A large majority own land (80 percent) or cattle, goats and sheep (63 percent). One-third of the households have cash crops. The proportion of households owning a television ranges from 24 percent in Nairobi to 2 percent in Eastern 15 Table 2.7 Household durable goods Percentage of households possessing various durable consumer goods, by urban-rural residence and region, Kenya 1993 Residence Province Rift Charac~ristie Urban Rural Nairobi Central Coast Eastern Nyanza Valley Western Total Radio 67.7 48.1 68.9 55.3 46.4 51.6 42.3 49.3 56.8 51.9 Television 22.0 2.4 24.0 6.0 6.7 2.1 2.0 6.8 4.0 6.1 Refrigerator 12.0 0.6 13.5 2.0 4.5 0.6 1.1 2.8 0.9 2.8 Bicycle 16.9 23.3 13.9 16.1 21.4 25.4 22.9 18.8 35.5 22.1 Land 43.0 88.8 49.6 75.5 59.4 90.9 88.5 81.5 91.4 80.0 Cattle/goats/sheep 29.6 70.7 36.7 63.5 36.3 74.4 65.7 70.1 64.8 62.8 Cash crops 16.4 38.3 23.4 44.0 26.1 41.5 35.8 24.0 39.5 34.1 Number of households 1527 6423 645 1282 678 1348 1245 1745 1007 7950 and Nyanza Provinces, while the proportion with bicycles ranges from 36 percent in Western Province to 14 percent in Nairobi. 2.3 Background Characteristics of Women Respondents General Characteristics Women were asked two questions in the individual interview to assess their age: "In what month and year were you bom?" and "How old were you at your last birthday?" Interviewers were trained to probe situations in which respondents did not know their age or date of birth, and they were instructed as a last resort to record their best estimate of the respondent's age. Table 2.8 shows the distribution of female respondents in five-year age groups. Table 2.8 indicates that 61 percent of female respondents are currently married or living with a man, ~ while about 30 percent have never been married (single). Three percent each are widowed, divorced, or separated. In the 1989 KDHS, 67 percent of women of childbearing age were married, 26 percent had never married, while 7 percent were either widowed, divorced, or separated. A large majority of the respondents have had some education; only 18 percent of women 15-49 have no formal education at all. Fifty-three percent have completed primary school (including those with secondary school), while 25 percent have gone to some secondary school. This shows considerable improvement since the 1989 KDHS. In 1989, 25 percent of women had never bcen to school, 47 percent had completed primary school and only 20 percent had continued to secondary school (NCPD, 1989, p.6). Kenya still remains predominantly rural, with less than one out of five women (18 percent) living in urban areas. The distribution of women by province is similar to that from other surveys and censuses. About one in five women are from each of Rift Valley and Eastern Provinces, approximately 15 percent are from each of Nyanza, Western and Central Provinces, while 10 percent are from Coast Province and 7 percent from Nairobi. ~Throughout this report, the term "married" includes both those in formal and informal marriages (living together). 16 Table 2.8 Background characteristics of respondents Percent distribution of women by selected background characteristics, Kenya 1993 Number of women Background Weighted Un- characteristic percent Weighted weighted Age 15-19 23.3 1754 1788 20-24 21.7 1638 1605 25-29 16.2 1221 1199 30-34 14.4 1088 1112 35-39 10.2 768 743 40-44 8.5 638 653 45-49 5.8 434 440 Marital status Never married 30.2 2280 2320 Married 58.3 4394 4329 Living together 3.1 235 254 Widowed 3.1 231 234 Divorced 2.7 202 198 Separated 2.6 198 205 Education No education 17.9 1352 1297 Primary incomplete 28.9 2179 2226 Primary complete 28.7 2166 2223 Secondary+ 24.5 1844 1794 Residence Urban 17.8 1339 1161 Rural 82.2 6201 6379 Province Nairobi 6.7 507 367 Central 14.5 1094 1075 Coast 9.5 717 1091 Eastern 18.6 1406 1044 Nyanza 15.4 1158 1264 Rift Valley 20.7 1562 1754 Western 14.5 1096 945 Religion Catholic 31.4 2368 2336 Protestant/other Christian 59.8 4509 4556 Muslim 4.9 370 366 No religion 2.7 204 201 Other 1.0 73 66 District t Mombasa 2.4 178 372 Murang'a 3.5 265 361 Nyeri 2.3 175 367 Kilifi 3.8 289 337 Taita Taveta 0.8 62 281 Machakos/Makueni 7.6 571 438 Meru/Tharaka-Nithi 5.2 388 364 Kisii/Nyamira 6.1 461 488 Siaya 2.6 196 408 South Nyanza 4.1 306 257 Kericho/Bomet 3.5 266 322 Nakuru 2.4 182 252 Nandi 2.2 164 403 Uasin Gishu 1.5 113 315 Bungoma 3.7 280 396 Kakamega/Vihiga 6.8 514 381 Total 100.0 7540 7540 IMombasa refers to the city; district refers only to rural areas of the district; the sum for districts does not add to I00.0. 17 Almost all the women interviewed report themselves as Christians (91 percent), either Protestant (60 percent) or Catholic (31 percent). Those who believe in Islam account for 5 percent, with about 3 percent reporting having no religion. Table 2.8 also presents the percentage of women who live in the rural areas of each of the 15 specially targetted districts and Mombasa. These figures provide a frame of reference for later statistics. Differentials in Educat ion Table 2.9 presents the distribution of female respondents by education according to selected characteristics. Results indicate that education is inversely related to age, that is, older women are less educated than younger women. For instance, only 4 percent of women age 15-19 have had no education, compared to 57 percent of women age 45-49. Rural women are more disadvantaged in education than urban women. Twenty percent of rural women have had no education at all, compared to 9 percent of urban women. Almost half (46 percent) of women in urban areas have attended secondary school, compared to 20 percent in the rural areas. There are also wide differentials in education attainment between regions. While less than 10 percent of the women in Nairobi and Central Province have no education, 37 percent of those in Coast Province are reported as having no education at all. Table 2.9 Level of education Percent distribution of women by the highest level of education attended, according to selected background characteristics, Kenya 1993 Highest level of education Number Background Primary Primary Second- of characteristic None incomplete complete ary+ Total women Age 15-19 4.2 33.5 41.0 21.4 100.0 1754 20-24 5.8 24.0 37.8 32.3 100.0 1638 25-29 12.7 25.4 26.0 35.9 100.0 1221 30-34 20.4 31.3 21.0 27.3 100.0 1088 35-39 36.9 30.6 15.7 16.8 100.0 768 40-44 43.2 30.8 17.6 8.4 100.0 638 45-49 57.0 26.6 11.3 5.1 100.0 434 Residence Urban 8.7 16.8 28.8 45.8 100.0 1339 Rural 19.9 31.5 28.7 19.9 100.0 6201 Province Nairobi 7.6 12.8 31.6 48.0 100.0 507 Central 9.5 22.5 36.6 31.3 100.0 1094 Coast 36.7 22.2 23.6 17.5 100.0 717 Eastern 16.6 32.2 29.9 21.3 100.0 1406 Nyanza 18.2 38.0 25.6 18.2 100.0 1158 Rift Valley 22.5 28.8 27.1 21.7 100.0 1562 Western 13.8 33.4 27.1 25.8 100.0 1096 Total 17.9 28.9 28.7 24.5 100.0 7540 18 Access to Media Women were asked if they usually read a newspaper, listen to the radio or watch television at least once a week. This information is crucial for planning the dissemination of family planning messages. Table 2.10 shows that two-thirds of women listen to the radio, one-third read a newspaper and 15 percent watch television at least once a week. Younger women have greater access to the media than older women. The higher the level of education the more access there is to the media; while only 2 percent of women with no education read a newspaper once a week, 61 percent of women with secondary education and above do so. Women in rural areas are more disadvantaged in access to media. While 80 percent of women in urban areas listen to the radio weekly, only 61 percent of rural women do so. Similarly, 56 percent of women in urban areas read a newspaper once a week, compared to only 26 percent of women in rural areas. Understandably, urban Nairobi enjoys more access to media than the other provinces. Fifty-nine percent of the women in Nairobi read a newspaper once a week, compared to only 23 percent of women in Westem Province. Women in Eastern and Nyanza Provinces are less likely to watch television than women in other provinces. Table 2.10 Access to mass media Percentage of women who usually read a newspaper once a week, watch television once a week, or listen to radio once a week, by selected background characteristics, Kenya 1993 Read Watch Listen to Number Background newspaper television radio of characteristic weekly weekly weekly women Age 15-19 38.1 18.6 67.7 1754 20-24 38.9 17.4 67.2 1638 25-29 33.3 14.5 68.7 1221 30-34 28.8 14.5 65.5 1088 35-39 22.7 10,5 56.5 768 40-44 17.3 9.5 55.0 638 45-49 13.7 8.2 59.2 434 Education No education 1.7 3.8 41.5 1352 Primary incomplete 18.7 8.4 56.6 2179 Primary complete 37.5 14.1 71.6 2166 Secondary+ 61.0 31.8 83.2 1844 Residence Urban 55.8 45.5 79.9 1339 Rural 26.2 8,3 61,4 6201 Province Nairobi 58.9 48.5 81.7 507 Central 29.7 17.0 69.0 1094 Coast 29.2 17.6 55.3 717 Eastern 26.8 6.2 64.4 1406 Nyanza 36.1 6.1 58.7 1158 Rift Valley 31.2 17.3 64.8 1562 Western 23.3 12.7 65.5 1096 Total 31.4 14.9 64.7 7540 19 CHAPTER 3 FERTILITY 3.1 Introduction The measurement of fertility levels, differentials and determinants has remained a major objective in all the demographic, contraceptive and health surveys that have been carried out in Kenya since 1977. The 1993 Kenya Demographic and Health Survey (KDHS) was no exception to this rule. The level of fertility has been a major factor underlying Kenya's high population growth rate. The understanding of fertility dynamics is therefore of paramount importance. The fertility indicators presented in this chapter are based on reports provided by women age 15-49 years regarding their reproductive histories. As in the 1989 KDHS, each woman was asked to provide information on the total number of sons and daughters to whom she bad given birth who were living with her, the number living elsewhere, and the number who had died. The women were also asked for a history of all live births, including such information as: name, month and year of birth, sex and survival status. For children who had died, information on age at death was solicited. The above information is analysed in the following sections to provide fertility levels and trends; fertility differentials by age, birth order, sex, residence, province and education; and limited measures of determinants. 3.2 Fertility Levels Table 3.1 gives the reported age-specific fertility rates for the three-year period preceding the survey per 1000 women. 1 The sum of the age-specific fertility rates (known as the total fertility rate) is a useful means of summarising the level of fertility. It can be interpreted as the number of children a woman would have by the end of her childbearing years if she were to pass through those years beating children at the currently observed rates. The general fertility rate represents the annual number of births in a population per 1,000 women age 15-44. The crude birth rate is the annual number of births in a population per 1,000 people. Both these measures are calculated using the birth history data for the three-year period before the survey and the age and sex distribution of the household population. The total fertility rate for the three years before the survey (representing early 1990 to early 1993) is 5.4 children per woman. The age pattem of fertility indicates that women in Kenya have children early in the childbearing period; by age 30, a Kenyan woman will have given birth to almost 60 percent of the children she will ever have. Table 3.1 and Figure 3.1 present fertility rates by urban-rural residence. These rates are higher in rural areas than in urban areas, a pattern that has persisted in various censuses and demographic surveys that have been carried out in the country. The total fertility rate is estimated at 5.8 in rural areas, about 70 percent ~Numerators of the age-specific fertility rates are calculated by summing the number of live births that occurred in the period 1-36 months preceding the survey (determined by the date of interview and the date of birth of the child), and classifying them by the age (in five-year groups) of the mother at the time of birth (determined by the mother's date of birth). The denominators of the rates are the number of woman-years lived in each of the specified five-year age gmups during the 1-36 months preceding the survey. 21 Table 3.1 Current fertility Age-specific and cumulative fertility rates and the crude birth rate for the three years preceding the survey, by urban-rural residence, Kenya 1993 Residence Age group Urban Rural Total 15-19 74 119 110 20-24 176 281 257 25-29 178 258 241 30-34 133 209 197 35-39 74 166 154 40-44 (34) 74 70 45-49 (21) 53 50 TFR 15-49 3.44 5.80 5.40 TFR 15-44 3.34 5.54 5.15 GFR 129 194 182 CBR 35.1 35.9 35.8 Note: Rates axe for the period 1-36 months preceding the survey. Rates for age group 45-49 may be slightly biased due to truncation. Rates in parentheses are based on fewer than 250 woman-yeaxs of exposure, TFR: Total fertility rate expressed per woman GFR: General fertility rate (births divided by number of women 15-44), expressed per 1,000 women CBR: Crude birth rate, expressed per 1,000 population Figure 3.1 Age-Specific Fertility Rates By Urban-Rural Residence Births per 1,000 Women 30o 2OO I I I I I 15-19 20-24 25-29 30-34 35-39 40-44 Age Group 45-49 KDHS 1993 22 higher than that in the urban areas (3.4). The difference in urban and rural fertility rates is particularly pronounced among women at older ages, when urban fertility rates are half those of rural women. 3.3 Fertility Differentials Table 3.2 and Figure 3.2 provide differentials in fertility by province and education. Western, Rift Valley, Nyanza and Eastern Provinces retain fertility rates that are above the national level. Nairobi and Central Province depict the lowest fertility levels. The total fertility rate in Westem Province (6.4) is almost twice the rate in Nairobi (3.4). Table 3.2 Fertility by background characteristics Total fertility rate for the three years preceding the survey and mean number of children ever born to women age 40-49, by selected background characteristics, Kenya 1993 Mean number of children Total ever born Background fertility to women characteristic rate 1 age 40-49 Residence Urban (3.44) 4.67 Rural 5.80 7.62 Province Nairobi (3.40) (4.26) Central (3.93) 6.89 Coast (5.25) 6.44 Eastern (5.89) 7.44 Nyanza (5.80) 7.69 Rift Valley (5.70) 7.91 Western (6.35) 7.86 Education No education (6.03) 7.58 Primary incomplete (6.18) 7.71 Primary complete (5.02) 6.83 Secondary+ (4.03) 4.99 Total 5.40 7.32 Note: Rates shown in parentheses indicate that one or more of the component age-specific rates is based on fewer than 250 woman-years of exposure. IRate for women age 15-49 years Female education apparently has a strong effect on fertility. Women with no education or only some primary school give birth to more than six children on average in their lifetime, compared to five children for women who completed primary school and four children for women with some secondary school. 23 Figure 3.2 Total Fertility Rate by Selected Background Characteristics RESIDENCE Urban Rural PROVINCE Nairobi Central Coast Eastern Nyanza R. Valley Western EDUCATION No Education Prim. Incomp. Prim. Comp. Secondary + 0 3.4 3.9 5.3 1 2 3 4 5 Total Fertility Rate 6,4 6 KDHS 1993 One way of examining trends in fertility over time is to compare the total fertility rates for the three years preceding the survey with the average number of children ever bom to women by the end of their childbearing period, age 40-49. The former is a measure of current fertility, while the latter is a measure of past or completed fertility. The data in Table 3.2 imply that there has been a decline of about two children over the past 10-20 years in Kenya. The decline has occurred across all the provinces and all education levels. 3.4 Fertility Trends Table 3.3 examines the trend in fertility in Kenya by comparing the results of the 1989 KDHS with those of earlier surveys. Fertility has declined from 8.1 births per woman in the mid-70s to 5.4 births for the period 1990-92. The decline has accelerated recently, with fertility dropping by 20 percent between 1984-88 and 1990-92. This is the most dramatic drop in fertility ever recorded in Kenya and one of the most dramatic recorded anywhere. As shown in Figure 3.3, the fertility decline has been experienced by women of all reproductive ages. Table 3.4 shows that, although all provinces in Kenya experienced a recent decline in fertility, some changed much more than others. The major declines--above 20 percent--in total fertility rates have been recorded in Central and Westem Provinces and in Nairobi. Central Province shows the largest decline in fertility, from a total fertility rate of 6.0 to 3.9 or a 35 percent decline. Meanwhile, fertility in Coast Province--traditionally an area of lower fertility--has hardly declined at all. 24 Table 3.3 Trends in current fertility rates Age-specific fertility rates and total fertility rates as reported in various surveys 1977/78 1984 1989 1993 KFS KCPS KDHS KDHS Agehime period 1975-77 1983 1984-88 1990-92 15-19 168 143 152 110 20-24 342 358 314 257 25-29 357 338 303 241 30-34 293 291 255 197 35-39 239 233 183 154 40-44 145 109 99 70 45-49 59 66 35 50 Total fertility rate 8.1 7.7 6.7 5.4 Note: The rates refer to the following periods prior to the survey: for 1977/78, a 3-ye~ period; for 1984, a 1-year period; for 1989, a 5-year period, and for 1993, a 3-year period. Sources: KFS--CBS. 1980, p. 87; KCPS--CBS. 1984, p. 50; 1989 KDHS--NCPD. 1989, p.18. Table 3.4 Trends in fertility by province Total fertility rates by province and percent change 1984-88 and 1990-92 1989 1993 KDHS KDHS Province/ Percent period 1984-88 1990-92 change Nairobi 4.6 3.4 -.26 Central 6.0 3.9 -.35 Coast 5.5 5.3 -.04 Eastern 7.0 5.9 -.16 Nyanza 7.1 5.8 -.18 Rift Valley 7.0 5.7 -.19 Western 8.1 6.4 -.21 Total 6.7 5.4 -.19 Note: Rates for the 1989 KDHS refer to the 5-year period prior to the survey, while those for the 1993 KDHS refer to the 3-year period prior to the survey. Source: NCPD. 1989, p. 22. Figure 3.3 Age-Specific Fertility Rates 1977/78 KFS, 1989 KDHS and 1993 KDHS Births per 1,000 Women 400 30o 2oo 100 ~ 0 I I I I I 15-19 20-24 25-29 30-34 35-39 40-44 Age Group 45-49 25 Table 3.5 shows trends in the percentage of women who reported that they were pregnant at the time of the interview, according to age group. The proportions show a steady decline, from 13 percent in 1977/78, to 11 percent in 1984, 9 percent in 1989, and 8 percent in 1993. The decline appears to have slowed somewhat between 1989 and 1993. Table 3.5 Percent pregnant Percentage of all women who are pregnant at the time of interview by age group as reported in various surveys 1977/78 1984 1989 1993 Age KFS KCPS KDHS KDHS 15-19 8 8 6.8 5.3 20-24 17 16 13.6 12.6 25-29 19 17 10.5 10.9 30-34 16 13 10.9 9.2 35-39 12 10 8.4 7.3 40-44 9 6 3.6 3.3 45-49 3 2 2.2 1.1 Total 13 11 8.9 8.2 Sources: KFS--CBS. 1980, p. 88; KCPS--CBS. 1984, p. 53; 1989 KDHS--NCPD. 1989, p. 21. Table 3.6 provides further insights into the apparent fertility decline documented above. The table gives the age-specific fertility rates for four-year periods preceding the survey, using data from respondents' birth histories. Figures in brackets represent partial fertility rates due to truncation; women 50 years of age and older were not included in the survey and the further back into time rates are calculated, the more severe is the truncation. For example, rates cannot be calculated for women age 45-49 for the period 8-11 years before the survey, because those women would have been over age 50 at the time of the survey and were not interviewed. It should also be noted that misreporting of the date of birth of children can result in the appearance of false trends in fertility. The data show declining fertility experienced by women in all age groups during the last two decades. Table 3.6 Age-specific fertility rates Age-specific fertility rates for four-year periods preceding the survey, by mother's age at the time of birth, Kenya 1993 Number of years preceding the survey Mother's age 0-3 4-7 8-11 12-15 16-19 15-19 118 143 166 184 187 20-24 266 312 333 353 334 25-29 256 304 333 342 [3441 30-34 204 268 281 325 [330] 35-39 152 195 [242] [303] 40-44 73 [141] [(245)] 45-49 [501 Note: Age-specific fertility rates are per 1,000 women. Estimates enclosed in brackets are truncated. Parentheses indicate that the figure is based on fewer than 250 woman-years of exposure. 26 Table 3.7 presents fertility rates for ever-married women by duration since first marriage for four-year periods preceding the survey. It is analogous to Table 3.6, but is confined to ever-married women and replaces age with duration since first marriage. The data confirm that the decline in fertility is apparent for all marriage durations. Table 3.7 Fertility by marital duration Fertility rates for ever-married women by duration since Ftrst marriage in years, for four-year periods preceding the survey, Kenya 1993 Marriage duration at birth Number of years preceding the survey 0-3 4-7 8-11 12-15 16-19 0-4 354 390 390 406 386 5-9 273 337 360 374 367 10-14 239 288 315 347 324 15-19 179 241 283 284 I302] 20-24 115 163 I189] [335] 25-29 63 [931 Note: Duration-specific fertility rates are per t,000 women. Estimates enclosed in brackets are truncated. 3.5 Children Ever Born The distribution of all women and currently married women by age and number of children ever bom is presented in Table 3.8. The table also shows the mean number of children ever born to women in each five-year age group, an indicator of the momentum of childbearing. The table shows that 17 percent of women age 15-19 years have given birth to a child. This represents a decline from the level of 21 percent reported in the 1989 KDHS and probably reflects a gradual increase in age at marriage which has been reinforced by rising school enrolment at these ages. However, high fertility is apparent for women age 25 years and over. Women in their early thirties have given birth to an average of 4.5 children and those age 45- 49 report an average of 7.9 children. This same pattem is reflected by currently married women with the exception that the percentage of currently married women age 15-19 who have had children is of course high (59 percent) compared to all women age 15-19. The percentage of women aged 45-49 who have never had children provides an indicator of the level of primary infertility---the proportion of women who are unable to bear children at all. Voluntary childlessness is rare in Kenya, and married women with no births are most likely unable to bear children. The KDHS results suggest that primary infertility islow, about 1 percent. This is slightly lower than that recorded in the 1989 KDHS (about 2 percent). It should be noted that this estimate of primary infertility does not include women who may have had one or more births but who are unable to have more (secondary infertility). 27 Table 3.8 Chi ldren ever born and living Percent distribution of all women and of currently married women by number of chi ldren ever born (CEB) and mean number ever born and living, according to five-year age groups, Kenya 1993 Number of children ever born (CEIt) Number Mean no. Mean no. Age of of of living group 0 1 2 3 4 5 6 7 8 9 10+ Total women CEB children ALL WOMEN 15-19 83.2 13.6 2.8 0.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100.0 1754 0.20 0.18 20-24 29.5 29.2 24.1 11.9 4.1 1.2 0.0 0.1 0.0 0.0 0,0 100.0 1638 1.36 1.25 25-29 5.8 10.3 21.0 21.6 21.0 13.I 4.8 2.0 0.4 0.1 0.0 100.0 1221 3.13 2.85 30-34 4.5 5.2 8.5 14.1 15.3 18.9 15.4 10.0 4.3 2.5 1.2 100.0 1088 4.53 4.06 35-39 2.5 1.5 3.4 5.8 11.1 14.8 17.1 13.0 13.8 9.0 8.0 100.0 768 6.13 5.45 40-44 2.7 3.1 3.6 4.5 7.8 8.5 9.7 13.0 13.7 14.1 19.2 100.0 638 6.95 6.11 45-49 1.1 2.6 4.1 2.4 5.1 6.6 9.1 11.2 14.3 12.8 30.8 100.0 434 7.87 6.72 Total 27.9 12.5 11.4 9.3 8.6 7.7 6.1 4.9 4.1 3.2 4.4 100.0 7540 3.17 2.82 CURRENTLY MARRIED WOMEN 15-19 41.0 42.9 14.3 1.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100.0 261 0.77 0.65 20-24 11.0 30.7 31.8 17.8 6.6 2.0 0.0 0.1 0.0 0.0 0.0 100.0 937 1.85 1.71 25-29 2.5 7.7 19.6 23.5 22.9 15.2 5.7 2.3 0.4 0.1 0.0 100.0 1003 3.40 3.10 30-34 2.5 3.7 7.0 13.9 16.3 20.3 16.4 10.9 4.9 2.6 1.5 100.0 918 4.79 4.30 35-39 1.5 1.3 3.1 5.4 10.2 15.3 16.2 13.8 14.7 9.7 8.9 100.0 644 6.32 5.67 40-44 2.2 2.3 2.8 4.7 6.2 8.4 8.8 13.3 13.9 15.0 22.3 100.0 519 7.28 6.45 45-49 1.2 1.4 4.3 2.5 3.5 6.6 8.9 11.2 14.2 14.9 31.4 100.0 348 8.04 6.94 Total 6.1 11.6 13.9 13.0 11.9 11.3 8.4 6.9 5.7 4.7 6.4 100.0 4629 4.40 3.94 A comparison of the mean number of children ever born reported in the 1993 KDHS and various other surveys is presented in Table 3.9. Except for the data from the 1984 KCPS, which are uniformly higher than the other sources, 2 the figures show a steady decline in completed fertility over time. The one exception is the apparent rise in mean children ever born to women age 45-49 between the 1989 and 1993 KDHSs. It is unlikely that this is actually true; a more likely explanation is that for some reason the figure was either underreported in the 1989 survey, overreported in the 1993 survey or both. 3.6 Birth Intervals Table 3.9 Trends in chi ldren ever born Mean number of clfildren ever born by age group as retmrted in various surveys 1977/78 1984 1989 1993 Age KFS KCPS KDHS KDHS 15-19 0.4 0.4 0.3 0.2 20-24 1.8 2.0 1.6 1.4 25-29 3.8 4.0 3.5 3.1 30-34 5.6 5.7 5.0 4.5 35-39 6.8 7.0 6.5 6.1 40-44 7.6 7.8 7.4 7.0 45-49 7.9 8.2 7.6 7.9 Sources: KFS- CI~S. 1980, p. 84; KCPS--CBS. 1984, p. 45; 1989 KDHS- -NCPD. 1989, p. 24. Information on birth intervals provides insight into birthspacing patterns which have far-reaching impact on both fertility and child mortality levels. Research has shown that children born too close to a previous birth are at increased risk of dying at an early age. ~l'he KCPS is the only one of these surveys in which a complete birth history was not asked for each respondent; instead, the survey relied on summary data on the number of children ever born. Studies have shown that summary data produce higher estimates of cumulative fertility than birth histories (Central Bureau of Statistics, 1975 and 1977). 28 As shown in Table 3.10, one in four births occurs after an interval of less than 24 months, about 40 percent take place 24-35 months (two years) after the previous birth and one-third occur three years or more after the previous birth. A shorter median interval prevails for children whose preceding sibling has died, compared to those whose prior sibling is alive. This pattern presumably reflects early resumption of sexual intercourse, shortened breastfeeding period, and minimal use of contraceptives. Birth intervals have lengthened slightly over time; the median birth interval was 29 months for births occuring in the five years prior to the 1989 KDHS, compared to 30 months for the 1993 KDHS (data not shown). Table 3.10 Birth intervals Percent distribution of births in the five years preceding the survey by number of months since previous birth, according to demographic and socioeconomic characteristics, Kenya 1993 Number of months since previous birth Characteris6c 7-17 18-23 24-35 36-47 48+ Median number of Number months since of Total previous birth births Age of mother 15-19 11.1 36.7 35.2 9.6 7.3 100.0 24.3 64 20-29 10.7 18.3 43.5 17.2 10.3 100.0 28.6 2385 30-39 8.1 12.8 40.9 18.7 19.6 100.0 31.8 1923 40 + 7.5 13.2 31.7 23.7 23.9 100.0 34.7 505 Birth order 2-3 10.3 17.2 40.8 16.7 14.9 I00.0 29.4 1892 4-6 7.9 15.0 42.0 19.3 15.8 100.0 30.7 1823 7 + 9.8 15.0 40.4 19.5 15.3 100.0 30.5 1162 Sex of prior birth Male 9.3 16.2 41.2 17.9 15.4 100.0 30.3 2362 Female 9.3 15.5 41.1 18.8 15.3 100.0 30.0 2515 Survival of prior birth Living 7.2 15.8 42.1 19.1 15.7 100.0 30.6 4405 Dead 29.1 16.2 32.0 11.1 11.6 100.0 24.8 472 Residence Urban 12.4 17,1 30.3 18.4 21.9 100.0 30.8 522 Rural 8.9 15.7 42.5 18.4 14.5 100.0 30.1 4356 Province Nalrobi 14.5 17.7 27.4 17.7 22.6 100.0 32.0 171 Central 7.2 18.3 40.8 16.9 16.8 100.0 30.0 541 Coast 7.7 13.0 40.1 20.7 18.6 100.0 33.1 443 Eastern 6.1 15.2 44.0 18.6 16.1 100.0 30.6 988 Nyanza 13.7 15.2 39.8 17.8 13.6 100.0 29.4 832 Rift Valley 7.9 17.2 40.8 18.5 15.6 100.0 30.2 1074 Western 11.8 15.2 43.2 18.2 11.6 100.0 28.7 827 Education No education 9.8 13.3 34.8 23.3 18.9 100.0 32.8 1112 Primary incomplete 8.4 14.3 48.7 16.3 12.2 100.0 29.7 1644 Primary complete 9.6 19.1 40.4 17.6 13.3 100.0 28.8 1190 Secondary+ 10.0 17.5 36.5 17.0 19.0 100.0 30.9 931 Total 9.3 15.9 41.2 18.4 15.3 100.0 30.1 4877 Not~: Firs~order births are excluded. The interval for multiple births is the number of months since the preceding pregnancy that ended in a live birth. 29 3.7 Age at First Birth The age at first birth is a crucial demographic indicator that usually reflects age at first marriage, level of contraceptive use and pre-marital sexual exposure. Early initiation into childbearing is generally a major determinant of large family size and rapid population growth, particularly in countries where family planning is not widely practised. Moreover, bearing children at a young age involves substantial risks to the health of both the mother and child. Early childbearing also tends to restrict educational and economic opportunities for women. Table 3.11 presents the percent distribution of women by age at first birth according to current age. Childbearing begins early in Kenya, with the majority of women becoming mothers before they reach the age of 20. The median age at first birth is 19. Moreover, the data show that median age at first birth has remained around 19 years for all women, irrespective of their current age, with the possible exception of women age 20-24, for whom the median age is 19.8. The median age at first birth does not appear to have changed significantly since the 1989 KDHS (with medians mostly around 19). Table 3.11 Age at first birth Percent distribution of women 1549 by age at first birth, according to current age, Kenya 1993 Curtentage Women Median with Age at first birth Number age at no of first births <15 15-17 18-19 20-21 22-24 25+ Total women birth 15-19 83.2 1.4 10.6 4.8 NA NA NA 100.0 1754 a 20-24 29.5 4.4 23.5 24.3 14.0 4.4 NA 100.0 1638 19.8 25-29 5.8 5.3 28.4 24.9 18.3 12.7 4.6 100.0 1221 19.3 30-34 4.5 6.2 30.0 25.4 16.5 11.4 6.0 100.0 1088 19.0 35-39 2.5 9.6 29.5 23.4 17.6 11.2 6.3 100.0 768 19.0 40-44 2.7 10.4 27.2 21.1 17.9 12.9 7.7 100.0 638 19.0 45-49 1.1 8.4 26.1 24.0 15.1 11.6 13.8 100.0 434 19.3 NA = Not applicable aLess than 50 percent of the women in the age group x to x+4 have had a birth by age x Differentials in median age at first birth are shown in Table 3.12. The most notable differentials are that urban women start childbearing later than rural women; educated women--particularly those with secondary education--start childbearing later than those with no education; and women in Nyanza Province have the lowest median age at first birth (18.2). 30 Table 3.12 Median age at first birth Median age at first birth among women age 20-49 years, by current age and selected background characteristics, Kenya 1993 Current age Background Ages Ages characteristic 20-24 25-29 30-34 35-39 40-44 45-49 20-49 25-49 Residence Urban a 20.7 20.0 19.7 21.0 (21.0) a 20.5 Rural 19.5 19.0 18.8 18.8 18.8 19.2 19.1 18.9 Province Nairobi a 20.4 (22.2) (18.5) (21.0) * a 20.6 Central a 19.7 19.5 19.9 18.9 19.5 19.7 19.6 Coast a 19.2 19.0 18.6 19.2 21.3 19.4 19.1 Eastern 19.8 19.6 19.1 19.1 18.9 18.9 19.3 19.2 Nyanza 18.6 18.0 18.2 18.4 18.3 19.1 18.3 18.2 Rift Valley 19.7 19.4 18.6 18.7 19.1 20.0 19.3 19.0 Western 19.6 19.2 19.2 18.6 19.2 17.9 19.2 18.9 Education No education 18.2 17.2 18.0 18.0 18.3 19.1 18.2 18.2 Primary incomplete 18.4 18.1 18.3 18.9 18.6 18.8 18.4 18.4 Primary complete 19.6 19.2 19.2 18.8 20.4 19.7 19.4 19.3 Secondary+ a 20.9 20.7 21.5 22.4 * a 21.1 Total 19.8 19.3 19.0 19.0 19.0 19.3 19.3 19.1 Note: The medians for cohort 15-19 could not be determined because half the women have not yet had a birth. aMedians were not calculated for these cohorts because less than 50 percent of women in the age group x to x+4 have had a birth by age x. Numbers in parentheses are based on 25-49 women; an asterisk indicates that the figure is based on fewer than 25 women and has been suppressed. 3.8 Teenage Fertility Early childbearing, particularly among teenagers (those under 20 years of age) has diverse negative demographic, socioeconomic and sociocultural consequences. Teenage mothers suffer most from severe complications during delivery, their socioeconomic advancement, such as educational attainment and accessibility to better job opportunities, is curtailed, and socially---especially if they are not married---~ey arc more likely to become outcasts and be relegated to ineffective roles in society. Table 3.13 shows the percentage of teenagers age 15-19 who arc mothers or pregnant with their first child, according to various background characteristics. Seventeen percent of teenage women arc mothers and another 4 percent arc pregnant with their first child. This represents a decline in teen childbearing--the 1989 KDHS showed that 21 percent of women 15-19 were already mothers. 31 Table 3.13 Teenage pregnancy and motherhood Percentage of teenagers 15-19 who are mothers or pregnant with their first child, by selected background characteristics, Kenya 1993 Percentage who are: Percentage who have Pregnant begun Number Background with first child- of characteristic Mothers child bearing teenagers Age 15 3.4 1.8 5.2 302 16 3.1 2.5 5.6 373 17 10.5 3.5 14.1 370 18 27.7 5.9 33.6 391 I9 39.5 4,4 43.9 318 Residence Urban 14.0 3.3 17.3 275 Rural 17.3 3.8 21.1 1479 Province Nairobi 15.5 3.4 19.0 80 Central 13.6 2.0 15.6 242 Coast 13,2 3.8 17.0 173 Eastern 18.0 1.8 19.8 296 Nyanza 21.6 6.4 28.0 299 Rift Valley 15.6 3.9 19.5 367 Western 17.4 4.1 21.5 297 Education No education 22.1 7.8 29.9 73 Primary incomplete 17.3 3.0 20.3 587 Primary complete 19.5 4.6 24.2 719 Secondary+ 9.8 2.4 12.1 375 Total 16.8 3.7 20.5 1754 As expected, the proportion of women who have begun childbearing rises rapidly with age, from 5 percent of those age 15 to 44 percent of those age 19. Those residing in rural areas, those with less than secondary education and those residing in Nyanza and Western Provinces are also more likely than others to have begun childbearing. These differentials parallel the differentials documented earlier about patterns in current and cumulative fertility. 32 Whereas most teenage women who have begun childbearing have given birth only once, a small proportion have had two births. As shown in Table 3.14, only 3 percent of women age 15-19 have had two or more births. The proportion rises to 11 percent among women age 19. Table 3.14 Children born to teenagers Percent distribution of teenagers 15-19 by number of children ever born (CEB), Kenya 1993 Age 0 1 2+ Number of Mean children ever born number Number of of Total CEB teenagers 15 96.6 3.1 0.3 100.0 0.04 302 16 96.9 3.0 0.1 100.0 0.03 373 17 89.5 10.0 0.5 100.0 0.11 370 18 72.3 22.7 5.0 100.0 0.33 391 19 60.5 28.8 10.7 100.0 0.52 318 Total 83.2 13.6 3.2 100.0 0.20 1754 33 CHAPTER 4 FERTILITY REGULATION 4.1 Knowledge of Contraception Determining the level of knowledge of contraceptive methods and accessibility of services was a major objective of the KDHS, since knowledge of specific methods and of the places where they can be obtained is a precondition for their use. Information about knowledge of contraceptive methods was collected by asking the respondent to name ways or methods by which a couple could delay or avoid pregnancy. If the respondent failed to mention a particular method spontaneously, the interviewer described the method and asked if she recognised it. Eight modem methods---the pill, IUD, injection, vaginal methods (diaphragm, foam tablets, jelly), condom, female sterilisation, male sterilisation (vasectomy), and Norplant--were described, as well as three traditional methods--the calendar rhythm method, natural family planning (temperature or mucus method), and withdrawal. Other methods mentioned by the respondent, such as herbs or breastfeeding, were also recorded. For each method recognised, the respondent was asked if she knew of a source or a person from whom she could obtain the method. If she reported knowing about calendar rhythm or natural family planning, she was also asked if she knew a place or a person where advice could be obtained on how to use the method. The KDHS results indicate that almost all (96 percent) Kenyan women know at least one method of family planning (Table 4.1). Knowledge of methods is slightly higher among married women than among all women. This chapter will focus primarily on married women since they are at greatest risk of pregnancy. A high proportion of married women (97 percent) report knowing a modem method and 76 percent of them have some knowledge about a traditional method. The methods most widely known by married women are the pill (95 percent), injection (93 percent), and female sterilisation (87 percent), followed by the condom (85 percent) and IUD (80 percent). Male sterilis0tion and vaginal methods are known by just over 40 percent of women. Due to its availability only recently and on a limited basis, Norplant is the least widely known method (14 percent). Considering the traditional methods included in the questionnaire, calendar rhythm (67 percent) is more widely known than withdrawal (34 percent), natural family plarming (31 percent), or any other traditional method (11 percent); the latter consist mostly of herbs, abstinence and breastfeeding. It should be noted that for all methods, knowledge is higher among currently married women than among all women. Knowledge of sources for obtaining family planning methods is widespread in Kenya. Ninety-four percent of currently married women know a source for a contraceptive method and 56 percent know a place to get information about bow to use the rhythm or natural family planning methods. Most married women (90 percent) know of a source where they can obtain the pill, 88 percent know where to obtain injectables, 81 percent know a source for female sterilisation and 76 percent know a source for the IUD. More than three quarters know where to obtain condoms. It is encouraging to note that 41 percent of currently married women know a source for male sterilisation, while 40 percent know where to obtain vaginal methods. In summary, for modem methods, over 90 percent of the women who have heard of a method also know where to obtain it. 35 Table 4.1 Knowledge of contraceptive methods and source for methods Percentage of all women and currently married women who know specific contraceptive methods and who know a source (for information or services), by specific methods, Kenya 1993 Know method Know a source 1 Currently Currently Con/xaceptive All married All married method women women women women Any method 95.6 97.2 88.2 93.5 Any modern method 95.2 96.9 87.6 93.1 Pill 91.9 95.1 82.7 89.7 IUD 73,3 80.4 67.4 76.0 Injection 87.6 93.1 80.1 87.9 Diaphragm/foam/jelly 38.3 42.3 35.2 39.8 Condom 83.4 85.1 73.3 78.3 Female sterilisation 81.1 87.4 72.7 80.9 Male sterilisation 41.3 45.4 36.9 41.4 Norplant 12.5 13.8 10.9 12.2 Any traditional method 71.9 75.9 51.8 55.6 Rhythrrffcounting days 64.2 66.7 48.6 52.0 Natural family planning 28.8 30.8 21.8 23.9 Withdrawal 29.5 33.6 NA NA Other traditional methods 9.3 11.2 NA NA Number of women 7540 4629 7540 4629 NA = Not applicable IFor modem methods, source refers to a place to obtain the method or procedure. For rhythm and natural family planning, refers to a place or person to obtain advice on practicing these methods. Trends in Knowledge of Methods and Sources Knowledge of family planning methods has increased considerably since the late 1970s. Table 4.2 shows comparable data for all women from the 1977/78 KFS, the 1984 KCPS and the 1989 and 1993 KDHSs. t While the level of knowledge of any method has increased only slightly (from 88 percent in 1977/78 to 96 percent in 1993), knowledge of many specific methods has increased more dramatically. For example, the proportion of women who have heard of contraceptive injections increased from 55 to 88 percent since 1977/78 and the proportion who have heard of condoms doubled from 40 to 83 percent. While some of the increase in knowledge of family planning methods occurred during the 1980s, there has been a large gain between 1989 and 1993, most notably in knowledge of condoms and male sterilisation. Knowledge of traditional methods has also become more widespread. ~AII four surveys used much the same techniques for probing knowledge of methods, however they included some different methods and used somewhat different terminology; for example, the 1977/78 KFS and the 1984 KCPS included douche, abortion, and abstinence, while the 1993 KDHS omitted these methods but included Norplant and natural family planning. 36 Table 4.2 Trends in knowledge of family planning methods and sources Percentage of women who know specific family planning methods and who know a source (for family planning information or services), Kenya, 1977/78, 1984, 1989, and 1993 Know method Know source 1977/78 1984 1989 1993 1989 1993 Method KFS l KCPS KDHS KDHS KDHS KDHS Any method 88.0 81.0 90.0 95.6 88.1 88.2 Any modern method 84.0 79.7 88.4 95.2 86.5 87.6 Pill 74.0 72.7 84.4 91.9 81.6 82.7 IUD 49.0 55.2 62.0 73.3 60.0 67.4 Injection 55.0 58.9 76.3 87.6 74.2 80.1 Diaphragm/foam/jelly 20,0 26.3 24.4 38.3 23.2 35.2 Condom 40.0 41.5 53.4 83.4 49.2 73.3 Female sterilisation 54.0 55.0 68.2 81.1 65.9 72.7 Male sterilisation 14.0 18.1 19.8 41.3 19.0 36.9 Any traditional method U 62.0 54.8 71.9 44.6 51.8 Rhythm 51.0 46.1 50.7 64.22 44.6 48.62 Withdrawal 24.0 24.6 16.8 29.5 NA NA Other U U 5. l 9.3 NA NA Number of women 8100 658l 7150 7540 7150 7540 NA = Not applicable U = No information 1Published data are presented in whole numbers; decimal was added to balance this table. 2Refers to calendar rhythm only, not to natural family planning Sources: KFS--CBS. 1980, p. 130, 132; KCPS--CBS. 1984, p.69; NCPD. 1989, p.29 Differentials in Knowledge of Methods and Sources KDHS data indicate that, in general, differences in knowledge of family planning methods by socioeconomic status or residence are minimal (see Table 4.3). Knowledge of any method is slightly lower among older women than younger women and the mean number of methods recognised is also lower (3.9 vs. 4.8). Urban women know slightly more methods on average than rural women. Provincial variations in contraceptive knowledge are rather small. The level of knowledge of family planning methods and places where they can be obtained increases with level of education; virtually all of the married women with secondary education know of a contraceptive method and can identify a source. Moreover, better educated women say they have heard of about 5 methods on average, compared to only about 4 methods for uneducated women. 37 Table 4.3 Knowledge of modern contraceptive methods and source for methods Percentage of currenfly married women who know at least one modem contraceptive method and who know a source (for information or services), by selected background characteristics. Kenya 1993 Mean Mean Know a Know no. of Know no. of source for Number Background any methods a modem modem modem of characteristic method known method 1 methods method women Age 15-19 98.1 4.8 97,0 3.7 89.6 261 20-24 97.8 4.7 97,5 3.5 94.1 937 25-29 98.1 4.7 98,1 3.5 95.6 1003 30-34 98.3 4.5 97,7 3.3 95.1 918 35-39 96.9 4.3 96.9 3.3 93.3 644 40-44 95.6 4.2 95,1 3.2 89.8 519 45-49 92.4 3.9 91,8 3.1 84.6 348 Residence Urban 98.5 4.8 98,3 3.5 95.7 697 Rural 97.0 4.4 96,6 3.4 92.6 3932 Province Nairobi 96.9 4.7 96,4 3.4 95.4 271 Central 99.8 4.7 99,8 3.4 97.1 610 Coast 94.6 4.3 933 3.2 85.7 445 Eastern 99.0 4.7 99,0 3.5 95.8 864 Nyanza 99.1 4.7 99,1 3.4 95,9 737 Rift Valley 92.9 4.1 92,1 3.1 87.4 992 Western 98.6 4.5 98,2 3.6 95.0 710 Education No education 91.5 3.8 90,3 3.0 81.9 1062 Primary incomplete 98,0 4.5 97,8 3.4 94.0 1411 Primary complete 99.2 4.8 99,2 3.6 96.8 1177 Secondary+ 99.8 4.9 99,8 3.5 99.4 980 Total 97.2 4.5 96,9 3.4 93.1 4629 llncludes pill. IUD, injection, vaginal methods (foaming tablets/diaphragm/foam/jelly), condom, female sterillsation, and male sterilisation. 4.2 Ever Use of Contraception All women interviewed in the KDHS who said that they had heard of a method of family planning were asked if they had ever used it. Forty-six percent of Kenyan women of repmducfive age have used a method of family planning at some time and over one third have used a modem method (Table 4.4). The corresponding proportions among currently married women are 55 and 43 percent, respectively. Ever use is lowest among the youngest age group (15-19 years), rising to a peak among those age 25-34, and then dropping among older women. Twice as many married women have used modem methods as traditional methods (43 vs. 22 percent). The most widely ever-used methods are the pill (28 percent), rhythm (19 percent), injection (14 percent) and the IUD (10 percent). Only seven percent of married women have used the condom (for family planning 38 Table 4.4 Ever use o f contraception Among all women and currently married women, the percentage who have ever used a contraceptive method, by specific method, according to age, Kenya 1993 Modern method Traditional method Any Dia- Female Male Natural modem phragm/ steri- steri- Any Rhythm/ fatal- With- Number Background Any meth- lnjec- foam/ Con- lisa- lisa- No¢- trad. counting ly draw- of characteristic method od Pill IUD tion jelly dora tioga tion plant method days planning al Other women ALL WOMEN 15-19 15.2 6.9 3.4 0.5 0.7 0.2 3.2 0.1 0.0 0.2 10,4 9.5 0.3 1.0 0.5 1754 20-24 47.3 31.5 20.7 2.8 6,6 0.9 9.1 0.2 0.2 0.2 25.9 24.3 1.3 2.8 0.9 1638 25-29 63.0 50.0 37.0 9.1 14.9 2.0 9.9 1.8 0.1 0.1 23.7 20.1 1,8 4.0 1.8 1221 30-34 61.7 50.2 32.9 12.3 21.3 4.8 7.8 5.2 0.1 0.2 23.5 19.2 2.6 4.7 2.4 10~8 35-39 57.5 45.3 28.1 14.7 17.1 3.7 6.4 10.3 0.3 0.3 20.9 16.0 1.0 3,1 3.9 768 40-44 51.5 42.3 21.8 12.1 14.7 2.4 5.3 12.3 0.0 0.4 16.4 13.7 1.3 1.8 2.2 638 45-49 43.9 35.4 21.0 11.3 12.2 3.5 4.7 11.5 0.2 0.3 16.6 12.6 0.7 2.3 4.6 434 Total 45.6 34.0 21.9 7.2 10.8 2.1 6.8 3.9 0.1 0.2 19.8 17.0 1.3 2.8 1.8 7540 CURRENTLY MARRIED WOMEN 15-19 31.0 14.1 9.5 0.5 2.0 0.5 4.9 0.0 0.0 0.3 21.9 20.3 1.0 2.5 0.6 261 20-24 49.4 34.8 24.2 4.1 8.3 0.7 7.5 0,1 0.1 0.0 24.7 229 1.5 3.1 0.9 937 25-29 62.9 50.0 36.7 9.8 14.4 2.0 9.9 1.9 0.2 0.1 23.4 19.9 1.9 4.1 2.0 10~3 30-34 62.6 50.3 32.0 11.6 21.4 4.5 7.5 6.0 0.2 0.3 23.8 19,2 3.0 4.8 2.7 918 35-39 57.6 45,3 27.1 14.5 15.8 3.8 6.4 11.1 0.3 0.3 21.4 15.8 0.8 3.2 4.3 644 40-44 53.6 43.5 21.9 13.0 15.6 2.3 5,3 13.0 0.0 0.3 17.4 14.2 1.0 1.8 2.2 519 45-49 45.8 37.2 22.2 13.4 13.4 3.7 5.5 12.3 0.2 0.4 18.1 14.3 0.9 2.5 4.6 348 Total 55.2 42.6 27.6 9,8 14.1 2.6 7.3 5.5 0.l 0.2 22.3 18.8 1.7 3.4 2.4 4629 purposes), while 6 percent have used female sterilisation and 3 percent have used withdrawal and vaginal methods. Ever use of family planning methods has increased greatly over the last 15 years, as evidenced by the data in Table 4.5. In 1977/78, less than one third of all women age 15-49 reported having ever used a method of family planning, compared to 46 percent in 1993. Ever use of modem methods has tripled over the same time period, with greater use of the pill and injection accounting for the bulk of the increase, especially in the four years between 1989 and 1993. 4.3 Current Use of Contracept ion Results from the 1993 KDHS indicate that 33 percent of m arried women in Kenya are currently using family planning (see Table 4.6). More women are using modem methods (27 percent) than traditional methods (6 percent). Thus, modem methods account for 83 percent of overall contraceptive use. The most widely used contraceptive method is the pill (10 percent), followed by injection (7 percent), female sterilisation (6 percent), IUD (4 percent) and rhythm (4 percent). Less than one percent of married women use other methods such as condom, vaginal methods, Norplant, natural family planning and withdrawal. Other traditional methods---mostly abstinence, herbs and breastfeeding--are used by less than one percent of married women. 39 Table 4.5 Trends in ever use of family planning methods Percentage of women who have ever used specific family planning methods, Kenya, 1977/78, 1984, 1989, and 1993 Contraceptive 1977/78 1984 1989 1993 method KFS 1 KCPS 1 KDHS KDHS Any method 29.0 28.5 39.1 45.6 Any modern method 11.0 14.1 24.1 34.0 Pill 7.0 9.0 15.1 21.9 IUD 2.0 4.0 6.8 7.2 Injection 2.0 2.0 5.5 10.8 Diaphragm/foam/jelly 1.0 1.0 1.8 2.1 Condom 3.0 2.0 3.6 6.8 Female sterilisation 1.0 2.0 3.8 3.9 Male sterilisation 0.0 0.0 0.2 0.1 Any tradit ional method U U 21.9 19.8 Rhytlun/counting days 13.0 13,0 19.4 17,02 Withdrawal 4.0 4.0 2.4 2.8 Other traditional methods U U 2.7 1.8 Number of women 8100 6581 7150 7540 U = No information tpublished data are presented in whole numbers; decimal was added to balance this table. ;~Refcrs to calendar rhythm only, not to natural family planning Sources: KFS--CBS. 1980, p. 130, 132; KCPS CBS. 1984, p. 78, 83; 1989 KDHS--NCPI). 1989, p.33. Table 4.6 Current use of contraception Percent distribution of all women and of currently married women by contraceptive method currently used, according to age, Kenya 1993 Modern method Traditional method Natural Any Dia- Female Rhythm fatal- Not modem phragm/ steri- Any count- ly With cur- Number Background Any meth- Injec- foam/ Con- lisa- Nor- trad. ing plan- draw- rently of ch~racteris6c method od Pill IUD tinn jelly dora tkm plant method days ning al Other using Total women ALL WOMEN 15-19 5.7 2.4 1.4 0.1 03 0.0 0.4 0.1 0.0 3.3 3.0 0.0 0.2 0.0 94.3 100.O 1754 20-24 23.0 16.2 9.7 1.3 3.6 0.0 1.4 0.2 0.0 6.7 63 0.2 0.1 0.1 77.0 100.0 1638 25-29 37.5 31.5 16.5 3.6 8.3 0.0 1.2 1.8 0.0 6.0 4.9 0.2 0.3 0.5 62.5 100.0 1221 30-34 38.3 32.2 8.9 5.2 11.5 0.1 l.I 5.2 0.0 6.2 4.9 0.4 0.3 0.6 61.7 100.0 1088 35-39 34.6 30.4 5.5 6.0 7.6 0.3 0.7 10.3 0.0 4.2 2.7 0.2 0.4 1.0 65.4 1130.0 768 40-44 34.2 29.4 4.8 4.5 7.0 0.1 0.8 12.3 0.0 4.9 3.4 0.3 0.6 0.6 65.8 100.0 638 45-49 26.7 22.3 1.8 3.2 5.2 0.0 0.5 I 1.5 0.0 4.4 3.1 0.0 0.0 1.3 73.3 leO.0 434 Total 25.9 20.7 7.5 2.8 5.5 0.1 0.9 3.9 0.0 5.2 4.3 0.2 0.3 0.4 74.1 leO.0 7540 CURRENTIN MARRIED WOMEN 15-19 10.3 6.1 4.6 0.0 0.8 0.0 0.4 0.0 0.3 4.1 3.0 0.0 1.1 0.0 89.7 1{30.0 261 20-24 23.6 18.3 11.5 1.8 4.0 0.0 08 0.1 0.0 5.3 4.9 0.2 0.2 0.0 76.4 lO0.O 937 25-29 37.2 31.5 17.0 3.9 7.6 0.0 11 1.9 0.0 5.7 4.5 0.3 03 0.5 62.8 100.0 1003 30-34 39.7 33.2 8.8 5.6 [1.9 0.0 0.9 6.0 0.1 6.5 5.0 0.4 0.3 0.7 60.3 100.0 918 35-39 35.9 31.6 5.2 7.0 7.3 0.3 0.7 11.1 0.0 4.3 2.5 0.2 0.4 1.2 64.1 I00.0 644 40-44 37.3 31.9 5.0 5.1 7.7 0.l 03 13.0 0.0 5.5 3.8 03 0.7 0.7 62.7 1130.0 519 45-49 30.4 24.9 2.2 4.0 5.7 0.0 0.7 12.3 0.0 5.5 3.8 0.0 0.0 1.7 69.6 1(20.0 348 Total 32.7 27.3 9.5 4.2 7.2 0.1 0.8 5.5 0.0 5.5 4.2 0.2 0.4 0.6 67.3 100.0 4629 40 The use of contraception increases steadily up through age group 30-34 and declines thereafter. The pill and calendar rhythm are the most commonly used methods among women age 15-24. There is a gradual shift to longer-term methods among older women, so that by age 25-29, injection has replaced rhythm as the second most popular method; by age 30-34, it is the most popular method. Above age 35, female sterilisation is the most widely used method, with the injection in second place. One in eight (12-13 percent) married women in their forties has been sterilised. Trends in Current Use of Family Planning Current use of family planning has shown a steady increase over the past nine years, from 17 percent of married women in 1984 to 33 percent in 1993 (see Table 4.7 and Figure 4.1). 2 Moreover, there has been a radical shift to greater use of modem methods. In 1984, 10 percent of married women were using modem methods, compared to 27 percent in 1993. Use of contraceptive injections has experienced a particularly steep increase, from less than one percent of married women in 1984 to 7 percent in 1993. Use of the pill has tripled and female sterilisation has doubled over the past 9 years, while IUD use showed more modest increases. Table 4.7 Trends in current use of family planning methods Percentage of currently married women age 15-49 who are currently using specific family planning methods, Kenya, 1984, 1989, and 1993 Contraceptive 1984 1989 1993 method KCPS KDHS KDHS Any method 17.0 26.9 32.7 Modern method 9.7 17.9 27.3 Pill 3.1 5.2 9.5 IUD 3.0 3.7 4.2 Injection 0.5 3.3 7.2 Diaphragm/foam/jelly 0.1 0.4 0.1 Condom 0.3 0.5 0.8 Female stefilisation 2.6 4.7 5.5 Male sterilisation 0.0 0.0 0.0 Any traditional method 7.3 9.0 5.5 Rhythm/counting days 3.8 7.5 4.41 Withdrawal 0.6 0.2 0.4 Other traditional methods 2.9 1.3 0.6 Number of women 4627 4765 4629 llncludes calendar rhythm and natural family planning Sources: KCPS--CBS. 1984, p. 85, 86; 1989 KDHS--NCPD. 1989, p. 35. 2In 1977/78, 7 percent of married women were using family planning (CBS. 1980, p. 133); data from the KFS regarding use of specific methods are not tabulated in a form that is readily comparable with data from the other surveys and thus are not shown in Table 4.7. 41 Figure 4.1 Trends in Contraceptive Use Currently Married Women 15-49 KDHS 1989 and KDHS 1993 Any Method Any Modern Method Any Traditional Method Pill IUD Injection Condom Female Sterilisation Periodic Abstinence 2~ F 8 10 20 30 Percent Using Method 40 Use of traditional methods declined sharply between 1989 and 1993. This was almost entirely due to a decrease in use of the rhythm method. There is reason to believe that some of the apparent decline is due to questionnaire design. In the 1989 KDHS, the term periodic abstinence was used to describe any method in which sexual intercourse was intentionally avoided on the days of the woman's menstrual cycle on which she thought she was most likely to be fertile. A sizeable proportion of women who reported that they were using periodic abstinence were actually either not sexually active in the month before the survey, abstaining after a birth, or amenorrhoeic. This implies that there was some confusion betweenperiodic abstinence and long-term or postpartum abstinence. In an attempt to avoid this problem, the 1993 KDHS questionnaire used the term rhythm~counting days to describe the calendar rhythm method. (The survey also introduced a separate category, "natural family planning" to describe the temperature and vaginal mucus methods of determining the fertile time.) To the extent that this modification reduced confusion of periodic with longer- term abstinence, it may account for some or all of the apparent decline in use of this method. Differentials in Current Use of Family Planning Table 4.8 and Figure 4.2 show the level of contraceptive use among currently married women by selected background characteristics. The table indicates that some women are much more likely to be using contraceptives than others. The level of contraceptive use is much higher in urban areas (43 percent) than rural areas (31 percent). The most popular methods among urban women are the pill, the IUD, and injection, while the most popular methods among rural women are the pill, injection and female sterilisation. 42 Table 4.8 Current use of family planning by method Percent distribution of currently married women by contraceptive method currently used, according to selected background characteristics, Kenya 1993 Any modem Background Any mcth- characterls6c method od Modern method Traditional method Natural Dia- Female Any farni- Not phragm/ steri- trad. Rhythm/ ly With- cut- Number Injec- foam/ Con- lisa- meth-countingplan- draw- rently of Pill IUD tinn jelly dora tinct od days ning al Other using Total women Residence Urban 43.4 37.9 15.7 9.5 6.2 0.0 1.3 5.2 5.5 4.6 0.7 0.2 0.0 56.6 100.0 697 Rural 30.9 25.4 8.4 3.2 7.4 0.1 0.8 5.6 5.4 4.1 0.2 0.4 0.7 69.1 100.0 3932 Province Nairobi 45,4 37,8 20A 9.7 4.6 0.0 1.0 2.0 7.7 6.6 0.5 0.5 0.0 54.6 100.0 271 Central 56.0 49,7 20.9 10.0 8.7 0.3 1.4 8.4 6.3 5.6 0.6 0.0 0.1 44.0 100.0 610 Coast 20,2 16.6 6.3 2.4 3.6 0.0 0.8 3,4 3,6 2.7 0.1 0.7 0.0 79.8 1(30.0 445 East~n 38.4 30.5 12.8 5.7 6.0 0.0 0.9 5.1 7.9 7.3 0.0 0.4 0.2 61.6 100.0 864 Nya~a 23.8 21.5 4.2 1.3 8.2 0.1 0.6 7.1 2.3 1.6 0.2 0.3 0.2 76.2 100.0 737 Rift Valley 27,8 21.0 4.3 2.0 7.9 0.0 0.7 5,9 6,9 4,7 0,3 0,7 1.3 72.2 100.0 992 Western 25.1 21.7 6.1 2.2 8.5 0.0 0.7 4,2 3,4 1,3 0,2 0,1 1.8 74.9 100.0 710 Education No education 19.5 15.3 2.9 1.1 5.4 0.0 0.3 5.7 4.2 2.6 0.0 0.4 1.2 80.5 100.0 IC62 Prima~y incomplete 27.9 22.7 6.8 2.5 7.3 0.0 0.5 5,4 5.2 3,8 0.1 0,5 0.8 72.1 100.0 1411 l~im sty complete 34.9 29,1 11.6 4.1 6.9 0.0 0.9 5.6 5.8 4.6 0.3 0.5 0.4 65.1 It0.0 1177 Secotadaty+ 51.6 44.9 17.9 10.0 9.4 0.2 1.9 5.5 6.7 6.0 0.6 0.1 0.1 48.4 1~.0 980 Number of living children 0 5.5 1.6 0.8 0.1 0,0 0.0 0.6 0.0 3.9 3.6 0.0 0.3 0.0 94.5 100.0 328 1 25.9 19.5 12.2 2.9 2.0 0.0 1.7 0.5 6.4 5.4 0,3 0.7 0.0 74.1 100.0 589 2 30.6 26.1 16.2 4.0 4.7 0.0 0.7 0.6 4.5 4.3 0.1 0.0 0.1 69.4 100.0 690 3 37.1 29.8 12.6 5.4 8.5 0,0 1.3 1.9 7.3 5.8 1.0 0.5 0.0 62.9 1430.0 668 4+ 37.7 32.5 7.1 4.7 9.8 0.1 0.6 tO.l 5.2 3.5 0.I 0.4 1.2 62.3 ICO.O 2353 Total 32.7 27.3 9.5 4.2 7,2 0.1 0.8 5.5 5.5 4.2 0.2 0.4 0.6 67.3 1~.0 4629 Note: Excludes Norplant There are large differences in levels of contraceptive use by province. Central Province has the highest level of current use (56 percent), followed by Nairobi (45 percent), and Eastern Province (38 percent). The contraceptive prevalence rate for the other provinces is below the national average. The pill is the most commonly used method in Nairobi and Central, Coast, and Eastern Provinces, while injection is the major method in the three western provinces--Rift Valley, Western and Nyanza. In all provinces, modem method use accounts for over 75 percent of all use. The largest differentials in current use of contraception are found for educational groups. Contraceptive use increases steadily with increasing level of education, from 20 percent of married women with no education to 52 percent of those with secondary education. The proportion of users who are using modem methods also increases with increasing level of education. Among women with no education, modem methods account for 78 percent of all use, compared to 87 percent for women with secondary education. Female sterilisation and injection are the favourite methods among women with no education, while injection and the pill are the favourite methods among women with some primary and completed 43 By Figure 4.2 Current Use of Contraception Selected Background Characteristics RESIDENCE Urban Rural PROVINCE / Na i rob i~ Central L. Coasl Eastern L - Nyanza R. Valley Western EDUCATION No Education Prim. Incomp. Prim, Comp. Secondary+ ~J~J J~ J~J~ 'T J~ J J~ J J~ J~43 ~ 45 ,~ 56 - -20 38 24 28 25 120 I 28 135 ] 52 10 20 30 40 50 60 Percent Using Any Method KDHS 1993 primary education. The pill, IUD and injection are favoured by those with secondary or higher education. Current contraceptive use increases with the number of children that a woman has, ranging from 6 percent among women with no children to 38 percent among those with four or more children. Table 4.9 shows data on contraceptive prevalence for the rural areas of 15 selected districts as well as for Mombasa. As mentioned above, these districts were selected because they are large in population size, most have been assigned a District Population Officer, and most were also targetted in the 1989 KDHS. The data should be viewed with caution, since the number of married women interviewed in many of the districts is small and the resulting figures are subject to higher sampling errors. It should also be noted that for the districts that were recently subdivided (i.e., Kakamega, Kericho, Kisii, Machakos, Meru, and South Nyanza), the former boundaries were used for the KDHS sample. Finally, except for Mombasa, the data for the individually targetted districts refer to rural women only. Of the districts studied, contraceptive prevalence is highest in Nyeri District; two thirds of the rural, married women in Nyeri District are using a family planning method (64 percent). This level is comparable to levels found in the most economically advanced developing countries and some developed countries. At the other end of the spectrum are rural South Nyanza (13 percent), Kilifi 04 percent) and Siaya (15 percent) Districts. Figure 4.3 shows trends in contraceptive prevalence rates for modem methods over the last four years for the rural areas of the districts that were targetted in both the 1989 and 1993 KDHSs. Extra caution is necessary in interpreting data on changes over time. Not only were the numbers of women interviewed in both surveys small, but they were also selected from independent samples and thus sampling errors are also independent. For this reason, for most districts, the contraceptive use rates in 1993 may not be significantly 44 Table 4.9 Current use of contraception by district Percent distribution of currently married women by contraceptive method currently used, according to selected districts. Kenya 1993 Any tandem Town/ Any meth- District method od Pill Modern method Traditional method Natural Diw Female Any fatal- Not ph'ngm/ steri- uad. Rhythm/ ly With- cur- Number Injec- foam/ Con- lisa- meth-countingplan- draw- rently of IUD tiota jelly d~n tion nd days ning al Other using Total women Mombasa 3 37.6 32.0 11.2 6.1 4.6 0.0 3.0 6.6 5.6 5.1 0.5 0.0 0.0 62.4 I00.0 94 Murang*a I 47.1 40.2 10.8 9.8 9.3 0.0 3.9 6.4 6.9 6.9 0.0 0.0 0.0 52.9 100.0 150 Nyeri I 64~2 60.3 18.1 12.3 15.2 0.0 2.9 11.8 3.9 2.9 0.5 0.0 0.5 35.8 100.0 97 Kilili ~ 13,8 10.3 3.0 0.9 3,0 0.0 0.0 3.4 3.4 3,0 0.0 0.4 0.0 86.2 100.0 199 Ta im Tavern I 33.7 28.7 11.9 3.1 10.6 0.0 2.5 0.6 5.0 3.7 0.0 1.2 0.0 66.2 I00.0 35 Maehakos 12 38.2 27.2 9.2 3.3 4.8 0.0 I.I 8.8 II.0 10.7 0.0 0.4 0.0 61.8 I00.0 355 Meru I 2 41.2 40.3 17.3 7.4 II.I 0.0 0.8 3.7 0.8 0.8 0.0 0.0 0.0 58.8 I00.0 259 Kisii I 2 40.3 37.9 5,5 1.7 16.2 0.0 1.7 12.8 2,4 1.7 0.0 0.3 0.3 59.7 100.0 274 Siaya ~ 15.2 10.9 2.3 0.4 4.7 0.4 0.0 3.1 4.3 3.5 0.4 0.0 0.4 84.8 100.0 124 South Nyanza I 2 12.8 11.3 3.6 0.5 3.6 0.0 0.0 3.6 1.5 0.5 0.5 0.5 0.0 87.2 100.0 232 Kericbo/23 26.4 23.6 2.4 0.0 13.9 0.0 0.0 6.7 2.9 1.9 0.5 0.5 0.0 73.6 100.0 172 Nakuru t 28.8 23.1 3.8 3.1 8.1 0.0 1.9 6,3 5.6 5.6 0.0 0.0 0.0 71.3 IC~.0 116 Nandi I 23.9 22.2 3.3 1.2 12.3 0.0 1.2 4.1 1.6 0.8 0.0 0.0 0.8 76.1 I00.0 99 Uasin Gishu 1 25.9 21.1 3.0 1.2 7.8 0.0 0.0 9,0 4.8 4.8 0.0 0.0 0.0 74.1 ICX).0 60 Bungoma I 20.8 16.9 3.9 0.8 8.2 0.0 0,4 3.5 3.9 1.2 0.0 0.4 2.4 79.2 100.0 180 Kakamega I 2 28.2 25.8 8.5 2.4 9.3 0.0 1.2 4.4 2.4 1.2 0,4 0.0 0.8 71.8 i00.0 334 ]Rural areas only 2Basnd on the fo~ner (undivided) boundaries of the district (see Chapter 1 for fuller explanation). ~Exclndes a tiny fraction of Norplant users Figure 4.3 Current Use of Modern Contraceptive Methods By District, KDHS 1989 and KDHS 1993 Murang 'a~ Nyer i~ Ma%; L~ Kisii ~ ' Siaya South Nyanza ~ Kericho Uasin Gishu Bungoma ~ Kakamega ~. , I M - - 0 10 20 30 40 50 Percent Using Modern Method 60 70 45 different (at a 95 percent level of confidence) from those in 1989, i.e., they may in fact be due to sampling errors. For example, in Meru District, 36 percent of rural married women interviewed in 1989 said that they were currently using family planning. The 95 percent confidence interval (within which the true prevalence rate could be said with 95 percent confidence to lie) for this figure ranged from 28 to 45 percent. In 1993, 41 percent of rural women in Meru District were using contraception; the 95 percent confidence intervals for this figure range from 34 to 49 (see Appendix Table B.14). Thus, since the intervals overlap, the apparent change from 36 percent in 1989 to 41 percent in 1993 cannot be said to be significant. Despite these cautionary notes, a number of points can be made. Prevalence of use of any method reportedly increased in the rural areas of every district studied except Machakos. The largest absolute increase was in Nyeri District, where the prevalence rate increased from 41 to 64 percent of married women. Even in districts in which the increase in overall prevalence was not large, the increase in use of modem methods was substantial (e.g., Machakos and Kisii Districts) (Figure 4.3). Finally, in terms of overall use, the districts are in roughly the same rank order in both surveys. 4.4 Number of Children at First Use Table 4.10 shows the number of living children at the time of first use of contraception among ever- married women. For the older cohorts (35-49 years), women generally started using contraception at higher parities than the younger women. For example, 21 percent of women aged 20-24 started using contraception after their first child, compared to only 5 percent of women 45-49. This probably reflects the fact that young women are more likely to use contraception to space births, while older women use it to limit births. Table 4.10 Number of children at first use of contraception Percent distribution of ever-married women by number of living children at the time of first use of contraception, according to current age, Kenya 1993 Number of living children at time Never of first use of contraception Number used of Current age contraception 0 1 2 3 4+ Missing Total women 15-19 68.4 20.1 9.9 1.0 0.0 0.0 0.6 100.0 284 20-24 48.3 14.9 20.9 10.5 3.2 1.4 0.8 100.0 1057 25-29 36.7 8.7 20.8 15.9 9.6 7.9 0.4 I00.0 1093 30-34 38.2 5.7 13.9 11.9 7.9 22.3 0.2 100.0 1030 35-39 43.0 2.7 8.6 5.9 7.6 31.6 0.6 100.0 748 40-44 48,5 2.4 5.9 4.0 5.4 32.7 1.1 100.0 627 45-49 56.1 1.9 4.9 4.6 4.6 27,7 0,2 100.0 422 Total 44.9 7.8 14.1 9.5 6.3 16.9 0.5 100.0 5260 4.5 Knowledge of Fertile Period A basic knowledge of reproductive physiology provides a useful background for successful practice of coital-related methods such as withdrawal, condom or barrier methods, but even more so for the calendar rhythm and natural family planning methods. The successful practice of these methods depends on an understanding of when, during the ovulatory cycle, a woman is most likely to conceive. Table 4.11 presents the percent distribution of all respondents and those who have ever used either the rhythm method or natural family planning by reported knowledge of the fertile period in the ovulatory cycle. 46 Table 4.11 Knowledge of fertile period Percent distribution of all women and of women who have ever used periodic abstinence by knowledge of the fertile period during the ovulatory cycle, Kenya 1993 Ever users Perceived All of periodic fertile period women abstinence t During menstrual period 2.3 3.3 Right after period has ended 32.6 34.8 In the middle of the cycle 20.0 29.0 Just before period begins 10.2 14.2 Other 1.4 1.7 Don't know 33.1 16.6 Missing 0.5 0.4 Total 100.0 100.0 Number 7540 1316 llncludes users of either calendar rhythm or natural family planning. One third of the women interviewed said a woman is most likely to conceive just after her period has ended, while the same proportion (33 percent) said they did not know when a woman is likely to conceive and 10peroentidentifiedthe fertile time tobejustbeforethe periodbcgins. Only2Opercentgavethecorrect response: that a woman is most likely to conceive in the middle of her ovulatory cycle. Women who have ever used either the calendar rhythm method or natural family planning are more knowledgable about the ovulatory cycle than women in general. Twenty-nine percent identified the fertile period as occurring in the middle of the cycle, and only 17 percent said they did not know when the fertile period occurred. It should be noted that the precoded response categories for this question are only one way of dividing the cycle into distinct periods. Women may actually have a more accurate understanding of their fertility cycles than is reflected by these categories, especially those who answered "right after her period has ended" (which could be interpreted as a correct response). However, it appears that over one third of all women and one fifth of those who have used periodic abstinence clearly do not understand the ovulatory process, since they either said they did not know when the fertile period is or they thought it occurred "during her period." 4.6 Timing of Sterillsation As mentioned above, more than I in 20 married women of reproductive age in Kenya has undergone a sterilisation procedure. This makes female sterilisation the third most popular contraceptive method after the pill and injection. Table 4.12 shows the distribution of sterilised women by the age at which they had the procedure, according to the number of years prior to the survey the procedure was done. The data show that the largest proportion--over one third---of sterilised women have the operation when they are in their early 30s, while almost the same proportion have the operation in their late 30s. The median age at sterilisation is 33. There does not seem to be any significant change in the recent past in the median age at which women have the operation. 47 Table 4,12 Timing of sterilisation Percent distribution of sterilised women by age at the time of sterilisation, according to the number of years since the operation, Kenya 1993 Age at time of sterillsation Number Years since of Median operation <25 25-29 30-34 35-39 40-44 45-49 Total women age I <2 6.6 19.6 35.9 20.0 9.7 8.2 100.0 86 32.5 2-3 4.9 11.6 32.6 37.6 13.3 0.0 100.0 61 34.3 4-5 1.8 25.4 32.7 28.5 11.7 0.0 100.0 54 32.2 6-7 (6.1) (25.8) (27.9) (32.3) (7.9) (0.0) 100.0 37 (32.7) 8-9 (0.0) (5.8) (48.6) (43.6) (2.0) (0.0) 100.0 25 (34.0) 10+ (5.3) (34.9) (42.9) (16.8) (0.0) (0.0) 100.0 29 Total 4.6 20.2 35.4 28.5 9.0 2.4 100.0 292 33.0 Note: Parentheses indicate that the figure is based on 25-49 women. IMedian age was calculated only for women less than 40 years of age to avoid problems of censoring. 4.7 Source of Supply In the KDHS, all current users of modem methods of family planning were asked to report the source from which they most recently obtained their methods. Since women often do not know exactly into which category the source they use falls (e.g. government hospital, mission health centre, etc.), interviewers were instructed to write the name of the source. Supervisors and field editors were instructed to verify that the name and the type of sources were consistent, asking cluster informants for the names of local family planning sources, if necessary. This practice was designed to improve the reporting of data on sources of family planning, although its actual effect is difficult to determine. The information in Table 4.13 and Figure 4.4 indicates that two thirds of women who use modem methods (68 percent) obtain their methods from public (government) sources, while 25 percent rely on private medical sources and 2 percent use other sources, such as shops or friends. Govemment hospitals are the single most frequently cited source, serving 30 percent of users, followed by government health centres (25 percent) and government dispensaries (14 percent). Private hospitals and clinics and facilities run by missions and churches each serve 8 percent of users. The source a woman uses to obtain contraceptive methods depends on many things, one of which is the type of method she may have chosen. Almost three quarters of pill users obtain their methods from public sources, mostly government health centres and hospitals; six percent obtain supplies from community-based distributors. Injections are also supplied mainly through government sources, with about one quarter supplied through the private sector. About two thirds of female sterilisations and IUD insertions are performed in govemment facilities, with one third occurring in private medical facilities. While condom users also rely heavily on government sources, many obtain supplies from pharmacies, shops and friends or relatives. The importance of various sources of contraceptives has not changed much since the 1989 KDHS, although modifications in the wording of the questionnaire hamper the comparison to some extent. 48 Table 4.13 Source of supply for modern contraceptive methods Percent distribution of current users of modem contraceptive methods by most recent source of supply, according to specific methods, Kenya 1993 Female All In jet- Con- sterili- modern Source of supply Pill 1UD tion dora sation methods Public 72.5 68.9 70.5 36.6 63.9 68.2 Government hospital 24.2 29.6 18.2 13.2 60.6 29.6 Government health centre 29.1 27.5 34.2 12.8 2.9 24.6 Government dispensary 19.1 11.8 18.2 10.6 0.4 13.9 Medical private 16.2 31.1 26.5 25.6 33.2 24.7 Mission/church hospital 4.5 5.6 8.8 2.4 15.1 7.7 FPAK clinic I 3.3 7.3 5.1 3.3 5.1 4.8 Other nongovernment service 0.4 0.6 1.6 0.0 0.9 0.8 Private hospital/clinic 4.5 11.6 8.8 3.3 11.2 7.8 Pharmacy 0.9 0.0 0.0 14.6 0.0 1.0 Privat~ doctor 2.6 6.0 2.2 2.0 0.9 2.6 Other private 2.5 0.0 0.0 21.9 0.0 1.9 Shop 0.0 0.0 0.0 9.2 0.0 0.4 Friends/relatives 2.5 0.0 0.0 12.7 0.0 1.5 Mobile clinic 1.0 0.0 1,8 1.9 0.4 1.0 Community distribution/ health worker 6.3 0.0 0.4 3.2 0.0 2.5 Other 0.6 0.0 0.3 0.0 0.0 0.3 Don't know 0.2 0.0 0.0 10.1 0.3 0.6 Missing 0.6 0.0 0.5 0.7 2.2 0.8 Total 100.0 100.0 100.0 100.0 100.0 100.0 Number 562 213 418 70 292 1560 Note: Total includes 4 users of foam tablets/jelly/diaphragm and 1 Norplant user. IFPAK = Family Planning Association of Kenya Figure 4.4 Percent Distribution of Current Users of Modern Methods by Most Recent Source of Supply Private Medical 25% CBD Workers 3% Other 5% uovernment Facilities 68% KDHS 1993 49 Women interviewed in the KDHS who were currently using a modem contraceptive method were asked how long it takes to travel from their home to the place where they obtain their method. Nonusers were asked if they knew a place where they could obtain a modem method and, if so, how long it would take to travel there. The results are presented in Table 4.14. Table 4.14 Time to source of supply for modem contraceptive methods Percent distribution of women who are currently using a modem contraceptive method, of women who are not using a modern method, and of women who know a method, by time to reach a source for family planning, according to urban-rural residence, Kenya 1993 Women who are currently Women who are not using Women who know a using a modem method a modem method contraceptive method Minutes to source Urban Rural Total Urban Rural Total Urban Rural Total Not applicable I 4.6 2.7 3.2 5.1 2.5 2.9 5.1 2.6 3.1 0-14 27.3 8.0 12.6 21.2 4.6 7.3 23.6 5.5 8.7 15-29 24.3 8.6 12.4 21.7 6.5 9.0 22.8 7.2 10.0 30-59 27.3 21.6 23.0 25.0 17.2 18.5 26.4 18.7 20.1 60 or more 14.2 57.8 47.3 9.4 50.3 43.8 10.9 53.9 46.1 Does not know time 1.1 1.0 1.0 1.8 0.9 1.0 1.7 0.9 1.1 Does not know source 0.0 0.0 0.0 15.1 17.6 17.2 8.7 10.7 10.4 Not stated 1.3 0.3 0.5 0.6 0.4 0.4 0.8 0.4 0.5 Total 100.0 100.0 100.0 100.0 I00.0 100.0 100.0 100.0 100.0 Median time to source 20.7 60.5 45.9 20.6 60.6 60.3 20.7 60.6 60.2 Number of women 376 t 184 1560 963 5017 5980 1301 5907 7208 IResponse was either "friends/relatives," "other," or "don't know." Among the women currently using a modem method, 25 percent are within 30 minutes (one-way travel time) of the place to which they go to get their method, while 23 percent are 30 minutes to one hour from their source. Almost half of users of modem methods are one hour or more from their source of supply. The median travel time for current users to reach their source is 46 minutes. As expected, urban users are generally closer than rural users to their supply sources; half of urban users are within 30 minutes of their supply sources, compared to one sixth of the rural users. Almost three fifths of the latter have to travel for one hour or more to get their supplies. Differences between the travel times reported by users and those reported by nonusers are not significant. This suggests that travel time to services is not a major barrier to use among Kenyan women. 4.8 Future Use To obtain information about potential demand for family planning services, all women who were not using contraception at the time of the survey were asked if they intended to use a method any time in the future. Table 4.15 shows the distribution of currently married women who were not using any contraceptive method at the time of the survey by their intention to use in the future, according to the number of living children. 50 Table 4.15 Future use of contraception Percent distribution of currently married women who are not using a contraceptive method by past experience with contraception and intention to use in the future, according to number of living children, Kenya 1993 Past experience with contraception and future intentions Number of living children I 0 1 2 3 4+ Total Never used contraception Intends to use in next 12 months 11.3 28.1 24.2 26.0 27.1 25.4 Intends to use later 16.9 11.3 8.4 7.7 3.8 7.0 Unsure as to timing 6,0 2.0 2.6 2.1 1.6 2.2 Unsure as to intention 12.3 7.5 7.0 6.1 5.2 6.4 Does not intend to use 31.6 24.8 22.9 21.7 26.4 25.4 Missing 0.4 0.0 0.2 0.0 0.1 0.1 Previously used contraception Intends to use in next 12 months 6.6 12.3 21.7 21.2 20.7 18.8 Intends to use later 5.1 6.3 4.5 4.0 3.0 3.9 Unsure as to timing 0.0 2.0 1.1 0.5 0.5 0.7 Unsure as to intention 0.2 0.9 0.8 1.9 1.6 1.3 Does not intend to use 9.8 4.9 6.3 8.7 9.6 8.3 Missing 0.0 0.0 0.4 0.3 0.4 0.3 Total 100.0 100.0 100.0 100.0 100.0 100.0 All currently married nonusers Intends to use in next 12 months 17.9 40.4 45.9 47.1 47.7 44.2 Intends to use later 22.0 17.6 12.9 11.7 6.8 11.0 Unsure as to timing 6.0 4.0 3.7 2.6 2.1 3,0 Unsure as to intention 12.5 8.3 7.8 8,0 6.8 7.8 Does not intend to use 41.3 29.6 29.2 30.3 36.1 33.7 Missing 0.4 0.0 0.5 0.3 0.5 0.4 Total 100.0 100.0 100.0 100.0 100.0 100.0 Number of women 226 406 512 423 1546 3113 llncludes current pregnancy. Almost three of five (58 percent) married nonusers say they intend to use family planning in the future, 44 percent within the next 12 months. One third of the women said they do not intend to use, while 8 percent are unsure. The proportion intending to use, and especially the timing of intended use, varies with the number of children. For example, the proportion who intend to use within the next 12 months is considerably lower among childless nonusers than among those with children, and the proportion who intend to use later is lower among women with four or more children. Looking at the relationship between previous use of family planning and intention to use in the future, those who intend to use in the future are more likely to have never used than to have used in the past. 51 4.9 Nonuse of Family Planning Reasons for Nonuse Table 4.16 presents the main reasons for not using family planning given by married nonusers who do not intend to use a contraceptive method in the future. One third of these women say they do not intend to use because of infecundity (either "difficult to get pregnant" or "menopausal"), while 22 percent cite the desire to have children as the reason. Other reasons given are side effects and other health concems (16 percent), opposition to family planning, either by the respondent, her husband, or someone else (8 percent), religion (7 percent), and lack of knowledge (5 percent). The younger cohort (under age 30) are more likely to say they do not to intend to use contraception in the future because they want more children, while those age 30 and over are more likely to cite reasons such as being menopausal or infecund. Table 4.16 Reasons for not using contraception Percent distribution of married women who are not using a contraceptive method and who do not intend to use in the future by main reason for not using, according to age, Kenya 1993 Age Reason for not using contraception 15-29 30-49 Total Wants children 38.8 15.1 22.1 Lack of knowledge 8,0 4.1 5.3 Partner opposed 6,2 3.0 3,9 Side effects 12.0 9.2 10.0 Fears sterility 2.3 1.1 1.5 Other health concern 4.2 4.1 4.2 Hard to get methods 0,4 0.1 0.2 Religion 5.3 7,1 6.6 Opposed to family planning 5.5 3.2 3.9 Fatalistic 1.5 1.0 1.2 Other people opposed 0.0 0.3 0.2 Infrequent sex 1.5 1.5 1.5 Difficult to get pregnant 7,0 28.3 22.0 Menopausal/had hysterectomy 0.6 16.8 12.0 Inconvenient 1.5 1,9 1.8 Other 2.5 2,1 2.2 Don't know 2.9 0,7 1.3 Missing 0.0 0,2 0.2 Total 100.0 100,0 100.0 Number 311 738 1049 Preferred Method Nonusers who said that they did intend to use family planning in the future were asked which method they preferred to use. Table 4.17 presents information on method preferences for currently married nonusers who say they intend to use in the future. The most popular method is injection (41 percent), followed by the pill (21 percent), and female sterilisation (13 percent). There is little difference in method preference according to timing of intended use, except that women who intend to use after 12 months and those who are unsure as to when they will use are more likely to be unsure of the method they will use. The method 52 Table 4.17 Preferred method of contraception for future use Percent distribution of currently married women who are not using a contraceptive method but who intend to use in the future by preferred method, according to whether they intend to use in the next 12 months or later, Kenya 1993 Intend to use In next After Unsure Preferred method 12 12 as to of contraception months months timing Total Pill 21.3 21.8 19.7 21.3 IUD 3.5 2.6 2.4 3.3 Injection 41.8 34.0 46.4 40.6 Diaphragm/foam/jelly 0.3 0.1 0.0 0.2 Condom 1.3 1.5 1.7 1.4 Female sterilisation 12.3 13.8 10.2 12.5 Norplant 1.7 2.0 0.0 1.6 Rhythm/counting days 3.2 2.0 2.4 2.9 Natural family planning 0.4 1.2 0.0 0.5 Withdrawal 0.3 0.0 0.0 0.2 Other 2.2 2.0 1.7 2.1 Unsure 11.7 19.0 15.5 13.3 Missing 0.1 0.0 0.0 0.2 Total 100.0 100.0 100.0 100.0 Number 1377 341 92 1812 preference has not changed since 1989, when nonusers who intended to use in the future indicated injection, pill and female sterilisation as their most preferred methods of contraception. 4.10 Exposure to Media Programmes on Family Planning Table 4.18 shows responses to a question on whether women have heard a family planning programme on the radio in the six months prior to the survey and if so, which one. Forty six percent of women have heard such a programme. Half of these women could not remember the name of the program they had heard. Of those who could remember the name, Panga Uzazi was by far the most widely heard programme. Differences by background characteristics are not large, except by education. More educated women are more likely to have heard a programme about family planning than less educated women. 53 Table 4.18 Heard family planning on radio Percentage of all women who have heard specific radio programs about family planning in the six months prior to interview, according to selected background characteristics, Kenya 1993 Name of program Maisha Kuclewa Ya Jifunze Ni Number Background Any Mwmda Panga Jamii Na Maisha Afya Daklari Kuzun- Do#t of charaeteristlc program Pole Uzazi Yako Ucndelea Bora Yako Akushauri gumza Other know women Age 15-19 42.5 1.3 I6.2 2.0 0.7 3.0 1.0 0.8 2.8 2.0 19.9 1754 20-24 50.3 3.0 18.8 2.8 0.9 3.4 1.7 1.1 5.2 1.4 22.9 1638 25-29 52.4 3.2 20.8 2.1 0.3 4.9 1.2 1.5 6.0 1.5 24.3 1221 30-34 48.9 2.0 18.5 2.2 0.8 3.5 1.9 3.0 4.5 1.2 22.7 1088 35-39 39.3 2.1 11.6 2.2 0.8 3.1 1.0 1.0 3.2 1.1 21.3 768 40-44 36.3 0.8 13.1 0.9 0.l 2.5 0.7 0.3 1.4 0.3 20.2 638 45-49 38.8 1.0 13.2 0.9 0.4 2.4 1.1 0.7 1.0 0.5 22.7 434 Res/dence Urban 49.9 2.4 16.9 3.6 0.9 2.9 1.1 2.4 8.3 1.2 22.8 1339 Rural 44.7 2.1 17.0 1.7 0.6 3.5 1.3 1.0 3.0 1.4 21.8 620l Provino~ Nairobi 45.5 1.9 12.5 4.1 0.5 2.7 1.9 2.7 7.1 0.8 21.5 507 Central 51.9 1.7 15.1 1.1 0.4 4.3 1.5 1.1 6.5 1.3 28.0 1094 Coast 32.8 1.7 12.5 1.7 0.9 2.9 0.9 1.1 4.2 0.8 12.9 717 Eastern 50.3 1.6 16.8 0.9 0.3 2.8 0.7 0.9 1.3 2.0 27.6 1406 Nyanza 44.2 3.4 24.7 3.2 1.4 5.3 2.5 1.9 4.3 1.6 14.0 1158 Rift Valley 40.7 2.5 15.8 2.1 0,6 3.1 0.6 1.0 2.4 1.6 19.7 1562 Western 50.6 1.7 17.3 2.7 0.3 2.2 1.6 1.1 4.7 0.6 26.7 1096 Education No education 24.7 0.3 8.3 0,9 0.2 1.4 0.4 0.2 0.8 0.6 14.2 1352 Primary incomplete 37.6 1.4 13.1 1.3 0.5 2.3 0.8 0.7 1.6 0.9 20.8 2179 Primary complete 50.8 2.3 19.6 2,1 0.7 3.6 1.6 1.1 3.7 1.4 24.5 2166 Secondary + 64.5 4.2 24.8 3,9 1.1 5.9 2.2 2.8 9.2 2.3 26.0 1844 Total 45.7 2.1 17.0 2,1 0.6 3.4 1.3 1.3 3.9 1.3 22.0 7540 4.11 Attitudes towards Family Planning Attitudes towards Family Planning for Youth In the KDHS, all women were asked if they thought that information about family planning or family planning services should be available for young people. Table 4.19 presents the percent distribution of women by their responses to these questions, according to background characteristics. Three quarters of women believe that family planning information should be available to young people, however, only half think that youth should be provided family planning services. Differentials by age and urban-rural residence are not large. It appears that women in Coast Province are more conservative than their counterparts in other provinces about making family planning information and services available to young people. Similarly, women with no formal education are least likely to approve of family planning information or supplies being available for Kenyan youth. 54 Table 4.19 Attitudes about family planning for youth Percent distribution of all women by whether they believe that family planning information and services should be available for young people by selected background characteristics, Kenya 1993 Believe information should be available Believe services should be available Number Background Don't Other/ Don't Other/ of characteristic Yes No know missing Total Yes No know missing Total women Age 15-19 71.0 21.4 7.2 0.4 100.0 47.8 42.6 9.1 0.5 100.0 1754 20-24 75.6 21.0 2.9 0.5 100.0 59.0 36.5 4.3 0.2 100.0 1638 25-29 76.3 19.8 3.3 0.6 100.0 56.2 38.5 4.4 0.9 100.0 1221 30-34 72.1 23.6 4.0 0.3 100.0 50.5 43.1 5.4 1.0 100.0 1088 35-39 70.7 23.7 5.3 0.3 100.0 47.5 45.5 6.7 0.3 100.0 768 40-44 72.5 21.3 5.5 0.8 100.0 51.5 41.5 6.1 0.9 100.0 638 45-49 66.8 26.0 7.0 0.2 100.0 43.7 44.7 10.9 0.6 100.0 434 Residence Urban 75.1 20.0 4.4 0.6 100.0 55.3 38.1 5.7 0.9 100.0 1339 Rural 72.4 22.3 4.9 0.4 100.0 51.3 41.7 6.5 0.5 100.0 6201 Province Nairobi 78.7 14.7 6.3 0.3 100.0 61.3 28.9 9.3 0.5 100.0 507 Central 81.4 15.3 2.6 0.8 100.0 59.3 36.4 3.8 0.5 100.0 1094 Coast 58.1 32.5 8.7 0.6 100.0 38.3 52.2 8.8 0.7 100.0 717 Eastern 78.6 17.3 3.9 0.2 100.0 62.9 32.8 4.4 0.0 100.0 1406 Nyanza 68.5 26.3 4.8 0.4 100.0 53.9 40.0 5.3 0.g 100.0 1158 Rift Valley 69.6 26.0 3.9 0.5 100.0 45.3 47.3 7.0 0.4 100.0 1562 Western 73.2 19.9 6.3 0.5 100.0 43.2 46.5 8.7 1.5 100.0 1096 Education No education 62.5 27.1 10.1 0.4 100.0 45.1 42.2 12.2 0.6 100.0 1352 Primary incomplete 70.0 23.9 5.7 0.4 100.0 50.8 41.4 7.3 0.5 100.0 2179 Primary complete 74.9 20.8 3.6 0.6 100.0 55.8 38.3 5.4 0.6 100.0 2166 Secondary + 81.5 16.8 1.4 0.3 100.0 54.2 42.9 2.2 0.8 100.0 1844 Total 72.9 21.9 4,8 0.5 100.0 52.0 41.0 6.4 0.6 100.0 7540 Approval of Family Planning An indication of the acceptability of family planning is the extent to which couples discuss the topic with each other. Table 4.20 indicates that, of married women who know a contraceptive method, roughly one third had never discussed family planning with their husbands in the year prior to the survey, one third bad discussed the topic only once or twice with their husbands, and one third had discussed family planning more often than once or twice. The tendency to discuss family planning with their husbands is greater among women in their 20s and 30s than among older or younger women. In order to obtain more direct information about the acceptability of family planning, respondents were asked if they approved or disapproved of couples using a method to avoid pregnancy. Although all women were asked this question, the data presented in Table 4.21 are confined to currently married women and exclude those women who had never heard of a contraceptive method. Currently married women were also asked if they thought that their husbands approved of the use of family planning. It should be noted that wives' opinions of their husbands' attitudes may be incorrect, either because they have misconstrued their husbands' true attitudes, or because of a tendency to report their husbands' attitudes as similar to their own. 55 Table 4,20 Discussion of family planning by couples Percent distribution of currently married women who know a contraceptive method by the number of times family planning was disctmsed with husband in the year preceding the survey, according to current age, Kenya 1993 Number of times family planning discussed Number Once or More of Age Never twice often Missing Total women 15-19 53.2 26,3 20.1 0.4 100.0 256 20-24 29.2 34.5 36.0 0,3 100.0 916 25 -29 25.1 31.6 43.1 0.1 100.0 984 30-34 30.4 29.8 39.6 0.0 100.0 903 35-39 35.1 29.0 35.5 0.0 100.0 624 40-44 41.2 32.0 26.4 0.0 100.0 496 45-49 55.1 16.4 28.1 0.1 I00.0 322 Total 33.9 30.1 35.7 0.1 100.0 4500 Table 4.21 Attitudes of couples toward famil~ planning Percent distribution of currently married women who know a contraceptive method by approval of family planning and by their perceplion of their husband's approval, according to selected background characteristics, Kenya 1993 Respondent Respondent approves disapproves Husband's Husband's Background Both Husband attitude is Husband attitude Both characteristic approve disapproves unknown approves unknown disapprove Missing Total Number Age 15-19 48.2 12.8 25.5 1.0 5.0 5.3 2,2 100.0 256 20-24 63.9 12.6 14.1 0.5 2,0 4,4 2.5 100.0 916 25-29 68.6 13.6 9.8 1.0 1.4 3.5 1,9 100.0 984 30-34 65.2 13.9 10.7 1.2 3.1 4.7 1.2 100.0 903 35-39 62.2 12.8 14.6 2.1 3.3 3.3 1.7 100.0 624 40-44 59.7 12.4 14,5 1,5 5.2 4.5 2.1 100.0 496 45-49 54.1 i2.0 15.7 1.0 5.6 9.9 1.7 100.0 322 Province Urban 74.0 8.1 9.7 0.9 2.1 3.1 2.1 100.0 686 Rural 60.9 14.0 14.0 1.2 3.2 4.8 1.8 100.0 3814 Region Nairobi 73.7 8.9 11.6 0.5 1,6 3.7 0.0 100,0 262 Central 72.9 10.2 6,5 1.6 2.2 3.7 2.9 100.0 609 Coast 48.8 11.1 18.7 1.3 7,7 10.8 1.7 100.0 421 Eastern 71.2 10.6 8.4 0.9 2.3 4.3 2.4 100.0 855 Nyanza 53.1 15.8 19.7 1.7 4.6 3.5 1.6 100.0 731 Rift Valley 62.5 12,9 13.0 1.5 2.3 5.1 2.7 100.0 922 Western 59.4 18.8 16.7 0.2 1.8 2.6 0.4 100.0 700 Education No education 44.1 16.6 20.6 1.6 6.0 9.0 2.2 100.0 971 Primary incomplete 57.9 14.3 16.9 1.4 3.4 4.4 1.8 100.0 1383 Primary complete 69.8 12,3 10.3 0.8 1.7 3.5 1.6 100,0 1168 Secondary+ 80.4 8.7 4.9 0,8 1.3 1.7 2.1 100.0 978 Total 62.9 13.1 13.4 1.2 3.1 4.6 1.9 100.0 4500 56 Overall, 89 percent ofm arried women who know a contraceptive method approve of family planning. Almost two thirds of the women say that their husbands also approve of family planning; 13 percent of women say that they approve of family planning and their husbands do not. Approval of family planning by married women does not vary much by age of the women except that women age 45-49 are less likely to approve than the younger cohorts. Married women who live in Coast Province and those who have no formal education are less likely than other women to approve of the use of family planning. They are also more likely than other married women not to know their husbands' attitudes towards family planning--another indication of the extent to which family planning is discussed among these couples. The proportion of wives who say they do not know their husbands' attitude towards family planning use is also high among women age 15-19. 4.12 Source of Family Planning Information In order to better assess where women learn about family planning, all women were asked how they first heard about family planning and from which place or person they leamed the most about family planning. Tables 4.22 and 4.23 show the percent distributions of women by these two sources, according to urban-rural residence and province. Friends and relatives are the most commonly reported first source of family planning information (31 percent), followed by health workers and clinics (29 percent). The radio was cited as a first source by one fifth of the women. Although friends and relatives are the most important first source of contraceptive information, more women said they got the most information from health workers and clinics (43 percent). Radio was cited as conveying the most information by 13 percent of women. These three sources were mentioned most often by women, regardless of their place of residence. Table 4.22 First source of family planning information Percent distribution of all women by source from which they first heard about family planning, according to urban-rural residence and province, Kenya 1993 Residence Province Rift Characteristic Urban Rural Nalrobi Central Coast Eastern Nyanza Valley Western Total Radio 26.3 19.0 29,4 12.4 18.3 10.2 26.0 24.7 25.8 20.3 Television 1.0 0.2 0.8 0,1 0.9 0.2 0.2 0.3 0.4 0.3 Newspapers 1.2 0,6 1.1 1.3 0.9 0.3 0.6 0.7 0.2 0.7 Posters 0.3 0.4 0.3 0.5 0.6 0.7 0.3 0.4 0.1 0.4 Husband 0.7 0.4 0.0 0.2 0.2 0.5 0.4 1.1 0.2 0.5 Friends/relatives 28,7 31.8 24.8 35.3 36,0 32.2 31.3 29.5 28,3 31.3 Health worker/clinic 23.3 29.8 23.2 33.3 25.6 34.9 26.6 23,7 29.9 28.7 Conununity distribution/ worker 2.2 3,2 2.7 1.5 4.3 3.1 2.9 4.0 2.4 3.0 Other 1.6 2.0 1.1 1.1 3.2 3,1 1.9 1.4 1.6 1.9 School/teacher 10.7 7.7 10.9 11.2 5.3 9.6 5.2 7,6 8.5 8.3 Church 0,2 0.4 0.3 0.4 0.1 0.7 0.6 0.1 0,1 0.3 Can't remember/ Don't know 3.5 4,4 5.2 2.8 4.2 4.4 3.8 6.5 2,5 4.3 Missing 0.3 0,0 0.3 0,0 0.4 0.0 0.0 0.2 0.0 0.I Total 100.0 100.0 I00.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Number 1339 6201 507 1094 717 1406 1158 1562 1096 7540 57 Table 4.23 Main source of family planning information Percent distribution of all women by source from which they got the most information about family planning, according to urban-rurnl residence and province. Kenya 1993 Residence Province Rift Chm'acleristic Urban Rural Nalxobi Central Coast Eastern Nyanza Valley Western Total Radio 16.2 12.7 14.7 8.2 13.7 5.9 16.7 19.7 14.8 13.4 Television 1.2 0.1 1.1 0.0 0.5 0.2 0.2 0.1 0.5 0.3 Newspapers 1.2 0.5 1.9 0.9 1.2 0.6 0.2 0.4 0.4 0.6 Posters 0.8 0.4 0.5 0.2 0.8 0.7 0,4 0.5 0.I 0.4 Husband 0.4 0.5 0,5 0.l 0.6 0.5 0,4 0.6 0.3 0.5 Friendsktelativas 21.0 24.2 21.5 19.0 27.6 24.8 25,5 22.8 24.3 23.6 Health worker/clinic 41.9 43.3 41.1 55.4 36.0 47.2 40,2 36.2 43.3 43.0 Community distribution/ worker 3.4 4.0 2.5 1.9 5.5 3.6 3,6 5.0 4.5 3.9 Other 1.8 2.4 2.2 2.5 3.4 3.5 1,7 1.7 1.6 2.3 School/teacher 8.3 6.7 9.0 8.0 5.9 7.8 5,8 6.4 6.7 7.0 Church 0.3 0.4 0.3 0.6 0.0 0.6 0,7 0.3 0.1 0.4 Can't remember/ Don't know 3.1 4.9 4.4 3.2 4.3 4.8 4,6 6.2 3.5 4.6 Missing 0.3 0.1 0.3 0.0 0.4 0.0 0,1 0.2 0.0 0.1 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Number 1339 6201 507 1094 717 1406 1158 1562 1096 7540 4.13 Community-Based Distribution As mentioned in Chapter 1, the Kenyan family planning programme has put special emphasis on establishing a network of community-based distributors (CBDs) as one arm of the supply system. CBD workers have several functions. They provide family planning information, motivation and, in most cases, supplies to the women and men living in their catchment area. CBDs operate under the auspices of a number of organisations, both govemment and nongovemmental. They are usually provided with training, supplies and, in some cases, a small stipend. It is estimated that there are over 10,000 CBD workers in 37 of the (then) 41 districts (Lewis et al., 1992). With the aim of evaluating the coverage of the CBD programme, the KDHS interviewers briefly described to respondents what a CBD worker does and then asked if there were such a person in the respondent's area. If the answer was affirmative, the interviewer asked how often the CBD worker visited the respondent's home in the six months prior to the interview. The data are presented in Table 4.24. Only one fifth of women said that there was a CBD worker in their area and only I in 10 said she had been visited by a CBD worker in the past six months. There are several possible explanations for these low figures. Women may know CBD workers but be unaware of their role in the community for some reason. Presumably, CBD workers target women who are at higher risk of a dangerous or unwanted pregnancy and they may avoid talking to women who are not married, menopausal, sterilised, using another method, or openly opposed to family planning. Older women and those living in rural areas are more likely than other women to know of a CBD worker in their area and to have been visited in the six months before the survey. CBD workers are either more prevalent and/or better known to women in Western, Coast and Nyanza Provinces and are relatively unknown to women in Nairobi. 58 Table 4.24 CommunitT-based distribution Percent distribution of all women by presence of community-based family planning worker (CBD) in their area and if so, number of visits in the past six months according to selected background characteristics, Kenya 1993 Presence of CBD Percent visited in past 6 months Number Not Twice Background Don't of at or characteristic Yes No know Missing Total women all Once more Missing Total Age 15-19 13,5 71.9 14.4 0.1 100,0 1754 6.2 3.1 3.8 0.5 13.5 20-24 18.3 74.8 6.8 0.1 100,0 1638 8.3 4.3 5.7 0.1 18.3 25-29 22.1 75.1 2.8 0.0 100.0 1221 11.2 3.4 7.1 0,3 22.1 30-34 26.3 70.8 2.8 0.0 |00.0 1088 12.5 5.9 7.6 0.4 26.3 35-39 25.9 71,0 3.1 0.0 100.0 768 11,8 6.9 7.1 0.1 25.9 40-44 27.1 69.8 3,1 0.0 I00.0 638 12.9 4.1 9.3 0.8 27.1 45-49 22.1 74.1 3.8 0.0 100.0 434 12.1 2.6 6.9 0,5 22.1 Residence Urban 14.9 77.0 8.0 0.2 100.0 1339 6.3 2.9 5.1 0.6 14.9 Rural 22.0 71.8 6.2 0.0 100.0 6201 10.6 4.5 6.5 0.3 22,0 Province Nairobi 9.5 83.1 7.4 0.0 100.0 507 1.4 2.7 4.9 0,5 9.5 Central 20.6 71.6 7.8 0.0 100.0 1094 14.1 2.4 3.4 0.7 20.6 Coast 29.3 62.8 7.6 0.3 100.0 717 12.8 4.9 11.0 0.5 29.3 • Eastern 13.3 81.8 4.9 0.0 100,0 1406 4.8 3.7 4.5 0.3 13.3 Nyanza 24.0 71.1 4.9 0.0 1{30.0 1158 9.0 5.0 9.8 0.1 24.0 Rift Valley 15.8 79.9 4.1 0.1 100,0 1562 7.2 3.8 4.4 0.4 15.8 Western 33.3 55.6 11.1 0.0 100.0 1096 18.6 6.9 7.8 0.1 33.3 Education No education 18.1 77.1 4.7 0.0 100.0 1352 8.5 3.7 5.7 0.3 18.1 Primary incomplete 21.7 71.5 6.8 0.1 100.0 2179 10.5 4.9 5.9 0.3 21.7 Primary complete 21.7 70.8 7.4 0.1 100.0 2166 9.5 4.4 7.4 0.5 21,7 Secondary + 20.3 73.4 6.3 0.0 100.0 1844 10.5 3.7 5,8 0.3 20.3 Total 20.7 72.8 6.5 0.1 100.0 7540 9.8 4.2 6.3 0.4 20.7 59 CHAPTER 5 OTHER PROXIMATE DETERMINANTS OF FERTILITY 5.1 Introduction Addressed in this chapter are the principal factors, other than contraception, that affect a woman's risk of becoming pregnant: nuptiality and sexual intercourse, postpartum amenorrhoea and abstinence from sexual relations, and termination of exposure to pregnancy. While it is not by any means exact, marriage is an indicator of exposure of women to the risk of pregnancy, and is therefore important for the understanding of fertility. Populations in which age at marriage is low also tend to experience early childbearing and high fertility; hence, the motivation to examine trends in age at marriage. This chapter also includes more direct measures of the beginning of exposure to pregnancy and the level of exposure: age at first sexual intercourse and the frequency of intercourse. Measures of other proximate determinants of fertility are the durations of postpartum amenorrhoea and postpartum abstinence. 5.2 Marital Status Data on the marital status of respondents at the time of the survey are shown in Table 5.1. As in other reports on demographic surveys and censuses in Kenya, this report defines marriage to include informal unions. Although shown separately in Table 5.1, the categories of "married" and "living together" are combined in subsequent tables and are referred to as "currently married." Respondents who are currently married, widowed, divorced or no longer living together (separated) are referred to as "ever married." Table 5.1 shows that 30 percent of women of childbearing age in Kenya have never married, 61 percent are currently married, and 8 percent are either widowed, divorced or no longer living with a partner. The proportion who have never married falls sharply from 84 percent of women age 15-19 to 3 percent in the age group 45-49. The universality of marriage is evident from the fact that, among women age 35 and over, 97 percent are, or have been, married. Table 5.1 Current marital status Percent distribution of women by current marital status, according to age, Kenya 1993 Marital status Number Never Living Not living of Age married Married together Widowed Divorced together Total women 15-19 83.8 13.6 1.2 0.2 0.4 0.7 100.0 1754 20-24 35.5 54.1 3.1 0.7 2.2 4.4 100.0 1638 25-29 10.4 78.7 3.5 1.3 3.5 2.6 100.0 1221 30-34 5.4 79.5 4.8 2.8 3.9 3.6 100.0 1088 35-39 2.6 81.0 2.8 6.6 3.8 3.1 100.0 768 40-44 1.7 76.5 4.7 10.1 4.9 2.1 100.0 638 45-49 2.8 76.8 3.5 12.3 3.0 1.7 100.0 434 Total 30.2 58.3 3.1 3.1 2.7 2.6 100.0 7540 61 While the proportions widowed, divorced and separated are almost identical at 3 percent each, they show different patterns by age group. As expected, the proportion widowed increases with age of women; however, the proportions divorced and separated are more even across age groups. Table 5.2 shows the trend in the proportion of women reported as never married by age group from past censuses and surveys in Kenya. It is evident that the proportion of women under 30 years of age who have never married has been increasing. The 1993 KDHS data show an increase for the youngest age groups 15-19 and 20-24 since the 1989 KDHS. In the age group 15-19, the proportion of women who have never married rose from 80 percent in 1989 to 84 percent in 1993, while the proportion in the age group 20-24 rose from 32 percent in 1989 to 36 percent in 1993. Above age 25, there are no substantial changes in the proportions never married. Increased involvement of women in higher education may explain the increasing proportions of single women age 15-24. Table 5.2 Trends in proportion never married Percentage of women who have never married at the tinae of various surveys and censuses by age group, Kenya 1969 1977/78 1979 1984 1989 1993 Age census KFS cereus KCPS KDHS KDHS 15-19 64 72 71 74 80 84 20-24 18 21 25 24 32 36 25-29 6 4 9 6 t 1 10 30-34 4 1 5 4 5 5 35 -39 3 1 3 2 3 3 40-44 3 l 3 1 2 2 45 -49 3 0 2 1 2 3 Sources: 1969--CBS, 1970, p. 79; KFS--CBS, 1980, p. 71; 1979--CBS, 1981b, p. 24B; 1984--CBS, 1984, p. 40; 1989--NCPD, 1989, p. 9. 5.3 Po lygyny In the 1993 KDHS, the extent of polygyny in Kenya was measured by asking married women whether their husbands had other wives and, if so, how many. Table 5.3 shows the proportion of currently married women who are in polygynous unions according to age group and selected background characteristics. Overall, 20 percent of currently married women in Kenya are in polygynous unions. The practice is more common among older than younger women, with almost one-third of women in their 40s reporting that their husbands have other wives. The survey found that polygyny is more prevalent in the rural than urban areas. Overall, Coast Province has the highest proportion of polygnous unions (29 percent), followed by Western and Nyanza Provinces (26 percent each). Central Province has by far the lowest proportion of such unions (8 percent). The differences become even more pronounced by age group, although the number of cases is small and so the data should be viewed with caution. There is an inverse relationship between education and polygyny. The proportion of married women in polygynous unions decreases from 33 percent for women with no education to 11 percent for women with at least some secondary education. 62 Table 5.3 Polygyny Percentage of currently married women in a polygynous union, by age and selected background characteristics, Kenya 1993 Background Age of woman All characteristic 15-19 20-24 25-29 30-34 35-39 40-44 45-49 ages Residence Urban (11.0) I1.8 14.9 12.7 9.4 (28.0) (14.9) 13.7 Rural 18.1 12.7 15.3 20.6 25.6 29.9 30.8 20.5 Province Nairobi * 8.2 7.7 (10.3) * * * 11.2 Central * 4.2 3.7 9.3 0.8 13.8 18.0 7.5 Coast 22.4 12.4 25.0 32.0 47.9 (45.6) (34.6) 29.0 Eastern * 5.2 12.5 14.3 15.3 25.9 23.3 14.5 Nyanza 29.6 19.6 22.4 28.2 27.3 38.3 24.4 26.1 Rift Valley 10.0 14.1 16.0 16.3 26.6 27.8 32.6 19.3 Western (17.9) 19,6 18.3 25.0 30.8 38.5 (46.5) 26.4 Education No education * 23.7 31.1 33.2 39.4 33.3 33.5 33.3 Primary incomplete 20.9 12.3 18.2 20.4 21.2 29.7 29.8 20.2 Primary complete 15.4 11.1 8.6 15.6 11.7 23.8 (16.1) 13.0 Secondary+ (12.3) 11.3 11.5 10.0 8.0 (25.0) * 11.4 Total 17.2 12.5 15.2 19.4 23.7 29.7 29.4 19.5 Note: Rates shown in parentheses are based on 25-49 women, whereas an asterisk means the rate is based on fewer than 25 women and has been suppressed. Comparison of the 1993 KDHS data with those from previous surveys indicates that the practise of polygyny has been declining over time. The proportion of married women who were in polygynous unions was 30 percent in 1977/78, 25 percent in 1984, 23 percent in 1989, and 20 percent in 1993 (CBS, 1980, p.80; CBS, 1984, p.43; NCPD, 1989, p.12). The general decline is apparent at all age groups of women--with some fluctuations---implying that the downward trend in the overall level of polygyny is not merely a result of changes in the age distribution of women. Previous data also show similar patterns of polygyny by background characteristics as the 1993 KDHS. Table 5.4 shows the distribution of currently married women by number of co-wives. A majority (80 percent) of currently married women are in monogamous unions, 14 percent are in polygynous unions with one co-wife, and a small proportion (5 percent) are in polygynous marriages with two or more co-wives. Currently married women above 35 years are not only more likely to be in a polygynous union than younger women, but they are also more likely to have two or more co-wives. This is also true for women in rural areas and those with no education. Women in Coast, Nyanza, and Western Provinces are more likely than women in the other provinces to have two or more co-wives. 63 Table 5.4 Number of co-wives Percent distribution of currently married women by number of co-wives, according to selected background chaxacterlstics, Kenya 1993 Number of co-wives Number Background of characteristic 0 1 2+ Missing Total women Age 15-19 82.8 10.9 5.0 1.3 100.0 261 20-24 87.5 8.6 3.3 0.7 100.0 937 25-29 84.8 10.7 3.7 0.8 100.0 1003 30-34 80.6 14.3 4.8 0.3 100.0 918 35-39 76.3 15.1 8.0 0.6 100.0 644 40-44 70.3 21.3 7.5 0.9 100.0 519 45-49 70.6 21.8 7.6 0.0 100.0 348 Residence Urban 86.3 10.1 3.3 0.4 100.0 697 Rural 79.5 14.3 5.6 0.7 100.0 3932 Province Nairobi 88.8 9.7 1.0 0.5 100.0 271 Central 92.5 5.8 0.8 0.9 100.0 610 Coast 71.0 18.3 10.6 0.0 100.0 445 Eastern 85.5 l 1.3 1.8 1.4 100.0 864 Nyanza 73.9 17.6 8.1 0.4 100.0 737 Rift Valley 80.7 15.5 3.8 0.0 100.0 992 Western 73.6 15.1 10.3 1.0 100.0 710 Education No education 66.7 22.6 10.2 0.5 100.0 1062 Primary incomplete 79.8 15.3 4.3 0.5 100.0 1411 Primary complete 87.0 8.6 3.7 0.6 100.0 1177 Secondary+ 88.6 7.7 2.9 0.8 100.0 980 Total 80.5 13.6 5.2 0.6 100.0 4629 5.4 Age at First Marriage Early marriage often leads to early childbearing and higher fertility for society as a whole. As shown in Table 5.5, most (58 percent) Kenyan women marry before they reach age 20. The median age at first marriage is 18.8 years. The median age at marriage has increased over time from 18.1 among women age 45-49 to 19.5 for those age 25-29 years. The proportion of women married by age 15 has declined from 16 percent among those aged 45-49 years to 1 percent among the 15 to 19 year-olds. Age at marriage has risen slightly since 1989. Table 5.6 presents the median age at first marriage by selected background characteristics for women age 25-49 years. The table shows large differentials in marriage behaviour patterns. It can be seen that in each age group, urban women marry later than their rural counterparts, with an overall difference of 2 years in the median age at marriage. Women in Coast and Nyanza Provinces have relatively early median ages at marriage (17.4), while those in Nairobi and Central Province marry the latest (21.0 and 20.1, respectively). These findings correspond with those of the 1989 KDHS. 64 Table 5.5 Age at first marriage Percentage of women who were first married by exact age 15, 18, 20, 22, and 25, and median age at first marriage, according to current age, Kenya 1993 Percentage of women who were first married by exact age: Current age 15 18 20 22 Percentage Median who had Number age at never of first 25 married women marriage 15-19 1.3 NA NA NA NA 83.8 1754 a 20-24 5.4 28.1 46.1 NA NA 35.5 1638 a 25-29 9.3 33.6 55.7 70.6 84.1 10.4 1221 19.5 30-34 11.9 41.1 62.7 77.3 86.7 5.4 1088 18.9 35-39 15.2 47.9 66.3 82.6 92.4 2.6 768 I8.2 40-44 16.9 46.9 67.2 82.2 91.4 1.7 638 18.3 45-49 15.7 47.8 68.9 80.4 89.0 2.8 434 18.1 20-49 10.8 37.9 58.0 72.4 81.3 14.0 5786 19.2 25-49 12.9 41.7 62.7 77.4 88.0 5.5 4148 18.8 NA = Not applicable aOmitted because less than 50 percent of the women in the age group x to x+4 were first married by age x. Table 5.6 Median age at first marriage Median age at first marriage among women age 25-49 years, by current age and selected background characteristics, Kenya 1993 Current age Women Background age characteristic 25-29 30-34 35-39 40-44 45-49 25-49 Residence Urban 21.5 19.9 19.9 20.4 (19.0) 20.6 Rural 19.1 18.7 18.0 18.0 18.1 18.5 Province Nairobi 23.0 (21.2) (18.9) * * 21.0 Central 20.5 20.9 19.7 19.2 18.9 20.1 Coast 18.3 17.3 15.9 17.0 18.1 17.4 Eastern 20.8 19.6 18.8 18.4 18.0 19.3 Nyanza 17.5 17.3 17.2 17.0 17.8 17.4 Rift Valley 19.1 18.1 17.9 18.4 18.6 18.6 Western 19.2 18.5 18.3 17.9 17.3 18.4 Education No education 16.3 16.8 16.9 17.0 17.7 17.0 Primary incomplete 17.7 17.8 17.7 17.8 17.9 17.8 Primary complete 19.5 19.3 18.9 20.1 18.9 19.4 Secondary+ 22.0 21.1 21.1 22.1 * 21.5 Total 19.5 18.9 18.2 18.3 18.1 18.8 Note: The medians for cohorts 15-19 and 20-24 could not be determined because some women may still get married before reaching age 20 and 25, respectively. Rates shown in parentheses are based on 25-49 women, whereas an asterisk means the rate is based on fewer than 25 women and has been suppressed. 65 A woman's education is highly correlated with her age at marriage. The median age at marriage increases with the level of education for all age groups of women in Kenya. For instance, the median age at first marriage for women age 25-49 increases steadily from 17.0 among women with no education to 21.5 for women with some secondary education. 5.5 Age at First Sexual Intercourse Age at marriage is often used as a proxy for the beginning of a woman's exposure to the risk of pregnancy. Although related, the two events do not always occur at the same time. Some women engage in sexual relations before marriage, especially if marriage is postponed. Others engage in sexual intercourse only when they marry. The 1993 KDHS gathered information on the age at which women first had sexual intercourse. The percentage of women who have ever had sexual intercourse by specific ages is given in Table 5.7 (Note that this information in Table 5.7 parallels the information on age at first marriage in Table 5.5). The proportion of women who have never had intercourse is of interest (Column 6 of Table 5.7). Slightly over half of women age 15 - 19 have not yet had sexual intercourse, meaning that just under half have been sexually active. Among women age 20-24,theproportionncverhavinghadsexdropsto 10percent and by age 25-29, almost all women have been sexually active. Sexual activity often preceeds marriage. This is evidenced by the fact that 64 percent of women age 25-49 report that they had had sexual intercourse by age 18, whereas only 42 percent had married by that age (see Table 5.5). Similarly, 83 percent of women report having had sex by the time they reached age 20, but only 63 percent reported having married by that age. The median age at first sexual intercourse is 16.6 years, 2 years earlier than the median age at first marriage of 18.8. Table 5,7 Age at first sexual intercourse Percentage of women who had first sexual intercourse by exact age 15, 18, 21), 22, and 25, and median age at In'st intercourse, according to current age, Kenya 1993 Percentage of women who had first intercourse by exact age: Current age 15 18 20 22 Percentage Median who Number age at never had of first 25 intercourse women intercourse 15-19 14.9 NA NA NA NA 53.9 1754 a 20-24 17.7 57,7 79,4 NA NA 10.3 1638 17.3 25-29 17,9 59.0 81.4 92.2 97.3 1.1 1221 17.0 30-34 23,2 67.1 84.2 92.5 95.8 0.6 1088 16.6 35-39 25,2 67.8 84.9 95.5 97.4 0.2 768 16.3 40-44 27.6 67.3 82,3 92,9 96.6 0.2 638 16.3 45-49 22.0 62.9 79.7 89,2 95.0 0.0 434 16.8 20-49 21.2 62.5 81.8 91.2 94.4 3.3 5786 16.8 25-49 22.6 64.4 82.7 92.7 96.6 0.5 4148 16.6 NA = Not applicable aOmitted because less than 50 percent of the women in the age group x to x+4 had had intercourse by age x. 66 Age at first intercourse has apparently been increasing slightly over time. For instance, women age 40-44 reported a median age at first intercourse of 16.3, while those age 20-24 reported a median age at first intercourse of 17.3 years. Comparison with similar data from the 1989 KDHS also indicates that there has been a small increase in age at first intercourse (data not shown). Differentials in median age at first sexual intercourse arc given in Table 5.8 by selected background characteristics. Rural women are more likely to engage in sexual intercourse earlier than women in urban areas. Women in Nyanza Province consistently have the lowest median age at first intercourse in each age group of women, while women in Nalrobi and Central Province generally delay initiating sexual relations. The median age at first intercourse for those with secondary and higher education is 3 years above that of women with no education; however, this difference is less than that of marriage--the median age at marriage for women with some secondary schooling is more than four years greater than those with no education. Table 5.8 Median age at first intercourse Median age at first sexual intercourse among women age 20-49 years, by current age and selected background characteristics, Kenya 1993 Current age Women Background age characteristic 20-24 25-29 30-34 35-39 40-44 45-49 20-49 Residence Urban 17.8 18.3 17.5 17.0 18.0 (18.6) 17.9 Rural 17.0 16.8 16.5 16.2 16.1 16.7 16.6 Province Nairobi 17.9 18.1 (17.7) (15.8) * * 17.9 Central 18.2 18.1 17.8 17.9 16.7 17.1 17.8 Coast 18.3 17.8 16.4 16.1 17.2 16.5 17.3 Eastern 17.3 16.9 16.5 16.4 15.9 16.4 16.7 Nyanza 15.7 15.7 15.3 15.2 15.0 16.0 15.5 Rift Valley 17.4 17.1 16.8 16.6 16.7 17.8 17.0 Western 16.7 17.2 16.7 16.3 16.3 16.3 16.6 Education No education 16.1 15.6 15.6 15.5 15.6 16.5 15.7 Primary incomplete 15.9 15.8 15.8 15.9 15.8 16.6 15.9 Primary complete 17.2 16.9 17.0 17.1 18.0 18.0 17.1 Secondary+ 18.8 18.7 18.5 18.8 20.3 * 18.8 Total 17.3 17.0 16.6 16.3 16.3 16.8 16.8 Note: The median for cohort 15-19 could not be determined because some women may still have intercourse before reaching age 20. Rates shown in parentheses are based on 25 -49 women, whereas an asterisk means the rate is based on fewer than 25 women and has been suppressed. 5.6 Recent Sexual Activity In the absence of contraception, the probability of becoming pregnant is related to the frequency of intercourse. Thus, infomaation on sexual activity can be used to refine measures of exposure to pregnancy. Although KDHS data indicate that all but a tiny fraction of women are sexually active at some time in their lives, not all women who have ever had intercourse are currently sexually active. Table 5.9 presents data on levels of sexual activity by background characteristics; the distributions are shown for women who have ever had intercourse. 67 Table 5.9 Recent sexual activity Percent dis~ibution of women who have ever had sexual intercourse by sexual activity in the four weeks preceding the survey and the duration of abstinence by whether or not postpartum, according to selected background characteristics, Kenya 1993 Not sexually active in last 4 weeks Sexually Abstaining Abstaining active (postpartum) (not postpartum) Number Background in last of characteristic 4 weeks 0-1 years 2+ years 0-1 years 2+ years Missing Total women Age of women 15-19 43.4 14.6 0.6 36.0 5.2 0,3 100.0 808 20-24 55,6 11.1 2.4 28.4 2,4 0,1 100.0 1469 25-29 66,9 8,5 1.5 21.6 1.5 0,0 100.0 1207 30-34 67.5 6.9 1,0 21.2 2,9 0.5 100.0 1082 35-39 58.5 7.3 1.4 29.2 3.3 0.3 100.0 766 40-44 57.4 3,6 2.0 28,8 7.0 1.2 100.0 637 45-49 49.1 2,5 1.1 32.6 13.9 0.9 100.0 434 Duration of union 0~4 years 70.2 8,9 0.8 19.6 0.4 0,0 100.0 1218 5~9 66.9 7.3 0.4 24.5 0.8 0.2 100.0 1021 10-14 69.6 8.2 0.8 19.0 2.0 0.3 100.0 989 15-19 64.2 7.5 1.0 24.3 2.4 0.6 100.0 743 20-24 59.1 4.5 1.3 29.1 5.3 0.7 100.0 629 25+ 51.4 3.0 1.6 32.3 10.9 0.8 100.0 660 Never in union 27.8 15.8 4.3 43.0 8.8 0.3 100.0 1143 Residence Urban 64.5 4.5 0.9 24.8 4.4 0.9 100.0 1119 Rural 57.0 9.4 1,6 27.8 3.9 0.2 100.0 5284 Province Nairobi 67,0 3.5 1.3 23.6 4.1 0.6 100.0 439 Central 60.5 5.8 1,6 25.1 6.6 0.4 100.0 921 Coast 62.6 5.7 1.5 26.8 3.0 0.4 100,0 585 Eastern 58.1 10.7 2.0 24,3 4.7 0.3 100,0 1220 Nyanza 56.6 7.4 1.3 31.5 3.2 0.1 100.0 1040 Rift Valley 57.9 12.8 1.9 23.3 3.5 0,6 100.0 1294 Western 52.0 8.1 0.7 36.6 2.5 O. 1 100.0 905 Education No education 54.6 8.6 2.0 28.5 5.8 0.5 100.0 1318 Primary incomplete 62.0 7.6 0,9 25.3 3.6 0.5 100.0 1831 Primary complete 58.2 9.6 1.5 27.8 2.9 0.1 100,0 1740 Secondary+ 57,1 8.5 1,8 28.0 4.2 0.4 100.0 1514 Current contraceptive No method 52.2 11.0 1.8 29.2 5,4 0.4 100.0 4457 Pill 76.9 2,3 0.4 20.0 0.2 0.1 100.0 562 IUD 80.2 0.8 0.2 17.7 0.2 1.0 100,0 213 Sterilisation 73.4 2.3 0.4 21,8 2.1 0.0 100.0 292 Rhythm/counting days 62.5 6.6 0,6 29.1 1.2 0.0 100.0 335 Other 69.7 2.9 1.8 24.6 0.6 0.3 100.0 544 Total 58.3 8.6 1.5 27.3 4.0 0.4 100.0 6402 68 Table 5.9 indicates that a majority (58 percent) of the women interviewed in the KDHS were sexually active in the four weeks prior to the interview. Ten percent had not had sexual relations since delivering a recent baby (postpartum) and 32 percent were abstaining for reasons other than having recently given birth. Most of these women had been sexually inactive for less than 2 years; only 6 percent of women had not had sexual intercourse for two years or longer. The likelihood that a woman has been sexually active in the last four weeks declines with marital duration. Single women who have ever had sexual intercourse are less likely to report that they have recently been sexually active. Urban women and women in Nairobi are more likely than other women to have been sexually active. 5.7 Postpartum Amenorrhoea and Insusceptibility The risk of pregnancy following a birth is largely influenced by two factors: breastfeeding and sexual abstinence. Postpartum protection from conception can be prolonged by breastfeeding through its effect on the length of amenorrhoea (the period prior to the return of menses). Protection can also be prolonged by delaying the resumption of sexual relations. Women are defined as insusceptible if they are not exposed to the risk of pregnancy, either because they are amenorrhoeic or abstaining following a birth. The percentage of births whose mothers are postpartum amenorrboeic, abstaining and postpartum insusceptible is shown in Table 5.10 by the number of months since birth. These distributions are based on current status data, i.e., on the proportion of births occurring x months before the survey for which mothers Table 5.10 Postpartum amenorrhoea, abstinence and imusceptibility Percentage of births whose mothers are postpartum amenorrhoeic, abstaining and insusceptible, by number of months since birth, and median and mean durations, Kenya 1993 Number Months Amenor- lnsus- of since birth rhoeic Abstaining ceptible births < 2 98.6 84.2 100.0 149 2-3 77.5 51.3 85.4 183 4-5 70.7 34.3 77.1 209 6-7 61.8 30.4 69.7 213 8-9 56.1 22.3 62.8 225 10-11 56.2 14.1 61.4 190 12-13 41.2 20.8 52.0 229 14-15 36.4 10.2 41.7 199 16-17 23.3 16.0 35.3 189 18-19 23.0 13.0 29.8 218 20-2l 10.4 9.0 19.4 193 22-23 17.6 10.3 25.0 179 24-25 7.5 5.3 12.4 217 26-27 5.7 5.5 10.4 197 28-29 3.7 2.4 5.7 208 30-31 0.6 3.4 4.0 207 32-33 1.9 2.8 4.7 217 34-35 3.1 6.7 9.7 176 Total 32.2 18.0 38.5 Median 10.8 3.0 12.9 Mean 12.2 7.2 14.4 Prevalence/incidence mean 11.4 6.4 13.7 3596 69 are still amenorrhoeic, abstaining or insusceptible. The estimates of the median and mean durations shown in Tables 5.10 and 5.11 are calculated from the current status proportions at each time period. The data are grouped in two-month intervals to minimise fluctuations in the estimates. The period of postpartum amenorthoea is considerably longer than the period of postpartum abstinence and is the major determinant of the length of postpartum insusceptibility to pregnancy. By 6-7 months following birth, 62 percent of women are still amenorrhoeic, while only 30 percent are still abstaining. Similarly, at 12-13 months postpartum, 41 percent of women are amenorrhoeic, compared to 21 percent still abstaining. The mean duration of postpartum amenorrhoea is 12 months; that of postpartum abstinence is 7 months. The combination of these two factors means that Kenyan women are insusceptible to the risk of pregnancy---either due to amenorrhoea or to abstinence--for an average of 14 months after giving birth. Table 5.11 displays median durations of postpartum amenorrhoea, abstinence and insusceptibility by various background characteristics. Women age 30 or older have a longer median duration of postpartum amenorrhoea 13 months---compared to 8 months for women under 30 years. Similarly, rural mothers wait considerably longer than urban mothers for their periods to return after birth (12 vs. 5 months). Table 5.11 Median duration of postpartum insusceptibility by background characteristics Median number of months of postpartum amenorrhoea, postpartum abstinence, and postpartum insusceptibility, by selected background characteristics, Kenya 1993 Postpartum Number Background Postpartum Postpartum insuseep- of characteristic amenorrhoea abstinence tibility women Age <30 8.1 3.1 11.3 2315 30+ 13.4 2.7 14.3 1282 Residence Urban 5.3 2.1 6.4 465 Rural 12.0 3.1 13.8 3132 Province Nairobi 4.5 0.8 5.4 174 Central 6.3 2.7 12.7 407 Coast 13.0 2.0 13.3 328 Eastern 12.6 2.9 16.1 707 Nyanza 10.4 2.5 10.9 614 Rift Valley 10.7 4.2 13.5 778 Western 12.4 3.1 14.5 589 Education No education 12.8 4.3 14.8 647 Primary incomplete 12.6 2.5 13.7 1135 Primapy complete 8.7 2.9 11.4 1021 Secondary+ 6.8 3.0 10.4 794 Total 10.8 3.0 12.9 3597 Note: Medians are based on current status. 70 Provincial differentials indicate that women in the Coast Province have the longest median duration of amenorrhoea ( 13 months), followed by women in Eastern Province (13 months) and Western Province ( 12 months); women in Nairobi have the shortest median duration of amenorrhoea (5 months). Table 5.11 further shows that the median duration of postpartum amenorrhoea is inversely related to education. It varies from 13 months for women with no education or only some primary education to 7 months for women with secondary education. Differences in median duration of postpartum abstinence are considerably smaller than those for amenorrhoea. Women in Nairobi appear to abstain from sexual intercourse for the shortest period of time after giving birth, while women in Rift Valley Province abstain the longest. Women with no education have a slightly longer median duration of abstinence than women with some education. 5.8 Termination of Exposure to Pregnancy The risk of pregnancy declines with age, as increasing proportions of women become infecund. While the onset ofinfecundity is difficult to determine for an individual woman, there are ways of estimating it for a population. Two indicators of decreasing exposure to the risk of pregnancy for women age 30 and older are displayed in Table 5.12. The first, an indicator of menopause, encompasses currently married women who are neither pregnant nor postpartum amenorrhoeic, but who have not had a menstrual period in the six months preceding the survey. The table shows that this proportion increases steadily with age, from 4 percent for women age 30-34 years to 27 percent for women age 48-49. The second is an indicator of long-term abstinence. This is the proportion of currently married women who did not have sexual intercourse in the last three years preceding the survey. As the table shows, long-term abstinence is not a major contributor to lower fertility. The proportion of women who have not had sexual intercourse for the last three years is less than one percent except among those age 46-49, where it is approximately 3 percent. Table 5.12 Termination of exposure to the risk of pregnancy Indicators of menopause, terminal infertility and long-term abstinence among currently married women age 30-49, by age, Kenya 1993 Long-term Menopause I abstinence 2 Age Percentage Number Percentage Number 30-34 4.1 946 0.1 1314 35-39 4.0 747 0.7 971 40-41 3.2 323 0.0 387 42-43 11.3 291 1.1 322 44-45 12.5 286 0.8 307 46-47 19.1 230 2.7 250 48-49 27.1 176 2.5 185 Total 8.0 2998 0.7 3735 IPercentage of non-pregnant, non-amenorrhoeic currently married women whose last menstrual period occurred six or more months l~preCeding the survey or who report that they are menopausal. ercentage of currently married women who did not have intercourse in the three years preceding the survey. 71 CHAPTER 6 FERTILITY PREFERENCES Several questions were asked in the KDHS concerning women's fertility preferences. The aim of this part of the interview was to establish the extent of unmet need for contraception and the number of unwanted or mistimed births. The KDHS questionnaire included questions on: 1) whether the respondent wanted another child, 2) i f so, how long she would like to wait to have the next child, and 3) how many children she would want in total i f she could start afresh. The usefulness of data on fertility preferences has been controversial. Critics consider the data misleading because of the fact that information gathered from women does not take into account the effect of social pressures or attitudes of other family members, particularly the husband, whose opinions on reproductive behaviour may be very influential. Another objection expressed by critics is that these preferences are usually held with weak intensity and little conviction, and consequently change with time. Others maintain that results obtained from these questions are important for assessing to what extent unwanted or mistimed pregnancies occur and the effect that would occur from prevention of such pregnancies. 6.1 Des i re fo r More Ch i ld ren In the KDHS, currently married women were asked "Would you like to have (a/another) child or would you prefer not to have any (more) children?" Interviewers were instructed to alter the wording depending on whether the respondent already had children or not. I f the woman was pregnant, she was asked if she wanted another child after the one she was expecting. Women who said they did want to have another child were then asked how long they would like to wait before the birth of the next child. Table 6.1 shows the percent distribution of currently married women by desire for another child, according to the number of l iving children. Almost half (46 percent) of married women want no more Table 6.1 Fertility preference by number o f living children Percent dis~ibution of currently married women by desire for more children, according to number of living children, Kenya 1993 Desire for Number of living children l children 0 1 2 3 4 5 6+ Total Have another soon 2 72.7 24.1 11.9 8.9 4.9 4.3 1.8 11.6 Have another later 3 9.6 60.5 51.3 33.5 18.9 13.1 4.6 26.0 Have another, undecided when 3.5 1.6 l.l 1.2 0.9 0.8 0.3 1.0 Undecided 3.0 3.7 5.9 8.7 8.5 7.9 5.2 6.3 Wants no more 0.4 7.0 25.7 44.7 59.9 63.3 71.0 46.2 Sterilised 0.0 0.6 0.6 1.9 5.4 8. l 12.6 5.5 Declared infecund 10.6 2.5 3.1 0.7 1.0 2.4 4.2 3.0 Missing 0.2 0.l 0.3 0.3 0.5 0.2 0.3 0.3 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Number of women 244 558 724 671 634 544 1254 4629 llncludes current pregnancy. 2Wants next birth within 2 years. 3Wants to delay next birth for 2 or more years. 73 Figure 6.1 Fertility Preferences of Currently Married Women 15-49 Undecided 7% Infeeund 3% • no more 52% Want child soon (<2 years) 12% Want child later (2+ years) 26% Note: 'Want no more' includes sterilised women. KDHS 1993 children and an additional 9 percent either have been sterilised or say that they cannot have any more children. Another 26 percent of women want another child, but they want to wait two or more years before having their next birth (Figure 6.1). Therefore, about three-quarters of married women in Kenya can be considered potential users of contraception for the purpose of either limiting their family size or spacing births. Not surprisingly, the desire for more children declines noticeably as the number of living children increases (Table 6.1 and Figure 6.2). Thus, 73 percent of married women with no children want to have a child soon (within two years), whereas only 2 percent of women with six or more children want to have another soon. Conversely, the percentage of women who want no more children rises from 7 percent for women with one child to 71 percent for women with six or more children. This indicates that a substantial proportion of married women are interested in limiting their fertility. The data also show that there is a desire among women to space births. For instance, 61 percent and 51 percent of women with one and two children respectively, want their next birth after two years. Table 6.2 shows the percent distribution of currently m arried women by desire for children according to age. The data show that the proportion of women who want no more children increases with age. Five percent of the women age 15-19 want no more children, compared to 64 percent of women age 45-49 years. The proportion who want to delay their next birth declines with age, as does the proportion of women who want the next birth within two years. 74 100 Figure 6.2 Fertility Preferences of Married Women by Number of Living Children 80 60 40 20 0 0 1 2 3 4 5 6+ NO. of L iv ing Ch i ld ren KDHS 1993 Table 6.2 Fertility preferences by age Percent distribution of currently married women by desire for more children, according to age, Kenya 1993 Desire for Age of woman children 15-19 20-24 25-29 30-34 35-39 40-44 45~.9 Total Have another soon x 34.2 16.8 11.5 11.6 5.7 3.9 2.8 11.6 Have another later 2 56.0 53.7 33.2 17.6 6.8 2.4 2.0 26.0 Have another, undecided when 1.5 0.9 0.9 1.7 0.8 0.4 0.9 1.0 Undecided 3.1 7.1 7.5 6.6 7.9 3.9 2.7 6.3 Wants no more 5.2 21.1 44.2 54.8 63.6 67.6 64.4 46.2 Sterilised 0.0 0.I 1.9 6.0 l l .1 13.0 12.3 5.5 Declared infecund 0.0 0.2 0,4 1.4 3.7 8.6 14.9 3.0 Missing 0.0 0.2 0.3 0.5 0.4 0.1 0.0 0.3 Total I00.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Number 261 937 1003 918 644 519 348 4629 IWants next birth within 2 years. 2Wants to delay next birth for 2 or more years. The proportion of women who want no more children is the most significant measure of fertility preference. Table 6.3 and Figure 6.3 show the percentage of currently married women who want no more children by number of living children and selected background characteristics. The proportion of women who want no more children is closely correlated with the number of living children as well as background 75 Table 6.3 Desire to limit (stop) childbearing Percentage of currently married women who want no more children, by number of living children and selected background characteristics, Kenya 1993 Number of living children 1 Background characteristic 0 1 2 3 4 5 6+ Total Residence Urban 0.0 14.8 43.1 67.0 74.2 (76.4) 89.4 47.9 Rural 0.5 4.8 21.3 42.1 64.4 70.8 83.2 52.5 Province Nairobi (0.0) 10.9 (53.5) (65.5) * * * 45.4 Centxal * 4.4 38.2 61.9 85.4 88.6 90.9 64.3 Coast 0.0 7.6 16.3 26.6 34.9 45.6 59.5 30.4 Eastern (0.0) 9.7 31.5 56.0 76.7 78.3 87.8 62.1 Nyanza 2.0 9.3 15.0 34.8 59.1 68.5 84.3 47.2 Rift Valley 0.0 4.8 21.2 44.3 51.2 63.6 79.8 48.5 Western (0.0) 6.7 20.6 39.3 67.9 73.1 88.4 53.7 Education No education (0.0) (5.4) 18.1 29.4 46.6 58.4 77.9 55.8 Primary incomplete 0.0 6.0 15.1 40.8 60.6 71.8 84.7 52.4 Primary complete 1.3 5.9 26.0 50.3 77.0 75.7 94.4 49.2 Secondary+ 0.0 11.1 37.6 57.7 72.4 83.7 87.7 49.7 Total 0.4 7.5 26.3 46.7 65.3 71.4 83.6 51.8 Note: Women who have been sterilised are comidered to want no more children. Parentheses indicate a figure based on 25-49 women. An asterisk indicates a figure based on fewer than 25 women and has been suppressed. ~Includes current pregnancy characteristics. For instance, overall, a larger proportion of rural than urban women want to stop childbearing; however, when the number of living children is taken into account, the reverse is true. This means that the overall figures result from the fact that on average, rural women have more children than urban women, since the proportion wanting no more children rises with the number of living children. Women in Nairobi and those in Central and Eastern Provinces are the least pronatalist. Over half of the married women in Nalrobi and about one-third of those in Central and Eastern Provinces want to stop childbearing after having two children. Among women with three children, two-thirds of those in Nairobi and Central Province want to stop, compared to only one-quarter of those in Coast Province. Women in Coast Province are the least likely to want to stop childbearing; only 60 percent of those with six or more children say they want to stop. The amount of education seems to have an effect on the desire to stop childbearing. For example, among women with three children, 29 percent of those with no education want to stop childbearing, compared to 58 percent of those with at least some secondary education. 76 Figure 6.3 Percentage of Currently Married Women Who Want No More Children by Background Characteristics RESIDENCE Urban Rural PROVINCE Nairobi Central Coast Eastern Nyanza R, Valley Western EDUCATION No Edt~cation Prim. Incomp, Prim. Comp. Secondary+ 0 [;i i ii E~i i ii E~i i il i~i:i:;:[~ :;: [:i :~: i:i:i :~:i:i:i:i:ii i i ii i!i !~![![!i!:!:i: !:~ :i: [:~ :i: i:[:~: i:i:i :~:i:i:i: i il :i: i ii i i~ii! i i: i:!:i:i: ~:i :i: [:i :~: [:~ :~: i il ::iil :iiil i i~ii!~![!~!:i:i:[:i:~: [ : i lE ~ ! : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : ; : : ; : l~ ' i l 56 . . . . . 152 = ¸¸¸¸¸¸¸¸¸¸¸¸¸¸¸¸¸¸¸¸¸¸¸¸¸¸¸¸¸¸¸¸¸¸¸¸¸¸¸¸¸¸¸¸¸¸¸¸¸¸¸¸t49 i i i i i i i i i i i i i i i i i i i i i i i i i i Y i i i i i I 50 10 20 30 40 50 60 70 Percent KDHS 1993 6.2 Demand for Family Planning Services Women who are currently married and who say either that they do not want any more children or that they want to wait two or more years before having another child, but are not using contraception, are considered to have an unmet need for family planning) Women who are using family planning methods are said to have a met need for family planning. Women with unmet and met need constitute the total demand for family planning. Table 6.4 presents data on unmet need, met need and total demand for family planning, according to whether the need is for spacing or limiting births. Over one-third of married women in Kenya have an unmet need for family planning services (Column 3 of Table 6.4)--22 percent for spacing purposes and 15 percent for limiting births. Combined with the 33 percent of married women who are currently using a contraceptive method, the total demand for family planning comprises almost 70 percent of married women in Kenya. Therefore, if all women who say they want to space or limit their children were to use methods, the contraceptive prevalence rate could be increased from 33 percent to 69 percent of married women. Currently, less than half of the demand for family planning is being met (next-to-last column in Table 6.4). The overall unmet need for family planning declines with age. As expected, unmet need for spacing purposes is higher among younger women, while unmet need for limiting childbearing is higher among older women. The level of unmet need among rural women is higher than that of urban women. It is higher among women in Western and Eastern Provinces and lowest in Central Province. Unmet need is lower among women with at least some secondary schooling than among less educated or uneducated women. ~For an exact description of the calculation, see footnote 1, Table 6.4. 77 Table 6.4 Need for family planning services Percentage of currently rnamod women with unmet need for family planning, met need for family planning, and the total demand for farrfily planning services, by selected background characteristics, Kenya 1993 Met need for Unmet need for family planning Total demand for Percentage family planning t (currently using) z family planning of demand Number Background For For For For For For saris- of characteristic spacing limiting Total spacing limiting Total spacing limiting Total fled women Age 15-19 37.5 4.4 41.9 9.7 0.5 10.3 47.2 4.9 52.1 19.7 261 20-24 35.6 5.0 40.6 17.5 6.0 23.6 53.1 11.0 64.1 36.7 937 25-29 28.5 11.4 39.9 16.4 20.8 37.2 44.9 32.2 77.1 48.3 1003 30-34 18.9 16.5 35.4 9.0 30.7 39.7 27.9 47.2 75.1 52.9 918 35-39 11.7 28.2 39.8 2.8 33.1 35.9 14.5 61.3 75.8 47.4 644 40-44 5.0 24.9 29.9 0.7 36.7 37.3 5.7 61.5 67.2 55.5 519 45-49 2.4 15.2 17.6 0.0 30.4 30.4 2.4 45.6 48.0 63.4 348 Residence Urban 15.3 10.2 25.5 16.3 27.1 43.4 31.6 37.3 68.9 63.0 697 Rural 22.7 15.7 38.4 8.8 22.1 30.9 31.5 37.8 69.2 44.6 3932 Province Nairobi 16.8 10.2 27.0 20.4 25.0 45.4 37.2 35.2 72.4 62.7 271 Central 11.9 13.2 25.1 14.8 41.2 56.0 26.7 54.4 81.1 69.0 610 Coast 25.3 8.0 33.3 9.3 10.9 20.2 34.5 18.9 53.5 37.7 445 Eastern 23.2 18.1 41.3 10.2 28.3 38.4 33.3 46.4 79.7 48.2 864 Nyanza 23.3 15.7 39.0 6.8 17.0 23.8 30.1 32.7 62.8 37.9 737 Rift Valhiy 21.4 15.1 36.5 9.0 18.8 27.8 30.4 33.9 64.3 43.3 992 Western 26.1 17.1 43.1 6.2 18.9 25.1 32.2 36.0 68.2 36.8 710 Education No educarion 17.4 19.1 36.5 2.6 16.9 19.5 20.0 36.0 56.0 34.8 1062 Primary incomplete 23.5 18.0 41.5 7.2 20.7 27.9 30.6 38.7 69.3 40.2 1411 Primary complete 26.8 11.7 38.4 10.3 24.6 34.9 37.0 36.2 73.3 47.6 1177 Secondary+ 17.3 9.5 26.8 21.2 30.4 51.6 38.5 39.9 78.4 65.8 980 Total 21.6 14.8 36.4 9.9 22.9 32.7 31.5 37.7 69.2 47.3 4629 tUnraet need for spacing includes pregnant women whose pregnancy was mistimed, amenorrhoeic women whose last birth was nfistimed, and women who are neither pregnant nor amenorrhoeic and who are not using any method of family planning and say they want to wait 2 or more years for their next birth. Also included in unmet need for spacing are women who ate unsure whether they want another child or who want another child but are unsure when to have the birth. Unmet need for limiting refers to pregnant women whose pregnancy was unwanted, amenorrhoeic women who6e last child wax unwanted and women who are neither pregnant nor amenorrhoeic aad who are not using any method of family planning and who want no mote children. ~[Jsing for spacing is defined as women who axe using some method of family planning and say they want to have another child or are undecided whether to have another. Using for limiting is defined as women who ale using and who want no more children. Note that the spaeifie methods used are not taken into account here. 6.3 Ideal Family Size In order to assess ideal fertility preferences, the KDHS included two questions. Women who had no children were asked, "If you could choose exactly the number of children to have in your whole life, how many would that be?" For women who had children, the question was rephrased as follows: "If you could go back to the time you did not have any children and could choose exactly the number of children to have in your whole life, how many would that be?" These questions on ideal family size aimed at two things: first, among women who have not started childbearing, the data provide an idea of the total number of children these women will have in the future (to the extent that women are able to realise their fertility desires). Secondly, among older, higher parity women, these data provide a measure of the level of unwanted fertility. It should be noted that some women, especially those for whom fertility control is an unfamiliar concept, may have had difficulty in answering this hypothetical question. 78 The data in Table 6.5 indicate that the vast majority of women were able to give a numeric answer to this question; only 6 percent of women gave a non-numeric answer such as "it is up to God," "any number," or "does not know." Those who gave numeric responses generally want to have small families. Only 10 percent of respondents said they would choose to have six or more children, while one-third favoured four children and one-quarter cited two children as ideal. Among women giving numeric responses, the mean ideal family size is 3.7 children. As expected, the ideal number of children increases with the number of living children; women with more living children are most likely to state four as their ideal number of children, while women with fewer children are as likely to state two or three children as ideal. The mean ideal family size increases from 3.4 among childless women to 4.5 among women with six or more children. There are several possible explanations for the relationship between ideal and actual number of children. First, to the extent that they are able to implement their preferences, women who want larger families will tend to actually have them. Secondly, women who have larger families may tend to rationalise their family size by reporting their actual number of children as their ideal number. Finally, women with larger families, being older, on average, than women with smaller families, have larger ideal family sizes, because of attitudes they acquired 20 to 30 years ago. Table 6.5 Ideal number of children Percent dlstiibudon of all women by ideal number of children and mean ideal number of children for all women and for currently married women, according to number of living children, Kenya 1993 Number of living children l Ideal number of children None 1 2 3 4 5 6+ Total 0 0.6 0.1 0.3 0.4 0.1 0.1 0.4 0.4 1 2.2 4.2 1.4 2.9 1.9 1.7 0.8 2.1 2 29.3 28.0 29.2 16.2 20.4 17.2 11.0 22.5 3 20.1 25.2 17.9 24.7 10.0 16.6 10.9 18.0 4 29.8 28.8 33.2 34.5 41.6 29.6 40.6 33.7 5 6.0 5.3 6.5 7.5 8.7 14.5 7.5 7.4 6+ 6.9 5.4 7.0 8.8 11.7 14.1 19.2 10.3 Non-numeric response 5.0 3.1 4.5 4,9 5.6 6.2 9.7 5.7 Total 109.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Number of women 2053 1006 939 789 723 617 1415 7540 Mean ideal number 3.4 3.3 3.5 3.6 3.9 4.0 4.5 3.7 Number of women 1950 975 897 750 682 579 1278 7111 Mean for women in union 4.0 3.5 3.6 3,7 3.9 4.0 4.5 3.9 Number of women in union 227 542 685 64l 601 512 1141 4348 Note: The means exclude women who gave non-numeric responses. 1Includes current pregnancy Despite the likelihood that some rationalisation of large families occurs, it is common for women to report ideal family sizes lower than their actual number of children. Two-thirds of the women with five children stated that they would ideally liked to have had fewer than five, and 71 percent of those with six or more children would have fewer if they could choose again. 79 There has been a large decline in ideal family size in Kenya over the past decade. In the 1984 KCPS, women reported a mean ideal family size of 5.8 children (CBS, 1984, p.61). It then declined to 4.4, as reported in the 1989 KDHS (NCPD, 1989, p.52), and then to the current figure of 3.7 children in 1993. Table 6.6 shows the mean ideal number of children for all women interviewed in the 1993 KDHS by age group and selected background characteristics. The mean ideal number of children increases with age from 3.5 among women age 15-19 to 4.5 among women age 45-49. At every age group, rural women have higher family size norms than urban women. This is reflected in the fact that women in Nairobi have the smallest ideal family size on average, regardless of age group; women in Coast Province have the highest. Ideal family size is negatively correlated with the level of education attained. Women with no education have the highest family size desires, while women with secondary education have the smallest; this is true for every age group. Table 6.6 Mean ideal number of children by background characteristics Mean ideal number of children for all women, by age and selected background characteristics, Kenya 1993 Background Age of woman characteristic 15-19 20-24 25-29 30-34 35-39 40-44 45-49 Total Residence Urban 2.9 2.8 2.8 3.1 3.5 3.0 (3.0) 2.9 Rural 3.6 3.5 3.8 4.1 4.2 4.2 4.7 3.9 Province Nairobi 2.5 2.6 2.6 (2.8) (3.4) * * 2.7 Central 2.8 2.7 3.1 3.2 3.4 3.7 4.0 3.1 Coast 4.1 4.1 4.2 5.1 5.2 5.0 5.8 4.5 Eastern 3.6 3.2 3.3 3.6 4.0 3.9 3.9 3.5 Nyanza 3.3 3.5 4.1 4.1 4.2 4.4 4.5 3.8 Rift Valley 3.7 3.7 3.9 4.6 4.4 4.5 5.8 4.1 Western 3.7 3.7 3.7 3.9 3.7 4.2 4.3 3.8 Education No education 5.2 4.9 5.0 5.3 4.7 4.4 4.9 4.9 Primary incomplete 3.8 3.7 4.0 4.1 4.0 4.2 4.4 4.0 Primary complete 3.4 3.3 3.4 3.5 3.8 3.7 3.7 3.4 Secondary+ 2.8 2.9 3.0 3.2 3.3 3.1 * 3.0 Total 3.5 3.4 3.6 4.0 4.1 4.1 4.5 3.7 Note: Rates shown in parentheses are based on 25-49 women, whereas an asterisk means the rate is based on fewer than 25 women and has been suppressed. 6.4 Fertility Planning There are two ways of estimating levels of unwanted fertility from the KDHS data. One is based on responses to a question as to whether each birth in the five years before the survey was planned (wanted then), mistimed (wanted, but at a later time), or unwanted (wanted no more children). These data are likely to result in underestimates of unplanned childbearing, since women may rationalise unplanned births and declare them as planned once they are born. Another way of measuring unwanted fertility utilises the data on ideal family size to calculate what the total fertility rate would be ifall unwanted births were avoided. This measure may also suffer from underestimation to the extent that women are unwilling to report an ideal family size lower than their actual family size. Data using these two approaches are presented below. 80 Table 6.7 shows the percent distribution of births in the five years before the survey by whether the birth was wanted then, wanted later, or not wanted. Seventeen percent of recent births were reported to be unwanted, while 34 percent were reported as mistimed (wanted later). Fourth and higher births are more likely than first, second and third births to be unwanted. Similarly, a much larger proportion of births to older women are unwanted--almost 50 percent among women in their early 40s. Table 6.7 Fertility planning status Percent distribution of births in the five years preceding the survey by fertility planning status, according to birth order and mother's age. Kenya 1993 Planning status of birth Birth order Wanted Number and mother's Wanted Wanted no of age then later more Missing Total births Birth order 1 55.3 37.1 6.1 1.5 100.0 1372 2 53.1 40.0 5.1 1.8 100.0 1158 3 54.0 37.1 7.3 1.6 100.0 945 4 49.5 38.0 11.3 1.2 100.0 819 5 46.1 33.3 19.7 0.9 100.0 689 6+ 34.7 25.3 39.3 0.8 100.0 1749 Age at birth <19 48.4 43.4 6.6 1.5 100.0 1124 20-24 53.2 39.1 6.3 1.4 100.0 2021 25-29 49.8 34.7 14.0 1.5 100.0 1709 30-34 40.6 27.5 31.5 0,4 100.0 994 35-39 38.6 19.9 40.3 1.1 100.0 595 40-44 32.1 16.3 49.2 2.4 100.0 240 45-49 64.2 7.0 28.8 0.0 100.0 50 Total 47.7 34.2 16.7 1.3 100.0 Note: Birth order includes current pregnancy. 6732 Table 6.8 presents "wanted" fenility rates calculated using the second approach to measuring unwanted fertility. The wanted fertility rate is calculated in the same manner as the total fertility rate, but unwanted births are excluded from the numerator. For this purpose, unwanted births are defined as those which exceed the number considered ideal by the respondent. (Women who do not report a numeric ideal family size are assumed to want all their births). This rate represents the level of fertility that would have prevailed in the three years preceding the survey if all unwanted births had been prevented. A comparison of the total wanted fertility rate and the actual fertility rate suggests the potential demographic impact of the elimination of unwanted births. The wanted fertility rate for Kenya as a whole was 3.4 two children lower than the actual total fertility rate. This implies that the total fertility rate is 60 percent higher than it would be if unwanted births could be avoided. The gap between the wanted and actual total fertility rates is greatest among rural women and those in Eastern and Western Provinces (where the wanted fertility rates are almost three children lower than the actual fertility rates), and among women with incomplete primary school. 81 Table 6,8 Wanted fertility rates Total wanted fertility rates and total fertility rates for the three years preceding the survey, by selected background characteristics, Kenya 1993 Total wanted Total Background fertility fertility characteristic rate rate Residence Urban 2.5 3.4 Rural 3.7 5.8 Province Nairobi 2.5 3.4 Central 2.6 3.9 Coast 4.3 5.3 Eastern 3.3 5.9 Nyanza 4.1 5.8 Rift Valley 3.6 5.7 Western 3.6 6.4 Education No education 4.2 6.0 Primary incomplete 3.7 6.2 Primary complete 3.0 5.0 Secondary+ 2.8 4.0 Total 3.4 5.4 Note: Rates are based on births to women 15-49 in the period 1-36 months preceding the survey. The total fertility rates are the same as those presented in Table 3.2. 82 CHAPTER 7 INFANT AND CHILD MORTALITY Presented in this chapter is information on mortality under age five in Kenya, specifically on levels, trends and differentials in neonatal, postneonatal, infant, and child mortality. The data are disaggregated by sex, socio-economic characteristics, provinces and other factors in order to identify segments of the population requiring special attention. This information is useful for monitoring and evaluating population and health programmes and policies, as well as for producing population projections. Mortality estimates can also prove useful in identifying populations that are at high risk and designing programmes that could reduce mortality. Infant and child mortality rates are basic indicators of a country's socio-economic situation and quality of life. 7.1 Assessment of Data Quality Estimates of infant and child mortality are based on information from the birth history section of the questionnaire administered to individual women. The section began with questions about the aggregate childbearing experience of respondents (i.e., the number of sons and daughters who live with the mother, the number who live elsewhere and the number who have died). For each of these births, information was then collected on the sex, the month and year of birth, survivorship status and current age, or, if the child had died, the age at death. This information has been used to calculate the following five rates: Neonatal mortality: the probability of dying within the first month of life; Postneonatal mortality: the difference between infant and neonatal mortality; Infant mortality: the probability of dying before the first birthday; Child mortality: the probability of dying between the first and fifth birthday; Under-five mortality: the probability of dying between birth and fifth birthday. All rates are expressed per 1,000 live births, except for child mortality, which is expressed per 1,000 children surviving to 12 months of age. The reliability of mortality estimates calculated from retrospective birth histories depends upon the completeness with which deaths are reported and the extent to which birth dates and ages at death are accurately reported and recorded. Omission of births and deaths directly affects mortality estimates, displacement of dates has an impact on mortality trends, and misreporting of the age at death may distort the age pattern of mortality. Undereporting of infant deaths is usually most severe for deaths which occur very early in infancy. If early neonatal deaths are selectively underreported, the result would be an abnormally low ratio of deaths under seven days to all neonatal deaths (deaths in the first month) and an abnormally low ratio of neonatal to infant mortality. Undcrreporting of early infant deaths is usually more common for births that occurred longer before the survey; hence, it is useful to examine the ratios over time. 83 It does not appear that early infant deaths have been severely underreported in the 1993 KDHS. First, the proportion of neonatal deaths that occur in the first week of life is quite high, about 75 percent t (see Appendix Table C.5). Furthermore, this proportion is roughly constant over the 20 years before the survey, which further supports the evidence that early infant deaths have not been grossly underreported. Second, the proportions of infant deaths that occur during the first month of life are plausible (45-50 percent--see Appendix Table C.6). This cursory inspection of data quality reveals no evidence of selective underreporting or age at death misreporting. 7.2 Levels and Trends in Infant and Chi ld Mor ta l i ty The various mortality rates described above are shown in Table 7.1 for five-year periods preceding the survey. Under five mortality for the most recent five-year period (which roughly corresponds to the years 1988-1993) is 96 per 1,000 births. This means that almost 10 percent of children bom in Kenya do not live until their fifth birthday. The infant mortality rate for the same period is 62 deaths per 1,000 births. Tab|e 7.1 Infant and child mortality Infant and child mortality rates by five-year periods preceding the survey, Kenya 1993 Years Approximare Neonatal Posmeonatal Infant Child Under-five preceding reference mortality mortality mortality mortality mortality survey period (NN) (PNN) (Iqo) (aql) (sqo) 0-4 1988-93 25.7 35.9 61.7 36.7 96.1 5-9 1983-87 28.2 35.2 63.4 28. l 89.7 10-14 1978-82 34.6 34.3 68.9 35.3 101.8 The estimatcs in Table 7.1 and Figure 7.1 indicate that child survival in Kenya has not improved during the last decade. Although there appears to have been a small decline from 1978-82 to 1983-87 for all but posmeonatal mortality, mo:~t of the rates show either no change or a small increase during the most recent period, 1983-87 to 1988-93. For example, child mortality evidently declined from 35 deaths per 1,000 children in 1978-82 to 28 in 1983-87 and then rose to 37 in 1988-93. Given the relatively high level of sampling error associated with mortality rates, this pattern should not be interpreted as indicating any real change in mortality over the periods examined. Further evidence of a stagnation in the decline in childhood mortality comes from a comparison of data from the 1993 KDHS with previous data sources. For example, for the period 1984-89, estimates from the 1989 KDHS were an infant mortality rate of 60 per 1,000 births, a child mortality rate of 32, and an under five mortality rate of 89, all of which are slightly lower than the analogous rates from the 1993 KDHS for 1988-93. 2 Thus it would appear that child survival in Kenya has shown little or no improvement in recent years. 1There are no model mortality patterns for the neonatal period. However, one review of data from several developing countries concluded that, at levels of neonatal mortality of 20 per 1000 or higher, approximately 70 percent of neonatal deaths occur within the first six days of life (Boerma, 1988, cited in Sullivan et al., 1990). 2Although the mortality rates estimated from the 1989 KDHS have been criticised as being underreported, it is the rates for the earlier periods which are most questionable; there is no evidence of underreporting of deaths of children born in the ten years bcfore the 1989 survey (Brass, W. et al., 1993, 34). 84 120 100 80 60 40 20 0 Figure 7.1 Trends in Infant and Child Mortality Kenya, 1978-1993 Deaths per 1,000 Live Births Child Mortality Under-five Mortality Infant Mortality KDHS 1993 It is beyond the scope of this report to investigate causes of the stagnation in mortality decline among young children. It could be related to the recent economic recession (see Chapter 1). Another possible explanation is an increase in mortality due to AIDS. 7.3 Socio-economic Differentials in Infant and Chi ld Morta l i ty Differentials in the various mortality rates by selected background characteristics are presented in Table 7.2. The table focuses largely on basic socioeconomic characteristics, including urban-rural residence, province, mother's educational level and maternal care prior to birth. A ten-year period is used to calculate the mortality estimates in order to have a sufficient number of cases in each category. Differences in under five mortality are presented in Figure 7.2. Children in the rural areas of Kenya experience a 27 percent higher risk of dying before age five than urban children. However, the urban-rural differential exists almost exclusively during infancy (43 percent higher) and fades away during the 1-4 year age group (5 percent). Whereas one in 15 children in rural areas die before their first binhday, the ratio for the urban areas is one in 22 children (see Table 7.2). Differences in mortality by province are also quite marked. Child mortality is highest in Nyanza Province, where about 19 percent of the children do not live to see their fifth birthday. Mortality is lowest in Central, Rift Valley and Eastern Provinces, where reported mortality rates for children under five years were less than 70 per 1,000 births. The pattern of higher infant than child mortality prevails in all provinces. The infant mortality rate in Nyanza Province is exceptionally high, almost twice that of the second highest rate (Coast Province). 85 Table 7,2 Infant and child mortality by background characteristics Infant and child mortality rates for the ten-year period preceding the survey, by selected background characteristics, Kenya 1993 Neonatal Postneonatal Infant Child Under-five Background mortality mortality mortality mortality mortality characteristic (NN) (PNN) (tqo) (aq,) (sq0) Residence Urban 23.0 22.5 45.5 31.3 75.4 Rural 27.5 37.3 64.9 32,8 95.6 Province Nairobi (33,8) (10.6) (44.4) (39.5) (82,1) Central 16.8 14.1 30.9 10.7 41.3 Coast 28.5 39.8 68.3 43.4 108.7 Eastern 24.1 23.3 47.4 19.4 65.9 Nyanza 38.5 89.4 127.9 67.5 186.8 Rift Valley 24.5 20.2 44,8 16.7 60.7 Western 26.9 36.6 63.5 49.3 109.6 Education No education 28.4 37.9 66.3 35.8 99.8 Primary incomplete 29.9 50.2 80.1 44.0 120.6 Primary complete 29,4 28.0 57.4 22.7 78.8 Secondary+ 17.5 17.4 34.8 19.6 53.7 Medical maternity care I No antenatal/delivery care (83.4) * * * * Either antenatal or delivery 24.2 39,8 64.0 54.5 115.0 Both antenatal & delivery 21.6 28.8 50.4 27.5 76.5 Total 27.0 35.6 62.5 32.7 93.2 Note: Rates shown in parentheses are based on 250-499 children exposed, whereas an asterisk means the rate is based on fewer than 250 children and has been suppressed. IRates for the five-year period preceding the survey. Early childhood mortality rates are higher for children of women who have no education and those who have not completed their primary education. Children bom to mothers who have some secondary education are half as likely to die before their fifth birthday as those bom to mothers who have no education or have not completed their primary education. This is probably because educated mothers are more likely to use the available health services and have greater knowledge of nutrition, hygiene and other practises relating to child care. However, the relationship between childhood mortality and maternal education is not straightforward. Surprisingly, children of mothers with incomplete primary education experience higher mortality before age five (121 per 1,000) than those whose mothers have no education (100 per 1,000). Matemal care during pregnancy and delivery is strongly associated with childhood mortality. Children born to women who obtained both antenatal and delivery care from medically trained persons have considerably lower mortality than children whose mothers received only antenatal or delivery care. (The proportion of births whose mothers receive neither type of care is too small to produce reliable estimates for most rates.) 86 Figure 7.2 Under-Five Mortality by Selected Background Characteristics RESIDENCE Urban Rural PROVINCE Nairobi Central E ~:~i~i Coast Eastern Nyanza R. Valley Western EDUCATION No Education Prim. Incomp. Prim. Comp. Secondary+ O ~ / ~ / / / / / / / / ~ 75 82 ~ 1 0 9 66 187 61 110 . ]tO0 1121 179 154 50 100 150 Deaths Per 1,000 Live Births Note: Rates are for the tO-year period preceding the survey. 2OO KDHS 1993 7.4 Demographic Differentials in Infant and Child Mortality The relationship between early childhood mortality and various demographic variables is examined in Table 7.3 and Figure 7.3 for the ten-year period preceding the survey. The results show that male children are 14 percent more likely to die in infancy than their female counterparts and experience about 9 percent higher mortality before their fifth birthday. Infant mortality rates for males and females are 67 and 59 per 1,000, respectively. Differences diminish as the children grow older; there is no gender difference in mortality rates at ages 1-4 years. The relationship between childhood mortality and mother's age at birth shows the expected U-shaped pattern with higher mortality for children of younger (less than 20) and older mothers. The mortality rate for neonates of mothers age 40-49 is more than twice that of younger mothers age 20-29 (i.e., 55 vs. 24 per 1,000, respectively). Birth order is correlated with mother's age so it is not surprising that mortality risks are elevated among first births (which are predominantly to younger mothers) and births of order seven or higher (which are generally to older mothers). This pattern is more pronounced for neonatal mortality, where first births are almost 60 percent more likely to die in the first month than second or third births. At ages 1-4, first birdas have lower mortality risks than children of higher birth orders. 87 Table 7.3 Infant and child mortality by demographic characteristics Infant and child mortality rates for the ten-year period preceding the survey, by selected demographic characteristics, Kenya 1993 Neonatal Posmeonatal Infant Child Under-five Demographic mortality mortality mortality mortality mortality characteristic (NN) (PNN) (lq0) (4ql) (sqo) Sex of child Male 28,7 37.8 66.6 32.8 97.1 Female 25.2 33.3 58.6 32.6 89.3 Age of mother at birth < 20 31.3 43.9 75.1 35.7 108.1 20-29 23.5 34.6 58.1 34.4 90.5 30-39 27.8 32.9 60.7 26.7 85.8 40-49 (55.0) (26.8) (81.8) * * Birth order 1 33.6 36.2 69.8 26.3 94.2 2-3 21.3 35.0 56.4 32.3 86.8 4 6 22.9 34.5 57.3 37.6 92.7 7+ 36.1 37.5 73.6 32.1 103.3 Previous birth Interval < 2 yrs 32.2 49.2 81.4 38.1 116.4 2-3 yrs 21.1 30.8 51.9 34.8 84.8 4 yrs + 21.1 23.4 44.5 20.8 64.4 Size at birth l Very small/small 48,2 37.3 85.5 (17.6) (101.6) Average or larger 19.8 34.1 53.9 44.3 95.8 Total 27.0 35.6 62.5 32.7 93.2 Note: Rates shown in parentheses are based on 250-499 children extx~sed, whereas an asterisk means the rate is based on fewer than 250 children and has been suppressed. 1Rates for the five-year period preceding the survey. The most consistent findings can be seen in the relationship between birth interval length and infant and child mortality. The data show that short birth intervals significantly reduce a child's chances of survival. Children born less than two years after their preceding siblings are nearly twice as likely to die in infancy as those born after an interval of four years or more (81 vs. 45 per 1,000). This relationship persists in all the age groups examined. This finding suggests the need to reduce mortality risks for Kenyan children by promoting family planning programmes and traditional practises such as breastfeeding, so as to space births more widely. A child's size (or weight, if measured) at birth is an important determinant of its survival, particularly during the first months of life. Neonates perceived by their mothers to be small or very small are twice as likely to die in their first month of life than those perceived as average or larger in size. 88 Figure 7.3 Under-Five Mortality by Selected Demographic Characteristics AGE OF MOTHEF <2(: 20-2~ 30-39 BIRTH ORDER 2-3 4-6 7+ PRIOR BIRTH INTERVAL < 2 years 2-3 years 4+ years Note: Rates are forthe lO-year period preceding the survey. 108 : ]116 50 100 150 Deaths Per 1,000 Live Bi~hs 2OO KDHS 1993 7.5 High-Risk Fertility Behaviour Previous research has shown the strong relationships between fertility pattems and children's survival chances. Typically, infants and children have a greater probability of dying if they are born to mothers who are especially young or old, if they are born after a short birth interval, or if they are of high birth order. Data to examine these relationships are presented in Table 7.4, which shows the distribution of births in the five years preceding the survey and of currently married women according to these categories of increased risk. In this analysis, a mother under the age of 18 years is classified as "too young," whereas if she is over 34 years of age she is classified as "too old." A "short birth interval" is defined by a birth occurring less than 24 months after a previous birth, and a child is of"high birth order" if the mother had previously given birth to three or more children (i.e., if the child is of birth order 4 or higher). The table is further divided into 2 categories, with births falling into either single risk categories (such as those born to mothers below the age of 18 or over the age of 34, those bom after an interval of less than 24 months and those of birth order higher than three) and those falling into a multiple high-risk category (e.g., those born after an interval of less than 24 months to mothers who are below the age of 18, or children of birth order 4 or higher who are born to mothers who are over 34 years, etc.). The results indicate that almost half (47 percent) of children bom in the five years before the survey fall into at least one risk category; one in six births is characterised by two or more risk factors. Also indicated in Table 7.4 is the relative risk of mortality of children born in the five years before the survey by comparing the proportions of births who have died in each risk category to the proportions of births with no risk factor who have died (Column 2). Two risk categories stand out--that of children born to older mothers and the multiple risk category of children born after a short interval to women under age 18. 89 Table 7.4 High-risk fertility behaviour Percent distribution of chiidren born in the five years preceding the survey by category of elevated risk of mortality, and the percent distribution of currendy married women at risk of conceiving a child with an elevated risk of mortality, by category of increased risk, Kenya 1993 Births in last 5 years Percentage preceding the survey of currendy Risk Percentage Risk married category of births ratio women a Not In any risk category Single risk categories Mother's age < 18 Mother's age > 34 Birth interval < 24 months Birth order > 3 Subtotal Multiple risk categories Age <18 & birth interval <24 e mos. Age >34 & birth interval <24 mos. Age >34 & birth order >3 Age >34 & birth interval <24 & birth order >3 Birth interval <24 & birth order >3 Subtotal In any risk category Total Number 52.9 1.00 39.3 6.0 1.92 0.5 1.2 2.52 6.1 12.8 1.35 14.6 10.5 0.80 7.6 30.5 1.32 28.9 0.5 2.28 0.2 0.2 1.90 0.3 9.3 1.08 19.7 2.4 1.40 5.0 4.2 2.13 6.6 16.6 1.44 31.8 47.1 1.36 60.7 100.0 NA 100.0 6072 NA 4629 Note: Risk ratio is the ratio of the proportion dead of births in a specific risk category to the pro.portion dead of births not in any risk category. Figures in parentheses are ratms based on fewer than 200 cases. "Women were assigned to risk categories according to the status they would have at the birth of a child, if the child were conceived at the time of the survey: age less than 17 years and 3 months, age older than 34 years and 2 months, latest birth less than 15 months ago, and latest birth of order 3 or ~igher. Includes sterilised women Clncludes the combined categories Age <18 and birth order >3. NA = Not applicable For example, babies bom to mothers over age 34 have a risk two and a half times higher than those who are not in any risk group. Fortunately, however, only a small proportion of recent births falls into either of these two categories, so that, even though the fertility behaviour results in much higher risk of death for the child, few children are subject to that higher risk. Of much greater practical importance are the categories of births that occur after an interval that is too short. Such births account for a total of 20 percent of all births and suffer mortality risks that are 35 percent higher than children who fall in the "not in any risk" category; children who fall in a multiple risk category that includes short intervals (e.g., "birth interval too short and birth order higher than 3") suffer even greater risks. Less than 7 percent of children in Kenya are exposed to a higher risk of mortality because they are born to mothers who are under 18 years. 90 Column 3 of Table 7.4 shows the proportion of currently married women who fall into the various risk categories. Overall, 61 percent of married women, if they became pregnant today, would conceive a child that would fall into a risk category. Thirty-nine percent of currently married women are at risk of bearing a child of birth order four or higher and 31 percent of women are at risk of delivering a child after age 34. In the light of this evidence, efforts should be concentrated on encouraging families to use available contraceptives to space and limit their births so as to reduce childhood mortality in Kenya. To conclude, it is important that family planning is made readily available to meet existing and future demand for this service. This, with other efforts (medical and social), can help reduce child mortality in Kenya. 91 CHAPTER 8 MATERNAL AND CHILD HEALTH Presented in this chapter are survey findings in the areas of maternal and child health. The topics under discussion are matemal care, characteristics of the newborn, vaccinations, and common childhood diseases and their treatment. This information can be used to identify groups of women whose babies are "at risk" because of nonuse of maternal health services. The information will assist policym akers in the planning of appropriate strategies to improve matemal and child care. Data were obtained from women who had had a live birth in the five years preceding the survey. 8.1 Antenatal Care Prevalence and Source of Antenatal Care Table 8.1 shows the percent distribution of births in the five years preceding the survey by source of antenatal care received during pregnancy, according to maternal and background characteristics. Interviewers were instructed to record all persons a woman may have seen for care, but in the table, only the provider with the highest qualifications is considered, if more than one person was seen. The data indicate that almost all pregnant women in Kenya receive antenatal care either from doctors (23 percent) or nurses or midwives (72 percent), with a small fraction receiving care from trained and untrained traditional birth attendants. Age of the woman and birth order of the child appear to have little, if any, effect on who the woman is likely to see for antenatal care. There is a slight difference in the sources of antenatal care for births in urban and rural areas. In urban areas, 30 percent of the women see a doctor, compared to 22 percent in rural areas. However, in rural areas, a higher percentage (73 percent) see nurses or midwives compared to urban women (68 percent). Women in Coast Province are more likely to receive antenatal care from a doctor than are women in other provinces. In the same province, though, the proportion of women who do not receive any care during their pregnancy is highest (8 percent). There is a positive relationship between education and receipt of antenatal care. The proportion of women who obtain antenatal care from a doctor increases from 20 percent of uneducated women to 26 percent of women with secondary education. Conversely, women with no education are more likely to receive no antenatal care than educated women. Number and Timing of Antenatal Visits Antenatal care is important to both the mother and child. The number and timing of antenatal care visits are considered to be important to preventing adverse pregnancy outcome. Care is most effective if the visits are started early during pregnancy and continue at regular intervals throughout the pregnancy. It is generally recommended that antenatal care visits be made monthly for the first 7 months, fortnightly in the 8th month, and then weekly until birth. I f the first visit is made at the third month of pregnancy, this schedule translates to a total of about 12 to 13 visits. 93 Table 8.1 Antenatal care Percent distribution of births in the five years preceding the survey, by source of antenatal care during pregnancy, according to selected background characteristics, Kenya 1993 Antenatal care provider I Trained Untrained Trained trad. trad. Background nurse/ birth birth No one/ characteristic Doctor Midwife attendant attendant Other Missing Total Number Mother's age at birth < 20 20.9 72.4 0.7 0.0 0.2 5.7 100.0 1026 20-34 23.8 71.6 0.3 0.5 0.2 3.7 100.0 4249 35+ 18.8 75.9 0.1 0.2 0.2 4.8 100.0 787 Birth order l 23.8 70,9 0.5 0.0 0.2 4.5 I00.0 1226 2-3 24.4 71,3 0.5 0.4 0.1 3.2 100.0 1885 4-5 21.8 73.9 0.3 0.6 0.2 3.3 100.0 1361 6+ 20.3 73.1 0.1 0.6 0.2 5.7 100.0 1591 Residence Urban 29.5 68.1 0.0 0.0 0.4 2.0 100.0 773 Rural 21.6 72.9 0.4 0.5 0.1 4.4 100.0 5289 Province Nairobi 27.0 70.5 0.0 0.0 0.0 2.5 100.0 276 Central 21.6 76.5 0.0 0.0 0.0 1.9 100.0 697 Coast 33.8 56.1 0.2 1.8 0.2 7.9 100.0 540 Eastern 20,3 76.4 0.1 0.0 0.5 2.7 100.0 1227 Nyanza 22.4 72.5 0.1 0.0 0.0 5.0 100.0 1011 Rift Valley 25.9 66.7 1.0 1.0 0.1 5.2 100.0 1309 Western 14.9 80.5 0.4 0.l 0.1 3.8 100.0 1001 Mother's education No education 19.9 68.4 0.5 1.7 0.0 9.5 100.0 115l Primary incomplete 21.2 74.0 0.4 0.0 0.1 4.3 100.0 1922 Primary complete 23.6 72.9 0.2 0,3 0.4 2.6 100.0 1680 Secondary+ 25.9 72.3 0.4 0.0 0.l 1.2 100.0 1309 All births 22.6 72.3 0.3 0.4 0.2 4.2 100.0 6062 Note: Figures are for births in the period 1-59 months preceding the survey. If the respondent mentioned more than one provider, only the most qualified provider is considered. Data on the number and timing of visits made by pregnant women are given in Table 8.2 and in Figure 8.1. For 64 percent of the births in the five years before the survey, mothers made 4 or more antenatal care visits, while 27 percent made 2-3 visits. Four percent of the women did not make any visits to health facilities for antenatal care during their pregnancies. The median number of antenatal care visits was 4.7, far fewer than the recommended 12 visits. Well over half (56 percent) of births in Kenya benefit from antenatal care before the sixth month of gestation. However, one third of pregnant women do not receive antenatal care until the sixth or seventh month of pregnancy. The median time at which mothers start antenatal visits is 5.6 months. 94 Table 8.2 Number of antenatal care visits and stage of pregnancy Percent distribution of live births in the five years preceding the survey by number of antenatal care visits, and by the stage of pregnancy at the time of the first visit, Kenya 1993 Characteristic Percent Number of visits 0 3.8 1 2.6 2-3 26.6 4+ 63.9 Don't know/missing 3.2 Total I00.0 Median 4.7 Months pregnant at time first visit No antenatal care 3.8 Less than 6 months 56.3 6-7 months 34.3 8+ months 4.1 Don't know/missing 1.5 Total 100.0 Median 5.6 Number of births 6062 Note: Figttres are for births in the period 1-59 months preceding the survey. Figure 8.1 Percent Distribution of Births by Number of Antenatal Care Visits and Timing of First Visit NUMBER OF VISITS ~2.301 34 4+ V~ Don't Know/Missingp 3 TIMING OF FIRSTo, IviS:i:~ 4 < 6 Months~ 6-7 Months ~ Don't Kn;w+/MM:;il:; ~22 4 YfJfJf~fJJ~fJJJJf~127 ~Jf~JfJ~fJ~J~JJJJ~J~J~f~fJ~J~J~Jf~J~J~J~f~fff/~164 56 34 I 10 20 30 40 50 60 Percent 70 KDHS 1993 95 Tetanus Toxoid Vaccinations Tetanus toxoid injections are given during pregnancy for prevention of neonatal tetanus. This is an often fatal disease caused by unhygienic conditions at childbirlh. For full protection, a pregnant woman needs two doses of the toxoid. However, i fa woman was vaccinated during a previous pregnancy, she may only require one dose during a subsequent pregnancy. Five doses are considered to provide lifetime protection. In order to estimate the extent of tetanus toxoid coverage during pregnancy, the KDHS collected data for each birth in the five years before the survey as to whether the mother had received tetanus toxoid vaccinations and, if so, how many. These results are presented in Table 8.3. Table 8.3 Tetanus toxoid vaccination Percent distribution of births in the five years preceding the survey, by number of tetanus toxoid injections given to the mother during pregnancy and whether the respondent received an antenatal card, according to selected background characteristics, Kenya 1993 Number of tetanus toxoid injections Percentage Two given Number Background One doses Don't know/ antenatal of characteristic None dose or more Missing Total card births Mother's age at birth < 20 11.0 31.4 56.5 1.0 100.0 91.4 1026 20-34 8.8 38.9 51.5 0.8 100.0 94.7 4249 35+ 13.3 37.9 48.0 0.8 100.0 93.0 787 Birth order 1 9.0 28.6 61.1 1.3 100.0 92.8 1226 2-3 8.6 39.7 51.0 0.7 100.0 94.8 1885 4-5 8.9 41.3 48.9 0.9 100.0 95.3 1361 6+ 12.6 38.5 48.4 0.5 100.0 92.5 1591 Residence Urban 6.0 36.2 56.7 1.1 100.0 96.7 773 Rural 10.4 37.7 51.2 0.8 100.0 93.5 5289 Province Nairobi 9.0 35.5 54.0 1.5 100.0 96.0 276 Central 8.4 33.7 57.1 0.9 100.0 96.1 697 Coast 14.5 30.6 52.7 2.1 100.0 88.7 540 Eastern 9.1 40.5 50.3 0.2 100.0 94.2 1227 Nyanza 7.9 43.7 47.4 1.0 100.0 94.3 101l Rift Valley 11.6 37.7 50.3 0.4 100.0 92.9 1309 Western 8.9 34.2 56.0 0.9 100.0 95.1 1001 Mother's education No education 16.3 36.9 46.0 0.8 100.0 87.7 1151 Primary incomplete 11.0 38.1 50.3 0.6 100.0 94.5 1922 Primary complete 7.5 36.8 55.1 0.6 100.0 95.2 1680 Secondary+ 5.3 38.2 55.3 1.3 100.0 96.9 1309 All births 9.8 37.5 51.9 0.8 100.0 93.9 6062 Note: Figures are for births in the period 1-59 months preceding the survey. 96 The data indicate that tetanus toxoid coverage is widespread in Kenya. For more than half of births, the mothers received two or more tetanus toxoid injections during pregnancy and for slightly less than 40 percent, the mothers received one dose. Only 10 percent of births did not benefit from any tetanus toxoid vaccination during pregnancy. Ninety-four percent of mothers said they received an antenatal card. Tetanus toxoid coverage is lower among older mothers, births of order 6 and over, and rural mothers. Provincial differentials show that the proportion of births to mothers who received two or more tetanus toxoid doses during pregnancy was highest in Central Province (57 percent) and lowest in Nyanza Province (47 percent). The proportion of births not protected by any tetanus toxoid during pregnancy is highest in Coast Province (15 percent) and lowest in Nyanza Province (8 percent). There is a positive relationship between the mother's education and tetanus toxoid coverage. The proportion of births whose mothers received 2 or more tetanus toxoid doses during pregnancy increases from 46 percent among women with no education to 55 percent among those with secondary school. Also, the proportion of births to women who did not receive any tetanus toxoid vaccine during pregnancy decreases as the level of education increases. Educated women may have greater accessibility to modem medical care, or they may be better informed of the benefits of vaccination, or they may be better able to utilise the services provided. 8.2 Del ivery Care Place of Delivery It is important that mothers deliver their babies in health facilities, where proper medical attention and hygienic conditions can reduce the risk of complication and infections which may cause death or serious illness to either the mother or the baby. In the KDHS, women were asked the type of place where they had delivered each of the children to whom they had given birth in the five years preceding the survey (Table 8.4 and Figure 8.2). Fifty-five percent of births to Kenyan women are delivered at home and 44 percent are delivered in health facilities (including public health facilities, mission hospitals/clinics and private hospitals/clinics). Deliveries at home are more common among women age 35 and older (67 percent) than among those age 20 and below (50 percent). Since first births have higher risks of complications than later births, it is encouraging that they are more likely to occur in health facilities. As expected, there is a large urban-rural differential in place of delivery. Sixty percent of rural births are delivered at home, compared to 21 percent of urban births. A much greater proportion of births in Coast and Western Provinces (68 percent and 66 percent, respectively) were delivered at home than in Nairobi and Central Province (20 percent and 27 percent, respectively). There is a strong relationship between mother's education and place of delivery. The proportion of births delivered at home decreases from 77 percent among mothers with no education to 28 percent among mothers with at least some secondary education. Conversely, the proportion of births delivered in health facilities increases from 22 percent among women with no education to 71 percent among women with secondary education. Women who visit health professionals during pregnancy are more likely to deliver at health facilities than women who do not; half of the women who make four or more antenatal visits deliver at health facilities, compared to only 13 percent of those who do not obtain any antenatal care. The likelihood of delivering in health facilities increases with the number of antenatal visits. 97 Table 8.4 Place of delivery Percent distribution of births in the five years preceding the survey, by place of delivery, according to selected background characteristics, Kenya 1993 Her home/ Public Mission Private Background other health hospital hospital characteTistic home facility clinic clinic Other Missing Total Number Mother's age at birth < 20 49.6 41.1 6.5 1.5 0.6 0.7 100.0 1026 20-34 53.5 34.1 8.9 2.3 0.8 0.5 100.0 4249 35+ 67.3 24.1 5.2 1.6 1.0 0.7 100.0 787 Birth order 1 39.3 47.0 10.3 2.3 0.4 0.6 100.0 1226 2-3 51.8 35.9 8.7 2.3 0.8 0.6 100.0 1885 4-5 56.6 32.4 7.4 2.4 0.9 0.3 100.0 1361 6+ 68.2 23.0 5.9 1.4 0.9 0.6 100.0 1591 Residence Urban 21.2 58.0 12.0 7.6 0.6 0.6 100.0 773 Rural 59.5 30.5 7.4 1.3 0.8 0.6 100.0 5289 Province Nairobi 19,5 60.0 11.5 7.5 0.5 1.0 100.0 276 Central 26.6 63.4 7.4 1.7 0.9 0.0 100.0 697 Coast 67.9 26.7 1.5 2.7 0.2 1.0 100.0 540 Eastern 52.6 32.4 12.8 0.7 0.7 0.8 100.0 1227 Nyanza 61.0 28.4 6.4 2.9 0.9 0.5 100.0 1011 Rift Valley 59.8 31.6 5.8 1.5 0.7 0.6 100.0 1309 Westem 66.0 20.8 9.6 2.2 1.1 0.3 100.0 1001 Mother 's education No education 76.7 16.9 3.8 1.2 0.4 1.1 100.0 1151 Primary incomplete 65.1 27.4 5.6 0.7 0.9 0.2 100.0 1922 Primary complete 48.6 40.4 7.9 1.7 0.5 0.9 100.0 1680 Secondary+ 27.5 50.4 15.4 5.4 1.2 0.1 100.0 1309 Antenatal care visits None 84.5 7.9 3.2 1.5 1.3 1.6 100.0 229 1-3 visits 62.1 29.5 6.2 1.2 0.9 0.0 100.0 1766 4 or more visits 50.2 37.3 9.2 2.5 0.7 0.1 100.0 3871 All births 54.6 34.0 8.0 2.1 0.8 0.6 100.0 6062 Note: Figures are for births in the period 1-59 months preceding the survey. Excludes those without information about antenatal visits. 98 Figure 8.2 Percent Distribution of Births in the Five Years Preceding the Survey by Place of Delivery Public health facility 34% Mission health facility 8% Private facility 2% Other 1% Home 55% KDHS 1993 Assistance During Delivery The type of assistance a woman receives during the birth of her child has health implications for the mother and child. Births that are delivered at home arc more likely to occur without assistance from a medically qualified person (or from anyone), whereas births delivered at health facilities are more likely to be delivered by trained medical personnel. Table 8.5 shows the percent distribution of births in the five years before the survey by type of assistance during delivery according to background characteristics. Forty-live percent of the births in Kenya are assisted by medically trained personnel---either doctors (12 percent) or nurses or midwives (33 percent). This corresponds almost exactly to the 44 percent of births that take place in medical facilities. One in live births (21 percent) is assisted by trained or untrained traditional birth attendants, while 23 percent are assisted by relatives. One in ten births is delivered by the mother without assistance from anyone. Except for a slight apparent decline in the proportion of births assisted by doctors and a consequent increase in the proportion assisted by traditional birth attendants, the pattem of assistance at deliveries has not changed significantly between 1989 and 1993. The 1993 KDHS data indicate that assistance at delivery varies according to characteristics of the mother. Births to mothers age 20 and below are more likely to be assisted by medically trained personnel than are births to mothers age 35 and over, one-quarter of which do not receive any assistance. Similarly, first births are more likely to be assisted by doctors, nurses or midwives (62 percent) than births of higher order. This is encouraging, since first births pose greater health risks than subsequent births. 99 Table 8.5 Assistance dur ing delivery Percent distribution of births in the five years preceding the survey, by type of assistance dur ing delivery, according to selected background characteristics, Kenya 1993 Attendant assisting durh~g delivery Trained Untrained Trained trad. trad. Background nurse/ birth birth characteristic Doctor Midwife attendant attendant Relative Other No one Missing Total Number Mother's age at birth < 20 13.5 37.8 9,1 13.6 21.8 0.2 3.5 0.5 100.0 1026 20-34 12,7 34.0 8,7 11.9 23.1 0.4 9.0 0.3 100.0 4249 35+ 8.7 22.2 8.5 13.2 22.1 0.2 24.6 0.5 100.0 787 Birth order 1 18.2 43.9 S.0 10.3 17.1 0.4 1.8 0.3 100,0 1226 2-3 12.0 36.9 8.6 11,9 23.9 0.5 5.9 0.4 100.0 1885 4-5 12.2 30.5 9.1 13,0 24.2 0.3 10.5 0.2 100.0 1361 6+ 8.2 22.4 9.2 14.0 24.5 0.2 21.0 0.4 100.0 1591 Residence Urban 23.7 56.2 4.2 2.4 10.4 0.2 2,9 0.0 100.0 773 Rural 10.6 29.7 9.4 13.8 24.6 0.4 11.1 0.4 100.0 5289 Province Nairobi 26.0 54.0 6.0 2.0 8.5 0.0 3.5 0.0 100.0 276 Central 19.3 54.5 0.4 1.6 15.6 0.6 8.0 0.0 100.0 697 Coast 12.3 20.1 5.9 18.6 34.3 0.4 7.8 0.7 100.0 540 Eastern 10.5 35.8 9.7 18.3 18.8 0.4 6.1 0.5 100.0 1227 Nyanza 12.5 26.8 9.2 11.3 23.1 0.0 16.5 0.5 100.0 1011 Rift Valley 11.6 28.4 6.0 19.6 28.2 0.3 5.5 0.3 100.0 1309 Western 6.6 28.5 18.9 3.6 22.7 0.6 19.0 0.1 1(30.0 1{301 Mother's education No eduealion 6.5 16.2 9.1 17.5 32.3 0.1 17.5 0.7 100.0 1151 Primary incomplete 8.6 25.7 10.6 15.1 26.7 0.3 12.9 0.1 100.0 1922 Primary complete 15.4 36.7 8.5 11.3 20.9 0.4 6.4 0.4 100.0 1680 Secondary+ 19.0 54.2 6.0 5.2 10.9 0.5 4.1 0.1 I00.0 1309 Antenatal care visits None 3.5 10.2 8.0 12.1 46.9 0,4 19.0 0.0 100.0 229 1-3 visits 9.8 28.2 8.0 14.0 27.2 0.2 12.4 0.0 100.0 1766 4 or more visits 13.9 36.4 9.3 11.7 19.9 0.3 8.6 0.0 100.0 3871 Tclal 12.3 33.1 8.7 12.4 22.8 0.3 10.1 0.3 100.0 6062 Note: Figures are for births in the period 1-59 months preceding the survey. If the respondent mentioned more than one attendant, only the most qualified attendant is considered. As might be expected, births in urban areas are more likely to be assisted by medical personnel (doctors, nurses or midwives) than mr'al births. Similarly, a higher proportion of births to women in Nairobi and Central Province are assisted by medical personnel than births to women in other provinces. Also notable is the relatively high proportion of births in Coast Province that are assisted by relatives (34 percent), and the high proportion of births in Western and Nyanza Provinces that do not benefit from any assistance at delivery (19 and 17 percent, respectively). Level of education of the mother is positively related to assistance by medical personnel. The proportion of births assisted by doctors, nurses and midwives increases from 23 percent of births to mothers with no education to 73 percent of births to women with at least some secondary education. Not surprisingly, the more antenatal visits a woman makes when pregnant, the greater the likeli- 100 hood that her baby will be delivered with assistance from medically trained staff. Of the births whose mothers received no antenatal care, only 14 percent were assisted by doctors, nurses or midwives, compared to half of the births whose mothers had four or more antenatal visits. Delivery Characteristics The KDHS collected information on several other aspects relating to the delivery of births, including the extent of caesarean section and premature deliveries. Questions on birth weight and the size of the baby at birth were included to estimate the proportion of low birth weight infants. Table 8.6 summarises the data on these delivery characteristics for births in the five years before the survey. The results indicate that 5 percent of births in Kenya are by caesarean section and, according to respondents, only 4 percent of births are delivered prematurely. Birth weights are not available for just over half of the births. Among the 44 percent for which data are available, 9 percent weighed less than 2.5 kilograms and thus can be classified as low birth weight infants. According to the re- spondent's own assessment of her infant's size, about 16 percent of births are smaller than average or very small in size and 32 percent are larger than average or very large. 8.3 Childhood Vaccination In order to assist in the evaluation of the Kenya Expanded Programme on Immunisation (KEPI) of the Ministry of Health, the KDHS collected information on vaccination coverage for all chil- dren born in the five years preceding the survey, although the data presented here are restricted to children who were alive at the time of the survey. KEPI recommends the following schedule of child- Table 8.6 Characteristics of delivery Percent distribution of births in the five years preceding the survey by whether the delivery was by caesarean section, whether premature, and by birth weight and the mother's estimate of baby's size at birth, Kenya 1993 Characteristic Percent Caesarean Yes 5.2 No 93.1 Missing 1.7 Total 100.0 Premature birth Yes 3.7 No 95.7 Don't know/Missing 0.7 Total 100.0 Birth weight Less than 2.5 kg. 3.8 2.5 kg. or more 40.1 Don't know/missing 3.8 Not weighed 52.3 Total 100.0 Size at birth Very large 3.7 Larger than average 28.0 Average 51.6 Smaller than average 13.5 Very small 2.1 Don't know/Missing 1.1 Total 100.0 Number of births 6062 Note: Figures are for births in the period 1-59 months preceding the survey. hood vaccinations: polio and BCG at birth; polio and DPT at 6, 10, and 14 weeks; and measles at 9 months of age. BCG is for protection against tuberculosis and DPT is for protection against diphtheria, pertussis, and tetanus. In order to be considered fully vaccinated, a child should receive the following vaccinations: BCG, measles and three doses each of DPT and polio, not including the dose of polio given at birth. Data Quality Information on vaccination coverage was collected in two ways in the KDHS: from vaccination cards shown to the interviewer and from mothers' verbal reports. The majority of health centres and clinics in Kenya provide cards on which vaccinations are recorded. If a mother was able to present such a card to the interviewer, this was used as the source of information, with the interviewer recording vaccination dates directly from the card. In addition to collecting vaccination information from cards, there were two ways of collecting the information from the mother herself. If a vaccination card was presented, but a vaccine had not been recorded on the card as being given, the mother was asked to recall whether that particular vaccine had been given. If the mother was not able to provide a card for the child at all, she was asked to recall whether the child had received BCG, polio (including the number of doses for polio), and measles 101 vaccinations. Since polio and DPT vaccines are usually administered together, the number of polio doses reported by the mother was assumed to equal the number of DPT doses. During the process of editing the data on computer, it was discovered that for a small number of children, there was evidently some confusion between the dose of polio vaccine given at birth (polio0) and that given at around six weeks of age (polio 1), since the dates given for polio0 and for the first dose of DPT (which is not to be administered at birth) were identical. This confusion could be due to the fact that polio0 has only recently been introduced in Kenya on a routine basis and consequently, not all children have received this vaccination. It is also possible that the vaccination cards used in some areas do not include space to list this vaccination. In any case, some interviewers evidently mistakenly wrote the date given on the vaccination card for poliol in the space on the questionnaire that was meant for listing polio0. For the purposes of this report, such children are assumed not to have received the dose of polio given at birth. Another minor complication arises in how to translate mothers' reports on the number of doses of polio into the specific doses their children have received, i.e., whether a report of three doses of polio means that the child received polio0, poliol and polio2, or poliol, polio2, and polio3. Since it is likely that mothers may not be aware of immunisations that their babies receive at birth---especially when delivery takes place in a health facility--it was decided to assume that polio0 was given only if the mother reported that her child received more than three doses of polio. To the extent that mothers do in fact remember the birth dose of polio, the coverage rates for polio0 presented here would be slightly underestimated. However, to the extent that some vaccinations reported as polio0 were given well after birth, at say, 4, 5 or even 6 weeks of age, they might have more of the effect of a polio I vaccination. Vaccination Coverage Information on vaccination coverage is presented in Table 8.7, according to the source of information used to determine coverage, i.e., the vaccination card or mother's report. Data are presented for children age 12-23 months, thereby including only those children who have reached the age by which they should be fully vaccinated. The first indicator shows the proportion of the children who had been vaccinated at any age up to the time of the survey. These results are presented according to the source of the information used to determine coverage, i.e., vaccination record or mother's report. The second indicator shows the proportion of children who had been vaccinated by age 12 months, the age at which vaccination coverage should be complete. Figure 8.3 presents coverage figures as assessed from both vaccination cards and mothers' reports. According to information from both the vaccination records and mothers' recall, 96 percent of children age 12-23 months have received a BCG vaccination and first doses of DPT and polio. Coverage declines for subsequent doses of DPT and polio. Only 87 percent of children receive the third doses of DPT and polio; dropout rates ~ between the first and third doses of DPT and of polio are about 10 percent. Given the recency of the addition of the birth dose of polio (polio0) to the recommended schedule of childhood vaccinations, it is encouraging that 62 percent of children have received it. The coverage rate for measles (84 percent) is only slightly lower than that for three doses of DPT and polio. Overall, 79 percent of children 12- 23 months are fully vaccinated; only 3 percent have not received any vaccinations at all. As mentioned earlier, it is recommended that children complete the schedule ofimmunisations during their first year of life, i.e., by 12 months of age. Table 8.7 shows that, among children age 12-23 months at thetimeofinterview, 71 percent had been fully vaccinated before their first birthday. With regard to specific vaccines, children were least likely to have received the measles vaccination by age 12 months. ~Dropout rate = (Dose 1 - Dose 3) * 100 / Dose 1 102 Table 8.7 Vaccinations by source ofinformailon Percentage of children 12-23 months who bed received specific vaccines at any time before the survey and the percentage vaccinated by 12 months of age, by whether the information was from a vaccination card or from the mother, Kenya 1993 Percentage of children who received: Percentage with DPT Polio vacci- Number Source of nation of information BCG 1 2 3+ Birth 1 2 3+ Measles All I None card children Vaccinated at any time before the survey Vaccination card 68.7 68.7 67.3 65.2 56.5 68.9 67.4 65.1 60.1 59.2 0.1 69.2 778 Mother's report 27.5 27.1 25.3 21.6 5.1 27.1 25.3 21.6 23.7 19.5 3.2 30.8 346 Either source 96.3 95.8 92.6 86.9 61.6 96.0 92.7 86.7 83.8 78.7 3.3 100.0 1124 Vaccinated by 12 months of age Vaccination card 94.7 95.3 91.9 85.3 61,4 95.3 91.5 85.4 76,3 70.7 4.3 1124 Note: The DPT coverage rate for children without a written record is assumed to be the same as that for polio vaccine since mothers were specifically asked wbethcr the child bed received polio vaccine. For children whose information was based on the mother's report, the proportion of vaccinations given during the frst year of life was assumed to be the same as for children with a written record of vaccination. See text for discussion of coverage for birth dose of polio. xChfldren who are fully vaccinated (i.e., those who have received BCG, measles and three doses of DPT and polio). Figure 8.3 Vaccination Coverage Among Children Age 12-23 Months Percent 100 9O 80 70 6O 5O 4O 3O 20 10 0 BCG 1 2 3 Polio Note: Based on health card information and mothers' reports I 2 3 Measles All None DPT KDHS 1993 103 Differentials in Vaccination Coverage Table 8.8 shows vaccination coverage rates among children age 12-23 months by selected background characteristics, including the child's sex and birth order, urban-rural residence, province, and the mother's education level. The figures refer to the proportion of children receiving the vaccinations at any time up to the date of the survey and they are based on information from both the vaccination records and mothers' reports. The table includes information on the proportion of children for whom a vaccination record was shown to the interviewer. The data indicate that male and female children have an equal chance of receiving vaccinations. Children of birth order 6 and above are less likely than children of lower birth orders to receive the basic childhood immunisations. The difference is particularly wide for the measles vaccine which is given to only 71 percent of children of birth order 6 and above, compared to 90 percent of children of birth order 3 or less. Table 8.8 Vaccinations by background characteristics Percentage of children 12-23 months who had received specific vaccines by the time of the survey (according to the vaccination card or the mother's report) and the percentage with a vaccination card, by selected background characteristics, Kenya 1993 Percentage of children who received: P¢i'ceR~ge with DPT Polio vacei- Number Background nation of characteristic BCG I 2 3+ Birth 1 2 3+ Measles All t None card children Sex Male 96.6 96.1 92.8 87.0 60.8 96.5 93.1 87.1 83.2 78.4 3.1 68.3 579 Female 95.9 95.5 92.4 86.7 62.4 95.5 92.2 86.3 84.4 79.0 3.5 70.1 545 Birth order 1 96.1 96.5 93.3 89.6 66.4 96.5 93.0 89.5 89.9 85.2 3.5 70.1 238 2-3 97.6 97.2 94.2 90.1 63.5 97.5 94.2 90.6 89.6 84.8 1.9 65.4 379 4-5 99.0 98.1 96.4 88.8 63.8 98.1 96.4 88.7 83.3 78.2 0.8 78.9 242 6+ 92.0 91.2 86.3 77.9 52.7 91.6 86.8 76.9 70.5 64.8 7.3 64.9 265 Residence Urban 98.9 98.6 93.7 92.5 62.3 98.6 93.7 92.5 84.0 80.9 1.1 58.7 177 Rural 95.8 95.3 92.4 85.8 61.5 95.6 92.5 85.6 83.7 78.3 3.7 71.1 947 Province Nairobi 100.0 100.0 100.0 100.0 57.8 100.0 100.0 100.0 86.7 86.7 0.0 53.3 62 Central 97.4 97.4 95.2 94.4 65.2 97.4 95.2 94.4 94.2 92.6 2.6 65,4 148 Coast 94.8 95.0 91.1 85.6 72.7 95.0 91.1 85.6 88.0 81.1 4.1 74.5 80 Eastern 99.0 97.8 96.1 90.8 72.4 98.4 96.1 90.7 90.0 85.0 1.0 76.6 209 Nyanza 93.6 91.9 88.6 79.6 56.4 91.9 88.6 79.9 76.1 69.7 6.1 60.8 174 Rift Valley 97.0 97.1 92.9 84.8 56.1 97.1 92.7 85.1 83.3 75.9 2.1 68.8 263 Western 93.1 93.1 88.1 82.3 55.8 93.9 88.8 80.9 73.8 69.5 6.1 75.3 188 Mother's education No education 89.1 87.6 83.2 74.0 49.5 88.1 83.2 73.8 68.9 63.3 10.6 58.7 188 Primary incomplete 96.9 96.3 91.4 85.8 60.0 96.7 91.8 85.8 78.4 74.5 2.4 73.8 331 Primary complete 98.2 97.9 96.3 89.9 67.1 97.9 96.1 90.1 89.4 83.6 1.7 72.6 331 Secondary+ 98.0 98.4 96.1 93.3 65.2 98.4 96.1 92.5 93.7 88.5 1.3 66.6 274 All children 96.3 95.8 92.6 86.9 61.6 96.0 92.7 86.7 83.8 78.7 3.3 69.2 1124 Note: The DPT coverage rate for children without a written record is assumed to be the same as that for polio vaccine since mothers were specifically asked whether the child had received polio vaccine. IChildren who are fully vaccinated (i.e., those who have received BCG, measles and three doses of DPT and polio). 104 It is notable that urban-rural differences in vaccination coverage are minimal. This implies that the KEPI programme has managed to penetrate the rural areas, making immunisations widely available. There are still some parts of Kenya, however, where vaccination coverage lags behind. In Nyanza and Western Provinces, only 70 percent of children age 12-23 months are fully immunised, compared to 93 percent of children in Central Province and 87 percent of the children in Nairobi. Although some of the provincial differences are due to slightly lower proportions of children in Nyanza and Westem Provinces receiving initial vaccinations such as BCG and DPT1, most of the difference is due to higher dropout rates between the first and third doses of DPT and polio and especially to lower proportions who receive the measles vaccine. As expected, the proportion of children who receive all the recommended vaccinations increases with the education level of the mother, from 63 percent of children of mothers with no education to 89 percent of those whose mothers have at least some secondary education. Overall, vaccination cards were produced for 69 percent of children age 12-23 months. Differentials in vaccination card levels generally follow those of the proportion fully immunised. Trends in Vaccination Coverage There are two ways to assess trends in vaccination coverage from KDHS data. One is to compare the data from the 1993 and 1989 surveys. This is made difficult by the fact that the questions were substantially altered between the two surveys. In the 1989 KDHS, if mothers could not produce a vaccination card for their children, they were merely asked if the child had ever been vaccinated, while in the 1993 survey, they were asked about specific vaccinations the child might have received. Rough estimates of what coverage rates would be if mothers had been asked to report on specific vaccinations have been produced indirectly (Boerma et al., 1990). This methodology results in an estimate that 63 percent of the children age 12-23 months in 1989 had been fully immunised, which implies that coverage has increased substantially between 1989 and 1993 (to 79 percent). While increases in the proportions of children age 12-23 months who receive BCG, DPT and polio were modest, the increase in the proportion receiving measles vaccine were much larger--from 72 to 84 percent. Another way to measure change in vaccination coverage is to compare coverage among children of different ages from the 1993 KDHS. Children age 24-35 months at the time of the survey were age 12-23 months one year before the survey, those age 36-47 months were age 12-23 months two years before the survey, etc. Of course, retrospective reporting has flaws, since vaccination cards are less likely to be available for older children and mothers' recall may be less accurate. However, the data can provide evidence of trends. Table 8.9 shows the proportion of children of various age cohorts who had received the various childhood vaccinations by 12 months of age (in order to maintain comparability). Data are derived from either the vaccination card or the mothers' reports. For children whose information was based on the mother's recall, the distribution of vaccinations during the first year of life was assumed to be the same as that for children for whom a vaccination record was available. The first row of Table 8.9 shows the proportion of children age 12-59 months for whom a vaccination card was seen by the interviewer. Overall, records were seen for 61 percent of the children, The percentage of children for whom a vaccination card was seen decreases with age, from 69 percent of children age 12-23 months, to 53 percent of those age 48-59 months. This decline is most likely due to a tendency to misplace or lose the cards over time and/or a tendency to discard the cards once children have been fully vaccinated. 105 Table 8.9 Vaccinations in the first year of life Percentage of children one to four years of age for whom a vaccination card was shown to the interviewer and the percentage vaccinated for BCG, DPT, polio, and measles during the first year of life, by current age of the child, Kenya 1993 Vaccine Current age of child in months All children 12-59 12-23 24-35 36-47 48~59 months Vaccination card shown to Interviewer 69.2 62.7 57.5 52.9 60.6 Percent vaccinated at 0 - l l months a BCG 94.7 93.5 92.6 92.9 93.4 DPT I b 95.3 93.6 91.6 91.1 92.9 DPT 2 91.9 89.5 89.0 87.9 89.6 DPT 3 85.3 80.4 81.9 78.1 81,5 Polio at birth 61.4 56.6 48.7 47.2 53.4 Polio 1 95.3 93.3 91.3 91.2 92.8 Polio 2 91.5 89.6 88.2 87.8 89.3 Polio 3 85.4 80.0 80.3 78.2 81.0 Measles 76.3 69.9 71.7 69.7 71.9 All vaccinations e 70.7 63.8 64.9 60.9 65.1 No vaccinations 4.3 5.6 6.1 6.8 5.7 Number of children 1124 1124 1231 1050 4529 alnformation was obtained either from a vaccination card or from the mother if there was no written record. For children whose information was based on the mother's report, the proportion of vaccinations given during the first year of life was assumed to be the same as that for children with a written vaccination record. bThe DPT coverage rate for children without a written record is assumed to be the same as that for polio vaccine, since mothers were specifically asked whether the child had received polio vaccine. CChildren who have received BCG, measles and three doses of DPT and polio vaccines. The data imply that the vaccination programme has gradually improved its coverage rates over the past few years. For example, the proportion of children who were fully immunised by their first birthday rose from 61 percent of those who were age 48-59 months old at the time of the survey to 71 percent for those age 12-23 months. As expected, gains were greatest for the birth dose of polio. Large gains were also made in the proportion who received the third doses of DPT and polio, as well as in coverage for measles. 8.4 Childhood Illness and Treatment Three illnesses that are of major importance for infant and child survival in Kenya are discussed in this section. They are acute respiratory infection, fever and diarrhoea. Estimates of the prevalence of these illnesses, as well as data concerning types of treatment, are presented. 106 Acute Respiratory Infection Acute respiratory infection (ARI) is one of the major causes of morbidity and mortality among children in Kenya. Common symptoms associated with severe respiratory infection include fever, cough, and difficult or rapid breathing. Early diagnosis and treatment with antibiotics can prevent a large proportion of deaths from respiratory infections. The prevalence of ARI was estimated in the KDHS by asking mothers if their children under age five had been ill with coughing accompanied by short, rapid breathing ~" during the two weeks preceding the survey. Mothers whose children had experienced these symptoms were asked what they had done to treat the illness. It bears mentioning that information on disease prevalence is more subjective than many other topics covered in the KDHS; it is highly dependent on what symptoms the mother considers serious. Similarly, reporting of treatment practices depends on how much mothers know about the medicines their children may receive. Mothers may not know whether the pills or syrups their children receive contain antibiotics or not. Thus reporting may vary widely within the country, due to cultural differences in reporting. Information on the prevalence and treatment of ARI is presented in Table 8.10. The KDHS results indicate that the prevalence of cough with rapid breathing in the two weeks before the survey was 18 percent among children under five. ARI is more common among children age 6-23 months, almost one-quarter of whom had a cough with rapid breathing in the two weeks before the survey. There is no significant difference in ARI prevalence by sex or birth order, but rural children are slightly more likely than urban children to have ARI symptoms. Nyanza and Central Provinces lead the other provinces in ARI prevalence, while Nairobi has the lowest prevalence of this disease. There is no clear relationship between ARI prevalence among children and education level of their mothers. Fifteen percent of children whose mothers had no education had ARI during the two weeks preceding the survey, compared to 21 percent of those whose mothers had incomplete primary education and 18 percent of those whose mothers had completed primary school. Overall, just over half of children who have symptoms of ARI are taken to a health facility for treatment. Of all children with symptoms, over 40 percent are given cough medicine, over 20 percent are given antibiotic pills or syrups, and almost 20 percent are given injections. Only 13 percent of children with ARI are not treated at all. Children of educated mothers are more likely to be taken to a health facility than those whose mothers had less education. Likewise, children in Coast Province who have symptoms of ARI are more likely to be taken to a health facility, than are children in urban areas. On the other hand, those in Nyanza Province are less likely to be taken to a health facility but more likely to receive a home remedy. It is also more common for children with ARI symptoms from Westem Province to receive antibiotics and for those in Nairobi to receive no treatment at all. Children from Coast and Western Provinces are more likely to receive injections than children from other provinces. 2Cough and short, rapid breathing are signs and symptoms of pneumonia. The KDHS estimate of ARt prevalence corresponds to an estimate of the prevalence of children who need treatment for presumed pneumonia and does not include other ARI-related conditions (coughs and colds, wheezing, ear infection, and streptococcal sore throat) covered under the WHO guidelines for ARI case management. 107 Table 8.10 Prevalence and treatment of acute respiratory infection Percentage of children under five years who were ill with a cough accompanied by rapid breathing during the two weeks preceding the survey, and the percentage of ill children who were ~eated with specific remedies, by selected background characteristics, Kenya 1993 Among children with cough and rapid breathing Percentage Percentage percentage treated with: of children taken to with cough a health Antibiotic None/ Number Background and rapid facility or pill or Cough llome Don't know/ of characteristic breathing provider I syrup Injection syrup remedy Other Missing children Child's age < 6 months 18.5 46.4 13.3 7.6 41.6 4.1 37.3 19.1 465 6-11 months 22.6 59.2 27.5 19.2 40.0 3.2 40.8 10.7 593 12-23 months 24.0 53.0 23.0 19.9 51.9 2.1 40.0 10.7 1124 24-35 months 19.0 49.6 21.0 16.4 42.2 4.6 36.5 13.8 1124 36-47 months 14.9 55.4 23.9 15.4 40.0 6.4 45.9 8.6 1231 48-59 months 13.2 43.7 22.3 15.3 35.8 0.5 42.3 1%0 1050 Sex Male 17.8 52.2 24.8 14.9 41.2 4.4 39.0 13.9 2792 Female 18.9 51.3 20.2 18.3 45.0 2.6 42.0 11.6 2795 Birth order 1 18,2 55,0 22.7 16.1 43,6 1.8 37.2 12,0 1116 2-3 18.6 53.3 20.5 20.1 51.6 4.9 38.8 11.0 1757 4-5 18.9 49.9 26.4 14.5 34.9 3.3 45.8 13.8 1247 6+ 17.7 48.9 21.1 14.7 39.7 3.2 40.6 14.4 1467 Residence Urban 14.8 66.7 23.9 21.8 45.2 2.9 39.1 11.7 720 Rural 18.9 50.0 22.3 16.0 42.9 3.5 40.7 12.8 4867 Province Nalrobi 12,4 60.9 17.4 8.7 43.5 0.0 52.2 21.7 257 Central 20.6 57.9 21.2 14,3 45.2 2.5 35,0 12.5 671 Coast 15.2 72.7 22.7 26.8 42.3 3,7 46.4 6.7 499 Eastern 19.8 55.1 24.0 15.0 45.8 1.1 40.8 9.7 1153 Nyanza 21.4 38.3 19.7 13.3 39.4 7.5 43.6 13.9 852 Rift Valley 15.9 46.4 16.7 11.5 43.8 2.1 31.1 17.9 1251 Western 18.8 51.9 31.9 27.2 41.6 5.2 47.5 10.4 905 Mother's education No education 15.0 47.3 19.8 13.5 37.0 3.9 41.5 14.2 ltJ65 Primary incomplete 20.5 45.0 22.1 18.5 39.0 6.4 37.1 14.2 1712 Primary complete 18.4 56.8 22.9 15.4 50.9 1.9 39.4 10.3 1564 Secondary+ 18.3 59.0 24.3 17.5 44.2 0.6 46.6 12.2 1246 All children 18.3 51.8 22.4 16.6 43.2 3.5 40.5 12.6 5587 Note: Figttres are for children born in the period 1-59 months preceding the survey. 1Includes health post, health centre, hospital, and private doctor. Fever Malaria is endemic in much of Kenya and accounts for a significant proportion of morbidity and mortality in certain areas. Since the major manifestation of malaria is fever, mothers were asked whether their children under age five had had a fever in the two weeks preceding the survey, and what type of treatment was sought, if any. 108 Table 8.11 shows that 42 percent of children under five years of age were reported to have had fever in the two weeks prior to the survey. Of these children, almost half were taken to a health facility and a little less than one-third were reported to have received antimalarial treatment. Fever is more prevalent among children age 6-23 months and among those in Western, Nyanza and Central Provinces. No pronounced differences were observed in the prevalence of fever by either sex, birth order, urban-rural residence or maternal education. Table 8.11 Prevalence and treatment of fever Percentage of children under five years who were ill with a fever during the two weeks preceding the survey, and the percentage of ill children who were treated with specific remedies, by selected background characteristics, Kenya 1993 Among children with fever Percentage Percentage treated with: Percentage taken to of children a health Don't Number Background with facility or Anti- Anti- Home know/ of characteristic fever provider I malarial biotic Injection remedy Other None Missing children Age of child <6 months 37.7 45.2 31.0 17.3 9.6 5.7 45.1 17.4 0.5 465 6-11 months 55.1 50.7 27.2 22.1 15.4 4.1 49.7 13.8 0.4 593 12-23 months 50.1 50.6 31.6 24.3 19.0 3.0 49.5 13.1 0.3 1124 24-35 months 42.5 45.9 28.6 21.2 14.5 2.8 46.3 15.8 0,5 1124 36-47 months 36.8 45.2 30.4 21.6 15.2 3.5 48.9 15.0 0.5 1231 48-59 months 32.2 44.9 32.3 17.0 16.9 4.2 46.4 14.4 1.1 1050 Sex or child Male 42.6 49.2 32.0 21.8 15.9 4.2 46.1 14.2 0.7 2792 Female 40.9 45.5 28.3 20.7 15.7 3.0 49.9 15.1 0.4 2795 Birth order 1 39.9 52.0 26.3 20.7 15.8 2.6 49.6 15.0 0.5 1116 2-3 40.6 48.2 30.5 19.3 17.9 4.2 51.6 13.9 0.6 1757 4-5 43.9 46.3 32.5 24.0 14.3 3.4 45.4 13.8 1.0 1247 6+ 42.7 44.1 30.6 21.4 14.9 3.8 45.0 15.9 0.1 1467 Residence Urban 38.8 59.8 24.4 23.1 21.0 2.2 52.1 14.7 0.7 720 Rural 42,2 45.7 31.0 21.0 15.1 3.8 47.4 14.6 0.5 4867 Province Nairobi 37.1 58.0 23.2 24.6 18.8 0.0 52.2 23.2 0.0 257 Central 44.9 44.1 12.0 16.5 13.6 2.6 62.3 14.8 0.5 671 Coast 38.2 63.3 30.8 20.2 23.2 1.9 51.7 12.8 0.7 499 Eastern 34.2 58.1 43.0 25.9 15.9 2.1 42.9 7.3 0.5 1153 Nyanza 48.6 38.4 43.8 16.9 12.6 6.1 46.1 12.7 0.5 852 Rift Valley 39.3 45.9 12.6 16.8 13.4 3.6 49.7 23.7 0.8 1251 Western 49.3 41.0 39.1 29.1 19.3 4.7 40.1 11.7 0.3 905 Education No education 41.3 41.2 27.7 17.4 13.2 3.6 44.6 21.2 0.3 1065 Primary incomplete 43.3 44.3 30.7 20.1 17.6 6.1 46.7 13.4 1.2 1712 Primary complete 40.5 49.4 31.3 22.7 16.0 2.5 48.9 13.4 0.4 1564 Secondary+ 41.6 54.6 30.2 24.4 15.4 1.3 51.6 12.5 0.0 1246 Total 41.8 47.4 30.2 21.3 15.8 3.6 48.0 14.6 0.5 5587 Note: Figures are for children born in the period 1-59 months preceding the survey. Includes health clinic, health cen~e, hospital, private doctor 109 There are also differences in treatment practices for those children who have fever. Treatment practices in- dicate that children in the two age groups that are most susceptible to fe- ver are more likely to be taken to a health facility than the others (see Table 8.11). Although prevalence of fe- ver is lower among firstboms than among children of higher birth order, firstboms with symptoms of fever are more likely to be taken to a health fa- cility. Among children with fever, those in urban areas tend to be taken to health facilities more often than their rural counterparts. Children in Coast, Nairobi and Eastern Provinces are more likely than children in other provinces to be taken to a health facility when they show signs of fever. Children in Nyanza Province are less likely to be taken to a health facility, but more like- ly to be given a home remedy than chil- dren in other provinces, while children in Central and Rift Valley Provinces are least likely to be given antimalarial drugs (probably due to the lower inci- dence of malarial fever in these prov- inces). Data indicate that the higher the education of the mother, the higher the likelihood that a child with fever would be taken to a health facility. Diarrhoea Dehydration engendered by severe diarrhoea is a major cause of morbidity and mortality among Kenyan chi ldren. One treatment for dehydration is oral rehydration therapy (ORT): a solution prepared from Table 8.12 Prevalence of diarrhoea Percentage of children under five years who had diarrhoea and diarrhoea with blood in the two weeks preceding the survey, and the percentage of children who had diarrhoea in the preceding 24 hours, by selected background characteristics, Kenya 1993 Diarrhoea in the All preceding 2 w~ks I diarrhoea in the Number Background All Dian'hoea pre~edin~ of characteristic diarrhoea with blood 24 hours" children Child's age < 6 months 15.0 1.6 7.8 465 6-11 months 23.8 3.6 10.6 593 12-23 months 24.4 3.4 10.2 1124 24-35 months 13.0 3.9 4.1 1124 36-47 months 8.0 1.4 2.4 1231 48-59 months 4.7 0.6 1.6 1050 Sex Male 14.3 2.3 5.5 2792 Female 13.6 2.5 5.5 2795 Birth order 1 15.3 2.3 6.1 1116 2-3 14.6 2.3 5.9 1757 4-5 12.0 2.4 4.6 1247 6+ 13.8 2.5 5.3 1467 Residence Urban 11.9 0.5 5.1 720 Rural 14.2 2.7 5.6 4867 Province Nairobi 10.8 0.5 5.4 257 Central 9.4 0.1 2.3 671 Coast 15.0 2.7 6.1 499 Eastern 12.2 2.3 4.8 1153 Nyanza 17.7 3.6 7.0 852 Rift Valley 11.8 2.2 4.3 1251 Western 19.2 3.8 8.7 905 Mother's education No education 15.1 2.9 6.7 11365 Primary incomplete 15.3 3.7 5.9 1712 Primary complete 13.9 2.2 5.7 1564 Secondary+ 11.2 0.5 3.6 1246 All children 13.9 2.4 5.5 5587 Note: Figures axe for children born in the period 1-59 months preceding the survey. llncludes diarrhoea in the past 24 hours 2Includes diarrhoea with blood commercially produced packets of oral rehydration salts (ORS---also called Oralite). ORS is distributed through health centres and pharmacies in Kenya. The Ministry of Health in Kenya no longer promotes any homemade solution prepared from sugar, salt and water, but instead recommends that any of various commonly taken liquids (soup, breastmilk, coconut milk, fresh fruit juices) be given to those with diarrhoea. Table 8.12 indicates the prevalence of diarrhoea in children under five years of age. Diarrhoea prevalence among children under five years of age was recorded at 14 percent within the two weeks prior to the interview and 6 percent within 24 hours before the survey. Only 2 percent of children under five had bloody diarrhoea (a sign of dysentery) in the two weeks prior to the survey. 110 As with fever and respiratory infection, diarrhoea is more common among children age 6-23 months than among older or younger children. The prevalence of diarrhoea is higher among children in Western, Nyanza and Coast Provinces, and lowest among children in Central Province. This corroborates previous findings (Ewbank et al., 1986, p.57). Diarrhoea prevalence is slightly higher among rural than urban children. It is lower among children of women with at least some secondary education than among those whose mothers have less education. Knowledge and Ever Use of ORS Packets In order to ascertain how widespread knowledge of ORS is in Kenya, the KDHS included a question for mothers of children born in the five years before the survey about whether they had ever heard of ORS or Oralite. The data arc shown in Table 8.13. Table 8.13 Knowledge and use of ORS packets Percentage of mothers with births in the five years preceding the survey who know about and have ever used ORS packets, by selected background characteristics, Kenya 1993 Know Have ever Number Background about ORS used ORS of characteristic packets packets mothers Age 15-19 61.7 34.0 292 20-24 78.7 55.0 1081 25-29 84.8 63.5 984 30-34 85.2 68.0 769 35+ 75.9 55.2 803 Residence Urban 83.7 58.0 561 Rural 79.0 58.2 3368 Province Nairobi 78.4 47.3 204 Central 81.4 51.1 502 Coast 86.8 69.3 359 Eastern 77.6 62.1 788 Nyanza 76.3 53.2 622 Rift Valley 79.4 60.1 841 Western 81.0 58.4 613 Education No education 73.4 55.8 726 Primary incomplete 78.5 58.8 1184 Primary complete 79.1 56.3 1108 Secondary+ 86.9 61.4 911 All mothers 79.7 58.2 3929 Note: Figures include mothers who have given ORS for diarrhoea during the preceding two weeks, although they were not asked about knowledge of ORS packets. Eighty percent of mothers with recent births have heard of oral rehydration salts (ORS) and almost 60 percent have ever used them to treat diarrhoea. Both knowledge and use of ORS rise significantly with age of the mother until age 30-34, before declining sharply among mothers age 35 and over. Although there 111 is a slight difference in knowledge of ORS by urban-rural residence, there is no difference in ever use between the two settings. Knowledge and ever use are highest among mothers in Coast Province (87 percent and 69 percent, respectively), while knowledge of ORS is lowest among mothers in Nyanza Province and ever use is lowest in Nairobi. Knowledge of ORS rises with increasing education, but ever use is more or less the same by education. Treatment of Diarrhoea Forty-one percent of children under five whose mothers reported that they had had diarrhoea in the two weeks before the survey were taken to a health facility for consultation (Table 8.14 and Figure 8.4). Of all children with diarrhoea, one-third were given ORS fluid, half were given more fluids than usual, and over one-third were given home remedies or herbs. About one in six children with diarrhoea was given antibiotics and the same number were given nothing to treat the diarrhoea. Attendance at health facilities was more or less the same for children regardless of age, except that children age 48-59 months were less likely to be taken to health facilities. These older children, as well as infants under six months of age, are also less likely to be given ORS fluid as treatment for their diarrhoea. Male children with diarrhoea are somewhat more likely than female children to be taken to a health facility. The data indicate some differences in the treatment of diarrhoea cases by urban-rural residence. Not only are urban children with diarrhoea more likely than rural children to be taken to a health facility, but they are also more likely to receive ORS fluid, increased fluids of any kind, and antibiotics. Rural children are more likely to be treated with home remedies or herbs. The proportion of children with diarrhoea who are taken to health facilities is highest in Coast Province and lowest in Nairobi. Children in Coast Province are 'also more likely to be given ORS solution. Injections are more commonly administered for diarrhoea in Western Province, while home remedies were more common in Central Province and least common in Rift Valley Province. As expected, children of mothers with at least some secondary education are more likely to be taken to a health facility when they have diarrhoea than are children whose mothers are less educated. They are also more likely to be treated with antibiotics. However, it is notable that other differences in diarrhoea treamrent by education level of the mother are minimal; for example, uneducated women are just as likely to treat thcir children by giving them ORS solution or by increasing their intake of fluids as are women with some secondary education. 112 Table 8,14 Treatment of diarrhoea Percentage of children under five years who had diarrhoea in the two weeks preceding the survey who were taken for treatment to a health facility or provider, the percentage who received increased fluids and oral rehydration solution (ORS), the percentage who received neither ORS nor increased fluids, and the percentage receiving other treatments, according to selected background characteristics, Kenya 1993 Percentage Percentage receiving Percentage Treat- Percentage receiving other treatments: taken to ment receiving neither Number of a health with in- ORS or Home No children Background facility or ORS creased increased Anti- In- remedy/ treat- with characteristic provider I packets fluids fluids biotics jecfion herbs ment Missing diarrhoea Child's age < 6 months 42.1 1%7 42.1 50.2 14.5 7.9 35.0 26.8 0.0 70 6-11 months 41.3 31.3 52.6 36.7 9.8 9.5 37.2 17,5 0.0 141 12-23 months 42.0 34.6 49.0 37.7 20.4 6.6 36.4 16.6 0.7 274 24-35 months 38.1 32.9 50.5 37.8 12.9 4.2 33.6 16.3 0.9 146 36-47 months 45.3 35.4 49.7 36.1 22.1 6.7 37.9 14.5 1.6 98 48-59 months 31.4 23.8 53.1 38.8 19.3 6.5 36.1 15.2 2.1 50 Sex Male 45.1 33.2 48.1 38.7 17.5 6.7 37.0 16.1 1.2 398 Female 36.5 29.9 51.4 38.4 15.9 6.9 35.0 18.5 0.3 381 Birth order 1 43.6 28.6 50.0 40.2 15.9 4.5 35.1 20.3 1.5 170 2-3 46.1 34.0 48.5 38.0 19.2 8.6 31.1 17.0 1.3 257 4-5 39.7 33.9 54.6 29.7 14.3 6.6 42.9 13.7 0.0 149 6+ 32.8 29.2 47.4 44.3 15.9 6.6 38.0 17.8 0.0 203 Residence Urban 52.5 40.4 58.5 30.3 29.0 4.5 26.5 12.5 3.4 86 Rural 39.4 30.5 48.6 39.6 15.2 7.1 37.2 17.9 0.4 693 Province Nalrobi 30.0 10.0 55.0 45.0 20.0 5.0 35.0 15+0 5.0 28 Centrdl 39.7 21.6 66.0 30.0 4.2 3.1 46.3 10.0 3.1 63 Coast 55.5 52.2 36.8 30.8 7.6 5.9 40.3 14,3 0.0 75 :Eastern 34.2 27.0 61.2 30.8 18.8 1.7 37.8 11.7 1.9 141 Nyanza 41.9 30.1 42.3 45.9 24.9 6.9 35.3 19.4 0.0 151 Rift Valley 44.6 42.5 48.9 38.3 6.7 4.5 24.5 22.4 0.0 148 Western 38.1 25.4 46.3 44.0 24.3 14.9 39.6 19.9 0.0 173 Mother's education No education 37.9 39.5 51.6 31.8 10.4 4.2 33.8 13.7 1.8 161 Primary incomplete 35.9 26.3 47.4 42.1 17.9 8.0 34.7 24.0 0.0 261 Primary complete 43.8 31.0 48.8 39.6 10.9 7.9 42.3 12.3 1.4 217 Secondary+ 49.1 33.2 53.2 37.9 30.7 5.8 31.5 16.7 0.0 140 All children 40.9 31.6 49.7 38.5 16.7 6.8 36.0 17.3 0.8 779 1Includes health post, health centre, hospital, and private doctor. 2Includes children born in the period 1-59 months preceding the survey. 113 Figure 8.4 Percentage of Children Under 5 Who Received Various Treatments for Diarrhoea in the Two Weeks Preceding the Survey Taken to Health Fac. 41 ORS Packet 32 i Increased Fluid Antibioti Injectio, Home Remed 0 10 20 30 40 50 Percent 60 KDHS 1993 Feed ing Pract ices The KDHS also directly investigated the extent to which mothers made changes in the amount of fluids that a child received during a diarrhoeal episode. To obtain these data, mothers who re- ported that they were still breastfeeding a child who had diarrhoea during the two-week period prior to the survey were asked whether they had increased the number of times they breastfed the child, de- creased the number, or made no change during this time. All moth- ers who had a child with diarrhoea were also asked whether they had changed the amount that the child was given to drink during the diarrhoeal episode. Table 8.15 shows that, among those chil- dren who were breastfed, 59 percent continued to breastfeed as usu- al and 22 percent were given increased feedings. However, 18 per- cent of such children had tbeir number of breastfeedings decreased or stopped altogether. Of all children with diarrhoea, 39 percent were given the same amount of fluids as usual and 44 percent received more fluids than usual; 14 percent received less fluids than usual. These results suggest that, although the benefit of increasing fluid intake during a diarrhoeal episode is quite widely understood in Kenya, about one in six mothers still curtail fluid intake and/or reduce breast- feeding frequency when their children have diarrhoea. Table 8.15 Feeding practices during diarrhoea Percent distribution of children under five years who had diarrhoea in the two weeks preceding the survey, by feeding practices during diarrhoea, Kenya 1993 Feeding practices Percent llreastfeedlng frequency l Same as usual 59.3 Increased 21.7 Reduced 16.2 Stopped 1.3 Don't know/missing 1.5 Total ] 00.0 Number of children 452 Amount or fluids given Same as usual 39.3 More 43.9 Less 14.3 Don't know/missing 2.5 Total 100.0 Number of children with diarrhoea 2 779 IApplies only to children who are still breast fed. 2Children born in the period 1-59 months preceding the survey. 114 CHAPTER 9 INFANT FEEDING AND CHILDHOOD AND MATERNAL NUTRITION Information on three related topics is presented in this chapter. One of these involves aspects of infant feeding: initiation of breastfeeding, pattems and duration of breastfeeding and patterns of supplementation. The second and third sections cover the nutritional status of children under five and their mothers based on anthropometric indicators (measurement of height and weight). These data can serve to evaluate the government's policy on infant feeding. That policy states that every institution providing matemity facilities and care for newborn infants should, among other things: • Encourage exclusive breastfeeding of infants below 4 to 6 months of age, • Help mothers initiate breastfeeding within half an hour of birth, • Encourage breastfeeding on demand, • Not give infants any foods in addition to breastmilk before 4 months, • Not give artificial teats or dummies to breastfeeding infants. 9.1 Breastfeeding and Supplementation The understanding of current feeding patterns and trends can provide information for assessing the determinants of nutritional status of infants and young children, and hence, identifying those women whose babies are undernourished. Breastfeeding influences a child's growth and development, and thus affects the child's risk of morbidity and mortality. Breastfeeding patterns also affect the mothers through the influence of breastfeeding on lactational amenorrhoea, which in tum affects the length of birth intervals, and thus fertility levels. These variables are influenced by both the intensity of breastfeeding and by the age at which children are introduced to food supplements and fluids other than breastmilk. Prevalence of Breastfeeding Table 9.1 presents data on the proportion of children born in the five years before the survey who were ever breastfed and the percentage of the most recent births who started breastfeeding within one hour and one day of birth. Almost all (97 percent) Kenyan children are breastfed for some period of time, and this is generally not influenced by sex of the child, mother's residence or education, or by place of delivery or assistance at delivery. Overall, 54 percent of newborn babies are put to the breast within the first hour of birth, and 84 percent within the first day of life. In Coast and Western Provinces and in Nairobi, breastfeeding is initiated later than elsewhere; only 33, 37 and 47 percent of children, respectively, are put to the breast within the first hour of life. 115 Table 9.1 Initial breastfeeding Percentage of children born in the five years preceding the survey who were ever breastfed, and the percentage of last-born children who started breastfeeding within one hour of birth and within one day of birth, by selected background characteristics, Kenya 1993 Among all children: Among last-bern children, percentage who started breasffeeding: Percentage Number Within Within Number Background ever of 1 hour 1 day of characteristic breastfed children of birth of birth children Sex Male 96.9 3051 54.3 83.3 1992 Female 97.2 3066 54.0 83.9 2000 Residence Urban 97.2 777 52.3 79.7 575 Rural 97.0 5341 54.5 84.3 3417 Province Nairobi 97.0 276 46.7 76.3 210 Central 97.9 703 61.9 85.9 504 Coast 96.1 544 33.0 67.6 366 Eastern 96.7 1233 60.6 90.8 800 Nyanza 96.9 1023 54.7 82.8 629 Rift Valley 96.8 1327 66.6 88.2 857 Western 97.9 1012 37.2 78.8 626 Mother's education No education 96.7 1167 54.1 81.3 740 Primary incomplete 96.9 1934 53.9 84.7 1202 Primary complete 96.5 1701 53.2 82.3 1124 Secondary+ 98.2 1316 55.8 85.5 925 Assistance at delivery Medically trained person 97.0 2765 55.0 83.5 1864 Traditional birth attendant 98.0 1299 51.7 85.3 813 Other or none 97.3 2033 54.8 82.8 1311 Place of delivery Health facility 97.2 2685 55.3 83.7 1803 At home 97.7 3352 53.7 83.7 2143 Other (96.4) 46 (35.6) (82.6) 37 All children 97.0 6118 54.2 83.6 3992 Note: Table excludes 20 children (3 last-born children) for whom information on assistance at delivery is missing and 34 (8 last-born children) for whom place of birth is missing. Table is based on all children bern in the five years preceding the survey, whether living or dead at the time of the interview. 116 Timing of Introduction of Supplementary Foods The timing of introduction of supplementary foods in addition to breastmilk has important implications for the child and the mother. Breastmilk is uncontaminated and contains all the nutrients needed by children in the first few months of life. In addition, it provides some immunity to disease through the mother's antibodies. Early supplementation, especially under unhygienic conditions, can result in infection with foreign organisms and lower immunity to disease. The timing of introduction of food supplements also has an impact on the length of the mother's postpartum amenorrhoea. Early initiation of supplementation results in earlier resumption of the mother's menses, since supplementation reduces infants' dependence on breastmilk and the frequency of suckling. In order to measure these variables in the KDHS, mothers were asked about the current breastfeeding status of all last-bom children under age five and, if the child was being breastfed, whether various types of liquids or solid foods had been given to the child "yesterday" or "last night." This information is used to derive the percentages of children breastfeeding that are shown in Table 9.2. Children who are exclusively breastfed receive breast milk only, while those who are fully breastfed include those who are exclusively breastfed and those who receive only plain water in addition to breast milk. According to Table 9.2, exclusive breastfeeding is uncommon. Among infants under two months of age, only 27 percent are exclusively breastfed and over half (54 percent) are already receiving supplements. This latter figure increases to 82 percent when infants are 2-3 months old. By age 6-7 months, exclusive breastfeeding is minimal and 97 percent of children are receiving food supplements. Table 9.2 Breastfeeding status Percent distribution of living children by breastfeeding status, according to child's age in months, Kenya 1993 Age in months Percentage of living children who are: Breastfeeding and: Number Not Exclusively Plain of breast- breast- water Supple- living feeding fed only ments Total children 0-1 1.5 26.8 17.7 54.0 100.0 148 2-3 2.8 9.4 5.8 82.1 I00,0 174 4-5 0.5 2.0 2.6 94,8 100.0 199 6-7 0.2 0.5 2.7 96.6 100.0 203 8-9 1.2 1.2 0.7 96.9 100.0 209 10-11 1.6 1.0 1.6 95.7 100.0 181 12-13 6.8 0.4 1.0 91.8 100.0 218 14-15 12.9 0.4 0.0 86.7 100.0 179 16-17 22.0 0.8 0.0 77,2 100.0 183 18-19 29.5 0.6 0.9 69.0 100.0 205 20-21 45.2 0.0 0.0 54.8 1130.0 180 22-23 47.8 1.4 0.0 50.8 100.0 159 24-25 71.7 0.0 0.7 27.6 1130.0 200 26-27 76.7 0.0 0.0 23.3 1(30.0 177 28-29 85.9 0.0 0.0 14.1 100.0 189 30-31 91.7 0.0 0.0 8.3 10O.0 195 32-33 94.3 0.0 0.0 5.7 100.0 200 34-35 91.9 0.0 0.0 8.1 1(30.0 163 Note: Breastfeeding status refers to preceding 24 hours. Children classified breast[eeding and plain water only receive no supplements. 117 Table 9.3 looks in more detail at the type of supplements received by breastfed children. The data show that in the first two months of life, 5 percent of breastfeeding children are receiving solid or mushy foods and 16 percent are being fed other milk. By 2-3 months, the propensity to supplement children with other milk and solid/mushy foods is almost equal, 45 percent and 49 percent, respectively. At 4-5 months, 80 percent have foods introduced in their diets. Bottlefeeding is not uncommon in Kenya; one in six infants under the age of 4 months is fed using a bottle with a nipple. However, use of infant formula is uncommon. Table 9.3 Breastfeeding and supplementation by age Percentage of breastfeeding children who are receiving specific types of food supplementation, and the percentage who are using a bottle with a nipple, by age in months, Kenya 1993 Age in months Percentage of breastfeeding children who are: Receiving supplement Using a bottle Number Infant Other Other Solid/ with a of formula milk liquid mushy nipple children 0-1 2.0 15.9 48.8 5.0 10.3 145 2-3 4.3 44.6 48.0 49.4 21.1 169 4-5 l+l 67.0 47.6 80.2 15.1 198 6-7 3.2 66.5 44.2 91.2 13.2 202 8-9 3.7 68.0 45.0 90.3 8.9 206 10-11 6.5 57.6 56.2 93.9 7.5 178 12-13 4.5 72.4 55.2 96.9 13.1 203 14-15 5.3 61.0 47.3 98.5 6.0 156 16-17 5.3 74.9 63.8 98.7 5.8 143 18-19 3.7 59.5 48.0 96.2 5.6 145 20-21 3.4 68.1 47.7 100.0 2.3 98 22-23 3.1 54.1 52.5 95.8 4.2 83 24-25 0.6 42.9 49.6 97.5 4.7 57 Note: Breastfeeding status refers to preceding 24 hours. Percents by type of supplement among breastfeeding children may sum to more than 100 percent, as children may have received more than one type of supplement. Duration and Frequency of Breastfeeding The median duration and the frequency of breastfeeding according to selected background characteristics are presented in Table 9.4. The estimates of mean and median durations are based on current status data, that is, the proportions of children under 3 years of age who were being breastfed at the time of the survey, as opposed to retrospective data on the length of breastfeeding for older children who are no longer breastfed. The prevalence/incidence mean is provided for the total population in order to allow for comparison with the results of earlier surveys in Kenya. I18 Table 9.4 Median duration and frequency of breastfeeding Median duration of any breastfeeding, exclusive breastfeeding, and full breastfeeding among children under 5 years of age, and the percentage of children under 6 months of age who were breasffed six or more times in the 24 hours preceding the interview, according to background characteristics, Kenya 1993 Children under 6 months Median duration in months 1 Number of Breasffed children 6+ times Any Exclusive Full under in Number Background breast- breast- breast- 3 years preceding of characteristic feeding feeding feeding 2 of age 24 hours children Residence Urban 19.6 (0.5) 0.5 472 76.9 62 Rural 21.5 0.5 0.7 3169 86.9 459 Province Nairobi (19.5) * 0.4 175 83.3 25 Central 20.3 (0.5) 1.4 410 81.9 46 Coast 21.1 0.4 0.6 329 90.1 55 Eastern 24.8 0.5 0.6 716 79.8 100 Nyanza 21.2 0.5 0.6 621 84.1 88 Rift Valley 19.5 0.5 0.6 791 88.9 124 Western 23.0 0.7 1.0 598 89.6 83 Education No education 23.4 0.6 1.0 655 91.1 98 Primary incomplete 21.0 0.5 0.6 1144 81.3 144 Primary complete 20.8 0.5 0.6 1035 87.7 153 Secondary+ 20.1 0.5 0.6 807 84.1 125 Assistance at delivery Medically trained 20.8 0.5 0.6 1598 82.6 209 Traditional midwife 22.0 0.5 0.6 795 90.5 115 Other or none 21.0 0.5 0.7 1243 86.1 196 Sex of child Male 22.0 0.5 0.6 1790 83.7 234 Female 20.4 0.5 0.7 1850 87.3 287 Total 21.1 0.5 0.7 3640 85.7 521 Mean 21.1 1.5 2.1 Prevalence/Incidence 3 20.4 0.7 1.3 Note: Excludes 4 children for whom information on assistance at delivery is missing. Rates shown in parentheses are based on 25-49 women, whereas an asterisk means the rate is based on fewer than 25 women and has been suppressed. 1Medians and means are based on current status 2Either exclusive breastfeeding or breasffeeding and plain water only 3Prey alence -incidence mean The median duration of breastfeeding in Kenya is 21 months. The durations of exclusive and full breastfeeding, i.e., children receiving only breastmilk or only water in addition to their diet of breastmilk, are both less than one month. This reflects the very early supplementation of breast milk with other liquids, as well as with solid and mushy foods. 119 The longest median duration of any breast feeding occurs in Eastern and Western Provinces. Children of mothers with no education tend to be breastfed longer than those whose mothers have primary and secondary education. Comparison of the prevalence/incidence mean with similar data from the 1989 KDHS shows that the mean duration of breastfeeding has increased by one month, from 19.4 to 20.4 months. The number of breastfeedings in a 24-hour cycle provides an indication of breastfeeding practices. From this information, it is possible to ascertain whether women practice demand feeding. Frequent demand feeding has been associated with a higher probability of continuation ofbreastfeeding and a lower probability of the retum of ovulation. As presented in Table 9.4 for children under 6 months, certain background characteristics are associated with the frequency of breastfeeding. While 86 percent of children under age 6 months are breastfed 6 or more times per day, rural children (87 percent) are more likely than urban children (77 percent) to be breastfed this often. At the provincial level, about 9 of every 10 children under 6 months in Coast, Westem and Rift Valley Provinces are breastfed six or more times a day. Breastfeeding frequency is also higher among children whose mothers have no education and those assisted by a traditional midwife at delivery. 9.2 Nutrit ional Status of Chi ldren Nutritional status of children under age five is a sensitive indicator of health status and reflects infant and child feeding practices. In this survey, anthropometric measurements, that is, the weight ~ and height, 2 were obtained for all children who had been born since January 1988 and whose mothers were interviewed in the KDHS. The anthropometric data, combined with data on the child's age, were used to derive the following three indices: • height-for-age • weight-for-height • weight-for-age Each of these indices gives different information about growth and body composition used to assess nutritional status. The nutritional status of children measured in the KDHS is evaluated by calculating the extent to which these three anthropometric indices deviate from measurements for a standard population of healthy, well-fed children. As recommended by the World Health Organisation (WHO), the international reference population, defined by the U.S. National Center for Health Statistics (NCHS) and accepted by the U.S. Center for Disease Control (CDC), is used as the reference population. The use of this reference population is based on the finding that well-nourished young children of all population groups (for which data exist) follow very similar growth patterns. Although there are inherent variations in height and weight, these variations approximate a normal distribution when the population is large. The height-for-age index is an indicator of cumulative growth deficit caused by chronically inadequate food intake, ill health, sustained incorrect feeding practices and low socio-economic status. ~For the measurement of weight, a bathroom-type scale with a digital display with accuracy of +/- 100 grams was used. :Although the term "height" is used throughout this analysis, children younger than 24 months were measured lying on a measuring board (recumbent length), while standing height was measured for older children. 120 Children falling below the cut-off point of minus two standard deviations (-2 SD) from the median of the reference population are classified as stunted or short for their age and are chronically undernourished. Children who are below minus three standard deviation (-3SD) are considered severely stunted. The weight-for-height index measures current nutritional status, or wasting. Wasting is a nutritional deficiency of recent onset of usually a few weeks to few months, that usually shows marked seasonal patterns associated with changes in food availability or disease prevalence. Causes of low weight-for-height include inadequate food intake, incorrect feeding practices and ill health. Children whose z-scores are below minus two standard deviations (-2 SD) from the median of the reference population are classified as wasted or acutely undemourished, while children whose weight-for-height fails below minus three standard deviations are severely wasted. The weight-for-age index is a composite of height-for-age and weight-for-height and, consequently, does not distinguish between wasting and stunting. Children whose weight-for-age index falls below minus two standard deviations from the median are classified as underweight. The validity of these indices is determined by the coverage of the population of children under study. Not all children eligible to be weighed and measured are included in the analysis; information is presented for 4,752 children under age five, which represents 84 percent of all eligible children. Eleven percent of eligible children were excluded from the analysis because information on either height or weight or both was missing. The most common reason for nonmeasurement was that the child was not at home when the interviewer visited the household. Also excluded from the analysis are children with grossly improbable weight or height measurements due to recording error or age misreporting (3 percent) and children whose month and year of birth were not reported by the mother (2 percent), which renders two of the indices (height- for-age and weight-for-age) incalculable. In a healthy, well-fed population of children, it is expected that only 2.3 percent of children will fall minus two standard deviations (-2SD) below the median of the reference population for each of the three indices. Less than one percent of children are expected to be below minus three standard deviations (-3SD). Table 9.5 shows the percent of children under five who fall below -3SD and -2SD on each of these three indicators, according to demographic and other background characteristics. Height-for-Age. Overall, 33 percent of Kenyan children are classified as stunted and 12 percent as severely stunted. The prevalence of stunting is low among children below six months, increases steadily with age and peaks at 12-23 months (40 percent), where the prevalence is twice what it is among children age 6-11 months (18 percent). There is little relationship between sex or birth order and chronic undemutrition. However, stunting is more prevalent among children bom less than 24 months after a previous sibling (36 percent) than among those born 48 months or more after a prior birth (27 percent). Stunting, especially severe stunting, is more prevalent among rural children than urban children; 13 percent of rural children are severely stunted, compared to 5 percent of urban children. The proportion of stunted children is highest in Coast (41 percent) and Eastern (39 percent) Provinces and lowest in Nairobi (24 percent). High rates of stunting in Coast Province have been observed in the four national child nutrition surveys conducted by the Central Bureau of Statistics (CBS, 1991b, pp. 20, 43). In Eastern Province, drought appears to have a negative effect on nutritional status, and may be associated with the current high level of stunting. The level of mother's education is associated with their children's nutritional status. The proportion of severely stunted children ranges from about 16 percent among children whose mothers did not complete primary education to 7 percent among those with at least some secondary education. 121 Table 9.5 Nutritional status by demographic and background characteristics Percentage of children under five years who are classified as undernourished according to three anthropometric indices of nutritional status: height-for-age, weight-for-height, and weight-for-age, by demographic and other background characteristics, Kenya 1993 Height-for-age Weight-for-height Weight-for-age Percentage Percentage Percentage Percentage Percentage Percentage Number Demographic/other below below below below below below of characteristic -3 SD -2 SD l -3 SD -2 SD I -3 SD - 2 SD 1 children Age Under 6 months 1.2 7.5 0.2 4.3 0.6 3.5 411 6-11 months 4.3 18.1 1.1 4.8 4.2 16.1 537 12-23 months 14.3 40.3 2.3 10.0 8.5 31.6 988 24-35 months 15.1 37.7 0.7 5.4 7.1 26.2 945 36~.7 months 14.9 37.3 1.4 4.7 5.4 22.0 1000 48-59 months 13.5 34.5 0.7 4.7 4.5 20.4 872 Sex Male 12.9 35.5 1.4 6.4 5.9 24.4 2374 Female 11.5 30.0 1.0 5.4 5.5 20.2 2379 Birth order 1 8.9 28.7 1.3 3.8 4.3 17.0 911 2-3 11.9 32.5 0.8 5.3 4.6 22.1 1509 4-5 13.2 34.3 1.2 6.5 7.0 23.8 1095 6+ 13.9 34.6 1.6 7.7 6.8 25.0 1237 Birth Interval 2 < 2 Years 15.0 35.6 2.0 7.5 7.3 27.4 913 2-3 Years 12.9 34.8 1.0 6.2 5.9 23.6 2329 4 or more years 10.2 27.0 0.5 5.6 4.4 17.2 594 Residence Urban 5.1 21.5 1.4 5.2 2.6 12.8 536 Rural 13.1 34.2 1.2 6.0 6.1 23.5 4216 Province Nairobi 4.2 24.2 0.0 0.8 0.8 9.2 166 Central 11.1 30.7 0.3 4.0 4.0 17.1 597 Coast 17.5 41.3 3.4 10.6 9.5 31.7 377 Eastern 14.7 39.4 1.2 6.8 6.7 28.8 983 Nyanza 12.5 32.1 1.1 4.7 5.3 20.3 773 Rift Valley 11.3 28.5 1.6 7.9 5.7 23.5 1027 Western 9.9 30.0 0.6 3.9 5.2 17.0 830 Education No education 15.3 36.7 1.8 9.3 8.1 28.4 828 Primary incomplete 16.3 39.7 1.1 6.1 7.8 26.8 1475 Primary complete 10.2 32.0 1.0 5.2 4.2 19.5 1364 Secondary+ 6.5 21.3 1.1 3.9 2.8 15.0 1086 Total 12.2 32.7 1.2 5.9 5.7 22.3 4752 Note: Figures are for children born in the period 1-59 months preceding the survey. Each index is expressed in terms of the number of standard deviation (SD) units from the median of the NCHS/CDC/WHO international reference population. Children are classified as undernourished if their z-scores are below minus two or minus three standard deviations (-2 SD or -3 SD) from the median of the reference population. 1Includes children who are below -3 SD 2Excludes first births 122 Weight-for-Height. Six percent of children under five are classified as wasted and 1 percent severely wasted. Variations in the level of wasting by demographic characteristics show that the proportion of wasted children is high in the 12-23 month age group, 10 percent of whom are wasted. This age group is critical as children are weaned and are prone to diarrhoeal diseases during this period (see Table 8.12). Wasting appears to increase with higher birth orders. The differences in the prevalence of acute undemutrition among rural and urban children are not as marked as they are for chronic undemutrition. The highest prevalence of wasting is reported in Coast Province, where the proportion of children who are classified as wasted (11 percent) is almost twice that of the national level (6 percent). Severe wasting is also highest in Coast Province. Prevalence of wasting is inversely related to the educational level of the mothers. Weight-forAge. Weight-for-age is widely used in Kenya for monitoring the growth of individual children. As seen in Table 9.5, 22 percent of children under five are underweight for their age, with 6 percent severely underweight. As with the other two anthmpometrie indices, children age 12-23 months are most likely to be underweight. The proportion of underweight children varies little by sex and increases with birth order. Children born less than 24 months after a sibling are more likely to be underweight (27 percent) than those born after an interval of four or more years (17 percent). The prevalence of underweight children is higher among children residing in rural than in urban areas (24 percent vs. 13 percent). Children in Coast (32 percent) and Eastern (29 percent) Provinces are much more likely to be underweight than children in other provinces. Also more likely to be underweight are children whose mothers have no formal education. Figure 9.1 shows the distribution of children by age and by the extent to which they deviate from the reference population in terms of the three indicators discussed above. The dip in height-for-age and weight- for-age that occurs at age 12-23 months is evident. Figure 9.1 Nutritional Status of Children Under Five Years, Mean Z-scores by Age in Months Z-score 2 1 d -1 -2 -3 6 12 18 24 30 36 42 48 Age in Months 54 60 Note: Compared to the median of the International Reference Population KDHS 1993 123 9.3 Nutritional Status of Mothers All mothers of children bom since January 1988 were eli- gible to be weighed and measured 3 in the KDHS. The objective was to obtain a picture of the nutritional status of women of repro- ductive age; however, since weighing and measuring all respond- ents would add considerably to the length and cost of the fieldwork, it was decided to limit the anthropometric section to women with young children who would be measured anyway. 4 In reviewing the results of the maternal anthropometric data collection, it is impor- tant to remember that the data are not representative of the entire KDHS sample of women. In particular, older women tend to be underrepresented in the group for which the height and weight measures arc available. The information on height and weight was used to compute indices used to evaluate the nutritional status of the mothers. These include: Mean height (in centimetres) Mean weight (in kilograms) Body mass index (BMI) Height and weight measurement is missing for just over 5 percent of eligible women. Furthermore, women who were preg- nant at the time of the survey and those who had delivered within the two months preceding the survey were excluded from the tables on weight and body mass index. Thus, data on height are available for 3,713 women, while data on weight are available for 3,156 women. Table 9.6 presents the distribution of mothers by height, weight and body mass index, along with the means and standard deviations for each of these measures. Overall, the mean height of the surveyed women is 159 centimetres. Height, as well as being a good indicator of socioeconomic status of the mother, is also used to identify mothers at nutritional risk. Height of mothers can be used to predict the risk of difficulty in delivering children, given the association between height and size of the pelvis. Also, the risk of giving birth to children of low weight is greater among women of small stature. Although the cut-offpoint below which the mother can be considered at risk varies between populations, it probably falls in the range of 140-150 centimetres. In Kenya, 6 percent of the mothers are shorter than 150 centimetres and less than one percent fall below 140 centimetres. Table 9.6 Anthropometxic indicators of maternal nutritional status Percent distribution and mean and standard deviation for women who had a birth in the five years preceding the survey by selected anthropometrie indicators (height, weight, and body mass index (BMI)), Kenya 1993 Indicator Percent Height (cm) < 140 0.l 140-144 0.6 145-149 5.1 150-159 47.5 160-169 37.2 170-179 4.0 >180 0.l ~lissing 5.5 Total 100.0 Mean 159.2 Standard deviation 6.1 Number of women 3929 Weight (kg) < 40 1.4 40-49 24.8 50-59 42.5 60-69 18.9 >70 6.7 ~lissing 5.6 Total 100.0 Mean 55.8 Standard deviation 9.4 Number of women 3343 BMI 12.0-15.9 0.5 16.0-16.9 1.3 17.0-18.4 7.4 18.5-20.4 23.6 20.5-22.9 33.7 23.0-24.9 14.4 25.0-26.9 6.4 27.0-28.9 3,5 29.0-29.9 1.0 -> 30.0 2.2 Missing 6.0 Total 100.0 Mean 22.0 Standard deviation 3.2 Number of women 3343 ~The measuring boards and scales used to measure the mothers were the same as those used to collect anthropometric measurements of children; as with older children, standing height was obtained for adults using a specially designed extension for the measuring board. 'Interviewers were instructed to weigh and measure all women who had a birth since January 1988, regardless of whether or not the child was still living. 124 Low pre-pregnancy weight is an important risk factor for unfavourable pregnancy outcomes, although height also needs to be taken into account. Excluding women who were pregnant or had had a recent birth, the mean weight of mothers is 56 kilograms, with 43 percent of mothers weighing 50-59 kilograms. Body mass index (BMI) is a useful measure of relative thinness in adults. BMI is calculated by dividing weight in kilograms by the square of height in metres. BMI levels of below 18.5 indicate chronic undernutrition, while a level of below 16.0 classifies severe undemutrition (James et al., 1988) which is associated with increased mortality. The mean BMI for mothers in Kenya is 22. Only 9 percent fall below the cut-off point of 18.5 and less than one percent fall below the cut-off point of 16.0 for severe undemutrition. Table 9.7 shows variations by selected socioeconomic characteristics for height and BMI. For each indicator, the mean is presented, as well as the proportion failing below the cut-off points described above. There are few variations in mean height or mean BMI of mothers. However, mothers with no education (17 percent) and those in rural areas (11 percent) are more likely to have low BMI. At the provincial level, the proportion of mothers with BMI less than 18.5 is comparatively high in Coast, Rift Valley and Eastern Provinces, and lowest in Nairobi. Table 9.7 Differentials in maternal anthropometrie indicators Mean height and percentage of women shorter than 145 centimetres, mean body mass index (BMI), and the percentage of women whose BMI is less than 18.5, according to selected background characteristics, Kenya 1993 Height BMI Background Percent Percent charac~fisfic Mean <145 cm Number Mean <18.5 Number Age < 20 158.8 1.3 276 21.2 9.0 223 20-34 159.3 0.7 2687 21.9 9.3 2246 >= 35 158.8 1.1 750 22.2 12.1 672 Chlldren ever born 1 159.4 0.9 712 22.0 7.2 582 ~3 159.5 0.6 1139 21.9 8.7 973 ~5 158.9 0.5 846 21.8 10.4 704 6+ 158.9 1.0 1016 22.1 12.4 884 Re~dence Urban 160.3 0.0 496 23.4 4.2 441 RurM 159.0 0.9 3217 21.7 10.8 2701 Province Nairobi 159.6 0.0 156 23.6 2.9 142 Central 157.5 1.2 478 22.3 6.7 425 Coast 156.7 2.2 340 21.6 13.4 262 "Eastern 157.3 1.1 732 21.5 12.7 633 Nyanza 160.9 0.6 603 21.7 9.9 504 Rift Valley 160.4 0.3 795 22.0 13.1 680 Western 160.7 0.3 609 22.1 4.5 495 Education No education 158.0 0.9 677 21.3 17.1 560 Primary incomplete 159.0 1.1 1124 21.6 11.0 935 Primary complete 159.4 0.8 1048 22.1 6.8 884 Secondary + 160.2 0.3 864 22.6 6.7 762 Total 159.0 1.0 3721 22.0 10.0 3153 125 CHAPTER 10 KNOWLEDGE OF AIDS AIDS and H1V infection have been identified as serious health and economic problems in Kenya. The HIV virus was probably first introduced in Kenya in the late 1970s or early 1980s (NACP, 1993); at that time it was generally confined to special groups, such as prostitutes, and hence did not pose an immediate threat to the rest of the population. As the virus spread rapidly among the heterosexual population, the Government realised the need to define AIDS as an issue of national priority. Consequently, the National Aids Control Programme (NACP) of the Ministry of Health, together with the National Council for Population and Development (NCPD) decided to work closely to monitor the data so as to design and evaluate the AIDS control programme in Kenya. A substantial amount of data on the actual number of AIDS cases is usually obtained from hospital records, while information on HIV prevalence and incidence is gathered annually from 13 sites throughout Kenya which were established as part of the sentinel surveillance system. Using the sentinel surveillance data and adjusting them to be representative of the total population, the NACP has estimated that there are about 700,000 people in Kenya who are infected with the HIV virus (NACP, 1993, p.5). Approximately 5.6 percent of the population age 15 and over are estimated to be infected--10-11 percent in urban areas and about 4-5 percent in rural areas (NACP, 1993, p.8). Although AIDS has touched every district in Kenya, there are certain parts of the country where HIV infection rates arc higher than others. Sites in western Kenya, as well as those in urban areas, report particularly high levels of infection among pregnant women. The KDHS included a section of questions on AIDS in order to assess the knowledge and attitude of respondents regarding transmission mechanisms and prevention of infection with the AIDS virus. Respondents were first asked if they had ever heard of AIDS and, if so, from what source they had heard information in the month preceding the survey. To assess the level of awareness about AIDS, respondents were asked to name the means of transmission of the AIDS virus. They were also asked if tbey thought it was possible to prevent AIDS, and if so, how. 10.1 AIDS Awareness and Knowledge Table 10.1 highlights the fact that both Kenyan men and women have knowledge of AIDS. In the KDHS, 99 percent of men and 98 percent of women said they had heard of AIDS. The high level of knowledge persists irrespective of age, urban-rural residence, education level and province of residence. Respondents who had heard of AIDS were asked to name all of the ways the virus could be transmitted. More men than women (96 vs. 90 percent) know that the AIDS virus is transmitted through sexual intercourse. The second most frequently cited mechanism of transmission was injections, reported by 35 percent of the men and 29 percent of the women. Other routes of transmission were mentioned less frequently. For example, only 5 percent of men and 7 percent of women mentioned that a baby could be infected in the uterus or during delivery. Circumcision was cited by only 5 percent of men (and 2 percent of women), while shaving razors was mentioned by only 17 percent and 13 percent of men and women, respectively. 127 Table 10.1 Knowledge of AIDS Percentage of men and women who have ever heard of AIDS and percentage reporting various modes of transmission, by selected background characteristics, Kenya I993 Mode of U'ansrrfission of the AIDS virus Ever Number heard Sexual Circum- Mother Blood of Background of inter- Shaving/ lnjec- cision/ to ~'ans- Don't respond- characteristic AIDS course razors tions tattoo child fusion Other know ents MALES Age 20-24 100.0 97.0 18.5 36.4 5.6 5.7 32.2 10.7 2.7 525 25-29 100.0 97.6 18.1 36.8 4.1 4.1 32.0 13.8 1.8 390 30-34 98.7 95.7 19.5 39.3 7.3 5.2 31.8 12.4 2.3 412 35-39 98.7 96.5 18.0 38.1 3.3 6.0 32.2 7.0 3.2 314 40-44 99.1 96.1 18.4 29.7 4.6 2.8 28.1 8.7 3.2 303 45-49 99.3 94.3 10.7 29.4 4.6 2.8 18.7 10.6 4.6 227 50-54 96.1 91.8 7.8 28.8 4.1 2.2 14.8 5.6 7.9 165 Residence Urban 99.7 97.1 18.9 36.1 7.2 7.9 39.9 9.2 2.5 566 Rural 98.9 95.7 16.4 34.8 4,3 3.4 25.6 10.8 3.3 1770 Province Nairobi 100.0 96.5 17.0 29.8 12.3 10.5 40.4 4.7 3.5 257 Cenmal 99.5 97.1 18.9 45.0 0.4 2.2 28.4 15.4 2.3 333 Coast 99.3 96.5 10.7 18.4 0.0 0.7 17.3 3.7 3.3 239 Eastern 99.4 96.7 14.0 27.2 7.6 0.9 24.4 8.3 2.4 389 Nyanza 99.0 92.5 17.7 40.3 9.5 9.4 30.5 17.9 4.7 287 Rift Valley 98.4 95.3 19.0 33.3 3.7 2.5 26.2 14.1 4.1 540 Western 98.9 98.3 19.9 51.2 2.6 8.4 39.5 3.9 1.3 292 Education No education 94.8 90.4 6.4 14.0 2.9 0.7 5.6 6.8 9.6 193 Primary incomplete 98.9 93.0 11.3 23.9 3.0 2.7 14.2 10.2 5.3 566 Primary complete 99.9 97.2 14.5 32.5 3.9 1.9 21.0 9.5 2.2 696 Secondary+ 99.6 98.3 24.9 48.8 7.6 8.4 49.8 12.0 1.2 882 Total 99.1 96.1 17.0 35.1 5.0 4.5 29.1 10.4 3.1 2336 FEMALES Age 15-19 98.0 87.2 13.2 25.6 1.5 6.2 18.2 7.7 9.6 1754 20-24 98.4 93.2 14.0 30.2 2.1 7.0 22.3 9.6 5.5 1638 25-29 98.5 92.9 14.9 28.8 2.0 7.5 21.5 10.4 5.6 1221 30-34 98.1 93.1 14.2 31.9 2.7 8.4 22.0 9.2 5.8 1088 35-39 97.5 88.4 11.4 27.7 2.8 5.8 16.5 8.1 10.2 768 40-44 96.3 89.7 13.5 28.1 0.3 6.1 16.6 7.0 8.5 638 45-49 94.4 82.3 9.5 25.6 1.0 3.7 15.4 5.3 14.2 434 Residence Urban 99.2 93.6 15.2 34.0 3.6 6.7 33.2 8.4 5.1 1339 Rural 97.5 89.6 13.0 27.3 1.5 6.7 16.8 8.7 8.3 6201 Province Nairobi 98.9 94.5 16.8 32.5 4.1 6.1 32.2 5.5 5.0 507 Central 99.I 93.4 16.1 38.2 3.3 3.0 25.4 7.9 4.1 1094 Coast 97.6 77.9 6.1 15.8 1.0 2.1 18.8 1.5 20.5 717 Eastern 97.4 93.4 13.3 28.7 0.7 1.5 17.0 6.8 5.6 1406 Nyanza 99.2 91.8 15.7 30.4 2.4 28.7 18.9 11.5 5.8 1158 Rift Valley 94.9 90.2 14.3 28.4 2.0 2.9 19.9 6.0 7.9 1562 Western 99.0 88.3 10.6 22.9 0.8 2.3 13.1 18.1 8.8 1096 Education No education 92.0 80.0 7.1 15.1 0.4 5.6 9.1 4.9 18.2 1352 Primary incomplete 97.9 86.7 11.0 19.7 1.2 6.4 11.3 10.0 10.3 2179 Primary complete 99.6 93.8 13.7 27.7 1.2 4.9 17.0 7.5 4.7 2166 Secondary+ 99.6 97.5 20.3 48.6 4.5 10.0 40.0 10.8 1.2 1844 Total 97.8 90.3 13.4 28.5 1.9 6.7 19.7 8.6 7.7 7540 128 Although there are slight variations by background characteristics in the proportion of respondents who cited common modes of transmission, this analysis clearly shows that men and women with higher education gave a wider range of correct information. In this analysis, half of the men and 40 percent of the women with secondary education mentioned contaminated blood as a route of transmission, compared to about 20 percent or less of those with primary level of education and below. Similarly, almost half the men and women with secondary education indicated that one can be infected with a contaminated needle. In short, the higher the level of education, the more likely a respondent is to cite a correct channel of transmission of the AIDS vires. Men are also more likely to name a transmission route than women. In this survey, 3 percent of men and 8 percent of women who had heard of AIDS could not name any method of transmission. There is therefore a need to intensify AIDS awareness campaigns to educate the general public, particularly on those modes of transmission that are less commonly known. 10.2 Sources of Information In Kenya, dissemination of AIDS information is a joint effort of the National AIDS Control Programme of the Ministry of Health and the National Council for Population and Development (NCPD). The messages channelled to the public include information about basic transmission modes and prevention strategies. This information is received by Kenyans through various channels as indicated in Table 10.2. The table shows the responses received when respondents who had heard of AIDS were asked to cite their source of information. Table 10.2 Sources of AIDS information Percentage of respondents who reported hearing of AIDS from various sources in the month before the survey according to urban-rural residence, Kenya 1993 Males Females Source of information Urban Rural Total Urban Rural Total Radio 90.6 87.0 87.9 78.7 64.2 66.8 TV 22,4 7,2 10.9 21.0 2.9 6.2 Newspapers 53.2 26.9 33.3 21.3 8.7 11.0 Health workers 13.9 14.7 14.5 17,7 14.4 15.0 Priests/preachers 5.0 3.0 3.5 3.1 2.7 2.8 Husband/wife 2.1 1.4 1.6 2,0 1.5 1,6 Friends/relatives 32.0 41.8 39.4 52,9 57.9 57.0 Schools 4.1 3.6 3.7 6.9 7.2 7.2 Booklets/posters 19.8 13.1 14.7 14.4 6.3 7.8 Barazas 3.5 9.I 7.7 1,3 2.5 2.3 Other 4.8 1,9 2.6 1.8 2.1 2.1 None 0.5 2.2 1,8 1.3 5.6 4.8 Missing 0.4 0.4 0.4 0.0 0.1 0.1 Number of respondents 564 1752 2316 1328 6043 7371 From the table it is evident that AIDS information is widely disseminated. The widest single source of information mentioned was the radio, with 88 percent of men and 67 percent of women citing it as a source, 129 More women than men had heard AIDS information from a relative (57 vs. 39 percent, respectively) and one-third of men and one in ten women heard about AIDS through reading a newspaper. About 15 percent of men and women said they heard about AIDS from a health worker. Men were twice as likely to have received the information from booklets and posters compared to women. In addition to this, only 2 percent of men and 5 percent of women said they had not heard any AIDS information in the month preceding the KDHS. People in urban areas tend to receive more information on AIDS from newspapers, television and posters compared to the rural dwellers. Friends and relatives are a more important source of AIDS information in the rural than the urban areas for beth men and women. There is a need for AIDS programmes to involve more actively institutions such as the church, chief's barazas and schools, because individuals holding positions in these institutions, namely church eiders, teachers and chiefs, are highly regarded as figures of authority. 10.3 Misconceptions About AIDS The 1993 KDHS also included a question asking respondents if they thought they could contract the AIDS virus from a variety of common social circumstances such as kissing, sharing clothing or eating utensils, and shaking hands with a person infected with AIDS. Although it has been shown that these situations pose almost no risks in spreading AIDS, in Kenya rumours have had it that one can actually contract this lethal virus under these circumstances. The level of ignorance/myth surrounding AIDS can be seen in Table 10.3. Over half of the respondents believe that one can contract AIDS from mosquito bites, whereas over one third of the respond- Table 10.3 AIDS transmission Percentage of respondents who reported possibility of transmitting the AIDS virus by various mearts and percent distribution of respondents by whether they think a healthy-looking person can be infected with AIDS and by whether they think a woman with AIDS can give birth to a child with the virus, according to urban-rural residence, Kenya 1993 Males Females Method of transmission Urban Rural Total Urban Rural Total Handsh',d<ing 10.8 15.7 14.5 Kissing 34.8 38.2 37.3 Sharing clothes 21.4 30.9 28.6 Sharing eating utensils 21.9 27.2 25.9 Touching the dead 22.4 29.3 27.6 Mosquito bites 51.5 61.4 59.0 Can a healthy person have AIDS? Yes 94.1 85.4 87.5 No 3.4 8.4 7.2 Don't know 2.5 6.2 5.3 6.8 15.5 13.9 24.6 34.0 32.3 14.1 26.0 23.8 14.6 27.0 24.7 14.6 27.6 25.3 39.3 58.6 55.1 87.1 73.4 75.8 7.3 13.9 12.7 5.4 12.6 11.3 Total 100.0 I00.0 100.0 100.0 100.0 100.0 Can a mother transmit to child? Yes 94.6 No 1.3 Don't know 4.1 88.9 90.3 91.6 85.3 86.5 3.5 2.9 2.1 4.2 3.8 7.6 6.8 6.1 10.4 9.7 100.0 100.0 100.0 100.0 100.0 1752 2316 1328 6043 7371 Total 100.0 Number of respondents 564 130 ents mentioned kissing as a way of transmitting AIDS. Whereas handshaking was cited by about 15 percent of men and women, touching the dead and sharing eating utensils and clothes was mentioned each by about a quarter of the respondents. Differences in responses by gender are particularly notable. Apparently men are more likely to cite that AIDS can be contracted from these common situations, Rural respondents are more likely than urban respondents to have misconceptions about transmission of the AIDS virus. Respondents were also asked if they thought it was possible for a healthy-looking person to be carrying the AIDS virus. As expected, more than three-quarters (88 percent of the men and 76 percent of the women) answered in the affirmative. It is generally thought that mother-to-child transmission of the HIV virus takes place during delivery. In the KDHS, men gave a slightly higher positive response (90 percent) than women (87 percent) when asked if a mother could give birth to a baby with the AIDS virus. Urban dwellers are more likely to know about the possibility of mother-to-child transmission than rural dwellers. Given that in Table 10.1, only 6 percent of respondents mentioned the possibility of a mother infecting the newborn child, it is surprising to find that over three-quarters of the respondents answered affirmatively to this question. It is possible that this variation was due to lack of probing by the interviewers. While it is true to say that Kenyans are generally informed about the major channel of AIDS transmission, it is also important to emphasise that a fairly large proportion of people believe that AIDS can be contracted through casual contact such as handshaking, hence highlighting the need for more AIDS information dissemination. 10.4 AIDS Prevention A question was also asked with regard to preventive measures. Table 10.4 shows the responses received when respondents were asked if they believed that AIDS could be prevented and, if so, how. It is Table 10.4 Protection against AIDS Percent distribution of respondents who believe Ihat people can protect themselves from getting AIDS and, of those who believe so, the percentage reporting various means of protection, according to urban-rural residence, Kenya 1993 Males Females Urban Rural Total Urb~a Rural Total Possibility of protection Yes 86.7 No 11.7 Don't know 1.6 Missing 0.0 Number of respondents 564 If so, how? Have no sex at all 14.2 Limit number of sexual partners 76.0 Use condoms during sex 45.2 Sterilise needles 20.3 Avoid prostitutes 11.0 Other 11.8 86.2 86.3 81.7 78.0 78.7 9.7 10.2 11.2 11.9 11.8 4.1 3.5 6.8 10.0 9.4 0.0 0.0 0.3 0.1 0.2 1752 2316 1328 6043 7371 8.9 10.2 15.9 19.3 18.6 74.5 74.9 68.7 70.6 70.2 32.6 35.6 39.4 16.6 20.8 18.3 18,8 20.9 16.5 17.3 16.4 15.1 16.4 16.9 16.8 I2.5 12.3 7.4 11.0 10.3 131 encouraging that 79 percent of women and 86 percent of men think AIDS can be prevented. As previously noted, more men than women believe that AIDS can be prevented. Secondly, three-quarters of men and women say that limiting the number of partners can help prevent the spread of the disease, whereas only about one in five respondents mention sterilised needles as a means of prevention. Thirty-six percent of men (versus 21 percent of women) believe condom use could prevent AIDS. Also, a larger proportion of urban than rural respondents mention condom use as an AIDS-prevention behaviour. 10.5 Personal Acquaintance With AIDS The KDHS included a question on whether respondents know somebody who has AIDS or who has died from AIDS. Table 10.5 shows the distribution of respondents by their responses to this question, according to selected background variables. Overall,just over 40 percent of men and women know someone with AIDS. Almost half (48 percent) of the urban respondents and about 40 percent of their rural counterparts say that they know someone who either has AIDS or who has died from AIDS. This residential difference supports the findings of other studies which show that AIDS is more prevalent in urban areas. Table 10.5 Personal knowledge of AIDS Percentage of men 20-54 and women 15-49 who know someone who has AIDS or who has died from AIDS according to selected background characteristics, Kenya 1993 Background characteristic Men Women Percentage Number Percentage Number Age 15-19 37.2 1754 20-24 38.7 525 43.2 1638 25 -29 40.6 390 45.4 1221 30-34 44.8 412 42.9 1088 35-39 40.4 314 41.4 768 40-44 37.8 303 44.3 638 45-49 4l .5 227 42.6 434 50-54 35.2 165 Residence Urban 47.7 566 47.5 1339 Rural 37.8 1770 40.8 6201 Province Nairobi 38.6 257 42.0 507 Central 34.1 333 39.5 1094 Coast 52.0 239 43.7 717 Eastern 30.9 389 31.8 1406 Nyanza 61.1 287 51.0 1158 Rift Valley 32.6 540 34.2 1562 Western 45.1 292 57.9 1096 Education No education 26.9 193 35.6 1352 Primary incomplete 34.4 566 42.8 2179 primary complete 39.0 696 41.8 2166 Secondary+ 47.8 882 45.9 1844 Total 40.2 2336 42.0 7540 132 The data show that 61 percent of male respondents and 51 percent of female respondents in Nyanza Province know someone who has AIDS or who has died from AIDS. Other provinces with high levels am Coast Province (52 percent of men and 44 percent of women) and Western Province (45 percent of men and 58 percent of women). These patterns reflect the fact that HIV prevalence is apparently higher in these provinces than in other parts of Kenya (NACP, 1993). Having at least some secondary education also increases the likelihood that a Kenyan knows somebody who has AIDS; almost half of the men and women with some secondary education know someone with AIDS, as opposed to only 27 percent of men and 36 percent of women with no education. In order to assess Kenyans' feelings of their own personal risk of acquiring the AIDS virus, the KDHS included a question as to whether the respondent thought he or she could "catch AIDS." If the answer was yes, they were asked how they thought they might catch AIDS. The data are shown in Table 10.6. Table 10.6 Personal risk of acquiring AIDS Percent distribution of men 20-54 and women 15-49 by whether they think they might acquire AIDS and, if so, how, Kenya 1993 Risk and means of acquiring Men Women Percentage Number Percentage Number Risk Think might acquire 65.6 1520 46.2 3406 Do not think acquire 24.0 556 34.5 2541 Don't know/Missing 10.4 240 19.3 1425 Total 100.0 2316 100.0 7371 Means of transmission From partner 70.6 1074 79.0 2692 From needles/injections 12.2 186 l l . l 379 From blood transfusion 4.3 66 3.2 108 Other 10.6 161 4,3 148 Don't know/Missing 2.2 34 2.3 80 Total 100,0 1520 100.0 3406 It is evident from the data that more men (66 percent) than women (46 percent) consider AIDS as a direct threat to them. This might be explained in part by the fact that men report having a greater number of recent sexual partners than women do (see Table 10.7). Of those who say they feel at risk of acquiring the AIDS virus, the vast majority think that their spouses or sexual partners are the means by which the virus might be transmitted to them--accounting for 71 percent of the men and 79 percent of the women who feel at risk. Needles (injections) and blood transfusions are perceived as major personal risks of transmission by only a small proportion of those who feel themselves to be at risk of contracting the AIDS virus. 10.6 Number of Sexual Partners and Condom Use Given the evidence that the vast majority of HIV infections in Kenya are contracted through heterosexual contact, information on sexual behaviour is important in designing and monitoring intervention programmes to control the spread of this fatal disease. In addition to the data on sexual activity in the four weeks before the survey (see Chapter 5), the KDHS included questions on the number of sexual partners in 133 the six months before the survey, as well as the lifetime number of partners. Respondents were also asked if they had used a condom with any parmer in the last six months. Data on number of sexual parmers should be viewed with some caution, since similar surveys have shown evidence of substantial misreporting, especially differential over- or under-reporting by sex of respondent. Table 10.7 shows the percent distribution of men and women by the number of sexual partners in the six months prior to the survey, according to selected background characteristics. Table 10.7.1 Number of recent sexual partners - male respondents Percent distribution of men and women by number of sexual partners in the six months preceding the survey according to selected background characteristics. Kenya 1993 Number of sexual partners Number Background of characteristic None I 2 3+ Missing Total men Type of union Unmarried 20. l 35.9 17.5 26,5 0.0 100,0 672 Married-monogamous 4.3 75.9 11.0 8,6 0.3 100.0 1470 Married-polygamous 2.2 16.3 67,3 14,2 0.0 100.0 192 Age 20-24 15.8 39.3 20,8 24.0 0.0 100.0 525 25-29 7.8 60.3 14.0 18.0 0.0 100.0 390 30-34 6.0 67.0 13.6 12.9 0.5 100,0 412 35-39 3.8 65.9 21.4 8.9 0.0 100.0 314 40-44 5.6 69.8 18,6 5.8 0.1 100.0 303 45-49 10.1 64,2 15.5 9.6 0,7 100.0 227 50-54 8.4 64.7 18,2 8.6 0.0 100.0 165 Residence Urban 8.9 58.6 16.0 16.3 0.3 100.0 566 Rural 8.7 59.7 18.0 13.5 0.1 100.0 1770 Province Nairobi 9.4 61.4 12.3 16.4 0.6 100.0 257 Central 8.5 57.0 14.9 19.7 0,0 100.0 333 Coast 10.4 52.3 19,3 18.0 0.0 100.0 239 Eastern 6.6 69.8 14.8 8.7 0.0 100.0 389 Nyanza 7.9 59.6 23.9 8.6 0,0 100.0 287 Rift Valley 7.7 62.5 16.9 12.9 0.l 100.0 540 Western 12,6 46.9 21.8 17.9 0.7 100,0 292 Education No education 7.3 56.6 23.7 12.4 0.0 100.0 193 Primary incomplete 8.1 56.4 22.5 12,7 0.3 100.0 566 Primary complete 7.7 61.3 15.5 15,4 0.1 100.0 696 Secondary+ 10.3 60.5 14.4 14.5 0.2 100.0 882 Total 8.7 59.4 17.5 14,2 0.2 100.0 2336 It is clear that men report having more sexual partners in the six months before the survey than women. Only 9 percent of men reported being abstinent during this period, compared to 30 percent of women. Conversely, 32 percent of men report having had two or more sexual partners in the six months before the survey, compared to only 4 percent of women. This gender differential is in part due to the fact that the male respondents were on average older than the women--age 20-54, as opposed to 15-49. However, comparison of men and women in the same age group shows that men still report more sexual parmers. For example, 45 percent of men age 20-24 report having two or more sexual partners in the six months before the survey, compared to 4 percent of women. The existence of polygynous marriages in Kenya also accounts 134 Table 10.7.2 Number of recent sexual partners - female respondents Percent distribution of men and women by number of sexual partners in the six months preceding the survey according to selected background characteristics, Kenya 1993 Number of sexual partners Number Background of characteristic None l 2 3+ Total women Type of union Unmarried 64,5 28.6 4.9 2.0 100.0 2911 Married-monogamous 7,2 91.0 1.3 0.6 100.0 3727 Married-polygamous 12.1 84.8 2.0 1.1 100.0 902 Age 15-19 65.7 29.3 3.6 1.3 100.0 1754 20-24 26.4 69.2 3.4 1.0 100.0 1638 25 -29 14. l 82. l 2.6 1.2 100.0 122 l 30-34 11.9 84.8 1.8 1.4 100.0 1088 35-39 17.5 79.4 1.6 1.4 100.0 768 40-44 19.1 78.8 1.9 0.2 100.0 638 45-49 26.3 70.2 2.4 1 .t 100.0 434 Residence Urban 28.4 64.8 4.7 2.1 100.0 1339 Rural 30.3 66.5 2.3 1.0 100.0 6201 Province Nairobi 24.3 67.6 4.9 3.3 100.0 507 Central 29.4 67.7 1.9 1.0 100.0 1094 Coast 29.0 67.0 2.7 1.2 100.0 717 Eastern 31.4 64.9 2.5 1.2 100.0 1406 Nyanza 26.2 68.6 3.6 1.5 100.0 1158 Rift Valley 34.6 63.0 1.9 0.5 100.0 1562 Western 29.1 67.0 3.1 0.8 100.0 1096 Education No education 22.6 73.6 2.7 1.1 100.0 1352 Primary incomplete 27.6 67.7 3.4 1.3 100.0 2179 Primary complete 34.5 61.5 2.8 1.2 100.0 2166 Secondary+ 32.7 64.4 2.0 1.0 100.0 1844 Total 29.9 66.2 2.7 1.2 100.0 7540 for some of the gender difference in number of sexual partners. As expected, the vast majority (82 percent) of the men in polygynous marriages report having two or more partners in the six months before the survey. It is notable that 80 percent of monogamously married men report having one or no sexual partner during the period. The largest differences between men and women occur among the unmarried. Two-thirds of unmarried women report that they were abstinent in the six months before the survey, compared to only 20 percent of men. Over one-quarter of unmarried men report having had three or more sexual parmers during this period. There are few differentials in number of sexual partners by urban-rural residence for either men or women. Similarly, differences by province for men and women are not large; differences for men by province are confounded by differences in the prevalence of polygyny, which is more common in Nyanza and Western Provinces (see Chapter 5). Among both men and women, there is a slight tendency for those with more education to have had fewer, if any, sexual partners in the recent past. This relationship, which 135 is stronger among women than men, may be due in part to the fact that those with more education are often younger and thus less likely to be as sexually active. In addition to asking respondents the number of sexual partners they had had in the six months prior to the survey, the KDHS included a question on the number of sexual partners respondents had had in their whole lives. The results are given in Table 10.8. These data reflect the previous finding that men report having a greater number of sexual partners on average than women. Less than two percent of men age 20-54 have never had sexual intercourse, while 62 percent have had six or more partners in their lifetime. In contrast, 15 percent of women age 15~.9 have never had sexual intercourse and only 4 percent report having had six or more lifetime sexual partners. One-third of women have had only one partner, while another one- third have had 2-3 partners. Table 10.8.1 Number of lifetime sexual partners - male respondents Percent distribution of men and women by number of sexual partners in characteristics. Kenya 1993 their life according to selected background Number of sexual partners Number Background of characteristic None 1 2-3 4-5 6+ Missing Total men Type of union Unmarried 5.6 6.1 16.1 16.1 50.2 6.0 I00.0 672 Married-monogamous 0.2 4.4 10.3 13.3 64.8 7.0 100.0 1470 Married-polygamous 0.0 0.0 6.5 8.8 76.4 8.2 I00.0 192 Age 20-24 5.5 7.1 18.4 15.6 46.9 6.4 100.0 525 25-29 1.3 4.3 10.4 15.9 61.9 6.2 100.0 390 30-34 0.3 3.4 10.0 13.4 66.0 6.8 100.0 412 35-39 0.0 4.4 8.7 10.5 69.8 6.6 I00.0 314 40-44 0.5 3.5 11.4 14.3 61.8 8.6 100.0 303 45-49 0.5 1.3 8.1 9.7 72.1 8.3 100.0 227 50-54 1.2 6.1 8.9 13.9 65.6 4.3 100.0 165 Residence Urban 1.7 5.2 12.7 12.7 60.3 7.3 100.0 566 Rural 1.7 4.3 11.4 14.0 61.9 6.6 100.0 1770 Province Nairobi 2.3 4.7 11.7 11.7 59.6 9.9 100.0 257 Central 1.1 6.7 12.2 15.2 59.7 5.1 100.0 333 Coast 0.7 5.9 19.1 14.2 53.5 6.6 100.0 239 Eastern 1.5 2.6 6.8 19.9 58.9 10.4 100.0 389 Nyanza 1.7 4.8 11.6 10.1 67.0 4.9 100.0 287 Rift Valley 2.2 5.9 13.7 15.9 55.6 6.6 100.0 540 Western 2.2 0.7 8.1 4.7 80.9 3.5 100.0 292 Education No education 1.7 4.2 13.0 14.4 55.2 11.4 100.0 193 Primary incomplete 2.1 4.6 9.1 9.8 67.7 6.6 100.0 566 Primal, complete 0.9 4.9 12.1 16.4 60.3 5.4 100.0 696 Secondary+ 2.1 4.2 12.8 14.0 59.9 7.0 100.0 882 Total 1.7 4.5 11.7 13.7 61.5 6.8 100.0 2336 136 Table 10.8.2 Number of lifetime sexual partners - female respondents Percent distribution of men and women by number of sexual parmers in their life according to selected background characteristics, Kenya 1993 Number of sexual partners Number Background of characteristic None l 2-3 4-5 6+ Missing Total women Type of union Unmarried 39.l 19.7 25.2 9.5 4.4 2.1 100.0 2911 Married-monogamous 0.3 42.8 39.2 12.4 3.5 1.8 100.0 3727 Married polygamous 0.4 41.6 38.7 12.5 5.3 1.5 100.0 902 Age 15-19 54.0 22.1 17.1 4.6 1.1 1.1 100.0 1754 20-24 10.4 32.3 38.9 13.2 3.5 1.6 100.0 1638 25-29 1.3 36.2 41.0 13.9 5.4 2.2 100.0 1221 30-34 0.8 34.6 39.4 15.5 6.6 3.2 100.0 1088 35-39 0.6 41.8 37.7 13.0 5.0 1.8 100.0 768 40-44 0.2 41.3 39.3 11.1 5.3 2.8 100.0 638 45-49 1.1 52.2 31.7 9.9 4.1 1.0 100.0 434 Residence Urban 16.5 29.5 33.8 11.4 5.8 2.9 100.0 1339 Rural 15.0 34.7 33.7 11.2 3.7 1.7 100.0 6201 Province Nairobi 13,4 26.7 34.1 13.9 9.0 3.0 I00.0 507 Central 15.9 34.7 34.2 10.3 3.4 1.6 100.0 1094 Coast 19.0 49.5 23.9 4.1 2.5 1.1 100.0 717 Eastern 13.4 25.3 34.4 16.8 7.0 3.1 100.0 1406 Nyanza 10.4 27.3 40.8 16.6 2.6 2.3 100.0 1158 Rift Valley 17.5 40.8 30.4 7.8 2.9 0.7 100.0 1562 Western 17.7 33.4 36.2 7.9 3.0 1.8 100.0 1096 Education No education 2.9 45.1 33.0 10.8 6.0 2.2 100.0 1352 Primary incomplete 16.2 30.9 34.6 12.0 4.1 2.2 100.0 2179 Primary complete 19.7 30.4 33.0 11.8 3.2 1.9 100.0 2166 Secondary+ 18.0 32.7 34.3 10.0 3.7 1.4 100.0 1844 Total 15.3 33.8 33.7 11.3 4.1 1.9 100.0 7540 Married men, especially those in polygamous relationships, are more likely to report having had six or more lifetime sexual partners. Men in Westem and Nyanza Provinces are also more likely to have had many partners. Among women, those who are unmarried are more likely to have never had sex than those who are married. As expected, younger women are less likely to have been sexually active than older women; over half of those age 15-19 report that they have never had sex. As for provincial differences, women in Nyanza and Eastem Provinces, as well as those in Nairobi, evidently have had more sexual partners on average than women in other provinces. Although it appears that better educated women are more likely to have had fewer sexual partners than uneducated women, this is probably more a function of the fact that those with higher education tend to be younger. Men and women who reported having had sex in the six months prior to the survey were asked if they had used a condom with any of these partners. As shown in Table 10.9, 20 percent of men reported having used a condom, compared to only 6 percent of women. It is encouraging that condom use is higher among those who report having a greater number of partners. Among those who report having had three or more partners in the last six months, 41 percent of men and 25 percent of women say they used a condom with at least one of these partners. Use of condoms is also higher among the unmarried than those who are married. 137 Table 10.9 Condom use Percentage of men and women who were sexually active in the six months prior to the survey by whether they used a condom with any partner according to number of partners and type of union, Kenya 1993 Partners/ union Men Women Percentage Number Percentage Number Number of partners 1 14.5 1389 5.4 4989 2 20.5 408 12.0 205 3+ 40.9 331 25.1 89 Type ol' union Unmarried 38.5 537 13.6 1033 M arried-monogamous 14.6 1402 4.3 3458 Married-polygamous 4.6 188 3.1 793 Total 19.8 2128 5.9 5284 138 CHAPTER 11 RESULTS OF THE MALE SURVEY In the KDHS, 2,336 men were interviewed individually to obtain information about their background and demographic characteristics, family planning knowledge and behaviour, fertility preferences, sexual activity, and awareness about AIDS. Data conceming all but the last topic are presented in this chapter;, results about AIDS knowledge and behaviour are covered in Chapter 10, Men were eligible for the individual interview if they were between 20 and 55 years of age. While women age 15-49 in every household selected in the KDHS were eligible for individual interviews, every second house- hold was designated as also falling into the male sample, and all men age 20-54 in these households were considered eligible. 11.1 Background Characteristics of the Male Survey Respondents General Characteristics Table 11.1 shows the percent distribu- tion of interviewed men by selected back- ground characteristics. The proportion of men in each age group declines with increasing age, due in part to mortality of older men, but even more to high fertility which produces ever larger cohorts over time. One-quarter (24 per- cent) of male respondents are unmarried and 71 percent are currently married (either in a formal or informal union). Almost all men have had at least some formal education, with only 8 percent having never been to school. Men have a clear educational advantage over women (see Table 2.8 and Figure 11.1). De- spite the fact that male respondents in the KDHS were on average somewhat older than female respondents (which should put them at an educational disadvantage), the proportion of women who have never been to school is twice that of men (18 vs. 8 percent). Men are much more likely to reach secondary school (38 per- cent) than women (25 percent). Almost one quarter of the men inter- viewed live in urban areas. This compares to only 18 percent of women, which is not Table 11.1 Background characteristics of respondents Percent distribution of men 20-54 by selected background characteristics, Kenya 1993 Number of men Background Weighted Un- characteristic percent Weighted weighed Age 20-24 22.5 525 526 25-29 16.7 390 396 30-34 17.6 412 417 35-39 13.4 314 298 40-44 13.0 303 305 45-49 9.7 227 228 50-54 7.1 165 166 Marital status Single 24.4 569 602 Married 62.4 1457 1424 Living together 8.9 207 213 Widowed 0.7 16 17 Divorced 1.6 37 35 Separated 2.1 49 45 Education No education 8.3 193 182 Primary incomplete 24.2 566 592 Primary complete 29.8 696 692 Secondary+ 37.7 882 870 Residence Urban 24.2 566 480 Rural 75.8 1770 1856 Province Nairnbi 11.0 257 171 Central 14.2 333 305 Coast 10.2 239 353 Eastern 16.7 389 308 Nyanza 12.3 287 302 Rift Valley 23.1 540 624 Western 12.5 292 273 Religion Catholic 35.2 823 823 Protestant/Other Christian 53.1 1241 1276 Muslim 4.7 110 100 No religion 5.3 124 112 Other 1.6 37 24 All men 100.0 2336 2336 139 50 40 30 20 10 O Figure 11.1 Level of Education Attained by Men Age 20-54 and Women Age 15-49 Percent NO Education Prim. Incomp. Prim. Comp. Secondary+ Level of Education KDHS 1993 surprising, since men are more likely to migrate to cities and towns in search of work. The distribution of men according to province of residence, religion and ethnic group parallels that of women, except that a higher proportion of men than women live in Nairobi. Differentials in Education Table 11.2 shows the distribution of men by education level, according to age, urban-roral residence and region. Compared to men in the younger age categories, older men are more likely to be uneducated. As expected, urban men are better educated than rural men; the proportion of urban men with some secondary school is almost twice that of rural men (58 vs. 31 percent). Men in Nairobi and in Western and Central Provinces on the whole receive more education than men in the other provinces. The data by age group given in Table 11.2 highlight the gender differentials in educational attainment, because they make it possible to compare men and women in the same age group (see Table 2.9). Differences are larger among older men and women. For example, 43 percent of women 40-44 have never been to school, compared to only 15 percent of men; only one quarter of these women have completed primary school, compared to over half of the men. Men's educational advantage over women seems to be gradually eroding as greater proportions of younger women are enrolled in school and stay there longer than before. 140 Table 11.2 Levelofeducation Percent distribution of men by highest level of education attended, according to selected background characteristics, Kenya 1993 Level of education Number Background Primary Completed Secondary/ of characteristic None incomplete primary Higher Total men Age 20-24 1.6 16,3 38.0 44.1 100.0 525 25-29 3.3 20,0 30.4 46.3 100.0 390 30-34 5.7 18.2 26.9 49,2 100,0 412 35-39 10.0 27.9 24.5 37.6 100,0 314 40-44 15.1 30,0 24,4 30.4 100.0 303 45-49 12.7 34,5 36,4 16.4 100.0 227 50-54 25,6 42.4 20,1 11.9 100.0 165 Residence Urban 4,3 13.5 23.8 58,4 100.0 566 Rural 9,5 27.7 31,7 31.2 100,0 1770 Province Nairobi 3.5 14,0 26.9 55.6 100.0 257 Central 3A 16.2 40.1 40.7 100.0 333 Coast 15.1 20.1 35,4 29,4 100.0 239 Eastern 7.4 31.9 31,6 29,1 100.0 389 Nyanza 9,1 31.0 28,1 31,8 100.0 287 Rift Valley 13.8 25,2 25.6 35.4 100,0 540 Western 2.8 26.9 23.0 47.4 100.0 292 Total 8.3 24.2 29,8 37,7 100.0 2336 Access to Mass Media All eligible men were asked if they usually listen to a radio, watch television, or read a newspaper at least once a week (Table 11.3). This information can be used to identify appropriate communication channels that can be used to reach men. Overall, 59 percent of men report that they read a newspaper once a week (vs. 31 percent of women), while 31 percent of men watch television once a week (vs. 15 percent of women), and 87 percent listen to the radio weekly (vs. 65 percent of women). Younger men are more likely to read newspapers, watch television and listen to the radio than older men. Access to all three media increases sharply as education increases. As expected, men in urban areas and in Nairobi are more likely than rural men to read newspapers, watch television and listen to the radio. 141 Table 11.3 Access to mass media Percentage of men who usually read a newspaper, watch television and listen to a radio weekly, by selected background characteristics, Kenya 1993 Read Watch Listen to Number Background newspaper television radio of characteristic weekly weekly weekly men Age 20-24 63.2 34.7 87.9 525 25-29 66.2 31.0 91.2 390 30-34 63.5 33.0 87.9 412 35 -39 60.7 32.9 89.5 314 40-44 54.2 27.7 84.8 303 45-49 48.3 24.6 81.3 227 50-54 38.8 21.3 82.0 165 55+ Education No education 9.9 16.2 60.6 193 Primary incomplete 34.2 21.9 83.2 566 Primary complete 62.4 26.0 88.8 696 Secondary+ 83.1 43.2 94.3 882 Residence Urban 84.6 54.0 93.6 566 Rural 50.9 23.2 85.2 1770 Province Nalrobi 82.5 53.2 92.4 257 Cen~al 61.3 27.3 84.6 333 Coast 65.1 38.2 88.5 239 Eastern 65.6 40.3 91.3 389 Nyanza 47.9 14.7 72.6 287 Rift Valley 48.2 25.4 85.4 540 Western 53.5 21.4 96.8 292 Total 59.1 30.7 87.2 2336 11.2 Fertility Regulation Knowledge of Contraception One of the main objectives of the KDHS was to determine the level of knowledge of contraceptive methods and the sources where they can be obtained. Although past programs have focused mainly on providing information about family planning to women, more recent programs have targeted men in Kenya. As for women respondents, information on contraceptive knowledge was obtained in the KDHS by asking men to name ways or methods that a couple could use to delay or avoid pregnancy. If the respondent failed to name a particular method spontaneously, the interviewer described the method and asked if he recognised it. Table 11.4 shows that knowledge of some contraceptive method is universal among men; 99 percent of all men and of currently married men age 20-54 know of at least one method of family planning. Knowledge of any method and of a modem method was equally high among currently married men as among all men. The pill and the condom are the most commonly known contraceptive methods, recognised by 94 and 93 percent of married men, respectively. Injections and female sterilisation are known by 88 percent of 142 Table 11.4 Knowledge of contraceptive methods and source for methods Percentage of all men and currently married men who know specific contraceptive methods and who know a source (for information or services), by specific methods, Kenya 1993 Know method Know a source Currently Currently Contraceptive All married All married method men men men men Any method 98.9 98.8 94.9 94.4 Any modern method 97.8 97.3 93.3 92.4 Modern method Pill 93.3 93.9 81.0 84.0 IUD 68.2 70.8 59.3 62.9 Injection 86.0 87.6 77.1 79.3 Diaphragm/foam/jelly 34.7 33.4 30.4 30.2 Condom 94.2 ~2.8 84.8 83.1 Female sterilisation 86.3 87.5 75.6 77.8 Male sterilisation 55.7 56.2 49.5 50.7 Norplant 12.9 13.9 10.5 l 1.4 Any traditional method 88.7 89.9 65.1 66.8 Rhythm/couming days 84.4 85.3 62.4 64.1 Natural family planning 35.6 36.6 26.5 27.9 Withdrawal 43.4 42.7 NA NA Other 12.0 13.7 NA NA Number of men 2336 1664 2336 1664 NA = Not applicable married men and calendar rhythm is known to 85 percent. Seven out of ten married men say they know the IUD. Interestingly, male sterilisation and withdrawal--both being "male methods"--are among the least widely known methods; just over half of married men say they know about male sterilisation and only 43 percent say they know about withdrawal. As with women respondents, the vaginal methods (diaphragm, foaming tablets, jelly), natural family planning, and Norplant were the least widely recognised methods among men. These findings indicate that the overall knowledge of contraception is extremely high among Kenyan men. However, knowledge of some specific methods such as vaginal methods, male sterilisation and to some extent, the IUD, is relatively low. More intensive information programs could help raise awareness of these methods. Overall, 94 percent of married men know of a place to obtain a method of family planning. Generally about 90 percent of the married men who know about a modem method also know of a place to obtain that method. As expected, knowledge of a source for information about the rhythm method or natural family planning is lower than that for the modem methods. The level of knowledge of contraceptive methods among currently married men and women can be compared in Figure 11.2. Women are slightly more likely than men to know about the female methods---the 143 Figure 11.2 Knowledge of Contraceptive Methods among Currently Married Men 20-54 and Women 15-49 Any Metho~ Any Modern Method . Any Traditional Method ::::::::::::::::::::::::::: Pill mu n ::::::::::::::::::::::: Injection ::::::::"::: ::: Condom Female Sterilisation Male Sterilisation Periodic Abstinence Withdrawal 0 I w 10 20 30 40 50 60 70 80 90 100 Percent Knowing Method pill, 1UD, injections and vaginal methods, while men are more likely to know about the male-oriented methods---condoms, male sterilisation and withdrawal. Interestingly, men are also more likely to know periodic abstinence (rhythm method and natural family planning). The same proportion of men as women report knowing about female sterilisation. The proportion of married men who know of at least one modem contraceptive method is extremely high (generally 95 percent or over) for all subgroups of men (Table 11.5). The only exception is men with no formal education, only 82 percent of whom recognise a modem method. Generally, around 90 percent of those who know a modem method also know of a place to obtain a modem method. Exceptions are men age 50-54, men in Rift Valley Province and men with no education, fewer of whom say they know a source for a modem method. Ever Use of Contraception All eligible men (age 20-54) interviewed in the KDHS who had heard of a particular method of family planning were asked if they had ever used it. It should be noted that many of the family planning methods asked about are used by women without requiring the participation or knowledge of men. To the extent that women use contraception without the knowledge of their partners, the results presented here may underestimate the true prevalence of contraceptive use. It should also be noted that the interpretation of these data is difficult in polygamous marriages (or any multi-partner relationships) where some of the wives may be using contraceptives, and others may not. 144 Table l l .5 Knowledge of modern contraceptive methods and source for methods Percentage of currently married men who know at least one modem contraceptive method and who know a source (for information or services), by selected background characteristics, Kenya 1993 Know a Know Know source for Number Background any a modem modem of characteristic method method 1 method men Age 20-24 99.3 99.3 94.8 101 25-29 99.5 98.0 96.7 257 30 34 99.1 98.4 93.5 359 35 39 98.8 97.1 91.2 299 40-44 99.2 97.0 93.9 285 45-49 98.0 96.8 93.1 211 50-54 97.1 94.1 80.1 153 Residence Urban 100.0 99.6 95.8 393 Rural 98.5 96.6 91.4 1271 Province Nairobi 100.0 100.0 98.3 179 Central 95.9 95.6 92.7 226 Coast 100.0 99.0 88.6 160 Eastern 99.6 99.6 97.8 283 Nyanza 99.6 98.8 94.1 208 Rift Valley 98.0 92.9 82.9 397 Westem 99.7 99.4 99.4 212 Education No education 95.2 82.2 65.1 170 Primary incomplete 99.0 98.5 91.6 429 Primary complete 99.3 99.3 95.5 471 Secondary+ 99.4 99.3 98.4 595 Total 98.8 97.3 92.4 1664 tlncludes pill, IUD, injection, vaginal methods (foaming tablets/diephragm/ foam/jelly), condom, female sterilisation, and male sterilisation. Table 11.6 indicates that almost three quarters (72 percent) of currently married men have used a family planning method at some time. A little less than half of married men have used a modem method and a little over half have used a traditional method. The calendar rhythm method is by far the most widely used method; half of the married men report that they have used it. Over one quarter (27 percent) of men report having used condoms and almost as many (24 percent) say they have relied on the contraceptive pill. Smaller proportions report ever use of the other methods. Although they have had less time in which to use family planning, men in their late 20s and early 30s are more likely to have used a method than older men. As expected, younger men are more likely than older men to have used temporary methods such as calcndar rhythm, condoms and the pill, while older men are more likely to have used longer-term methods such as female sterilisation and the IUD. Since most mcn intcfviewed were married, ever use of contraceptive methods among all men is comparable to that of currently marricd men. 145 Table 11.6 Ever use o f contraception Among all men and current ly marr ied men, the percentage who have ever used a contraceptive method, by specific method, according to age, Kenya 1993 Modern method Traditional method Any Dia- modem plcagm/ Background Any meth- Injec- foam/ Con- characteristic method od Pill IUD lion jelly dora Fcanale Nal~ral steri- Any Rhythm/ fatal- With- Number lisa- Nor- trad, counting ly draw- of lion plant method days planning al Other men ALL MEN 20-24 68.5 55.3 11.7 2.0 1.7 1.9 52.0 0.3 0.3 47.8 45.7 2.7 10.7 1.3 525 25-29 76.8 59.3 21.1 3.6 6.4 2.4 46.3 0.3 0.5 58.1 54.6 5.0 14.4 2.4 390 30-34 72.1 51.I 29.5 6.8 11.2 3.5 36.4 2.1 0.3 53.0 46.9 7.5 12.7 3.7 412 35°39 71.7 51.2 26.2 10.6 12.1 4.3 25.2 4.3 0.2 54.4 51.0 5.6 9.6 8.7 314 40-44 71.2 45.0 21.8 7.2 10.9 1.3 19.9 7.9 0.0 53.0 46.4 4.9 10.0 7.1 303 45-49 65.6 40.1 23.1 7.5 9.3 0.7 14.2 12.8 0.4 51.4 44.8 2.8 5.4 9.6 227 50-54 64.9 31.1 15.7 8.5 8.4 3.9 8.7 13.3 1.1 51.3 46.0 6.6 4.8 11.6 165 Total 70.8 50.2 21.1 5.9 8.0 2.5 33.8 4.3 0.3 52.6 48.1 4.9 10.5 5.2 2336 CURRE.NTLY MARRIED MEN 20-24 65.1 50.4 11.2 4.2 3.1 2.2 42.7 0.0 0.0 48.5 47.0 5.6 7.8 2.0 101 25-29 78.4 55.5 23.7 3.2 6.9 0.8 38.6 0.0 0.3 62.8 59.6 5.6 13.9 3.6 257 30-34 74.6 51.8 30.2 6.8 12.5 3.8 36.3 2.4 0.3 55.3 48.6 8.3 13.2 3.7 359 35-39 72.4 51.2 26.9 10.7 12.7 4.5 24.7 4.5 0.2 56.0 52.6 5.9 9.6 9.0 299 40-44 72.2 47.1 22.6 7.7 11.6 1.4 20.9 8.1 0.0 53.2 46.2 5.1 10.0 7.0 285 45-49 67.4 42.2 24.1 8.1 9.8 0.8 15.3 13.8 0.4 52.1 45.0 3.0 5.0 10.3 211 50-54 63.1 31.8 17.0 9.2 9.1 2.9 7.7 14.1 1.2 48.3 42.6 5.8 5.3 11.2 153 Total 71.8 48.3 24.2 7.3 10.3 2.5 27.0 5.7 0.3 54.8 49.5 5.8 10.0 6.6 1664 Current Use of Contraception Well over half (54 percent) of currently married men reported that they were using family planning methods at the time of the survey (see Table 11.7). Almost one-third of married men are using a modem method, while 23 percent are using traditional methods. Calendar rhythm is reported to be the most widely used method among married men (19 percent), with the pill the second most popular method (11 percent). Seven percent of married men say they are using condoms and 6 percent rely on injections. Current use of contraception is well over 50 percent among men in all age groups except the youngest (20-24) and the oldest (50-54), where it is lower. As with ever use, younger men are more likely than older men to be using temporary methods like the pill and condom, while older men are more likely to rely on longer-term methods such as female sterilisation, the IUD, and injections. Figure 11.3 shows current use of contraceptive methods among currently married men and women. Men are much more likely than women to report that they are currently using contraceptive methods (54 percent vs. 33 percent). The reported current use of modem contraceptives among men is much closer to that of women (32 percent vs. 27 percent). The largest differences are in the use of traditional methods; men are four times more likely to report that they are using traditional methods than women. Differences by individual method are small, except for the condom and calendar rhythm methods, for which married men report considerably higher levels of use than married women. 146 Table 11.7 Current use of contraception Percent distzlbution of all men and of currently married men by contraceptive method cun'ently used, according to age, Kenya 1993 Modern method Traditional method Any Dia- F~rnal¢ Natural modem phragm/ steri- Any Rhythm/ fatal* With- Not Number Background Any meth* Injec- foam/ Con- lisa- trod. counting ly draw- currently of characteristic method od Pill IUD tion jelly dora tion method days planning al Other using Total men ALL MEN 20-24 44.5 26.8 2.3 0.3 0.6 0.2 23.5 0-0 17.7 17,0 0.0 0.5 0.2 55.5 100,0 525 25-29 54.0 34.8 12.4 1.6 2.7 0.8 17.3 0-0 19.2 17,8 0.6 0.7 0.1 46.0 100.0 390 30-34 52.0 33.8 13.0 2.7 6.5 0.0 10.4 1.2 18.2 15.2 1.7 0.4 0.9 48.0 I00.0 412 35-39 56.3 29.5 9.1 4.1 6.3 0.2 5.7 4.3 26.7 22.8 1.3 0.1 2.5 43.7 100.0 314 40-44 57.2 35.3 8.5 3.7 8.1 0.0 7.4 7.5 22.0 18.1 0.4 0.l 3.3 42.8 100.0 303 45-49 49.7 28.0 6.4 2.7 5.2 0.0 0.9 12.8 21.7 15.7 0.1 1.4 4.5 50.3 100.0 227 50-54 40.1 24.0 5.0 3.9 3.1 0.0 0.5 11.6 16.0 11.9 1.3 0.2 2.6 59.9 100-0 165 Total 50.8 30.8 8.2 2.4 4.3 0.2 11.8 3.8 20.1 17.3 0.7 0.5 1.6 49.2 100.0 2336 CURRENTLY MARRIED MEN 20-24 35.5 21.0 6.5 1.5 3.1 0.0 9.9 O.O 14.4 13.9 0.0 0.0 0.5 64.5 100.0 101 25-29 56.4 31.5 16.9 1.1 3.2 0-0 10,2 0.0 24.9 23.3 0.9 0.6 0.1 43.6 100.0 257 30-34 55.7 35,6 13.9 2.7 7.4 0-0 10.2 1.4 20.0 16.6 2.0 0.5 1,0 44.3 I00.0 359 35-39 58.4 30.3 9.5 4.3 6.6 0.2 5.3 4.5 28.1 24.0 1.3 0.I 2.6 41.6 1130.0 299 4~44 60.6 37.2 8.9 4.0 8.7 0.0 7.7 7.9 23.4 19.2 0.5 0.1 3.5 39.4 100.0 285 45-49 52.9 30.1 6.9 2.9 5.6 0-0 0.9 13.8 22.8 16.4 0.1 1.5 4.8 47.1 100.0 211 50-54 43.5 26.1 5.4 4.2 3.3 0-0 0.5 12.5 17.4 12.9 1.4 0.2 2.9 56.5 100.0 153 Total 54.4 31.9 10.6 3.0 6.0 0.0 6.8 5.4 22.6 18.9 1.0 0.5 2.2 45.6 100.0 1664 Figure 11.3 Current Use of Contraceptive Methods among Currently Married Men 20-54 and Women 15-49 Any Method I Any Modern Method' I Any Traditional Method' Pill IUD Injection Condom Female Sterilisation Periodic Abstinence Withdrawal II m I 10 20 30 40 Percent Us ing Method 50 60 147 These observed differences may be due to a number of reasons. The higher prevalence among men may indicate use of contraceptives by men with women other than their wives. It is also possible that women did not mention some of the methods that were primarily used by their husbands, either due to shyness or because they did not know that their husbands were using them. This may explain the difference in reported condom use; however, it is difficult to see how it would explain the difference in use of the rhythm method for which the woman's participation is necessary. Differences in reported use of the rhythm method might be due to misunderstanding of the method among men who might mistake occasional abstinence for whatever reason for the more systematic abstinence during the most fertile time of the woman's ovulatory cycle. Table 11.8 shows the percent distribution of married men age 20-54 years by the contraceptive method currently used, according to rural-urban residence, province and highest level of education attained. Men in urban areas are more likely to use contraceptive methods, especially modem methods, than their counterparts in rural areas. Conversely, reported use of traditional methods is more common in rural areas than in urban areas. Table 11.8 Current use of family planning by background characteristics Percent distribution of all men and currently married men by contraceptive method currently used, according to selected background characteristics, Kenya 1993 Any modem Background Any meth- characteristic method od Pill Modern method Traditional method Natural Din- Female Any fatal- No~ phragm/ stxri- tred. Rhythm/ ly With- cur- Number injec- foam/ Con- lisa- meth-cotmtingplan- draw- rently of IUD tion jelly dora tion od days ning al Other using Total men Residence Urban Rural Province Nairobi Central C~st Eastern Nyanza Rift Valley Western Educaaon No education Primly incomplete Primary complete Secondary+ 60.5 42.4 14.7 6.1 7.6 0.1 8.1 5.7 18.1 14.5 0.8 0.0 2.8 39.5 100.0 393 52.5 28.6 9.3 2.1 5.5 0.0 6.4 5.3 24.0 20.2 I.l 0.6 2.0 47.5 100.0 1271 58.0 37.0 15.1 3.4 5.9 0.0 7.6 5.0 21.0 18.5 0.0 0.0 2.5 42.0 100.0 179 58.9 44.5 16.4 5.1 3.5 0.0 13.5 5.9 14.5 10.8 0.3 0.0 3.4 41.1 100.0 226 42.5 19.0 8.1 1.2 3.1 0.3 4.5 1.6 23,5 21.6 0.7 0.9 0.3 57.5 100.0 160 84.9 43.2 14.7 5.2 7.3 0.0 8.7 7.4 41.7 38.7 1.2 0.4 1.5 15.1 100.0 283 36.5 21.5 5.4 0.4 7.7 0.0 1.2 6.8 15.0 12.7 0.7 0.4 1.2 63.5 100.0 208 50.0 24.6 5.0 3.2 6.4 0.0 4.8 5.3 25.4 20.0 0.5 1.0 3.9 50.0 100,0 397 40.8 32.4 12.6 1,5 6.6 0.0 7.8 3.9 8.4 3,3 4.1 0.0 1.0 59.2 100.0 212 32.0 11.4 4.8 0.6 1.3 0.0 1.7 2.9 20.6 15.6 0.5 0.8 3.8 68,0 100.0 170 46.9 22.4 6,2 0.6 4.1 0.0 4.7 6.7 24.6 21.0 0.3 0.8 2.5 53.1 100.0 429 54.4 31.4 10.5 2.6 5.2 0.0 7.2 5.8 23.1 19.1 0.7 0.2 3.1 45.6 100,0 471 66.2 44.9 15.5 5.9 9.2 0.1 9.5 4.8 21.3 18.2 1.9 0.3 0.9 33.8 100.0 595 Total 54.4 31.9 10.6 3.0 6.0 0.0 6.8 5.4 22.6 18.9 1.0 0.5 2.2 45.6 100.0 1664 There are quite large differences in the prevalence of current contraceptive use among men in the various provinces. For example, 85 percent of the married men in Eastern Province report using family planning methods, compared to only 37 percent of those in Nyanza Province. Other provinces with relatively low male contraceptive use are Western and Coast Provinces. As for use of modem methods, men in Central and Eastem Provinces and those in Nairobi report the highest levels (around 40 percent), while those in Coast, Nyanza and Rift Valley Provinces report the lowest (around 20 percent). These differences need to be interpreted cautiously because of the small numbers of men covered. 148 Contraceptive use increases regularly with increasing educational attainment, from 32 percent of married men with no formal education, to 47 percent of those with some primary school, to 54 percent of those who completed primary school, and to 66 percent of those with at least some secondary school. A similar pattem is observed for use of modem contraceptive methods; however, there is little difference in use of traditional methods by education level. Sources of Family Planning Methods All current users of modem methods of family planning were asked to report the source from which they most recently obtained their supplies. Table 11.9 shows that 48 percent of male users of modem contraceptives obtained their last supplies from public (government) health facilities, of which half were government hospitals. Thirty-one percent of male users obtain contraceptives from private medical facilities, and 19 percent obtain their contraceptive supplies from other private sources. The importance of public and private sources in providing family planning services differs between men and women. Almost half of male users of modem contraceptives say their methods are obtained from public sources, compared to 68 percent of women (see Table 4.13). Most of the difference stems from the fact that men whose partners are using the pill, IUD and injection are more likely than women using these methods to say that the method was obtained from private as opposed to public sources. It is interesting to note that 9 percent of men who use either pills or condoms say they obtain them from the community-based distributor (CBD). Table l 1.9 Source of supply for modern contraceptive methods Percent distribution of current male users of modem contraceptive methods by most recent source of supply, according to specific methods. Kenya 1993 Female In jet- sterili- All Source of supply Pill IUD tion Condom salSon methods Public sector 54.4 53.9 60.7 32.9 61.0 48.0 Government hospital 22.4 34.6 17.0 14.9 61.0 24.5 Government health centre 21.2 19.3 26.2 13.6 0.0 16.5 Government dispensaz 3, 10.9 0.0 17.6 4.3 0.0 7.0 Medical private sector 32.2 41.6 34.4 25.4 39.0 31.4 Mission/church hospital 9.1 10.4 8.1 3.0 19.0 7.9 FPAK health centre/clinic 11.8 16.1 9.0 5.7 7.0 8.7 Other non-government 0.4 0.0 0.5 3.0 0.0 1.3 Private hospital/clinic 7.5 12.4 9.6 6.9 12.6 8.6 Pharmacy 0.8 0.0 0.0 6.1 0.0 2.5 Private doctor 2.6 2.7 7.3 0.8 0.4 2.3 Other private sector 10.8 1.8 2.6 39.9 0.0 18.7 Mobile clinic 0.2 1.8 2.6 5.6 0.0 2.7 Community distributor/ health worker 9.0 0.0 0.0 9.3 0.0 6.0 Shop 1.6 0.0 0.0 16.1 0.0 6.6 Friends/relatives 0.0 0.0 0.0 8.8 0.0 3.4 Other 0.8 0.0 1.5 0.2 0.0 0.5 Don't know 0.6 2.8 0.0 0.0 0.0 0.4 Missing 1.2 0.0 0.8 1.6 0.0 1.1 Total 100.0 100.0 100.0 100.0 100.0 100.0 Number of men 191 55 101 277 89 719 FPAK = Family Planning Association of Kenya 149 Intention to Use Family Planning Methods Among Nonusers Currently married men who were not using a modem contraceptive method at the time of the survey were asked if they intended to use a method to delay or avoid a pregnancy at any time in the future. According to the results, which are shown in Table 11.10, more than half (52 percent) of men not currently using contraception intend to use contraceptive methods in the future, while 36 percent do not intend to use and 10 percent are unsure. The proportion of men not intending to use contraceptives in the future increases with increasing number of living children among those with at least one child. These data on intentions for future contraceptive use closely mirror those obtained for female nonusers (see Table 4.15). Table l l .10 Future use of contraception Percent distribution of currently married men who are not using a contraceptive method by intention to use in the future, according to number of living children, Kenya 1993 Number of living children Future intentions 0 1 2 3 4+ Total All currently married nonusers Intend to use in next 12 months 23.6 33.6 31.7 26.8 29.1 29.2 Intend to use later 37.0 30.7 21.2 20.2 11.1 18.1 Unsure as to timing 7.0 0.6 4.8 4.6 4.6 4.4 Unsure as to intention 1.0 5.1 12.1 15.4 10.2 9.5 Do not intend to use 31.4 24.0 28.3 28.4 42.7 36.1 Missing 0.0 6.0 1.9 4.6 2.3 2.7 Total 100.0 100.0 100.0 100.0 100.0 100.0 Number of men 72 92 98 76 421 758 Currently married men who indicated that they did not intend to use any contraceptive in the future were asked to state their reasons. Almost one-third (31 percent) of these men said that they do not intend to use because their wives are menopausal or are infertile (have difficulty in getting pregnant), and one-quarter (26 percent) said that they do not intend to use contraception because they want children (see Table 11.11). Other reasons given are "lack of knowledge" (10 percent), "opposed to family planning" (10 percent), "religion" (5 percent) and "parmer opposed" (4 percent). The reasons for not intending to use contraception given by male nonusers are similar to those given by female nonusers, except that women are more likely to cite side effects, fear of sterility and other health concerns, while men are more likely to cite lack of knowledge and opposition to family planning. Nonusers who said that they did intend to use family planning in the future were asked to state the method they would prefer to use. Table 11.12 shows that almost one in five (18 percent) is unsure of the method. Another one in five prefers to use injections, while the rest are almost equally divided in preferring to use the pill, condoms, female sterilisation and the rhythm method. Men who intend to use in the next 12 months are more likely than those who intend to use later to say they want to use the rhythm method and less likely to say they are unsure of which method they intend to use. 150 Table 11.11 Reasons for not using contraception Percent distribution of cun'ently married men who are not using a contraceptive method and who do not intend to use in the future by main reason for not using, according to age, Kenya 1993 Reason for not using contraception Total Wants children 25.7 Lack of knowledge 10.0 Partner opposed 4.4 Side effects 2.6 Fears sterility 0.4 Other health concems 2.5 Hard to get methods 1.3 Religion 5.1 Opposed m family planning 9.8 Fatalistic 1.7 Infrequent sex 0.6 Difficult to be pregnant 6.0 Menopausal/had hysterectomy 25.4 Inconvenient 0.6 Other 2.3 Don't know 1.5 Total 100.0 Number 274 Radio Messages About Family Planning Table 11.12 Preferred method of contraception for future use Percent distribution of currently married men who are not using a contraceptive method but who intend to use in the future by preferred method, according to whether they intend to use in the next 12 months or later, Kenya 1993 Intend to use In next After Preferred method 12 12 of contraception months months Total Pill 13.5 17.5 14.8 1UD 3.7 2.4 2.9 Injections 21.9 17.3 19.0 Diaphragm/foam/jelly 0.3 0.0 0.2 Condom 13.8 16.5 13.9 Female sterilisation 13.8 12.6 12.9 Male sterilisation 0.0 0.4 0.5 Norplant 1.3 0.0 0.7 Rhythm/counting days 14.0 7.9 10.6 Natural method 1.2 1.4 1.6 Withdrawal 0.2 0.0 0.8 Other 4.3 0.9 3.4 Unsure 11.8 23.2 18.0 Missing 0.2 0.0 0.7 Total 100.0 100.0 100.0 Number 222 138 397 Note: Table excludes those who are unsure when they might use. All male respondents were asked if they had heard a family planning message on the radio in the six months preceding the survey. A majority of men (67 percent) said they did hear such a message on the radio (Table 11.13). This proportion remains remarkably constant across subgroups of men according to urban- rural residence, province of residence and education level attained. The only exceptions are among men in Eastem Province, a larger proportion of whom report having heard a family planning message, and among men with no education, a smaller proportion of whom have heard a message. Men are much more likely to have heard a family planning message on the radio than women (67 vs. 46 percent). Discussion and Approval of Family Planning One indicator of the extent of knowledge and acceptance of family planning is the proportion of married couples who discuss the topic. In the KDHS, both married men and women were asked whether they had ever discussed family plarming with their spouses and if so, how many times they had discussed it in the year prior to the survey. As shown in Table 11.14, over two-thirds of married men have discussed family planning with their wives in the past year and most of these men have discussed the topic not only once or twice, but on at least three occasions. Men in their late 20s and early 30s are more likely than older or younger men to have discussed contraception with their wives. 151 Table 11,13 Heard family planning on radio Percentage of all men who have he~d specific radio programs about family planning in the six months prior to interview, according to selected background characteristics, Kenya 1993 Name of program Maisha Kuelewa Ye Jifunze Ni Number Background Any Mwenda I'~nga J~.rnii Na Maisha Afya Daktari Kuzun- Don*t of ch~acteristic program Pole Uzazi Yako Ucndelea Bora Y~,ko Akushauri gumza Other know me~ Residence Urban 68.2 3.8 21.4 4.4 0.9 2.5 2.1 2.0 12.0 1.3 30,8 566 Rural 66.5 2.6 28.2 5.5 0.5 2.8 2.4 1.8 7.0 2.4 27.1 1770 Province Nalrobi 64.9 2.9 19.9 1.8 0.0 1.2 1.2 0.6 10.5 0.6 32.2 257 Central 65.4 5.2 15.0 4.2 0.1 2.8 1.1 0.2 4.6 3.5 34.5 333 Coast 60.2 2.0 21.2 3.6 0.6 1.6 2.0 1.5 8.4 0.7 25.3 239 Eastern 80.0 1.1 38.9 4.8 0.0 0.8 1.0 0.9 2.3 0.5 36.1 389 Nyanza 62.4 1.8 28.5 3.0 0.3 2.3 2.8 2.9 2.8 4.3 30.4 287 Rift Valley 65.8 4.4 30.1 7,0 0.9 1.7 0.2 1.5 4.1 3.2 23.4 540 Western 65.1 1.3 24.9 10.4 2.0 10.0 10.4 5.8 311.8 1.1 14.5 292 Education No education 41.2 0.2 8.4 2.3 0.0 0.0 0.0 0.0 0.9 0.6 30.1 193 Primary incomplete 60.0 1.3 26.2 5.1 0.2 1.7 2.2 0.9 5.9 1.5 24.9 566 Primary complete 66.9 1.9 26.7 3.0 0.2 2.0 1.3 1,0 6.6 2.9 29.3 696 Secondary + 77.1 5.1 30.6 7.7 1.2 4,7 3.8 3.4 12.5 2.2 28.5 882 Total 66.9 2.8 26.5 5.2 0.6 2.8 2.3 1.8 8.2 2.1 28.0 2336 Table 11.14 Discussion of family planning with wife Percent distribution of currently married non-sterilised men knowing a contraceptive method by the number of times they discussed family planning with their wives in the past yem', according to current age, Kenya 1993 OnEe or More Age Never twice often Missing Total Number 20-24 39.8 18.6 40.9 0.6 100.0 101 25-29 23.3 27.0 49.8 0.0 100,0 255 30-34 21.2 19.5 59,1 0.2 100,0 356 35 39 27,3 20.4 52.0 0,3 100.0 295 40-44 30.6 19.2 49.8 0.4 100.0 282 45 49 43.3 12.5 42.6 1.6 100.0 2(17 50-54 49.9 20.2 28.0 1.9 100.0 148 Total 30.7 19.9 48.8 0.6 100.0 1644 152 To obtain more direct information about the acceptability of family planning methods, currently married men who had not been sterilised and who knew at least one contraceptive method were asked if they approved of couples' using family planning methods. They were also asked whether they thought their wives approved of family planning. The percent distribution of men according to their attitudes and their perception of their wives' attitudes is presented in Table I 1.15, according to selected background characteristics. Table 11.15 Attitudes of couples towards family planning Percent distribution of currently married non-sterilised men who know a contraceptive method, by approval of family planning and by their perception of their wives' attitudes, according to selected background characteristics, Kenya 1993 Respondent Respondent approves disapproves Unsuro Unsur¢~ Background Both Wife of Wife of Both characteristic approve disapproves wife approves wife disapprove Missing Total Number Age 20-24 68.3 5.8 20.8 0.0 0.0 2.1 2.9 100.0 101 25-29 82.2 3.7 9.6 0.6 2.4 1.2 0.4 100.0 255 30-34 80.7 2.4 8.4 3.5 2.4 1.2 1.3 100.0 356 35-39 74.9 2.1 11.7 1.7 5.4 2.7 1.6 100.0 295 40-44 73.5 4.7 11.7 1.4 2.2 5.6 1.0 100.0 282 45-49 73.0 2.0 8.2 2.4 6.2 6.6 1.5 100.0 207 50 54 65.6 4.2 12.5 1.1 6.3 8.6 1.7 100.0 148 Residence Urban 79.1 3.7 7.1 2.2 2.2 4.3 1.4 100.0 393 Rural 74.4 3.1 12.1 1.7 4.0 3.4 1.3 100.0 1251 Province Nairobi 79.0 3.4 9.2 1.7 2.5 2.5 1.7 100.0 179 Central 73.9 2.4 17.1 0.0 2.6 4.0 0.0 100.0 217 Coast 70.5 4.3 9.5 4.0 2.8 7.0 1.9 100.0 160 Eastern 82.5 5.3 5.9 1.9 1.4 2,2 0.8 100.0 282 Nyanza 72.6 2.4 15.7 0.9 3.8 1.4 3.3 100.0 208 Rift Valley 75.8 1.8 7.8 2.5 4.3 6.1 1.7 100.0 389 Western 71.4 3.9 14.4 1.5 7.5 1.2 0.0 100.0 211 Education No education 54.4 1.2 14.6 0.9 14.6 11.9 2.5 100.0 162 Primary incomplete 69.6 4.6 I2.5 2.5 4.8 3.5 2.5 100.0 424 Primary complete 74.1 3.4 12.9 2.4 2.0 4.0 1.1 100.0 468 Secondary+ 86.7 2.7 7.1 1.0 1.0 t.1 0.3 100.0 591 Total 75.6 3.2 10.9 1.8 3.6 3.6 1.3 100.0 1644 The data indicate that 90 percent of married men in Kenya approve of family planning use. Moreover, according to the men's perceptions, in three quarters of Kenyan couples, both the husband and wife approve of family planning, whereas in only 4 percent of couples, both the man and wife disapprove of family planning. Approval of family planning varies with background characteristics. Older men are less likely than younger men to approve of family planning. Men in Coast and Rift Valley Provinces are also somewhat less likely to approve of family planning than men in other provinces. Approval of contraceptive use increases steadily with increasing education. 153 11.3 Nuptiality and Sexual Intercourse Factors other than contraception that affect men's fertility are considered in this section. These include nuptiality, as well as more direct measures of the beginning of exposure to pregnancy and the level of exposure, i.e., age at first sexual intercourse and the frequency of intercourse. Marital Status Table I 1.16 shows the current marital status of eligible men at the time of the survey. The term "married" refers to civil, religious, or traditional marriage, as well as consensual unions. Men who are widowed, divorced, or not living together (separated) are classified as "ever married" or "ever in union." Table 11.16 Current marital status by age Percent distribution of men by current marital status, according to age, Kenya 1993 Marital status Not Number Age Never Living living of group married Married together Widowed Divorced together Total men 20-24 78.3 16.6 2.7 0.0 0.4 2.1 100.0 525 25-29 29.5 57.2 8.7 0.1 0.8 3.7 100.0 390 30-34 8.3 77.2 10.0 0.2 1.7 2.7 100.0 412 35-39 1.7 82.2 13.0 0.6 1.9 0.6 100.0 314 40-44 0.0 82.1 11.8 2.3 2.4 1.5 100.0 303 45-49 0.7 78.7 14.4 1.3 2.0 2.8 100.0 227 50-54 1.0 86.7 5.6 2.0 4.5 0.4 100.0 165 Total 24.4 62.4 8.9 0.7 1,6 2.1 100.0 2336 Overall, 71 percent of men age 20-54 are married, about one quarter have never married and the remaining 4 percent are either widowed, divorced or separated. Kenyan men do not marry at particularly young ages; a large majority (78 percent) of those age 20-24 are still single and even at age 25-29, 30 percent have not yet married. These figures are considerably higher than those for women, indicating the tendency for men to marry later than women. Polygyny Table 11.17 shows the percentage of currently married men who are in a polygynous union by background characteristics. Overall, 12 percent of currently married men are in polygynous marriages. This proportion increases sharply with age of the respondent, from 5 percent of those under age 35 to 25 percent of those age 50-54. Polygynous unions are more common in rural than urban areas and in Nyanza, Rift Valley and Western Provinces. Men who have completed primary school are less likely to have more than one wife than those who either have no education or only some primary school. Almost all polygynously married men have only two wives. Of the 12 percent of married men who have more than one wife, 90 percent have two wives and less than 10 percent have three or more wives (not shown). Age at First Marriage Table 11.18 shows the percentage of men by specific exact ages at marriage according to their current age. The overall median age at first marriage among men age 25-54 years is 25 years. Only about 10 percent of men marry before reaching 20 years of age, while about half marry before age 25. 154 Table 11.17 Polygyny Percentage of currently married men in a polygynous union, by selected background character- istics, Kenya 1993 Background characteristic Total Age 20-24 5.0 25-29 5.2 30-34 5.4 35-39 16.5 40-44 13.8 45-49 13.1 50-54 25.2 Residence Urban 8.6 Rural 12.5 Province Nairobi 7.6 Cenlral 3.7 Coast 8.8 Eastern 7.8 Nyanza 19.2 Rift Valley 16.3 Western 14.0 Education No education 22.9 Primary incomplete 16.2 Primary complete 7.5 Secondary+ 8.2 Total 11.6 Table 11.18 Age at first marriage Percentage of men who were first married by exact age 15, 18, 20, 22, and 25, and median age at first marriage, according to current age, Kenya 1993 Current age 15 18 20 22 Percentage of men who were Percentage Median first married by exact age: who had Number age at never of first 25 married men marriage 20-24 0.4 1.1 4.7 NA NA 78.3 525 a 25-29 0.1 2.7 6.5 17.4 46.8 29.5 390 a 30-34 0.2 2.8 9.2 25.0 55.9 8.3 412 24.5 35-39 0.8 3.8 10.9 27.3 57.5 1.7 314 24.4 40-44 1.0 5.0 14.2 28.0 54.5 0.0 303 24.6 45-49 0.2 7.4 14.7 25.9 52.4 0.7 227 24.7 50-54 2.6 9.4 15.5 26.1 51.1 1.0 165 24.9 20-54 0.6 3.7 9.6 21.5 46.1 24.4 2336 a 25-54 0.6 4.5 11.0 24.5 53.1 8.7 1811 24.7 NA = Not applicable aOmitted because less than 50 percent of the men in the age group x to x+4 were first married by age x 155 The median age at first marriage has not changed appreciably across age cohorts. This suggests that the age at first marriage for Kenyan men has not changed significantly over time. However, these findings may be affected by recall problems among older men who are likely to have been married many years ago and hence, less likely to remember the exact age at the time of the first marriage. A comparison of the median age at first marriage for women and men shows that women tend to marry earlier than men. The median age at first marriage is 19 years for women age 25-49 (Table 5.6), compared to 25 years for men age 25-54. Age at First Sexual Intercourse Although age at first marriage is commonly used as a proxy for exposure to sexual intercourse, the two events do not necessarily coincide exactly. Sexual relations may begin prior to marriage or be delayed after marriage. To obtain more objective information about this topic, the KDHS asked men to report the age at which they first had sexual intercourse (see Table 11.19). The proportion of men who have never had intercourse is of interest (Column 6 of Table 11.19). Only 6 percent of men age 20-24 have not yet had sexual intercourse. This proportion falls to less than one percent among older men. Further evidence of the pattern of early sexual activity is the fact that about one- quarter of men report that they had sexual intercourse before they reached 15 years of age. The median age at first sexual intercourse is about 17 years. Sexual activity often preceeds marriage. This is evidenced by the fact that 64 percent of men age 20- 54 report that they have had sexual intercourse by age 18, whereas only 4 percent have married by that age (see Table 11.18). It is also interesting that, although men tend to marry at much older ages than women, both men and women report having their first sexual experience at about the same ages (see Table 5.7). In contrast to women, age at first intercourse among men has apparently been decreasing over time. For instance, men age 50-54 years report a median age at first intercourse of 18.0, while those age 20-24 report a median age at first intercourse of 16.3 years. Table 11.19 Age at first sexual intercourse Percentage of men ever having sexual intercourse by exact specified ages and median age at first sexual intercourse, according to current age, Kenya 1993 Cu~entage 15 18 20 22 Percentage of men who Percentage Median first had sex by exact age: who have Number age at never of first 25 had sex men sex 20-24 27.7 72.8 89.1 NA NA 5.5 525 16.3 25-29 26.6 68.7 89.5 94.1 98.3 0.8 390 16.4 30-34 22.3 61.4 84.4 92.6 96.3 0.3 412 17.0 35-39 21.1 65.0 88.1 95.1 97.6 0.0 314 16.8 40-44 23.3 56.5 82.1 90.9 96~3 0.0 303 17.3 45-49 22.1 60.5 81.9 90.3 92.7 0.5 227 17.1 50-54 15.5 49.4 71.2 89.9 95.1 0.7 165 18.0 20-54 23.7 64.1 85.3 92.6 95.9 1.5 2336 16.7 NA = Not applicable aOmitted because less than 50 percent of the men in the age group x to x+4 had first had intercourse by age x 156 11.4 Fertility Preferences In the KDHS, currently married men were asked about their desire for more children and how long they would like to wait before the birth of the next child. They were also asked about the number of children they would ideally want to have in their whole life. Desire for More Children As Table 11.20 shows, Kenyan men are almost equally divided between those who want more children and those who do not. Almost half (47 percent) of married men want to have another child either in the next two years, later or whenever, while 44 percent want no more children (38 percent who want no more and 5 percent whose wives are sterilised). Seven percent of men are undecided about having another child. As expected, the proportion who want no more children increases with the number of living children and the proportion who want another child soon decreases with number of children. Table 11.21 presents similar information on fertility preferences according to age instead of number of children. Table 11.20 Fertility preference by number of living children Percent distribution of currently married men by desire for more children, according Kenya 1993 to number of living children, Number of living children Desire for children 0 1 2 3 4 5 6+ Total Want another soon I 72.6 29.1 24.6 19.3 16.0 14.8 8.2 19.4 Want another late~ 10.7 59.1 41.l 34.9 18.5 11.6 11.4 24.3 Want another, undecided when 7.5 3.7 3.7 3.3 5.0 1.3 2.8 3.4 Undecided 7.6 1.5 7.6 4.3 10.7 9.4 8.5 7.3 Want no more 0.0 5.3 21.1 33.2 43.8 49.0 58.1 38.4 Wife sterilised 0.0 0.2 0.4 4.4 5.8 11.5 8.0 5.4 Wife declared infecund 1.6 1.1 1.3 0.7 0.2 2.5 2.7 1.7 Missing 0.0 0.0 0.2 0.0 0.0 0.0 0.4 0.2 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Number of men 95 182 213 221 179 184 586 1664 tWant next birth within 2 years 2Want to delay next birth for 2 or more years It is interesting to compare these data with analogous information from the female KDHS respondents (Table 6.1). Overall, more women than men want to stop childbearing. Moreover, this is true regardless of the number of children women and men already have. For example, 44 percent of married men with four children say they do not want any more, compared to 60 percent of women. Ideal and Actual Number of Children Though men do not bear children, they can have a strong influence on, and in many cases, make the final decision on childbearing issues. Thus their views on the number of children they would like to have is of interest to policymakers. In order to assess ideal fertility preferences in Kenya, the KDHS included two questions. Men who had no children were asked, "If you could choose exactly the number ofchildren to have in your whole life, how many would that be?" For men who had children, the question was rephrased as follows: "If you could go back to the time you did not have any children and could choose exactly the number of children to have in your whole life, how many would that be?" 157 Table 11.21 Fertility preferences by age Percent distribution of currently married men by desire for more children, according to age, Kenya 1993 Desire for Current age children 20-24 25-29 30-34 35-39 40-44 45-49 50-54 Total Have another soon I 43.4 31.9 24.2 14.7 12.2 8.1 9.0 19.4 Have another latex ~ 43.0 44.0 30.4 26.2 11.6 9.0 5.5 24.3 Another/undecided when 2.1 4.2 5.3 3.3 2.8 1.6 2.2 3.4 Undecided 3.5 2.7 5.3 11.4 10.0 12.1 2.6 7.3 Want no more 8.0 I6.4 32.9 39.1 53.2 51.6 60.9 38.4 Wife sterilised 0.0 0.0 1.4 4.5 7.9 13.8 12.5 5.4 Wife declared infecund 0.0 0.0 0.5 0.7 2.2 3.8 6.6 1.7 Missing 0.0 0.7 0.0 0.0 0.0 0.0 0.7 0.2 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Number 101 257 359 299 285 211 153 1664 1Want next birth within 2 years 2Want to delay next birth for 2 or more years The data in Table 11.22 indicate that the vast majority of men were able to give a numeric answer to this question; only 7 percent of men gave a non-numeric answer such as "it is up to God," "any number," or "do not know." Those who gave numeric responses generally want to have small families. Only 12 percent of respondents said they would choose to have six or more children, while one-third favoured four children and one-fifth cited two children as ideal. Among men giving numeric responses, the mean ideal family size is 3.8 children. Table 11.22 Ideal number of children Percent distribution of all men by ideal number of children and mean ideal number of children for all men and for currently married men, according to number of living children, Kenya 1993 Number of living children Ideal number of children 0 1 2 3 4 5 6+ Total 0 0.1 0.0 1.7 1 1.9 2.9 O.5 2 28.4 19.5 18.8 3 28.6 32.9 22.7 4 25.1 30.5 32.3 5 6.1 4.6 6.7 6+ 5.4 6.8 11.5 Non-numeric response 4.4 2.8 5.8 Total 100.0 100.0 100.0 Number of men 630 243 241 Mean ideal number 3.3 3.5 3.7 Number of men 602 236 227 Mean for men in union 3.5 3.5 3.7 Number of men in union 89 177 200 1.3 0.0 0.0 0.0 0.3 2.9 2.l 2.2 1.2 1.8 l l .8 18.3 22.5 14.2 19.8 22.3 8.5 16.2 15.9 21.8 40.8 35.4 16.2 34.9 30.8 6.7 9.6 11.6 4.8 6.5 7.1 17.4 19.3 18.0 11.7 7.1 8.6 12.0 10.9 7.3 100.0 100.0 100.0 100.0 100.0 241 187 186 602 2336 3.7 4.2 4.0 4.6 3.8 224 171 164 536 2164 3.7 4.2 4.0 4.6 4.1 205 163 163 522 1522 Note: The means exclude men who gave non-numeric responses. 158 As expected, the ideal number of chil- dren increases with the number of living chil- dren; men with more living children are likely to state four or more as their ideal number of children, while men with fewer children are as likely to state two or three children as ideal. The mean ideal family size increases from 3.3 among childless men to 4.6 among men with six ormore children. There are several possible explanations for the relationship between ideal and actual number of children. First, to the ex- tent that they are able to implement their pref- erences, men who want larger families will tend to actually have them. Secondly, men who have larger families may tend to rationalise their family size by reporting their actual num- ber of children as their ideal number. Finally, men with larger families, being on average old- er than men with smaller families, have larger ideal family sizes, presumably because of atti- tudes they acquired 20 to 30 years ago. Despite the likelihood that some ra- tionalisation of large families occurs, it is com- mon for men to report lower ideal family sizes lower than their actual number of children. Al- most 60 percent of the men with five children stated that they would ideally have liked to have had fewer than five and 71 percent of those with six or more children would have fewer if they could choose again. Men and women are remarkably simi- lar in the number of children they consider ideal. The distribution of men and women by the ideal number of children is almost identical (see Table 6.5) and the mean ideal number according to the actual number of children is only slightly higher among men than women. Table 11.23 Background characteristics of husbands Percent distribution of husbands by selected background characteristics, Kenya 1993 Number of man Background Weighted Un- characteristic percent Weighted weighted Age 20-24 4.9 62 57 25-29 15.8 199 196 30-34 21.6 274 276 35-39 19.3 244 228 40-44 16.7 212 223 45-49 13.4 170 171 50-54 8.3 105 106 Number of children <2 27,2 344 341 3-4 25,6 323 320 5+ 47,0 594 593 Missing 0,3 4 3 Type of union Monogamous 86.4 1093 1094 Polygamous 13.3 168 157 Missing information 0.4 5 6 Education No education 11.1 141 127 Primary incomplete 26.8 339 355 Primary complete 27.7 351 350 Secondary+ 34.4 435 425 Residence Urban 14.7 186 159 Rural 85.3 1079 1098 Province Nairobi 6.3 80 58 Central 13.3 169 16 l Coast 7.6 96 142 Eastern 19.4 246 190 Nyanza 13.1 166 188 Rift Valley 24.4 309 342 Westem 15.7 199 176 All men 100.0 1265 1257 11.5 Couples Background Characteristics The KDHS made use of the fact that both women and men were interviewed to link currently married men with their wives. Thus it is possible to study a subset of married couples, both of whom were interviewed individually. This yields a total of 1,257 couples (weighted number is 1,265). Due to polygyny, a few husbands have been counted more than once, if two or more of their wives were interviewed. Table 11.23 presents the percent distribution of the husbands of these matched couples by age, number of children, type of union, level of education, urban-rural residence, and region. Relatively few 159 husbands (5 percent) are under 25 years of age, and only 21 percent are under 30 years of age. Almost half of the husbands have five or more children. One in seven of the unions considered here are polygynous. Table 11.24 shows that men tend to marry younger women. In 96 percent of the couples observed, husbands are older than wives. In about one-quarter of couples, husbands are at least 10 years older than their wives. Overall, husbands are a median of 7 years older than their wives. This differential is wider for younger than older wives, presumably in part due to polygyny, in which men take second wives who are considerably younger than they. Table 11.24 Age difference between spouses Percent distribution of couples by age difference between husband and wife and median age difference between spouses, according to wife's age, Kenya 1993 Median age difference llusbaed's age - wife's age (in years) First Second+ Wife's age Negative 0-4 5-9 10-14 15+ Total wives wives Total Number 15-19 0.0 11.4 57.8 17.4 13.3 100.0 8.7 20.8 9.7 72 20-24 2.0 22.9 44.1 19.2 11.8 100.0 8.1 13.9 8.3 273 25-29 3.4 26.4 44.6 16.0 9.6 100.0 7.0 13.0 7.0 309 30-34 1.9 29.0 44.3 17.2 7.6 100.0 7.0 16.0 7.0 266 35-39 6.2 24.6 47.5 19.4 2.2 100.0 6.2 a 6.2 168 40-44 7.6 27.8 46.2 18.4 0.0 100.0 5.5 10.0 5.6 109 45-49 24.3 44.2 31.5 0.0 0.0 100.0 2.5 3.2 2.5 68 Total 4.4 26.2 45.0 16.8 7.6 100.0 6.9 14.8 7.0 1265 aCannot be calculated due to small number of observations Knowledge and Use of Contraception Table 11.25 shows the percent distribution of married couples by knowledge of specific contraceptive methods. There is generally high correlation between spouses; if one partner knows a method, the other is likely to know it as well, or, if one partner does not know a method, the other is also likely not to know it. Exceptions to this are vaginal methods (diaphragm, foam, jelly), male sterilisation, and the traditional methods, for which there is less consistency of knowledge between spouses. For most methods, if only one spouse knew the method, it was more likely to be the husband, except in the case of pill, IUD, injections and vaginal methods (diaphragm, foam, jelly). Husbands are especially likely to know about traditional methods more often than their wives. Approval of Family Planning Family planning use is facilitated when both husband and wife approve of its use. Table I 1.26 shows the percent distribution of couples by approval of family planning of both spouses, according to selected background characteristics. Overall, among 80 percent of the couples, both husband and wife approve of family planning, and among 3 percent of the couples, both disapprove. Among 6 percent of the couples, the wives approve but their husbands do not, while among 8 percent of the couples, the husbands approve of family planning but their wives do not. Variations in the data by background characteristics are not large, although there is a tendency for couples in which neither spouse has any formal education 1o disagree about approval of family planning use. 160 Table 11.25 Knowledge of methods among mantled couples Percent distribution of married couples by contraceptive knowledge, according to specific method, Kenya 1993 Both Husband, Wife, know not not Method method wife husband Neither Total Any method 95.9 3.2 0.9 0.1 100.0 Any modern method 95.0 2,2 1.5 1.3 100.0 Pill 90.6 3.2 4.1 2.2 100.0 IUD 63.2 9.9 15.6 11.3 100.0 Injection 82.6 5.6 9.7 2.2 100.0 Diaphragm/foam/jelly 18.2 14.9 24.1 42.8 100.0 Condom 81.0 11.4 3.7 4.0 100.0 Female sterilisation 79.1 8.7 8.2 3.9 100.0 Male sterilisation 30.5 26.7 15.2 27.6 100.0 Norplant 3.8 9.1 8.3 78.8 100.0 Any traditional method 67.1 22.5 5,9 4.5 100.0 Rhythm/counting days 57.2 28.2 7.7 7.0 1130.0 Natur',d family planning 15.0 21.5 16.5 47.0 100.0 Withdrawal 15.6 26.8 15.9 41.7 100.0 Other 1.5 11.6 8.5 78.4 100.0 Table 11.26 Attitudes of couples towards family planning Percent distribution of couples by approv,,d of family planning, according to age difference between spouses, type of marriage and education level, Kenya 1993 Wife, Husband, Number Background Both Both not not Percent of characteristic approve disapprove husband wife Missing Total agree couples Age difference between spouses Wife older 72.1 0.7 6.8 16.9 3.6 100.0 72.8 56 Husband 0-4 years older 86.2 3.6 3.1 4.8 2.2 100.0 89.8 331 Husband 5-9 years older 77.8 1.9 7.2 9.9 3.1 100.0 79.8 569 Husband 10-14 years older 79.8 4.7 6.0 7.8 1.7 100.0 84.5 213 Husband 15+ years older 73.6 2.9 14.2 5.0 4.2 100.0 76.6 96 Type of union Monogamous 80.6 2.0 6.9 7.8 2.8 100.0 82.5 1054 Polygamous 74.2 8.7 3.1 11.1 2.9 100.0 82.9 154 Different 1 80.2 3.4 7.6 7.2 1.6 100.0 83.6 58 Couple's education Both none 50.6 14.4 18.8 12.2 3.9 100.0 65.1 101 Wife some, husband none (76.5) (3.3) (16.7) (3.4) (0.0) 100.0 79.8 40 Husband some, wife none 73.1 4.7 5.7 12.8 3.7 100.0 77.8 191 Both some 84.4 1.2 4.8 7.0 2.6 100.0 85.6 933 Total 79.8 2.9 6.4 8.2 2.8 100.0 82.6 1265 Note: Parentheses indicate a figure is based on 25-49 cases. When asked t ~ define type of union, each partner responded differently. 161 Because both men and women interviewed in the KDHS were asked whether they approved of family planning and, if married, whether they thought their spouses approved of family planning, it is possible to examine the extent to which husbands and wives report accurately on their spouses' attitudes. Table 11.27 shows the percent distribution of couples by husband's and wife's actual attitude toward family planning, according to their spouse's perception of their attitude. Table 11.27 Spouse's perception of other spouse's approval of family planning Percent distribution of couples by husband's and wife's actual attitude towards family planning according to their spouse's perception of their attitude, Kenya 1993 Husband Wife's perception Approves Disapproves Unsure Total Number Believe husband approve 94.0 4.8 1.2 100.0 812 Believe husband disapproves 79.4 19.9 0.8 100.0 220 Don't know 80.5 16.4 3.0 100.0 233 Total 89.0 9.6 1.5 100.0 1265 Husband's perception Wife Approves Disapproves Unsure Total Number Believe wife approves 90.2 8.6 1.2 100.0 991 Believe wife disapproves 67.7 29.3 3.0 100.0 86 Don't know 82.0 16.7 1.3 100.0 188 Total 87.4 11.2 1.4 100.0 1265 The data indicate that when husbands and wives report that their spouses approve of family planning, they are generally accurate. For example, in 94 percent of the cases in which wives reported that their husbands approved of family planning, the husbands also said they approved. However, when husbands and wives report that their spouses disapprove of family planning, in most cases, the opposite is true, that is, the spouse actually approves of family planning. A conclusion from these data that there is a considerable lack of communication between spouses about attitudes towards family planning should be taken with caution. It is also likely that at least some respondents report more favourable attitudes towards family planning than they in fact hold, perhaps in an attempt to please the interviewer or to appear more sophisticated. Such a pattem of misreporting could produce the results in Table 11.27. Desire for More Children It is also possible to compare the fertility preferences of husbands and wives. Table 11.28 shows the percent distribution of couples by desire for more children, according to the number of living children each parmer has. Overall, there is a high degree of agreement between spouses. For example, among 30 percent of the couples, both spouses want more children, while among 28 percent, both want no more children. The proportion of couples in which the husband wants more children and the wife does not (12 percent) exceeds the proportion in which the wife wants more and the husband does not (7 percent). Among 22 percent of the couples, one or both of the spouses is undecided about whether they want to have more children. 162 Table 11.28 Desire for more children among couples by number of living children Percent distribution of couples by desire for more children, according to number of living children, Kenya 1993 Husband Wife Husband wants wants wants more, more, Both Both more, wife husband want One Number Number of want wife does does no or both of living children more infecund not not more missing Other Total couples Husband 0 75.0 0.0 5.7 0.0 0.0 0.0 19.3 100.0 65 1-3 52.1 1.5 I2.5 8.7 12.0 0.0 13.2 I00.0 456 4-6 14.2 0.6 11.9 6.3 34.6 3.0 29.4 100.0 414 7-9 7.6 0.4 10.5 4.7 50.7 2.8 23.3 100.0 226 10+ 11.9 0.0 14.9 7.2 38.8 0.0 27.2 100.0 104 Wife 0 69.1 2.1 4.7 5.0 0.0 0.0 19.2 100.0 90 I-3 47.6 1.2 13.0 10.5 12.3 0.3 15.2 100.0 540 4-6 12.4 0.4 12.0 5.1 39.1 3.4 27.5 100.0 412 7+ 1.8 0.0 11.4 0.8 56.3 1.7 28.0 100.0 223 Total 29.6 0.8 11.8 6.6 27.9 1.5 21.7 100.0 1265 Th is genera l ly h igh degree o f agreement between spouses is true, regard less o f the number o f ch i ldren the husband or wi fe has. O f course, when the number o f ch i ldren is smal l , both spouses are more l ike ly to want more ch i ldren and when the number o f ch i ldren is h igh, they are l ike ly to both want no more. When there is d isagreement , it is usual ly the husband who wants more chi ldren. 163 Table I 1.29 shows the percent distribution of couples by the extent to which they agree on ideal number of children, according to selected background characteristics. Overall, only just over one-quarter (28 percent) of the couples report the same ideal number of children. Among 31 percent of the couples, the husband wants more children than the wife, and among 28 percent of the couples, the wife wants more children than the husband. This shows that there is little agreement between husbands and wives on the ideal number of children a couple would like to have. Variations in this pattern according to background characteristics are not large, considering the relatively small samples for some of the categories. Table 11.29 Spouse's agreement on ideal number of children Percent distribution of couples by extent of agreement on ideal number of children, according to selected background characteristics, Kenya 1993 Ideal Husband Wife number wants wants same for more more Non- Number Background husband than than numeric of characteristic and wife wife husband response Total couples Age difference Wife older 21.1 33.2 30.4 15.3 100.0 56 Husband 0-4 older 31.8 32.3 27.2 8.7 100.0 331 Husband 5-9 older 24.7 31.1 31.3 12.9 100.0 569 Husband 10-14 older 36.3 25.2 25.4 13.1 100.0 213 Husband 15+ older 21.7 39.4 19.9 19.0 100.0 96 Type of union Monogamous 29.3 29.6 29.7 11.4 100.0 1054 Polygamous 19.3 43.1 18.7 19.0 100.0 154 Different I 30.1 27.7 29.4 12.8 100.0 58 Education Both none 16.7 29.9 24.0 29.4 100.0 101 Wife some, husband none (26.1) (13.7) (27.8) (32.4) 100.0 31 Husband some, wife none 20.7 27.2 30,8 21.4 100.0 115 Both some 30.1 32.2 28.5 9.1 100.0 1019 Total 28.1 31.1 28.4 12.4 100.0 1265 Note: Parentheses indicate a figure is based on 25-49 cases. lWben asked to define type of union, each partner responded differently. 164 CHAPTER 12 LOCAL AVAILABILITY OF FAMILY PLANNING AND HEALTH SERVICES Use of family planning and health services is determined by supply as well as demand. The 1993 KDHS included a Services Availability Questionnaire (reproduced in Appendix E) to assess the availability, or supply, of family planning and health services. The main reason for implementing the services availability study in Kenya was to determine what proportion of women live in areas that are covered by community- based family planning distributors (CBDs) and whether they are working under government or non- government auspices. The Services Availability Questionnaire was applied at the cluster (community) level, that is, one questionnaire was filled for each selected enumeration area. Information was gathered by the team supervisors. They were instructed to gather the information by first contacting the local chief, sub-chief or other local official and asking him or her to assemble a group of knowledgeable informants in the community. The supervisor was then meant to ask the appropriate questions of this group, facilitating a discussion and encouraging a consensus. Alternative respondents for the Services Availability Questionnaire were either a local CBD worker and/or staff at a health centre or dispensary located in or near the selected cluster. Data on type of informants were not tabulated. The information collected in the Services Availability Questionnaire is assigned to each respondent (individual questionnaire) to obtain population-based estimates. The number of independent data points, however, remains the same as the number of clusters (sample points) for which the information was collected: 29 points for Nairobi, 72 points for Central Province, 82 for Coast Province, 65 for Eastern Province, 83 for Nyanza Province, 120 points for Rift Valley Province and 57 data points for Western Province (a total of 508)) Due to the small number of data points, the service availability estimates are subject to larger sampling errors than are the estimates based on data from individual women in the main survey. This analysis focuses just on the seven provinces, including Nairobi. Moreover, although the results in this chapter are presented for women, it should be kept in mind that these were actually the results of 508 interviews at the cluster level. One interview was held per cluster and therefore all service availability data are the same for all women in the cluster. Intracluster variability is not taken into account. 12.1 Service Availability Questionnaire The Services Availability Questionnaire was designed to provide a picture of the family planning and health service environment available to Kenyan women. There are two types of mechanisms for providing services---outreach programmes and stationary facilities. The former deliver services directly to people in their communities, whereas the latter function as repositories of services, relying on people to come to them to obtain services. Outreach family planning services are provided by community-based distributor (CBD) workers and mobile clinic and outreach health services by health workers. The informants in each cluster were asked whether their communities are served by such services and, if so, the nature of these services. For example, ~The 1993 KDHS included 520 clusters. No service availability data were available for twelve clusters. 165 i fa CBD worker visits the community, the informants were asked whether he or she provides family planning methods, specifically, the pill, condom and foaming tablets. Many types of stationary facilities exist. Community informants were asked to identify the nearest hospital, health centre and dispensary. Information was also collected for a second health centre and dispensary if ones existed that served the people living in the cluster. Despite attempts to explain to community informants the differences between the various types of facilities, there were some instances in which informants and/or supervisors misidentified facilities. For example, there were cases in which information about "Dispensary X" was filled in under the space left for the nearest health centre. For this reason, health centres, clinics, and dispensaries were classified into one group in the following tables. 12.2 Availability of Family Planning Services Outreach Programs As mentioned previously, community-based distribution (CBD) of family planning information and services has been an important part of the national family planning programme. A survey conducted by the NCPD and the United Nations Population Program (UNFPA) found that there were over 10,000 CBD workers in Kenya (Barasa and Kanani, 1991). One of the primary reasons for implementing the Services Availability Survey was to estimate the coverage of both government- and non-govemment-sponsored CBD workers. In most cases, community informants were able to identify the type (government or non- government) of CBD worker. Table 12.1 shows the percent distribution of women by presence of a CBD worker who provides family planning methods and by distance and time to the nearest hospital or health centre/dispensary providing family planning services, according to province. Half of women (48 percen0 live in communities served by CBD workers, Of these, half (23 percent of all women) are covered by government-sponsored CBDs and half by CBDs sponsored by non-governmental organisations. These levels are considerably higher than the level estimated from the data in the woman's individual questionnaire, which shows only 21 percent of women reporting that thcy live in areas covered by CBD workers (see Table 4.24). The higher coverage estimate from the Services Availability Survey lends weight to the hypothesis of underreporting of CBDs by individual women. It should be noted, however, that the estimate from the Services Availability survey is that half of the women live in areas that are covere d by CBDs; it does not mean that half of the women are visited regularly or visited at all. More than half of the women in Central Province and two-thirds of the women in Nyanza and Westem Provinces live in areas that are covered by CBD workers who provide family planning methods. CBD coverage is lowest among women in Nairobi (24 percent) and Coast Province (29 percent). Approximately one-quarter of women live in areas that are reportedly covered by mobile clinics that provide family planning methods. Mobile clinics are available to about 40 percent of women in Eastem and Nyanza Provinces and to only 9 percent of women in Nairobi and Central Province. Stationary Facilities Half of currently married women live within 5 kilometres of a facility that provides family planning services (see column 3 in Table 12.1). As expected, health centres and dispensaries are considerably closer than hospitals; 45 percent of women live within 5 kilometres of a health centre/dispensary that provides 166 Table 12.1 Distance and time to nearest facility providing family planning services according m type of facility and residence Percent distribution of currently married women by distance and time to the nearest facility/worker providing family ~lanning services, according to facility and province, Kenya 1993 Type of facility Province Health Distance to centre/ All Rift neaxest facility Hospital Dispensary facilities Nairobi Cen~al Coast Eastern Nyanza Valley Western DISTANCE TO NEAREST FACILITY Mobile clinic UBD worker Government Non-Government Unknown None Total CBD 100.0 KIIometres <1 2.6 1-4 8.5 5-9 15.6 10-14 9.0 15-29 23.6 30+ 29.0 None 1 6.9 Missing 4.8 Total 100.0 Number of women 4629 Median distance 17.1 24.2 8.7 8.8 23.8 38.8 40.4 20.2 14.5 48.1 24.0 51.2 28.7 45.7 65.2 37.7 66.6 23.3 5.1 34.2 1.9 14.8 26.0 17.8 49.2 23.8 14.3 16.9 26.0 30.9 37.2 18.4 16.8 1.1 4.6 0.0 0.8 0.0 1.9 1.6 0.6 51.9 76.0 48.8 71.3 54.3 34.8 62.3 33.4 1~.0 1~.0 1~.0 1~.0 1~.0 1~.0 1~.0 1~.0 1~.0 10.8 12.8 34.0 37.2 25.2 27.0 9.2 9.2 5.1 7.2 0.9 1.9 11.0 1.0 3.9 3.7 25.0 5.6 20.5 8.5 18.0 8.1 16.1 66.8 41.8 31.7 39.8 34.9 25.2 41.6 8.2 28.8 20.8 27.7 31.4 25.6 32.8 0.0 12.8 4.1 8.6 8.5 12.8 9.5 0.0 7.6 11.5 6.4 3.8 15.4 0.0 0.0 0.2 1.5 0.0 2.9 5.9 0.0 0.0 1.2 0.0 0.0 0.5 3.5 0.0 0.0 2.0 9.8 9.1 0.0 3.6 0.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 4629 4629 4.8 4.8 271 610 445 864 737 992 710 1.8 5.1 4.2 4.7 4.3 6.6 4.7 TIME TO NEAREST FACILITY Minutes <15 5.3 13.1 16.9 30.6 13.2 28.0 15.7 14.4 12.7 17.7 15-29 10.9 15.0 20.2 46.4 25.7 23.5 16.0 15.5 25.2 6.5 30-59 24.7 24.5 29.3 23.0 43.8 9.0 32.7 46.6 21.3 20.8 60-119 21.9 21.5 19.6 0.0 14.1 17.1 17.6 13.9 19.4 41.8 120+ 26.0 10.6 9.3 0.0 0.0 12.5 8.1 9.1 14.3 13.3 None I 6.9 11.0 1.1 0.0 L2 0.0 0.7 0.5 3.5 0.0 Missing 4.3 4.3 3.7 0.0 2.0 9.8 9.1 0.0 3.6 0.0 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 I00.0 100.0 100.0 Number of women 4629 4629 4629 271 610 445 864 737 992 710 1No facility mentioned on questionnaire; evidently, service is so distant it is not considen:d available or is not known. family planning, while only 11 percent of women live within 5 kilometres of a hospital that does so. Overall, the median distance to any facility providing family planning is 5 kilometres. However, health centres and dispensaries with family planning services are closer than h0spitals--the median distance for women is 5 kilometres for health centre and dispensary, compared to 17 kilometres for hospital. 167 As expected, women living in Nairobi are generally closer to a source of family planning than women in the provinces. Ninety-two percent of women in Nairobi live within 5 kilometres of a family planning outlet, compared to only one-third of women in Rift Valley Province. Table 12.1 also shows the distribution of women by one-way travel time to the nearest facility providing family planning. Two-thirds of Kenyan women live within one hour's travel time to a source of family planning; 37 percent live within 30 minutes of a family planning outlet. The closest facility with family planning services is generally a health centre or dispensary--53 percent of women live within one hour of a health centre or dispensary, and 41 percent are within one hour of a hospital. As expected, women living in Nairobi and in Coast Province are on average closer to a family planning outlet than women living in other provinces. Clearly, health centres and dispensaries are potentially important suppliers of contraceptive methods. Their role in contraceptive supply has already been shown in Chapter 4, where it was established that government health centres and dispensaries together account for the sources of supply for about 40 percent of current users of modem contraceptives (see Table 4.13). Availability of Specific Methods Women cannot use modem family planning methods unless they are available. Tabulations on distance to the nearest source providing specific methods show that not all methods are equally accessible (Table 12.2). Including CBD workers, over half of Kenyan women live within one kilometre of a modem method provider. As might be expected, supply methods like the pill, condoms and foaming tablets are generally more readily available to women than are clinical methods like the IUD and sterilisation. The median distance to obtain a cycle of pills is one kilometcr, compared to 18 kilometres for sterilisation. Table 12.2 Distance to nearest family planning method Percent distribution of currently married women by distance to nearest source of specific family planning methods, Kenya 1993 Family planning method Distance to nearest family Foaming Slerili- Any planning method Pill Condom tablets IUD sation method Kllometres <1 49.4 46.5 38.6 18.4 5.4 52.4 1-4 15.0 16.7 16.6 22.7 8.0 14.9 5-9 15.8 16.8 17.4 24.0 14.8 14.9 10 14 7.7 6.6 8.9 11.3 8.7 6.6 15-29 6.1 6.6 9.9 11.1 23.8 5.3 30+ 2.2 2.9 4.5 7.8 3,1.0 2.1 Unknown distance 0.0 0.0 0.0 0.1 0.4 0.0 Missing 3.8 4.0 4.1 4.5 5.0 3.7 Total 100.0 100.0 I(X).O 100.0 100.0 100.0 Number 4629 4629 4629 4629 4629 4629 Median distancc 1.0 1.5 3.6 5.9 18.2 0.9 168 Availability of Family Planning by User Status If, as many believe, wider availability of family planning services leads to higher levels of contraceptive use, then the question arises as to how different users and nonusers are with regard to access. Do contraceptive users live in communities with better access? Table 12.3 shows the percent distribution of all women, all users, users of clinical, supply and traditional methods of family planning and all nonusers by presence of a CBD worker and by distance to the nearest stationary facility providing family planning services. I Table 12,3 Distance to nearest facility providing family planning services for users/nonusers Percent distribution of currently married women by presence of community-based distributor (CBD) and the percent distribution by distance to the nearest facility providing family planning services, according to use of family planning and type of method used, Kenya 1993 CBD worker and distance to nearest facility Type of method used: ~roviding family All Non- All planning service Clinical Supply Traditional users users women CBD worker Government 25.7 26.8 25.3 26.2 21.8 23.3 Non-Government 24.9 30.4 14.5 26.1 22.6 23.8 Unknown 0.0 1.2 0.5 0.7 1.2 1. I No CBD worker 45.6 37.4 52.8 42.4 51.2 47.6 No information 3.8 4.2 6.9 4.6 3.2 3.7 Total 100.0 100.0 100.0 100.0 100.0 100.0 Kllometres <1 17.2 16.3 12.4 15.9 11.3 12.8 I-4 40.5 42.6 37.8 41.2 35.3 37.2 5-9 23.3 23.7 23.1 23.5 28.7 27.0 10-14 7.1 7.4 6.8 7.2 10.2 9.2 15-29 7.5 5.0 6.2 5.9 7.8 7.2 30+ 0.0 0.3 2.3 0.5 2.6 1.9 Service not provided 0.6 0.4 4.3 1.1 0.9 1.0 Missing 3.8 4.2 6.9 4.6 3.2 3.7 Total 100.0 1(30.0 I00.0 100.0 100.0 100.0 Number of women 451 813 252 1516 3113 4629 Median distance 4.1 3.9 4.2 4.0 5.1 4.8 The data show that users are slightly more likely than nonusers to live in areas that are covered by CBD workers (53 percent of users vs. 46 percent of nonusers). Moreover, a somewhat larger proportion of women who are using clinical methods (51 percent) and especially those using supply methods (58 percent) live in areas covered by CBD workers, compared to those using traditional methods (40 percent). This would be the expected pattern, since CBD workers generally provide only pills, condoms and foaming tablets, all of which are supply methods. A similar pattern is observed for distance to the nearest fixed facility offering family planning. Fifty- seven percent of all users live within 5 kilometres of such a facility, compared to 47 percent of nonusers. The differential is not large and is probably not statistically significant. 169 12.3 Availability of Maternal and Child Health (MCH) Services Antenatal Care Although the major impetus for the Services Availability study related to evaluating the family planning service environment, the questionnaire also collected information related to availability of general health services. Table 12.4 shows the percent distribution of women by distance and time to the nearest facility providing antenatal care, according to type of facility and province. The data indicate that half of women in Kenya live within 5 kilometres of a facility that offers antenatal services (Column 3). Moreover, 43 percent of women live within 5 kilometress of a health centre or dispensary with antenatal services, compared to only 12 percent who live within 5 kilometres of a hospital with these services. Women in Nalrobi are more proximate to antenatal services than women in other provinces; 86 percent live within 5 kilometres of antenatal care services, compared to only 36 percent of women in Rift Valley Province. More than two-thirds of Kenyan women live within one hour's travel time to antenatal services; 38 percent live within 30 minutes' travel time of antenatal services. Table 12.4 Distance and time to nearest facility providing antenatal care according to type of facility and province Percent distribution of currently married women by distance and time to the nearest facility providing antenatal care services, according to type of facility and province, Kenya 1993 Type of facility Province Health Distance to centre/ All Rift ne~cest facility Hospital Dispensary facilities Nairobi Central Coast Eastern Nyanza Valley Western DISTANCE TO NEAREST FACILITY KIIometres <1 1-4 5-9 10-14 15-29 30+ None 1 Missing Total Number of women Median distance 2.4 10.3 12.2 18.9 4.0 20.1 5.6 17.4 8.9 19.1 9.5 33.1 37.8 66.8 41.9 31.0 37.3 35.7 27.5 44.8 16.8 25.4 27.4 8.2 30.0 22.1 20.8 30.6 34.0 31.2 10.0 9.4 8.5 0.0 12.0 4.4 11.3 9.0 10.4 4.9 26.0 6.9 8.5 0.0 10.1 11.0 13.7 3.8 13.9 0.0 29.6 1.4 1.4 0.0 0.0 1.5 2.3 2.9 1.8 0.0 0.8 9.9 0.4 6.1 0.0 0.0 0.0 0.5 0.0 0.0 4.8 3.7 3.7 0.0 2.0 9.8 9.1 0.0 3.6 0.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 4629 4629 4629 271 610 445 864 737 992 710 17.1 5.0 4.8 2.3 5.2 4.3 5.4 4.3 6.3 4.3 TIME TO NEAREST FACILITY Minutes <15 5.2 12.8 16.5 26.5 11.6 28.0 15.7 14.4 12.8 17.7 15.29 11.5 16.1 21.8 44.4 29.7 24.0 15.5 14.4 27.5 12.6 30-59 25.8 24.4 29.3 23.0 42.9 8.0 28.8 46.1 22.7 25.5 60-119 24.7 21.6 19.9 0.0 13.9 15.1 19.3 15.5 23.6 35.8 120+ 27.7 10.9 8.3 0.0 0.0 15.2 10.8 9.1 9.9 8.4 Service not provided 0.8 9.9 0,6 6.1 0.0 0.0 0.7 0.5 0.0 0.0 Missing 4.3 4.3 3.7 0.0 2.0 9.8 9.1 0.0 3.6 0.0 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Number of women 4629 4629 4629 271 610 445 864 737 992 710 1No facility mentioned on questionnaire; evidently, service is so distant it is not considered available or is not known. 170 Delivery Care Table 12.5 shows the distributions of currently married women by distance and one-way travel time to the nearest facility providing delivery care. Overall, 32 percent of women live within 5 kilometres of a facility that offers delivery care. As expected, health centres and dispensaries are closer to the women than hospitals; 22 percent live within 5 kilometres of a health centre that provides delivery care, while 12 percent live within 5 kilometres of a hospital providing delivery assistance. In terms of travel time, 35 percent of women have a health centre with delivery care within one hour's travel time, compared to 43 percent with a hospital within one hour's travel. It is important to note that one-third of the women live in communities where the health centre/dispensary does not provide delivery care. Table 12.5 Distance and time to nearest facility providing delivery care according to type of facility and province Percent distribution of currently married women by distance and time to nearest facility providing delivery care services, according to type of facility and province, Kenya 1993 Type of facility Province Health Distance to centre/ All Rift newest facility Hospital Dispensary facilities Nairobi Central Coast Eastern Nyanza Valley Western DISTANCE TO NEAREST FACILITY Kilometres <1 2.6 4.9 7.0 14.8 1.5 9.6 5.0 10.8 3.9 9.7 1-4 9.3 17.1 24.8 30.6 25.5 24.6 21.5 17.4 23.5 35.4 5-9 16.8 19.4 29.3 30.1 31.9 18.4 23.3 31.6 26.6 42.4 10-14 10.0 9.2 13.1 22.4 18.8 7.7 11.8 17.5 10.3 8.9 15-29 26.4 9.7 14.6 0.0 16.7 17.8 17.4 11.2 24.5 2.5 30+ 29.6 2.2 6.9 0.0 3.6 11.1 10.9 10.5 7.6 0.0 Service not provided 0.5 33.8 0.7 2.0 0.0 1.0 1.0 1.0 0.0 1.1 Missing 4.8 3.7 3.7 0.0 2.0 9.8 9. ! 0.0 3.6 0.0 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Number of women 4629 4629 4629 271 610 445 864 737 992 710 Median distance 17.1 7.4 7.9 5.4 7.7 6.3 8.7 8.3 9.0 5.8 TIME TO NEAREST FACILITY Minutes <15 5.4 6.9 11.0 15-29 11.5 9.5 17.6 30-59 25.7 18.5 31.6 60-119 24.6 16.0 21.6 120+ 28.0 I 1.5 13.9 Service not provided 0.5 33.8 0.6 Missing 4.3 3.9 3.7 Total lff0.0 100.0 100.0 Number of women 4629 4629 4629 14.8 9.0 17.0 11.3 10.0 8.7 11.2 23.0 24.9 21.0 11.5 10.5 22.2 15.5 59.2 45.0 17.5 26.8 40.1 23.4 27.3 1.0 19.1 11.7 18.5 21.0 29.0 31.7 0.0 0.0 22.0 21.1 18.0 13.0 14.5 2.0 0.0 1.0 1.7 0.5 0.0 0.0 0.0 2.0 9.8 9.1 0.0 3.6 0.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 271 610 445 864 737 992 710 171 Immunisation Data on distance and travel time to child immunisation services are presented in Table 12.6. More than half of married women live within 5 kilometres of a facility that provides imrnunisations and 46 percent live within 5 kilometres of a health centre providing immunlsations. The closest facilities are health centres----55 percent of women live within one hour of a health centre and 43 percent live within one hour of a hospital that provides immunisations. Table 12.6 Distance and time to nearest facility providing child immunisation services according to type of facility and province Percent dista'ibution of currently married women by distance and time ~ nearest facility providing child immunisation services, according to type of facility and province, Kenya 1993 Type of facility Province Health Distance to centre/ All Rift nearest facility Hospital Dispensary facilities Nairobi Central Coast Eastern Nyanza Valley Western DISTANCE TO NEAREST FACILITY KIIom~r~ <1 2.6 11.9 13.8 23.0 5.6 20.9 9.3 18.0 11,7 17.0 1-4 9.5 33.6 38.1 66.8 43.2 33.3 35.9 36.4 26,0 46.9 5-9 16.8 25.4 26.6 8.2 28.4 19.4 21.1 30.0 32,9 31.2 1~14 10.0 9.5 8.5 0.0 12.0 4.1 9.8 8.5 12,1 4.9 15-29 26.4 6.4 7.8 0.0 8.8 11.0 12.5 3.8 12,5 0.0 30+ 29.6 1.3 1.3 0.0 0.0 1,5 2.3 2.9 1,2 0.0 Se~ice not pmvided 0.2 8.3 0.2 2.0 0.0 0.0 0.0 0.5 0,0 0.0 Missing 4.8 3.7 3.7 0.0 2.0 9.8 9.1 0.0 3.6 0.0 To~ 1~.0 1~.0 1~.0 1~.0 1~.0 1~.0 1~.0 1~.0 1~,0 1~.0 Numberofwomen 4629 4629 ~29 271 610 445 864 737 992 710 TIME TO NEAREST FACILITY Minutes <15 15-29 30-59 60-119 120+ Service not provided Missing Total Number of women 5.4 13.7 17.4 30.6 13.2 28.4 18.5 13.1 13.6 I7.7 11.5 16.7 21.8 44.4 28.1 25.3 13.8 16.8 28.7 10.5 25.8 24.9 28.7 23.0 42.9 6.6 25.3 46.6 23.3 25.5 24.7 21.2 19.8 0.0 13.9 17.4 19.3 13,9 21.9 37.9 28.0 10.7 8.3 0.0 0.0 12.5 13.4 9.1 8.9 8.4 0.2 8.3 0.3 2.0 0.0 0.0 0.7 0.5 0.0 0.0 4.3 4.3 3.7 0.0 2.0 9.8 9.1 0.0 3.6 0.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 4629 4629 4629 271 610 445 864 737 992 710 It is of interest to see if those who use MCH services are more likely to live closer to them than those who do not use them. Table 12.7 shows the percent distribution of children under age five by distance to the nearest facility providing MCH services, according to whether their mothers received antenatal and/or delivery care and according to whether they themselves received all or only some or no vaccinations against childhood diseases. 172 Table 12.7 Distance to nearest maternal end child health services for children Percent distaibution of children by distance to the nearest facility providing maternal and child health services, according to maternal care and vaccination coverage, Kenya 1993 Maternal care received 1 Vaccination coverage 2 Distance to nearest ANC ANC All Some/No facility providing All and or vacci- vacci- MCH services children DA DA Neither nations nations Total KIIometres <1 12.7 15.3 12.8 11.0 15.2 12.1 14.5 1~. 36.9 42.3 37.5 22.6 38.2 36.0 37.7 5-9 27.5 24.6 27.4 28.8 25.1 29.3 26.1 10-14 8.9 8.0 9.1 5.3 8.9 9.3 9.0 15-29 9.0 6.6 8.6 17.9 8.1 8.3 8.2 30+ 1.6 1.1 1.5 3.9 1.4 1.4 1.4 Service not provided 0.2 0.3 0.2 1.2 0.2 0.0 0.2 Missing 3.1 2.0 2.9 9.3 2.8 3.6 3.0 Total 100.0 100.0 100.0 I00.0 100.0 100.0 100.0 Number of children 6062 3236 5836 226 3483 1046 4529 Median distance 4.9 4.2 4.8 6.3 4.6 5.0 4.7 1Figures are for children age 0-4 years 2Figures are for children age 1-4 years ANC = Antenatal care by doctor, nurse, or trained midwife DA = Delivery assistance by doctor, nurse, U'ained midwife, or delivered in a health facility The data show that the hypothesis is true, that is, children whose mothers received both antenatal and delivery care are more likely to live within 5 kilometres of a facility providing MCH services (58 percent) than either those whose mothers received only one of these services (50 percent) or those whose mothers received neither antenatal nor delivery care (34 percent). Children who are fully vaccinated are only slightly more likely than those not fully vaccinated to live within 5 kilometres of a facility providing MCH services. 173 REFERENCES Barasa, J. and S. Kanani. 1991. An Inventory Study of CBD Family Planning Services. Nairobi, Kenya: National Council for Population and Development (NCPD) and United Nations Population Fund (UNFPA). Boerma, J. Ties, A. Elisabeth Sommerfelt, Shea O. Rutstein, and Guillermo Rojas. 1990. Immunization: Levels, Trends and Differentials, DHS Comparative Studies No. 1. Columbia, Maryland: Institute for Resource Development/Macro Systems Inc. Boerma, J. Ties. 1988. Monitoring and Evaluation of Health Interventions: Age- and Cause-Specific Mortality and Morbidity in Childhood. In Research and Intervention Issues Concerning Infant and Child Mortality and Health, 195-218. Proceedings of the East Africa Workshop, International Development Research Centre, Manuscript Report 200e. Ottawa, Canada. Brass, William and Carole L. 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AIDS in Kenya: Background, Projections, Impact, Interventions. Nairobi, Kenya: NACP and NCPD. National Council for Population and Development (NCPD), Ministry of Home Affairs and National Heritage, and Institute for Resource Development/Macro Systems Inc. (IRD/Macro). 1989. Kenya Demographic and Health Survey, 1989. Columbia, Maryland: NCPD and IRD/Macro. Republic of Kenya [1994[. Development Plan, 1994-1996. Nairobi: Government Printer. Rutstein, Shea Oscar and George T. Bicego. 1990. Assessment of the Quality of Data Used to Ascertain Eligibility and Age in the Demographic and Health Surveys. lnAnAssessmentofDHS-IData Quality, 3-37. DHS Methodological Reports No. I. Columbia, Maryland: Institute for Resource Development/Macro Systems Inc. Statistics Division, Ministry of Finance and Economic Planning [Kenya[. 1970. Kenya Population Census, 1969. Vol. 1. Nairobi: Statistics Division. Statistics Division, Ministry of Finance and Economic Planning IKenya]. 197t. Kenya Population Census, 1969. Vol. 2, Data on Urban Population. Nairobi: Statistics Division. Sullivan, Jeremiah M., George T. Bicego, and Shea Oscar Rutstein. 1990. Assessment of the Quality of Data Used for the Direct Estimation of Infant and Child Mortality in the Demographic and Health Surveys. In An Assessment of DHS-I Data Quality, 115-137. DHS Methodological Reports No. 1. Columbia, Maryland: Institute for Resource Development/Macro Systems Inc. World Bank. 1991. Worm Tables 1991. Baltimore: Johns Hopkins University Press. 176 APPENDIX A SURVEY DESIGN APPENDIX A SURVEY DESIGN A.I Questionnaires Four types of questionnaires were used for the KDHS: a Household Questionnaire, a Woman's Questionnaire, a Man's Questionnaire and a Services Availability Questionnaire. The contents of these questionnaires were based on the DHS Model B Questionnaire, which is designed for use in countries with low levels of contraceptive use. Additions and modifications to the model questionnaires were made during a series of meetings organised around specific topics or sections of the questionnaires (e.g., fertility, family planning). The NCPD invited staff from a variety of organisations to attend these meetings, including the Population Studies Research Institute and other departments of the University of Nairobi, the Woman's Bureau, and various units of the Ministry of Health. The questionnaires were developed in English and then translated into and printed in Kiswahili and eight of the most widely spoken local languages in Kenya (Kalenjin, Kamba, Kikuyu, Kisii, Luhya, Luo, Meru, and Mijikenda). The Household Questionnaire was used to list all the usual members and visitors of selected households. Some basic information was collected on the characteristics of each person listed, including his/her age, sex, education, and relationship to the head of the household. The main purpose of the Household Questionnaire was to identify women and men who were eligible for individual interview. In addition, information was collected about the dwelling itself, such as the source of water, type of toilet facilities, materials used to construct the house, and ownership of various consumer goods. The Woman's Questionnaire was used to collect information from women aged 15-49. These women were asked questions on the following topics: Background characteristics (age, education, religion, etc.), Reproductive history, Knowledge and use of family planning methods, Antenatal and delivery care, Breastfeeding and weaning practices, Vaccinations and health of children under age five, Marriage, Fertility preferences, Husband's background and respondent's work, Awareness of AIDS. In addition, interviewing teams measured the height and weight of children under age five (identified through the birth histories) and their mothers. Information from a subsample of men aged 20-54 was collected using a Man's Questionnaire. Men were asked about their background characteristics, knowledge and use of family planning methods, marriage, fertility preferences, and awareness of AIDS. The Services Availability Questionnaire was used to collect information on the health and family planning services obtained within the cluster areas. One service availability questionnaire was to be completed in each cluster. 179 A.2 Sample Design and Implementation The sample for the 1993 KDHS was national in scope, with the exclusion of all three districts in Northeastern Province and four other northern districts (Isiolo and Marsabit from Eastern Province and Samburu and Turkana from Rift Valley Province). Together the excluded areas account for less than four percent of Kenya's population. The KDHS sample points were selected from a national master sample maintained by the Central Bureau of Statistics, the third National Sample Survey and Evaluation Programme (NASSEP-3), which is an improved version of NASSEP2 used in the 1989 survey. This master sample follows a two-stage design, stratified by urban-rural residence, and within the rural stratum, by individual district. In the first stage, 1989 census enumeration areas (EAs) were selected with probability proportional to size. The selected EAs were segmented into the expected number of standard-sized clusters to form NASSEP clusters. The entire master sample consists of 1,048 rural and 325 urban ~ sample points ("clusters"). A total of 536 clusters---92 urban and 444 rural--were selected for coverage in the KDHS. Of these, 520 were successfully covered. Sixteen clusters were inaccessible for various reasons. As in the 1989 KDHS, selected districts were oversampled in the 1993 survey in order to produce more reliable estimates for certain variables at the district level. Fifteen districts were thus targetted in the 1993 KDHS: Bungoma, Kakamega, Kericho, Kilifi, Kisii, Machakos, Meru, Murang'a, Nakuru, Nandi, Nyeri, Siaya, South Nyanza, Taita-Taveta, and Uasin Gishu; in addition, Nairobi and Mombasa were also targetted? Although six of these districts were subdivided shortly before the sample design was finalised) the previous boundaries of these districts were used for the KDHS in order to maintain comparability with the 1989 survey. About 400 rural households were selected in each of these 15 districts, just over 1000 rural households in other districts, and about 18130 households in urban areas, for a total of almost 9,000 households. Due to this oversampling, the KDHS sample is not self-weighting at the national level. After the selection of the KDHS sample points, fieldstaff from the Central Bureau of Statistics conducted a household listing operation in January and early February 1993, immediately prior to the launching of the fieldwork. A systematic sample of households was then selected from these lists, with an average "take" of 20 households in the urban clusters and 16 households in rural clusters, for a total of 8,864 households selected. Every other household was identified as selected for the male survey, meaning that, in addition to interviewing all women age 15-49, interviewers were to also interview all men age 20-54. It was expected that the sample would yield interviews with approximately 8,000 women age 15-49 and 2,500 men age 20-54. Tables A.IA and A.I.2 provide details about response rates among households and eligible women and men. ~The NAS SEP-3 (and therefore the KDHS) defines urban as centers of population of 10,000 or more, plus all district headquarters, regardless of size. This definition differs from that used in the 1989 census which included all municipalities, town councils, urban councils, and trading centres as urban. 2With the exception of Nandi, Nakuru, and Taita-Taveta, all the above districts were also targeaed in the 1989 KDHS; Kirinyaga District was targetted in 1989 and not in 1993. Also, Nairobi was specifically targetted in 1989, but Mombasa was not. 3Kericho was divided into Kericho and Bomet; Machakos into Machakos and Makueni; Mera into Mera and Tharaka-Nithi; Kisii into Kisii and Nyamira; South Nyanza into Homa Bay and Migori; and Kakamega into Kakamega and Vihiga. 180 TableA.l . l Sample implementation: Women Percent distribution of households mad eligible women and men in the DHS sample by result of the interview and household, eligible women, eligible men eaad overall response rates, according to province and urban rural area, Kenya 1993 Province Residence Rift Result Nalrobi Central Coast Eastern Nyanza Valley Western Urban Rural Total Selected households Completed (C) 80.5 93.6 87.8 93,4 89.8 89.3 95.0 83.4 91.9 90.3 ttousehold present but no competent respondent at home (HP) 3.8 1.0 2.7 1,2 1.4 1.3 O. 1 3.7 1.0 1.5 Postponed (P) 0.0 0.1 0.1 0.0 0.0 0.0 0.0 0.1 0.0 0,0 Refused (R) 3.1 0.7 0.5 0.7 0.2 1.4 0.0 2.2 0.5 0.8 Dwelling not found (DNF) 0.7 0.1 0.2 0.0 0.3 0.5 0.0 0.4 0.3 0.3 tlousehold absent (ItA) 3.1 0.9 3.5 2.9 5.3 2.7 1.5 3.9 2.7 2.9 Dwelling vacant/address not a dwelling (DV) 3.6 2.7 4,4 1.5 2.5 3.3 3.1 4.4 2,7 3.0 Dwelling destroyed (DD) 1.0 0.4 0.7 0.3 0.2 1.3 0.2 0.5 0.7 0.6 Other(O) 4.1 0.4 0.2 0.1 0.3 0.2 0.1 1.5 0.3 0.5 Total percent 100.0 100.0 100.0 100.0 100,0 100.0 100.0 100.0 100.0 100.0 Number 606 1339 1260 1068 1523 2116 893 1654 7151 8805 Household response rate (HRR) 1 91.4 97.9 96.2 98.0 97,9 96.6 99.9 93.0 98.0 97.1 Eligible women Completed (EWC) 90.2 95.2 94.2 95.6 93,6 94.7 98.1 91.7 95.4 94.8 Not at home (EWNH) 4.7 2.8 3.0 2.7 3.6 2.8 0.7 3.6 2.6 2,8 Postponed (EWP) 0.0 0.0 0,0 0.0 0.2 0.0 0.0 O. 1 0.0 0.0 Refused (EWR) 1.5 0.8 0.3 0.5 0.6 1.0 0.3 1,4 0.5 0.7 Partly completed (EWPC) 1.0 0.4 0.7 0.5 0.7 0.5 0.3 0.9 0.5 0.5 Incapacitated (EWI) 0.2 0.6 0.7 0.4 1.0 0.6 0.5 0.4 0.7 0.6 Other (EWO) 2.5 0.2 1.0 0,3 0.3 0.5 0.0 1,8 0.3 0.5 Total percent 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Number 407 1129 1158 1092 1350 1853 963 1266 6686 7952 Eligible woman response rate (EWRR) 2 90.2 95.2 94.2 95.6 93.6 94.7 98.1 91.7 95.4 94.8 Overall response rate (ORR) 3 82.4 93.2 90.6 93.7 91.6 91.4 98.0 85.3 93.5 92.1 Note: The household response rate is calculated for completed households as a proportion of completed, no competent respondent, postponed, refused, and dwelling not found. The eligible woman response rate is calculated for completed interviews as a proporfon of completed, not at home, postponed, refused, partially completed, incapacitated and "other," The overall response rate is the ~roduct of the household and woman response rates. Using the number of households falling into specific response categories, the household response rate (IIRR) is calculated as: C C+HP+P+R+DNF ZUsing the number of eligible women falling into specific response categories, the eligible woman response rate (EWRR) is calculated as: EWC EWC + EWNII + EWP + EWR + EWPC + EWI + EWO ~The overall response rate (ORR) is calculated as: ORR = IIRR * EWRR 181 Table A.I.2 Sample implementation: Men Percent dist~ibutlon of households and eligible women and men in the DHS sample by result of the interview and household, eligible women, eligible men and overall response rates, according to province and urban rural area, Kenya 1993 Province Residence Rift Result Nairobi Centrul Co~t Eastern Nyanza Valley Western Urban Rural Total Selected households Completed (C) llousehold present but no competent respondent at home (tiP) Refused (R) Dwelling not found (DNF) 11ousehold absent (HA) Dwelling vacant/address not a dwelling (DV) Dwelling destroyed (DD) Other (O) Total percent Number 81.5 92.8 87.2 94.7 89.8 89.0 94,8 82.6 92.0 90.2 3.0 1.6 2.5 0.9 1,9 0.9 0.2 3.8 1.0 1.5 2.7 1,0 0.5 0.8 0.1 1.4 0.0 2.2 0.6 0.9 1.3 0.0 0.2 0.0 0.3 0.6 0.0 0.6 0.2 0.3 2.4 1.0 3.5 1.9 5.4 3.1 1.8 4.0 2.6 2.9 4.7 2.7 5.4 1.3 2.3 3.6 2.9 4.9 2.8 3.2 1.0 0,6 0.5 0.4 0.1 0.9 0.2 0.6 0.5 0,5 3.4 0.1 0.3 0.0 0.1 0.4 0.0 1.2 0.2 0.4 100.0 100.0 100.0 100.0 100,0 100.0 100.0 100.0 100.0 100.0 297 667 632 531 744 1057 442 818 3552 4370 Household response rate (HRR) t 92.0 97.2 96.5 98.2 97.5 96.8 99.8 92.6 98.1 97.1 Eligible men Completed (EMC) 73.7 84.5 81.9 88.5 79,1 86.5 95.1 77.9 86.5 84.6 Not at home (EMNtt) 15.1 12.7 9.3 7.5 15.4 10.5 3.5 12.7 10.0 10.6 Postponed (EMP) 0.0 0.0 0.2 0,0 0.0 0.1 0.0 0.2 0.0 0.1 Refused (EMR) 5.6 0.6 1.4 0.9 2.1 1.1 0.0 3.2 0.9 1.4 Partly completed (EMPC) 0.9 0.6 0.7 0.3 0.0 0.4 0.0 1.0 0.2 0.4 Incapacitated (EMI) 0.4 1.1 1.9 1.7 0.8 1.0 0.7 0.2 1.4 1.1 Other (EMO) 4.3 0.6 4.6 1.1 2.6 0.3 0.7 4.9 0.9 1.8 Total percent 100.0 100.0 100.0 100.0 1(30.0 100.0 100.0 100.0 100.0 100.0 Number 232 361 431 348 382 721 287 616 2146 2762 Eligible man reSponse rate 0EMRR) 2 73.7 84.5 81.9 88.5 79.1 86.5 95.1 77.9 86.5 84.6 Overall resEonse rate (ORR) 3 67.8 82.1 79.0 86.9 77.1 83.8 94.9 72.2 84.9 82.1 Note: The household response rate is calculated for completed households as a proportion of completed, no competent respondent, refused, and dwelling not found. The eligible nmn response rate is calculated for completed interviews as a proportion of completed, not at home, postponed, refused, partially completed, incapacitated and "other." The overull response rate is the product of the household and man response rates. tUsing the number of households fulling into specific response categories, the household response rate 01RR) is calculated as: C C +l IP+ R + DNF 2Using the number of ehgible men fulling into specific response categories, the eligible man response rate (EMRR) is calculated as: EWC EMC + EMNIt + EMP + EMR + EMPC + EMI + EMO 3The overall response rate (ORR) is calculated as: ORR = IIRR * EMRR 182 A.3 Training and Fieldwork The KDHS questionnaires were pretested in October 1992. Sixteen interviewers (one woman and one man for each of the eight local languages) were trained for two weeks at the Masaku County Training Centre in Machakos town. Four of the 16 had participated in the 1989 KDHS and several others had other experience with fieldwork, Trainers included several officers from the NCPD, the CBS, Macro, and several guest lecturers from other agencies (e.g., the District Public Health Nurse, the District Statistical Officer). Since the main purpose of the pretest was to check the translations, trainees were asked to compare the English version with that in their own languages and to make back translations into English of key questions. After training, the eight teams spent eight days in the field conducting interviews under the observation of six officers from NCPD headquarters. Altogether, 185 Woman's and 183 Man's Questionnaires were completed. In addition, several of the NCPD officers tried filling in a preliminary version of the Services Availability Questionnaire. The field teams then spent two days in Nairobi in debriefing meetings, describing the fieldwork and suggesting modifications to the questionnaires. On the basis of these suggestions, revisions in the wording and translations of the questionnaires were made. In November 1992, NCPD officers visited several districts to recruit candidates for fieldstaff positions for the main survey. Recruitment criteria included ability to speak at least one of the eight local languages in which the survey was conducted, educational attainment, maturity, ability to spend one month in training and at least four months in the field and experience in other surveys. A total of 102 trainees were recruited. Training for the main survey was conducted at the Mathari Pastoral Centre in Nyeri for four weeks (from 18 January to 12 February 1992). In order to facilitate training, participants were divided into two groups and almost all of the classroom training was done separately. A plenary hall was used for the opening and closing ceremonies and for short lectures when it was beneficial to have the whole group together. Two NCPD officers were assigned full-time to each group, with several other officers assisting periodically. Lectures on family planning were presented by two women from the Nyeri branch of the Family Planning Association of Kenya. Two staff from Macro assisted full-time, while one Macro consultant assisted a CBS officer in the anthropometric measurement training for one week. Most of the first week of training consisted of lectures on how to fill the questionnaires, with mock interviews between participants after each section was explained. The second week was divided between completing the explanation of the questionnaire and training on how to take height and weight measurements. Generally, one group would spend half the day on anthropometric training and the otherhalfin the classroom. Anthropometric training consisted of explanations of how to use the equipment, followed by practice within the group of trainees, and then practice on children during visits to two nearby nursery schools and the Provincial Hospital. The third week was spent in mock interviews on the whole questionnaire, discussion of the local language versions of the questionnaires, and two days of field practice interviewing in the community. The fourth week was spent in another day of field practice, training supervisors and field editors in questionnaire editing and filling out the services availability questionnaire, administering a test, checking and dividing the questionnaires and other field equipment by team, and the closing ceremony. In addition, during the last three days, a separate training course was held for all the District Population Officers and several of the District Statistical Officers. Trainees who performed satisfactorily in the training programme were selected as interviewers, while those whose performance was rated as superior were selected as supervisors and/or field editors. Those whose performance was satisfactory, but who either could not travel in the field or whose native language was one in which there was a surfeit of interviewers, were selected as data processing staff. 183 The fieldwork for the KDHS was carded out by 12 interviewing teams. Each consisted of one supervisor, one field editor, 4-7 female interviewers, one male interviewer and one driver;, however, due to its lighter workload, the Masai team consisted of one supervisor/editor, two female interviewers, one male interviewer and one driver. In total, there were 12 supervisors, 11 field editors, 60 female interviewers, 12 male interviewers and 12 drivers. In addition, each team was assigned a fieldwork coordinator, generally one of the trainers, who spent a considerable amount of time in the field starting the team off and periodically checking on them. In addition, the District Population Officers assisted in the logistical aspects of fieldwork. Fieldwork commenced on 18 February and was completed on 15 August 1993. Data on the time the interview began and ended are available from the Woman's Questionnaires for most respondents. The data indicate that interviews with eligible women took an average of 42 minutes, excluding the time taken to fill the household questionnaire and to take anthropometric measurements. Slightly over one-quarter took less than 30 minutes to complete, while one-third took 30-44 minutes; 23 percent took 45-59 minutes and 15 percent took more than one hour. A.4 Data Processing All questionnaires for the KDHS were returned to the NCPD headquarters for data processing. The processing operation consisted of office editing, coding of open-ended questions, data entry, and editing errors found by the computer programs. One NCPD officer, one data processing supervisor, one questionnaire administrator, two office editors, and initially four data entry operators were responsible for the data processing operation. Due to attrition and the need to speed up data processing, another four data entry operators were later hired temporarily. The data were processed on seven microcomputers, two of which were supplied specifically for the survey. The DHS data entry and editing programs were written in ISSA (Integrated System for Survey Analysis). Data processing commenced in early March and was completed by mid-September 1993. 184 APPENDIX B ESTIMATES OF SAMPLING ERRORS APPENDIX B ESTIMATES OF SAMPLING ERRORS The estimates from a sample survey are affected by two types of errors, nonsampling error and sampling error. Nonsampling error is the result of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the KDHS to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically. Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the KDHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. The sampling error is a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results. Sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design. If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the KDHS sample is the result of a two-stage stratified design, and, consequently, it was necessary to use more complex formulas. The computer software used to calculate sampling errors for the KDHS is the ISSA Sampling Error Module (ISSAS). This module used the Taylor linearization method of variance estimation for survey estimates that are means or proportions. The Jacknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates. ISSAS treats any percentage or average as a ratio estimate, r = y/x, where y represents the total sample value for variable y, and x represents the total number of cases in the group or subgroup under consideration. The variance of r is computed using the formula given below, with the standard error being the square root of the variance: mr(r) - - 1 - f m~ ~ 2 zs x 2 h=t mh-1 2"~Z~- - - t=l mh in which Z~ = yta-r.xla , and z h -- yh-r.xk where h mk Yhi represents the stratum which varies from I to H, is the total number of enumeration areas selected in the h ~ stratum, is the sum of the values of variable y in EA i in the h th stratum, 187 XAi f is the sum of the number of cases in EA i in the h th stratum, and is the overall sampling fraction, which is so small that it is ignored. In addition to the standard errors, ISSAS computes the design effect (DEFT) for each estimate, which is defined as the ratio between the standard error using the given sample design and the standard error that would result if a simple random sample had been used. A DEFT value of 1.0 indicates that the sample design is as efficient as a simple random sample, while a value greater than 1.0 indicates the increase in the sampling error due to the use of a more complex and less statistically efficient design. ISSAS also computes the relative error and confidence limits for the estimates. Sampling errors for the KDHS are calculated for selected variables considered to be of primary interest. The results are presented in this appendix for the country as a whole, for urban and rural areas, and for the nine provinces. In addition, sampling errors for contraceptive variables are calculated for certain smaller subsamples of female respondents, namely, Mombasa and the rural areas of the special districts. For each variable, the type of statistic (mean, proportion, or rate) and the base population are given in Table B. 1. Tables B.2 to B. 17 present the value of the statistic (R), its standard error (SE), the number of unweighted (N) and weighted (WN) cases, the design effect (DEFT), the relative standard error (SE/R), and the 95 percent confidence limits (R+2SE), for each variable. In general, the relative standard error for most estimates for the country as a whole is small, except for estimates of very small proportions. There are some differentials in the relative standard error for the estimates of sub-populations such as geographical areas. For example, for the variable Children ever born to women aged 15-49, the relative standard error as a percent of the estimated mean for the whole country, for urban areas and for Nairobi is 1.3 percent, 4.3 percent, and 7.9 percent, respectively. The confidence interval (e.g., as calculated for Children ever born to women aged 15-49) can be interpreted as follows: the overall average from the national sample is 3.167 and its standard error is .042. Therefore, to obtain the 95 percent confidence limits, one adds and subtracts twice the standard error to the sample estimate, ie. 3.167+.084. There is a high probability (95 percent) that the true average number of children ever born to all women aged 15 to 49 is between 3.083 and 3.251. 188 Table B. 1 List of selected variables for sampling errors T Kenya 1993 Variable Description Base population WOMEN No education Proportion With secondary education or higher Proportion Never married (in union) Proportion Currently married (in union) Proportion Married before age 20 Proportion Had first sexual intercourse before 18 Proportion Children ever born Mean Children ever born to women over 40 Mean Children surviving Mean Knowing any contraceptive method Proportion Knowing any modern contraceptive method Proportion Knowing source for any modem method Proportion Ever used any contraceptive method Proportion Currently using any method Proportion Currently using a modem method Proportion Currently using pill Proportion Currently using IUD Proportion Currently using injections Proportion Currently using condom Proportion Currently using female sterilisatinn Proportion Currently using periodic abstinence Proportion Using public sector source Proportion Want no more children Proportion Want to delay at least 2 years Proportion Ideal number of children Mean Mothers received tetanus injection Proportion Mothers received medical care at birth Proportion Had diarrhoea in the last 24 hours Proportion Had diarrhoea in the last 2 weeks Proportion Treated with ORS packets Proportion Consulted medical personnel Proportion Having health card, seen Proportion Received BCG vaccination Proportion Received DPT vaccination (3 doses) Proportion Received polio vaccination (3 doses) Proportion Received measles vaccination Proportion Fully immunised Proportion Total fertility rate (3 years) Rate Infant mortality rate (0-4 years) Rate Infant mortality rate (0-9 years) I Rate All women All women All women All women Women aged 20 and older Women aged 20 and older All women Women aged 40-49 All women Currently married women Currently married women Currently married women Currently married women Currently married women Currently married women Currently married women Currently married women Currently married women Currently married women Currently married women Currently married women Current users of modem method Currently married women Currently married women All women Births in last 5 years Births in last 5 years Children under 5 Children under 5 Children under 5 with diarrhea in last 2 weeks Children under 5 with diarrhea in last 2 weeks Children 12-23 months Children 12-23 months Children 12-23 months Children 12-23 months Children 12-23 months Children 12-23 months All women Births in last 5 years Births in last 10 years MEN No education Proportion With secondary education or higher Proportion Never married (in union) Proportion Currently married (in union) Proportion Knowing any contraceptive method Proportion Knowing any modern contraceptive method Proportion Knowing source for any modem method Proportion Ever used any contraceptive method Proportion Currently using any method Proportion Currently using a modem method Proportion Currently using pill Proportion Currently using IUD Proportion Currently using injections Proportion Currently using condom Proportion Currently using female sterilisation Proportion Currently using periodic abstinence Proportion Want no more children Proportion Ideal number of children Mean All men All men All men All men Currently married men Currently married men Currently married men Currently married men Currently married men Currently married men Currently married men Currently married men Currently married men Currently married men Currently married men Currently married men Currently married men All men ~For national, urban and rural samples only. 2For the national sample only. 189 Table B.2 Sampling errors - National sample, Kenya 1993 Number of cases Standard Design Relative Confidence limits Value error Unweighted Weighted effect error Variable (R) (SE) (N) (WN) (DEFI') (SE/R) R-2SE R+2SE No education With secondary education or higher Never married (in union) Currently nma'fied (in union) Married before age 20 Had first sexual intercourse before 18 Children ever born Children ever born to women over 40 Children surviving Knowing any conlxaceptive method Knowing any modern method Knowing source for any modem method Ever used any cona'aceptive method Currendy using any method Currently using a modern method Currently using pill Currendy using IUD Currently using injections Currently using condom Currently using female sterilisation Currently wing periodic abstinence Using public sector source Want no more children Want to delay at least 2 years Ideal number of children Mothers received tetanus injection Mothers received medical care at birth Had diarrhoea in the last 24 hours Had diarrhoea in the last 2 weeks Treated with ORS packets Consulted medical personnel Having health card, seen Received BCG vaccination Received DPT vaccination (3 doses) Received polio vaccination (3 doses) Received measles vaccination Fully immunised Total fertility rate (3 years) Infant mortality rate (0-4 years) Infant mortality rate (0-9 years) .179 .006 7540 7540 1.433 .035 .167 .192 .245 .009 7540 7540 1.783 .036 .227 .262 .302 .007 7540 7540 1.285 .022 .289 .316 .614 .007 7540 7540 1.306 .012 .599 .629 .580 .009 5752 5786 1.428 .016 .561 .598 .625 .009 5752 5786 1.464 .015 .607 .644 3.167 .042 7540 7540 1.168 .013 3.083 3.252 7.323 .117 1093 1072 1.223 .016 7.089 7.557 2.818 .037 7540 7540 1.162 .013 2.743 Z892 .972 .004 4583 4629 1.471 .004 .965 .979 .969 .004 4583 4629 1.421 .004 .961 .976 .931 .005 4583 4629 1.268 .005 .921 .940 .552 .010 4583 4629 1.406 .019 .532 .573 .327 .010 4583 4629 1.402 .030 .308 .347 .273 .009 4583 4629 1.411 .034 .254 .292 .095 .006 4583 4629 1.395 .064 .083 .107 .042 .004 4583 4629 1.293 .092 .034 .049 .072 .004 4583 4629 1.168 .062 .063 .081 .008 .001 4583 4629 .979 .157 .006 .011 .055 .004 4583 4629 1.069 .065 .048 .G63 .044 .003 4583 4629 1.124 .077 .038 .051 .682 .014 1552 1560 1.198 .021 .653 .710 .462 .009 4583 4629 1.221 .019 .444 .480 .260 .007 4583 4629 1.150 .029 .245 .274 3.698 .036 7115 7111 1.708 .010 3.626 3.770 .894 .006 6052 6062 1.199 .006 .883 .905 .541 .010 6052 6062 1.339 .019 .520 .562 • 055 .004 5583 5587 1.144 .067 .048 .062 • 139 .005 5583 5587 1.115 .039 .129 .150 .316 .021 755 779 1.160 .066 .274 .357 .409 .021 755 779 1.125 .051 .367 .451 • 692 .015 1115 1124 1.119 .022 .661 .723 • 963 .008 1115 1124 1.378 .008 .947 .978 • 869 .011 1115 1124 1.120 .013 .846 .891 • 867 .011 1115 1124 1.099 .013 .845 .889 • 838 .013 1115 1124 1.191 .016 .812 .864 • 787 .014 1115 1124 1.103 .018 .760 .814 5.403 .130 7540 7540 1.332 .024 5.143 5.662 61.649 4.321 6271 6286 1.310 .070 53.008 70.290 62.532 3.521 12500 12566 1.435 .056 55.489 69.575 190 Table B.3 Samplinl~ errors - Urban sample, Kenya 1993 Number o[ cases Standard Design Relative Confidence limits Value error Unweighted Weighted effect error Variable (R) (SE) (N) (WN) (DEFT) (SEJR) "R-2SE R+2SE No education .087 .010 With secondary education or higher .458 .026 Never married (in union) .378 .021 Currently mat~od (in union) .520 .021 Married be fore age 20 .416 .024 Had first sexual intercourse befor~ 18 .505 .026 Children ever born 1.959 .084 Children ever born to women over 40 4.670 .365 Children surviving 1.783 ,076 Knowing any contraceptive method ,985 .005 Knowing any modern method .983 .005 Knowing source for any modem method ,957 .009 Ever used any contraceptive method ,701 .026 Currently using any method ,434 .025 Curtenily using a modem method .379 .026 Currently using pill .157 .020 Currently using IUD .095 ,015 Currently using injections .062 .010 Currently using condom .013 .004 Currently using female sterilisation .052 .008 Currently using periodic abstinence .053 .009 Using public sector sottrce .541 .033 Want no more children .427 .025 Went to delay at Iea~t 2 years .262 .023 Ideal number of cbildren 2.936 .043 Mothers ~ceived tetanus injection .929 .014 Mothers received medical care at birth .841 .017 Had diarrhoea in the last 24 hours .051 .011 Had diarrhoea in the l&st 2 weeks .119 .018 Treated with ORS packets .404 .060 Consulted medical personnel .525 .059 Having hcahh card. seen .587 .047 Received BCG vaccination .989 .009 Received DPT vaccination (3 doses) .925 .022 Received polio vaccination (3 doses) .925 .022 Received measles vaccination .840 .030 Fully immunisad .809 .035 Total fertility rate (3 years) 3.440 .219 Infant mortality rate (0-9 years) 45.525 7.810 1161 1339 1.209 .115 .067 .107 1161 1339 1.767 .056 .406 .509 1161 1339 1.473 .056 .336 .420 1161 1339 1.458 .041 .478 .563 915 1064 1.459 .057 .369 .464 915 1064 1.550 .051 .453 .556 1161 1339 1.281 .043 1.791 2.127 92 108 1.295 .078 3.939 5.401 1161 1339 1.271 ,043 1.632 1.935 607 697 .960 .005 .975 .994 607 697 .984 .005 .972 .993 607 697 1.131 .010 .939 .976 607 697 1.406 .037 .649 .754 607 697 1.260 .058 .383 .485 607 697 1.294 .067 .328 .430 607 697 1.368 .129 .116 .197 607 697 1.242 .156 .065 .124 607 697 1.063 .168 .041 .083 607 697 .950 .343 .004 .021 607 697 .905 .156 .036 .069 607 697 1.018 .175 .035 .072 315 376 1.179 .061 .475 .607 607 697 1.266 .060 ,376 .477 607 697 1.299 .089 .215 .308 1088 1273 1.101 .015 2.849 3.023 662 773 1.343 .015 .901 .958 662 773 1.021 .021 .806 .876 616 720 1.168 .217 .029 .073 616 720 1.313 .147 .084 .154 77 86 1.040 .148 .285 .523 77 86 .998 .112 .407 .643 148 177 1.170 .081 .492 .682 148 177 1.076 .009 .970 1.000 148 177 1.020 .023 .882 .969 148 177 1.020 .023 .882 .969 148 177 1.010 .036 .780 .900 148 177 1.101 .043 .738 .879 1161 1339 1.213 ,063 3.036 3.913 1308 1516 1.320 .172 29.905 61.145 191 Table B.4 Sampling errors - Rural sampl% Kenya 1993 Number of cases Standard Design Relative Confidence limits Value error Unweighted Weighted effect error Variable (R) (SE) (N) (WN) (DEFI') (SE/R) R-2SE R+2SE No education .199 .007 6379 6201 1.441 .036 .185 .214 With secondax~ education or higher .199 .008 6379 6201 1.566 .039 .183 .214 Never married (in union) .286 .007 6379 6201 1.194 .024 .273 .300 Currently mar'ned (in union) .634 .007 6379 6201 1.231 .012 .619 .649 Married before age 20 .616 .009 4837 4722 1.310 .015 .598 .635 Had first sexual intercourse before 18 .653 .009 4837 4722 1.378 .014 .634 .672 Children ever born 3.428 .044 6379 6201 1.068 .013 3.341 3.515 Children ever born to women over 40 7.620 .118 1001 964 1.214 .015 7.384 7.856 Children surviving 3.041 .039 6379 6201 1.073 .013 2.964 3.118 Knowing any conwaceptive method .970 .004 3976 3932 1.521 .004 .962 .978 Knowing any modern method .966 .004 3976 3932 1.464 .004 .958 .975 Knowing source for any modem method .926 .005 3976 3932 1.280 .006 .915 .937 Ever used any contraceptive method .526 .011 3976 3932 1.418 .021 .503 .548 Currently using any method .309 .010 3976 3932 1.425 .034 .288 .329 Currently using a modern method .254 .010 3976 3932 1.428 .039 .234 .274 Currently using pill .084 .006 3976 3932 1.368 .072 .072 .096 Currently using IUD .032 .003 3976 3932 1.250 .109 .025 .039 Currently using injections ,074 .005 3976 3932 1.180 .066 .064 .083 Currently using condom .008 .001 3976 3932 .982 .176 .005 .010 Currently using female sterilisation .056 .004 3976 3932 1.097 .071 .048 .064 Currently using periodic abstinence .043 .004 3976 3932 1.144 .086 .036 .050 Using public sector source .726 .014 1237 1184 1.119 .020 .698 .755 Want no more children .469 .010 3976 3932 1.208 .020 .450 .488 Want to delay at least 2 years .259 .008 3976 3932 1. I 14 .030 .244 .275 Ideal number of children 3.864 .042 6027 5838 1.788 .011 3.780 3.948 Mothers received tetanus injection .889 .006 5390 5289 1.190 .007 .877 .901 Mothers received medical care at birth .497 .011 5390 5289 1.359 .022 .475 .520 Had diarrhoea in the last 24 hours .056 .004 4967 4867 I. 142 .070 .048 .063 [tad diarrhoea in the last 2 weeks .142 .006 4967 4867 1.086 .040 .131 .154 Treated with ORS packets .305 .022 678 693 1.170 .072 .261 .349 Consulted medical personnel .394 .022 678 693 1.138 .056 .350 .438 ttaving health card. seen .711 .016 967 947 1.104 .023 .679 .744 Received BCG vaccination .958 .009 967 947 1.411 .009 .940 .976 Received DPT vaccination (3 doses) .858 .013 967 947 1.131 .015 .833 .883 Received polio vaccination (3 doses) .856 .013 967 947 1.108 .015 .831 .881 Received measles vaccination .837 .015 967 947 1.227 .017 .808 .867 Fully immunised .783 .015 967 947 1.103 .019 .754 .813 Total fertility rate (3 years) 5.804 .136 6379 6201 1.306 .023 5.533 6.076 In rant mortality rate (0-9 years) 64.852 3.850 11192 11050 1.455 .059 57.153 72.551 192 Table B.5 Sampling errors - Nairobi, Kenya 1993 Number of cases Standard Design Relative Confidence limits Value error Unweighted Weighted effect error Variable (R) (SE) (N) 0VN) (DEFT) (SE/R) R-2SE R+2SE No education .076 .013 With secondary education or higher .480 .033 Never married (in union) .354 .044 Currently manned (in union) .534 .047 Married before age 20 .395 .039 tied first sexual intercourse before 18 .511 .042 Children ever born 1.787 .142 Children ever born to women over 40 4.265 .521 Children surviving 1.646 .136 Knowing any contxaceptlve method .969 .011 Knowing any modern method .964 .013 Knowing source for any modem method .954 .017 Ever used any contraceptive method .714 .050 Currently using any method .454 .040 Currently using a modern method .378 .038 Currently using pill .204 .037 Currently using IUD .097 .017 Currently using injcelions .046 .019 Currently using condom .010 .007 Currently using female sterilisatlon .020 .010 Currently using periodic abstinence .071 .019 Using public sector source .531 .044 Want no more children .434 .052 Want to delay at least 2 years .306 .040 Ideal number of children 2.655 .076 Mothers received tetanus injection .895 .032 Mothers received medical care at birth .860 .031 llad diarrhoea in the last 24 hours .054 .022 llad diarrhoea in the I~t 2 weeks .108 .029 Treated with ORS packets .100 .082 Consulted medical personnel .300 .079 flaying health card, seen .533 .065 Received BCG vaccination 1.000 .000 Received DPT vaccination (3 doses) 1.000 .000 Received polio vaccination (3 doses) 1.000 .000 Received measles vaccination .867 .055 FuUy immunised .867 .055 367 507 .950 .173 .050 .103 367 507 1.272 .069 .413 .546 367 507 1.757 .124 .266 .442 367 507 1.786 ~087 .441 .627 309 427 1.403 .099 .317 .473 309 427 1.485 .083 .427 .596 367 507 1.323 .079 1.504 2.071 34 47 1,198 .122 3.222 5.307 367 507 1.348 .082 1.374 1.917 196 271 .910 .012 .947 .992 196 271 .944 .013 .939 .989 196 271 1.105 .017 .921 .987 196 271 1.534 .069 .615 .814 196 271 1,131 .089 .373 .535 196 271 1.103 .101 .301 .454 196 271 1.295 .183 .129 .279 196 271 .791 .173 .063 .130 196 271 1.255 .410 .008 .084 196 271 1.011 .713 .000 .025 196 271 1.019 .506 .000 .041 196 271 1.047 .270 .033 .110 113 156 .941 .084 .442 .620 196 271 1.453 .119 .331 .537 196 271 1.219 .131 .226 .387 351 485 1.381 .028 2.504 2.806 200 276 1.306 .035 .832 .958 200 276 .990 .037 .797 .923 186 257 1.209 .403 .010 .097 186 257 1.246 .269 .050 .165 20 28 1.221 .825 .000 .265 20 28 .751 ,262 .143 .457 45 62 .841 .122 .403 .663 45 62 NP .000 1.000 1.000 45 62 NP .000 1.000 1.000 45 62 NP .000 1.000 1.0OO 45 62 1.068 .063 .758 .976 45 62 1.068 .063 .758 .976 NP = Not possible to calculate 193 Table B.6 Sampling errors - Central Province~ Kenya 1993 Number of cases Standard Design Relative Confidence limits Value error Unweighted Weighted effect emar Variable (R) (SE) (N) (WN) (DEFT) (SE/R) R-2SE R+2SE No education .095 .009 With secondary education or higher .313 .024 Never married (in union) .358 .017 Currently manSed (in union) .558 .017 Married before age 20 .451 .023 Had first sexual intercourse before 18 .520 .028 Children ever born 2.885 .098 Children ever born to women over 40 6.887 .256 Children surviving 2.700 .095 Knowing any contraceptive method .998 .001 Knowing any modern method .998 .001 Knowing source for any modem method .971 .006 Ever used any contraceptive method .747 .021 Currently using any method .560 .027 Currently using a modern method ,497 .024 Currently using pill .209 .016 Currently using IUD .100 .015 Currently using injections .087 .014 Currently using condom .014 .005 Cum~ntly using female sterilisation .084 .010 Currently using periodic abstinence .062 .010 Using public sector source .752 .029 Want no more children .560 .018 Want to delay at least 2 years .214 .018 Ideal number of children 3.108 .050 Mothers received tetanus injection .908 .013 Mothers ~ceived medical care at birth .742 .031 Had diarrhoea in the last 24 hours .023 .007 Had diarrhoea in the last 2 weeks .094 .018 Treated with ORS packets .216 .058 Consulted medical personnel .397 .079 llavlng health card, seen .654 .045 Received BCG vaccination .974 .017 Received DPT vaccination (3 doses) .944 .021 Received polio vaccination (3 doses) .944 .021 Received measles vaccination .942 .022 Fully immunised .926 .021 1075 1094 1075 1094 1075 1094 1075 1094 837 851 837 851 1075 1094 190 180 1075 1094 600 610 600 610 600 610 600 610 600 610 600 610 600 610 600 610 600 610 600 610 600 610 600 610 370 376 600 610 600 610 1038 1055 675 697 675 697 650 671 650 671 59 63 59 63 143 148 143 148 143 148 143 148 143 148 143 148 1.062 .100 .076 .114 1.716 .077 .265 .362 1.178 .048 .323 .392 1.095 .030 .525 .591 1.341 .051 .405 .497 1.593 .053 .465 .575 1.114 .034 2.689 3.080 1.217 .037 6.376 7.399 1.177 .035 2.510 2.891 .783 .001 .995 LO00 .783 .001 .995 1.000 .924 .006 .959 .984 1.188 .028 .704 .789 1.337 .048 .506 .614 1.168 .048 .449 .545 .944 .075 .177 .240 1.234 .151 .070 .130 1.173 .155 .060 .114 .930 .315 .005 .023 .904 .122 .063 .104 1.042 .166 .041 .082 1.311 .039 .693 .811 .899 .033 .523 .596 1.048 .082 .179 .249 1.329 .016 3.008 3.207 1.120 .015 .881 .934 1.622 .041 .680 .803 1.221 .313 .009 .037 1.563 .187 .059 .129 1.089 .271 .099 .332 1.256 .199 .239 .555 1.148 .069 .563 .745 1.289 .018 .940 1.000 1.115 .023 .901 .986 1.115 .023 .901 .986 1.122 .023 .899 .985 .978 .023 .883 .968 194 Table B.7 Sampling errors - Coast Province r Kenya 1993 Number of cases Standard Design Relative Confidence limits Value error Unweighted Weighted effect error Variable (R) (SE) (N) OVN) (DEFT) (SE/R) R-2SE R+2SE No education .367 ,020 With secondary education or higher .175 .020 Never mamed (in union) .277 .016 Currently married (in union) .621 .017 Married before age 20 .670 .030 Had first sexual intercourse before 18 .572 .026 Children ever bern 2.887 .116 Children ever born to women over 40 6.436 .434 Children surviving 2.465 .083 Knowing any contraceptive method .946 .027 Knowing any modern method .937 .023 Knowing source for any modem method .857 .020 Ever used any contraceptive method .385 ,022 Currently using any method .202 .020 Currently using a modern method .166 .020 Currently using pill .063 .014 Currently using IUD ,024 ,008 Currently using injections .036 ,007 Currently using condom .008 .002 Currently using female sterilisatlon .034 .008 Currently using periodic abstinence .028 .006 Using public sector source .600 .052 Want no more children .270 .021 Want to delay at least 2 years .324 .020 Ideal number of children 4.506 .113 Mothers received tetanus injection .834 .030 Mothers received medical care at birth .382 .030 ttad diarrhoea in the last 24 hours .061 .017 itad diarrhoea in the last 2 weeks .150 .014 Treated with ORS packets .522 .056 Consulted medical personnel .555 .048 ltaving health card, seen .745 .043 Received BCG vaccination .948 .028 Received DPT vaccination (3 doses) .856 .043 Received polio vaccination (3 doses) .856 .043 Received measles vaccination .880 .041 Fully immunised .811 .048 1091 717 1.389 .055 .326 .407 1091 717 1.750 .115 .135 .216 1091 717 1.189 .058 .244 .309 1091 717 1.155 .027 .587 .655 813 544 1.796 .044 .611 .729 813 544 1.524 .046 .519 .625 1091 717 1.296 .040 2.656 3.118 119 79 1.538 .067 5.567 7.304 1091 717 1.105 .034 2.299 2.631 651 445 3.083 .029 .891 1.000 651 445 2.460 .025 .890 .984 651 445 1.446 .023 .817 .896 651 445 1.172 .058 .340 .430 651 445 1.294 .101 .161 .243 651 445 1.375 .121 .126 .206 651 445 1.505 .228 .034 .092 651 445 1.332 .336 .008 .039 651 445 .898 .182 .023 .049 651 445 .473 .201 .005 .012 651 445 1.109 .231 .018 .050 651 445 .908 .209 .016 .040 187 103 1.461 .087 .495 .705 651 445 1.199 .077 .228 .312 651 445 1.O85 .061 .284 .364 948 598 1.571 .025 4.281 4.732 766 540 1.902 .036 .773 .894 766 540 1.472 .079 .322 .442 713 499 1.940 .282 .027 .096 713 499 1.124 .096 .121 .179 95 75 1,172 .107 .410 .634 95 75 1.008 .086 .459 .651 124 80 1,081 .058 .659 .831 124 80 1.412 .030 .892 1.000 124 80 1.357 .051 .770 .943 124 80 1.357 .051 .770 .943 124 80 1.383 .046 .799 .962 124 80 1.352 .059 .715 ,908 195 Table B,8 Sampling errors - Eastern Province, Kenya 1993 Number of eases Standard Design Relative Confidence fimits Value error Unweighted Weighted effect error Variable (R) (SE) (N) (WN) (DEFT) (SE/R) R-2SE R+2SE No education .166 .015 With secondary education or higher .213 .018 Never roamed (in union) .292 ,014 Currently mamnd (in union) ,614 .016 Man-led before age 20 .527 .022 Had first sexual intercourse before 18 .636 .020 Children ever born 3.447 .095 Children ever born to women over 40 7.443 ,235 Children surviving 3.125 .087 Knowing any contraceptive method .990 ,004 Knowing any modern method .990 .004 Knowing source for any modem method .958 .007 Ever used any contraceptive method .670 .024 Currently using any method .384 .024 Currently using a modern method .305 .023 Currently using pill .128 .018 Currently using IUD .057 ,012 Currenily using injections .060 .012 Currently using condom .009 .004 Currently using female sterilisadon .051 .007 Currently using periodic abstinence .073 .011 Using public sector sottrce .727 .026 Want no more children .570 .019 Want to delay at least 2 years .206 .012 Ideal number of children 3.549 .066 Mothers received tetanus injection .907 .012 Mothers received medical care at birth .559 .022 1lad diarrhoea in the last 24 hours .048 .008 Had diarrhoea in the last 2 weeks .122 .011 Treated with ORS packets .270 .047 Consulted medical personnel .342 .051 Having health card, seen .766 .034 Received BCG vaccination .990 .007 Received DPT vaccination (3 doses) ,908 .021 Received polio vaccination (3 doses) .907 .024 Received measles vaccination .900 .020 Fully immunised .850 .024 1044 1406 1.331 .092 .135 .197 1044 1406 1.405 .084 .178 .249 1044 1406 .990 .048 .264 .320 1044 1406 1.078 .026 .582 .647 823 1110 1.240 .041 .484 .570 823 1110 1.189 .031 .597 .676 1044 1406 ,974 .028 3,256 3,637 182 241 1.025 .032 6.973 7.913 1044 1406 .987 .028 2,951 3.298 649 864 .937 ,004 .983 .997 649 864 .937 .004 .983 .997 649 864 ,848 .007 .945 .972 649 864 1,314 .036 .622 .719 649 864 1.253 .062 .336 .432 649 864 1.284 .076 .259 .352 649 864 1.337 ,137 .093 .164 649 864 1.264 .201 .034 .081 649 864 1.250 .195 .036 .083 649 864 1.076 .449 ,(301 .017 649 864 ,861 .146 .036 .066 649 864 1.O63 .149 .051 ,095 240 311 .887 .035 .676 .779 649 864 .963 .033 .533 .608 649 864 .741 .057 ,183 .230 1017 1370 1.381 .019 3.416 3.682 905 1227 1.098 .014 .883 .932 905 1227 1.129 .040 .515 .604 849 1152 1.017 .160 .032 .063 849 1152 .942 .089 ,100 .144 105 141 1.076 .174 ,176 .363 105 141 1.077 .150 .240 .444 154 209 .992 .044 .698 .834 154 209 .895 .007 .975 1.000 154 209 .902 ,023 .866 .950 154 209 1.006 .026 .860 .954 154 209 .816 .022 .860 .939 154 209 .847 .028 .801 .899 196 Table B.9 Sampling errors - Nyanza Province~ Kenya 1993 Number of eases Standard Design Relative Confidence limits Value error Unweighted Weighted effect error Variable (R) (SE) (N) 0,VN) (DEFT) (SE/R) R-2SE R+2SE No education .182 .012 1264 1158 1.118 .067 .158 .206 With secondary education or higher .182 .016 1264 1158 1.437 .086 .151 .214 Never married (in union) .262 .017 1264 1158 1.384 .065 .228 .297 Currently ma~ied (in union) .636 .019 1264 1158 1.390 .030 .599 .674 Married before age 20 .714 .020 946 860 1.356 .028 .674 .753 Had first sexual intercourse before 18 .796 .019 946 860 1.465 .024 .758 .835 Children ever born 3.442 .092 1264 1158 .999 .027 3.259 3.626 Children ever bern to women over 40 7.693 .248 198 171 1.163 .032 7.197 8.189 Children surviving 2.813 .079 1264 1158 1.044 .028 2.654 2.972 Knowing any contraceptive method ,991 .003 802 737 1,041 .003 .984 .998 Knowing any modern method .991 .003 802 737 1.O41 .003 ,984 .998 Knowing source for any modem method ,959 ,008 802 737 1.104 ,008 .944 ,975 Ever used any contraceptive method ,444 .023 862 737 1,290 .O51 .399 ,490 Currently ualng any method .238 ,020 802 737 1,311 ,083 ,199 .277 Currently using a modern method ,215 ,021 802 737 1.415 ,096 ,174 .256 Currendy using pill ,042 ,009 802 737 1.236 .208 .025 ,060 Currendy using IUD .013 .005 802 737 1.275 .386 .003 .024 Curren fly using injecilons .082 .Oll 802 737 1.189 .141 .059 .104 Currently using condom ,006 ,003 802 737 .975 ,429 .OO1 .012 Currently using female ster/lisation .071 .011 802 737 1,265 .162 .048 ,094 Currently using periodic abstinence .018 .005 802 737 1.011 .262 .009 ,028 Using public sector source .699 .031 193 179 ,930 ,044 .637 ,760 Want no more children ,401 ,022 802 737 1,293 ,056 .356 .446 Want to delay at least 2 years .267 .022 802 737 1.408 .082 ,223 .311 Ideal number of children 3.831 .046 1187 1082 1.064 .012 3.740 3,922 Mothers received tetanus injection .911 .008 11 G6 1011 .811 .009 .894 ,927 Mothers received medical care at birth ,485 ,O17 1106 1011 .919 .035 .451 .519 Ilad diarrhoea in the last 24 hours .070 ,008 930 852 .972 .116 ,054 ,086 1tad diarrhoea in the last 2 weeks ,177 ,012 930 852 .943 .069 ,153 .201 Treated with ORS packets .301 .049 169 151 1.310 .162 .203 .398 Consulted medical personnel ,419 ,040 169 151 .981 ,095 .340 .499 [laving health card, seen .608 ,043 190 174 1.222 .071 ,521 .694 Received BCG vaccination .936 .028 190 174 1,592 .030 ,879 .992 Received DPT vaccination (3 doses) .796 .039 190 174 1.345 .049 .717 ,874 Received polio vaccination (3 doses) ,799 .039 190 174 1.345 ,049 .721 .877 Received measles vaccination .761 .039 190 174 1.265 .051 .683 .839 Fully immunised ,697 .041 190 174 1.239 ,059 .615 .780 197 Table B.10 Sampling errors. Rift Valley Province I Kenya 1993 Number of cases Standard Design Relmive Confidence limits Value error Unweighted Weighted effect error Variable (R) (SE) (N) (WN) (DEFT) (SE/P.) R-2SE R*2SE No education .225 .020 With secondary education or higher .217 .023 Never married (in union) .305 .014 Currently married (in union) .635 .015 Ma~ied before age 20 .600 .019 Had first sexual intercourse before 18 .605 .021 Children ever born 3.342 .111 Children ever born to women over 40 7.908 .246 Children surviving 3.091 .099 Knowing any contraceptive method .929 .009 Knowing any modern method .921 .011 Knowing source for any modem method .874 .015 Ever used any contraceptive method .495 .022 Currently using any method .278 .016 Cun'endy using a modern method .210 .014 Currendy using pill .043 .006 Currently using IUD .020 .007 Currendy using injections .079 .009 Currently using condom .007 .003 Currendy using female sterilisation .059 .008 Currently using periodic abstinence .049 .008 Using public sector source .624 .040 Want no more children .425 .018 Want to delay at least 2 years .280 .015 Ideal number of children 4.104 .117 Mothers received tetanus injection .880 .013 Mothers received medical care at birth .460 .024 ilad dim'rhoea in the last 24 hours .043 .008 Had dima'hoea in the last 2 weeks .118 .011 Treated with ORS packets .425 .049 Consulted medical personnel .446 .059 Having health card, seen .688 .029 Received BCG vaccination .970 .014 Received DPT vaccination (3 doses) .848 .024 Received polio vaccination (3 doses) .851 .022 Received measles vaccination .833 .025 Fully immunised .759 .030 1754 1562 2.006 .089 .185 .265 1754 1562 2.349 .107 .171 .263 1754 1562 1.285 .046 .277 .333 1754 1562 1.267 .023 .606 .664 1331 1195 1.389 .031 .563 .638 1331 1195 1.576 .035 .563 .648 1754 1562 1.413 .033 3.120 3.564 241 206 1.216 .031 7.416 8.400 1754 1562 1.375 .032 2.892 3.289 1074 992 1.119 .009 .912 .947 1074 992 1.300 .012 .899 .942 1074 992 1.495 .017 .844 .905 1074 992 1.454 .045 .450 .539 1074 992 1.176 .058 .246 .311 1074 992 1.109 .066 .182 .237 1074 992 .975 .140 .031 .055 1074 992 1.602 .342 .006 .034 1074 992 1.066 .111 .062 .097 1074 992 1.122 .409 .001 .013 1074 992 1.085 .132 .044 .075 1074 992 1.194 .160 .034 .065 289 247 1.413 .065 .543 .705 1074 992 1.170 .042 .390 .461 1074 992 1.112 .054 .249 .310 1669 1476 2.173 .028 3.871 4.338 1509 1309 1.265 .014 .855 .905 1509 1309 1.503 .052 .413 .508 1447 1251 1.162 .175 .028 .058 1447 1251 1.113 .091 .097 .140 155 148 1.111 .116 .327 .524 155 148 1.338 .131 .329 .563 294 263 1.060 .042 .630 .745 294 263 1.425 .014 .942 .999 294 263 1.145 .029 .799 .896 294 263 1.065 .026 .806 .896 294 263 1.159 .030 .782 .883 294 263 1.207 .040 .698 .819 198 Table B.11 Sampling errors - Western Province r Kenya 1993 Number of cases Standard Design Relative Confidence limits Value error Unweighted Weighted effect error Variable (R) (SE) (N) O,VN) (DEFT) (SE/R) R-2SE R+2SE No education .138 .010 945 1096 .872 .071 .118 .158 With secondary education or higher .258 .027 945 1096 1.867 .103 .205 .311 Never married (in union) .292 .016 945 1096 1.065 .054 .260 .323 Currently married (in union) .648 .019 945 1096 1.220 .029 .610 .686 Married before age 20 .651 .027 693 799 1.481 .041 .598 .705 Had first sexual ina:rcourse before 18 .666 .025 693 799 1.385 .037 .616 .715 Children ever born 3.372 .093 945 1096 .834 .028 3.187 3.558 Children ever born to women over 40 7.856 .330 129 149 1.110 .042 7.197 8.515 Children sm'viving 2.929 .070 945 1096 .732 .024 2.789 3.069 Knowing any contraceptive method .986 .005 611 710 1.015 .005 .976 .996 Knowing any modern method .982 .005 611 710 1,015 .006 .971 .993 Knowing source for any medem methed .950 .009 611 710 1.042 .010 .931 .968 Ever used any conlraceptive method .477 .024 611 710 1,182 .050 .429 .525 Currently using any method .251 .025 611 710 1,405 .098 .201 .300 Currendy using a modern method .217 .025 611 710 1,496 .115 .167 .267 Currently using pill .061 .016 611 710 1,640 .261 .029 .092 Currently using IUD .022 .006 611 710 ,925 .249 .011 .033 Currently using injections .085 .011 611 710 1,011 .134 .062 .108 Currently using condom .007 .002 611 710 ,655 .323 .002 .011 Currently using female sterilisation .042 .009 611 710 1.138 .220 .023 .060 Currently using periodic abstinence .015 .004 611 710 .804 .264 .007 .023 Using public sector source .693 .040 160 187 1.089 .057 .613 .773 Want no more children .495 .026 611 710 1.268 .052 .444 .546 Want to delay at least 2 years .269 .021 611 710 1.197 .080 .226 .312 Ideal number of children 3.798 .095 905 1046 1.662 .025 3.608 3.988 Mothers rcceivad tetanus injection .902 .011 891 1001 .916 .012 .881 .924 Mothers received medical care at birth .539 .027 891 1001 1.267 .049 .486 .592 Had disxrhoea in the last 24 hours .087 .009 808 905 .885 .106 .068 .105 Had diaxrhova in the In,st 2 weeks .192 .016 808 905 1,059 .081 .161 .223 Treated with ORS packets .254 .050 152 173 1.288 .195 .155 .354 Consulted medical personnel .381 .045 152 173 1.074 .118 .291 .471 Having health card. seen .753 .038 165 187 1.126 .051 .676 .830 Received BCG vaccination .931 .027 165 187 1.339 .029 .878 .985 Received DPT vaccination (3 doses) .823 .030 165 187 1.004 .037 .763 .884 Receivad polio vaccination (3 doses) .809 .030 165 187 .954 .037 .750 .868 Received measles vaccination .738 .044 165 187 1.255 .060 .649 .827 Fully immunised .695 .037 165 187 1.008 .053 .621 .769 199 Table B.12 Sampling errors - Mombasa City I Murangia rural and Nyeri rural r Kenya 1993 NulDber of cases Standard Design Relative Value error Unweighted Weighted effect error Variable (R) (SE) (N) (WN) (DEFI') (SE/R) Confidence fimits R-2SE R+2SE MOMBASA CITY No education .183 With secondary education or higher .352 Never married (in union) .360 Currently married (in union) ,530 Knowing any contraceptive method .990 Knowing any modem method .990 Knowing source for any modem method .949 Ever used any contraceptive method ,650 Currently using any method .376 Currently using a modern method .320 Currently using pill .112 Currently using IUD .061 Currently using injections .046 Currently using condom .030 Currently using female sterilisation .066 Currently using periodic abstinence .056 .028 372 178 1.382 .152 .127 .238 .041 372 178 1.640 .115 .271 .433 .030 372 178 1.184 .082 .301 .419 .033 372 178 1.270 .062 .464 .595 ,007 197 94 .955 ,007 .976 1,000 .007 197 94 .955 ,007 .976 1.000 .016 197 94 1.040 .017 ,917 .982 .033 197 94 ,980 .051 ,583 .717 .043 197 94 1,240 .114 ,290 .461 .045 197 94 1.345 .140 .230 ,409 ,033 197 94 1.450 .292 .046 .177 .025 197 94 1.483 .416 .010 .112 .009 197 94 .622 .203 .027 .064 ,007 197 94 .553 .223 .017 .044 .017 197 94 .945 .254 .032 .100 .013 197 94 .789 .232 .030 ,082 MURANG'A RURAL No education .102 With secondary education or higher .230 Never married (in union) .338 Currently married (in union) .565 Knowing any contraceptive method .995 Knowing any modern method .995 Knowing source for any modern method .956 Ever used any contraceptive method .701 Currently using any method .471 Currently using a modern method ,402 Currently using pill .108 Currently using IUD .098 Currently using injections .093 Currently using condom .039 Currently using female sterilisation .064 Currently using periodic abstinence .069 .011 361 265 .681 .106 .081 .124 .025 361 265 1,138 .110 .179 .280 .019 361 265 ,754 .056 .300 .376 .024 361 265 .920 ,043 .517 .613 .005 204 150 .978 .005 .986 1.000 .005 204 150 ,978 ,005 .986 1.000 .016 21)4 150 1.136 .017 .923 .989 .022 204 150 .685 .031 .657 .745 .027 204 150 .779 .058 .416 .525 .022 204 150 .627 .054 .359 .445 .015 204 150 .691 .140 .078 .138 .031 204 150 1.508 .321 .035 .161 .018 204 150 .863 .189 .058 .128 • 016 204 150 1.161 .403 .008 .071 .011 204 150 .659 .177 .041 .086 ,022 204 150 1.264 .327 ,024 .113 NYER1 RURAL No education .054 With secondary education or higher .365 Never married (in union) .384 Currently married (in union) .556 Knowing any contraceptive method .995 Knowing any modern method .995 Knowing source for any modern method ,985 Ever used any contraceptive method .833 Currently using any method .642 Currently using a modern method .603 Currently using pill .181 Currently using IUD .123 Currently using injections .152 Currently using condom .029 Currently using female sterilisatinn .118 Currently using periodic abstinence .034 .009 367 175 .732 .159 .037 .072 .042 367 175 1.665 .115 .281 .449 .016 367 175 .642 .042 .352 .417 .022 367 175 .860 ,040 .511 .601 .005 204 97 .949 .005 .986 1,000 .005 204 97 ,949 .005 ,986 1.000 .008 204 97 .910 .008 .970 1.1300 .031 204 97 1.199 .038 .771 .896 .044 204 97 1.310 .069 ,554 .730 .041 204 97 1.185 ,068 .522 .684 .018 204 97 .679 .101 .145 .218 .037 204 97 1.628 .306 .048 .197 .021 204 97 .849 .141 .109 .195 .013 204 97 1.130 .456 .003 .056 .024 204 97 1.040 .200 .071 .165 • 014 204 97 1.111 .413 .006 .063 200 Table B.13 Sampling errors - Kilifi rural r Taita Taveta rural and Machakos rural t Kenya 1993 Number of cases Standard Design Relative Confidence limits Value error Unweighted Weighted effect error Variable (R) (SE) (N) OVN) (DEFT) (SE/R) R-2SE R+2SE KILIFI RURAL No education .510 With secondary education or higher .089 Never married (in union) .208 Currently married (in union) .688 Knowing any contraceptive method .953 Knowing any modern method .944 Knowing source for any modern method .836 Ever used any contraceptive method .306 Currently using any method .138 Currently using a modern method .103 Currently using pill .030 Currently using 1UD .009 Currently using injections .030 Currently using condom .000 Currently using female sterilisation .034 Currently using periodic abstinence .030 .036 337 289 1.310 .070 .439 .582 .034 337 289 2.162 .377 .022 .156 .020 337 289 .920 .098 .167 .248 .021 337 289 .840 .031 .646 .731 .013 232 199 .920 .013 .927 .978 .014 232 199 .918 .015 .916 .972 .033 232 199 1.357 .040 .770 .902 .035 232 199 1.148 .114 .236 .376 .022 232 199 .953 .157 .095 .181 .016 232 199 .806 .156 .071 .136 .012 232 199 1.106 .413 .005 .055 .006 232 199 1.044 .736 .000 .021 .007 232 199 .665 .248 .015 .045 .000 232 199 NP .000 .000 .000 .013 232 199 1.084 .377 .008 .061 .011 232 199 1.014 .378 .007 .053 TAITA TAVETA RURAL No education .114 With secondary education or higher .221 Never married (in union) .352 Currently married (in union) .569 Knowing any contraceptive method .994 Knowing any modern method .994 Knowing source for any modem method .969 Ever used any contraceptive method .550 Currently using any method .338 Currently using a modern method .287 Currently using pill .119 Currently using IUD .031 Currently using injections .106 Currently using condom .025 Currently using female sterilisation .006 Currently using periodic abstinence .037 .016 281 62 .854 .142 .081 .146 .035 281 62 1.421 .160 .150 .291 .034 281 62 1.194 .097 .284 .420 .037 281 62 1.234 .064 .496 .642 .006 160 35 .992 .006 .981 1.000 .006 160 35 .992 .006 .981 1.000 .011 160 35 .804 .011 .947 .991 .058 160 35 1.464 .105 .434 .666 .044 160 35 1.173 .130 .250 .425 .042 160 35 1.172 .146 .203 .372 .023 160 35 .906 .196 .072 .165 .010 160 35 .759 .335 .010 .052 .024 160 35 .989 .227 .058 .155 .009 160 35 .706 .350 .008 .042 .006 160 35 .986 .986 .000 .019 .012 160 35 .771 .310 .014 .061 MACHAKOSRURAL No education .084 With secondary education or higher .258 Never married (in union) .290 Currently married (in union) .621 Knowing any contraceptive method .996 Knowing any modem method .996 Knowing source for any modem method .967 Ever used any contraceptive method .710 Currently using any method .382 Currently using a modem method .272 Currently using pill .092 Currently using IUD .033 Currently using injections .048 Currently using condom .011 Currently using female sterilisation .088 Currently using periodic abstinence .107 .016 438 571 1.229 .194 .052 .117 .030 438 571 1.423 .115 .198 .318 .022 438 571 1.016 .076 .246 .334 .029 438 571 1.245 .047 .563 .679 .004 272 355 1.029 .004 .989 1.000 .004 272 355 1.029 .004 .989 1.000 .011 272 355 1.020 .011 .945 .989 .034 272 355 1.227 .048 .642 .777 .034 272 355 1.156 .089 .314 .451 .032 272 355 1.167 .116 .209 .335 .023 272 355 1.291 .247 .047 .137 .010 272 355 .900 .296 .014 .053 .020 272 355 1.523 .413 .008 .087 .007 272 355 1.148 .661 .000 .026 .018 272 355 1.052 .206 .052 .125 .016 272 355 .874 .154 .074 .139 NP = Not pessible to calculate 201 Table B.14 Sampling errors - Meru rural T Kisii/Nyamira rural and Siaya rural T Kenya 1993 Number of eases Standard Design Relative Confidence limits Value error Unweighted Weighted effect error Variable (R) (SE) (N) (WN) (DEFT) (SE/R) R-2SE R+2SE MERU RURAL No education .223 .027 364 With secondary education or higher .151 .020 364 Nevea" married (in union) .286 .025 364 Currently married (in union) .668 .025 364 Knowing any contraceptive method .979 .008 243 Knowing any modern method .979 .008 243 Knowing source for any modem method .942 .012 243 Ever used any contraceptive method .646 .051 243 Currently using any method .412 .038 243 Currently using a modern method .403 .040 243 Currently using pill .173 .030 243 Currently using IUD .074 .018 243 Currently using injections .111 .023 243 Currently using condom .008 ,006 243 Currently using female sterilisation .037 .008 243 Currently using periodic abstinence .008 .006 243 388 1.253 ,123 .168 .277 388 1,062 .132 A l l .191 388 1.067 ,089 .235 .336 388 .997 .037 .618 .717 259 .874 .008 .963 .995 259 .874 .008 .963 .995 259 .784 .012 .919 .966 259 1.661 .079 .544 .748 259 1.206 .093 .335 .488 259 1.255 .098 .324 .482 259 1.252 .176 .112 .234 259 1,064 .242 .038 .110 259 1.149 .209 ,065 .158 259 .959 .677 .0OO .019 259 .655 .215 .021 .053 259 .959 .677 .000 .019 KISI]/NYAMIRA RURAL No education .152 .014 488 With secondary education or higher .207 .023 488 Never married (in union) .320 .024 488 Currently married (in union) .594 .026 488 Knowing any contraceptive method ,997 .004 290 Knowing any modern method .997 .004 290 Knowing source for any modem method .976 .010 290 Ever used any contraceptive method .628 .025 290 Currently using any method .403 .034 290 Currently using a modern method .379 .038 290 Currently using pill .055 .014 290 Currently using IUD .017 .010 290 Currently using injections .162 .022 290 Currently using condom .017 .007 290 Currently using female sterilisation .128 .028 290 Currently using periodic abstinence .017 .008 290 461 .879 ,094 ,123 .180 461 1.239 .110 .161 .252 461 1.150 .076 .271 .368 461 1,170 .044 ,542 .646 274 1.021 .004 .990 1.0OO 274 1.021 .004 .990 1.000 274 1.118 .010 .956 .996 274 .895 .041 .577 .678 274 1.171 .084 .336 .471 274 1,343 .101 .303 .456 274 1.019 .248 .028 .083 274 1.276 .567 .0OO .037 274 .994 .133 .119 .205 274 ,892 .396 .004 .031 274 1.405 .216 .072 .183 274 .989 .439 .002 .032 SIAYA RURAL No nducadon .240 .019 With secondary education or higher .147 .019 Never married (in union) .221 .020 Currently married (in union) .630 .026 Knowing any contraceptive method .984 .009 Knowing any modern method .984 .009 Knowing source for any modem method .942 .013 Ever used any cona'aceptive method .397 .028 Currently using any method .152 .027 Currently using a modem method .109 .024 Currently using pill .023 .011 Currently using IUD .004 .004 Currently using injections .047 .011 Currently using condom .000 .000 Currently using female sterilisation .031 .013 Currently using periodic abstinence .039 .012 408 196 .893 .079 .202 .278 408 196 1.095 .131 .109 .185 408 196 .982 .092 .180 .261 408 196 1.072 .041 .579 .681 257 124 1.113 .009 .967 1.0(30 257 124 1.113 .009 .967 1.000 257 124 .878 .014 .916 ,967 257 124 .901 .069 ,342 .452 257 124 1.205 .178 .098 .206 257 124 1.245 .223 .060 .157 257 124 1,120 .453 .002 .044 257 124 1.046 1,046 .000 .012 257 124 ,828 ,234 .025 .069 257 124 NP ,000 .0OO .0(30 257 124 1.174 .409 .006 .057 257 124 1.003 .312 .015 .063 NP = Not possible to calculate 202 Table B.15 Sampling errors - South Nyartza rural, Kedcho rural and Nakuru ruralv Ken'?a 1993 Variable Number o f cases Standard Design Relative Value error Unweighted Weighted effect error (R) (SE) (N) (WN) (DEFT) (SE/R) Confidenc~ fimiu R-2SE R+2SE SOUTH NYANZA RURAL No education .206 .027 With secundary education ur higher .117 .021 Never married (in union) .140 .034 Currently married (in union) .759 .045 Knowing any contraceptive method .985 ,009 Knowing any modern method .985 .009 Knowing source for any modem method .938 .018 Ever used any contraceptive method ,323 .041 Currently using any method .128 .018 Currently using a modem method .I 13 .019 Currently using pill .036 .008 Currently using IUD .005 .005 Currently using injections .036 .016 Currently using condom .000 .000 Currently using female sterilisation .036 .008 Currently using periodic abstinence .010 .007 257 306 1.053 .129 .153 ,259 257 306 1.044 .180 ,075 ,159 257 306 1.581 .245 .072 .209 257 306 1.685 .059 ,669 .849 195 232 1.023 .009 .967 1.000 195 232 1.023 .009 ,967 1.000 195 232 1.049 .019 .902 .975 195 232 1.224 .127 .241 .405 195 232 .741 .139 .093 .164 195 232 .828 .167 .075 .150 195 232 ,563 .209 .021 .051 195 232 1.008 1,008 .000 .015 195 232 1.168 ,435 .005 .067 195 232 NP ,000 .000 .000 195 232 .617 .229 .019 .052 195 232 1.008 .711 .000 ,025 KERICHO RURAL No education .174 .027 With secondary education or higher .165 .026 Never married (in union) .311 .033 Currently married (in union) .646 .035 Knowing any contraceptive method .923 .021 Knowing any modern method ,923 .021 Knowing source for any modem method .889 .032 Ever used any contraceptive method .394 .045 Currently using any method .264 .039 Currently using a modem method .236 .041 Currently using pill .024 .013 Currently using IUD .000 .000 Currently using injections .139 .026 Currently using condom .000 .000 Currently using female sterilisatinn .067 .022 Currently using periodic abstinence .024 .014 322 266 1.256 .153 .121 .227 322 266 1.250 .157 .113 .216 322 266 1.283 .107 .244 .377 322 266 1.322 .055 .575 .717 208 172 1.142 .023 .881 .965 208 172 1.142 .023 .881 .965 208 172 1.477 .036 .825 .954 208 172 1.314 .113 .305 ,483 208 172 1.288 .149 .185 .343 208 172 1.385 .173 .154 .317 208 172 1.225 .543 .000 .050 208 172 NP .000 .000 .000 208 172 1.070 .185 .088 .191 208 172 NP .000 .000 .000 208 172 1.266 .327 .023 Al l 208 172 1.331 .589 .000 .052 NAKURU RURAL No education .163 .021 With secondary education or higher .206 ,028 Never married (in union) .290 .017 Currently married (in union) .635 .023 Knowing any contraceptive method .981 .010 Knowing any modern method .981 .010 Knowing source for any modem method .944 .018 Ever used any contraceptive method .506 .049 Currently using any method .287 .045 Currently using a modem method .231 .041 Currently using pill .038 .014 Currently using IUD .031 .019 Currently using injections .081 .028 Currently using condom .019 .011 Currently using female sterilisation .063 .013 Currently using periodic abstinence .056 .015 252 182 .915 .131 .120 .205 252 182 1.108 .137 .150 .263 252 182 .593 .059 .256 .324 252 182 .748 .036 .589 .680 160 116 .963 .011 .961 1.000 160 116 .963 .011 .961 1.000 160 116 .971 .019 .908 .979 160 116 1.230 .096 .409 .604 160 116 1.241 .155 .198 .377 160 116 1.237 .179 .148 .314 160 116 .951 .382 .009 .066 160 116 1.363 .602 .000 .069 160 116 1.314 .350 .024 .138 160 116 1.069 .613 .000 .042 160 116 .677 .208 .037 .088 160 116 .820 .266 .026 .086 NP = Not possible to calculate 203 Table B. 16 Sampling errors - Nandi rural r Uasin Gishu rural and Bungoma rural T Kenya 1993 Number of cases Standard Design Relative Confidence limits Value error Unweighted Weighted effect error Variable (R) (SE) (N) (WN) (DEFF) (SE/R) R-2SE R+2SE NANDIRURAL No education .166 With secondary education or higher .181 Nev~ married (in union) .325 Currently married (in union) .603 Knowing any contraceptive method .992 Knowing any modern method .992 Knowing source for any modem method .959 Ever used any contraceptive method .432 Currently using any method .239 Currently using a modem method .222 Currently using pill .033 Currently using IUD .012 Currently using injections .123 Currently using condom .012 Currently using female sterilisation .041 Currently using periodic abstinence .008 .022 403 164 1.177 .131 .123 .210 .025 403 164 1.292 .137 .132 .231 .020 403 164 .837 .060 .286 .364 .021 403 164 .844 .034 .562 .644 .006 243 99 1.052 .006 .980 1.000 .006 243 99 1.052 .006 .980 1.000 .013 243 99 1.053 .014 .932 .986 .044 243 99 1.394 .103 .343 .521 .030 243 99 1.087 .125 .179 .298 .031 243 99 1.145 .138 .161 .283 .006 243 99 .532 .185 .021 .045 .007 243 99 .926 .532 .000 .025 .020 243 99 .940 .161 .084 .163 .009 243 99 1.279 ,736 .000 .031 .013 243 99 1.027 ,319 .015 .067 .I]06 243 99 1.076 .759 .000 ,021 UASIN GISHU RURAL No education .152 With secondary education or higher .171 Never married (in union) .400 Currently married (in union) .527 Knowing any contraceptive method 1.000 Knowing any modem method .994 Knowing source for any modem method .934 Ever used any contraceptive method .410 Currently using any method .259 Currently using a modem method .211 Currently using pill .030 Currently using IUD .012 Currently using injections .078 Currently using condom .000 Currently using female sterilisation .090 Currently using periodic abstinence .048 .028 315 113 1.403 .187 .095 .209 .021 315 113 1.008 .125 .129 .214 .033 315 113 1.176 .081 .335 A65 .031 315 113 1.114 .060 .464 ,590 .000 166 60 NP .000 1.000 1,000 .005 166 60 .852 .005 .984 1.000 .620 166 60 1.030 .621 .894 ,974 .042 166 60 1.101 .103 .325 ,494 .037 166 60 1.097 .144 .184 ,334 .027 166 60 .857 .129 .156 ,265 .014 166 60 1.042 .460 .002 .058 .009 166 60 1.011 .713 .000 ,029 .021 166 60 .981 .262 .037 ,119 .000 166 60 NP .000 .000 ,000 .025 166 60 1.109 .274 .041 ,140 .021 166 60 1.284 .444 .005 ,091 BUNGOMARURAL No education .116 With secondary education or higher .270 Never married (in union) .283 Currently married (in union) .644 Knowing any contraceptive method .996 Knowing arty modem method .996 Knowing source for any modem method .957 Ever used any contraceptive method .486 Currently using any method .208 Currently using a modem method .169 Currently using pill .039 Currently using IUD .008 Currently using injections .082 Currently using condom .004 Currently using female sterilisation .035 Currently using periodic abstinence .012 .019 396 280 1.171 .162 .078 .154 .021 396 280 .961 .079 .227 ,313 .024 396 280 1.068 .086 .234 .331 .025 396 280 1.033 .039 .594 ,694 .004 255 180 1.002 .004 .988 1.000 .004 255 180 1.002 .004 .988 L000 .012 255 180 .934 .012 .933 .981 .023 255 180 .726 .047 .441 .532 .016 255 180 .645 .079 .175 .241 .023 255 180 .967 .135 .123 .214 .012 255 180 .945 .294 .016 .062 .005 255 180 .986 .696 .000 .019 .024 255 180 1.385 .290 .035 .130 .004 255 180 1.006 1.000 .000 .012 .010 255 180 .831 .273 .016 .055 .007 255 180 1.037 .596 .000 .026 NP = Not possible to calculate 204 Table B.17 Sampling errors - Kakamega rural~ Kenya 1993 Number of cases Standard Design Relative Confidence limits Value enor Unweighted Weighted effect error Variable (R) (SE) (N) (WN) (DEBT) (SE/R) R-2SE R+2SE No education .123 .016 With secondary education or higher .241 .038 Never married (in union) .294 .019 Currently married (in union) .651 .026 Knowing any contraceptive method .984 .006 Knowing any modern method .976 .008 Knowing so~ce for any modem method .940 .014 Ever used any contraceptive method .500 .028 Currently using any method .282 .037 Currently using a modem method .258 .035 Currently using pill .085 .028 Currently using 1UD .024 .009 Currently using injectiom .093 .017 Currently using condom .012 .004 Currently using female sterilisation .044 .015 Currently using periodic abstinence .016 .007 381 514 .969 .132 .091 .156 381 514 1.718 .156 .166 .317 381 514 .828 .066 .255 .333 381 514 1.074 .040 .598 .703 248 334 .758 .006 .972 .996 248 334 .822 ,008 .960 .992 248 334 .913 .015 .912 .967 248 334 .869 .055 .445 .555 248 334 1.296 .132 .208 .356 248 334 1.267 .137 .188 .329 248 334 1.563 .327 .629 .140 248 334 .960 .388 .005 .043 248 334 .948 .189 .058 .128 248 334 .576 .331 .004 .020 248 334 1.109 .328 .o15 .073 248 334 .901 .448 .002 .031 205 APPENDIX C DATA QUALITY TABLES APPENDIX C DATA QUALITY TABLES Table C.1 Household age distribution Single-year age distribution of the de facto household population by sex (weighted), Kenya 1993 Males Females Males Females Age Number Percent Number Percent Age Number Percent Number Percent 0 562 3.1 596 3.0 1.2 1 594 3.2 535 2.7 0,7 2 620 3.4 602 3.0 0,6 3 631 3.4 695 3.5 0.8 4 648 3.5 593 3.0 0.6 5 563 3.1 597 3.0 1.3 6 722 3.9 822 4.1 0.6 7 640 3.5 715 3.6 0.7 8 649 3.5 705 3.5 0,6 9 579 3.2 623 3.1 0.4 10 715 3.9 710 3.6 0.9 11 499 2.7 461 2.3 0.6 12 606 3.3 667 3.3 0.5 I3 593 3.2 693 3.5 0.5 14 508 2.8 627 3.1 0.4 15 500 2.7 337 1.7 0.8 16 423 2,3 421 2.1 0,2 17 383 2.1 390 2,0 0.5 18 398 2.2 408 2.0 0.3 19 312 1.7 311 1.6 0.3 20 346 1.9 496 2.5 0.5 21 234 1.3 291 1.5 0.6 22 240 1.3 307 1.5 0.4 23 265 1.4 328 1,6 0.3 24 201 1.1 314 1,6 0,3 25 238 1.3 316 1.6 0.9 26 198 1.1 260 1.3 0,2 27 185 1.0 233 1.2 0.3 28 230 1.3 309 1,6 0,3 29 177 1.0 228 1.1 0.2 30 325 1.8 398 2.0 0.5 31 127 0.7 181 0,9 0,2 32 234 1.3 212 1.1 0.2 33 170 0,9 167 0.8 0.3 34 172 0.9 161 0,8 0,2 2.4 35 221 223 1,1 36 126 181 0.9 37 105 124 0.6 38 152 183 0,9 39 114 100 0,5 40 236 226 1,1 41 106 92 0.5 42 124 114 0.6 43 116 140 0.7 44 78 103 0.5 45 168 149 0.7 46 105 86 0.4 47 88 72 0.4 48 85 77 0.4 49 82 62 0,3 50 154 202 1.0 51 39 106 0.5 52 82 155 0.8 53 64 130 0,7 54 58 119 0.6 55 95 106 0.5 56 104 105 0.5 57 66 87 0.4 58 64 82 0.4 59 63 43 0.2 60 158 174 0.9 61 43 51 0,3 62 57 87 0.4 63 60 71 0.4 64 38 48 0.2 65 87 98 0.5 66 33 36 0.2 67 33 40 0,2 68 50 64 0.3 69 37 26 0.1 70+ 444 423 2.1 Don't know/ missing 69 0.4 41 0.2 Total 18289 100,0 19935 100.0 Note: The de facto population includes all residents and nonresidents who slept in the household the night before the interview. 209 Table C.2 Age distribution of eligible and interviewed women Percent distribution in five-year age groups of the de facto household population of women aged 10-54 and of interviewed women aged 15-49, and percentage of eligible women who were interviewed (weighted), Kenya 1993 Household population Interviewed of women women age 15-49 Age Number Percent Number Percent Percentage interviewed (weighted) 10-14 3158 NA NA NA NA 15-19 1867 23.3 1724 22.9 92.3 20-24 1736 21.7 1667 22.1 96.0 25-29 1346 16.8 1258 16.7 93.4 30-34 1118 14.0 1067 14.2 95.4 35-39 811 10.1 755 10.0 93.0 40-44 675 8.4 639 8.5 94.7 45-49 446 5.6 424 5.6 95.1 50-54 712 NA NA NA NA 15-49 7999 100.0 7533 100.0 94.2 Note: The de facto population includes all residents and nonresidents who slept in the household the night before interview. NA = Not applicable 210 Table C.3 Completeness of reporting Percentage of observations missing information for selected demographic and health questions (weighted), Kenya 1993 Percentage Number missing of Subject Reference group information cases Birth date Births in last 15 years Month only 8.3 17549 Month and year 0.3 17549 Age at death 0.5 1652 Age/date at first union I 2.6 5260 Respondent's education 0.0 7540 Child's size at birth 1.0 2839 Anthropometry Height missing 10.7 5643 Weight missing 8.8 5643 Height and weight missing 10.8 5643 Diarrhoea in last 2 weeks Deaths to births in last 15 years Ever-married women All women Births in last 59 months Living children 0-59 months Living children age 0-59 months 2.4 5643 IBoth year and age missing 211 Table C.4 Births by calendar year since birth Distr ibution of births by calendar years since birth for l iving (L), dead (D), and all (T) chi ldren, according to report ing completeness, sex ratio at birth, arid ratio of births by calendar year, Kenya 1993 Percentage with Sex ratio Number of bi~hs complete bil~h date t at birth 2 Calendar ratio 3 Male Female Year L D T L D T L D T L D T L D T L D T 93 397 23 421 100.0 91.7 99.5 77.0 162.8 80.3 NA NA NA 173 15 187 225 9 233 92 1205 85 1289 98.9 95.3 98.6 108.9 71.0 105.9 164.3 121.0 160.5 628 35 663 577 50 626 91 1069 117 1186 98.3 96.9 98.2 100.2 75.8 97.5 89.5 125.8 92.1 535 50 585 534 67 601 90 1185 101 1286 97.1 92.4 96.7 103.0 110.1 103.5 106.1 91.4 104.8 601 53 654 584 48 632 89 1163 104 1267 96.0 77.1 94.4 91.5 129.5 94.1 103.5 108.8 103.9 556 59 614 608 45 653 88 1064 90 1154 97.1 82.8 95.9 107.0 95.3 106.0 87.8 73.2 86.5 550 44 594 514 46 560 87 1259 143 1402 94.0 80.2 92.6 89.3 129.4 92.7 110.3 127.8 111.9 594 81 675 665 62 728 86 1220 133 1353 91.8 85.3 91.1 86.2 115.2 88.7 107.1 101.5 106.5 565 71 636 655 62 717 85 1019 120 1139 91.7 80.5 90.5 101.0 108.6 101.8 85.9 93.0 86.6 512 62 574 507 57 564 84 1152 124 1276 87.3 85.5 87.1 88.6 115.4 90.9 NA NA NA 541 67 608 611 58 668 89-93 5019 430 5449 97.8 90.5 97.2 98.7 97.0 98.5 NA NA NA 2493 212 2705 2526 218 2745 84-88 5714 610 6324 92.3 82.8 91.4 93.5 113.8 95.3 NA NA NA 2762 325 3086 2952 286 3238 79-83 4619 547 5166 88.4 78.6 87.4 96.8 111.6 98.2 NA NA NA 2272 288 2560 2348 259 2606 74-78 3095 444 3539 86.0 70.2 84.0 99.3 113.0 101.0 NA NA NA 1542 236 1778 1552 208 1761 <74 2798 605 3403 82.7 68.4 80.2 96.3 108.5 98.4 NA NA NA 1373 315 1688 1425 290 1715 All 21245 2637 23881 90.6 77.8 89.2 96.6 109.1 97.9 NA NA NA 10441 1376 11817 10804 1261 12065 NA = Not applicable 1Both year and month of biv.h given 2(B,,/B,)* 100, where B= and Bf are the numbers of male and female births, respectively 3[2B./(B,a+B,.t)]* 100 where B, is the number of births in calendar year x 212 Table C.5 Reporting of age at death in days Distribution of reported deaths under 1 month of age by age at death in days and the percentage of neonatal deaths reported to occur at ages 0-6 days, for five-year periods of birth preceding the survey, Kenya 1993 Number of years preceding the survey Age at death Total (in days) 0-4 5-9 10-14 15-19 0-19 <1 55 60 60 37 212 1 33 32 38 17 120 2 12 26 12 10 59 3 8 8 13 8 38 4 5 2 3 3 13 5 3 1 3 3 10 6 1 3 1 4 10 7 15 17 19 12 63 8 0 1 1 0 3 9 0 2 2 1 6 10 1 3 2 1 7 11 0 1 1 0 2 13 1 0 0 0 1 14 15 11 11 5 42 15 0 1 0 1 2 17 0 1 0 0 1 18 1 0 0 0 1 20 0 1 l 1 3 21 2 2 1 4 10 22 0 0 0 1 1 23 1 0 0 0 1 26 2 2 0 0 4 29 0 0 0 2 2 30 3 4 2 0 9 Missing 0 2 0 0 2 Total 0-30 159 179 171 111 620 Percent early neonatal 73.5 73.5 76.3 73.9 74.4 1(0-6 days/0-30 days) * 100 213 Table C.6 Reporting of age at death in months Distribution of reported deaths under 2 years of age by age at death in months and the percentage of infant deaths reported to occur at ages under one month, for five-year periods of birth preceding the survey, Kenya 1993 Number of years preceding the survey Age at death Total (in months) 0-4 5-9 10-14 15-19 0-19 <1 a 159 180 171 111 622 1 19 14 23 9 65 2 25 24 15 6 70 3 24 28 30 17 99 4 17 25 16 15 73 5 16 26 13 17 72 6 34 26 22 17 99 7 17 14 11 17 59 8 15 34 21 5 75 9 13 18 10 11 52 10 3 4 5 6 17 11 12 6 8 2 27 12 29 31 36 31 126 13 5 4 3 4 16 14 4 3 3 8 18 15 6 3 3 5 16 16 5 3 4 2 13 17 1 3 4 1 9 18 11 11 13 17 52 19 1 0 0 1 2 20 1 4 2 0 7 21 1 5 3 1 10 22 2 1 0 0 4 24 0 0 1 1 2 Missing I 1 0 0 2 1 year 1 0 2 1 4 Total 0-11 355 399 344 233 1331 Percent neonatal b 44.8 45.3 49.8 47.7 46.7 alncludes deaths under 1 month reported in days b(Under 1 month/under 1 year) * 100 214 APPENDIX D PERSONS INVOLVED IN THE KENYA 1993 DHS APPENDIX D PERSONS INVOLVED IN THE KENYA 1993 DHS Administrative Amb. S.B.A. Bunut, Director NCPD Jotham A. Mwaniki, Director CBS Pius P. KaUaa, Director CBS National Council for Population and Development Margaret K. Chemengich Kimeli Chepsiror Michael K.M. Mbayah Charles Oisebe Peter W. Thumbi Paul M.L. Kizito Timothy Takona Kagwirla Kioga Central Bureau of Statlstics Zachary E. Gichohi R.S. Ahluwalia S. O. Akach J. Owuor Questionnaire Design Task Force J. Liku J. Wakajuma J. Barasa L.N. Thaim J. Kekovole F. Njui Field Officers Margaret F. Mukabana Emmanuel S. Adienge George A. Kichamu Ben O. Osindo Nelson N. Nyaga Churchil O. Ndire Nzioka K. Munge Edward K. Kisaka District Statistical Officers P.V. Omukule A.N. Njoroge J.C. Amoi C.N. Omolo A.V. Mulewa N. Mugo F.C. Ochollo A.O. Sunga R.T. Kiai S.E. Onsare G.O. Ogwang A.M. Nyamari S.K. Ndungu J.N. Gichimu F.K. Ng'ang'a F.M. Kamau M. Musyoka J.O. Opiyo J.A. Were R.M. Nzuli P.M. Mutual O. Okuthe R.C. Buluma G.O. Otieno D. Okumu Z. Muganzi Willie M.O. Nyambati Peter Reriani Joseph Omagwa Alex Juma P.R. Muriithi E.K. Gitahi S.A. Ndili S.R. Nthenge D. Ngesa C.O. Obiero J.O. Odero R.K. Tanui M.O. Ogot J.E. Owour L.A. Lugadim 217 Field Staff Kalenjin Charlotte Chelangat 0dilia Masionge Margaret Kinutai Selly Cbebon Magdalene Chepkirwok Lilian Langat Betsy Kiptanui Peter K. Keter Ephantus Imwara Chep Ngeno Gloria Florence Kaptich Caroline Chepkoech Christine Kepkemei Anne Jepkemboi Dominic Kipkoech Kamba Agnes Kisese Winfred Nzioki Lucy M. Mangati Jane N. Josiah Agnes Mutuo Kimatu Esther Nzisa Mutua Secret Mathii Siku Jane Mwikaii Ndambuki Robert M. Musii Data Processing Staff Chilson S. Wamoja Shamton W. Kimenyi Teresia Wanjiru Jairus M. Ounza Transport&Finance N.K. Waweru Moses Webuye Morris Oruoch Kikuyu Jane W. Njuguna Margaret W. Kung'u Jane N. Kamau Mary W. Ndegwa Mercy W. Njeri Jane H.W. Muriuki Mercy W. Njoka Martha G. Gathiru Violet W. Karongo Samuel M. Kamunya Ruth N. Waweru Faith W. Nderitu Nancy M. Wamae Beatrice W. Njiru Mary W. Kanyingi Juliana Githui Bilha W. Waweru John M. Kariuki Meru/Embu Agnes Rinyiru Jane Ntinyari Purity Gacheri Loise N. Mutea Clara Guantai Pamela Nkirote Timothy Kanake Mijikenda Jane H. Lumwe Eddah R. Lumbukeni Lizzie M. Ngao Rachel F. Mwasindo Tabia N. Katana Constance D. Mnazi Gladys N. Chamli John Mwongolo Kisii Alice Omariba Jannes Mandieka Doreen Arita Florence Nyakundi Margaret Kwamboka Joyce Mayiyette Wilchester Ondimu Zebedeo Makori Luo Florine Ndire Jane P. Okoth Dorothy A. Otieno Naomi A. Were Carolyne A. Yaia Jael Achieng Roselyne Midega Noah Ochiel Charles N. Momanyi Grace Gitonga Andrew Ndirangu Drake Chelangat Beth Wambari Festo Mukolwe Peter Kekovole Swahili Deborah Iminza Caroline Njem Emily Kandi Grace M. Mwema Elizabeth Mwanzia Ruwaida A. Mohammed Patricia Mwakondi Henry Mchapo Masai Eva Nyaga Catherine M. Nooseli Rebecca P. Leseketeti Peter L. Lengewa Luhya Beverly Opwora Salma N. Musa Madline ljayi Everline Muyome Jane Osindo Violete Kahomhi Damaris K. Sianzu Joan Liula Joan Musimbi John Lusinde 218 APPENDIX E QUESTIONNAIRES 25 NOV/92 NAT IONAL COUNCIL FOR POPULAT ION AND DEVELOPMENT CONFIDENTIAL CENTRAL BUREAU OF STAT IST ICS Data used KENYA DEMOGRAPHIC AND HEALTH SURVEY 2 for research HOUSEHOLD SCHEDULE purposes on ly IDENTIF ICAT ION PROVINCE D ISTR ICT LOCATION/TOWN SUBLOCATION/WARD NASSEP CLUSTER NUMBER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . KDHS CLUSTER NUMBER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . HOUSEHOLD NUMBER . . . . . . . . . . . . . . . . NA IROBI /MOMBASA=I , SMALL CITY=2, TOWN=3, RURAL=4. . . NAME OF HOUSEHOLD HEAD HOUSEHOLD SELECTED FOR MALE SURVEY? (YES=l, NO-2) INTERVIEWER VIS ITS DATE INTERVIEWER'S NAME RESULT * NEXT VISIT: DATE T IME 1 2 3 * RESULT CODES: 1 COMPLETED 2 NO HOUSEHOLD MEMBER AT HOME OR NO COMPETENT RESPONDENT AT HOME AT T IME OF V IS IT 3 ENT IRE HOUSEHOLD ABSENT FOR EXTENDED PERIOD 4 POSTPONED 5 REFUSED 6 DWELL ING VACANT OR ADDRESS NOT A DWELL ING 7 DWELL ING DESTROYED 8 DWELL ING NOT FOUND 9 OTHER (SPECIFY) F INAL V IS IT DAY MONTH YEAR NAME I II RESULT NO.OF V IS ITS I' TOTAL IN . ~ HOUSEHOLDI L 'i TOTAL ~ WOMEN 15-49 MEN 20-54~- - - - ] L INE NO.I----T----- ] oF aEsp I 1 l TO HOUSE HOLD SCHEDULE LANGUAGE OF QUEST IONNAIRE: ENGL ISH NAME DATE F IELD EDITED BY OFF ICE EDITED BY KEYED BY I l l ° KEYED BY 223 EN HH NOW we would Like nmme tn for lmt i~ about the people Who usuatL (1) . l i E USUAL RESIDENTS AND iO. VISITORS i PLease give i the i r~mm| of the pecler~ j Me usua l ly Live (n j your household and i guests of the house- j ho ld tdto stayed here : L is t h (ght , stort~ng ] u i th the head of the household. (2) 01 02 03 04 05 06 07 0~ 09 10 RELATIONSHIP RESIDENCIE SEX TO HEAD OF NOU~I~I.O* a Mat is Does Did the reLot io~ip usua l ly ste~p of (II/~E) Live here or to the head here? of the he,toehold? (3) IS (NAME) ( kiN4E ] (NNqE) ro le Lost fem0te night1 ? (4) (5) (6) YES NO YES NO M F I 2 1 2 I 2 • i 1 Z 1 2 1 2 1 Z I 2 1 2 • m 1 2 2 2 | i 1 2 2 2 i i 1 2 2 2 1 2 2 2 1 2 i 2 2 I 2 i 2 2 J 1 2 i 2 2 i s (NMIE)? ever (7) (8) IN YEARS YES NO - - -7 - - - -7 . 1 2 - - - - -~ . 1 2 HOUSEHOLD SCHEDULE Live In your household or M/no i re stuy(ng w|th you ~ov. AGE EDUCATION PARENTAL SURVIVt)ISNIP SSID IItESIDENCE FOR PERSOMS LESS TNNI 15 ~r.ARS IF AC, F.D 6 YEARS OR OLDER | i No~ oLd Has IF ATTENOED SCHiX~ Is IF ALIVE Is e l f ALIVE . / M i l l * . . l i d l | . I ] I_ 1 Z - ~ . 1 2 (lIME) , Uhat is IF the h tg l~t LESS to leveL of school ? school 25 (NAME) YEARS attended? , Mot Is the highest Is standard or (NA~) form (NNqE) s t i l l in completed school? at that LeveL?** (9) (10) LEVEL FORM YES NO , 1 , THAN (ILq~)'$ natura l mother ol ive? (11) ( I INE) ' s rmtt ra( mother l i ve in th i s house- hold? IF YES: Vnet i s ~er rmle? RECORD MOTHER f S LINE NUMBER (12) (NARE)'s ~tur lL fether o l ive? (13) natural father Live in this ] h~- hold? IF YEs: uhet is h is nee1 RECORD FATNER't LINE I~ER (14) 1 2 2 2 2 YES NO DK i 28 '~T- - - 1 ' i i 2 8 ' [ ~ 1 ' L i 28 ' [ ' - '~ ' i t 'E l l - - l - -1 ,,i--[-11 i 2 0 . ~ ' ~ ] . 2 8 ~ " ~ 1 EL IG I - B IL ITY WOMEN CIRCLE Li l le ~ R OF ALL WOIqEN 15-49 (15) NUSINID ELLGI" LINE IIILITY IItJiPl~R MEN | mITE CIRCLE Li l le L IE BJmER ~ R OF THE OF ALL NEll OF EACH ELIGIBLE 20-54 WOFAN ( IF HOUSE- MtlTE O0 HOLD IF NOT FALLS MARRIED IN MALE Oil IF SNqPLE ) HUSlUkNO NOT IN HOUSE - HOLD. (16) (17) YES NO DK ' ' . f - - [ - - ] . 01 . I - - [ - -1 . 01 ~ ' l - - F1 . 03 . i - - I - -1 . O~ '~ I - - l - -1 . ~ . i - - [ - - ] . , , r- l -1 = , , I - i - i - Z [ - I - l i - EIIG HN 2 HOUSEHOLD SCHEDULE COMTIMU[D i(1) 1 (2) I (3) ( ' ) I ( ' ) I ( s ) I (7) (8) I ( ' ) I (lO) I YES NO YES NO fl F IN YEARS YES NO LEVEL FOR4 YES NO | , ; ; : ." : : : : , , , , , , | l I I , , ! ' .- : .- . ! , I ! , I I , , I , | , , ! ' ] , I I , , | n I | , , I . . . . 18 2 2 2 2 2 , l q I ' l l I l l I ] , I I TICK HERE iF CONTINUATION SHEET USED I---7 TOTAL NLII4R~R OF ~LI~IRiF (11) I (12) I (13) YES NO OK ] YES NO DR 2 8 ~-~i 2 8 I I 28 ~ 28 n n 2 8 [ ~ 2 8 I J 2 8 ~ 2 8 EE .r -tJ ,8 2 8 ~ ' ~ 2 8 n n 2 8 ~ 2 8 , n 2 8 ~ 2 8 i i TOTAL HUHBER OF ELIGIBLE WOREN[--[--~ Just to make sure that I have a complete l ist in<j: 1) Are there any other persons such as sml t ch i ldren or infants that ue have r~t l isted? In addit ion; are there any other peopLe uho may" not be members of your family, such as domestic servants, Lodgers or fr iends vho usuatLy Live here? Do you have any" guests or t rap | ra fT v i s i to rs staying here, or anyone etse ~ho slept here test night? 2) 3) i (14) , IT M-7 TOTAL N~BER OF ELIGIBLE ~N YES ~ r ENTER EACH IN TABLE YES N J- ESTER EACH IR TABLE YES ~-~ r ENTER EACH IN TABLE I ( is) I (16) I (17) • 11 11 m m 12 ~ 12 13 ~ 13 I • | 14 ~ 14 • 15 . ~ . 13 .16 16 17 IF-- I 1, | i 18 18 i .1 , 1' 20 ~ 20 i I I No [ ] No [ ] .o [ ] RELATIONSHIP TO HEAD OF HOUSEHOLD: 01= HEAD O51, GRANDCHILD 02# WIFE OR HUSBAND 06= PARENT Q3: SON OR DAUGHTER OT = PADENT'IM'LA~ 04= SON OR DAUGHTER-IN|LAW C6: EROTHER OR SISTER O: NURSERY 09= OTHER RELATIVE 1= P~qlP~qY 10" N)OPTED/FOSTER CHILD 21 SECONDARy 11z IlOT RELATED 3 z UNIVERSITY DK 8z OK 00= LESS THEN 1 TEAR CCflPLET[D 98= DON'T s~* These questions refer to the b ioL~icaL parents of the ch i ld , record O0 i f parent not m r of household. EM6 NH 3 NO. OOESTIORS AND FILTERS 18 What i s the gOurCe of uster your household uses fo r h ind~lh l r~ and d | l lh~ld l lng fo r most of the year? CODING CATEGORIES ~GO TO PIPED UATER J PIPED INTO HOOSE/CONPOUND/PLOT.11 L-'~O PUBLIC TAP . . . . . . . . . . . . . . . . . . . . . 12 | WELL UATER I WELL WITH PUMP . . . . . . . . . . . . . . . . . Zl WELL WITHOOT PUI4P . . . . . . . . . . . . . . 22 SURFACE WATER LAKE, POND . . . . . . . . . . . . . . . . . . . . . 31 RIVER/STREAM . . . . . . . . . . . . . . . . . . . 32 RAINWATER . . . . . . . . . . . . . . . . . . . . . . . 41 ¢20 OTHER 51 | 19 | Bow lone does I t take to 9o there, get uater , I and ccem beck? I :OTE, . I I l l I PREMISES . . . . . . . . . . . . . . . . . . . 996 20 I PoD= your houBehotd get d r ink ing umter I YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 r22 I f ro= th i s sm source? I NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 | 21 Bhat | l the IoUrc l of d r ink ing ~ater fo r mmber l of your household? PIPED WATER PIPED INTO HOOSE/CQMPOUNO/PLOT.11 PUBLIC TAP . . . . . . . . . . . . . . . . . . . . . 12 WELL WATER WELL WITH PUMP . . . . . . . . . . . . . . . . . 21 NELL UITH(~T PUMP . . . . . . . . . . . . . . 22 SURFACE WATER LAKE, POND . . . . . . . . . . . . . . . . . . . . . 31 R%VER/STREAM . . . . . . . . . . . . . . . . . . . 32 RAINWATER . . . . . . . . . . . . . . . . . . . . . . . 41 OTHER 51 221 What k ind of to i le t fac i l i ty does your household have? I FLUSH TOILET OWN FLUSH TOILET . . . . . . . . . . . . . . . 11 SHARED FLUSH TOILET . . . . . . . . . . . . 12 PIT TO%LET/LATRINE TRADitiONAL PIT TOILET . . . . . . . . . 21 VENTILATED IMPROVED PIT TOILET,22 NO FACILITY/RUSH/FIELD . . . . . . . . . . 31 OTHER 41 23 DOes your household have: ELect r i c i ty? A radio? A te lev i s ion? A re f r igerator? I YES '0 i ELECTRICITY . . . . . . . . . . . . . . . . 1 2 RADIO . . . . . . . . . . . . . . . . . . . . . . 1 2 TELEVIS%ON . . . . . . . . . . . . . . . . . 1 2 REFRIGERATOR . . . . . . . . . . . . . . . 1 2 24 25 26 27 28 go~ mmny rooms in your household are used for s leeping? MAIN MATERIAL OF THE FLOOR. RECORD OBSERVATioN. NAIN MATERIAL OF THE WALL. REC(~D OBSERVATioN. MAIN MATERIAL OF THE ROOF. RECCIID ONSERVATIUM. Does Imy m r of your household own: A b icyc le? Lind? Cat t le , goats or sheep? Cash crops such as team, cof fee, cotton? . I I I EARTH/DUNG . . . . . . . . . . . . . . . . . . . . 11 RUOIMENTARY FLOOR WOO0 PLANKS . . . . . . . . . . . . . . . . . . . 21 F]NISHED FLO0~ PARQUET OR POLISHED EE~O0 . . . . . . ]1 VINYL/LINOLEUM/ASPHALT STRIPS.32 CERAMIC TILES . . . . . . . . . . . . . . . . . 33 CEMENT . . . . . . . . . . . . . . . . . . . . . . . . OTHER ~1 HOO/DUNG . . . . . . . . . . . . . . . . . . . . . . 11 RUDIMENTARY WALLS L,~OO/T]MBER . . . . . . . . . . . . . . . . . . . Zl FINISHED WALLS BRICKS . . . . . . . . . . . . . . . . . . . . . . . . 31 CEMENT/STONE BLOCKS . . . . . . . . . . . 32 OTHER 41 GRASS/THATCH . . . . . . . . . . . . . . . . . . 11 RUOIMENTARY ROOF CORRUGATED IRON (NABATI) . . . . . . 21 FINISHED ROOF TILES . . . . . . . . . . . . . . . . . . . . . . . . . ]1 OTHER 41 YES NO BICYCLE . . . . . . . . . . . . . . . . . . . . 1 2 LAND . . . . . . . . . . . . . . . . . . . . . . . 1 2 CATTLE, GOATS, ON SHEEP.1 2 CASH CROPS . . . . . . . . . . . . . . . . . 1 2 ENG HB 4 226 NATIONAL COUNCIL FOR POPULAT ION AND DEVELOPMENT CENTRAL BUREAU OF STAT IST ICS KENYA DEMOGRAPHIC AND HEALTH SURVEY 2 WOMAN'S QUEST IONNAIRE IDENTIF ICAT ION CONFIDENTIAL Data used for research purposes on ly PROVINCE D ISTR ICT LOCATION/TOWN SUBLOCATION/WARD NASSEP CLUSTER NUMBER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . KDHS CLUSTER NUMBER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . HOUSEHOLD NUMBER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . NA IROBI /MOMBASA=I , SMALL CITY=2, TOWN=3, RURAL=4. . . NAME OF HOUSEHOLD HEAD NAME AND L INE NUMBER OF WOMAN INTERVIEWER V IS ITS i 2 3 DATE INTERVIEWER'S NAME F INAL V IS IT DAY MONTH YEAR NAME RESULT * NEXT VIS IT: DATE T IME * RESULT CODES: RESULT 1 COMPLETED 2 NOT AT HOME 3 POSTPONED iiiiiiiiii!i!iiiiiii!ii!!!! TOTAL NUMBER[----] ~!!!!~!!!!~!~i~!~!~!~!!! OF V IS ITS i i 4 REFUSED 5 PARTLY COMPLETED 6 INCAPACITATED 7 OTHER (SPECIFY) LANGUAGE OF QUEST IONNAIRE: ENGL ISH LANGUAGE USED IN INTERVIEW** . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . RESPONDENT'S LOCAL LANGUAGE** . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TRANSLATOR USED (NOT AT ALL=I ; SOMETIMES=2; ALL THE TIME=3) . ** LANGUAGE CODES: 01 KALENJ IN 05 LUHYA 09 K ISWAHIL I 02 KAMBA 06 LUO i0 ENGL ISH 03 K IKUYU 07 MERU/EMBU i i OTHER 04 K IS I I 08 MI J IKENDA i 0 NAME DATE F IELD EDITED BY OFF ICE EDITED BY KEYED BY KEYED BY 227 SECTIOR 1. RESPONDENT*S 6ACKGROUNO SKIP .0. m QUESTIONS AND FILTERS m CODING CATEGORIES m TO IIECQIID TIlE TILE. I 102 I F i r s t I would Like to ask sew4 questions about you and I yo~ hcxdeehotd. For BOOt of the time unt i l you were 12 yelrs old, d id you Llve In Nllrobi or NOmbasl, in another c i ty or tmm or In the countryside? NAIROBI/W]MSASA . . . . . . . . . . . . . . . . . I OTHER CITY/TOWN . . . . . . . . . . . . . . . . . 2 COUNTRYSIDE . . . . . . . . . . . . . . . . . . . . . ] I F ~ 103 I Hou tong have you ~ Living continuously in (NAME OF YEARS . . . . . . . . . . . . . . . . . . . . I I ~ I gUOLOCATIGR t TOWN OR CITY)? I I l ALWAYS . . . . . . . . . . . . . . . . . . . . . . . . . VISITOR . . . . . . . . . . . . . . . . . . . . . . . . 1GS lO& I Just before you moved here, did you Live in Nairobi or I HAIRORI/NONSASA . . . . . . . . . . . . . . . . . 1 I I4~gb~s, in another c i ty or town or in The countryside? I OTHER CITY/TOWN . . . . . . . . . . . . . . . . . 2 COUHTRYSIDE . . . . . . . . . . . . . . . . . . . . . 5 1G'J In MI I t Ionth mnd year were you born? HONTH . . . . . . . . . . . . . . . . . . . . DOES NOT KNOW MONTH . . . . . . . . . . . . 98 YEAR . . . . . . . . . . . . . . . . . . . . . [ ~ DOES NOT KMO~/ YEAR . . . . . . . . . . . . . 98 AGE %N CORPLETED YEARS. CONP/LRE ~ CORRECT 105 AND/OR 106 IF INCONSISTENT, 107 Hive yo*J ever stteflded school? I YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 I I | HO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 L l l1 1 -+*+ **°+°°*+**+ I+ . I primery, sKonCk|ry, or un ivers i ty? SECONDARY . . . . . . . . . . . . . . . . . . . . . . . 2 UNIVERSITY . . . . . . . . . . . . . . . . . . . . . . ] +1 -,+,++-**.,.++++.,,,ouo+.,o,+*,+ I . 109A ~l l t is the highest cer t i f i ca te you obtained? NO CERTIFICATE . . . . . . . . . . . . . . . . . O0 CEE (Std. 4) . . . . . . . . . . . . . . . . . . . 01 CPE/KPE (Std,7) . . . . . . . . . . . . . . . . 02 KAPE/KCPE (Std, 8) . . . . . . . . . . . . . O] KJSE (Form 2) . . . . . . . . . . . . . . . . . . 04 0 LEVEL . . . . . . . . . . . . . . . . . . . . . . . . 05 KCSE . . . . . . . . . . . . . . . . . . . . . . . . . . . 06 A LEVEL . . . . . . . . . . . . . . . . . . . . . . . . 07 ANY UNIVERSZTY DEGREE . . . . . . . . . . 08 OTHER 09 (SPECIFY) 111 Can you reed a Letter or neuspaper in any Language eas i ly , u i th d i f f i cu l ty , or not at s i t ? 112 I Do y~Jusuat ty reed • newspaper or magazine at Least J YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 I ~e • ueek? I NO . . . . . . . . . . . . . . . . . . . . . . . . . . * . . .2 EMG ~ 2 / EASILY . . . . . . . . . . . . . . . . . . . . . . . . . . 1 | WITH DIFFICULTY . . . . . . . . . . . . . . . . . 2 L NOT AT ALL . . . . . . . . . . . . . . . . . . . . . . 3--- 11] 228 RO i 113 I QUESTIONS AND FILTERS DO yo~ uauaLLy Listen to • radio at Least once a week? I CODING CATEGORIES ~ GO TO I YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 I NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2 I I I 114 i Do you usuaLLy witch te tev is im at Least o~e a week? I YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 I I I I NO . . . . . . . . . . . . . . . . . o . .++*. . . . . . °2 I I cAT °Lc . I PROTESTANT/OTHER CHRISTIAN . . . . . . 2 NUSLIN . . . . . . . . . . . . . . . . . . . . . . . . . . 3 NO RELLGION . . . . . . . . . . . . . . . . . . . . . 4 OTHER 5 (SPECIFY) 116 Hi+at Is your ethnic Group/tribe? KALERJIN . . . . . . . . . . . . . . . . . . . . . . . 01 KAMBA . . . . . . . . . . . . . . . . . . . . . . . . . . 02 KIKUYU . . . . . . . . . . . . . . . . . . . . . . . . . 03 KIS I I . . . . . . . . . . . . . . . . . . . . . . . . . . 04 LUHYA . . . . . . . . . . . . . . . . . . . . . . . . . . 05 LUO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 06 NERU/EMBU . . . . . . . . . . . . . . . . . . . . . . 07 MIJ[KENOA/SWAHILI . . . . . . . . . . . . . . 08 SONALI . . . . . . . . . . . . . . . . . . . . . . . . . 09 TAITA/TAVETA . . . . . . . . . . . . . . . . . . . 10 OTHER 11 (SPECIFY) i 116AI 118 DO you belong tO ~ uomenis organisat ion or group? YES . . . . . . . . . . ** . . . . . . . . . . . . . . . * .1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 CHECK Q.& IN THE HOUSEHOLD OUESTIONNA|RE THE WCI(AN LNTERVIEWED LS NOT A USUAL RESIDENT I THE WOI4AN [NTERV%EUED IS A USUAL RESIOEHT I I r-1 Ram I vm*JLd Like to ask about the place in which you usuaLLy Live. DO you usuaLLy Live in Nairobi or Manta•s•, in a small c i ty , in • town or in the countryside? RA I ROB I/ROHBASA . . . . . . . . . . . . . . . . . 1 SHALL CITY . . . . . . . . . . . . . . . . . . . . . . 2 TO, JR . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 COUNTRYSIDE . . . . . . . . . . . . . . . . . . . . . 4 ~201 119 In Nhich d i s t r i c t is that Located? i~RITE MANE OF OISTRICT CLEARLY. DISTRICT 120 N~ I ~td Like to ask a l :~t the ho~ehotd in ~hich y~J usuaLLy Live. What is the source of aater your household uses for hard,washing arKd dishwashing for toast of the year? PIPED WATER PIPED INTO RPAJSE/CORPOUND/PLOT.11 PUBLIC TAP . . . . . . . . . . . . . . . . . . . . . 12 UELL UAFER WELL WITH pU/4p . . . . . . . . . . . . . . . . . Zl WELL WITHOUT PUttP . . . . . . . . . . . . . . 22 SURFACE HATER LAKE, POND . . . . . . . . . . . . . . . . . . . . . 51 RIVER/STREAN . . . . . . . . . . . . . . . . . . . 32 RAINWATER . . . . . . . . . . . . . . . . . . . . . . . 41 OTHER 51 I ~12Z ~122 I HOW Long does i t take to go there, get water, MINUTES . . . . . . . . . . . . . . 121 and cam I~ck? OH PREMISES . . . . . . . . . . . . . . . . . . . 996 I I ' 122 Does your household set dr ink ing water YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 ~124 from th i s sm source? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 | ERC WON 3 229 NO. GAIESTIORS AND FILTERS 123 What Is the laure l of d r ink iN eater for m r s of your household? COOING CATEGORIES PIPED MATER PIPED INTO HOUSE/CONPOUi~/PLOT.11 PUBLIC TAP . . . . . . . . . . . . . . . . . . . . . 12 WELL MATER WELL UITH ~ . . . . . . . . . . . . . . . . . 21 NELL WITHOUT PUMP . . . . . . . . . . . . . . 22 SURFACE WATER LAKE, P(~4D . . . . . . . . . . . . . . . . . . . . . 31 RIVER/STREAM . . . . . . . . . . . . . . . . . . . 32 RAINWATER . . . . . . . . . . . . . . . . . . . . . . . 41 OTHER 51 ~0 TO 124 What kind of to i le t f l c i [ i ty does your hounhotd have? FLUSH TOILET OWN FLUSH TOILET . . . . . . . . . . . . . . . 11 SHARED FLUSH TOILET . . . . . . . . . . . . 12 PIT TOILET/LATRIBE TRADITIONAL PIT TOILET . . . . . . . . . 21 VENTILATED IMPROVED PIT TOILET.22 N0 FACILITY/BUSH/FIELD . . . . . . . . . . ]1 OTHER 41 125 DOeS your ho~z4ehoLd huva: YES NO E leCtr ic i tY? ELECTR] CITY . . . . . . . . . . . . . . . . 1 2 A r"41o? RADIO . . . . . . . . . . . . . . . . . . . . . . 1 2 A tetevIBion? TELEVISION . . . . . . . . . . . . . . . . . 1 2 A re f r igerator? REFRIGERATOR . . . . . . . . . . . . . . . 1 2 127 CouLd y~a describe the mein mater ia l of the f loor of your h~te? NATURAL FLC4~R EARTH/DUNG . . . . . . . . . . . . . . . . . . . . 11 RUDIMENTARY FLOOR 5/000 PLANKS . . . . . . . . . . . . . . . . . . . 21 FZHISHED FLOOR PARQUET OR POLISHED ~ . . . . . . 31 VINYL/LIBOLEUI4/ASPHALT STRIPS.32 CERAMIC TILES . . . . . . . . . . . . . . . . . 33 CEMENT . . . . . . . . . . . . . . . . . . . . . . . . 34 UTHER 41 I 127A| CouLd you describe the man mater ia l of the ~aIIs ( of your holm? NATURAL WALLS MUD/DUNG . . . . . . . . . . . . . . . . . . . . . . 11 RUDIMENTARY WALLS MOCO/TINBER . . . . . . . . . . . . . . . . . . . 21 fINISHED MALLS BRICKS . . . . . . . . . . . . . . . . . . . . . . . . 31 CEMENT/STORE BLOCKS . . . . . . . . . . . 32 UTHER 41 I IZ?B I CouLd you describe the main material of the roof I of your home? NATURAL ROOF GRASS/THATCH . . . . . . . . . . . . . . . . . . 11 RUDIMENTARY RCOF CORRUGATED IRON (MABATI) . . . . . . 21 FINISHED ROOF TILES . . . . . . . . . . . . . . . . . . . . . . . . . 31 OTHER 41 128 I DO4I any m r of your household ow~: A bl cycle? Land? CattLe. goats or sheep? Caah ¢rol~ such ms tal l , coffee or cotton? YES MO BICYCLE . . . . . . . . . . . . . . . . . . . . 1 2 LAND . . . . . . . . . . . . . . . . . . . . . . . ] 2 CATTLE, GOATS, OR SHEEP.,1 2 CASH CROPS . . . . . . . . . . . . . . . . . 1 2 ENG ~ k 230 SECTION 2. REPROOUCTION NO. I QUESTIONS AND FILTERS m 201 I Nou I u(xald l i ke to I l k about a l l the b i r ths you have I hod dur ing your l i fe . Have you ever Oivett b i r th? SKIP I COOING CATEGORIES I TO ,0,1 I " ' . 't NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 r204 01 w i . And how warty daughters l i ve u i th you? DAUGHTERS AT HOME . . . . . . . . IF NOIdE RECORD I 00% 2~ I oo ,., h .v . .oy - - ° r daughte'~s '° " " You h"ve IYES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 I given b i r th who mre mltva but do not l i ve u i th you? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 r206 205 HOU ~y SOnS are aL ive but do not Live ~ i th you? ~ SONS ELSEt/HERE . . . . . . . . . . . And hou many daughters are a l i ve b~t do not Live wi th I you? DAUGHTERS ELSEUHERE . . . . . . IF IIG~E RECORD '00 ' . 2O6 YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . t Sometimes i t happens that ch i ld ren d ie . I t may be very pa in fu l to ta lk WQout and 1 am sorry to ask you about pa in fu l lmor les~ but i t i s important to Bet the r ight In fo r l t ion . Have you ever g iven b i r th to a bay or g i r t ~ho was barn a l i ve but la ter died? IF NO, PRORE: Any baby Nho c r ied or showed any s ign of l i fe but ~ty surv ived • few hours or days? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 ~2ON I 207 In mLt, how many bays have died? And ha many g i r l s hmve died? IF NONE RECORD JO0 t . BOYS DEAD. GIRLS DEAD . . . . . . . . . . . . . . . 208 SUM AMSUERS TO 203, 205, AND 207, AND ENTER TOTAL. IF NONE RECORD IO0% TOTAL 209 F CHECK 208: JUSt to make sure that 1 have th i s r ight : yc~ have had In TOTAL b i r ths dur ing your l i fe . Is that cor rKt? PRONE kRD NO I I ~ CORRECT 201-208 AS NECESSARY CHECK 20di: E~ I ONEBIRTHSOil 14~qE i NO BIRTHS [~ .2 ~t , m , ~ , m m v I ENG ~ 5 231 211 NOV I we ld l i ke Re ta lk to you about I l l of your b i r ths , whether s t i l l a l ive or not, I t s r t l~ one you had. RECORD MAJ4EB OF ALL THE BIRTHS IN 212. RECORD TWINS AND TRIPLETS OH SEPARATE LINES, 212 Uhet r~ NIl ll¥1n to your ( f l r l t , rmxt ) b~n 213 214 215 216 0, I (MANE) (NAME) (NAME) (WANE) (V,A~) (k~E) (NAME) (Wd4E) Is (HA~) m boy BECOItO or • SINGLE g i r t ? MULT- IPLE BIRTH STATUS In what mo~th Is end yeer was (HN4E) (Itl~qE) born? s t i l l al ive? PROBE: Whet is h i s / her birthday? OR: In what |eeeOrl WaS ha/she born? 217 IF ALIVE: How old was (NAME) I t his/her Last birthday? RECORD AGE IN COMPLETED YEARS, AGE IW YEARS 218 219 IF ALIVE; IF LESS THAN 15 YRS. OF AGE: Is (NAME) Living With whom with you? does he/she I Live? IF 15+: GO TO NEXT BIRTH. YES . . . . . 111! FATHER . . . . . . . . . I (GO NEXT GRANDPARENTS.2 BIRTH)* I OTHER RELATIVE.] I NON-RELATIVE.4 NO . . . . . . 2 SCHOOL . . . . . . . . . 5 (GO NEXT BIRTH) with the f i r s t 220 IF DEAD: HOW old Was he/she when he/she dlad? IF "1 YEAA"sPRCmB: NOW ~ monthl o ld wau (~)7 RECORD DAYS IF UNOER 1 140UTH, 140UTNS IF UNDER 2 YEARS, OR YEARS. DAYS.I MONTHS.2 YEARS.3 AGE IN YEARS NO . . . . . . Z FATHER . . . . . . . . . 11 GRANDPARENTS.21 OTHER RELATIVE.3 NON-RELATIVE.6 SCHOOL . . . . . . . . . 5 (GO NEXT BIRTH DAYS.,,1 MO~ITHH,2 YEARS.] AGE IN YEARS NO . . . . . . 2 FATHER . . . . . . . . . 11 GRANDPARENTS.2 OTHER RELATIVE.3 NON-RELATIVE.6 SCHOOL . . . . . . . . . 5 (GO NEXT BIRTH DAYS.1 MONTHS.2 YEARS.3 AGE IW YEARS NO . . . . . . 2 FATHER . . . . . . . . . I I DAYS.1 GRANDPARENTS.2 OTHER RELATIVE.3 MONTHS,2 NON-RELATIVE.,.4 SCHOOL . . . . . . . . . 5 YEARS.,3 (GO NEXT BIRTH) "TH yEAR. AGE IN YEARS YES . . . . . 1 (GO NEXT 81RTH)~] NO . . . . . . 2 FATHER . . . . . . . . . 1 | DAYS.1 GRANDPARENTS.2 I OTHER RELATIVE.3 MONTHS.2 NON'RELATIVE.4 SCHOOL . . . . . . . . . 5 YEARS.3 (GO NEXT BIRTH) AGE I H YEARS YES . . . . . I (GO NEXT BIRTHI~ ] NO . . . . . . 2 GRANDPARENTS.2 OTHER RELATIVE.3 NONTNB.2 NON-RELAT IVE. .4 SCHOOL . . . . . . . . . 5 YEARS.] (GO NEXT BIRTH) AGE IN YEARS YES . . . . . 1 (GO NEXT BIRTH)* ] NO . . . . . . 2 GRANDPARENTS.2 OTHER RELATIVE.3 NOI4TH5.2 MO~-RELATIVE.4 SCHOOL . . . . . . . . . 5 YEARS.3 (GO NEXT BIRTH) AGE IN YEARS NO . . . . . . 2 FATHER . . . . . . . . . 1 GRANDPARENTS.2 OTHER RELATIVE.3 NON-RELATIVE.4 SCHOOL . . . . . . . . . 5 (GO NEXT BIRTH) 232 ew~ ~oN 6 212 i l lv~n to your ( f I rst ,mext) (N~qE) (NAME) (N~4E) (BANE) (NAV~) '21 l ~ ) 213 REC~ID ~N¢4.B MULT - IRLB BIRTH STATUS 214 Is (NAME) eboy or • g i r l ? 215 216 In shmt month I s lllcl yesr was (NAME) (kW4E) born? s t i l l al ive? PRORE: whet is h i s / her birthday? OR: In ~hat sENior1 wee he/she born? 217 IF ALIVE: How old was (NAME) st h is /her Last birthday? RECORD AGE IN CONPLETED YEARS. 218 IF ALIVE: Is (NAME) l i v ing u i th youT 219 IF LESS THAN 15 YRS. OF AGE: With ~h~ does he/she Live? IF 15+: GO TO NEXT BIRTH. 220 IF DEAD: Ho~ old was he/she when he/she died? IF "1YEAA#,PRO~E: HOd ~ IIIO~ths old was (MANE)? RECORD DAYS IF UMOER 1 MONTH, MONTHS IF UMOER 2 YEARS, OR YEARS. YEARS (GO NEXT GRANDPARENTS.2 BIRTH) OTHER RELATIVE.3 NON-RELATIVE.4 NO . . . . . . 2 SCHOOL . . . . . . . . . 5 (GO NEXT BIRTH) AGE IB I YES . . . . . 1 FATHER . . . . . . . . . 1 YEA. I 'O : :;31GRABOPARB"YB'''B I OTHER RELATIVE.3 - ~ NON'RELATIVE*.*4 NO . . . . . . 2 SCHOOL . . . . . . . . . 5 (GO NEXT BIRTH) AGE IN I YES . . . . . ,]1 I FATHER . . . . . . . . . I YEARS I (GO NEXTI I GRANDPARENTS.2 BIRTH)4 J OTHER RELATIVE.3 ~ [ ~ NON'RELATIVE.4 NO . . . . . . 2 SCHOOL . . . . . . . . . 5 : (GO NEXT BIRTH) YEARS (GO NEXT ~] GRANDPARENTS.2 BIRTH) OTHER RELATIVE.] NON-RELATIVE.4 NO . . . . . . 2 SCHOOL . . . . . . . . . 5 (GO NEXT BIRTH) TEARS (GO NEXT' 1 GRANDPARENTS. 2 BIRTH) OTHER RELATIVE.3 NON'RELATIVE.4 NO . . . . . . 2 SCHOOL . . . . . . . . . 5 (GO NEXT BIRTH) (GO NEXT GRANDPARENTS.2 N%RTH) OTHER RELATIVE.] 1 ~ NON'RELAT IV [ . . ,4 NO . . . . . . 2 SCHOOL . . . . . . . . . 5 (GO TO 221) CCMPARE 208 WITH NUMBER OF DIRTHS IN HISTORY ABOVE AND MARK: NUMBERS E l NUMBERS ARE F7 ARE ~ ~ DIFFERENT , , ~ (PROBE AND RECONCILE) v CHECK: FOR EACH BIRTH: YEAR OF BIRTH I$ RECORDED. FOR EACH LIVING CHILD: CURRENT AGE IS RECORDED. FOR EACH BIRTH INTERVAL 4 YEARS OR MORE: WRITE THE REAS(~. FOR AGE AT DEATH 12 MONTHS: PROBE TO DETERMINE EXACT NUMBER OF MONTHS. ENG WON 7 233 223 QUESTIONS AND FILTERS i m B Not* I tloutd t i ke to ask you about some current events i |n your L|fe. Are yc.a pregnant? I SKIP CODING CATEGORIER I TO YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 I I ; ;; iiiiiiiiiiiiiiiiiiiiiiiiiii', 2,, ! 22, I I . . . . . . . . . . . . . . . . . . . or did you not uent to be¢~ pregnant at alL? LATER . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 NOT AT ALL . . . . . . . . . . . . . . . . . . . . . . 3 - -7 !26 k(hen did your Last mer4truat period s tar t? i DAYS AGO . . . . . . . . . . . . . . . 1 WEEKS AGO . . . . . . . . . . . . . . 2 MONTHS AGO . . . . . . . . . . . . . 3 YEARS AGO . . . . . . . . . . . . . . 4 IN MENOPAUSE . . . . . . . . . . . . . . . . . . 994 MENSTRUATION NOT YET RESUMED SINCE HER LAST BIRTH . . . . . . . . . 995 HAS NEVER MENSTRUATED IN HER ~HOLE LIFE . . . . . . . . . . . . . . . 996 228 During ~h|ch times of the monthly cycle does a woman have the greatest chance of becoming pregnant? DURING HER PERIOD . . . . . . . . . . . . . . . 1 RIGHT AFTER HER PERIOD HAS ENOED . . . . . . . . . . . . . . . . . . . . . . 2 IN THE MIDDLE OF TEE CYCLE . . . . . . 3 JUST BEFORE HER PERIOD BEGINS.4 OTHER 5 (SPECIFY) DOES ROT KN(~ . . . . . . . . . . . . . . . . . . . 8 ENG WON 8 234 SECTIOB 3. CONTRACEPTION 302 Have yc~J ever heard oFJ 303 Have you ever 304 Do you kno~ I~hlre O11 P ILL Mom~l ca~ take a p i l l every day . 02• IUO Uom~n can have • Loop or co l t p ra ted ins ide them by • doctor o r •nurn . INJECTIOBS I~men can have an in jec t ion by s doctor o r nurse k~ich s top• that f roabe¢o~ing pregnant fo r severs [ months° ~_~ FOAM TABLET|/JELLY/NEO'SN'IPOON U ~ can p |ece Foam tobLet• . d laphr•~l , spongoo je l ty o r cream ins ide th l~before In tercourse° sheath dur ing sexua l |n ter - 61 FEMALE STERILISATIOB Momen can have an oper•t ton to avo id h•v lng Iwymore ch | [dr~. 07• HALE STERILISATIOII Men can have ~n operat ion to •vo id hav ing w~y ~or l ch i ld ren . 81NOBPLAMT ~ can have some m~L rods put under the i r sk in tn the i r am. 09• RNYTi~I, COUIITING DAYS A u~an cam count the days o f her cyc te ar¢l avo id hav ing sexua l In ter - course on the days uhen she i s more L ike ly to become pregr~nt . 01 NATURAL FANILY PLANNING A wo~l~ can t l ke her temperature every d~y or check her v•g ina I mucus to te l l uh lch days to avo id h•v lng aexuat In tarcourae . 11 UITHDRAIdAL Men c~ be care fu l i~d put t out be fore cL |~x . N•ve you heard o f any o ther way= or methods that ~om=n or Ben ca~ use to avo id pregnancy? (SPECIFY) 2 (SPECIFY) (SPECIFY) (HIETNGO) ? READ DESCRIPT]O~ OF EACH NETHQO. YES/SPONT . . . . . . . . . . . . . . . 1 YES/PROBED . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . , o . . , . .~ V YES/SPONT . . . . . . . . . . . . . . . 1 YES/PROBED . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . 37 v YES/SPONT . . . . . . . . . . . . . . . 1 YES/PROBED . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . 3} v YES/SPOBT . . . . . . . . . . . . . . . 1 YES/PROBED . . . . . . . . . . . . . . 2 BO . . . . . . . . . . . . . . . . . . . . . . 3] v YES/SPOBT . . . . . . . . . . . . . . . I YES/PROBED . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . 3~ v YES/EPONT . . . . . . . . . . . . . . . 1 YES/PROBED . . . . . . . . . . . . . . 2 BO, . , . . . . . . . . . . . . . . . . . . . ~] / v YES/SPONT . . . . . . . . . . . . . . . 1 YES/PROBED . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . 3q V YES/SPOBT . . . . . . . . . . . . . . . I YES/PROBED . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . 31 v YES/SPONT . . . . . . . . . . . . . . . I YES/PROBED . . . . . . . . . . . . . . E NO . . . . . . . . . . . . . . . . . . . . . . 31 v YES/SPONT . . . . . . . . . . . . . . . 1 YES/PROSED . . . . . . . . . . . . . . 2 I v YES/SPONT . . . . . . . . . . . . . . . 1 YES/PROBED . . . . . . . . . . . . . . NO . . . . . . . . . . . . . . . . . . . . . . v YES/SPOBT . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . 3- used (NETHOD)? ml~m~m~mmmm YES . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . 2 • per•on c~id go to get (WETNCO)? YES . . . . . . . . . . . . . . . . . . . . 1 NO. oo . . , . . . , , , , oo . . . . . . 2 YES . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . 2 YES . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . . t NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . 2 YES . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . 2 i YES . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . 2 Have you ever had an YES . . . . . . . . . . . . . . . . . . . . 1 operat ion to •vo id hav ing shy more NO . . . . . . . . . . . . . . . . . . . . . Z ch i ld ren? YES . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . 2 YES . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . 2 YES . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . 2 YES . . . . . . . . . . . . . . . 1 Do you kno~ uhere • per•on cmn obta in ~dv lce NO . . . . . . . . . . . . . . . . 2 o~ h~ to use th i s method? YES . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . 2 YES . . . . . . . . . . . . . . . 1 Do you kr~0~ where • per•on can obta in adv ice NO . . . . . . . . . . . . . . . . 2 on hou to use rmturs t fami ly p lann ing? YES . . . . . . . . . . . . . . . . . . . . 1 NO. .o . . . . . . . . . . . . . . . . . .~ n n YES . . . . . . . . . . . . . . . ~ mltUtI,fllIlimI~IHIW~IWIW~mHli~ ~1 Iltl'i;li illi ~liltIlllllllll;lll ~ I ~i ifll NO . . . . . . . . . . . . , , • * ~ ~ ~ fl ! ~ llllBHB ~ IJi~ U I Uila m m re•was =L~ m U ! ~ YES . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . 2 YES . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . 2 YES . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . 2 Hllilgiili,~li,li:Idlr, lglU:'~l,",l,%~?,%'l,1~%"~ HIIIIIliilllilllllllmlillitllllJliillUl~ ~ BIIIIII flIIIIIIIUIIIIItUtUlHIEttItlIIHILUmlnlI~ BU ~ UIEIRtRI I i I tlIIRUlEI3EIB~HilI~IIJ~F II!!M!l!!Uf!]llllllll L~lllm fl~u!~Illl!tmtlt J~J~!~ I mlmll illltWitlllll l l IBU HIIIillJlllllU;nlllltll~ll~llld:1t II; II I ~111 BI;llllllitlllltlllfl I U=~B~/~3~ IIIItllll!lllllll"MIHllt!llWIllilllttMi~t~"Jtl 1311 II H IIIIIIIlUtttIIIII;HIUi UlUlIJIHIII~IIt~ IIlIII~Ult;ItlIIIIIntlItIItltlIIRIIItlIt~E'IIIEEi~BIEdlI~ illtlJiJlttllltlltlllllt!Uill~lUiltlt!ltI!l~M~qmlliU~ I l I~B I IllfllflllltUllltltllJ. • I Itll n IIImltll AT LEAST ONE "YES" (EVER USED) I - - ' J ' - -~ SKIP TO 307A ENG ~ 9 235 NO. J QUESTIONS AND FILTERS 306 i Nave you ever used anything or t r |od In any ~ay to m I delay or mvoid get t ing pregnant? SKIP I CODING CATEGORIES I TO YES . . . . . . . . . . . . . . . . . . . . . . . . . . . ~1 I I No . 307 What have you usod or done? CORRECT 303-305 (AND 30Z IF NECESSARY). V i 3OTJJ The leSt t im y~ used r~turat family planning, how I d(d you determine on Which days to avoid having sexual tnterc~Jrse? I I / NEVER USED NATURAL [~]~J 308 FN41LY PLANNING l I TOOK BODY TEMPERATURE . . . . . . . . . . . 1 CHECKED CERVICAL MUCUS . . . . . . . . . . 2 BODY TEMPERATURE ANO 14J(~JS . . . . . . 3 COUNTING DAYS . . . . . . . . . . . . . . . . . . . 4 OTHER 5 (SPECIFY) 308 309 310 311 Nou I .o~Ld Like to ask you •bout the tame Nhen you f i r s t d id s~mth ing or wed • method to avoid get t ing pregnant. N¢x4 mwty t |v lng children did you have at that time, i f w~t IF NONE I RECORD *OO'. CI~C~ 223: NOT PREEJ4AN T [~ OR UNSURE V CHECK 303: ~ouul NOT E~3 STERILISED PREGNANT ROMAN STERILISED ~-~ Are y~J cur rent ly doing something or using any ~thod to detmy or •void gett ing pregnant? RL~4BER OF CHILOREN . . . . . . . ~ T ~ YES . . . . . . . . . . . . . . . , . . . . . . . . . . . . ,1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2 ,324 I ~324 312 3124 Which method are you using? CIRCLE =06 ~ FOR FEKALE STERILiZATiON. I PILL . . . . . . . . . . . . . . . . . . . . . . . . . . . 01 | ]~ . . . . . . . . . . . . . . . . . . . . . . . . . . . . 02-- t INJECTIONS . . . . . . . . . . . . . . . . . . . . . 03 FOAM TABLETS,JELLY,DIAPHRACd4.04 CONDOM . . . . . . . . . . . . . . . . . . . . . . . . . 05 318 FEMALE STERILISATIO~ . . . . . . . . . . . 06 KALE STERILISATIGN . . . . . . . . . . . . . 07 NORPLART . . . . . . . . . . . . . . . . . . . . . . . 08 RHYTHM e COUNTING DAYS . . . . . . . . . . 09 / NATURAL FP, NJCUSj TENPERATURE,IO~323 WITHDRAWAL . . . . . . . . . . . . . . . . . . . . . 11 _J OTHER 12 (SPECIFY) J 313 At the t im you ftrtt startod using the pitt, did you have a physical checkup by • doctor or r~rse? PRO6E: Did you have your blood pressure checked or an interne| examination? i YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 I NO . . . . . . . . . . . . . ,*o . . . . . . . o , ,o . . .2 DOES NOT RNOtJ . . . . . . . . . . . . . . . . . . . a s ,LL,,0s . . . . . . . . . . . . FREE . . . . . . . . . . . . . . . . . . . . . . . . . . 996 DOES NOT KRObl . . . . . . . . . . . . . . . . . ENG WON 10 236 NO* 316 OUESTIORS AND FILTERS CNECM 312: S~/NE STERILISED E~ Yhere d id the s ter l t l se t |on take peace? USING ANOTHER METHOD [~ i i v Wt~ere d id you obta in (METNOD) The l i s t t ime? (HANE OF PLACE) CODING CATEGORIES PUBLIC SECTOR GOVERNMENT HOSPITAL . . . . . . . . . . . . 11 GOVERNMENT HEALTH CENTRE . . . . . . . 12 GOVERNMENT DISPENSARY . . . . . . . . . . 13 MEDICAL PRIVATE SECT(X1 MISSION,CHURCH IIOSPITAL/CLINIC.21 FPAK HEALTH CENTRE/CLINIC . . . . . . 22 OTHER NON'GOVENI~NTAL fdERVICE*2] PRIVATE HOSPITAL OR CLINIC . . . . . 24 PHARMACY . . . . . . . . . . . . . . . . . . . . . . . 25 PRIVATE DOCTOR . . . . . . . . . . . . . . . . . 26 MO61LE CLINIC . . . . . . . . . . . . . . . . . . . 31 COMV~JNITY BASED DISTRIBUTOR/ COI~qUNITY HEALTH ~ORNER . . . . . . . 41 SBOR . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 FRIENDS/RELATIVES . . . . . . . . . . . . . . . 61 - - OTHER 71 (SPECIFY) DOES MOT KNOW . . . . . . . . . . . . . . . . . . ,9~ - - SKIP TO P321 319 HOW long does | t take to t rave l f ro lyour home to th i s place? IF LESS THAN 2 HOURS, RECORD MINUTES. OTNESWIfdE, RECOMD H~UllS. MINUTES . . . . . . . . . . . . 1 J J J J H~RS . . . . . . . . . . . . . . 2 DOES NOT KNOi~ . . . . . . . . . . . . . . . . 999B 320 Do you ~stk or use s~ i l r l of t ranspor ta t ion to get there? 321 I CHECK 312: USING salE/HE (~ ANOTHER STERILISED METHO0 V 322 In k/let month and year k~la the e ter l t i se t lon operat ion performed? WALK . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 USE TRANSPORT . . . . . . . . . . . . . . . . . . . 2 DOES NOT KNOW . . . . . . . . . . . . . . . . . . . 8 ~323 I I MONTH . . . . . . . . . . . . . . . . . . . . 33SA YEAR . . . . . . . . . . . . . . . . . . . . . 3~.J (CUIEENTF°P h°wllnY IIl°¢~th" have Y°~'lbeen u s i r l i l ( T N O D ) ¢. t ln l~w; I )e / I :::::: IF LENS THAN I MONTH, REC(~D IO0*, I I ' 324 Do yOU intend to use a method to delay or avoid YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 ~326 pregnar~y at any t ime in the future? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 I 325 Uhat i s the min reason you do not intend to use • l int hod? IF SHE SAYS SHE IS TOO YOUNG, ASK WHAT SHE WILL DO UHEN SHE IS OLDER. IF SHE SAYS SHE IS BREASTFEEDING OR HER PERIQO HAS NOT YET RETURNED, ASK WHAT SHE WILL DO WHEN SHE STOPS BREASTFEEDIMG Gq HER PERIODS RESUINE. IF ANSI~RS TO THESE PROSES REGAJIRE CHANGING 0324, DD SO. WANTS CHILDREN . . . . . . . . . . . . . . . . . 01 - LACK OF KNOWLEDGE . . . . . . . . . . . . . . 02 HUSBAND OPPOSED TO USING . . . . . . . 03 COST TOO MUCH . . . . . . . . . . . . . . . . . . 04 SIDE EFFECTS . . . . . . . . . . . . . . . . . . . 05 FEARS IT WILL MAKE HER STERILE.06 OTHER HEALTH CONCERNS . . . . . . . . . . 07 HARD TO GET METHODS . . . . . . . . . . . . 08 RELIGION . . . . . . . . . . . . . . . . . . . . . . . 09 OPPOSED TO FAHZLy PLANNING . . . . . 10 FATALISTIC . . . . . . . . . . . . . . . . . . . . . 11 OTHER PEOPLE OPPOSED . . . . . . . . . . . 12 INFREQUENT SEX . . . . . . . . . . . . . . . . . 13 DIFFICULT TO GET PREGNANT . . . . . . 14 MENOPAUSAL/HAD HYSTERECTOMY.15 INCONVENIENT . . . . . . . . . . . . . . . . . . . 16 OTHER 17 (SPECIFY) DOES NOT KNOW . . . . . . . . . . . . . . . . . . 98 ~-330 "261 DO to . " 'h* I "S . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ' l ~lthln the next 12 months? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 DOES NOT KNOM . . . . . . . . . . . . . . . . . . . 8 ENG UOM 11 237 NO, 327 QUESTIONS AND FILTERS When yam use • mthod, Which mthod ~J td you prefer to use? CODING CATEGORIES PILL . . . . . . . . . . . . . . . . . . . . . . . . . . . 01 IUO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 02 INJECTIONS . . . . . . . . . . . . . . . . . . . . . 03 FOAM TABLETS, JELLY ,O IAPHRAGN.O& CORDOR . . . . . . . . . . . . . . . . . . . . . . . . . 05 FEMALE STERILISAT IOR . . . . . . . . . . . 06 MALE STEHI LIBATION . . . . . . . . . . . . . 07 NORPLANT . . . . . . . . . . . . . . . . . . . . . . . 08 RHYTHM, COUNTING DAYS . . . . . . . . . . 09 NATURAL FP, MUCUS, TENPERATURE.IO WI THDRASAL . . . . . . . . . . . . . . . . . . . . . 11 OTHER 12 (SPECIFY) UNSURE . . . . . . . . . . . . . . . . . . . . . . . . . 98 l l[ IP 70 t 330 328 329 33O Where can you get (NETH~O NENTIORED IN 327)? (NAME OF PLACE) CHEC[ 312: USING NHTHYN, COUNTING DAYS. WITHGRAMAL OR OTHER TRADITIONAL NETHOU v DO you know of • piece ~ere you can obtain • method of fml ty planning? PUBLIC SECTOR GOVERNMENT HOSPITAL . . . . . . . . . . . . 11 - - GOVERNMENT HEALTH CENTRE . . . . . . . 12 GOVERNMENT DISPENSARY . . . . . . . . . . 13 MEDICAL PRIVATE SECTOR MISSION.CHURCH HOSPITAL/CLINIC.21 FPAH HEALTH CENTRE/CLINIC . . . . . . 22 OTHERNOR-GOVERNMENTAL SERVICE.Z3 PRIVATE HOSPITAL OR CLINIC . . . . . 24 PHARMACY . . . . . . . . . . . . . . . . . . . . . . . 25 PRIVATE DOCTOR . . . . . . . . . . . . . . . . . 26 MOBILE CLINIC . . . . . . . . . . . . . . . . . . . 31 COMMUNITY BASED DISTRIBUTOR/ COMMUNITY HEALTH WG~KER . . . . . . . 41 SHOP . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 FRIENDS/RELATIVES . . . . . . . . . . . . . . . 61 OTHER ~I (SPECIFY) DOES NOT KBOR . . . . . . . . . . . . . . . . . . . 9B USING A MODERN hETHGO [----I .332 ~333A '~333A I ~33~ II ~333~ I I YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I I I NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 ~333A 331 Where is that? PUBLIC BECTO~ GOVERMMEHT HOSPITAL . . . . . . . . . . . . 11 GOVERNMENT HEALTH CENTRE . . . . . . . 12 GOVERNMENT DISPENSARY . . . . . . . . . . 13 MEDICAL PRIVATE SECTOR MISSION,CHURCH HOSPITAL/CLINIC.21 FPAK HEALTH CENTRE/CLINiC . . . . . . 22 OTHER NON-GOVERNMENTAL SERVICE.23 PRIVATE HOSPITAL Oil CLINIC . . . . . 24 PHARMACY . . . . . . . . . . . . . . . . . . . . . . . 25 PRIVATE DOCTOR . . . . . . . . . . . . . . . . . 26 MOBILE CLINIC . . . . . . . . . . . . . . . . . . . 31 COR4UNITY BASED DISTRIBUTOR/ COMMUNITY HEALTH WORKER . . . . . . . 41 SHOP . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 FR]ENDS/RELAT]VEB . . . . . . . . . . . . . . . 61 OTHER T1 (IIAME OF PLACE) ~ ~333A (SPECIFY) 333 Do you walk or use s~ae means of t ransportat ion to I WALK . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 I Eat there? I USE TRANSPORT . . . . . . . . . . . . . . . . . . . 2 I DOES HOT KNOW . . . . . . . . . . . . . . . . . . . 8 ENG ~ 12 238 NO. OUESTIO#dS AND FILTERS 333A ReAd did you f i rmt hear ebeut fmi ty planning? CODING CATEGORIES RADIO . . . . . . . . . . . . . . . . . . . . . . . . . . 01 TELEVISION . . . . . . . . . . . . . . . . . . . . . 02 NEWSPAPERS . . . . . . . . . . . . . . . . . . . . . 0.3 POSTERS . . . . . . . . . . . . . . . . . . . . . . . . 04 HUSBAND . . . . . . . . . . . . . . . . . . . . . . . . 05 FRIENDS/RELATIVES . . . . . . . . . . . . . . 06 HEALTH ~(X~KER/CLINIC . . . . . . . . . . . 07 COMMUNITY BASED DISTRIBUTOa/ COMMUNITY HEALTH ~R[ER . . . . . . 08 OTHER 09 (SPECIFY) CANIT REMEMBER/DOER NOT V, MOW, , .98 GO TO 33311 Fr~whlch place or person did you Bet the most In fomat lon? RADIO . . . . . . . . . . . . . . . . . . . . . . . . . . 01 TELEVISIOD . . . . . . . . . . . . . . . . . . . . . 02 NEWSPAPERS . . . . . . . . . . . . . . . . . . . . . 03 POSTERS . . . . . . . . . . . . . . . . . . . , . , . .0~ HUSBAND . . . . . . . . . . . . . . . . . . . . . . . . 05 FRiENDS/RELATIVES . . . . . . . . . . . . . . 06 HEALTH ~O~KER/CLINLC . . . . . . . . . . . 07 CCMMUNITY BASED DLBTRIBLJTOR/ COMMUNITY HEALTH MORKER . . . . . . 08 OTHER 09 (SPECIFY) CAN'T REMEMBER/DOES NOT KNOB* . .9 It ~out fmi ty p ly ing? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2- - - ; DUES NOT KNOW . . . . . . . . . . . . . . . . . . . D B335 I 334A ~lch pro l r lm have you heard? Any others? DO NOT READ CODES TO RESPONDENT, CIRCLE ALL MENTIONED. MWEMDA POLE . . . . . . . . . . . . . . . . . . . . . A PANGA UZAZI . . . . . . . . . . . . . . . . . . . . . i HAISHA YA JA~ll YAKO . . . . . . . . . . . . C JIFUBZE NA UENDELEA . . . . . . . . . . . . . D MAISHA BORA . . . . . . . . . . . . . . . . . . . . . E AFYA TAKO . . . . . . . . . . . . . . . . . . . . . . . F DAKTARI AKUSRAURI . . . . . . . . . . . . . . . S KUELEWANA Nl KOZUBGUMZA . . . . . . . . . H OTHER I (SPECLFY) DOES NOT KNOW/CANNOT REMEMBER,.J 335 I °y°thinkh--° °ut ing I YEs . I should be ava i lab le to young people? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 OTHER 3 (SPECIFY) DOES MOT KN(3~J . . . . . . . . . . . . . . . . . . . 8 3 AI °° he Y--ngservce sh°uLd I YEs . I be ava i lab le for young people? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 OTHER 3 (SPECIFY) DUES NOT KNO~ . . . . . . . . . . . . . . . . . . . 8 I 3~ In R~e coemunitlem there is a woman or mn who is YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 | t r l ined to t l Lk to fmiL ie l in that area abeut f~ i iy I pfenn ig . Sometime they v i s i t each h~se e~ ta lk NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 - - -P401 about f ru i ty pLann i~ a~ give out supplies. Other | t fml they hive lul~ptlea in the i r h~$es. Is there any DOESN'T KN~ . . . . . . . . . . . . . . . . . . . . B ~401 k ~ or mn Like that in y~r area7 I I ' - - - - ' ' r~°°r - - ° l " ' - - - . ~ 1 ENG ~ 13 239 40Z 403 SECTION 4A. PREGNANCY AND BREASTFEEBING CNE rv 222: OEN OIIE I~1 NO giRTHS BIRTHS SLICE JAN. 1968 Lr--J SINCE JAN. 1988 • (SKIP TO 501) ENTER THE LIME hUMBER, NA~, AJ~ UVIVAL STATUS OF EACH BIRTH SINCE JANUARY 1988 IN THE TABLE. I ASK TND QUESTIOIli MNXIT ALL OF THESE BIRTHS. BEGIN UITH THE LAST BIRTH. ( IF THERE ARE NOllE THA~ 3 NIRTNEe I USE ADOITIOE~L FCANS). NO~ i I~u ld l i ke tO msk you BoW sore quest ion~ about the hea l th of a l l yo~r ch i ld ren born in the ~st 5 ye l r l . We ~t i i ta lk about o~e ch i ld st s t ime. LIND EUNOER FROM d. 212 FROM 0. 212 AND 0. 216 At the t im yma bec l p rWt wi th (NNI [ ) , d id yO~ Want to bec~m prsgnamt thsn, d id yOU ~nt to ua l t unt i l l a te r or d id you u~nt r~ (more) ch i ld ren at mi t t I LAST BIRTH NAME ALIVE ? DEAD [~ i r a THEN . . . . . . . . . . . . . . . . . . . 1 1 (SKIP TO 40S)~ / LATER . . . . . . . . . . . . . . . . . . 2 NO NONE . . . . . . . . . . . . . . . . 3 1 (SKIP TO 405)~ l NEXT'TO'LAST BIRTH NAME ALIVE [ ] m I l l THEN . . . . . . . . . . . . . . . . . . . 1 1 (SKIP TO 405)4 l LATER . . . . . . . . . . . . . . . . . . 2 NO MORE . . . . . . . . . . . . . . . . (SKIP TO 405)4 l I•SECOND" FROI4 - LAST BIRTH ALi. 0 v THEN . . . . . . . . . . . . . . . . . . . I 1 (SEIP TO 40S)4 [ LATER . . . . . . . . . . . . . . . . . . 2 NO MORE . . . . . . . . . . . . . . . . (SKIP TO 405)~ / 404 I NO~ much Longer t~utd you I Like to hsve ~ l ted? MONTHS . . . . . . . . 1 N4~JTHS . . . . . . . . 1 YEARS . . . . . . . . . 2 YEARS . . . . . . . . . 2 YEARS . . . . . . . . . 2 DOES NOT KNON . . . . . . . . 998 DOES NOT KNON . . . . . . . . 998 DOES NOT KNOW . . . . . . . . 998 I 405 Nhen you v~re preg~t N l th (NAME), d id you see w lymle forantenetmi care fo r th i s p~egnancy? IF YES, Uhomdid you see? Anymw else? RECOIU) ALL PERSONS SEEN. HEALTH PROFESSIONAL DOCTON . . . . . . . . . . . . . . . . A NURSE/M IDN [ FE . . . . . . . . . 6 OTHER PERSON TRAINED TRAD[TIONAL BIRTH ATTENDANT . . . . . . C UNTRAINED TRADITIONAL BIRTH ATTENDANT . . . . . . D OTHER E (SPECIFY) NO ONE . . . . . . . . . . . . . . . . . . F (SKIP TO &09)~ / HEALTH PROFESSIONAL DOCTOR . . . . . . . . . . . . . . . . A HURSE/NIDU%FE . . . . . . . . . S bTHER PERSON TRAINED TRADITIONAL BZRTH ATTENDANT . . . . . . C UHTRA[HED TRADITIONAL BIRTH ATTEHDANT . . . . . . D ITHER E HEALTH PROFESSIONAL DlXlTOR . . . . . . . . . . . . . . . . A NURSE/MIDWIFE . . . . . . . . . O OTHER PERSON TRAINED TRADITIONAL BIRTH ATTENOANT . . . . . . C UNTRAINED TRADITIONAL BIRTH ATTENDANT . . . . . . O 3THER (SPECIFY) ~o ONE . . . . . . . . . . . . . . . . . . E (SKIP TO 409}~ ] (SPECIFY) NO ONE . . . . . . . . . . . . . . . . . . F 1 (SKIP TO 409) , | I I~terk~tat card fo r I th i s pregnancy? NO . . . . . . . . . . . . . . . . . . . . . Z NO . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . 2 DOES NOT KNON . . . . . . . . . . B DOES NOT KNO~/ . . . . . . . . . . 8 DOES NOT KNON . . . . . . . . . . 8 . T H S . . . . . . . . . . .DHtHS . . . . . . . . . . . . ,HS . . . . . . . . . . r- l_ check on th l s pregnamcy? DOES NOT KNOb/ . . . . . . . . . 98 DOES HOT KHON . . . . . . . . . 9B DOES NOT ENON . . . . . . . . . 98 I ,-i,-- I th ts pregnancy? DOES NOT KNON . . . . . . . . . 98 DOES NOT KNO~ . . . . . . . . . 9B DOES NOT KNOW . . . . . . . . . 98 | 409 I Idhen you were pregnant E~ 8 ] ~ I u l th (I~JIE) were you g iven YES . . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . . 1 an in jec t ion in the am , to pr - - t t h . . from NO . . . . . . . . . . . . . . . . . . . . . 21 NO . . . . . . . . . . . . . . . . . . . . . 21 NO . . . . . . . . . . . . . . . . . . . . . 2 I ~t t lnN team, that i s , (SKIP TO /.11)4 (SKIP TO 411)4 (SKIP TO 411)4 cm~vulsions a f te r b i r th? DOES NOT KNON . . . . . . . . . . DOES NOT KNO~ . . . . . . . . . . DOES NOT KNOt/ . . . . . . . . . . th ta in jec t ion? DOES NOT KNO~ . . . . . . . . . . 8 ODES NOT KNON . . . . . . . . . . 8 DOES NOT KNOW . . . . . . . . . . 8 | ENG NON 14 240 411 Idhere d id you B lv l b i r th to (MALE)? LAST 81RTH MANE NER HORE, OTHER I t0~. . .11 GOVERNMENT NOSPITAL/ NLTH CENT./MATERNITY.21 PRIVATE HECTOR NIE$1ON NOSP/CLIRIC.31 PRIVATE ROSP./CLINIC.32 OTkfR 41 (SPECIFY) NEXT'TO'LAST 81RTH NAME HER HOME, OTHER HCI4E. . I I GOVERHMEMT ROSPITAL/ HLTH CENT,/MATERRITY.21 PRIVATE SECTOR NISSION HOSP/CLIRIC*.~I PRIVATE HOSP./CLINIC.32 OTHER 41 (SPECIFY) SEC~D- FB~q-LAST BIRTH RAME HER 1~4E, OTHER HEI4E.11 GOVERNMENT IIOSP I TAL/ ELTH DENT ,/MATERNITY. 21 PRIVATE HECTOR NiBS ioN i IOSP/CL IN iC . .31 PRIVATE NOSP./CLiKIC.32 OTHER 41 (SPECIEY) 412 Vno ass i s ted u i th the • l - l i very o f ( IN l l ) ? Anyone else? PROBE FOR THE TYPE OF PERSON AND RECORD ALL PERS~IS ASSISTING* RF.ALTH PROFESEiORAL DOCTOR . . . . . . . . . . . . . . . . A NURSE/NIObilEE . . . . . . . . . B OTHER PERSOM TRAINED TRADITioNAL 81RTH ATTENDANT . . . . . . C UNTRAINED TRADITICIIAL BIRTH ATTENDANT . . . . . . D RELATIVE/FRIEHD . . . . . . . E OTHER F ~EALTH PROFESSIONAL DOCTOR . . . . . . . . . . . . . . . . A NURSE/RIDUIFE . . . . . . . . . B 3THER PERSON TEAiRED TRADITioNAL BIRTH ATTENDANT . . . . . . C UNTRAINED TRADITIORAL BIRTH ATTENDAHT . . . . . . D RELATIVE/FRIEMD . . . . . . . E OTHER F (SPECIFY) HO ORE . . . . . . . . . . . . . . . . . . G HEALTH PROFESSIONAL DOCTOR . . . . . . . . . . . . . . . . A NURGE/MIDbilFE . . . . . . . . . I OTHER PERSON TRAINED TRADitiONAL BIRTH ATTEROANT . . . . . . C UNTRAINED TRADITICIIIAL BIRTH ATTEROART . . . . . . O RELATIVE/FRIENO . . . . . . . E OTHER F (SPECIFY) (SPECIFY) NO ONE . . . . . . . . . . . . . . . . . . G NO ONE . . . . . . . . . . . . . . . . . . G 41~ i U l l (KAJIE) born oi l t im OR TIME . . . . . . . . . . . . . . . . I ON TIME . . . . . . . . . . . . . . . . 1 ON TiME . . . . . . . . . . . . . . . . 1 | J o t p r i tu re ly? PREMATURELY . . . . . . . . . . . . 2 PREMATURELY . . . . . . . . . . . . 2 PREMATURELY . . . . . . . . . . . . 2 I DOES NOT KHObi . . . . . . . . . . 8 DOES NOT KH04/ . . . . . . . . . . 8 DOES NOT KMOU . . . . . . . . . . B 414 I Ue l ( lAME) de l ivered I YES . . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . . 1 ] YES . . . . . . . . . . . . . . . . . . . . 11 by cN lar lan lec t ion? NO . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . 2 Ale bihlm (NAME) ll l l i l born , il l l l l h l /eh l l very le rge , VERY LARGE . . . . . . . . . . . . . 1 VERY LARGE. ; . . . . . . . . . . 1 VERY LARGE . . . . . . . . . . . . . 1 t er l le , LARGE . . . . . . . . . . . . . . . . . . 2 LARGE . . . . . . . . . . . . . . . . . . 2 LARGE . . . . . . . . . . . . . . . . . . 2 average, AVERAGE . . . . . . . . . . . . . . . . ~ AVERAGE . . . . . . . . . . . . . . . . 3 AVERAGE . . . . . . . . . . . . . . . . 3 l i lmi l ~ SMALL . . . . . . . . . . . . . . . . . . 4 SMALL . . . . . . . . . . . . . . . . . . 4 SHALL . . . . . . . . . . . . . . . . . . 4 o r very mi l ? VERY SMALL . . . . . . . . . . . . . $ VERY SHALL . . . . . . . . . . . . . 5 VERY SMALL . . . . . . . . . . . . . S DOES NOT KRON . . . . . . . . . . 8 DOES HOT KRON . . . . . . . . . . 8 DOES NOT KNObi . . . . . . . . . . . I NO . . . . . . . . . . . . . . . . . . . . . 21 YES . . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . . 1 I / 416 blue (MANE) lleIRbod YES . . . . . . . . . . . . . . . . . . . . 1 (SKIP TO 419)~ 1 NO . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . 2~ et b i r th? (SK P TO 418)~ (SKIP TO 419)~ | , , , . . . . . l i l . . . . . I If--IlK'L--' . I r - l i DOES HOT KHO~ . . . . . . ~ . .98 DOES HOT KNO~ . . . . . . i . .98 I DOES NOT KMObi . . . . . . . . 9~ | I Xoll l l~h d id (MAl l l ) t l i i b? i~ill ili~Ji~ii~Eii~ii~J~i J~!ili~i~iEiill i~!l!ill i ii!lq ! it • I i i Bt L E E ~. l I I Aio .e, your period returr~ I YES . . . . . . . . . . . . . . . . . . . 111 ,~,~ll~llll~;~,~.~.,.~.,ti.,t.l,t IIIItllililllllltlttlltllll~l'tlltlllMI RO (SK IP TO 4 ;10)4 ~| ' I!iB[!~i!i![!i!!i!Bi~!!i!i~ii!i]!Bii~Ellillllllillltltlltlltltllllt ~ltlltlltllllllll I s ince the b i r th o f (RRIIE)? I~;~ii;~ . hllh,ltill'lhllll Ullltnllllmi~"~l~ I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . YES . . . . . . . . . . . . . . . . . . . 1 419 Did y(x l r per iod re turn IltllI~qllt,ql,Ii!,ll,l,.I.,I,il.,.,t,,I,,,I.II.,,,,,,,,,,,l NO . . . . . . . . . . . . . . . . . . . . . 2 betueen the b i r th o f (NAME) UllllHiliilii,.i.,tllii.iilliii.i.iill~i. iillllt.tll,iilliiiii,i ] I I~ ~OUr I l l p l ' l l~y~ i !lulliilim,lqthlPJillll~liillllttllllWiilUlliitliiiilll ISKIP TO 47] ) I (SKIP TO i l3 ) i the b i r th o f (NAME) d id NORTHS . . . . . . . . . . MONTHS . . . . . . . . . . HORTHS . . . . . . . . . . DOES NOT KNO~ . . . . . . . . . 98 DOES ROT KRO~ . . . . . . . . . 98 DOES NOT KMObi . . . . . . . . . 98 | Oil UNSURE !ii!~i!!!!i!!i!i[]i!i!!i!i!iN[~![llfi!!!!!!!!!!!lllllll IIIII lillllli!! III ii,i~i,,,li,,,,,ili,ii,il,il ,i,,,t,i,,tii,iil,i,iltUtUll! IIII III,lUl J~ JE iiiiii~ilFiiii[iil!ii~lii!ii!il! i, !iII 1![ii 11111t II t1111 t 11111 IIt II II111 I (SKIP TO 423) ,~F~,,~ . ~,~ll~.~,~,,i,~,,,i,,,pB,l,,il.,llt,, .",~lll"lll~.'l. I . . . . . . . . . . . . . . . . • . . . . :)3 '22 Hive yo~ relcmd sexual YES . . . . . . . . . . . . . . . . . . . . ' i,ll,,ll,,ll.,,ll<ll,.,,,,~ll,.,,.,,,.ll.d,.ll,i.iilli]illlilllillIIU i i l l ' i l i l i i i "~ I Iliil!lF!!iltiliiiillill~t!~l!llillil!iill!~ ~liilll111tlt iII II! IIIII tllll II re l l t i o i~e s ince the b i r th NO / tlii!~i~i!ii~lli[i~iiii~i~iiii!iiiiiiiiiiiiitiiilltlilltl]tltiitllllllll I I llllllBJlllllfllUlllllllllllltllllllllllllill I d H m n ~ l • * (SK P TO 4z4), the b i r th o f ( lU l l ) d id NOI~TRS . . . . . . . . . . MONTHS . . . . . . . . . . NObiTH$ . . . . . . . . . . yo~ not h ive sexual re lu t lon~? DOES NOT IHON . . . . . . . . . 98 DOES NOT KNOW . . . . . . . . . 96 DOES NOT l ION . . . . . . . . . 98 | Ells WON 15 241 I LAST BIRTH NEXT'TO'LAST BIRTH NAME NAME 424 I Old You ever bree l t f l l c l ( I IA~)? I xEsNo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (SKIP T0.~426). 1 YES]_ NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (SKIP TO 433)~ 21jill SFCOMO'FROR'LAST I I I YN I NAME YES . . . . . . . . . . . . . . . . . . . . 11 m I ( IK IP TO 433) J . o . . . . . . . . . . . . . . . . . . . . . 2 I 425 Why d id you not breeet feed (IU~IE)? MOTHER ILL/WEAX . . . . . . . 01 CNILD ILL/WEAK . . . . . . . . 02 CHILD DIED . . . . . . . . . . . . 03 NIPPLE/BREAST PEORLEM.04 IMSUEEICIENT MILK . . . . . OS MOTHER ~ORKING . . . . . . . . 06 CHILD REFUSED . . . . . . . . . 07 OTNEN .08 (SPECIFY) ISKIP TO 435)~ MOTHER ILL/VEAK . . . . . . . 01 CHILD ILL/WEAK . . . . . . . . 02 CHILD DIED . . . . . . . . . . . . 03 NIPPLE/BREAST PROBLEM.O~ INSUFFICIENT MILX . . . . . 05 MOTHEE ~ORKING . . . . . . . . 06 CHILD REFUSED . . . . . . . . . 07 OTHER 08 (SPECIFY) (SKIP TO G3S)~ MOTHER ILL/MEAK . . . . . . . 01- CHILD ILL/WEAK . . . . . . . . DE CHILD O ED O]: NiPPLE/BREAST PEORLEN.04 INSUFFICIENT MILK . . . . . 0~i R~TNER hi'KING . . . . . . . . 06 CHILD REFUSED . . . . . . . . . 07 OTHER .01~ (SPECIFY) (SHIP TO 43§)~ 426 I Holl Long e f te r b i r th d id I you f l r l t put (M~qE) to the bremst? IF LESS THAN 1 NOUR, RECORO 'OO' i~S . IF LESS THAN 24 NOUIS, RECORD H~UIS. OTHERWISE, RECORD DAYS. I: I: INMED lATELY . . . . . . . . . . ODD HQORS . . . . . . . . . 1 [ ~ DATI . . . . . . . . . . 2 DEAD (SKIP EL TO 4331 YEN . . . . . . . . . . . . . . . . . . . . I NO . . . . . . . . . . . . . . . . . . . . . 2] (SHIP TO 433)~ 429 No~ l lny t im d id you I b reut feed Lut n ight I bltk~tn sunlit ~ l~tlrile? IF ANSWER IS HOT MLN4ERIC, PROIE FOR APPMORIMATE NUMBER NI.IIBER OF NIGHTTIME FEEDINGS 430 I No~ l lny t l lm l d id yo~ IM~4BER OF I breeet feed ~sterdey DAYLIGHT dur ing the d ly l lght hour i? FEEDINGS I IF ANSWER IS NOT MLJI4ERIC~ PRONE FOil APPROXIMATE NUMBER 431 432 At z~y Eime yesterday or ta l l n ight ~ I (NAME) Q Iv~ any of the folLo~lr41?: PLain uater? $~ar water? Juice? B~oy formula? Fresh m| lk? Tlnnl~l or powdered milk? Other l iqu id~? Por r idge , uJ i? o ther io ( |d or iLmhy food? CHECK k31: FC~O OR LIQUID GIVEN YESTERDAY? YES NO PLAIN WATER . . . . . . . . 1 2 f~IGAR WATER . . . . . . . . 1 Z JUICE . . . . . . . . . . . . . . 1 2 DABY FORMULA . . . . . . . 1 2 FRESH MILK . . . . . . . . . 1 Z T I NNED/P(X~ORD . MI LX . 1 2 OTHER L I~JIDS . . . . . . 1 2 PORRIDGE, UJI . . . . . . 1 2 SOL ID/NUSHY F(XX). 1 2 "YES" TO ONE OR "NO" TO ALL V V (SK IP TO G37) (SX IP TO 436) li~i]ii~iiiiiiiiiiiiiii~iiiiii~iiiiii~iiiiiii~J~iiiiiiJfillii'Jlg'ili iiiiiiiiiliNiiiiiiiiiiiiiiiiiiiiiiiiiii!H!iliiiii!iii!!ii!!l!Uil!iill tl!i,i,~iiiii:iiiiii!iiiiiiiiiiiii!i!ii:iii!ii~:i,!lllll I,d,tl,,dltl li~]iiiii!]iiiii~iiiii[]iiiii~ii!iii~]~iiiii~iiiii~ili JlJfl J 'I!JiU! i[iiiiiiiiii!i~iiiiiiiiiiiili[iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii m;;~::::~!l;!!;:;:~:::;l:~;;::m~:~:!::!::~::l:~:~l:ll[m%Ig~]gl:ll iiiiiiiiiiiiiiii~iiiiiii~iiiiiiiiiiii~iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiilI !ii!ili!!!ii!ii!!i!!i~iiii!iiiiiiiliiii!!!!!~!!!!i!!ii!T!II~!!T!l!!~!l Jl I I I I III qlIIHP m#Pl ,mnmplq IH N II~IHI~I~IttlJHW[IIWFltGIUUlII~H[I~UII~IliHW I~[BW fi!Jth~lIfltlIiIZltIII,tiltilfl!tlhll,lllUIm~ El~fllm [IH[tlt~lllll;Hi?lltllqlU~U~ii}ttiill?~l?ll~l?lllllpl~llllHl?l~ , E , Fq Uz ~,~, i i qm ft ? tttl,.iii.tLtttt . ? . l i . . . . . . .Lt l! . . . .~ tl IlJllH?~llltl~l~Elt H,r l II t"~illlll~lr ~ ' . ~H. t,E~itiilIttltPLlllUliitllllJr ~ ,, .~; , #,lliiililttJllilml~ ~ IlitEtltiiUltilt]!lllliillEt~iHIUttltlli~iHliWWII ~llllBnlll i l l JtlUlIIIIIliltlIUtilttllilIiI~ItUG H; . t I~I I~I~I~F~ , , t l~fl lL~[ tl , I , I l l l l~fl . . . . . . . ~ , IIII III111111111 IltnlltlR111114 IlJlt[I IIII Bil l Illllt!tldlhlU*tllfllllltlllitiIUUhl!,I~llililf~,tl ~11~ II ~ I!t Il l H !hi ?illl I ~ ~ !!i!it!t!i!i[*!! ! ~![ill ~ l~ ]! ff gl~ e I iiilJliJ !it I J~ ~itlJlU liJ U i!i~iitikl Jt iiLil llliJ HlUlfi~lliilJi~lJl~ Ifill;t [lilt till; tiJilJ Illilill{il Ji~;li;i~;IH Z UillEim lIBSili;~i] , el IJ IIHIIIUIilIIIUII31WIIilIIIIUlIIHIIWIIIIIIUlIII~ i a I I el IIIIIIIULIIIL lUllllllll~llillU l~i.,ill llill II II311111WI IH I~I L L llli!.~ILililliiiilIL,!!i L~!FI~I~ILII ~II ;IIIL Lil II J Ill II I lliillillil IIIIII H III UiH I~ U I H I llill lliU I H I ~II~IHIIWH ~ l*ii~,llll~iUlllflUllflmJ~,~lilW~Ul~lll~N II II Ill III II III III II I~ IiI~ I WII III Ill l~ill HI II IIWfllIIIIW IIW II I I13 I;I hlII III Hill III ~#W lllJ~l Ill III II~ U~P~m ~ II R~illillnlWlllllllll~51111li~i!llllil~nil~l~ll~ BIWIII3UIIIWIBIIIIImlWII III~HU EWI mrouluitllulnl,muln, H i l ! lmf~, l~E~, , ENG 14011 16 242 I 433 [ ~ ho~ shy ~onthl d id breastfeed (MN4E)2 LAST BIRTH RAKE MONTHS . . . . . . . . . . ~ - ~ UNT|L DIED . . . . . . . . . . . . 9~ (SKIP TO 436)~ i NEXT-TO-LAST BIRTH NAME MONTHS . . . . . . . . . . UNTIL DIED . . . . . . . . . . . . 96~ (SKIP TO 436)~ l SECCID - FRGI4" LAST DLRTM MANE MONTHS . . . . . . . . . . UNTIL DIED . . . . . . . . . . . . 961 (SKIP TO 436). / 434 Why did you stop breat feed ing (IMNE)T 436 I t . s (NAME) ever given I ~ater or w4yth|rq| else to d r ink or eat (other th~ ~e l l tmiLk)? MOTHER ILL/MEAK . . . . . . . 01 CHILD ]LL/~EAK . . . . . . . . 02 CHILD DiED . . . . . . . . . . . . O] NIPPLE/BREAST PROELEN.O~ INSUFFIC[ENT MILK . . . . . 05 MOTHER WORKING . . . . . . . . 06 CHILD REFUSED . . . . . . . . . 07 UEANING AGE . . . . . . . . . . . OD BECAME PREGNANT . . . . . . . 09 STARTED USING CONTRACEPTION . . . . . . . . 10 OTHER 11 (SPECIFY) YES . . . . . . . . . . . . . . . . . . . . 1 Re . . . . . . . . . . . . . . . . . . o*.21 (SK P TO 4401~ MOTHER ILL/WEAK . . . . . . . 01 CHILD ILL/~EAK . . . . . . . . 02 CHILD DiED . . . . . . . . . . . . 03 NIPPLE/BREAST PROBLEM.e4 INSUFFICIENT MILK . . . . . 05 MOTHER WORKING . . . . . . . . 06 CHILD REFUSED . . . . . . . . . 07 WEANING AGE . . . . . . . . . . . 08 BECAME PREGNANT . . . . . . . 09 STARTED USING CONTRACEPTION . . . . . . . . 10 OTHER ~1 (SPECIFY) ALIVE ? (SKIP TO 437) YES . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . 2 (SKIP TO 4401~ MOTHER ILL/WEN( . . . . . . . 01 CHILD ILL/~EAJ( . . . . . . . . 02 CHILD DIED . . . . . . . . . . . . 03 NIPPLE/BREAST pMnlLF, N.04 INSUFFICIENT MILE . . . . . O~ MOTHER I~KLNG . . . . . . . . 06 CHILD REFUBRD . . . . . . . . . 07 WEANING AGE . . . . . . . . . . . 08 EECNCE PREGNANT . . . . . . . 09 STARTED UilNG CORTRADEPTION . . . . . . . . 1Q OTHER I1 (SPECIFY) ALIVE , DE y (SKIP TO 437) YES . . . . . . . . . . . . . . . . . . . . 1 RO*.o*. . . . ,o**. . . , . . . .o~ (SKIP TO 440)~--~ i 437 HOW many months old ues (N~GE) when you started g iv ing the foLLowing on • r lg~L l r beei l? : FormuLa or mi lk other thw~ brea•tmllk? P la in ~mter? Other Liquids? Porridge or u j i ? Any sot|d or mushy food? IF LESS THAN 1 MONTH. ~A~[TE *DO ~ AGE IN NONTHS. . .~[~ NOT GIVEN . . . . . . . . . . . . . 96 AGE IN MONTNS. . .~ NOT GIVEN . . . . . . . . . . . . . 96 AG~ IN NONTHS. . .~r ~ NOT GIVEN . . . . . . . . . . . . . 96 AGE IN HONTHS. . . [~ NOT GIVEN . . . . . . . . . . . . . 96 AGE IN MONTHS.F-- ~ NOT GIVEN . . . . . . . . . . . . . 96 DEAD [~ e (SKIP TO 440) AGE IN MONTHS. . .~ NOT GIVEN . . . . . . . . . . . . . 96 AGE IN MONTHS. . .~[~ NOT GIVEN . . . . . . . . . . . . . 96 AGE IN MONTHS. . *~[~ NOT GIVEN . . . . . . . . . . . . . 96 AGE iN MONTHS. NOT G[VEN . . . . . . . . . . . . . 96 AGE IN MONTHS. NOT GIVEN . . . . . . . . . . . . . 96 (SKIP TO 440) 439 | Did (NAME) dr ink ~yth |ng YES . . . . . . . . . . . . . . . . . . . . I from a bot t le ~|th • nipple NO . . . . . . . . . . . . . . * *** . . .~ II!JJ~!s!'~']~!~ i'~II~!IN~"HJ'~!I'LI['ZlHI~IH{ yesterday or Last n|ghtT DOES NOT KR(~4~ . . . . . . . . . . 8 I /~0 | GO BACK TO 403 FOR NEXT BIRTH; OR, IF NO MORE BIRTHS, GO TO FIRST COLUMN OF 441 AGE IN MONTHS. . .~ NOT GIVEN . . . . . . . . . . . . . 96 AGE IN MONTNS. . .~ NOT GIVEN . . . . . . . . . . . . . 96 AGE IN MONTHS. NOT GIVEN . . . . . . . . . . . . . 96 AGE IN KONTH$.[-~--~ NOT GIVER . . . . . . . . . . . . . 96 AGE IN MI~ITHS.~-~ ROT GIVER . . . . . . . . . . . . . 96 (SKIP TO 440) IW tll [llll IN H I H [ H N I W H W W !11 Ill. W U W I ~I~W ~11~]~ ii i IlU,ll~lUmNNHmlNImm~ltmm IZUZJIHZHZJIHZilIH~IIBIFIW]Iff]~Zlt~HBZl]9]HIHZflF~HF~II nflillU;HWHIWUWiHUWWWIHWWI~ ~l~J ll~lIHZlll~HI,Wl~lllIXilliHi~llliql gBI ,,.,.,,.,.,,.,.,.,.,.,,_.+ fi~iiillillllmlmIW~iP,~l~M Ilfll~I.JHIH.IHIH~HWHHIIH~HHIHII~filIW I~IilW ERG ~ 17 243 441 SECTION 4B. INI~JNISATtON AND HEALTH J ENTER TRE LIME NUI4mER AND NAME Of EACH BIRTH SINCE JANUARY 1988 IN THE TABLE. ASK THE QUEBTIORS AIkOUT ALL OF THESE NIRTNB, BEGIN klITN THE LAST BIRTH. {IF THERE ARE FK)RE THAN 3 BIRTHS, US8 ADDITIONAL EOIU4S). I L .E - -E - - . '2 I I i Oo y~ have • heaLth cord Where (NAME'S) vocc lnet lons BPo wr lEten dmm? IF YES:Nly I lee tE, p le l le? LAST BIRTH NANE ALIVE [~ DEAD [~ V immmmm V TEl. SEEM . . . . . . . . . . . . . . . 1 1 (sKip TO 444)~ i YB$, NOT SEEN . . . . . . . . . . . 2 1 (SEIP TO ~)~ / NO CARD . . . . . . . . . . . . . . . . . 3 NEXT-TO'LAST BIRTH NAME ALIVE [~ DEAD [~ V M i l l ) V YES, SEEN . . . . . . . . . . . . . . . 11 (SKIP TO 444)~ / YES, NOT SEEN . . . . . . . . . . . 2 (SKIP TO 446)J ] NO CARD . . . . . . . . . . . . . . . . . 3 NAN•DONO'FR•'LAST MIRTH ALI 0 DEAN O-- I v iml~qUNv l lml YES, SEEN . . . . . . . . . . . . . . . I (SEIP TO t~) j ] i YES+ NOT SEEN . . . . . . . . . . . 2 (BKIP TO 446)~ ] I NO c~m . . . . . . . . . . . . . . . . . 3 I i YEN . . . . . . . . . . . . . . . . . . . . . i YES . . . . . . . . . . . . . . . . . . . . . 12] j YES . . . . . . . . . . . . . . . . . . . . . 1 443 D id you ever h i ,e l R 1 1 V I~c |Mt ton cord fo r (SEIP TO /~66)4 / (SKIP TO 446)4 / (SKIP TO 446)~ (NNqE)? NO . . . . . . . . . . . . . . . . . . . . . . 2J NO . . . . . . . . . . . . . . . . . . . . . . 2J I NO . . . . . . . . . . . . . . . . . . . . . . 445 (1) COPY VACCINATION DATES FOR EACH VACCINE FRUM THE CARD. m (2) MIITE '~* IN *BAY +COLUNB IF CARD SNOBS THAT k VACCINE WAS GIVEN BUT NO DATE NAB RECORDED. I i DAY MONTH YEAR I DAY MONTH YEAR I DAY MOI4TH YEAR TUiSERCULOSIB (BCG) |CG OPT 1St DOSE )1 DPT 2ndDGSE P2 DPT 3rdOOSE ~3 POI.IO'BIRTN~3SE PO POLIO-tat DOSE Pl PQL I 0-2rid DOSE 72 POLlO-3rd DOSE P3 MEASLES HEA Nee (NN4E) rece ived any vacc l r tot lons that I re not recorded on th i s cmrd? RE~D 'YES' ONLY IF RESPONOENT MENTIONS BCG, DPT 1"3, POLIO 0"3 AND/OR HEASLEB VACCIHE(S). YES . . . . . . . . . . . . . . . . . . . . . 1 (PROBE FOR VACCIHAT]ORS AND NRITE '66' IN TEE COERESPO~DING DAY ~- COLUMN IN 444) - - YES . . . . . . . . . . . . . . . . . . . . . I (PROSE FOR VACCINATIONS AND NR[TE ~66' IM THE CORRESPONDING DAY ~r- COLUMN IN 444) - - NO . 2 DK . . . . . . . . . . . . . . . . . . . . . . 8 (SKIP TO 448) 4 YES . . . . . . . . . . . . . . . . . . . . . 1 (PROBE FOR VACCIBATIOES AND EEtlTE '66' IM THE CORRESPONDING DAY ~- GOLUMI4 IN 444) - - NO . . . . . . . . ° . . , , . o . . . . . . . 2 DE . . . . . . . . . . ° , , ° . . . . . . . . B (SKIP TO 448) 4 BO . . . . . . . . . . . . . . . . . . . . . . 2 DK . . . . . . . . . . .oo+, , , . . . . . B (SKIP TO 448) 4 B id (MANE) ever rece ive YES . . . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . . . I YES . . . . . . . . . . . . . . . . . . . . . I • ny v .cc inat lons to NO . . . . . . . . . . . . . . . . . . . . . . 21 NO . . . . . . . . . . . . . . . . . . . . . . 21 NO . . . . . . . . . . . . . . . . . . . . . . 21 prevent h im/her from (SKIP TO 448)4 ~ (SKIP TO 448)4 8J (SKIP TO 448)4 gett ing diseases? DOES NOT KNOW . . . . . . . . . . . DOES NOt kNO~ . . . . . . . . . . . DOES NOT KNOt/ . . . . . . . . . . . 447 PLease te l l m I f (NAME) (h i s ) rece ived any of the fo lLowino vacc inat ion4: A BCG vecc in4t ion mgeir~t t~rcu lo l ia , that i s , an In jec t ion in the le f t forearm thee mode • scar? Po l io vacc ine, that i s , drops In the mouth? IF YES: HOW ~ times? An in jec t ion ~ la lns t IMe$ies, thmt I I . In the t r t of the r ight mrm? CHECK 216: CHILD ALIVE? YES . . . . . . . . . . . . . . . . . . . . . 1 NO° . . . . . . . . . . . , . . . . . . . . . 2 DOES NOT KNOW . . . . . . . . . . . B YES . . . . . . . . . . . . . . . . . . . . . 1 ~0° . . . . . . . . . . . + . . . . . . . . . ;) DOES NOT KNOW . . . . . . . . . . . 8 NUMBER OF TIMES . . . . . . i YES . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . , . . . . . . . , ,2 DOES NOT KNOW . . . . . . . . . . . D ALIVE (SKIP TO 4S0) GO BACK TO 442 FOR NEXT BIRTH; YES . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . 2 DOES NOT KN(~+# . . . . . . . . . . . 8 YES . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . 2 DOES NOT KNOW . . . . . . . . . . . B NUMBER OF TINES . . . . . . i YES . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . 2 DOES NOT KNOW . . . . . . . . . . . B DEAD [~ ALIVE (SKIP DEAD [~ E~ TO 450) v v OR, IF NO MORE BIRTHS, SKIP TO 480. YES°°° . °° . . . . ° . ° . ° . . . . . . 1 MO . . . . . . . . . . . . . . . . . . . . . . 2 DOES NOT KW)W . . . . . . . . . . . 8 YES . . . . . . . . oo . . . .oo . .o .o l NO.° . . . . . . . . . . . . . . . . . . . °2 DOES NOT KNOW . . . . . . . . . . . 8 NUMBER OF TINES . . . . . . Y~S . . . . . . . . . . . . . . . . . . . . . 1 NO.o . . . . . . . . . . . . o . . °o . .2 DOES NOT KNOW . . . . . . . . . . . B AL IVE~(BK IP OEAD TO 450) 244 ENOm,~ m LAST BIRTH NEXT-TO-LAST B IRTH SECONO-FROI-LAST BIRTH NAME HAME NAME 4SO I H~ (NA)iE) I~ | I t wi th YES . . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . . 1 I I • fever i t Mly t i lm In NO . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . 2 I the List Z ~mlce? DOES NOT KMOW . . . . . . . . . . 8 DOES NOT KNOW . . . . . . . . . . B DOES NOT KNOt/ . . . . . . . . . . 8 I u cough et any time In NO . . . . . . . . . . . . . . . . . . . . . 2, NO . . . . . . . . . . . . . . . . . . . . . 21 NO . . . . . . . . . . . . . . . . . . . . . 2 l the test 2 weeks? (SKIP TO 455)1 (SKIP TO 45S)~ (K IP TO 455)~ OOES NOT KNOW . . . . . . . . . . & DOES NOT KNOW . . . . . . . . . . DOES GOT KM~J . . . . . . . . . . 65z I g. ( .~) .an ILL Math YES . . . . . . . . . . . . . . . . . . . . I YES . . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . . 1 I i • cough tn the List NO . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . 2 I I 24 hours? DOES NOT KgOkt . . . . . . . . . . 8 DOES NOT ICNOW . . . . . . . . . . D DOES NOT KMOW . . . . . . . . . . 8 453 I For h, ~ days (has the DAYS . . . . . . . . . . . . ~ r ' ~ [ - ~ I , COUp4 teated/did the ¢oush DAYS . . . . . . . . . . . . t , , DAYS . . . . . . . . . . . . t I I I I J I I iMt )? I I I LdsE,Y ' *A'" ASA 456 Whe~ (NN4E) hid the I ILlness with = cough, i d id he/Ihe breathe Fester than u~t Math nhort , r lp id breaths? I ~la Inyth f~ given to t reat the fever/c~Jilil? YES . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . o° . . . . . , . . o . . . . .2 DOES NOT KNOM . . . . . . . . . . D w IN EITHER C)R 451 ~THER (SKIP TO 460) YES . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . 2 (SKIP TO 458), / DOES NOT KNOW . . . . . . . . . . YES . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . 2 DOES NOT KNOW . . . . . . . . . . 8 "YES" IN EITHER "YES" IN EITHER 450 OR 451 450ON 451 (SKIP (SKIP TO 460) TO 460) YES . . . . . . . . . . . . . . . . . . . . I YES . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . "12 Ha . . . . . . . . . . . . . . . . . . . . . 2] (SKIP TO 458)4 / (SKIP TO 458)~ DOES NOT KNOW . . . . . . , . . .~ J DOES NOT KNOM . . . . . . . . . . YES. ,° . . . . . . . . . . . , . . ° . . t HO. . , ° . . . . . . . . . , . . , . o .o~ D~S NOT KNOb/ . . . . . . . . . . B 457 What WOI i J v l l l to t reat the fever/ccxJgh? Anythino else? RED, ORe ALL MENTIOMED. INJECTIOR . . . . . . . . . . . . . . A ANTIBIOTIC PILL, SYRUP.B ANTIKALARIAL PILL OR SYRUP . . . . . . . . . . . . . . . . C COUGH SYRUP . . . . . . . . . . . . D OTHER PILL OR SYRUP.E HONE REMEDY/ HERBAL MEDICINE . . . . . . . F OTHER G INJECTION . . . . . . . . . . . . . . A ANTIBIOTIC PILL, SYRUP.B ANTIMALARIAL PILL OR SYRUP . . . . . . . . . . . . . . . . C COUGH SYRUP . . . . . . . . . . . . D OTHER PILL OR SYRUP.,,.E HOME REMEDY/ HERBAL MEDICINE . . . . . . . F OTHER G IWJECTI(~I . . . . . . . . . . . . . . A ANTIBIOTIC PILL, SYROP.B ANTIMALARIAL PILL OR SYRUP . . . . . . . . . . . . . . . . C COUGH SYRUP . . . . . . . . . . . . D OTHER PILL OR SYRUP.E HONE REMEDY/ HERBAL MEDICINE . . . . . . . F OTHER G (SPECIFY) (SPECIFY) (SPECIFY) I NO . . . . . . . . . . . . . . . . . . . . . 2] NO . . . . . . . . . . . . . . . . . . . . . 2] YES . . . . . . . . . . . . . . . . . . . . 1 I &58 Did you seek advice or YES . . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . . I t reater For the HO . . . . . . . . . . ° °°° . . . . . ° .2 fever/cm41h? (SK P TO 46D)~ SX P TO 660)4 (SKIP TO &60)l ] 459 I/here did you seek KIvice or t reatm~t? /b~ytdlere else? RECORD ALL MENTIONED. PUBLIC SECTOR GOVERNMENT HOSPITAL.°A GOVT.HEALTH CENTRE.B GOVT. DISPENSARY . . . . . . C MEDICAL PRXVATE SECTOR NIBBleR,CHURCH HOSPITAL OR CLINIC.D OTHER HOR'GOVT.SERVIC.E PVT. HOSPITAL/CLINIC.F PHARMACY . . . . . . . . . . . . . . G PRIVATE DOCTOR . . . . . . . . H MOEiLE CLINIC . . . . . . . . . . . [ COMMUNITY HLTH WORKER.J OTHER PRIVATE SECTOR SHOP . . . . . . . . . . . . . . . . . . K HERBALIST . . . . . . . . . . . . . L RELATIVE/FRIEHD . . . . . . . M OTHER N (SPECIFY) PUBLIC SECTOR GOVERNMENT BOSPITAL.,A GOVT.NEALTB CENTRE.B GOVT, DISPENSARY . . . . . . C HED[CAL PRIVATE SECTOR MISSION,CHURCH HOSPITAL OR CLINIC.D OTHER NON'GOVT.SEHVIC.E PVT. HOSPXTAL/CL)HIC.F PHARMACY . . . . . . . . . . . . . . G PRIVATE DOCTOR . . . . . . . . H MOBILE CLINIC . . . . . . . . . . . l COEHUNITY HLTH UORKEH,,.J OTHER PRIVATE SECTOR SHOP . . . . . . . . . . . . . . . . . . K HERBALIST . . . . . . . . . . . . . L RELATIVE/FRIEND . . . . . . . M OTHER W (SPECIFY) PUBLIC SECTOR GOVERNMENT HOSPITALo.A GOVToHEALTH CENTRE.R GOVT. DISPENSARY . . . . . . C HEDICAL PRIVATE SECTOR MISSIO$1,CHURCH HOSPITAL OR CLINIC.D OTHER NON'GOVT.SERVIC.E PVT. HOSPITAL/CLJNIC.F PHARMACY . . . . . . . . . . . . . . G PRIVATE DOCTOR . . . . . . . . B MOBILE CLINIC . . . . . . . . . . . i COMHUNITY HLTH ~KER. . . J OTHER PRIVATE SECTOR SHOP . . . . . . . . . . . . . . . . . . K HERBALIST . . . . . . . . . . . . . L RELATIVE/FRIERO . . . . . . . N OTHER N (SPECIFY) EHG WON 19 245 I I X l l ( IA I ( ) h id d iar rhoea in the L~lt two Imeks? LAST BIRTH NEXT-TO-LAST BIRTH MNkE NAME J YER . . . . . . . . . . . . . . . . . . . . 11 YES . . . . . . . . . . . . . . . . . . . . I 1 (SKIP TO 462)~ (SKIP TO 462)a NO . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . 2 ~ O O E S NOT KNOW . . . . . . . . . . 8 ~ D O E S NOT KHOIJ . . . . . . . . . . 8 GO lACK TO 4~2 FOR MEXT BIRTH OR, ~F NO NORE BIRTNS, SKIP TO 480 N i l ( lOllS) h~d d iar rhoea YES . . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . . I i n the LUE 24 hour i? NO . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . 2 DOES NOT KNOW . . . . . . . . . . 8 DOES NOT KNOW . . . . . . . . . . B SEGONO-FRGM-LAST BIRTH I NkJ~ YES . . . . . . . . . . . . . . . . . . . . ( sK iP TO '11 NO°.°°°°**°°°°° , °°°°° , , ;) DOES NOT KNOW . . . . . . . . . . 8 YES . . . . . . . . . . . . . . . . . . . . 1 I NO . . . . . . . . . . . . . . . . . . . . . 2 I DOES NOT KN~/ . . . . . . . . . . 8 For ho l l i ny days (ha l Ehe NOW l i~ s tog ie d id (MANE) " ' " ° ' ,h . - -EROF STDOLS~-~ NUMBER OF BTOOL$~-~ MUNBER OF BTOO~.S~] | ~ss there any b lood YES . . . . . . . . . . . . . . . . . . . . 1 I i n the s t~Ls? NO . . . . . . . . . . . . . . . . . . . . . 2 DOES NOT KNOW . . . . . . . . . . 8 I.,ic,EcK,,,,,., YEB NO ,BKIP I LAST CHILD STILL RREASTFED? - - TO 4~) v 466 I Our l~ (NANE)~I dtz r rhoez , YES . . . . . . . . . . . . . . . . . . . . 1 d fd you cha~e the f r l~lum~y ~ . . . . . . . . . . . . . . . . . . . . . 2] I of ~aasEfe ld i~? (SKIP TO 4~)4 / YES . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . 2 DOES NOT KNOW . . . . . . . . . . 8 (SKIP TO L,68) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iUl ilil![li!i[11[ H iii!!!!iiiiil!l!i~l~i;.i~l!tii!il!lttililhtlitllll YEB . . . . . . . . . . . . . . . . . . . . 1 m DOES NOT KNOW . . . . . . . . . . O (SK IP TO 4M) BB311H~IIH JIlIHWII1w~wiH.l~tllrlH]mmB~imlmii. I~llJ~UJlZllZlZlll~lZl~liEllUlllE ~1 ElJUll IWBIBmBII I mliliHUllliHZE X "H B UH H UmmB~alBZEP~ I L.67 Did yOU Ih ( ; re l ie the number INCREASED . . . . . . . . . . . . . . 1 ii!iGl[illWElil[iiilZl~tltilWZ]Wtl~iUl;ilt~tLitl~l~F~ ~HHtEWm[~[tlnilHIIII~EIIII]IHIHHII~F318WliHHt]IHH I of b reast feed l , reduce them REDUCEO . . . . . . . . . . . . . . . . 2 !~i~iiiltl[~i!i!:~!~:.!F!~iiHi!iliiiii!iiiiiii!i!!iiiiiiiUii~!i~ ltUl~ilit~il~Wl~!t,ifl~i~lt[l~ 468 I (Aside f r~ breesEmtlk) I Mas he/she Bive~ the la~e SNkE . . . . . . . . . . . . . . . . . . . 1 SAME . . . . . . . . . . . . . . . . . . . 1 SAME . . . . . . . . . . . . . . . . . . . 1 mounE EO dr ink as before HORE . . . . . . . . . . . . . . . . . . . 2 MORE . . . . . . . . . . . . . . . . . . . 2 NORE . . . . . . . . . . . . . . . . . . . 2 the d ie r rhoe l , or mre , or LESS . . . . . . . . . . . . . . . . . . . 3 LESS . . . . . . . . . . . . . . . . . . . 3 LESS . . . . . . . . . . . . . . . . . . . 3 tess? Ol~S NOT XNO~ . . . . . . . . . . B DOES NOT KNOW . . . . . . . . . . B DQES HOT KNOiJ . . . . . . . . . . O 469 Wee Inyth lno g iven TO treeE YES . . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . . YES . . . . . . . . . . . . . . . . . . . . 1 I the d iarrhoea? NO . . . . . . . . . . . . . . . . . . . . . ~2 NO . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . 2 (SKIP TO 4711~ | (SKIP TO ~71)4 (SKIP TO 471)~ DDEB NOT KNOW . . . . . . . . . . ~J DOES NOT KNOW . . . . . . . . . . D DOES ROT KNO~ . . . . . . . . . . 67O Whet was Qlv~ to t reat the dierrhoeaT Anyth ing else? RECORD ALL MENTIONED. FLUID FROM ORS PACKET.A ANTIBIOTIC PILL, SYRUP,B OTHER PILL ON SYRUP.C INJECTION . . . . . . . . . . . . . . D I ,V . (IHTRAVEH(~JS) . . . . . E BONE REMEDY OR HERBS,,,F OTHER G FLUID FRON ORS PACKET,,A ANTIBIOTIC PILL, SYRUP.B OTHER PILL OR SYRUP.( INJECTION . . . . . . . . . . . . . . D ] .V. (INTRAVENOUS) . . . . . E HORE REMEDY OR NERBS.F OTHER G (SPECIFY) FLUID FRON ORS PACKET, .A ANTIBIOTIC PILL, BYRUP.B OTHER PILL ON $YRUP.C INJECTION . . . . . . . . . . . . . . D I ,V . (INTRAVEMOUSl . . . . . E HOME REHEDY OR HERRS.F OTHER G (SPECIFY) (SPECIFY) I NO . . . . . . . . . . . . . . . . . . . . . 2] YES . . . . . . . . . . . . . . . . . . . . 1 I 471 Old you seek advice or YES . . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . . 1 treatment fo r the NO . . . . . . . . . . . . . . . . . . . . . 2] NO . . . . . . . . . . . . . . . . . . . . . 2 d iarrhoea? (SKIP TO 47~ u (SKIP TO 4~)4 (SKIP TO 47] )4 ] k72 Sere d id you seek advice or zreezw~nt? Anywhere else? RECORD ALL NEBTIONED. PUBLIC SECTOR GOVERNMENT HOSPITAL.A GOVT.HEALTH CENTRE.B GOVT. DISPENSARY . . . . . . C ~ED]CAL PRIVATE SECTOR MISS[ON,CHURCH HOSPITAL O~ CLZNIC.D OTHER NON-GOVT.SERVIC.E PVT. NOSP]TAL/CL]NiC,,F PflARHACY . . . . . . . . . . . . . . G PRIVATE DOCTOR . . . . . . . . H N(~[LE CLINIC . . . . . . . . . . . l COHMUNITY HLTH bN~KER.J OTNER PRIVATE SECTOR SHOP . . . . . . . . . . . . . . . . . . K HERBALIST . . . . . . . . . . . . . L RELATIVE/FRZEND . . . . . . . M OTHER N (SPECIFY) PUBLIC SECTOR GOVERNNENT HOSP]TAL.A GOVT.HEALTH CENTRE.,B GOVT. DISPENSARY . . . . . . C MEDICAL PRIVATE SECTOR NISSION,CHURCH HOSPITAL OR CLIN]C.D OTHER NON-GOVT.SENVIC.E PVT. HOSPlTAL/CLIN[C,.F PHARNACY . . . . . . . . . . . . . . G PRIVATE DOCTOR . . . . . . . . H MOBILE CLIKIC . . . . . . . . . . . l COIkHUN]TY HLTH 5A3RXER.J OTHER PRIVATE SECTOR SHOP . . . . . . . . . . . . . . . . . . K HERBALIST . . . . . . . . . . . . . L RELATIVE/FRIEND . . . . . . . N OTHER N (SPECIFY) PURLIC SECTOR C'OVERNNERT HOSPITAL.*.A GOVT,HEALTH CENTRE.B r.~VT. DISPENSARY . . . . . . C MEDICAL PRIVATE SIECTC~ MISSION,CHURCH HOSPITAL ON CLINIC.D OTHER NON-GOVT.SERV]C.E PVT, HOSPITALJCLINIC.F PHARMACY . . . . . . . . . . . . . . G PRIVATE DOCTQ~ . . . . . . . . H MOBILE CLINIC . . . . . . . . . . . I C~NITY HLTX IJORKER.,.J OTHER PRIVATE SECTOR SHOP . . . . . . . . . . . . . . . . . . K HERBALIST . . . . . . . . . . . . . L RELATIVE/FR[END . . . . . . . R OTHER B (SPECIFY) 246 ENB ~,~ 20 473 I CHECK 470: ORS FLUID FROM PACKET MENTIONED? 474 I Uu (MANE) Birch ~eter mixed I with Orat l te or OBS •aches when he/she h id the diarrhoea? LAST BIRTH NkNE NO, YES, ORS FLUID ORS FLUID NOT MENTIONED MENTIOMED v (SKIP TO 4~) YES . . . . . . . . . . . . . . . . . . . . ( NO., . . . . . . . . . . ° . . . . . . . 2 "1 (SKIP TO 479)4 DOES NOT KNOW . . . . . . . . . . 8 J NEXT-TO*LAST BIRTH NAME NO, YES, ORS FLUID ORS FLUID NOT MENTIONED MENTIORED (SKIP TO 4~) YES . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . 2 (SNIP TO 479)~ / DOES NOT KN~ . . . . . . . . . . SECOND" FROM" LAST BIRTH NAME NO, YES, ORS FLUID ORS FLUID MOT MEN?loBED NENTIOMEO (SKIP TO 4731 YES . . . . . . . . . . . . . . . . . . . . 1 NO,.+.* . . . . . . . . . . ° ,+. .2 -1 (SKIP TO 4~9)~ DORS NOT KNOt/ . . . . . . . . . . 8 j 475 For how ~ days Was (NbJiE) given the Ors t l te / ORS? IF LESS THAN ( DAY, ~IRITE SACK TO 442 FOM BENT DAYS . . . . . . . . . . . . ~ - ~ DAYS . . . . . . . . . . . . [ ~ DOES NOT NMO~ . . . . . . . . . 98 DOES ROT KNO~ . . . . . . . . . ON, IF NO MORE BIRTHS, GO TO 480 DAYS . . . . . . . . . . . . DOES NOT KNOW . . . . . . . . . 98 SKIP 481 | HmVe you ever heard of • special product catted ORS or | YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1--~-~_4~ I Orat | ta you clm get for the treatment of diarrhoea? I / NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 *'*.'h'. i . ' l SHOU SACHETS. NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 ~501 sachets to t reat d i•rrhoea in yoursel f or s~reone eLse? SHOd/ SACHETS. NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 D486 k84 I Th" t'st t i " YOU preparedOr'Iite (ORS)" d i d y o u p r . r , th . .o te • .ch . t . t once or onty part of I wHOLE SACHET AT OBCE . . . . . . . . . . . . 1 I the sachet? PART OF SACHET . . . . . . . . . . . . . . . . . . Z ;486 485 Uhlt con(siPper did yo4J use to measure the water the Ia•t t im you made Orat i te (ORS)? SMALL KIHBO (1/2 KG) . . . . . . . . . . . 01 LARGE KIN80 (1KG) . . . . . . . . . . . . . 02 BEER BOTTLE (TUSKER) . . . . . . . . . . . 03 BEER BOTTLE (PREMIUM) . . . . . . . . . . 06 TREETOP BOTTLE (750 ML) . . . . . . . . 05 SOOA BOTTLE (250 NL) . . . . . . . . . . . 06 TEACUP . . . . . . . . . . . . . . . . . . . . . . . . . 07 GLASS . . . . . . . . . . . . . . . . . . . . . . . . . . 0~ OTHER 09 (SPECIFY) I content• of the ~S sachet? I 486 Uhere can you get OraL|Re/ORS s•chets? PR~: Anywhere else? RECORD ALL PLACES MENTIONED. PUBLIC SECTOR GOVERNMENT HOSPITAL . . . . . . . . . . . . A GOVERNMENT HEALTH CENTRE . . . . . . . N GOVERNMENT DISPENSARY . . . . . . . . . . C MEDICAL PRIVATE BECTOI~ MISSION,CHURCH HOSPiTAL,CLINIC.D OTHER NON*GOVERNMENTAL SERVICE.E PRIVATE HOSPITAL/CLINIC . . . . . . . . F PHARMACY . . . . . . . . . . . . . . . . . . . . . . . G PRIVATE DOCTOR . . . . . . . . . . . . . . . . . N MOBILE CLINIC . . . . . . . . . . . . . . . . . . . . J COMMUNITY HLTN ~R3MKER . . . . . . . . . . . . J OTHER PRIVATE SECTOR SHOP . . . . . . . . . . . . . . . . . . . . . . . . . . . K HERBALIST . . . . . . . . . . . . . . . . . . . . . . L RELATiVE/FRIEND . . . . . . . . . . . . . . . . N OTHER N (SPECIFY) 247 ENG NON 21 SECTION 5. MARRIAGE SKIP " I CutsY i .S AND FILTERS I CODING CATEGORIES I TO ~0 [ . - - - ,o - , , . , o - , .e . - ,oo * 'n I * ~ - * * - o* ' - - ,~ - - . I NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 ~512 ,o21Ar.* ,rr. or vir, i*h.n o*.r*ouno I,REO . ' wldolIQd, dlvorcKI e or no torqler Living together? L%VING TOGETHER . . . . . . . . . . . . . . . . . 2 WIDOWED . . . . . . . . . . . . . . . . . . . . . . . . . DIVONCED . . . . . . . . . . . . . . . . . . . . . . . . 507 NO LO~GER LIVING TOGETHER . . . . . . . S I I S03 DOes your husb lnd / l~r t r4r usua l ly l i ve wtth you or does LIVES WITH HER . . . . . . . . . . . . . . . . . . 1 ~504 he usueity stay a~mdhere else? m STAYS SONEMHERE ELSE . . . . . . . . . . . . 2 °1 I . I NAIROG[ . . . . . . . . . . . . . . . . . . . . . . . . . 2 MOMBASA . . . . . . . . . . . . . . . . . . . . . . . . . 3 OUTSIDE DISTRICT . . . . . . . . . . . . . . . . 4 DOES NOT KNOW . . . . . . . . . . . . . . . . . . . 8 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 ~507 ~O~lN'--v°th'*"'--''*t'r'--heD'v" m -ER0~S RDT . *NCU . ~--71'8 I or -- . , . - - , I . *-- . ' 1 508 In whet month and year did you s tar t l i v ing with MONTH . . . . . . . . . . . . . . . . . . . . your ( f i r s t ) husband/partner? I L l DOES NOT KNOW VX)NTN . . . . . . . . . . . . 98 YEAR . . . . . . . . . . . . . . . . . . . . . [ ~ DOES NOT KNOG YEAR . . . . . . . . . . . . . No~ old were you when you started r iv ing with him? ,°, I 511 NO R AOE . [ - -~1 DOES NOT KNOW AGE . . . . . . . . . . . . . . 98 I ~513 CHECK CC~SISTENCY OF 508 AND 509: YEAR OF BIRTH (105) PLUS + AGE AT MARRIAGE (500) CALCULATED ~ - ~ YEAR OF MARRIAGE IF NECESSARY. CALCULATE YEAR OF UIRTH CURRENT YEAR MINUS CURRENT AGE (1061~-~ CALCULATED ~ - - ~ YEAR OF BIRTH IS THE CALCULATED YEAR OF MARRIAGE WITHIN ONE YEAR OF THE REPORTED YEAR OF MARRIAGE (508) ? YES I--7 NO [~ ,PROBE AND CORRECT 508 AND S09. (SKIP TO 513) ENG WON 22 248 NO. I ~JESTIONS ANO FILTERS i 512 ~ IF NEVER MARRIED OR LIVED WITH A MAN: I Nave you ever had sexual InTercourse? SKIP I COOING CATEGORIES j TO I - * -y* c*v*y°l . . . . . . . . . . . . . . . . . . . . . . order to get • better understanding of fami ly planning fe r t i l i ty , Sou oLd were you when you f i r s t had FIRST TIME WHEN NARRIEO . . . . . . . . 96 sexum I I nter¢o~rse? Hxuat Intercourse? DAYS . . . . . . . . . . . . . . . . . . . . . IF NOllE, WRITE mOO1. I .~--.-,--.~oo~-~,~o-ooo~,. I - -o , - . ~1 with in the test 6 months? IF 00, SKIP TO 518, 51, I D,d v~ ~. . *~- .ith --Y of t"--.--o? eYES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 NO. , , , ° °° , . . . . . . . . . . . , . ° .o . . . ° . °~ ,,.I .--o---,--.~--,o,.~ooo-.,,~,~,~,.o,.,., I - -o , - . c -~ l 519 When uas the ru t t im you had sexual intercourse? DAYS AGO . . . . . . . . . . . . . , .1 i l l WEEKS AGO . . . . . . . . . . . . . . 2 HONTHS AGO . . . . . . . . . . . . . 3 YEARS AGO . . . . . . . . . . . . . . & BEFORE LAST BIRTH . . . . . . . . . . . . . 996 .01 . . , . . . , . _ , ,n_ , . v.ry,~**.o,,op,c I , . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . , J Have you heard of I disease cal led AIDS? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 ~531 521 From which sources of Inforn~tlon or persons have you heard Woout AIDS In the Imat n~onthT Any others? CIRCLE ALL HENTIORED. RADIO . . . . . . . . . . . . . . . . . . . . . . . . . . . A TV . . . . . . . . . . . . . . . . . . . ,o .ooo. . .o .D NEWSPAPERS . . . . . . . . . . . . . . . . . . . . . . C HEALTH WORKERS . . . . . . . . . . . . . . . . . . D PRIESTS/PREACHERS/KN)HIS . . . . . . . . E HUSBAND . . . . . . . . . . . . . . . . . . . . . . . . . F FRIENDS/RELATIVES . . . . . . . . . . . . . . . G SCHOOLS . . . . . . . . . . . . . . . . . . . . . . . . . H 6QOKLETS/PAI4PHLETSIPOSTERS . . . . . . I 6ARAZAS . . . . . . . . . . . . . . . . . . . . . . . . . J OTHER K (SPECIFY) HONE . . . . . . . . . . . . . . . . . . . . . . . . . . . . L 522 Nou is AIDS t ro l l i ed? Any other ~ye? DO NOT READ CODES. CIRCLE ALL MENTIONED. SEXUAL INTERC(X~SE . . . . . . . . . . . . . . A SHAVING/RAZORS . . . . . . . . . . . . . . . . . . l ]NJECTIOES . . . . . . . . . . . . . . . . . . . . . . C CIRCt.qCISIOR, TATTOOS . . . . . . . . . . . D MOTHER TO CHILD . . . . . . . . . . . . . . . . . E TRANSFUSION OF INFECTED DLOOD.F OTHER G (SPECIFY) DOES HOT KNOW . . . . . . . . . . . . . . . . . . . B EHG ~ 23 249 523 QUESTIONS AND FILTERS Do ~ th ink that you cam Rat AIDS from shaking hands with someone Mho has AIDS? k iss ing sGlm~ne who has AIDS? Bar | r ig the clothes of s~e k~o has AIDS? sharing eat ing utlee~it~ with someone ~o has AIOS? to~chlng SGlmOr~ NhO has died from AIDS? mosquito, f lee or bedbug bites? COOING CATEGORIES YES NO DK HARDSHAKING . . . . . . . . . . . . . . . . 1 2 8 KISSING . . . . . . . . . . . . . . . . . . . . 1 2 B SHARING CLOTHES . . . . . . . . . . . . 1 2 8 SHARING EATING UTENSILS.,.t 2 8 TOUCHING SOMEONE WHO OlEb.,1 2 8 NOSG4JITO/FLER/REDBUO $ITES.1 2 8 SKIP TO 21 *b °r h h*°°ki rs°n I . I to be ln f~ted with the AIDE virus? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Z DOES NOT KNOW . . . . . . . . . . . . . . . . . . . 8 ,2 , I i . i t possible for a Koman who Has the AIDS v i rus to ] YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 I RiVe b i r th to a ch i ld with the AIDS virus? I NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 DOES NOT KNOM . . . . . . . . . . . . . . . . . . . 8 there nothing that people can do? NOTHING THEY CAM DO . . . . . . . . . . . . . 2 ~528 DOES ROT KNOU . . . . . . . . . . . . . . . . . . . ~ ~528 I I s27 I . . cNi people protect ThemSelves from get t in 9 AIDS? DO NOT HAVE SEX AT ALL . . . . . . . . . . A I LIMIT NUMBER OF SEXUAL PARTNERR.R DO NOT READ CODES TO RESPONDENT. USE CONDOkiS DURING SEX . . . . . . . . . . C STERILIZE SYRiNGES/NEEDLES . . . . . . D Any other waysT AVOID PROSTITUTES . . . . . . . . . . . . . . . E OTHER F CIRCLE ALL MERTIUMEO. (SPECIFY) I [ . I died f r~AIOS? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 ~531 DOES NOT KNOW . . . . . . . . . . . . . . . . . . . 8 ~531 PRESERCE OF OTHERS AT THIS POINT. YES NO CHILDREN UNDER 10 . . . . . . . . . . 1 2 HUSSANO . . . . . . . . . . . . . . . . . . . . 1 2 OTHER MALES . . . . . . . . . . . . . . . . 1 2 OTHER FEMALES . . . . . . . . . . . . . . 1 2 ENG kOI 2k 250 NO. 601 602 603 CHECK 312: NEITHER .EHILLEED [~ SECTION 6. FERTLLLTY PREFERENCES DUESTIGNS AND FILTERS I COD IJ HE OR SHE $TERILISED r~ CRECK 502: CURRENTLY NARRLED OR L|VLNG TOGETHER ? CHECK 223: NOT PREGNANT OR UN~HE (~ / Nou I have iome ~Nestto~z ebout the future . bloutd you Like to have ( I /another) ch i ld or v~uLd you prefer not to hew any (more) chitdrcm? NOT MARRIED/ NOT LIVING TOGETHER I-'-I PREGNANT J v Ro~ Z have some questions about the future. After the ch i ld you are expecting, would you Like to have another ch i ld or would you prefer not to have any more chi ldren? HAVE A (ANOTHER) CHILD . . . . . . . . . . 1 NO HORE/MONE . . . . . . . . . . . . . . . . . . . . 2 - - SAYS SHE ~NIT GET PREGNANT . . . . . 3 UNDECIDED, DOES NOT KNOW . . . . . . . . B EI(IP I TO I L614 I .610 CHECK ZZ3: NOT PREGNANT OR UNSURE [~ / HOW ior4l ~td you Llke to va i l from now before the b i r th of (e/another) chi ld? 605 J CHECK Z16 AND Z2~: HAS LIVLNG CRILDCREN) YES PREr~t~RT? v 606 CRECK Z23: NOT PREGN~T OR U~NIE [~ ! R~ old would you l ike ymJr youngest ch i ld to be uhen your next ch i ld ia born? PREGNANT [~ Now tong would you Like to wait a f ter the b i r th of the ch i ld you are expecting before the b i r th of another chi ld? NO F-1 PREGNANT [~ ho~ old ucutd you Like the ch i ld you are expecting to be uhen your next ch i ld is born? "~rHS . . . . . . . . . . . . . . . . . 1 ~1112 YEARS . . . . . . . . . . . . . . . . . . 2 SOON/ROW . . . . . . . . . . . . . . . . . . . . . . SAYS SHE CARAT GET PREGNANT.99+J - OTHER (SPECLFY) DOES NOT KNOA . . . . . . . . . . . . . . . . . 998 AGE OF CHILD I YEARS . . . . . . . . . . . . . . . . . . . . ~- J "~- - DOES NOT KNOW . . . . . . . . . . . . . . . . . . ,610 t 610 ~610 I I i 607 I~utd you rec~wmend to a f r iend or re lat ive in your YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 ~610 clr©Lamtarce= to have an operatio~ not to have any more ¢hf tdren? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 608 I Why not? ERG I~N 251 SKIP BO" I C~IESTIORS AHD FILTERS I CODING CATEGORIES I TO dlsq:lDrovea of coqptes u6Jne a method to avoid DISAPPROVES . . . . . . . . . . . . . . . . . . . . . 2 pt'~y? DOES NOT KHQW . . . . . . . . . . . . . . . . . . . 8 - i - . ° - - - - r " n " ° * _ _ - - o o * - - . - - . r I . . I i . ,i NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2~612 family ptlmning in the N i t year? ONCE ON TWICE . . . . . . . . . . . . . . . . . . . 2 NORE OFTEN . . . . . . . . . . . . . . . . . . . . . . 3 i - . o - - - o ° - r - - o o Or , - - o - - I . . I 613 J Do you think your husband/partner uants the same SAME NUMBER . . . . . . . . . . . . . . . . . . . . . 1 I I tvJber of children that you want, or does he uant more NORE CHILDREN . . . . . . . . . . . . . . . . . . . 2 I or fewer than you Mint? FEWER CHILDREN . . . . . . . . . . . . . . . . . . ] DOES NOT KNOW . . . . . . . . . . . . . . . . . . . 8 Now Lor~ should I couple vai l before starting sexual Interc~Jrse after the b i r th of • baby? NONTHS . . . . . . . . . . . . . . . . . 1 [ ~ I YEARS . . . . . . . . . . . . . . . . . . 2 OTHER (SPECIFY) I 615 Should i mother uait unt i l she has completely stopped WAlt . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 I breestfeedJng before starting to have sexual relations I again, or doesn*t i t mmtter? DOESN'T MATTER . . . . . . . . . . . . . . . . . . 2 616 in generlt, do you IJpprove or disapprove of couples I APPROVE . . . . . . . . . . . . . . . . . . . . . . . . . 1 using a mthod to avoid getting pregnant? I DISAPPROVE . . . . . . . . . . . . . . . . . . . . . . 2 617 CHECK 216: HAS LIVING CHILD(REN) [~ / I f you could go back to the time yc~ did not hive any children and could choose exlctty the number of children to hive in your whole Life, how Itlny Mould that be? NO LIVING CHILDREN~ If you could choose exactly the number of children to have in your whole t i le , hou nlany ~outd that be? RECORD SINGLE NUMBER OR OTHER ANSWER. NUMBER . . . . . . . . . . . . . . . . . . . OTHER ANSWER 96 ~618 (SPECifY) J "1 Hou many boys? go~ l I I my gir ls? I NUNSER OF BOYS . . . . . . . . . . . ~ I NUNBER OF GIRLS . . . . . . . . . . OTHER (SPECIFY) 96 618 I ~nmt do you think is the bast number of months or yelrm between the birth of one chi ld and the birth of the next child? J MONTHS . . . . . . . . . . . . . . . . . 1 ~ J TEARS . . . . . . . . . . . . . . . . . . 2 OTHER 996 (SPECIFY) ENG WON 26 252 SECTICK 7. HUSBAND'S BACKGROUND AND ~14kN'S liORK SKIP HQ. J QUESTIONS AND FILTERS I CODING CATEGORIES I TO 170' i L~)tl ! CHECK SOls EVER MARRIED NEVER HARR]ED/ OR LIVED NEVER LLVED TOGETHER [~ TOGETHER ¥ ASK QUEST|O~S ABOUT CURRENT OR MOST RECENT HUSBAND/PARTNER. I 702 Did your (Lest) hL-___.~rd/l~rtner ever attend school? ~3 I . , , w l l the highest Level of school he attended: I PRIHANY . . . . . . . . . . . . . . . . . . . . . . . . 1 I pr|aery, eecondory, or university? I SECONDARY . . . . . . . . . . . . . . . . . . . . . . . 2 UNIVERSITY . . . . . . . . . . . . . . . . . . . . . . 3 DOES NOT KNOW . . . . . . . . . . . . . . . . . . . 8 ~704E ~ I ~" - Eh" ~" "t--rO/'°r'"r' ~" °~"tid I B T ' A R D ' / ' E A B . , t . ,.v.L, DO. NOT *NO~ . . . . . . . . . . . . . . . . . . . . . . . . . ~- - l I , , I 704E I CIn (CouLd) he reid • Letter or newspeper in any I tangull l l easily, with d i f f i cu l ty , or not at al l? I EASILY . . . . . . . . . . . . . . . . . . . . . . . . . . I WITH DIFFICULTY . . . . . . . . . . . . . . . . . 2 NOT AT ALL . . . . . . . . . . . . . . . . . . . . . . 3 DOES NOT KNOtJ . . . . . . . . . . . . . . . . . . . 8 705 706 rinse kind of work does (did) your (List) huil~nd/plrtner mlinLydo? [-~ LEAVE BOXES BLANK 70T 70'AI '°--'°°' h'e'*°'*e~L'r"B'°r"L'r*' I *ESDOESNo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . HOT KH~ . . . . . . . . . . . . . . . . . . . " . . . . . . . ,2'1 CHECK 705: v ~ I ~OFLKS (WORKED) ~ DOES (DID) IN AGRICULTURE HOT I~O~K (-'-] L708 IN AGRICULTURE | (Does/did) your hus l~/par tner ~ork mainly on his I HIS/FAHILY LAND . . . . . . . . . . . . . . . . . 1 I oMn L~d or family tw~d. or (does/did) he rent land, I RENTED LAND . . . . . . . . . . . . . . . . . . . . . 2 I or (dOel/d|d) hi ~ork on someone eLseJl Land? SOMEONE ELSE*S LAHD . . . . . . . . . . . . . 3 EMG El(~4 27 253 .o. I QUIESTIORS AND FILTERS Aalcie from your oun houseuork, are y~ current ly working? SKIP CODING CATEGORIES I TO I ; YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 ~710 NO . . . . . . .~ . . . . . . . . . . . . o . .H .oo . .~ 709 As you knou, some t~wen take up jobs for uhich they ere la id in cash or k ind. Others seLL things, have a mi i I~ le |~ l or work on the f lm i ty farm or in the fami ly bum|nice, Are you curref l t iy dolnO any of these th i rds or any other work? I YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 I I NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 ~717 710 I i~mt ts your occupation, that is, t lh l t k i rd of v~rk do you do? LEAVE BOXES BLAME. falsity, for scQeon4 else, or are you seLf-lmlpLoyed? FOR SONEONE ELSE . . . . . . . . . . . . . . . . 2 SELF*EHPLOYED . . . . . . . . . . . . . . . . . . . 3 "t I . I PROBE: Do yo~ Bake money for uorking? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Z I 7t ] i Do you do th is wrk st home or a~ay f r~ ho~e? I 716 I CHECK E15/216/Z18: HAS CHILD EOEN SINCE YES JAN. 19U AMO LIVING AT ItCNE? v [~ 7151 . i L . ,ou ere uork l r~, do you usuaLLy I have (ItANE OF YOUNGEST CHILD AT HERE) with you, sometime hive h|m/h i r u i th you, or r~y~r have him/her u i th you? HOHE . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 AWAY . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 NO I--1 USUALLY . . . . . . . . . . . . . . . . . . . . . . . . . 1 SOHETIHES . . . . . . . . . . . . . . . . . . . . . . . 2 NEVER . . . . . . . . . . . . . . . . . . . . . . . . . . . ] [ II r717 I I ~717 I 716 Mho us~iiy takes care of (MANE OF Y(XJUGEST CHILD AT HOME) uhi te yo~ ere ~ork|ne? REC~O(tD THE TIRE HUSSARD/PARTNER . . . . . . . . . . . . . . . . 01 OLDER CHILD(REN) . . . . . . . . . . . . . . . 02 OTHER RELATIVES . . . . . . . . . . . . . . . . 03 NEIGHBORS . . . . . . . . . . . . . . . . . . . . . . 04 FRIENDS . . . . . . . . . . . . . . . . . . . . . . . . 05 SERVANTS/HIRED HELP . . . . . . . . . . . . 06 CHILD IS IN SCHOOL . . . . . . . . . . . . . 07 INSTITUTIORAL CHiLDCARE . . . . . . . . 08 OTHER 09 (SPECIFY) £#G MZlq 28 254 SECTION 8. HEIGHT AND HEIGHT CHECK 222: ONE OM NG~E BIRTHS ~ NO BIRTHS SINCE JAR, 1988 L,r,-d SINCE JAN, 1988 ~1 > END INTERVIEWER: IN 802 (COLLI~S 2 -4) RECORD THE LINE RU#IBER FOR EACH CHILD BORN SINCE JANUARY 19B8 AND STILL ALIVE. IN 803 ~ 806 RECORD THE NAME AND BIRTH DATE FOR THE RESPONDENT AND FOR ALL LIVING CHILDREN BOBN RINCE JANUARy 1988. IN 806 AND 1108 RECORD HEIGHT AND UE]GAT OF THE RESPONDENT AND THE LIVING CHILDREN. (NOTE: ALL RESPORDENTS MITN ONE OR ;40RE BIRTHS SINCE JANUARY 1988 SHOULD BE MEIGAED ANO MEASURED EVEN IF ALL OF THE CHILDREN HAVE DIED. IF THERE ARE MORE THAN ] L IVIRG CHILDREN ROAN SINCE JANUARY 1988. USE ADDITIONAL FCRRS). 802 LIME NO. FROM D.212 8O3 VANE FROM D.212 FOR CHILDREN 80G DATE OF BIRTH FROM 0.105 FOM RESP~ROENT FROM D.21~ EOM CHILDRER~ ~ ASK FOR DAY OF BIRTH 8O5 RCG SCAR ON LOWER LEFT ARM 806 HEIGHT ( in cent t l te re ) 807 UAS HEIGHT/LENGTH OF CHILD MEASURED LYING OO~ CR STANOING UP? 808 WEIGHT ( in k l tvErm) B09 RID-UPPER ARM CIRCUMFERENCE ( in mi t t imeters ) 810 ~4TE WEIGHED ARO NEASUItED RE SPCddOENT • r . . . ; . i .~:lii .,,~#q IHIII~ pp l~l l l in l l l i t l lq i f l liJ u ii i i]i ltUiJi (RAHE) J,~ YOUNGEST LIVING CHILD n-] ( NAME ) DAY . . . . . . MONTH . . . . YEAR . . . . . I,I;Ut~ItWLt!tltI,~I,I,~,,~.,.,z.,I,,,,,I SCAR SEEN . . . . . . 1 W; Illll]itlll~ltltIHH~;~ll~il~l~HI][]tl RO SCAR . . . . . . . . ~1 811 RESULT DAY . . . . . . NORTH . . . . YEAR . . . . . MEASURED . . . . . . . 1 NOV PRESENT.,. .3 REFUSED . . . . . . . . 4 OTHER . . . . . . . . . . 6 (SPECIFY) LYING . . . . . . . . . . 1 STANDING . . . . . . . 2 FrF]D I I I DAY . . . . . . MONTH . . . . YEAR . . . . . CHILD MEASURED.1 CHILD S IC[ . . . . . 2 CHILD NOT PRESENT . . . . . . . 3 CHILD REFUSED.4 MOTHER REFUSED.S OTHER . . . . . . . . . . 6 (SPECIFY) NEXT-TO- ~ SECOND- TO- Y~NGEST Y~NGEST LIVING CHILD LIVING CHILD (NAME) (NAME) MONTH . . . . MONTH . . . . YEAR . . . . . SCAR SEEN . . . . . . 1 S~R SEEM . . . . . . 1 NO S~R . . . . . . . . 2 NO S~R . . . . . . . . 2 LYING . . . . . . . . . . 1 LYING . . . . . . . . . . 1 STANDING . . . . . . . 2 STANDING . . . . . . . 2 I l l l ~NTH . . . . NORTH . . . . YEAR . . . . . CHILD MEASURED,1 CHILD ~ASURED.1 CHILD SICK . . . . . 2 CHILD SICK . . . . . 2 CHILD ROT CHILD NOT PRESENT . . . . . . . 3 PRESENT . . . . . . . 3 CHILD REFUSED.,4 CHILD REFUSED,.4 MOTHER REFUSED.5 MOTHER REFUHED,5 OTHER . . . . . . . . . . 6 OTHER . . . . . . . . . . 6 (SPECIFY) (SPECIFY) 812 NAME Of ~ RARE OF MEASURER : ASS I START : * * Adapt que l t ton tOcet ty a f te r determin ing the most common in jec t ion s i te (usua lLy the Le f t arm or shou lder ) . ENG WON 29 255 Comments About Respondent: INTERVIEWER'S OBSERVATIONS (To be fi l led in after complet ing interview) Comments on Speci f ic Questions: Any Other Comments: SUPERVISOR'S OBSERVATIONS Name of Supervisor: Date: EDITORtS OBSERVATIONS ENG WOM 3 0 256 NATIONAL COUNCIL FOR POPULAT ION AND DEVELOPMENT CONFIDENTIAL 2 Nov/92 CENTRAL BUREAU OF STAT IST ICS KENYA DEMOGRAPHIC AND HEALTH SURVEY 2 - -MAN'S QUEST IONNAIRE IDENTIF ICAT ION PROVINCE DISTRICT LOCATION/TOWN SUBLOCATION/WARD NASSEP CLUSTER NUMBER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . KDHS CLUSTER NUMBER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . HOUSEHOLD NUMBER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . NA IROBI /MOMBASA=I , SMALL CITY=2, TOWN=3, RURAL=4. . . NAME OF HOUSEHOLD HEAD NAME AND L INE NUMBER OF MAN NAME AND L INE NUMBER OF WIFE NAME AND L INE NUMBER OF SECOND WIFE INTERVIEWER V IS ITS 1 2 3 DATE INTERVIEWER IS NAME RESULT * NEXT VIS IT: DATE T IME * RESULT CODES: 1 COMPLETED 2 NOT AT HOME 3 POSTPONED I F INAL V IS IT DAY MONTH YEAR NAME RESULT iiiiii i~iiiiiiiii TOTAL NU.EER iiiii~iiiiiiiiiiiiii~iiiiii OF V IS ITS 4 REFUSED 5 PARTLY COMPLETED 6 INCAPACITATED 7 OTHER (SPECIFY) LANGUAGE OF QUEST IONNAIRE: ENGL ISH 1 0 LANGUAGE USED IN INTERVIEW** . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . RESPONDENTIS LOCAL LANGUAGE** . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . TRANSLATOR USED (NOT AT ALL=l ; SOMETIMES=2; ALL THE TIME=3) . ** LANGUAGE: 01 KALENJ IN 04 K IS I I 07 MERU/EMBU i0 ENGL ISH 02 KAMBA 05 LUHYA 08 MI J IKENDA 11 OTHER 03 K IKUYU 06 LUO 09 K ISWAHIL I NAME F IELD EDITED BY OFF ICE EDITED BY KEYED BY IKEYED BY DATE II - - - - - - - - Z., LI I 257 SECTION 1. RESPONDENT'S BACKGROUND SKIP 11102 I F i rs t I tioutd Like to i l k 10w questions about you and you¢ household. For most of the time unt i l you were 12 years o ld, d id you Live In Nalrobi or Mombasa, in I~other c i ty or town or In the countryside? i NAIRO~I/14QI4BASA . . . . . . . . . . . . . . . . . 1 OTHER CITY/TOWN . . . . . . . . . . . . . . . . . 2 COUNTRYSIDE . . . . . . . . . . . . . . . . . . . . . ] N10.3 SUELOCATION,H°W tong haVeTouNyou oRbe~nCITY)?tlvlng continuously in (HARE OF ii YEARS . . . . . . . . . . . . . . . . . . . . I ALWAYS . . . . . . . . . . . . . . . . . , . . . . . . . 95 VISITOR . . . . . . . . . . . . . . . . . . . . . . . . . . . MlO§ N104 I Just before you moved here. d id you Live in Nairobi or I NAIROBI/ROI4BASA . . . . . . . . . . . . . . . . . 1 I Noalb41ll, In Iwlother c i ty oP toun, or in the I OTHER CITY/TOWN . . . . . . . . . . . . . . . . . 2 cMt ry l Jde? COUNTRYSIDE . . . . . . . . . . . . . . . . . . . . . 3 N105 In Idlst month and year were you born? I MONTH . . . . . . . . . . . . . . . . . . . . I DOES ROT KNOW MONTH . . . . . . . . . . . . 98 YEAR . . . . . . . . . . . . . . . . . . . . . ~- -~ DOES NOT KNO~ YEAR . . . . . . . . . . . . . 98 #106 Hou old were you st your List birthday? i ~ I I AGE IN COMPLETED YEARS., I I I I COMPARE AND CORRECT NIO§ AND/ON MlO6 IF INCONS]STENT. M107 HIVe yOU ever I t tended school? I YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 I I | NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 ~Nl l l primary, secm~imry, or university? SECONDARY . . . . . . . . . . . . . . . . . . . . . . . 2 UNIVERSITY . . . . . . . . . . . . . . . . . . . . . . ] NIOgA tdilst Is the highest cer t i f i ca te you obtained? Nl l l SECONDARY ON ABOVE ~1 I Can you reed • Letter or newspaper in any Language | I eas i ly e u i th d i f f i cu l ty , or not at aLL? I NO CERTIFICATE . . . . . . . . . . . . . . . . . O0 CEE (Std. 6) . . . . . . . . . . . . . . . . . . . 01 CPE/KPE (Std.7) . . . . . . . . . . . . . . . . 02 KAPE/KCPE (Std, 8) . . . . . . . . . . . . . 03 KJSE (Form 2) . . . . . . . . . . . . . . . . . . 04 O LEVEL . . . . . . . . . . . . . . . . . . . . . . . . 05 KCSE . . . . . . . . . . . . . . . . . . . . . . . . . . . 06 A LEVEL . . . . . . . . . . . . . . . . . . . . . . . . 07 ANY UNIVERSITY DEGREE . . . . . . . . . . 08 OTHER 09 (SPECIFY) EASILY . . . . . . . . . . . . . . . . . . . . . . . . . . I UITH DIFFICULTY . . . . . . . . . . . . . . . . . 2 NOT AT ALL . . . . . . . . . . . . . . . . . . . . . . 3 ~N112 I I ~#113 I I Nl12 I Do you usual ly reed = newspaper or magazine at tease I I o~¢e m ~k? I I YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 I NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Z 258 NO i Nl131 QUESTIONS AND FILTERS DO you usua l ly Listen to • radio at LeasE once a week? COOING CATEGORIES YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . oo° . . . . . . 2 GO TO Nl14 I De you usua l ly watch teLevleio~ et LeasE once a week? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Nl15 116 Nl17 t/neE kind of work do yOU mlnLy do? CHECK Nl15: NO'KS IN AGRICULTURE [~ v DOES NOT UORK IN AGRICULTURE Do you work IminLy on your o~ Lend or fami ly Land or do you rent Lind or work on someone ekse's Land? DO you earn a regular wade or salary? r-l-] I *N"8 J I HIS/FAMILY LAND . . . . . . . . . . . . . . . . . 1"~--PMI19 RENTED LAND . . . . . . . . . . . . . . . . . . . . . 2 I SONEONE ELEE'S LAND . . . . . . . . . . . . . 3 I Nl181 YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 DOES NOT KNOW . . . . . . . . . . . . . . . . . . . 8 Nl19 ~aE is your re l ig ion? CATHOLIC . . . . . . . . . . . . . . . . . . . . . . . . 1 PROTESTANT/OTHER CHRISTIAN . . . . . . 2 MUSLIM . . . . . . . . . . . . . . . . . . . . . . . . . . 3 NO RELIGION . . . . . . . . . . . . . . . . . . . . . 4 OTHER 5 (SPECIFY) N120 r l21 N122 UhIE i ! your ethnic group/tr ibe? CHECK 0.4 IN THE HOUSEHOLD QUESTIONNAIRE THE NAN INTERVIE~D IS NOT A E~ USUAL RESIDENT v NOW I would Like to ask about Ehe place in which you usua l ly Live, Do you ~u~tLy Live in Nairc~i or Mombasa. in a small c i ty . in a to~l or in the countryside? KALENJ[N . . . . . . . . . . . . . . . . . . . . . . . 01 KAMHA . . . . . . . . . . . . . . . . . . . . . . . . . . 02 KIKUYU . . . . . . . . . . . . . . . . . . . . . . . . . 03 KIS I I . . . . . . . . . . . . . . . . . . . . . . . . . . LUHYA . . . . . . . . . . . . . . . . . . . . . . . . . . 05 LUO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 06 MERU/EMBU . . . . . . . . . . . . . . . . . . . . . . 07 NIJ|KENDA/SWAHIL] . . . . . . . . . . . . . . 08 SOMALI . . . . . . . . . . . . . . . . . . . . . . . . . 09 TAITA/TAVETA . . . . . . . . . . . . . . . . . . . 10 OTHER 11 LSPECIFT) THE MAN INTERVIEWED IS A USUAL RESIDENT NAIROBI/HDMBASA . . . . . . . . . . . . . . . . . 1 SMALL CITY . . . . . . . . . . . . . . . . . . . . . . 2 TOWN . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 COUNTRYSIDE . . . . . . . . . . . . . . . . . . . . . 4 I,N2o11 M123 In which d i s t r i c t is Ehat |ocmted? ~RITE NAME OF DISTRICT CLEARLY. DISTRICT ENG MAN 3 259 N124 OUESTIO#S ANO FILTERS Nou I MouLd Like to uk ~bout the household in uhich y~ usua l ly Live. Whet Is the source of water your household uses for h~ndushinQ and dl lhuashlng for most of the year? CODING CATEGORIES J GO TO PIPED WATER m PIPED INTO HOUSE/COMPQUNO/PLOT,11~126 PUBLIC TAP . . . . . . . . . . . . . . . . . . . . . 12 MELL WATER biELL WITH PUMP . . . . . . . . . . . . . . . . . 21 WELL WITHOUT ~ . . . . . . . . . . . . . . 22 SURFACE WATER LAKE . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 RIVER/STREAM . . . . . . . . . . . . . . . . . . . 32 PORO . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 RAINWATER . . . . . . . . . . . . . . . . . . . . . . . 41----~N126 OTHER 51 I (SPECIFY) 14125 Hou tong does i t take to go there, get water, and ¢~ beck? Does your ho~ehoLd Bet dr ink ing uater from thLR S i source? I I YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 =14128 RO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Z I N127 I~at Im the source of dr ink ing ~ter for mrR of your ho~aehoLd? PIPED WATER PIPED INTO HOUSE/CONPOUND/PLOT,11 PUBLIC TAP . . . . . . . . . . . . . . . . . . . . . 12 WELL WATER WELL WITH PIJ4p . . . . . . . . . . . . . . . . . 21 WELL WITHOUT PUMP . . . . . . . . . . . . . . 22 SURFACE WATER LAKE . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 R]VER/SFREAN . . . . . . . . . . . . . . . . . . . 32 POND . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 RAINWATER . . . . . . . . . . . . . . . . . . . . . . . 41 OTHER 51 (SPECIFY) X12B Mh=t k|nd of to i le t fac i l i ty does your househotd h=ve? FLUSH TOILET O~N FLUSH TOILET . . . . . . . . . . . . . . . 11 SHARED FLUSH TOILET . . . . . . . . . . . . 12 PIT TOILET/LATRINE TRADITIONAL PIT TOILET . . . . . . . . . 21 VENTILATED INPROVED PIT TOILET.22 NO FACILITY/BUSH/FIELD . . . . . . . . . . 3t OTHER 41 (SPECIFY) N129 Does your ho~M~otd have: YES NO Et tc t r i c i ty? ELECTRICITY . . . . . . . . . . . . . . . . 1 2 A rKlto? RADIO . . . . . . . . . . . . . . . . . . . . . . 1 2 A teLevisio~? TELEVISION . . . . . . . . . . . . . . . . . 1 Z A re f r igerator? REFRIGERATOR . . . . . . . . . . . . . . . 1 2 .,3o I r--oy--H eho*Oareos For leo, ng, I . . . . . . . . . . . . . . . . . . . . R131 CouLd you describe the main mater ia l of the f loor of your home? NATURAL FLOUR EARTH/DUNG . . . . . . . . . . . . . . . . . . . . 11 RUDIMENTARY FLOOR WOO0 PLANKS . . . . . . . . . . . . . . . . . . . 21 FIN%SHED FLOOR PARQUET OR POLISHED E;OOD . . . . . . 31 VINYL/LINOLEUM/ASPHALT STRIPS.32 CERAMIC TILES . . . . . . . . . . . . . . . . . 33 CEHEMT . . . . . . . . . . . . . . . . . . . . . . . . OTHER 41 (SPECIFY) EWG ~ 4 260 140. QUESTIONS AND FILTERS M132 Could you describe the wain material of the NuLLs of your h~? COOING CATEGORIES NATURAL WALLS I#,JO/DUNG . . . . . . . . . . . . . . . . . . . . . . 11 RUOIMENTARY WALLS kQOO/TIMBER . . . . . . . . . . . . . . . . . . . 21 FINISHED WALLS BRICKS . . . . . . . . . . . . . . . . . . . . . . . . 31 CENENT/STONE BLOCKS . . . . . . . . . . . 32 OTHER 41 (SPECIFY) GO TO N133 Could you describe the main material of the roof of your home? I NATURAL ROOF GRASS/THATCH . . . . . . . . . . . . . . . . . . 11 RUOIMENTARY ROOF CORRUGATED IRO#d (HABAT]) . . . . . . 21 FINISHED ROOF TILES . . . . . . . . . . . . . . . . . . . . . . . . . ]1 OTHER 41 #134 Does any amber of your household o~m: A bicycle? A wotorcyc re? A car? Land? CuttLe, goats or sheep? Cmeh cropa? YES NO BICYCLE . . . . . . . . . . . . . . . . . . . . 1 2 MOTORCYCLE . . . . . . . . . . . . . . . . . 1 2 CAR . . . . . . . . . . . . . . . . . . . . . . . . 1 2 LAND . . . . . . . . . . . . . . . . . . . . . . . 1 2 CATTLE t GOATSf OR SHEEP.1 2 CASH CROPS . . . . . . . . . . . . . . . . . 1 2 ENG MAN 5 261 SECTION 2. " I QUESTIONS AND FILTERS m M201 ~ H,ve you ever been married or l ived with = woman7 I MARRIAGE SKIP I COOING CATEGORIES I TO I YES . 1 I HO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2----~M301 ~?.02 I Are you nou mrr ied or | iv ing with • ~oman, or are you wldoued0 divorced, or no longer l iv ing together? I MANR]ED . . . . . . . . . . . . . . . . . . . . . . . . . 1 I LIVING TOGETHER . . . . . . . . . . . . . . . . . 2 WIDONED . . . . . . . . . . . . . . . . . . . . . . . . . ] DIVORCED . . . . . . . . . . . . . . . . . . . . . . . . 4~M~OS NO LONGER LIVING TOGETHER . . . . . . . 5 ~ N203 I HOW ~y uivea do you have? I HUMBER . . . . . . . . . . . . . . . . . . . ~ I 1@.04 I DO you Stay together uith your wife (any of your ~,iv.)? I YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 HO~ W%TH NONE . . . . . . . . . . . . . . . . . . . 2 .,051 "rr' °r 'iv °°'Y °°ca I ON Eor . r . ,h. . THAN . , ' 1 1~.06 In ~at ~th and year did you start l iv ing ~ith your ( f i r=t ) wife/l~rtner? MONTH . . . . . . . . . . . . . . . . . . . . DOES ROT KHOU MONTH . . . . . . . . . . . . 98 YEAR . . . . . . . . . . . . . . . . . . . . . ~ DOES NOT KNOW YEAR . . . . . . . . . . . . . 98 N207 IZ09 I OW Did uere you ~ yo~ started l iv ing with her? I CHECK NZ06 AND M207: YEAR AND AGE GIVER? YES 0 .o I AOE . ~- - l ] DOEs NOT KNO~,/ AGE . . . . . . . . . . . . . . 98 I ~M]01 CHECK CONSISTENCY OF M206 AND M207: YEAR OF BIRTH (MlOS) [~ PLUS + AGE AT MARRIAGE (M207) CALCULATED YEAR OF MARRIAGE IF NECESSARY, CALCULATE YEAR OF gIRTH CURRERT YEAR ~- -~ MINUS CURRENT AGE ( I06)~ CALCULATED YEAR OF BIRTH ]g THE CALCULATED YEAR OF MARRIAGE ~[TH]N ONE YEAR OF THE REPORTED YEAR OF MARRIAGE (M206)? YES E~ No [~ rPROBE AND CORRECT M206 AND M207. ENG MAN 6 262 SECTION 3, CONTRACEPTIOB i N3Ol I NOW I ueutd Like to ta lk M~ut f ru i ty pLanning - the various ways or a~thode that a couple can me to belay or ~vold I pregfwr~y. I/nlch t~ys or methods have you heard atx~t? CIRCLE CODE 1 IN H30;7 FOB EACH NETNOD NENTIOBED SPONTANEOUSLY° THEN MOVE D4~MM THE COLUMN, READING THE NN4E AND DESCRIPTION OF EACH METHOD NOT MENTIONED SPONTAMEQUSLY. CIRCLE C~DE 2 IF N~TNCO IS RECOG~ISED, AND CODE ?J IF HOT RECOGNIZED. THEN, FOR EACH HETHOD UXTN CODE 1 OB ;7 CIRCLED IN /430;7, ASK N303"M304 BEFORE PNOGEEDING TO THE NEXT 14ETHCO 14.1102 Rave you ever heard of (HETNOG)? READ DESCRIPTION OF EACH METHOD. 0• PILL UcIr~n ceff take • p i l l everyday . 021 IUD ~ elk'1 have a LOOp or co i l placed inside the= by a doctor or • nurse. N303 Have you ever used (METRO0)? 3J INJECTIOBR ~ can hive an In ject |~n I~f I doctor or nurse Which S t~ (h i f r~ becoming pregeant for several months. 0 4 1 FOAM TABLETS/JELLY/NEO'SANP(~II can pt lca fou l t lbLat l , • d lq~hri im, =pe~Ne,jetty or cre l~ Inalbe th~ before intercourse. I•J C(~O01 Neff CMI U~e a rubber shaath during sexuaL In ter - cour I I . 061 FEMALE STERILISATIOB Woey~n can hive =n o~eratton to ivo ld having any more chlLdrefl. HALE STEEILISATIOB Nan can hive 1/1 o~er•tlo(1 to avoid having any amre ch i ld ren . 08• R(~PLANT ~ can have •ome mL( rod~ put under the i r sk in In the i r am. 91RNYTHN, COUNTING DAYS A woman can count the day~ of her cycle Bid •void having sexual in ter - course On the day1 when she IS mor• LikeLy to beco~m prngmmt. tO I NATURAL FAMILY PLANNING A woman can take her temperature avery bey or check her vaginaL muc~ to te l l ~hlch day~ to ivo id having sexuaL Intercourse. 111MITHOILAMAL Him can be careful and pu l l out before cl imax. lZ I Nive you heard of Imy other u~yl or mthode that v~tn or mn can use to avoid pregnancy? (SPECIFY) 2 (S~ECIFY) 3 N304 Do you knou uhere a pera~ could Do to Det (HETROD)? YES/SPONT . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . . 1 YES/PROBED . . . . . . . . . . . . . . ;7 NO . . . . . . . . . . . . . . . . . . . . . . 3] NO . . . . . . . . . . . . . . . . ;7 NO . . . . . . . . . . . . . . . . . . . . . 2 V YES/SPeNT . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . . 1 YES/PROBED . . . . . . . . . . . . . . ;7 NO . . . . . . . . . . . . . . . . . . . . . . 31 RO . . . . . . . . . . . . . . . . ;7 NO . . . . . . . . . . . . . . . . . . . . . ;7 YES/SPORT . . . . . . . . . . . . . . . 1 YES/PROSED . . . . . . . . . . . . . . ;7 NO . . . . . . . . . . . . . . . . . . . ° . .~] I v YES/SPONT . . . . . . . . . . . . . . . 1 YES/PROBED . . . . . . . . . . . . . . ;7 NO . . . . . . . . . . . . . . . . . . . . . . 3] V YES/SPOILY . . . . . . . . . . . . . . . 1 YES/PROBED . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . 3] V YES/SPONT . . . . . . . . . . . . . . . 1 YES/PROBED . . . . . . . . . . . . . . ;7 NO. . . . . . . . . . . . . . , o° . . . . . ~1 [ V YES/SPORT . . . . . . . . . . . . . . . 1 YES/PROBED . . . . . . . . . . . . . . 2 NO. . . . . . . . . . , . . , . . , . . . . . 3 ] / v YES/SPOBT . . . . . . . . . . . . . . . 1 YES/PROBED . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . 3~ v YES/SPONT . . . . . . . . . . . . . . . 1 YES/PROBED . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . 3] v YES/SPONT . . . . . . . . . . . . . . . 1 YES/PROBED . . . . . . . . . . . . . . ;7 NO . . . . . . . . . . . . . . . . . . . . . . 31 v YES/SPONT . . . . . . . . . . . . . . . 1 YES/pNO~ED . . . . . . . . . . . . . . Z NO. . , . . . . . . . . . . . . . . . . . . . v YES . . . . 1 YES . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . Z YES . . . . 1 YES . . . . . . . . 1 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . 2 YES, 1 YES . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . 2 Has your wife/woman YES . . . . . . . . . . . . . . . . . . . . 1 ever had an operation to avoid having any NO . . . . . ;7 more chi ldren? YES . . . . . . . . . . . . . . . 1 RO . . . . . . . . . . . . . . . . ;7 Have you ever had YES . . . . . . . . 1 th is operation? YES . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . ;7 NO . . . . . . . . . . . . . . . . ;7 YES . . . . 1 YES . . . . . . . . 1 NO . . . . . . . . . . . . . . . . ;7 NO . . . . . . . . . . . . . . . . . . . . . 2 YES . . . . . . . . . . . . . . . 1 DO yOU know kCere • per- son c in obtain advice on NO . . . . . . . . . . . . . . . . ;7 ham to use th i s method? YES . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . ;7 YES . . . . . . . . . . . . . . . 1 Do yc~ kneed where a p obtain ~dvfce NO . . . . . . ;7 on hou to u~e natura l f l i ty planning? YES . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . ;7 Iiti!tJi!!EllilJlJ~J~l!ll~llll!ll~!!!llI!lllilliIlJl~Ht~I Ilttlt~tltllllllUX NO . . . . . . . . . . . . . . . . ;7 IIUtiitltiltHllllEIU~llJtgiEIglitlill~llltliU~ll~tiitili YES . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . 2 YES . . . . . . . . . . . . . . . 1 140 . . . . . 2 YES . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . 2 Fi ~=:=::: == i -i "=~=i = H:::j I i r Lt,.=l,~LIIHII, I,UlII,II~l,Itllllll,Ih~lWhlL,~lN I~UI!I~, , * " q '!IU li',,'~ ~ Bqlll!t!ltJlll~lll~l~Illl t II, I U tl i~fl~lllllllUllltUl[l:JltB Hill I1~ 1~ , IIi!Ut!illllUiltiliiN~]lllilt~l!lUiUlUililUIIINtll JiEti~tlittiliE ilLltil~iJtllll;E~;lit;;I hill}Eli i ti!tllfitl!U; fl fl I1 II ! 'Jl~lllllllltllllllllll~ ~ II IIII tl It IIIilIIIEIIfllIIIIIIIII~IIIIII~,I~E~I IlI'~tX'II'~'H'H" ~ '1 I WtWJlt !=ltlI~ l~l"ultl Ilslittllll In IIIlIII~II~UlUIN I ItlII l i;IIHI ; ; IJJl iltlli];tiJll~ll~tilil~Htil[;tl~l] ~Hll I'lll ItNIl,tllhfllfllilml I ~.~ AT LEAST ONE "YES" (EVER USED) - - TO N3OB YES/SPOBT . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . 3 ENG IMN 7 263 NO. I GUESTIORS AKO FILTERS m m 14306 I Nave you or your wi fe or partner ever used mnythlng or I t r ied in ~y way to de isy or mvo|d having • baby? K IP I COOING CATEGORIES I TO I YES . . . . . . . . . . . . . . . . . . . . . . . . . . . [---] I NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . ~ -~18 COIRECT 14303-K3G5 (AMO 14302 IF NECESSARY). N308 i~ 1 ~mutd t i ke to msk you mbout the t ime When yo~ f i r s t d id SOlmthtrq or used I mthod tO i~ ld l~ss~ncy . ROW Imny t tv lng ¢hJtdrqm d id you have st thmt t inw, NUI4BER OF CHILDREN . . . . . . . i f eny~ I L I IF NONE, RECORD ~00 ~. I ' " . ,i No . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 ~H.316 N310 Vhich mthod mre you using? iF USING 14Q~E THAN ORE METHOD, CIRCLE CODE FOR METHOD THAT CCI4ES FIRST OR THE LIST (MITH LOi~ST CODE NUMBER), AND la lTE NAJqE OF OTHER METHOD HERE: PILL . . . . . . . . . . . . . . . . . . . . . . . . . . . 01 IUD . . . . . . . . . . . . . . . . . . . . . . . . . . . . OZ INJECTIONS . . . . . . . . . . . . . . . . . . . . . 03 FOAN TAMLETR~JELLY,DIAPRRAGN.04 CONDOR . . . . . . . . . . . . . . . . . . . . . . . . . OE FENALE STERILISAYION . . . . . . . . . . . 06 MALE STERILISAYIOR . . . . . . . . . . . . . 07 RORPLART . . . . . . . . . . . . . . . . . . . . . . . 08 RHYTHM, COURTING DAYS . . . . . . . . . . NATURAL FP, HUCUR, TEMPERAYURE.IO UITHDRAUAL . . . . . . . . . . . . . . . . . . . . . 11 OTHER 12- (SPECIFY) t,H315 N311 CHECK N310: SHE/HE SYER|LISED E~ USIHG ANOTHER NETHOD r v v Vhere d id the ghere d id you (or your s te r i t l se t ion take w i fe /per t r~r ) obta in ptKOT ( l~T i '~) the las t time? (NAI4E OF PLACE) PUBLIC SECTOR GOVERNMENT HOSPITAL . . . . . . . . . . . . 11 GOVERNMENT HEALTH CENTRE . . . . . . . 12 GOVERNMENT DISPENSARY . . . . . . . . . . 13 MEDICAL PRIVATE SECTOR MISSION,CHURCH HOSPITAL/CLINIC,21 EPAK HEALTH CENTRE/CLINIC . . . . . . 22 OTHER NOR-CK)VERNMENTAL SERVICE.Z3 PRIVATE HOSPITAL OR CLINIC . . . . . 24 PHARMACY . . . . . . . . . . . . . . . . . . . . . . . 25 PRIVATE DOCTOR . . . . . . . . . . . . . . . . . Z6 MOBILE CLINIC . . . . . . . . . . . . . . . . . . . 31 CORHUH[TY RASEO DISTRIBUTOR/ COMI4UN[TY HEALTH ~O~KER . . . . . . . 41 SHOP . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 FRIENDS/RELAT;VES . . . . . . . . . . . . . . . 61 - OTHER 71 (SPECIFY) DOES HOT KRO~ . . . . . . . . . . . . . . . . . . . 98 i ,.,.N313 14312 M314 o _ ,,., L. , . - - .c D 2 - - , ,o - - , ,h. ,L. , . , - - s I M* uT s DOES NOT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . USING J ANOTHER METHOD I~ ~N31 in k~mt month and year was J MONTH . . . . . . . . . . . . . . . . . . . . 16 the s tor i t l sa t ion Operation performed? I YEAR . . . . . . . . . . . . . . . . . . . . . ENG IMli 8 264 " " I .TiURs .NG EILTE. I m 1(315 I For h~ meny ~the h ive you be~ us ing (CURRENT ~THOD) I I cOnE I nuo~ty? I IF LESS THAN I HONTB, RECOND '00 ' . CODING CATEGORIES I TG .THS . I--T--I I GTEA. UR LOROEH . . . . . . . . . . . . . . m16 I H,v. yo. u*.d • condo* In the t*et four week•? | YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 | I I NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 BN317A N317 i HOUm~,ch d id the corN~myo. Last used cost? N318 COST IN SHILLINGS . . . . . . . . I I I PARTNER OBTAINED . . . . . . . . . . . . . . . 9S FREE . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 DOES NOT KNOW . . . . . . . . . . . . . . . . . . 9e ~N323 I I Do you in tend to use • method to delay or avo id | YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 ~'.320 pregnancy i t imy t im In the future? I NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 | DOES HOT KNOW/NOT SURE . . . . . . . . . . 8 ~N324 - I N319 what ie the ml in reason you do not intend to use • method? IF HE SAYS BE IS TOO YOUNG, ASK MHAT HE MILL DO MILER HE IS 04.DER* UANTS CHILDREN . . . . . . . . . . . . . . . . . G1- - LACK OF KNOWLEDGE . . . . . . . . . . . . . . 02 HUSBAND OPPOSED TO USING . . . . . . . 03 COST TOC MUCH . . . . . . . . . . . . . . . . . . 04 SIDE EFFECTS . . . . . . . . . . . . . . . . . . . 05 FEARS IT MILL MAKE HER STERILE.06 OTHER HEALTH CONCERNS . . . . . . . . . . G7 HARD TG GET NETHOO$ . . . . . . . . . . . . 01~ RELIGION . . . . . . . . . . . . . . . . . . . . . . . 09 OPPOSED TO FAMILY PLANNING . . . . . 10 FATALISTIC . . . . . . . . . . . . . . . . . . . . . I1 OTHER PEOPLE OPPOSED . . . . . . . . . . . 12 INFREQUENT SEX . . . . . . . . . . . . . . . . . 13 DIFFICULT TO GET PREGNANT . . . . . . 14 mENOPAUSAL/HAD HYSTERECTONY.15 INC~VEM[ENT . . . . . . . . . . . . . . . . . . . 16 OTHER 17 (SPECIFY) DOES HOT KNOW . . . . . . . . . . . . . . . . . . 98 1.N324 J Do you intend to use • method ~I th in the next 12 months? I YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 I NO . . . . . . . . . . . . . . . . * , , ° , , . . . . . . * *2 DOES NOT KROM . . . . . . . . . . . . . . . . . . . 8 14321 Nhen you use • method, ~hich method ~ouLd you pre fer to tale? PILL . . . . . . . . . . . . . . . . . . . . . . . . . . . 01 IUO . . . . . . . . . . . . . . . . . . . . . . . . . . . . G2 INJECTIONS . . . . . . . . . . . . . . . . . . . . . 03 FOAM TABLETS,JELLY,DIAPBBACJ4.04 CONDOM . . . . . . . . . . . . . . . . . . . . . . . . . 05 FEMALE STERILISATION . . . . . . . . . . . 06 HALE STERILISATION . . . . . . . . . . . . . G7 NORPLABT . . . . . . . . . . . . . . . . . . . . . . . 06 RHYTHM, COUNTING DAYS . . . . . . . . . . 09 NATURAL FP, MUCUS, TEMPERATURE.IG WITHDRAWAL . . . . . . . . . . . . . . . . . . . . . 11 OTHER 12 (SPECIFY) UNSUBE . . . . . . . . . . . . . . . . . . . . . . . . . 98~ 14322 Mllere can you get (NETHOD BENT]OWED IN 14321)? (I~M4E OF PLACE) PUBLIC SECTOR GOVERHHENT HOSPITAL . . . . . . . . . . . . 1L - - GOVERBMENT HEALTH CERTRE . . . . . . . 12 GOVERNMENT DISPENSARY . . . . . . . . . . 1] MEDICAL PRIVATE SECTOR MISSION,CHURCH HGSPITAL/CLINIC.21 ~M.326 FPAK HEALTH CENTRE/CLINIC . . . . . . 22 OTHER NON'GOVERNMENTAL SERVICE.2] PRIVATE HOSPITAL OR CLINIC . . . . . 24 PHARMACY . . . . . . . . . . , . . . . . . . . . . . . 2S PRIVATE DOCTOR . . . . . . . . . . . . . . . . . 26 MOBILE CLIRIC . . . . . . . . . . . . . . . . . . . ]1 COI4~UN]TY BASED DISTRIBUTGR/ COMHURITY HEALTH ~/ORKIER . . . . . . . 41 SHOP . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 - FRIENDS/RELATIVES . . . . . . . . . . . . . . . 6~ ~P~27 OTHER 7¶ ~-.327 (SPECIFY) DOES HOT KNOW . . . . . . . . . . . . . . . . . . . ~ .N327 ENG IMM 9 265 NO. X324 QUESTIONS AND FIL CNECK fl310: USING RHYTHM, CUT ING DAYS, WITHDRAMAL C~ OTHER TIIL~DITIOMAL METNOD Do you knov of • p lace Where you can obta in a method of fami ly p lanning? COOING CATEGORIES USING A HOOERN NETH~O J : : i : :_ lo 14325 kqlere le that? (NAME OF PLACE) PUBLIC SECTOR GOVERNMENT HOSPITAL . . . . . . . . . . . . 11 GOVERNMENT HEALTH CENTRE . . . . . . . 12 GOVERNMENT DISPENSARY . . . . . . . . . . 13 NEDICAL PRIVATE SECTOA MISSION,CHURCH HOSPITAL/CLINIC.21 FPAK HEALTH CENTRE/CLINIC . . . . . . 22 OTHER MOS*GOVERWiENTAL SERVICE.23 PRIVATE HOSPITAL ON CLINIC . . . . . 24 PHARMACY . . . . . . . . . . . . . . . . . . . . . . . ~5 PRIVATE DOCTOR . . . . . . . . . . . . . . . . . 26 MOBILE CLINIC . . . . . . . . . . . . . . . . . . . 31 COMMUNITY BASED DISTRIBUTON/ COMMUNITY HEALTH ~ORKER . . . . . . . 41 SHOP . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 FRIENDS/RELATIVES . . . . . . . . . . . . . . . 61 OTHER 71----1~14327 (SPECIFY) | N326 I NOV toNI does I t take to grmve( f roayour home to th i s place? IF LESS TITAN 2 HQU~$, RECQIID MINUTES, OTHERWISE, RECORD flOURS. MX U ES . . . . . . . . . . . . HOURS . . . . . . . . . . . . . . 2 DR . . . . . . . . . . . . . . . . . . . . . . . . . . . 9998 14327 Nov d id yc~J f i r s t he i r mbout fami ly p lanning? RADIO . . . . . . . . . . . . . . . . . . . . . . . . . . 01 TELEVISION . . . . . . . . . . . . . . . . . . . . . 02 NEWSPAPERS . . . . . . . . . . . . . . . . . . . . . 03 POSTERS . . . . . . . . . . . . . . . . . . . . . . . . 04 WIFE . . . . . . . . . . . . . . . . . . . . . . . . . . . 05 FRIENDS/RELATIVES . . . . . . . . . . . . . . 06 HEALTH I~ORKER/CLINIC . . . . . . . . . . . 07 CBD/CHW . . . . . . . . . . . . . . . . . . . . . . . . O~ BARAZAS . . . . . . . . . . . . . . . . . . . . . . . . 09 OTHER tO (SPECIFY) CAN'T REMEHBER/DO~S NOT KNOW,.98 N328 f remtdn|ch p lace or person d id you Bet the most Inforwat lonT RAOIO . . . . . . . . . . . . . . . . . . . . . . . . . . 01 TELEVISION . . . . . . . . . . . . . . . . . . . . . O~ NEWSPAPERS . . . . . . . . . . . . . . . . . . . . . 03 POSTERS . . . . . . . . . . . . . . . . . . . . . . . . 04 WIFE . . . . . . . . . . . . . . . . . . . . . . . . . . . 05 FR%EBDS/RELAT]VE$ . . . . . . . . . . . . . . 06 HEALTH ~ORKER/CLIRIC . . . . . . . . . . . 07 CBO/CHW . . . . . . . . . . . . . . . . . . . . . . . . 08 BARAZAB . . . . . . . . . . . . . . . . . . . . . . . . 09 OTHER 10 (SPECIFY) CAN'T REMEMBER/DOES MOT KNOB.98 • l~t fami ly ptmm.tng? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1~N331 DOES BOT KNO~ . . . . . . . . . . . . . . . . . . . ~ m ENG MAN 10 266 DO° 14]]0 QU£SFIONS AND FILTERS ~l i ch prPOr lh lVe you hemrd? Any others? DO MOT READ CCOES TO RESPOr~ENT. CIACLE ALL NENTIIMED. CODING CATEGORIES 14UENDA POLE . . . . . . . . . . . . . . . . . . . . . A PANGA UZAZI . . . . . . . . . . . . . . . . . . . . . l kiAISNA YA JN411YAKO . . . . . . . . . . . . C JIFUNZE MA UENDELEA . . . . . . . . . . . . . O 14AISNA GORA . . . . . . . . . . . . . . . . . . . . . E AFYA YAKO . . . . . . . . . . . . . . . . . . . . . . . F DAKTARI AIOJSNAI)Ri . . . . . . . . . . . . . . . G KUELEk~HA MI KUZL~GUNT"A . . . . . . . . . N OTHER ] (SPECIFY) DOES NOT gRIM/CANNOT REMENSER.J I~Yo 14331 I I + . 1 shouLd be avaiLabLe to young people? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 OTHER 3 (SPECIFY) DOES ROT RNOI/ . . . . . . . . . . . . . . . . . . . 8 N332 I DO y~ th ink that fami ly p lann ing serv ices should I YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 I I I 1341 IVllLlbtl for young people? I N0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 I OTHER 3 (SPECIFY) DOES NOT KNOW . . . . . . . . . . . . . . . . . . . 8 I 14333 In |m comi t ies there lm • womn or t~ who is YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 ] t ra ined to ta lk to fa i l l es tn that area ilbo4Jt fami ly I p lann ing . S~letlmes they v i s i t each ho4~e and ta lk NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 +14335 a~ut f i l l y p lann ing and g ive out supp l ies . Other I t i l l they have 1uppL le l in the i r houses. Is there any DOESN'T KNOW . . . . . . . . . . . . . . . . . . . . 8 ~14335 + or ~ Like that in your opel? I °+I . - - , ' - . - ,+ . -++,o+- 'o ,+ ,., .,, -+ I T`-++ . ~i 14335 Dur ing Millch t i l l of the ~thty cyc le does a Noman hove the grNteat charge of becoming pregmmt? DURING HER PERIOD . . . . . . . . . . . . . . . 1 RIGHT AFTER HER PERIOD HAS ENOED . . . . . . . . . . . . . . . . . . . . . . 2 IN THE MIDDLE OF THE CYCLE . . . . . . ] JUST BEFORE HER PERIOD BEGINS.4 OTHER 5 (SPECIFY) DOES NOT KNOW . . . . . . . . . . . . . . . . . . . 8 END MAN 11 267 SO. QIJESTIORE AMP FILTERS CNECI( M201: NEVER MARRIED OR LIVED r -~ TOGETHER WITH A ~ Lr-J SECTION 4. SEXUAL ACTIVITY AND AIDS | COOING CATEGORIES EVER MARRIED OR LIVED WiTH A~I~I4AN I I I - , I . - ~- , h , - , 'o,.ro-r., I*Es . , I NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 ¢N410 order to get • better underst•nding of fmi ly plsnrlirlg w~d fe r t i l i ty . Row old were you when you f i r s t hed FIRST TIME WHEN MARRIED . . . . . . . . 96 leXt;e I I rlterco~,lr le? l i xua t I htercouree? DAYS . . . . . . . . . . . . . . . . . . . . . IF m~E. WRITE IDOl. u l th ln the Last 6 months? IF DO. SKIP TO M408. -0' i 0 0 . - - ' - - "~ 'o ,o ' - -o ' i Y~ . '1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Z - - i . , ,~ ' - -o - ' .~- ' -~ ' - ,oo~. .= o . = o f l , o , - . - , . , , . , --E, OF- - . ~ i k%09 Hen use the List time you had sexual intercourse? DAYS AGO . . . . . . . . . . . . . . . 1 WEEKS AGO . . . . . . . . . . . . . . MONTHS AGO . . . . . . . . . . . . . YEARS AGO . . . . . . . . . . . . . . 4 BEFORE LAST BZRTH . . . . . . . . . . . . . 996 I I I 11410 I i o , I h ive I fwa questions sb~Jt a very important topic. I YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 I I Rive you heird of • d i le i se celt•el AIDS? I NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2~--~14421 W411 From ~hlch sources of Information or persons have you heard about AIDS in the last month? Any others? CIRCLE ALL NENTIONED. RADIO . . . . . . . . . . . . . . . . . . . . . . . . . . . A TV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S NEWSPAPERS . . . . . . . . . . . . . . . . . . . . . . C HEALTH WORKERS . . . . . . . . . . . . . . . . . . O PRIESTS/PREACHERS/KADHIE . . . . . . . . E WIFE . . . . . . . . . . . . . . . . . . . . . . . . . . . . F FR % ENDS/RELAT ]VES . . . . . . . . . . . . . . . G SCHOOLS . . . . . . . . . . . . . . . . . . . . . . . . . R BOOKLETS/PANPHLETS/POSTERS . . . . . . I DARAZAE . . . . . . . . . . . . . . . . . . . . . . . . . J OTHER K (SPECIFY) NONE . . . . . . . . . . . . . . . . . . . . . . . . . . . . L How i l AIDS transmitted? Any other Miys? DO #OT READ CODES. CIRCLE ALL MENTIONED. SEXUAL INTERCOURSE . . . . . . . . . . . . . . A SHAVING/RAZORS . . . . . . . . . . . . . . . . . . S INJECTIONS . . . . . . . . . . . . . . . . . . . . . . C CIRCLLMCISION, TATTOOS . . . . . . . . . . . D MOTHER TO CHILD . . . . . . . . . . . . . . . . . E TRANSFUSION OF INFECTED BLOCO.F OTHER G (SPECIFY) DOES NOT KNOW . . . . . . . . . . . . . . . . . . . H ERG NAN 12 268 NO, HA13 CUESTIONS AND FILTERS Do ~ th ink the( ~ can get AIDS fram ~ek| rq hmrtJs wi th someof~ who has AIDS? ktaalrto soB4on4 t4~o hu AIDS? weerlr~ the c lo thes of someone who has AIDS? s J tmr l~ ~t i r~ ute f ts l I s wi th someone who has AIDS? touching s~4tmor~ who has d ied from AIDS? mosquito, f l ea or ~ b i tes? CODING CATEGORIES YEN NO DK HANDSHAKING . . . . . . . . . . . . . . . . 1 2 8 KISSING . . . . . . . . . . . . . . . . . . . . 1 2 8 SHARING CLOTHES . . . . . . . . . . . . 1 2 8 SHARING EATING UTENSILS.1 2 8 TOUCHING SONEC~IE WI~ DIED*.1 2 8 NOSQUITO/FLEA/BEDBUG BLTES.I 2 8 t r lp TO 1 I I . '1 to be Infected wi th the AIDS v i rus? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 DOES NOT KNOW . . . . . . . . . . . . . . . . . . . 8 I I I kvt15 | Is I t poss ib le fo r a k~en who has the AIDS v i rus to | YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 | I g ive b i r th to I ch i ld wi th the AIDS v i rus? I NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Z I DOES NOT KNOU . . . . . . . . . . . . . . . . . . . 8 14416 I Cam people protect thsmetves from Gett ing AIDS or i s | CAN PROTECT THEMSELVES . . . . . . . . . . 1 | I I there noth ing that ~ le can ~7 I NOTHING THEY CAN DO . . . . . . . . . . . . . 2 ~N418 DOES NOT KN~ . . . . . . . . . . . . . . . . . . . 8 ~14418 I M417 NOW ten pedDLe protect themselves from set t ing AIDS? DO NOT READ CODES TO RESPONDENT, Any other ways? CIRCLE ALL MENTIUMED. DO NOT HAVE SEX AT ALL . . . . . . . . . . A LIMIT NUMBER OF SEXUAL pARTNERS.D USE CONDOMS DURING SEX . . . . . . . . . . C STERILIZE SYRINGES/NEEDLES . . . . . . D AVOID PROSTITUTES . . . . . . . . . . . . . . . E OTHER F (SPECIFY) ""1 I . '1 d ied from AIDS? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 11619 J DO you th ink that you yourse l f can catch AIDS? I YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 J I I NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2---.~14421 DOES NOT KNOW . . . . . . . . . . . . . . . . . . . 8 ~1~21 HA'° I ou'Y~ h nk*ou Ht"tcHAO0? I FR~ ~''E'PART'ER NOT OTHER FRD#t FROM SURE/DOES BLOOD NEEDLES/INJECTIOES TRANSFUSIONS (SPECIFY) NOT . . . . . . . . . . . . . . . ~Now . . . s 4321 I 14421 In the L i l t 12 months, have you not iced any discharge from your i~mls? ¼22 PRETd~NCE OF OTHERS AT THIS POINT. YES . . . . . . . . . . . . . . . . . , . . . . . . . . . . .1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 NOT SURE/DOES NOT KNOW . . . . . . . . . . D YES NO CHILDREN UNDER 10 . . . . . . . . . . 1 2 WIFE . . . . . . . . . . . . . . . . . . . . . . . 1 2 OTHER HALES . . . . . . . . . . . . . . . . 1 2 OTHER FEMALES . . . . . . . . . . . . . . 1 2 END NAN 13 269 SECTION 5. ,o . I GUESTICedS AND FILTERS m m ~01 I HOW ~ ecw~ do you h ive who are your ow~? I IF WO~ilE. whITE =CW):. FERT|LITY PREFERENCES l COOING CATEGOItIES l TO I N~S. OF S=~ . ~- - ] I W502 4503 4504 M505 HOM Imply dl~Jghtere ¢k) you have t~lo are your o~? iF MOILE r ~ITE +D(] + • CHECK ~10: MEITHEK [~ HE C41 SHE STERILIFAED $TERILISED ! ¥ CHECK M202: CUItREMTLY ~RRIED MOT MARR%ED/ (311 LIVING MOT LIVING TOCJETHER E~ TOGETHER II NOu I h ive I~ ¢ lu l l s t l~ I=~w:~Jt the fu ture . WouLd you [ i ke to have •~ther (a) ch i ld or woutd you pee ler not to have ~ (more) ch i ld ren? I HU.°ER OF O~, ,S , . . . . ~- -11 I .M507 I I .M515 I J ";%:E):Mo~y~.'.).c".I.~.::::::::::; I SU~;ICSI2EEDIA~'~sGERToTPRKEO~AMT.:::: :3B'~MS[]Q I M506 Hou tcw~g WouLd you t i ke to wait before the b i r th of another (m) chito~t YEARS . . . . . . . . . . . . . . . . . . Z SOON/MOW . . . . . . . . . . . . . . . . . . . . . . 994 SAYS WIFE CANIT GET PREGNANT.095 OTHER 996 (SPECIFY) DOES NOT KNO~ . . . . . . . . . . . . . . . . . 908 ~M509 I ; M507 WOULd you reccu~nd to m f r iend or re la t ive in your YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1---~N509 c iPc~t~e• to have an operat ion not to have any Bore chl idren? No . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 M508 Why not? I I I~09 0o you th ink that your w l fa /par tner approves or | APPROVES . . . . . . . . . . . . . . . . . . . . . . . . I | disq~proves of coup(as us ing • method to avoid I DISAPPROVES . . . . . . . . . . . . . . . . . . . . . 2 I pregnarcy? DOES ROT KMOU . . . . . . . . . . . . . . . . . . . 8 +0 + + +. o+r++++O°oh__°++ + + + + . + + + +h + h° I++ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . l M511 HmVe yOU ever teiked to your uifell~rtner about I T~S . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I f i i ty p lann ing? I I NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2----*N513 ENG l+JkN 14 270 ND I QUESTIONS AND FILTERS N512 I Hov often have you ta lked to your u i fe /partner about I f lm i ty p( imnlng in the past year? I COOING CATEGOItIEB l NEVER . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 ONCE ON TWICE . . . . . . . . . . . . . . . . . . . 2 BORE OFTEN . . . . . . . . . . . . . . . . . . . . . . ] KIP I TO I 14513 I Have you and your wi fe/partner ever discussed I the ~ r of ch i ldren you would l i ke to have? I YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I J NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ""1 I E - . '1 nmloer of ch i ldren that you want, or does she want more BORE CHILDREN . . . . . . . . . . . . . . . . . . . 2 or fe~ar then you want? FEWER CHILDREN . . . . . . . . . . . . . . . . . . ] DOES NOT KNOM . . . . . . . . . . . . . . . . . . . 8 N515 I Row Long should a couple wmlt before stmrtinQ sexual Intercour#e af ter the b i r th of • baby? I MONTHS . . . . . . . . . . . . . . . . . 1 ~ I YEARS . . . . . . . . . . . . . . . . . . 2 OTHER 996 (SPECIFY) "" I ' - - ' - " ' - " "h - c - "y -° I "IT . '1 breestfe4KItng before etmrtinQ to have sexual re lat ions ag i le , or doesn't i t INttar? DOESN'T HATTER . . . . . . . . . . . . . . . . . . 2 " '1 Lo mL, ba y~' - r - or oi-~ro.e o' o o u ~ L e s . l ~ . . ,h~ ,o --Lo B.,tL~ ore.n. I RppR°VE0,sAppRoVE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ~'1 M518 CHECK N501 AND M502: NA$ LIVLNG CRILD(REN) [~ / I f yo~ could go back to the time you d id not have any ch i ldren and could choose exact ly the number of ch i ldren to have In your t~hoLa Life, how many would that be? NO LIVING CHILDREN E~ i f you could choose exactly the nclnber of chi ldren to have in your whole Life, how many would that be? RECORD SINGLE NUMBER Oe OTHER ANSUER. NUMBER . . . . . . . . . . . . . . . . . . . OTHER ANSWER 96 (SPECIFY) N520 I M519 ~ Ho~ many bays? I Hou mny g i r l s? I NUMBER OF BOYS . . . . . . . . . . . ~ I NUMBER OF GIRLS . . . . . . . . . . OTHER 96 (SPECIFY) years pat ten the b i r th of one ch i ld and the b i r th of the next chi ld? YEARS . . . . . . . . . . . . . . . . . . 2 OTHER 9 9 6 ENG NAN 15 271 Comments About Respondent: INTERVIEWER'S OBSERVATIONS (To be fil led in after completing interview) Comments on Specif ic Questions: Any Other Comments: SUPERVISOR'S OBSERVATIONS Name of Supervisor: Date: EDITOR'S OBSERVATIONS ENG MAN 16 272 KENYA DEMOGRAPHIC AND HEALTH SURVEY 1993 NAT IONAL COUNCIL FOR POPULAT ION AND DEVELOPMENT CENTRAL BUREAU OF STAT IST ICS SERVICES AVAILABIL ITY QUEST IONNAIRE PROVINCE DISTRICT LOCATION/TOWN SUBLOCATION/WARD NASSEP CLUSTER NUMBER . . . . . . . . . . . . . . . . . . . . . . . . . . . . KDHS CLUSTER NUMBER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . NA IROBI /MOMBASA=I , SMALL C ITY=2, TOWN=3, RURAL=4 SUPERVISOR NAME AND NUMBER DATE QUEST IONNAIRE IS COMPLETED . . . . . . . . . . . . . . . . . . . INFORMANTS WHO PROVIDED INFORMATION: (WRITE POSIT ION, E.G. , NURSE IN HEALTH CENTER, CBD) i. DAY MONTH YEAR . 3. 4. 5. NAME F IELD EDITED BY OFF ICE EDITED BY KEYED BY KEYED BY 273 i . COIg~JNITY-IASED SERVICES QU(STION$ CODING CATEGORIES SKiP TO In some ¢¢JmiJnltlel there Is B t or men who is t ra ined YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 to ta lk to fami l ies In that ores about fami ly ptarming. $mmt lm they v i s i t each ho~ee and ta lk abo~t fami ly NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 -- • ? planning end g lv t out eupptleo. Other times they have fami ly plwnning m~pptle= In the i r h~Bn. Is there any woman or man Like th i s Id~ cmmr• th i s c luster? tO. 1 I / l i t Is th i s I~rSOfl=s h i l t ? TRY TO GET MANE. IF klOT, IWflITE DOES NOT KNOW. IF NORE THAM ONE PERSON, WRITE NAPIE OF OTHER PERSON . . . . . . . . . Dots th f• casmunity-besed fu t l ty planning d i s t r ibutor work for the Kenya IoverrB int (h in t l t ry of Health) or doel she/he Kork for • church orgtn ieat ion or another o rgan l la t lan Like th t F la l ty PLanning Association of Kenya or N~mdeLeo ye k~aueke, or NECK? Can you te l l me the name of the orgenisat ion that she/he uorkm for? (NAJ4E OF CBD WORKER) (NN4E OF OTHER CBO IJORRIER) GOVERNMENT . . . . . . . . . . . . . . . . . . . . . . I NON-GOVERNMENT . . . . . . . . . . . . . . . . . . 2 ROT SURE/OOES MOT K#OM . . . . . . . . . . 8 YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1 - - L5 IF YES, WIITE ~ ON THE LiNE NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 i DOeS th i s {oerlmct pcovlde fsaiJiy planning methods or only PROVIDES METHODS . . . . . . . . . . . . . . . . 1 In for lmt l0~ I/oout fami ly plwlnlng? ONLY INFORMATioN . . . . . . . . . . . . . . . . 2 - ; 7 Dose th i s c~wmJntty fami ly planning d i s t r ibutor provide: PILL: a: the p l ( | ? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . DO NOT KNOW . . . . . . . . . . . . . . . . . . . . . 8 b: Condom? c: FOaming tablets? ]e th i s • re l v i s i ted by • mobile c l in i c that supplies fmt ty ptmnning math•d=? How often dots the ~ iLe fau l ty planning c l in i c v i s i t ? Do4s the wd) l i t fami ly planning c l in i c provide: a: PILLs? b: lUD? COtIDOM: YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 DO NOT KNOi~ . . . . . . . . . . . . . . . . . . . . . 8 FOAMING TABLETS: YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . o°**.o.~) DO NOT KNOW . . . . . . . . . . . . . . . . . . . . . 8 YES . . . . . . . . . . . . . . . . . . . . . . . . . . . .1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 - = 10 NO. OF TINES i l I PER NORTH.1 I L J YEAR. . .2 PILL: YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 DO NOT KNO~ . . . . . . . . . . . . . . . . . . . . . 8 IUD: YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 00 NOT KNOW . . . . . . . . . . . . . . . . 8 INJECTIOn: YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . . . oo .o .~ DO NOT KNOW . . . . . . . . . . . . . . . . . . . . . 8 C: In ject ion7 2of 6 274 10 11 12 13 QUESTIONS CODING CATEGORIES SKIP TO I f t~men de not 9o to the hospitaL or the health centre to YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . I de l iver , Who hetde thus to del iver? Is there • t rad i t iona l NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 b i r th i t t lmdamt In th i s i res to help ~ deLiver? DO NOT KNOW . . . . . . . . . . . . . . . . . . . . . 8 Is th i s c tustar covered by s celmJnJty health worker? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 - • 16 DO ROT KNOU . . . . . . . . . . . . . . . . . . . . . 8 -- • 14 i i Does the commanity health worker provide any medications PROVIDES MEDICATIONS . . . . . . . . . . . . 1 such IS (]aS, imt l r le p i l l s , or only Information? ONLY INFORMATION . . . . . . . . . . . . . . . . 2 -- L 14 Boee th i s ccmmanity health uorker provide: l : OreL rehydrmtlon sa l t s (ONE) packets? b: H i i s r l l mKIIcine? C: Condom? d: Anything eLse? (WHITE OM LINE) I I . HOSPITALS ORS: YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 DO NOT KNOW . . . . . . . . . . . . . . . . . . . . . 8 MALAR]A MEDICINE: YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 RO . . . . . . . . . . . . . . . . , * *H . . . ,o ,H .2 DO NOT KNOU . . . . . . . . . . . . . . . . . . . . . B CONDOM: YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . , , ° . * . . . . . . . . . . . . . . . 2 i DO NOT KNOW . . . . . . . . . . . . . . . . . . . . . 8 16 15 16 17 18 19 What Is the nlmm of the nearest hospital that provides health services to people in th i s c luster? IF DOES NOT KNOW NJU4E, NRITE "DOES NOT ERO~". IS that s Bovernl lnt hospitaL, or Is t t operated by a church or Is I t pr ivate? GOVERNMENT . . . . . . . . . . . . . . . . . . . . . . 1 MISSION/CHURCH . . . . . . . . . . . . . . . . . . 2 PRIVATE EMPLOYER . . . . . . . . . . . . . . . . 3 OTHER PRIVATE . . . . . . . . . . . . . . . . . . . 4 OTHER 5 (SPECIFY) HOW far is the hospita l from here in kilometers? i KILORETRES . . . . . . . . . . . . . . IF LESS THAN 11~. , RECORD tOOl. IF 97 KM.OR MORE, WRITE 97 IF UNKN(Mad RECORD mEBILBUT TRY TO GET AN ESTIMATE) Roe do mmst people tn th i s c lus ter get from here to th i s ho lp J t i t ? HOW Long does i t tmke to get from here to th i s hoopJtiL u#irNI (MEANS NENTLOMED ABOVE)? RECORD IN MINOTES IF LESS THAN 2 HOURS AND IN HOURS IF 2 R(X~S OR HONE. Does th i s hosplt iL provide: IntimataL care? deLivery care? ch i ld I imJnlmetlonl? f l l l t y planning services? CAR / MOT(~CYCLE . . . . . . . . . . . . . . . . . 1 PUBLIC TRANSPORT (BUS,TAXI) . . . . . . 2 BICYCLE . . . . . . . . . . . . . . . . . . . . . . . . . . 3 ANIMAL / ANIMAL CART . . . . . . . . . . . . . & WALKING . . . . . . . . . . . . . . . . . . . . . . . . . . 5 OTHER 6 (SPECIFY) NOUNS . . . . . . . . . . . . . . 1 MINUTES . . . . . . . . . . . . 2 YES NO OK ANTENATAL CARE . . . . . . . . . I 2 8 DELIVERY CARE . . . . . . . . . . 1 2 8 CHILD IMMUNISATIONS.1 2 S FAN]LY PLANNING . . . . . . . . 1 2 S 3o f6 275 I l l , HEALTH CENTRES JO. 2O 21 2Z 23 24 25 26 27 28 29 OUESTIONS CODING CATEGORIES SKIP TO What is the mml of the nearest health centre thst provides heal th service8 to people In th i s c luster? IF DOES NOT i(N(~ SAME, WRITE "DOES NOT KBO~/". Is that a goverrmmnt health centre or Is i t oper•ted by a church or Is I t pr ivate? HO~ far Is the health centre from here in kiLometres? IF LESS THAN 1KN. , RECORD *00' IF 97 104 OR 140lIE RECORD 197', IF UMKNOIdN RECOIW '98 ~ (BUT TRY TO GET AN EST]MATE) BOW do most PeoPLe In th i s c lus ter get from here to th i s heal th centre? Ho~ Long does I t take to get free here to th i s health centre u l in l l (RANt MENTIONED ABOVE)? RECORD IN MINOTES IF LESS THAN 2 HOURS AND IN HOURS IF 2 HOJRS OR MOllE. Does th i s health centre provide: antermtat care? de l ivery care? ch i ld lammleatlonB? fml ty planning services? Is there another heal th centre th•t provides services to I~te in th i s c luster? IF YES: Mhat IS the ~ of th i s place? IF DOES NOT KNOW NN4E, I~ITE "DOES NOT KNOtd", Is th•t a goverrment health centre or is i t operated by s church or Is I t pr ivate? (iOVERNMERT . . . . . . . . . . . . . . . . . . . . . . . 1 MISSION/CHURCH . . . . . . . . . . . . . . . . . . . 2 PRIVATE EMPLOYER . . . . . . . . . . . . . . . . . 3 OTHER PRIVATE . . . . . . . . . . . . . . . . . . . . 4 OTHER 5 (SPECIFY) NILONETRES. CAR / MOFORCYCLE . . . . . . . . . . . . . . . . . 1 PUBL%C TRANSPORT (BUS,TAXI) . . . . . . 2 BICYCLE . . . . . . . . . . . . . . . . . . . . . . . . . . ANIMAL ! ANIMAL CART . . . . . . . . . . . . . 4 WALKING . . . . . . . . . . . . . . . . . . . . . . . . . . 5 OTHER 6 (SPECIFY) HOURS . . . . . . . . . . . . . . 1 MINUTES . . . . . . . . . . . . 2 YES NO OK ANTENATAL CARE . . . . . . . . . 1 2 8 DELIVERY CARE . . . . . . . . . . 1 2 8 CHILD ]NMUNISATIORS.1 2 8 FAMILY PLANNZRG . . . . . . . . I 2 YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 (gAME) NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 -- GOVERNMENT . . . . . . . . . . . . . . . . . . . . . . 1 MISSION/CHURCH . . . . . . . . . . . . . . . . . . 2 PRIVATE EMPLOYER . . . . . . . . . . . . . . . . 3 OTHER PRIVATE . . . . . . . . . . . . . . . . . . . 4 OTHER 5 (SPECIFY) CAR / MOTORCYCLE . . . . . . . . . . . . . . . . . I PUBLIC TRANSPORT (BUS,TAXI) . . . . . . 2 BICYCLE . . . . . . . . . . . . . . . . . . . . . . . . . . ANIMAL / AN(HAL CART . . . . . . . . . . . . . 4 WALKING . . . . . . . . . . . . . . . . . . . . . . . . . . 5 OTHER 6 NOW f i r ts the health centre from here in kiloereters? NILOHETRES . . . . . . . . . . . . . . I l l IF LESS THAM 1 104,. RECORD ,00 ' . IF 97 134 OR NOllE RECORD '97 J. IF UNgBOk~, RECORD ' 98 ' . BUU do most peqpie in th i s c l~ l te r get from here to th i s heal th centre? (SPECIFY) / *o f 6 t- 32 276 IO. 30 31 QUESTIORS CODING CATEGORIES S4(iP TO H~ tcmg does I t take to get from here to the health centre using (NIEAMS NENTIORED ABOVE)? HOURS . . . . . . . . . . . . . . 1 m o B i L RECORD IN HIBUTES IF LESS THAN 2 HOURS AND IN HOURS [F 2 HOURS rmNORE. H]NUTES . . . . . . . . . . . . 2 I I I I i DOeS th i s heaLth centre provide: YES NO DK antenatal care? ANTENATAL CARE . . . . . . . . . 1 2 8 de l ivery care? DELIVERY CARE . . . . . . . . . . 1 2 8 ch i ld l lmunisation4? CHILD IHHUNISATIORS.,I 2 8 famiLy planning services? FAMILY PLANNING . . . . . . . . 1 2 8 IV. DISPENSARIES YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 32 33 34 35 36 37 38 39 Is there a d|spe~zsry thee provides services to the people in th i s c luster? iF YES: Whet 18 the ~ of th i s dispensary? IF DOES NOT KNOW NAME. WRITE "OOES NOT KNOW". Is that a Iloverrment dispensary or is i t operated by a church or is i t pr ivate? How far is the dispensary from here in kiL~e~tres? IF LESS THAN 1KJ4., RECORD IO0' IF 97 134 OR NGQE RECORD '97 ' , iF UNKNOMN RECORO '915' (BUT TRY TO GET AN ESTIMATE). Hou do most people in th i s c tuster get from here to th is dispensary? How Long does i t take to get from here to th is dispensary using (NEAXE MENTIONED ABOVE)? RECORD IN MINUTES IF LESS THAN 2 HOURS AND IN HOURS IF 2 V~UR| OR NORE. Does th i s d lmsry provide: antenatal care? deLivery caret ch i ld tmiunisat ion l? fami ly pLannin 9 services? (NAME) NO . . . . . . . . . . . . . . . . . . . . . , .H . . . . .~ - GOVERNMENT . . . . . . . . . . . . . . . . . . . . . . . 1 MISSION/CHURCH . . . . . . . . . . . . . . . . . . . 2 PRIVATE EMPLOYER . . . . . . . . . . . . . . . . . 3 OTHER PRIVATE . . . . . . . . . . . . . . . . . . . . 4 OTHER § (SPECIFY) KiL(~ETRES . . . . . . CAR / MOTORCYCLE . . . . . . . . . . . . . . . . . 1 PUBLIC TRANSPORT (BUS.TAXi) . . . . . . 2 BICYCLE . . . . . . . . . . . . . . . . . . . . . . . . . . 3 AN%HAL / ANIMAL CART . . . . . . . . . . . . . 4 WALKING . . . . . . . . . . . . . . . . . . . . . . . . . . 5 OTHER 6 (SPECIFY) HOURS . . . . . . . . . . . . . . 1 MINUTES . . . . . . . . . . . . 2 YES NO DK ANTENATAL CARE . . . . . . . . . 1 2 8 DELIVERY CARE . . . . . . . . . . 1 2 8 CHILD ]HMUNISATIQIdS.1 2 8 FAM]LY PLANNING . . . . . . . . 1 2 a 39 i s there Iny other dispensary that people in th is ctuster YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 use? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2- ~ 39 IF YES: Ho~ shy dispensaries do people in th is c luster use? NLYADER . . . . . . . . . . . . . . . . . . . . . . I I i i Do ahopl in th i s ares se l i coq~lems? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 HD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 DO NOT KNOW . . . . . . . . . . . . . . . . . . . . . 8 5o f6 277 V. AVAILABIL[TY BY NETHO0 40 41 42 4] 44 45 46 47 48 49 50 51 52 Whlt | l the n4me of the neares t p lace to th i s c lus ter where NEAREST PILL PROVIDER MARE cont racept ive p i l l s can be obta ined? Now fa r i s i t ( in ks~) from here? I F LESS THAN 1 101., RECORD 'DO' KILORETRES . . . . . . . . . . . . . . I I I IF 97 KN OR NORE RECORD '97 ' I I I IF ORKNO~ RECORD 'Q6' I /nat ia the r~am of the nearest p lace to th i s c lus ter where NEAREST CONDOR PROVIDER condom can be o~ta ined? HOW fs r IS i t ( it1 kirk) froth here? IF LESS THAN 1 KN., RECORD 'DO' KILORETRES . . . . . . . . . . . . . . J ] J I F 97 104 OR NORE RECORD '97 ' , IF UNKNOk/M RECORD '98' (BUT TRY TO GET AN ESTIMATE). What i s the ~ of the neerest p lace to th i s c lus ter where NEAREST INJECTABLE PROVIDER MANE • womln cou ld get • fami ly p tan~ing in jec t ion? How fa r is i t ( in lures) f rom here? IF LESS THAN 1 i04., RECORD 'DO' KILOMETRES . . . . . . . . . . . . . . J J J IF 97 VJ4 OR NORE RECORD '97 a, L I I IF URKNO,~ RECORD +98' (BUT TRY TO GET AN EST%HATE). Mhet i s the name of the nearest p lace to th i s c lus ter where NEAREST FO/~41NG TABLET PROVIDER foaming tab le ts can be obta ined? How f i r i i i t ( in lul~) frown here? IF LESS THAN 1 I04., RECORD 'DO I KILOMETRES . . . . . . . . . . . . . . I l l IF 97 KN OR )(ORE RECORD '97% I f UNKNOI~ RECORD '98 ~ (BUT TRY TO GET AN ESTINATE). Whlt iS the r~ of the nearest p lace to th i s c lus ter where NEAREST IUO PROVIDER NAME IUDs can be inser ted? How fa r is i t ( in knm) from here? IF LESS THAN I KN. , RECORD '00 ' KILOHETRES . . . . . . . . . . . . . . I L I I f 97 KN OR MORE RECORD '9 ; '~, IF UNKNOWN RECORD ~9 R' (BUT TRY TO GET AN ESTIMATE). i I Nhl t i s the mime of the nearest p lace to th i s C lus ter where NEAREST S IERIL ISATIOR PROVIDER MANE • ~ n cou ld go to get a s te r i t i sa t ion operat ion? How f i r i i i t ( in kms) f rom here? I F LESS THAN I KN. , RECORD =DO a KILONETRES . . . . . . . . . . . . . . I1 IF 97 lot OR ROBE RECORD 197 ' , IF U#dKI~t RECORD =981 (BUT TRY TO GET AN ESTIMATE). Has there been any spec ia l educat iona l campaign in YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 th i s c lus ter over the past 6 months that was in tended to inc rease awareness about the problem of AIDS? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 6o f6 278 Front Matter Title Page Survey Information Table of Contents List of Tables List of Figures Foreword Acknowledgments Executive Summary Map of Kenya Chapter 1 - Introduction Chapter 2 - Characteristics of Households and Respondents Chapter 3 - Fertility Chapter 4 - Fertility Regulation Chapter 5 - Other Proximate Determinants of Fertility Chapter 6 - Fertility Preferences Chapter 7 - Infant and Child Mortality Chapter 8 - Maternal and Child Health Chapter 9 - Infant Feeding and Childhood and Maternal Nutrition Chapter 10 - Knowledge of AIDS Chapter 11 - Results of the Male Survey Chapter 12 - Local Availability of Family Planning and Health Services References Appendix A - Survey Design Appendix B - Estimates of Sampling Errors Appendix C - Data Quality Tables Appendix D - Persons Involved Appendix E Page 221-250 - Questionnaires Appendix E Page 251-278

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