Kenya - Demographic and Health Survey -1989

Publication date: 1989

Demographic and Health Survey 1989 National Council for Population and Development Ministry of Home Affairs and National Heritage ®DHS / Demographic and Health Surveys Institute for Resource Development/Macro Systems. Inc. Kenya Demographic and Health Survey 1989 National Council for Population and Development Ministry of Home Affairs and National Heritage Nairobi, Kenya Institute for Resource Development/Macro Systems, Inc. Columbia, Maryland USA October 1989 This report presents the findings of the Kenya Demographic and Health Survey (KDHS). The survey was a collaborative effort between the National Council for Population and Development and the Institute for Resource Development/Macro Systems, Inc. (IRD). The survey is part of the worldwide Demographic and Health Surveys Program, which is designed to collect data on fertility, family planning, and maternal and child health. Funding for the survey was provided by the U.S. Agency for International Development (Contract No. DPE-3023-C-00-4083-00) and the Government of Kenya. Additional information on the KDHS can be obtained from the Kenya National Council for Population and Development, Ministry of Home Affairs and National Heritage, P.O. Box 30478, Nairobi, Kenya. Additional information about the DHS Program can be obtained by writing to: DHS Program, 1RD/Macro Systems, Inc., 8850 Stanford Blvd., Suite 4000, Columbia, MD 21045, USA (Telephone: 301-290-2800; Telex: 87775; Fax: 301-290-2999). CONTENTS Page CONTENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii L IST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v L IST OF F IGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv FOREWORD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvii SUMMARY OF F INDINGS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xix MAP OF KENYA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxii 1. BACKGROUND . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 Geography, History and Economy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Populat ion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Populat ion and Family Planning Policies and Programmes . . . . . . . . . . . . . 2 Heal th Priorit ies and Programmes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Object ives of the Kenya Demograph ic and Health Survey . . . . . . . . . . . . . 3 Survey Organisat ion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Background Characterist ics of Women Respondents . . . . . . . . . . . . . . . . . 4 2. NUPT IAL ITY , BREASTFEEDING AND POSTPARTUM INSUSCEPT IB IL ITY . . . 9 2.1 2.2 2.3 2.4 2.5 Introduct ion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Marital Status . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Polygyny . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Age at First Marr iage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Breast feeding and Postpartum Insusceptibil ity . . . . . . . . . . . . . . . . . . . . 14 3. FERT IL ITY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3.1 3.2 3.3 3.4 3.5 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 Levels and Trends in Fertility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 Fertil ity Dif ferentials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 Cumulat ive Fertility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 Age at First Birth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 4. FERT IL ITY REGULAT ION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 4.1 4.2 4.3 Contracept ive Knowledge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 Acceptabi l i ty of Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 Knowledge of Supply Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 iii Page 4.4 4.5 4.6 4.7 4.8 4.9 4.10 4.11 4.12 Ever Use of Contracept ion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 Current Contracept ive Use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 Current Use by Background Characterist ics . . . . . . . . . . . . . . . . . . . . . . 36 Number of Chi ldren at First Use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 Knowledge of Ferti le Per iod . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 Sources for Contracept ive Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 Att i tude Toward Pregnancy and Reason for Nonuse . . . . . . . . . . . . . . . 42 Intent ion to Use in the Future . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 Approval of Family Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 5. FERT IL ITY PREFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 5.1 5.2 5.3 Des i re for More Children . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 Ideal Number of Chi ldren . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 Unwanted Fertility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 6. MORTAL ITY AND HEALTH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 6.1 6.2 6.3 6.4 Chi ldhood Mortal ity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 Materni ty Care . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 Child Health Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 Household Sanitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 7. HUSBAND'S SURVEY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 7.1 7.2 7.3 7.4 7.5 7.6 7.7 7.8 Characterist ics of the Sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 Marr iage and Fertil ity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 Knowledge and Use of Family Planning . . . . . . . . . . . . . . . . . . . . . . . . 76 Sources for Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 Intent ion to Use Family Phmning in the Future . . . . . . . . . . . . . . . . . . 80 Att i tudes Toward Family Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 Des i re for More Children . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 Ideal Number of Chi ldrcn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 APPENDIX A SURVEY DES IGN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 APPENDIX B EST IMATES OF SAMPL ING ERROR . . . . . . . . . . . . . . . . . . . . . . 101 APPENDIX C NOTE ON AGE REPORTING . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 APPENDIX D L IST OF PERSONS INVOLVED IN THE KDHS . . . . . . . . . . . . . . . 117 APPENDIX E SURVEY QUEST IONNAIRES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 iv LIST OF TABLES Page Table 1.1 Table 1.2 Table 1.3 Table 1.4 Table 2.1 Table 2.2 Table 2.3 Table 2.4 Table 2.5 Table 2.6 Table 2.7 Table 3.1 Table 3.2 Percent distribution of all women and currently married women by background characteristics, Kenya, 1989 . . . . . . . . . . . . . . . . . . 6 Percent distribution of all women by background characteristics, 1977/78 Kenya Fertility Survey, 1984 Kenya Contraceptive Prevalence Survey, and 1989 KDHS . . . . . . . . . . . . . . . . . 7 Percent distribution of women by level of education, according to background characteristics, Kenya, 1989 . . . . . . . . . . . . . . . . 8 Percentage of women who live in households with selected amenities, according to urban-rural rcsidcnce, Kenya, 1989 . . . . . . . . . . . . 8 Percent distribution of women by current marital status, according to age, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Percentage of womcn who have never married at the time of various surveys and ccnsuses, by age group, Kenya, 1989 . . . . . . . . . . 10 Percentage of currently married women in a polygynous union, by age, according to background characteristics, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Percent distribution of womcn by age at first marriage and median age at first marriage, according to current age, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Median age at first marriage among women age 20-49 years, by current age and background characteristics, Kenya, 1989 . . . . . . . . . . 13 Percentage of births whose mothers are still breastfeeding, postpartum amenorrhocic, abstaining, and insusceptible, by number of months since birth, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . 14 Mean number of months of breastfeeding, postpartum amenorrhoea, postpartum abstinence, and postpartum insusceptibility, by background characteristics, Kenya, 1989 . . . . . . . . . . . 15 Age-specific fertility rates from various surveys and censuses, Kenya . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 Age-period fertility rate by age of woman at birth, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 V Page Table 3.3 Table 3.4 Table 3.5 Table 3.6 Table 3.7 Table 3.8 Table 3.9 Table 3.10 Table 4.1 Table 4.2 Table 4.3 Table 4.4 Table 4.5 Age-specific fertility rates and total fertility rates for three periods before the survey, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . 20 Percentage of all women who are currently pregnant by age, Kenya, 1977/78 KFS, 1984 KCPS and 1989 KDHS . . . . . . . . . . . . . . . . 21 Total fertility rates for calendar year periods and for five years preceding the survey, and mean number of children ever born to women 40-49 years of age, by background characteristics, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 Percent distribution of fill women and currently married women by number of children ever born (CEB), according to age, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 Mean number of children ever born as reported in various surveys and censuses, by age group, Kenya . . . . . . . . . . . . . . . . . . . . . . 25 Mean number of children ever born to ever-married women, by age at first marriage and years since first marriage, Kenya 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Percent distribution of women by age at first birth, according to current age, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . 26 Median age at first birth among women aged 20-49 years, by current age and background characteristics, Kenya, 1989 . . . . . . . . . . . . . 27 Percentage of all women and currently married women knowing contraceptive method and knowing a source by specific method, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 Percentage of currently married women knowing at least one modern method, knowing a source for a modern method, by background characteristics, Kenya, 1989 . . . . . . . . . . . . . . . . 30 Percent distribution of women who have ever heard of a contraceptive method by main problem perceived in using the method, according to specific method, Kenya, 1989 . . . . . . . . . . . . . 32 Percent distribution of women knowing a contraceptive method by supply source they say they. would use, according to specific method, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 Percentage of all women and currently married women who have ever used a contraceptive method, by specific method and age, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 vi Page Table 4.6 Table 4.7 Table 4.8 Table 4.9 Table 4.10 Table 4.11 Table 4.12 Table 4.13 Table 4.14 Table 4.15 Percent distribution of all women and currently married women, by contraceptive method currently being used, according to age, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 Percent distribution of currently married women by contraceptive method currently being used, according to background characteristics, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . 37 Percent distribution of ever-married women by number of living children at time of first use of contraception, according to current age, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . 38 Percent distribution of all women and women who have ever used periodic abstinence by knowledge of the fertile period during the ovulatory cycle, Kenya, 1989 . . . . . . . . . . . . . . . . . . . 39 Percent distribution of current users of modcrn methods by most recent source of supply or information, according to specific method, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 Percent distribution of current users of modern methods of family planning, nonusers of modern methods, and all women knowing a method, by time to reach source of supply and transport to source, according to urban-rural residence, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 Percent distribution of nonpregnant women who are sexually active and who are not using any contraceptive method by attitude toward becoming pregnant in the next few weeks, according to number of living children, Kenya, 1989 . . . . . . . . . . . . . . . . 42 Percent distribution of nonprcgnant women who are sexually active, not using any contraceptive method and who would be unhappy if they became pregnant by main reason for nonuse, according to age, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . 43 Percent distribution of currently married women who are not currently using any contraceptive method, by intention to use in the future, according to number of living children, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 Percent distribution of currently married women who are not using a contraceptive method but who intend to use in the future, by preferred method, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . 44 vii Page Table 4.16 Table 4.17 Table 4.18 Table 4.19 Table 5.1 Table 5.2 Table 5.3 Table 5.4 Table 5.5 Table 5.6 Table 5.7 Table 6.1 Percent distribution of all women by whether they feel it is acceptable to have family planning information presented on the radio, by age and background characteristics, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 Percent distribution of currently married women knowing a contraceptive method by the husband's and wife's attitude toward the use of family planning, Kenya, 1989 . . . . . . . . . . . . . . . . . . . 45 Percentage of currently married women knowing a contraceptive method who approve of family planning and who say their husband approves of family planning by background characteristics, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . 46 Percent distribution of currently married women knowing a contraceptive method by number of times discussed family planning with husband, according to current age, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 Percent distribution of currently married women by desire for children, according to number of living children, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 Percent distribution of currently married women by desire for childrcn, according to age, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . 49 Percentage of currently married women who want no more children (including those steriliscd) by number of living children and background charactcristics, Kenya, 1989 . . . . . . . . . . . . . . . 50 Percentage of currently married women who are in need of family planning by background characteristics, Kenya, 1989 . . . . . . . . . . . 51 Percent distribution of all women by ideal number of children and mean ideal number of children for all women and currently married women, according to number of living children, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 Mean ideal number of children for all women by age and background characteristics, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . 52 Percent distribution of women who had a birth in the last 12 months by fertility planning status according to birth order, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 Infant and childhood mortality rates by five-year calendar periods, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 viii Page Table 6.2 Table 6.3 Table 6.4 Table 6.5 Table 6.6 Table 6.7 Table 6.8 Table 6.9 Table 6.10 Infant and childhood mortality rates by background characteristics of the mother for the ten-year period preceding the survey, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 Infant and childhood mortality rates by selected demographic characteristics, for the ten-year period preceding the survey, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 Mean number of children ever born, surviving and dead, and proportion of children dead among those born, by age of women, Kenya, 1989 . . . . . . . . . . " . . . . . . . . . . . . . . . . . . . . . . . . . . 60 Percent distribution of births in the last 5 years by type of ante-natal care for the mother and percentage of births whose mother received a tetanus toxoid injection, according to background characteristics, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . 61 Percent distribution of births in the last 5 years by type of assistance during delivery, according to background characteristics, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 Percentage of currently married women and births in the 12 months prior to the survey to women who fall in various categories of high health risk, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . 63 Among all children under 5 years o1" age, the percentage with health cards seen by interviewer, the percentage who are immunised as recorded on a health card or as reported by the mother and, among children with health cards, the percentage for whom BCG, DPT, polio and measles immunisations are recorded on the health card, according to age, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 Among all children aged 12-23 months, the percentage with health cards seen by interviewer, the percentage who arc immunised as recorded on a health card or as reported by the mother and, among children with health cards, the percentage for whom BCG, DPT, polio and measles immunisations are recorded on the health card, according to background characteristics, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . 65 Among children under 5 years of age, the percentage reported by the mother to have had diarrhoea in the past 24 hours and the past two weeks, according to background characteristics, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 ix Page Table 6.11 Tablc 6.12 Table 6.13 Table 6.14 Table 6.15 Table 7.1 Table 7.2 Table 7.3 Table 7.4 Table 7.5 Among children under 5 years of age who had diarrhoea in the past two weeks, the percentage receiving different treatments as reported by the mother, and the percentage not consulting a medical facility and not receiving treatment, according to background characteristics, Kenya, 1989 . . . . . . . . . . . . . . . 67 Among children under 5 years of age, the percentage who are reported by the mother as having had fever in the past four weeks, and, among children under 5 who had fever in the past four weeks, the percentage consulting a medical facility, according to background characteristics, Kenya, 1989 . . . . . . . . . . 68 Among children under 5 years of age, the percentage who are reported by the mother as having suffered from severe cough or difficult or rapid breathing in the past four weeks, and, among children under 5 who suffered from severe cough or difficult brcathing, the percentage consulting a medical facility, the percentage receiving various treatments, and the percentage not consulting a medical facility and not receiving treatment, according to background characteristics, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 Percent distribution of all women by source of water for drinking, washing, and cooking, according to urban-rural residence and province, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . 70 Percent distribution of women by type of toilet facility in the household, according to urban-rural residence and province, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 Percent distribution of husbands by background characteristics, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 Percent distribution of husbands by level of education, according to background characteristics, Kenya, 1989 . . . . . . . . . . . . . . . 74 Percentage of husbands in polygynous union, according to background characteristics, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . 75 Percent distribution of husbands by number of current wives, according to age, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 Percent distribution of married couples by number of years husband is older than his interviewed wife(ves), according to wife's age, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 Page Table 7.6 Table 7.7 Table 7.8 Table 7.9 Table 7.10 Table 7.11 Table 7.12 Table 7.13 Table 7.14 Table 7.15 Table 7.16 Percent distribution of husbands by number of living children, according to age, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . 76 Percentage of husbands who know contraceptive methods, who know a source for methods, who have ever used and who are currently using, by method, Kenya, 1989 . . . . . . . . . . . . . . . . . . 77 Percent distribution of married couples by knowledge of contraception, according to method, Kenya, 1989 . . . . . . . . . . . . . . . . . . 78 Percentage of husbands who are currently using any method and any modern method of contraceptkm, by background characteristics, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ~¢9 Percent distribution of husbands who have ever heard of condom, male sterilisation, or withdrawal, by main problem perceived in using the method, according to specific method, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 Percent distribution of husbands knowing a contraceptive method by supply source they say they would use, according to specific method, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 Percent distribution of husbands by number of living children at time of first use of contraception, according to current age, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 Percent distribution of husbands who are not currently using any contraceptive method, by intention to use in the future, according to number of living children, Kenya, 1989 . . . . . . . . . . . . . . . . 82 Percent distribution of husbands who are not using a contraceptive method but who intend to use in the future, by preferred method, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 Percentage of all husbands who believe it acceptable to have messages about family planning on the radio and percentage of husbands knowing a contraceptive method who approve of family planning, by background characteristics, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 Percent distribution o f husbands knowing a contraceptive method by number of times discussed family planning with wife, according to current age, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . 84 xi Table 7.17 Table 7.18 Table 7.19 Table 7.20 Table 7.21 Table 7.22 Table 7.23 Table A.1 Table A.2 Table B.1 Table B.2 Table B.3 Table B.4 Table B.5 Table B.6 Table B.7 Page Percent distribution of married couples by wife's perception of husband's attitude toward family planning, according to husband's actual attitude, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . 84 Percent distribution of husbands by desire for children, according to number of living children, Kenya, 1989 . . . . . . . . . . . . . . . . 85 Percentage of husbands who want no more children by background characteristics, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . 85 Percent distribution of married couples by desire for more children, according to the number of living children, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 Percent distribution of husbands by ideal number of children and mean ideal number of children, according to number of living children, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 Percent distribution of married couples by whether husband's ideal number of children is less than, the same as, or higher than the wife's according to wife's ideal number, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 Mean ideal number of children of husbands by background characteristics, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 Sampling results for the whole country, Kenya, 1989 . . . . . . . . . . . . . . . . . Response rates for households, eligible women and eligible husbands, by urban-rural residence and province, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 List of selected variables with sampling errors, Kenya, 1989 . . . . . . . . . . 97 Sampling errors for the total population, Kenya, 1989 . . . . . . . . . . . . . 103 Sampling errors for the urban population, Kenya, 1989 . . . . . . . . . . . . 104 Samping errors for the rural population, Kenya, 1989 . . . . . . . . . . . . . . 105 Sampling errors for women in Nairobi, Kenya, 1989 . . . . . . . . . . . . . . . 106 Sampling errors for women in Central Province, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 Sampling errors for women in Coast Province, Kenya, 1989 . . . . . . . . . 108 xii Table B.8 Table B.9 Table B.10 Table B.11 Table B.12 Table C.1 Table C.2 Page Sampling errors for women in Eastern Province, Kenya 1989 . . . . . . . . 108 Sampling errors for women in Nyanza Province, Kenya, 1989 . . . . . . . . 109 Sampling errors for women in Rift Valley, Kenya, 1989 . . . . . . . . . . . . 109 Sampling errors for women in Western Province, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 Sampling errors for current contraceptive use among women by district, Kenya, 1989 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 Percent distribution of the de facto population enumerated in various censuses and surveys by age group, according to sex, Kenya . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 Whipple's indices of age misreporting from various censuses and surveys, by sex, Kenya . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 xiii Figure 2.1 Figure 2.2 Figure 2.3 Figure 3.1 Figure 3.2 Figure 3.3 Figure 4.1 Figure 4.2 Figure 4.3 Figure 5.1 Figure 5.2 Figure 6.1 Figure 6.2 Figure 7.1 Figure 7.2 Figure C.1 LIST OF FIGURES Page Marital Status of Women by Current Age . . . . . . . . . . . . . . . . . . . . . 10 Percent of Women Never Married by Age, KFS, KCPS and KDHS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Duration of Breastfeeding, Amenorrhoea and Postpartum Abstinence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 Age-Specific Fertility Rates, KFS, KCPS and KDHS . . . . . . . . . . . . . . 19 Total Fertility Rate (TFR) and Mean Number of Children Ever Born (CEB) to Women 40-49 by Residence and Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 Total Fertility Rates by Province, KFS, KCPS and KDHS . . . . . . . . . . 24 Trends in Contraceptive Use Among Currently Married Women 15-49 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 Current Contraceptive Use by Province, Currently Married Women 15-49 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 Source of Family Planning Supply, Current Users of Modern Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 Fertility Preferences, Currently Married Women 15-49 . . . . . . . . . . . . . 48 Fertility Preferences by Number of Living Children . . . . . . . . . . . . . . . 49 Trends in Infant and Child Mortality . . . . . . . . . . . . . . . . . . . . . . . . . 56 Infant Mortality by Province and Education . . . . . . . . . . . . . . . . . . . . 58 Family Planning Knowledge and Use Among Husbands . . . . . . . . . . . . 77 Mean Ideal Number of Children Among Husbands . . . . . . . . . . . . . . . 89 Percent Distribution of De Facto Household Population by Single Year of Age and Sex . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 XV FOREWORD The Kenya Demographic and Health Survey is a welcome addition to demographic and health data sources in Kenya. It provides us with a complete set of relevant data to evaluate population, health and family planning programmes and to assess the overall demographic situation in the country. Given the scope and representativeness, it can stand with census and intercensal survey data to provide the National Council for Population and Development, social scientists and other policymakers with a clear picture about Kenya's demographic trends in the recent past and likely directions for the future. The KDHS is an addition to previous surveys that have been conducted by the Central Bureau of Statistics and have utilised the CBS sample survey programme. Demographic surveys that have been conducted by CBS in the past include: the Kenya Fertility Survey (KFS) in 1977/78; the National Demographic Survey I (NDS I) in 1977; NDS II (1978); NDS llI (1983); and the Kenya Contraceptive Prevalence Survey (KCPS) (1984). The Kenya Demographic and Health Survey is the most complex survey to have been undertaken by NCPD. The KDHS findings provide the first evidence of a major decline in fertility and an increase in the use of family phmning. It further reveals that the infant mortality rate has dcclined between 1978 and 1989. I would like to acknowledge assistance by the United States Agency for International Development for financial support, IRD/DHS (Columbia, Maryland, USA) which provided technical assistance, the Central Bureau of Statistics, and the other members of the National Population Council who contributed to the success of the KDHS project. S. W. Ndirangu Director, National Council for Population and Development xvii SUMMARY OF FINDINGS The Kenya Demographic and Health Survey (KDHS) was conducted between December 1988 and May 1989 to collect data regarding fertility, family planning and maternal and child health. The survey covered 7,150 women aged 15-49 and a subsample of 1,116 husbands of these women, selected from a sample covering 95 percent of the population. The purpose of the survey was to provide planners and policymakers with data useful in making informed programme decisions. The survey data can also be used to evaluate Kenya's efforts to reduce fertility and the picture that emerges shows significant strides have been made toward this goal. KDHS data provide the first evidence of a major decline in fertility. If young women continue to have children at current rates, they will have an average of 6.7 births in their lifetime. This is down considerably from the average of 7.5 births for women now at the end of their childbearing years. The fertility rate in 1984 was estimated at 7.7 births per woman. A major cause of the decline in fertility is increased use of family pIanning. Twenty-seven percent of married women in Kenya are currcntly using a contraceptive method, compared to 17 percent in 1984. Although periodic abstinence continues to he the most common method (8 percent), of interest to programme planners is the fact that two-thirds of marricd women using contraception have chosen a modern method--either the pill (5 percent) or female sterilisation (5 percent). Contraccptive use varies by province, with those closest to Nairobi having the highest levels. Further evidence of the success in promoting family planning is the fact that more than 90 percent of married women know at least one modern method of contraception (and where to obtain it), and 45 percent have used a contraceptive method at some time in their life. The survey indicates a high level of knowledge, use and approval of family planning by husbands of interviewed women. Ninety-three percent of husbands know a modern method of family planning. Sixty-five percent of husbands have used a method at some time and almost 49 percent are currently using a method, half of which are modern methods. Husbands in Kenya are strongly supportive of family planning. Ninety-one percent of those surveyed approve of family planning use by couples, compared to 88 percent of married women. If couples are able to realise their childbearing preferences, fertility may continue to decline in the future. One half of married women say that they want no more children; another 26 percent want to wait at least two years before having another child. Husbands report similar views on limiting births--one-half say they want no more children. The desire to limit childbearing appears to be greater in Kenya than in other subSaharan countries. In Botswana and Zimbabwe, for example, only 33 percent of married women want no more children. Another indicator of possible future decline in fertility in Kenya is the decrease in ideal family size. According to the KDHS, the mean ideal family size declined from 5.8 in 1984 to 4.4 in 1989. The KDHS indicates that in the area of health, government programmes have been effective in providing health services for womcn and children. Eight in ten births benefit from ante-natal care from a doctor, nurse, or midwife and one-half of births are assisted at delivery by a doctor, nurse, or midwife. At least 44 percent of children 12-23 months of age are fully immunised against the major childhood diseases, Almost all children benefit from an extended period of breastfeeding. The average duration of breastfeeding is 19 months and the practice does not appear to be waning among either younger women or urban women. Another encouraging xix piece of information is the high level of ORT (oral rehydration therapy) use for treating childhood diarrhoea. Among children under five reported to have had an episode of diarrhoea in the two weeks before the survey, half were treated with a homemade solution and almost one-quarter were given a solution prepared from commercially prepared packets. The survey indicates several areas where there is room for improvement. Although young women are marrying later, many are still having births at young ages. More than 20 percent of teen-age girls have had at least one child and 7 percent were pregnant at the time of the survey. There is also evidence of an unmet need for family planning services. Of the births occurring in the 12 months before the survey, over half were either mistimed or unwanted; one fifth occurred less than 24 months after a previous birth. Hopefully, the data in this report will be useful to those making decisions regarding the future direction of health and family planning programmes. Total Fert i l i ty Rate KFS, KCPS and KDHS Women 15-49 Births per woman JI 7f~ 6.7 1977-78 KFS 1984 KCPS 1989 KDH8 Kenya DH8 1989 Current Contracept ive Use KFS, KCPS and KDHS Currently Marr ied Women 15-49 Percent 1977-78 1984 KFS KCP8 1989 KDHS Kenya DHS 1989 XX Kenya P JFTV~ UGAN ;0MALIA ) tr~CE W~STER I~Y& PROV ~CE F/Is .rAN 21 , NNROBI CE~ PROVIN~ 22 KIambu 26 K1dnyago 23 Muranga 17 Nyanda~o 2# Ny~ COAST pROVINCE 33 KJllfl 31 Kwale 55 Lomu 32 Mombcmo 30 TaRa 34. Tana E/~zcJ~N PROVINCE RIFT VALLEY PROVINCE 27 Embu 15 8adngo 37 lelolo 3 Flgeyo Momkwot 29 KlbJI 20 KaJlado 28 Mochakoa 14 Kedcho 3g Manmblt 16 Lolklpla 2.5 Meru 18 Nakuru 9 Nandl NORTH--~I~Z~N pROVINCE 19 Narok 36 God~a 38 Somburu 41 Monden3 4 Trane Nzo|o 40 Wo]Ir I Turkana 10 UasIn G18hu NYANZA PROVINCE 2 Wemt Pokot 13 Kla~ 11 KJsumu W~ilu~N pROVINCE 7 5[oya 5 Bun~oma 12 Sou~n Nyanza 6 Busla 8 Kakomega xxii 1 BACKGROUND 1.1 Geography, History and Economy Kenya is located in East Africa and lies between a longitude of 34 degrees and 42 degrees east and a latitude of 4 degrees north and 4 degrees south. It covers an area of approximately 582,646 sq. kilometres and is bordered by Ethiopia and Sudan to the north, Somalia and the Indian Ocean to the east, Uganda to the west and Tanzania to the south (see map). Kenya consists of eight areas called provinces. The next lower administrative units are districts, followed by divisions, locations, sublocations, and villages. Altogether, there are 41 districts in the country, in addition to Nairobi. The climate varies throughout the country and is determined by topography, altitude and precipitation. Most of the northern and eastern part of the country is semi-arid and less than one-third of the country is arable. Kenya achieved her independence in 1963 after a bitter and protracted struggle during which the indigenous people regained self determination and control of their destiny from the British colonial administration. Since then, the country has enjoyed political stability. In the past decade, the government began a new era with the establishment of the District Focus for Rural Development, which emphasizes the development of all parts of thc country. The Government has been committed to the provision of equal educational opportunities for all by providing free primary education. In 1985, the government introduced the 8-4-4 system of education (8 years of primary, 4 years of secondary and 4 years of university) that places emphasis on vocational and technical training at all levels. In addition to private universities, Kenya has established four public universities since independcnce. In Kenya, agriculture remains the leading sector in stimulating economic growth. The most important foreign exchange earners are coffee and tea in the agricultural sector and tourism in the non-agricultural sector. The country registered an impressive growth performance over the period 1964-71, with an average gross domestic product (GDP) of 6.5 percent. The oil crisis, that was caused by a steep rise in the price of crude oil, resulted in a drop in the GDP from an average of 6.7 percent in 1972 to 3.1 percent in 1975. In 1975, a severe frost which affected the coffee crop in Brazil led to an unexpected increase in the price of coffee and tea on the world market from 1976 to 1978. However, another oil crisis in 1979 dampened economic growth until 1983. 1.2 Population On the basis of census statistics, Kenya's population increased from 5.4 million in 1948 to 16.1 million in 1979 (Republic of Kenya, 1989). Estimates from the 1979 population census indicated that the population growth rate in Kenya was 3.8 percent per annum (Central Bureau of Statistics, no date). At this rate, the population is expected to increase to 35 million by the year 2000 (Central Bureau of Statistics, 1983). As a result of high fertility and declining mortality, Kenya is characterised by a young population. Almost 50 percent of Kenya's population is less than 15 years of age. The momentum generated by high fertility and declining mortality implies that the population growth rate will remain high for some time. The crude birth rate increased from 50 per thousand in 1948 to 52 per thousand in 1979, whereas the crude death rate decreased from 25 to 14 in the same period. The infant mortality rate decreased from 184 deaths per thousand births in 1948 to 104 in 1979 (Republic of Kenya, 1989). The 1984 Kenya Contraceptive Prevalence Survey (KCPS) showed some evidence of a possible decline in fertility, from a total fertility rate of 8.1 children per woman in 1977/78 (Kenya Fertility Survey) to 7.7 in 1984 (Central Bureau of Statistics, 1984). The population growth rate in the urban areas is more than 7 percent per year (Republic of Kenya, 1989). The population of the capital, Nairobi, has increased from 897,000 in 1980 to an estimated 1,429,000 in 1989. This increase can be attributed in large part to rural-urban migration. 1.3 Population and Family Planning Pofieies and Programmes The Government of Kenya became concerned about the high rate of population growth after the 1962 population census. During the early 1960s, the Family Planning Association of Kenya (FPAK) was established by private individuals, but it was not until 1967 that the official national family phmning programme was launched. Family planning was integrated into the Maternal and Child Health Division of the Ministry of Health. At first, due to lack of an effective health infrastructure and adequate skilled manpower, the Ministry of Health relied mainly on FPAK and expatriate staff for technical assistance. After the 1969 census provided evidence of a high level of fertility, the government decided to launch a five-year (1975-1979) family planning programme. The specific goals of the programme were to reduce the high annual rate of natural population increase from 3.3 percent (in 1975) to 3.0 percent (in 1979) and to improve the health of mothers and their children under the age of five, Initially, however, the family planning component of the Maternal and Child Health Programme had limited success. The 1979 census results indicated a population growth rate of 3.8 percent per annum, which was higher than the projected growth rate of 3.0 percent. This failure in achieving the population growth rate target could be attributed to shortfalls in the assumptions used to arrive at the target. The plan to reduce the growth rate concentrated on the supply side of family planning instead of putting emphasis on programmes aimed at changing family size norms. It was with the realisation of the need to improve on the earlier weaknesses of the family planning programme that the government of Kenya approved the establishment of the National Council for Population and Development (NCPD) in 1982. The Council's mandate is to formulate population policies and strategies and to co-ordinate the activities of government ministries, non- governmental organisations, and donors involved in population, integrated rural health, and family planning programmes. 1.4 Health Priorities and Programmes The 1989-1993 Kenya Development Plan emphasises the government's commitment to developments in the health sector that are geared toward the attainment of "Health for All by the Year 2000". The government encourages an integrated approach to the health system that involves such essential components as appropriate health education, provision of proper nutrition, basic sanitary facilities, and maternal and child health, including family planning and immunisation against major infectious diseases, among others. In 1981, the Ministry of Health started a major programme in preventive health, the Kenya Expanded Programme on Immunisation (KEPI). Several other government programmes aimed at the reduction of diseases, improvement of nutrition, and provision of maternal and child health services have also been launched. 1.5 Objectives of the Kenya Demographic and Health Survey On March 1, 1988, 'on behalf of the Government of Kenya, the National Council for Population and Development (NCPD) signed an agreement with the Institute for Resource Development (IRD) to carry out the Kenya Demographic and Health Survey (KDHS). The KDHS is intended to serve as a source of population and health data for policymakers and for the research community. In general, the objectives of the KDHS are to: assess the overall demographic situation in Kenya, assist in the evaluation of the population and health programmes in Kenya, advance survey methodology, and assist the NCPD strengthen and improve its technical skills to conduct demographic and health surveys. The KDHS was specifically designed to: provide data on the family planning and fertility behaviour of the Kcnyan population to enable the NCPD to evaluate and enhance the National Family Planning Programme, measure changes in fertility and contraceptive prevalence and at the same time study the factors which affect these changes, such as marriage patterns, urban/rural residence, availability of contraception, breastfeeding habits and other socioeconomic factors, and examine the basic indicators of maternal and child health in Kenya. 1.6 Survey Organisation The KDHS was a national survey that was carried out by NCPD in collaboration with the Central Bureau of Statistics (CBS) and the Institute for Resource Development (IRD). Funds for the survey came from three sources--the Government of Kenya, the United States Agency for International Development (USAID) office in Kenya, and IRD, through its contract with USAID/Washington. IRD also provided technical assistance throughout all stages of the survey. The sample for the KDHS is based on thc National Sample Survey and Ewduation Programme (NASSEP) master sample maintained by the CBS. The KDHS sample is national in coverage, with the exclusion of North Eastern Province and four northern districts which together account for only about five percent of Kenya's population. The KDHS sample was designed to produce completed interviews with 7,500 women aged 15-49 and with a subsample of 1,000 husbands of these women. The NASSEP master sample is a two-stage design, stratified by urban-rural residence, and within the rural stratum, by individual district. In the first stage, 1979 census enumeration areas (EAs) were selected with probability proportional to size. The selected EAs were segmented into the expected number of standard-sized clusters, one of which was selected at random to form the NASSEP cluster. The selected clusters were then mapped and listed by CBS field staff. In rural areas, household listings made betwecn 1984 and 1985 were used to select the KDHS households, while KDHS pretest staff were used to relist households in the selected urban clusters. Despite the emphasis on obtaining district-level data for phmning purposes, it was decided that reliable estimates could not be produced from the KDHS for all 32 districts in NASSEP, unless the sample were expanded to an unmanageable size. However, it was felt that reliable estimates of certain variables could be produced lbr the rural areas in the 13 districts that have been initially targeted by the NCPD: Kilifi, Machakos, Meru, Nyeri, Murang'a, Kirinyaga, Kericho, Uasin Gishu, South Nyanza, Kisii, Siaya, Kakamega, and Bungoma. Thus, all 24 rural clusters in the NASSEP were selected for inclusion in the KDHS sample in these 13 districts. About 450 rural households were selected in each of these districts, just over 1000 rural households in other districts, and about 3000 households in urban areas, for a total of almost 10,000 households. Sample weights were used to compensate for the unequal probability of selection between strata, and weighted figures are used throughout the remainder of this report. The KDHS utilised three questionnaires: one to list members of the selected households (household questionnaire); another to record information from all women aged 15-49 who were present in the selected households the night before the interview (woman's questionnaire); and the third to record information from the husbands of interviewed women in a subsample of households (husband's questionnaire). The questionnaires were pretested in August 1988. Copies of lhe final versions appear in Appendix E. The field staff for the KDHS consistcd of nine teams, each of which was fluent in one of the major indigenous languages. The teams were composed of four or five female interviewers, one editor, one supervisor, and a male interviewer. There was a smaller tenth team that had three interviewers for the Narok-Kajiado region. The teams were supervised by the local District Population Officer, the District Statistical Officer, or in some cases, an officer from NCPD headquarters. A more complete description of the survey design appears in Appendix A. Interviewers and data entry staff were recruited in October 1988 and trained in November 1988. The training included practice interviewing both in the classroom and in the field. Data collection began on 1 December and was completed during tile last week of May. The proportion of women interviewed by month was: December 1988 (7 percent); January 1989 (13 percent); February (14 percent); March (24 percent); April (25 percent); and May (17 percent). 1.7 Background Characterist ics of Women Respondents A total of 9,836 households were selected in the Kenya Demographic and Health Survey. Of these, 8,343 were identified as occupied households during the fieldwork and 8,173 were successfully interviewed. Respondents for the individual interview were women aged 15-49 who had spent the night before the interview in the selected household. In the interviewed households, 7,424 eligible women were identified and 7,150 were successfully interviewed. In general, few problems were encountered during the interviewing and the response rate was high--98 percent for households and 96 percent for individual female respondents. In addition, 1,116 husbands were interviewed out of a total of 1,397 eligible, for a response rate of 81 percent. Eligible husbands were defined as those who spent the night before the interview in the selected households and whose wives were successfully interviewed. Every other household was considered eligible for the husband interview. Details on nonresponse appear in Appendix A. This section of the report briefly examines the background characteristics of the female respondents. Knowledge of these characteristics provides a crude measure of the representativeness of KDHS data and facilitates the interpretation of other survey findings. Table 1.1 presents the distribution of all women and currently married women by selected background characteristics. Table 1.2 indicates that the distribution for all women generally fits the pattern established by the 1977/1978 KFS and 1984 KCPS (Central Bureau of Statistics, 1980; Central Bureau of Statistics, 1984). The proportion of the respondents in the 15-19 age group is slightly lower in the KDHS than in the 1977/1978 and 1984 surveys, and there has been a steady increase over time in the proportion living in urban areas to 17 percent in 1989. The distribution of all women by province indicates only minor differences among the three sources of data. For purposes of comparison, in Table 1.2 respondents are classified into 4 educational categories, according to the highest grade attained at each level. These categories are: no education, 1-4 years, 5-8 years, and 9 or more years. 1 The data show a strong increase in the educational attainment of women over time. The proportion of women with no education declined from 44 percent in 1977/78 to 25 percent in 1989. The proportion of women who have 5 to 8 years of education is higher in 1989 (43 percent) than in 1984 (32 percent) and 1977/78 (27 percent). Women interviewed in the survey were classified into five religious groups: Catholic, Protestant, Moslem, Other, and no religion. More than half of the interviewed women are Protestant. The distribution of the respondents according to religion has changed little over time. There are also inter-relationships between various background characteristics. Table 1.3 shows the distribution of the surveyed women by education, according to other selected characteristics. Nearly one-quarter of the women in the KDHS sample have never attended school, about 28 percent have some primary education only, 27 percent graduated from primary school with no further education, and 1 out of 5 women attended secondary school or higher education. Education is inversely related to age; that is, older women are generally less educated than younger women. For examplc, whereas only 5 percent of women aged 15-19 have had no formal education, more than 50 per cent of the women aged 40 and over have never been to school. 1 For the remainder of the report, respondents are classified into the categories: no education, some primary (Standard 1-6), primary complete (Standard 7 or 8), and secondary or higher (Form 1 and above). Although the introduction of the 8-4-4 system has changed the definition of primary complete, the new system came in 1986 and has not had a chance to affect respondents age 15 and above. Tab[e 1.1 Percent d i s t r ibut ion of a l l women and cur rent ly marr ied women by background characteristics, Kenya, 1989 AIL Women Currently Married Women Weighted Unwtd. Weighted Unwtd. Background Weighted no. of no. of Weighted no. of no. of characteristic percent women women percent women women Age 15-19 20.9 1497 1481 5,8 276 300 20-24 18.5 1321 3482 17,3 827 882 25-29 18,7 1334 1357 23.2 1104 1126 30-34 13.7 982 1007 17.5 833 853 35-39 12.6 898 830 16.4 781 720 40-44 9.4 674 646 12.1 576 544 45-49 6.2 445 427 7.7 369 353 NO. Living children 0-2 47.0 3364 3506 29.4 1400 1535 3-4 20,7 1477 1499 27.1 1291 1314 5 or more 32.3 2310 2145 43.5 2075 1929 Residence Urban 17,3 1236 1917 15.7 748 1160 Rural 82.7 5914 5233 84.3 4018 3618 Province Nairobi 7.7 554 859 7.0 335 519 Central 15.7 1120 1281 13.6 648 787 Coast 7.0 498 720 7.3 350 529 Eastern 17.8 1269 898 16,9 804 561 Nyanza 17.0 1218 1268 18.3 872 895 Rift Valley 21.2 1519 1100 22.0 1047 742 Western 13.6 971 1027 14.9 710 745 Education No educat ion 25.1 1797 1702 31.6 1506 1438 Some primary 27,7 1977 1888 30.7 1462 1394 Primary complete 26,7 1910 1938 20.7 987 1026 Secondary + 20.4 1457 1612 16.9 804 914 Missing 0,1 9 10 0.1 6 6 Religion Catholic 34.7 2480 2390 34.8 1656 1589 Protestant 57,4 4107 4075 56.8 2706 2670 Muslim 3.5 253 317 3,5 165 213 Other 1.6 115 104 1.7 79 77 No religion 2.6 184 254 3.2 151 222 Missing 0.2 12 18 0,2 9 7 Total 100.0 7150 7150 100.0 4765 4778 Womcn who reside in the urban areas have considerably more education than those living in the rural areas. In urban areas, the percentage of women who never attended school is lower than in rural areas and the percentage who have secondary or higher education is more than twice as high as in the rural areas. Looking at the data by province, Nairobi, the capital, has the smallest proportion of uneducated women (9 percent), compared to 47 percent in Coast Province, and 13 percent in Central Province. There is little difference among the othcr four provinccs in terms of the 6 Table 1.2 Percent d is t r ibut ion of all women by background characteristics, 1977/78 Kenya Fertility Survey, 1984 Kenya Contraceptive Prev- alence Survey, and 1989 KDHS Background 1977/78 1984 1989 characteristic KFS KCPS KDHS Age 15-19 23.8 25.9 20.9 20-24 17.9 19.6 18.5 25-29 18.4 15.8 18.7 30-34 12.5 12.7 13.7 35-39 11.5 10.5 12.6 40-44 7.7 8.5 9.4 45-49 8.0 7.0 6.2 Residence Urban 12.3 14.0 17.3 Rural 87.7 86.0 82.7 Province Nairobi 5.4 4.7 7.7 Central 15.2 16.3 15.7 Coast 8.4 10.1 7.0 Eastern 17.2 16.7 17.8 Nyanza 21.9 18.8 17.0 Rift Val ley 18.3 20.9 21.2 Western 13.4 12.5 13.6 Education No education 44.2 34.8 25.1 1-4 years 18.4 16.1 13.6 5-8 years 27.4 32.1 42.6 9+ years 9.8 16.8 18.3 Missing 0.2 0.2 0.3 Religion Catholic 36.2 36.5 34.7 Protestant 53.1 52.8 57.4 Muslim 4.8 3.7 3.5 Other 0.4 1.9 1.6 No religion 5.4 5.1 2.6 Missing 0.I 0.1 0.2 I o ta [ 100.0 100.0 100.0 proportion who are uneducated. The KDHS data show that educational achievement of women in Nairobi is highest among all the provinces, with 30 percent having completed primary school, and 43 percent having attained sccondary or higher education. Among the other provinces, Central Province shows the highest level of educational achievement. The proportions of women who have attended secondary or higher education in the other regions are very similar--16 percent in the Coast, 15 percent in Eastern, 17 percent in Nyanza, 16 percent in Rift Valley, and 20 percent in Western Province. Table 1.4 provides information regarding certain household amenities available to women. Overall, 10 percent of women live in households that have electricity and 61 percent have radios. Only 5 percent of women's households have televisions and 4 percent have refrigerators. As for means of transportation, 28 percent of women live in households in which some member owns a bicycle, 7 percent a car, and just over 1 percent either a motorcycle or tractor. Almost 90 percent 7 Table 1.3 Percent distribution of women by level of education, according to background characteristics, Kenya, 1989 Level of education Wtd. number Background Some Primary Second- Miss- of characteristfc None primary complete ary + ing Total women Age 15-19 4.7 23.5 50.4 21.4 0.I 100.0 1497 20-24 8.5 25.6 30,7 35.0 0.1 100.0 1321 25-29 18.2 30.3 23.2 28.1 0.2 100.0 1334 30-34 36.8 28.7 16.9 17.3 0.2 100.0 982 35-39 42.7 28.6 19.0 9.7 0.I 100.0 898 40-44 50.4 34.0 10.9 4.6 0.1 100.0 674 45-49 64.6 25.8 7.2 2.4 0.1 100.0 445 Residence Urban 12.3 18.6 27,6 41.4 0.1 100.0 1236 Rural 27.8 29.5 26.5 16.0 0.1 100.0 5914 Province Nairobi 8.5 18.2 30,2 43.0 0.2 100.0 554 Central 12.8 26.9 33.1 26.7 0.5 100.0 1120 Coast 47.5 15.5 21,5 15.5 0.0 I00.0 498 Eastern 23°6 32.7 28.3 15.3 0.1 100.0 1269 Nyanza 27.4 31.7 23.9 16.9 0.1 100.0 1210 Rift Valley 32.1 20.2 23.4 16.2 0.0 100.0 1519 Western 25.5 27.6 26.6 20.2 0.1 100.0 971 Total 25.1 27.7 26,7 20.4 0.1 100.0 7150 of women live in households with land, 76 percent live in households with cattle, sheep or goats, and 40 percent live in households where cash crops are grown. Only 35 percent live in permanent houses. It should be noted that interviewers usually relied on personal observation of the respondent's house and did not ask whether the house was permanent or not. Thus, the definition of what constitutes a "permanent" house may have varied by interviewer and/or by team. Ownership of household amenities varies tremendously by urban-rural residence. As expected, the proportion of urban women living in households with these amenities is higher than the proportion of rural women for all items except bicycles, land, animals, and cash crops. The urban-rural differential is particularly strong for electricity, televisions, and refrigerators. Table 1.4 Percent of women who live in households with selected amenities, according to urban-rural residence, Kenya, 1989 Household amenity Residence Urban Rural Total Electricity 45.2 2.8 10.1 Radio 77.6 58.0 61.4 Television 22.4 1.5 5.1 Refrigerator 16.4 0.9 3.6 Bicycle 24.8 28.7 28.0 MotorcycLe 3.1 1.1 1.5 Car 18.1 4.7 7.0 Tractor 1.8 1.2 1.3 Land 61.3 92.7 87.3 Cattle, sheep, goats 47.4 81.4 75.5 Cash crops 25.5 43.5 40.4 Permanent house 65.5 28.5 34.9 Number of Women 1236 5914 7150 2 NUPTIALITY, BREASTFEEDING AND POSTPARTUM INSUSCEPTIBILITY 2.1 Introduct ion Fertility levels and trends depend in part on the extent of and age at marriage among women. From past demographic surveys and censuses, Kenya has reliable indices on nuptiality. Such data show that age at first marriage for Kenyan women has been rising. As in other demographic surveys and censuses carried out in Kenya, marriage is defined in the KDHS to include informal unions. This chapter investigates the trends in age at marriage of different cohorts. Other proximate determinants of fertility that render a woman to be at risk of pregnancy, such as breastfeeding, postpartum amenorrhoea and postpartum sexual abstinence are also explored. 2.2 Mar i ta l Status In this report, the terms "living together" and "married" are combined and referred to as "currently married". Women who are currently married, widowed, divorced or no longer living together are referred to as "ever-married". Table 2.1 Percent distribution of women by current marital status, according to age, Kenya, 1989 Current marita[ status Wtd. Never Living no. mar- Mar- to- Widow- Oi- Separ- of Age ric~J ried gether c~d vorced ated Total worn 15-19 79.9 17.2 1.2 0.0 1.1 0.5 100.0 1497 20-24 31.8 58.6 4.0 0.7 2.7 2.3 100.0 1321 25-29 10.7 78.6 4.1 1.5 3.6 1.3 100.0 1334 30-34 5.4 79.6 5.3 2.1 5.4 2.2 100.0 982 35-39 3.2 02.4 4.5 4.7 3.8 1.4 I00.0 898 40-44 1.5 82.4 3.1 8.2 3.0 1.8 100.0 674 45-49 2.4 79.7 3.2 11.0 3.1 0.6 100.0 445 Total 26.0 63.1 3.6 2.7 3.1 1.5 I00.0 7150 Table 2.1 shows that 26 percent of women of childbearing age have never married, 67 percent are currently married and 7 percent are either widowed, divorced, or no longer living together (separated). The proportion never married falls sharply from 80 percent in the age group 15-19 to 11 percent in the age group 25-29 (see Figure 2.1). This proportion declines to 2 percent in the age group 45-49, reinlbrcing the findings of other demographic surveys--that most women in Kenya marry early. The proportion currently married rises steeply in the first two age groups reaching a high of 87 percent among women 35-39. As expected, widowhood increases steadily with age, varying from none for women aged 15-19 years to 11 percent for the age group 45-49. 9 The proport ion divorced or separated increases from 2 percent for women aged 15-19 years to 8 percent for women aged 30-34 years, and dcclines to 4 percent for women in their forties. 100% Figure 2.1 Marital Status of Women by Age 75% 50% 25% 0% 15-19 20-24 25-29 30-34 Age l Never in union Married I 35-39 40-44 45-49 Widowed/div./sep. / Kenya DH8 1989 Table 2.2 Percent of women who have never married at the time of various surveys and censuses, by age group, Kenya, 1989 1962 1969 1977 1977/78 1979 1984 1989 Age Census Census NDS KFS Census KCPS KDHS 15-19 55 64 71 72 71 74 80 20-24 13 18 22 21 25 24 32 25-29 5 6 6 4 9 6 11 30-34 3 4 3 I 5 4 5 35-39 2 3 2 I 3 2 3 40-44 2 3 I I 3 I 2 45-49 2 3 I 0 2 I 2 Source: Centrat Bureau of Statistics, 1984, Tabte 4.4 Table 2.2 shows the trend in the proportion of women reported as never married by age from past censuses and surveys in Kenya. It is evident that the proportion of women under 30 who have never married has been increasing (Figure 2.2). The KDHS data show an increase for every age group over the KCPS data. For example, the proportion of women 15-19 who have never married increased from 74 percent in 1984 to 80 percent in 1989 and the proportion in age group 20-24 rose from 24 percent in 1984 to 32 percent in 1989. There is also a notable increase in the proportion never married for women aged 25-29, from 6 in 1984 to 11 percent in 1989. These observations suggest that age at first marriage in Kenya is increasing. Above age 25, the proportions of women remaining single are too small to discern any trend over time. 10 As has been observed in other surveys, increased enrolment of women in higher education may be the major cause of the increasing proportions of single women aged 15-24 years. Figure 2.2 Percent of Women Never Married by Age KFS, KCPS and KDHS Percent , o is-19 2o-24 2s-29 Age l mm KFS, 1977-78 ~ KCPS, 1984 ~ KDHS, 1989 Kenya DHS 1989 2.3 Polygyny In order to measure the extent of polygyny in Kenya, married women in the KDHS were were asked if their husbands had other wives. Table 2.3 displays the answers to this question by age of the woman. Overall, 23 percent of currently married women arc in polygynous unions. This is a slight decline from the 25 percent in the 1984 KCPS and the 30 percent in the 1977/78 KFS. The table indicates that polygyny is more common among older than younger women, which may reflect a trend away from this traditional practice. Polygyny is more common in the rural areas than in the urban areas. This is true for women in most age groups. Nyanza Province has the highest proportion of women in polygynous unions (37 percent), with Central Province having the lowest (8 percent). There is considerable provincial variation in polygyny according to the age of the woman. Twenty-nine percent of married women aged 15-19 in Nyanza Province are in polygynous unions, compared to only 4 percent of women in Rift Valley. Among women in the age group 40-44, Coast Province shows 54 percent in polygynous marriages, while Nairobi has only 10 percent. Except for Nyanza and Eastern Provinces, polygyny decreases slightly among women aged 45-49 years. 11 As in other surveys carried out in Kenya, the KDHS found that there is a negative relationship between education and polygyny. The percentage in polygynous unions decreases from 35 percent among women with no education to 12 percent among women with secondary education and higher. Table 2.3 Percentage of currently married women in a polygynous union, by abe, according to background characteristics, Kenya, 1989 Age Background characteristic 15-19 20-24 25-29 30-34 35-39 40-44 45-49 Total Residence Urban 14.5 14.6 14.6 22.9 27.6 18.5 17.5 17.7 Rural 11.9 18.4 18.2 28.4 26.1 34.1 32.2 24.4 Province Nairobi 15.1 10.3 14.6 18.0 34.0 10.3 6.3 15.4 Central 5.3 3.3 2.5 13.6 4.7 18.8 14.8 8.3 Coast 16.0 24.0 30.8 35.8 37.7 53.7 45.3 34.1 Eastern 9.3 14.7 13.6 20.4 11.8 34.0 36.2 19.5 Nyanza 28.7 24.9 32.5 44.1 50.0 34.9 47.3 37.4 Rift Valley 4.0 18.8 13.9 23°6 20.0 34.0 20.2 19.8 Western 5.7 22.3 19.3 29.3 41.8 40.2 39.6 28.0 Education No education 16.3 39.2 25.8 37.2 35.7 44.5 30.2 35.3 Some primary 14.2 17.6 21.2 26.9 21.4 22.8 38.3 22.5 Primary con~lete 12.7 16.3 11.2 20.6 17.5 22.4 21.3 15.8 Secondary + 5.8 10.0 13.1 13.0 16.3 10.1 (0.0) 11.9 Total 12.7 17.5 17.6 27.6 26.3 33.0 31.2 23.4 Note: Numbers in parentheses are based on fewer than 20 unweighted cases. 2.4 Age at First Marriage Table 2.4 shows that the proportion of women who marry before age 15 has declined from 25 percent of women 40-44 to only 4 percent of women 15-19. This suggests a rising age at first union in Kenya. As the table shows, 75 percent of women aged 40-44 married before the age of 20, compared to only 52 percent of women aged 20-24. With the exception of women 45-49, the median age at marriage has increase d over time, from 17.3 among women 40-44 to 19.8 for those aged 20-24. That the median age of marriage for women aged 45-49 years is higher than expected (18.5) could be explained by recall lapse. Table 2.5 presents the median age at first marriage by selected background characteristics. Only women aged 20-49 are included in the table since median age at marriage for younger women is influenced by the large proportion that have not yet married. In general, urban women marry later than their rural counterparts. This is true for all age groups, with an overall difference of 1.5 years in the median age at marriage. 12 Tabte 2.4 Percent d i s t r ibut ion of women by age at f i r s t marriage and median age at f i r s t marriage, according to current age, Kenya, 1989 Curr- Age at first marriage Wtd. Med- ent Never no.of Jan* age married <15 15-17 18-19 20-21 22-24 25+ Totat women age 15-19 79.8 3.5 11.8 4.9 0.0 0.0 0.0 100.0 1497 20-24 31.8 5.6 25.9 20.3 12.0 4.4 0.0 100.0 1321 19.8 25°29 10.7 15.7 27.5 22.0 11.3 8.5 4.2 100.0 1334 18.6 30-34 5.4 23.0 27.7 17.2 13.6 8.5 4.7 100.0 982 17.9 35-39 3.2 20.1 31.3 20.0 12.4 7.1 5.9 I00.0 898 17.9 40-44 1.5 25.0 30.3 19.7 11.9 7.1 4.4 I00.0 674 17.3 45-49 2.4 17.7 28.0 20.5 13.4 10.1 7.9 100.0 445 18.5 Total 26.0 13.8 24.7 16.9 9.7 5.8 3.1 100.0 7150 - Some data for women age 15-19 and the median for all women have been omitted, since a substantial proportion of these women have not yet married. * Defined as the exact age by which 50 percent of women have experienced marriage. Table 2.5 Median age at first marriage among women age 20-49 years, by current age and background characteristics, Kenya, 1989 Current age Background Characteristic 20-24 25-29 30-34 35-39 40-44 45-49 Total Residence Urban 20.3 19.9 19.6 18.7 18.7 19.5 19.8 Rural 19.7 18.3 17.7 17.8 17.3 18.4 18.3 Province Nairobi 20.5 20.1 19.9 19.5 19.4 22.6 20.2 Central 21.9 20.2 19.3 19.3 18.2 19.1 19.9 Coast 19.5 17.1 16.3 16.2 15.1 16.3 17.0 Eastern 22.5 19.2 20.0 19.1 18.3 18.9 19.5 Nyanza 17.7 17.1 16.4 16.6 16.4 17.1 16.9 Rift Valley 19.3 17.6 17.2 18.3 17.3 20.4 18.1 Western 19.0 18.5 17.7 17.1 16.9 15.4 17.9 Education No education 16.7 16.7 16.2 17.1 16.7 18.6 16.9 Some primary 18.3 17.4 17.5 17.5 17.1 17.6 17.6 Primary con~piete 19.7 18.6 19.3 19.0 19.5 19.9 19.2 Secondary + 22.6 21.0 21.0 21.2 22.6 (22.4) 21.6 Total 19.8 18.6 17.9 17.9 17.3 18.5 18.5 Note: Numbers in parentheses are based on fewer than 20 unweighted cases. Provincial differentials in age at marriage also exist in Kenya. Women in Coast and Nyanza Provinces marry the earliest, with a median age of about 17 years, while women in Nairobi and Central Province marry the latest, with a median age of about 20 years. Differences in age at marriage have perhaps been influenced most by increased education of women. As Table 2.5 shows, the median age at marriage increases with the level of education 13 for every cohort of women. Those with a secondary school or higher education have the highest median age at marriage (21.6). The difference in the median age at marriage between this group and women who have no education is 4.7 years over all ages and is almost 6 years for those aged 20-24. Perhaps Kenya's 8-4-4 system of education will effect further increases in age at marriage for younger women now in school. 2.5 Breastfeeding and Postpartum Insusceptibility Breastfeeding, postpartum amenorrhoea and postpartum sexual abstinence are factors related to the risk of pregnancy. The duration of amenorrhoea (the period following a birth before the return of the menstrual cycle) is directly related to breastfeeding--the longer a woman breastfeeds, the longer she is likely to be amenorrhoeic. Table 2.6 shows that over 95 percent of babics in Kenya are breastfed for at least some time. Over 80 percent are breastfed to their first birthday, and almost three-fifths are breastfed for at least 18 months. Table 2.6 Percentage of births whose mothers are still breast- feeding, postpartum amenorrhoeie, abstaining, and insusceptible, by number of months since birth, Kenya 1989 Months Breast- Amenor- Abstain- Insus- No. of since birth feeding rhoeic ing ceptible* births Less than 2 96.0 95.6 88.6 97.3 194 2-5 94.4 85.7 50.8 89.2 244 4-5 91.9 69.8 22.2 74.7 262 6-7 92.2 64.1 21.6 69.2 252 8-9 87.2 56.2 15.4 60.3 260 10-11 91.0 51.4 15.2 54.5 241 12-13 81.7 42.4 17.0 46.0 246 14-15 68.3 28.9 9.1 31.0 263 16-17 63.4 19.5 5.8 22.2 257 18-19 55.9 11.4 6.0 15.7 246 20-21 42.3 10.0 10.1 17.2 211 22-23 40.9 9.2 9.9 16.8 200 24-25 21.7 6.4 5.4 9.8 256 26-27 14.6 2.0 6.1 8.1 235 28-29 15.7 0.7 5.4 6.1 234 30-31 12.5 3.5 3.3 6.4 276 32-33 4.0 0.5 1.3 1.6 233 34-35 4.1 0.4 2.9 3.3 278 Iota[ 54.0 30.6 15.6 34.5 4387 Median 19.4 10.8 2.6 11.6 Note: Includes births 0-35 months before survey * Either amenorrhoeic or abstaining at the time of the survey More than 85 percent of Kenyan women experience amenorrhoea for at least two months after birth, with this percentage dropping rapidly to about 42 percent still amenorrhoeic one year after giving birth. The proportion of amenorrhoeic women decreases faster than that of the breastfeeding women, reaching 11 percent by the 18-19 months after birth. 14 There is a sharper decline in the practice of sexual abstinence after a birth than the decline of either breastfeeding or postpartum amenorrhoea. Only 51 percent of women are abstaining 2- 3 months after birth, whereas 17 percent abstain for at least one year and 5 percent abstain for two years after birth. Table 2.6 also shows the proportion of women who are insusceptible to pregnancy due to either amenorrhoea or the practice of sexual abstinence. One year after giving birth, 46 percent of the women are insusceptible. Table 2.7 provides estimates of the mean duration 1 in months of the four birth-related variables by selected background characteristics. As the table and Figure 2.3 show, there is not much difference in any of the variables by age of the woman. Rural women have slightly longer mean durations of breastfeeding, postpartum amenorrhoea and sexual abstinence than their urban counterparts. As a result, they have a longer period of insusceptibility to pregnancy. Table 2.7 Mean number of months of breastfeeding, postpartum amenorrhoea, postpartum abstinence, and postpartum insusceptibitity, by background characteristics, Kenya, 1989 Background Breast- Amenor- Abstain- Insus- No. of characteristic feeding rhoeic ing ceptibte* births Age <30 19.4 10.5 6.3 12.5 2760 30+ 19.5 11.6 5.2 12.9 1689 Residence Urban 18.8 9.1 5.2 11.0 612 Rural 19.5 11.2 6.0 12.9 3837 Province Nairobi 19.9 9.1 6.3 11.5 264 Central 18.4 10.7 7.7 13.9 619 Coast 17.7 9.4 2.6 9.9 255 Eastern 20,9 9.3 6.4 11.2 794 Nyanza 19.3 11.5 3.9 13.0 789 Rift Valley 19.1 12.2 8.2 14.1 1007 Western 19.7 11.7 3.4 12.1 721 Education NO education 20.9 13.4 6.8 14.7 1144 Some primary 19.1 11.0 4.7 12.1 1395 Primary complete 19.4 I0.0 6.5 12.4 1088 Secondary + 18.0 8.5 5.7 10.8 819 Total 19.4 I0,9 5.9 12,6 4449 Note: /nciudes births 1-36 months before survey. Estimates are based on prevalence/incidence method (see text). Three women with education level not stated are omitted. * Either amenorrheoic or abstaining at the time of the survey 1 Estimates of mean duration are calculated using the prevalence/incidence method, borrowed from epidemiology. The duration of breastfeeding, for example, is defined as the prevalence (number of children whose mothers are breastfeeding at the time of the survey), divided by the incidence (average number of births per month over the last 36 months). 15 Figure 2.3 Duration of Breastfeeding, Amenorrhoea and Postpartum Abstinence Mean Duration in Months 241 21 18 15 12 9 6 3 0 (30 30* Urban Rural AGE RESIDENCE [ ~Breast feed ing Amenorrhoea None Some PrL Sec.* pri. comp. EDUCATION m Abstinence Kenya DHS 1989 Eastern Province recorded the highest mean duration of breastfeeding (21 months), with Coast Province having the shortest, about 18 months. Mean duration of sexual abstinence is particularly short for women in Coast, Western and Nyanza Provinces. In Coast Province, this may be attributed to Islamic religious practiccs. There is an inverse relationship between education and the mean duration of brcastfeeding, amenorrhoea and insusceptibility. The higher the level of education, the shortcr the mean duration of these variables. This may be attributed to the fact that better educated women are more likely to work in jobs that make brcasffeeding more difficult. 16 3 FERTILITY 3.1 Background Everything affecting the demographic character of a population--its size, rate of increase, geographic distribution, age and sex structure, life expectation and family composition--must work through one of three demographic variables: fertility, mortality and migration. Of these, fertility is the major dynamic element. In most instances it is the prime determinant of age structure, family composition and population growth rates. To understand fertility is, therefore, to understand not only a major portion of all demographic behaviour, but a fundamental element in social structure. The fertility measures presented in this chapter are based on the reported reproductive histories of women aged 15-49 interviewed in the KDHS. Each woman was asked the number of sons and daughters living with her, the number living elsewhere, and the number who had died. She was then asked for a history of all her births, including the month and year each was born, the sex, the name, and if dead, the age at death, and if alive, whether he/she was living with the mother. Based on this information, fertility measures like completed fertility (number of children ever born) and current fertility (total fertility rate, or TFR) are examined. These measures are also analyzed in connection with different background characteristics. Thus, the chapter contains a discussion of levels, trends and differentials in fertility of Kenyan women. It is appropriate to mention that the birth history approach has some limitations and is susceptible to data collection errors. Data on the total number of children ever born may be distorted due to socio-cultural factors. Women are likely to include relatives' children among their own children, due to the extended family system in the country. Also, babies who die very young are more likely to be omitted from reporting. Another source of error in the reported number of children could be the inclusion of stillbirths. Women in older age groups also tend to forget grown children, especially those who have left the household. Finally, misreporting of the dates of birth is common in many cultures. 'So, fertility levels can be affected by underreporting, while misreporting of dates of births can seriously distort estimates of fertility trends. There is no complete solution to the above problems, but the interviewers were instructed to do all they could to facilitate respondents' recall, probe for early infant deaths, and avoid including stillbirths. Furthermore cross-checks were built into the questionnaires. Interviewers were instructed to probe for reasons for longer birth intervals and to compare ages, dates, etc., for inconsistencies. Despite these safeguards, there are indications in the KDHS results that births occurring five and six years prior to the survey were shifted to seven years before the survey, presumably to avoid the necessity of filling in the health section for the children. In order to obtain data for all children under age five, questions in the health section were asked for all children born since January 1, 1983. KDHS data on births by year show that there are roughly 30 percent more births reported as occurring in 1982 than in 1983. Similar displacement of births has been found in other DHS surveys. For the purpose of this report, data on trends in fertility that involve the year 1982 or 1983 should he regarded with caution. However, this problem most likely does not affect the rates for the five-year period prior to the survey. 17 3.2 Levels and Trends in Ferti l i ty The total fertility rate (TFR) for the five-year period prior to the survey is 6.7, which represents the total number of births a woman would have by the time she reached age 50 if she had children at the same rate as women are currently having at each age group. As shown in Table 3.1, the KDHS data are the first evidence of a major decline in fertility in Kenya. The total fertility rate was about 8 children per woman in the late 1970s, and although the 1984 Kenya Contraceptive Prevalence Survey (KCPS) showed some slight evidence of decline (to a TFR of 7.7), the KDHS rate of 6.7 represents a substantial decline in fertility. It should be noted that the estimates are not strictly comparable. For example, the rates from the 1962 and 1969 censuses are based on reported data, without the upward adjustment for underreporting that is common for census data. Data from the censuses, the 1977 NDS and the 1984 KCPS refer to rates in the 12-month period before the survey, while the rates from the KDHS refer to the five- year period prior to the survey. rabte 3.1 Age-specific fertility rates from various surveys and censuses, Kenya 1962 1969 1977 1977/78 1979 1984 1989 Age Census Census NDS KFS Census KCPS KDHS 15-19 83 111 135 177 179 143 152 20-24 207 284 365 369 368 358 314 25-29 223 290 361 356 372 338 303 30-34 203 253 316 284 311 291 255 35-39 163 200 231 216 226 233 183 40-44 109 121 133 132 105 109 99 45-49 63 60 56 51 14 66 35 Total fer- tility rate 5.3 6.6 8.0 7.9 7.9 7.7 6.7 Source: Central Bureau of Statistics, 1984, Table 4.13 and Central Bureau of Statistics, no date, Table 6.15. Data from the 1979 Census have been adjusted for underreportin9 of births. Age-specific fertility rates for the KDHS show that the rate increases from 152 births per 1000 women in the youngest age group to over 300 for women aged 20-29 and then decreases steadily to 35 for women aged 45-49. Figure 3.1 gives a graphical representation of the KDHS age-spccific fertility rates for comparison with other survey results. For every age group except the youngest the age-specific fertility rates recorded in the KDHS are lower than those recorded in the other surveys. Further evidence of a fertility decline appears in Table 3.2, which presents age-specific fertility rates for five-year periods prior to the survey, based on data from the KDHS birth histories. In reading the table, one should notc that the figures in parenthesis represcnt partial fertility rates due to truncation. Women 50 ycars and over were not included in the survey and the further back into time rates are calculated, the more severe is the truncation. For example, rates cannot be calculated for women aged 45-49 for the period 5-9 years before the survey, 18 because those women would have been aged 50-54 at the time of the survey and were not interviewed. Figure 3.1 Age-Specific Fertility Rates KFS, KCPS and KDHS Birth8 per 1,000 women 400 300 200 100 0 15-19 • - 4 5'- 30'-34 20-24 2 29 35-39 40-44 Age 45-49 - - - - KFS 1977-78 +- KCPS 1984 ~ KDHS 19B9 i Kenya DH$ 1989 Tabte 3.2 Age-period fertitity rate by age of wc~nan at birth, Kenya, 1989 Nun~)er of years preceding survey Age at birth 0-4 5-9 I0-14 15-19 20-24 25-29 30-34 15-19 152 189 203 216 190 192 (108) 20-24 314 338 357 334 347 (322) 25-29 303 314 337 332 (329) 30-34 255 Z93 304 (285) 35-39 183 241 (306) 40-44 99 (158) 45-49 (35) Note: Figures in parentheses are partially truncated rates. Not avaitable due to age truncation. 19 The data show a decline in fertility rates at all ages from those prevailing 10-14 years before the survey. It is interesting to note that, if one assumes that the same fertility rate at age 45-49 prevailed 5-9 years before the survey as 0-4 years before the survey, the total fertility rate 5-9 years before the survey (approximately 1979-1984) would be 7.8, which is equivalent to the rates recorded in Table 3.1 for the late 1970s and 1984. The data in Table 3.2 show a peak in fertility 10-14 years prior to the survey for all but the youngest age group. While it is possible that fertility has risen and then fallen, another possible explanation is a shifting of births from the period 15-19 and/or 5-9 years prior to the survey. It is important to note that the decline in fertility is consistent with the increase in age at marriage discussed in Chapter 2, as weI1 as the increase in contraceptive use recorded by the KI)HS (see Chapter 4). Another factor relating to the decline could be the recent extension of a comprehensive health care system, which makes it easy to promote population programmes. The fall in the rate of infant mortality could also have contributed to the fertility decline. Several studies have shown that there is a close relationship between the infant mortality rate and the fertility rate. When the probability of child survival increases, couples need to have only that number of children which they actually desire, especially when childbearing involves both physical and mental strain and childrearing is expensive. By achieving major reductions in infant mortality in Kenya, a similar decline in fertility has bccn possible. Perhaps the largest single factor that has contributed to fertility decline is education, cspccially education of women. Age at marriage increases with education, hence delaying the start of childbearing. Tabte 3.3 Age-specific fe r t i l i ty rates and total fe r t i l i ty rates for three periods before the survey, Kenya, 1989 Months prior to sucvey Age at 12 24 60 birth months months months 15-19 139 148 152 20-26 302 317 314 25-29 305 297 303 30-34 250 235 255 35-39 192 180 183 40-44 95 87 99 45-49 12 21 35 Total fer- titity rates: 15-49 6.5 6.4 6.7 15-44 6.4 6.3 6.5 Table 3.3 presents age-specific fertility rates for the 12-month, 24-month and 60-month periods prior to thc survey. They indicate a slight decline between the 60-month period and the 24-month period prior to the survey and an increase at certain ages and a decline at other ages between the 24-month and 12-month period before the survey. The most that can be concluded from these data is that fertility has probably continued to decline in the few years before the survey. 20 In the KDHS, all women were asked whether or not they were pregnant at the time of the survey. The percentage of women pregnant in each age group is shown in Table 3.4 along with comparable information from the 1977/78 KFS and the 1984 KCPS. Table 3.4 Percentage of art women who are currently pregnant by age, Kenya, 1977/78 KFS, 1984 KCPS and 1989 KDHS 1977/78 1984 1989 NO. of Age KFS KCPS KDHS women 15-19 8 8 6.8 1497 20-24 17 16 13.6 1321 25-29 19 17 10.5 1334 30-34 16 13 I0.9 982 35-39 12 10 8.4 898 40-44 9 6 3.6 674 45-49 3 2 2.2 445 Total 13 11 8.9 7150 The data provide further corroboration of a recent fertility decline. Only 9 percent of women in the KDHS said they were pregnant, compared to 11 percent in 1984 and 13 percent in 1977/78. Moreover, the KDHS results reveal a consistent decline in the proportion pregnant in all age groups. 3.3 Ferti l i ty Differentials Knowledge of differential fertility provides valuable information about the relative contributions of different socio-economic and cultural factors to the overall level of fertility, thus providing an indication of future fertility rates. Only after these differences have been ascertained is it possible to investigate the pattern of causation underlying it. Questions asked in the 1989 KDHS make it possible to study differentials by urban-rural residence, province, and education; these are presented in Table 3.5. Table 3.5 also presents thc total fertility rates for two calendar year periods (1986-88) and (1983-1985) and for the five-year period preceding the survey, as well as the mean number of children ever born to women 40-49 years old, according to background characteristics of women. Caution should be cxercised in comparing these rates, as the average number of births to women 40-49 refers to past or completed fertility, while total fertility in the preceding five years refers to a more current measure of fertility. A.s mentioned above, the rates for 1983-85 are especially suspect, as they include the year 1983, from which a number of births were evidently displaced back to 1982. This has the effect of reducing the apparent decline in fertility between the two 3-year periods. Comparing the last two columns of Table 3.5 reveals that there has been a major decline in fertility, from 7.5 children ever born to women 45-49 to a total fertility rate of 6.7. This is consistent with the other evidence of fertility decline. 21 ~abke 3.5 Total fe r t i l i ty rates for calendar year per iods and for f i ve years preceding the survey, and mean number of ch i ld ren ever born to women 40-49 years of age, by background character i s t i cs , Kenya, 1989 Total fertility rates* Mean number of 0-4 ch i ld ren years ever born Background 1986- 1983- before to women character i s t i c 1988"* 1985 survey age 40-49 Residence Urban 4.5 5,8 4.8 5.1 Rural 7.1 7,1 7.1 7.7 Province Nairobi 4.2 7.0 4.6 4.9 Central 6,0 6,4 6,0 7,3 Coast 5,4 6.0 5.5 7,3 Eastern 7.2 7,0 7.0 7.4 Nyanza 6,9 7.1 7.1 7,9 Ri f t Va l ley 7.0 6.7 7.0 7.4 ~estern 8.1 7.9 8.1 8.2 Education No education 7.5 7.2 7.2 7.4 some primary 7.5 7.5 7,5 8.0 Primary co~otete 6.4 6.4 6.5 7.3 Secondary + 4.8 5,0 4.9 4.7 Total 15-49 6.7 6,8 6.7 7.5 Total 15-44 6.5 6.5 6.5 * Based on women 15-49 ** Includes exposure in 1989 up to the time of interview. There is a considerable difference in fertility betwcen rural and urban areas. Based on births in the five years before the survey, women in urban areas have a total fertility rate of approximately 5 live births, compared to about 7 for rural women. This difference also exists in the mean number of children ever born to women 40-49 (Figure 3.2). Much of the observed urban-rural differences in fertility are probably due to the differential practice of birth control, which spread outward from urban to rural areas. Urban areas usually have the most educated, highest income population, as well as the best medical facilities. The difference could also be attributed to urban-rural differences in three inter-related factors that determine the ability to control fertility: knowledge about birth control, skill in its practice and degree of access to the most effective means. A/though fertility rates are still high in Kenya, there is considerable variation between provinces (Figure 3.3). For the five years prior to the survey, Nairobi had the lowest total fertility rate (4.6), while Western Province had the highest (8.1). This is also consistent with the mean number of children ever born to women 40-49 years old. The observed regional differentials could be due to the highly diverse physical and climatic environment, reflecting diverse modern and traditional systems of land use. The geographic distribution of Kenyan tribes, with particular tribes being concentrated in certain provinces, leads to variation in cultural practices among the provinces. The large difference in the total fertility ratcs for Nairobi for 1983-85 and 1986-88 is most probably 22 due to a combination of sampling error due to small sample sizes in certain age groups of women and to misreporting of dates of birth. Figure 3.2 Total Ferti l ity Rate (TFR) and Mean Number of Children Ever Born (CEB) to Women 40-49 by Residence and Education RESIDENCE Urban Rural EDUCATION None Some Primary Primary Comp. Secondary* i~\\~\\\\\\\\\\\\\\\\\\\\\\~\\\\\\\\\\~ k\~\\\\\\~\\\\\\\\\\~'~k'~ ~\\\\\\\\\\\\\\\\X~ ~\\\\\\\~kk\~kkk\\\t 2 4 6 8 10 Number of Children TFR i CEB Kenya DHS 1989 No other social variable has been as frequently associated with fertility differentials as education. The total fertility rate of women with complete primary education (6.5) is lower than the one for women with no education (7.2). Women with secondary and higher education have the lowest total fertility rate of 4.9 live births. A negative association between schooling and fertility has been widely observed. Schooling may have its own independent effect on fertility, through raising the age at marriage or it may be an indicator of the existence of certain elements that are correlated with lower fertility, such as higher socio-economic status. 3.4 Cumulat ive Ferti l i ty The number of children ever born (cumulative fertility) is one of the basic measures of fertility. As pointed out above, it is subject to possible errors such as omission of births and inclusion of stillbirths and children of relatives. Table 3.6 shows that the level of fertility in Kenya is still high. It should be noted that just as marriage occurs relatively early, childbearing also occurs early with teenage girls (15-19) reporting an average of 0.3 births (last column of Table 3.6). Women in their early twenties have had an average of more than one and half births each. This increases rapidly to 3.5 births among women in their late twenties and 6.5 births for women in their late thirties. By the time women reach the end of their childbearing years (45-49), they have had, on average, 7.6 live births. 23 10 Figure 3.3 Total Fertility Rates by Province, KFS, KCPS and KDHS Births per woman 8 6 4 2 O i Nairobi Central Coast Eastern Nyanza Rift Valley Western Province m KFS 1977-78 ~ KCPS 1984 ~ KDHS 1989 / I Kenya DHS 1989 Table 3.6 Percent distrib~Jtion of all women and currently married women by number of children ever born (CEB}, according to age, Kenya, 1989 Nun~oer of children ever born Wtd. Mean no. of no. Total women CEB Age 0 I 2 3 4 5 6 7 8 9 10+ At[ Women 15-19 78.6 15.9 4.4 0.9 0.2 0.0 0.0 0.0 0.0 0.0 0.0 100.0 1497 0.3 20-24 21.5 30.0 25.4 16.3 5.5 1.0 0.2 0.0 0.0 0.0 0.0 100.0 1321 1.6 25-29 5.3 9.3 13.7 20.3 24.2 14.7 8.9 2.6 0.8 0.0 0.1 100.0 1334 3.5 30-34 2.9 4.1 6.7 10.8 16.0 14.8 19.0 12.9 8.5 3.3 1.2 100.0 982 5.0 35-39 2.2 1.4 4.9 4.7 7.4 12.9 14.3 15.1 16.2 9.0 11.9 100.0 898 6.5 40-44 2.3 1.7 3.2 3.1 5.6 8.4 12.2 10.9 14.8 14.1 23.6 100.0 674 7.4 45-49 2.8 1.9 1.4 4.3 4.8 6.2 11.8 12.2 14.0 13.7 26.7 100.0 445 7.6 Total 22.5 11.6 10.1 9.6 9.5 7.8 8.0 5.9 5.6 3.9 5.6 100.0 7150 3.7 Curently Married Women 15-19 32.8 42.8 19.2 4.3 0.9 0.0 0.0 0.0 0.0 0.0 0.0 100.0 276 1.0 20-24 8.4 25.0 32.6 23.9 8.3 1.5 0.3 0.0 0.0 0.0 0.0 100.0 827 2.0 25-29 3.0 6.7 12.5 21.8 24.7 16.9 10.2 5.0 1.0 0.0 0.1 100.0 1104 3.7 30-34 1.8 3.2 4.9 10.0 16.5 15.3 19.6 14.3 8.9 3.8 1.4 100.0 833 5.2 35-39 1.3 0.9 4.5 3.7 7.1 12.2 14.8 16.1 15.9 10.3 13.1 100.0 781 6.7 40-44 2.1 1.3 3.0 2.6 5.0 6.8 11.4 11.5 15.4 15.5 25.5 100.0 576 7.6 45-49 2.6 0.8 1.4 4.5 4.7 5.6 9.0 10.7 15.7 15.7 29.4 100.0 369 7.9 Total 5.0 9.3 11.7 12.5 12.2 10.1 10.4 8.1 7.5 5.4 7.6 100.0 4765 4.6 24 The distribution of women by number of births reveals that almost 78 percent of women 20-24 have had at least one child. By the time Kenyan women reach the end of childbearing, 27 percent have had ten or more live births. Primary infertility--the proportion of married women aged 45-49 who have ncver had children--is quite low at 3 percent. This confirms the 1984 KCPS finding that only 3 percent of women aged 40 years and above reported never having given birth (Central Bureau of Statistics, 1984, Table 4.8). Information on cumulative fertility from past surveys and censuses can be compared with the KDHS results. Table 3.7 indicates that fertility generally increased between 1962 and 1984 and declined thereafter; however, these trends should be interpreted cautiously because different methods were employed in eliciting information on births. For example, KFS and KDHS questionnaires employed a birth history approach, while the KCPS collected only summary data on the number of children ever born. The data on children ever born from the censuses may be biased downwards due to difficulty in obtaining high quality data on such a large scale. Table 3.7 Mean number of children ever ~rn as reported in various surveys and censuses, by age group, Kenya 1962 1969 1977 1977/78 1979 1984 1989 Age Census Census NDS KFS Census KCPS KDHS 15-19 0.4 0.4 0.3 0.4 0.3 0.4 0.3 20-24 1.7 1.9 1.8 1.8 1.9 2.0 1.6 25-29 3.0 3.7 3.7 3.8 3.7 4.0 3.5 30-34 4.2 5.1 5.6 5.6 5.4 5.7 5.0 35-39 5.1 6.0 6.7 6.8 6.5 7.0 6.5 40-44 5.6 6,4 7.3 7.6 7.0 7.8 7.4 45-49 5.9 63 7.5 7.9 7.2 8.2 7.6 Source: Central Bureau of Statistics, 1984, Table 4.9 Looking only at the data from the surveys, the mean number of children ever born from the 1984 KCPS is higher for each age group than from either the 1977/78 KFS or the 1989 KDHS. The investigation of possible overreporting of fertility in the KCPS or underreporting in the KDHS could be a useful topic for further analysis. Previous methodological research in Kenya (Central Bureau of Statistics, 1975 and 1977) has shown that birth histories rcsult in lower estimates of cumulative fertility than summary data, though the cause is unclear. In any case, the figures from the KDHS are lower than those from the two surveys in the late 1970s, which is consistent with othe'r evidence of a rccent decline in fertility. The mean number of children ever born by age at first marriage and duration of marriage is given in Table 3.8. As expected, the mean number of childrcn born rises with increasing marital duration. The results indicate that irrespective of the age at first marriage, a Kenyan woman would have given birth to an average of 3.3 children during the first 5-9 years of her marriage. At shorter marriage durations, the mean number of children ever born increases with age at marriage, which is unexpected. This could be due to a greater possibility that late-marrying women experience pre-marital conceptions or births, thus artificially raising the tempo of early marital fertility. Another possibility could be that late-marrying women have shorter birth intervals due to shorter breastfeeding durations. At longer durations of marriage, the relationship between 25 Table 3.8 Mean number of ch i ld ren ever born to ever-marr ied women, by age at f i r s t marriage and years s ince f i r s t marr iage, Kenya, 1989 Years Age at first marriage s ince f i r s t marriage <15 15-17 18-19 20-21 22-24 25+ Total 0-4 1.2 1.3 1.3 1.6 1.4 2.9 1.5 5-9 2.7 3.2 3.2 3.5 3.5 3.8 3.3 10-14 4.4 4.6 4.7 4.7 4.9 5.3 4.7 15-19 5.5 6.3 6.0 6.3 6.5 4.6 6.0 20-24 6,9 7.3 7.1 6.8 6.5 16,&) 7.0 25-29 7.5 8.4 7.8 8.2 (5.3) 7.9 30+ 8.2 8.6 (8 .7) 8.3 Total 6.0 4.9 4.3 4.3 3.8 3.9 4.8 ( ) Fewer than 20 unweighted cases. Ho cases, s ine ~ definition these women would ~ age 50 or over. children born and age at marriage is erratic. The data in Table 3.8 may also reflects adolescent subfecundity, in that women who marry when they are under 15 years generally have a lower mean number of children cvcr born. Caution should be exercised in interpreting the data in Table 3.8 because the data on age at first marriage are subject to reporting errors. 3.5 Age at First Birth The onset of childbearing is art important demographic indicator. In many countries, postponement of first births, rcflecting a rise in age at marriage, has made a large contribution to the overall fertility decline. Also, the proportion of women who become mothers in their teenage years, before the age of 20, is a basic indicator of maternal and child health. Table 3.9 shows the distribution of women by age at first birth and current age. Table 3.9 Percent distribution of women by age at first birth, according to current age, Kenya, 1989 Wtd. Median Age at first birth nun~)er age at Current No of first age births <15 15-17 18-19 20-21 22-24 25+ Total w(~en birth* 15-19 78.6 2.3 14.0 5.1 100.0 1497 20-24 21.5 4.0 28.0 26.4 15.3 4.8 100o0 1321 19.3 25-29 5.3 11.1 29.2 27.2 16.4 8.3 2.5 100.0 1334 18.7 3D-34 2.9 15.1 32.0 22.4 14.0 9.4 4.3 100.0 982 18.2 35-39 2.2 11.3 28.7 27.5 16.6 9.8 3.9 100.0 898 18.6 40-44 2.3 14.7 29.2 20.7 15.8 I0.6 6.6 100.0 674 18.6 45-49 2.8 10.2 21.2 22.7 17.7 13.4 11.9 I00.0 445 19.7 Total 22.5 8.8 25.6 20.9 12.5 6.8 2.9 100.0 7150 * Def ined as the exact age by which 50 percent of wo~n have had a b i r th . 26 The data show that 55 percent of Kenyan women become mothers before they reach age 20. This finding has serious health implications, since young mothers suffer more health problems than older mothers, and their children have higher mortality rates. The data imply that the median age at first birth has been relatively constant over time, with younger women having almost the same median ages at first birth as the older women. However, it should be noted that the data are heavily dependent on correct reporting of dates of birth of both the woman and her first birth. Table 3.10 presents data on differentials in median age at first birth among women aged 20-49 years by background characteristics of women. The table reveals that urban women start childbearing late compared to their rural counterparts. Nyanza Province has the lowest median age at first birth (17.8), while Nairobi has the highest median age at first birth (19.9). A~s expected, women with no education report the lowest median age at first birth (18.1), which is almost 3 years earlier than their secondary and higher educated counterparts (20.7). Table 3.10 Median age at first birth among women aged 20-49 years, by current age and background characteristics, Kenya, 1989 Current age Background characteristics 20-24 25-29 30-34 35-39 40-44 45-49 Total Residence Urban 19.9 19,9 19.7 19.2 19,9 21.2 19,8 Rural 19.1 18.4 18.0 18.6 18.5 19.6 18.6 Province Nairobi 19.9 19,9 19.4 18.0 20.5 23.2 19.9 Central 19.7 19.1 18.6 19.2 19.1 19.7 19.2 Coast 20.5 18.3 19.3 17,5 17.4 18,7 18.7 Eastern 19,5 19.2 18.5 19,4 19.2 19.8 19.3 Nyanza 18.0 17.8 17.3 17.7 17.5 18.2 17.8 Rift Valley 19.1 18,2 17.9 18.7 18.6 22.0 18.6 Western 19,2 18.7 18.1 10.3 18.7 17.9 18.6 Education No education 17.7 17.3 17.6 18.2 18,1 20.0 18.1 Some primary 18.3 17.8 17.6 18.5 18.6 18.9 18,2 Primary complete 18.9 18.7 16.5 18.8 20.2 20.6 18.8 Secondary + 20.9 20.3 20.8 20.9 22.4 (23.3) 20.7 Total 19.3 10,7 18.2 18.6 18.6 19.7 18.8 Note: Median is defined as the exact age by which 50 percent of women have had a birth. Numbers in parentheses are based on fewer than 20 unweighted cases, 27 4 FERTILITY REGULATION 4.1 Contracept ive Knowledge Determining the level of knowledge of contraceptive methods and services was a major survey objective, since knowledge of contraceptive methods and of places where these methods can be obtained are preconditions for their use. Information about knowledge of contraceptive methods was collected by asking the repondent to name ways by which a couple could delay or avoid pregnancy. If a respondent failed to mention any particular method spontaneously, the method was described by the interviewer and then the respondent was asked if she recognized the method. In the questionnaire, seven modern methods--pill, IUD, injection, condom, .barrier methods (diaphragm, foam and jelly), female sterilisation, and male sterilisation--were described, as well as two traditional methods--periodic abstinence (or rhythm) and withdrawal. Any other methods mentioned by the respondent, such as herbs or breastfeeding, were also recorded. For any method that she recognised, the respondent was asked if she knew of a source or a person from whom she could obtain the method. If she reported knowing about rhythm she was also asked if she knew a place or person from whom she could get information on the method. Table 4.1 Percentage of all women and currently married women knowing a contraceptive method and knowing a source by specific method, Kenya, 1989 Knows Knows Knows Knows method method source source Method AW CMW AW CMW Any method 90.0 92.4 88.1 90.8 Any modern method 88.4 91.3 86.5 89.9 Pill 84.4 88.4 81.6 86.3 IUD 62.0 67.0 60.0 65.1 Injections 76.3 81.9 74.2 79.9 Diaphragm/Foam/Jelly 24.4 26.7 23.2 25.5 Condom 53.4 55.7 49.2 51.7 Female steritisation 68.2 72.5 65.9 70.6 Male sterilisation 19.8 21.7 19.0 21.2 Any traditional method 54.8 55.8 44.6 44.8 Periedic abstinence 50.7 50.8 44.6 44.8 Withdrawal 16.8 18.2 Other 5.1 6.3 AW = All women (7150); CMW = Currently married women (4765) The KDHS results indicate that 90 percent of Kenyan women know at least one contraceptive method (Table 4.1). This is an increase from the levels reported in the 1977/78 Kenya Fertility Survey (88 percent) and the 1984 Kenya Contraceptive Prevalence Survey (81 percent). More women indicate that they know a modern method (88 percent) than a traditional method (55 percent). 29 The pill, recognised by 84 percent of women interviewed, is the most widely known method. In the 1984 KCPS, only 73 percent of women interviewed recognised the pill. Injection is the second most widely known method (76 percent). The other better known methods are female sterilisation (68 percent) and the IUD (62 percent). Fifty-three percent of all women report knowing about the condom and 24 percent know about barrier methods. The least known method is male sterilisation (20 percent). Lack of knowledge of condom and male sterilisation may be attributed to the fact that they are male oriented. Considering the traditional methods included in the questionnaire, periodic abstinence (51 percent) is better known than withdrawal (17 percent) or any other traditional method (5 percent). It should be noted that for all methods knowledge is higher among currently married women than among all women. In order for women to adopt family planning, they need to know about the available methods as well as to be aware of where they can obtain contraceptive information and services. More currently married women (91 percent) know a source for a contraceptive method than do all women (88 percent). While only 45 percent of all the women interviewed know a source for a traditional method, 87 percent know of a source where they can obtain a modern contraceptive method. Most women (82 percent) know a source where they can obtain the pill, 74 percent know where to obtain injections, 66 percent know a source for female sterilisation and 60 percent know a source for the IUD. Forty-nine percent know where to obtain condoms, 45 percent know where to obtain information on periodic abstinence, and less than 25 percent know a source for either the barrier methods or male sterilisation. TabLe 4.2 Percentage of currentLy married women knowing at teast one modern method, knowing a source for a modern method, by back- ground character i s t i cs , Kenya, 1989 Background Knows Wtd. character- modern Knows no. of i s t ie method source women Age 15-19 86.0 84.5 276 20-24 94.5 93.3 827 25-29 93.9 92.4 1104 30-34 92.3 91.1 833 35-39 92.9 92.1 781 40-44 85.4 83.8 576 45-49 83.1 80,6 369 Residence urban 95.2 94.1 748 RuraL 90.5 89.1 4018 Province Rairobi 94.8 93.8 335 CentraL 95.8 95.2 648 Coast 92.3 89.2 350 Eastern 92.7 90.1 804 Nyanza 93.3 91.6 872 Rift VaLLey 84.6 84.0 1047 Western 90.6 89.7 711 Education No education 82.8 80.6 1506 Some primary 92.0 90.9 1462 Primary co(npLete 96.9 95.8 987 Secondary + 98.8 98.1 804 ReLigion CathoLic 90.7 89.2 1656 Protestant 93.1 92.0 2706 MusLim 94.7 92.0 165 other 79.3 79.3 79 No reLigion 65.5 62.0 151 Total 91.3 89.9 4765 ~ote: Excludes a smaLL number of women not stated as to education and reLigion. Some interesting differences are revealed when knowledge of methods and sources is considered in connection with background characteristics of the respondents (Table 4.2). Over 80 percent of currently married women in each age group know at least one modern contraceptive method. Knowledge of modern methods is lowest (83 percent) among women aged 45-49 and highest (95 percent) among women aged 20-24. Similarly, knowledge of a source for contraceptive information or services is lowest among women 45-49 (81 percent) and highest among .women 20- 24 (93 percent). 30 Although urban women are more likely than rural women to know about a method of contraception or a source of information or services, the difference is not pronounced. Ninety- five percent of currently married urban women know at least one modern contraceptive method, compared to 91 percent of their rural counterparts. While 94 percent of urban women could name a source, almost as many (89 percent) rural women could do the same. Provincial variations in contraceptive knowledge are rather small. Central Province has the highest level of contraceptive knowledge (96 percent), followed closely by Nairobi (95 percent), Eastern and Nyanza Provinces (93 percent), Coast Province (92 percent) and Western Province (91 percent). Contraceptive knowledge is lowest in Rift Valley Province (85 percent). Both knowledge of a modern method and knowledge of a source of information or services increase with higher levels of education. While 83 percent of the currently married women with no education know at least one modern contraceptive method, 97 percent of women with some primary education and 99 percent of those who had acquired secondary and higher education know at least one modern method. The same is true with knowledge of a source of contraceptive information or services--knowledge goes up with increased education. Often contraceptive knowledge is associated with religious affiliation. The results from this survey, however, indicate that the differentials in knowledge by religion are small; over 90 percent of Catholic, Protestant and Muslim women know of a modern method and almost as many know of a source. Knowledge of both method and source is lowest among currently married women with no religion. 4.2 Acceptabi l i ty of Methods The women interviewed during the KDHS were asked to report problems in connection with contraceptive methods that they had heard about. Table 4.3 shows that the proportion of women who give "no problem" or "don't know" as answers is high for most methods, which may reflect a lack of depth of knowledge of many methods. "Health concerns" were cited frequently for most modern methods, especially the pill (39 pcrcent), IUD (29 percent) and injcction (26 percent). Substantial proportions reported ineffectiveness as the major problem with periodic abstinence (20 percent), withdrawal (18 percent), barrier methods (17 percent), and the condom (15 percent). It is somewhat surprising that ineffectiveness was also cited by 10 percent of women knowing the IUD. Disapproval of the partner was cited more frequently for the male-oriented methods (withdrawal, condom, and male sterilisation) and inconvenience was cited more frequently for withdrawal, condom, barrier methods, and periodic abstinence. 4.3 Knowledge of Supply Sources Information on knowledge of sources of contraceptive methods was obtained by asking women who know a method about where they could obtain the method if they wanted to use it. Results show that government institutions are perceived as the primary source of contraceptive services for most methods (Table 4.4). Over 85 percent of respondents said they would go to either a government hospital or health center to obtain the pill, IUD, injection, and fcmale and sterilisation, while over 70 percent said they would go to one of these sources to obtain barrier methods and condoms. The Family Planning Association of Kenya (FPAK) was the next most frequently named potential source for most of the modern methods, except condoms, which a 31 Table 4.3 Percent distril~tion of women who have ever heard of a contraceptive method by main problem perceived in using the method, according to specific method, Kenya, 1989 Contraceptive method Main Diaphragm/ Female Male Per iodic problem In jec - foam Con- s te r i [ - s te r i [ - abs t in - With- perceived P i l l IUD t ion je l l y dom i sa t ion i sa t ion ence drawat None 21.9 17.0 24.2 16.7 25.3 34.7 27.0 51.1 22.7 Not effective 2.7 9.7 3.0 16.5 14.8 2.9 0.7 19.8 18.3 Partner disapproves 0.2 0.4 0.3 2.1 5.2 2.0 5.5 1.2 11.0 Community disapprove 0.1 0.2 0.4 0.0 0.0 0.2 0.5 0.0 0.2 Rel ig ion disapproves 0.3 0.3 0.2 0.1 0.0 0.3 0.4 0.1 0.0 Health concerns 38.8 28.9 25.9 6.4 1.7 17.3 10.3 0.6 0.3 Access/Avai t ab i l i ty 0.1 0.2 0.2 0.1 0.0 0.1 0.3 0.1 0.0 Costs too much 0.2 0.0 0.1 0.2 0.1 0.2 0.1 0.1 0.2 Inconvenient to use 1.5 3.7 1.1 5.0 5.5 1.2 1.3 5.3 12.6 other 1.3 1.4 2.0 0.8 0.4 1.4 0.7 0.3 2.3 Don't know 32.6 37.8 42.2 51.7 46.2 39.0 52.4 20.1 28.3 Missing 0.2 0.4 0.5 0.5 0.8 0.8 0.8 1.3 4.1 Total 100.0 100.0 100.0 100.0 100.0 I00,0 100.0 100.0 100.0 Number of women 6032 4436 5459 1747 3815 4874 1413 3624 1202 Table 4.4 Percent distrib~Jtion of women knowing a contraceptive method by supply source they say they would use, according to specific method, Kenya, 1989 Contraceptive method Supply source Diaphragm/ Female Male Per iodic that would In jec - foam Con- s te r i l - s te r i l - abs t in - be used Pill IUO l ion je l l y dom i sa t ion i sa t ion once Nowhere 0.1 0.1 0.1 0.3 0.1 0.2 0.I 7.9 Govt. hospital 56.7 59.5 59.1 48.5 42.8 80.7 82.1 11.3 Govt. health center 29.1 27.9 27.9 29.4 28.4 8.9 7.4 10.4 FPAK* 5.0 5.1 5.4 8.1 6.6 3.1 2.4 5.6 Mobile clinic 1.1 0.4 8.6 1.1 1.2 0.1 0.0 1.5 Field educator 0.2 0.0 0.0 0.I 0.3 0.0 0.0 1.6 Pharmacy/Shop 0.7 0.2 0.1 3.4 8.1 0.1 0.3 0.0 Private hospital 1.4 1.6 1.8 2.0 1.9 1.7 1.6 1.3 Mission hospital/dispen. 1.9 1.4 1.7 1.6 1.1 1.7 0.9 1.9 Emp[oyer,s c l in i c 0.1 0.1 0.1 0.1 0.1 0.0 0.1 0.2 Private doctor 0.4 0.5 0.4 0.7 0.5 0.4 0.6 1.7 Trad i t iona l hea ler 0,1 0.0 0.0 0.0 0.0 0.0 0.0 0.2 Husband would oet 0.0 0.0 0.0 0.0 1.0 0.0 0.1 2.4 Friends/Relatives 0.1 0.0 0.0 0.0 0.1 0.0 0.6 43.2 Other 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6.8 Don't know 3.0 3.1 2.4 4.4 7.1 2.6 3.1 3.4 Missing 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.7 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Number 6032 4436 5459 1747 3815 4874 1413 3624 * Family Planning Association of Kenya 32 larger proportion of respondents said they would obtain at a pharmacy or shop. Those who know about periodic abstinence were most likely to say they would go to friends or relatives for advice about the method. 4.4 Ever Use of Contracept ion Ever use of contraception is one of the most important items of information in the Kenya Demographic and Health Survey. The survey asked women if they had ever used any of the contraceptive methods they said they knew, Table 4.5 ~ercentage of all women and currently married wome~ who have ever used a contraceptive method, by 8peclfic method and age, Kenya, 1989 Contraceptive method Dla- Peri- Wtd. Any phragm/ Female Male Any odic With- number Any modern In Jec- foam Con- sterll- sterll- trad' 1 abstl- draw- of Age method method Pill IUD tlon Jelly dom isation iaatlon method hence al Other women All Women 15-10 14.9 4.2 2.2 0.6 0.5 0.3 1.5 0.0 0.I 12,1 11.4 1.0 0.9 I~97 20-24 40.5 21.2 14.9 3.6 2.9 1.5 5.8 0.8 0.0 25,9 25.6 2.4 3.0 1321 25-29 47,1 30.6 21.0 7.9 6.~ 1.8 5.1 1.3 0.i 25.6 23.6 3.1 1.4 1334 30-34 50.5 35,9 23.8 11.7 0.9 2.7 4.4 6,3 0.2 24,5 21.9 3.4 3.5 982 35-30 49.8 34.4 18.0 II.4 8.5 3.2 5,0 8.8 '0.4 24,6 20.3 2.9 3.8 898 40-44 43.9 29.2 15.7 9.6 9.8 3.0 2.6 9.1 0.5 23.3 18.6 2.1 4.9 674 45-49 39.4 26.2 15.9 9.2 5.1 1.6 2.3 10.3 0.i 19,6 14.6 2,4 5.9 445 Total 39.1 24.1 15.1 6.8 5.5 1.8 3.6 3.6 0.2 21.9 19.4 2.4 2.7 7150 Currently Marrled Women 15-19 26.2 Ii.i 8.2 1.6 0.5 0.3 4.2 0.0 0.0 19.0 18.4 2.5 0.3 276 20-24 41.7 22.8 16.2 4.1 3.2 1.3 4.4 0.7 0.0 25.7 22.6 2.7 3.6 827 25-29 46.5 30,0 1~.0 8.1 6.7 1.8 5.2 1.4 0.I 25.2 23.3 3.0 1.3 1104 30-34 48.2 35,1 22.6 II.I 9.4 2.5 4.2 6.4 0.5 22.7 19.6 3.7 3.9 835 35-39 49.8 33.7 17.3 11.4 8.2 3.4 5.3 8.9 0.4 25.7 21.2 3.4 3.8 781 40-44 46.3 30,I 16.2 9.4 I0.0 3.i 2.6 9.8 0.0 25.1 20.2 2.2 5.2 576 45-49 ~2.6 27,6 16.9 0.8 4.9 1.8 2,7 i0,0 0.2 20.7 15.3 2.6 4,1 369 Total 45.0 29.0 18.0 8.4 6.7 2.1 4,3 5,0 0.i 24.2 20.9 3.0 3,2 4765 Table 4.5 shows the proportion of all women and currently married women who have had experience with contraceptive methods. The level of ever use of any method among all women is 39 percent, higher than the level of 29 percent reported in both the 1977/78 Kenya Fertility Survey and the 1984 Kenya Contraceptive Prevalence Survey. KDHS data also show that the level of ever-use among all women (39 percent) is lower than that of currently married women (45 percent). Ever use of modern methods of contraception is slightly higher (24 percent) than that of traditional methods (22 percent) among all women and among the currently married women, 29 percent of whom had used at least one modern method and 24 percent of whom had used a traditional method. Three observations are that: 33 ever-use has increased over the past decade, ever-use is higher among currently married women than all women, and ever-use is slightly higher for the modern methods. The KDHS results reveal that the ever-use rate for any method is low for all women aged 15-19 (15 percent), and then it increases for women in the age groups 20-24 (40 percent) and 25- 29 (47 percent). Ever-use is highest among women aged 30-39 (50 percent), but then declines slightly for all women aged 40-44 (44 percent) and 45-49 (39 percent). Periodic abstinence has been used by more women than any other mcthod (19 percent), followed by the pill (15 percent). Seven percent of all women have used the IUD, 6 percent have used injection, and 4 percent have used either female sterilisation or condoms. The proportion of women who have been sterilised increases with age. 4.5 Cur rent Contracept ive Use The level of current use of contraceptives is the most widely used measure of the success of a family planning programme. The KDHS results show that 27 percent of currently married Kenyan women are currently using a contraceptive method (Table 4.6). As in the case of ever- use, the contraceptive prevalence rate among currently married women is higher (27 percent) than among all women (23 percent). Current use of contraceptives is usually presented tbr currently married women, because they are likely to be more consistently exposed to the risk of pregnancy. More currently married women are using modern contraceptives (18 percent) than traditional methods (9 percent). Nevertheless, periodic abstinence is the single most widely used method--used by. 8 percent of currently married women. The next most popular method is the pill, used by 5 percent of married women. Current use for other methods include female sterilisation (5 percent), IUD (4 percent) and injection (3 percent). Less than one percent of married womcn rely on barrier methods, condoms or withdrawal. The 27 percent level of contraceptive use recorded in the KDHS represents an increase of more than 50 percent over the rate from the 1984 KCPS (17 percent) and almost four times the rate from the 1977/78 KFS (7 percent). Use of modern methods has doubled since 1984, from 9 to 18 percent of currently married women (Figure 4.1). Injectible contraceptivcs have shown the biggest gain, from less than 1 percent of married women in 1984 to over 3 percent in 1989. Use of periodic abstinence and female sterilisation has almost doubled since 1984. An inverted U-pattern of prevalence by age was observed for the currently married sample. As in the case of ever-use, the current use rate for any method is low for the currently married women aged 15-19 (13 percent), but rises steadily, reaching 34 percent among those aged 35-39. Current use then falls to 31 percent in the 40-44 age group and 24 percent for women aged 45- 49. Current use is probably lower among younger women because many of them are interested in starting their families and among older women, because some are no longer fecund. 34 Table 4.6 Percent dls~rlbutlon of all women and currently married women, by contraceptive me~hod currently being used, according to age r Kenya t 1989 Contraceptive method Weigh- Dla- Female Perl- Not ted Any phragm/ st~r- Any odic With- curt- number Any modern InJec- foam/ Con- illsa- trad'l abstl- draw- ently of Age method method Pill IUD tlon ~elly dom tion method nence al Other using Total women All Women 15-19 7.5 1,0 1.3 0.3 0.i 0,0 0.i 0.0 5.7 4.0 0.O 0.8 92.5 i00.0 1497 20-24 20.7 11.5 6,6 2.0 1.4 0.3 0.8 0.4 9.2 7.8 O.l 1.2 79.3 i00.0 1321 25-29 27.2 17.5 8,i 3.7 3.4 0.i 0.9 1,2 9.7 8,6 0,3 0.8 72,0 I00.0 1334 30-34 32.1 22.5 5,6 4.7 5.6 0.6 0.i 5.8 9.6 7,0 0,2 2.4 67.9 i00.0 982 35-39 34.1 23.1 3.7 5.7 4.5 0.4 0.4 8.4 I0.9 8.9 0.2 1.8 65.9 1O0.0 899 40-44 27.8 19.7 2.3 3.6 3.6 1.0 0,I 8.5 8.2 6.9 0.3 1.5 72.2 I00.0 674 45-49 22.3 16.7 1.9 2.0 1,3 0.5 0,I I0.i 5.6 4.0 0.0 1.6 77.7 i00,0 445 Total 23.2 14.7 4,6 3.0 2.7 0.3 0.4 3.6 8.5 7,0 0.2 1,3 76.8 100.0 7150 Currently Married Women 15-19 13,0 6.7 5.1 1.3 0.3 0.0 0,0 0.0 6.3 6.1 0.0 0.3 87.0 i00.0 276 20-24 20.1 11.8 6.7 2.2 1,2 0.i I,i 0.6 8.3 7.1 0.2 1.0 79.9 100.0 827 25-29 26.1 16.8 7.4 3.5 3,5 0.2 0.8 1.3 9.3 8.i 0.4 0.7 73.9 i00.0 1104 30-34 51.5 22.2 5.6 4.9 5.1 0.6 0.i 5,8 9.2 6,4 0.2 2.6 69.5 i00.0 833 35-39 34.2 22.9 3.4 5.4 4.8 0.5 0.3 8.4 11.3 9.6 0.2 1.5 65.8 I00.0 781 40-44 30.6 21.2 2.7 3.0 4.2 1.2 0.I 9.1 9.4 7.9 0.3 i.I 69.4 I00.0 576 45-49 23.7 17.5 1.8 3,~ 1.5 0.6 0.2 10.0 6.2 4.7 0.0 1.6 76.3 i00.0 369 Total 26.9 17.9 5.2 3.7 3.1 0.4 0.5 4.7 9.0 7.5 0,2 1.3 73.1 1O0.0 4765 Figure 4.1 Trends in Contraceptive Use Among Currently Married Women 15-49 Any Method Any Modern Method Pill IUD Injection Female Sterilisation Periodic Abstinence ~\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \~ ~\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \~ 0 5 10 15 20 25 30 35 Percent using I mKCPS 1984 ~KDHS 1989 / Kenya DHS 1989 35 4.6 Current Use by Background Characterist ics Table 4.7 presents the relationship between the level of contraceptive use and background characteristics for currently married women. The percent of women using contraceptives is somewhat higher among urban women (31 percent) than rural women (26 percent). However, while use of modern methods is higher for urban women (26 percent) than for rural women (16 percent), the reverse is observed for traditional methods. The percentage using traditional methods among rural women (10 percent) is twice that of urban women (5 percent). As for provincial differentials, Eastern and Central Provinces have the highest level of current use (40 percent) followed by Nairobi (34 percent), Rift Valley (30 percent), and Coast (18 percent). Nyanza and Western Provinces (14 percent) lag behind, creating a three-fold differential in use by province from highest to lowest (Figure 4.2). The mix of methods also varies substantially by province. In most provinces, about 75 percent of current users are using modern methods, however, in Eastern Province, the figure is less than 50 percent. The pill is the most commonly used method in Nairobi, Coast and Western Provinces, the IUD in Central Province, female sterilisation in Nyanza, and periodic abstinence in Eastern and Rift Valley Provinccs. Figures are also presented in Table 4.7 for the rural areas of the 13 individual districts that were targetted in the sample design lbr the survey. The results should be viewed with caution since the number of women interviewed in each district is not large (see discussion of sampling errors in Appendix B). The data show that there is a nine-fold difference in contraceptive use by district, ranging from a high of 52 percent of married rural women in Kirinyaga District to a low of 6 percent of women in South Nyanza District. In addition to Kirinyaga District, Nyeri, Machakos, Meru and Murang'a Districts all show high levels of use, while Siaya, Bungoma, and Kilifi Districts have levels only slightly higher than South Nyanza. The level of use of modern methods in Meru District (34 percent) is identical to the level found for the Chogoria Hospital catchment area in 1985 (Chogoria Hospital, 1987). The method mix also varies; in some districts, periodic abstinence is the most widely used method, while in others it is the pill, female sterilisation, IUD, or injection. For example, it is clear that the high level of use of periodic abstinence in Eastern Province mentioned above is due almost entirely to the prominence of that method in Machakos District, not Meru District, the other targetted district from Eastern Province, where use of the pill and IUD predominate. Also notable is the high level of IUD use in Kirinyaga and Murang'a Districts and the extent of female sterilisation in Nyeri District. Regarding education levels, the major observation is that the percent of currently married women using any method increases directly with education. Use among women with secondary and higher education is more than double (40 percent) that of women with no education (18 percent). Current contraceptive use directly increases with the number of living children that a woman has, ranging from 5 percent among women with no children to 31 percent among those with four or more. Christian women are more likely to be using contraceptives than are Muslim women and women of other religious groups, or those with no religious affiliation. There is, however, slightly more current use among Protestant women (29 percent) than among Catholic women (26 percent). 36 Table 4.7 Percent distribution of currently married won~n by centracQptivemethod currently being used, according to background characterla~Ic$, Kenya, 1989 Contraceptive mQthod currently being used Weigh- Die- Female Peri- Not ted Background ~ny phragm/ star- Any odic With- curt- number character- Any modern InJec- foam/ Con- ilisa- trad'l absti- draw- en~ly of Istlcs method method Pill IUD tion Jelly dom tlon method nence al Oth@r using Total women Residence Urban 30.5 25.5 9.8 8.0 2°8 0.5 8.8 3.6 5.0 4.0 0.4 0.6 69.5 i00.0 748 Rural 26.2 16.4 4.3 2.9 3.4 0.4 0.4 4.9 9.8 8.i 0.2 1.4 73.8 i00.0 4018 Province Nalrobi 33.5 27.9 ii.9 7.9 2.3 1.2 0.4 4.4 5.6 4.0 0.8 0.8 66.5 I00.0 335 Central 39,5 30.8 9.1 i0.0 3.6 0.3 1.3 7.7 8.7 7.1 0.3 1.3 60.5 I00.0 648 Coast 18.1 14.8 5.5 1.7 3.6 0.i 0.3 3.6 3.3 3.0 0.3 0.0 91.9 i00.0 350 Eastern 40.2 19.5 5.9 4.7 3.5 8.4 0.4 4.5 20.8 17.9 0.3 2.5 59.8 I00.0 804 Nyanza 13.8 10.2 2.7 0.9 2.5 0.0 0.3 3.9 3.5 3.0 0.0 0.5 86.2 I00.0 872 Rift Valley 29.6 18.1 3.6 2.3 5.5 1.0 0.5 5.5 11.5 9.0 0.3 2.3 70.4 I00.0 1047 Western 13.7 i0.0 3.8 1.6 1.6 0.2 0.2 2.6 3.7 3.0 0.0 0.7 86.3 i00.0 711 Dis~riot (Rural) Kilifi 9.7 8.5 3.7 0.7 2.3 0.3 0.3 1.0 1.3 1.0 0.3 0.0 90.3 i00.0 500 Machakos 40.4 12,1 5.3 1,4 i.i 0.0 0,7 5.5 28.4 24.5 0.7 3.2 59.6 i00.0 282 Meru 36.3 34.2 12.4 8.3 5.7 1,6 0.5 5.7 2.1 2.1 0.5 0.0 63.7 i00.0 193 Nyerl 41,2 35.3 7.8 9.3 2.9 0.5 0.5 14.2 5.9 4.9 0.5 0.5 58.8 100.0 204 Muranga 31.3 24.2 2.8 i0.0 2.4 0.5 0.9 7.6 7.1 6.6 0.5 0.0 68.7 i00.0 211 Kirinyaga 52.2 44.2 12.4 18.6 8.0 0.4 0.9 4.0 8.0 7.1 0.4 0.4 47.8 i00.0 226 Kerlcho 23.2 15.2 3.4 0.9 5.3 0.4 0.0 5.3 8.0 6.8 0.0 1.1 78.8 i00.0 263 Oasln Cishu 14.9 I0.I 4.3 0.7 4.1 0.0 0.0 1.4 4.7 3.d 0.7 0.7 85.1 I00.0 148 South Nyanza 5.9 5.3 1.8 0.O O.0 0.0 0.0 1.5 2.6 1.8 0.0 0.7 94.1 100.0 272 Kisii 20.2 15.5 1.7 1.7 5.6 0.0 0.4 6.0 4.7 4.3 0.0 0.4 79.8 i00.0 253 siaya 8.8 5.6 0°6 O.0 1.3 0.0 1.3 2.5 5.i 2.5 0.0 0.6 91.2 i00.0 160 Ka kamega 14.5 10.2 3.2 0,3 2.2 0.5 0.3 3.8 4.1 3.9 0.0 0.3 85.7 i00.0 315 Bungoma 8.5 5.0 1.6 6.9 0.6 0.0 0.3 1.6 3.5 1.3 0.0 2.2 91.5 100.0 317 Education No education 18.3 9.7 2.1 1.5 2.2 0.1 0.3 3.7 8.6 6.9 0.0 1.7 81.7 I00.0 1506 Some primary 26.1 17.3 4.5 2.9 4.1 0.3 0.i 5.7 9.8 7.3 0°2 1.3 73.9 100.0 1462 Primary temp. 30.4 22.0 7.2 4.3 4.5 0.7 0.3 4.9 8.4 6.9 0.3 1.2 69.6 i00.0 987 Secondary + d0.4 29.3 10.2 9.3 2,6 1.0 1.7 4.5 II.i 9.8 0,7 0.7 59.6 I00.0 804 NO. of children None 4.7 0.8 0.6 0.0 0.2 0.0 0.0 0.0 5.8 3.4 0.4 0.O 95.3 i00.0 290 1 16.9 8.6 5.2 1.8 8.9 0.0 0.5 0.3 8.3 B.l 0.0 0.2 85.1 I00.0 497 2 24.2 16.0 6.7 4.d 1.6 0.2 1.2 1.9 8.2 7.2 0.i 0.9 75.8 i00.0 613 3 28.5 18.5 9.3 3.3 2.4 0.3 0.5 2.7 9.9 8.4 0.9 0.6 71.5 i00.0 649 d+ 31.4 21.7 4.3 4.4 4.8 0.7 0.4 7.1 9.7 7.7 0.I 1.9 68.6 100.0 2716 Religion Catholic 25.8 14.4 4.4 3.2 2.3 8.7 0.6 3.2 11.4 9.8 0.4 1o2 74.2 100.0 1656 Protestant 29.3 20.9 5.8 4.1 4.3 0.3 0.d 6.0 9.4 6.7 0.i 1.6 70.7 i00.0 2706 Muslim 16,7 13.9 4.6 2.9 2.7 0.0 1.0 2.6 2.8 2.6 0.2 0.0 83.3 I00.0 165 Other 20.8 15.9 5.5 8.1 0,0 0.0 0.8 1.6 4.9 3.3 1.6 0.0 79.2 i00.0 79 No religion 9.8 6.3 2.3 0.9 1.0 0.0 0.0 2.1 3.5 3.5 0.0 0.0 90.2 i00.0 151 To~al 26.9 17.9 5.2 3.7 3.3 0.4 0.5 4.7 9.0 7.5 0.2 1.3 73.1 I00.0 4765 Note: Excludes a few women not stated as to education and religion, since the sample within individual districts was self-welghtlng, n%Imbers of women in each district are unwQighted. 37 60" 50 40 30 20 10 0 / Percent Figure 4.2 Current Contraceptive Use by Province Currently Married Women 15-49 39.5 40.2 Nairobi Centrar Coast Eastern Nyanza Rift V. Western Province L ~ Traditional Methods I I Modern Methods i Kenya DHS 1989 4.7 Number of Children at First Use Table 4.8 shows the number of living children at the time of first use of contraception among ever-married women. Generally, younger women are starting to use contraception at lower parities than the older women did. For example, 19 percent of women 20-24 started using contraception after their first child, compared to only 4 percent of women 45-49. This probably reflects the fact that younger women are more likely to use contraception to space births, while older women use it to limit births. rabte 4.8 Percent distribution of ever-married women by number of living children at time of first use of contraception, ~ccording to current age, Kenya, 1989 Number of living children at time first used Wtd. Never no. of Age used None I 2 3 4+ Missing Total women 15-19 73.6 12.9 11.9 0.8 0.5 0.0 0.3 100.0 302 20-24 58.8 9.3 18.8 7.5 4.4 0.5 0.6 I00.0 901 25-29 53.9 3.7 15.6 10.7 6.9 7.9 1.2 100.0 1191 30-34 50.3 2.5 8.2 10.3 7.0 21 .I 0.5 100.0 928 35-39 49.9 2.7 6.2 6.5 8.0 24.9 1.6 100.0 869 40-44 55.}" 3.5 4.2 6.5 3.4 25.0 0.8 100.0 664 45-49 59.7 1.8 4.0 1.6 3.9 28.3 0.7 100.0 434 rotat 55.3 4.6 10.7 7.6 5.6 15.2 0.9 100.0 5289 38 4.8 Knowledge of Fert i le Per iod An elementary knowledge of reproductive physiology provides a useful background for successful practice of coital-related methods such as withdrawal, condom or barrier methods, but more so for periodic abstinence. Successful practice of periodic abstinence is dependent on a correct understanding of when in the ovulatory cycle a woman is most likely to conceive. Table 4.9 presents the distribution of all respondents and the small number of respondents who had ever used periodic abstinence by knowledge of the period in the ovulatory cycle when a woman is fertile. Table 4.9 Percent distr ibut ion of all women and women who have ever used periodic abstinence by knowledge of the fertile period during the ovulatory cycle, Kenya, 1989 Ever users of Air periodic Fer t i le period women abstinence During her period 1.2 0.9 Right after period has ended 40.8 46.8 Middle of the cycle 22.4 32.7 Just before period begins 9.0 10.5 At any time 5.3 3.1 Other 0.7 0.7 Don't know 20.5 5.2 Missing 0.2 0.1 Total 100.0 100.0 Number of women 7150 1386 Over 40 percent of the women interviewed said a woman is most likely to conceive just after her period has ended, while 21 percent did not know when a woman is likely to conceive and a small number (9 percent) identified the fertile time to be just before the period begins. Only 22 percent gave the "correct" responsc--that a woman was most likely to conceive in the middle of the cycle. Ever-users of periodic abstinence seem to be more knowledgeable about the ovulatory cycle, since 33 percent identificd the fertile time as occurring in the middle of the cycle (between two periods), and only 5 percent said they did not know. It should be noted that the response categories developed for this question are one attempt at dividing the ovulatory cycle into distinct periods. It is possible that women who gave an answer of, say, "one week after her period" were coded in the category "just after her period has ended," instead of in the category "in the middle of her cycle." Thus, women may actually have a more accurate understanding of their fertility cycles than is reflected in Table 4.9. 4.9 Sources for Contracept ive Methods Information on the source for contraceptive methods was obtained by asking women using modern methods where they obtained their methods the last time and by asking women relying on periodic abstinence where they received advice about the method. The results are presented in Table 4.10 and Figure 4.3. 39 Table 4.10 Percent distribution of current users of modern methods by most recent source of supply or information, according to specific method, Kenya, 1989 Total D iaph. / In- Total Female Source of supply foam/ jec - c l in i c s te r i t - Total supply methods P i t t Condom je t ty t ion methods IUD i sa t ion users Govt. hospital 46.1 44.4 27.8 53.4 50.7 67.4 58.9 75.4 55.7 Govt.ctiniclheatth centre 20.5 24.1 26.9 8.4 15.0 7.9 15.7 1.5 14.8 FPAK clinic 13.0 14.6 9.1 3.1 12.1 6.7 11.4 2.8 10.1 Other hospital/clinic 7.5 6.4 4.1 25.5 7.6 5.7 1.5 9.3 6.7 Mobile c l in i c 1.6 1.2 0.0 0.0 2.7 0.6 0.5 0.7 1.1 F ie ld educators 1.3 2.2 0.0 0.0 0.0 0.1 0.3 0.0 0.8 Pr ivate doctor 7.1 4.9 4.5 9.6 10.8 9.9 10.4 9.5 8.3 Pharmacy 1.4 1.0 17.6 0.0 0.0 0.0 0.0 0.0 0.8 Husband obta ins 0,4 0.0 7.7 0.0 0.0 0.1 0.3 0.0 0.3 F r iends / re la t ives " 0.1 0.0 0.0 0.0 0.3 0.7 0.0 0.0 0.4 Other 0.7 0.8 0.0 0.0 0.8 0.7 0.9 0.5 0.7 Missing 0.4 0.4 2.3 0.0 0.0 0.1 0.0 0.3 0.3 Total 100.0 100.0 100,0 100.0 100.0 100.0 100.0 100.0 100.0 Number of users 574 328 29 25 192 475 215 256 1048 Note: Total inc ludes 3 users of male s ter i t i sa t ion . Figure 4.3 Source of Family Planning Supply Current Users of Modern Methods FPAK 10% Pr ivate Source Other Hoep. /C l in i c 8% Other 2% nment 71% • Pr ivate doctor /pharmacy Kenya DHS 1989 According to Table 4.10, the most frequently mentioned source for both supply methods and clinic methods is the government hospital, which supplies 56 percent of all users. This is followed by government clinics and health centres, which supply 15 percent of users, and the Family Planning Association of Kenya (FPAK) clinics, which supply 10 percent of users. Nine percent of users obtain their methods from private doctors or pharmacies, while 7 percent depend on non- 40 governmental hospitals and"clinics, such as those run by private doctors' and church missions. Users of clinic methods, such as sterilisation and the IUD, are more likely to depend on government hospitals than users of supply methods, such as the pill and condom. Each current user of a modern method of family planning was askcd how much time it takes for her to get from her home to the place she obtained her method and whether she walks or uses some means of transport to get there. These same questions were also asked of nonusers and users of traditional methods. The results are shown in Table 4.11. Table 4.11 Percent dfstr~b~tion of current users of modern methods of family planning, nonusers of modern methods, and all women knowing a method, by time to reach source of supply and transport to source, according to urban-rural residence, Kenya, 1989 Current users of Nonusers of • All women who know a Time to source/ modern methods modern methods contraceptive method transport to source Urban Rural Total Urban Rural rotai Urban Rural Total Minutes to source "" 0-14 16.4 2.6 6.1 14.8 3.3 5.2 15.2 3.2 5.3 15-29 32.3 30.9 31.2 35.2 29.8 30.7 34.6 30.0 30.8 30-59 45.2 49.8 48.7 42.1 44.0 43.7 42.8 44.8 44.5 60 or more 3.4 15.8 12.7 3.9 19.4 16.8 3.8 18.9 16.2 Does not know 0.7 0.1 0.2 2.7 1.8 1.9 2.2 1.5 1.6 Not stated 2.0 0.7 1.0 1.2 1.8 1.7 1.4 1.6 1.6 Transport to source Walk 54.0 45.9 47.9 67.7 65.1 65.5 ' '64.5 62.3 62.7 Use transport 43.8 53.3 50.9 29.9 32.3 31.9 33.1 35.4 35.0 Does not know 2.2 0.8 1.1 2.5 2.6 2.6 2.4 2.4 2.4 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 ~umber of women 264 785 1048 889 4497 5386 1153 5281 6434 The results show that most (45 percent) women knowing a contraceptive method report that they are 30 to 60 minutes from a place they would go to or do go to for family planning services. A sizable proportion (31 percent) are 15 to 30 minutes from a family planning source. As expected, urban women are more likely to be closer to a source than rural women. However, there is surprisingly little difference between users and nonusers of modern methods in terms of distance from a family planning source. Regarding type of transport to reach family planning sources, roughly two-thirds of women knowing a method either walk to the source they use or say they would walk to a source if they were to use a method in the future; one-third say they would use transport. Users of modern methods are much more likely to use transport to get to their source than nonusers. Users are evenly split between those who walk and those who use transport, while nonusers of modern methods are more likely to say they would walk than use transport. 41 4.10 Att i tude Toward Pregnancy and Reason for Nonuse In the KDHS, nonpregnant women who were sexually active and who were not using any contraceptive method were asked their attitude toward becoming pregnant in the next few weeks. Table 4.12 presents information on the attitude toward becoming pregnant among these women. Sixty-two percent of nonusers exposed to the risk of pregnancy report that they would be unhappy if they got pregnant in the next few weeks, 31 percent say they would be happy, and 5 percent say it would not matter. The percentage who say that they would be unhappy increases with the number of living children, ranging from 43 percent among women with no children to 74 percent among those with 4 or more children. Table 4.12 Percent distribution of nonpregnant wo~en who are sexually active and who are not using any contraceptive method by attitude toward becoming pregnant in the next few weeks, according to number of Living children, Kenya, 1989 Attitude toward becoming pregnant in next few weeks Wtd. Nu~nber of WouLd Nun~oer Living not of ch i ld ren Happy Unhappy matter Missing TotaL women Hone 51.0 42.6 4.9 1.6 ,100.0 550 I 49.1 45.0 4.8 1.0 100.0 400 2 33.4 60.5 2.4 3.8 I00.0 403 3 33.7 60.4 4.3 1.6 100.0 378 4+ 17.5 73.8 5.1 3.7 100.0 1614 Table 4.13 examines the reasons for not using family planning Totat 30.5 62.1 4.6 2.8 100.0 3345 given by exposed nonusers who say that they would be unhappy if they became pregnant right away. Twenty-three percent of the women cite lack of knowledge as the primary reason they are not contracepting, 12 percent cite factors relating to access and availability, whereas 11 percent cite infrequent sex as the reason for not using contraceptives. A further 10 percent of these women say they are not using contraception because their husbands disapprove. It is interesting to note that health concerns and religious beliefs do not appear to be major obstacles to use of family planning. Differences by age are not large, except that older women are more likely than younger women to cite inconvenience as the reason for non-use. 4.12 Intent ion to Use in the Future Married women who were not using a contraceptive method at the time of the KDHS interview were asked if they thought that they would do something to keep from getting pregnant at any time in the future. Data obtained from this question are shown in Table 4.14. About 53 percent of nonusers intend to use a contraceptive method in the future, 12 percent are unsure, and 34 percent do not intend to do anything to avoid future pregnancy. The percentage of women intending to use is lowest for those with no children (41 percent), increases for those with one child (53 percent), and is highest for the women with 2 children (61 percent). The percentage decreases again for women who have 3 or more children. Table 4.15 presents information on method preferences for currently married nonusers who say they intend to use in the future. The most popular method is injection (37 percent), followed by the pill (24 percent), and female sterilisation (13 percent). 42 Table 4.13 Percent distribution of non-pregnant women who are sexua l ly ac t ive , not using any contracept ive method and who would be unhappy i f they became pregnant, by main reason for nonuse, according to age, Kenya, 1989 Main reason for nonuse Age <30 30+ Total Lack of knowledge 23.9 21.2 22.5 Opposed to family planning 3.7 3.7 3.7 Husband disapproves I0.9 8.8 9.8 Others disapprove 0.8 0.8 0.8 Infrequent sex 8.9 13.8 11.4 Postpartum/breastfeeding 1.4 0.7 1.0 Menopausal/subfecund 0.1 0+8 0.5 Heatth concerns 1.5 1.8 1.7 Access~availability 14.2 10.2 12.1 Costs too much 1.4 2.3 1.9 Fatalistic 0.8 1.7 1.2 Religion 7.0 3.1 5.0 Inconvenient to use 0.5 15.6 8.3 Other 18.1 9.9 13.9 Don't know 6.2 4.6 5.4 Missing 0.6 1.2 0.9 Total 100.0 100.0 100.0 Number 1011 1075 2086 Table 4.14 Percent distribution of currently married women who are not currently using any contraceptive method, by intention to use in the future, according to number of tiring children, Kenya, 1989 Intention Number of living chitdren* to use in fu ture None 1 2 3 4+ Total PLan fu ture use 41.1 53.2 60.7 55.7 51.4 53.2 Unsure about use 22.0 9.7 9.7 12.7 11.2 11.7 Does not intend 36.9 37.1 29.5 30.8 36.2 34.3 Missing 0.0 0.0 0.1 0.8 1.2 0.8 Total 100.0 I00.0 I00.0 100.0 100.0 100.0 Number 199 385 484 478 1871 3483 *Inctudes current pregnancy 4.13 Approval of Family Planning Table 4,16 shows responses to a question on whether women believe it acceptable to have family planning messages on the radio. The table shows that almost 90 percent of respondents believe that radio messages are acceptable. Over 80 percent of women in each age group find the idea acceptable, though women in their 20s are more likely than those in their 40s to accept radio messages. 43 Table 4.15 Percent d i s t r ibut ion of currentty married women who are not using a contraceptive method but who intend to use in the future, by preferred method, Kenya, 1989 Preferred method Percent Pitt 24.4 IUD 7.1 injections 37.0 Diaphragm/Foam/Jelly 0.4 Condom 0.9 Female steritisation 12.7 Male sterilisation 0.I Periodic abstinence 4.3 Other 1.8 Don't know 11.2 Missing 0.1 Total 100.0 Number 1852 Table 4.16 Percent distribution of all women by whether they feet it is acceptable to have family planning information presented on the radio, by age and background characteristics, Kenya, 1989 Age of woman Background characteristic 15-19 20-24 25-29 30-34 35-39 40-44 45-49 Total Urban 89.5 92.5 92.8 96.6 93.4 91.7 82.5 92.2 Rural 83.7 92.7 91.7 87.0 90.4 81.5 83.9 87.8 Bairobi 91.6 91.7 91.3 97.5 94.7 98.0 77.3 92.7 Central 86.3 95.3 93.6 94.4 91.7 92.8 94.9 92.2 Coast 63.6 86.1 81.8 81.0 71.0 56.2 58.2 74.4 Eastern 86.2 94.3 98.5 95.9 91.7 87.2 89.2 92.0 Nyanza 90.0 95.7 92.4 92.9 95.9 79.5 90.3 91.7 Rift Valley 86.4 91.0 91.4 81.5 89.3 80.3 83.7 87.1 Western 76.9 90.6 86.9 8O.5 95.8 76.5 67.4 83.0 No education 59.8 72.0 81.1 81.3 86.1 76.3 80.4 79.8 Some primary 78.4 93.3 93.2 90.4 92.8 85.3 87.7 88.9 Primary complete 86.2 92.6 94.6 95.6 94.9 95.6 96.1 91.0 Secondary + 93.8 97.3 95.3 94.2 97.1 96.8 (93.9) 95.6 Total 84.8 92.7 91.9 88.6 90.7 82.4 83.8 88.6 Note: Numbers in parentheses are based on fewer than 20 unweighted cases. Urban-rural differentials in the acceptability of family planning messages on radio are small. Variations by province are also small, except that women in Coast Province are less likely to find radio messages acceptable. The proportion who believe that radio messages on family planning are acceptable increases with educational attainment, from 80 percent of women with no education to 44 96 percent of women with secondary education. It is notable that high proportions of women in almost all categories find radio messages on family planning to be acceptable. This fact encourages increased use of radio for family planning messages. To obtain information about attitudes toward family planning, the respondents were asked whether they approved of couples using something to avoid pregnancy. Although all women were asked the question on approval, the 'analysis presented here is focused on currently married women and excludes those women who have never heard of a contraceptive method. Currently married women were further asked whether they thought that their husbands approved of the use of family planning. Table 4.17 presents information obtained from answers to these questions. Tabie 4.17 Percent distribution of currentiy married women knowing a contraceptive method by the husbar~d's and wife's attitude toward the use of family planning, Kenya, 1989 Wife's Husband's attitude toward ramify planning attitude toward fam- ily ptanning Disap- Ap- Don't proves proves know Missing Totat Number Disapproves 4.5 0.9 3.2 0.0 8.7 382 Approves 14.0 57.5 t6.4 0.3 88.2 3885 Missing 0.5 2.1 0.5 0.1 3.1 138 rotat 19.1 60.4 20.1 0.4 100.0 4405 Number 841 2661 888 16 4405 4405 Overall, 88 percent of married women say they approve of family planning, while 9 percent disapprove. Sixty percent say that their husbands approve of family planning, while 19 percent say their husbands disapprove, and 20 percent say they do not know their husband's attitude. According to the wife's report, only 58 percent of couples jointly approve of family planning, while 5 percent jointly disapprove. Approval of family planning is also discussed in Chapter 7, where the responses of husbands are compared with their wives' perceptions of their beliefs. Table 4.18 shows that there are few differentials in approval of family planning by married women or their husbands by age of the wife, urban-rural residence, or province, except that Coast province has the lowest percentage of women and husbands approving of family planning (78 and 45 percent respectively). Approval by women and their husbands increases with education of the woman. A good indication of the acceptability of family planning is the extent to which couples discuss the subject with each other. Table 4.19 indicates that one-third of currently married women had never talked about family planning with their husbands in the year preceding the survey. About one-third said that they discussed the subject once or twice with their husbands in the past year, while another one-third said they had discussed it more often. 45 Table 4.18 Percentage of currently married women knowing a contraceptive method who approve of family planning ar~J who say their husbar~d approves of family planning by background charscte~stics, Kenya, 1989 Background Woman Husband characteristics Approves Approves Total Age 15-19 87.8 53.8 241 20-24 89.3 61.5 784 25-29 90.3 62.1 1044 30-34 88.9 61.2 779 35-39 90,7 62.2 731 40-44 80.7 56.6 508 45-49 83.4 57.2 318 Residence Urban 90.6 65.4 716 Rural 87.7 59.4 3689 Province Nairobi 92.1 68.5 319 Central 92.0 69.9 628 Coast 77.7 44.8 323 Eastern 91.0 68.6 763 Nyanza 93.8 49.8 814 Rift Valley 81.1 63,9 910 Western 87.7 53.7 648 Education No education 81.4 45.4 1289 $om~ pr i~rv 89,7 59.1 1357 Primary co~plete 92.0 68,4 958 Secondary + 92.4 77.6 795 Total 88.2 60.4 4405 Note: Excludes a small number of women with education not stated. Table 4.19 Percent distribution of currently married women knowing a contraceptive method by number of times discussed family planning with husband, according to current age, Kenya, 1989 Nun~er of times discussed Wtd. nu~nber Once or More of Age Never twice often Missing Total women 15-19 42.1 32.0 25.9 0,0 100.0 241 20-24 30.0 36.0 33.4 0.6 100.0 784 25-29 29.0 33,0 37.7 0.3 100.0 1044 30-34 35,0 31.2 33,1 0.7 100.0 779 35-39 31,5 32.5 35.6 0.4 I00,0 T31 40-44 41.4 24.9 33.4 0,2 100.0 508 45-49 44.1 22,4 33.2 0.3 100.0 318 Total 33.9 31.4 34.3 0,4 100.0 4405 46 5 FERTILITY PREFERENCES In the KDHS, women were interviewed about their fertility preferences. The aim of this part of the interview was to establish the extent of unmet need for contraception and the number of unwanted or mistimed births. This information can be used to assist family planning programmes to carry out their services more effectively. The KDHS questionnaire included a number of questions about fertility preferences. All currently married women were asked if they wanted to have another child (after the current pregnancy if the woman was pregnant) and if so, they were asked how long they wanted to wait before having their next child. All women regardless of marital status were asked how many children they would like to have altogether, assuming they could go back to the time when they did not have any children ("ideal" number of children). Also, women with a birth in the five years before the survey were asked if, at the time they got pregnant, they wanted to have that child then, wait till later, or not have the child at all. 5.1 Desire for More Chi ldren Table 5.1 shows the desire for children among currently married women by the number of living children. Almost 50 percent of married women want no more children and 26 percent want another child, but only after two or more years (Figure 5.l). Thus, three-quarters of married women can be considered potential users of contraception for the purpose of either limiting their family size or spacing births. Table 5.1 Percent distribution of currently married women by desire for children, according to number of living children, Kenya, 1989 Number of living children* Desire for more children 0 I 2 3 4 5 6+ Total Want within 2 years 47.8 31.7 16.7 14.3 9.9 7.5 2.4 12.4 Want after 2+ years 14.0 55.6 49.0 42.7 29.8 15.9 6.1 26.4 Want, unsure when 21.5 5,7 3.3 Io7 2.4 0.4 0.9 2.9 Undecided 4.7 2.6 5.5 6.8 6.3 10.2 5.6 6.0 Want no more** 0.9 3.1 23.1 32.6 49.0 63.5 81.7 49.4 Declared infecund 9.3 1.3 2.2 1.1 2.5 1.6 3.3 2.6 Missing 1.8 0.0 0.1 0.8 0.2 0.8 0.1 0.3 Total 100.0 100.0 100.0 100.0 100.0 100.0 100,0 100.0 ~un~ber 213 468 633 663 648 507 1634 4765 * Includes current pregnancy "" Includes sterilised women The desire to limit childbearing appears to be considerably greater in Kenya than in other sub-Saharan countries where DHS surveys have been conducted. For example, the proportion of married women who want no more children is 33 percent in Botswana and Zimbabwe, 23 percent in Ghana and 19 percent in Uganda, compared to 49 percent in Kenya. This suggests that many women in Kenya may be candidates for more long-term methods of family planning, such as sterilisation or the IUD. 47 Figure 5.1 Fertility Preferences Currently Married Women 15-49 Want Wi th in 2 ¥r~ t~ Want, Unsure When ,3 Undec ided 6% In fecund/Miss ing 3% t After 2+ Yrs. 26% Want No More 49% Kenya DHS 1989 The desire for more children declines with the number of living children (see Figure 5.2). While more than 90 percent of marricd women with one child want another, only 9 percent of women with six or more children want another child. Conversely, the percentage of women who want no more children rises from 3 percent for women with one child to 82 percent for women with six or more children. This indicates substantial interest in limiting fertility among married women. The table also points to a desire among women to space births. For instance, 56 percent and 49 percent of women with one and two children respectively want their next births after two years. Table 5.2 shows the percent distribution of currently married women by desire for children according to age. The data show that the proportion of women who want no more children increases with age. Nine percent of the women aged 15-19 years want no more children, compared to 81 percent of the women aged 45-49 years. The proportion who want to delay their next birth declines with age, as does the proportion of women who want the next birth within two years. Table 5.3 shows the percentage of currently married women who want no more children by number of living children and selected background characteristics. In terms of fertility preference measures, the proportion of women who want no more children is the most significant figure. Therefore, it has been selected as an indicator for studying differentials in fertility preference by background characteristics of women. The proportion of women who want no more children is closely correlated with number of living children as well as background characteristics. For instance, overall, a larger proportion of rural than urban women want to stop childbearing, however, when the number of living children is taken into account, the reverse is true, which means that the overall figures result from the fact that a greater percentage of rural women have more children than urban women, since the proportion wanting no more children rises with the number of living children. 48 Percent 100 80 60 40 20 0 o Figure 5.2 Fertility Preferences by Number of Living Children 1 2 3 4 5 6+ Number of living children m Want No More ~ Want to Space ~ Want Soon i I Undecided ~ Infecund/Missing Kenya DHS 1989 Table 5.2 Percent distribution of currently married women by desire for children, according to age, Kenya, 1989 Age Desire for more children 15-19 20-24 25-29 30-34 35-39 40-44 45-49 Total Want within 2 years 25,4 15.3 14,3 12.9 8.6 6.6 6.6 12.4 Want after 2+ years 53,8 55.2 35,9 17.8 11.8 2.0 0.8 26.4 Wants, unsure when 7.9 4.4 2.6 2.5 1.5 2.0 1.6 2,9 Undecided 3.3 6,1 6.8 8,4 6.5 4.7 1.0 6,0 Want no more* 9.3 18.3 39,3 56.0 67.0 78.4 81.4 49.4 Declared infeeund 0.2 0.4 0.5 2.2 3.9 6.1 8.5 2.6 Missing 0.0 0.2 0,6 0.2 0.6 0.1 0.1 0.3 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 I00.0 Number 276 827 1104 833 781 576 369 4765 * Includes sterilised women Central Province has the highest proportion of women who want to stop childbearing; half of the women with three children and 95 percent of the women with six or more children want to have no more. On the other hand, women in Coast Province seem to be the most pronatalist; only 55 percent of those with six or more children say they want to stop. The relationship between education and the desire to stop childbearing is somewhat erratic. The biggest differences are between women with no education and those with some education; the amount of education seems to have little effect on desire to stop childbearing. 49 Table 5.3 Percentage of currently married women who want no more children (including those sterilised) by ntanber of tiring children and background characteristics, Kenya, 1989 Nuldoer of living children* Background characteristic 0 I 2 3 4 5 6+ Total Residence Urban 2.5 5.4 31.1 47.1 62.9 67.4 85.3 39.6 Rural 0.0 2.1 20.7 29.0 46.3 62.9 81.5 51.2 Province Nairobi 6.1 6.1 34.2 55.8 63.5 79.5 89.3 43.7 Central 0.0 7.2 25.8 49.7 72.2 81.2 94.6 67.3 Coast 0.0 1.8 12.8 28.5 29.7 26.4 55.3 28.0 Eastern (0.0) 4.0 24.6 44.6 54.6 81.8 86.9 59.7 Nyanza 0.0 1.1 15.3 18.4 45.1 55.8 77.5 41.7 Rift Valley 0.0 1.2 28.5 24.5 42.2 56.8 81.5 49.7 Western 0.0 3.2 19.9 10.6 32.0 50.7 76.8 43.2 Education No education 1.2 0.9 26.0 25.8 41.4 52.5 77.3 54.4 Some primary 1.1 3.4 21.9 30.1 46.5 70.6 85.1 53.4 Primary cocaplete 0.0 3.7 20.3 41.6 52.2 68.4 86.5 46.2 Secondary + 1.6 3.4 25.2 35.0 58.1 65.2 84.6 36.5 Total 0.9 3.1 23.1 32.6 49.0 63.5 81.7 49.4 Note: Numbers in parentheses are based on fewer than 20 unweighted cases. Includes current pregnancy Table 5.4 examines the need for family planning among currently married women. Women are considered to be in need if they arc not contracepting and either want no more births or want to postpone their next birth. Overall, 60 percent of currently married Kenyan women are in need of family planning. Of these, 32 percent are in need because they do not want another child, while 28 percent are in need because they want to postpone their next birth. The proportion in need is slightly higher for rural women and women in Western Province. Need is also higher among women with less education. 5.2 Ideal Number of Children In order to assess fertility preferences in Kenya, all KDHS rcspondents regardless of marital status were asked: "(If you could go back to the time when you did not have any children) and if you could choose the number of children to have in your whole life. how many would that be?" Women with children were asked the entire question while those with no children were asked the part excluding the phrase in parentheses. This question aimed at two things--first, among women who have just started childbearing, the data will give an idea of the total numbcr of children these women will have in the future (to the extent that women are able to realise their fertility desires); secondly, among older, higher parity women, the data provide an idea of the level of unwanted fertility. It is important to note that some women have difficulty answering a hypothetical question of this type, especially women for whom control over fcrtility is not culturally acceptable. There 50 is also a possibility that some women report their actual number of children as their ideal since they find it difficult to admit that they would not want some of their children if they could choose again. Table 5.4 Percentage of currently married women who are in need of family planning by back- ground chBracteristics, Kenya, 1989 In need* and: Want Want to Number Background no post- of characteristics more pone** Total women Residence Urban 22.4 31.8 54.2 748 Rural 33,7 27.7 61,4 4018 Province Nairob~ 22,9 30.3 53.2 335 Central 37.9 16.5 54.3 648 Coast 18.4 36.8 55.2 350 Eastern 33.5 15.7 49.2 804 Nyanza 34.6 31.5 66.0 872 Rift Volley 31.0 30.5 61.5 1047 ~estern 33.8 41.4 75.1 771 Education No education 40.4 25,5 65.9 1506 Some primary 35.9 26,1 62.0 1462 Primary complete 25.5 32,4 57.9 987 Secondary + 16.9 32,9 49.8 804 Total 31.9 28,3 60.3 4765 * Includes women who are not using contraception and who either want no more children or want to postpone their next birth for 2 or more years ** Want next birth after two or more years Table 5.5 shows the percent distribution of sill women by ideal number of children and mean ideal number of children for all women and currently married women according to the number of living children. Four children is the most commonly reported ideal family size among all women; overall, 40 percent of women state four as their ideal number. This percentage is high, considering that another 30 percent consldcr five or more children as ideal. However, it is encouraging that while women with more living children are likely to state five or more as their ideal number of children, women with fewer children ~lre more likely to state two or three children as ideal. Thus, the mean ideal number of children increases with the number of living children. This may be due to the fact that women who want more children actually end up having them, or to the fact that women rationalise their family size by reporting their actual number of children as their ideal number. The KDHS data show a large decline in ideal family size from the 1984 KCPS results. The mean ideal number of children for all women was 5.8 in 1984, compared to 4.4 in 1989; for currently married women, the figures sire 6.3 in 1984 and 4.8 in 1989 (Central Bureau of Statistics, 1984, p.61). 51 Table 5.6 shows the mean ideal number of children for all women interviewed in the KDHS by age and selected background characteristics. The mean ideal number of children increases with age from 3.7 among women aged 15-19 to 5.5 among women aged 40-44, implying that if younger women succeed in having only those children they want, then fertility rates may fall in future. Table 5,5 Percent d i s t r ibut ion of a l l women by ideal number of ch i ld ren and mean ideal number of ch i ld ren for a l l women and cur rent ly marr ied women, according to number of l i v ing ch i ld ren , Kenya, 1989 Number of l i v ing ch i ld ren* Ideal number of ch i ld ren 0 1 2 3 4 5 6+ Total 0 0.3 0.3 0.0 0.2 0.2 0.0 0.0 0.1 I 1.3 2.1 1.0 0.9 0,5 0.5 0.3 0.9 2 20.3 13.0 15.7 4.7 9.1 7.0 3.2 10.7 3 17.0 20.8 11.4 20.7 5.2 6.8 5.8 ~2.¢ 4 40.6 42.7 52.2 38.6 45.1 28.0 36.3 40.3 5 9.4 9.2 8.0 12.5 12.0 21.7 8.2 10.5 6+ 8.1 8.4 9.5 19.0 24.4 31.8 40.3 21.1 Non-numeric response 3.1 3.5 2.2 3.2 3.6 4.2 6.0 3.9 ~otal 100.0 100.0 100.0 100.0 100.0 100.0 lO0.O 100.0 Nua~ber of women 1566 890 813 773 724 601 1783 7150 Mean (all women) 3.7 3.8 3.9 4.4 4.6 5.1 5.4 4.4 Mean (currently married) 4.4 4.1 4.1 4.5 4.6 5.2 5.5 4.8 Base (all women) 1516 858 796 749 698 576 1677 6870 Base (currently married) 197 443 616 641 624 488 1532 4540 * Includes current pregnancy Table 5.6 Mean ideal number of children for all women by age and background characteristics, Kenya, 1989 Age Background characteristics 15-19 20-24 25-29 30-34 35-39 40-44 45-49 Total Residence Urban 3.5 3.6 3.8 3.8 4.5 4.2 4.4 3.8 Rural 3.8 4.1 4.6 5.0 5.0 5.6 5.3 4.6 Province Nairobi 3.3 3.5 3.6 3.7 4.1 3.9 4.2 3.6 Central 3.2 3.2 3.8 4.0 4.4 4.7 4.2 3.8 Coast 4.4 4.7 5.6 6.2 6.3 6.4 8.0 5.6 Eastern 3.5 3.7 4.0 4.4 4.7 5.3 4.7 4.2 Nyanza 3.9 4.3 4.6 5.0 4.9 5.5 4.8 4.6 Rift Valley 4.1 4.1 4.9 4.8 5.0 6.0 5.2 4.7 Western 4.0 4.4 4.6 5.2 5.2 5.9 7.5 4.9 Education No education 5.6 5.4 5.4 5.4 5.3 5.8 5.2 5.4 Some primary 3.9 4.1 4.6 4.7 4.9 5.2 5.5 4.6 Primary co~o[ ete 3.7 4.0 4.2 4.3 4.6 5.2 5.4 4.1 Secondary + 3.3 3.4 3.8 3.9 4.0 4.0 3.2 3.6 Total 3.7 3.9 4.4 4.8 4.9 5.5 5.3 4.4 52 The mean ideal number of children is higher lbr rural women than for urban women regardless of age. Coast Province has the highest average ideal family size (5.6), while Nairobi has the lowest (3.6). Also, women with no education have a higher mean ideal family size (5.4) than women with primary (4.1) or secondary or higher education (3.6). 5.3 Unwanted Fertil ity Table 5.7 shows the percent distribution of women who had a birth in the last twelve months by fertility planning status and birth order. Over 50 percent of the births in the last 12 months were either mistimed or unwanted, Forty-two percent of births were wanted at a later time (mistimed), while 11 percent were not wanted (unwanted). This indicates that a substantial proportion of women need family planning services, espccially for spacing births. Table 5.7 Percent distribution of women who had a birth in the Last 12 months by fertility planning status, according to birth order, Kenya, 1989 gir th order PLanning status of birth 1-2 3+ Total Wanted then 52.6 43,5 46.3 Wanted later 43.1 41,7 42.1 got wanted 3.9 14.2 II,0 Not classifiable 0.4 0,6 0.5 Total 100.0 100.0 100o0 Number 477 1062 1539 53 6 MORTALITY AND HEALTH 6.1 Chi ldhood Mortafity The government of Kenya has long been concerned about the high rates of infant and childhood mortality in the country and has made considerable efforts to reduce them. The infant mortality rate, especially, is often cited as a basic indicator of general health and welfare. In the KDHS, data on mortality were collected for the purpose of estimating infant and childhood mortality rates. This focus is a result of the fact that data appropriate for adult mortality estimation require very large samples and are difficult to collect by the retrospective household survey approach. In this section mortality rates are presented for three age intervals: Infant mortality--the probability of dying between birth and exact age one (lq0), Childhood mortality--the probability of dying between age one and age five(4q~), Under 5 mortality--the probability o[" dying between birth and exact age five (No). Mortality rates are calculated on a period basis (i.e., utilising information on deaths and exposure to mortality by age during a specific time period) rather than on a birth cohort basis. The pcriod approach is preferred for two reasons: first, period-specific rates are more appropriate for programme evaluation and second, the data necessary for the calculation of cohort-based childhood mortality rates are only partially available for the five-year period immediately preceding the survey. For a complete description of the methodology for computing period-spccific mortality probabilities, see Rutstein, 1984. Birth History Survivorship Data The data for the estimation of mortality rates were collected in the reproduction section of the individual woman questionnaire. The section began with questions about the aggregate childbearing experience of respondents (i.e., the number of sons and daughters who live in the household, who live elsewhere, and who died). Those questions were followed by a retrospective birth history in which data were obtained on sex, date of birth, survivorship status and current age or age at death of each of the respondents' live births. The data obtained from these questions are used to calculate infant and childhood mortality rates. A retrospective birth history, in which data are collected from respondents aged 15-49 as of the survey date is susceptible to truncation bias and other data collection errors. Truncation bias refers to the fact that for any time period prior to the year of survey, data are not available for women at the oldest ages of childbearing (e.g., for the period 10-15 years prior to the survey, there is no information about births to women aged 40-49). Other data collection errors involve underreporting of events, misreporting of age at death, and misreporting of date of birth. In general, all these data problems are less serious for time periods close to the survey date. 55 Mortality Levels and Trends 1974-1989 Table 6.1 and Figure 6.1 display infant and childhood mortality rates for the five-year period preceding the survey (1984-89) and for two previous five-year time periods (1974-78 and 1979-83). Table 6.1 Infant and childhood mortality rates by five-year calendar periods, Kenya, 1989 Infant Childhood Under 5 mortality mortality mortality rate rate rate Period (lqO) (4qi) (SqO) 1984-1989" 59.6 31.5 89.2 1979-1983 57.6 37.8 93.1 1974-1978 64.1 44.2 105.5 Percent decline 1974-78 to 1984o89 7,0 28.7 15.4 * Includes calendar year 1989 up to the month preceding date of interview. 120 100 80 Figure 6.1 Trends in Infant and Child Mortality Rate per 1,000 64 ~ RQ Infant Mor ta l i ty lm 44 1974-78 Child Mor ta l i ty 1979-83 Under 5 Mor ta l i ty 1984-89 Kenya DHS 1989 56 The infant mortality rate for Kenya for the period 1984-89 is 60 per thousand live births and the childhood mortality rate is 32 per thousand. The overall probability of dying between birth and exact age five is 89 per thousand. While the KDHS rates indicate a decline in mortality, it is important to note that the decline is small. During the ten-year interval between 1974-78 and 1984-89, infant mortality declined by only 7 percent, childhood mortality by 29 percent and the overall probability of dying between birth and age five, by 15 percent. When KDHS rates are compared to data from previous sources, they imply a substantial decline in infant and childhood mortality. For example, the infant mortality rate reported in the 1977/78 KFS was 96 per thousand births (Central Bureau of Statistics, 1980, p.105) and the rate estimated from 1979 census data was 104 per thousand (Central Bureau of Statistics, no date, p.103). This magnitude of decline is large but certainly possible. Howevcr, the fact that the infant mortality rate from the KDHS for the period 1974-78 (64) is also much lower than the rate in either the 1979 census or the 1977/78 KFS, suggests that children who died might have been underreported in the KDHS. An investigation of this possibility is beyond the scope of this report. Mortality DifferentiaLs 1979-89 Mortality differentials by province:, mothcrs' level of cducation and urban-rural residence are presented in Table 6.2. In order to have a sufficient number of births to calcuIate reliable rates for the study of mortality differentials across population sub-groups, period-specific rates are presented for the ten-year period 1979-1989. Table 6.2 Infant and childhood mortality rates by background characteristics of the mother for the ten-year period preceding the survey, Kenya, 1989 Infant Childhood Under 5 mortality mortality mortality rate rate rate Background (lqO) (4qi) (5qO) characteristics 1979-89 1979-89 1979-89 Residence Urban 56.8 34.2 89.0 Rural 58.9 34.3 91.2 Region Nairobi 46.3 35.T 80.4 Central 37.4 10.0 47.0 Coast 107.3 54.5 156.0 Eastern 43.1 22.2 64.3 Nyanza 94.2 60.0 148.5 Rift Valley 34.6 16.9 50.9 Western 74.6 62.9 132.8 Education None 71.7 39.9 108.7 Some prLmary 59.1 38.3 95.2 Primary co~tete 49.3 24.4 72.5 Secondary + 41.8 23.4 64.2 Total 58.6 34.3 90.9 Note: Rates include calendar year 1989 up to the month preceding date of interview. 57 Curiously, mortality is only slightly higher in rural areas than in urban areas. The provincial rates display marked differentials. The infant mortality rate is highest for Coast Province (107 per thousand live births), followed by Nyanza (94), Western (75), Nairobi (46), Eastern (43) and Central (37) (Figure 6.2). Rift Valley has the lowest infant mortality rate (35). Childhood mortality differentials are even larger, with the ratcs in Western and Nyanza Provinces (63 and 60, respectively) being six times the rate in Central Province (10). PROVINCE Nairobi Centra l Coast Eastern Nyanza Rift V. Western EDUCATION None Some Pri. Pri. Comp. See.÷ Figure 6.2 Infant Mortal ity by Province and Education 46 37 107 J 75 ,I 94 ,. ) 49 42 , i 72 ,, 59 0 20 40 60 80 100 120 Rate per 1,000 Kenya DHS 1989 Mortality differentials by mother's level of formal education display expected differentials. Mortality is highest for children whose mothers have no education, declines for children whose mothers have some primary education and is lowest for children whose mothers have attained secondary education and above. Mortality differentials by sex, mother's age at birth, birth order, and length of the previous birth interval are shown in Table 6.3. As expected, mortality rates are lower for females than for males. Infant mortality differentials by age of mother are moderate, but show higher levels for children born to mothers under age 20. Childhood mortality declines steeply as age of the mother increases. Infant mortality estimates by birth order also display the expected differentials. Infant mortality is higher for first births (65 per thousand), declines for second and third births (55) and births 4-6 (50), then rises sharply for births 7 and above (72). The length of birth intervals also has a strong effect on infant and child mortality levels. The infant mortality rate estimates are 76 per thousand for births occurring after intervals of less than 2 years, 48 per thousand for births after intervals of 2-3 years and 36 per thousand for births 58 Table 6.3 Infant and childhood mor ta l i ty rates by selected demographic character is t ics , fo r the ten-year period preceding the survey, Kenya, 1989 Infant Chitdhood Under 5 morta l i ty mor ta l i ty mor ta l i ty rate rate rate Demographic (lqO) (4q l ) (5qO) character i s t i cs 1979-89 1979-89 1979-89 Sex of ch i ld Mate 63.0 35.4 96.1 Female 54.3 33.2 85.7 Age of rm)ther at birth Less than 20 67.5 43.8 108.3 20-29 54.8 35.8 88.6 30-39 60.2 26.4 85.0 40-49 58.3 15.0 72.5 Birth order F i rs t 65.3 37.5 100.3 2-3 54.8 32.5 85.5 4-6 49.7 33.4 ~ 81.5 7+ 71.9 36.4 105.6 Previous b i r th in terva l <2 years 75.6 41.1 113.6 2-3 years 47.7 32.6 78.7 4 years or more 35.9 17.9 53.2 Note: Rates include calendar year 1988 up to the month preceding date of interview. after intervals of 4 years or more. There are also substantial differentials in childhood mortality by length of the preceding birth interval, in the same direction as the infant mortality differentials. These differentials suggest that a change in birth spacing practices would by itself, have a favourable impact on mortality levels. Additional evidence regarding childhood mortality levels in Kenya can be obtained from the proportion of children ever born who have died, tabulated by age of woman (Table 6.4). Just over 10 percent of all children born to women 15-49 have died. The proportion dead by age of mother shows an unusual pattern; it is very high for women 15-19 and falls for women 20-24 and 25-29, before showing the expected increase with age of mother. 6.2 Matern i ty Care The health care that a mother receives during pregnancy and at the time of delivery is important to the survival and well-being of the child as well as the mother. To obtain data on the type of maternity care that Kenyan women receive, KDHS respondents who had given birth in the five years preceding the interview were asked if they had seen anyone for an ante-natal checkup before the birth and if anyone had assisted with delivery of that child. If they had had an ante- natal checkup or received assistance at delivery, they were asked who provided the care.' In cases where the maternity care was received from more than one provider, the most qualified provider was recorded by the interviewer. 59 Table 6.4 Mean number of ch i ld ren ever born, surv iv ing , and dead, and proportion of chitdren dead among those born, by age of wonmn, Kenya, 1989 Mean number of children: ~td. number of wc41)en Propor- Ever Sur- t ion Age born r iv ing Dead dead 15-19 0.28 0.25 0.03 0.117 1497 20-24 1.58 1.44 0.14 0.088 1321 25-29 3.47 3,19 0.28 0.082 1334 30-34 5.01 4.49 0.52 0.104 981 35-39 6.48 5.80 0.67 0.104 898 40-44 7.36 6.53 0.84 0.114 674 45-49 7.63 6.55 1.08 0.142 445 Total 3,67 3.28 0.39 0.106 7150 Since neonatal tetanus has been shown to be a major cause of infant deaths in developing countries like Kenya, mothers were also asked if they had received an injection before the birth to keep the baby from getting tetanus. The responses to this question are affected by the mother's recall of events during pregnancy and, particularly by her ability to distinguish the tetanus toxoid vaccination from other injections she may have received. Moreover, the failure of a respondent to be immunised against tetanus during any particular pregnancy does not necessarily mean that the mother and child were exposed to the risk of tetanus, since protection may have been provided by tetanus toxoid vaccinations before that pregnancy. Despite these drawbacks, the proportion of women receiving a tetanus toxoid vaccination during pregnancy provides an indicator of the success of maternal and child health efforts. Table 6.5 presents data on the type of ante-natal care obtained for births that occurred in the five years before the survey. The results suggest that the majority of mothers in Kenya receive at least some maternity care. For 77 percent of births, mothers had seen a doctor or trained nurse/midwife to check the pregnancy and for 89 percent of births, mothers had had a tetanus toxoid injection. Tetanus toxoid coverage may be overrcported, since it seems doubtful that in 12 percent of cases, mothers received a tetanus injection without obtaining any other ante-natal care, however, the level of 89 percent is close to the rate of 83 percent of women reported in the 1987 national coverage survey for the Kenya Expanded Programme on Immunisation (KEPI) as having a tetanus injection during either of their last two pregnancies (Ministry of Health, 1987). The authors of the KEPI study believed that respondents in their survey had confused other injections for tetanus toxoid. There are few differences in ante-natal care by age or residence of mother. By province, ante-natal care from either a doctor, trained nurse, or midwife is most prevalent for births to women interviewed in Nyanza Province and Nairobi and least prevalent among births to women interviewed in Central and Coast Provinces (69 percent). The rates for Nairobi and Coast Province are in the expected direction, however, that the rate for Nyanza is higher than the rate for Central Province is unusual. About one-quarter of births to women in Coast, Central and Western Provinces do not receive ante-natal care. Better educated women are slightly more likely than less educated women to obtain ante-natal care from trained professionals. 60 Table 6.5 Percent d i s t r ibut ion of b i r ths in the las t 5 years by type of ante -nata l care fo r the mother and percentage of b i r ths whose mother received a tetanus toxoid in jec t ion , according to background character i s t i cs , Kenya, 1989 Type of ante-natal care Percentage receiving tetanus Number toxoid of injection births Trained Trad'l Background 0oc- nurse/ birth characteristics tor midwife attend. Other None Missing Totat Age of mother <30 28.8 49.4 1.7 0.7 18.4 0.9 I00,0 89.3 4081 30+ 27.9 47.8 2.2 0.2 21.0 0.8 I00,0 88,0 2969 Residence Urban 28.5 53.1 0.9 0.8 16.1 0.6 100.0 92.2 979 Rural 28.4 48.1 2.1 0.5 20.0 0.9 100.0 88.2 6072 Province Nairobi 27.5 55.8 0.6 1.5 13.9 0.6 100.0 90.3 417 Central 52,7 16.2 0.4 1.0 28.8 0.8 100.0 89.9 969 Coast 35.6 33.7 0.4 0.1 29.9 0.3 100.0 89.1 423 Eastern 31.5 48.9 0.9 0.7 17.6 0.4 100.0 88.4 1233 Nyanza 22,9 60.6 1.2 0,3 13.4 1.5 100.0 90.7 1283 Rift Valley 25.7 53.6 4.6 0.0 15.6 0.5 100.0 86.4 1593 Western 12.2 59.4 2.4 0.5 24.1 1.4 100.0 88.5 1133 Education NO education 24.3 48.0 4.4 0.2 22.3 0.9 I00.0 84,8 1888 Some primary 26.2 49.9 1,3 0.3 21.4 0,9 100.0 88,9 2234 Primary co(nplete 33.3 46.6 0.8 0.9 17.5 1.0 100.0 90.0 1655 Secondary + 32.3 50.6 0,7 0,9 14.8 0.7 100.0 92.7 1268 Total 28.4 48.8 1.9 0.5 19.5 0.9 100.0 88.7 7050 Table 6.6 presents data on the type of assistance mothers received at delivery for all births in the five years before the survey. Half of the births in the last five years were assisted at delivery by a doctor or trained nurse/midwife and 14 percent by a traditional birth attendant. A substantial proportion of births were assisted by relatives and friends of the mother (21 percent) or by no one (12 percent). Births to older women, rural women, and women with no education are less likely to benefit from assistance at delivery by trained medical personnel. The results also show that Nairobi leads in maternity care, with over 80 percent of births being assisted by a doctor or trained nurse/midwife, followed by Central Province, where 73 percent of births are assisted by professionals. Presumably, this is because Nairobi is an urban area where medical facilities are more available. Also notable are the higher proportion of births in Central Province that are assisted by doctors (35 percent), the higher proportion of births in Coast Province that are assisted by relatives and friends (44 percent), and the higher proportion of births in Western Province that do not benefit from any assistance at delivery (31 percent). It is also important to note that traditional birth attendants play a more significant role in delivering babies in Rift Valley, Eastern, and Nyanza Provinces. An important indicator of maternal and child health is the proportion of Women and recent births that fall into certain high risk categories. It has been shown that the risk of serious illness and/or death for both mother and child is related to the age and parity of the mother, as well as 61 Table 6.6 Percent d i s t r ibut ion of b i r ths in the last 5 years by type of assistance dur ing de l ivery , according to background character is t ics , Kenya, 1989 Type of assistance at de l ivery Trained Trad'[ Rela- Number Background Doc- nurse/ b i r th t i re / Miss- of character is t ics tor midwife attend, f r iend Other None ing Total b i r ths Age of mother <30 17.9 37.5 14.2 20.2 1.7 7.5 0,9 100.0 4081 30+ 14,4 28.3 14,4 22.8 1,9 17.5 0.7 100.0 2969 Residence Urban 23,1 54.4 5.0 9,9 1.0 6.0 0.6 100.0 979 Rural 15.4 30,3 15,8 23.1 1.9 12.7 0.9 100.0 6072 Province Nairobi 19.8 63.4 2.5 8.8 1.1 4.2 0.3 100.0 417 Central 34.9 38.4 5.9 12,0 2.3 5.6 0.9 100.0 969 Coast 13.7 27.2 4.7 44.1 1.2 8.4 0.6 100.0 423 Eastern 12.8 28,0 19.6 29.1 1,8 8.3 0.4 100.0 1233 Nyanza 14.4 39.4 17,4 14.9 1.8 10.6 1.5 100.0 1283 Rift Valley 16.8 27.9 20,8 23,4 2.3 8.0 0.7 I00,0 1593 Western 6.2 28.6 10.8 21.1 0.9 31,3 1.1 100.0 1133 Education No education 9.5 24.0 17.2 27.6 1.6 19,1 1.0 100.0 1888 Some primary 14,5 30.5 15,2 24,5 1.9 12.7 0.7 100.0 2234 Primary complete 19.5 34.9 15.9 18.6 2.4 7.8 0.9 I00,0 1655 Secondary + 26.3 51.9 6.2 9.7 0.9 4.2 0.8 100.0 1268 Tota[ 16.4 33.6 14.3 21.3 1,8 11.7 0.8 100.0 7050 to the interval between births. Risk is higher for births to younger (under age 18) and older (age 35 or over) mothers, those who have had a prior birth recently (within the previous 24 months), and those of higher parity (four or more births). Table 6.7 indicates that 85 percent of currently married women fall into at least one of the high health risk categories and over half fall into two or more categories. Most married women have had 4 or more births, and almost half have had a birth in the past 24 months. With regard to recent births, two-thirds fall into one category and 28 percent fall into two or more categories. Over half (56 percent) of Kenyan births occur to high parity mothers, while one-fifth are born less. than 24 months after a previous sibling. 6.3 Child Health Indicators The KDHS included a series of questions intended to provide information on immunisation coverage and on the occurrence and treatment of diarrhoea, fever and respiratory illness among children under age five. Strictly speaking, these data do not represent all children under five in Kenya, but only those children of women who were interviewed in the KDHS. Thus, no information was obtained for children of women who had died, who were institutionalised, or who, for some other reason were not interviewed in the survey. Although the immunisation status and the morbidity experience of the latter children are likely to differ from that of children whose mothers were interviewed, their numbers are not large, so the results presented below can be considered as generally describing the health status of children under five years of a~e in Kenya. 62 Table 6.7 Percentage of currently married women and births in the 12 months prior to the survey to women who faLL in various categories of high health risk, Kenya, 1989 Currently Births Health risk married in past category woc~en 12 months Under age 18 1.4 2.9 Age 35 oP older 36.2 18.2 Last birth occurred within past 24 months 48.3 20.5 Four b i r ths or more 61.5 55.8 In at Least one category 85.2 66.4 In 2 or more categories 52.1 28.1 Weighted number 4765 1484 lmmunisation of Children In the KDHS, women who had children under the age of five were asked if the children had health cards. If the health card was available, the interviewers copied from the card the dates on which the child had received immunisations against the following diseases: tuberculosis (BCG); diphtheria, whooping cough (pertussis) and tetanus (DPT); polio; and measles. If the child had no card or the interviewer was not able to examine the card, the mother was asked if the child had ever received a vaccination. However, no information was obtained on specific vaccinations for these children because of doubts about the reliability of the mother's rccall. In examining these data, it should be borne in mind that as of January 1986, the Kenya Expanded Programme of Immunisation (KEPI) recommended that children be immunised according to the following schedule (Ministry of Health, 1987, p.20): Age Immunisation Birth BCG, polio 6 weeks DPT, polio 10 weeks DPT, polio 14 weeks DPT, polio 9 months measles The data in Table 6.8 indicate that immunisation cards were seen for 50 percent of all the children under age five. The proportion of children with health cards seen is highest for children 6-11 months of age. Of children with cards, almost all had received at least one immunisation. This is not surprising since one of the major reasons lbr issuing a health card is to record immunisations. Forty-three percent of children did not have a health card available, but were reported by their mothers to have been immunised. The information on specific immunisations collected for children with health cards is also presented in Table 6.8. In interpreting the data in the table, it is important to bear in mind that the figures are based on children whose health cards were seen by the interviewers. Thus, the 63 Table 6.8 Among a l l ch i ld ren under 5 years of age, the percentage with hea l th cards seen by interv iewer, the percentage who are immunised as recorded on a health card or as reported by the mother and, among ch i ldren with hea l th cards, the percentage fo r whom BCG, DPT, po l io and measles immunisations are recorded on the health card, by age, Kenya, 1989 Among ch i ldren under 5 Among ch i ldren under 5 with health cards seen, the percentage wi th : the percentage who have received: Num- Ui tb Some [ramun. per hea l th irnmun- reported of Age in cards isat ion by DPT DPT DPT Pol io Pol io Pol io Meas- ch i t - months seen on card mother BCG I 2 3÷ I 2 3+ tes ALL* dren ,6 52.7 51.7 2a.4 93.3 62.1 55.5 26.9 89.5 67.2 41.5 4.G 1.8 601 6-11 67.5 67.3 26.2 96.7 98.6 91.5 82.6 99.0 94.0 85.8 25.7 23.1 710 12-17 60.3 59.9 35.2 9S.5 98.4 94.3 88.6 99.4 94.3 91.3 77.0 71.0 703 18-23 61.9 61.8 34.9 98.1 99.4 98.1 93.0 99.2 97.4 93.5 79.1 74.8 612 24-59 43.7 43.4 51.5 96.4 98.0 93.0 86.6 97.4 92.8 86.3 81.0 72.9 3889 Total 50.6 50.3 43.3 96.2 96.8 90.0 81.5 97.3 91.2 83.4 64.8 58.8 6514 * BeG, at Least 3 doses of DPI and poLio, and measles results cannot be interpreted as coverage rates for the entire population of children of that age, but rather, should be viewed as providing measures of drop-out rates, since virtually all children with cards received at least one immunisation. The KDHS found that among children aged 1-5 years for whom health cards were available, more than 95 percent had received a BCG vaccination and at least one dose of DPT and polio. Almost all of those who have the first dose of DPT and polio receive the second and third doses, however, only about 80 percent of children aged 1-5 with health cards have been immunised against measles. Since it is customary to report immunisation coverage based on one-year olds, Table 6.9 presents data on the proportion of children 12-23 months with cards who have received specific immunisations, according to selected background characteristics. The data show that there is a slight difference in immunisation coverage for boys and girls--76 percent of girls whose cards were available bad received all immunisations, compared to 70 percent of boys. This differential is due almost entirely to the differential in measles coverage. Rural and urban differentials on immunisation coverage are modest, with the urban children having higher coverage than their rural counterparts. Rural children are also more likely to have health cards available to show the interviewer. There seem to be some marked variations in coverage by province, with Central Province having the highest proportion fully immunised (88 percent), and Western Province the lowest (57 percent). There is also a much steeper drop-out rate between the three doses of DPT and polio among children in Western Province than for children in other provinces. Considering differentials by educational status of the child's mother, full immunisation coverage is much higher among children whose mothers have attained secondary education (86 percent) than for those whose mothers have no education (55 percent). Estimates of coverage for all children, including those whose health cards were not seen, can be derived by multiplying the proportion of children with particular immunisations recorded on health cards by the proportion of children whose health cards were seen. For example, 64 Table 6.9 Among a l l ch i ld ren aged 12-23 months, the percentage with health cards seen by interv iewer, the percentage who are immunised as recorded on a health card or as reported by the mother and, among ch i ldren with health cards, the percentage for whofn BCG, DPT, po l io and measles immunisations are recorded on the health card, by background character is t ics , Kenya, 1989 Among ch i ldren 12-23 Among ch i ldren 12-23 months with health cards seen, months, percent w i th : the percent who have received: Back- With Some Immun. No. ground health immun, reported of character- card on by DPT DPT DPT Pol io Pol io Pol io Meas- ch i t - istics seen card mother BCG I 2 3+ I 2 3+ les ALl* dren Sex Male 63.4 63.2 32.7 96.9 98.7 96.6 90.3 99.4 96.2 92.6 75.8 70.0 653 Female 58.7 58.4 37.5 96.5 99.1 95.4 91.0 99.2 95.3 92.1 80.3 75.9 662 Residence Urban 49.5 48.9 46.9 96.7 98.7 98.7 94.7 98.7 98.0 94.7 86.1 82.1 197 Rural 63.1 62.9 33.0 96.7 98.9 95.7 90.I 99.4 95.5 92.0 76.8 71.5 1118 Province Nairobi 47.9 46.5 47.9 92.8 97.1 97.1 94.2 97.1 95.7 94.2 85.5 79.7 93 Central 61.0 61.0 36.7 95.6 I00.0 99.4 98.2 00.0 100.0 97.9 93.6 87.7 203 Coast 66.2 65.7 32.0 96.4 99.3 97.1 85.6 99.3 97.1 93.6 71.6 68.7 73 Eastern 73.I 73.1 24.1 97.4 100.0 98.6 92.4 00.0 98.6 96.8 82.0 79.4 241 Nyanza 55.0 55.0 39.7 97.8 98.6 95.2 91.7 99.3 96.6 93.3 67.4 64.8 226 Rift V. 62.3 62.3 33.9 97.8 97.8 94.4 89.5 99.8 94.6 90.8 77.2 70.7 290 Western 55.5 54.6 38.4 95.5 98.3 90.8 80.6 97.4 86.5 783 66.2 56.5 189 Education None 53.0 52.9 38.1 96.3 98.8 91.6 77.0 98.7 92.7 86.9 58.4 55.1 306 Some prim. 65.3 64.7 30.8 94.7 98.8 96.3 91.2 99.4 95.5 91.8 76.8 70.5 392 Prm. cornp. 66.5 66.5 32.5 98.0 100.0 98.8 96.0 99.8 97.8 95.0 84.7 79.4 355 Second.+ 56.7 56.5 41.2 98.6 97.4 96.3 95.4 99.0 96.3 95.2 90.7 85.8 259 Total 61.0 60.8 35.1 96.7 98.9 96.1 90.7 99.3 95.8 92.4 78.0 72.8 1315 * BCG, at least 3 doses of OPT and polio, and measles multiplying the 73 percent of children 12-23 months who are fully immunised according to their health cards by the 61 percent who produced health cards for the interviewer gives an estimate of 44 percent of all children 12-23 months who are fully immunised. This compares closely with the estimate of 41 percent fully immunised according to cards from the Kenya Expanded Programme of Immunisation (KEPI) survey (Ministry of Health, 1987). These are minimum estimates of coverage, since they assume that all children without cards have not received any immunisations. If one assumes that all children without cards whose mothers say they have received some immunisation(s) have received the same immunisations as those with cards, the estimate in the KDHS increases to 70 percent fully immunised among children 12-23 months. This is probably on the high side and the true coverage is most likely between 44 and 70 percent. In the KEPI survey, information on specific immunisations received was asked of the mothers of children without cards; using this information, the proportion of children 12-23 months fully immunised was 51 percent. Child Morbidity and Treatment In addition to the immunisation data, information was collected for all children under age five on the occurrence of diarrhoea, fever and respiratory illness in the weeks preceding the interview and treatment provided for children experiencing these illnesses. The data on diarrhoea, 65 lever and respiratory illness cannot be used to measure incidence of these ailments. However, they provide a basis for a period prevalence estimate for each illness, i.e., the percentage of children under 5 years whose mothers report that they had the illness in question during the weeks preceding the survey. In considering the morbidity information, it is important to remember that the measures are influenced by the mother's subjective evaluation of whether the child experienced the illness in question. For example, the question on diarrhoea simply asked the mother if the child had diarrhoea during the last 24 hours or two weeks. The responses to the question are clearly dependent on what the mother understood by the term diarrhoea and thus there may be considerable variation in the length and severity of the diarrhoea episodes reported in response to the question. The morbidity measures are also affected by the reliability of the mother's recall as to when the episode of the illness in question occurred. Both the failure to report illness occurring within the reference period (two weeks for diarrhoea and four weeks for fever and cough) and the reporting of episodes that occurred prior to the period affect the accuracy of the prevalence estimate. In interpreting the morbidity data, it should be kept in mind that the majority of interviews took place during the dry season, when the number of cases of illness in question--diarrhoea, fever and respiratory problems--would be expected to be somewhat lower than at other times of the year. Table 6.10 Among children under 5 years of age, the percentage reported by the mother to have had diarrhoea in the past 24 hours and the past two weeks, by background characteristics, Kenya, 1989 Percent of children under No. 5 with diarrhoea in: of chit - Background Past Past dren character- 24 two under istics hours weeks 5 Age in months <6 11.0 18.0 601 6-11 12.9 25.3 710 12-17 13.6 25.6 703 18-23 9.2 18.3 612 24-59 3.2 6.4 3889 Sex Male 6.8 12.9 3210 Female 6.5 12.6 3305 Residence Urban 5.4 10.8 899 Rural 6.9 13.1 5615 Province Nairobi 7.3 13.0 386 Central 4.7 10.0 927 Coast 4.3 10.1 378 Eastern 7.8 15.1 1174 Nyanza 7.0 15.5 1106 Rift Valley 4.6 7.4 1533 Western 10.5 18.6 1011 Education No education 7.5 13.2 1725 Some primary 6.9 12.9 2043 Primary comp. 6.7 12.7 1546 Secondary + 5,0 11.9 1194 Total 6.7 12.7 6514 Diarrhoea Table 6.10 shows the percentage of childrcn under age five reported as having had diarrhoea in the two weeks preceding the survey, whereas Table 6.11 shows the kind of treatment they received. Seven percent of children under five were reported to have had diarrhoea in the 24 hours before the survey and 13 percent were reported to have had diarrhoea in the two weeks before the survey. Diarrhoea prevalence varies with the age of the child; the rates are greatest for children aged between 6 and 17 months (when weaning usually takes place), whereas it is lowest for children 24 months or older. The sex differential is insignificant--13 percent of both boys and girls are reported as having had diarrhoea in the previous two weeks. Diarrhoea prevalence is slightly higher for rural than urban children. By province, diarrhoea prevalence is 66 highest for Western Province, followed by Nyanza and Eastern Provinces, in that order; it is lowest for Rift Valley Province. There are no substantial differences in prevalence of diarrhoea by education of mother. For the children who had an episode of diarrhoea in the two weeks preceding the survey, Table 6.11 indicates what, if anything, mothers did to treat the diarrhoea. About 47 percent consulted medical personnel, 21 percent used ORS packets, 49 percent used a homemade rehydration solution, and 84 percent used other treatment. It is important to note that only 10 percent did nothing to control the diarrhoea. Table 6.11 Among children under 5 years of age who had diarrhoea in the post two weeks, the percentage consulting a medical fac i l i ty , the percentage receiving dif ferent treatments as reported by the mother, and the percentage not consulting a medical fac i l i ty and not receiving treatment, according to background characteristics, Kenya, 1989 Percent of children with Not con- No.of Percent diarrhoea treated with*: sulting chil- consult- facility dren Background ing a Home Uther and no with character- medical ORS solu- treat- treat- diar- istics facility packets tion ment ment rhoea Age in months ,6 55.1 9.4 55.8 85.7 6.7 108 6-11 49.0 26.1 40.9 84.0 10.2 180 12-17 53.2 24.6 61.3 92.3 3.2 180 18-23 44.6 21.8 44.5 84.3 14.2 112 24-59 38.0 19.6 44.7 76.3 14.8 250 Sex Male 47.0 16.6 46.8 85.5 9.1 413 Female 46.6 25.4 50.9 82.0 11.2 417 Residence Urban 58.7 20.7 52.0 84.0 12.0 97 Rural 45.2 21.1 48.5 83.7 9.9 733 Province Nairobi 66.7 23.1 57.7 87.2 5.1 50 Central 31.9 19.6 71.4 94.7 3.7 92 Coast 58.2 38.0 35.1 86.6 10.6 38 Eastern 48.4 13.8 54.1 87.2 10.2 177 Nyanza 49.8 24.6 41.0 77.1 13.9 171 Rift Valley 35.8 29.0 27.5 69.3 18.6 113 Western 48.9 16.6 53.5 88.4 6.0 188 Education NO education 42.3 18.6 42.6 77.2 16.3 228 some primary 49.2 21.3 51.7 86.0 7.6 264 Primary con~. 49.2 21.2 55.1 86.9 8.0 196 Secondary + 46.3 24.3 45.4 85.6 8.1 142 Total 46.8 21.1 48.9 83.7 10.2 830 * Percents may add to more than I00, since children may receive more than one treatment. 67 The type of treatment given varies somewhat according to background characteristics. For example, small infants are less likely than older children to receive oral rehydration solution made from packets as treatment for diarrhoea, while older children are more likely tb.an younger children not to receive any treatment at all. Urban children are more likely than rural children to consult a medical facility. Children in Nairobi are more likely to be taken for medical consultation than children in other provinces and children in Central Province are more likely to be given home solution when they have diarrhoea, while those in Coast Province are more likely to be given solutions made from ORS packets. Differences in treatment by education of the mother are small, except that children of mothers with no education are more likely to receive no treatment. Fever In Table 6.12, information is presented on the percentage of children under age five reported to have had fever during the four weeks prior to the KDHS interview. Fever is a specific symptom of many infectious diseases, but increased prevalence of fever may indicate a higher prevalence of malaria. Forty-two percent of children under age five had fever during the month before the survey. The age of the child is related to the reported episode of lever, with prevalence peaking at 55 percent among children aged 6-11 months, whereas it is lowest for children aged 24-59 months (37 percent). There is no evidence of strong differentials in fever prevalence by sex, urban-rural residence, province, or education of the mother, except that prevalence is lowest in Rift Valley Province. Table 6.12 further shows that of the reported children with fever, 56 percent consulted a medical facility, which is higher than the percentage of children with diarrhoea who consulted a medical facility. The percentage of children with fever who receive medical consultation varies little by age or sex of child, or mother's education. Children of urban women are more likely to Table 6.12 Among children under 5 years of age, the percentage who are reported by the mother as having had fever in the past four weeks, and, among children under 5 who had fever in the past four weeks, the percentage consulting a medical facility, according to background characteristics, Kenya, 1989 Percentage Percentage Number Background with fever consulting of characteD- in past medical children istics four weeks facility under 5 Age in months <6 45.5 63.6 601 6-11 55.2 56.8 710 12-17 47.7 60.6 703 18-23 50.5 59.6 612 24-59 36.8 51.5 3889 SeK Male 41.4 55.8 3210 Female 42.8 55.3 3305 Residence Urban 41.5 71.5 899 Rural 42.2 53.0 5615 Province Nairobi 45.9 69.8 386 Central 50.2 52.7 927 Coast 44.1 76.9 378 Eastern 43.7 55.9 1174 Nyanza 50.0 56.4 1106 Rift Valley 29.0 49.9 1533 Western 41.6 48.5 1011 Education NO education 38.6 54.2 1725 Some primary 44.2 52.0 2043 Primary comp. 42.2 56.1 1546 Secondary + 43.3 62.7 1194 Total 42.1 55.5 6514 receive medical consultation (72 percent) than children of rural women (53 percent) and children in Nairobi and Coast Province are more likely to be taken to a medical facility for consultation than children in other provinces. 68 Table 6.13 Among ch i ldren under 5 years of age, the percentage who are reported by the mother as having suffered from severe cough or d i f f i cu l t or rapid breathing in the past four weeks, and, among ch i ldren under 5 who suffered from severe cough or d i f f i cu t t breathing, the percentage consult ing a medical fac i l i ty , the percentage receiving various treatments, and the percentage not consult ing a medical fac i l i ty and not receiving treatment, according to background character is t ics , ~enya, 1989 Percent- Percent- Percentage Percent Number age age with with cough not con- of with cough treated by*: su i t ing chit- Background cough consulting facility dren character- in past medical Cough and no under istics 4 weeks facility syrup Other treatment 5 Age in months <6 24.1 64.9 53.5 63.9 10.6 601 6-11 24.7 72.8 57.9 50.1 9.3 710 12-17 18.3 72.1 49.6 44.6 9.6 703 18-23 19.3 65.9 60.1 68.5 4.2 612 24-59 16.0 61.5 49.8 61.5 7.9 3889 Sex Mate 18.0 61.8 51.8 57.4 9.9 3210 Female 18.5 68.4 53.1 60.4 6.6 3305 Residence Urban 14.8 78.7 66.2 56.0 4.8 899 Rural 18.8 63.4 50.7 59.3 8.7 5615 Province Nairobi 13.7 76.8 67.1 56.1 9.8 386 Central 16.3 73.1 63.9 52.7 5.0 927 Coast 18.0 72.5 31.9 44.1 2.6 378 Eastern 18.7 56.9 40.5 66.5 2.5 1174 Nyanza 21.7 72.1 59.3 70.6 8.0 1106 Rift Valley 20.3 59.9 50.0 45.1 14.3 1533 Western 14.5 61.6 56.7 72.3 9.6 1011 Education No education 18.1 65.7 53.2 59.2 7.5 1725 Sofne primary 20.6 61.7 48.1 57.2 12.8 2043 Primary complete 18.8 65.2 50.8 61.3 4.9 1546 Secondary + 13.6 T3.3 65.4 58.4 3.9 1194 rotal 18.2 65.2 52.4 59.0 8.2 6514 * Percents may add to more than 100, since children may receive more than one treatment. Cough/Difficult Breathing An attempt was made in the survey to obtain information on the prevalence of respiratory illness by asking for each child under age five whether the child had had cough or difficulty breathing in the four weeks before the survey. The combination of cough and rapid breathing is considered an indication of lower respiratory tract infection, particularly pneumonia. Data from these questions are presented in Table 6.13. The data indicate that of all children under five, 18 percent had had cough or difficulty breathing in the four weeks before the survey. There exists 69 little difference in the percentage who had a cough by sex, urban-rural residence, or province. Younger children and those whose mothers are less educated are more likely to have had respiratory problems. Of the children experiencing cough or difficulty breathing, 65 percent consulted a medical facility, 52 percent used cough syrup, 59 percent used other medicine, and 8 percent did nothing to treat the cough. Table 6.13 also shows how treatment regimes varied with sex of child, age of child, province, residence and mother's education. Children in urban areas are more likely to be taken to a medical facility than children in rural areas. 6.4 Household Sanitation Table 6.14 presents information about the source of water used by female respondents in the KDHS. The most common source of water for drinking, washing, and cooking is rivers (37 percent of women). Almost one-third (31 percent) of women have access to piped water, either inside their house (19 percent) or from a public tap ( l l percent), while 16 percent of women rely on wells for water. There is considerable difference in water sources by urban-rural and provincial residence. As expected, urban women are much more likely to have piped water than rural women. Women in Western, Rift Valley, Central and Eastern Provinces tend to obtain water from rivers, while those in Nairobi and Coast Province arc likcly to have pipcd water, mainly because of the large urban population in these two areas. Table 6.14 Percent distribution of all women by source of water for drinking, washing, and cooking, according to urban-rural residence and province, Kenya, 1989 Source of water Piped Welt Welt Residence/ into Public with without Rain- No. of province house tap pump , pump Lake River Pond water Other Total women Residence Urban 56.1 34.7 2.1 2.3 0.2 3,0 1.0 0.2 0.4 100.0 1236 Rural 11.6 6.6 5.9 12.5 1.9 43.9 7.4 1.6 8.6 100.0 5914 Province Nairobi 57.7 38.I 0.8 1.3 0.0 1.7 0.0 0.2 0.1 100.0 554 Central 34.0 3.9 6.3 7.6 0.0 38.8 2.7 4.9 1.8 100.0 1120 Coast 24.4 32.7 4.0 6.1 0.1 15.7 15.7 0.0 1.2 100.0 498 Eastern 15.6 8.8 3.9 22.5 0.1 38.0 3.6 1.0 6.5 100.0 1269 Nyanza 7.6 9.2 6.5 12.1 8.8 27.1 3.4 0.4 25.0 100.0 1218 Rift Valley 9.0 5.9 7.0 10.2 0.2 51.9 9.2 1.1 5.6 100.0 1519 Western 13.7 8.8 4.9 5.8 0.2 52.5 11.5 1.0 1.5 100.0 971 Total 19.3 11.4 5.3 1D.7 1.6 36,8 6.3 1.4 7.2 100.0 7150 Table 6.15 shows data on the types of toilet facilities for KDHS respondents. Three- quarters of the women have pit latrincs, 9 percent have flush toilets, and 15 percent have no facilities. Urban women and women in Nairobi are much more likely to have flush toilets than rural women or women in other provinces. Despite the fact that Coast Province has the second largest city in Kenya (Mombasa), over one-third of the respondents report that they have no toilet facilities. 70 Table 6.15 Percent distribution of women by type of toitet facility in the household, according to urban-rural residence and province, Kenya, 1989 Type of to i le t Residence/ Flush P i t NO fac- NO. of province to i le t Lat r ine Other i L i t ies Total women Residence Urban 44.3 50.1 2.5 3.1 100.0 1236 Rural 1.5 80.5 1.3 16.8 100.0 5914 Province Nairobi 46.6 47.1 4.0 2.2 100.0 554 Central 7.7 90.0 1.9 0.4 100.0 1120 Coast 14.5 48.6 0.5 36.4 100.0 498 Eastern 0.8 83.8 0.2 15.2 I00.0 1269 Nyanza 4.9 77.6 0.5 17.0 100.0 1218 Rift Valley 5.0 70.4 1.2 23.4 100.0 1519 Western 7.4 81.1 3.2 8.3 100.0 971 Total 8.9 75.2 1.4 14.5 100.0 7150 7] 7 HUSBAND'S SURVEY The Kenya Demographic and Health Survey also interviewed husbands of some of the female respondents. The husband questionnaire was designed to provide information on the husbands' background, fertility, fertility preferences, and contraceptive knowledge, use and attitudes. The information obtained from the husband's questionnaire will assist in planning and managing population and family planning programmes. In some tables in this chapter, husbands are matched with their wives to provide information on a sample of married couples. 7.1 Characteristics of the Sample The KDHS was designed to interview 1000 husbands. Respondents were husbands who spent the night before the interview in the household in which his wife or wives were interviewed. In order to produce the required number of husbands, every other household selected in the KDHS was considered eligible for the husband's interview. During the data collection, 1,116 husbands were successfully interviewed. Table 7.1 presents the percent distribution of husbands in the sample by age, number of children, region, level of education, and occupation. All data have been weighted to produce a representative sample. About 13 percent of the husbands are less than 30 years of age, one out of three is 30 to 39, and more than 50 percent are 40 years or older. Husbands are older than currently married women in general, since 46 percent of married women are less than 30 years of age and only 20 percent are 40 or older. The distribution of husbands by province is similar to that of married women. At least 50 percent of the husbands have completed primary or higher education, whereas 17 percent have no education. Husbands are better educated than currently married women, only 38 percent of whom have completed primary 'education and 32 percent of whom have no education (Table 1.1). The majority of the husbands are employed in agriculture (52 percent), compared to 13 percent employed in professional and technical occupations. Table 7.1 Percent d i s t r ibut ion of husbands by background character is t ics , Kenya, 1989 Weighted Unwtd, Background Weighted no. of no. of characteristic percent husbands husbands Age Less than 30 12.8 150 160 30-39 32.7 383 379 40-49 28.6 335 311 50 or over 25.8 302 266 Residence Urban 13.4 157 244 Rural 86.6 1013 872 Province Rairobi 5.5 65 100 Central 14.1 165 214 Coast 5.9 69 99 Eastern 21.6 253 177 Nyanza 16,3 190 188 Rift Valley 25.2 295 207 Western 11,4 134 131 Education No education 17.2 201 185 some primary 32.5 381 352 Primary complete 23.3 273 247 Secondary + 27,0 316 332 Occupation Never worked 0.2 2 3 Prof/Tech/Manag 12.6 148 123 Cler ica l 6.6 77 83 Sales 6.6 78 83 Agric-self employed 44.2 517 456 Agric-c~ployee 8.2 96 90 Household/domestic 8.7 102 120 Skilled manual 9.2 108 125 Unski l led manual 3.6 42 31 Total 100.0 1170 1116 73 Table 7.2 presents the distribution of husbands by level of education and background characteristics. Younger husbands and those who live in urban areas have higher levels of education than older husbands and their rural counterparts. The education distribution by province shows that the husbands living in Nairobi are the most highly educated while those in the Coast Province are the least educated. This could be partly due to the rural-urban migration of educated husbands into Nairobi. Husbands working in professional, technical, and clerical occupations are the most educated, while those in agricultural occupations are the least educated. Table 7.2 Percent distribution of husbands by level of education, according to background characteristics, Kenya, 1989 Level of education Wtd. no. of Background Some Primary Second- hus- character i s t i c None pr imary complete ary ÷ Totat bands Age Less than 30 5.9 16.7 29.8 47.6 100.0 150 50-39 12.9 28.4 24.0 34.6 100.0 383 40-49 12.7 31.3 29.3 26.7 100.0 335 50 or over 33.0 47.0 12.7 7.4 100.0 302 Residence Urban 9.4 11.9 22.5 56.1 100.0 157 Rura[ 18.4 35.7 23.5 22.4 100.0 1013 Province Nairobi 7.0 10.0 22.0 61.0 100.0 65 Central 6.7 33.7 24.9 34.7 100.0 165 Coast 52.5 16.4 12.1 19.0 100.0 69 Eastern 17.2 42.8 23.4 16.6 100.0 253 Nyanza 11.0 26.3 32.0 30.7 100.0 190 Rift Valley 20.1 37.5 18.8 23.6 100.0 295 Western 18.6 29.0 25.4 26.9 100.0 134 Occupation Never worked 0.0 66.7 0.0 33.3 100.0 2 Prof/Tech/Manag. 3.4 8.6 18.3 69.7 100.0 148 Clerical 2.5 6.5 21.2 69.8 100.0 77 Sales 17.2 23.9 29.2 29.7 100.0 78 Agric-seif c~pioyed 24.1 41.9 22.2 11.9 I00.0 517 Agric-employee 32.6 41.0 19.1 7.4 100.0 96 Household/domestic 9.1 29.5 31.3 30.1 100.0 102 Skilled manual 10.1 26.5 33.9 29.5 100.0 108 Unskilled manual 10.9 68.9 10.4 9.8 100.0 42 Total 17.2 32.5 23.3 27.0 100.0 1170 7.2 Marr iage and Ferti l i ty The KDHS husband questionnaire included a question about the number of wives a husband had. Table 7.3 displays responses to this question. About 20 percent of husbands have more than one wife. Polygyny increases with age; only 3 percent of husbands under 30 years were in a polygynous union, compared with 45 percent of 74 those 50 or over. The proportion of rural husbands who are polygynous is higher than for urban husbands. Table 7.3 Percentage of husbands in a polygynous union, according to background character is t ics , Kenya, 1989 Weighted Background no. of character is t ic Percent husbands Age Less than 30 3.3 150 30-39 10.3 383 40-49 17.7 335 50 or over 44.9 302 Residence Urban 17.6 157 Rural 20.9 1013 Province Nairobi 17.0 65 Central 7.6 165 Coast 41.4 69 Eastern 14.2 253 Nyanza 29.4 190 Ri f t Val ley 17.3 295 Western 33.4 134 Education No education 37.9 201 Some primary 19.5 381 Primary complete 17.4 275 Secondary + 13.2 316 Total 20.5 1170 Provincial differentials show that Coast Province has the highest proportion of husbands in polygynous unions (41 percent). This is followed by Western Province (33 percent), and Nyanza Province (29 percent), with Central Province having the smallest proportion of such unions (8 percent). Polygyny decreases with increasing level of education. Tabte 7.4 Percent d i s t r ibut ion of husbands by number of current wives, according to age, Kenya, 1989 Number of current wives Wtd. no. of husb. Age 1 2 3+ Total Less than 30 96.7 3.3 0.0 100.0 150 30-39 89.7 10.1 0.2 100.0 383 40-49 82.3 15.1 2.5 100.0 335 50 or over 55.1 30.5 14.5 I00.0 302 Total 79.6 15.9 4.5 I00.0 1170 Table 7.4 shows the percent distribution of husbands by number of wives according to age of the husband. The data show that about three-quarters of the polygynous husbands have 2 wives, while one-quarter have three or more wives. The proportion with three or more wives increases with age of the husband. 75 Table 7.5 shows the mean age difference between spouses. As expected, wives tend to be younger than their husbands. The mean age difference is about 10 years. The difference increases to 18 years between husbands and second wives. Table 7.5 Percent distribution of married couples by number of years husband is older than his interviewed wife(yes), according to wifefs age, Kenya, 1989 Age of Husband's age - wi fe 's age Mean Wtd, in ter - no. of number viewed years of wife Total older couples Nega- 0-4 5-9 I0-14 15+ tive yrs yrs yrs yrs 15-19 0.0 19.9 40.5 31.2 8.5 100.0 9.7 36 20-24 0.4 23.4 46.5 12.3 17.4 100.0 9.3 178 25-29 2.4 27.8 38.2 14.2 17.5 100.0 9.8 262 30-34 3.2 25.9 37.4 13.1 20.4 100.0 10.2 213 35-39 5.1 22.5 31.8 22.2 10.4 100.0 10.3 228 40-44 8.0 19.7 27.0 25.2 20.1 100.0 10.8 171 45-49 10.1 32.9 22.3 22.3 12.4 100.0 8.2 102 Total 4.1 24.8 35.2 18.0 17.8 100.0 9.9 1189 Note: The number of married couples is greater than the number of hus- bands because several husbands had more than one wife interviewed. Table 7.6 presents the mean number of living children, by age of husband. The number increases dramatically with age, from 2.1 children for husbands under age 30, to 9.6 children for husbands age 50 or over. Forty-nine percent of husbands have six or more children and 42 percent of husbands age 50 or over have 10 or more children. Table 7.6 Percent d is t r ibut ion of husbands by number of l i v ing ch i ldren, according to age, Kenya, 1989 Number of living children Wtd. Mean no. of no. Age 0 1 2 3 4 5 6 7 8 9 10+ Total husb. a l ive < 30 6.9 26.1 32.1 25.2 6.6 2.0 0.7 0.4 0.0 0.0 0.0 100.0 150 2.1 30-39 1.5 6.5 12.1 17.8 21.5 14.8 15.6 4.0 2.9 2.3 1.0 I00.0 383 4.2 40-49 0.9 0.6 4.8 6.1 10.1 12.4 14.3 14.1 11.5 7.5 17.6 100.0 335 6.9 50 + 0,3 0.9 1.1 4.7 4.8 5.1 10.3 10.3 9.5 10.9 42.1 100.0 302 9.6 Total 1.7 5.9 9.8 12.1 12.0 9.9 11.9 8.1 6.7 5.7 16.3 100.0 1170 6.1 7.3 Knowledge and Use o f Fami ly P lann ing Table 7.7 and Figure 7.1 show the percentage of husbands who know a family planning method, know a a source for a method, who have ever used a method, and who are currently using a method. 76 Table 7.7 Percentage of husbands who know contraceptive methods, who know a source for methods, who have ever used and who are currently using, by method, Kenya, 1989 Knows Knows Ever Currently Method method source used using Any method 94,7 92.7 65.0 49.3 Any modern method 93.1 91.6 35.1 24.6 Pill 87.5 83.7 16.9 7.8 iUD 67.4 64.1 8.8 5.3 Injection 79.8 77.0 6.3 3.5 Diaphragm/foam/jelly 29.2 28,0 2.4 0.7 Condom 81.5 74.3 16.7 3.2 Female ster i l isat ion 83.0 79.8 7.1 6.3 Male ster i l isat ion 35,0 32.2 1,0 0.3 Any traditional method 82.6 53.9 54.4 29.0 Periodic abstinence 76.5 53.9 48.1 25.8 Withdrawal 47.4 0.0 15.3 2.5 Other methods 18.1 0.8 8.2 2.8 Note: Husbands may report current use of more than one method. Figure 71 Family Planning Knowledge and Use Among Husbands Percent 100 80 60 40 20 0 Know Method ~ 2 / 65 Ever Used Currently Using ~Modern Method mBI IAny Method / Kenya DHS 1989 ?7 As the table shows, knowledge of contraceptives by Kenyan husbands is high. While 95 percent know of at least one method, 93 percent know a source, 65 percent have used a method at some time, and 49 percent are currently using a method. Over 93 percent of the husbands have heard of at least one modern method of contraception. While 92 percent know a source for modern methods, only 35 percent have ever used a modern method and 25 percent are currently using one. Knowledge of specific methods is greatest for the pill, followed by female sterilisation, condom, injection, periodic abstinence and the IUD in that order. The table further shows that traditional methods, specifically, periodic abstinence, (26 percent) are the most widely used by husbands. Eight percent of husbands say they rely on the pill, while 6 percent rely on female sterilisation. There are sharp differences between ever-use and current use of contraceptive methods, especially for modern methods. For example, of the 17 percent of husbands who have ever used the condom, only 3 percent are currently using the method. Table 7.8 shows that husbands are more knowledgeable about contraceptive methods than their wives. For almost all methods, the proportion of husbands who know the method when the wife does not is higher than the proportion of wives who know the method when the husband does not. Table 7.8 Percent d is t r ibut ion of married couples by knowledge of contraception, according to r~thed, Kenya, 1989 Both Only Only know husband wife Neither Method method knows knows knows Total Pitt 78.7 9.0 8.8 3.4 100.0 IUD 52.3 15.1 14.2 18.3 100.0 Injections 68.6 11.4 14.2 5.8 100.0 Diaphragm/foam/jel ly 11.6 17.9 17.3 53.3 100.0 Condom 49.7 32.1 6.7 11.5 100.0 Female s ter i t i sa t ion 65.6 17,7 9.6 7.1 100.0 Male s ter i l i sa t ion 11.5 23.3 12.7 52.5 100.0 Periodic abstinence 40.7 35.6 10.8 12.9 100.0 Withdrawal 11.6 35.7 6.8 45.8 100.0 Other 1.8 16.6 5.1 76.5 100.0 Note: Table is based on 1187 married couples. Table 7.9 below shows the differentials in current contraceptive use. The table shows that use of any method and modern methods is higher among husbands in their 30s and 40s than among older and younger husbands. Use is also highest among husbands with 3-4 living children and lower among husbands with less than three or more than four children. Rural-urban differentials exist, especially for use of modern methods. Forty percent of urban husbands are currently using a modern method, almost twice the proportion among rural husbands (22 percent). As expected, variation by province shows that Nairobi has the highest level of current use, followed by Central, Eastern, and Rift Valley Provinces. 78 Table 7.9 Percentage of husbands who are currently using any method and any modern method of contracep- t ion, by background character- i s t ics , Kenya, 1989 Any Wtd. Background Any modern no. of characteristic method method husb. Age Less than 30 44.2 20.5 150 30-39 53.0 28.7 383 40-49 57,2 29.1 335 50 or over 39,0 16.3 302 Residence Urban 55,7 39.8 157 Rural 48.5 22.2 1013 Province Nairobi 66.0 46,0 65 Central 64.3 39.5 165 Coast 37.0 18.8 69 Eastern 60.7 22.3 253 Nyanza 43.7 19.4 190 Rift Valley 46.8 21.8 295 Western 22.7 16.7 134 Education No education 38.5 16.9 201 Some primary 45.0 18.6 381 Primary complete 47.7 18.3 273 Secondary + 63.3 42.1 316 Re. of living children 0-2 43.6 23.6 203 3-4 52.3 28.7 281 5 or more 50.1 23.2 Total 49.5 24.6 1170 Current use increases with educational attainment. It varies from 39 percent for any method for the husbands with no education to 63 percent for those with secondary and higher education. The same pattern is seen among husbands who are currently using a modern method. As with female respondents, husbands were also asked about problems they perceived in using contraceptive methods. However, husbands were asked only about problems perceived for male-oriented methods: condom, male sterilisation and withdrawal. The results are shown in Table 7.10. The table shows that the most common response regarding problems in using the condom was "no problem" or "don't know", which together constitute almost 70 percent of the responses. Substantial proportions say that the condom is not effective or that it is inconvenient to use. Regarding male sterilisation, the most common answer was "don't know" and "other" (much of which refers to the permanency of the method). Health concerns and community disapproval were also mentioned by a number of husbands. Husbands were most likely to say there were no problems with withdrawal, however, almost 20 percent said the main problem with the method was that it was ineffective. Inconvenience was also mentioned. 79 TabLe 7.10 Percent d ist r ibut ion of husbands who have ever heard of condom, nmte ster i t isat ion, or withdrawal, by main problem perceived in using the method, according to spec i f i c method, Kenya, 1989 Contraceptive method Main problem Male ster- With- perceived condom ilisation drawat None 43.7 10.1 45.7 Not effective 8.8 0.1 18.8 Partner disapproves 2.4 2.3 1.5 Community disapproves 0.7 10.9 2.6 ReLigion disapproves 1.9 7.3 1.5 Health concerns 4.0 18.5 4.6 Access/AvaiLabiLity 1.7 0.1 0.0 Costs too much 0.0 0.2 0.0 Inconvenient to use B.2 5.0 12.7 Other 3.1 22.6 3.6 Don't know, not stated 25.6 23.0 8.9 Total 100.0 I00.0 100.0 Weighted number 954 410 554 7.4 Sources for Methods Table 7.11 shows that for all methods except periodic abstinence, the majority of husbands (generally over 70 percent) would use government sources--especially hospitals--to obtain family planning methods if they wanted to use them. After government sources, the next most commonly cited source is the Family Planning Association of Kenya, followed by mission hospitals and dispensaries. For periodic abstinence, husbands are most likely to say they would not go anywhere for information; a smaller'proportion would go to friends or relatives, or to government hospitals for information. Table 7.12 shows that younger husbands start using contraception when they have fewer children than older husbands. For example, while only 3 percent of husbands aged 50 started using contraception before they had any children, 14 percent of husbands less than 30 years of age started before having their first child. 7.5 Intention to Use Family Planning in the Future As with female respondents, all husbands who were not current users of family planning were asked whether they intended to use a method at any time in the future. As Table 7.13 shows, of husbands who are not currently using contraception, 48 percent say they intend to use, 42 percent do not intend to use, and 10 percent are unsure. As shown in Table 7.14, the most preferred method husbands say they intend to use is injection, followed by pill and female sterilisation. From the table, it is evident that the husbands interviewed intended to use female-oriented contraceptives. 80 Table 7.11 Percent d i s t r ibut ion of husbands knowing a contraceptive method by supply source they say they would use, according to speci f ic method, Kenya, 1989 Contraceptive methed Supply source Diaphragm/ Female Mate Periodic that would In jec- foam Con- s te r i l - s te r i l - abst in- be used P i l l IUD t ion je t ty dom isat ion isat ion ence Nowhere 0.5 0.6 0.7 0.2. 1.7 0.6 2.3 23.8 Govt. hospital 53.8 58.6 56.9 57.1 42.6 75.3 71.7 14.9 Govt. health center 22.0 17.1 20.5 17.1 18.1 5.9 2.9 7.3 FPAK* 9.0 10.2 9.4 11.7 11.0 5.9 4.8 8.4 Mobile clinic 1.6 1.2 2.2 2.0 2.5 0.1 0.2 4.3 Field educator 1.3 0.2 0.1 0.5 1.8 0.1 0.2 2.7 Pharmacy/Shop 0.6 0.1 0.0 0.7 7.1 0.0 0.0 0.1 Private hospital 1.7 1.4 1.3 3.0 1.4 1.8 2.2 0.5 Mission hospital/dispen. 3.4 4.8 4.6 2.0 3.2 5.2 5.6 1.0 Employer's clinic 0.2 0.6 0.5 0.9 0.7 0.3 0.4 0.I Private doctor 1.0 0.7 0.4 0.7 0.2 0.1 0.5 0.6 Traditional healer 0.0 0.0 O.O 0.0 0.0 0.0 1.6 0.0 Partner would get 0.I 0.0 0.0 0.3 0.3 0.1 0.0 8.6 Friends/Relatives 0.4 0.2 0.2 0.0 1.8 0.7 0.8 18.8 Other 0.6 0.1 0.3 0.0 0.4 0.5 1.1 3.0 Don't know/not stated 3.9 4.5 2.9 3.9 7.1 3.2 5.7 5.8 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Weighted no. of husbands 1024 789 934 342 954 971 410 895 * Family Planning Association of Kenya Table 7.12 Percent distribution of husbands by number of living children at time of first use of contraception, according to current age, Kenya, 1989 Number of living children at time first used Wtd. no. of husb. Never Age used None I 2 3 4+ Missing Total Less than 30 38.5 14.3 30.6 10.2 4.8 1.6 0.0 I00.0 150 30-39 31.7 5.7 25.6 15.8 8.6 11.6 1.1 100.0 383 40-49 30.5 4.0 11.9 10.2 12.5 28.2 2.8 100.0 335 50 or over 42.7 3.2 12.4 9.5 9.5 21.7 1.1 100.0 302 Total 35.0 5.6 " 18.9 11.8 9.5 17.7 1.4 100.0 1170 7.6 Attitudes Toward Family Planning The KDHS asked husbands if they thought it acceptable to have family planning messages on the radio. Table 7.15 shows that 92 percent of husbands believe it is acceptable to have family planning messages on the radio. With radios becoming universal in Kenyan households, this augurs well for family planning education on radio. Differentials in acceptability of radio messages by age and urban-rural residence are small. The greatest differentials occur by educational group, where 77 percent of those with no education find it acceptable to have family planning messages on radio, compared to 97 percent of those with 81 Table 7.13 Percent d i s t r ibut ion of husbands who are not cur rent ly using any contrsceptive method, by in tent ion to use in the future , according to number of l i v ing ch i ldren , Kenya, 1989 in tent ion Number of l i v ing ch i ldren to use in future None 1 2 3 4+ Total Intends to use 46.7 73.1 47.9 46.8 45.3 47.5 Unsure about using 18.9 18.8 8,5 20.1 6.9 9.7 Does not intend to use 36.5 8.1 42.6 31.4 46.7 41.9 Missing O.O 0.0 1.1 1.8 1.1 1.1 Total 100.0 100.0 100.0 100.0 100.0 I00.0 Wtd. no. of husbands 20 37 58 64 413 593 secondary and higher education. Variation by province shows that Central Province is highest with 99 percent and Coast lowest with 81 percent. Perhaps the lack of acceptability of family planning messages on radio in Western and Coast Provinces is influenced by cultural conservatism. In certain ethnic groups in Kenya, sexual matters are relegated to certain age groups and are not to be discussed publicly, let alone be broadcast on radio. Table 7.14 Percent distribution of husbands who are not using a contraceptive method but who intend to use in the future, by preferred method, Kenya, 1989 Preferred method Percent Pill 20.2 IUD 3.1 Injections 23.1 Diaphragm/foam/Jelly 0.6 Condom 6.1 female steritisation 20.2 Male steritisation 0.2 Periodic abstinence 11.6 ~ithdrawat 0.2 Other 6.2 Unsure/not stated 8.5 Total 100.0 Number 281 Table 7.15 also shows the percentage of husbands knowing a method who approve of family planning according to background characteristics. The table shows that 91 percent of Kenyan husbands who know a method approve of family planning. Approval decreases with age, with those husbands aged above 50 years approving least (86 percent). Urban-rural differentials in approval are small. 82 Table 7.15 Percentage of at l husbands who believe i t ecceptabte to have rm~ssages about family ptanning on the rad io and percentage of husbands knowing a contraceptive method who approve of famity ptanning, by background characteristics, Kenya, 1989 Of art husbands, percent who find FP messages on rad io acceptable Of husbands who know a method, percent who approve of FP Background characteristics Percent Nucdoer Percent Number Age Less than 30 95.5 150 97.0 140 30-39 93.4 383 92.8 377 40-49 94.1 335 91.6 322 50 or ever 87.1 302 85.9 269 Residence Urban 94.7 157 93.2 152 Rural 91.9 1013 91.0 957 Province Nairobi 94.0 65 91.8 63 Central 98.9 165 98.5 164 Coast 81.0 69 73.5 65 Eastern 95.2 253 95.1 252 Nyanza 92.3 190 89.8 190 Rift Valley 91.7 295 92.0 262 gestern 84.5 134 83.3 113 Education No education 76.6 201 77.9 173 Sofne primary 93.2 381 90.7 361 Primary complete 96.3 273 96.5 263 Secondary + 97.4 316 95.1 312 Total 92.2 1170 91.3 1106 Less educated husbands are less likely to approve of family planning than their more educated counterparts, although even among husbands with no education, over three-quarters approve of family planning. As with acceptibility of radio messages, approval of family planning is lower among husbands in Coast and Western Provinces. Discussion of family planning between husbands and wives is instrumental in the decision to control fertility. As shown in Table 7.16, 36 percent of the Kenyan husbands in KDHS who know at least one contraceptive method say they have not talked with their wives about family planning in the past year, about 14 percent say they have talked about it once or twice in the past year and 51 percent say they have discussed family planning three or more times. Except for husbands aged 50 and over, there are few differences by age of the husband. Table 7.17 shows the level of communication about family planning among married couples. This table compares the husband's own report of his attitude toward family planning with his wife's perception of his attitude. Among husbands who reported that they approve of family planning, 14 percent of their wives believe that their husbands disapprove and 20 percent do not know. Of 83 husbands who disapprove of family planning, 27 percent of their wives believe they approve, 37 percent of wives believe that their husbands disapprove and 36 percent do not know. Table 7.16 Percent distr ib~Jt ion of husbands knowing a contraceptive method by number of times discussed fami ly planning with wi fe, according to current age, Kenya, 1989 Number of times discussed Wtd. nun~er Once or More of Age Never twice often Total husbands <30 30.6 17.8 51.7 100.0 140 30"39 27. I 16.3 56.6 100.0 377 40"49 33.9 10.7 55.4 100.0 322 50 + 51.7 11.3 37.0 100.0 269 Totai 35.5 13.6 51.0 100.0 1108 Table 7.17 Percent distribution of married couples by wife's perception of husband,s attitude toward family planning, according to husband=s actuai attitude, Kenya, 1989 Of husbands who: Wife,s perception of husband's Approve Disapprove at t i tude of FP of FP Wife thinks husband approves 65.8 27.3 Wife thinks husband disapproves 13.9 37.2 Wife doesn't know 20.3 35.5 Tota( 100.0 100.0 Weighted number ~065 116 Note: Excludes 8 couples where husband's attitude toward family planning is missing. 7.7 Desire for More Children Husbands interviewed in this survey were asked whether they wanted more children. Table 7.18 shows their answers. The table shows that almost half of husbands want no more children and one-quarter want to space their next child. The proportion who want no more rises with the number of living children, while the proportion who want their next child within two years decreases with number of living children. The table further shows that irrespective of the number of living children, a large proportion of husbands want to have their next child after two years, indicative of consciousness of childspacing. 84 Table 7.1B Percent d i s t r ibut ion of husbands by desire for ch i ld ren , according to number of l i v ing chiLdren, Kenya, 1989 Number of l i v ing ch i ldren Desire for more children 0 I 2 3 4 5 6+ Total Wants within 2 years 4O.Q 31.A 20.9 18.5 9.2 5.5 6.8 11.9 Wants after 2+ years 10.3 52.2 49.4 38.8 36.4 15.9 11.7 24.3 Wants, unsure timing 41.5 9.5 5.6 3.9 3.7 3.6 4.6 5.3 Undecided 5.0 0.9 4.0 16.0 7.9 10.4 11.2 9.9 Wants no more 3.2 5.9 20.2 22.8 42.8 64.6 65.7 48.6 Total 100.0 100.0 100.0 100.0 300.0 100.0 100.0 100.0 Wtd. number of husbands 20 69 114 140 140 116 569 1170 Note: Excludes 2 husbands with number of Living children missing Table 7.19 shows that the desire to stop childbearing is higher among rural than urban husbands which is probably due to the fact that they have more children than urban husbands. There is no clear relationship between level of education and the desire to have no more children. Table 7.19 Percentage of husbands who want no more children by background characteristics, Kenya, 1989 Background NO, of characteristics Percent husbands Residence Urban 35.7 157 Rural 50.6 1013 Education NO education 45.0 201 Some primary 56.8 381 Primary compiete 40.9 273 Secondary + 47.5 316 No.of living children None 3.2 20 I 5.9 69 2 20.2 114 3 22.8 140 4 42.8 140 5 64.6 116 6+ 65.7 569 Total 48.6 1170 85 Table 7.20 compares husbands' and wive's views regarding future childbearing. In general, there is a fairly high degree of correlation between husband and wife on this matter. In 38 percent of couples, neither spouse wants more children, while in 27 percent, both spouses want another child. The proportion of couples in which the husband wants another child and the wife does not (11 percent) only slightly exceeds the proportion in which the wife wants another child and the husband does not (7 percent). Table 7.20 Percent d i s t r ibut ion of marr ied couples by desire for more ch i ld ren , according to the number of living children, Kenya, 1989 Husband Husband Wife 8oth One or Wtd. Humber of Both wants, wants, wants, want both no. of living want wife wife husband no unde- married children more infecund doesn't doesn't more cided Total couples Husband None 82.1 6.5 0.0 3.2 0.0 8.2 I00.0 20 I-3 54.9 0.9 12.5 5.1 12.1 14.4 100.0 324 4-6 21.2 0.9 7.4 9.6 42.8 18.0 100.0 404 7 or more 8.8 2.7 13.5 5.5 54.9 14.5 I00.0 440 Wife None 59.0 8.2 0.0 7.2 1.9 23.8 100.0 35 I-3 52.3 1.1 10.5 8.3 14.2 13.6 100.0 422 4-6 17.4 0.3 11.9 8.0 41.8 20.6 100.0 424 7 or more 1.6 3.6 11.2 2.8 70.2 10.6 100.0 307 Total 26.9 1.7 10.9 6.8 38.1 15.6 100.0 1189 7.8 Ideal Number of Chi ldren Husbands were asked the same question as female respondents about the number of children they would want if they could choose exactly (ideal family size). The results are shown in Table 7.21. It is clear from the tables that regardless of the number of living children, the modal response among husbands was 4. Even among husbands with six or more children, 41 percent choose 4 children as the ideal number. This is possibly due to a preference for equity by sex-- two boys and two girls. The mean ideal number of children among husbands in this survey was 4.8, which is identical to the mean for currently married women (see Chapter 5). Table 7.22 shows that the mean ideal number of children increases with the actual number of living children. Table 7.22 shows the comparison of ideal number of children according to husband and wife. The data indicate that exact agreement regarding ideal number of children is not common among married couples. Table 7.23 and Figure 7.2 show the mean ideal number of children among husbands according to selected background characteristics. The mean ideal number of children increases with age, from 3.9 for the husbands less than 30 to 6 children for husbands who are 50 years old and above. The table shows that the mean ideal number of children for urban husbands is 4.0, while it is 4.9 children for the rural husbands. The mean ideal number of children decreases with 86 increasing education of husband. Regional differentials also exist. The Coast region recorded a mean ideal number of 12 children with Nairobi recording the lowest mean of 3.8 children. Nyanza and the Rift Valley recorded intermediate values of 5.1 and 4.6, respectively. Table 7.21 Percent distr ib~Jt ion of husbands by ideal number of ch i ldren and mean ideal number of ch i ld ren , according to nun~er of l i v ing ch i ldren, Kenya, 1989 Nut,her of l i v ing ch i ldren Ideal number of children 0 I 2 3 4 5 6+ Total I 0.0 1.8 0.0 0.0 1.4 0.0 0.5 0.5 2 20.7 4.7 17.6 8.7 6.5 8.9 4.8 7.4 3 9.7 15.7 24.4 15.4 8.0 9.1 11.6 12.8 4 28.2 51.2 40.8 50.5 45.7 38.0 41.4 42.9 5 12.4 4.5 7.6 6.0 15.3 19.5 5.7 8.5 6 9.7 8.0 4.9 13.2 15.7 6.4 11 l 0 10.5 7 0.0 0.9 0.7 0.5 1.0 0.4 1.7 1.2 8 or more 1.8 5.6 1.0 1.8 2.2 9.8 9.2 6.5 gon-nta~er ic response 17.6 5.7 2.5 3.0 4.2 6.1 10.4 7.4 Missing 0.0 1.9 0.6 1.0 0.0 1.8 3.6 2.3 Total I00.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Mean ideal nu~ber 3.9 4.2 3.6 4.1 4.4 4.8 5.4 4.8 Wtd. no. of husbands 20 69 114 140 140 116 569 1170 Base for mean* 17 64 111 135 134 107 489 1056 * Means are based on nLmneric answers only. Table 7.22 Percent distribution of married couples by whether husband's ideal number of children is less than, the same as, or higher than the wife's, according to wife's ideal number, Kenya, 1989 Husband's ideal number of children Wife.s Wtdi ideal no. of number of Less than same as More than Non- married children wife=s wife's wife's numeric Total couples I 0.0 0.0 93.3 6.7 100.0 5 2 2.3 36.2 56.8 4.7 100.0 90 3 12.8 28.0 53.0 6.1 100.0 122 4 22.0 47.4 23.8 6.8 100.0 563 5 56.5 13.2 23.8 6.5 100.0 186 6 or more 57.4 25.7 3.3 13.5 100.0 390 Total 34.7 33.8 23.0 8.5 100.0 1357 87 Table 7.23 Mean ideal number of ch i ldren of husbands by background character is t ics , Kenya, 1989 Background Mean No. of character is t ics ideal husbands Age <30 3.9 141 30-39 4.5 361 40-49 4.5 306 50 + 6.0 248 Residence Urban 4.0 139 Rural 4.9 917 Province Nairobi 3.8 57 Central 4.0 162 Coast 12.4 47 Eastern 4.2 240 Nyanza 5.1 163 Rift Valley 4.6 281 Western 4.3 106 Education No education 7.1 154 Some primary 4.6 348 Primary co~plete 4.4 250 Secondary + 4.1 304 No.of l i v ing ch i ldren None 3.9 17 1 4.2 64 2 '3 .6 111 3 4.1 135 4 4.4 134 5 4.8 107 6+ 5.4 489 Total 4.8 1056 88 Figure 7.2 Mean Ideal Number of Children Among Husbands Mean number of children 8 6 4 2 0 None 4.6 4.4 4.1 Some Pri. See.÷ Pri. Comp. EDUCATION 4.9 H Urban Rural RESIDENCE Kenya DHS 1989 89 REFERENCES Central Bureau of Statistics. n.d. 1979 Populat ion Census. Vol. 2, Analytical Report. Nairobi: Ministry of Finance and Planning. Central Bureau of Statistics. 1986. Kenya Contraceptive Prevalence Survey 1984: Prov inc ia l Report. Nairobi: Ministry of Planning and National Development. Central Bureau of Statistics. 1984. Kenya Contracept ive Prevalence Survey 1984: F i r s t Report. Nairobi: Ministry of Planning and National Development. Central Bureau of Statistics. 1983. Populat ion Pro jec t ions for Kenya 1980-2000. Nairobi: Ministry of Economic Planning and Development (courtesy of UNICEF). Central Bureau of Statistics. 1980. Kenya Fer t i l i ty Survey 1977-1978: F i r s t Report. 2 Vols. Nairobi: Ministry of Economic Planning and Development. Central Bureau of Statistics. 1977. Demographic Surveys i973-1976: Methodological Report. Nairobi: Ministry of Finance and Planning. Central Bureau of Statistics. 1975. Demographic Basel ine Survey Report 1973. Nairobi: Ministry of Finance and Planning. Chogoria Hospital and Centers for Disease Control (CDC). 1987. 1985 Chogoria Community Health Survey: Report of Pr inc ipa l Findings. Chogoria, Kenya: Community Health Department, P.C.E.A. Chogoria Hospital and Division of Reproductive Health, CDC. Ministry of Health. 1987. Innnunizatlon Coverage in Kenya 1987. Kenya Expandcd Programme on Immunization. Nairobi: Ministry of Health. National Council for Population and Development. 1984. Population Po l icy Guidel ines. Sessional Paper No. 4 of 1984. Nairobi: Office of the Vice-President, Ministry of Home Affairs and National Heritage. Rutstein, Shea O. 1984. In fant and Child Morta l i ty : Levels , Trends, and Demographic D i f fe rent ia l s . World Fertility Survey Comparative Study Number 43. Voorburg, Netherlands: International Statistical Institute. Republic of Kenya. 1989. Development Plan 1989-1993. Nairobi: Ministry of Planning and National Development. 91 " APPENDIX A SURVEY DES IGN APPENDIX A. SURVEY DESIGN A.1 Quest ionnai re Design and Training The KDHS utilised three questionnaires: a household questionnaire, a woman's questionnaire, and a husband's questionnaire. The first two were based on the DHS Programme's Model "B" Questionnaire that was designed for low contraceptive prevalence countries, while the husband's questionnaire was based on similar questionnaires used in the DHS surveys in Ghana and Burundi. A two-day seminar was held in Nyeri in November 1987 to develop the questionnaire design. Participants included representatives from the Central Bureau of Statistics (CBS), the Population Studies Research Institute at the University of Nairobi, the Community Health Department of Kenyatta Hospital, and USAID. The decision to include a survey of husbands was based on the recommendation of the seminar participants. The questionnaires were subsequently translated into eight local languages (Kalenjin, Kamba, Kikuyu, Kisii, Luhya, Luo, Meru and Mijikenda), in addition to Kiswahili. In order to test the quality of the translations, as well as to check other aspects of survey design, a pretest was conducted in July and August 1988. Sixteen female and 8 male interviewers were recruited and trained for two weeks in July 1988 by NCPD, CBS and IRD/DHS staff. They were then grouped into teams, one for each of the eight local languages, and travelled to selected areas in various parts of the country where those languages are spoken. Officers from NCPD and CBS accompanied the teams as supervisors. The interviewers carried out about 200 pretest interviews with women and somewhat fewer with husbands. After the pretest, the questionnaires were modified slightly based on the pretest comments. Training for the main survey was held in Machakos from October 26 to November 17. Participants included 26 people who had conducted the pretest and 55 new recruits, for a total of 81. Most of the trainees had "O" level education, while a few had "A" level. Training consisted of a combination of classroom lectures, demonstration interviews in front of the whole group, mock interviews in smaller groups, practice in interviewing in the local languages, a written examination, and, during the final three days, field practice interviews in households outside the town center. Training was conducted by 5 officers from the NCPD and one from the CBS. Towards the end of the course, the trainers met and determined who would be the supervisors, field editors, interviewers and data processing staff. For the most part, the former pretest interviewers were selected as supervisors and field editors. They rcceived special training in how to scrutinise questionnaires for accuracy, completeness, and consistency, while supervisors were taught how to read maps and use the household listing form to find the selected households. A.2 F ie ldwork KDHS field staff were divided into 9 full-sized teams (one for each of the eight vernaculars and two for the Kikuyu language), each with a supervisor, a field editor, 4 or 5 female interviewers, and one male interviewer. Although the questionnaires were not translated into Maasai, a special small team, consisting of a supervisor and two Maasai-speaking interviewers was formed to cover the few clusters selected in Narok and Kajiado Districts. 95 The first three teams began data collection in December 1988. The delay in sending out the other teams was due to the lack of vehicles. By mid-February 1989, all the teams had been launched. Field work was co-ordinated by NCPD Headquarters and most teams were accompanied at least initially by NCPD officers, who also made periodic supervisory field trips. The CBS full- time enumerators and supervisors were also utilized to help locate the selected sample points and households and in some areas, the District Statistical Officers assisted in supervising the teams and providing communication and logistical support.' Due to attrition in field staff during the first few months of the survey, NCPD recruited some eight replacements in early February 1989. After a one-week training at NCPD Headquarters, the new recruits were sent to their respective teams to observe their colleagues and conduct some practice interviews before being fully integrated into the team. Tables A.1 and A.2 provide a summary of the outcome of the field work. Fourteen percent of the 9836 selected households were either vacant, destroyed or not found in the field. Of the households that existed, 98 percent were interviewed. The response rate of 96 percent among eligible women was also high, however, the response rate for eligible husbands was somewhat lower (81 percent), due to the fact that husbands were often away from the house during the day. Response rates were higher in rural than in urban areas, especially for husbands. There was little difference in response rates by province, except that the rates for husbands were higher in Eastern and Western Provinces than in Nairobi, Coast and Central Provinces. Table A.I Sampling results for the whole country, Kenya, 1989 Results Number Percent HOUSEHOLDS SELECTED 9836 100.0 Occupied 8343 84.8 Vacant/Destroyed/Not found 1408 14.3 Househotd absent 85 0.9 HOUSEHOLDS OCCUPIED 8343 100.0 Interviewed 8173 98.0 Not interviewed 170 2.0 ELIGIBLE WOMEN IDENTIFIED 7424 100.0 Interviewed 7150 96.3 Not interviewed 274 3.7 ELIGIBLE HUSBANDS IDENTIFIED 1397 100.0 Interviewed 1129 80.8 Not interviewed 268 19.2 A.3 Data Processing Data processing staff for the KDHS consisted of five data entry clerks, two data entry supervisors and a control clerk who logged in questionnaires when they arrived at the office. The staff was supervised by two NCPD officers with periodic assistance from IRD staff. All the data processing staff completed the interviewer training course in November 1988 and received further instruction in data processing from the IRD staff. Three IBM-compatible desktop microcomputers were installed in a temporary office on the Kenyatta National Hospital compound and wcre used to process the data. The Integrated System for Survey Analysis (ISSA) program was used for data entry, editing and tabulations. The supervisors and the NCPD officers were responsible for supervising data entry, and for resolving inconsistencies in questionnaires detected during secondary machine editing. Data processing started in February 1989, once a sufficient number of questionnaires had been returned to Nairobi. Data entry was completed in early June and tabulations for the preliminary report were run in mid-June, two wceks after the last interview took place. The preliminary report was printed in July, tabulations for the final report were also produced in July, and this report was drafted in August and September. 96 Tabte A.2 Response rates for households, eLigibte women and e l ig ibte husbands, by urban-rural residence and province, Kenya, 1989 Househotds Et igibte Women EligibLe Husbands Residence/ Number Percent Number Percent Number Percent region occupied compteted ident i f ied compteted ident i f ied completed Residence Urban 2755 96,6 2008 95.5 347 70.6 Rural 5586 98.6 5416 96.6 1050 84.2 Province Nairobi 1195 97.7 908 94.6 143 70.6 Centre[ 1639 98.1 1352 94.7 299 71.6 Coast 862 94.2 734 98.1 136 73.5 Eastern 813 99.1 911 98.6 186 95.7 Nyanza 1502 98.9 1351 93.6 250 77.2 gift Valley 1375 97.5 I~24 97,9 244 85.2 Western 937 99.7 1044 98.4 139 97.1 Total 8343 98.0 7424 96.3 1397 80.8 97 APPENDIX B ESTIMATES OF SAMPLING ERROR APPENDIX B. ESTIMATES OF SAMPLING ERROR The results from sample surveys are affected by two types of errors: (1) nonsampling error and (2) sampling error. Nonsampling error is due to mistakes made in carrying out field activities, such as failure to locate and interview the correct household, errors in the way questions are asked, misunderstanding of the questions on the part of either the interviewer or the respondent, data entry errors, etc. Although efforts were made during the design and implementation of the KDHS to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate analytically. The sample of women selected in the KDHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each one would have yielded results that differed somewhat from the actual sample selected. The sampling error is a measure of the variability between all possible samples; although it is not known exactly, it can be estimated from the survey results. Sampling error is usually measured in terms of the "standard error" of a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which one can be reasonably assured that, apart from non-sampling errors, the true value of the variable for the whole population falls. For example, for any given statistic calculated from a sample survey, the value of that same statistic as measured in 95 percent of all possible samples with the same design (and expected size) will fall within a range of plus or minus two times the standard error of that statistic. If the sample of women had been selected as a simple random sample, it would have been possible to use strightforward formulas for calculating sampling errors. However, the KDHS sample design depended on stratification, stages, and clusters; consequently, it was necessary to utilize more complex formulas. The computer package CLUSTERS was used to assist in computing the sampling errors with the proper statistical methodology. The CLUSTERS program treats any percentage or average as a ratio estimate, r=y/x. where both x and y are considered to be random variables. The variance of r is computed using the formula given below, with the standard error being the square root of the variance: 1 - f H mh mh zZh - - )] x 2 h=l mh-1 i= l mh where in which, zb, = Yh~ " r xh,, and zh = Yh - rxh, h mb Yb, Xbl represents the stratum and varies from 1 to H, is the total number of EAs selected in the h-th stratum, is the sum of the values of variable y in cluster i in the h-th stratum, is the sum of the number of cases (women) in cluster i in the h-th stratum, and 101 f is the overall sampling fraction, which is so small that the CLUSTERS program ignores it. In addition to the standard errors, CLUSTERS computes the design effect (DEFT) for each estimate, which is defined as the ratio between the standard error using the given sample design and the standard error that would result if a simple random sample had been used. A DEFT value of 1.0 indicates that the sample design is as efficient as a simple random sample; a value greater than 1.0 indicates the increase in the sampling error due to the use of a more complex and less statistically efficient design. Sampling errors are presented in Table B.2 through B.4 for 45 variables considered to be of major interest. Results are presented for the whole country and for urban and rural areas. In Tables B.5 through B,11, results are presented by province for 30 variables. Finally, Table B.12 contains sampling errors for current contraceptive use for the 13 targctted districts. For each variable, the type of statistic (mean, proportion) and the base population are given in Table B.1. For each variable, Tables B.2 through B.12 present the value of the statistic, its standard error, the number of unwcighted and weighted cases, the design effect, the relative standard error, and the 95 percent confidence limits. The confidcnce interval has the following intcrprctation. For currcnt use of family planning (CURUSE), the overall proportion of married women using is 0.269 or 26.9 percent and its standard error is 0.010. Therefore, to obtain the 95 percent confidence limits, one adds and subtracts twice the standard error to the sample estimate, i.e., 0.269 + or - (2 x 0.010), which means that there is a high probability (95 percent) that the true contraceptive prevalence rate falls within the interval of 0.250 to 0.288 (25 to 29 percent). The relative standard error for most estimates for the country as a whole is not large, except for estimates of very small proportions. The magnitude of the error increases as estimates for subpopulations such as particular provinces or districts are considered. For contraceptive prevalence, for example, the relative standard error (as a percentage of the cstimated proportion) for the whole country, urban areas, Nairobi and Kilifi District is, respectively, 3.6 percent, 6.2 percent, 7.6 percent, and 23.3 percent. By district, this means that the prevalence rate of 31.3 for Murang'a District cannot be said with certainty to differ from the rate of 20.2 for Kisii District, since the confidence intervals overlap. Similarly, the difference between the rates for Kirinyaga (52.2 percent) and Machakos Districts (40.4 percent) might be explained by sampling error. 102 Table B.1 L i s t of se lected var iab les wi th sampling er rors , Kenya, 1989 Var iab le Type Descr ip t ion Populat ion NOEDUC Proportion SECONDARY Proportion MARRIED Proportion MBEF18 Proportion BBEF18 Proportion CEB Mean CEB4D Mean CBUR Mean PREGNANT Proportion KNOW Proportion KNOWMOD Proportion KNWSRC Proportion KNOWOV Proportion EVERUBE Proportion CURUSE Proportion MODUSE Proportion APPRFP Proportion WANTNM Proportion WANT2 Proportion IDEAL Mean BREASTF Mean AMEN Mean ABSTAIN Mean TETANU Proportion ATTE Proportion WCARD Proportion BCG Proportion DPT Proport ion POL Proport ion MEASL Proportion FULLIM Proportion DIAR Proportion PACKET Proportion HOMSOL Proportion DIARF Proportion FEVER Proportion FEVERF Proportion COUGH Proportion COUGHF Proportion POLYG Proportion CHILDREN Mean USINGFP Proportion NOMORE Proportion HIDEAL Mean HUSAPR Proportion With no educat ion With secondary or more Cur rent ly marr ied Marr ied before age 18 Had a b i r th before age 18 Number of ch i ld ren ever bern Number of ch i ld ren ever bern Nunt:x~r of ch i ld ren surv iv ing Currently pregnant Knowing any contraceptive method Knowing any modern method Knowing source of family planning Knowing fertile period in cycle Ever using any method Currently using any method Currently using a modern methed Approving of family planning Who want no more ch i ld ren Who want next ch i ld a f te r 2+ yrs . Ideal nu~lber of ch i ld ren Months of breast feeding Months of amenorrhoea Months of postpartum abstinence Whose mothers received tetanus immunisation during pregnancy Attended by doctor/nurse/midwife With hea l th cards ava i lab le With BCG immunisation on card With 3+ doses of DPT on card With 3+ doses of po l io on card With measles immunisation on card Fu l ly immunised on card With d iarrhoea in las t 2 weeks Treated wi th ORS packet Treated wi th home so lu t ion Consulted a medical fac i l i ty w i th fever in past 2 weeks Consulted a medical fac i l i ty With cough in past 2 weeks Consulted a medical fac i l i ty In polygynous unions Number of l i v ing ch i ld ren Cur rent ly using contracept ion Who want no more ch i ld ren Ideal number of ch i ld ren Approving of fami ly p lanning All worn 15-49 All women 15-49 All women 15-49 All women 15-49 All wcelen 15-49 All women 15-49 Worn 40-49 All women 15-49 ALL women 15-49 Currently married women 15-49 Currently married women 15-49 Currently marrind women 15-49 All w(~en 15-49 Currently married women 15-49 Currently married women 15-49 Currently married women 15-49 Currently married women 15-49 who know a method Currently married women 15-49 Currently married women 15-49 All women 15-49 Births in last 3 years Births in last 3 years Births in last 3 years Births in last 5 years Bi r ths in las t 5 years Ch i ld ren 12-23 months Ch i ld ren 12-23 months wi th cards Ch i ld ren 12-23 months wi th cards Ch i ld ren 12-23 months wi th cards Ch i ld ren 12-23 months w i th cards Ch i ld ren 12-23 months w i th cards Ch i ld ren under 5 Ch i ld ren under 5 w i th d iarrhoea Ch i ld ren under 5 w i th d iar rhoea Ch i ld ren under 5 w i th d iar rhoea Children under 5 Children under 5 with fever Children under 5 Children under 5 with cough Al l husbands A l l husbands A l l husbands A l l husbands A l l husbands Husbands who know a method 103 Tabte B.2 Sampting er rors for the to ta l populat ion , Kenya, 1989 Var iab[e Vatue Stan- Unwei o Weight- Reta- Confidence l im i ts dard ghted ed Design r ive e r ror number number effect error R-2SE R+2SE NOEDUC .252 .010 7140 7140.8 1.978 .040 .231 .272 SECONDARY .204 ,011 7140 7140.8 2.328 .054 .182 .226 MARRIED .666 .009 7150 7150.0 1.590 .013 .649 .684 MBEF18 .365 .010 7150 7150.0 1,730 .026 .366 .405 BBEF18 .344 .008 7150 7150.0 1.339 .022 .329 .359 DES 3.669 .058 7150 7150.0 1.505 .016 3.553 3.785 CEB40 7.470 .123 1073 1118.7 1,335 .017 7.223 7,717 CBUR 3.281 .051 7150 7150.0 1.475 .016 3.179 3.382 PREGNANT .089 .005 7150 7150.0 1.544 .058 .079 .100 KNOW .924 .009 4778 4765.4 2.368 .010 .906 .942 KNOWMOD .913 .011 4778 4765.4 2.664 .012 ,891 .934 KNOWSRC ,899 .011 4778 4765.4 2.513 .012 .877 .921 KNOWOV .223 .009 4778 4765.4 1.535 .041 .205 ,242 EVERUSE .450 .012 4778 4765.4 1,703 .027 ,425 ,474 CURUSE .269 .010 4778 4765.4 1.491 .036 .250 .288 MOOUSE ,179 .007 4778 4765.4 1.289 .040 ,164 .193 APPRFP .882 .006 4466 4404.9 1.194 .007 .870 .894 WANTRM .494 .010 4778 4765.4 1.349 .020 .474 .513 WANT2 .264 .008 4778 4765.4 1.303 .032 .247 .280 IDEAL 4.432 .051 6836 6870.0 2.105 .012 4.330 4.534 BREASTF 19.428 .272 4361 4448.7 1.122 .014 18.884 19.973 AMEN 10.910 .314 4361 4448,7 1.366 ,O29 10.282 11.537 ABSTAIN 5.855 .284 4361 4448.7 1.417 .048 5.288 6,423 TETANU .887 .006 6912 7050.2 1.417 .007 .875 .900 ATTE .501 .016 6912 7050,2 2.099 ,031 ,470 .532 WCARD .610 .014 1302 1314.6 1.013 .023 ,583 .638 BCG .967 .008 781 802.3 1.229 .008 .952 .983 DPT .907 ,016 781 802.3 1.523 .017 .875 ,938 POL .924 ,012 781 802.3 1,255 .013 .900 ,947 MEASL .780 .020 781 802.3 1.325 ,025 .740 .819 FULLIM .728 .020 781 802.3 1.240 .027 .689 .768 DIAR .127 .005 6341 6514.1 1.065 ,036 .118 .136 PACKET .211 .015 829 829.7 1.002 .070 .181 .240 HC~SOL .489 .022 829 829.7 1.188 .044 .446 .532 DIARF .468 .020 829 829.7 1.072 .042 .429 .507 FEVER .421 ,009 6341 6514.1 1.231 .021 .403 .438 FEVERF .555 ,016 2739 2740.8 1.471 .029 ,523 .587 COUGH ,182 .008 6341 6514.1 1.389 ,042 .167 .198 COUGHF ,652 .025 1169 1188.6 1.542 .038 .602 .701 POLYG .205 .016 1116 1170.1 1.307 ,077 .173 .236 CHILDREN 6.108 .190 1113 1167.9 1.528 .031 5.729 6.487 USINGFP .495 .016 1116 1170.1 1.087 .033 .462 .527 NOMORE .486 .018 1116 1170.1 1.195 .037 .450 .521 HIDEAL 4.763 .089 999 1056.1 .672 ,019 4.584 4.942 HUSAPR .913 .009 1069 1108.3 1.094 ,010 .894 .932 104 Table B.3 Sampling errors For the urban population, Kenya, 1989 Variable Stan- Unwei- Weight- Reta- Confidence l imits dard ghted ed Design t i re Value error number number effect error R-2SE R+2SE NOEDUC .123 .014 1915 1234.6 1.906 .117 .094 .151 SECONDARY .414 .022 1915 1234.6 1.974 .054 .370 .459 MARRIED .605 .016 1917 1235.9 1.433 .026 .573 .637 MBEF18 .307 .016 1917 1235,9 1.559 .053 .274 .340 BBEF18 .279 .013 1917 1235,9 1.251 .046 .253 .304 CEB 2.322 ,069 1917 1235.9 1.259 .030 2.184 2.459 CEB40 5.065 .271 153 98.6 1.166 .053 4.524 5.607 CSUR 2.108 .059 1917 1235.9 1,189 .028 1.990 2.225 PREGNANT .091 .006 1917 1235.9 .983 ,071 .078 .104 KNOW .957 .006 1160 747.8 .942 ,006 ,946 .968 KNO~4MOD .952 ,006 1160 747.8 .913 ,006 .940 .963 KNOWSRC .941 .007 1160 747,8 .999 .007 .927 ,954 KNOt40V .247 .022 1160 747,8 1.706 .087 .204 .291 EVERUSE .515 .020 1160 747.8 1.362 .039 .475 .555 EURUSE .305 ,019 1160 747.8 1.391 .062 .268 ,343 MOOUSE ,255 .018 1160 747,8 1.438 .072 .218 .292 APPRFP ,906 .010 1110 715.6 1,138 .011 .886 .926 WANTNM .396 .016 1160 747.8 1.093 .040 .364 .427 WANT2 .317 .016 1160 747.8 1.155 .050 .286 .349 IDEAL 3.753 .049 1847 1190.7 1.345 .013 3.656 3.851 DREASTF 18.778 .602 949 611.8 1,097 .032 17.573 19.983 AMEN 9,104 .575 949 611.8 1.182 .063 7.955 10.253 ABSTAIN 5,159 .458 949 611.8 1.104 .089 4.243 6.076 TETANU .922 .O09 1518 978.6 1.102 .009 .905 .940 ATTE .775 .026 1518 978.6 2.035 .034 .723 .820 WCARD .495 .031 305 196.6 1,077 .064 .432 .558 BCG ,967 .012 151 97.3 .803 .012 ,943 .990 DPT .947 .021 151 97,3 1.167 .023 .904 .990 POL ,947 .019 151 97.3 1.022 ,020 ,910 .984 MEASL .861 .027 151 97.3 .950 .031 ,807 .915 FULLIM ,821 .029 151 97.3 .927 .035 .763 .879 DIAR .108 .009 1395 899.3 .969 .080 .090 .125 PACKET .207 .049 150 96.7 1.411 .238 .108 .305 HOMSOL .520 .038 150 96,7 .884 .074 .443 .597 DIARF .587 ,042 150 96.7 .958 .071 .503 .670 FEVER .415 ,019 1395 899.3 1.263 .045 .378 .452 FEVERF .715 .021 579 373.3 .967 .029 .674 .756 CO¢JGH .148 .013 1395 899,3 1.181 ,086 .123 .174 COUGHF .787 .031 207 133,5 .968 .039 .725 .850 POLYG .176 .029 244 157.3 1.191 .165 .118 .234 CHILDREN 3.889 .247 243 156.7 1.174 .063 3.396 4.382 USINGFP .557 .039 244 157.3 1.213 .069 .480 .635 NOMORE .357 .040 244 157.3 1.288 ,111 .277 .436 HIDEAL 4.046 .124 216 139,3 .979 .031 3.798 4.294 HUSAPR .932 .017 235 151.5 1.015 .018 .898 .965 105 Table B.4 Sampling errors for the rura l populat ion, Kenya, 1989 Variable Start- Unwei- Weight- Rela- Confidence l im i ts dard ghted eel Design t i re Value error number number ef fect error R-2SE R+2SE NOEDUC .279 .012 5225 5906.2 SECONDARY .160 .012 5225 5906.2 MARRIED .679 .010 5233 5914.1 MBEF18 .402 .012 5233 5914.1 BBEF18 .358 °009 5233 5914.1 CEB 3.950 .069 5233 5914.1 CEB40 7.703 .130 920 1020.1 CSUR 3.526 .059 5233 5914.1 PREGNANT .089 .006 5233 5914.1 KNOW .918 .011 3618 4017.5 KNOWMO0 .905 .013 3618 4017.5 KNOWSRC .891 .013 3618 4017.5 KNOt~OV ,219 .010 3618 4017.5 EVERUSE ,438 .014 3618 4017.5 CURUSE .262 .011 3618 4017.5 MOOUSE .164 .008 3618 4017,5 APPRFP ,877 .007 3356 3689.3 WANTNM .512 .011 3618 4017.5 WART2 .254 .009 3618 4017.5 IDEAL 4.574 .063 4989 5679.3 BREASTF 19.532 .302 3412 3836.9 AMEN 11.198 .356 3412 3836,9 ABSTAIN 5,966 .320 3412 3836.9 TETANU .882 ,007 5394 6071,6 ATTE. .457 ,017 5394 6071,6 WCARD ,631 .015 997 1117.9 BCG ,967 .009 630 705.0 OPT ,901 .018 630 705.0 POL .920 ,013 630 705,0 MEASL .768 .022 630 705.0 FULLIM .715 .022 630 705,0 D%AR .131 .005 4946 5614.7 PACKET ,211 .015 679 733.0 HOMSOL ,485 .024 679 733.0 DIARF .452 .022 679 733.0 FEVER .422 .010 4946 5614.7 FEVERF .530 .018 2160 2367.6 COUGH .188 .009 4946 5614.7 COUGHF ,634 .027 962 1055.1 1.986 .044 .254 .303 2.389 .076 .136 ,184 1,603 .015 .659 .700 1.717 .029 .378 .425 1.340 .025 ,340 .376 1.497 .018 3.812 4.088 1.344 .017 7.442 7.963 1.432 .017 3.407 3,644 1.563 .069 .077 .I01 2.361 .012 .897 .940 2.659 .014 .879 .931 2.509 .015 .865 .917 1.496 .047 .198 ,239 1.707 .032 .410 .466 1.484 .041 ,241 ,284 1,261 .047 .149 .180 1.158 .008 .864 .890 1.329 .022 .490 .534 1.301 .037 .235 .273 2.153 .014 4.449 4.700 1.097 .015 18.927 20.137 1.352 .032 10.485 11.910 1.388 .054 5.326 6.607 1.366 .008 .867 .896 1.999 ,037 .423 .490 .976 .024 .600 .661 1.213 .009 .950 .984 1.472 .020 .866 .937 1.215 .015 .894 .947 1.291 ,029 .724 .813 1,216 .031 ,671 .760 1.036 ,039 .120 .141 .925 ,072 .181 .242 1.173 .050 .437 .533 1.049 .048 .409 .495 1.185 .023 .402 .441 1,414 .033 .495 ,565 1.337 °046 .171 .205 1.467 .043 .580 .689 POLYG .209 .018 872 1012.8 1.285 .085 .174 .244 CHILDREN 6.452 .211 870 1011.3 1.496 .033 6.031 6.873 USINGFP ,485 .018 072 1012.8 1.048 .037 .450 .521 NOMORE .506 .020 872 1012.8 1,172 .039 .466 .545 HIDEAL 4.872 .101 783 916.9 .636 .021 4.670 5,074 HUSAPR .910 .011 834 956.8 1,069 .012 .889 .931 106 Table 8.5 Sampling errors for women in Nairobi, Kenya, 1989 Variable Value Stan- Unwei- Weight- Reta- Confidence limits dard ghted ed Design live error number number effect error R-2SE R+2$E NOEOUC .085 .009 857 552.5 .969 .I09 .067 .104 SECONDARY .431 .023 857 552.5 1.369 .054 .384 .477 MARRIED .604 .021 859 553.8 1.265 .035 .562 .646 CEB40 4.861 .300 72 46.4 1.023 .062 4.261 5.462 KNOWMO0 .948 .009 519 334.6 .884 .009 .931 .965 KNOWSRC .938 .012 519 334.6 1.095 .012 .915 .961 CURUSE .335 .025 519 334.6 1.222 .076 .285 .386 MOOUSE .279 .027 519 334.6 1.346 .095 .226 .332 APPRFP .921 .015 495 319.1 1.218 .016 .892 .951 WANTNM .437 .025 519 334.6 1.137 .057 .388 .487 IDEAL 3.566 .051 842 542.8 .982 .014 3.483 3.689 BREASTF 19.932 .968 410 264.3 1.126 .049 17.996 21.867 AMEN 9.132 °983 410 264.3 1.302 .108 7.166 11.897 ABSTAIN 6.322 .660 410 264.3 .951 .I04 5.003 7.641 IETANU .903 .016 647 417.1 1.198 .018 .870 .936 ATTE .832 .031 647 417.1 1.827 .038 .769 .894 WCARD .479 .038 144 92.8 .907 .079 .403 .555 BCG .928 .027 69 44.5 .855 .029 .874 .981 DPT .942 .034 69 44.5 1.212 .036 .874 1.010 POL .942 .033 69 44.5 1.187 .035 .875 1.009 MEASL .855 .037 69 44.5 .876 .044 .781 .930 FULLIM .797 .041 69 44.5 .833 .051 .716 .878 DIAR .130 .014 599 386.2 .926 .I07 .102 .158 PACKET .231 .079 78 50.3 1.538 .344 .072 .389 HOMSOL .577 .061 78 50.3 1.020 .107 .454 .700 DIARF .667 .046 78 50.3 .778 .068 .575 .758 FEVER .459 .032 599 386.2 1.394 .070 .395 .523 FEVERF .698 .028 275 177.3 .885 .041 .641 .755 COUGH .137 .018 599 386.2 1.171 .135 .I00 .174 COUGHF .768 .066 82 52.9 1.219 .086 .636 .900 Table 8.6 Sampling errors for women in Central Province, Kenya, 1989 Stan- Unwei- Weight- Reta- Confidence l im i ts dard ghted ed Design t ive Variable "Value error nLmlber number ef fect error R-2SE R+2SE NOEDUC .129 .019 1277 1115.3 2.055 .150 .090 .167 SECONDARY .268 .050 1277 1115.3 4.014 .186 .168 .367 MARRZED .578 .022 1281 1120.4 1.564 .037 .535 .622 CEB40 7.308 .207 224 182.9 1.110 °028 6.893 7.722 KNOWMOD .958 .008 787 648.1 1.060 .008 .942 .973 KNOUSRC .952 .008 787 648.1 1.062 .008 .936 .969 CURUSE .395 .022 787 648.1 1.284 .057 .351 .440 MOOUSE .308 .020 787 648.1 1.217 .065 .268 .348 APPRFP .920 .015 770 628.4 1.501 .016 .890 .949 WANTNM .673 .030 787 648.1 1.800 .045 .613 .734 IDEAL 3.756 .074 1250 1095.4 1.900 .020 3.607 3.904 BREASTF 18.358 .733 702 619.2 1.216 .040 16.891 19.825 AMEN 10.670 .731 702 619.2 1.311 .068 9.209 12.132 ABSTAIN 7.730 .619 702 619.2 1.108 .080 6.491 8.969 TETANU .899 .010 1129 968.8 1.010 .011 .878 .920 ATTE .733 .029 1129 968.8 1.903 .039 .676 .790 WCARD .610 .039 225 203.3 1.185 .063 .533 .687 8CG .956 .030 133 124.0 1.711 .031 .897 1.015 DPT .982 .010 133 124.0 .928 .011 .961 1.003 POL .979 .011 133 124.0 .893 .011 .957 1.000 MEASL .936 .018 133 124.0 .862 o019 .901 .972 FULLIM .877 .037 133 124.0 1.332 .042 .803 .951 OIAR .100 .015 1076 926.5 1.706 .155 .069 .131 PACKET .196 .051 107 92.4 1.315 .259 .094 .298 HOMSOL .714 .036 107 92.4 .801 .050 .642 .786 OIARF .319 .042 107 92.4 .923 .132 .235 .403 FEVER .502 .019 1076 926.5 1.073 .038 .464 .540 FEVERF .527 .033 508 465.2 1.334 .063 .461 .593 COUGH .163 .016 1076 926.5 1.229 .098 .131 .195 CO(JGHF .731 .042 184 150.7 1.129 .057 .647 .614 107 Table B.7 Sampling errors for women in Coast Province, Kenya, 1989 Variable Bran- Unwei- Weight- Rela- Confidence l im i ts dard ghted ed Design t i re Value error number nuznber effect error R-2SE R+2BE NOEDUC .475 .045 720 498.4 2.436 .096 .384 .565 SECONDARY .155 .018 720 498.4 1.298 .113 .120 .190 MARRIED .702 .021 720 498.4 1.233 .030 .660 .744 CEB40 7.296 .577 112 64.9 1.716 .079 6.142 8.449 KNO~JMO0 .923 .018 529 350.0 1.574 .020 .886 .959 KNOt~BRC .892 .026 529 350.0 1.926 .029 .840 .944 CURUSE .181 .016 529 350.0 .963 .089 .149 .214 MOOUSE .148 .016 529 350.0 1.056 .110 .115 .181 APPRFP .777 .015 476 323.3 .811 .020 °746 .807 gANTNM .280 .025 529 350.0 1.299 .091 .229 .331 IOEAL 5.602 .288 624 443.5 2.327 .051 5.026 6.179 BREASTF 17.667 1.263 391 254.6 1.413 .071 15.141 20.192 AMEN 9.440 .599 391 254.6 .759 .063 8.242 10.638 ABSTAIN 2.606 .547 391 254.6 1.124 .210 1.513 3.699 TETANU .891 .013 631 423.1 .883 .015 .865 .918 ATTE .410 .031 631 423.1 1.276 .076 .348 .472 WCARD .662 .034 115 73.4 .737 .052 .593 .731 BCG .964 .024 77 48.6 1.088 .025 .915 1.012 DPT .856 .089 77 48.6 2.126 .104 .677 1.035 POL .936 .025 77 48.6 .865 .027 .885 .987 MEASL .716 .059 77 48.6 1.080 .082 .598 .833 FULLIM .687 .062 77 48.6 1.114 .091 .562 .811 DIAR .I01 .018 556 377.8 1.299 .177 .065 .136 PACKET .380 .044 53 38.0 .646 .117 .291 .468 HOMSOL .351 .045 53 38.0 .760 .129 .260 .441 DIARF .582 .079 53 38.0 1.084 .136 .424 .740 FEVER .441 .033 556 377.8 1.459 .076 .374 .508 FEVERF .769 .033 261 166.7 1.161 .042 .704 .834 COUGH .180 .036 556 377.8 1.979 .199 .109 .252 COUGHF .725 .036 112 68.1 .797 .049 .654 .796 Table B.8 Sampling errors for women in Eastern Province, Kenya, 1989 Variable Value Stan- Unwei- Weight- Reta- Confidence Limits dard ghted ed Design tire error number number effect error R-2SE R+2SE NOEDUC .237 .012 897 1268.4 .862 .052 .212 .261 SECONDARY .153 .016 897 1268.4 1.339 .105 .121 .185 MARR%ED .633 .026 898 1269.4 1.595 .041 .582 .684 CEB40 7.425 .297 159 227.3 1.240 .040 6.831 8.018 KNOt#MOO .927 .023 561 803.7 2.127 .025 .880 .974 KNOWSRC .901 .020 561 803.7 1.581 .022 .861 .941 CURUSE .402 .030 561 803.7 1.427 .073 .343 .461 MOOUSE .195 .024 561 803.7 1.405 .121 .148 .242 APPRFP .910 .014 532 762.8 1.121 .015 .882 .938 WANTNM .597 .021 561 803.7 1.024 .036 .555 .640 IDEAL 4.172 .081 890 1260.4 1.393 .019 4.010 4.334 BREASTF 20.898 .832 553 794.0 1.209 .040 19.234 22.562 AMEN 9.303 .739 553 794.0 1.167 .079 7.825 10.781 ABSTAIN 6.404 .650 553 794.0 1.092 .I02 5.103 7.704 TETANU .884 .012 858 1232.9 .956 .014 .860 .908 ATTE .408 .023 858 1232.9 1.119 .055 .363 .453 WCARD .731 .035 175 240.5 1.032 .046 .660 .801 BCG .974 .009 127 175.7 .623 .009 .957 .992 DPT .924 .026 127 175.7 1.102 .026 .871 .976 POL .968 .015 127 175.7 .961 .016 .937 .998 MEASL .820 .045 127 175.7 1.293 .055 o731 .910 FULLIM .794 .043 127 175.7 1.187 .055 .707 .881 DIAR .151 .010 813 1174.1 .781 .064 .131 .170 PACKET .138 .024 126 176.8 .757 .176 .089 .187 HOMBOL .541 .057 126 176.8 1.226 .105 .427 .655 DIARF .484 .051 126 176.8 1.099 .I05 .382 .586 FEVER .437 .029 813 1174.1 1.496 .067 .378 .495 FEVERF .559 .042 368 512.6 1.481 .076 .475 .644 COUGH .187 .022 813 1174.1 1.338 .117 .143 .231 COUGHF .569 .084 144 219.7 1.7"35 .148 .400 .738 108 Table B.9 Sampling er rors for women in Nyanza Province, Kenya, 1989 Var iab le Value Stan- Unwei- Weight- Rela- Confidence Limits dard ghted ed Design tive error number number effect error R-2SE R+2SE NOEDUC .274 .015 1264 1216.8 1.192 .055 .245 .304 SECONDARY .169 .012 1264 1216.8 1.132 .071 .145 .193 MARRIED .716 .013 1265 1217.7 .998 .018 .691 .741 CEB40 7.903 .198 194 188.1 .930 .025 7.507 8.298 KNOt/MOO .933 .008 895 872.0 1.016 .009 .916 .950 KNO~SRC .916 .008 895 872.0 .890 .009 .899 .932 CURUSE .138 .009 895 872.0 .747 .063 .120 .155 MOOUSE .102 .013 895 872.0 1.269 .126 .077 .128 APPRFP .938 .009 831 813.6 1.127 .010 .919 .956 WANTMM .417 .016 895 872.0 .955 .038 .386 .449 IDEAL 4.578 ,067 1196 1148.3 1.301 .015 4.445 4.711 BREASTF 19.292 .426 803 788.9 .763 .022 18.440 20.145 AMEN 11.483 .608 803 788.9 1.135 .053 10.267 12.699 ABSTAIN 3.926 .373 803 788.9 .943 .095 3.180 4.673 TETANU .907 .013 1284 1283.3 1.280 .014 .881 .933 ATTE ,538 .O4O 1284 1283.3 2.419 .075 .457 .619 WCARD .550 .029 221 226.3 .885 .054 .491 .608 BCG .978 .013 128 124.4 .995 .013 .952 1.004 OPT .917 .024 128 124.4 .966 .026 .870 .964 POL .933 .021 128 124.4 .938 .022 .892 .974 MEASL .674 ,043 128 124.4 .993 .063 .589 .759 FULLIM .648 .044 128 124.4 1.002 .068 .560 335 DIAR ,155 .010 1107 1105.8 .943 .067 .134 .175 PACKET .246 .029 173 170.9 .871 .118 .188 .305 HOMSOL .410 .032 173 170.9 .837 .079 .345 .474 DIARF .498 .032 173 170.9 .800 .064 .434 .561 FEVER ,500 .018 1107 1105.8 1.008 .035 .465 .535 FEVERF .564 .047 557 553.1 1.960 .084 .469 .658 COUGH .217 .027 1107 1105.8 1.923 .125 .162 .271 COUGHF .721 .036 247 239.5 1.150 .050 .649 .794 Table B.IO Sampling errors for women in Rift Valley Province, Kenya, 1989 Var iab le Value Stan- Unwei- Weight- Reta- Confidence limits dard ghted ed Design tire error nutnber number effect error R-2SE R+2SE NOEOUC .321 .040 1099 1518.5 2.840 .124 .241 .402 SECONDARY .162 .021 1099 1518.5 1.892 .130 .120 .204 MARRIED .689 .027 1100 1518.9 1.929 .039 .635 .743 CEB48 7.354 .333 144 239.4 1.382 .045 6.688 8.020 KBOWMOO .8/+6 .044 742 1046.5 3.354 .053 .757 .935 KNOWSRC .840 .046 742 1046.5 3.389 .054 .749 .931 CURUSE .296 .031 742 1046.5 1.857 .105 .234 .358 MOOUSE .181 .016 742 1046.5 1.116 .087 .150 .213 APPRFP .811 .015 678 910.0 .995 .019 .780 .841 WANTNM .497 .030 742 1046.5 1.660 .061 .436 .558 IDEAL 4.731 .159 1086 1486.6 2.442 .034 4.414 5.049 BREASTF 19.130 .733 728 1006.6 1.198 .038 17.665 20.596 AMEN 12.179 .860 728 1006.6 1.451 ,071 10.460 13.899 ABSTAIN 8.217 .880 728 1006.6 1.583 .107 6.457 9.976 TETANU .864 .021 1168 1592.5 1.734 .025 .821 ,906 ATTE .448 .039 1168 1592.5 2.063 .086 .371 .525 WCARD ,623 .034 225 289.5 1,020 .055 .554 .691 BCG ,978 .019 140 180.3 1.461 .019 .941 1.016 DPT .895 .047 140 180.3 1.710 .053 .800 .990 POL .908 .043 14O 180.3 1.629 .O47 .822 .994 MEASL ,772 .043 140 180.3 1.155 .056 .686 .858 FULLIM .707 .049 140 180.3 1.219 .069 .609 .805 DIAR .074 .006 1128 1532.8 .729 .079 .062 .086 PACKET .290 .057 98 113.4 1.119 .197 .176 .404 HOMSOL .275 .049 98 113.4 .965 .179 .177 .373 DIARF .358 .047 98 113.4 .857 .131 .264 .451 FEVER .290 .017 1128 1532.8 1.047 .059 .256 .325 FEVERF ,499 .037 320 445,2 1.110 .074 .425 .572 COUGH ,203 .012 1128 1532,8 .858 .058 ,179 .227 COUGHF .599 .034 257 311.1 .912 .057 ,530 .668 109 Tabte B.11 Sampting errors for women in Western Province, Kenya, 1989 Stan- Unwei- Weight- Rela- Confidence Limits dard ghted ed Design rive Var iab le Value er ror number number e f fec t e r ro r R-2SE R+2SE NOEDUC .255 .015 1026 970.8 1.119 .060 .225 .286 SECONDARY .202 .026 1026 970.8 2.035 .126 .151 .253 MARRIED .732 .017 1027 971.3 1.256 .024 .697 .766 CE840 8.171 .318 168 169.6 1.335 .039 7.536 8.807 KNOi~MO0 .906 .011 745 710.6 1.061 .013 .883 .928 KNO~SRC .897 .011 745 710.6 .989 .012 .875 .919 CURUSE .137 .022 745 710.6 1.732 .159 .093 .181 MDOUSE .100 .016 745 710.6 1.455 .160 .068 .132 APPRFP .877 .017 684 647.6 1.374 .020 .842 .911 WANTNM .432 .015 745 710.6 .811 .034 .402 .461 IDEAL 4.877 .098 948 892.9 1.449 .020 4.680 5.074 BREASTF 19.731 .521 774 721.1 .963 .026 18.688 20.774 AMEN 11.656 .493 774 721.1 .913 .042 I0.669 12.642 ABSTAIN 3.433 .471 774 721.1 1.239 .137 2.492 4.375 TETANU .885 .011 1195 1132.5 .934 .012 .864 .907 ATTE .348 .031 1195 1132.5 1.788 .089 .286 .409 WCARD .555 .041 197 188.7 1.154 .074 .4T3 .638 BCG .955 .023 107 104.8 1.172 .024 .908 1.001 DPT .806 .052 107 104.8 1.367 .064 .702 .909 POL .787 .031 107 I04.8 .779 .039 .726 .848 MEASL .662 .079 107 104.8 1.739 .120 .503 .821 FULLIM .565 .052 107 104.8 1.078 .092 .461 .669 DIAR .186 .012 1062 1010.9 .981 .064 .162 .210 PACKET .166 .027 194 187.8 .978 .163 .112 .220 HOMSOL .535 .046 194 187.8 1.179 .086 .443 .627 DIARF .489 .046 194 187.8 1.205 .095 .396 .582 FEVER .416 .015 1062 1010.9 .871 .036 .386 .446 FEVERF .485 .026 450 420.8 .966 .054 .433 .538 COUGH .145 .018 1062 1010.9 1.337 .122 .110 .180 COUGHF .616 .063 143 146.6 1.276 .103 .489 .742 Table B.12 Sampling errors for current contraceptive use among rural women by district, Kenya, 1989 Dis t r i c t Value Stan- Unwei- Weight- Reta- Confidence L imits dard ghted ed Design r ive error number number effect error R-2SE R+2SE KILIFI .097 .023 300 105.5 1.319 .233 .052 .142 MACHAKOS .404 .032 282 337.3 1.093 .079 .340 .468 MERU .363 .042 193 201.9 1.210 .116 .279 .447 NYERI .412 .042 204 155.9 1.203 .101 .329 .495 MURANG'A .313 .054 211 158.4 1.701 .174 .204 .422 KIRINYAGA .522 .037 226 81.7 1.099 .878 .449 .595 KERICHO .232 .026 263 195.5 1.016 .114 .179 .285 U. GISHU .149 .035 148 59.1 1.182 .233 .079 .218 S. NYANZA .059 .010 272 253.6 .706 .172 .039 .079 KISII .202 .022 233 210.3 .823 .I07 .158 .245 SIAYA .087 .020 160 129.8 .886 .227 .048 .127 KAKAMEGA .143 .020 315 393.7 .994 .137 .104 .182 BUNGOMA .085 .015 317 157.4 .945 .174 .056 .115 110 APPENDIX C NOTE ON AGE REPORTING APPENDIX C. NOTE ON AGE REPORTING The KDHS household questionnaire contains information on the de facto population, that is, those who slept in the household the previous night. The de facto population enumerated in the household questionnaire was 42,615 persons. Table C.1 presents the percent distribution of the de facto population by age and sex from the KDHS, along with comparable information for the 1977/78 KFS, the 1979 census, and the 1984 KCPS. The proportion of the population age 0-4 in the KDHS is lower than the percent age 5- 9. It is also lower than the proportion age 0-4 from the other sources. This might be due in part to the decline in fertility discussed in Chapter 3, but it could also be partly attributed to displacement of younger children into the 5-9 age group in order to reduce interviewers' workload. It is interesting that all three surveys show evidence that women age 15-19 were displaced to age group 10-14, also presumably to reduce interviewers' workload. The 1979 census shows a much more even decline in proportions of women at these two age groups. The two later surveys also show some displacement of women from age group 45-49 to 50-54 relative to the census. Table C.1 Percent d i s t r ibut ion of the de facto popu lat ion enumerated in var ious censuses and surveys by age group, according to sex, Kenya 1977/78 KFS 1979 Census 1984KCPS 1989 KDBS Age Fe- Both Fe- Both Fe o goth Fe- Both group Mate male sexes Male mate sexes Mate male sexes Mate mate sexes 0-4 20.I 19.7 19.9 18.7 18.4 18.6 20.3 19.5 19.9 17.6 17.6 17.6 5-9 17.8 17.3 17,5 16.4 16.1 16.3 17.2 17,2 17.2 18.1 17.7 18.0 10-14 14,7 15.4 15,1 13.8 13.3 13.5 14.6 15,1 14,9 16.4 17.4 16,9 15-19 9.7 9.0 9.4 11.2 11,5 11.4 9.6 8.2 8.9 11.0 7,8 9.4 20-24 5.9 6.7 6.3 8,4 8.9 8.7 7.0 8.4 7.7 6.6 6.7 6,6 25-29 6.3 7.0 6.6 6.8 7,0 6.9 5.6 6.6 6.1 5.9 6.8 6.4 30-34 4.4 4.7 4.6 5.3 5,4 5.3 4.9 5.4 5,2 4,5 5.0 4.8 35-39 4.0 4,3 4.2 3.8 4.2 4.0 4.2 4.0 4.1 3,9 4,4 4.1 40-44 3.5 2.9 3.2 3.4 3.6 3.5 3,4 3.1 3.3 3.1 3,4 3.2 45-49 3.1 3.0 3.0 2,9 2.9 2.9 3.0 2,2 2.6 2.9 2.2 2.6 50-54 2.6 2.9 2.7 2.4 2.5 2.4 2.8 3,7 3.2 2,4 3.8 3.1 55-59 1.9 2.4 2.1 1.9 1.7 1.8 2.0 2,1 2.1 1,9 2.1 2,0 60-64 1.9 1.7 1,8 1.4 1.4 1,4 1.8 1.6 1.7 1.9 1,8 1.8 65-69 1.3 1.2 1.3 1.3 1.1 1.2 1.1 0.9 1,0 1,3 1.1 1,2 70-74 1.2 0.9 1.0 0.9 0,8 0.8 1.1 0.7 0,9 1.0 0.9 1.0 75+ 1.4 1.0 1.2 1.2 1,1 1.1 1.0 0.9 0.9 1,5 1.1 1.3 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 In Kenya, as in most societies, there is a tendency to report ages that end in preferred digits, usually 0 and 5. This tendency is known as age "heaping". In an effort to measure the extent of age heaping in the KDHS, an indicator known as Whipple's index was calculated and compared with similar data from the 1962, 1969 and 1979 censuses, and the 1984 KCPS. The index generally varies between 100, representing no preference for the digits 0 and 5, to 500, indicating that all ages were reported as ending in 0 or 5. As shown in Table C.2, the indices for the KDHS are 134 for males and 121 for females, which are slightly higher (indicating more 113 age heaping) than in the 1984 KCPS. Indices from both surveys, however, are considerably lower than those from the censuses, which is probably due to the fact that they are much smaller and more controlable operations. Also, the fact that the indices for females in both surveys are lower than those for males, while the opposite is true for the censuses, is most likely due to the fact that the two surveys focused on interviewing women, many of whom may have had to estimate the ages of the men in the household. Tabte C.2 Whippte's indices of age misreporting from various censuses and surveys, by sex, Kenya 1962 1969 1979 1984 1989 Sex Census Census Census KCPS KDHS Mate 203.4 157.5 146.2 128.5 134.0 Femate 294.9 158.5 162.9 112.9 121.0 Figure C.1 shows the distribution of the KDHS de facto household population by single year of age according to sex. The preference for ages ending in 0 and 5 and, to a lesser extent, 2 and and 8 is apparent for both males and females. The precipitous decline in the proportion of women age 14 to age 15 that was mentioned above, is also obvious; the figure shows that it is not limited to these two ages alone, but also affects the number of girls age 16, 17 and 18, relative to boys. Figure C.1 Distribution of De Facto Household Population by Single Year of Age and Sex Percent 4 3 0 I d ~ I I d I d I I I 5 10 15 20 25 30 35 40 45 50 55 60 65 Single year of age l Male ~ Female i Kenya DHS 1989 114 APPENDIX D PERSONS INVOLVED IN THE KDHS APPENDIX D. PERSONS INVOLVED IN THE KDHS A. ADMINISTRATIVE STAFF Mr. Leonard Arap Sawe, Permanent Secretary, Ministry of Home Affairs and National Heritage Mr. Johnson Hungu, Permanent Secretary, Ministry of Planning and National Development Mr. Simon Ndirangu, Director, National Council for Population and Development Mr. Jotham A. Mwaniki, Director, Central Bureau of Statistics Mr. G. H. Olum, Deputy Director, Central Bureau of Statistics Mr. Peter Ondieki, Senior Assistant Director, National Council for Population and Development B. TECHNICAL STAFF Mr. Walter Obungu, Demographer, NCPD Mr. Paul M. Kizito, Demographer, NCPD Mr. Michael K. M. Mbayah, Demographer, NCPD Mr. Kennedy Ondimu, Demographer, NCPD Mrs. Jenipher Liku, Sociologist, NCPD Mr. David Ojakaa, Demographer, NCPD (formerly) Mr. John Wakajumah, Demographer, NCPD (formerly) Mr. Zakary E. Gichohi, Senior Economist-Statistician, Central Bureau of Statistics Ms. Anne R. Cross, Regional Coordinator for Anglophone Africa, DHS/IRD Dr. Ann Blanc, Country Monitor, DHS/IRD Ms. Jeanne Cushing, Data Processing Coordinator, DHS/IRD C. FIELD COORDINATORS Mr. Njagi Nyaga, Planning Officer, NCPD Mr. George Kahuthia, Demographer, NCPD Mr. Charles Erigori, Demographer, NCPD Mr. Charles Oisebe, Planning Officer, NCPD Mrs. Maria Musomi, Demographer, NCPD Mr. P. V. L. Omokule, Provincial Statistical Officer, Nairobi Mr. Ogola-Soti, Provincial Statistical Officer, Mombasa Mr. Peter Reriani, District Population Officer, Kericho Mr. G. Gichamu, District Population Officer, Kakamega Mr. A. Adienge, District Population Officer, Kisii Mr. Mbatha, District Population Officer, Mombasa Mr. Achoki, District Statistical Officer, Kisii Mr. Mutoro, District Statistical Officer, Kakamega Mr. Bulemi/Mr. Gondi, District Statistical Officer, Bungoma Mr. J. A. Were, District Statistical Officer, Siaya Mr. S. M. Kamau, District Statistical Officer, Nyeri Mr. F. K. Ndungu, District Statistical Officer, Meru Mr. A. V. Mulewa, District Statistical Officer, Machakos Mr. E. O. Okute, District Statistical Officer, Kisumu Mr. M. M. Masegwa, District Statistical Officer, Kilifi 117 Mr. J. K. Bii, District Statistical Officer, Uasin Gishu Miss R. N. Ngara, District Statistical Officer, Kiambu Mr. P. R. Mureithi, District Statistical Officer, Kirinyaga Mr. A. O. Sunga, District Statistical Officer, South Nyanza/Migori Mr. R. IC Tanui, District Statistical Officer, Kericho Mr. P. M. Muturi, District Statistical Officer, Nakuru Mr. S. N. Ndhenge, District Statistical Officer, Kitui Mr. J. Odero, District Statistical Oficer, Homa Bay Mr. J. W. Githinji, District Statistical Officer, Embu Mr. Mark Otieno, District Statistical Officer, Nandi Mr. C. N. Omolo, District Statistical Officer, Taita-Taveta D. FIELD STAFF Kalenjin Dinah Jeruto Kirwa Elizabeth Kiplagat Lilian Terer Jane Chelangat Jane Lagat Grace Cheruiyot Rita Ngeno Philip Rono Kamba Jane Ndabuki Jane Francis Kitala Tabitha Nguli Elizabeth Mwikali Nzeli Nzoka Florence Mwei Rachel Mukulu Kimuyu Michael Mutisya Kisii Rose Nyamoita Grace IC Nyakeruma Agnes Onwonga Janet N. Nyangwono Jannes Nyarinda Sarah Rioba Jane Ondieki Patrick Osoro rdkle Faith Wairimu Nderitu Teresia W. Kariuki Cecilia W. Gachira Phylis Wangui Gitonga Muiruri P. Muthoni Jane Wangui Simon Wamae Mary Kanyingi Eunice Gitari Gladys Njeri Mwangi Esther Ndirangu Margaret W. Mureu Rosemary Wanjiku Ephantus Wambugu Luhya Roseline Mutenyo Faustine Nabwire Doris Omunga Judith Wanjala Gladys Odanga Jaqueline Kesenwa Mary Manyasa John Lusinde Luo Anne Akinyi Thabita Odingo Bentah B. Aoko Benita A. Omondi Susan Achiro Roseline Oyare Rose Abondo Mathew Oyolo Meru Lucy Karimi Japheth Njiru Mary Mati Lucy Silas Beatrice Ivara Purity Munene Isabella G. Muthamia Kaburu Nyaga Mijikenda Olive Shume Edith Mbeyu Japhet Julitha Sharif Jane Lumwe Joyce Kalenga Mercy Kahaso Kenga Fatuma Mwasuche Chimbeja Emwasambu E. DATA PROCESSING STAFF Joseph Owiti Lucy Nganga Charles Momanyi Julius Majale Salma Musa Mildred Agwanda Monica Kananu Ronald Kilele 118 Eunice Wanjiru Claire Mokeira APPENDIX E SURVEY QUESTIONNAIRES NATIONAL COUNCIL FOR POPULAT ION AND DEVELOPMENT MIN ISTRY OF HOME AFFA IRS AND NAT IONAL HERITAGE KENYA DEMOGRAPHIC AND HEALTH SURVEY HOUSEHOLD SCHEDULE CONFIDENTIAL Data used fo r research Ipurposes on ly IDENTIF ICAT ION PROVINCE D ISTR ICT LOCATION/TOWN SUBLOCATION/WARD CLUSTER NUMBER . . . . . . . . . . . . . . . HOUSEHOLD NUMBER . . . . . . . . . . . . . STRUCTURE NUMBER . . . . . . . . . . . . . URBAN/RURAL (urban=l , NAME OF HOUSEHOLD HEAD rura l=2) . . . . . . . . . . . . . . . . . . . . . . . . . . INTERVIEWER V IS ITS 1 2 3 F INAL V IS IT DATE INTERVIEWER'S NAME RESULT* MONTH YEAR NEXT V IS IT : DATE T IME *RESULT CODES: 1 COMPLETED 2 HOUSEHOLD PRESENT BUT NO COMPETENT RESP . 3 HOUSEHOLD ABSENT N IGHT BEFORE INTERVIEW 4 POSTPONED 5 REFUSED 6 DWELL ING VACANT OR ADDRESS NOT A DWELL ING 7 DWELL ING DESTROYED 8 DWELL ING NOT FOUND 9 OTHER AT HOME INTER- V IEWER NO. F INAL RESULT TOTAL NUMBER OF V IS ITS TOTAL IN HOUSEHOLD TOTAL EL IG IBLE WOMEN TOTAL EL IG IBLE (SPECIFY) HUSBANDS ' ' ' LANGUAGE OF QUEST IONNAIRE: ENGL ISH F IELD EDITED BY OFF ICE EDITED BY KEYED BY KEYED BY NAME - DATE 121 HOUSEHOLD SCHEDULE Now we would Like some information about the people who usually l ive in your household or who are staying with yOU nOW. NO, USUAL RESIDENTS AND VISITORS RELATIONSHIP RESIDENCE SEX AGE LINE PLease give me the names of No. the persons who usually l ive in your household or are staying with you now, start - ing with the head of the household. (I) (2) 01 02 03 04 05 06 07 08 09 10 11 12 13 14 1 Head 2 Spouse Did 3 Son/daugh. Does (NAME) 4 Broth/sis. (NAME) sleep 5 Grandchild usually here 6 Parent l ive last 7 Other rel . here? night? 8 Unrelated (3) (4) (5) YES NO I YEIS NO F- - - I _ _ , 1 2 2 I 2 1 2 - - - - i i I 2 I 2 - - - - | [ ] 1 2 1 2 - - - - m I 2 1 2 - - - - m [ ] 2 1 2 - - - - i [ ] 2 I 2 - - - - i 2 1 2 - - - - m 2 1 2 - - - - m [ ] 2 1 2 - - - - m 2 I 2 2 I 2 2 I 2 - - - - m [ ] __ 2, 1 2 Is (NAME) How mate or Did is female? he/she? hold?* (6) (7) M F IN YEARS 2 M m m I • = 2 F-F] 2 . ~ . FOSTERING ELIGIBILITY l ONLY FOR CHILDREN CIRCLE LINE UNDER 15 TEARS OLD: NUMBER OF I~)MEN AND DO any of his/her HUSBANDS parents usually ELIGIBLE Live in this house- FOR INDIVIDUAL INTERVIEW (8) (9) m YES NO I 2 01 1 2 02 l 1 2 03 1 2 04 2 05 2 06 2 07 2 08 2 09 2 10 2 11 2 12 2 13 2 14 2 15 122 NO. LINE NO. (1) 16 17 18 19 20 21 22 23 24 25 USUAL RESIDENTS AND VISITORS RELATIONSHIP RESIDENCE Ptease give me the names of the persons who usuaLly t i re in your household or are s tay ing wi th you now, s ta r t - ing w i th the head of the household. (2) 1 Head 2 Spouse 3 Son/daugh. 4 Broth /s i s . 5 Grandchi ld 6 Parent 7 Other re l . 8 UnreLated (3) I I i Did Does i (NAME) (NAME) i s leep usuaLLy here live [ast here? n ight? (4) (5) YES NO YES 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 SEX I (~ME) m le or f ~ate? (6) NO F 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 AGE How old i~ he/she? (7) H YEARS I I -1 7 I FOSTERING ELIGIBILITY ONLY FOR CHILDREN CIRCLE LINE UNDER 15 YEARS OLD: NUMBER OF WOMEN AND DO any of h i s /her HUSBANDS parents usua l ly ELIGIBLE Live in th i s house- FOR hold?* INDIVIDUAL INTERVIEW (8) (9) YES NO 1 2 16 1 2 17 I 2 18 I 2 19 I 2 20 I 2 21 I 2 22 I 2 23 1 2 24 I 2 25 TICK HERE IF CONTINUATION SHEET USED F--l / I I TOTAL NUMBER OF ELIGIBLE WOMEN [ 1 1 TOTAL NUMBER OF ELIGIBLE HUSBANDS Just to make sure that I have a compLete l i s t ing : 1) Are there any other persons such as small ch i ld ren or in fants that we have not Listed? 2) In add i t ion , are there any other people who may not be members of your fami ly , such as domestic servants, tc<Jgers or f r iends who usua l ly l i ve here? 3) Do you have any 9uests or temporary v i s i to rs s tay ing here, or anyone e lse who s lept here Last n ight? YES ~ YES YES [~ > ENTER EACH IN TABLE NO E l > ENTER EACH IN TABLE NO > ENTER EACH IN TABLE NO 123 NATIONAL COUNCIL OF POPULAT ION AND DEVELOPMENT MIN ISTRY OF HOME AFFA IRS AND NAT IONAL HERITAGE KENYA DEMOGRAPHIC AND HEALTH SURVEY WOMAN'S QUEST IONNAIRE (For Women Aged 15-49 Who S lept There Las t N ight ) CONFIDENTIAL Data used fo r research purposes on ly IDENTIF ICAT ION PROVINCE D ISTR ICT LOCATION/TOWN SUBLOCATION/WARD CLUSTER NUMBER . . . . . . . . . . . . . . HOUSEHOLD NUMBER . . . . . . . . . . . . STRUCTURE NUMBER . . . . . . . . . . . . URBAN/RURAL (urban=l , NAME OF HOUSEHOLD HEAD o t o , , e e , , o o o o , l o o o . i . . . . . . . . rura l=2) . . . . . . . . . . . . . . . . . . . . . . . . . . L INE NUMBER OF WOMAN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . INTERVIEWER V IS ITS 1 2 3 F INAL V IS IT DATE INTERVIEWER'S NAME RESULT* NEXT V IS IT : DATE T IME MONTH YEAR INTER- VIEWER NO. F INAL RESULT TOTAL NUMBER OF V IS ITS *RESULT CODES: 1 COMPLETED 2 NOT AT HOME 3 POSTPONED 4 REFUSED 5 PARTLY COMPLETED 6 OTHER LANGUAGE OF QUEST IONNAIRE** ENGL ISH LANGUAGE USED IN INTERVIEW** . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . RESPONDENT'S LOCAL LANGUAGE** . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . - TRANSLATOR USED ( I=NOT AT ALL ; 2=SOMETIME; 3=ALL THE T IME) . . * *LANGUAGE CODES: 01 KALENJ IN 05 LUHYA 09 K ISWAHIL I 02 KAMBA 06 LUO i0 ENGL ISH 03 K IKUYU 07 MERU/EMBU i i OTHER 04 K IS I I 08 MI J IKENDA 1[ F~ELD EDITED BY OFFICE EDITED BY ]I KEYED BY I[ NAME DATE 124 104 I SECTION 1. RESPONDENT'S BACKGROUND QUESTIONS AND FILTERS I COOING CATEGORIES SKIP I TO 103 I RECORD THE TIME, MINUTES . 105 F i r s t I woutd t i ke to ask some questions about you and your househotd. For most of the time unt i l you were 12 years otd, did you r ive in the countryside, in Rairobi or Mo~ioasa, or in another town? I COUNTRYSIDE . . . . . . . . . . . . . . . . . . . . . I I NAIROBIIMOMBASA . . . . . . . . . . . . . . . . . 2 OTHER TOWN . . . . . . . . . . . . . . . . . . . . . . 3 106 How tong have you been r iv ing continuousty in (NAME OF SUBLOCATION, TCYdR, CITY)? I I ALWAYS . . . . . . . . . . . . . . . . . . . . . . . . . 95 VISITOR . . . . . . . . . . . . . . . . . . . . . . 96--~>107 YEARS . . . . . . . . . . . . . . . . . . . . ~ I 107 Just before you moved here, did you l i ve in the coun- t rys ide , in Nairobi or Mombasa, or in another town? I COUNTRYSIDE . . . . . . . . . . . . . . . . . . . . . 1 J I NAIROBI/MOMBASA . . . . . . . . . . . . . . . . . 2 OTHER TOWN . . . . . . . . . . . . . . . . . . . . . . 3 >114 I t i s important to know your exact age. and year were you born? In what month MONTH . . . . . . . . . . . . . . . . . . . . DK MONTH . . . . . . . . . . . . . . . . . . . . . . . 98 YEAR . . . . . . . . . . . . . . . . . . . . . ~ DK YEAR . 98 I 108 How old were you at your fast birthday? AGE IN COMPLETED YEARS. . . I I I | INTERVIEWER: COMPARE AND CORRECT 107 AND/OR 108 IF I I I I INCONSISTENT. 109 Have you ever attended schoot? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 I NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 >112A I 110 What was the highest fever of school you attended: primary, secondary, higher or un ivers i ty? PRIMARY . . . . . . . . . . . . . . . . . . . . . . . . . 1 SECONDARY . . . . . . . . . . . . . . . . . . . . . . . 2 HIGHER . . . . . . . . . . . . . . . . . . . . . . . . . . 3 UNIVERSITY . 4 OTHER 5 (SPECIFY) 112 INTERVIEWER: CHECK 110: I I SECONDARY PRIMARY 9 OR ABOVE I V 2 NO. I 125 SKIP NO. I QUESTIONS AND FILTERS | COOING CATEGORIES | TO 112A I Have you ever attended an adul t L iteracy class? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 113 Can you read a le t te r or newspaper in any Language eas i ly , with d i f f i cu l ty , or not at a i l ? EASILY . . . . . . . . . . . . . . . . . . . . . . . . . . 1 WITH DIFFICULTY . . . . . . . . . . . . . . . . . 2 NOT AT ALL . . . . . . . . . . . . . . . . . . . . . . 3 114 J Do you usuaLLy Listen to a radio at Least once a week? I YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 J I NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 115 Where does your household get most of i t s water for d r ink ing , handwashing, and cooking most of the year? I PIPED INTO HOUSE/COHPOUND/PLOT.01 >117 PUBLIC TAP . . . . . . . . . . . . . . . . . . . . . 02 I WELL WITH HANDPUHP . . . . . . . . . . . . . O] I WELL WITHOUT HANDPUNP . . . . . . . . . . 04 LAKE . . . . . . . . . . . . . . . . . . . . . . . . . . . 05 RIVER . . . . . . . . . . . . . . . . . . . . . . . . . . 06 POND . . . . . . . . . . . . . . . . . . . . . . . . . . . 07 RAINWATER . . . . . . . . . . . . . . . . . . . . . . 08 >117 OTHER 09 J (SPECIFY) ,I A I How,ong doos,t usua,,y takeyou to go to that place, r - rT -1 I get water, and return? HIHUTES . . . . . . . . . . . . . . 117 What kind of to i le t Fac iL i ty does your househoLd have? FLUSH TOILET . . . . . . . . . . . . . . . . . . . . 1 BUCKET . . . . . . . . . . . . . . . . . . . . . . . . . . 2 PIT LATRINE . . . . . . . . . . . . . . . . . . . . . 3 OTHER 4 (SPECIFY) NO FACILITIES . . . . . . . . . . . . . . . . . . . 5 118 I At what age do ch i ldren in th i s household s tar t using AGE IN YEARS . . . . . . . . . . . . . ~ I I the same to i le t fac i l i ty as adults? I NO CHILDREN . . . . . . . . . . . . . . . . . . 119 I DO you have, r ight now, bathing soap or washing soap on YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 I I the premises? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 I 3 I >119 ! 126 SKIP NO. I QUESTIONS AND FILTERS I COOING CATEGORIES I TO 120 Does your house have: E lec t r i c i ty? A rad io? A te lev i s ion? A re f r igerator? YES NO ELECTRICITY . . . . . . . . . . . . . . . . 1 2 RADIO . . . . . . . . . . . . . . . . . . . . . . 1 2 TELEVISION . . . . . . . . . . . . . . . . . 1 2 REFRIGERATOR . . . . . . . . . . . . . . . 1 2 121 Does any ~ r of your household own: A b icyc le? A motorcyc le? A car? A t rac tor? Land? Cat t le , goats or sheep? Cash crops? A permanent house? YES NO BICYCLE . . . . . . . . . . . . . . . . . . . . 1 Z MOTORCYCLE . . . . . . . . . . . . . . . . . 1 2 CAR . . . . . . . . . . . . . . . . . . . . . . . . I Z TRACTOR . . . . . . . . . . . . . . . . . . . . I 2 LAND . . . . . . . . . . . . . . . . . . . . . . . I 2 CATTLE, GOATS, SHEEP . . . . . . . 1 E CASH CROPS . . . . . . . . . . . . . . . . . 1 2 PERMANENT HOUSE . . . . . . . . . . . . 1 2 122 INTERVIEWER: INQUIRE OR OBSERVE MAIN MATERIAL OF THE FLOOR. PARQUET/POLISHED WOO0 PIECES . . . . 1 VINYL/LINOLEUM/ASPHALT STRIPS. . .2 TILES . . . . . . . . . . . . . . . . . . . . . . . . . . . ] t~30O PLANKS . . . . . . . . . . . . . . . . . . . . . 4 CEMENT . . . . . . . . . . . . . . . . . . . . . . . . . . 5 EARTH . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 OTHER 7 (SPECIFY) 130 What is your re l ig ion? CATHOLIC . . . . . . . . . . . . . . . . . . . . . . . . 1 PROTESTANT/OTHER CHRISTIAN . . . . . . 2 MUSLIM . . . . . . . . . . . . . . . . . . . . . . . . . . ] OTHER (SPECIFY) 4 NO RELIGION . . . . . . . . . . . . . . . . . . . . . 5 140 What i s your ethnic group/tribe? KALENJIN . . . . . . . . . . . . . . . . . . . . . . . 01 KAMBA . . . . . . . . . . . . . . . . . . . . . . . . . . 02 KIKUYU . . . . . . . . . . . . . . . . . . . . . . . . . O] K IS I I . . . . . . . . . . . . . . . . . . . . . . . . . . 04 LUHYA . . . . . . . . . . . . . . . . . . . . . . . . . . 05 LUO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 06 MERU/EMBU . . . . . . . . . . . . . . . . . . . . . . 07 MIJIKENDA/SWAHIL] . . . . . . . . . . . . . . 08 SOMALI . . . . . . . . . . . . . . . . . . . . . . . . . 09 OTHER 10 (SPECIFY) 150 To which womenJs o rgan izat ion or assoc ia t ion do you betong? CIRCLE CODES FOR ALL ORGANIZATIONS MENTIONED. MAENDELEO YA WARAWAKE . . . . . . . . . . . 1 MOTHERS' UNION OR ANY OTHER RELIGIOUS ASSOCIATION . . . . . . . . . 1 LOCAL ~EN'S GROUP/WELFARE ASS.1 OTHER 1 (SPECIFY) NONE . . . . . . . . . . . . . . . . . . . . . . . . . . . . I 127 SECTION 2. REPROOUCTION ] SKIP NO. | QUESTIONS AND FILTERS I COOING CATEGORIES l TO +, i ++, I + . , l had during your l i fe . Have you ever given b i r th? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 >206 202 1 go yoo have e°Y so°s °r daughters you h°ve given birth I Y E S t o who are oow living with you? . ' 1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 >204 And how many daughters Live with you? DAUGHTERS AT HOME . IF NONE ENTER '00 f. ++ i 0o + ++ + +s +++, +u ++ ++ i +o ++ +,+v+ do ,+v+ w++ +u, . , l NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 >206 And how many daughters are a l ive but do not Live with you? DAUGHTERS ELSEWHERE . . . . . . IF NONE ENTER =001. ~°+ I +e +u°v°r +n ++ ~°' +~°~ ~ ~+~' +°w" I ~" . '1 born a l ive but Later died? IF NO, PROBE: Any (other) boy or g i r t who cr ied or showed any sign of L i fe but NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 >208 only survived a few hours or days? I + I +M + +° ++' + ' +° ++ + ,oo,. I +o+ . . . . . . . . . . . . . . . . . . . . 209 CHECK 208: Just to make sure that I have th is r ight : you have had in TOTAL Live b i r ths during your Li fe. Is that correct? PROBE AND YES ? NO [-~> CORRECT 201-209 AS NECESSARY v 210 I CHECK 208: ? ORE OR MORE BIRTHS v NO BIRTHS I >220 I 128 211 Now I would l i ke to ta lk to you about a l l of your b i r ths , whether s t i l l a l i ve or not, s ta r t ing with the f i r s t one you had. (RECORD NAMES OF ALL THE BIRTHS IN 212. RECORD TWINS ON SEPARATE LINES. COOE TYPE OF BIRTH.) 212 What name was given to your ( f i r s t , next) baby? (NAME) (NAME) (NAME) (NAME) ojD (NAME) (NAME) (NAME) (NAME) 213 Is (NAME) boy or a i r t ? BOY GIRL I 2 OY GIRL 2 OY GIRL 2 BOY GIRL 2 BOY GIRL 2 BOY GIRL 2 BOY GIRL 2 BOY GIRL 2 214 In what month and year was (NAME) born? PROBE: What is h is /her birthday? OR: In what season? MONTH.~ YEAR,. MONTH.~ YEAR. MONTH.~ YEAR. MONTH.~ YEAR. MONTH.~ YEAR. MONTH. [~ YEAR. MONTH.~ YEAR. MONTN.~ YEAR. 215 Is (NAME) stil l alive? YES NO --1 2 >(GO TO 217) YES NO - -1 2 >(GO TO 217) YES NO - -1 2 >(GO TO 217) YES NO - -1 2 >(GO TO 217) YES NO - -1 2 >(GO TO 217) YES NO - -1 2 >(GO TO 217) YES NO - -1 2 >(GO TO 217) YES NO 2 GO TO 217) 216 IF DEAD: HOW old was (NAME) when he/she died? RECORD DAYS IF LESS THAN ONE MONTH, MONTHS IF LESS THAN T~ YEARS, OR YEARS. 217 IF ALIVE: How old was (NAME) at h i s / her last birthday? RECORD AGE IN COMPLETED YEARS. DAYS . I MONTHS.2 YEARS.,3 (GO TO NEXT BIRTH) DAYS . I AGE MONTHS.2 ~ARS~F~ YEARS. .] (GO TO NEXT BIRTH) DAYS . . . . . 1 MONTHS.2 YEARS. .] (GO TO NEXT BIRTH) DAYS . I AGE MONTHS,.2 IN YEARS YEARS.3 (GO TO NEXT BIRTH) DAYS . I AGE MONTHS.2 ~ARS YEARS. .] (GO TO NEXT BIRTH) DAYS . I AGE MONTHS.2 IN ~ YEARS YEARS. .] (GO TO NEXT BIRTH) DAYS . . . . . 1 AGE MONTHS2 YEARS.3 (GO TO NEXT BIRTH) DAYS . . . . . 1 MONTHS.2 YEARS,,.,3 (GO TO NEXT BIRTH) 218 IF ALIVE: Is he/she l i v ing with you? AGE YES NO 1 2 YES NO I 2 AGE r ~ IN I I YES NO t I YEARS 1 2 YES NO 1 2 YES NO 1 2 YES NO 1 2 YES NO 1 2 lACE IN ~ YES NO I I I YEARS 1 2 129 6 212 What name was given to your next baby? (NAME) (NAME) ;Fl (NAME) (NAME) sE] (NAME) (NAME) (NAME) 213 Is (NAME) a boy or a gir l? BOY GIRL I 2 BOY GIRL I 2 BOY GIRL I 2 BOY GIRL 1 2 BOY GIRL I 2 BOY GIRL 1 2 BOY GIRL I 2 214 In what month and year was (NAME) born? PROBE: What is his/her birthday? OR: In what season? MONTH.~ YEAR. MONTH.~ YEAR. MONTH. YEAR. MONTH,~ YEAR. 215 Is (NAME) s t i l l ative? YES NO 1 2 2 >~(GO TO 217) YES NO - - I 2 >(GO TO 217) YES NO 1 2 2 [CGOTO 17) I YES NO 1 2 >~(GO TO 217) YES NO --1 2 >(GO TO 217) 216 IF DEAD: How old was (NAME) when he/she died? RECORD DAYS IF LESS THAN ONE MONTH, MONTHS IF LESS THAN TWO YEARS, OR YEARS. DAYS . . . . . 1 MONTHS.2 YEARS.3 217 IF ALIVE: How old was (NAME) at his/ her last birthday? RECORD AGE 1N COMPLETED YEARS. AGE (GO TO NEXT BIRTH) DAYS1 . IAGE M TNS.2 YEARS.3 (GO TO NEXT BIRTH) DAYS . . . . . 1 - - [ AGE F ' ~ MONTHS.2 I IN YEABSI I I YEARS.3 (GO TO REXT BIRTH) DAYS . . . . . 1 AGE MONTHS.2 ~ARS YEARS.3 (GO TO NEXT BIRTH) DAYS . . . . . 1 MONTHS,.2 YEARS.3 (GO TO NEXT BIRTH) DAYS . . . . . I MONTHS.2 YEARS.3 (GO TO NEXT BIRTH) BAYS . . . . . I MONTHS.2 AGE ABSIrl YEARS,.3 (GO TO 219) I AGE - - I AGE ;;ARB 218 IF ALIVE: Is he/she l iving with you? YES NO 1 2 YES NO 1 2 YES NO 1 2 YES NO 1 2 YES NO 1 2 YES NO 1 2 YES NO I 2 219 COMPARE 208 WITH NUMBER OF BIRTHS IN HISTORY ABOVE AND MARK: NUMBERS r'--l NUMBERS ARE ARE SAME L~ DIFFERENT V > (PROBE AND RECONCILE) 130 SNIP NO. I QUESTIONS AND FILTERS | CODING CATEGORIES | TO 220 I Now I would l i ke to ask you about some current events I YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 I i I in your L i fe . Are you pregnant now? I NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2~ UNSURE . . . . . . . . . . . . . . . . . . . . . . . . . . 8 / >225 MONTHS . . . . . . . . . . . . . . . . . . . i n jec t ion to prevent the baby from Bett ing tetanus? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2~ DK . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 >223 I 222B Where did you go to get the (Last) in ject ion? HOSPITAL . . . . . . . . . . . . . . . . . . . . . . . . 1 HEALTH CENTER/CLINIC/ DISPENSARY . . . . . . . . . . . . . . . . . . . . . 2 MOBILE CLINIC . . . . . . . . . . . . . . . . . . . 3 VILLAGE HEALTH WORKER . . . . . . . . . . . 4 PRIVATE DOCTOR . . . . . . . . . . . . . . . . . . 5 SPECIAL CAMPAIGN . . . . . . . . . . . . . . . . 6 OTHER 7 (SPECIFY) DK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B 223 I Did Y°U see any°ne f ° r advice °n th is pregnancy? I YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 I • NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 >226 224 Whom did you see? DOCTOR . . . . . . . . . . . . . . . . . . . . . . . . . . 1 TRAINED NURSE/MIDWIFE . . . . . . . . . . . 2~>226 PROBE FOR TYPE OF PERSON AND RECORD MOST QUALIFIED. TRADITIONAL BIRTH ATTENDANT . . . . . 3 ~ OTHER .4 (SPECIFY) | 225 How tong ago did your Last menstrual per i~ start? DAYS AGO . I WEEKS AGO . 2 MONTHS AGO . 3 YEARS AGO . 4 BEFORE LAST BIRTH . . . . . . . . . . . . . 995 NEVER MENSTRUATED . . . . . . . . . . . . . 996 226 From the time a woman gets her period unti l the time she gets her next period, when do you think she has the greatest chance of becoming pregnant? PROSE: What are the days during the month when a woman has to be careful to avoid becocMng pregnant? DURING HER PERIOD . . . . . . . . . . . . . . . I RIGHT AFTER HER PERIOD HAS ENDED . . . . . . . . . . . . . . . . . . . . . . 2 IN THE MIDDLE OF THE CYCLE . . . . . . 3 JUST BEFORE HER PERIOD BEGINS.4 AT ANY 71ME . . . . . . . . . . . . . . . . . . . . . 5 OTHER .6 (SPECIFY) DR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S 227 PRESENCE OF OTHERS AT THIS POINT. I YES NO I CHILDREN UNDER 10 . . . . . . . . . . I 2 HUSBAND . . . . . . . . . . . . . . . . . . . . I 2 OTHER MALES . . . . . . . . . . . . . . . . I 2 OTHER FEMALES . . . . . . . . . . . . . . 1 2 B 131 I SECTION 3. CONTRACEPTION [ 3S1 Now 1 would Like to ta lk about a d i f fe rent topic. There are various ways or methods that a couple can use to delay or avoid a pregnancy. Which of these ways or methods have you heard about? CIRCLE CODE 1 IN 302 FOR EACH METHOD MENTIONED SPONTANEOUSLY. THEN PROCEED DOWN THE COLUMN, READING THE NAME AND DESCRIPTION OF EACH METHOD NOT MENTIONED SPONTANEOUSLY. CIRCLE CODE 2 IF METHOD IS RECOGNIZED, AND CODE 3 IF NOT RECOGNIZED. THEN FOR EACH METHOO WITH CODE 1 OR 2 CIRCLED IN 302, ASK 302A-305 BEFORE PROCEEDING TO THE NEXT METHOD. 01 PILL Women can take a p i l l every day. 302 Have you ever 302A Do you heard of (METHOD)? know how to use READ DESCRIPTION. (METHOD)? 02 IUD Women can have a loop or co i l placed inside them by a doctor or a nurse. 03 INJECTIONS Women can have an injection by a doctor or nurse which stops them from becoming pregnant for severe[ months. 04 DIAPHRAGM/FOAM/JELLY Women can place a diaphragm, tampon, sponge, foam tablets, j e l l y or cream in themselves before sex. 05 CONDOM Men can use a rubber sheath during sexual inter- course. 06 FEMALE STERILIZATION women can have an operation to avoid having any more children. 07 MALE STERLLIZATION Men can have an operation to avoid having any more children. 08 PERIODIC ABSTINENCE Couples can avoid having sexual in ter - course on certa in days of the month when the woman is more Likely to become pregnant. $9 WLTHDRAWAL Men can be careful and pull out before climax. 10 ANY OTHER METHODS? Have you heard of any other ways or methods that women or men can use to avoid pregnancy? (SPECIFY) YES/SPONT . . . . . . . . 1->YES . . . . . 1 YES/PROBED . . . . . . . 2-> NO . 3 INO . 2 I V YES/SPONT . 1 7 YES/PROBED . 2 m NO . . . . . . . . . . . . . 3 V YES/BPONT . . . . . . . . 1 - YES/PROBED . . . . . . . 2 NO . . . . . . . . . . . . . 3 V YES/SPONT . . . . . . . . 1->YES . . . . . 1 YES/PROBED . . . . . . . 2-> NO . . . . . . . . . . . . . 3 NO . . . . . . 2 v YES/SPOHT . . . . . . . . I-7 YES/PROBED . . . . . . . 2 m NO . . . . . . . . . . . . . 3 v YES/SPOHT . . . . . . . . 19 YES/PROBED . . . . . . . 2 m NO . . . . . . . . . . . . . 3 V YES/SPOUT . . . . . . . . 1- 7 YES/PROBED . . . . . . . 2 i NO . 3 v YES/SPONT . . . . . . . . 1-> YES . . . . . 1 YES/PROBED . . . . . . . 2-> NO . 3 NO . 2 v I YES/SPONT I YES/PROBED. NO . . . . . . . . . . . . . v3 YES,. . .1 YES/SPOHT . . . . . . . . 1-> NO . . . . . . 2 NO . . . . . . . . . . . . . 3 303 Rave]3O4 Where would you go I 305 In your opinion, you ever l IB obtain (METHOD) i f I what is the main used you wanted to use it? problem, if any, with (METHOD) using (METHOD)? partner?With any (CODES BELOW) (CODES BELOU) YES. 1 I f NO. . 2 OTHER YES., I f NO.2 OTHER YES., t{ NO.2 OTHER YES., I l l NO.2 OTHER YES, I I NO. .2 OTHER YES. 1 LI NO. .2 OTHER YES, I I NO.2 OTHER Where would you go to obtain advice on perio- YES.1 dic abstinence? NO.2 OTHER YES.1 I NO. 2] I NO. . . > t I I i YES.I 101 CODES FOR 304 GOVERNMENT HOSPITAL NO.2 02 GOVMENT HEALTH CNTR 03 FPAK 04 MOBILE CLINIC 05 FIELD EDUCATOR $6 PHARMACY/SHOP 07 PRIVATE HOSPITAL $8 MISSION HOSP/DISP 09 EMPLOYER'S CLINIC 10 PRIVATE DOCTOR 11 TRADITIONAL HEALER 12 HUSS/PRTNR tVOULD GO 13 FRIENDS/RELATIVES 14 OTHER (SPECIFY) 15 NO~HERE 980K • SKIP TO 309 I11 OTHER I I I OTHER OTHER OTHER E7 OTHER LI OTHER OTHER II OTHER OTHER CODES FOR 305 01 NONE 02 NOT EFFECTIVE 03 PARTNER DISAPPROVES 04 COMMUNITY DISAPPRVS 05 RELIGION DISAPPRVES 06 HEALTH CONCERN 07 ACCESS/AVAILABILITY 08 COSTS TOO MUCH 09 INCONVENIENT TO USE 10 OTHER (SPECIFY) 98 DK 306 CHECK 303: NOT A SINGLE "YES +' ~ AT LEAST ONE "YES'* (NEVER USED) LT--J (EVER USED) v 132 9 SKIP NO I QUESTIONS AND FILTERS I CODING CATEGORIES I TO 307 J Just t ° make sure' have Y°u ever used anything °r t r ied l i n any way to delay or avoid get t ing pre9nant? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . [~ I NO . . . . . . . . . . . . . . . . . . . . . . . . . . ~ >315G MARK APPROPRIATE BOX WITH AN 'X'. I 3081 What have you used or done? I 1 I CORRECT 302-303 AND OBTAIN INFORMATION FOR 304 TO 306 I AS NECESSARY. I 309 I CHECK 303: I EVER USED NEVER USED PERIODIC ~ PERIODIC ABSTINENCE ABSTINENCE F'~ v >311 I 310 The Last time you used periodic abstinence, how did you determine on which days you had to abstain? BASED ON CALENDAR . . . . . . . . . . . . . . . 1 BASED ON BODY TEMPERATURE . . . . . . . 2 BASED ON CERVICAL MUCUS (BILLINGS) METBOD . . . . . . . . . . . . . . 3 BASED ON BODY TEMPERATURE AND MUCUS . . . . . . . . . . . . . . . . . . . . . . . . . . 4 OTHER .S (SPECIFY) NO SPECIFIC SYSTEM . . . . . . . . . . . . . . 6 311 How nY vin ch °ten any OOY°uareadY v I when you f i r s t d id something or used a method to avoid NUMBER OF CHILDREN . . . . . . get t ing pregnant? IF NONE ENTER 600'. 312 I CHECK 220: NOT PREGNANT [~ OR HOT SURE v PREGNANT [~ >315H ! 313 l Are you currentty doing something or using any method I I to avoid getting pregnant? I I YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 I I NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 >315H 314 Which method are you using? P ILL . . . . . . . . . . . . . . . . . . . . . . . . . . . 01 IUD . . . . . . . . . . . . . . . . . . . . . . . . . . . . 02 INJECTIONS . . . . . . . . . . . . . . . . . . . . . 03 DIAPHRAGM/FOAM/JELLY . . . . . . . . . . . 04 CORDON . . . . . . . . . . . . . . . . . . . . . . . . . 05 FEMALE STERILIZATION . . . . . . . . . . . 06 J MALE STERILIZATION . . . . . . . . . . . . . 07 1>315A I PERIODIC ABSTINENCE . . . . . . . . . . . . 08 ~315B I WITHDRAWAL . . . . . . . . . . . . . . . . . . . . . 0 9 ~ OTHER .101>315H (SPECIFY) I 315 315A 315B Where d id you obta in (METHOD) the tast time? Where did the s ter i l i za t ion take place? Where d id you obta in inst ruct ions for th i s method? HOSPITAL . . . . . . . . . . . . . . . . . . . . . . . 01 | HEALTH CENTER/CLINIC . . . . . . . . . . . 02 I MOBILE CLINIC . . . . . . . . . . . . . . . . . . 03 FIELD EDUCATOR . . . . . . . . . . . . . . . . . 04 PHARMACY/SHOP . . . . . . . . . . . . . . . . . . 05~ PRIVATE DOCTOR . . . . . . . . . . . . . . . . . 06 / TRADITIONAL HEALER . . . . . . . . . . . . . 07~>315D HUSBAND/PARTNR OBTAINS METHO0.08 m FRIENDS/RELATIVES . . . . . . . . . . . . . . 09 OTHER ~0 [>315R (SPECIFY) I 10 133 SKIP NO. I QUESTIONS AND FILTERS I CODING CATEGORIES 1 TO 315C I What agency or organization(~3erates the service? GOVERNMENT . . . . . . . . . . . . . . . . . . . . . . 1 FPAK . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 CHURCH/MISSION . . . . . . . . . . . . . . . . . . 3 EMPLOYER . . . . . . . . . . . . . . . . . . . . . . . . 4 OTHER PRIVATE . . . . . . . . . . . . . . . . . . . 5 OTHER 6 (SPECIFY) DK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 315D I How much time does i t take to get from your home to I HOURS . . . . . . . . . . . . . . . . . . . . . . I this place? I MINUTES . . . . . . . . . . . . . . . . . . . . IF TIME EXACTLY I, 2, 3 ETC. HOURS, ENTER '00' MINUTES. 315F For how tong have you been using (CURRENT METHOD) MONTHS . . . . . . . . . . . . . . . . . . . cont ir~ous/y? >317A YEARS . . . . . . . . . . . . . . . . . . . . I I / | 315G I CHECK 302: ~ F~ I HEARD OF AT LEAST [~ NEVER HEARD OF ONE METHOD ANY METHOD , , >316 -v place where you could obtain family planning services? IF TIME EXACTLY I, 2, 3 ETC. HOURS, ENTER 'OO' MINUTES. IF 'DE', WRITE '98' HOURS. Iou°° °u° ° °°°I . 1 get there? USE TRANSPORT . . . . . . . . . . . . . . . . . . . 2 DE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 time i n the future? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 ~ DK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S >317A 317 Which method woutd you prefer to use? HOURS . . . . . . . . . . . . . . . . . . . . MINUTES . . . . . . . . . . . . . . . . . . PILL . . . . . . . . . . . . . . . . . . . . . . . . . . . 01 IUD . . . . . . . . . . . . . . . . . . . . . . . . . . . . 02 INJECTIONS . . . . . . . . . . . . . . . . . . . . . 03 DIAPHRAGM/FOAM/JELLY . . . . . . . . . . . 04 CONDOM . . . . . . . . . . . . . . . . . . . . . . . . . 05 FEMALE STERILIZATION . . . . . . . . . . . 06 MALE STERILIZATION . . . . . . . . . . . . . 07 PERIODIC ABSTINENCE . . . . . . . . . . . . 08 WITHDRAWAL . . . . . . . . . . . . . . . . . . . . . 09 OTHER 10 (SPECIFY) UNSURE/DE . . . . . . . . . . . . . . . . . . . . . . Q8 317A| In the last s ix months, have you heard or read about | fami ly planning: I On the radio? On the te tev is ion? In a newspaFer or magazine? From a poster? FrOm friends or reLatives? YES NO RADIO . . . . . . . . . . . . . . . . . . . . . . I 2 TELEVISION . . . . . . . . . . . . . . . . . I 2 NEWSPAPER/MAGAZINE . . . . . . . . . 1 2 POSTER . . . . . . . . . . . . . . . . . . . . . I 2 FRIENDS/RELATIVES . . . . . . . . . . 1 2 I I 319 | IS i t acceptable or not acceptable to you that fami ly | ACCEPTABLE . . . . . . . . . . . . . . . . . . . . . . 1 I planning informat ion is provided on radio or tete- I NOT ACCEPTABLE . . . . . . . . . . . . . . . . . . 2 v is ion? DK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 134 SECTION 4. HEALTH AND BREASTFEEDING I 401 CHECK 214: ONE OR MORE LIVE BIRTHS D NO LIVE BIRTHS SINCE JAN. 1983 1 SINCE JAN. 19~ ~ > (SKIP TO 428K) v 402 ENTER THE NAME, LINE NUMBER, AND SURVIVAL STATUS OF EACH BIRTH SINCE JAN. 1983 IN THE TABLE. BEGIN WITH THE LAST BIRTH. ASK THE QUESTIONS ABOUT ALL OF THESE BIRTHS. 403 When you were pregnant with (NAME) were you given any inject ion to prevent the baby from getting tetanus? ~04 When you were pregnant with (NAME), did you see anyone for advice on this pregnancy? IF YES: Whom did you see? PROBE FOR THE TYPE OF PERSON AND RECORD THE MOST QUALIFIED. LAST BIRTH NAME ALIVE [~ DEAD [~ v - - v - - YES . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . 2 DK . . . . . . . . . . . . . . . . 8 DOCTOR . . . . . . . . . . . . 1 TRAINED NURSE/ MIDWIFE . . . . . . . . . . 2 TRADITIONAL BIRTH ATTENDANT . . . . . . . . 3 OTHER 4 (SPECIFY) NAMENEXT'TO'LAST__ --BIRTH-- ALIVE DEAD V - - V -- YES . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . 2 DK . . . . . . . . . . . . . . . . 8 DOCTOR . . . . . . . . . . . . I TRAINED NURSE/ MIDWIFE . . . . . . . . . . 2 TRADITIONAL BIRTH ATTENDANT . . . . . . . . 3 OTHER 4 (SPECIFY) SECOND'FROM'LAST NAME . . . . . ALIVE [~ DEAD [~ - - v - - v YES . . . . . . . . . . . . . . . 1 NO . 2 DK . . . . . . . . . . . . . . . . 8 DOCTOR . . . . . . . . . . . . I TRAINED NURSE/ MIDWIFE . . . . . . . . . . 2 TRADITIONAL BIRTH ATTENDANT . . . . . . . . 3 OTHER 4 (SPECIFY) THIRD-FROM-LAST NAME ~OS Who assisted with the deLivery of (NAME)? PROBE FOR THE TYPE OF PERSON AND RECORD THE MOST QUALIFIED. ~05A Where d id you deliver (NAME)? ALIVE ~ DEAD [~ v v - YES . . . . . . . . . . . . . . . 1 NO . 2 DK . 8 DOCTOR . . . . . . . . . . . . I TRAINED NURSE/ MIDWIFE . . . . . . . . . . 2 TRADITIONAL BIRTH ATTENDANT . . . . . . . . 3 OTHER 4 (SPECIFY) NO ONE . . . . . . . . . . . S NO ONE . . . . . . . . . . . S NO ONE . . . . . . . . . . . 5 NO ONE . . . . . . . . . . . 5 DOCTOR . . . . . . . . . . . . I TRAINED NURSE/ MIDWIFE . . . . . . . . . . 2 TRADITIONAL BIRTH ATTENDANT . . . . . . . . 3 RELATIVE . . . . . . . . . . 4 OTHER .5 (SPECIFY) NO ONE . . . . . . . . . . . . 6 DOCTOR . . . . . . . . . . . . I TRAINED NURSE/ MIDWIFE . . . . . . . . . . 2 TRADITIONAL BIRTH ATTENDANT . . . . . . . . 3 RELATIVE . . . . . . . . . . 4 OTHER .S (SPECIFY) NO ONE . . . . . . . . . . . . 6 NOSPI TAL . . . . . . . . . . 1 CLINIC . . . . . . . . . . . . 2 HOME . . . . . . . . . . . . . . 3 OTHER .4 (SPECIFY) DOCTOR . . . . . . . . . . . . I TRAINED NURSE/ MIDWIFE . . . . . . . . . . 2 TRADITIONAL BIRTH ATTENDANT . . . . . . . . 3 RELATIVE . . . . . . . . . . 4 OTHER .5 (SPECIFY) NO ONE . . . . . . . . . . . . 6 HOSPITAL . . . . . . . . . . I CLINIC . . . . . . . . . . . . 2 HOME . . . . . . . . . . . . . . 3 OTHER .4 (SPECIFY) DOCTOR . . . . . . . . . . . . I TRAINED NURSE/ MIDWIFE . . . . . . . . . . 2 TRADITIONAL BIRTH ATTENDANT . . . . . . . . 3 RELATIVE . . . . . . . . . . 4 OTHER .5 (SPECIFY) NO ONE . . . . . . . . . . . . 6 HOSPITAL . . . . . . . . . . ] CLINIC . . . . . . . . . . . . 2 HOME . . . . . . . . . . . . . . 3 OTHER .4 (SPECIFY) HOSPITAL . . . . . . . . . . I CLINIC . . . . . . . . . . . . 2 HOME . . . . . . . . . . . . . . 3 OTHER .4 (SPECIFY) ~06 Did you ever feed YES . . . . . . . . . . . . . . . '11 YES . . . . . . . . . . . . . . . '11 YES . . . . . . . . . . . . . . . '11 YES . . . . . . . . . . . . . . . 1 '1 (NAME) at the breast? (SKIP TO 407)< - j (SKIP TO 408)< J (SKIP TO 408)< J (SKIP TO 408)< J NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 INCONVENIENT . . . . . 01 HAD TO WORK . . . . . . 02 INSUFFICNT MILK.03 BABY REFUSED . . . . . 04 CHILD DIED . . . . . . . 05 CHILD SICK . . . . . . . 06 OTHER 07 (SPECIFY) (ALL SKIP TO 408C)<- MONTHS . . . . . I l l UNTIL DEATH . . . . . . 96] (SKIP TO 408C)< -J INCONVENIENT . . . . . 01- HAD TO ~RK . . . . . . 02- INBUFFICNT MILK.O3- BABY REFUSED . . . . . 04- CHILD DIED . . . . . . . O5- CHILD SICK . . . . . . . 06 OTHER O~ (SPECIFY) (ALL SKIP TO 408C)<-- MONTHS . I I I UNTIL DEATH . . . . . . 96] (SKIP TO 408C) <-J INCONVENIENT . . . . . 01 HAD TO WORK . . . . . . 02 INSUFFICNT MILK.O3 BABY REFUSED . . . . . 04 CHILD DIED . . . . . . . 05 CHILD SICK . . . . . . . 06 OTHER 07 (SPECIFY) (ALL SKIP TO 408C)<- GO6A Why did you never feed (NAME) at the breast? INCONVENIENT . . . . . 01 HAD TO WORK . . . . . . 02 INSUFFICNT MILK.O3 BABY REFUSED . . . . . 04 CHILD DIED . . . . . . . 05 CHILD SICK . . . . . . . 06 OTHER 07 (SPECIFY) (ALL SKIP TO 408C)<- MONTHS . I I I UNTIL DEATH . 9 (SKIP TO 408C)< 6] ~07 Are you still breast- feeding (NAME)? (IF DEAD, CIRCLE '2') ~08 How many months Did was (NAME) when you stopped breastfeeding? YES . . . . . . . . . . . . . . . 1 (SKIP TO 408B)< ~3 NO (OR DEAD) . 2 MONTHS . I I I UNTIL DEATH . 9 (SKIP TO 408C) <6] 12 135 qO8A Why d id you stop breast feed ing (NAME)? 408B Do you ever give (NAME) anyth ing to dr ink or eat other than breastmitk? INCONVENIENT . . . . . 01 HAD TO WORK . . . . . . 02 [MSUFFICNT MILK.03 BABY REFUSED . . . . . O4- CHILD DIED . . . . . . . 05 + CHILD SICK . . . . . . . O& CH HAD DIARRHEA.O~ CH WEARING AGE,.O~ BECAME PREGNANT,.O~ OTHER ,10 (SPECIFY) (ALL SKIP TO 408C)<-- YES . . . . . . . . . . . . . . . 1 NO . . . . . , o . . . . . . . . . 21 (SKIP TO 409)< -J 408C HOW many months o ld J I was (NAME) when you MONTHS . . . . f i r s t gave h im/her anyth ing to dr ink or eat DIED BEFORE other than breastmitk? OTHER FOOO/ DRINK GIVEN . . . . . 96 409 How many months after I I J the b i r th of (NAME) did MONTHS . . . . . your per iod return? MOT RETURNED . . . . . 96 410 Have you resumed YES (OR PREGN.). . I sexual re la t ions s ince NO . . . . . . . . . . . . . . . . 2 the birth of (HARE)? (GO TO NEXT COL)< I 411Howmanymonths after J I I the birth of (NAME) MONTHS . . . . . did you resume sexuaL relations? (GO TO NEXT COLUMN) NO. | QUESTIONS AND FILTERS INCONVENIENT . . . . . 01- HAD TO WORK . . . . . . 02- [NSUFFICNT MILK.O~ BABY REFUSED . . . . . 04- CHILD DIED . . . . . . . 05- CHILD SICK . . . . . . . O& CH HAD DIARRHEA,.O~ CH WEANING AGE.O~ BECAME PREGNANT.09- OTHER .10- (SPECIFY) (ALL SKIP TO 408C)<- MONTNS . I I DIED BEFORE OTHER F(X30/ DRINK GIVEN . . . . . 96 MOHTHS . I I NEVER RETURNED.96 MONTHS . . . . . J I (GO TO NEXT COLUMN) INCONVENIENT . . . . . 01. HAD TO WORK . . . . . . 02, IRSUFFICNT MILK,,OT- BABY REFUSED . . . . . 04. CHILD DIED . . . . . . . 05. CHILD SICK . . . . . . . 06 CH HAD DIARRHEA.OT CH WEANING AGE. . .O~ BECAME PREGNANT.09 OTHER .10 (SPECIFY) (ALL SKIP TO 408C)<- INCONVENIENT . . . . . 01 HAD TO WORK . . . . . . 02 INSUFFICNT MILK.O3 BABY REFUSED . . . . . 04 CHILD DIED . . . . . . . 05 CHILD SICK . . . . . . . Ob CH HAD DIARRHEA.,OT CH WEANING AGE.O8 BECAME PREGNANT.09 OTHER .10 (SPECIFY) (ALL SKIP TO 408C)<- MONTHS . . . . . MONTHS . I I I DIED BEFORE DIED BEFORE OTHER FOOO/ OTHER FO00/ DRINK GIVEN . . . . . 96 DRINK GIVEN . . . . . 96 MONTHS . . . . . F~-~' MONTHS . I I I NEVER RETURNED.96 NEVER RETURNED. ,96 MONTHS . . . . . I I (GO TO NEXT COLUMN) MONTNS . I I I (GO TO 412) SKIP I COOING CATEGORIES I TO 412 I CHECK 407 FOR LAST gIRTEd: LAST CHILD STILL BREASTFED v ALL OTHERS HUMBER OF TIMES . . . . . . . . . . I I I AS OFTEN AS CHILD RANTED . . . . . . . 96 413 How many times d id you breastfeod Last n ight between sundown and sunr ise? 415 I >418 At any t ime yesterday or las t n ight , was (NAME OF LAST CHILD) g iven any of the fo l low ing : PLain water? Juice? Powdered mi lk? Cow's or goat ' s mi lk? Porr idge or u j i ? Any other Liquid? Any so l id or mushy food? YES NO PLAIN UATER . . . . . . . . . . . . . . . . 1 2 JUICE . . . . . . . . . . . . . . . . . . . . . . 1 2 POUDERED MILK . . . . . . . . . . . . . . 1 2 COW'S OR GOAT'S MILK . . . . . . . I 2 PORRIDGE OR UJI . . . . . . . . . . . . 1 2 ANY OTHER LIQUID 1 2 (SPECIFY) ANY SOLID OR MUSHY FOOD . . . . 1 2 416 I CHECK 415: WAS GIVEN NO FO00 FO00 OR F~ OR LIQUID LIQUID GIVEN ~-~ [ v 13 136 I >418 NO. J QUESTIONS AND FILTERS J COOING CATEGORIES J TO 417J Were any of these g iven in a bot t te w i th a rubber J YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 I nil~ote? I No . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 418 J At the t ime you became pregnant w i th (NAME OF LAST J THEN . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 I BIRTH), d id you want to have that ch i td then, d id you J LATER . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 want to wa i t unt iL la ter , or d id you want no (more) NO MORE . . . . . . . . . . . . . . . . . . . . . . . . . 3 ch i td ren at a( (? 419 ENTER THE NAME, LINE NUMBER, AND SURVIVAL STATUS OF EACH BIRTH SINCE JAN. 1983 BELOW. BEGIN WITH THE LAST BIRTH. THE HEADINGS IN THE TABLE SHOULD BE EXACTLY THE SAME AS THOSE AFTER Q. 402. ASK THE QUESTIONS ONLY FOR LIVING CHILDREN. LINE HOMSEB I I I FROM O. 212 ~ ~ ~ 420 Do you have a ch i td hea l th card fo r (NAME)? IF YES: May I see i t p(ease? ~21 RECORD IMMUNIZATION DATES FROM CHILD HEALTH CARD. BCG DPT 1 OPT 2 OPT 3 DPT 4 POLIO 1 POLIO 2 POLIO 3 POLIO 4 MEASLES ~22 Has (NAME) ever had a vacc inat ion to pre- vent h im/her f rom get t ing diseases? LAST BIRTH NAME ALIVE v YES, SEEN . . . . . . . . . . . 1 YES, HOT SEEN . . . . . . . 2 (SKIP TO 422)< NO CARD . . . . . . . . . . . . . 3 NEXT-TO-LAST BIRTH SECOND-FROM-LAST THIRD-FROM-LAST NAME NAME NAME DEAD n ~AL,VE ~ DEAD n ,ALIVE ~ DEAD F7 ,ALIVE ~ DEAD V V V - - (GO TO 427) YES, SEEN . I YES, NOT SEEN . .2] (SKIP TO 422)< 4 NO CARD . .3J HOT GIVEN DAY MO YR (SKIP TO 423) V YES, SEEN . . . . . . . . . . . 1 YES, NOT SEEN . . . . . . . 2 =l (SKIP TO 422)< _1 NO CARD . . . . . . . . . . . . . 53 NOT GIVEN DAY MO YR 1 1 1 1 1 1 1 1 1 1 (SKIP TO 423) YES . . . . . . . . 1 NO . . . . . . . . . 2 DK . . . . . . . . . . . . . . . . . . 8 YES, SEEN . . . . . . . . . . . 1 YES, NOT SEEN . . . . . . . 2 (SKIP TO 422)<-~ NO CARD . . . . . . . . . . . . .3 J l NOT GIVEN DAY MO YR I i I = I I i i l ! 1 1 1 i 1 1 I (SKIP TO 423) YES . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . 2 DK . . . . . . . . . . . . . . . . . . 8 YES . . . . ,o° . . . . . . . . , ,1 NO . . . . . . . . . . . . . . . . . . 2 OK . . . . . . . . . . , , . . . . . . 8 NOT GIVEN DAY MO YR (SKIP TO 423) YES . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . 2 OK . . . . . . . . . . . . . . . . . . 8 14 137 623 Has (NAME) had d ia r rhea in the las t 24 hours? 424 Has (NAME) had diarrhea in the l as t two weeks? 424A Now l have some quest ions about (NAME's) Last ep isode o f d ia r rhea . How many days ago d id the d ia r - rhea s tar t? 424B CHECK 407: LAST CHILD STILL BREASTFED? 424C Did you breast feed (NAME) when he/she had d ia r rhea then? 424D When (NAME) had diarrhea then, was he/ she given more, Less, or the same amount to drink as before the diarrhea, or d id yOU stop giving anything to drink? 424E Was (NAME) g iven more, Less, o r the same amount of solid food as was given before he/she had diarrhea or did you s top g iv ing so l id food a l together? ~ES . I (SKIP TO 424A)< ''~ NO . 2 YES . . . . . . . . . . . . . . . . . I (SKIP TO 424D)< ' ' l NO . . . . . , . . . . . . . . . , ° .~ i (GO TO NEXT COL)<~ OK . 8 J DAYS . . . . . . . . . DK . . . . . . . . . . . . . . . . . 98 ,ES RO v (SKIP TO 424D) V YES . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . , . . . . . . . . . 2 MORE . . . . . . . . . . . . . . . . 1 LESS . . . . . . . . . . . . . . . . 2 SAME . . . . . . . . . . . . . . . . 3 STOPPED . . . . . . . . . . . . . 4 DX . . . . . . . . . . . . . . . . . . 8 MORE . I LESS . 2 SAME . 3 STOPPED SOLID FOODS.4 SOLID FOODS NOT YET GIVER . . . . . . . . . . . . . 5 DK . 8 YES . t- (SKIP TO 424A)<- - NO . . . . . . . o . °° , . . . . . . 2 YES . I- (SKIP TO 4240)<-- NO . 2- (GO TO NEXT COL)<-- OK . DAYS . ~ - - ~ DK . 98 MORE . . . . . . . . . . . . . . . . 1 LESS . . . . . . . . . . . . . . . . 2 SAME . . . . . . . . . . . . . . . . 3 STOPPED . . . . . . . . . . . . . 4 DK . . . . . . . . . . . . . . . . . . 8 MORE . . . . . . . . . . . . . . . . I LESS . . . . . . . . . . . . . . . . 2 SAME . . . . . . . . . . . . . . . . 3 STOPPED SOLID FO00S.4 DK . . . . . . . . . . . . . . . . . . B YES . I (SKIP TO 424A)<-- NO . 2 YES . I (SKIP TO 424D)<-- MOo, . . . . . . . . . * * , . . . . 2, (GO TO NEXT COL)<- - OK . . . . . . . . . . . . . . . . . . & OAYS . . . . . . . . . OK 98 MORE . . . . . . . . . . . . . . . . 1 LESS . . . . . . . . . . . . . . . . 2 SAME . . . . . . . . . . . . . . . . 3 STOPPED . . . . . . . . . . . . . 4 DK . . . . . . . . . . . . . . . . . . 8 MORE . . . . . . . . . . . . . . . . I LESS . . . . . . . . . . . . . . . . Z SAME . . . . . . . . . . . . . . . . 3 STOPPED SOLID F000S.4 OK. . . . . . . . , . . . . . . . . 8 YES . I (SKIP TO 424A)<-- NO . 2 YES . I l (SKIP TO 424D)<-- NO . 2 (SKIP TO 427)<-~ OK . . . . . . . . . . . . . . . . . .SJ DAYS . . . . . . . . . DK . . . . . . . . . . . . . . . . . 98 MORE . . . . . . . . . . . . . . . . 1 LESS . . . . . . . . . . . . . . . . 2 SAME . . . . . . . . . . . . . . . . 3 STOPPED . . . . . . . . . . . . . 4 DK . . . . . . . . . . . . . . . . . . 8 MORE . . . . . . . . . . . . . . . . 1 LESS . . . . . . . . . . . . . . . . 2 SAME . . . . . . . . . . . . . . . . 3 STOPPED SOLID FOODS.4 DX . . . . . . . . . . . . . . . . . . 8 15 138 424G Was (NAME) g iven e i ther a home so lu t ion of sugar, sa l t , and water to d r ink , or a so lu t ion made f rom a spec ia l packet? IF YES: Which? 424N The las t t ime (NAME) was g iven (home so lu t ion /spec ia l pack- e t ) , d id he/she get better within a day, worse, or was there no change? 4241 HOW much of the (home so lu t ion /spec iaL packet) was (NAME) g iven every 24 hours? 424J For how many days was (NAME) given (home solution/ special packet)? 425 Was (NAME) taken to a pr ivate doctor , a hosp i ta l or c l in i c , a t rad i t iona l hea le r , or any o ther p lace dur ing the las t episode of d ia r rhea? IF YES: Where was he/she taken ( the las t t ime)? 426 What treatments did (NAME) receive there (the last time)? CIRCLE ALL TREAT- MENTS MENTIONED. 426A Why was (NAME) not taken somewhere for treatment during the l as t episode of d ia r rhea? HOME SOLUTION OF SALT, SUGAR, WATER.1 ORS PACKET SOLUTIOX.2 BOTH GIVEN . . . . . . . . . . 3 NEITHER GIVEN . . . . . . . 4 (SKIP TO 425)<-- BETTER . . . . . . . . . . . . . . I WORSE . . . . . . . . . . . . . . . 2 NO CHANGE . . . . . . . . . . . 3 . I -T - I DK . . . . . . . . . . . . . . . . . 98 DAYS . F ' ~ DK . 98 PRIVATE DOCTOR . . . . . . I HOSPITAL/CLINIC . . . . . 2 TRADITIONAL HEALER.3 OTHER .4 (SPECIFY) CHILD NOT TAKEN. . . .5] (SKIP TO 426A)< ~ INJECTION . . . . . . . . . . . I IV (INTRAVENOUS) . . . . I TABLETS OR CAPSULES.I SYRUPS . . . . . . . . . . . . . . I ORS . . . . . . . . . . . . . . . . . 1- HERBS . . . . . . . . . . . . . . . 1 OTHER 1~ (SPECIFY) NOTHING GIVEN . . . . . . . 1- (ALL GO TO NEXT COL)<- ILLNESS WAS MILD . . . . I- MOTHER TOO BUSY . . . . . 2- MOTHER IJORKING . . . . . . 3 ~ RELIGION FORBIDS.4 NO FACILITIES AVAIL.S- OTHER .& (SPECIFY) (ALL GO TO NEXT COL)<- HONE SOLUTION OF SALT, SUGAR, WATER.1 ORS PACKET SOLUTION.2 BOTH GIVEN . . . . . . . . . . ] NEITHER GIVEN . . . . . . -41 (SKIP TO 425)< .--J BETTER . . . . . . . . . . . . . . 1 WORSE . . . . . . . . . . . . . . . 2 NO CHANGE . . . . . . . . . . . 3 DK . . . . . . . . . . . . . . . . . 98 DAYS . . . . . . . . . DK . . . . . . . . . . . . . . . . . 98 PRIVATE DOCTOR . . . . . . 1 HOSPITAL/CLINIC . . . . . 2 TRADITIONAL HEALER.3 OTHER .4 (SPECIFY) CHILD NOT TAKEN . . . . . 5- (SKIP TO 426A)<-- INJECTION . . . . . . . . . . . 1- IV (INTRAVENOUS) . . . . I- TABLETS OR CAPSULES.I- SYRUPS . . . . . . . . . . . . . . I- ORS . . . . . . . . . . . . . . . . . I- HERBS . . . . . . . . . . . . . . . I OTHER .t- (SPECIFY) NOTHING GIVEN . . . . . . . I- (ALL GO TO NEXT COL)<- ILLNESS WAS MILD . . . . I- MOTHER TOO BUSY . . . . . 2" MOTHER WORKING . . . . . . 3- RELIGION FORBIDS.4 NO FACILITIES AVAIL .5 OTHER .~ (SPECIFY) (ALL GO TO NEXT COL)<. HO~E SOLUTION OF SALT, SUGAR, WATER.1 ORS PACKET SOLUTION.2 BOTH GIVEN . 3 NEITHER GIVEN . . . . . . . 4- (SKIP TO 425)<-- SETTER . . . . . . . . . . . . . . I WORSE . 2 NO CHANGE . 3 NUMBER OF F - ~ GLASSES . . . . . . DK . . . . . . . . . . . . . . . . . 98 DAYS . . . . . . . . . DK . . . . . . . . . . . . . . . . . 98 PRIVATE DOCTOR . . . . . . I HOSPITAL/CLINIC . . . . . 2 TRADITIONAL HEALER.3 OTHER .4 (SPECIFY) CHILD NOT TAKEN . . . . . 5 (SKIP TO 426A)<- - INJECTION . . . . . . . . . . . I IV (INTRAVENOUS) . . . . I TABLETS OR CAPSULES.I SYRUPS . . . . . . . . . . . . . . I ORB . . . . . . . . . . . . . . . . . 1 HERBS . . . . . . . . . . . . . . . 1 OTHER .1 (SPECIFY) NOTHING GIVEN . . . . . . . 1 (ALL GO TO NEXT COL)< ILLNESS WAS MILD . . . . I MOTHER TOO BUSY . . . . . 2 MOTHER WORKING . . . . . . 3 RELIGION FORBIDS . . . . 4 NO FACILITIES AVAIL.5 OTHER .6 (SPECIFY) (ALL GO TO NEXT COL)< BONE SOLUTION OF SALT, SUGAR, WATER.1 ORS PACKET SOLUTION.2 BOTH GIVEN . . . . . . . . . . 3 NEITHER GIVEN . . . . . . . 4- (SKIP TO 425)<-- BETTER . . . . . . . . . . . . . . I WORSE . . . . . . . . . . . . . . . 2 NO CHANGE . . . . . . . . . . . 3 DK . . . . . . . . . . . . . . . . . 98 DAYS . . . . . . . . . DK . . . . . . . . . . . . . . . . . 98 PRIVATE DOCTOR . . . . . . I HOSPITAL/CLINIC . . . . . 2 TRADITIONAL HEALER.,3 OTHER .4 (SPECIFY) CHILD NOT TAKEN . . . . . 5 (SKIP TO 426A)<-- INJECTION . . . . . . . . . . . I IV (INTRAVENOUS) . . . . I TABLETS OR CAPSULEB,I SYRUPS . . . . . . . . . . . . . . I ORS . . . . . . . . . . . . . . . . . 1 HERBS . . . . . . . . . . . . . . . 1 OTHER .1 (SPECIFY) NOTHING GIVEN . . . . . . . 1 (ALL GO TO 427)<- - ILLNESS WAS MILD . . . . I MOTHER TOO BUSY . . . . . 2 MOTHER WORKING . . . . . . 3 RELIGION FORBIDS.4 NO FACILITIES AVAIL.5 OTHER .6 (SPECIFY) (ALL GO TO 427)<-- 16 139 SKIP NO. | QUESTIONS AND FILTERS m CODING CATEGORIES | TO 427 427A CHECK 424G: HOME SOLUTION HOME SOLUTION NOT MENTIONED MENTIONED 9 ASKEDOR Q424G NOT v Where did you Learn how to prepare the sugar, salt and water solution given to (NAME)? GOVERNMENT HOSPITAL . . . . . . . . . . . . 01 GOVERNMENT HEALTH CENTER/ CLINIC/DISPENSARY . . . . . . . . . . . . . 02 PRIVATE HOSPITAL/CLINIC/ DISPENSARY . . . . . . . . . . . . . . . . . . . 03 VILLAGE HEALTH WORKER . . . . . . . . . . 04 PRIVATE DOCTOR . 05 PHARMACY . . . . . . . . . . . . . . . . . . . . . . . 06 TRADITIONAL HEALER . . . . . . . . . . . . . 07 OTHER .08 (SPECIFY) MOTHER DID MOT ADMINISTER . . . . . . 96 DK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 I >428 I 428 428A CHECK 424G: ORS PACKET ORS PACKET NOT MENTIONED MENTIONED [~ ASKEDOR Q424G NOT [~ v Where did you get the packet of ORS (the last time)? I >428K GOVERNMENT HOSPITAL . . . . . . . . . . . . 01 GOVERNMENT HEALTH CENTER/ CLINIC/DISPENSARY . . . . . . . . . . . . . 02 PRIVATE WOSPITAL/CLINIC/ DISPENSARY . . . . . . . . . . . . . . . . . . . . 03 VILLAGE HEALTH WORKER . . . . . . . . . . 04 PRIVATE DOCTOR . . . . . . . . . . . . . . . . . 05 PHARMACY . . . . . . . . . . . . . . . . . . . . . . . 06 SHOP . . . . . . . . . . . . . . . . . . . . . . . . . . . 07 TRADITIONAL HEALER . . . . . . . . . . . . . 08 RELATIVE/FRIEND . . . . . . . . . . . . . . . . 09 OTHER 10 (SPECIFY) MOTHER DID NOT ADMINISTER . . . . . . 96 OK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 >428K I 428B[ How much did the packet cost? KSH CENTS COST . D FT3 FREE . . . . . . . . . . . . . . . . . . . . . . . . . . DK . . . . . . . . . . . . . . . . . . . . . . . . . . . . ~S 428C I Do you have one of these packets in your house now? I YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 I NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 >428E I Can l see the packet? CODE TYPE OF PACKET. I UNICEF . . . . . . . . . . . . . . . . . . . . . . . . . . I ORALYTE . . . . . . . . . . . . . . . . . . . . . . . . . 2 D.T.S . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 OTHER PACKET . . . . . . . . . . . . . . . . . . . . 4 PACKET NOT SHOWN . . . . . . . . . . . . . . . . 5 17 140 SKIP NO. I QUESTIONS AND FILTERS I COOING CATEGORIES I TO 428E Do you th ink the contents of the packet are used to cure the d ia r rhea , or that they are used to prevent the ch i ld f rom dry ing out? CURE DIARRHEA . . . . . . . . . . . . . . . . . . . 1 PREVENT DRYING OUT . . . . . . . . . . . . . . 2 BOTH . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 OTHER ,4 (SPECIFY) OK. . . . . . . . . . . . . . . . . . . . . , . . . . . . . .8 428F I Did you use ba i ted water , bet t ted water , or o ther water to mix the contents of the packet ( the Last t ime)? I BOILED WATER . . . . . . . . . . . . . . . . . . . . I I BOTTLED WATER . . . . . . . . . . . . . . . . . . . 2~ OTHER . . . . "31->428H (SPECIFY) j OK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 I 428G I I Did you mix the contents of the packet with the water I be fore you boiLed the water or a f te r you ba i led the I water ( the (ast t ime)? MIXED BEFORE BOILING WATER . . . . . . I MIXED AFTER BOILING WATER . . . . . . . 2 DK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 428H What kind of container did you use to measure the correct amount of water (the last time)? LARGE KIMBO . . . . . . . . . . . . . . . . . . . . . I SMALL KIMBO . . . . . . . . . . . . . . . . . . . . . 2 BEER BOTTLE . . . . . . . . . . . . . . . . . . . . . 3 SOOA BOTTLE . . . . . . . . . . . . . . . . . . . . . 4 TEACUP . . . . . . . . . . . . . . . . . . . . . . . . . . 5 GLASS . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 OTHER .7 (SPECIFY) 4281 In what k ind of conta iner d id you mix the contents of the packet and the water? COOKING POT . . . . . . . . . . . . . . . . . . . . . I SUFURIA . . . . . . . . . . . . . . . . . . . . . . . . . 2 EARTHEN JAR . . . . . . . . . . . . . . . . . . . . . 3 EMPTY BOTTLE . . . . . . . . . . . . . . . . . . . . 4 CALABASH . . . . . . . . . . . . . . . . . . . . . . . . 5 OTHER .6 (SPECIFY) 4 8J I Did you prepare a new mixture every day or d id you use the same mixture fo r more than one day? I HEW MIXTURE EACH DAY . . . . . . . . . . . . 1 I USE SAME FOR MORE THAN 1 DAY . . . . 2 I OTHER .3 (SPECIFY) 428K Which ptaces can you go i f you want to get a vacc inat ion fo r a ch i ld? CIRCLE ALL PLACES MENTIONED. HOSPITAL . . . . . . . . . . . . . . . . . . . . . . . . 1 HEALTH CENTER/CLINIC DISPENSARY . . . . . . . . . . . . . . . . . . . . . I MOBILE CLINIC . . . . . . . . . . . . . . . . . . . I VILLAGE HEALTH ~RXER . . . . . . . . . . . I PRIVATE DOCTOR . . . . . . . . . . . . . . . . . . I SCHOOL . . . . . . . . . . . . . . . . . . . . . . . . . . 1 OTHER 1 (SPECIFY) 18 141 429 ENTER THE NAME, LINE NUMBER, AND SURVIVAL STATUS OF EACH BIRTH SINCE JAN. 1983 BELOW. BEGIN WITH THE LAST BIRTH. THE HEADINGS IN THE TABLE SHOULD BE EXACTLY THE SAME AS THOSE AFTER O. 419. ASK THE QUESTIONS ONLY FOg LIVING CHILDREN. IF NO CHILDREN SINCE JAN. 1983, SKIP TO 501, LINE NUMBER I J FROM Q. 212 ~ ~ ~ r - i I 430 Has (NAME) had fever in the fast four weeks? LAST BIRTH NEXT'TO'LAST BIRTH SECOND-FROM'LAST THIRD-FROM-LAST NAME NAME _ [~ D-~AD ~ NAME _~ D-~AD NAME ALIVE DEAD ,ALIV ,ALIV n ,ALIVE DEAD V v V V - - V - - YES . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . "211 NO . . . . . . . . . . . . . . . . . "211 NO . . . . . . . . . . . . . . . . . . .2q No . . . . . . . . . . . . . . . . . . 2 (SNIP TO 433)<~i (SNIP TO 4]3)< (SNIP TO 433)< . DK . . . . . . . . . . . . . . . . . . . DK . OK . . . . . . . . . . . . . . . . . . B~[ DK (SKIP TO 433)<--~ 430A Was the fever due to ma lar ia , meastes, or so~ other cause? MALARIA . . . . . . . . . . . . . 1 MEASLES . . . . . . . . . . . . . 2 OTHER CAUSE . . . . . . . . . 3 DK . . . . . . . . . . . . . . . . . . 8 MALARIA . . . . . . . . . . . . . 1 MEASLES . . . . . . . . . . . . . 2 OTHER CAUSE . . . . . . . . . ] DK . . . . . . . . . . . . . . . . . . 8 MALARIA . . . . . . . . . . . . . 1 MEASLES . . . . . . . . . . . . . 2 OTHER CAUSE . . . . . . . . . 3 DK.o , , , . . . . . . . . , . . . . 8 MALARIA . . . . . . . . . . . . . 1 MEASLES . . . . . . . . . . . . . 2 OTHER CAUSE . . . . . . . . . ] DK . . . . . . . . . . . . . . . . . . 8 431 Was (NAME) taken to a private doctor, a hospital or clinic, a traditional healer, or any other place to treat the fever? IF YES: Where was he/ she taken? PRIVATE DOCTOR . . . . . . 1 HOSPITAL/CLINIC . . . . . 2 TRADITIONAL HEALER.3 OTHER 4 (SPECIFY) CRILO NOT TAKEN . . . . . 5 PRIVATE DOCTOR . . . . . . 1 HOSPITAL/CLINIC . . . . . 2 TRADITIONAL HEALER.] OTHER 4 (SPECIFY) CHILD NOT TAKEN . . . . . 5 PRIVATE DOCTOR . . . . . . I HOSPITAL/CLINIC . . . . . 2 TRADITIONAL HEALER.] OTHER 4 (SPECIFY) CHILD NOT TAKEN . . . . . 5 PRIVATE DOCTOR . . . . . . I HOSPITAL/CLINIC . . . . . 2 TRADITIONAL HEALER.3 OTHER 4 (SPECIFY) CHILD NOT TAKEN . . . . . S 433 Has (NAME) su f fe red f rom severe cough or d i f f i cu l t or rap id breath ing in the las t four weeks? YES . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . 2 (GO TO NEXT COL) < DX . . . . . . . . . . . . . . . . . YES . . . . . . . . . . . . . . . . . 1 DK . . . . . . . . . . . . . . . . . . 8J I YES . . . . . . . . . . . . . . . . . I NO. . , . . . . . . . . . . , . . . . 2 (GO TO NEXT COL) < DN . . . . . . . . . . . . . . . . . YES . . . . . . . . . . . . . . . . . I NO . . . . . . . . . . . . . . . . . . 2 (SKIP TO 501)<~ DN . . . . . . . . . , . . . . . . . . . 434 Was (NAME) taken to a private doctor, a hospital or c t in ic , a traditional heater , or any other place to treat the problem? IF YES: Where was he/ she taken? PRIVATE DOCTOR . . . . . . I HOSPITAL/CLINIC . . . . . 2 TRADITIONAL HEALER.5 OTHER 4 (SPECIFY) CHILD NOT TAKER . . . . . 5 PRIVATE DOCTOR . . . . . . I HOSPITAL/CLINIC . . . . . 2 TRADITIONAL HEALER.] OTHER 4 (SPECIFY) CHILD NOT TAKEN . . . . . 5 PRIVATE DOCTOR . . . . . . 1 HOSPITAL/CLINIC . . . . . 2 TRADITIONAL HEALER.3 OTHER 4 (SPECIFY) CHILD NOT TAKEN . . . . . 5 PRIVATE DOCTOR . . . . . . I HOSPITAL/CLINIC . . . . . 2 TRADITIONAL HEALER.] OTHER 4 (SPECIFY) CHILD NOT TAKEN . . . . . 5 435 Was there anyth ing (e tse) you or some- body d id to t reat the probiem? IF YES: What was done? CIRCLE CODE 1 FOR ALL MENTIONED. CAPSULES . . . . . . . . . . . . 1 LIQUID OR SYRUP . . . . . 1 ASPIRIN . . . . . . . . . . . . . 1 OTHER TABLETS . . . . . . . I INJECTION . . . . . . . . . . . I UVULECTOMY . . . . . . . . . . I OTHER 1 (SPECIFY) NOTHING . . . . . . . . . . . . . 1 (ALL GO TO NEXT COL) CAPSULES . . . . . . . . . . . . 1 LIOUID OR SYRUP . . . . . 1 ASPIRIN . . . . . . . . . . . . . 1 OTHER TABLETS . . . . . . . 1 INJECTION . . . . . . . . . . . 1 UVULECTOMY . . . . . . . . . . 1 OTHER .1 (SPECIFY) NOTHING . . . . . . . . . . . . . I (ALL GO TO NEXT COL) CAPSULES . . . . . . . . . . . . 1 LIQUID OR SYRUP . . . . . 1 ASPIRIN . . . . . . . . . . . . . 1 OTHER TABLETS . . . . . . . 1 INJECTION . . . . . . . . . . . 1 UVULECTONy . . . . . . . . . . 1 OTHER 1 (SPECIFY) NOTHING . . . . . . . . . . . . . I (ALL GO TO NEXT COL) CAPSULES . . . . . . . . . . . . 1 LIQUID OR SYRUP . . . . . I ASPIRIN . . . . . . . . . . . . . I OTHER TABLETS . . . . . . . I INJECTION . . . . . . . . . . . I UVULECTOMY . . . . . . . . . . I OTHER 1 (SPEC|FY) NOTHING . . . . . . . . . . . . . I (ALL GO TO 501) 19 142 I SECTION 5. MARRIAGE [ SKIP NO. I QUESTIONS AND FILTERS I C~ING CATEGORIES I TO NO . 2 >519 I 502 Are you now ~rr i~ or living with a ~n, or are you MARR]ED . I I widow~, d ivorc~ or not now living together? LIVING TOGETHER . 2 I WID~ED . DIVORCED . . . . . . . . . . . . . . . . . . . . . . . . ~>507 NOT N~ LIVING TOGETHER . . . . . . . . . 5 ~ I 503 I Does your hus~/par tner l i ve with you or is he now I LIVING WITH HER . . . . . . . . . . . . . . . . . 1 I staying elsewhere? I STAYING ELSEWHERE . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 >507 I DK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . >507 I I . 507 I Have you ~en ~rr ied or l i v~ with a man only once, I ONCE . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 I or ~re than once? I M~E THAN ONCE . . . . . . . . . . . . . . . . . . 2 508 In what ~nth an year did you s tar t L iving with your ( f i r s t ) hus~ or ~r tner? MONTH . . . . . . . . . . . . . . . . . . . . ~ - ~ DK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 YEAR . ~-~->509A DK YEAR . 98 I 20 143 SKIP NO. I QUESTIONS AND FILTERS I CODING CATEGORIES I TO ~o~ I "ow °'°"re ~°°"n ~°° '~"v ' ° ' w~th h'°~ I 'o~ . ~- -~1 509A I At the time that you married him, did your (first) I YES . I I I husband/partner have any other living wives besides I I yourse[ f? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 >518 I ~o~1 .o.oo~o~e~,~,n~.,~o~o,o~e~,~~,~ou°rr~°, I "" 0L . . . . . . . . . . . . . . . . . . ~ 1 518 In how many towns and d is t r i c ts have you l i ved for s ix I NUMBER OF T(~4NS . . . . . . . . . . ~ ' ~ / months or more s ince you were f i r s t marr ied (s ta r ted I ~- -~- - - >520 IivinotoBether) incIu, inBthispI°ce, NUMBER OF DISTRICTS . . . . . . l l~ l 519 Now we need some deta i l s about your sexual ac t iv i ty I in order to get a batter understanding of contraception I and fe r t i l i ty . Have you ever had sexual intercourse? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 - ->520A I NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 - ->528 I 520 Now we need some details about your sexua[ activity I I in order to get a better understanding of contraception I I and ferti[ity. 523 When was the las t t ime you had sexual intercourse? DAYS AGO . . . . . . . . . . . . . . . 1 WEEKS AGO . . . . . . . . . . . . . . 2 MONTHS AGO . . . . . . . . . . . . . 3 YEARS AGO . . . . . . . . . . . . . . 4 BEFORE LAST BIRTH . . . . . . . . . . . . . 996 >528 21 144 SKIP NO. I QUESTIONS AND FILTERS I CODING CATEGORIES I TO 524 I CHECK 220: 9 HOT PREGNANT OR NOT SURE V PREGNANT I >528 I 525 I CHECK 313: 9 NOT USING USING [~ CONTRACEPTION CONTRACEPTION V I >528 I 526 i f you become pregnant in the next few weeks, woutd you feet happy, unhappy, or would it not matter very much? I i HAPPY . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 >528 UNHAPPY . . . . . . . . . . . . . . . . . . . . . . . . . 2 L~JLD NOT MATTER . . . . . . . . . . . . . . . . 3 527 ~hat is the main reason that you are not using a method to avoid pregnancy? LACK OF KNOWLEDGE . . . . . . . . . . . . . . 01 OPPOSED TO FAMILY PLANNING . . . . . 02 HUSBAND DISAPPROVES . . . . . . . . . . . . O] OTHERS DISAPPROVE . . . . . . . . . . . . . . 04 HEALTH CONCERNS . . . . . . . . . . . . . . . . 05 ACCESS/AVAILABILITY . . . . . . . . . . . . 06 COSTS TOO MUCH . . . . . . . . . . . . . . . . . 07 INCONVENIENT TO USE . . . . . . . . . . . . 08 INFREQUENT SEX . . . . . . . . . . . . . . . . . 09 FATALISTIC . . . . . . . . . . . . . . . . . . . . . 10 RELIGION . . . . . . . . . . . . . . . . . . . . . . . 11 POSTPARTUM/gREASTFEEDIHG . . . . . . . 12 MEHOPAUSAL/SUBFECURD . . . . . . . . . . . 13 OTHER 14 (SPECIFY) DK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 528 PRESENCE OF OTHERS AT THIS POINT. YES NO CHILDREN UNDER 10 . . . . . . . . . . 1 2 HUSBAND . . . . . . . . . . . . . . . . . . . . 1 2 OTHER HALES . . . . . . . . . . . . . . . . 1 2 OTHER FEMALES . . . . . . . . . . . . . . 1 2 22 145 NO. | J SECTION 6. FERTILITY PREFERENCES ] QUESTIONS AND FILTERS | SKIP COOING CATEGORIES | TO 601 602 CHECK 502: [~ CURRENTLY MARRIED OR ALL OTHERS I I LIVING TOGETHER v CHECK 220 AND MARK BOX. Now I have some questions about the future. NOT PREGNANT OR UNSURE [~] Would you l i ke to have a (another) ch i ld or would you prefer not to have any (more) chi ldren? PREGNANT Af ter the ch i ld you are expecting, would you l i ke to have another ch i ld or would you prefer not to have any (more) chi ldren? HAVE ANOTHER . . . . . . . . . . . . . . . . . . . . I >609 NO MORE . . . . . . . . . . . . . . . . . . . . . . . . . 2 SAYS SHE CANtT GET PREGNANT . . . . . 3~>605 UNDECIDED OR DK . . . . . . . . . . . . . . . . . 8 ~ i I 603 Bow Long wouid you Like to wait from now before the birth of a (another) child? DURATION MONTHS . . . . . . . . . . . . . . . . . 1 >605 YEARS . . . . . . . . . . . . . . . . . . 2 DK . . . . . . . . . . . . . . . . . . . . . . . . . . . . 998 604 I CHECK 215: HOW old would your youngest child be then? IF NO LIVING CHILDREN, CIRCLE '96 i. I AGE OF Y(XJNGEST YEARS . . . . . . . . . . . . . . . . . . . . NO LIVING CHILDREN . . . . . . . . . . . . . 96 DK . 98 605 For how Long should a couple wait before starting sex- ual intercourse after the birth of a baby? DURATION F ~ MONTHS . . . . . . . . . . . . . . . . . I I I I YEARS . . . . . . . . . . . . . . . . . . 2 OTHER 996 (SPECIFY) oo61 Shou,o o other wait until sho has c~,eTeIy s to~ I WAIT . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 I breastfeeding before starting to have sexual re la t ions again, or doesn't i t matter? DOESN'T MATTER . . . . . . . . . . . . . . . . . . 2 6°' I D° Y°u think that Y°°r h°sba~'bart°er a~r°ves °r I APPR°VES . . . . . . . . . . . . . . . . . . . . . . . . '1 disapproves of couples using a method to prevent DISAPPROVES . . . . . . . . . . . . . . . . . . . . . 2 or delay pregnancy? DK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 I = I . th is subject in the past year? ONCE OR TWICE . . . . . . . . . . . . . . . . . . . 2 MORE OFTEN . . . . . . . . . . . . . . . . . . . . . . 3 610 CHECK 202 AND 204: f~ NO LIVING CHILDREN I f yOU could choose exactly the number of children to have in your whote l i fe , how many wouId that be? HAS LIVING CHILDREN D I f you could go back to the t in~ you did not have any ch i ldren ar~ could choose exact ly the number of ch i ldren to have in your whole l i fe , how many would that be? RECORD SINGLE NUMBER OR OTHER ANSWER. NUMBER . . . . . . . . . . . . . . . . . . OTHER ANSWER (SPECIFY) 611 I HOW many boys? [ NUMBER OF BOYS . . . . . . . . . . . HOW many g i r l s? NUMBER OF GIRLS . . . . . . . . . . OTHER ~6 (SPECIFY) 146 23 No. I SECTION 7. HUSBAND'S BACKGROUND AND WOMAN'S WORK ] QUESTIONS AND FILTERS COOING CATEGORIES SKIP TO 701 CHECK 501: WITHOREVERLIVEDAMARRIEDMAN ~ ALL OTHERS V ASK QUESTIONS ABOUT CURRENT OR MOST RECENT HUSBAND/PARTNER. >715 702 Now I have some quest ions about your (most recent) husband/partner. Did your husband/partner ever attend school? I YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 ! NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 >706 I 703 What was the highest level of school he attended: primary, secondary, higher, or university? PRIMARY . . . . . . . . . . . . . . . . . . . . . . . . I SECONDARY . . . . . . . . . . . . . . . . . . . . . . . 2 HIGHER . . . . . . . . . . . . . . . . . . . . . . . . . . 3 UNIVERSITY . . . . . . . . . . . . . . . . . . . . . . 4 OTHER 5 (SPECIFY) DK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 >706 I 704 What was the highest (standard, form, year) he coe~leted at that Level? STANDARD/FORM/YEAR . . . . . . . [ ~ I OK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 705 706 CHECK 703: SECONDARY OR PRIMARY HIGHER v Can (could) he read a letter or newspaper in any language? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 I >707 I 707 What kind of work does (did) your husband/partner mainly do? NEVER WORKED . . . . . . . . . . . . . . . . . . . 96 I >712 24 147 SKIP NO. I QUESTIONS AND FILTERS I COOING CATEGORIES I TO 708 708A I CHECK 707: DOES (DID) MOTE l WORKS ~RK IN AGRI" LI~ (I¢ORKED) ~ i CULTURE J IN AGRICULTURE I v Does he work for someone else or for himself? I >710 I I FOR SOMEONE ELSE . . . . . . . . . . . . . . . . 1 J I FOR HIMSELF . . . . . . . . . . . . . . . . . . . . . 2 >712 I I 709 Does (d id ) he earn a regu lar wage or sa la ry? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 / NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 ~>712 DK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 I I I 710 Does (d id ) your husband/par tner work main ly on h i s or HIS/FAMILY LAND . . . . . . . . . . . . . . . . . 1 >712 fami ly land , or on someone etse 's Land? | SOMEONE ELSEIS LAND . . . . . . . . . . . . . 2 I 711 Does (d id ) he lease the land or does (d id ) he work fo r LEASES THE LAND . . . . . . . . . . . . . . . . . 1 [ wages? I WORKS FOR WAGES . . . . . . . . . . . . . . . . . 2 712 Before you marr ied your ( f i r s t ) husband, d id you your* YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 ] se l f ever work regu lar ly to earn money, other than on a I farm or in a bus iness run by your fami ly? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 I 714 S ince you were f i r s t marr ied , have you ever worked YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 >717 regu lar ly to earn money o ther than on a farm or in a | bus iness run by your fami ly? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 >718 I 715 Have you ever worked regu lar ly to earn money, o ther YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 I than on a farm or in a bus iness run by your fami ly? I NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 >718 I 717 Are you now working to earn money other than on a farm YES . I I or in a business run by your family? I NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 = I . MINUTES . . . . . . . . . . . . . . . . . . 25 148 Person Interviewed: (To be INTERVIEWER'S OBSERVATIONS fi l led in after complet ing interview.) Specif ic Questions: Other Aspects: Name of Interviewer: Date: SUPERVISOR'S OBSERVATIONS Name of Supervisor: Date: EDITOR'S OBSERVATIONS Name of Field Editor: Name of Keyer: 26 Date: Date: 149 NATIONAL COUNCIL FOR POPULAT ION AND DEVELOPMENT MIN ISTRY OF HOME AFFA IRS AND NAT IONAL HERITAGE KENYA DEMOGRAPHIC AND HEALTH SURVEY HUSBAND'S QUEST IONNAIRE CONFIDENTIAL Data used fo r research purposes on ly IDENTIF ICAT ION PROVINCE D ISTR ICT LOCATION/TOWN SUBLOCATION/WARD CLUSTER NUMBER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . HOUSEHOLD NUMBER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . STRUCTURE NUMBER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I URBAN/RURAL (urban=l , ru ra l=2) . . . . . . . . . . . . . . . . . . . . . . . . . . NAME OF HOUSEHOLD HEAD L INE NUMBER OF HUSBAND . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . L INE NUMBER OF WIFE INTERVIEWED . . . . . . . . . . . . . . . . . . . . . . . . . L INE NUMBER OF WIFE INTERVIEWED . . . . . . . . . . . . . . . . . . . . . . . . . I L INE NUMBER OF WIFE INTERVIEWED . . . . . . . . . . . . . . . . . . . . . . . . . INTERVIEWER V IS ITS 1 2 3 DATE INTERVIEWER'S NAME RESULT* NEXT V IS IT : DATE T IME F INAL V IS IT MONTH YEAR INTER- V IEWER NO. F INAL RESULT ~ TOTAL NUMBER OF V IS ITS *RESULT CODES: 1 COMPLETED 2 NOT AT HOME 3 POSTPONED 4 REFUSED 5 PARTLY COMPLETED 6 OTHER (SPECIFY) LANGUAGE OF QUEST IONNAIRE** ENGL ISH LANGUAGE USED IN INTERVIEW** . . . . . . . . . . . . . . RESPONDENT'S LOCAL LANGUAGE** . . . . . . . . . . . . . TRANSLATOR USED ( I=NOT AT ALL ; 2=SOMETIME; 3=ALL THE T IME) . . * *LANGUAGE CODES: 01 KALENJ IN 02 KAMBA 03 K IKUYU 04 K IS I I 05 LUHYA 06 LUO 07 MERU/EMBU 08 MI J IKENDA 09 K ISWAHIL I i0 ENGL ISH i i OTHER NAME DATE F IELD EDITED BY OFF ICE EDITED BY KEYED BY KEYED BY 150 NO. I SECTION H1 RESPONDEHT'S BACKGROUND QUESTIONS AND FILTERS ] COOING CATEGORIES SKIP | TO HlOO I RECORD THE TIME. MINUTES . . . . . . . . . . . . . . H1011 I Fi rst I would l ike to ask some questions about you and I your household. For most of the time unt i l you were 12 I years old, did you l ive in the countryside, in Nairobi or MoMoasa, or in another town? I COUNTRYSIDE . 1 I HAIROBI/MOMBASA . . . . . . . . . . . . . . . . . 2 I OTHER TO~N . . . . . . . . . . . . . . . . . . . . . . 3 I H102| How tong have you been l iv ing continuously in ) • (NAME OF SUBLOCATION, TOWN, CITY)? I ALWAYS . . . . . . . . . . . . . . . . . . . . . . . . . 95 I VISITOR . . . . . . . . . . . . . . . . . . . . . . . . 96 YEARS . . . . . . . . . . . . . . . . . . . . H103 I t is important to know your exact age. and year were you b~rn? In what month MONTH . . . . . . . . . . . . . . . . . . . . ~ 1 ~ DK MONTH . . . . . . . . . . . . . . . . . . . . . . . 98 YEAR . . . . . . . . . . . . . . . . . . . . . ~] DK YEAR . . . . . . . . . . . . . . . . . . . . . . . . 98 I H1041 How Did were you at your last birthday? I INTERVIEWER: COHPARE AND CORRECT H103 AND/OR H1D4 IF INCONSISTENT. )AOE "LETOYR I H105 I What is your rel igion? I CATHOLIC . . . . . . . . . . . . . . . . . . . . . . . . 1 I PROTESTANT/OTHER CHRISTIAN . . . . . . 2 MUSLIM . . . . . . . . . . . . . . . . . . . . . . . . . . 3 OTHER (SPECIFY) 4 NO RELIGION . . . . . . . . . . . . . . . . . . . . . S HI06' What is your ethnic group or tr ibe? KALENJIN . . . . . . . . . . . . . . . . . . . . . . . 01 KAMBA . . . . . . . . . . . . . . . . . . . . . . . . . . 02 KIKUYU . . . . . . . . . . . . . . . . . . . . . . . . . 03 KISII . . . . . . . . . . . . . . . . . . . . . . . . . . 04 LUHYA . . . . . . . . . . . . . . . . . . . . . . . . . . 05 LUO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 06 MERU/EMBU . . . . . . . . . . . . . . . . . . . . . . 07 MIJIKENDA/SWAHILI . . . . . . . . . . . . . . 08 SOMALI . . . . . . . . . . . . . . . . . . . . . . . . . 09 OTHER 10 (SPECIFY} 151 SKIP NO. | Q~ESTIOH$ ARD FILTERS | ~ODINO CATEGORIES | TO HlOTI Now we c~ to matters of marriage. I marrled only once or more than once? Have you Ioeen I i ONCE . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 >HlO9 MORE THAN ONCE . . . . . . . . . . . . . . . . . . 2 H1081 How many wives or partners do you currently have? H109 1 HOW many wives or partners did your father have? I NUMBER . ~1 Hl10 1 Would you like to have an additional wife in the future? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 DON'T KN~ . . . . . . . . . . . . . . . . . . . . . . 8 H l l l Have you ever lost a wife or portner: Through death? Through divorce? (She is not coming back) Through separation? (She might cc~ back) DEATH . . . . . . . . . . . . . . . . . . . . DIVORCE . . . . . . . . . . . . . . . . . . SEPARATION . . . . . . . . . . . . . . . YES NO .,1 2 .1 2 .1 2 Hl12 In what month and year did you start l iving with your ( f i r s t ) wife or partner? MONTH . . . . . . . . . . . . . . . . . . . . DK . . . . . . . . . . . . . . . . . . . . . . . YEAR . . . . . . . . . . . . . . . . . . . . . DK YEAR . . . . . . . . . . . . . . . . . . . . . . . . 98 I H~131 ~o~ o,~ ~ere yoo when you stort~,,v,n~ w,th her~ I AO~ . . . . . . . . . . . . . . . . . . . . . . ~ 1 Hl141 Do you approve or disapprove of divorce? APPROVE . . . . . . . . . . . . . . . . . . . . . . . . . 1 DISAPPROVE . . . . . . . . . . . . . . . . . . . . . . 2 DONbT MIND . . . . . . . . . . . . . . . . . . . . . . 3 Hl15 1 Generally, do you approve or disapprove of polygamy? I APPROVE . . . . . . . . . . . . . . . . . . . . . . . . . 1 DISAPPROVE . . . . . . . . . . . . . . . . . . . . . . 2 DON'T MIND . . . . . . . . . . . . . . . . . . . . . . 3 152 SKIP NO. J QUESTIONS AND FILTERS J COOING CATEGORIES | TO H11 lHveyoueveratte' oh°° I . I NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 >H120 I I I H117J What was the highest [eve[ of school you attended: J PRIMARY . . . . . . . . . . . . . . . . . . . . . . . . . 1 I primary, secondary, higher or university? I SECONDARY . . . . . . . . . . . . . . . . . . . . . . . 2 HIGHER . . . . . . . . . . . . . . . . . . . . . . . . . . 3 UNIVERSITY . . . . . . . . . . . . . . . . . . . . . . 4 OTHER 5 (SPECIFY) Hl19 1 INTERVIEWER: CHEC~ 117: SECONDARY J PRIMARY OR ABOVE ~ >H122 I V l °lv° °ver' u I . . I . l easi ly, with d i f f i cu l ty , or not at att? WITH DIFFICULTY . . . . . . . . . . . . . . . . . 2 NOT AT ALL . . . . . . . . . . . . . . . . . . . . . . 3 H1221 Do you usuatly l isten to a radio at least once a week? J YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 J I I I NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 NEVER WORKED . . . . . . . . . . . . . . . 96 -->H201 I H124 I H125[ CHECK H123: DOES NOT WORK IN AGRI- t~3RKS CULTURE IN AGRICULTURE V *H127 I I I DO you work for someone etse or for yourself? l FOR SO$1EORE ELSE . . . . . . . . . . . . . . . . I l I | FOR HIMSELF . . . . . . . . . . . . . . . . . . . . . 2 >H201 H126 Do you earn a regular wage or salary? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 H201 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 I I ; H127 DO you work mainly on your own or family land, HIS/FAMILY LAND . . . . . . . . . . . . . . . . 1 >H201 or on someone etse's land? SOMEONE ELSE'S LAND . . . . . . . . . . . . . 2 H128J Do you tease the land or do you work for wages? J LEASES THE LAND . . . . . . . . . . . . . . . . . 1 I I WORKS FOR WAGES . . . . . . . . . . . . . . . . . 2 153 4 SECTION H2: CONTRACEPTION ] H201 Now I would L ike to ta lk about a d i f fe rent top ic . There are var ious ways or methods that a coup le can use to detay or avo id a pregnancy . Which o f these ways or methods have you heard about? CIRCLE CODE 1 IN H202 FOR EACH METHOD MENTIONED SPONTANEOUSLY. THEN PROCEED DOWN THE COLUMN, READING THE NAME AND DESCRIPTION OF EACH METHOD NOT MENTIONED SPONTANEOUSLY. CIRCLE COOE 2 IF METHOD IS RECOGNIZED, AND CODE 3 IF NOT RECOGNIZED. THEN FOR EACH METHOD WITH COUE I OR 2 CIRCLED IN H202 ASK H202A'H204 BEFORE PROCEEDING TO THE NEXT METHOD. H2OZ Have you ever heard o f (METHO0)? READ DESCR PT ON. PILL Wofnen can take a p i I I every day. IUD Women can have a Loop or co iL p laced ins ide them by a doctor or a nurse. INJECTIONS Women can have an in jec t ion by a doctor or nurse which stops them from becoming pregnant for severai months. DIAPHRAGM/FOAM/JELLY Women can place a diaphraBm, tampon, sponge, foam tablets, jetly or cream in themselves before sex. CONDigN Men can use a rui:~oer sheath during sexual inter- course. FEMALE STERILIZATION Women can have an operation to avoid having any more children. MALE STERILIZATION Men can have an operation to avoid having any more children. PERIODIC ABSTINENCE Couples can avoid having sexua( inter- course on certain days of the month when the woman is more [ikety to become pregnant. WITHDRAWAL Men can be careful and ;:wJ[l out before climax. H202A Do I know I obta in (METHOD) i f you lyou H203 Have you H204 Where would you go to ever used how to use (METHOD) with I wanted to use it? (METHOD)? any partner? i (CODES BELOW) YES/SPONT . . . . . . . . 1 7 YES/PROBED . . . . . . . 2 NO . . . . . . . . . . . . . v3 I YES/SPONT . . . . . . . . 17 YES/PROBED . . . . . . . 2 NO . . . . . . . . . . . . . 3 v YES/SPONT . . . . . . . . 17 YES/PROBED . . . . . . . 2 m NO . . . . . . . . . . . . . 3 v YES/SPONT . . . . . . . . 17 YES/PROBED . . . . . . . 2 m .o . . . . . . . . . . . : I YES/SPONT . . . . . . . . I -> YES/PROBED . . . . . . . 2-> NO . 3 . v I YES/SPONT . . . . - YES/PROBED . . . . . . . 2- NO . . . . . . . . . . . . . 3 v YES/SPONT . . . . . . . . 1- YES/PROBED . . . . . . . 2- NO . . . . . . . . . . . . . 3 v YES . I NO . 2 YES . . . . . . . . . 1 NO . . . . . . . . . . 2 YES . I NO . 2 YES . . . . . . . . . 1 NO . . . . . . . . . . 2 YES . . . . . . . . . 1 NO . . . . . . . . . . 2 YES . I NO . 2 YES . . . . . . . . . 1 NO . . . . . . . . . . 2 YES . . . . . . . . . 1 NO . . . . . . . . . . Z OTHER OTHER I ~ OTHER OTHER OTHER ~ 1 OTHER OTHER Where would you go to obtain advice on periodic ANY OTHER METHODS? Have you heard of any other ways or methods that women or men can use to avoid pregnancy? (SPECIFY) YES/SPONT . . . . . . . . 1-> YES . . . . . 1 YES/PROBED . . . . . . . 2-> NO . 3 J NO . 2 v YEB/SPONT . I-> YES . I YES/PROBED . . . . . . . 2-> NO . . . . . . . . . . . . . ] v YES/BPONT . I-> NO . . . . . . . . . . . . . 3 YES . . . . . . . . . 1 RO . . . . . . . . . . 2 YES . . . . . . . . . I NO . . . . . . 2 NO . . . . . . . . . . 2 YES . . . . . 1 I H205 CHECK H203: NOT A SINGLE "YES" (NEVER USED) NO . . . . . . 2 v AT LEAST ONE "YES" (EVER USED) v 154 5 YES . 1 NO . 2 F~ > SKIP TO abst inence? OTHER CODES FOR H204 01 GOVERNMENT HOSPITAL 02 GOVERNMENT HEALTH CENTER 03 EPAK 04 MOBILE CLINIC 05 FIELD EDUCATOR 06 PHARMACY/SHOP 07 PRIVATE HOSPITAL 08 MISSION HOSPITAL/DISPENSARY 09 EMPLOYER'S CLINIC 10 PRIVATE DOCTOR 11 TRADITIONAL HEALER 12 WIFE/PARTNER WOULD GO 13 FRIENDS/RELATIVES 14 OTHER (SPECIFY) 15 NOWHERE 98 DK H208 I SKIP NO. | QUESTIONS AND FILTERS | CODING CATEGORIES | TO NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . F~ ,H211 MARK APPROPRIATE BOX WITH AN 'X i . I CORRECT H202-H204 AS NECESSARY. when you f i r s t d id someth ing or used a method to avo id NUMBER OF CHILDREN . . . . . . . hav ing a ch i td? IF NONE ENTER 'OO'. NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 ,H211 I ff210 Which method(s ) are you us ing? CIRCLE ALL MENTIONED PILL . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 IUD . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 INJECTIONS . . . . . . . . . . . . . . . . . . . . . 1 DIAPHRAGM/JELLY/FOAM . . . . . . . . . . . I CONDOM . . . . . . . . . . . . . . . . . . . . . . . . . I FEMALE STERILIZATION . . . . . . . . . . . I MALE STERILIZATION . . . . . . . . . . . . . I PERIODIC ABSTINENCE . . . . . . . . . . . . I WITHDRAWAL . . . . . . . . . . . . . . . . . . . . . I OTHER ,1 (SPECIFY) ~H215 ( I H211 Do you intend to use a method to avo id pregnancy at any YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I ,H213 t ime in the future? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 I DK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 ~H215 I H212 Why not? LACK OF KNOWLEDGE . . . . . . . . . . . . . . 01 - OPPOSED TO FAMILY PLANNING . 02 PARTNER DISAPPROVES . . . . . . . . . . . . 03 OTHERS DISAPPROVE . . . . . . . . . . . . . . 04 HEALTH CONCERNS . . . . . . . . . . . . . . . . 05 ACCESS/AVAILABILITY . . . . . . . . . . . . 06 COSTS TOO MUCH . . . . . . . . . . . . . . . . . 07 INCONVENIENT TO USE . . . . . . . . . . . . 08 INFREQUENT SEX . . . . . . . . . . . . . . . . . 09 FATALISTIC . . . . . . . . . . . . . . . . . . . . . 10 RELIGIOM . . . . . . . . . . . . . . . . . . . . . . . 1~ WANTS CHILDREN . . . . . . . . . . . . . . . . . 12 OTHER 13 (SPECIFY) DK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 ~H215 H213 Which method wou ld you pre fer to use? PILL . . . . . . . . . . . . . . . . . . . . . . . . . . . 01 IUD . . . . . . . . . . . . . . . . . . . . . . . . . . . . 02 INJECTIONS . . . . . . . . . . . . . . . . . . . . . 03 DIAPHRAGM/JELLY/FOAM . . . . . . . . . . . 04 CONDOM . . . . . . . . . . . . . . . . . . . . . . . . . 05 FEMALE STERILIZATION . . . . . . . . . . . 06 NALE STERILIZATION . . . . . . . . . . . . . 07 PERIODIC ABSTINENCE . . . . . . . . . . . . 08 WITHDRAWAL . . . . . . . . . . . . . . . . . . . . . 09 OTHER .10 (SPECIFY) UNSURE . . . . . . . . . . . . . . . . . . . . . . . . . 98 155 6 SKIP NO. I OUESTIONS AND FILTERS I CODING CATEGORIES | TO I ,o . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 I . 11 information is provided on radio or television? NOT ACCEPTABLE . . . . . . . . . . . . . . . . . . 2 DE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 L- . about fs~ity plBnni~ in the past year? O~iCE OR TWICE . . . . . . . . . . . . . . . . . . . 2 THREE OR MORE . . . . . . . . . . . . . . . . . . . 3 I . disajc~oroves of couptes using a methcw:~ to prevent DISAPPROVES . . . . . . . . . . . . . . . . . . . . . 2 or delay pregnancy? DK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 HZ18J In generat, do you al~orove or disaRorove of c o u p t e S u s i n g a method to prevent or delay pregnancy? I APPROVEDISAPPROVE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 I N219 APPR DISAPPR DK ' I f couptee wish to avoid pregnancy, do you approve or disapprove of the i r using: The corw~o~? Mate sterilisation? ~ithdrawat? CONDOM . . . . . . . . . . . . . . I 2 8 MALE STERILIZATION.I 2 8 WITHDRAWAL . . . . . . . . . . I 2 8 H220 In your opinion, what is the main probtem, i f any, with using: The cor~dom? Male s ter i l i sa t ion? Withdrawal? ENTER CODE FOR EACH METHOD FROM LIST BELOW. 01 NONE 02 NOT EFFECTIVE 03 WIFE/PARTNER DISAPPROVES 04 COMMUNITY DISAPPROVES 05 RELIGION DISAPPROVES 06 HEALTH CONCERN 07 ACCESS/AVAILABILITY 08 COSTS TOO MUCH 09 INCONVENIENT TO USE 10 OTHER (SPECIFY) 98 DK CONDO#4 . . . . . . . . . . . . . . . . . . (OTHER " SPECIFY) MALE STERILIZATION . . . . . . (OTHER - SPECIFY) WITHDRAWAL . . . . . . . . . . . . . . ~] (OTHER - SPECIFY) H2Z1 Now many own sons do you have? Arw~ how many own daughters do you have? IF NONE ENTER '00 ' . 156 SKIP NO. J QUESTIONS AND FILTERS i COOING CATEGORIES I TO H222 Now 1 have some questions about the Future. Would you l i ke to have a (another) ch i ld or would you prefer not to have any (more) chi ldren? HAVE ANOTHER . . . . . . . . . . . . . . . . . . . . 1 NO MORE . . . . . . . . . . . . . . . . . . . . . . . . . 2----~ UNDECIDED OR DK . 31"H224 I H2231 How tong woutd you want to wait from now before the birth of a (another) child? I TIME TO WAIT: MONTHS . . . . . . . . . . . . . . . . . 1 YEARS . . . . . . . . . . . . . . . . . . 2 DK . . . . . . . . . . . . . . . . . . . . . . . . . . . . 998 NZZ41 For how tong should a couple wait before s tar t ing sex- uat intercourse a f ter the b i r th of a baby? I DURATION MONTHS . . . . . . . . . . . . . . . . . I YEARS . . . . . . . . . . . . . . . . . . 2 OTHER 996 (SPECIFY) H225 1 Should a mother wait until she has completely stopped breastfeeding before starting to have sexual relations again, or doesn't i t matter? J WAIT . . . . . . . . . . . . . . . . . . . . . . . . . . . . I J DOESN'T MATTER . . . . . . . . . . . . . . . . . . 2 H226 From the time a woman gets her period until the time she gets her next period, when do you think she has the greatest chance of boc~ing pregnant? PROBE: What are the days during the month when a woman has to be careful to avoid becoming pregnant? DURING HER PERIO0 . . . . . . . . . . . . . . . I RIGHT AFTER HER PERIO0 HAS ENDED . . . . . . . . . . . . . . . . . . . . . . 2 IN THE MIDDLE OF THE CYCLE . . . . . . 3 JUST BEFORE HER PERIO0 BEGINS.4 AT ANY TIME . . . . . . . . . . . . . . . . . . . . . 5 OTHER .6 (SPECIFY) DK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 H227 CHECK H221: NO C~VN CHILDREN If you could choose exactly the number of children to have in your whoLe Life, how many wouLd that be? HAS OWN CHILDREN [ I I f you could go back to the time you did not have any ch i ldren and could choose exact ly the number of ch i ldren to have in your whole l i fe , how many would that be? RECORD SINGLE NUMBER OR OTHER ANSWER. NUMBER . . . . . . . . . . . . . . . . . . OTHER ANSWER (SPECIFY) H228 1 HOW many boys? I Now many g i r l s? NUMBER OF ~ I BOYS . . . . . . . . . . . NUMBER OF GIRLS . . . . . . . . . . OTHER 996 (SPECIFY) H2291 RECORD THE TIME. MINUTES . . . . . . . . . . . . . . . 157 INTERVIEWER°S OBSERVATIONS: Name of Interviewer: SUPERVISOR'S OBSERVATIONS: Date: Name of Supervisor: EDITOR'S OBSERVATIONS: Date : Name of Edi tor : 9 Date: 158 Front Matter Title Page Contact Information Table of Contents List of Tables List of Figures Foreword Summary of Findings Map of Kenya Chapter 01 - Background Chapter 02 - Nuptiality, Breastfeeding and Postpartum Insusceptibility Chapter 03 - Fertility Chapter 04 - Fertility Regulation Chapter 05 - Fertility Preferences Chapter 06 - Mortality and Health Chapter 07 - Husband's Survey References Appendix A - Survey Design Appendix B - Estimates of Sampling Error Appendix C - Note on Age Reporting Appendix D - Persons Involved in the KDHS Appendix E - Questionnaires Household Questionnaire Woman's Questionnaire Husband's Questionnaire

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