Kenya - Demographic and Health Survey - 2010

Publication date: 2010

Kenya 2008-09Demographic and Health Survey K enya 2008-09 D em ographic and H ealth Survey KENYA DEMOGRAPHIC AND HEALTH SURVEY 2008-09 Kenya National Bureau of Statistics Nairobi, Kenya National AIDS Control Council Nairobi, Kenya National AIDS/STD Control Programme Nairobi, Kenya Ministry of Public Health and Sanitation Nairobi, Kenya Kenya Medical Research Institute Nairobi, Kenya National Coordinating Agency for Population and Development Nairobi, Kenya MEASURE DHS, ICF Macro Calverton, Maryland, U.S.A. U.S. Agency for International Development (USAID) Nairobi, Kenya United Nations Population Fund Nairobi, Kenya United Nations Children’s Fund Nairobi, Kenya June 2010 KENYANS AND AMERICANSIN PARTNERSHIP TO FIGHT HIV/AIDS This report summarises the findings of the 2008-09 Kenya Demographic and Health Survey (KDHS) carried out by the Kenya National Bureau of Statistics (KNBS) in partnership with the National AIDS Control Council (NACC), the National AIDS/STD Control Programme (NASCOP), the Ministry of Health and Sanitation, the Kenya Medical Research Institute (KEMRI), and the National Coordinating Agency for Population and Development (NCAPD). ICF Macro provided technical assistance for the survey through the USAID-funded MEASURE DHS programme, which is designed to assist developing countries to collect data on fertility, family planning, and maternal and child health. Funding for the KDHS was received from USAID/Kenya, the United Nations Population Fund (UNFPA), the United Nations Children’s Fund (UNICEF), UNAIDS, and the World Bank. The opinions expressed in this report are those of the authors and do not necessarily reflect the views of the donor organisations. Additional information about the survey may be obtained from the Kenya National Bureau of Statistics (KNBS), P.O. Box 30266, Nairobi (Telephone: 254.20.340.929; Fax: 254.20.315.977, email: director@cbs.go.ke). Additional information about the DHS programme may be obtained from MEASURE DHS, ICF Macro, 11785 Beltsville Drive, Suite 300, Calverton, MD 20705, U.S.A. (Telephone: 1.301.572.0200; Fax: 1.301.572.0999; e-mail: reports@macrointernational.com). Recommended citation: Kenya National Bureau of Statistics (KNBS) and ICF Macro. 2010. Kenya Demographic and Health Survey 2008-09. Calverton, Maryland: KNBS and ICF Macro. Contents | iii CONTENTS TABLES AND FIGURES . ix FOREWORD . xvii ACKNOWLEDGMENTS . xix SUMMARY OF FINDINGS . xxi MAP OF KENYA . xxvi CHAPTER 1 INTRODUCTION 1.1 Geography, History, and the Economy . 1 1.1.1 Geography . 1 1.1.2 History. 1 1.1.3 Economy . 2 1.2 Population . 3 1.3 Population and Family Planning Policies and Programmes . 3 1.4 Health Priorities and Programmes . 5 1.5 Strategic Framework to Combat the HIV/AIDS Epidemic . 6 1.6 Objectives of the Survey . 6 1.7 Survey Organisation . 7 1.8 Sample Design . 8 1.9 Questionnaires . 8 1.10 HIV Testing . 9 1.11 Training . 10 1.12 Fieldwork . 11 1.13 Data Processing . 12 1.14 Response Rates . 12 CHAPTER 2 HOUSEHOLD POPULATION AND HOUSING CHARACTERISTICS 2.1 Population by Age and Sex . 13 2.2 Household Composition . 14 2.3 Education of the Household Population . 15 2.3.1 Educational Attainment . 15 2.3.2 School Attendance Rates . 17 2.4 Household Environment . 20 2.4.1 Drinking Water . 20 2.4.2 Household Sanitation Facilities . 22 2.4.3 Housing Characteristics . 23 2.5 Household Possessions . 24 2.6 Wealth Index . 25 2.7 Birth Registration . 26 iv | Contents CHAPTER 3 CHARACTERISTICS OF RESPONDENTS 3.1 Characteristics of Survey Respondents . 29 3.2 Educational Attainment by Background Characteristics . 31 3.3 Literacy. 32 3.4 Access to Mass Media . 34 3.5 Employment . 37 3.6 Occupation . 39 3.7 Earnings and Type of Employment . 41 3.8 Health Insurance Coverage . 43 3.9 Knowledge and Attitudes Concerning Tuberculosis . 43 3.10 Smoking . 45 CHAPTER 4 FERTILITY LEVELS, TRENDS, AND DIFFERENTIALS 4.1 Introduction . 47 4.2 Current Fertility . 47 4.3 Fertility Trends . 50 4.4 Children Ever Born and Children Surviving . 52 4.5 Birth Intervals . 53 4.6 Age at First Birth . 54 4.7 Teenage Fertility . 55 CHAPTER 5 FAMILY PLANNING 5.1 Knowledge of Contraceptive Methods . 57 5.2 Ever Use of Family Planning Methods . 59 5.3 Current Use of Contraceptive Methods . 61 5.4 Differentials in Contraceptive Use by Background Characteristics . 64 5.5 Number of Children at First Use of Contraception . 66 5.6 Knowledge of Fertile Period . 66 5.7 Timing of Sterilisation . 67 5.8 Source of Contraception . 67 5.9 Cost of Contraceptive Methods . 68 5.10 Informed Choice . 69 5.11 Contraceptive Discontinuation . 70 5.12 Future Use of Contraception . 71 5.13 Reasons for Not Intending to Use. 71 5.14 Exposure to Family Planning Messages . 72 5.15 Contact of Non-users with Family Planning Providers . 75 5.16 Husband/Partner’s Knowledge of Women’s Contraceptive Use . 76 CHAPTER 6 OTHER PROXIMATE DETERMINANTS OF FERTILITY 6.1 Current Marital Status . 79 6.2 Polygyny . 80 6.3 Age at First Marriage . 82 6.4 Age at First Sexual Intercourse . 84 6.5 Recent Sexual Activity . 86 6.6 Postpartum Amenorrhoea, Abstinence, and Insusceptibility . 89 6.7 Menopause . 90 Contents | v CHAPTER 7 FERTILITY PREFERENCES 7.1 Desire for More Children . 93 7.2 Desire to Limit Childbearing by Background Characteristics . 95 7.3 Need for Family Planning Services . 96 7.4 Ideal Number of Children . 97 7.5 Mean Ideal Number of Children by Background Characteristics . 99 7.6 Fertility Planning Status . 99 7.7 Wanted Fertility Rates . 101 CHAPTER 8 INFANT AND CHILD MORTALITY 8.1 Levels and Trends in Infant and Child Mortality . 103 8.2 Data Quality . 105 8.3 Socioeconomic Differentials in Infant and Child Mortality . 106 8.4 Demographic Differentials in Infant and Child Mortality . 108 8.5 Perinatal Mortality . 109 8.6 High-risk Fertility Behaviour . 110 CHAPTER 9 MATERNAL HEALTH 9.1 Antenatal Care . 113 9.1.1 Antenatal Care Coverage . 113 9.1.2 Source of Antenatal Care . 115 9.1.3 Number and Timing of Antenatal Care Visits . 116 9.1.4 Components of Antenatal Care . 116 9.2 Tetanus Toxoid Injections . 118 9.3 Place of Delivery . 119 9.4 Assistance during Delivery . 122 9.5 Postnatal Care . 123 CHAPTER 10 CHILD HEALTH 10.1 Weight and Size at Birth . 127 10.2 Vaccination Coverage . 128 10.3 Acute Respiratory Infection . 132 10.4 Fever . 134 10.5 Diarrhoeal Disease . 135 10.6 Knowledge of ORS Packets . 139 10.7 Stool Disposal . 139 CHAPTER 11 NUTRITION OF WOMEN AND CHILDREN 11.1 Nutritional Status of Children . 141 11.1.1 Measurement of Nutritional Status among Young Children . 141 11.1.2 Results of Data Collection . 142 11.1.3 Levels of Malnutrition . 142 11.2 Initiation of Breastfeeding . 146 11.3 Breastfeeding Status by Age . 148 vi | Contents 11.4 Duration and Frequency of Breastfeeding . 150 11.5 Types of Complementary Foods . 151 11.6 Infant and Young Child Feeding Practices . 152 11.7 Micronutrient Intake among Children . 154 11.8 Nutritional Status of Women . 157 11.9 Micronutrient Intake among Mothers . 158 CHAPTER 12 MALARIA 12.1 Introduction . 161 12.2 Household Ownership of Mosquito Nets . 162 12.3 Use of Mosquito Nets . 164 12.4 Intermittent Preventive Treatment of Malaria in Pregnancy . 167 12.5 Malaria Case Management among Children . 168 CHAPTER 13 HIV/AIDS-RELATED KNOWLEDGE, ATTITUDES, AND BEHAVIOUR 13.1 Introduction . 173 13.2 HIV/AIDS Knowledge of Transmission and Prevention Methods . 173 13.2.1 Awareness of HIV/AIDS . 173 13.2.2 Knowledge of HIV Prevention . 174 13.2.3 Rejection of Misconceptions about HIV/AIDS . 177 13.2.4 Knowledge of Mother-to-Child Transmission of HIV . 180 13.3 Attitudes towards People Living with AIDS . 181 13.4 Attitudes Towards Condom Education for Youth . 185 13.5 Higher Risk Sex . 186 13.5.1 Multiple Partners and Condom Use . 186 13.5.2 Transactional Sex . 190 13.6 Coverage of HIV Counselling and Testing . 190 13.6.1 General HIV Testing . 190 13.6.2 HIV Counselling and Testing during Pregnancy . 193 13.7 Male Circumcision . 194 13.8 Self-Reporting of Sexually Transmitted Infections . 194 13.9 HIV/AIDS Knowledge and Sexual Behaviour among Youth . 195 13.9.1 HIV/AIDS-Related Knowledge among Young Adults . 196 13.9.2 Trends in Age at First Sex . 197 13.9.3 Condom Use at First Sex . 199 13.9.4 Abstinence and Premarital Sex . 200 13.9.5 Higher-Risk Sex and Condom Use among Young Adults . 202 13.9.6 Cross-generational Sexual Partners . 205 13.9.7 Drunkenness during Sex among Young Adults . 206 13.9.8 Voluntary HIV Counselling and Testing among Young Adults . 207 CHAPTER 14 HIV PREVALENCE AND ASSOCIATED FACTORS 14.1 Coverage of HIV Testing . 209 14.2 HIV Prevalence by Age . 213 14.3 Trends in HIV Prevalence . 214 Contents | vii 14.4 HIV Prevalence by Socioeconomic Characteristics . 215 14.5 HIV Prevalence by Demographic Characteristics and Sexual Behaviour . 217 14.6 HIV Prevalence among Youth . 220 14.7 HIV Prevalence by Other Characteristics . 223 14.8 HIV Prevalence by Male Circumcision . 224 14.9 HIV Prevalence among Couples . 226 14.10 Distribution of the HIV Burden in Kenya . 227 CHAPTER 15 WOMEN’S EMPOWERMENT AND DEMOGRAPHIC AND HEALTH OUTCOMES 15.1 Employment and Form of Earnings. 229 15.2 Controls over Earnings . 230 15.2.1 Control over Wife’s Earnings . 230 15.2.2 Control over Husband’s Earnings . 231 15.3 Women’s Participation in Decision-making . 233 15.4 Attitudes towards Wife Beating . 236 15.5 Men’s Attitudes towards Wife’s Refusing Sex . 239 15.6 Women’s Empowerment Indicators . 241 15.7 Current Use of Contraception by Women’s Status . 241 15.8 Ideal Family Size and Unmet Need by Women’s Status . 242 15.9 Women’s Status and Reproductive Health Care . 243 CHAPTER 16 GENDER-BASED VIOLENCE 16.1 Introduction . 245 16.2 Data Collection. 245 16.3 Experience of Physical Violence . 247 16.4 Experience of Sexual Violence . 249 16.5 Marital Control . 251 16.6 Marital Violence . 253 16.7 Frequency of Spousal Violence . 258 16.8 Physical Consequences of Spousal Violence . 259 16.9 Violence Initiated by Women Against Husbands . 260 16.10 Response to Violence . 262 16.11 Female Genital Cutting . 264 CHAPTER 17 ADULT AND MATERNAL MORTALITY 17.1 Data . 269 17.2 Estimates of Adult Mortality . 270 17.3 Estimates of Maternal Mortality . 272 REFERENCES . 275 APPENDIX A SAMPLE IMPLEMENTATION . 283 APPENDIX B ESTIMATES OF SAMPLING ERRORS . 289 APPENDIX C DATA QUALITY . 305 APPENDIX D LIST OF 2008-09 KDHS PARTICIPANTS . 311 APPENDIX E QUESTIONNAIRES . 319 Tables and Figures | ix TABLES AND FIGURES CHAPTER 1 INTRODUCTION Table 1.1 Basic demographic indicators. 3 Table 1.2 Results of the household and individual interviews . 12 CHAPTER 2 HOUSEHOLD POPULATION AND HOUSING CHARACTERISTICS Table 2.1 Household population by age, sex, and residence . 13 Table 2.2 Household composition . 15 Table 2.3.1 Educational attainment of the female household population . 16 Table 2.3.2 Educational attainment of the male household population . 17 Table 2.4 School attendance ratios . 18 Table 2.5 School attendance . 19 Table 2.6 Household drinking water . 21 Table 2.7 Household sanitation facilities . 22 Table 2.8 Household characteristics . 23 Table 2.9 Household durable goods . 25 Table 2.10 Wealth quintiles . 26 Table 2.11 Birth registration of children under age five . 27 Table 2.12 Reason for not registering birth . 28 Figure 2.1 Population Pyramid . 14 Figure 2.2 Age-specific Attendance Rates of the de-facto Population 5 to 24 Years . 20 CHAPTER 3 CHARACTERISTICS OF RESPONDENTS Table 3.1 Background characteristics of respondents . 30 Table 3.2.1 Educational attainment: Women . 31 Table 3.2.2 Educational attainment: Men . 32 Table 3.3.1 Literacy: Women . 33 Table 3.3.2 Literacy: Men . 34 Table 3.4.1 Exposure to mass media: Women . 35 Table 3.4.2 Exposure to mass media: Men . 36 Table 3.5.1 Employment status: Women . 37 Table 3.5.2 Employment status: Men . 38 Table 3.6.1 Occupation: Women . 40 Table 3.6.2 Occupation: Men . 41 Table 3.7 Type of employment among women . 42 Table 3.8.1 Knowledge and attitude concerning tuberculosis: Women . 44 Table 3.8.2 Knowledge and attitude concerning tuberculosis: Men . 45 Table 3.9 Use of tobacco: Men . 46 Figure 3.1 Access to Mass Media . 36 Figure 3.2 Women’s Employment Status in the Past 12 Months . 39 Figure 3.3 Employment Characteristics among Working Women . 42 Figure 3.4 Health Insurance Coverage . 43 x | Tables and Figures CHAPTER 4 FERTILITY LEVELS, TRENDS, AND DIFFERENTIALS Table 4.1 Current fertility . 47 Table 4.2 Fertility by background characteristics . 48 Table 4.3 Trends in age-specific fertility rates . 50 Table 4.4 Trends in fertility by background characteristics . 51 Table 4.5 Trends in age-specific fertility rates . 51 Table 4.6 Children ever born and living . 52 Table 4.7 Birth intervals . 53 Table 4.8 Age at first birth . 54 Table 4.9 Median age at first birth . 55 Table 4.10 Teenage pregnancy and motherhood . 56 Figure 4.1 Total Fertility Rates by Background Characteristics . 49 Figure 4.2 Trends in Total Fertility Rate, Kenya 1975-2008 . 50 CHAPTER 5 FAMILY PLANNING Table 5.1 Knowledge of contraceptive methods . 58 Table 5.2 Trends in contraceptive knowledge . 59 Table 5.3 Ever use of contraception . 60 Table 5.4 Current use of contraception by age . 63 Table 5.5 Current use of contraception by background characteristics . 65 Table 5.6 Number of children at first use of contraception . 66 Table 5.7 Knowledge of fertile period . 66 Table 5.8 Timing of sterilisation . 67 Table 5.9 Source of modern contraception methods . 68 Table 5.10 Cost of modern contraceptive methods . 69 Table 5.11 Informed choice . 70 Table 5.12 First-year contraceptive discontinuation rates . 70 Table 5.13 Future use of contraception . 71 Table 5.14 Reason for not intending to use contraception in the future . 71 Table 5.15 Preferred method of contraception for future use . 72 Table 5.16 Exposure to family planning messages . 73 Table 5.17 Exposure to condom messages . 74 Table 5.18 Acceptability of condom messages . 75 Table 5.19 Contact of nonusers with family planning providers . 76 Table 5.20 Husband/partner’s knowledge of women’s use of contraception . 77 Table 5.21 Men’s attitudes toward contraception . 78 Figure 5.1 Trends in Contraceptive Use, Kenya 1978-2008 (percentage of currently married women using any method) . 61 Figure 5.2 Trends in Current Use of Specific Contraceptive Methods among Currently Married Women Age 15-49, Kenya 1998-2008 . 62 Figure 5.3 Current Use of Any Contraceptive Method among Currently Married Women Age 15-49, by Background Characteristics . 64 Tables and Figures | xi CHAPTER 6 OTHER PROXIMATE DETERMINANTS OF FERTILITY Table 6.1 Current marital status . 79 Table 6.2.1 Number of women’s co-wives . 80 Table 6.2.2 Number of men’s co-wives . 81 Table 6.3 Age at first marriage . 83 Table 6.4 Median age at first marriage . 84 Table 6.5 Age at first sexual intercourse . 85 Table 6.6 Median age at first intercourse . 86 Table 6.7.1 Recent sexual activity: Women . 87 Table 6.7.2 Recent sexual activity: Men . 88 Table 6.8 Postpartum amenorrhoea, abstinence and insusceptibility . 89 Table 6.9 Median duration of amenorrhoea, postpartum abstinence, and postpartum insusceptibility . 90 Table 6.10 Menopause . 91 Figure 6.1 Percentage of Currently Married Women Whose Husbands Have At Least One Other Wife . 81 CHAPTER 7 FERTILITY PREFERENCES Table 7.1 Fertility preferences by number of living children . 94 Table 7.2 Desire to limit childbearing . 95 Table 7.3 Need and demand for family planning among currently married women . 97 Table 7.4 Ideal number of children . 98 Table 7.5 Mean ideal number of children by background characteristics . 99 Table 7.6 Fertility planning status . 100 Table 7.7 Wanted fertility rates . 101 Figure 7.1 Fertility Preferences among Currently Married Women Age 15-49 . 94 Figure 7.2 Planning Status of Births . 100 CHAPTER 8 INFANT AND CHILD MORTALITY Table 8.1 Early childhood mortality rates . 104 Table 8.2 Early childhood mortality rates by socioeconomic characteristics . 107 Table 8.3 Early childhood mortality rates by demographic characteristics . 108 Table 8.4 Perinatal mortality . 109 Table 8.5 High-risk fertility behaviour . 110 Figure 8.1 Trends in Infant and Under-Five Mortality 2003 KDHS and 2008-09 KDHS . 104 Figure 8.2 Under-Five Mortality by Background Characteristics . 107 CHAPTER 9 MATERNAL HEALTH Table 9.1 Antenatal care . 114 Table 9.2 Source of antenatal care . 115 Table 9.3 Number of antenatal care visits and timing of first visit . 116 Table 9.4 Components of antenatal care . 117 Table 9.5 Tetanus toxoid injections . 119 Table 9.6 Place of delivery . 120 xii | Tables and Figures Table 9.7 Reason for not delivering in a health facility . 121 Table 9.8 Assistance during delivery . 122 Table 9.9 Timing of first postnatal checkup. 124 Table 9.10 Type of provider of first postnatal checkup . 125 Figure 9.1 Trends in Receipt of Antenatal Care from a Skilled Medical Provider, Kenya 2003-2008 . 114 Figure 9.2 Components of Antenatal Care . 118 Figure 9.3 Trends in Delivery Care . 123 CHAPTER 10 CHILD HEALTH Table 10.1 Child’s weight and size at birth . 128 Table 10.2 Vaccinations by source of information . 129 Table 10.3 Vaccinations by background characteristics . 131 Table 10.4 Prevalence and treatment of symptoms of ARI . 133 Table 10.5 Prevalence and treatment of fever . 134 Table 10.6 Prevalence of diarrhoea . 135 Table 10.7 Diarrhoea treatment . 136 Table 10.8 Feeding practices during diarrhoea . 138 Table 10.9 Knowledge of ORS . 139 Table 10.10 Disposal of children’s stools . 140 Figure 10.1 Percentage of Children Age 12-23 Months with Specific Vaccinations. 130 Figure 10.2 Trends in Childhood Vaccination Coverage . 132 CHAPTER 11 NUTRITION OF WOMEN AND CHILDREN Table 11.1 Nutritional status of children . 143 Table 11.2 Trends in nutritional status of children . 145 Table 11.3 Initial breastfeeding . 147 Table 11.4 Breastfeeding status by age . 149 Table 11.5 Median duration and frequency of breastfeeding . 150 Table 11.6 Foods and liquids consumed by children in the day or night preceding the interview . 152 Table 11.7 Infant and young child feeding (IYCF) practices . 153 Table 11.8 Micronutrient intake among children . 155 Table 11.9 Presence of iodized salt in household . 157 Table 11.10 Nutritional status of women . 158 Table 11.11 Micronutrient intake among mothers . 159 Figure 11.1 Nutritional Status of Children by Age . 144 Figure 11.2 Proportion of Underweight Children by Province, 2003 and 2008-09 . 146 Figure 11.3 Prelacteal Liquids . 148 Figure 11.4 Infant Feeding Practices by Age . 149 Figure 11.5 Infant and Young Child Feeding (IYCF) Practices . 154 Tables and Figures | xiii CHAPTER 12 MALARIA Table 12.1 Ownership of mosquito nets . 163 Table 12.2 Use of mosquito nets by children . 165 Table 12.3 Use of mosquito nets by women . 166 Table 12.4 Prophylactic use of antimalarial drugs and use of intermittent preventive treatment (IPT) by women during pregnancy . 168 Table 12.5 Prevalence and prompt treatment of fever . 169 Table 12.6 Type and timing of antimalarial drugs . 170 Table 12.7 Availability at home of antimalarial drugs taken by children with fever . 171 Figure 12.1 Ownership of Mosquito Nets, 2003-2009 . 163 Figure 12.2 Use of Mosquito Nets by Children under Five . 165 Figure 12.3 Use of Mosquito Nets by Women Age 15-49 . 167 CHAPTER 13 HIV/AIDS-RELATED KNOWLEDGE, ATTITUDES, AND BEHAVIOUR Table 13.1 Knowledge of AIDS. 174 Table 13.2 Knowledge of HIV prevention methods . 175 Table 13.3.1 Comprehensive knowledge about AIDS: Women . 177 Table 13.3.2 Comprehensive knowledge about AIDS: Men . 178 Table 13.4 Knowledge of prevention of mother to child transmission of HIV . 181 Table 13.5.1 Accepting attitudes toward those living with HIV/AIDS: Women . 182 Table 13.5.2 Accepting attitudes toward those living with HIV/AIDS: Men . 184 Table 13.6 Adult support of education about condom use to prevent AIDS . 186 Table 13.7.1 Multiple sexual partners and higher-risk sexual intercourse in the past 12 months: Women . 188 Table 13.7.2 Multiple sexual partners and higher-risk sexual intercourse in the past 12 months: Men . 189 Table 13.8 Payment for sexual intercourse: Men . 190 Table 13.9.1 Coverage of prior HIV testing: Women . 191 Table 13.9.2 Coverage of prior HIV testing: Men . 192 Table 13.10 Pregnant women counselled and tested for HIV. 193 Table 13.11 Male circumcision . 194 Table 13.12 Self-reported prevalence of sexually-transmitted infections (STIs) and STIs symptoms . 195 Table 13.13 Comprehensive knowledge about AIDS and of a source of condoms among youth . 196 Table 13.14 Age at first sexual intercourse among youth . 198 Table 13.15 Condom use at first sexual intercourse among youth . 200 Table 13.16 Premarital sexual intercourse and condom use during premarital sexual intercourse among youth . 201 Table 13.17.1 Higher-risk sexual intercourse among youth and condom use at last higher-risk intercourse in the past 12 months: Women . 203 Table 13.17.2 Higher-risk sexual intercourse among youth and condom use at last higher-risk intercourse in the past 12 months: Men . 204 Table 13.18 Age-mixing in sexual relationships among women age 15-19 . 205 Table 13.19 Drunkenness during sexual intercourse among youth . 206 Table 13.20 Recent HIV tests among youth . 207 xiv | Tables and Figures Figure 13.1 Trends in Knowledge of HIV Prevention Methods: Women . 176 Figure 13.2 Trends in Knowledge of HIV Prevention Methods: Men . 176 Figure 13.3 Comprehensive Knowledge about AIDS . 179 Figure 13.4 Accepting Attitudes towards Those with HIV: Women . 183 Figure 13.5 Accepting Attitudes towards Those with HIV: Men . 185 Figure 13.6 Comprehensive Knowledge about AIDS and Source of Condoms among Youth . 197 Figure 13.7 Age at First Sexual Intercourse among Youth . 199 Figure 13.8 Abstinence, Being Faithful and Condom Use (ABC) among Young Women and Men . 205 CHAPTER 14 HIV PREVALENCE AND ASSOCIATED FACTORS Table 14.1 Coverage of HIV testing by residence and region . 211 Table 14.2 Coverage of HIV testing by selected background characteristics . 212 Table 14.3 HIV prevalence by age . 213 Table 14.4 Trends in HIV prevalence by age . 214 Table 14.5 HIV prevalence by socioeconomic characteristics . 216 Table 14.6 HIV prevalence by demographic characteristics . 218 Table 14.7 HIV prevalence by sexual behaviour . 219 Table 14.8 HIV prevalence among young people by background characteristics . 221 Table 14.9 HIV prevalence among young people by sexual behaviour . 222 Table 14.10 HIV prevalence by other characteristics . 223 Table 14.11 Prior HIV testing by current HIV status . 224 Table 14.12 HIV prevalence by male circumcision . 225 Table 14.13 HIV prevalence among couples . 226 Figure 14.1 Coverage of HIV Testing by Gender . 210 Figure 14.2 HIV Prevalence by Age Group and Sex . 214 Figure 14.3 Trends in HIV Prevalence among Women 15-49 . 215 Figure 14.4 Trends in HIV Prevalence among Men 15-49 . 215 Figure 14.5 HIV Prevalence by Gender and Province . 217 CHAPTER 15 WOMEN’S EMPOWERMENT AND DEMOGRAPHIC AND HEALTH OUTCOMES Table 15.1 Employment and cash earnings of currently married women and men . 230 Table 15.2.1 Control over women’s cash earnings and relative magnitude of women’s earnings: Women . 231 Table 15.2.2 Control over men’s cash earnings . 232 Table 15.3 Women’s control over her own earnings and over those of her husband . 233 Table 15.4.1 Women’s participation in decision-making . 233 Table 15.4.2 Women’s participation in decision-making according to men . 234 Table 15.5.1 Women’s participation in decision-making by background characteristics . 235 Table 15.5.2 Men’s attitude toward wives’ participation in decision-making . 236 Table 15.6.1 Attitude toward wife beating: Women . 237 Table 15.6.2 Attitude toward wife beating: Men . 238 Table 15.7 Men’s attitudes toward a husband’s rights when his wife refuses to have sexual intercourse . 240 Table 15.8 Indicators of women’s empowerment . 241 Table 15.9 Current use of contraception by women’s status . 242 Tables and Figures | xv Table 15.10 Women’s empowerment and ideal number of children and unmet need for family planning . 243 Table 15.11 Reproductive health care by women’s empowerment . 244 Figure 15.1 Number of Decisions in Which Women Participate . 234 CHAPTER 16 GENDER-BASED VIOLENCE Table 16.1 Experience of physical violence . 248 Table 16.2 Persons committing physical violence . 249 Table 16.3 Force at sexual initiation . 249 Table 16.4 Experience of sexual violence . 250 Table 16.5 Persons committing sexual violence . 251 Table 16.6 Experience of different forms of violence . 251 Table 16.7 Degree of marital control exercised by husbands . 252 Table 16.8 Forms of spousal violence . 254 Table 16.9 Spousal violence by background characteristics. 256 Table 16.10 Spousal violence by husband’s characteristics and empowerment indicators . 257 Table 16.11 Frequency of spousal violence among those who report violence . 259 Table 16.12 Injuries to women due to spousal violence . 260 Table 16.13 Violence by women against their spouse . 261 Table 16.14 Help seeking to stop violence . 263 Table 16.15 Sources from where help was sought . 264 Table 16.16 Knowledge and prevalence of female circumcision . 265 Table 16.17 Age at circumcision . 266 Table 16.18 Person performing circumcisions among women by residence . 267 Table 16.19 Benefits of circumcision . 267 Table 16.20 Attitudes about female circumcision . 268 Figure 16.1 Domestic Violence . 254 CHAPTER 17 ADULT AND MATERNAL MORTALITY Table 17.1 Data on siblings . 270 Table 17.2 Adult mortality rates . 271 Table 17.3 Maternal mortality . 273 Figure 17.1 Trends in Adult Mortality, Kenya 1996-2002 and 2002-2008 . 272 APPENDIX A SAMPLE IMPLEMENTATION Table A.1 Sample implementation: women . 283 Table A.2 Sample implementation: men . 284 Table A.3 Coverage of HIV testing among interviewed women by social and demographic characteristics . 285 Table A.4 Coverage of HIV testing among interviewed men by social and demographic characteristics . 286 Table A.5 Coverage of HIV testing among interviewed women by sexual behaviour characteristics . 287 Table A.6 Coverage of HIV testing among interviewed men by sexual behaviour characteristics . 288 xvi | Tables and Figures APPENDIX B ESTIMATES OF SAMPLING ERRORS Table B.1 List of selected variables for sampling errors, Kenya 2008-09 . 292 Table B.2 Sampling Errors for Kenya . 293 Table B.3 Sampling Errors for Urban . 294 Table B.4 Sampling Errors for Rural . 295 Table B.5 Sampling Errors for Nairobi . 296 Table B.6 Sampling Errors for Central Province . 297 Table B.7 Sampling Errors for Coast Province . 298 Table B.8 Sampling Errors for Eastern Province . 299 Table B.9 Sampling Errors for Nyanza Province . 300 Table B.10 Sampling Errors for Rift Valley Province . 301 Table B.11 Sampling Errors for Western Province . 302 Table B.12 Sampling Errors for North Eastern Province . 303 APPENDIX C DATA QUALITY Table C.1 Household age distribution . 305 Table C.2.1 Age distribution of eligible and interviewed women . 306 Table C.2.2 Age distribution of eligible and interviewed men . 306 Table C.3 Completeness of reporting . 307 Table C.4 Births by calendar years . 307 Table C.5 Reporting of age at death in days . 308 Table C.6 Reporting of age at death in months . 309 Table C.7 Nutritional status of children . 310 Foreword | xvii FOREWORD The primary objective of the 2008-09 KDHS, like its predecessors, is to provide up-to-date information for policymakers, planners, researchers, and programme managers. This information guides the planning, implementation, monitoring, and evaluation of population and health programmes in Kenya. Specifically, the survey collects data on the following: fertility levels, marriage, sexual activity, fertility preferences, awareness and use of family planning methods, breastfeeding practices, nutritional status of women and young children, childhood and maternal mortality, maternal and child health, malaria and use of mosquito nets, domestic violence, awareness and behaviour regarding HIV/AIDS and other sexually transmitted infections (STIs), and HIV prevalence among adults. The results of the current survey present evidence of a resumption of the fertility decline observed in the 1980s and the 1990s in Kenya. The total fertility rate (TFR) of 4.6 children per woman is the lowest rate ever recorded for Kenya. This decline in fertility could be attributed to an increase in the proportion of currently married women using contraception, which rose from 7 percent in 1978 to 46 percent in 2008-09. Survey results also indicate a resumption in the decline of childhood mortality. The under- five-mortality rate decreased to 74 deaths per 1,000 live births in 2008-09, down from 115 deaths in 2003, while the infant mortality rate was 52 deaths per 1,000 live births, down from 77 deaths reported in 2003. The improvement in child survival is corroborated by increases in child vaccination coverage, in ownership and use of mosquito bednets, and in antenatal care coverage, all of which have been shown to reduce child mortality. Overall, 77 percent of children age 12-23 months are fully vaccinated, and only three percent have not received any vaccines. Use of mosquito nets is considered to be one of the strongest strategies in the fight against malaria. The survey found that 61 percent of households own at least one mosquito net (treated or untreated), and 56 percent report owning at least one insecticide-treated net (ITN). Fifty-one percent of children under five years and 53 percent of pregnant women slept under a mosquito net the night prior to the interview. The results also indicate that 9 in 10 mothers visited a health professional at least once for antenatal care for the most recent birth in the five-year period preceding the survey. These trends and a plethora of other important findings imply that the deterioration in the quality of life among the Kenyan population seen in earlier surveys has been reversed. The Kenya National Bureau of Statistics (KNBS) wishes to acknowledge the contributions of the various agencies and institutions that culminated in the compilation of the 2008-09 Kenya Demographic and Health Survey (KDHS). The survey was conducted in close collaboration with the National Public Health Laboratory Services (NPHLS), the National Coordinating Agency for Population and Development (NCAPD), the Kenya Medical Research Institute (KEMRI), the National AIDS Control Council (NACC), ICF Macro, the United Nations Fund for Population Activities (UNFPA), the United Nations Children’s Fund (UNICEF), and the United States Agency for International Development (USAID). These institutions provided technical, administrative, and logistical support to the process, for which we are exceedingly grateful. Special thanks go to staff of the Kenya National Bureau of Statistics, Ministry of Public Health and Sanitation, National AIDS Control Council (NACC), National Coordinating Agency for Population and Development (NCAPD), and Kenya Medical Research Institute (KEMRI) who coordinated the survey. Lastly, we acknowledge the financial support provided by USAID, UNFPA, the World Bank, and UNICEF. Summary of Findings | xix SUMMARY OF FINDINGS The 2008-09 Kenya Demographic and Health Survey (KDHS) is a nationally represen- tative sample survey of 8,444 women age 15 to 49 and 3,465 men age 15 to 54 selected from 400 sample points (clusters) throughout Kenya. It is designed to provide data to monitor the population and health situation in Kenya as a follow-up to the 1989, 1993, 1998, and 2003 KDHS surveys. The survey utilised a two-stage sample based on the 1999 Population and Hous- ing Census and was designed to produce sepa- rate estimates for key indicators for each of the eight provinces in Kenya. Data collection took place over a three-month period, from 13 No- vember 2008 to late February 2009. The survey obtained detailed information on fertility levels, marriage, sexual activity, fertility preferences, awareness and use of family plan- ning methods, breastfeeding practices, nutri- tional status of women and young children, childhood and maternal mortality, maternal and child health, and awareness and behaviour re- garding HIV/AIDS. The survey also included collection information on ownership and use of mosquito nets, domestic violence, and HIV test- ing of adults. The 2008-09 KDHS was implemented by the Kenya National Bureau of Statistics (KNBS) in collaboration with the Ministry of Public Health and Sanitation (including the National AIDS and STIs Control Programme-NASCOP), the Ministry of Medical Services, the Ministry of Gender, the Kenya Medical Research Institute (KEMRI), the National Coordinating Agency for Population Development (NCAPD), and the Na- tional AIDS Control Council (NACC). The Na- tional Public Health Laboratory Services assisted in recruitment and training of the health field workers, supported the voluntary counselling and testing of respondents, and implemented the HIV testing in the laboratory. Technical assis- tance was provided through the international MEASURE DHS programme at ICF Macro and NCAPD. Financial support for the survey was provided by the Government of Kenya and the U.S. Agency for International Development (USAID), the United Nations Population Fund (UNFPA), and the United Nations Children’s Fund (UNICEF). FERTILITY Fertility Levels and Trends. One of the most important findings from the 2008-09 KDHS is that fertility rates—which had stag- nated in the late 1990s—have declined some- what. The total fertility rate of 4.6 children per woman for the three-year period preceding the survey (2006-2008) is lower than the rate of 4.9 derived from the 2003 KDHS and the rate of 5.0 from the 1999 Population and Housing Census. Fertility Differentials. There are substantial differences in fertility levels throughout Kenya. The total fertility rate is considerably higher in the rural areas (5.2 children per woman) than in the urban areas (2.9 children per woman). Re- gional differences are also marked. Fertility is lowest in Nairobi province (2.8 children per woman) and highest in North Eastern province (5.9 children per woman). Fertility in Central province is also relatively low (3.4), compared with Western (5.6) and Nyanza (5.4) provinces. Education of women is strongly associated with low fertility. The total fertility rate (TFR) decreases dramatically from 6.7 for women with no education to 3.1 for women with at least some secondary education. Over time, fertility has actually increased among women with no education and has only declined among those with primary incomplete education. Unplanned Fertility. Despite a relatively high level of contraceptive use, the 2008-09 KDHS data indicate that unplanned pregnancies are common in Kenya. Overall, 17 percent of births in Kenya are unwanted, while 26 percent are mistimed (wanted later). Overall, the propor- tion of births considered unwanted has decreased slightly, compared with the 2003 KDHS, while the proportion mistimed has hardly changed at all. xx | Summary of Findings Fertility Preferences. There have been some changes in fertility preferences since 2003. The proportion of currently married women who want another child soon has declined slightly (from 16 to 14 percent), as has the proportion who want another child later in life (from 29 to 27 percent). The proportion of married women who either want no more children or who have been sterilised increased from 49 percent in 2003 to 54 percent in 2008-09. The mean ideal family size among currently married women has de- clined from 4.3 to 4.0. FAMILY PLANNING Knowledge of Contraception. Knowledge of family planning is nearly universal, with 95 percent of all women and 97 percent of men age 15 to 49 knowing at least one modern method of family planning. Among all women, the most widely known methods of family planning are male condoms, injectables, and pills, with about 89 percent of all women saying that they know these methods. Around 6 in 10 women have heard of female sterilisation, the IUD, implants, and the female condom. With regard to tradi- tional methods, about two-thirds of women have heard of the rhythm method, and just under half know about withdrawal, while folk methods are the least likely to be mentioned. There has been little change in levels of knowledge of contraceptive methods among all women since 2003. The level of knowledge of female and male sterilisation and of the IUD has declined since 2003, while knowledge of im- plants and withdrawal has increased slightly. Use of Contraception. Slightly less than half of married women (46 percent) in Kenya are using a method of family planning. Most are using a modern method (39 percent of married women), but 6 percent use a traditional method. Injectables are by far the most commonly used contraceptive method; they are used by 22 per- cent of married women, while pills are used by 7 percent of women, and female sterilisation and periodic abstinence are each used by 5 percent of married women. Trends in Contraceptive Use. Contracep- tive use has increased since 2003, from 39 to 46 percent of married women. Between 2003 and 2008-09, use of modern methods increased from 32 to 39 percent of married women, while use of traditional methods over the same time period actually decreased from 8 to 6 percent of mar- ried women. The 2008-09 KDHS corroborates trends in method mix, namely, a continuing in- crease in use of injectables and decrease in use of the pill as was the case in earlier KDHS sur- veys. Differentials in Contraceptive Use. As ex- pected, contraceptive use increases with level of education. Use of any method increases from 14 percent among married women with no educa- tion to 60 percent among women with at least some secondary education. Urban women (53 percent) are more likely to use contraception than rural women (43 percent). Source of Modern Methods. In Kenya, public (government) facilities provide contracep- tives to more than half (57 percent) of modern method users, while 36 percent are supplied through private medical sources, and 6 percent are supplied through other sources. Contraception Discontinuation. Overall, more than one in three women (36 percent) dis- continue use within 12 months of adopting a method. The 12-month discontinuation rates for injectables (29 percent) and periodic abstinence (33 percent) are lower than the rates for the pill (43 percent) and for the male condom (59 per- cent). Unmet Need for Family Planning. One- quarter of currently married women in Kenya have an unmet need for family planning, which remains unchanged since 2003. Unmet need is evenly split between women who want to wait two or more years before having their next child (spacers) and those who want no more children (limiters). MATERNAL HEALTH Antenatal Care. The 2008-09 KDHS data indicate that 92 percent of women in Kenya re- ceive antenatal care from a medical professional, either from doctors (29 percent) or nurses or midwives (63 percent). The 2008-09 data indi- cate a slight increase since 2003 in medical an- tenatal care coverage, from 88 percent to 92 per- cent. Just over half of women (55 percent) re- ceived two or more tetanus toxoid injections dur- Summary of Findings | xxi ing pregnancy for their most recent birth in the five years preceding the survey, slightly higher than the 52 percent level in 2003. Taking into account previous injections, almost three in four births are protected against tetanus. Delivery Care. Proper medical attention and hygienic conditions during delivery can reduce the risk of serious illness among mothers and their babies. The 2008-09 KDHS found that two out of five births (43 percent) are delivered in a health facility, while 56 percent are delivered at home. This represents a slight improvement in the proportion of births occurring at a health fa- cility, from 40 percent in 2003 to 43 percent in 2008-09. Similarly, 44 percent of births in Kenya are delivered under the supervision of a health pro- fessional, mainly a nurse or midwife. Traditional birth attendants continue to play a vital role in delivery, assisting with 28 percent of births. Relatives and friends assist in 21 percent of births. The proportion of births assisted by medically trained personnel increased slightly since 2003. Only 6 percent of births are deliv- ered by Caesarean section, a slight increase since 2003. Maternal Mortality. Data on the survival of respondents’ sisters were used to calculate a ma- ternal mortality ratio for the 10-year period be- fore the survey, which was estimated as 488 ma- ternal deaths per 100,000 live births. This is sta- tistically insignificantly different from the rate of 414 maternal deaths per 100,000 live births for the ten-year period prior to the 2003 KDHS Thus, it is impossible to say with confidence that maternal mortality has changed. CHILD HEALTH Childhood Mortality. Data from the 2008- 09 KDHS show remarkable declines in child mortality levels compared with the 2003 survey. Comparing data for the five-year period before each survey, under-five mortality has declined from 115 to 74 deaths per 1,000 births, while infant mortality has dropped from 77 to 52 deaths per 1,000 live births. Childhood Vaccination Coverage. In the 2008-09 KDHS, mothers were able to show a health card with immunisation data for 70 per- cent of children age 12-23 months. Accordingly, estimates of coverage are based on both data from health cards and mothers’ recall. The data show that 77 percent of children 12-23 months are fully vaccinated against the major childhood illnesses. Only 3 percent of children 12-23 months have not received any of the recom- mended immunisations. These results represent an improvement in immunisation coverage for children since 2003 when only 57 percent of children age 12-23 months were fully immu- nised. Child Illness and Treatment. Among chil- dren under five years of age, 8 percent were re- ported to have had symptoms of acute respira- tory illness in the two weeks preceding the sur- vey, 24 percent had a fever in the two weeks preceding the survey, and 17 percent had diar- rhoea. Around half of children with symptoms of acute respiratory illness, fever, or diarrhoea were taken to a health facility or provider for treat- ment. For example, 49 percent of children with diarrhoea were taken to a facility for treatment, while 78 percent were given either a solution prepared from oral rehydration salt (ORS) pack- ets or increased fluids. NUTRITION Breastfeeding Practices. Breastfeeding is nearly universal in Kenya; 97 percent of children are breastfed. The median duration of breast- feeding is 21 months, similar to the duration documented in the 2003 KDHS. The 2008-09 KDHS data indicate that complementary feeding of children begins early. For example, among newborns less than two months of age, 24 per- cent are receiving complementary foods or liq- uids other than water. The median duration of exclusive breastfeeding is estimated at less than one month. Bottle-feeding is common in Kenya; 25 per- cent of children under 6 months are fed with bot- tles with teats. Nevertheless, use of infant for- mula milk is minimal; only a tiny fraction of children below six months receive commercially produced infant formula. Intake of Vitamin A. Ensuring that children between six months and 59 months receive enough vitamin A may be the single most effec- tive child survival intervention, since deficien- cies in this micronutrient can cause blindness and can increase the severity of infections such xxii | Summary of Findings as measles and diarrhoea. Overall, 77 percent of children age 6-35 months consumed vitamin A- rich foods in the day before the survey, and 30 percent of children age 6-59 months received a vitamin A supplement in the six months preced- ing the survey. Nutritional Status of Children. Survey data show that the nutritional status of children under five has improved only slightly in the past few years. At the national level, 35 percent of children under five are stunted (low height-for- age), while 7 percent of children are wasted (low weight-for-height) and 16 percent are under- weight (low weight-for-age). Nutritional Status of Women. The mean body mass index (BMI) for women age 15-49 is 23, identical to what it was in 2003. MALARIA The country has witnessed an impressive rise in household ownership of insecticide- treated mosquito nets (ITNs). The 2008-09 KDHS shows that 56 percent of households have at least one ITN, up from 48 percent recorded in the 2007 Kenya Malaria Indicator Survey and 6 percent recorded in the 2003 KDHS. Just under half of children under five (47 percent) were reported to have slept under an ITN the night before the survey, compared with only five percent in 2003. The 2008-09 KDHS data show that 49 percent of pregnant women slept under an ITN the night before the survey, and 14 percent received intermittent preventive treatment with antimalarial medication during antenatal care visits. Among children with fever in the two weeks preceding the survey, 8 percent were given the recommended medicine, ACT, while 3 percent were given the second-line drug, sulfadoxine- pyrimethamine or SP. Only about half of chil- dren receive these drugs within a day of the on- set of the fever. HIV/AIDS Awareness of AIDS. Almost all Kenyan women and men (more than 99 percent) have heard of AIDS. More than 90 percent of women and men indicate that the chances of getting the AIDS virus can be reduced by limiting sex to one faithful partner. Similarly, 75 percent of women and 81 percent of men age 15-49 know that using condoms can reduce the risk of con- tracting the HIV virus. As expected, the propor- tion of both women and men who know that ab- staining from sex reduces the chances of getting the AIDS virus is high—88 percent among women and 90 percent among men. Almost 9 in 10 women and men (87 percent) know that HIV can be transmitted by breastfeed- ing, and 7 in 10 know that the risk of maternal- to-child transmission can be reduced by the mother taking certain drugs during pregnancy. Ninety percent of women and 92 percent of men age 15-49 are aware that a healthy-looking per- son can have the AIDS virus. Attitudes towards HIV-Infected People. Large majorities of Kenyan women and men (90 and 94 percent, respectively) express a willing- ness to care for a relative sick with AIDS in their own household, while far fewer (68 and 80 per- cent, respectively) say they would be willing to buy fresh vegetables from a vendor who has the AIDS virus. Survey results further indicate that 76 and 80 percent of women and men, respec- tively, believe that a female teacher who has the AIDS virus should be allowed to continue teach- ing in school. Finally, 54 percent of women and 69 percent of men say that if a member of their family got infected with the virus that causes AIDS, they would not necessarily want it to re- main a secret. HIV-Related Behavioural Indicators. Comparison of data from the 2008-09 KDHS with similar data from the 2003 KDHS indicates that there has been a slight increase in the age at first sexual experience. The median age at first sex has increased from 17.8 to 18.2 among women age 20-49 and 17.1 to 17.6 among men aged 20-54. Since the most important mecha- nism of HIV transmission is sexual intercourse, it is important to know the extent of multiple sexual partners. The 2008-09 KDHS data show that only 1 percent of women and 9 percent of men report having had more than one sexual partner in the 12 months prior to the survey. HIV Prevalence. In the one-half of the households selected for the man’s survey, all women and men who were interviewed were asked to voluntarily provide some drops of blood for HIV testing in the laboratory. Results indi- Summary of Findings | xxiii cate that 6 percent of Kenyan adults age 15-49 are infected with HIV, only slightly lower than the level of 7 percent measured in the 2003 KDHS and the 2007 Kenya AIDS Indicator Sur- vey (KAIS). HIV prevalence is 8 percent among women age 15-49 and 4 percent among men 15- 49. The peak prevalence among women is at age 40-44 (14 percent), while prevalence among men is highest at age 35-39 (10 percent). Patterns of HIV Prevalence. The HIV epi- demic shows regional heterogeneity. Nyanza province has an overall prevalence of 14 percent, double the level of the next highest provinces— Nairobi and Western, at 7 percent each. All other provinces have levels between 3 percent and 5 percent overall, except North Eastern province where the prevalence is about 1 percent. HIV prevalence is by far the highest among women who are widowed (43 percent). Both women and men who are divorced or separated also have relatively high HIV prevalence (17 and 10 per- cent, respectively). Survey findings indicate that there is a strong relationship between HIV prevalence and male circumcision; 13 percent of men who are uncircumcised are HIV infected compared with 3 percent of those who are cir- cumcised. Among couples who are married or living together, 6 percent are discordant, with one partner infected and the other uninfected. GENDER-RELATED VIOLENCE Violence Since Age 15. In the 2008-09 KDHS, women were asked if they had experi- enced violence since age 15. The data show that 39 percent of women have experienced violence since they were 15 and one in four reported ex- periencing violence in the 12 months preceding the survey. The main perpetrators are husbands, and to a lesser extent, teachers, mothers, fathers, and brothers. Marital Violence. Thirty percent of ever- married women report having experienced emo- tional violence by husbands, 37 percent report physical violence, and 17 percent report sexual violence. Almost half (47 percent) of ever- married women report suffering emotional, physical, or sexual violence, while 10 percent have experienced all three forms of violence by their current or most recent husband. The factor most strongly related to marital violence is hus- band’s alcohol use; violence is 2-3 times more prevalent among women who say their husbands get drunk often compared with those whose hus- bands do not drink. Attitudes Towards Marital Violence. To gauge the acceptability of domestic violence, women and men interviewed in the 2008-09 KDHS were asked whether they thought a hus- band would be justified in hitting or beating his wife in each of the following five situations: if she burns the food; if she argues with him; if she goes out without telling him; if she neglects the children; and if she refuses to have sexual rela- tions with him. Results show that 53 percent of Kenyan women and 44 percent of men agree that at least one of these factors is sufficient justifica- tion for wife beating. Female Genital Cutting. Survey data show that there has been a gradual decline in the pro- portion of Kenyan women who are circumcised, from 38 percent in 1998 to 32 percent in 2003 and to 27 percent in 2008-09. xxiv | Map of Kenya EASTERN RIFT VALLEY COAST NORTH EASTERN NYANZA CENTRAL WESTERN NAIROBI MAP OF KENYA BY PROVINCE 0 140 28070 Kilometers ³ SUDAN ETHIOPIA SOMALIA TANZANIA UGANDA Source:1999 Kenya Population Census Introduction | 1 INTRODUCTION 1 Collins Opiyo, Christopher Omolo, and Macdonald Obudho 1.1 GEOGRAPHY, HISTORY, AND THE ECONOMY 1.1.1 Geography Kenya is situated in the eastern part of the African continent. The country lies between 5 degrees north and 5 degrees south latitude and between 24 and 31 degrees east longitude. It is almost bisected by the equator. Kenya is bordered by Ethiopia (north), Somalia (northeast), Tanzania (south), Uganda and Lake Victoria (west), and Sudan (northwest). It is bordered on the east by the Indian Ocean. The 536-kilometre coastline, which contains swamps of East African mangroves and the port in Mombasa, enables the country to trade easily with other countries. The country is divided into 8 provinces and 158 districts (as of the 2009 Population and Housing Census). It has a total area of 582,646 square kilometres of which 571,466 square kilometres form the land area. Approximately 80 percent of the land area of the country is arid or semiarid, and only 20 percent is arable. The country has diverse physical features, including the Great Rift Valley, which runs from north to south; Mount Kenya, the second highest mountain in Africa; Lake Victoria, the largest freshwater lake on the continent; Lake Nakuru, a major tourist attraction because of its flamingos; Lake Magadi, famous for its soda ash; a number of rivers, including Tana, Athi, Yala, Nzoia, and Mara; and numerous wildlife reserves containing thousands of different animal species. The country falls into two regions: lowlands, including the coastal and Lake Basin lowlands, and highlands, which extend on both sides of the Great Rift Valley. Rainfall and temperatures are influenced by altitude and proximity to lakes or the ocean. The climate along the coast is tropical with rainfall and temperatures being higher throughout the year. There are four seasons in a year: a dry period from January to March, the long rainy season from March to May, followed by a long dry spell from May to October, and then the short rains between October and December. 1.1.2 History Kenya is a former British colony. The independence process was met with resistance and an armed struggle by Kenyans against the British colonial rulers. The Mau Mau rebellion in the 1950s paved the way for constitutional reform and political development in the following years. The country achieved self-rule in June 1963 and gained independence (Uhuru) on December 12, 1963. Exactly one year later, Kenya became a republic. The country was a multiparty state until 1981, when the relevant parts of the constitution were amended to create a one-party state. However, in the early 1990s, the country reverted to a multiparty state. From independence until December 2002, the country was ruled by the Kenya African National Union (KANU). During the 2002 general elections, the National Alliance of Rainbow Coalition ascended to power through a landslide victory. Currently, the country is run by a coalition government that brings together the Party of National Unity (PNU) and the Orange Democratic Movement (ODM). Various ethnic groups are distributed throughout the country. The major tribes include Kikuyu, Luo, Kalenjin, Luhya, Kamba, Kisii, Mijikenda, Somali, and Meru. In Kenya, English is used as the official language, and Kiswahili is the national language. The main religions in the country are Christianity and Islam. 2 | Introduction 1.1.3 Economy The Kenyan economy is predominantly agricultural with a strong industrial base. There has been a gradual decline in the share of the gross domestic product (GDP) attributed to agriculture, from over 30 percent during the period 1964-1979 to 25 percent in 2000-2002. The agricultural sector directly contributed 22 and 23 percent of the GDP in 2007 and 2008 respectively. Coffee, tea, and horticulture (flowers, fruits, and vegetables) are the main agricultural export commodities; in 2008, these three commodities jointly accounted for 45 percent of the total export earnings (Kenya National Bureau of Statistics, 2009). The manufacturing sector contributes significantly to export earnings, especially from the Common Market for Eastern and Southern Africa (COMESA) region. The manufacturing sector has increased slightly from about 10 percent of the GDP in 1964-1973 to 11 percent of the GDP in 2008. The performance of the Kenyan economy since the country became independent has been mixed. In the first decade after the country’s independence, the economy grew an average of 7 percent per annum, with the growth attributed to expansion in the manufacturing sector and an increase in agricultural production. Since then, there has been a consistent decline in the economy, which reached its lowest GDP growth level of about 0.2 percent in 2000. The consistently poor growth performance has failed to keep pace with population growth. The weak performance has been caused by external shocks and internal structural problems, including the drought of the 1980s, low commodity prices, world recession, bad weather, and poor infrastructure. The poor growth of the economy has contributed to deterioration in the overall welfare of the Kenyan population. Similarly, the economy has been unable to create jobs at a rate to match the rising labour force. To reverse the trend of decline, the government prepared the Economic Recovery Strategy (ERS) for Wealth and Employment Creation in 2003 with the objectives of restoring economic growth and creating employment and social development. During implementation from 2003 to 2007, the ERS evolved a four-pillar strategy to meet the following objectives: • Restoring economic growth within the context of a sustainable macroeconomic framework • Strengthening the institutions of governance • Restoring and expanding the infrastructure • Investing in human capital The ERS enabled the economy to grow steadily from 0.5 percent in 2002 to 7 percent in 2007. One of the lessons learned through the implementation of the ERS was that employment creation is the most effective strategy for halting increasing poverty. In 2008, the government of Kenya launched Vision 2030 and its Medium Term Plan (MTP) to provide continuity by consolidating the gains made under the ERS. The goal was to transform Kenya into a newly industrialized middle-income country by 2030. After remarkable growth, which averaged 6 percent in the period 2004-2007 and peaked at 7.1 percent in 2007, real GDP growth slowed to 1.7 percent in 2008. The slowdown resulted from both domestic and external shocks, including post-election violence, high food and fuel prices, drought, and the global financial crisis. These shocks had a negative impact on key sectors of the economy, including tourism, manufacturing, transport, and agriculture. They weakened the country’s balance of payments position. These factors dampened prospects for growth in 2009 and beyond. Specifically, the slow and fragile recovery in more advanced countries is likely to continue to lower demand for Kenya’s main exports and to reduce earnings from tourism, remittances, and private capital flows. On the domestic front, while the short rains have helped to reduce the magnitude of shortages of food, water, and Introduction | 3 energy, the negative effects of climate change are likely to worsen unless deliberate and appropriate policy measures are taken to reverse environmental degradation. 1.2 POPULATION Kenya’s population was 10.9 million in 1969, and by 1999 it had almost tripled to 28.7 million (Central Bureau of Statistics, 1994, 2001a) (see Table 1.1). The country’s population is projected to reach 39.4 million in 2009. Results of previous censuses indicate that the annual population growth rate was 2.9 percent per year during the 1989-1999 period, down from 3.4 percent reported for the 1979-1989 inter-censal period. Currently, growth is estimated to be about 2.8 percent. The decline in population growth is a realisation of the efforts called for by the National Population Policy for Sustainable Development (National Council for Population and Development, 2000) and is a result of the decline in fertility rates over recent decades. Fertility levels have declined from 8.1 births per woman in the late 1970s to the current level of 4.6 births per woman. The decline in fertility levels is expected to be manifested in the age distribution of the country’s population. Mortality rates also have risen since the 1980s, presumably due to increased deaths from the HIV/AIDS epidemic, deterioration of health services, and widespread poverty (National Council for Population and Development, 2000). The crude birth rate increased from 50 births per 1,000 population in 1969 to 54 per 1,000 in 1979 but thereafter declined to 48 and 41 per 1,000 in 1989 and 1999, respectively. The crude death rate increased from 11 per 1,000 population in 1979-1989 to 12 per 1,000 for the 1989-1999 period. The infant mortality rate, which had steadily decreased from 119 deaths per 1,000 live births in 1969 to 88 deaths per 1,000 live births in 1979, and then to 66 deaths per 1,000 live births in 1989, increased briefly in 1999 to 77 per 1,000 but then resumed its decline in 2009. Table 1.1 Basic demographic indicators Selected demographic indicators for Kenya, 1969, 1979, 1989, 1999, and 2009 Indicator 1969 1979 1989 1999 2009 Population (millions) 10.9 16.2 23.2 28.7 39.4a Density (pop./km2) 19.0 27.0 37.0 49.0 67.7a Percent urban 9.9 15.1 18.1 19.4 21.0a Crude birth rate 50.0 54.0 48.0 41.3 34.8b Crude death rate 17.0 14.0 11.0 11.7 u Inter-censal growth rate 3.3 3.8 3.4 2.9 2.8a Total fertility rate 7.6 7.8 6.7 5.0 4.6b Infant mortality rate (per 1,000 births) 119 88 66 77.3 52.0b Life expectancy at birth 50 54 60 56.6 58.9a a Revised projection figures b KDHS results (see later chapters) u = unknown Source: CBS, 1970; CBS, 1981; CBS, 1994; CBS, 2002a 1.3 POPULATION AND FAMILY PLANNING POLICIES AND PROGRAMMES Owing to its high fertility and declining mortality, Kenya is characterised by a youthful population. Projections show about 43 percent of the population is younger than 15 years (CBS, 2006). This implies that over three-fifths of Kenya’s population, or about 25 million people in 2009, are less than 25 years old. Consequently, Kenya faces the formidable challenge of providing its youth with opportunities for a safe, healthy, and economically productive future. The 1994 International Conference on Population and Development (ICPD) endorsed the right of adolescents and young adults to obtain the highest levels of health care. In line with the ICPD recommendations, Kenya has put in place an Adolescent Reproductive Health and Development policy (ARH&D). Broadly, the policy addresses the following adolescent reproductive health issues and challenges: adolescent sexual health and reproductive rights; harmful practices, including early marriage, female genital cutting, and gender-based violence; drug and substance abuse; socioeconomic factors; and the special needs of adolescents and young people with disabilities (Odini, 2008). 4 | Introduction The Ministry of Health (MOH) formally approved and adopted the National Reproductive Health Policy with the theme: ‘Enhancing the Reproductive Health Status for all Kenyans’. The policy provides a framework for equitable, efficient, and effective delivery of quality reproductive health services throughout the country and emphasises reaching those in greatest need who are most vulnerable. Its aim is to guide planning, standardisation, implementation, and monitoring and evaluation of reproductive health care provided by various stakeholders. The new policy will allow the government to incorporate and address key issues such as security of reproductive health commodities, prevention of mother-to-child transmission of HIV, emergency obstetric care, adolescent reproductive health issues, gender-based violence, reproductive health needs of persons with disabilities, and integration of reproductive and HIV health care (Health Policy Initiative, 2009). This policy emphasises priority actions for the achievement of the ICPD goals and the Millennium Development Goals (MDGs) of improving maternal health, reducing neonatal and child mortality, reducing the spread of HIV/AIDS, and achieving women’s empowerment and gender equality. Attainment of sexual and reproductive health and rights will have positive effects on poverty reduction and reduction of infant mortality, maternal mortality, and new cases of HIV/AIDS. A key challenge to attainment of the MDGs will be strengthening the health system by building the capacity to manage programmes and addressing critical bottlenecks, especially a shortage of skilled health workers, an inadequate budget for the health sector, poor procurement and supply systems, and other critical management problems (Division of Reproductive Health, 2005). In 2000, the government of Kenya launched the National Population Policy for Sustainable Development (National Council for Population and Development, 2000). This policy builds on the strength of Kenya’s first national population policy outlined in Sessional Paper No. 4 of 1984 on Population Policy Guidelines. The current policy—whose implementation period ends in 2010— outlines ways to implement the programme of action developed at the 1994 International Conference on Population and Development in Cairo, Egypt. The implementation of this policy is being guided by national and district plans of action. The policy also addresses the issues of environment, gender, and poverty, as well as the problems facing certain segments of the Kenyan population, such as its youth. Goals of the population policy include the following: • Improvement of the standard of living and quality of life • Improvement of the health and welfare of the people through provision of information and education on how to prevent illness and premature deaths among risk groups, especially among mothers and children • Sustenance of the ongoing demographic transition to further reduce fertility and mortality, especially infant and child mortality • Continuing motivation and encouragement of Kenyans to adhere to responsible parenthood • Promotion of stability of the family, taking into account equality of opportunity for family members, especially the rights of women and children • Empowerment of women and the improvement of their status in all spheres of life and elimination of all forms of discrimination, especially against the girl child • Sustainability of the population programme • Elimination of retrogressive sociocultural practices through education. The policy has the following targets, some of which have been achieved according to the current KDHS results: • Reduction of the infant mortality rate (deaths per 1,000 live births) from 71 in 1998 to 67 by 2005 and to 63 by 2010 • Reduction of the under-five mortality rate (deaths per 1,000 live births) from 112 in 1998 to 104 by 2005 and to 98 by 2010 • Reduction of the maternal mortality rate (deaths per 100,000 live births) from 590 in 1998 to 230 by 2005 and to 170 by 2010 • Maintenance of the crude death rate at 12 per 1,000 population up to the year 2000 and reduction to 10 by 2005 and to 9 by 2010 Introduction | 5 • Minimisation of the decline in life expectancy at birth for both sexes, from age 58 in 1995 to age 53 in 2010; • Stabilisation of the population growth rate at 2.1 percent per year by 2010. 1.4 HEALTH PRIORITIES AND PROGRAMMES The major health care providers in Kenya are the Ministry of Public Health and Sanitation and the Ministry of Medical Services. These two ministries operate more than half of all health facilities in the country. The public delivery system is organised in a traditional pyramidal structure. First-level care is provided at dispensaries and medical clinics. The next level comprises health centres and sub-district hospitals. Third-level care is provided at district hospitals and provincial general hospitals. There are two national hospitals—Moi Referral and Teaching Hospital in Eldoret and Kenyatta National Hospital in Nairobi. Resources for health are scarce, and the disease burden is high in the country, just as in other countries in the region (Glenngård, A.H. and T.M. Maina, 2007) Making adequate health care services universally available requires striking a delicate balance between a population’s health needs and available resources. It also requires the equitable and efficient allocation of resources. Without proper health care financing strategies, no government can hope to successfully meet the health needs of its citizens. In 1989, the Kenyan government introduced cost sharing in an effort to bridge the growing gap between health sector expenses and available resources. Since then, the government has strived to achieve a mix of health care financing strategies and systems that will enable the country to provide its citizens with universal access to adequate basic health services (Health Policy Initiative, 2009). Since attaining independence, the government has prioritized the improvement of the health status of Kenyans. It recognises that good health is a prerequisite to socioeconomic development. A number of government policy documents and successive national development plans have stated that the provision of health services should meet the basic needs of the population, place health services within easy reach of Kenyans, and emphasize preventive, promotive, and rehabilitative services without ignoring curative services. Perhaps as a result of these policies, both infant mortality and life expectancy at birth, which are basic indicators of health status, have improved significantly (Ngigi and Macharia, 2006). The second National Health Sector Strategic Plan (NHSSP II) by the MOH aims to reverse the downward trends in health indicators observed during the years of the first strategic plan (NHSSP I, 1999–2004), while applying the lessons learned and searching for innovative solutions. NHSSP II re-invigorates the Kenya Health Policy Framework elaborated in 1994. The health goals formulated in the framework underlined the need to pursue the principles of primary health care to improve the health status of the Kenyan population. The Kenya Health Policy Framework set the following strategic imperatives: 1. Ensure equitable allocation of government of Kenya resources to reduce disparities in health status. 2. Increase cost-effectiveness and efficiency of resource allocation and use. 3. Manage population growth. 4. Enhance the regulatory role of the government in health care provision. 5. Create an enabling environment for increased private sector and community involvement in service provision and financing. 6. Increase and diversify per capita financial flows to the health sector. 6 | Introduction The policies that the government has pursued over the years have had a direct impact on improving the health status of Kenyans. Despite a decline in economic performance, cumulative gains have been made in the health sector as evidenced by the improvement in the basic health indicators (Odini, 2008). 1.5 STRATEGIC FRAMEWORK TO COMBAT THE HIV/AIDS EPIDEMIC To meet the challenge of the HIV/AIDS epidemic in the country, in September 1997, the government of Kenya approved Sessional Paper No. 4 on AIDS in Kenya. The government clearly intended to support effective programmes to control the spread of AIDS, to protect the human rights of those with HIV or AIDS, and to provide care for those infected and affected by HIV/AIDS. The goal set forth by the paper is to ‘provide a policy framework within which AIDS prevention and control efforts will be undertaken for the next 15 years and beyond’. Specifically, it has the following objectives: • Give direction on how to handle controversial issues while taking into account prevailing circumstances and the sociocultural environment • Enable the government to play the leadership role in AIDS prevention and control activities (Challenges posed by AIDS call for a multisectoral approach, necessitating involvement from a diversity of actors) • Recommend an appropriate institutional framework for effective management and coordination of HIV/AIDS programme activities The sessional paper recognises that responding effectively to the HIV/AIDS crisis will require a strong political commitment at the highest level; implementation of a multisectoral prevention and control strategy focused on young people; mobilisation of resources for financing HIV prevention, care, and support; and establishment of a National AIDS Control Council (NACC) to provide leadership at the highest level. Kenya is experiencing a mixed and geographically heterogeneous HIV epidemic. Its characteristics are those of both a generalised epidemic among the mainstream population and a concentrated epidemic among the most at risk population. The HIV epidemic affects all sectors of the economy. It is equally a developmental and an epidemiological challenge, encompassing identification and development of a series of appropriate sectoral responses and their applications at the local level. Nationally, most new infections (44 percent) occur in couples who engage in heterosexual activity within a union or regular partnership (National AIDS Control Council, 2009). Men and women who engage in casual sex contribute 20 percent of the new infections, while sex workers and their clients account for 14 percent. Men who have sex with men and prison populations contribute 15 percent, and injecting drug users account for 4 percent. Health facility-related infections contribute 3 percent of new cases. The NACC launched the third Kenya National AIDS Strategic Plan (KNASP III) in 2009 to address the challenges posed by HIV infection. The KNASP III aims to achieve Kenya’s universal access targets for quality integrated services at all levels to prevent new HIV infections, reduce HIV-related illnesses and deaths, and mitigate the effects of the epidemic on households and communities (National AIDS Control Council, 2009). 1.6 OBJECTIVES OF THE SURVEY The 2008-09 Kenya Demographic and Health Survey (KDHS) is a population and health survey that Kenya conducts every five years. It was designed to provide data to monitor the population and health situation in Kenya and also to be used as a follow-up to the previous KDHS surveys in 1989, 1993, 1998, and 2003. From the current survey, information was collected on fertility levels; marriage; sexual activity; fertility preferences; awareness and use of family planning methods; breastfeeding practices; nutritional status of women and young children; childhood and maternal mortality; maternal and child Introduction | 7 health; and awareness and behaviour regarding HIV/AIDS and other sexually transmitted infections. The 2008-09 KDHS is the second survey to collect data on malaria and the use of mosquito nets, domestic violence, and HIV testing of adults. The specific objectives of the 2008-09 KDHS were to: • Provide data, at the national and provincial levels, that allow the derivation of demographic rates, particularly fertility and childhood mortality rates, to be used to evaluate the achievements of the current national population policy for sustainable development • Measure changes in fertility and contraceptive prevalence use and study the factors that affect these changes, such as marriage patterns, desire for children, availability of contraception, breastfeeding habits, and other important social and economic factors • Examine the basic indicators of maternal and child health in Kenya, including nutritional status, use of antenatal and maternity services, treatment of recent episodes of childhood illness, use of immunisation services, use of mosquito nets, and treatment of children and pregnant women for malaria • Describe the patterns of knowledge and behaviour related to the transmission of HIV/AIDS and other sexually transmitted infections • Estimate adult and maternal mortality ratios at the national level • Ascertain the extent and pattern of domestic violence and female genital cutting in the country • Estimate the prevalence of HIV infection at the national and provincial levels and by urban-rural residence, and use the data to corroborate the rates from the sentinel surveillance system The 2008-09 KDHS information provides data to assist policymakers and programme implementers as they monitor and evaluate existing programmes and design new strategies for demographic, social, and health policies in Kenya. The data will be useful in many ways, including the monitoring of the country’s achievement of the Millennium Development Goals. As in 2003, the 2008-09 KDHS survey was designed to cover the entire country, including the arid and semi-arid districts, and especially those areas in the northern part of the country that were not covered in the earlier KDHS surveys. The survey collected information on demographic and health issues from a sample of women at the reproductive age of 15-49 and from a sample of men age 15-54 years in a one-in-two subsample of households. 1.7 SURVEY ORGANISATION The Kenya National Bureau of Statistics (KNBS) implemented the 2008-09 KDHS in collaboration with the Ministry of Public Health and Sanitation, including the National AIDS and STIs Control Programme (NASCOP), the Ministry of Medical Services, the Ministry of Gender, the Kenya Medical Research Institute (KEMRI), the National Coordinating Agency for Population Development (NCAPD), and the National AIDS Control Council (NACC). The National Public Health Laboratory Services assisted in recruitment and training of the health field workers, supported the voluntary counselling and testing of respondents who wanted to know their HIV status, and implemented the HIV testing in the laboratory. As in the previous surveys, technical assistance was provided through the international MEASURE DHS programme at ICF Macro. This is a project sponsored by the United States Agency for International Development (USAID) to carry out population and health surveys in developing countries. 8 | Introduction Financial support for the KDHS was provided by the government of Kenya, the U.S. Government (USAID), UNICEF, and UNFPA. UNICEF provided vehicles and drivers for use in the arid and semi-arid lands (ASAL) districts. 1.8 SAMPLE DESIGN The survey is household-based, and therefore the sample was drawn from the population residing in households in the country. A representative sample of 10,000 households was drawn for the 2008-09 KDHS. This sample was constructed to allow for separate estimates for key indicators for each of the eight provinces in Kenya, as well as for urban and rural areas separately. Compared with the other provinces, fewer households and clusters were surveyed in North Eastern province because of its sparse population. A deliberate attempt was made to oversample urban areas to get enough cases for analysis. As a result of these differing sample proportions, the KDHS sample is not self-weighting at the national level; consequently, all tables except those concerning response rates are based on weighted data. The KNBS maintains master sampling frames for household-based surveys. The current one is the fourth National Sample Survey and Evaluation Programme (NASSEP IV), which was developed on the platform of a two-stage sample design. The 2008-09 KDHS adopted the same design, and the first stage involved selecting data collection points (‘clusters’) from the national master sample frame. A total of 400 clusters—133 urban and 267 rural—were selected from the master frame. The second stage of selection involved the systematic sampling of households from an updated list of households. The Bureau developed the NASSEP frame in 2002 from a list of enumeration areas covered in the 1999 population and housing census. A number of clusters were updated for various surveys to provide a more accurate selection of households. Included were some of the 2008-09 KDHS clusters that were updated prior to selection of households for the data collection. All women age 15-49 years who were either usual residents or visitors present in sampled households on the night before the survey were eligible to be interviewed in the survey. In addition, in every second household selected for the survey, all men age 15-54 years were also eligible to be interviewed. All women and men living in the households selected for the Men’s Questionnaire and eligible for the individual interview were asked to voluntarily give a few drops of blood for HIV testing. 1.9 QUESTIONNAIRES Three questionnaires were used to collect the survey data: the Household, Women’s, and Men’s Questionnaires. The contents of these questionnaires were based on model questionnaires developed by the MEASURE DHS programme that underwent only slight adjustments to reflect relevant issues in Kenya. Adjustment was done through a consultative process with all the relevant technical institutions, government agencies, and local and international organisations. The three questionnaires were then translated from English into Kiswahili and 10 other local languages (Kalenjin, Kamba, Kikuyu, Kisii, Luhya, Luo, Maasai, Meru, Mijikenda, and Somali). The questionnaires were further refined after the pretest and training of the field staff. In each of the sampled households, the Household Questionnaire was the first to be administered and was used to list all the usual members and visitors. Basic information was collected on the characteristics of each person listed, including age, sex, education, and relationship to the head of the household. The main purpose of the Household Questionnaire was to identify women age 15-49 and men age 15-54 who were eligible for the individual interviews. The questionnaire also collected information on characteristics of the household’s dwelling unit, such as the source of water, type of toilet facilities, materials used for the floor, walls, and roof of the house, ownership of various durable goods, ownership of agricultural land, ownership of domestic animals, and ownership and use of mosquito nets. In addition, this questionnaire was used to capture information on height and weight Introduction | 9 measurements of women age 15-49 years and children age five years and below, and, in households eligible for collection of blood samples, to record the respondents’ consent to voluntarily give blood samples. A detailed description of HIV testing procedures is given in Section 1.10 below. The Women’s Questionnaire was used to capture information from all women age 15-49 years and covered the following topics: • Respondent’s background characteristics (e.g., education, residential history, media exposure) • Reproductive history • Knowledge and use of family planning methods • Antenatal, delivery, and postnatal care • Breastfeeding • Immunisation, nutrition, and childhood illnesses • Fertility preferences • Husband’s background characteristics and woman’s work • Marriage and sexual activity • Infant and child feeding practices • Childhood mortality • Awareness and behaviour about HIV/AIDS and other sexually transmitted diseases • Knowledge of tuberculosis • Health insurance • Adult and maternal mortality • Domestic violence • Female genital cutting The set of questions on domestic violence sought to obtain information on women’s experience of violence. The questions were administered to one woman per household. In households with more eligible women, special procedures (use of a ‘Kish grid’) were followed to ensure that the woman interviewed about domestic violence was randomly selected. The Men’s Questionnaire was administered to all men age 15-54 years living in every second household in the sample. The Men’s Questionnaire collected information similar to that collected in the Women’s Questionnaire, but it was shorter because it did not contain questions on reproductive history, maternal and child health, nutrition, maternal mortality, and domestic violence. Two pilot projects were conducted in 12 districts for the KDHS, the first from July 1-7, 2008, and the second from October 13-17, 2008, to test the questionnaires, which were written in English and then translated into eleven other languages. The pilot was repeated because the first pilot did not include the HIV blood testing component. Twelve teams (one for each language) were formed, each with one female interviewer, one male interviewer, and one health worker. A total of 260 households were covered in the pilots. The lessons learnt from the pilot surveys were used to finalise the survey instruments and set up strong, logistical arrangements to ensure the success of the survey. 1.10 HIV TESTING As was done in the previous KDHS, in all households selected for the Men’s Questionnaire, all eligible women and men who were interviewed were asked to voluntarily provide some drops of blood for HIV testing. The protocol for blood specimen collection and analysis was based on the anonymous linked protocol developed by the DHS programme and was revised and enhanced by KEMRI and NACC. It was reviewed and approved by the Scientific and Ethical Review Committee of KEMRI. The protocol allowed for the linking of the HIV results to the sociodemographic data collected in the individual questionnaires, provided that the information that could potentially identify an individual was destroyed before the linking took place. This required that identification codes be 10 | Introduction deleted from the data file and that the part of the Household Questionnaire containing the barcode labels and names of respondents be destroyed prior to merging the HIV results with the individual data file. Considerable care was necessary to prepare respondents for the blood sample, and for this reason, two health workers were assigned to each of the 23 survey teams. They were recruited through the Ministry of Public Health. To obtain informed consent for taking blood for HIV testing, the health worker explained the procedures, the confidentiality of the data, and the fact that test results could not be traced back to or made available to the subject. For those who were interested in knowing their HIV status, the health worker provided information about how they could obtain it through voluntary counselling and testing (VCT) services. If consent was granted, the health worker then collected a dried blood spot (DBS) sample on a filter paper card from a finger prick, using a single-use, spring- loaded, sterile lancet. Each DBS sample was given a barcode label, with a duplicate label attached to the Household Questionnaire on the line showing consent for that respondent. The health worker affixed a third copy of the same barcode label to a Blood Sample Transmittal Form in order to track the blood samples from the field to the laboratory. Filter papers were dried overnight in a plastic drying box after which the health worker packed them in individual Ziploc bags with desiccant and a humidity indicator card and placed them in a larger Ziploc bag with other blood spots for that particular cluster. Blood samples were periodically collected in the field along with the completed questionnaires and transported to KNBS headquarters in Nairobi for logging in, after which they were taken to the National Public Health Laboratory Services headquarters in Nairobi for HIV testing. At the laboratory, the DBS samples were each assigned a laboratory number and kept frozen until testing was started in early June 2009. After the samples were allowed to attain room temperature, hole punches were used to cut a circle at least 6.3 mm in diameter. The blots were placed in cryo-vials that contained 200 µl of elution PBS buffer and were labelled with the laboratory number. The vials were left to elute overnight at 4°C, then they were centrifuged at 2,500 rpm for 10 minutes. These eluates were then tested with a Vironostika Anti-HIV-1/2 Plus enzyme-linked immunosorbent assay (ELISA) test kit (DADE Behring HIV-1/2) for verification purposes. All positive samples and 5 percent of negative samples were then tested with a Murex HIV-1/2 MicroELISA System. For quality assurance, all positive samples and a 10 percent random sample of the negative samples were retested at the KEMRI HIV laboratory using the same testing algorithm of both Vironostika and Murex MicroELISA systems. Finally, 30 discrepant samples were tested by polymerase chain reaction (PCR) DNA at KEMRI laboratory. 1.11 TRAINING KNBS recruited research assistants and supervisors in the month of October 2008 based on a set of qualifications and experience, especially in past KDHSs or other health-related sample surveys, such as the Kenya Aids Indicator (KAIS) Survey, the Kenya Malaria Indicator Survey (KMIS), and the Multiple Indicator Cluster Survey (MICS). The process brought on board a number of qualified people with the skills necessary to undertake the survey. Different categories of personnel were recruited and trained to undertake the KDHS. These included 23 supervisors, 52 health workers, 92 female research assistants, 23 male research assistants, 23 field editors, 6 office editors, 4 quality assurance personnel, and 5 reserves. A three-week training course was conducted from October 21 to November 8 in Nakuru. Because of the large number of people involved, trainees were divided into five groups and trained in three different locations on questionnaire administration. They came together in plenary sessions for special lectures. Four trainers were assigned to each group. The trainers were officers of KNBS, the Ministry of Public Health, and NCAPD, as well as staff from ICF Macro. The training team developed a programme that allowed for some topics to be shared in plenary sessions while others were conducted in the smaller classes to allow for better explanation of technical details. In addition to the main regular trainers, guest lecturers gave presentations in plenary sessions on specialised Introduction | 11 topics such as family planning, anthropometric measurements, HIV/AIDS, and Kenya’s VCT programme. The DHS standard approach to training was used, including class presentations, mock interviews in class, and practice interviews in the field. Participants were also given tips on interviewing techniques. Three tests were given to help participants understand the survey concepts and how to complete each of the three questionnaires. Anthropometric measurement was given special attention by inviting an expert who conducted training and also provided many hours of demonstrations and practical exercises to each group. A separate class was organised for the health workers. Staff from KEMRI and NACC trained the health workers on how to administer the consent procedures, how to take blood spots for HIV testing, and how to minimise risks in handling blood products (‘universal precautions’). All trainees were taken for practice interviews in households in selected areas in the town of Nakuru. Towards the end of training, the final field teams were formed and supervisors, enumerators, editors, and quality assurance personnel were identified. This was based on performance both in class and in the field, as well as on the leadership skills displayed during training. Both supervisors and editors were taken through further training on how to supervise fieldwork and edit questionnaires in the field. 1.12 FIELDWORK Fieldwork started on 13 November 2008 and was completed in late February 2009. Each of the 23 field teams was composed of one supervisor, one field editor, four female interviewers, one male interviewer, two health workers, two VCT counsellors, and one driver. There were a few teams that had two vehicles and two drivers. Staff from KNBS and ICF Macro participated in field supervision. In related surveys, many respondents expressed interest in learning their HIV status, so to ensure that this need would be met, the National AIDS Control Programme (NASCOP) engaged a parallel team of two VCT counsellors to work with each of the data collection teams. The mobile VCT teams followed the same protocol applied in fixed VCT sites, according to the National Guidelines for Voluntary Counselling and Testing for HIV (Ministry of Health, 2003). This included pretest counselling of the clients followed by anonymous testing for HIV for those requesting the service. A finger prick was performed to collect drops of blood for simultaneous (parallel) testing performed with two simple, rapid HIV test kits (Abbott Determine HIV 1/2 and Trinity Biotech Uni- Gold); for quality control, a dried blood spot filter paper was collected on every tenth client for testing in the laboratory. During the 15 minutes while the test was developing, prevention counselling was provided. If the two test results were discrepant, a third test (Instascreen) was performed as a ‘tiebreaker’. Post-test counselling was then provided. The sensitivity of the survey required a good plan for social mobilisation in areas where the survey was conducted. NACC organised and implemented a series of mobilisation activities in the clusters sampled for the KDHS before the survey teams moved in to conduct interviews. This process appeared to have had a positive impact on the survey, likely contributing to the high response rates. NACC also printed a brochure on HIV/AIDS and VCT for the team’s health workers to provide to all households and survey respondents. Similarly, numbered vouchers were printed and left with eligible respondents. The vouchers were to be given to the mobile VCT teams or the fixed VCT site when the eligible respondents went for VCT. NASCOP also made arrangements with the fixed VCT sites charging for services, so that they would provide free services to KDHS clients. Finally, although the VCT teams were to give priority to clients presenting the KDHS vouchers, they also accepted any other clients from the sampled communities. 12 | Introduction 1.13 DATA PROCESSING A data processing team was constituted and trained at the KNBS offices in Nyayo House in Nairobi after the data collection teams started fieldwork. This team was supported by technical assistance from ICF Macro. Data processing commenced at the beginning of December 2008 and was finalised in early March 2009. Tabulation of the results was done by June 2009 by KNBS in collaboration with ICF Macro. Data processing for blood draws was delayed at the National HIV Reference Laboratory to allow for completion of data cleaning and validation and to remove all personal identifiers from the stored questionnaires. The KDHS preliminary report was prepared and launched in November 2009. 1.14 RESPONSE RATES A total of 9,936 households were selected in the sample, of which 9,268 were occupied at the time of fieldwork and thus eligible for interviews (Table 1.2). Of the eligible households, 9,057 households were successfully interviewed, yielding a response rate of 98 percent. The shortfall in the number of households was largely due to structures that were found to be vacant or destroyed and households whose members were absent for an extended period during data collection. From the households interviewed, 8,767 women were found to be eligible and 8,444 were interviewed, giving a response rate of 96 percent. Interviews with men covered 3,465 of the eligible 3,910 men, yielding a response rate of 89 percent. The response rates are generally higher in rural than in urban areas. The main reason for no response among both eligible men and eligible women was the failure to find individuals at home despite repeated callbacks made to the household by the interviewers. On some occasions the interviewers would visit respondents at their work places without success. The lower response rates for men are a result of their more frequent absences from home. Table 1.2 Results of the household and individual interviews Number of households, number of interviews, and response rates, ac- cording to residence (unweighted), Kenya 2008-2009 Result Residence Total Urban Rural Household interviews Households selected 3,286 6,650 9,936 Households occupied 3,015 6,253 9,268 Households interviewed 2,910 6,147 9,057 Household response rate1 96.5 98.3 97.7 Interviews with women age 15-49 Number of eligible women 2,735 6,032 8,767 Number of eligible women interviewed 2,615 5,829 8,444 Eligible women response rate2 95.6 96.6 96.3 Interviews with men age 15-54 Number of eligible men 1,269 2,641 3,910 Number of eligible men interviewed 1,084 2,381 3,465 Eligible men response rate2 85.4 90.2 88.6 1 Households interviewed/households occupied 2 Respondents interviewed/eligible respondents Household Population and Housing Characteristics | 13 HOUSEHOLD POPULATION AND HOUSING CHARACTERISTICS 2 John Bore and James Ng’ang’a This chapter summarizes demographic and socioeconomic characteristics of the population in the households sampled in the 2008-09 KDHS. For the purpose of the 2008-09 KDHS, a household was defined as a person or a group of persons, related or unrelated, who live together and who share a common source of food. The Household Questionnaire (see Appendix E) included a schedule collecting basic demographic and socioeconomic information (e.g., age, sex, education attainment, and current school attendance) for all usual residents and visitors who spent the night preceding the interview in the household. This method of data collection allows analysis of the results for either the de jure (usual residents) or de facto (those present at the time of the survey) populations. The household questionnaire also obtained information on housing facilities (e.g., sources of water supply and sanitation facilities) and household possessions. The information presented in this chapter is intended to facilitate interpretation of the key demographic, socioeconomic, and health indices presented later in the report. It is also intended to assist in the assessment of the representativeness of the survey sample. 2.1 POPULATION BY AGE AND SEX Age and sex are important demographic variables and are the primary basis of demographic classification. The distribution of the de facto household population in the 2008-09 KDHS is shown in Table 2.1 by five-year age groups, according to sex and residence. The household population constitutes 38,019 persons, of which 49 percent are male and 51 percent are female. There are more persons in the younger age groups than in the older age groups for both sexes, with those age 0-19 accounting for more than half of the population. Table 2.1 Household population by age, sex, and residence Percent distribution of the de facto household population by five-year age groups, according to sex and residence, Kenya 2008-09 Age Urban Rural Total Male Female Total Male Female Total Male Female Total <5 14.3 13.0 13.6 17.1 15.4 16.2 16.6 14.9 15.7 5-9 12.4 11.0 11.6 17.2 15.1 16.1 16.3 14.3 15.2 10-14 8.1 9.6 8.9 15.2 14.8 15.0 13.8 13.7 13.8 15-19 8.0 9.0 8.5 11.2 9.7 10.4 10.6 9.5 10.0 20-24 10.8 14.6 12.8 7.3 8.0 7.6 8.0 9.3 8.6 25-29 10.9 13.5 12.2 5.6 6.5 6.0 6.6 7.8 7.2 30-34 10.3 8.1 9.1 4.7 5.9 5.3 5.8 6.3 6.0 35-39 7.1 6.1 6.6 4.1 4.5 4.3 4.7 4.8 4.7 40-44 6.6 4.6 5.6 3.6 3.9 3.8 4.2 4.1 4.1 45-49 4.1 2.8 3.5 3.2 3.8 3.5 3.4 3.6 3.5 50-54 2.2 2.9 2.6 2.7 3.3 3.0 2.6 3.2 2.9 55-59 2.1 1.9 2.0 2.0 2.3 2.2 2.0 2.3 2.2 60-64 1.5 1.4 1.4 2.0 2.1 2.0 1.9 1.9 1.9 65-69 0.5 0.5 0.5 1.4 1.6 1.5 1.2 1.4 1.3 70-74 0.5 0.1 0.3 1.1 1.4 1.3 1.0 1.1 1.1 75-79 0.3 0.6 0.4 0.7 0.7 0.7 0.6 0.7 0.6 80 + 0.1 0.4 0.3 1.0 1.2 1.1 0.8 1.1 1.0 Don’t know/missing 0.0 0.1 0.1 0.1 0.1 0.1 0.0 0.1 0.1 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Number 3,586 3,830 7,416 14,917 15,686 30,602 18,503 19,516 38,019 14 | Household Population and Housing Characteristics Figure 2.1 illustrates the age-sex structure of the Kenyan population in a population pyramid. As was the case in 2003, the household population age-sex structure is still wide at its base, as depicted by the population pyramid. The share of the Kenyan population under 15 years of age is 45 percent; those age 15-64 constitute 51 percent, and those age 65 years and older make up 4 percent of the total Kenyan household population. This means that the age dependency ratio in Kenya has increased slightly, from 92 in 2003 to 96 in 2008-09.1 The pyramid shows a rather sharp drop in population between women age 10-14 and those age 15-19. This may be partly due to a possible tendency on the part of some interviewers to estimate the ages of women to be under the cutoff age of 15 for eligibility for the individual interview, thus reducing their workload. Figure 2.1 Population Pyramid Kenya 2008-09 0.5 0.3 0.6 0.7 1 1.2 1.6 1.8 2.1 2.5 3.2 4 4.7 4.9 7.1 7.3 7.7 0.4 0.3 0.5 0.6 0.9 1 1.3 1.7 2 2.3 2.8 3.2 3.9 5.1 6.7 7.9 8.1 80+ 75-79 70-74 65-69 60-64 55-59 50-54 45-49 40-44 35-39 30-34 25-29 20-24 15-19 10-14 5-9 <5 Age group (years) 0246810 0 2 4 6 8 10 .0 Percent Female Male .0 1.0 4.0 2.2 HOUSEHOLD COMPOSITION Information on key aspects of the composition of households, including the sex of the head of the household and the size of the household, is presented in Table 2.2. These characteristics are important because they are associated with the welfare of the household. Households headed by women are, for example, typically poorer than households headed by men. Economic resources are often more limited in large households than in small households. Moreover, where the size of the household is large, crowding can lead to health problems. The data for household composition show that, at the national level, women head 34 percent of Kenyan households, a slightly higher proportion than was observed in the 2003 KDHS (32 percent). There are modest differences in female-headed households between urban (29 percent) and rural areas (36 percent). The data also show that the mean size of a Kenyan household is 4.2 persons, slightly fewer than the mean household size of 4.4 found in the 2003 KDHS. As expected, rural households are larger on average (4.6 persons) than are urban households (3.1). 1 The dependency ratio is defined as the sum of all persons under 15 years or over 64 years of age, divided by the number of persons age 15-64, multiplied by 100. Household Population and Housing Characteristics | 15 Table 2.2 Household composition Percent distribution of households by sex of head of household and by household size (mean size of household), according to residence), Kenya 2008-09 Characteristic Residence Total Urban Rural Household headship Male 71.4 64.2 66.1 Female 28.6 35.8 33.9 Total 100.0 100.0 100.0 Number of usual members 1 23.9 11.7 14.9 2 18.3 10.7 12.6 3 20.3 13.9 15.5 4 17.7 16.5 16.8 5 9.2 15.0 13.5 6 5.5 11.7 10.1 7 2.0 8.1 6.5 8 1.5 5.2 4.2 9+ 1.5 7.2 5.8 Total 100.0 100.0 100.0 Mean size of households 3.1 4.6 4.2 Number of households 2,350 6,707 9,057 Note: Table is based on de jure household members, i.e., usual residents. 2.3 EDUCATION OF THE HOUSEHOLD POPULATION Education is a key determinant of the lifestyle and status an individual enjoys in a society. Studies have consistently shown that educational attainment has a strong effect on health behaviours and attitudes. Results from the 2008-09 KDHS can be used to look at educational attainment among household members and school attendance ratios among youth. For the analysis presented here, the official age for entry into the primary level is six years. The official duration of primary school is eight years (i.e., from standard 1 to standard 8), and the number of years assumed for completion of secondary school is four years. 2.3.1 Educational Attainment Tables 2.3.1 and 2.3.2 present data on educational attainment of household members age six and older for each sex. The data show a slight decrease in the proportion of women and men with no education (19 percent for women and 13 percent for men) compared with the 2003 KDHS (23 percent for women and 16 percent for men). As expected, more men have either completed secondary (12 percent) or attained more than secondary (6 percent) compared with 9 percent and 5 percent of women who have completed secondary or attained more than secondary, respectively. Compared with the 2003 KDHS, there has been a slight decrease in the proportion of children and young adults who have never attended school, particularly among those age 10-14 years and 15-19 years. In most of the age groups, there are fewer men than women who have no education at all, a pattern that was observed in the 2003 KDHS. The gap between the proportion of men who have no education and women who have no education increases with age. For instance, in the 6-9 age group, male children are actually more likely than female children to have no education, while in the 65 and over age group, 77 percent of women have never been to school, compared with only 40 percent of men. 16 | Household Population and Housing Characteristics Table 2.3.1 Educational attainment of the female household population Percent distribution of the de facto female household populations age six and over by highest level of schooling attended or completed and median grade completed, according to background characteristics, Kenya 2008-09 Background characteristic No education Some primary Completed primary1 Some secondary Completed secondary2 More than secondary Don’t know/ missing Total Number Median years completed Age 6-9 39.7 60.1 0.1 0.0 0.0 0.0 0.0 100.0 2,242 0.3 10-14 4.5 90.0 4.3 1.0 0.0 0.0 0.2 100.0 2,680 3.7 15-19 4.5 43.6 22.0 21.7 7.3 0.8 0.1 100.0 1,862 7.1 20-24 7.5 25.2 29.4 11.0 18.1 8.7 0.0 100.0 1,806 7.6 25-29 8.3 24.0 33.0 9.6 15.2 10.0 0.0 100.0 1,529 7.5 30-34 7.9 32.2 24.8 9.6 16.2 9.2 0.1 100.0 1,232 7.4 35-39 11.0 32.0 22.9 7.6 17.0 9.1 0.4 100.0 933 7.3 40-44 14.5 20.7 29.1 8.3 19.4 7.8 0.2 100.0 791 6.6 45-49 22.0 25.9 24.2 9.4 13.6 4.9 0.0 100.0 697 6.1 50-54 34.5 27.5 18.1 4.7 8.3 6.6 0.4 100.0 625 3.9 55-59 43.9 24.7 16.6 3.6 4.5 6.2 0.6 100.0 439 2.2 60-64 55.6 23.7 10.8 1.4 2.8 4.9 0.8 100.0 380 0.0 65+ 76.8 16.7 3.2 0.4 0.5 1.3 1.1 100.0 828 0.0 Residence Urban 11.3 27.2 18.1 9.6 20.2 13.4 0.1 100.0 3,257 7.6 Rural 21.3 47.3 16.7 6.5 5.7 2.2 0.2 100.0 12,805 4.5 Province Nairobi 6.1 20.4 17.8 9.6 20.5 25.3 0.3 100.0 1,014 9.6 Central 10.9 38.2 25.3 10.0 11.2 4.2 0.1 100.0 1,726 6.5 Coast 33.1 36.8 13.1 5.3 8.5 3.2 0.0 100.0 1,265 3.4 Eastern 20.8 45.9 17.3 6.5 6.6 2.7 0.3 100.0 2,847 4.5 Nyanza 13.4 49.3 17.4 9.3 6.4 4.0 0.2 100.0 2,594 5.7 Rift Valley 21.5 43.7 16.9 5.7 9.0 3.1 0.2 100.0 4,369 4.9 Western 14.2 55.4 14.2 7.3 6.9 1.6 0.4 100.0 1,833 5.0 North Eastern 69.6 23.9 2.4 1.4 1.6 1.0 0.1 100.0 413 0.0 Wealth quintile Lowest 40.2 46.5 9.5 2.4 0.7 0.3 0.3 100.0 3,089 1.5 Second 20.0 55.0 15.8 5.6 3.1 0.3 0.2 100.0 3,154 4.2 Middle 17.1 48.7 19.4 7.3 6.1 1.2 0.2 100.0 3,238 5.1 Fourth 12.9 40.8 20.3 10.7 11.0 4.0 0.3 100.0 3,270 6.3 Highest 7.5 26.0 19.7 9.4 21.4 15.9 0.1 100.0 3,310 7.8 Total 19.3 43.2 17.0 7.2 8.7 4.5 0.2 100.0 16,061 5.2 Note: Total includes 17 women whose age was not stated. 1 Completed Grade 8 at the primary level, for those under age 40; because of the change in the school system in the 1980s, those age 40 and above are considered to have completed primary if they completed Grade 7. 2 Completed Form 4 at the secondary level About twice as many women and men in rural areas have no education at all compared with those in urban areas. Although North Eastern province has the highest proportion of those without education, a significant drop was observed between 2003 and 2008-09 for both genders in the province. As expected, the proportion with no education decreases dramatically as wealth increases. Nationally, the median number of years of schooling completed is slightly higher for males (6.0 years) than females (5.2 years). Over the years, the median number of years of schooling completed has been increasing, although the increase has been small. For example, the median number of years of schooling increased from 4.3 in 2003 to 5.2 in 2008-09 for the female population age 6 and above and from 5.0 in 2003 to 6.0 in 2008-09 for the male population. Household Population and Housing Characteristics | 17 Table 2.3.2 Educational attainment of the male household population Percent distribution of the de facto male household populations age six and over by highest level of schooling attended or completed and median grade completed, according to background characteristics, Kenya 2008-09 Background characteristic No education Some primary Completed primary1 Some secondary Completed secondary2 More than secondary Don’t know/ missing Total Number Median years completed Age 6-9 42.9 56.9 0.0 0.1 0.0 0.0 0.1 100.0 2,453 0.2 10-14 4.9 91.5 2.6 1.0 0.0 0.0 0.0 100.0 2,557 3.6 15-19 1.6 49.4 18.4 23.7 6.5 0.4 0.1 100.0 1,952 6.9 20-24 3.2 22.6 26.3 13.6 23.9 10.2 0.1 100.0 1,472 8.0 25-29 4.1 23.8 26.2 9.4 23.3 13.2 0.1 100.0 1,226 7.8 30-34 4.4 24.2 28.8 7.1 22.1 13.1 0.2 100.0 1,067 7.7 35-39 4.0 22.4 25.2 9.1 27.5 11.7 0.1 100.0 863 8.0 40-44 6.0 14.8 31.1 6.5 26.1 15.3 0.1 100.0 774 7.8 45-49 7.9 18.7 30.9 6.3 24.1 12.0 0.1 100.0 629 6.9 50-54 12.6 18.6 30.5 9.5 18.9 9.5 0.4 100.0 476 6.7 55-59 13.9 22.2 26.5 7.9 17.5 10.6 1.3 100.0 378 6.7 60-64 21.4 18.6 27.0 9.5 11.6 11.4 0.5 100.0 347 6.7 65+ 39.6 33.5 12.3 3.4 6.4 4.6 0.2 100.0 678 2.3 Residence Urban 6.8 24.3 16.2 9.0 23.9 19.5 0.3 100.0 2,997 8.8 Rural 14.7 48.3 17.1 7.7 9.4 2.7 0.1 100.0 11,884 5.2 Province Nairobi 4.6 16.9 12.7 7.4 27.0 31.1 0.4 100.0 1,002 11.1 Central 5.3 41.8 23.7 9.1 14.7 5.3 0.1 100.0 1,568 6.7 Coast 17.6 34.1 19.6 7.5 14.7 6.4 0.1 100.0 1,145 6.4 Eastern 14.2 48.6 17.7 7.2 9.2 2.8 0.3 100.0 2,638 5.2 Nyanza 8.9 48.4 17.6 9.7 9.3 5.9 0.2 100.0 2,461 6.1 Rift Valley 16.5 43.4 15.4 6.6 13.5 4.4 0.1 100.0 3,897 5.4 Western 9.9 53.6 15.3 10.3 8.5 2.3 0.1 100.0 1,754 5.3 North Eastern 49.1 35.7 6.6 3.1 3.2 2.3 0.1 100.0 417 0.0 Wealth quintile Lowest 29.6 50.9 11.8 4.5 2.5 0.4 0.2 100.0 2,702 2.8 Second 14.0 53.0 18.2 7.4 6.5 0.9 0.1 100.0 2,986 4.8 Middle 11.0 49.8 19.6 8.6 9.5 1.3 0.1 100.0 3,000 5.6 Fourth 8.3 41.8 18.2 9.9 16.1 5.3 0.4 100.0 3,048 6.6 Highest 4.6 23.6 16.2 8.9 25.1 21.4 0.2 100.0 3,145 9.6 Total 13.1 43.5 16.9 7.9 12.3 6.1 0.2 100.0 14,881 6.0 Note: Total includes 9 men whose age was not stated. 1 Completed Grade 8 at the primary level, for those under age 40; because of the change in the school system in the 1980s, those age 40 and above are considered to have completed primary if they completed Grade 7. 2 Completed Form 4 at the secondary level 2.3.2 School Attendance Rates Table 2.4 presents the primary school and secondary school net and gross attendance ratios (NAR and GAR) for the school year that started in 2008 by household residence and zones. The NAR for primary school is the percentage of the primary-school-age (6-13 years) population that is attending primary school. The NAR for secondary school is the percentage of the secondary-school- age (14-17 years) population that is attending secondary school. By definition, the NAR cannot exceed 100 percent. The GAR for primary school is the total number of primary school students, of any age, expressed as a percentage of the official primary-school-age population. The GAR for secondary school is the total number of secondary school students, of any age, expressed as a percentage of the official secondary-school-age population. If there are significant numbers of over- age and under-age students at a given level of schooling, the GAR can exceed 100 percent. Youth are considered to be attending school currently if they attended formal academic school at any point during the given school year. The gender parity index (GPI) assesses sex-related differences in school attendance rates and is calculated by dividing the GAR for the female population by the GAR for the male population. A GPI less than 1 indicates a gender disparity in favour of the male population, i.e., a higher proportion of males than females attends that level of schooling. A GPI greater than 1 indicates a gender disparity in favour of females. A GPI of 1 indicates parity or equality between the rates of participation for the sexes. 18 | Household Population and Housing Characteristics Table 2.4 School attendance ratios Net attendance ratios (NAR) and gross attendance ratios (GAR) for the de facto household population by sex and level of schooling; and the gender parity index (GPI), according to background characteristics, Kenya 2008-09 Background characteristic Net attendance ratio1 Gross attendance ratio2 Male Female Total Gender parity index3 Male Female Total Gender parity index3 PRIMARY SCHOOL Residence Urban 85.4 82.5 83.9 0.97 106.8 100.4 103.5 0.94 Rural 76.4 79.6 77.9 1.04 114.3 111.2 112.8 0.97 Province Nairobi 89.8 92.1 91.1 1.03 102.7 100.2 101.3 0.98 Central 89.4 90.5 89.9 1.01 117.5 117.1 117.3 1.00 Coast 69.4 73.1 71.4 1.05 106.3 95.3 100.6 0.90 Eastern 80.3 84.7 82.5 1.06 116.9 114.6 115.8 0.98 Nyanza 85.3 87.5 86.3 1.03 128.7 121.5 125.3 0.94 Rift Valley 70.5 73.2 71.9 1.04 100.8 101.0 100.9 1.00 Western 79.0 82.0 80.5 1.04 127.8 128.5 128.1 1.01 North Eastern 55.7 50.5 53.4 0.91 88.0 66.6 78.3 0.76 Wealth quintile Lowest 62.8 66.2 64.5 1.05 95.9 93.8 94.9 0.98 Second 77.0 84.3 80.5 1.09 120.9 124.0 122.4 1.03 Middle 81.8 84.0 82.9 1.03 127.0 118.1 122.6 0.93 Fourth 84.7 82.7 83.7 0.98 116.2 109.4 112.8 0.94 Highest 88.7 86.4 87.5 0.97 106.9 102.9 104.8 0.96 Total 77.6 80.0 78.8 1.03 113.3 109.6 111.5 0.97 SECONDARY SCHOOL Residence Urban 43.7 32.0 37.5 0.73 79.4 61.1 69.8 0.77 Rural 13.1 16.0 14.5 1.23 46.6 34.3 40.5 0.73 Province Nairobi 55.0 51.1 53.0 0.93 102.0 84.7 92.9 0.83 Central 18.6 31.2 25.3 1.68 56.8 57.5 57.2 1.01 Coast 22.1 14.6 18.5 0.66 34.2 29.9 32.1 0.87 Eastern 15.8 17.8 16.7 1.13 50.9 43.2 47.4 0.85 Nyanza 16.1 23.6 19.6 1.46 47.8 42.5 45.3 0.89 Rift Valley 14.4 14.4 14.4 1.00 58.3 31.8 43.8 0.55 Western 14.0 6.2 10.3 0.44 43.9 22.2 33.6 0.51 North Eastern 10.6 10.0 10.4 0.95 24.1 17.9 21.4 0.74 Wealth quintile Lowest 7.2 5.6 6.4 0.78 26.7 12.5 19.8 0.47 Second 9.3 7.8 8.6 0.83 41.9 24.3 33.4 0.58 Middle 12.7 19.1 15.6 1.49 45.6 40.4 43.2 0.89 Fourth 19.7 28.2 24.3 1.43 73.6 56.8 64.5 0.77 Highest 53.3 37.0 44.7 0.69 86.8 64.3 74.9 0.74 Total 17.0 18.4 17.7 1.08 50.8 38.2 44.6 0.75 1 The NAR for primary school is the percentage of the primary-school age (6-13 years) population that is attending primary school. The NAR for secondary school is the percentage of the secondary-school age (14- 17 years) population that is attending secondary school. By definition the NAR cannot exceed 100 percent. 2 The GAR for primary school is the total number of primary school students, expressed as a percentage of the official primary-school-age population. The GAR for secondary school is the total number of secondary school students, expressed as a percentage of the official secondary-school-age population. If there are significant numbers of overage and underage students at a given level of schooling, the GAR can exceed 100 percent. 3 The Gender Parity Index for primary school is the ratio of the primary school NAR(GAR) for females to the NAR(GAR) for males. The Gender Parity Index for secondary school is the ratio of the secondary school NAR(GAR) for females to the NAR(GAR) for males. The data for NAR in Table 2.4 indicates that 79 percent of children of primary school age are attending school, which is identical to the finding of the 2003 KDHS. Surprisingly, the NAR is slightly higher for girls (80 percent) than for boys (78 percent). As expected, the NAR for primary school is higher in urban (84 percent) than in rural (78 percent) areas. The NAR for primary school increases with the increase in the wealth quintile, from 65 percent at the lowest wealth quintile to 88 percent at the highest wealth quintile. Household Population and Housing Characteristics | 19 The GAR indicates that there are children in primary school who are not of primary school age, with ratios of 113 and 110 for males and females, respectively. As expected, the NAR and GAR are lower at the secondary school level than at the primary level. However, there has been a considerable improvement in the secondary school NAR in 2008-09 compared with the 2003 KDHS, where the 2008-09 NAR is 5 percentage points higher than the one observed in the 2003 KDHS. The gap between the secondary school NAR in the lowest wealth quintile and that in the highest wealth quintile is very wide, ranging from 6 percent to 45 percent. The gender parity index shows the ratio of the female to male GARs. In primary school, there is parity between the sexes because the index is close to 1. However, the GPI for secondary school drops to 0.75, indicating a bias in favour of males. Comparison with data from the 2003 KDHS shows that the GPI for primary school has not changed much, though there is a notable increase in parity between the sexes in North Eastern province since 2003. For the secondary school level, the GPI in 2008-09 is lower than the one observed in 2003. Table 2.5 shows the percentage of the population age 6-24 who attended school in 2008 by age, sex and residence. Ninety-three percent of those age 6-15 attended school in 2008, with urban attendance accounting for a higher proportion (96 percent) than rural attendance (93 percent). Attendance by those age 21-24 is relatively low, with about one of five (18 percent) having attended school in 2008. Table 2.5 School attendance Percentage of the de facto population age 6-24 years who attended school in 2008 by age, sex and residence, Kenya 2008-09 Age Male Female Residence Urban Rural Total Urban Rural Total Urban Rural Total 6-10 96.5 90.4 91.3 97.7 90.2 91.3 97.1 90.3 91.3 11-15 97.1 95.1 95.4 94.2 95.2 95.0 95.4 95.2 95.2 6-15 96.7 92.5 93.1 96.2 92.5 93.0 96.4 92.5 93.0 16-20 66.7 74.0 72.7 42.1 61.5 57.1 52.5 67.9 64.9 21-24 19.2 29.8 26.9 10.7 10.6 10.6 14.4 19.5 18.0 Attendance rates for both male and female youth are at par at the age groups 6-10 (91 percent) and 11-15 (95 percent). However, at age group 16-20, there is a noticeably big gap in attendance between males and females; 73 percent of males attended school in 2008 compared with 57 percent of females. This pattern continues in the 21-24 age group in which more than twice as many males attended school in 2008 as females (27 percent for males and 11 percent for females). Urban attendance is higher (96 percent) compared with rural attendance (93 percent) only for the 6-15 age group. In the 16-20 and 21-24 age groups, more people in rural areas than in urban areas attended school in 2008. Figure 2.2 illustrates age-specific attendance rates, i.e., the percentage of a given age cohort who attend school, regardless of the level attended (primary, secondary, or higher). At age 5, attendance by males is twice that of females (12 percent for males versus 6 percent for females). However, from age 6 to age 10, female attendance is higher than that of males. Attendance peaks at age 13 for males and 14 for females where the peak attendance rate is identical (96 percent) for both genders. From age 14 onward, as school attendance begins to decline, the gender differential increases, with more male than female youths attending. 20 | Household Population and Housing Characteristics Figure 2.2 Age-specific Attendance Rates of the de-facto Population 5 to 24 Years Kenya 2008-09 6 29 66 84 92 94 95 95 96 96 91 85 76 55 44 25 22 8 6 7 12 29 62 79 90 93 96 96 96 94 95 92 87 73 62 44 40 32 23 10 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Age Percent Female Male 2.4 HOUSEHOLD ENVIRONMENT The physical characteristics of the dwelling in which a household lives are important determinants of the health status of household members, especially children. They can also be used as indicators of the socioeconomic status of households. Respondents in the 2008-09 KDHS were asked a number of questions about their household environment, including questions on the source of drinking water; type of sanitation facility; type of flooring, walls, and roof; and number of rooms in the dwelling. The results are presented here in terms of households and of the de jure population. 2.4.1 Drinking Water Increasing access to improved drinking water is one of the Millennium Development Goals that Kenya along with other nations worldwide has adopted (United Nations General Assembly 2001). Table 2.6 includes a number of indicators that are useful in monitoring household access to improved drinking water (WHO and UNICEF, 2005). The source of drinking water is an indicator of whether it is suitable for drinking. Sources that are likely to provide water suitable for drinking are identified as improved sources in Table 2.6. They include a piped source within the dwelling or plot, public tap, tube well or borehole, protected well or spring, and rainwater.2 Lack of ready access to a water source may limit the quantity of suitable drinking water that is available to a household. Even if the water is obtained from an improved source, moreover, water that must be fetched from a source that is not immediately accessible to the household may be contaminated during transport or storage. Another factor in considering the accessibility of water sources is that the burden of going for water often falls disproportionately on female members of the household. Finally, home water treatment can be effective in improving the quality of household drinking water. As shown in Table 2.6, three out of five households in Kenya (63 percent) get drinking water from an improved source. However, disparities exist by residence, with a higher proportion of urban households (91 percent) having an improved source of drinking water compared with rural households (54 percent). Among the improved sources, piped water into the plot accounts for the highest proportion (15 percent) of households, but mainly in urban areas (33 percent), while the most common improved category for rural households is a protected dug well (12 percent). 2 The categorization into improved and non-improved follows that proposed by the WHO/UNICEF Joint Monitoring Programme for Water Supply and Sanitation (WHO and UNICEF, 2004). Household Population and Housing Characteristics | 21 More than one-third of Kenyan households get their drinking water from a non-improved source, mainly surface water from lakes, streams, and rivers (24 percent of households). Although only 6 percent of urban households use non-improved sources for drinking water, the proportion is far higher for rural households (46 percent). Table 2.6 Household drinking water Percent distribution of households and de jure population by source, time to collect, and person who usually collects drinking water; and percentage of households and the de jure population by treatment of drinking water, according to residence, Kenya 2008-09 Characteristic Households Population Urban Rural Total Urban Rural Total Source of drinking water Improved source 89.3 53.8 63.0 89.7 53.1 60.2 Piped water into dwelling 22.8 4.7 9.4 24.4 3.5 7.5 Piped water into plot 33.1 9.0 15.2 30.7 7.5 12.0 Public tap/standpipe 19.8 6.1 9.7 21.3 6.2 9.1 Tube well or borehole 6.7 9.5 8.8 5.6 9.7 8.9 Protected dug well 4.7 11.6 9.8 5.1 12.9 11.4 Protected spring 1.6 10.2 8.0 1.9 11.0 9.2 Rainwater 0.6 2.7 2.2 0.7 2.4 2.1 Non-improved source 6.3 45.8 35.5 6.2 46.5 38.7 Unprotected dug well 1.3 6.2 4.9 1.5 6.4 5.5 Unprotected spring 0.9 7.2 5.6 1.0 7.7 6.4 Tanker truck/cart with small tank 2.1 1.1 1.3 1.8 1.1 1.2 Surface water 1.9 31.3 23.7 2.0 31.3 25.6 Bottled water, improved source for cooking/washing1 1.4 0.0 0.4 1.2 0.0 0.2 Bottled water, non-improved source for cooking/washing1 0.1 0.0 0.1 0.1 0.1 0.1 Other 2.8 0.4 1.0 2.7 0.3 0.8 Total 100.0 100.0 100.0 100.0 100.0 100.0 Percentage using any improved source of drinking water 90.8 53.8 63.4 91.0 53.1 60.4 Time to obtain drinking water (round trip) Water on premises 64.7 26.2 36.2 64.1 23.4 31.2 Less than 30 minutes 26.9 33.9 32.1 26.1 34.4 32.8 30 minutes or longer 6.3 39.3 30.7 7.8 41.9 35.3 Don’t know/missing 2.2 0.5 1.0 2.1 0.4 0.7 Total 100.0 100.0 100.0 100.0 100.0 100.0 Person who usually collects drinking water Adult female 15+ 21.5 58.0 48.5 24.9 64.3 56.7 Adult male 15+ 9.8 9.1 9.2 6.6 5.4 5.7 Female child under age 15 0.7 3.9 3.1 0.9 4.5 3.8 Male child under age 15 0.3 1.9 1.5 0.4 1.8 1.6 Other 2.9 0.9 1.4 3.1 0.5 1.0 Water on premises 64.7 26.2 36.2 64.1 23.4 31.2 Total 100.0 100.0 100.0 100.0 100.0 100.0 Treatment of water2 Boiled 38.0 25.4 28.6 37.6 24.0 26.6 Bleach/chlorine 21.5 16.3 17.6 22.9 17.0 18.2 Strained through cloth 0.3 1.5 1.2 0.4 1.7 1.4 Ceramic, sand, or other filter 1.7 0.5 0.8 1.6 0.6 0.8 Solar disinfection 0.0 0.2 0.1 0.0 0.2 0.1 Allowed to settle 0.2 0.5 0.4 0.1 0.4 0.4 Other 0.3 0.1 0.1 0.3 0.1 0.1 No treatment 42.5 59.1 54.8 42.1 59.7 56.3 Percentage using an appropriate treatment method3 57.1 40.2 44.6 57.5 39.8 43.2 Number 2,350 6,707 9,057 7,365 30,704 38,069 1 Because the quality of bottled water is not known, households using bottled water for drinking are classified as using an improved or non-improved source according to their water source for cooking and washing. 2 Respondents may report multiple treatment methods, so the sum of treatment may exceed 100 percent. 3 Appropriate water treatment methods include boiling, bleaching, straining, filtering, and solar disinfecting. 22 | Household Population and Housing Characteristics Almost one-third of households are within 30 minutes of the source of their drinking water, and more than a third have water on the premises. The remaining 31 percent of households must travel 30 minutes or longer to get drinking water. Notably, 39 percent of rural households travel at least 30 minutes to obtain drinking water, compared with only 6 percent of urban households. Similarly, a huge urban-rural disparity exists in the proportion of households with water on the premises, with nearly two-thirds (65 percent) of urban households having water compared with 26 percent of rural households. Women in Kenya, especially those in rural areas, bear the burden of collecting drinking water. In nearly half of Kenyan households (49 percent), adult women are responsible for water collection. In rural households, adult women are six times more likely than adult men to be the ones to fetch water (58 percent of households compared with 9 percent, respectively). Even in urban households, women are more than twice as likely as men to collect water (22 and 10 percent of households). It is encouraging to note that children under age 15 are usually responsible for fetching drinking water in only 5 percent of households. Less than half of Kenyan households (45 percent) treat their drinking water. The main method of treatment is boiling (29 percent of households), while 18 percent of households add bleach or chlorine to make water safer for drinking. Appropriate water treatment methods are more common among urban households (57 percent) than among rural households (40 percent). 2.4.2 Household Sanitation Facilities Ensuring adequate sanitation facilities is a Millennium Development Goal that Kenya shares with other countries. A household is classified as having an improved toilet if the toilet is used only by members of one household (i.e., it is not shared) and if the facility used by the household separates the waste from human contact (WHO/UNICEF Joint Monitoring Programme for Water Supply and Sanitation, 2004). As shown in Table 2.7, less than one-quarter of households use an improved toilet facility that is not shared with other households. Urban households are only slightly more likely than rural households to have an improved toilet facility (30 percent and 20 percent, respectively). The most common type of toilet facility in rural areas is an open pit latrine or one without a slab (47 percent of rural households), while in urban areas toilet facilities are mainly shared with other households (52 percent). Overall, 12 percent of households have no toilet facility at all; they are almost exclusively rural, accounting for 16 percent of rural households. Table 2.7 Household sanitation facilities Percent distribution of households and de jure population by type of toilet/latrine facilities, according to residence, Kenya 2008-09 Type of toilet/latrine facility Households Population Urban Rural Total Urban Rural Total Improved, not shared facility 29.8 20.1 22.6 34.3 21.8 24.3 Flush/pour flush to piped sewer system 18.7 0.6 5.3 20.2 0.6 4.4 Flush/pour flush to septic tank 5.5 0.3 1.7 6.6 0.3 1.5 Flush/pour flush to pit latrine 1.5 0.2 0.5 1.6 0.2 0.5 Ventilated improved pit (VIP) latrine 2.3 9.0 7.3 3.4 9.5 8.4 Pit latrine with slab 1.8 10.0 7.8 2.5 11.2 9.5 Non-improved facility 70.1 79.8 77.3 65.7 78.1 75.7 Any facility shared with other households 52.2 16.7 25.9 47.6 13.5 20.1 Flush/pour flush not to sewer/ septic tank/pit latrine 3.3 0.2 1.0 3.3 0.1 0.7 Pit latrine without slab/open pit 13.5 46.5 37.9 13.3 46.4 40.0 Bucket/Hanging toilet, latrine 0.2 0.4 0.4 0.4 0.4 0.4 No facility/bush/field 0.9 16.0 12.1 1.1 17.7 14.5 Total 100.0 100.0 100.0 100.0 100.0 100.0 Number 2,350 6,707 9,057 7,365 30,704 38,069 Household Population and Housing Characteristics | 23 2.4.3 Housing Characteristics Table 2.8 presents characteristics of the dwellings in which Kenyan households live. These characteristics reflect the household’s socioeconomic situation. They also may influence environmental conditions—for example, in the case of the use of biomass fuels, exposure to indoor pollution—that have a direct bearing on the health and welfare of household members. Table 2.8 Household characteristics Percent distribution of households and de jure population by housing characteristics and percentage using solid fuel for cooking, according to residence, Kenya 2008-09 Housing characteristic Households Population Urban Rural Total Urban Rural Total Electricity Yes 65.6 8.1 23.0 64.8 6.9 18.1 No 34.3 91.9 76.9 35.1 93.1 81.9 Total 100.0 100.0 100.0 100.0 100.0 100.0 Flooring material Earth, sand 8.0 44.1 34.8 8.8 44.7 37.7 Dung 2.4 26.6 20.3 2.6 29.0 23.9 Wood planks 0.4 0.3 0.3 0.4 0.3 0.3 Parquet, polished wood 1.6 0.1 0.5 1.5 0.1 0.3 Vinyl, asphalt strips 1.4 0.1 0.4 1.0 0.1 0.3 Ceramic tiles 3.6 0.2 1.1 3.8 0.2 0.9 Cement 77.8 27.9 40.8 76.6 25.1 35.0 Carpet 4.9 0.5 1.7 5.2 0.5 1.4 Other/missing 0.1 0.1 0.0 0.0 0.1 0.1 Total 100.0 100.0 100.0 100.0 100.0 100.0 Rooms used for sleeping One 65.6 41.9 48.0 53.9 34.1 37.9 Two 22.5 36.1 32.6 28.1 39.0 36.9 Three or more 11.8 22.0 19.4 17.9 26.9 25.1 Missing 0.1 0.1 0.1 0.2 0.1 0.1 Total 100.0 100.0 100.0 100.0 100.0 100.0 Place for cooking In the house 84.5 33.1 46.4 80.4 27.7 37.9 In a separate building 7.3 59.5 46.0 9.9 65.1 54.4 Outdoors 6.7 6.7 6.7 9.0 7.1 7.4 Other 0.1 0.1 0.1 0.0 0.0 0.0 Missing 1.4 0.6 0.8 0.6 0.2 0.3 Total 100.0 100.0 100.0 100.0 100.0 100.0 Cooking fuel Electricity 1.6 0.1 0.5 1.4 0.0 0.3 LPG/Natural gas 21.7 1.2 6.5 21.2 0.9 4.8 Kerosene 26.9 1.5 8.1 20.9 0.6 4.5 Coal, lignite 0.1 1.1 0.8 0.0 1.1 0.9 Charcoal 41.1 10.8 18.7 45.2 8.7 15.8 Wood 6.1 83.3 63.3 9.4 87.0 71.9 Straw/shrubs/grass 0.8 1.4 1.2 1.0 1.6 1.5 Agricultural crop 0.0 0.1 0.1 0.0 0.1 0.1 No food cooked in household 1.4 0.6 0.8 0.6 0.2 0.3 Other/missing 0.3 0.0 0.1 0.4 0.0 0.1 Total 100.0 100.0 100.0 100.0 100.0 100.0 Percentage using solid fuel for cooking1 48.0 96.6 84.0 55.6 98.4 90.1 Number 2,350 6,707 9,057 7,365 30,704 38,069 LPG = Liquid petroleum gas 1 Includes coal/lignite, charcoal, wood, straw/shrubs/grass, and agricultural crops Almost one-quarter (23 percent) of Kenyan households have electricity, an increase from the 16 percent recorded in the 2003 KDHS. There is a large imbalance between urban and rural areas, with two-thirds (66 percent) of urban households having electricity compared with only 8 percent of rural households. 24 | Household Population and Housing Characteristics More than half of Kenyan households (55 percent) live in dwellings with floors made of earth, sand, or dung. The next most common type of flooring material is cement, accounting for 41 percent of households. Most urban households have floors made of cement (78 percent), while those in rural areas mainly have floors made from earth, sand, or dung (71 percent). The number of rooms used for sleeping is an indicator of the extent of crowding in households. Overcrowding increases the risk of contracting diseases like acute respiratory infections, tuberculosis, and skin diseases. Overall, almost half of Kenyan households use only one room for sleeping, while one-third use two rooms and the remainder use three or more rooms for sleeping. Urban households tend to have fewer rooms for sleeping; two-thirds use only one room for sleeping, compared with 42 percent of rural households. With regard to cooking arrangements, Kenyan households are evenly divided between cooking in the house and cooking in a separate building (46 percent each). Seven percent of households do their cooking outdoors. There is large variation in the place of cooking by residence, with urban households mostly cooking in the house (85 percent) and rural households mainly cooking in a separate building (60 percent). Cooking and heating with solid fuels can lead to high levels of indoor smoke, a complex mix of health-damaging pollutants that could increase the risks of acute respiratory diseases. Solid fuels are defined as coal, charcoal, wood, straw, shrubs, and agricultural crops. In the 2008-09 KDHS, households were asked about their primary source of fuel for cooking. Their answers show that 84 percent of households use solid fuel for cooking. The use of solid fuel is nearly universal in households in rural areas (97 percent), compared with less than half of those in urban areas. The most common cooking fuel in Kenya is wood, used by close to two-thirds (63 percent) of households. Although wood is widely used in rural areas (83 percent of households), urban households rely mainly on charcoal (41 percent), kerosene (27 percent), and liquid petroleum gas or natural gas (22 percent). 2.5 HOUSEHOLD POSSESSIONS The availability of durable consumer goods is a useful indicator of a household’s socioeconomic status. Moreover, particular goods have specific benefits. For instance, having access to a radio or a television exposes household members to innovative ideas; a refrigerator prolongs the wholesomeness of foods; and a means of transport allows greater access to many services away from the local area. Table 2.9 shows the availability of selected consumer goods by residence. Ownership of durable goods varies according to residence and the nature of the asset. Of the 16 selected items asked about in the survey, radios, homes, agricultural land, farm animals, and the land on which the dwelling is located stand out as the assets most commonly owned by households. Seventy-four percent of Kenyan households own a radio, while about two-thirds own land, their dwelling, and agricultural animals. Notably, 62 percent of households own a mobile telephone. Somewhat fewer households own a clock (45 percent), a bicycle (30 percent), or a television (28 percent), while fewer than one in ten households own a refrigerator, car, truck, motorcycle, animal cart, motorboat, solar panel, or land-line telephone. There is noticeable urban-rural variation in the proportion of households owning specific goods. Most of the electronic goods are considerably more prevalent in urban areas, but farm-oriented possessions are more commonly found in rural areas. For example, 21 percent of urban households own a refrigerator compared with only 1 percent of rural households. Similarly, 57 percent of urban households own a television compared with 18 percent of rural households. Differentials in ownership of mobile phones are also apparent (86 percent for urban households and 53 percent for rural households). Radio possession is high among both urban and rural households (82 percent and 71 percent, respectively). It is evident that less than 20 percent of households in urban areas own the dwelling in which they reside or the land on which the dwelling is built, compared with over 80 percent of rural households. As expected, ownership of farm animals (cattle, cows, bulls, horses, donkeys, mules, goats, sheep, or chickens) is high in rural areas (80 percent of households). Household Population and Housing Characteristics | 25 Table 2.9 Household durable goods Percentage of households and de jure population possessing various household effects, means of transportation, agricultural land, and livestock/farm animals by residence, Kenya 2008-09 Possession Households Population Urban Rural Total Urban Rural Total Clock 65.3 37.5 44.7 67.3 39.1 44.5 Radio 82.0 70.6 73.6 82.6 72.3 74.3 Television 57.1 17.9 28.1 60.1 18.8 26.8 Mobile telephone 85.6 53.0 61.5 85.2 55.5 61.3 Non-mobile telephone 6.6 0.5 2.0 8.1 0.5 2.0 Refrigerator 21.2 1.2 6.4 24.0 1.2 5.6 Solar panel 2.9 5.8 5.0 3.3 6.5 5.9 Bicycle 17.7 34.4 30.1 20.6 38.3 34.9 Animal drawn cart 1.0 3.2 2.6 1.5 3.7 3.3 Motorcycle/scooter 2.8 1.9 2.1 3.7 2.1 2.4 Car/truck 13.4 2.9 5.6 14.4 2.9 5.2 Boat with a motor 0.3 0.3 0.3 0.2 0.3 0.3 Ownership of dwelling 17.9 84.4 67.2 23.4 88.8 76.1 Ownership of land on which dwelling is built 16.4 81.2 64.4 21.1 85.5 73.0 Ownership of agricultural land 35.3 78.1 67.0 39.7 80.3 72.4 Ownership of farm animals1 27.2 79.6 66.0 30.6 84.7 74.3 Number 2,350 6,707 9,057 7,365 30,704 38,069 1 Cattle, cows, bulls, horses, donkeys, mules, goats, sheep, or chickens There has been an increase in the percentage of households owning some items since the 2003 KDHS. The most dramatic increase has been with ownership of telephones; the proportion of households with either a mobile or non-mobile telephone has increased from 13 percent in 2003 to at least 62 percent in 2008-09.3 This increase could be a result of increased availability of affordable phones together with an increase in the number of service providers and the extent of geographical coverage. The proportion of households owning televisions also increased, from 18 percent in 2003 to 28 percent in 2008-09. Ownership of other items increased minimally. 2.6 WEALTH INDEX The wealth index is a background characteristic that is used throughout the report as a proxy for the long-term standard of living of the household. It is based on the data from the household’s ownership of consumer goods; dwelling characteristics; type of drinking water source; toilet facilities; and other characteristics that relate to a household’s socioeconomic status. To construct the index, each of these assets was assigned a weight (factor score) generated through principal component analysis, and the resulting asset scores were standardised in relation to a standard normal distribution, with a mean of zero and a standard deviation of one (Gwatkin et al., 2000). Each household was then assigned a score for each asset, and the scores were summed for each household. Individuals were ranked according to the total score of the household in which they resided. The sample was then divided into quintiles from one (lowest) to five (highest). A single asset index was developed on the basis of data from the entire country sample, and this index is used in all the tabulations presented. Table 2.10 shows the distribution of the de jure household population into five wealth levels (quintiles) based on the wealth index by residence. These distributions indicate the degree to which wealth is evenly (or unevenly) distributed by geographic areas. 3 In the 2003 KDHS, mobile and non-mobile telephones were combined into one category, but in the 2008-09 KDHS, the two were separated. 26 | Household Population and Housing Characteristics Table 2.10 Wealth quintiles Percent distribution of the de jure population by wealth quintiles, according to residence and region, Kenya 2008-09 Residence/region Wealth quintile Total Number of population Lowest Second Middle Fourth Highest Residence Urban 0.5 1.5 2.7 16.8 78.5 100.0 7,365 Rural 24.7 24.4 24.2 20.7 6.0 100.0 30,704 Province Nairobi 0.0 0.0 0.2 4.2 95.5 100.0 2,352 Central 2.2 11.1 29.2 36.0 21.4 100.0 3,870 Coast 26.3 12.4 9.4 16.2 35.7 100.0 2,953 Eastern 16.7 22.3 27.6 26.3 7.1 100.0 6,629 Nyanza 17.6 28.4 23.9 17.7 12.3 100.0 6,324 Rift Valley 28.2 19.2 16.1 17.8 18.7 100.0 10,375 Western 17.9 33.0 25.1 18.8 5.2 100.0 4,506 North Eastern 75.9 5.6 5.5 4.9 8.0 100.0 1,059 Total 20.0 20.0 20.0 19.9 20.1 100.0 38,069 Wealth is concentrated in the urban areas, with 79 percent of the urban population falling in the highest wealth quintile. In contrast, those in rural areas are poorer, with one quarter in the lowest wealth quintile and only 6 percent in the highest quintile. Nairobi province, which is entirely urban, has 96 percent of its population in the highest quintile, while North Eastern province has over three- quarters of its population in the lowest quintile. Other provinces have varying distributions of population in different wealth quintiles. Coast, Eastern, Nyanza, Rift Valley, and Western provinces show a substantial distribution across all the quintiles. On the other hand, Central province has most of its population within the highest three quintiles. 2.7 BIRTH REGISTRATION The registration of births is the inscription of the facts of the birth into an official log kept at the registrar’s office. A birth certificate is issued at the time of registration or later as proof of the registration of the birth. Birth registration is basic to ensuring a child’s legal status and, thus, basic rights and services (UNICEF, 2006; United Nations General Assembly, 2002). Table 2.11 gives the percentage of children under five years of age whose births were officially registered and the percentage who had a birth certificate at the time of the survey. Not all children who are registered may have a birth certificate because some certificates may have been lost or never issued. However, all children with a certificate have been registered. Three of every five children in Kenya under age five have been registered with civil authorities, and close to one-quarter (24 percent) have a birth certificate. The distribution by age brackets and gender shows a nearly equal proportion of birth registration. However, differentials exist according to residence, province, and wealth quintile. For example, the births of 76 percent of children in urban areas have been registered, compared with only 57 percent in rural areas. Nairobi province leads in the proportion of children registered (86 percent), followed by Central province (81 percent), with Nyanza province having the lowest proportion registered (42 percent). The results show that children in higher wealth quintiles are more likely to be registered and to possess a birth certificate than those in lower wealth quintiles. Household Population and Housing Characteristics | 27 Table 2.11 Birth registration of children under age five Percentage of de jure children under five years of age whose births are registered with the civil authorities, according to background characteristics, Kenya 2008-09 Background characteristic Percentage of children whose births are registered Number of children Had a birth certificate Did not have a birth certificate Total registered Age <2 20.7 38.9 59.5 2,303 2-4 26.0 34.3 60.3 3,653 Sex Male 24.9 36.0 60.9 3,051 Female 22.9 36.2 59.1 2,904 Residence Urban 37.1 39.2 76.3 996 Rural 21.3 35.4 56.7 4,960 Province Nairobi 38.5 47.9 86.4 307 Central 24.9 55.9 80.9 471 Coast 31.6 35.9 67.5 488 Eastern 16.2 49.1 65.3 932 Nyanza 24.2 17.9 42.1 1,090 Rift Valley 23.2 40.7 63.9 1,708 Western 23.2 22.2 45.4 778 North Eastern 24.1 23.7 47.8 184 Wealth quintile Lowest 19.0 29.2 48.2 1,490 Second 18.3 35.4 53.7 1,247 Middle 20.3 39.1 59.4 1,150 Fourth 29.9 35.9 65.8 1,040 Highest 35.8 43.8 79.6 1,028 Total 23.9 36.1 60.0 5,956 Various reasons were given for failure to register births. These reasons are presented in Table 2.12. More than one-quarter (27 percent) of mothers or caretakers cited a lack of awareness of the existence of registration, and 16 percent said that registration is not necessary. Nine percent said that they did not register the child’s birth because the registration required them to travel too far, and an equal percentage gave insecurity or nomadic lifestyle as the reason the birth was not registered. Reasons presented do not vary much according to age and sex of the child. However, differentials exist according to residence, province, and wealth index. For example, 10 percent of children in rural areas are not registered because the place of registration was too far, as compared with 4 percent of children in urban areas. As expected, for a majority of children in North Eastern province, insecurity or nomadic lifestyle was cited as the key hindrance to their registration. Notably, for nearly half (48 percent) of those children in Coast province whose births were not registered, the main reason given was lack of awareness about registration. This reason is also commonly cited in Eastern and Western provinces (33 percent and 34 percent, respectively). Lack of awareness of registration and the perception that registration is not necessary feature prominently across all the wealth quintiles as reasons for not registering births. 28 | Household Population and Housing Characteristics Table 2.12 Reason for not registering birth Percent distribution of de jure children under five years of age whose birth was not registered with the civil authorities, according to background characteristics, Kenya 2008-09 Background characteristic Reason child’s birth was never registered Total Number of children Too far Lacked money Not aware Not necessary Nomadic life/ insecurity Other Missing Age <2 8.8 4.4 25.4 16.0 7.4 36.9 1.2 100.0 875 2-4 9.1 5.7 28.1 15.8 9.6 29.9 1.7 100.0 1,241 Sex Male 8.2 5.7 28.8 15.8 9.3 30.9 1.4 100.0 1,059 Female 9.8 4.6 25.1 16.0 8.1 34.7 1.6 100.0 1,057 Residence Urban 3.9 5.8 24.5 20.7 1.5 42.6 1.1 100.0 203 Rural 9.5 5.1 27.2 15.4 9.4 31.8 1.5 100.0 1,913 Province Nairobi (0.9) (3.9) (17.8) (29.9) (0.0) (47.6) (0.0) 100.0 28 Central 7.5 11.1 14.4 23.5 0.0 43.4 0.0 100.0 70 Coast 13.8 2.0 47.9 19.8 0.2 15.7 0.6 100.0 138 Eastern 6.1 4.6 33.0 27.1 3.5 21.8 4.0 100.0 276 Nyanza 7.9 4.0 24.6 10.1 0.2 51.2 2.0 100.0 577 Rift Valley 10.3 4.8 18.9 20.8 21.7 23.1 0.4 100.0 550 Western 9.1 7.9 34.3 8.8 0.2 38.1 1.5 100.0 384 North Eastern 12.2 5.9 21.9 3.2 56.4 0.0 0.4 100.0 92 Wealth quintile Lowest 9.8 5.1 32.3 12.0 21.4 18.9 0.6 100.0 730 Second 10.1 6.6 28.2 16.3 1.1 36.1 1.6 100.0 505 Middle 9.2 4.2 22.3 14.6 2.5 45.3 2.0 100.0 413 Fourth 7.8 5.8 17.5 22.0 3.1 40.9 2.9 100.0 302 Highest 4.2 2.4 28.4 24.4 1.3 38.1 1.2 100.0 167 Total 9.0 5.2 26.9 15.9 8.7 32.8 1.5 100.0 2,116 Note: Numbers in parentheses are based on 25-49 unweighted cases. Characteristics of Respondents | 29 CHARACTERISTICS OF RESPONDENTS 3 Vivianne Nyarunda and Gladys Mbaluku This chapter provides a profile of the respondents who were interviewed in the 2008-09 Kenya Demographic and Health Survey (KDHS), i.e., women age 15-49 and men age 15-54. First, information is presented on a number of basic characteristics, including age at the time of the survey, religion, marital status, residence, education, literacy, and media access. Then, the chapter explores adults’ employment status, occupation, and earnings. This information is useful for understanding the factors that influence reproductive behaviour and contraceptive use as they provide a context for the interpretation of demographic and health indices. An analysis of these variables provides the socioeconomic context within which demographic and reproductive health issues are examined in the subsequent chapters. 3.1 CHARACTERISTICS OF SURVEY RESPONDENTS Table 3.1 presents the distribution of 8,444 women and 3,256 men, age 15-49, by age, marital status, residence, province, education, wealth, religion, and ethnicity. The distribution of the respondents according to age shows a generally similar pattern for men and women. As expected in Kenya’s age structure, the proportion of respondents in each age group declines with increasing age for both sexes. Forty-one percent of women and 43 percent of men are in the 15-24 age group, 32 percent of women and 29 percent of men are age 25-34, and the remaining respondents are age 35- 49. Fifty-eight percent of the women are currently married or living with a partner, compared with 49 percent of the men. The proportion of men who have never married is almost equal to those in some form of union (47 percent), but only 31 percent of the women have never married. Whereas 4 percent of the women are widowed and 6 percent are either divorced or separated, less than 1 percent of the men are widowed, and 4 percent are divorced or separated. The proportion of men in urban areas (27 percent) is just slightly above that of the women (25 percent). Within the eight provinces in Kenya, the Rift Valley province has the largest proportion of respondents (27 percent), and the North Eastern province has the smallest (2 percent). As shown in Table 3.1, 9 percent of women have no education, compared with 3 percent of their male counterparts. Furthermore, 30 percent of the men have completed secondary or higher education, compared with 22 percent of the women. About half of those interviewed (47 percent of the female and 51 percent of the male populations) are in the two highest wealth quintiles, and the smallest proportions are in the lowest quintile (17 percent of the women and 14 percent of the men). Overall, the proportions increase across the quintiles for both men and women. The distribution of respondents by religion shows a pattern similar to that seen in the 2003 KDHS, with nine of ten respondents being Christian. There is a slight decline in the proportion of men who report that they practice no religion (4 percent, compared with 7 percent in 2003), but for women, the proportion remains at 2 percent. Ethnic affiliation is associated with various demographic behaviours because of differences in cultural beliefs. For example, in Kenya, certain ethnic groups encourage the practice of female genital cutting. Survey data show that the Kikuyu and Luhya ethnic groups are the largest, accounting for about 16-19 percent. 30 | Characteristics of Respondents Table 3.1 Background characteristics of respondents Percent distribution of women and men age 15-49 by selected background characteristics, Kenya 2008-09 Background characteristic Women Men Weighted percent Weighted Unweighted Weighted percent Weighted Unweighted Age 15-19 20.8 1,761 1,767 23.8 776 763 20-24 20.3 1,715 1,744 19.3 630 620 25-29 17.2 1,454 1,423 14.8 483 488 30-34 14.3 1,209 1,180 14.2 461 482 35-39 10.4 877 930 10.6 344 359 40-44 9.1 768 730 9.4 306 291 45-49 7.8 661 670 7.9 257 253 Marital status Never married 31.2 2,634 2,540 46.8 1,524 1,501 Married 54.2 4,578 4,682 46.9 1,527 1,574 Living together 4.1 350 359 2.0 65 58 Divorced/separated 6.1 512 512 3.8 123 107 Widowed 4.4 369 351 0.6 19 16 Residence Urban 25.4 2,148 2,615 26.6 866 1,023 Rural 74.6 6,296 5,829 73.4 2,392 2,233 Province Nairobi 8.6 728 952 9.6 314 399 Central 10.7 905 973 10.7 347 365 Coast 8.0 674 1,149 7.7 252 419 Eastern 16.3 1,376 1,127 16.3 530 417 Nyanza 16.4 1,389 1,318 16.0 520 511 Rift Valley 26.8 2,262 1,278 27.2 885 517 Western 11.0 927 1,039 10.7 349 429 North Eastern 2.2 184 608 1.9 62 199 Highest level of schooling No education 8.9 752 1,242 3.4 112 171 Some primary 29.9 2,526 2,431 27.1 883 889 Completed primary 26.9 2,272 1,973 24.7 804 800 Some secondary 12.2 1,030 961 14.6 477 429 Completed secondary 14.7 1,243 1,123 20.5 666 612 More than secondary 7.3 620 714 9.7 316 355 Wealth quintile Lowest 16.5 1,393 1,699 14.0 457 543 Second 17.6 1,483 1,284 17.7 577 543 Middle 19.1 1,613 1,455 17.6 574 547 Fourth 20.6 1,736 1,617 22.3 725 675 Highest 26.3 2,220 2,389 28.4 926 948 Religion Roman Catholic 21.9 1,852 1,684 25.6 834 775 Protestant/other Christian 68.1 5,748 5,152 63.4 2,065 1,892 Muslim 7.4 626 1,358 6.2 204 421 No religion 2.2 185 184 4.1 133 127 Missing 0.0 3 9 0.2 7 2 Ethnicity Embu 1.4 120 145 2.1 70 80 Kalenjin 13.2 1,115 750 13.3 432 297 Kamba 10.9 923 666 11.6 378 274 Kikuyu 19.4 1,642 1,504 17.5 569 545 Kisii 6.9 579 447 7.0 228 179 Luhya 16.3 1,373 1,266 17.7 578 539 Luo 13.0 1,098 1,113 13.0 425 458 Maasai 1.3 113 124 1.2 39 42 Meru 4.9 415 367 5.1 168 155 Mijikenda/Swahili 5.1 430 717 4.0 131 240 Somali 2.8 240 679 2.1 69 202 Taita/Taveta 0.9 79 124 1.1 37 48 Other 3.7 317 542 4.2 136 197 Total 15-49 100.0 8,444 8,444 100.0 3,258 3,256 Men age 50-54 na na na na 207 209 Total men 15-54 na na na na 3,465 3,465 na = Not applicable Characteristics of Respondents | 31 3.2 EDUCATIONAL ATTAINMENT BY BACKGROUND CHARACTERISTICS Tables 3.2.1 and 3.2.2 present an overview of the relationship between the respondent’s level of education and other background characteristics. As mentioned, male respondents are more educated than their female counterparts. The proportion of men with no education is less than 10 percent in all age groups. The proportion of women in groups age 35 and above with no education are all greater than 10 percent; indeed, one of every five women age 45-49 has no education. Educational attainment by type of place of residence shows that respondents in urban areas are more educated than their rural counterparts. Fifty-three percent of males in urban areas have completed secondary school or higher, compared with 22 percent in the rural areas. Among women, 45 percent of those in urban areas have completed secondary education or higher, compared with only 14 percent of their counterparts in rural areas. Educational attainment by province shows an improvement from the 2003 levels. For example, North Eastern province still has the largest proportion of respondents with no education (41 percent for males and 78 percent for females). This is, however, an improvement from 71 percent and 93 percent for the men and women in 2003, respectively. Educational attainment by wealth quintile shows that there is improvement with higher wealth status for both men and women. For example, only 2 percent of women in the highest quintile have no education, compared with 31 percent in the lowest quintile. On the other hand, only 3 percent of women in the lowest quintile have completed secondary school, compared with 28 percent in the highest quintile. This pattern is similar to that depicted among men, with only 9 percent of the men in the lowest quintile having completed at least secondary education, but 55 percent of those in the highest quintile having attained that level. Table 3.2.1 Educational attainment: Women Percent distribution of women age 15-49 by highest level of schooling attended or completed, and median grade completed, according to background characteristics, Kenya 2008-09 Background characteristic Highest level of schooling Total Median years completed Number of women No education Some primary Completed primary1 Some secondary Completed secondary2 More than secondary Age 15-24 5.6 33.1 26.1 16.9 12.9 5.3 100.0 7.4 3,475 15-19 4.1 41.2 22.9 22.7 8.2 0.9 100.0 7.2 1,761 20-24 7.2 24.9 29.4 11.0 17.8 9.8 100.0 7.6 1,715 25-29 8.3 24.5 32.6 10.7 13.7 10.2 100.0 7.5 1,454 30-34 7.8 31.6 25.7 8.4 16.9 9.6 100.0 7.4 1,209 35-39 10.7 33.9 21.9 6.4 17.3 9.8 100.0 7.3 877 40-44 14.1 20.4 30.0 8.5 19.6 7.4 100.0 6.7 768 45-49 21.3 27.8 23.9 9.5 13.3 4.2 100.0 6.0 661 Residence Urban 4.7 14.0 23.7 12.8 27.2 17.6 100.0 9.8 2,148 Rural 10.4 35.3 28.0 12.0 10.5 3.8 100.0 7.0 6,296 Province Nairobi 2.5 7.9 21.5 12.2 24.8 31.1 100.0 11.1 728 Central 0.7 21.4 36.5 16.6 19.0 5.8 100.0 7.7 905 Coast 24.3 25.6 21.6 9.3 14.2 4.9 100.0 6.7 674 Eastern 5.7 36.1 29.8 12.1 11.6 4.7 100.0 7.2 1,376 Nyanza 2.1 37.4 27.4 15.8 11.0 6.4 100.0 7.3 1,389 Rift Valley 12.2 28.8 27.8 9.7 16.2 5.3 100.0 7.2 2,262 Western 4.1 45.2 22.7 12.7 11.8 3.4 100.0 6.9 927 North Eastern 77.7 8.7 5.5 2.9 3.5 1.8 100.0 0.0 184 Wealth quintile Lowest 31.1 41.7 19.4 4.7 2.4 0.8 100.0 4.9 1,393 Second 6.5 46.8 28.1 11.3 6.1 1.2 100.0 6.7 1,483 Middle 5.5 35.4 32.2 13.5 11.2 2.2 100.0 7.1 1,613 Fourth 4.7 24.7 28.8 17.6 17.6 6.5 100.0 7.6 1,736 Highest 2.4 11.3 25.5 12.3 28.4 20.0 100.0 10.2 2,220 Total 8.9 29.9 26.9 12.2 14.7 7.3 100.0 7.3 8,444 1 Completed Grade 8 at the primary level, for those under age 40; because of the change in the school system in the 1980s, those age 40 and above are considered to have completed primary if they completed Grade 7. 2 Completed Form 4 at the secondary level 32 | Characteristics of Respondents Table 3.2.2 Educational attainment: Men Percent distribution of men age 15-49 by highest level of schooling attended or completed, and median grade completed, according to background characteristics, Kenya 2008-09 Background characteristic Highest level of schooling Total Median years completed Number of men No education Some primary Completed primary1 Some secondary Completed secondary2 More than secondary Age 15-24 1.9 34.8 21.8 19.6 16.2 5.7 100.0 7.6 1,406 15-19 1.0 46.1 18.7 25.0 8.9 0.3 100.0 7.2 776 20-24 3.1 20.9 25.6 13.0 25.2 12.3 100.0 8.6 630 25-29 3.0 23.1 25.6 11.3 25.3 11.7 100.0 8.0 483 30-34 3.9 26.3 26.6 10.6 19.2 13.3 100.0 7.8 461 35-39 3.5 18.3 22.8 14.1 27.2 14.2 100.0 9.1 344 40-44 6.2 19.4 30.3 11.7 21.3 11.0 100.0 7.6 306 45-49 8.2 15.0 31.2 5.4 26.6 13.6 100.0 6.9 257 Residence Urban 1.4 11.6 16.1 18.3 31.4 21.2 100.0 10.7 866 Rural 4.2 32.7 27.8 13.3 16.5 5.5 100.0 7.4 2,392 Province Nairobi 1.5 5.2 15.2 8.2 37.7 32.2 100.0 11.5 314 Central 1.1 22.3 33.7 13.4 22.0 7.6 100.0 7.8 347 Coast 3.1 20.5 27.0 14.3 25.6 9.4 100.0 8.3 252 Eastern 1.3 37.0 24.1 15.8 15.3 6.4 100.0 7.4 530 Nyanza 0.8 30.5 25.6 17.9 16.1 9.1 100.0 7.7 520 Rift Valley 6.0 26.4 23.5 15.8 21.6 6.8 100.0 7.7 885 Western 1.7 39.5 27.1 13.3 13.3 5.2 100.0 7.3 349 North Eastern 41.2 18.9 14.6 9.8 8.1 7.4 100.0 4.5 62 Wealth quintile Lowest 12.9 45.0 23.4 9.3 8.0 1.4 100.0 6.4 457 Second 1.8 39.7 30.0 12.5 13.5 2.6 100.0 7.1 577 Middle 2.4 29.7 31.1 14.1 19.3 3.3 100.0 7.5 574 Fourth 2.8 27.3 21.5 19.3 21.0 8.0 100.0 8.0 725 Highest 0.9 8.6 20.6 15.2 31.2 23.5 100.0 10.9 926 Total 15-49 3.4 27.1 24.7 14.6 20.5 9.7 100.0 7.8 3,258 Men age 50-54 14.2 18.3 34.8 7.6 13.5 11.6 100.0 6.5 207 Total men 15-54 4.1 26.6 25.3 14.2 20.0 9.8 100.0 7.7 3,465 1 Completed Grade 8 at the primary level, for those under age 40; because of the change in the school system in the 1980s, those age 40 and above are considered to have completed primary if they completed Grade 7. 2 Completed Form 4 at the secondary level 3.3 LITERACY The ability to read and write is an important personal asset, allowing individuals increased opportunities in life. Knowing the distribution of the literate population can help programme managers, especially those in health and family planning, decide how to reach women and men with their messages. The 2008-09 KDHS assessed the ability to read among women and men who had never been to school or who had attended only primary school by asking respondents to read a simple, short sentence.2 Tables 3.3.1 and 3.3.2 show the percent distribution of female and male respondents, respectively, by level of literacy and overall percentage of literacy, according to background characteristics. Data reveal that the proportion of illiterate women is double that of men; 14 percent of Kenyan women age 15-49 cannot read at all, compared with 7 percent of men in the same age group. Literacy levels among women decrease with increasing age, from 92 percent for women age 15-19 to 62 percent for those in the 45-49 age group. This pattern is less pronounced among men as literacy in all age groups is above 85 percent. 2 These sentences include the following: 1. The child is reading a book. 2. Farming is hard work. 3. Parents should care for their children. 4. The rains were heavy this year. Characteristics of Respondents | 33 Literacy levels for respondents in urban areas are higher than those in rural areas, and the gap between men and women is narrower. One of every five women (21 percent) in North Eastern province is literate, which is the lowest level compared with the other provinces that all have levels above 72 percent. The highest literacy levels are observed among women in Nairobi and Central provinces (96 percent and 95 percent, respectively). Literacy levels for men are also lowest for North Eastern province (64 percent), but they are three times the literacy level among the women from this province. Women in the lowest wealth quintile have the lowest level of literacy (59 percent) compared with those from higher quintiles (e.g., 95 percent among those in the highest quintile). Men from the lowest wealth quintile (with a literacy level of 80 percent) are not as disadvantaged as their female counterparts. Table 3.3.1 Literacy: Women Percent distribution of women age 15-49 by level of schooling attended and level of literacy, and percentage literate, according to background characteristics, Kenya 2008-09 Background characteristic Secondary school or higher No schooling or primary school Total Percentage literate1 Number Can read a whole sentence Can read part of a sentence Cannot read at all No card with required language Blind/ visually impaired Missing Age 15-19 31.9 51.8 8.3 7.2 0.6 0.0 0.2 100.0 92.0 1,761 20-24 38.6 38.9 11.5 10.7 0.0 0.1 0.2 100.0 89.0 1,715 25-29 34.6 40.7 12.1 12.0 0.5 0.0 0.1 100.0 87.4 1,454 30-34 34.9 38.9 11.4 14.2 0.4 0.1 0.1 100.0 85.2 1,209 35-39 33.5 33.9 14.8 16.2 1.2 0.3 0.2 100.0 82.2 877 40-44 35.6 30.6 10.6 22.1 0.5 0.6 0.0 100.0 76.8 768 45-49 27.0 19.0 16.2 35.2 0.9 1.4 0.3 100.0 62.2 661 Residence Urban 57.6 27.8 7.2 6.9 0.1 0.2 0.3 100.0 92.6 2,148 Rural 26.3 42.9 13.0 16.7 0.7 0.2 0.1 100.0 82.3 6,296 Province Nairobi 68.1 20.5 7.3 3.6 0.0 0.1 0.5 100.0 95.8 728 Central 41.4 44.8 9.1 3.9 0.1 0.4 0.3 100.0 95.3 905 Coast 28.4 38.7 5.2 27.4 0.0 0.2 0.1 100.0 72.4 674 Eastern 28.4 51.1 7.5 12.8 0.0 0.2 0.1 100.0 86.9 1,376 Nyanza 33.2 45.1 11.5 9.4 0.1 0.5 0.1 100.0 89.8 1,389 Rift Valley 31.2 34.5 16.4 15.9 1.8 0.2 0.0 100.0 82.1 2,262 Western 28.0 38.6 17.8 15.3 0.0 0.0 0.3 100.0 84.4 927 North Eastern 8.2 9.0 4.1 78.4 0.0 0.2 0.2 100.0 21.2 184 Wealth quintile Lowest 7.8 35.3 15.8 38.5 2.2 0.3 0.1 100.0 58.9 1,393 Second 18.6 50.6 16.0 14.4 0.3 0.2 0.0 100.0 85.2 1,483 Middle 26.9 47.5 12.3 12.6 0.3 0.3 0.1 100.0 86.8 1,613 Fourth 41.8 39.5 9.0 9.1 0.2 0.1 0.3 100.0 90.3 1,736 Highest 60.8 27.2 7.4 4.0 0.0 0.3 0.2 100.0 95.4 2,220 Total 34.3 39.1 11.5 14.2 0.5 0.2 0.1 100.0 84.9 8,444 1 Refers to women who attended secondary school or higher and women who can read a whole sentence or part of a sentence 34 | Characteristics of Respondents Table 3.3.2 Literacy: Men Percent distribution of men age 15-49 by level of schooling attended and level of literacy, and percentage literate, according to background characteristics, Kenya 2008-09 Background characteristic Secondary school or higher No schooling or primary school Total Percentage literate1 Number Can read a whole sentence Can read part of a sentence Cannot read at all No card with required language Blind/ visually impaired Missing Age 15-19 34.2 51.2 9.4 4.0 1.0 0.1 0.1 100.0 94.8 776 20-24 50.4 30.6 10.1 8.0 0.6 0.0 0.2 100.0 91.2 630 25-29 48.4 27.0 15.7 7.0 1.3 0.2 0.4 100.0 91.1 483 30-34 43.1 34.0 12.8 9.4 0.4 0.0 0.3 100.0 89.9 461 35-39 55.4 28.6 9.5 5.3 0.8 0.0 0.4 100.0 93.5 344 40-44 44.1 29.9 15.7 9.7 0.5 0.0 0.0 100.0 89.7 306 45-49 45.6 30.8 8.9 12.8 1.5 0.4 0.0 100.0 85.3 257 Residence Urban 70.9 20.4 5.4 3.2 0.0 0.0 0.1 100.0 96.7 866 Rural 35.3 40.6 13.7 8.9 1.2 0.1 0.2 100.0 89.6 2,392 Province Nairobi 78.1 12.5 7.0 2.2 0.0 0.0 0.2 100.0 97.7 314 Central 43.0 43.2 7.2 6.3 0.4 0.0 0.0 100.0 93.3 347 Coast 49.4 42.7 4.9 2.8 0.0 0.0 0.2 100.0 97.0 252 Eastern 37.6 43.1 14.6 3.9 0.0 0.1 0.7 100.0 95.3 530 Nyanza 43.1 38.7 10.0 7.9 0.0 0.2 0.1 100.0 91.8 520 Rift Valley 44.1 29.4 14.4 8.9 3.0 0.0 0.1 100.0 88.0 885 Western 31.7 41.4 14.9 11.8 0.0 0.2 0.0 100.0 88.0 349 North Eastern 25.3 27.5 10.7 36.4 0.0 0.0 0.0 100.0 63.6 62 Wealth quintile Lowest 18.7 44.5 16.5 17.7 2.4 0.2 0.0 100.0 79.7 457 Second 28.6 41.6 21.3 8.0 0.3 0.2 0.0 100.0 91.5 577 Middle 36.8 48.4 7.8 5.7 1.1 0.0 0.3 100.0 92.9 574 Fourth 48.3 31.8 11.1 7.3 0.8 0.1 0.6 100.0 91.2 725 Highest 69.9 21.1 5.6 2.9 0.4 0.0 0.0 100.0 96.7 926 Total 15-49 44.8 35.2 11.5 7.4 0.9 0.1 0.2 100.0 91.5 3,258 Men age 50-54 32.7 32.9 14.0 18.6 1.4 0.2 0.3 100.0 79.6 207 Total men 15-54 44.1 35.1 11.7 8.0 0.9 0.1 0.2 100.0 90.8 3,465 1 Refers to men who attended secondary school or higher and men who can read a whole sentence or part of a sentence 3.4 ACCESS TO MASS MEDIA Information access is essential for increasing people’s knowledge and awareness of what is taking place around them, which may eventually affect their perceptions and behaviour. It is important to know the types of persons who are more or less likely to be reached by the media for purposes of planning programmes intended to spread information about health and family planning. In the survey, exposure to the media was assessed by asking how often a respondent reads a newspaper, watches television, or listens to a radio. Tables 3.4.1 and 3.4.2 show the percentage of women and of men who were exposed to different types of media by age, urban or rural residence, province, level of education, and wealth quintile. As has been the case in previous surveys, men have more access to all forms of mass media than women. For example, only 24 percent of women read a newspaper at least once a week, compared with 46 percent of men (Figure 3.1). The tables show that radio is the most popular medium for both women and men, while newspapers are the least popular medium. The proportion of women who are not exposed to any type of media at least once a week generally increases gradually with age. The largest proportion of women who do not have access to any media at least once a week are those age 45-49 (27 percent). Among men, those age 15-19 are the largest proportion with no access to any form of media at least once a week. Characteristics of Respondents | 35 Table 3.4.1 Exposure to mass media: Women Percentage of women age 15-49 who are exposed to specific media on a weekly basis, by background characteristics, Kenya 2008-09 Background characteristic Reads a newspaper at least once a week Watches television at least once a week Listens to the radio at least once a week All three media at least once a week No media at least once a week Number Age 15-19 26.3 31.6 76.6 14.0 19.5 1,761 20-24 27.3 37.2 80.6 17.7 15.8 1,715 25-29 26.2 39.4 78.5 20.7 18.0 1,454 30-34 24.4 31.7 77.0 15.9 19.1 1,209 35-39 22.5 34.5 73.9 16.2 20.3 877 40-44 20.0 33.0 76.0 15.5 21.3 768 45-49 14.2 26.7 70.7 10.4 26.6 661 Residence Urban 49.2 69.3 83.1 41.5 9.3 2,148 Rural 15.8 22.1 74.9 7.6 22.6 6,296 Province Nairobi 59.1 86.1 87.8 52.5 5.0 728 Central 22.9 44.7 91.0 14.6 6.3 905 Coast 25.7 34.6 65.2 16.7 28.3 674 Eastern 14.0 23.8 68.7 8.1 27.6 1,376 Nyanza 24.6 26.8 80.5 12.9 15.0 1,389 Rift Valley 23.6 30.2 77.1 16.7 20.7 2,262 Western 17.4 23.4 80.4 7.6 16.4 927 North Eastern 6.9 9.5 25.7 4.3 71.9 184 Education No education 0.8 8.6 35.4 0.3 62.3 752 Primary incomplete 6.9 16.3 72.3 2.6 24.9 2,526 Primary complete 16.3 32.0 83.1 8.7 13.8 2,272 Secondary+ 51.9 58.0 87.2 38.3 7.4 2,894 Wealth quintile Lowest 4.3 2.9 42.3 0.4 56.3 1,393 Second 9.7 6.8 77.5 1.5 20.3 1,483 Middle 15.1 19.8 84.9 5.2 13.5 1,613 Fourth 24.6 42.1 85.3 15.0 11.1 1,736 Highest 53.0 76.1 86.2 45.1 5.9 2,220 Total 24.3 34.1 77.0 16.3 19.2 8,444 Urban women have more access to all forms of mass media compared with their rural counterparts; for example, only 16 percent of women in rural areas read a newspaper at least once a week, compared with 49 percent of women in urban areas. Although 69 percent of women in urban areas watch television at least once a week, only 22 percent of those residing in rural areas do so. Access to all three forms of mass media is highest among residents of Nairobi and lowest among residents of North Eastern province for both women and men. Access to mass media increases with educational attainment and wealth quintile for both women and men. For example, the proportion of women who listen to the radio at least once a week increases from 35 percent of women with no education to 87 percent of those with at least some secondary schooling. Similarly, the proportion of women who watch television at least once a week increases from only 3 percent of those in the poorest wealth quintile to 76 percent of those in the highest quintile. The percentage of women who access all three types of mass media at least once a week has increased since 2003 from 13 percent to 16 percent for women age 15 to 49 and from 27 percent to 31 percent for men age 15-49. 36 | Characteristics of Respondents Table 3.4.2 Exposure to mass media: Men Percentage of men age 15-49 who are exposed to specific media on a weekly basis, by background characteristics, Kenya 2008-09 Background characteristic Reads a newspaper at least once a week Watches television at least once a week Listens to the radio at least once a week All three media at least once a week No media at least once a week Number Age 15-19 37.9 40.2 87.2 20.9 8.7 776 20-24 48.4 53.7 90.9 33.5 4.7 630 25-29 48.6 54.4 90.9 35.5 6.5 483 30-34 47.2 53.5 90.0 35.8 6.5 461 35-39 51.2 56.8 92.3 41.2 5.9 344 40-44 49.7 45.9 92.6 31.4 5.2 306 45-49 46.9 44.2 90.1 28.4 6.4 257 Residence Urban 72.5 78.3 92.5 59.9 1.7 866 Rural 36.5 38.9 89.3 21.0 8.2 2,392 Province Nairobi 84.5 89.7 92.9 75.0 0.3 314 Central 63.3 57.6 95.0 43.0 1.4 347 Coast 40.1 41.2 87.9 23.0 9.5 252 Eastern 39.6 39.6 88.4 21.4 7.4 530 Nyanza 45.8 49.6 95.4 29.4 2.9 520 Rift Valley 38.0 45.9 87.1 26.0 10.0 885 Western 32.8 38.2 87.5 20.6 9.0 349 North Eastern 25.9 26.5 85.9 17.4 12.0 62 Education No education 0.7 15.4 63.3 0.0 32.3 112 Primary incomplete 19.4 28.6 86.0 7.8 12.0 883 Primary complete 39.5 43.7 92.2 22.5 5.5 804 Secondary+ 69.3 67.7 93.6 52.9 1.7 1,459 Wealth quintile Lowest 15.3 16.8 75.2 5.0 23.9 457 Second 23.7 22.0 90.1 7.8 7.6 577 Middle 42.7 38.6 93.3 21.3 4.1 574 Fourth 48.9 56.9 92.4 32.2 4.4 725 Highest 74.9 83.3 93.7 64.6 0.3 926 Total 15-49 46.1 49.4 90.1 31.3 6.5 3,258 Men age 50-54 40.3 41.2 84.6 25.9 8.8 207 Total men 15-54 45.7 48.9 89.8 31.0 6.6 3,465 Figure 3.1 Access to Mass Media Kenya 2008-09 24 34 77 16 46 49 90 31 Reads newspaper weekly Watches television weekly Listens to radio weekly All three media 0 20 40 60 80 100 Percent Women Men Characteristics of Respondents | 37 3.5 EMPLOYMENT Respondents were asked whether they were employed at the time of the survey and, if not, whether they were employed in the 12 months that preceded the survey. Because employment is viewed as a stock concept (measured at a particular point in time), the corresponding statistics must, in principle, refer to a precise instant in time. Respondents are asked a number of questions to elicit their current employment status and continuity of employment in the 12 months prior to the survey. Employed individuals are those who say that they are currently working (i.e., worked or held a job in the past 7 days) and those who worked at any time during the 12 months prior to the survey (referred to as usual employment). Table 3.5.1 Employment status: Women Percent distribution of women age 15-49 by employment status, according to background characteristics, Kenya 2008-09 Background characteristic Employed in the 12 months preceding the survey Not employed in the 12 months preceding the survey Total Number of women Currently employed1 Not currently employed Age 15-19 19.3 1.8 78.9 100.0 1,761 20-24 50.5 3.7 45.7 100.0 1,715 25-29 66.6 3.1 30.3 100.0 1,454 30-34 71.4 2.1 26.5 100.0 1,209 35-39 73.7 1.5 24.8 100.0 877 40-44 78.7 1.8 19.5 100.0 768 45-49 74.2 1.3 24.5 100.0 661 Marital status Never married 35.4 1.8 62.8 100.0 2,634 Married or living together 63.9 2.7 33.4 100.0 4,928 Divorced/separated/ widowed 79.5 2.2 18.3 100.0 881 Number of living children 0 31.8 1.9 66.3 100.0 2,397 1-2 61.9 3.1 35.0 100.0 2,579 3-4 70.5 2.1 27.4 100.0 1,899 5+ 69.1 2.2 28.7 100.0 1,569 Residence Urban 59.7 2.2 38.1 100.0 2,148 Rural 55.5 2.5 42.0 100.0 6,296 Province Nairobi 58.5 2.4 39.2 100.0 728 Central 66.4 2.5 31.1 100.0 905 Coast 48.3 4.2 47.5 100.0 674 Eastern 55.4 4.4 40.1 100.0 1,376 Nyanza 65.6 1.7 32.5 100.0 1,389 Rift Valley 57.6 1.8 40.6 100.0 2,262 Western 45.2 0.9 54.0 100.0 927 North Eastern 17.1 0.3 82.6 100.0 184 Education No education 50.6 3.0 46.4 100.0 752 Primary incomplete 52.7 2.6 44.7 100.0 2,526 Primary complete 60.1 2.9 37.0 100.0 2,272 Secondary+ 58.8 1.7 39.5 100.0 2,894 Wealth quintile Lowest 48.4 3.6 48.0 100.0 1,393 Second 54.0 1.7 44.2 100.0 1,483 Middle 57.2 2.1 40.7 100.0 1,613 Fourth 55.8 2.4 41.8 100.0 1,736 Highest 63.7 2.3 34.1 100.0 2,220 Total 56.6 2.4 41.0 100.0 8,444 1 ‘Currently employed’ is defined as having done work in the past seven days. Includes persons who did not work in the past seven days but who are regularly employed and were absent from work for leave, illness, vacation, or any other such reason. 38 | Characteristics of Respondents Table 3.5.2 Employment status: Men Percent distribution of men age 15-49 by employment status, according to background characteristics, Kenya 2008-09 Background characteristic Employed in the 12 months preceding the survey Not employed in the 12 months preceding the survey Total Number of men Currently employed1 Not currently employed Age 15-19 59.9 3.9 36.3 100.0 776 20-24 86.9 2.8 10.3 100.0 630 25-29 97.4 0.7 1.9 100.0 483 30-34 95.4 2.5 2.1 100.0 461 35-39 98.6 0.5 0.9 100.0 344 40-44 98.6 1.0 0.4 100.0 306 45-49 97.9 0.9 1.2 100.0 257 Marital status Never married 73.8 3.0 23.2 100.0 1,524 Married or living together 97.8 1.2 0.9 100.0 1,592 Divorced/separated/ widowed 94.4 3.1 2.5 100.0 142 Number of living children 0 75.3 3.0 21.7 100.0 1,626 1-2 97.2 1.1 1.7 100.0 691 3-4 97.2 1.7 1.1 100.0 559 5+ 98.5 1.1 0.4 100.0 381 Residence Urban 85.8 1.5 12.7 100.0 866 Rural 86.7 2.4 11.0 100.0 2,392 Province Nairobi 86.7 1.3 12.0 100.0 314 Central 91.4 2.0 6.7 100.0 347 Coast 79.6 1.4 19.0 100.0 252 Eastern 94.2 0.3 5.6 100.0 530 Nyanza 83.2 5.7 11.0 100.0 520 Rift Valley 86.2 1.2 12.6 100.0 885 Western 84.5 2.5 13.0 100.0 349 North Eastern 59.8 7.3 33.0 100.0 62 Education No education 93.7 3.2 3.1 100.0 112 Primary incomplete 81.7 3.2 15.1 100.0 883 Primary complete 93.9 0.7 5.4 100.0 804 Secondary+ 84.6 2.2 13.2 100.0 1,459 Wealth quintile Lowest 82.6 3.3 14.1 100.0 457 Second 86.1 2.2 11.7 100.0 577 Middle 89.0 1.8 9.2 100.0 574 Fourth 85.1 1.9 13.0 100.0 725 Highest 88.0 1.8 10.2 100.0 926 Total 15-49 86.4 2.1 11.4 100.0 3,258 Men age 50-54 97.6 1.5 1.0 100.0 207 Total men 15-54 87.1 2.1 10.8 100.0 3,465 1 ‘Currently employed’ is defined as having done work in the past seven days. Includes persons who did not work in the past seven days but who are regularly employed and were absent from work for leave, illness, vacation, or any other such reason. Tables 3.5.1 and 3.5.2 and Figure 3.2 show the percent distribution of adult women and men according to current and usual employment. As shown, 57 percent of women and 86 percent of men age 15-49 are categorised as currently employed. The proportion of women currently employed increases with age up to 44 years and then declines slightly for those in the 45-49 age group. The data on men show little variation with age over age 25 or by type of place of residence, education level, or wealth status. The proportion of currently employed women and men increases with number of living children except for women with five or more children. Characteristics of Respondents | 39 Figure 3.2 Women’s Employment Status in the Past 12 Months Kenya 2008-09 Label 57% Label 2% Label 41% Currently employed Not currently employed but worked in past 12 months 2% Did not work in past 12 months 41% Current employment status for women by province shows that women from North Eastern province have the least chance of being employed (17 percent are currently employed) and those from Central and Nyanza have the highest proportions employed (66 percent). Women who are divorced, separated, or widowed have the highest proportions employed (80 percent), followed by those who are married (64 percent), and only 35 percent of the never-married are employed. There is little variation in employment of women by urban or rural residence. 3.6 OCCUPATION The term occupation refers to the job held or the kind of work performed during the reference period. Respondents who were currently employed were asked to state their occupation, and the results are presented in Tables 3.6.1 and 3.6.2 for women and men, respectively. Thirty-nine percent of working women and men age 15-49 are engaged in agricultural occupations, a drop from the 49 percent and 42 percent, respectively, recorded in 2003. The next major occupation category among working women and men is that of professional, technical, or managerial occupations, which accounts for 31 percent of working women and 19 percent of working men age 15-49. Among employed women, the next largest category is sales and services, accounting for 13 percent of employment, followed by domestic service (7 percent) and skilled manual labour (6 percent). Among working men age 15-49, however, 16 percent are employed in unskilled manual jobs, while 9 percent each are employed in sales and services and skilled manual jobs. There has been a shift in the distribution by occupation since 2003. The proportion of working women and men who are employed in professional, technical, and managerial occupations appears to have increased since 2003 as the proportion employed in agriculture has declined. Occupations in the sales and services sector accounted for a larger share of employment in 2003 than in 2008-09. 40 | Characteristics of Respondents Table 3.6.1 Occupation: Women Percent distribution of women age 15-49 employed in the 12 months preceding the survey by occupation, according to background characteristics, Kenya 2008-09 Background characteristic Professional/ technical/ managerial Clerical Sales and services Skilled manual Unskilled manual Domestic service Agri- culture Missing Total Number of women Age 15-19 12.9 0.4 11.2 6.6 3.0 14.6 49.9 1.4 100.0 371 20-24 31.5 2.2 16.0 6.9 1.4 10.3 31.4 0.4 100.0 931 25-29 29.4 3.6 13.6 8.8 2.9 7.9 33.3 0.6 100.0 1,014 30-34 34.0 1.4 13.5 4.8 3.2 3.4 39.5 0.3 100.0 889 35-39 31.7 3.3 12.8 4.4 2.7 4.6 39.8 0.7 100.0 659 40-44 36.8 1.7 8.2 4.0 2.5 6.1 40.7 0.1 100.0 618 45-49 27.9 1.2 8.9 4.0 1.8 3.8 52.5 0.0 100.0 499 Marital status Never married 28.6 4.7 11.8 8.9 2.8 16.3 26.1 0.9 100.0 980 Married or living together 32.4 1.7 12.2 5.2 1.9 2.9 43.3 0.4 100.0 3,280 Divorced/separated/ widowed 24.4 0.8 15.6 5.0 4.6 12.8 36.6 0.1 100.0 720 Number of living children 0 30.6 5.0 12.1 7.5 2.0 14.7 27.2 0.9 100.0 807 1-2 32.4 3.1 15.7 7.1 1.9 7.2 32.1 0.6 100.0 1,676 3-4 31.6 1.1 12.0 4.8 2.9 5.0 42.3 0.2 100.0 1,379 5+ 26.0 0.2 9.0 4.4 3.1 3.4 53.7 0.2 100.0 1,118 Residence Urban 45.7 6.5 18.0 5.4 3.3 14.5 5.9 0.7 100.0 1,330 Rural 24.9 0.6 10.6 6.1 2.2 4.2 51.0 0.4 100.0 3,650 Province Nairobi 45.4 10.2 14.6 4.9 5.7 15.0 2.7 1.5 100.0 443 Central 19.9 1.4 12.5 5.3 2.8 6.2 51.5 0.5 100.0 624 Coast 38.1 5.8 16.2 6.0 2.1 13.2 18.7 0.0 100.0 354 Eastern 22.0 0.6 8.8 1.5 3.1 6.8 56.9 0.4 100.0 824 Nyanza 27.5 0.7 12.8 5.4 1.1 4.4 47.9 0.3 100.0 936 Rift Valley 33.0 1.5 14.0 8.4 1.5 5.1 36.1 0.4 100.0 1,343 Western 37.3 0.6 9.9 10.1 3.8 5.2 33.0 0.1 100.0 427 North Eastern 52.5 3.2 16.4 0.3 2.3 23.8 0.0 1.6 100.0 32 Education No education 24.2 0.4 13.1 10.5 5.4 5.8 40.5 0.1 100.0 403 Primary incomplete 21.4 0.0 11.9 2.8 2.8 6.4 54.5 0.3 100.0 1,395 Primary complete 24.1 0.5 13.1 10.1 2.2 8.9 40.5 0.6 100.0 1,431 Secondary+ 44.4 5.7 12.6 3.9 1.8 6.1 25.0 0.5 100.0 1,751 Wealth quintile Lowest 18.3 0.0 8.5 7.1 4.0 4.0 58.1 0.1 100.0 725 Second 24.6 0.2 8.9 5.1 2.2 4.3 54.4 0.3 100.0 827 Middle 20.1 0.8 8.2 6.6 1.8 3.8 58.4 0.2 100.0 956 Fourth 32.0 0.9 13.3 6.5 1.3 4.9 40.5 0.5 100.0 1,010 Highest 45.5 6.2 19.2 4.9 3.1 13.4 7.0 0.8 100.0 1,463 Total 30.5 2.2 12.6 5.9 2.5 7.0 39.0 0.4 100.0 4,981 Characteristics of Respondents | 41 Table 3.6.2 Occupation: Men Percent distribution of men age 15-49 employed in the 12 months preceding the survey by occupation, according to background characteristics, Kenya 2008-09 Background characteristic Professional/ technical/ managerial Clerical Sales and services Skilled manual Unskilled manual Domestic service Agri- culture Missing Total Number of men Age 15-19 2.8 0.0 5.5 3.3 9.7 2.3 56.6 19.9 100.0 495 20-24 15.6 0.3 10.9 9.4 13.7 2.9 44.2 2.8 100.0 565 25-29 24.5 1.0 13.5 8.5 18.5 4.6 29.5 0.0 100.0 474 30-34 22.3 1.3 7.8 14.0 20.1 2.6 31.9 0.0 100.0 451 35-39 31.9 2.2 7.5 7.8 18.9 1.5 30.3 0.0 100.0 341 40-44 25.0 1.5 10.7 9.1 15.1 2.2 36.5 0.0 100.0 305 45-49 22.2 1.3 8.5 7.3 13.0 4.5 42.9 0.1 100.0 254 Marital status Never married 10.0 0.7 9.5 6.3 11.8 3.1 49.0 9.6 100.0 1,169 Married or living together 26.8 1.3 9.0 9.7 18.1 2.7 32.4 0.1 100.0 1,578 Divorced/separated/ widowed 14.3 0.3 11.2 14.4 17.1 4.2 38.4 0.1 100.0 138 Number of living children 0 12.8 0.6 9.7 6.3 11.8 3.6 46.4 8.8 100.0 1,273 1-2 28.7 1.4 8.8 10.6 18.2 2.8 29.2 0.3 100.0 679 3-4 24.1 1.7 9.7 11.6 20.0 2.0 30.9 0.1 100.0 553 5+ 17.9 0.4 8.4 7.5 16.9 2.2 46.6 0.0 100.0 380 Residence Urban 39.1 2.6 13.4 11.0 22.1 5.6 4.3 1.9 100.0 756 Rural 12.4 0.4 7.9 7.6 13.2 1.9 51.9 4.7 100.0 2,129 Province Nairobi 38.1 5.8 12.6 15.7 20.0 5.4 2.2 0.2 100.0 276 Central 14.7 0.0 11.4 9.7 20.4 2.5 40.9 0.4 100.0 324 Coast 30.6 2.2 17.9 11.8 15.8 8.0 13.8 0.0 100.0 204 Eastern 14.4 0.1 5.8 3.4 7.2 5.2 43.5 20.4 100.0 500 Nyanza 14.4 0.9 6.8 10.9 15.1 0.9 49.6 1.3 100.0 462 Rift Valley 21.2 0.3 10.1 5.1 15.5 1.3 46.2 0.3 100.0 774 Western 8.9 0.0 5.0 12.3 20.3 1.5 51.8 0.3 100.0 303 North Eastern 36.5 1.4 15.3 5.1 17.2 0.0 21.9 2.6 100.0 41 Education No education 9.9 0.0 9.7 8.6 12.5 2.4 57.0 0.0 100.0 108 Primary incomplete 4.9 0.1 9.1 6.8 16.8 2.9 53.6 5.8 100.0 749 Primary complete 10.1 0.4 12.7 13.8 17.7 2.4 39.8 2.9 100.0 761 Secondary+ 34.4 1.9 7.3 6.3 13.7 3.3 29.2 3.9 100.0 1,266 Wealth quintile Lowest 8.2 0.3 5.5 6.0 14.5 1.2 58.6 5.7 100.0 392 Second 10.0 0.5 5.8 8.9 13.1 1.3 56.6 3.8 100.0 509 Middle 7.6 0.0 6.3 7.7 15.8 1.2 55.3 6.0 100.0 521 Fourth 16.3 0.9 11.9 7.0 12.7 4.5 42.7 4.0 100.0 631 Highest 40.2 2.2 13.1 11.1 19.5 4.5 7.4 2.0 100.0 832 Total 15-49 19.4 1.0 9.3 8.5 15.5 2.9 39.4 4.0 100.0 2,885 Men age 50-54 28.2 1.8 8.1 6.4 7.0 1.1 46.9 0.4 100.0 205 Total men 15-54 20.0 1.0 9.2 8.4 15.0 2.8 39.9 3.7 100.0 3,090 3.7 EARNINGS AND TYPE OF EMPLOYMENT Table 3.7 presents the percent distribution of employed women age 15-49 by type of earnings and employer characteristics, according to type of employment (agricultural or non-agricultural). Seventy-five percent of women receive cash for their work. This is similar to the percentage recorded in 2003. Almost one-quarter of working women are not paid (Figure 3.3). Women employed in agricultural work are much more likely to be unpaid and much less likely to be paid in cash only, compared with women employed in non-agricultural occupations. More than three in five working women (62 percent) are self-employed. Thirty percent are employed by a non-family member, and 9 percent are employed by a family member. Those working in agricultural jobs are more likely to be self-employed or employed by a family member than are women working in non-agricultural jobs. 42 | Characteristics of Respondents Table 3.7 Type of employment among women Percent distribution of women age 15-49 employed in the 12 months preceding the survey by type of earnings, type of employer, and continuity of employment, according to type of employment (agricultural or non- agricultural), Kenya 2008-09 Employment characteristic Agricultural work Non- agricultural work Total Type of earnings Cash only 33.2 84.6 64.5 Cash and in-kind 14.7 8.3 10.7 In-kind only 3.7 0.5 1.7 Not paid 48.5 6.7 23.0 Total 100.0 100.0 100.0 Type of employer Employed by family member 16.8 3.4 8.7 Employed by non-family member 14.8 39.1 29.6 Self-employed 68.3 57.4 61.6 Total 100.0 100.0 100.0 Continuity of employment All year 49.9 70.1 62.2 Seasonal 44.9 23.4 31.7 Occasional 5.2 6.5 6.1 Total 100.0 100.0 100.0 Number of women employed during the last 12 months 1,941 3,017 4,981 Note: Total includes women with information missing on type of employment who are not shown separately. Figure 3.3 Employment Characteristics among Working Women Kenya 2008-09 65 11 2 23 9 30 62 62 32 6 Type of earnings Cash only Cash and in-kind In-kind only Not paid Type of employer Employed by family member Employed by non-family member Self-employed Continuity of employment All year Seasonal Occasional 0 10 20 30 40 50 60 70 80 Percent Sixty-two percent of working women are employed all year; another 32 percent have seasonal jobs and 6 percent work only occasionally. Women who are engaged in non-agricultural work (70 percent) are more assured of continuity in employment than those engaged in agricultural activities, whose employment is more prone to consist of seasonal work. Characteristics of Respondents | 43 3.8 HEALTH INSURANCE COVERAGE Medical insurance provides peace of mind, and most important, necessary care to save the life and/or well-being of the enrolee. In the 2008-09 KDHS, women and men were asked if they were covered by any health insurance and, if so, what type of insurance. Results shown in Figure 3.4 indicate that few Kenyans have health insurance. Only 7 percent of women and 11 percent of men age 15-49 are covered by medical insurance. The largest category of insurance is employer-based policies. Figure 3.4 Health Insurance Coverage Kenya 2008-09 1 4 1 7 2 8 2 11 Social security Employer-based insurance Privately purchased commercial insurance Any insurance 0 5 10 15 Percent Women Men 3.9 KNOWLEDGE AND ATTITUDES CONCERNING TUBERCULOSIS The 2008-09 KDHS collected data on women’s and men’s knowledge and attitudes concerning tuberculosis (TB). Tables 3.8.1 and 3.8.2 show the percentage of women and men who have heard of TB, and among those who have heard of TB, the percentage who know that TB is spread through air by coughing, the percentage who believe that TB can be cured, and the percentage who would want a family member’s TB to be kept a secret. Results show that awareness of TB is almost universal in Kenya; 98 percent of women and 99 percent of men have heard about TB. Moreover, knowledge of other aspects of TB is also widespread. Seventy-six percent of women and 80 percent of men age 15-49 who have heard of TB know that it is spread through the air by coughing, and 89 percent of women and 92 percent of men know that TB can be cured. Finally, stigma related to TB is not so common in Kenya. Only one in four women and less than one in ten men say that, if a family member had TB, they would want to keep it a secret. Differences by background characteristics show that, as expected, rural women and men are less likely than urban residents to know that TB is spread through the air by coughing and to believe that TB can be cured. Among women who have heard about TB, women in Coast province are least likely to know that TB is spread through the air by coughing (66 percent), while those in Nyanza province are most likely to want a family member’s TB kept a secret. Similarly, men in Coast province are least likely to know that TB is spread through the air by coughing, and men in Eastern province are most likely to want a family member’s TB kept a secret. 44 | Characteristics of Respondents Table 3.8.1 Knowledge and attitude concerning tuberculosis: Women Percentage of women age 15-49 who have heard of tuberculosis (TB), and among women who have heard of TB, the percentages who know that TB is spread through the air by coughing, the percentage who believe that TB can be cured, and the percentage who would want to keep secret that a family member has TB, by background characteristics, Kenya 2008-09 Background characteristic Among all respondents Among respondents who have heard of TB Percentage who have heard of TB Number Percentage who report that TB is spread through the air by coughing Percentage who believe that TB can be cured Percentage who would want a family member’s TB kept secret Number Age 15-19 96.6 1,761 74.4 81.4 33.4 1,701 20-24 98.3 1,715 75.7 89.8 25.8 1,687 25-29 98.5 1,454 74.2 89.7 20.5 1,432 30-34 98.5 1,209 79.2 94.1 22.0 1,190 35-39 98.8 877 78.6 93.8 20.6 867 40-44 99.4 768 81.0 90.6 17.9 763 45-49 98.7 661 71.7 91.1 24.7 652 Residence Urban 99.1 2,148 85.2 92.2 24.3 2,128 Rural 97.9 6,296 73.0 88.3 24.6 6,164 Province Nairobi 98.5 728 90.1 96.6 19.2 717 Central 99.5 905 81.5 91.3 29.0 900 Coast 98.9 674 65.9 93.6 20.3 666 Eastern 98.9 1,376 72.4 87.1 20.4 1,361 Nyanza 97.9 1,389 77.0 89.4 33.2 1,359 Rift Valley 97.4 2,262 75.3 87.8 24.1 2,203 Western 97.4 927 73.3 84.1 23.0 902 North Eastern 99.3 184 79.3 94.2 17.8 183 Education No education 94.2 752 58.4 84.9 20.4 709 Primary incomplete 96.9 2,526 64.8 83.5 28.2 2,449 Primary complete 99.4 2,272 77.5 90.1 26.4 2,258 Secondary+ 99.4 2,894 89.2 94.7 20.9 2,877 Wealth quintile Lowest 95.8 1,393 62.7 83.9 20.7 1,334 Second 98.2 1,483 71.2 87.3 24.2 1,456 Middle 99.0 1,613 74.1 87.7 27.7 1,597 Fourth 98.3 1,736 80.9 91.0 25.7 1,707 Highest 99.0 2,220 85.5 93.7 23.8 2,198 Total 98.2 8,444 76.2 89.3 24.5 8,292 Characteristics of Respondents | 45 Table 3.8.2 Knowledge and attitude concerning tuberculosis: Men Percentage of men age 15-49 who have heard of tuberculosis (TB), and among men who have heard of TB, the percentages who know that TB is spread through the air by coughing, the percentage who believe that TB can be cured, and the percentage who would want to keep secret that a family member has TB, by background characteristics, Kenya 2008-09 Background characteristic Among all respondents Among respondents who have heard of TB Percentage who have heard of TB Number Percentage who report that TB is spread through the air by coughing Percentage who believe that TB can be cured Percentage who would want a family member’s TB kept secret Number Age 15-19 98.5 776 79.6 87.8 14.2 765 20-24 96.8 630 80.0 92.5 10.8 610 25-29 99.2 483 77.4 93.5 4.8 479 30-34 99.6 461 79.1 93.9 8.5 459 35-39 99.9 344 87.0 90.9 5.5 344 40-44 99.9 306 80.7 91.2 5.6 306 45-49 99.2 257 81.9 94.3 8.2 255 Residence Urban 99.8 866 88.4 95.8 8.3 864 Rural 98.4 2,392 77.4 90.0 9.4 2,353 Province Nairobi 99.7 314 94.4 95.6 6.8 313 Central 99.3 347 76.6 89.3 9.9 344 Coast 100.0 252 73.7 96.7 5.5 252 Eastern 99.6 530 85.1 91.6 16.6 528 Nyanza 99.3 520 84.4 91.9 9.0 516 Rift Valley 97.8 885 75.4 90.8 5.8 866 Western 96.7 349 76.1 87.0 11.2 337 North Eastern 100.0 62 74.9 96.7 1.9 62 Education No education 91.7 112 66.9 84.3 5.7 103 Primary incomplete 97.4 883 71.9 87.2 10.7 860 Primary complete 99.1 804 78.0 92.9 9.3 797 Secondary+ 100.0 1,459 87.5 94.0 8.3 1,459 Wealth quintile Lowest 96.8 457 75.0 89.5 5.0 442 Second 98.1 577 75.9 90.6 9.8 566 Middle 98.9 574 79.2 89.2 10.2 567 Fourth 99.5 725 79.5 90.6 10.9 721 Highest 99.5 926 87.0 95.4 8.6 922 Total 15-49 98.8 3,258 80.3 91.6 9.1 3,218 Men age 50-54 98.5 207 72.8 93.5 8.4 204 Total men 15-54 98.8 3,465 79.9 91.7 9.1 3,422 Among women who have heard about TB, those with no education (58 percent) and those in the lowest wealth quintile (63 percent) are less likely to know that TB is spread through the air by coughing than women with some education and those in the higher wealth quintiles. Similarly, among men who have heard about TB, men with no education (67 percent) and in the lowest wealth quintile (75 percent) are less likely to know that TB is spread through the air by coughing than men with some education and those in the higher wealth quintiles. 3.10 SMOKING In order to measure the extent of smoking among Kenyan adults, women and men who were interviewed in the 2008-09 KDHS were asked if they currently smoked cigarettes or used tobacco. Less than 2 percent of women said they used tobacco of any kind, and less than 1 percent said they smoked cigarettes (data not shown). Nineteen percent of men age 15-49 use tobacco products, with 18 percent saying that they smoke cigarettes. The proportion of women who smoke is too small to show details, but differentials in smoking among men can be shown (Table 3.9). 46 | Characteristics of Respondents Table 3.9 Use of tobacco: Men Percentage of men age 15-49 who smoke cigarettes or a pipe or use other tobacco products and the percent distribution of cigarette smokers by number of cigarettes smoked in preceding 24 hours, according to background characteristics, Kenya 2008-09 Background characteristic Cigarettes Pipe Other tobacco Does not use tobacco Number of men Number of cigarettes in the last 24 hours Total Number of cigarette smokers 0 1-2 3-5 6-9 10+ Don’t know/ missing Age 15-19 2.7 0.0 0.3 97.1 776 * * * * * * 100.0 21 20-24 15.1 0.4 1.5 84.3 630 0.0 19.0 45.4 11.4 23.8 0.5 100.0 95 25-29 20.1 1.1 2.2 78.9 483 0.0 25.3 33.9 16.9 23.3 0.6 100.0 97 30-34 25.5 1.6 3.4 73.1 461 0.0 19.9 31.9 14.3 32.2 1.7 100.0 118 35-39 24.5 1.7 4.1 73.2 344 0.4 8.6 44.2 8.2 38.6 0.0 100.0 84 40-44 25.9 3.0 4.7 71.2 306 3.8 15.3 36.5 15.8 28.6 0.0 100.0 79 45-49 29.6 2.7 9.1 66.2 257 1.1 10.4 27.5 25.3 35.5 0.2 100.0 76 Residence Urban 17.2 0.3 0.8 82.7 866 1.7 12.3 28.6 19.4 36.1 1.9 100.0 149 Rural 17.6 1.5 3.5 80.6 2,392 0.6 19.7 38.3 14.1 27.0 0.4 100.0 421 Province Nairobi 17.1 0.7 0.9 82.7 314 3.2 2.5 39.6 18.0 36.4 0.3 100.0 54 Central 30.4 2.4 2.2 69.3 347 0.0 16.7 41.6 16.3 25.0 0.4 100.0 105 Coast 22.6 0.0 1.8 76.3 252 0.0 24.5 19.2 11.9 44.3 0.0 100.0 57 Eastern 26.0 0.0 4.6 72.8 530 0.6 22.2 31.8 23.2 22.2 0.0 100.0 138 Nyanza 7.9 0.0 2.0 91.1 520 (0.0) (35.2) (34.1) (11.2) (11.8) (7.7) 100.0 41 Rift Valley 14.3 2.7 3.8 82.9 885 0.6 10.7 41.6 9.4 37.2 0.5 100.0 127 Western 11.2 0.0 1.4 88.3 349 4.4 23.7 40.1 10.7 21.2 0.0 100.0 39 North Eastern 15.6 4.4 2.4 82.8 62 (0.0) (4.5) (16.6) (20.8) (58.1) (0.0) 100.0 10 Education No education 19.8 1.2 18.3 68.4 112 (0.0) (13.8) (38.7) (1.8) (45.8) (0.0) 100.0 22 Primary incomplete 19.1 2.3 4.0 79.1 883 1.5 17.4 41.3 15.8 24.0 0.0 100.0 169 Primary complete 19.8 1.0 3.0 78.7 804 0.5 15.7 37.6 14.4 31.9 0.0 100.0 160 Secondary+ 15.1 0.5 0.7 84.7 1,459 0.8 19.9 29.9 17.4 30.1 2.0 100.0 220 Wealth quintile Lowest 16.7 2.9 6.3 78.6 457 1.1 31.3 38.1 6.7 22.8 0.0 100.0 76 Second 17.1 1.0 3.9 81.4 577 0.8 20.6 26.9 18.2 32.5 1.2 100.0 99 Middle 19.3 1.6 3.8 79.3 574 0.8 9.8 52.7 12.5 23.8 0.4 100.0 110 Fourth 19.6 0.9 1.8 79.7 725 0.6 19.8 30.7 15.3 33.5 0.0 100.0 142 Highest 15.4 0.2 0.4 84.5 926 1.2 12.6 32.5 20.8 30.9 2.0 100.0 143 Total 15-49 17.5 1.1 2.8 81.1 3,258 0.9 17.7 35.8 15.5 29.4 0.8 100.0 570 Men age 50-54 28.6 5.1 9.7 62.2 207 0.0 11.2 26.3 26.1 36.4 0.0 100.0 59 Total men 15-54 18.2 1.4 3.2 80.0 3,465 0.8 17.1 34.9 16.5 30.0 0.7 100.0 630 Note: Numbers in parentheses are based on 25-49 unweighted cases; an asterisk denotes a figure based on fewer than 25 unweighted cases that has been suppressed. Men in the highest wealth quintile and those with secondary and higher education are less likely to smoke cigarettes than are men with less education and in the lower wealth quintiles. This is in contrast to what was recorded in 2003, where men in the lowest wealth quintile and with no education were reported to be less likely to smoke cigarettes. In addition, men in the highest wealth quintile are more likely not to use tobacco products (85 percent) than men in the lower wealth quintiles. Conversely, in 2003, men in the lowest and middle wealth quintiles were reported as less likely to use tobacco products (77 percent did not use). Among the provinces, men in Central province have the highest level of smoking cigarettes, whereas men in Nyanza province have the lowest level of smoking. In 2003, men in Eastern province had the highest level of smoking. Men in Nyanza have over time continued to be the least likely to smoke. Among men age 15-49 who smoke cigarettes, the largest proportion said they smoked 3-5 sticks in the previous 24 hours (36 percent), followed by those who smoked 10 or more sticks in a day (29 percent). Fertility Levels, Trends, and Differentials | 47 FERTILITY LEVELS, TRENDS, AND DIFFERENTIALS 4 James N. Munguti and Robert Buluma 4.1 INTRODUCTION This chapter analyses the fertility data collected in the 2008-09 KDHS. Levels, trends, and differentials in fertility are described by selected background characteristics; lifetime fertility (children ever born and living); and age at first birth and birth intervals. Thereafter, a brief discussion on teenage fertility, which has become critical to the changes in fertility in Kenya, is presented. The 2008-09 KDHS was conducted against the backdrop of a stall in an ongoing fertility decline. Indeed, Bongaarts (2006), while examining fertility trends in countries with multiple DHS surveys, found that Kenya was one among seven countries where fertility had stalled in mid-transition in the 1990s. Fertility was high (more than six births per woman) in the 1950s in each of these countries but declined to fewer than five births per woman in the early or mid-1990s, before coming to a standstill. The fertility levels varied by country, ranging from 4.7 births per woman in Kenya to 2.5 births per woman in Turkey. An analysis of trends in the determinants of fertility in these countries revealed a systematic pattern of leveling or near leveling in a number of determinants, including contraceptive use, demand for contraception, and number of wanted births, but no significant increases in unwanted births or in the unmet need for contraception. 4.2 CURRENT FERTILITY Findings on measures of current fertility are presented in Table 4.1. These include the total fertility rate (TFR), general fertility rate (GFR), and crude birth rate (CBR). The point estimates refer to the three-year period immediately preceding the 2008-09 survey, or approximately 2006-08. Age-specific fertility rates (ASFRs) are calculated by dividing the number of births to women in a specific age group by the number of woman-years lived during a given period.1 The total fertility rate (TFR) is defined as the average number of children a woman would have if she went through her entire reproductive period, from 15 to 49 years, reproducing at the prevailing ASFRs. The general fertility rate (GFR) represents the annual number of births per 1,000 women age 15-44, and the crude birth rate (CBR) represents the annual number of births per 1,000 population. The CBR was estimated using the birth history data in conjunction with the population data collected in the household schedule. A TFR of 4.6 children per woman was reported for the three years before the survey (see Table 4.1) compared with a TFR of 4.9 children reported for the period 2000-02 based on the 2003 KDHS. As expected, rural areas recorded higher fertility than urban areas (TFR of 5.2 and 2.9, respectively). This pattern is reflected by every age group, and the difference increases with the age of the women, with the fertility 1 Numerators for the age-specific fertility rates are calculated by summing all births that occurred duri

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