Kenya: Demographic and Health Survey Volume 1 (2022)

Publication date: 2023

Kenya Demographic and Health Survey 2022 D em ographic and H ealth S urvey • V olum e 1 K enya 2022 Volume 1 Kenya Demographic and Health Survey 2022 Volume 1 Kenya National Bureau of Statistics Nairobi, Kenya Ministry of Health Nairobi, Kenya The DHS Program ICF Rockville, Maryland, USA June 2023 The 2022 Kenya Demographic and Health Survey (2022 KDHS) was implemented by the Kenya National Bureau of Statistics (KNBS) in collaboration with the Ministry of Health (MoH) and other stakeholders. Funding for the survey was provided by the Government of Kenya, the United States Agency for International Development (USAID), the Bill & Melinda Gates Foundation, the World Bank, the United Nations Children’s Fund (UNICEF), the United Nations Population Fund (UNFPA), Nutrition International, the World Food Programme (WFP), the United Nations Entity for Gender Equality and the Empowerment of Women (UN Women), the World Health Organization (WHO), the Clinton Health Access Initiative, and the Joint United Nations Programme on HIV/AIDS (UNAIDS). The UN Resident Coordinator office assured the coordination of UN agencies supporting the 2022 KDHS. ICF provided technical assistance through The DHS Program, a USAID-funded project providing support and technical assistance in implementing population and health surveys in countries worldwide. Additional information about the 2022 KDHS may be obtained from Kenya National Bureau of Statistics (KNBS), P.O. Box 30266-00100, GPO Nairobi, Kenya; telephone: +254-20-3317583, +254-20-2911000/1, +254-20- 3317612/22/23/51; email: directorgeneral@knbs.or.ke, info@knbs.or.ke; website: www.knbs.or.ke. Information about The DHS Program may be obtained from ICF, 530 Gaither Road, Suite 500, Rockville, MD 20850, USA; telephone: +1-301-407-6500; fax: +1-301-407-6501; email: info@DHSprogram.com; internet: www.DHSprogram.com. The contents of this report are the sole responsibility of KNBS and ICF and do not necessarily reflect the views of USAID, the United States Government, or other donor agencies. ISBN: 978-9914-49-610-9 Recommended citation: KNBS and ICF. 2023. Kenya Demographic and Health Survey 2022: Volume 1. Nairobi, Kenya, and Rockville, Maryland, USA: KNBS and ICF. Contents  iii CONTENTS LIST OF TABLES, FIGURES, AND MAPS . ix FOREWORD . xxv ACRONYMS AND ABBREVIATIONS . xxvii READING AND UNDERSTANDING TABLES FROM THE 2022 KENYA DEMOGRAPHIC AND HEALTH SURVEY (KDHS) . xxix SUSTAINABLE DEVELOPMENT GOALS . xxxvii MAP OF KENYA . xl 1 INTRODUCTION AND SURVEY METHODOLOGY . 1 1.1 Survey Objectives . 1 1.2 Sample Design . 1 1.3 Questionnaires . 4 1.4 Anthropometry Measurements . 5 1.5 Training of Trainers and Pretest . 6 1.6 Pretest . 6 1.7 Training of Field Staff . 6 1.8 Fieldwork . 7 1.9 Data Processing . 7 1.10 Response Rates . 7 2 HOUSING CHARACTERISTICS AND HOUSEHOLD POPULATION . 9 2.1 Housing Characteristics . 9 2.1.1 Use of Clean Fuels and Technologies . 10 2.1.2 Cooking . 10 2.1.3 Heating and Lighting . 10 2.1.4 Primary Reliance on Clean Fuels and Technologies . 10 2.2 Household Wealth . 12 2.2.1 Household Durable Goods . 12 2.2.2 Wealth Index . 12 2.3 Household Population and Composition . 13 2.4 Children’s Living Arrangements and Parental Survival . 13 2.5 Birth Registration . 14 2.6 Education . 15 2.6.1 Educational Attainment . 16 2.6.2 Primary and Secondary School Attendance . 17 2.6.3 Participation Rate in Organised Learning among Children Age 5 . 18 2.7 Disability . 18 2.7.1 Disability by Domain and Age . 19 2.7.2 Disability among Adults by Other Background Characteristics . 19 2.8 Deaths and Injuries from Road Traffic Accidents . 19 2.9 Food Security Status . 20 2.10 COVID-19 Testing, Vaccination, and Deaths . 21 2.11 Health Insurance Coverage . 21 2.12 Outpatient and Inpatient Health Expenditures . 23 2.13 Social Protection . 23 iv  Contents 3 CHARACTERISTICS OF RESPONDENTS . 67 3.1 Basic Characteristics of Survey Respondents . 67 3.2 Education and Literacy . 68 3.3 Mass Media Exposure and Internet Usage . 69 3.4 Employment . 70 3.5 Occupation . 71 3.6 Type of Employment for Women . 72 3.7 Tobacco Use . 72 3.8 Alcohol Consumption . 73 3.9 Place of Birth and Recent Migration . 74 3.9.1 Type of Migration . 76 3.9.2 Reason for Migration . 76 4 MARRIAGE AND SEXUAL ACTIVITY . 127 4.1 Marital Status . 127 4.2 Marriage Registration . 128 4.3 Polygyny . 128 4.4 Age at First Marriage . 130 4.5 Age at First Sexual Intercourse . 132 4.6 Recent Sexual Activity . 132 5 FERTILITY . 151 5.1 Current Fertility . 151 5.2 Children Ever Born and Living . 153 5.3 Birth Intervals . 153 5.4 Insusceptibility to Pregnancy . 155 5.5 Age at First Menstruation . 155 5.6 Arrival of Menopause . 156 5.7 Age at First Birth . 156 5.8 Teenage Pregnancy . 157 5.9 Pregnancy Outcomes and Induced Abortion Rates . 158 6 FERTILITY PREFERENCES . 175 6.1 Desire for Another Child . 175 6.2 Ideal Family Size . 177 6.3 Fertility Planning Status . 178 6.4 Wanted Fertility Rates . 180 7 FAMILY PLANNING . 191 7.1 Contraceptive Knowledge and Use . 191 7.1.1 Use of Emergency Contraception . 194 7.1.2 Knowledge of the Fertile Period . 194 7.2 Source of Modern Contraceptive Methods . 194 7.3 Informed Choice . 195 7.4 Discontinuation of Contraceptives . 196 7.5 Demand for Family Planning . 196 7.5.1 Decision Making about Family Planning and Opinion about Using Family Planning . 198 7.5.2 Pressure to Become Pregnant and Future Use of Contraception . 199 7.5.3 Exposure to Family Planning Messages . 199 7.6 Contact of Nonusers with Family Planning Providers . 200 Contents  v 8 INFANT AND CHILD MORTALITY . 233 8.1 Infant and Child Mortality . 234 8.2 Perinatal Mortality . 235 8.3 High-risk Fertility Behaviour . 236 9 MATERNAL AND NEWBORN HEALTH CARE . 243 9.1 Antenatal Care Coverage and Content . 244 9.1.1 Skilled Providers . 244 9.1.2 Timing and Number of Antenatal Care Visits . 245 9.2 Components of Antenatal Care . 245 9.2.1 Deworming and Iron-containing Supplementation during Pregnancy . 246 9.2.2 Source of Iron-containing Supplements . 247 9.3 Protection against Neonatal Tetanus . 247 9.4 Delivery Services . 248 9.4.1 Institutional Deliveries . 248 9.4.2 Delivery by Caesarean . 250 9.4.3 Skilled Assistance during Delivery . 251 9.5 Postnatal Care . 253 9.5.1 Postnatal Health Check for Mothers . 253 9.5.2 Postnatal Health Check for Newborns . 254 9.5.3 Postnatal Health Checks for Mothers and Newborns . 255 9.6 Men’s Involvement in Maternal Health Care . 256 9.7 Problems in Accessing Health Care . 257 9.8 Distance and Means of Transport to the Nearest Health Facility . 257 9.9 Community Health Care Visits and Services . 258 10 CHILD HEALTH . 303 10.1 Child’s Size and Birth Weight . 304 10.2 Vaccination of Children . 304 10.2.1 Vaccination Card Ownership and Availability . 304 10.2.2 Basic Antigen Coverage . 305 10.2.3 National Schedule Coverage . 306 10.3 Symptoms of Acute Respiratory Infection and Careseeking Behaviour . 308 10.4 Fever and Careseeking Behaviour . 309 10.5 Diarrhoeal Disease . 309 10.5.1 Diarrhoea and Careseeking Behaviour . 309 10.5.2 Feeding Practices . 310 10.5.3 Oral Rehydration Therapy, Zinc, Continued Feeding, and Other Treatments . 311 10.6 Treatment of Childhood Illness . 312 10.7 Early Childhood Development . 312 11 NUTRITION OF CHILDREN AND ADULTS . 337 11.1 Nutritional Status of Children . 337 11.2 Growth Monitoring and Promotion . 341 11.3 Infant and Young Child Feeding Practices . 342 11.3.1 Ever Breastfed, Early Initiation of Breastfeeding, and Exclusive Breastfeeding for the First 2 Days after Birth . 342 11.3.2 Exclusive Breastfeeding and Mixed Milk Feeding . 343 11.3.3 Continued Breastfeeding and Bottle Feeding . 344 11.3.4 Introduction of Complementary Foods . 344 11.3.5 Minimum Dietary Diversity, Minimum Meal Frequency, Minimum Milk Feeding Frequency, Minimum Acceptable Diet, and Egg and/or Flesh Food Consumption . 344 vi  Contents 11.3.6 Sweet Beverage Consumption, Unhealthy Food Consumption, and Zero Vegetable or Fruit Consumption among Children . 347 11.3.7 Infant and Young Child Feeding (IYCF) Indicators . 348 11.4 Infant and Young Child Feeding Counselling . 348 11.5 Micronutrient Supplementation and Deworming among Children . 349 11.6 Adults’ Nutritional Status . 351 11.6.1 Nutritional Status of Women . 352 11.6.2 Nutritional Status of Men . 354 11.7 Women’s Dietary Practices . 355 11.8 Presence of Iodised Salt in Households . 356 12 MALARIA . 389 12.1 Ownership of Insecticide-treated Nets . 390 12.2 Household Access and Use of ITNs . 393 12.3 Use of ITNs by Children and Pregnant Women . 395 12.4 Reasons Mosquito Nets Were Not Used . 396 12.5 Malaria in Pregnancy . 397 12.6 Case Management of Malaria in Children . 398 12.6.1 Care Seeking and Diagnosis of Malaria in Children Under 5 with Fever . 398 12.6.2 Use of Recommended Antimalarials . 398 13 KNOWLEDGE, ATTITUDES, AND BEHAVIOUR RELATED TO HIV, AIDS, AND TUBERCULOSIS . 421 13.1 Tuberculosis: Knowledge, Diagnosis, and Preventive Treatment . 422 13.1.1 Knowledge and Beliefs about Tuberculosis . 422 13.1.2 Tuberculosis Diagnosis and Preventive Treatment . 422 13.2 Knowledge and Attitudes about Medicines to Treat or Prevent HIV . 422 13.3 Discriminatory Attitudes towards People Living with HIV . 424 13.4 Multiple Sexual Partners . 425 13.5 Coverage of HIV Testing Services . 426 13.5.1 HIV Testing of Pregnant Women . 426 13.5.2 Experience with Prior HIV Testing . 427 13.6 Disclosure, Shame, and Stigma among Self-reported HIV Positive . 431 13.7 Male Circumcision . 432 13.8 Self-reporting of Sexually Transmitted Infections . 433 13.9 Knowledge and Behaviour Related to HIV and AIDS among Young People . 433 13.9.1 Knowledge about HIV Prevention . 433 13.9.2 First Sex . 434 13.9.3 Premarital Sex . 435 13.9.4 Multiple Sexual Partners . 435 13.9.5 Recent HIV Testing . 436 14 CHRONIC CONDITIONS . 471 14.1 Physical Activity . 472 14.2 High Blood Pressure . 473 14.3 High Blood Sugar . 473 14.4 Heart Disease or Chronic Heart Disease . 474 14.5 Lung Disease or a Chronic Lung Condition . 474 14.6 Mental Health Conditions, Depression, and Anxiety . 474 14.7 Breast and Cervical Cancer Examinations . 475 14.8 Arthritis . 478 14.9 Cancer of the Prostate . 479 Contents  vii 15 WOMEN’S EMPOWERMENT . 497 15.1 Married Women’s and Men’s Employment . 498 15.2 Control over Women’s Earnings . 499 15.3 Control over Men’s Earnings . 499 15.4 Women’s and Men’s Ownership of Assets . 500 15.4.1 Ownership of a House or Land . 500 15.4.2 Documentation of House or Land Ownership . 502 15.4.3 Ownership and Use of Mobile Phones and Bank Accounts . 502 15.5 Participation in Decision Making . 504 15.6 Attitudes toward Wife Beating . 505 15.7 Negotiating Sexual Relations . 505 15.8 Women’s Participation in Decision Making about Sexual and Reproductive Health . 506 16 HOUSEHOLD WATER AND SANITATION . 553 16.1 Drinking Water Sources, Availability, and Treatment . 553 16.1.1 Drinking Water Service Ladder . 554 16.1.2 Person Collecting Drinking Water . 557 16.1.3 Availability of Drinking Water . 557 16.1.4 Treatment of Drinking Water . 558 16.2 Sanitation . 558 16.2.1 Sanitation Service Ladder . 559 16.2.2 Removal and Disposal of Excreta . 560 16.3 Disposal of Children’s Stools . 561 16.4 Handwashing . 561 16.5 Menstrual Hygiene . 562 17 GENDER-BASED VIOLENCE . 585 17.1 Measurement of Violence . 587 17.2 Women’s Experience of Physical Violence . 588 17.2.1 Perpetrators of Physical Violence . 589 17.2.2 Experience of Physical Violence during Pregnancy . 589 17.3 Experience of Sexual Violence . 590 17.3.1 Prevalence of Sexual Violence . 590 17.3.2 Perpetrators of Sexual Violence . 590 17.3.3 Experience of Sexual Violence by a Non-intimate Partner . 590 17.3.4 Age at First Experience of Sexual Violence . 590 17.4 Experience of Different Forms of Violence . 590 17.5 Forms of Controlling Behaviours and Intimate Partner Violence . 591 17.5.1 Prevalence of Controlling Behaviours . 591 17.5.2 Prevalence of Intimate-partner Violence Perpetrated by Current or Most Recent Spouse/Intimate Partner . 593 17.5.3 Intimate-partner Violence in the Last 12 Months Perpetrated by Any Husband/Intimate Partner . 595 17.6 Injuries to Women and Men due to Intimate Partner Violence . 597 17.7 Violence Initiated by Women and Men against Spouses/Intimate Partners . 597 17.8 Help Seeking among Women Who Have Experienced Violence . 598 17.8.1 Prevalence of Help Seeking . 598 17.8.2 Sources for Help . 598 18 FEMALE GENITAL MUTILATION . 635 18.1 Respondents’ Knowledge of Female Genital Mutilation. 636 18.2 Female Genital Mutilation among Women . 636 18.2.1 Prevalence and Type of FGM . 636 18.2.2 Age at Circumcision among Women . 637 viii  Contents 18.3 Female Genital Mutilation among Daughters . 638 18.4 Person Who Performed the Circumcision . 638 18.5 Female Circumcision Over Time. 638 18.6 Attitudes towards Female Genital Mutilation . 639 18.7 Effects of Female Genital Mutilation . 639 18.8 Help-seeking Behaviours . 639 REFERENCES . 653 Tables, Figures, and Maps  ix TABLES, FIGURES, AND MAPS 1 INTRODUCTION AND SURVEY METHODOLOGY . 1 Table 1.1 Results of the household and individual interviews . 8 Figure 1.1 2022 Kenya DHS Sample Design . 3 2 HOUSING CHARACTERISTICS AND HOUSEHOLD POPULATION . 9 Table 2.1 Household characteristics: Housing . 26 Table 2.2 Household characteristics: Cooking . 27 Table 2.3 Household characteristics: Heating and lighting . 28 Table 2.4 Primary reliance on clean fuels and technologies . 29 Table 2.4C Primary reliance on clean fuels and technologies by county . 30 Table 2.5 Household possessions . 31 Table 2.6 Wealth quintiles . 32 Table 2.7 Household population by age, sex, and residence . 33 Table 2.8 Household composition . 34 Table 2.9 Children’s living arrangements and orphanhood . 35 Table 2.9C Children’s living arrangements and orphanhood by county . 36 Table 2.10 Birth registration of children under age 5 . 37 Table 2.10C Birth registration of children under age 5 by county . 38 Table 2.11.1 Educational attainment of the female household population . 39 Table 2.11.1C Educational attainment of the female household population by county . 40 Table 2.11.2 Educational attainment of the male household population . 41 Table 2.11.2C Educational attainment of the male household population by county . 42 Table 2.12 School attendance ratios . 43 Table 2.12C School attendance ratios by county . 44 Table 2.13 Participation rate in organised learning . 46 Table 2.13C Participation rate in organised learning by county . 47 Table 2.14 Disability by domain and age . 48 Table 2.15.1 Disability among adults according to background characteristics: Women . 49 Table 2.15.1C Disability among adults according to county: Women . 50 Table 2.15.2 Disability among adults according to background characteristics: Men . 51 Table 2.15.2C Disability among adults according to county: Men . 52 Table 2.16 Deaths and injuries from road traffic accidents . 53 Table 2.16C Deaths and injuries from road traffic accidents by county . 54 Table 2.17 Food security status . 55 Table 2.17C Food security status by county . 56 Table 2.18.1 COVID-19 diagnosis and vaccination . 57 Table 2.18.1C COVID-19 diagnosis and vaccination by county . 58 Table 2.18.2 COVID-19 cases and deaths . 59 Table 2.18.2C COVID-19 cases and deaths by county . 60 Table 2.19 Health insurance coverage . 61 Table 2.19C Health insurance coverage by county . 62 Table 2.20.1 Average annual expenditure on inpatient admissions [in Kenyan shillings] . 63 Table 2.20.2 Average monthly expenditure on outpatient visits [in Kenyan shillings] . 63 Table 2.21.1 Cash transfer: All households . 64 x  Tables, Figures, and Maps Table 2.21.2 Cash transfer: Households receiving cash transfer or social assistance. 64 Table 2.21.3 Cash transfer by residence and household wealth . 64 Table 2.21.3C Cash transfer or social assistance by county . 65 Figure 2.1 Primary reliance on clean fuels and technologies by residence . 11 Figure 2.2 Household wealth by residence . 12 Figure 2.3 Population pyramid . 13 Figure 2.4 Children’s living arrangements by residence . 14 Figure 2.5 Birth registration by household wealth . 14 Figure 2.6 Birth registration: trends . 15 Figure 2.7 Educational attainment of the household population . 16 Figure 2.8 Secondary school attendance by household wealth . 18 Map 2.1 Primary reliance on clean fuels and technologies by county . 11 Map 2.2 Birth registration by county . 15 Map 2.3 Health insurance coverage by county . 22 3 CHARACTERISTICS OF RESPONDENTS . 67 Table 3.1 Background characteristics of respondents . 79 Table 3.1C Background characteristics of respondents by county . 80 Table 3.2.1 Educational attainment: Women . 81 Table 3.2.1C Educational attainment by county: Women . 82 Table 3.2.2 Educational attainment: Men . 83 Table 3.2.2C Educational attainment by county: Men . 84 Table 3.3.1 Literacy: Women . 85 Table 3.3.1C Literacy by county: Women . 86 Table 3.3.2 Literacy: Men . 87 Table 3.3.2C Literacy by county: Men . 88 Table 3.4.1 Exposure to mass media: Women . 89 Table 3.4.1C Exposure to mass media by county: Women . 90 Table 3.4.2 Exposure to mass media: Men . 91 Table 3.4.2C Exposure to mass media by county: Men . 92 Table 3.5.1 Internet usage: Women . 93 Table 3.5.1C Internet usage by county: Women . 94 Table 3.5.2 Internet usage: Men . 95 Table 3.5.2C Internet usage by county: Men . 96 Table 3.6.1 Employment status: Women . 97 Table 3.6.1C Employment status by county: Women . 98 Table 3.6.2 Employment status: Men . 99 Table 3.6.2C Employment status by county: Men . 100 Table 3.7.1 Occupation: Women . 101 Table 3.7.1C Occupation by county: Women . 102 Table 3.7.2 Occupation: Men . 103 Table 3.7.2C Occupation by county: Men . 104 Table 3.8 Type of employment: Women . 105 Table 3.9.1 Tobacco smoking: Women . 105 Table 3.9.1C Tobacco smoking by county: Women . 106 Table 3.9.2 Tobacco smoking: Men . 107 Table 3.9.2C Tobacco smoking by county: Men . 108 Table 3.10 Average number of cigarettes smoked daily: Men . 109 Table 3.11 Smokeless tobacco use and any tobacco use . 110 Table 3.12 Any tobacco use by background characteristics . 110 Tables, Figures, and Maps  xi Table 3.12C Any tobacco use by county . 111 Table 3.13.1 Alcohol consumption: Women . 112 Table 3.13.1C Alcohol consumption by county: Women . 113 Table 3.13.2 Alcohol consumption: Men . 114 Table 3.13.2C Alcohol consumption by county: Men . 115 Table 3.14.1 Usual number of alcoholic drinks consumed: Women . 116 Table 3.14.2 Usual number of alcoholic drinks consumed: Men . 117 Table 3.15.1 Residence at birth and recent migration: Women . 118 Table 3.15.1C Residence at birth and recent migration by county: Women . 119 Table 3.15.2 Residence at birth and recent migration: Men . 120 Table 3.15.2C Residence at birth and recent migration by county: Men . 121 Table 3.16 Type of migration . 122 Table 3.17.1 Reason for migration: Women . 123 Table 3.17.1C Reason for migration by county: Women . 124 Table 3.17.2 Reason for migration: Men . 125 Table 3.17.2C Reason for migration by county: Men . 126 Figure 3.1 Education of survey respondents . 68 Figure 3.2 More than secondary education by household wealth . 68 Figure 3.3 Exposure to mass media . 69 Figure 3.4 Trends in exposure to media . 69 Figure 3.5 Internet usage by residence . 70 Figure 3.6 Employment status by number of living children . 71 Figure 3.7 Occupation . 72 Map 3.1 Lifetime migration by county . 75 4 MARRIAGE AND SEXUAL ACTIVITY . 127 Table 4.1 Current marital status . 134 Table 4.2 Marriage registration . 135 Table 4.2C Marriage registration by county . 136 Table 4.3.1 Number of women’s co-wives . 137 Table 4.3.1C Number of women’s co-wives by county . 138 Table 4.3.2 Number of men’s wives . 139 Table 4.3.2C Number of men’s wives by county . 140 Table 4.4 Age at first marriage . 141 Table 4.5 Median age at first marriage by background characteristics . 142 Table 4.5C Median age at first marriage by county . 143 Table 4.6 Age at first sexual intercourse . 144 Table 4.7 Median age at first sexual intercourse according to background characteristics . 145 Table 4.7C Median age at first sexual intercourse according to county . 146 Table 4.8.1 Recent sexual activity: Women . 147 Table 4.8.1C Recent sexual activity by county: Women . 148 Table 4.8.2 Recent sexual activity: Men . 149 Table 4.8.2C Recent sexual activity by county: Men . 150 Figure 4.1 Marital status . 128 Figure 4.2 Trends in polygyny . 129 Figure 4.3 Median age at first sex and first marriage . 131 Figure 4.4 Trends in early marriage . 131 Figure 4.5 Median age at marriage by household wealth . 131 Figure 4.6 Trends in early sexual intercourse . 132 Map 4.1 Polygyny by county . 130 xii  Tables, Figures, and Maps 5 FERTILITY . 151 Table 5.1 Current fertility . 160 Table 5.2 Fertility by background characteristics . 160 Table 5.2C Fertility by county . 161 Table 5.3.1 Trends in age-specific fertility rates . 162 Table 5.3.2 Trends in age-specific and total fertility rates . 162 Table 5.4 Children ever born and living . 162 Table 5.5 Birth intervals . 163 Table 5.5C Birth intervals by county . 164 Table 5.6 Postpartum amenorrhoea, abstinence and insusceptibility . 165 Table 5.7 Median duration of amenorrhoea, postpartum abstinence and postpartum insusceptibility . 165 Table 5.8 Age at first menstruation . 166 Table 5.9 Menopause . 166 Table 5.10 Age at first birth . 166 Table 5.11 Median age at first birth . 167 Table 5.11C Median age at first birth by county . 168 Table 5.12 Teenage pregnancy . 169 Table 5.12C Teenage pregnancy by county . 170 Table 5.13 Sexual and reproductive health behaviours before age 15 . 170 Table 5.14 Pregnancy outcome by background characteristics . 171 Table 5.14C Pregnancy outcome by county . 172 Table 5.15 Induced abortion rates . 173 Figure 5.1 Trends in fertility by residence . 152 Figure 5.2 Fertility by education . 152 Figure 5.3 Fertility by education . 154 Figure 5.4 Trends in birth interval . 154 Figure 5.5 Trends in age at first birth. 156 Figure 5.6 Median age at first birth by household wealth . 156 Figure 5.7 Teenage pregnancy by household wealth . 157 Figure 5.8 Pregnancy outcome . 159 Map 5.1 Fertility by county . 153 Map 5.2 Teenage pregnancy by county . 158 6 FERTILITY PREFERENCES . 175 Table 6.1 Fertility preferences by number of living children . 182 Table 6.2.1 Desire to limit childbearing: Women . 182 Table 6.2.2 Desire to limit childbearing: Men . 183 Table 6.3 Ideal number of children by number of living children . 184 Table 6.4 Mean ideal number of children according to background characteristics . 185 Table 6.4C Mean ideal number of children by county . 186 Table 6.5 Fertility planning status . 187 Table 6.6 Wanted fertility rates . 188 Table 6.6C Wanted fertility rates by county . 189 Figure 6.1 Trends in desire to limit childbearing by number of living children . 176 Figure 6.2 Desire to limit childbearing by number of living children . 176 Figure 6.3 Ideal family size . 177 Figure 6.4 Ideal family size by number of living children . 177 Figure 6.5 Trends in mean ideal family size . 178 Tables, Figures, and Maps  xiii Figure 6.6 Fertility planning status . 178 Figure 6.7 Trends in fertility planning status . 179 Figure 6.8 Fertility planning status . 179 Figure 6.9 Trends in wanted and total fertility . 180 Map 6.1 Wanted fertility rates by county . 181 7 FAMILY PLANNING . 191 Table 7.1 Knowledge of contraceptive methods . 202 Table 7.2 Knowledge of contraceptive methods according to background characteristics . 203 Table 7.2C Knowledge of contraceptive methods according to county . 204 Table 7.3 Current use of contraception by age . 205 Table 7.4.1 Trends in the current use of contraception . 206 Table 7.4.2 Current use of contraception according to background characteristics . 207 Table 7.4.2C Current use of contraception according to county . 208 Table 7.5 Timing of sterilisation . 209 Table 7.6 Use of DMPA-SC/Sayana Press . 209 Table 7.6C Use of DMPA-SC/Sayana Press by county . 210 Table 7.7 Use of emergency contraception . 211 Table 7.7C Use of emergency contraception by county . 212 Table 7.8 Knowledge of fertile period . 213 Table 7.9 Knowledge of fertile period by age . 213 Table 7.10 Source of modern contraception methods . 214 Table 7.11 Use of social marketing brand pills and condoms . 215 Table 7.12 Informed choice . 216 Table 7.13 Twelve-month contraceptive discontinuation rates . 217 Table 7.14 Reasons for discontinuation. 218 Table 7.15.1 Need and demand for family planning among currently married women . 219 Table 7.15.1C Need and demand for family planning among currently married women by county . 220 Table 7.15.2 Need and demand for family planning for all women and for sexually active unmarried women . 221 Table 7.16 Decision-making about family planning . 222 Table 7.17 Decision-making about family planning by background characteristics . 222 Table 7.17C Decision-making about family planning by county . 223 Table 7.18 Pressure to become pregnant . 224 Table 7.18C Pressure to become pregnant by county . 225 Table 7.19 Future use of contraception . 226 Table 7.20.1 Exposure to family planning messages: Women . 226 Table 7.20.1C Exposure to family planning messages by county: Women . 227 Table 7.20.2 Exposure to family planning messages: Men . 228 Table 7.20.2C Exposure to family planning messages by county: Men . 229 Table 7.21 Contact of nonusers with family planning providers . 230 Table 7.21C Contact of nonusers with family planning providers by county . 231 Figure 7.1 Contraceptive use . 192 Figure 7.2 Trends in contraceptive use . 192 Figure 7.3 Source of modern contraceptive methods . 195 Figure 7.4 Contraceptive discontinuation rates . 196 Figure 7.5 Demand for family planning . 197 xiv  Tables, Figures, and Maps Figure 7.6 Trends in demand for family planning . 197 Figure 7.7 Unmet need by education . 197 Map 7.1 Modern contraceptive use by county . 193 Map 7.2 Unmet need by county . 198 8 INFANT AND CHILD MORTALITY . 233 Table 8.1 Early childhood mortality rates . 237 Table 8.2 Five-year early childhood mortality rates according to background characteristics . 237 Table 8.3 Ten-year early childhood mortality rates according to additional characteristics . 237 Table 8.3C Ten-year early childhood mortality rates by county . 238 Table 8.4 Perinatal mortality . 239 Table 8.4C Perinatal mortality by county . 240 Table 8.5 High-risk fertility behaviour . 241 Figure 8.1 Trends in early childhood mortality rates . 234 Figure 8.2 Under-5 mortality by mother’s education . 235 Figure 8.3 Perinatal mortality by mother’s age at birth . 235 9 MATERNAL AND NEWBORN HEALTH CARE . 243 Table 9.1 Antenatal care . 260 Table 9.1C Antenatal care by county . 261 Table 9.2 Number of antenatal care visits and timing of first visit . 262 Table 9.2C Number of antenatal care visits and timing of first visit by county . 263 Table 9.3.1 Components of antenatal care among women receiving ANC . 264 Table 9.3.1C Components of antenatal care among women receiving ANC by county . 265 Table 9.3.2 Components of antenatal care among all women . 266 Table 9.3.2C Components of antenatal care among all women by county . 267 Table 9.4 Deworming and iron-containing supplementation during pregnancy . 268 Table 9.4C Deworming and iron-containing supplementation during pregnancy by county . 269 Table 9.5 Source of iron-containing supplements . 270 Table 9.6 Tetanus toxoid injections . 271 Table 9.6C Tetanus toxoid injections by county . 272 Table 9.7 Place of delivery . 273 Table 9.7C Place of delivery by county . 274 Table 9.8 Caesarean section . 275 Table 9.8C Caesarean section by county . 276 Table 9.9 Assistance during delivery. 277 Table 9.9C Assistance during delivery by county . 278 Table 9.10 Duration of stay in health facility after birth . 279 Table 9.11 Timing of first postnatal check for the mother . 280 Table 9.11C Timing of first postnatal check for the mother by county . 281 Table 9.12 Type of provider of first postnatal check for the mother . 282 Table 9.12C Type of provider of first postnatal check for the mother by county . 283 Table 9.13 Content of postnatal care for the mother . 284 Table 9.13C Content of postnatal care for the mother by county . 285 Table 9.14 Timing of first postnatal check for the newborn . 286 Table 9.14C Timing of first postnatal check for the newborn by county . 287 Table 9.15 Type of provider of first postnatal check for the newborn . 288 Tables, Figures, and Maps  xv Table 9.15C Type of provider of first postnatal check for the newborn by county . 289 Table 9.16 Content of postnatal care for newborns . 290 Table 9.16C Content of postnatal care for newborns by county . 291 Table 9.17 Postnatal checks on mother and newborn . 292 Table 9.17C Postnatal checks on mother and newborn by county . 293 Table 9.18 Men’s involvement in maternal health care . 294 Table 9.18C Men’s involvement in maternal health care by county . 295 Table 9.19 Problems in accessing health care . 296 Table 9.19C Problems in accessing health care by county . 297 Table 9.20 Distance from health care . 298 Table 9.20C Distance from health care by county . 299 Table 9.21 Community health care visits and services . 300 Table 9.21C Community health care visits and services by county . 301 Figure 9.1 Trends in antenatal care coverage . 244 Figure 9.2 Components of antenatal care . 246 Figure 9.3 Trends in antenatal care services . 246 Figure 9.4 Trends in protection against neonatal tetanus . 248 Figure 9.5 Trends in place of birth . 249 Figure 9.6 Health facility births by birth order . 249 Figure 9.7 Caesarean section by household wealth . 251 Figure 9.8 Assistance during delivery. 251 Figure 9.9 Postnatal care by place of delivery . 255 Map 9.1 Health facility births by county . 250 Map 9.2 Skilled assistance at delivery by county . 252 10 CHILD HEALTH . 303 Table 10.1 Child’s size and weight at birth . 315 Table 10.1C Child’s size and weight at birth by county . 316 Table 10.2 Possession and observation of vaccination cards, according to background characteristics . 317 Table 10.2C Possession and observation of vaccination cards, according to county . 318 Table 10.3 Vaccinations by source of information . 319 Table 10.4 Vaccinations by background characteristics . 320 Table 10.4C Vaccinations by county . 321 Table 10.5 Source of vaccinations . 323 Table 10.5C Source of vaccinations by county . 324 Table 10.6 Children with symptoms of ARI and careseeking for symptoms of ARI . 325 Table 10.6C Children with symptoms of ARI and careseeking for symptoms of ARI by county . 326 Table 10.7 Source of advice or treatment for children with symptoms of ARI . 327 Table 10.8 Children with fever and careseeking for fever . 328 Table 10.8C Children with fever and careseeking for fever by county . 329 Table 10.9 Children with diarrhoea and careseeking for diarrhoea . 330 Table 10.9C Children with diarrhoea and careseeking for diarrhoea by county . 331 Table 10.10 Feeding practices during diarrhoea . 332 Table 10.10C Feeding practices during diarrhoea by county . 333 Table 10.11 Oral rehydration salts, zinc, continued feeding and other treatments for diarrhoea . 334 Table 10.12 Source of advice or treatment for children with diarrhoea . 335 Table 10.13 Early Childhood Development Index 2030 . 336 xvi  Tables, Figures, and Maps Figure 10.1 Trends in childhood vaccinations . 305 Figure 10.2 Childhood vaccinations . 307 Figure 10.3 Vaccination coverage by birth order . 307 Figure 10.4 Diarrhoea prevalence by age . 310 Figure 10.5 Feeding practices during diarrhoea . 310 Figure 10.6 Treatment of diarrhoea . 311 Figure 10.7 Symptoms of childhood illness and careseeking . 312 11 NUTRITION OF CHILDREN AND ADULTS . 337 Table 11.1 Nutritional status of children . 358 Table 11.1C Nutritional status of children by county . 359 Table 11.2 Child growth monitoring . 360 Table 11.2C Child growth monitoring by county . 361 Table 11.3 Early breastfeeding . 362 Table 11.3C Early breastfeeding by county . 363 Table 11.4 Breastfeeding status according to age. 364 Table 11.5 Infant feeding practices by age . 364 Table 11.6 Liquids consumed by children in the day or night preceding the interview . 365 Table 11.7 Foods consumed by children in the day or night preceding the interview . 366 Table 11.8 Minimum dietary diversity, minimum meal frequency, and minimum acceptable diet among children . 367 Table 11.9 Egg and/or flesh food consumption and unhealthy feeding practices among children age 6–23 months . 368 Table 11.10 Infant and young child feeding (IYCF) indicators . 369 Table 11.11 Infant and young child feeding counselling . 370 Table 11.12 Micronutrient supplementation and deworming among children . 371 Table 11.12C Micronutrient supplementation and deworming among children by county . 372 Table 11.13.1 Nutritional status of women age 20–49 . 373 Table 11.13.1C Nutritional status of women age 20–49 by county . 374 Table 11.13.2 Nutritional status of adolescent women age 15–19 . 375 Table 11.13.2C Nutritional status of adolescent women age 15–19 by county . 376 Table 11.13.3 Nutritional status of men age 20–49 . 377 Table 11.13.3C Nutritional status of men age 20–49 by county . 378 Table 11.13.4 Nutritional status of adolescent men age 15–19 . 379 Table 11.13.4C Nutritional status of adolescent men age 15–19 by county . 380 Table 11.14 Foods and liquids consumed by women in the day or night preceding the interview . 381 Table 11.14C Foods and liquids consumed by women in the day or night preceding the interview by county . 382 Table 11.15 Minimum dietary diversity and unhealthy food and beverage consumption among women . 384 Table 11.15C Minimum dietary diversity and unhealthy food and beverage consumption among women by county . 385 Table 11.16 Presence of iodised salt in household . 386 Table 11.16C Presence of iodised salt in household by county . 387 Figure 11.1 Trends in child growth measures . 339 Figure 11.2 Stunting in children by household wealth . 340 Figure 11.3 Infant feeding practices by age . 343 Tables, Figures, and Maps  xvii Figure 11.4 IYCF indicators on Minimum Acceptable Diet (MAD) by breastfeeding status . 346 Figure 11.5 IYCF indicators on Minimum Acceptable Diet (MAD) by mother’s education . 347 Figure 11.6 Unhealthy feeding practices among children age 6–23 months by breastfeeding status . 348 Figure 11.7 Unhealthy feeding practices among children age 6–23 months by mother’s education . 348 Figure 11.8 Nutritional status of adolescent and adult women and men . 352 Figure 11.9 Trends in women’s nutritional status . 353 Figure 11.10 Minimum dietary diversity among women by education . 356 Map 11.1 Stunting in children by county . 340 12 MALARIA . 389 Table 12.1 Household possession of mosquito nets . 400 Table 12.1C Household possession of mosquito nets by county . 402 Table 12.2 Source of mosquito nets . 403 Table 12.2C Source of insecticide-treated nets (ITNs) by county . 404 Table 12.3 Access to an insecticide-treated net (ITN) . 405 Table 12.3C Access to an insecticide-treated net (ITN) by county . 406 Table 12.4 Use of mosquito nets by persons in the household . 407 Table 12.4C Use of mosquito nets by persons in the household by county . 408 Table 12.5 Use of existing ITNs . 409 Table 12.5C Use of existing ITNs by county . 410 Table 12.6 Use of mosquito nets by children . 411 Table 12.6C Use of mosquito nets by children according to county . 412 Table 12.7 Use of mosquito nets by pregnant women . 413 Table 12.8 Main reason mosquito net was not used the night before the survey . 414 Table 12.8C Main reason mosquito net was not used the night before the survey by county . 415 Table 12.9 Use of Intermittent Preventive Treatment (IPTp) by women during pregnancy . 416 Table 12.9C Use of Intermittent Preventive Treatment (IPTp) by women during pregnancy according to county . 417 Table 12.10 Children with fever and careseeking, prompt treatment, and diagnosis . 418 Table 12.10C Children with fever and careseeking, prompt treatment, and diagnosis by county . 419 Table 12.11 Source of advice or treatment for children with fever . 420 Table 12.12 Type of antimalarial drugs used . 421 Figure 12.1 Household ownership of ITNs . 390 Figure 12.2 Trends in household ownership of ITNs . 391 Figure 12.3 Source of ITNs . 392 Figure 12.4 Access to and use of ITNs . 393 Figure 12.5 Trends in ITN access and use . 394 Figure 12.6 ITN use . 395 Figure 12.7 Trends in use of ITNs by children and pregnant women in households with at least one ITN . 396 Figure 12.8 Reason ITN was not used . 396 Figure 12.9 Trends in IPTp use by pregnant women . 397 Figure 12.10 Trends in ACT use by children with fever . 399 xviii  Tables, Figures, and Maps Map 12.1 ITN ownership by county . 392 Map 12.2 Use of INT by persons in the household by county . 394 13 KNOWLEDGE, ATTITUDES, AND BEHAVIOUR RELATED TO HIV, AIDS, AND TUBERCULOSIS . 421 Table 13.1 Knowledge of and beliefs about tuberculosis . 438 Table 13.1C Knowledge of and beliefs about tuberculosis by county . 439 Table 13.2.1 Tuberculosis diagnosis and preventive treatment: Women . 440 Table 13.2.2 Tuberculosis diagnosis and preventive treatment: Men . 441 Table 13.3 Knowledge of and attitudes about medicines to treat HIV or prevent HIV transmission . 442 Table 13.4 Discriminatory attitudes towards people living with HIV . 443 Table 13.4C Discriminatory attitudes towards people living with HIV by county . 444 Table 13.5.1 Multiple sexual partners and higher-risk sexual intercourse in the last 12 months: Women . 445 Table 13.5.1C Multiple sexual partners and higher-risk sexual intercourse in the last 12 months by county: Women . 446 Table 13.5.2 Multiple sexual partners and higher-risk sexual intercourse in the last 12 months: Men . 447 Table 13.5.2C Multiple sexual partners and higher-risk sexual intercourse in the last 12 months by county: Men . 448 Table 13.6 Pregnant women tested for HIV . 449 Table 13.7.1 Coverage of prior HIV testing: Women . 450 Table 13.7.1C Coverage of prior HIV testing by county: Women. 451 Table 13.7.2 Coverage of prior HIV testing: Men . 452 Table 13.7.2C Coverage of prior HIV testing by county: Men . 453 Table 13.7.3 Coverage of prior HIV testing: Women and men . 454 Table 13.7.3C Coverage of prior HIV testing by county: Women and men . 455 Table 13.8 Number of times tested for HIV in lifetime . 456 Table 13.9 Knowledge and coverage of self-testing for HIV . 456 Table 13.10.1 Disclosure, shame, and stigma experienced by people living with HIV: Women . 457 Table 13.10.2 Disclosure, shame, and stigma experienced by people living with HIV: Men . 458 Table 13.11 Male circumcision . 459 Table 13.11C Male circumcision by county . 460 Table 13.12 Self-reported prevalence of sexually-transmitted infections (STIs) and STIs symptoms . 461 Table 13.12C Self-reported prevalence of sexually-transmitted infections (STIs) and STIs symptoms by county . 462 Table 13.13.1 Knowledge about HIV prevention among young people: Women . 463 Table 13.13.1C Knowledge about HIV prevention among young people by county: Women . 464 Table 13.13.2 Knowledge about HIV prevention among young people: Men . 465 Table 13.13.2C Knowledge about HIV prevention among young people by county: Men . 466 Table 13.14 Age at first sexual intercourse among young people . 467 Table 13.15 Premarital sexual intercourse among young people . 467 Table 13.16.1 Multiple sexual partners and higher-risk sexual intercourse in the last 12 months among young people: Women . 468 Tables, Figures, and Maps  xix Table 13.16.2 Multiple sexual partners and higher-risk sexual intercourse in the last 12 months among young people: Men . 469 Table 13.17 Recent HIV tests among young people . 470 Figure 13.1 Knowledge of medicines to treat HIV or prevent HIV transmission . 423 Figure 13.2 Trends in knowledge of mother-to-child transmission (MTCT) . 423 Figure 13.3 Discriminatory attitudes towards people living with HIV . 424 Figure 13.4 Discriminatory attitudes towards people living with HIV education . 425 Figure 13.5 Sex and condom use with non-cohabiting partners . 425 Figure 13.6 HIV testing . 427 Figure 13.7 Trends in HIV testing . 427 Figure 13.8 HIV testing by wealth quintile . 428 Figure 13.9 Disclosure, shame, and stigma experienced by people living with HIV . 432 Figure 13.10 Knowledge about HIV prevention among young people . 434 Figure 13.11 Trends in age at first sexual intercourse among young people . 435 Map 13.1 HIV testing by county . 429 14 CHRONIC CONDITIONS . 471 Table 14.1 Physical activity. 480 Table 14.1C Physical activity by county . 481 Table 14.2.1 Blood pressure diagnosis and treatment: Women. 482 Table 14.2.1C Blood pressure diagnosis and treatment by county: Women . 483 Table 14.2.2 Blood pressure diagnosis and treatment: Men . 484 Table 14.2.2C Blood pressure diagnosis and treatment by county: Men . 485 Table 14.3.1 Blood sugar diagnosis and treatment: Women . 486 Table 14.3.2 Blood sugar diagnosis and treatment: Men . 487 Table 14.4 Heart disease and chronic heart condition diagnosis and treatment . 488 Table 14.5 Lung disease and chronic lung condition diagnosis and treatment . 489 Table 14.6 Depression diagnosis and treatment . 490 Table 14.6C Depression diagnosis and treatment by county . 491 Table 14.7 Examinations for breast and cervical cancer . 492 Table 14.7C Examinations for breast and cervical cancer by county . 493 Table 14.8 Arthritis diagnosis and treatment . 494 Table 14.8C Arthritis diagnosis and treatment by county . 495 Table 14.9 Prostate cancer diagnosis and treatment . 496 Figure 14.1 Blood pressure and blood sugar diagnosis and treatment . 473 Figure 14.2 Breast and cervical cancer exams by education . 476 Map 14.1 Breast and cervical cancer exams by county . 477 15 WOMEN’S EMPOWERMENT . 497 Table 15.1 Employment and cash earnings of currently married women and men . 510 Table 15.1.1C Employment and cash earnings of currently married women by county . 511 Table 15.1.2C Employment and cash earnings of currently married men by county . 512 Table 15.1.3 Average monthly earnings . 513 Table 15.2.1 Control over women’s cash earnings and relative magnitude of women’s cash earnings . 514 Table 15.2.1C Control over women’s cash earnings and relative magnitude of women’s cash earnings by county . 515 Table 15.2.2 Control over men’s cash earnings . 516 Table 15.2.2C Control over men’s cash earnings by county . 517 xx  Tables, Figures, and Maps Table 15.3.1 House and land ownership: Women . 518 Table 15.3.1C House and land ownership by county: Women . 519 Table 15.3.2 House and land ownership: Men . 520 Table 15.3.2C House and land ownership by county: Men . 521 Table 15.4.1 House ownership and documentation of ownership: Women . 522 Table 15.4.1C House ownership and documentation of ownership by county: Women . 523 Table 15.4.2 House ownership and documentation of ownership: Men . 524 Table 15.4.2C House ownership and documentation of ownership by county: Men . 525 Table 15.5.1 Agricultural land ownership and documentation of ownership: Women . 526 Table 15.5.1C Agricultural land ownership and documentation of ownership by county: Women . 527 Table 15.5.2 Non-agricultural land ownership and documentation of ownership: Women . 528 Table 15.5.2C Non-agricultural land ownership and documentation of ownership by county: Women . 529 Table 15.5.3 Agricultural land ownership and documentation of ownership: Men . 530 Table 15.5.3C Agricultural land ownership and documentation of ownership by county: Men . 531 Table 15.5.4 Non-agricultural land ownership and documentation of ownership: Men . 532 Table 15.5.4C Non-agricultural land ownership and documentation of ownership: Men . 533 Table 15.6.1 Ownership and use of mobile phones and bank accounts: Women . 534 Table 15.6.1C Ownership and use of mobile phones and bank accounts by county: Women . 535 Table 15.6.2 Ownership and use of mobile phones and bank accounts: Men . 536 Table 15.6.2C Ownership and use of mobile phones and bank accounts by county: Men . 537 Table 15.7 Participation in decision making . 538 Table 15.8.1 Women’s participation in decision making by background characteristics . 538 Table 15.8.1C Women’s participation in decision making by background characteristics . 539 Table 15.8.2 Men’s participation in decision making by background characteristics . 540 Table 15.8.2C Men’s participation in decision making by county . 541 Table 15.9.1 Attitude toward wife beating: Women . 542 Table 15.9.1C Attitude toward wife beating by county: Women . 543 Table 15.9.2 Attitude toward wife beating: Men . 544 Table 15.9.2C Attitude toward wife beating: Men . 545 Table 15.10 Attitudes toward negotiating safer sexual relations with husband . 546 Table 15.10C Attitudes toward negotiating safer sexual relations with husband by county . 547 Table 15.11 Ability to negotiate sexual relations with husband . 548 Table 15.11C Ability to negotiate sexual relations with husband by county . 549 Table 15.12 Women’s participation in decision making about sexual and reproductive health . 550 Table 15.12C Women’s participation in decision making about sexual and reproductive health by county . 551 Figure 15.1 Control over women’s earnings . 499 Figure 15.2 Ownership of a house or land . 501 Figure 15.3 Ownership of assets . 503 Tables, Figures, and Maps  xxi Figure 15.4 Participation in decision making . 504 Figure 15.5 Attitudes towards wife beating . 505 Figure 15.6 Women’s participation in decision making regarding sexual and reproductive health by education . 507 Map 15.1 Women’s participation in decision making about sexual and reproductive health by county . 508 16 HOUSEHOLD WATER AND SANITATION . 563 Table 16.1 Household drinking water . 564 Table 16.2 Drinking water service ladder . 564 Table 16.2C Drinking water service ladder by county . 565 Table 16.3 Person collecting drinking water . 566 Table 16.3C Person collecting drinking water by county . 567 Table 16.4 Availability of sufficient drinking water . 568 Table 16.4C Availability of sufficient drinking water by county. 569 Table 16.5 Treatment of household drinking water . 570 Table 16.5C Treatment of household drinking water by county . 571 Table 16.6 Household sanitation facilities . 572 Table 16.7 Sanitation service ladder . 572 Table 16.7C Sanitation service ladder by county . 573 Table 16.8 Emptying and removal of wastes from on-site sanitation facilities . 574 Table 16.8C Emptying and removal of wastes from on-site sanitation facilities by county . 575 Table 16.9 Management of household excreta . 576 Table 16.9C Management of household excreta by county . 577 Table 16.10 Disposal of children’s stools. 578 Table 16.10C Disposal of children’s stools by county . 579 Table 16.11 Handwashing . 580 Table 16.11C Handwashing by county . 581 Table 16.12 Menstrual hygiene . 582 Table 16.12C Menstrual hygiene by county . 583 Figure 16.1 Household with improved source of drinking water service by residence . 554 Figure 16.2 Household population drinking water service by residence . 555 Figure 16.3 Person collecting drinking water . 557 Figure 16.4 Availability of sufficient quantities of drinking water by wealth quintile . 558 Figure 16.5 Household population sanitation service by residence . 559 Figure 16.6 Management of household excreta . 560 Figure 16.7 Management of household excreta by household wealth . 561 Figure 16.8 Menstrual hygiene by education . 563 Map 16.1 At least basic service for drinking water by county . 556 17 GENDER-BASED VIOLENCE . 585 Table 17.1 Experience of physical violence by any perpetrator . 600 Table 17.1C Experience of physical violence by any perpetrator according to county . 601 Table 17.2 Persons committing physical violence. 602 Table 17.3 Experience of physical violence during pregnancy . 603 Table 17.3C Experience of violence during pregnancy by county . 604 Table 17.4 Experience of sexual violence by any perpetrator . 605 xxii  Tables, Figures, and Maps Table 17.4C Experience of sexual violence by any perpetrator according to county . 606 Table 17.5 Persons committing sexual violence . 607 Table 17.6 Experience of sexual violence by any non-intimate partner . 608 Table 17.6C Experience of sexual violence by any non-intimate partner according to county . 609 Table 17.7 Age at first experience of sexual violence . 610 Table 17.8 Experience of different forms of violence . 611 Table 17.9.1 Forms of controlling behaviours and intimate partner violence: Women . 612 Table 17.9.2 Forms of controlling behaviours and intimate partner violence: Men . 613 Table 17.10.1 Controlling behaviours of spouse/intimate partner by background characteristics: Women . 614 Table 17.10.1C Controlling behaviours of spouse/intimate partner by county: Women . 615 Table 17.10.2 Controlling behaviours of spouse/intimate partner by background characteristics: Men . 616 Table 17.11.1 Intimate partner violence by background characteristics: Women . 617 Table 17.11.1C Intimate partner violence by county . 618 Table 17.11.2 Intimate partner violence by background characteristics: Men . 619 Table 17.12.1 Intimate-partner violence by husband’s/intimate partner’s characteristics and women’s empowerment indicators: Women . 620 Table 17.12.2 Intimate partner violence by wife’s/intimate partner’s and respondent’s characteristics: Men . 622 Table 17.13.1 Violence by any husband or intimate partner in the last 12 months: Women . 623 Table 17.13.1C Violence by any husband or intimate partner in the last 12 months by county: Women . 624 Table 17.13.2 Violence by any wife or intimate partner in the last 12 months: Men . 625 Table 17.14 Injuries to women and men due to intimate partner violence . 626 Table 17.15 Physical violence by respondent against their spouse/intimate partner by respondent’s background characteristics . 627 Table 17.15C Violence by respondent against their husband/intimate partner by county . 628 Table 17.16 Violence by respondent against their spouse/intimate partner by spouse/ intimate partner characteristics and women’s empowerment indicators . 629 Table 17.17.1 Help seeking to stop violence: Women . 631 Table 17.17.1C Help seeking to stop violence by county: Women . 632 Table 17.17.2 Help seeking to stop violence: Men . 633 Table 17.18 Sources for help to stop the violence . 634 Figure 17.1 Trends in physical violence . 588 Figure 17.2 Forms of controlling behaviours . 592 Figure 17.3 Prevalence of intimate partner violence among women . 593 Figure 17.4 Prevalence of intimate partner violence among men . 594 Figure 17.5 Intimate partner violence by husband’s/intimate partner’s alcohol consumption . 595 Figure 17.6 Help seeking by type of violence experienced . 598 Map 17.1 Intimate partner violence by any partner in the last 12 months by county . 596 18 FEMALE GENITAL MUTILATION . 635 Table 18.1 Knowledge of female circumcision . 641 Table 18.2 Prevalence of female circumcision . 642 Table 18.3 Age at circumcision . 643 Table 18.4 Prevalence of circumcision and age at circumcision: Girls 0–14 . 643 Tables, Figures, and Maps  xxiii Table 18.5 Circumcision of girls age 0–14 by mother’s background characteristics . 644 Table 18.6 Infibulation among circumcised girls age 0–14 . 645 Table 18.7 Aspects of circumcision among circumcised girls age 0–14 and women age 15–49 . 646 Table 18.8.1 Opinion of women and men about whether circumcision is required by religion . 647 Table 18.8.2 Opinion of women and men about whether circumcision is required by culture . 648 Table 18.8.3 Opinions of women and men about whether circumcision is required by society . 649 Table 18.9 Opinion of women and men about whether the practice of circumcision should continue . 650 Table 18.10 Effect of female circumcision . 651 Table 18.11 Help seeking behaviours among circumcised women . 652 Figure 18.1 Type of FGM . 636 Figure 18.2 Trends in FGM . 637 Figure 18.3 FGM by age . 637 Figure 18.4 Age at circumcision . 637 Figure 18.5 Age at circumcision among women and girls . 638 Figure 18.6 Attitudes about FGM by circumcision status . 639 Foreword  xxv FOREWORD he Kenya Demographic and Health Survey (KDHS) was the 7th to be carried out in Kenya, following similar surveys conducted in 1989, 1993, 1998, 2003, 2008–09 and 2014. The survey’s objective was to provide up to-date information on socio-economic, demographic, nutrition and health indicators for planning, monitoring and evaluation of various health programmes and policies. The 2022 KDHS was implemented in DHS-8 series that expanded some of the questions and had additional modules like early childhood development and chronic diseases. Further, the survey obtained information on indicators of interest as related to Universal Health Coverage, and these included healthcare financing and utilisation of community health services. The survey also provided an opportunity to obtain information on key indicators related to the COVID-19 pandemic, the latest global health threat. This report provides baseline indicators that will be used in monitoring and evaluation of the progress of implementation of the Bottom-up Economic Transformation Agenda (BETA) and its commitments to the citizens as far as health support systems are concerned. The information in the report provides benchmark statistics on demographic profiles and health care status of households in the implementation of the Medium-Term Plan IV (MTP IV) and the country’s economic blueprint, Vision 2030. Besides this, the report provides indicators to monitor and evaluate Kenya’s achievements towards Agenda 2030 on Sustainable Development Goals and aspirations of the Africa Agenda 2063. It is worth noting that for a number of indicators, the information in the report has been provided at county level to enable the county governments to adequately plan, monitor, and evaluate their respective health programmes and projects. Generally, the 2022 KDHS shows an improvement in many of the health indicators. The total fertility rate (TFR) was 3.4 children per woman, having declined from 3.9 as reported in 2014 KDHS. The TFR has been on a declining trend from a high of 8.1 recorded in 1977–78 Kenya Fertility Survey (KFS). The use of modern methods of family planning among currently married women in Kenya has been on the rise, increasing from 18% in 1989 to 57% in 2022. There is notable improvement in maternal health indicators with more women attending four or more antenatal visits for their most recent live birth, at 66% in 2022 compared to 58% in 2014. Additionally, eight in ten live births were delivered in a health facility in 2022, up from six in ten recorded in 2014. Mortality rates among infants and under-fives have also improved over time in Kenya. Infant mortality rate decreased from 61 deaths per 1,000 live births in 1989 to 32 deaths per 1,000 live births in 2022. Similarly, under-five mortality rate declined from 90 deaths per 1,000 live births in 1989 to 41 deaths per 1,000 live births in 2022. Prevalence of stunting among children under five years was 18% in 2022, representing a significant decrease from 35% in 2008–09. This indicates a reduction in chronic undernutrition. Moreover, 80% of children aged 12–23 months received all basic vaccinations (BCG, measles, three doses each of DPT and polio vaccine, excluding polio vaccine given at birth) in 2022. The percentage of women age 15– 49 who reported being circumcised declined from 38% in 1998 to 15% in 2022. This portrays a gradual shift in cultural practices and attitudes towards female genital mutilation in the country. The 2022 KDHS was implemented by Kenya National Bureau of Statistics (KNBS) in collaboration with the Ministry of Health (MoH), Directorate of Monitoring and Evaluation in The National Treasury and Economic Planning, National Council for Population and Development (NCPD), Kenya Medical Research Institute (KEMRI), National Syndemic Diseases Control Council (NSDCC), Council of Governors (CoG), Population Studies and Research Institute (PSRI) of University of Nairobi, State Department for Social Security and Protection in the Ministry of Labour and Social Protection (SDSSP-MLSP), State Department for Gender and Affirmative Action in the Ministry of Public Service, Gender and Affirmative Action (SDGAA-MPSGAA), Anti-FGM Board, National Gender and Equality Commission (NGEC), United States Agency for International Development-Kenya (USAID/Kenya), ICF, The United Nations Resident T xxvi  Foreword Coordinator’s Office (UNRCO), United Nations Children’s Fund (UNICEF), Bill & Melinda Gates Foundation (BMGF), United Nations Population Fund (UNFPA), United Nations AIDS (UNAIDS), United Nations Development Programme (UNDP), World Food Programme (WFP), UN Women, The World Bank, Clinton Health Access Initiative (CHAI), Nutrition International–Kenya, and the World Health Organization (WHO). KNBS provided leadership in the overall survey planning, development of survey tools, training of personnel, data collection and analysis. KNBS appreciates the support provided by the various stakeholders, the steering committee, the technical committee, the secretariat, and all the survey personnel who worked tirelessly, through a period when the COVID-19 pandemic was at its peak and, in most cases, for very long hours to collect the data and to develop this report. Additionally, we are grateful to the respondents for taking their time to provide valuable information that has made this report possible. MACDONALD G. OBUDHO, MBS DIRECTOR GENERAL KENYA NATIONAL BUREAU OF STATISTICS Acronyms and Abbreviations  xxvii ACRONYMS AND ABBREVIATIONS ACT artemisinin-based combination therapy AIDS acquired immunodeficiency syndrome AL artemether-lumefantrine ANC antenatal care ARI acute respiratory infection ART antiretroviral therapy BCG bacillus Calmette-Guérin BMI body mass index CAPI computer-assisted personal interviewing CBO community-based organisation CBR crude birth rate CEDAW Convention on the Elimination of All Forms of Discrimination against Women CHAI Clinton Health Access Initiative CHW community health worker COVID-19 coronavirus disease 2019 CSI Coping Strategies Index CSPro Census and Survey Processing DHAP dihydroartemisinin-piperaquine DHS Demographic and Health Survey DMPA-SC subcutaneous depot medroxyprogesterone acetate DPT diphtheria, pertussis, and tetanus vaccine EA enumeration area ECD early childhood development ECDI Early Childhood Development Index EPSEM Equal Probability Selection Method FBO faith-based organisation FCS Food Consumption Score FGM female genital mutilation FP2030 Family Planning 2030 GAR gross attendance ratio GBV gender-based violence GFR general fertility rate GPI Gender Parity Index GPS global positioning system HepB hepatitis B Hib Haemophilus influenzae type B HIV human immunodeficiency virus IPTp intermittent preventive treatment during pregnancy IPV inactivated polio vaccine ITN insecticide-treated net IUD intrauterine contraceptive device IYCF infant and young child feeding xxviii  Acronyms and Abbreviations JMP Joint Monitoring Programme for Water Supply, Sanitation, and Hygiene KDHS Kenya Demographic and Health Survey K-HMSF Kenya Household Master Sample Frame KMIS Kenya Malaria Indicator Survey KNBS Kenya National Bureau of Statistics KPHC Kenya Population and Housing Census KSh Kenya shilling LAM lactational amenorrhoea method LLIN long-lasting insecticidal net LPG liquefied petroleum gas METS metabolic equivalents MoH Ministry of Health MR measles-rubella MTCT mother-to-child transmission MTP Medium-Term Plan MUAC mid-upper arm circumference NAR net attendance ratio NCD non-communicable disease NGAO National Government Administration Officer NGO nongovernmental organisation NHIF National Hospital Insurance Fund OPV oral polio vaccine ORS oral rehydration salts ORT oral rehydration therapy PCV pneumococcal conjugate vaccine PNC postnatal care RHF recommended homemade fluids OR government recommended homemade fluids SD standard deviation SDG Sustainable Development Goal SDM standard days method SP sulfadoxine-pyrimethamine STI sexually transmitted infection TB tuberculosis TFR total fertility rate UN Women United Nations Entity for Gender Equality and the Empowerment of Women UNAIDS Joint United Nations Programme on HIV/AIDS UNFPA United Nations Population Fund UNICEF United Nations Children’s Fund UNSCR United Nations Security Council Resolution USAID United States Agency for International Development VIP ventilated improved pit WFP World Food Programme WG Washington Group on Disability Statistics WHO World Health Organization Reading and Understanding Tables from the 2022 KDHS • xxix READING AND UNDERSTANDING TABLES FROM THE 2022 KENYA DEMOGRAPHIC AND HEALTH SURVEY (KDHS) he 2022 Kenya DHS final report is based on approximately 200 tables of data. For quick reference, they are located at the end of each chapter and can be accessed through links in the pertinent text (electronic version). Additionally, this more reader-friendly version features about 90 figures that clearly highlight trends, subnational patterns, and background characteristics. Large colourful maps display breakdowns for 47 counties in Kenya. The text has been simplified to highlight key points in bullets and to clearly identify indicator definitions in boxes. While the text and figures featured in each chapter highlight some of the most important findings from the tables, not every finding can be discussed or displayed graphically. For this reason, KDHS data users should be comfortable reading and interpreting tables. The following pages provide an introduction to the organisation of KDHS tables, the presentation of background characteristics, and a brief summary of sampling and understanding denominators. In addition, this section provides some exercises for users as they practice their new skills in interpreting KDHS tables. T xxx • Reading and Understanding Tables from the 2022 KDHS Example 1: Exposure to mass media: Women A Question Asked of All Survey Respondents Table 3.4.1 Exposure to mass media: Women Percentage of women age 15–49 who are exposed to specific media on a weekly basis, according to background characteristics, Kenya DHS 2022 Background characteristic Reads a newspaper at least once a week Watches television at least once a week Listens to the radio at least once a week Accesses all three media at least once a week Accesses none of the three media at least once a week Number of women Age 15–19 9.3 50.0 56.6 5.0 24.6 6,025 20–24 8.0 56.2 63.5 5.1 19.4 6,001 25–29 7.8 61.3 64.4 5.5 18.7 5,687 30–34 7.3 57.8 63.9 5.0 20.3 4,530 35–39 7.7 55.2 62.7 5.4 22.9 4,311 40–44 9.5 53.1 63.7 6.3 21.3 3,084 45–49 8.9 49.4 61.7 5.9 24.9 2,518 Residence Urban 10.4 74.1 61.1 7.2 14.3 13,143 Rural 6.8 42.1 62.9 4.1 26.4 19,013 Education1 No education 0.1 13.6 26.9 0.0 66.4 1,770 Primary 3.1 42.5 60.6 1.5 26.9 11,687 Secondary 9.0 62.1 66.6 5.6 15.4 12,550 More than secondary 19.2 77.1 66.4 13.8 10.5 6,150 Wealth quintile Lowest 3.0 10.1 44.3 0.9 52.7 5,019 Second 4.9 26.2 65.8 1.8 27.4 5,698 Middle 7.1 54.7 67.3 4.2 19.0 6,069 Fourth 8.5 71.2 64.7 5.9 14.4 7,139 Highest 14.6 89.2 64.7 11.0 6.2 8,231 Total 8.3 55.2 62.2 5.4 21.5 32,156 1 No education includes informal education (Madrassa/Duksi/adult education), and more than secondary includes middle-level colleges and universities. Secondary includes individuals who reported vocational training as the highest education level attended. Step 1: Read the title and subtitle, highlighted in orange in the table above. They tell you the topic and the specific population group being described. In this case, the table is about women age 15–49 and their exposure to different types of media. All eligible female respondents age 15–49 were asked these questions. Step 2: Scan the column headings—highlighted in green in Example 1. They describe how the information is categorised. In this table, the first three columns of data show different types of media that women access at least once a week. The fourth column shows women who access all three types of media, while the fifth column shows women who do not access any of the three types of media on a weekly basis. The last column lists the number of women age 15–49 interviewed in the survey. Step 3: Scan the row headings—the first vertical column highlighted in blue in Example 1. These show the different ways the data are divided into categories based on population characteristics. In this case, the table presents women’s exposure to media by age, urban-rural residence, level of education, and wealth quintile. Most of the tables in the KDHS report will be divided into these same categories. Step 4: Look at the row at the bottom of the table highlighted in pink. These percentages represent the totals of all women age 15–49 and their weekly access to different types of media. In this case, 8.3% of 1 2 3 4 5 Reading and Understanding Tables from the 2022 KDHS • xxxi women age 15–49 read a newspaper at least once a week, 55.2% watch television at least weekly, and 62.2% listen to the radio on a weekly basis.1 Step 5: To find out what percentage of women in the rural areas listen to the radio at least once a week, draw two imaginary lines, as shown on the table. This shows that 62.9% of women in the rural areas listen to the radio at least once a week. By looking at patterns by background characteristics, we can see how exposure to mass media varies across Kenya. Mass media are often used to communicate health messages. Knowing how mass media exposure varies among different groups can help programme planners and policy makers determine how to most effectively reach their target populations. *For the purpose of this document, data are presented exactly as they appear in the table including decimal places. However, the text in the remainder of this report rounds data to the nearest whole percentage point. Practice: Use the table in Example 1 to answer the following questions: a) What percentage of women in Kenya do not access any of the three media at least once a week? b) Which age group of women is most likely to watch television at least once a week? c) Which women read a newspaper at least once a week by education level? d) Which age group is the least exposed to newspapers at least once a week? e) What are the lowest and the highest percentages (range) of women who accesses none of the three media at least once a week by education level? f) Is there a clear pattern in women who accesses all three media at least once a week by wealth quintile? 1 For the purpose of this document data are presented exactly as they appear in the table including decimal places. However, the text in the remainder of this report rounds data to the nearest whole percentage point. Answers: a) 21.5% b) Women age 30–34 are more likely to watch television at least once a week. c) Women with more than secondary level education read a newspaper at least once a week; 19.2% compared to 0.1% for women with no education, 3.1% for women with primary education, and 9.0% for women with secondary education. d) Women age 30–34 are the least exposed to newspaper at least once a week; 7.3%. e) The range for women who accesses none of the three media at least once a week by education level is 10.5% for women with more than secondary education and 66.4% for women with no education. f) Yes, weekly exposure to all three media increases with the household quintile; 0.9% for women in the lowest quintile, 1.8% for the second lowest, 4.2% for the third, 5.9% for the fourth, and 11.0% for the highest quintile. xxxii • Reading and Understanding Tables from the 2022 KDHS Example 2: Children with symptoms of ARI and careseeking for symptoms of ARI A Question Asked of a Subgroup of Survey Respondents Table 10.6 Children with symptoms of ARI and careseeking for symptoms of ARI Among children under age 5, percentage who had symptoms of acute respiratory infection (ARI) in the 2 weeks before the survey; and among children with symptoms of ARI in the 2 weeks before the survey, percentage for whom advice or treatment was sought, according to background characteristics, Kenya DHS 2022 Among children under age 5: Among children under age 5 with symptoms of ARI: Background characteristic Percentage with symptoms of ARI1 Number of children Percentage for whom advice or treatment was sought2 Percentage for whom advice or treatment was sought the same or next day2 Number of children Age in months <6 1.4 1,735 (75.1) (48.0) 24 6–11 2.1 1,838 (84.0) (41.7) 39 12–23 1.9 3,324 87.0 46.1 65 24–35 1.7 3,230 88.2 53.9 56 36–47 1.8 3,435 81.8 30.5 61 48–59 1.5 3,321 72.5 43.5 49 Sex Male 1.9 8,589 84.7 43.9 161 Female 1.6 8,294 79.4 43.0 132 Mother’s smoking status3 Smokes cigarettes/tobacco 0.0 55 * * 0 Does not smoke 1.7 8,683 84.5 46.1 147 Cooking fuels and technologies Clean fuel and technology4 1.4 4,556 (96.7) (46.0) 64 Solid fuel5 1.9 11,875 77.8 44.0 222 Kerosene/paraffin 1.7 432 * * 7 No food cooked in household * 21 * * 1 Residence Urban 1.7 6,316 89.5 38.4 109 Rural 1.7 10,567 78.1 46.5 184 Mother’s education6 No education 1.9 1,738 69.3 36.5 34 Primary 2.1 6,374 80.7 46.6 135 Secondary 1.5 5,719 84.0 33.5 85 More than secondary 1.3 3,053 (95.2) (59.9) 40 Wealth quintile Lowest 2.5 3,784 78.5 44.6 96 Second 2.1 3,038 73.0 38.8 63 Middle 1.4 2,955 82.6 56.7 40 Fourth 1.4 3,410 88.6 34.8 48 Highest 1.2 3,697 (96.3) (45.4) 46 Total 1.7 16,883 82.3 43.5 293 Note: Figures in parentheses are based on 25–49 unweighted cases. An asterisk indicates that a figure is based on fewer than 25 unweighted cases and has been suppressed. Step 1: Read the title and subtitle. In this case, the table is about two separate groups of children: all children under age 5 (a) and children under age 5 with symptoms of acute respiratory infection (ARI) in the 2 weeks before the survey (b). Step 2: Identify the two panels. First, identify the columns that refer to all children under age 5 (a), and then isolate the columns that refer only to children under age 5 with symptoms of ARI in the 2 weeks before the survey (b). Step 3: Look at the first panel. What percentage of children under age 5 had symptoms of ARI in the 2 weeks before the survey? It is 1.7%. Now look at the second panel. How many children under age 5 had 1 2 3 4 a b Reading and Understanding Tables from the 2022 KDHS • xxxiii symptoms of ARI in the 2 weeks before the survey? It’s 293 children or 1.7% of the 16,883 children (with rounding). The second panel is a subset of the first panel. Step 4: Only 1.7% of children under age 5 had symptoms of ARI in the 2 weeks before the survey. Once these children are further divided into the background characteristic categories, there may be too few cases for the percentages to be reliable. ▪ What percentage of children under age 5 with symptoms of ARI under 6 months had advice or treatment sought the same or next day? 48%. This percentage is in parentheses because there are between 25 and 49 children (unweighted) in this category. Readers should use this number with caution—it may not be reliable. (For more information on weighted and unweighted numbers, see Example 4.) ▪ What percentage of children under age 5 with symptoms of ARI had advice or treatment sought and had no food cooked in household? There is no number in this cell—only an asterisk. This is because there are fewer than 25 children. Results for this group are not reported. The subgroup is too small, and therefore the data are not reliable. Note: When parentheses or asterisks are used in a table, the explanation will be noted under the table. If there are no parentheses or asterisks in a table, you can proceed with confidence that enough cases were included in all categories that the data are reliable. xxxiv • Reading and Understanding Tables from the 2022 KDHS Example 3: Understanding Sampling Weights in KDHS Tables A sample is a group of people who have been selected for a survey. In the KDHS, the sample is designed to represent the national population age 15–49. In addition to national data, most countries want to collect and report data on smaller geographical or administrative areas. However, doing so requires a large enough sample size in each area. For the 2022 KDHS, the survey sample is representative at the national and county levels, and for urban and rural areas. To generate statistics that are representative of the country as a whole and the 47 counties, the number of women surveyed in each county should contribute to the size of the total (national) sample in proportion to size of the county. However, if some counties have small populations, then a sample allocated in proportion to each county’s population may not include sufficient women from each county for analysis. To solve this problem, counties with small populations are oversampled. For example, let’s say that you have enough money to interview 32,156 women and want to produce results that are representative of Kenya as a whole and its counties (as in Table 3.1C). However, the total population of Kenya is not evenly distributed among the counties: some counties, such as Nairobi City, are heavily populated while others, such as are not. Thus, Lamu must be oversampled. A sampling statistician determines how many women should be interviewed in each county in order to get reliable statistics. The blue column (1) in the table at the right shows the actual number of women interviewed in each county. Within the counties, the number of women interviewed ranges from 483 in Taita/Taveta to 944 in Nairobi City. The number of interviews is sufficient to get reliable results in each county. With this distribution of interviews, some counties are overrepresented and some counties are underrepresented. For example, the population in Nairobi City is about 13.2% of the population in Kenya, while Lamu’s population contributes only 0.3% of the population in Kenya. But as the blue column shows, the number of women interviewed in Nairobi City accounts for only about 2.9% of the total sample of women interviewed (944 /32,156) and the number of women interviewed in Lamu accounts for almost the same percentage of the total sample of women interviewed (2%, or 675 /32,156). This unweighted distribution of women does not accurately represent the population. Table 3.1C Background characteristics of respondents by county Percent distribution of women and men age 15–49 by county, Kenya DHS 2022 Women County Weighted percent Weighted number Unweighted number Mombasa 2.9 947 749 Kwale 1.5 498 711 Kilifi 2.9 928 742 Tana River 0.5 149 641 Lamu 0.3 101 675 Taita/Taveta 0.7 234 483 Garissa 0.9 290 641 Wajir 0.5 160 745 Mandera 0.6 206 723 Marsabit 0.4 129 535 Isiolo 0.4 137 623 Meru 3.0 979 602 Tharaka-Nithi 0.8 271 535 Embu 1.1 358 584 Kitui 2.3 735 671 Machakos 3.1 992 699 Makueni 2.1 683 720 Nyandarua 1.3 409 590 Nyeri 1.6 501 529 Kirinyaga 1.5 481 605 Murang’a 2.2 692 557 Kiambu 6.5 2,094 668 Turkana 1.0 331 644 West Pokot 1.2 384 756 Samburu 0.5 156 615 Trans Nzoia 2.1 675 713 Uasin Gishu 3.1 983 731 Elgeyo/Marakwet 0.7 228 591 Nandi 1.9 622 721 Baringo 1.2 378 687 Laikipia 1.0 332 576 Nakuru 5.2 1,658 782 Narok 2.2 718 744 Kajiado 2.8 887 660 Kericho 2.3 729 779 Bomet 2.0 650 778 Kakamega 4.0 1,283 810 Vihiga 1.2 371 721 Bungoma 3.5 1,138 841 Busia 1.9 622 768 Siaya 1.7 537 674 Kisumu 2.4 771 761 Homa Bay 2.1 662 712 Migori 2.1 674 777 Kisii 2.6 831 708 Nyamira 1.0 327 635 Nairobi City 13.2 4,235 944 Total 15-49 100.0 32,156 32,156 1 2 3 Reading and Understanding Tables from the 2022 KDHS • xxxv In order to get statistics that are representative of Kenya, the distribution of the women in the sample needs to be weighted (or mathematically adjusted) such that it resembles the true distribution in the country. Women from a small county, like Lamu, should only contribute a small amount to the national total. Women from a large county, like Nairobi City, should contribute much more. Therefore, DHS statisticians mathematically calculate a “weight” which is used to adjust the number of women from each county so that each SUBUNIT’s contribution to the total is proportional to the actual population of the county. The numbers in the purple column (2) represent the “weighted” values. The weighted values can be smaller or larger than the unweighted values at the county level. The total national sample size of 32,156 women has not changed after weighting, but the distribution of the women in the counties has been changed to represent their contribution to the total population size. How do statisticians weight each category? They take into account the probability that a woman was selected in the sample. If you were to compare the green column (3) to the actual population distribution of Kenya, you would see that women in each county are contributing to the total sample with the same weight that they contribute to the population of the country. The weighted number of women in the survey now accurately represents the proportion of women who live in Nairobi City and the proportion of women who live in Lamu. With sampling and weighting, it is possible to interview enough women to provide reliable statistics at national and county levels. In general, only the weighted numbers are shown in each of the KDHS tables, so don’t be surprised if these numbers seem low: they may actually represent a larger number of women interviewed. Sustainable Development Goal Indicators • xxxvii SUSTAINABLE DEVELOPMENT GOAL INDICATORS Sustainable Development Goal Indicators, Kenya DHS 2022 Residence Total Indicator Urban Rural 1. No poverty 1.4.1 Proportion of population living in households with access to basic services a) Access to basic drinking water services 90.6 56.3 67.9 b) Access to basic sanitation services 47.3 37.7 40.9 c) Access to basic hygiene services 67.6 43.0 51.3 d) Access to electricity1 88.6 29.9 49.6 e) Access to clean fuels and technologies2 53.4 4.9 21.2 Sex Total Indicator Male Female 2. Zero hunger 2.2.1 Prevalence of stunting among children under 5 years of age 19.6 15.6 17.6 2.2.2 Prevalence of malnutrition among children under 5 years of age 8.3 7.9 8.1 a) Prevalence of wasting among children under 5 years of age 5.4 4.3 4.9 b) Prevalence of overweight among children under 5 years of age 2.9 3.6 3.2 3. Good health and well-being 3.1.2 Proportion of births attended by skilled health personnel na na 89.3 3.2.1 Under-5 mortality rate3 45.0 38.0 41.0 3.2.2 Neonatal mortality rate3 24.0 19.0 21.0 3.7.1 Proportion of women of reproductive age (age 15–49 years) who have their need for family planning satisfied with modern methods na 74.6 na 3.7.2 Adolescent birth rates per 1,000 women a) Girls age 10–14 years4 na 2.0 na b) Women age 15–19 years5 na 73.0 na 3.a.1 Age-standardised prevalence of current tobacco use among persons age 15 years and older6 12.4 1.3 6.8a 3.b.1 Proportion of the target population covered by all vaccines included in their national programme a) Coverage of DPT containing vaccine (3rd dose)7 88.8 89.5 89.2 b) Coverage of measles containing vaccine (2nd dose)8 69.7 63.8 66.8 c) Coverage of pneumococcal conjugate vaccine (last dose in schedule)9 91.2 91.1 91.2 4. Quality education 4.2.1 Proportion of children age 24–59 months who are developmentally on track in health, learning and psychosocial well-being 76.4 79.8 78.0 4.2.2 Participation rate in organised learning (one year before the official primary entry age) 86.9 88.8 87.9 5. Gender equality 5.2.1 Proportion of ever-partnered women and girls age 15 years and older subjected to physical, sexual or psychological violence by a current or former intimate partner in the previous 12 months10,11 na 28.1 na a) Physical violence na 16.0 na b) Sexual violence na 7.1 na c) Psychological violence na 22.1 na 5.2.2 Proportion of women and girls age 15 years and older subjected to sexual violence by persons other than an intimate partner in the previous 12 months12 na 0.6 na 5.3.1 Proportion of women age 20–24 years who were married or in a union before age 15 and before age 18 a) before age 15 na 2.2 na b) before age 18 na 12.5 na 5.3.2 Proportion of girls and women age 15–49 years who have undergone female genital mutilation/cutting na 14.8 na 5.6.1 Proportion of women age 15–49 years who make their own informed decisions regarding sexual relations, contraceptive use and reproductive health care13 na 64.8 na 5.b.1 Proportion of individuals who own a mobile telephone14 80.4 77.5 79.0a 6. Clean water and sanitation 6.1.1 Proportion of population using safely managed drinking water services a) Proportion with basic drinking water services 90.6 56.3 67.9 b) Proportion with water available when needed 64.1 65.8 65.2 6.2.1 Proportion of population using (a) safely managed sanitation services and (b) hand-washing facility with soap and water a) Proportion using basic sanitation service 47.3 37.7 40.9 b) Proportion in which excreta are safely disposed of in situ or treated off site 89.6 53.8 65.9 c) Proportion using a hand-washing facility with soap and water 67.6 43.0 51.3 d) Proportion using open defecation 0.9 10.4 7.2 7. Affordable clean energy 7.1.1 Proportion of population with access to electricity1 88.6 29.9 49.6 7.1.2 Proportion of population with primary reliance on clean fuels and technology2 53.4 4.9 21.2 Continued… xxxviii  Sustainable Development Goal Indicators Continued Sex Total Indicator Male Female 8. Decent work and economic growth 8.10.2 Proportion of adults (15 years and older) with an account at a bank or other financial institution or with a mobile-money-service provider14 39.1 28.5 33.8a 16. Peace, justice, and strong institutions 16.2.3 Proportion of young women and men age 18–29 years who experienced sexual violence by age 18 2.6 4.9 na 16.9.1 Proportion of children under 5 years of age whose births have been registered with a civil authority 76.3 75.6 76.0 17. Partnerships for the goals 17.8.1 Proportion of individuals using the Internet15 56.2 44.2 50.2a na = not applicable 1 Persons living in households that report the primary source of lighting is electricity 2 Persons living in households that report no cooking, no space heating, or no lighting are not excluded from the numerator. 3 Expressed in terms of deaths per 1,000 live births for the 5-year period preceding the survey 4 Equivalent to the age-specific fertility rate for girls age 10–14 for the 3-year period preceding the survey, expressed in terms of births per 1,000 girls age 10–14 5 Equivalent to the age-specific fertility rate for women age 15–19 for the 3-year period preceding the survey, expressed in terms of births per 1,000 women age 15–19 6 Data are not age-standardised and are available for women and men age 15–49 only. 7 The percentage of children age 12–23 months who received three doses of DPT-HepB-Hib 8 The percentage of children age 24–35 months who received two doses of measles rubella (MR) 9 The percentage of children age 12–23 months who received three doses of pneumococcal conjugate vaccine (PCV) 10 Data are available for women age 15–49 who have ever been in union only. 11 In the DHS, psychological violence is termed emotional violence. 12 Data are available for women age 15–49 only. 13 Data are available for currently married women only. 14 Data are available for women and men age 15–49 only. 15 Data are available for women and men age 15–49 who have used the Internet in the last 12 months. a The total is calculated as the simple arithmetic mean of the percentages in the columns for males and females. xl  Map of Kenya Introduction and Survey Methodology • 1 INTRODUCTION AND SURVEY METHODOLOGY 1 he 2022 Kenya Demographic and Health Survey (2022 KDHS) was implemented by the Kenya National Bureau of Statistics (KNBS) in collaboration with the Ministry of Health (MoH) and other stakeholders. This is the 7th KDHS implemented in the country. Data collection took place from 17 February to 31 July 2022. ICF provided technical assistance through The Demographic and Health Surveys (DHS) Program, which is funded by the United States Agency for International Development (USAID) and offers financial support and technical assistance for population and health surveys in countries worldwide. Other agencies and organisations that facilitated the successful implementation of the survey through technical or financial support include: The Bill & Melinda Gates Foundation, the World Bank, the United Nations Children’s Fund (UNICEF), the United Nations Population Fund (UNFPA), Nutrition International, the World Food Programme (WFP), the United Nations Entity for Gender Equality and the Empowerment of Women (UN Women), the World Health Organization (WHO), the Clinton Health Access Initiative (CHAI), and the Joint United Nations Programme on HIV/AIDS (UNAIDS). 1.1 SURVEY OBJECTIVES The primary objective of the 2022 KDHS is to provide up-to-date estimates of basic sociodemographic, nutrition and health indicators. Specifically, the 2022 KDHS collected information on: ▪ Fertility levels and contraceptive prevalence ▪ Childhood mortality ▪ Maternal and child health ▪ Early Childhood Development Index (ECDI) ▪ Anthropometric measures for children, women, and men ▪ Children’s nutrition ▪ Woman’s dietary diversity ▪ Knowledge and behaviour related to the transmission of HIV and other sexually transmitted diseases ▪ Noncommunicable diseases and other health issues ▪ Extent and pattern of gender-based violence ▪ Female genital mutilation. The information collected in the 2022 KDHS will assist policymakers and programme managers in monitoring, evaluating, and designing programmes and strategies for improving the health of Kenya’s population. The 2022 KDHS also provides indicators relevant to monitoring the Sustainable Development Goals (SDGs) for Kenya, as well as indicators relevant for monitoring national and subnational development agendas such as the Kenya Vision 2030, Medium Term Plans (MTPs), and County Integrated Development Plans (CIDPs). 1.2 SAMPLE DESIGN The sample for the 2022 KDHS was drawn from the Kenya Household Master Sample Frame (K-HMSF). This is the frame that KNBS currently uses to conduct household-based sample surveys in Kenya. The frame is based on the 2019 Kenya Population and Housing Census (KPHC) data, in which a total of 129,067 enumeration areas (EAs) were developed. Of these EAs, 10,000 were selected with probability proportional to size to create the K-HMSF. The 10,000 EAs were randomised into four equal subsamples. A survey can utilise a subsample or a combination of subsamples based on the sample size requirements. The 2022 KDHS sample was drawn from subsample one of the K-HMSF. The EAs were developed into clusters through a process of household listing and geo-referencing. The Constitution of Kenya 2010 established a devolved system of government in which Kenya is divided into 47 counties. To design the T 2 • Introduction and Survey Methodology frame, each of the 47 counties in Kenya was stratified into rural and urban strata, which resulted in 92 strata since Nairobi City and Mombasa counties are purely urban. The 2022 KDHS was designed to provide estimates at the national level, for rural and urban areas separately, and, for some indicators, at the county level. The sample size was computed at 42,300 households, with 25 households selected per cluster, which resulted in 1,692 clusters spread across the country, 1,026 clusters in rural areas, and 666 in urban areas. The sample was allocated to the different sampling strata using power allocation to enable comparability of county estimates. The 2022 KDHS employed a two-stage stratified sample design, where in the first stage 1,692 clusters were selected from the K-HMSF using the Equal Probability Selection Method (EPSEM). The clusters were selected independently in each sampling stratum. Household listing was carried out in all the selected clusters, and the resulting list of households served as a sampling frame for the second stage of selection, where 25 households were selected from each cluster. However, after the household listing procedure, it was found that some clusters had fewer than 25 households; therefore, all households from these clusters were selected into the sample. This resulted in 42,022 households being sampled for the 2022 KDHS. Interviews were conducted only in the pre-selected households and clusters; no replacement of the pre- selected units was allowed during the survey data collection stages. Household listing was done with computer assisted personal interviews (CAPI) with the data transmitted to a central server for processing. During the listing exercise, geo-data were collected to assist in identifying the selected households. All clusters were standardised to have one Measure of Size (MoS) defined as an average of 100 households with a lower limit of 50 households and an upper limit of 149 households. Large EAs with more than 149 households were standardised by splitting them into nearly equal segments with one segment randomly selected and listed to form a cluster. There was no merging of EAs with less than 50 households. The listing exercise used interactive maps that ensured that in every EA, all listed households were within the EA boundaries. The 2022 KDHS was successfully implemented in 1,691 clusters; one cluster in Mandera County could not be visited due to insecurity. As a result of the non-proportional allocation of the sample to the sampling strata and due to nonresponse, the survey was not self-weighting. Therefore, the resulting data have been weighted to be representative nationally as well as for other survey domains. Survey weights were based on the selection probabilities for each sample selection stage. Refer to Appendix A for detailed information on the 2022 KDHS sample design. Introduction and Survey Methodology • 3 Figure 1.1 2022 Kenya DHS sample design All women age 15–49 who were usual members of the selected households or who had slept in the households the night before the survey were eligible for interviews (Figure 1.1). The men’s interview was conducted in half of the sampled households, where all men age 15–54 who were usual members of the selected households or who had slept in those households the night before the survey were eligible to be interviewed. In a half of the men’s subsample, one man per household was randomly selected for the gender-based violence module. In the other half of the men’s subsample and in the sample of households not selected for the men’s interview, one woman per household was randomly selected for the gender- based violence module. Thus, in three quarters of the sample, the gender-based violence module was administered to women, and in one quarter of the sample, the module was administered to men. Half of Households: 21,996 Full Questionnaire ▪ Core modules plus: ▪ Disability ▪ Health expenditure and insurance cover ▪ Food consumption ▪ Social protection ▪ Mobile money ▪ Biomarker (Children <5, Women 15–49, Men 15–54) All Households: 42,300 Core modules: ▪ Characteristics of household members ▪ Birth registration ▪ Household characteristics ▪ Household possession ▪ Possession and use of mosquito nets ▪ Housing characteristics ▪ COVID-19 Traffic Accidents Women (15–49) ▪ Sociodemographic characteristics ▪ Reproduction ▪ Family planning ▪ Maternal health care and breastfeeding ▪ Vaccination and health of children ▪ Children’s nutrition ▪ Woman’s dietary diversity ▪ Early childhood development ▪ Marriage and sexual activity ▪ Fertility preferences ▪ Husband’s background characteristics and woman’s work ▪ HIV/AIDS, other STIs, and TB ▪ Other health issues ▪ Chronic diseases ▪ Female genital mutilation Men (15–54) ▪ Sociodemographic characteristics ▪ Reproduction ▪ Family planning ▪ Marriage and sexual activity ▪ Fertility preferences ▪ Employment and gender roles ▪ HIV/AIDS, other STIs, and TB ▪ Other health issues ▪ Chronic diseases ▪ Female genital mutilation Women (15–49): 10,152 Households ▪ Gender-based violence Men (15–49): 11,844 Households ▪ Gender-based violence Women (15–49) ▪ Sociodemographic characteristics ▪ Reproduction ▪ Family planning ▪ Maternal health care and breastfeeding ▪ Vaccination and health of children ▪ Children’s nutrition ▪ Marriage and sexual activity ▪ Husband’s background characteristics and woman’s work ▪ Gender-based violence Half of Households: 20,304 Short Questionnaire ▪ Core modules ▪ Biomarker (Children <5) 4 • Introduction and Survey Methodology The Biomarker Questionnaire, which included height and weight measurements, was administered in all households with children age 0–59 months and in the men’s subsample, the Biomarker Questionnaire was administered with men age 15–54 and women age 15–49. Modules on disability, COVID-19, health insurance, health expenditures, road traffic accidents, household food expenditure, early childhood development (ECD), chronic diseases, and female genital mutilation (FGM) were administered in half of the households sampled for the 2022 KDHS. The GPS coordinates for all interviewed households were selected during data collection. 1.3 QUESTIONNAIRES Four questionnaires were used in the 2022 KDHS: Household Questionnaire, Woman’s Questionnaire, Man’s Questionnaire, and the Biomarker Questionnaire. The questionnaires, based on The DHS Program’s model questionnaires, were adapted to reflect the population and health issues relevant to Kenya. In addition, a self-administered Fieldworker Questionnaire was used to collect information about the survey’s fieldworkers. The Household Questionnaire identified women and men who were eligible for the individual interviews and women age 15–49, men age 15–54, and children age 0–59 months for anthropometry. The Household Questionnaire also collected information on: ▪ Basic information on each person in the household (name, sex, age, education, relationship to the household head, survival of parents for children under age 18) ▪ Disability ▪ Assets, land ownership, and housing characteristics ▪ Sanitation, water, and other environmental health issues ▪ Health expenditures ▪ Traffic accident and injury ▪ COVID-19 (prevalence, vaccination, and related deaths) ▪ Household food consumption The Woman’s Questionnaire collected information from women age 15–49 on: ▪ Sociodemographic characteristics ▪ Reproduction ▪ Family planning ▪ Maternal health care and breastfeeding ▪ Vaccination and health of children ▪ Children’s nutrition ▪ Woman’s dietary diversity ▪ Early childhood development ▪ Marriage and sexual activity ▪ Fertility preferences ▪ Husband’s background characteristics and woman’s employment activity ▪ HIV/AIDS, other STIs, and TB ▪ Other health issues ▪ Chronic diseases ▪ Female genital mutilation ▪ Gender-based violence Introduction and Survey Methodology • 5 The Man’s Questionnaire was administered to men age 15–54. The questionnaire collected information on: ▪ Sociodemographic characteristics ▪ Reproduction ▪ Family planning ▪ Marriage and sexual activity ▪ Fertility preferences ▪ Employment and gender roles ▪ HIV/AIDS, other STIs, and TB ▪ Other health issues ▪ Chronic diseases ▪ Female genital mutilation ▪ Gender-based violence The Biomarker Questionnaire collected information on anthropometry (weight and height measurements). The full Biomarker Questionnaire collected anthropometry measurements on children age 0–59 months, women age 15–49, and men age 15–54, while the short Biomarker Questionnaire collected weight and height measurements only on children age 0–59 months. There were two versions of the questionnaires for the Household, the individual Woman’s, and the Biomarker: the full and short questionnaires. The full version of the questionnaire contained all the modules and was administered to half of the household sample, while the short version of the questionnaire contained only core modules as indicated in Figure 1.1 and was administered to the other half of the household sample that did not receive the full version of the questionnaire. All questions in the short questionnaires were also contained in the full questionnaires. This provides adequate sample size to enable county level estimations of some indicators. The purpose of the Fieldworker Questionnaire was to collect basic background information on the individuals who were collecting data in the field. This included the Team Supervisor, CAPI Supervisor, Interviewer, and Biomarker Technician. All questionnaires except the Fieldworker Questionnaire were translated into Kiswahili to make it easier for interviewers to ask questions in a language that respondents could understand. All questionnaires were programmed into tablet computers to allow for computer assisted personal interviewing (CAPI) for data collection purposes, with the capability to choose Kiswahili or English. The protocol for the 2022 KDHS was reviewed by the ICF Institutional Review Board. Country-specific reviews were not done because the survey was conducted in accordance with the Statistics Act, 2006. 1.4 ANTHROPOMETRY MEASUREMENTS Children under age 5, women age 15–49, and men age 15–54 had their weight and height measured in order to provide information on their nutritional status. Weight measurements were taken using SECA scales with a digital display (model number SECA 874). Height and length were measured with a ShorrBoard® measuring board. Children younger than age 24 months are measured lying down (recumbent length), while older children and adults were measured standing (height). To assess the precision of measurements, two children were randomly selected in each cluster for remeasurement. The 2022 KDHS adopted the guidelines of The DHS Program, which define a difference of less than one centimetre between the two height measurements as an acceptable level of precision. The data collection application was programmed to calculate anthropometric z scores automatically. Children found to have a z score of less than negative three (–3) or more than three for height-for-age, weight-for- 6 • Introduction and Survey Methodology height, or weight-for-age were flagged as having unusual measurements and were measured a second time. Remeasurement of flagged cases was performed to ensure accurate reporting of height and weight measurements. Children whose second measurement indicated severe wasting (weight-for-height z score less than –3) were referred for treatment to the nearest health facility, and the field team supervisor or another survey team member informed the caretaker of the affected child about the referral for treatment before the team left the cluster. 1.5 TRAINING OF TRAINERS AND PRETEST A total of 45 trainers from KNBS, MoH, other government departments and agencies, universities, and development partners participated in the training of trainers. The training was supported by ICF and was held from 29 November to 3 December 2021. The objectives of the training were to: ▪ Equip trainers with adult learning principles and effective facilitation methods ▪ Review and finalise the 2022 KDHS questionnaires ▪ Familiarise trainers with the 2022 KDHS CAPI system ▪ Prepare and finalise materials for training of survey personnel (interviewers, supervisors, and biomarker technicians) 1.6 PRETEST The pretest included classroom training and field practice for interviewers and biomarker technicians. The training took place from 11 December 2021 to 18 January 2022. The objectives of the pretest were to: ▪ Test the adequacy of the training agenda for the main survey ▪ Test the data collection instruments (questionnaires, manuals, and forms) ▪ Test the suitability of the CAPI data collection approach ▪ Evaluate the competence of personnel ▪ Assess the workload of field interviewers and biomarker technicians ▪ Test the adequacy of training procedures for the field personnel ▪ Test the adequacy of the planned duration of data collection ▪ Evaluate the overall administrative and financial structure and other general logistics issues ▪ Test the reliability of the central server data transmission mechanisms and the robustness of the system established to monitor the quality of data from the field ▪ Test the effectiveness of the publicity and advocacy strategy and data processing strategies The training for the pretest included all aspects of the questionnaire content, interviewing procedures, and anthropometry practice with children. Two days were used for field practice, and then the field teams were sent to eight counties to pilot the survey tools and procedures. The pretest clusters were selected to include different geographical areas and different languages. These clusters were not part of the 2022 KDHS sample. After the fieldwork, a debriefing was held to assess issues from the pretest. The resolutions from the debriefing were used to finalise the questionnaires, CAPI programme, and field logistics before the implementation of the main training and data collection. 1.7 TRAINING OF FIELD STAFF A total of 314 personnel (48 supervisors, 48 biomarker technicians, 144 female interviewers, 48 male interviewers, and 26 reserves) were trained at a central location from 17 January to 13 February 2022. The training included a detailed question-by-question explanation of the questionnaires, accompanied by explanations from the interviewer’s manual, role-play demonstrations, group discussions, in-class practice interviewing in pairs, and assessment tests. Anthropometry training provided the biomarker technicians with instruction, demonstrations, and practice in length/height and weight measurements for children and adults. The technicians completed a Introduction and Survey Methodology • 7 standardisation exercise with measurements of children that were intended to gauge and improve accuracy and precision. Restandardisation exercises were conducted for those who did not pass the standardisation exercises. The biomarker technicians had a medical or health background. Appendix C Table C.7 provides the standardisation results. 1.8 FIELDWORK Data collection for the 2022 KDHS was conducted by 48 teams from 17 February to 13 July 2022. Each team included one supervisor, one biomarker technician, three female interviewers, one male interviewer, and a driver. At the county level, the KDHS field teams were assisted by KNBS county statistical officers who provided links to National Government Administration Officers (NGAOs). Prior to the data collection, a county mobilisation team conducted targeted publicity within the clusters to prepare for the fieldwork. The KNBS field staff and village elders assisted in identifying the sampled clusters and households. Monitoring of data collection was undertaken by Technical Working Committee and Steering Committee members throughout the data collection period. The aim of monitoring was to ensure that the survey was conducted according to protocol and to provide real-time solutions to any challenges that were encountered. 1.9 DATA PROCESSING CAPI was used during data collection. The devices used for CAPI were Android-based computer tablets programmed with a mobile version of CSPro. The CSPro software was developed jointly by the U.S. Census Bureau, Serpro S.A., and The DHS Program. Programming of questionnaires into the Android application was done by ICF, while configuration of tablets was completed by KNBS in collaboration with ICF. All fieldwork personnel were assigned usernames, and devices were password protected to ensure the integrity of the data. Work was assigned by supervisors and shared via Bluetooth® to interviewers’ tablets. After completion, assigned work was shared with supervisors, who conducted initial data consistency checks and edits and then submitted data to the central servers hosted at KNBS via SyncCloud. Data were downloaded from the central servers and checked against the inventory of expected returns to account for all data collected in the field. SyncCloud was also used to generate field check tables to monitor progress and identify any errors, which were communicated back to the field teams for correction. Secondary editing was done by members of the KNBS and ICF central office team, who resolved any errors that were not corrected by field teams during data collection. A CSPro batch editing tool was used for cleaning and tabulation during data analysis. 1.10 RESPONSE RATES Table 1.1 presents the response rates for the 2022 KDHS. A total of 42,022 households were selected for the survey, of which 38,731 (92%) were found to be occupied. Among the occupied households, 37,911 were successfully interviewed, yielding a response rate of 98%. The response rates for urban and rural households were 96% and 99%, respectively. In the interviewed households, 33,879 women age 15–49 were identified as eligible for individual interviews. Of these, 32,156 women were interviewed, yielding a response rate of 95%. The response rates among women selected for the full and short questionnaires were similar (95%). In the households selected for the men’s survey, 16,552 men age 15–54 were identified as eligible for individual interviews and 14,453 were successfully interviewed, yielding a response rate of 87%. 8 • Introduction and Survey Methodology Table 1.1 Results of the household and individual interviews Number of households, number of interviews, and response rates, according to residence (unweighted), Kenya DHS 2022 Residence Total Result Urban Rural ALL HOUSEHOLDS Household interviews Households selected 16,610 25,412 42,022 Households occupied 14,869 23,862 38,731 Households interviewed 14,329 23,582 37,911 Household response rate1 96.4 98.8 97.9 Interviews with women age 15–49 Number of eligible women 13,129 20,750 33,879 Number of eligible women interviewed 12,386 19,770 32,156 Eligible women response rate2 94.3 95.3 94.9 HOUSEHOLDS SELECTED FOR FULL QUESTIONNAIRES Household interviews Households selected 8,657 13,312 21,969 Households occupied 7,725 12,469 20,194 Households interviewed 7,429 12,318 19,747 Household response rate1 96.2 98.8 97.8 Interviews with women age 15–49 Number of eligible women 6,911 10,914 17,825 Number of eligible women interviewed 6,517 10,384 16,901 Eligible women response rate2 94.3 95.1 94.8 Interviews with men age 15–54 Number of eligible men 6,134 10,418 16,552 Number of eligible men interviewed 5,232 9,221 14,453 Eligible men response rate2 85.3 88.5 87.3 HOUSEHOLDS SELECTED FOR SHORT QUESTIONNAIRES Household interviews Households selected 7,953 12,100 20,053 Households occupied 7,144 11,393 18,537 Households interviewed 6,900 11,264 18,164 Household response rate1 96.6 98.9 98.0 Interviews with women age 15–49 Number of eligible women 6,218 9,836 16,054 Number of eligible women interviewed 5,869 9,386 15,255 Eligible women response rate2 94.4 95.4 95.0 1 Households interviewed/households occupied 2 Respondents interviewed/eligible respondents Housing Characteristics and Household Population • 9 HOUSING CHARACTERISTICS AND HOUSEHOLD POPULATION 2 Key Findings ▪ Electricity: The proportion of households with electricity increased from 36% in 2014 to 58% in 2022. ▪ Primary reliance on clean fuels and technologies: 21% of the population relies on clean fuels and technologies for cooking, space heating, and lighting. ▪ Orphanhood: 9% of children under age 18 are orphans. ▪ Birth registration: Three in four (76%) children under age 5 are registered with the civil registration authority. ▪ Education: 88% of children who were age 5 at the beginning of the school year participated in organised learning. ▪ Disability: 5% of the population age 5 and over are reported to have either a lot of difficulty functioning in at least one domain or could not function in a domain at all. ▪ Food security: Overall, 3 in 10 households reported not having enough food or money to buy food in the seven days before the survey. ▪ COVID-19: 30% of the household population received COVID-19 vaccinations. nformation on the socioeconomic characteristics of the household population in the 2022 KDHS provides context for interpreting demographic and health indicators and furnishes an approximate indication of the representativeness of the survey. The information also sheds light on the living conditions of the population. This chapter presents information on housing characteristics and household possessions; use of clean fuels and technologies (related to cooking, heating, and lighting); wealth; household population composition; children’s living arrangements and orphanhood; birth registration; educational attainment and school attendance; disability; deaths and injuries from road accidents; COVID-19; food security status; health insurance and expenditure; and social protection. Results from this chapter show progress towards achieving the SDG targets on the proportion of the population with access to electricity (Indicator 7.1.1) and the proportion of the population with primary reliance on clean fuels and technology (Indicator 7.1.2). 2.1 HOUSING CHARACTERISTICS Nationally, more than half of the households (58%) have electricity, with the majority (90%) in urban areas compared to 36% in rural areas (Table 2.1). I 10 • Housing Characteristics and Household Population Patterns by background characteristics ▪ In urban and rural areas, the dominant flooring material is cement at 59% and 39%, respectively. ▪ A higher percentage of urban households (61%) compared to rural households (32%) have one room used for sleeping. However, a higher percentage of rural households (30%) compared to urban households (14%) have three or more rooms used for sleeping. ▪ Nationally, 9% of people smoke daily in the home. A higher percentage of those in rural areas (11%) smoke daily in the home compared to the urban areas (5%). 2.1.1 Use of Clean Fuels and Technologies Primary reliance on clean fuels and technologies The percentage of the population that use clean fuels and technologies for cooking, heating, and lighting where each component is defined as follows: Clean cooking fuels and technologies Includes stoves/cookers that use electricity, LPG/natural gas/biogas, solar, and alcohol/ethanol Clean heating fuels and technologies Includes central heating, electricity, LPG/natural gas/biogas, solar air heater, and alcohol/ethanol Clean lighting fuels and technologies Includes electricity, solar lantern, battery powered or rechargeable flashlight/torch/lantern, and biogas lamp Sample: Households and de jure population 2.1.2 Cooking Half of the households in Kenya cook indoors, with 30% having no separate room or kitchen for cooking. The majority of rural households (63%) have a separate building for cooking, compared to 8% in urban areas (Table 2.2). Nationally, 24% of the population use clean fuels and technology for cooking. A higher percentage of the population in urban areas (59%) use clean fuels and technology for cooking, compared to 6% of the population in rural areas. For the population that uses solid fuels for cooking, majority (62%) use wood as the source of fuel. 2.1.3 Heating and Lighting The majority (77%) of households do not heat their homes. For those who do, 16% use manufactured cook stoves and only 1% use clean fuels and technologies for heating (Table 2.3). Nine in ten people use clean fuels and technologies for lighting. The most common source of clean fuels and technologies are electricity (50%) and solar lanterns (34%). 2.1.4 Primary Reliance on Clean Fuels and Technologies Nationally, 21% of the population relies on clean fuels and technologies for cooking, space heating, and lighting. There is a pattern between wealth status and primary reliance on clean fuels and technologies. The percentage of the population relying on clean fuels and technologies for cooking, space heating, and lighting increases with wealth quintile. Eight in ten people in the highest wealth quintile rely primarily on clean fuels and technologies for cooking, space heating, and lighting (Table 2.4). Housing Characteristics and Household Population • 11 More than half (53%) of the urban population relies on clean fuels and technologies for cooking, space heating, and lighting compared to 5% in rural areas (Figure 2.1). There are differences across counties in the proportion of the population primarily relying on clean fuels and technologies for cooking, space heating, and lighting. Nairobi City (76%), Kiambu (57%), Kajiado (49%), Mombasa (42%), Kirinyaga (31%), Machakos (29%), and Nyeri (28%) counties have a higher percentage of the population relying primarily on clean fuels and technologies than the national average (21%). Mandera County has the lowest percentage (1%) of the population primarily relying on clean fuels and technologies, followed by Wajir, Tana River, Marsabit, Baringo, West Pokot, Turkana, Samburu, and Elgeyo/Marakwet counties (2%) (Table 2.4C and Map 2.1). Map 2.1 Primary reliance on clean fuels and technologies by county Percentage of de jure population relying on clean fuels and technologies for cooking, space heating, and lighting The boundaries used in this map are not an authority on administrative units. Figure 2.1 Primary reliance on clean fuels and technologies by residence 24 5 92 21 59 20 96 53 6 1 90 5 Cooking Space heating Lighting Cooking, space heating, and lighting Percentage of de jure population relying on clean fuels and technologies for: Total Urban Rural 12 • Housing Characteristics and Household Population 2.2 HOUSEHOLD WEALTH 2.2.1 Household Durable Goods Household possessions reflect a household’s economic status. The most commonly found item in households is a mobile phone (94%), followed by a bed (93%) (Table 2.5). Television and/or radio provide a means of information access in households. More households own a radio than a television. Nationally, 66% of the households own a radio with 71% in urban areas and 62% in rural areas. Half of the households own a television, with 68% in urban areas and 38% in rural areas. Nationally, 11% of the households own a computer with a wide variation between urban (21%) and rural (4%). The majority of households own a mobile phone, both in urban (97%) and rural (91%) areas. Seventy-one percent of rural households own agricultural land as compared to 33% of urban households. In addition, 78% of rural households own farm animals as compared to 41% of urban households. Trends: Possession of household ownership of mobile phones increased from 86% in 2014 to 94% in 2022. 2.2.2 Wealth Index Wealth index Households are assigned scores based on the number and kinds of consumer goods they own; these range from a television to a bicycle or car, and housing characteristics such as source of drinking water, toilet facilities, and flooring materials. These scores are derived with principal component analysis. National wealth quintiles are compiled by assigning the household score to each usual (de jure) household member, ranking each person in the household population by their score, and then dividing the distribution into five equal categories, each with 20% of the population. Sample: Households Wealth index is a composite measure of a household’s cumulative living standard. In this report, the wealth index is used as a background characteristic to compare the influence of wealth on various population, health, and nutrition indicators. More than half (54%) of the population in urban areas falls in the highest wealth quintile, as compared to 3% in the rural households (Table 2.6 and Figure 2.2). In Turkana County, 75% of the population falls within the lowest wealth quintile, while in Nairobi City almost no one does. In Nairobi City County, 71% of the population is in the highest wealth quintile. Mandera, Marsabit, and Bomet counties have the smallest proportion (2%) of the population in the highest wealth quintile. Figure 2.2 Household wealth by residence 2 294 28 9 26 32 14 54 3 Urban Rural Percent distribution of de jure population by wealth quintiles Wealthiest Fourth Middle Second Poorest Housing Characteristics and Household Population • 13 2.3 HOUSEHOLD POPULATION AND COMPOSITION Household A person or group of related or unrelated persons who live together in the same dwelling unit(s), who acknowledge one adult male or female as the head of the household, who share the same housekeeping arrangements, and who are considered a single unit. De facto population All persons who stayed in the selected households the night before the interview (whether usual residents or visitors). De jure population All persons who are usual residents of the selected households, whether or not they stayed in the household the night before the interview. How data are calculated All tables are based on the de facto population, unless specified otherwise. Figure 2.3 shows the population pyramid that depicts the age-sex structure of the population. The broad base depicts the youthfulness of the Kenyan population. Nationally, the dependent population who are age 0–14 and 65+ account for 45% of the population with 37% in urban areas and 48% in rural areas (Table 2.7). A third of households in Kenya are headed by women. In the rural areas, 36% of households are headed by women compared to 31% in the urban areas (Table 2.8). Nationally, the mean household size is four persons. Rural households have a mean household size of about four compared to urban households with about three members. Nationally, only 1% of households with children under age 18 have both parents who are dead. Eight percent of all households with children under age 18 are single orphans. Rural areas have a greater proportion (10%) of households with a single orphan than urban areas (4%). In rural areas, 26% of households with children under age 18 have orphans and/or children who are not living with their biological parents compared to 10% in urban areas. 2.4 CHILDREN’S LIVING ARRANGEMENTS AND PARENTAL SURVIVAL Orphan A child with one or both parents who are dead. Sample: Children under age 18 Figure 2.3 Population pyramid 8 6 4 2 0 2 4 6 8 <5 5–9 10–14 15–19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 55–59 60–64 65–69 70–74 75–79 80+ Age Percent distribution of the household population Male Female 268 4 14 • Housing Characteristics and Household Population Nine percent of children under age 18 are orphans. Nationally, 53% of children under age 18 live with both parents. Even with both parents still alive, 11% of children do not live with any of their parents, while 23% live with their mothers as their fathers live elsewhere. Fifty-nine percent of children living in urban areas live with both their parents compared with 50% in rural areas (Table 2.9 and Figure 2.4). Patterns by background characteristics ▪ Percentage of orphans was high in the lowest wealth quintile at 12% and low in the highest wealth quintile at 4%. ▪ Homa Bay County has the highest proportion of children who are double orphans at 3% (Table 2.9C). ▪ Vihiga County has the highest percentage of children (27%) not living with a biological parent, while Kiambu County has the least (5%). 2.5 BIRTH REGISTRATION Registered birth Child has a birth certificate or child does not have a birth certificate, but the birth is registered with the civil registration authority. Sample: De jure children under age 5 Birth registration is the process of officially recording the birth of a child with the office of the registrar. This process is important for establishing legal identity, accessing government services, and protecting the rights of children. Three in four (76%) children are registered with the civil registration authority. Thirty four percent of children whose births are registered have a birth certificate (Table 2.10). As household wealth rises, there is a corresponding increase in the registration of births. A higher proportion of children in the highest wealth quintile (88%) than those in the lowest wealth quintile (63%) have their births registered (Figure 2.5). Urban areas have a greater proportion of registered children’s births than rural areas (81% versus 73%). Nyeri County has the highest proportion of children under age 5 whose birth are registered with civil authority (96%) whereas Wajir County has the lowest (50%) (Map 2.2). Turkana County has the lowest percentage of children with a birth certificate (8%), while Nyeri County has the highest (58%) (Table 2.10C). Figure 2.4 Children’s living arrangements by residence Figure 2.5 Birth registration by household wealth 53 59 50 Total Urban Rural Percentage of children under age 18 living with both parents 63 73 78 81 88 Lowest Second Middle Fourth Highest Percentage of de jure children under age 5 whose births are registered with the civil authority Poorest Wealthiest Housing Characteristics and Household Population • 15 Map 2.2 Birth registration by county Percentage of de jure children under age 5 whose births are registered with the civil registration authority The boundaries used in this map are not an authority on administrative units. Trends: The proportion of de jure children under age 5 whose births were registered has increased from 60% in 2008–09 to 67% in 2014 and 76% in 2022. The percentage of children registered with civil registration authority in rural areas increased from 57% in 2008–09 to 73% in 2022 (Figure 2.6). 2.6 EDUCATION Education is a vital element in Kenya’s socio- economic development. Education facilitates improvement in health and also serves as a powerful tool in building correct civic attitudes and reducing conflicts. The level of educational attainment is highly correlated with individuals’ attitude on health seeking behaviours and in solving other societal problems. The 2022 KDHS results are instrumental in providing information on educational attainment among household members and indicators on school attendance among the population. Figure 2.6 Birth registration 60 67 76 76 79 81 57 61 73 2008–09 KDHS 2014 KDHS 2022 KDHS Percentage of de jure children under age 5 whose births are registered with the civil registration authority Rural Total Urban 16 • Housing Characteristics and Household Population 2.6.1 Educational Attainment Median educational attainment Half of the population has completed less than the median number of years of schooling, and half of the population has completed more than the median number of years of schooling. Sample: De facto household population age 6 and older Overall, about 12% of women and 13% of men have more than secondary education. Thirteen percent of women compared to 10% of men have no education, while about 15% of both women and men have completed only primary education. The median years completed is seven for both women and men (Table 2.11.1 and Table 2.11.2). Trends: Between 2003 and 2022, there is a declining trend in the proportion of women and men age 6 and older with no education from 23% to 13% among of women and 16% to 10% among men (Figure 2.7). Patterns by background characteristics ▪ Women and men in urban areas have the highest median number of years of education completed (7.7 years for women and 8.0 years for men) compared to those in rural areas (6.1 years for women and 6.3 years for men) (Table 2.11.1 and Table 2.11.2). ▪ The proportion of the population with more than secondary education increases with an increase in wealth, with the highest percentage of women (33%) and men (38%) are in the highest wealth quintile. ▪ Thirty-seven percent of girls and 40% of boys age 6–9 have no education. ▪ Nairobi City has the highest median number of years of schooling at 9 years for women and men. In the arid and semi-arid counties of Tana River, Garissa, Mandera, Wajir, Turkana, Samburu, and Marsabit, the median number of years of schooling for women is zero (Table 2.11.1C and Table 2.11.2C). Figure 2.7 Educational attainment of the household population 27 19 23 19 16 1317 10 16 13 11 10 1993 KDHS 1998 KDHS 2003 KDHS 2008–09 KDHS 2014 KDHS 2022 KDHS Percentage of the household population age 6 and above with no education Women Men Note: Data from 2003 and later are nationally representative, while data collected before 2003 exclude the North Eastern region and several northern districts in the Eastern and Rift Valley regions. Housing Characteristics and Household Population • 17 2.6.2 Primary and Secondary School Attendance Net attendance ratio (NAR) Percentage of the school-age population that attends primary or secondary school. Sample: Children age 6–13 for primary school NAR and children age 14–17 for secondary school NAR Gross attendance ratio (GAR) The total number of children attending primary school divided by the official primary school age population and the total number of children attending secondary school divided by the official secondary school age population. Sample: Children age 6–13 for primary school GAR and children age 14–17 for secondary school GAR There is no major difference in NAR for girls (87%) and boys (86%) for primary school children age 6–13. Overall, almost half (49%) of children within the official secondary school age (14–17) are attending secondary school. About 53% of the girls within the official secondary school age are attending secondary school compared to 45% of the boys within the same age (Table 2.12). The GAR for primary school exceeds 100% (105% for girls and 109% for boys), which indicates that the system has both underage and overage learners. Conversely, the GAR in secondary school is less than 100% (84% for girls and 81% for boys), which implies that all children age 14–17 are not in school. Gender Parity Index (GPI) The ratio of female to male students attending primary school and the ratio of female to male students attending secondary school. The index reflects the magnitude of the gender gap. A GPI of less than one means there are more boys than girls in school. A GPI greater than one means there are more girls than boys. A GPI of one indicates equality between the two. Sample: Primary school students and secondary school students Nationally, the GPI for NAR is 1.02 for primary and 1.16 for secondary. This indicates that there is parity in overall school attendance by primary school-age girls and boys, while for secondary, more girls than boys are attending. Patterns by background characteristics ▪ The NAR for primary school is higher in urban areas (89%) than in rural areas (85%). However, GAR in primary school is higher in rural areas at 108% compared to 104% in urban areas. ▪ Primary school NAR increases with increasing household wealth. The NAR for primary school increases from 75% in the lowest wealth quintile to 92% in the highest wealth quintile. ▪ The NAR for secondary school in rural areas is lower than that of urban areas at 46% and 58%, respectively. ▪ Turkana County has the lowest NAR (44%) and GAR (57%) for primary school. Nyandarua County has the highest NAR for primary school at 95% (Table 2.12C). 18 • Housing Characteristics and Household Population ▪ The NAR for secondary school increases from 27% in the lowest wealth quintile to 69% for the highest wealth quintile. The NAR for the female population increases from 23% in the lowest wealth quintile to 71% in the highest wealth quintile (Figure 2.8). ▪ Tana River County has the lowest GAR in secondary school at 34%, while Kirinyaga County has the highest GAR at 113%. 2.6.3 Participation Rate in Organised Learning among Children Age 5 Participation rate in organised learning—adjusted net attendance ratio (NAR) Percentage of children of age one year younger than official primary school entry age (at the beginning of school year) who are attending an early childhood education programme or primary school. The ratio is termed adjusted because it includes children in primary school. Sample: Children age 5 at the beginning of the school year Participation rate in organised learning (one year before the official primary entry age) is an indicator that measures the exposure of children to organised learning activities one year before they start primary school. Eighty-eight percent of children who were age 5 at the beginning of the school year participated in organised learning; 66% attended an early childhood education programme, and 22% attended primary school (Table 2.13 and Table 2.13C). Patterns by background characteristics ▪ Urban areas have a higher participation rate (91%) in organised learning among children age 5 at the beginning of the school year than rural areas (86%). ▪ Children age 5 at the beginning of the school year in the lowest wealth quintile have the lowest participation rate (73%) in organised learning, while those in the highest wealth quintile have the highest participation rate (95%). 2.7 DISABILITY Functional Domains Seeing, hearing, communicating, remembering or concentrating, walking or climbing steps, washing all over, or dressing. Persons with disability Those with a lot of difficulty and those who cannot do at all in any of the domains. Sample: De facto household population age 5 and older Figure 2.8 Secondary school attendance by household wealth 31 50 60 62 68 23 42 50 59 71 Lowest Second Middle Fourth Highest Net attendance ratio for secondary school among children age 14–17 Girls Boys WealthiestPoorest Housing Characteristics and Household Population • 19 The 2022 KDHS included a Disability Module with a series of questions based on the Washington Group on Disability Statistics (WG) questions, which are based on the framework of the World Health Organization’s International Classification of Functioning, Disability, and Health. The questions address six core functional domains—seeing, hearing, communication, cognition, walking, and self-care—and provide the basic necessary information on disability. This information is comparable to that collected worldwide via the WG disability tools. 2.7.1 Disability by Domain and Age Five percent of the population age 5 and over are reported to have either a lot of difficulty functioning in at least one domain or could not function in a domain at all. However, 15% of the population age 5 and older are reported to have some level of difficulty in at least one domain. The most common type of disability is disability in mobility (2%) followed by disability in vision (2%) (Table 2.14). 2.7.2 Disability among Adults by Other Background Characteristics Patterns by background characteristics ▪ The prevalence of disability is similar among women and men age 15 and older; 7% of women and 5% of men have disability ▪ Disability is more prevalent among widowed women and men than among others; 28% of widowed women have disability whereas the prevalence is 8% among divorced and separated, 5% among married, and 4% among never married women. Similarly, 21% of widowed men have disabilities compared to 8% of divorced or separated, 6% of married, and 3% of never married men (Tables 2.15.1, 2.15.1C, 2.15.2 and 2.15.2C). 2.8 DEATHS AND INJURIES FROM ROAD TRAFFIC ACCIDENTS Traffic Accident An accident involving at least one vehicle on a road open to public traffic in which at least one person is injured or killed. Severe Injury Injuries resulting from traffic accidents that render one unable to carry out activities of daily life for at least a day. Sample: De facto household population Road traffic injuries cause considerable economic losses to individuals, their families, and nations as a whole. These losses arise from the cost of treatment as well as lost productivity for those killed or disabled by their injuries, and for family members who must take time off work or school to care for the injured. In the 12 months before the survey, 1,466 persons per 100,000 population had severe injuries, while 135 persons per 100,000 population died due to road traffic injuries. Overall, 1,601 persons per 100,000 had severe injuries and died due to road traffic accidents (Table 2.16). Patterns by background characteristics ▪ Urban areas had the highest number (190) of deaths due to traffic injuries per 100,000 population, as compared to rural areas (107). ▪ Individuals in the fourth wealth quintile have the highest number of severe road traffic accident injuries and deaths per 100,000 population (2,123), while those in the lowest wealth quintile have the lowest (1,104). 20 • Housing Characteristics and Household Population ▪ The counties with the highest number of deaths due to road traffic injuries per 100,000 population are Murang’a (365), Kisumu (307), West Pokot (264), Elgeyo/Marakwet (261), and Vihiga (247), while some counties such as Garissa, Mandera, Tana River, and Trans Nzoia did not report any deaths (Table 2.16C). ▪ The counties that reported the highest numbers of persons with severe injuries due to road traffic accidents per 100,000 population are Bungoma (3,036), Kisumu (2,868), Murang’a (2,577), Homa Bay (2,488), and Migori (2,302), while the counties with the lowest are Mandera (191), Tana River (282), Marsabit (284), Samburu (548), and Kwale (571). ▪ The counties with the highest deaths and severe injuries due to road traffic accidents per 100,000 population are Kisumu (3,175), Bungoma (3,062), Murang’a (2,943), Homa Bay (2,697), Migori (2,364), Tharaka-Nithi (2, 321), Busia (2,310), and Laikipia (2,246). 2.9 FOOD SECURITY STATUS Food Consumption Score (FCS) A composite score based on dietary diversity, food frequency, and relative nutrition importance of different food groups. ▪ Poor: Thresholds between 0–21 ▪ Borderline: Thresholds between 21.5–35 ▪ Acceptable: Thresholds >35.5 Households in the poor and borderline food consumption group are considered to have insufficient dietary intake. Coping Strategies Index (CSI) An indicator of household stress due to a lack of food or money to buy food. The CSI measures the behaviours adopted by households when they have difficulties in meeting their food needs. The strategies adopted are: 1. Relying on less preferred and less expensive foods; 2. Borrowing food or relying on help from friends or relatives; 3. Limiting portion size at mealtimes; 4. Restricting consumption by adults in order for children to eat; and 5. Reducing the number of meals in a day. ▪ Minimal: Thresholds between 0–3 ▪ Stressed: Thresholds between 4–18 ▪ Crisis: Thresholds >19 Sample: Households The majority of households (85%) in Kenya had acceptable food consumption scores in the seven days before the survey. Four percent of households had poor food consumption, and 11% had borderline scores. Overall, 3 in 10 households in Kenya reported not having enough food or money to buy food in the seven days before the survey (Table 2.17). Among households that reported not having food or enough money to purchase food, the mean coping strategy index CSI score was 20.1. Eleven percent of households reported minimal coping strategies, 44% reported stressed coping strategies, and 45% reported crisis coping strategies. Patterns by background characteristics ▪ The likelihood of lacking food or money to purchase food decreased with increasing household wealth. More than half (53%) of households in the lowest wealth quintile reported not having enough food or money to buy food, whereas 12% of households in the highest wealth quintile did report not having enough food or money to buy food. Housing Characteristics and Household Population • 21 ▪ The proportion of households that reported lacking food or money to purchase food is higher in rural areas (33%) than in urban areas (23%). ▪ The highest proportions of households reporting lacking food or money to purchase food were recorded in Turkana (80%), Vihiga (59%), Marsabit (58%), Busia (57%), Homa Bay (57%) and Samburu (55%) counties (Table 2.17C). 2.10 COVID-19 TESTING, VACCINATION, AND DEATHS Kenya reported its first case of COVID-19 on 13th March 2020. Public health measures adopted to reduce the spread and impact of COVID-19 included hand hygiene, social distancing, mask-wearing, isolation, quarantine, and targeted lockdown measures. In March 2021, Kenya introduced 5 types of COVID-19 vaccines: Astra Zeneca, Johnson & Johnson, Pfizer, Moderna, and Sinopharm. Twenty-seven percent of households had one or more persons who were tested for COVID-19, while 63% of households had one or more persons who received vaccination against COVID-19 (Table 2.18.1). Twelve percent of household population had ever tested for COVID-19, while 30% had received vaccination against COVID-19. The number of persons who tested positive for COVID-19 is 621 per 100,000 population, while the number of deaths due COVID-19 related complications is 64 deaths per 100,000 population (Table 2.18.2). Patterns by background characteristics ▪ COVID-19 testing and vaccination against COVID-19 is higher in urban areas than in rural areas; 20% of household population in urban areas have tested for COVID-19 compared with 8% in rural areas, while 34% of household population in urban areas have received vaccination against COVID-19 compared with 28% in rural areas (Table 2.18.1). ▪ The number of persons who tested positive for COVID-19 per 100,000 population is higher in urban areas (1,347 per 100,000 population) than in rural areas (254 per 100,000 population) (Table 2.18.2). ▪ Cases of COVID-19 per 100,000 population increases with household wealth. The cases vary from 31 per 100,000 population in the lowest wealth quintile to 2,134 per 100,000 population in highest wealth quintile. ▪ Percentage of household population tested for COVID-19 is highest in Nairobi City (27%), Kajiado (23%), Nyeri (23%), Mombasa(21%) and Kisumu(20%), and lowest in Mandera, Tana River and Wajir and West Pokot counties at 3% each (Table 2.18.1C). ▪ Vaccination coverage is highest in Nyeri (48%), Kirinyaga (47%), Kiambu (39%), Nairobi City (38%) and Embu (38%) counties, while the lowest was in Tana River and Garissa counties at 5% each. ▪ Counties with the highest cases of COVID-19 per 100,000 population are Kajiado (2,522), Nairobi City (1,821), Kisumu (1,173), Nakuru (1,000), Kiambu (977), Murang’a (977) and Embu at 966. Elgeyo /Marakwet (0), Mandera (20) and Tana River (24) counties have the lowest cases COVID-19 per 100,000 population (Table 2.18.2C). 2.11 HEALTH INSURANCE COVERAGE Health insurance for the population ensures that people are protected against unforeseen expenditures that may arise. Health insurance is one of the key components of financial protection for the population, because the cost associated with health payments during a disease episode can drive families into poverty. 22 • Housing Characteristics and Household Population Nationally, one in four persons (26%) have some form of health insurance. The National Hospital Insurance Fund is the most common type of health insurance (Tables 2.19 and 2.19C). Patterns by background characteristics ▪ Health insurance coverage is twice as high as in urban areas than in rural areas; 40% of household populations in urban areas have some form of health insurance compared to 19% in rural areas. ▪ Health insurance coverage increases with increasing wealth, from 5% the lowest wealth quintile to 58% in the highest wealth quintile. ▪ Health insurance coverage varies across the 47 counties from a low of 5% and 6%, respectively in Tana River and Mandera counties to a high of 46% and 44% in Nairobi City and Laikipia counties, respectively (Map 2.3). Map 2.3 Health insurance coverage by county Percentage of de jure household population with any form of health insurance The boundaries used in this map are not an authority on administrative units. Housing Characteristics and Household Population • 23 2.12 OUTPATIENT AND INPATIENT HEALTH EXPENDITURES Outpatient An outpatient is someone who received healthcare without having stayed overnight at a health facility. Inpatient An inpatient is someone who stayed overnight at a health facility. Out of Pocket Expenditure Payments made to health care providers after a service has been rendered in the form of either cash or in-kind. Sample: de facto household members. In Kenya, people pay an average of KSh 37,362 for each in-patient visit per year and an average of KSh 1,735 for each outpatient visit per month (Tables 2.20.1 and 2.20.2). Patterns by background characteristics ▪ Males spend twice as much as females on inpatient admissions. Males spend an average of KSh 52,924 per year for inpatient admissions whereas females spend an average of KSh 27,536 per year. ▪ Average monthly expenditure for outpatient visits is also slightly higher among males than females (KSh 1,858 among males and KSh 1,637 among females). ▪ Outpatient health expenditure increases with an increase in age, with the oldest respondents reporting the highest outpatient health expenditure (KSh 898 for age 0–4 and KSh 4,078 for age 60 and older). ▪ Generally, persons in urban areas spend, on average, twice the amount spent by those in rural areas on inpatient (KSh 59,493 in urban areas and KSh 24,731 in rural areas) and outpatient (KSh 2,281 in urban areas and KSh 1,455 in rural areas) health expenditures. ▪ Cash payments, followed by NHIF payments, are the most common means of payment for both inpatient and outpatient expenditures. Household members paid by cash for inpatient expenditures an average of KSh 13,621 per year and KSh 9,330 through NHIF compared to KSh 6,202 paid through private insurance. 2.13 SOCIAL PROTECTION Social protection Social protection covers the range of policies and programmes needed to reduce the lifelong consequences of poverty and exclusion. Social protection systems help individuals and families, especially the poor and vulnerable, cope with crises and shocks, find jobs, improve productivity, invest in the health and education of their children, and protect the aging population. In Kenya, current delivery instruments of social protection include cash transfers, food distribution, school-based feeding programmes, social health insurance, retirement benefits, price subsidies and public works among others. Nationally, 17% of households receive cash transfer or social assistance; mainly from the government (national or county) at 11% followed by assistance received from friends, relatives, and neighbours (6%). Households receive cash transfer or social assistance mostly for supporting older persons (4%) and for food for work or cash for work (3%) (Table 2.21.1). 24 • Housing Characteristics and Household Population Patterns by background characteristics ▪ Nine in ten households (91%) receiving cash transfer or social assistance for elderly persons receive it from the government; 86% from the national government and 9% from the county government (Table 2.21.2). ▪ Twenty percent of households in rural areas receive cash transfers or other social assistance compared to 13% households in urban areas (Table 2.21.3). ▪ Households in rural areas are more likely to receive cash transfer or other social assistance to support older persons (30%) than households in urban areas (9%). ▪ One in five households (20%) in the lowest wealth quintile receive food aid for persons in arid and semi-arid lands. LIST OF TABLES For more information on household population and housing characteristics, see the following tables: ▪ Table 2.1 Household characteristics: Housing ▪ Table 2.2 Household characteristics: Cooking ▪ Table 2.3 Household characteristics: Heating and lighting ▪ Table 2.4 Primary reliance on clean fuels and technologies ▪ Table 2.4C Primary reliance on clean fuels and technologies by county ▪ Table 2.5 Household possessions ▪ Table 2.6 Wealth quintiles ▪ Table 2.7 Household population by age, sex, and residence ▪ Table 2.8 Household composition ▪ Table 2.9 Children’s living arrangements and orphanhood ▪ Table 2.9C Children’s living arrangements and orphanhood by county ▪ Table 2.10 Birth registration of children under age 5 ▪ Table 2.10C Birth registration of children under age 5 by county ▪ Table 2.11.1 Educational attainment of the female household population ▪ Table 2.11.1C Educational attainment of the female household population by county ▪ Table 2.11.2 Educational attainment of the male household population ▪ Table 2.11.2C Educational attainment of the male household population by county ▪ Table 2.12 School attendance ratios ▪ Table 2.12C School attendance ratios by county ▪ Table 2.13 Participation rate in organised learning ▪ Table 2.13C Participation rate in organised learning by county ▪ Table 2.14 Disability by domain and age ▪ Table 2.15.1 Disability among adults according to background characteristics: Women ▪ Table 2.15.1C Disability among adults according to county: Women ▪ Table 2.15.2 Disability among adults according to background characteristics: Men ▪ Table 2.15.2C Disability among adults according to county: Men ▪ Table 2.16 Deaths and injuries from road traffic accidents ▪ Table 2.16C Deaths and injuries from road traffic accidents by county ▪ Table 2.17 Food security status ▪ Table 2.17C Food security status by county ▪ Table 2.18.1 COVID-19 diagnosis and vaccination ▪ Table 2.18.1C COVID-19 diagnosis and vaccination by county ▪ Table 2.18.2 COVID-19 cases and deaths ▪ Table 2.18.2C COVID-19 cases and deaths by county ▪ Table 2.19 Health insurance coverage Housing Characteristics and Household Population • 25 ▪ Table 2.19C Health insurance coverage by county ▪ Table 2.20.1 Average annual expenditure on inpatient admissions [in Kenyan shillings] ▪ Table 2.20.2 Average monthly expenditure on outpatient visits [in Kenyan shillings] ▪ Table 2.21.1 Cash transfer: All households ▪ Table 2.21.2 Cash transfer: Households receiving cash transfer or social assistance ▪ Table 2.21.3 Cash transfer by residence and household wealth ▪ Table 2.21.3C Cash transfer or social assistance by county 26 • Housing Characteristics and Household Population Table 2.1 Household characteristics: Housing Percent distribution of households and de jure population by housing characteristics and percent distribution by frequency of smoking in the home, according to residence, Kenya DHS 2022 Households Population Characteristic Urban Rural Total Urban Rural Total Electricity Yes 90.4 35.7 57.8 89.7 31.5 51.1 No 9.6 64.3 42.2 10.3 68.5 48.9 Total 100.0 100.0 100.0 100.0 100.0 100.0 Flooring material Earth, sand 5.5 35.0 23.1 6.7 36.6 26.5 Dung 0.8 17.6 10.8 1.1 20.0 13.6 Wood/planks 0.4 0.2 0.3 0.5 0.1 0.2 Parquet or polished wood 0.7 0.0 0.3 0.8 0.0 0.3 Vinyl or asphalt strips 0.8 0.0 0.3 0.6 0.0 0.2 Ceramic tiles 26.8 7.0 15.0 28.5 6.7 14.0 Cement 58.7 38.6 46.7 56.0 35.2 42.2 Carpet 6.2 1.5 3.4 5.7 1.3 2.8 Other 0.1 0.1 0.1 0.1 0.1 0.1 Total 100.0 100.0 100.0 100.0 100.0 100.0 Rooms used for sleeping One 61.2 32.0 43.8 45.0 21.8 29.6 Two 25.1 38.4 33.0 32.9 40.6 38.0 Three or more 13.6 29.6 23.1 22.1 37.6 32.4 Total 100.0 100.0 100.0 100.0 100.0 100.0 Frequency of smoking in the home Daily 5.1 11.3 8.8 4.7 11.0 8.9 Weekly 1.2 2.0 1.7 1.1 2.0 1.7 Monthly 0.3 0.3 0.3 0.2 0.3 0.3 Less than once a month 0.6 0.6 0.6 0.5 0.6 0.5 Never 92.8 85.9 88.7 93.4 86.1 88.6 Total 100.0 100.0 100.0 100.0 100.0 100.0 Number of households/ population 15,277 22,634 37,911 47,730 94,296 142,026 Housing Characteristics and Household Population • 27 Table 2.2 Household characteristics: Cooking Percent distribution of households and de jure population by place for cooking, cooking technology, and cooking fuel, according to residence, Kenya DHS 2022 Households Population Characteristic Urban Rural Total Urban Rural Total Place for cooking In the house 84.9 26.0 49.7 80.0 20.4 40.5 Separate room/kitchen 31.9 11.4 19.7 38.0 10.0 19.4 No separate room/kitchen 52.9 14.6 30.1 42.0 10.4 21.0 In a separate building 7.7 63.1 40.8 12.4 69.4 50.3 Outdoors 5.5 9.6 7.9 6.9 9.7 8.7 No food cooked in household 1.9 1.3 1.6 0.7 0.4 0.5 Total 100.0 100.0 100.0 100.0 100.0 100.0 Main cooking technology Clean fuels and technologies 63.3 9.6 31.2 58.7 6.0 23.7 Electric stove 0.4 0.1 0.2 0.3 0.1 0.2 LPG/natural gas stove 59.9 8.8 29.4 55.7 5.5 22.4 Piped natural gas stove 2.1 0.4 1.1 1.9 0.2 0.8 Biogas stove 0.3 0.2 0.2 0.3 0.1 0.2 Liquid fuel stove using alcohol/ ethanol 0.6 0.0 0.2 0.5 0.0 0.2 Other fuels and technologies 34.8 89.1 67.2 40.6 93.6 75.8 Liquid fuel stove not using alcohol/ethanol 8.4 0.6 3.7 6.5 0.2 2.3 Manufactured solid fuel stove 16.9 7.4 11.3 20.2 6.2 10.9 With a chimney 2.4 1.3 1.7 3.1 1.1 1.8 Without a chimney 14.5 6.2 9.5 17.1 5.1 9.1 Traditional solid fuel stove 0.7 4.7 3.1 0.8 5.5 3.9 With a chimney 0.2 1.5 1.0 0.2 1.8 1.3 Without a chimney 0.5 3.2 2.1 0.6 3.7 2.7 Three stone stove/open fire 8.8 76.3 49.1 13.1 81.7 58.6 No food cooked in household 1.9 1.3 1.6 0.7 0.4 0.5 Total 100.0 100.0 100.0 100.0 100.0 100.0 Cooking fuel Clean fuels and technologies1 63.3 9.6 31.2 58.7 6.0 23.7 Solid fuels for cooking 26.2 88.4 63.3 34.0 93.3 73.4 Charcoal 16.9 7.7 11.4 20.1 6.5 11.0 Wood 9.2 80.1 51.6 13.8 86.3 61.9 Straw/shrubs/grass 0.1 0.3 0.2 0.1 0.3 0.2 Agricultural crop 0.0 0.1 0.1 0.0 0.1 0.1 Sawdust 0.0 0.1 0.1 0.1 0.1 0.1 Other fuels 8.6 0.7 3.9 6.6 0.3 2.4 Gasoline/diesel 0.1 0.0 0.0 0.1 0.0 0.0 Kerosene/paraffin 8.4 0.7 3.8 6.5 0.3 2.4 No food cooked in household 1.9 1.3 1.6 0.7 0.4 0.5 Total 100.0 100.0 100.0 100.0 100.0 100.0 Number of households/population 15,277 22,634 37,911 47,730 94,296 142,026 LPG = liquefied petroleum gas 1 Includes stoves/cookers using electricity, LPG/natural gas/biogas, solar, and alcohol/ethanol 28 • Housing Characteristics and Household Population Table 2.3 Household characteristics: Heating and lighting Percent distribution of households and de jure population by heating technology, heating fuel, and main lighting fuel or technology, according to residence, Kenya DHS 2022 Households Population Characteristic Urban Rural Total Urban Rural Total Heating technology Central heating 2.4 0.3 1.2 2.8 0.2 1.1 Manufactured space heater 0.0 0.0 0.0 0.1 0.0 0.0 Without a chimney 0.0 0.0 0.0 0.1 0.0 0.0 Traditional space heater 0.3 0.7 0.5 0.3 0.6 0.5 Without a chimney 0.3 0.6 0.5 0.3 0.5 0.5 Manufactured cookstove 11.8 19.1 16.1 13.3 19.4 17.4 With a chimney 1.6 2.8 2.3 1.8 2.9 2.5 Without a chimney 10.2 16.3 13.8 11.6 16.6 14.9 Traditional cookstove 0.4 7.4 4.6 0.6 8.1 5.6 Without a chimney 0.4 7.4 4.6 0.6 8.1 5.6 Three stone stove/open fire 0.3 1.4 1.0 0.3 1.5 1.1 Other 0.2 0.1 0.2 0.2 0.2 0.2 No heating in household 84.6 71.0 76.5 82.4 70.0 74.2 Total 100.0 100.0 100.0 100.0 100.0 100.0 Heating fuel Clean fuels and technologies1 2.9 0.5 1.4 3.4 0.3 1.4 Central heating 2.4 0.3 1.2 2.8 0.2 1.1 Electricity 0.4 0.1 0.2 0.5 0.1 0.2 Solar air heater 0.0 0.0 0.0 0.1 0.0 0.0 Liquified petroleum gas (LPG)/cooking gas 0.0 0.0 0.0 0.1 0.0 0.0 Charcoal 11.8 18.6 15.8 13.3 18.8 16.9 Wood 0.7 9.8 6.1 0.9 10.6 7.4 Straw/shrubs/grass 0.0 0.1 0.0 0.0 0.0 0.0 Agricultural crop 0.0 0.1 0.0 0.0 0.1 0.0 Sawdust 0.0 0.0 0.0 0.0 0.1 0.0 No heating in household 84.6 71.0 76.5 82.4 70.0 74.2 Total 100.0 100.0 100.0 100.0 100.0 100.0 Main lighting fuel or technology Clean fuels and technologies 95.2 88.2 91.0 96.0 89.2 91.5 Electricity 89.5 34.2 56.5 88.6 29.9 49.6 Solar lantern 3.9 44.0 27.9 5.1 49.1 34.3 Rechargeable flashlight/torch/ lantern 1.2 6.2 4.2 1.4 6.6 4.8 Battery powered flashlight/ torch/lantern 0.6 3.7 2.5 0.9 3.7 2.8 Gasoline lamp 0.1 0.2 0.1 0.1 0.1 0.1 Kerosene/paraffin lamp 1.6 7.8 5.3 1.5 7.0 5.2 Charcoal 0.1 0.1 0.1 0.1 0.1 0.1 Wood 0.0 0.7 0.4 0.0 0.8 0.6 Straw/shrubs/grass 0.0 0.1 0.1 0.0 0.1 0.1 Oil lamp 0.3 1.1 0.8 0.2 1.0 0.7 Candle 2.5 0.9 1.6 2.0 0.6 1.1 Other fuel 0.0 0.1 0.1 0.0 0.1 0.1 No lighting in household 0.2 0.8 0.5 0.2 0.8 0.6 Total 100.0 100.0 100.0 100.0 100.0 100.0 Number of households/ population 15,277 22,634 37,911 47,730 94,296 142,026 LPG = liquefied petroleum gas 1 Includes central heating, electricity, LPG/natural gas/biogas, solar air heater, and alcohol/ethanol Housing Characteristics and Household Population • 29 Table 2.4 Primary reliance on clean fuels and technologies Percentage of de jure population relying on clean fuels and technologies for cooking, percentage relying on solid fuels for cooking, percentage relying on clean fuel and technologies for space heating, percentage relying on clean fuel and technologies for lighting, and percentage relying on clean fuels and technologies for cooking, space heating, and lighting, according to background characteristics, Kenya DHS 2022 Background characteristic Primary reliance on clean fuels and techno- logies for cooking1 Primary reliance on solid fuels for cooking2 Number of persons in households that reported cooking in the house Primary reliance on clean fuels and techno- logies for space heating3 Number of persons in households that reported use of space heating Primary reliance on clean fuels and techno- logies for lighting4 Number of persons in households that reported use of lighting Primary reliance on clean fuels and techno- logies for cooking, space heating, and lighting5 Number of persons Residence Urban 59.1 34.3 47,374 19.5 8,423 96.1 47,658 53.4 47,730 Rural 6.0 93.7 93,891 1.1 28,257 89.9 93,562 4.9 94,296 Wealth quintile Lowest 0.0 99.9 28,301 0.0 7,443 78.0 27,708 0.3 28,409 Second 0.2 98.7 28,249 0.1 7,720 87.7 28,357 0.6 28,408 Middle 4.0 93.3 28,198 0.9 8,774 95.2 28,362 3.2 28,404 Fourth 29.7 63.1 28,152 2.6 7,720 99.1 28,395 24.8 28,406 Highest 84.9 13.9 28,365 33.0 5,023 99.9 28,399 77.1 28,400 Total 23.8 73.8 141,265 5.3 36,680 92.0 141,221 21.2 142,026 1 Includes stoves/cookers using electricity, LPG/natural gas/biogas, solar, and alcohol/ethanol 2 Includes coal/lignite, charcoal, wood, straw/shrubs/grass, agricultural crops, and animal dung/waste, processed biomass (pellets) or woodchips, garbage/plastic, and sawdust 3 Includes electricity, LPG/natural gas/biogas, solar air heater, and alcohol/ethanol 4 Includes electricity, solar lantern, rechargeable flashlight/torch/lantern, battery powered flashlight/torch/lantern, and biogas lamp 5 In order to calculate SDG indicator 7.1.2, persons living in households that report no cooking, no space heating, or no lighting are not excluded from the numerator. 30 • Housing Characteristics and Household Population Table 2.4C Primary reliance on clean fuels and technologies by county Percentage of de jure population relying on clean fuels and technologies for cooking, percentage relying on solid fuels for cooking, percentage relying on clean fuel and technologies for space heating, percentage relying on clean fuel and technologies for lighting, and percentage relying on clean fuels and technologies for cooking, space heating, and lighting, according to county, Kenya DHS 2022 County Primary reliance on clean fuels and techno- logies for cooking1 Primary reliance on solid fuels for cooking2 Number of persons in households that reported cooking in the house Primary reliance on clean fuels and techno- logies for space heating3 Number of persons in households that reported use of space heating Primary reliance on clean fuels and techno- logies for lighting4 Number of persons in households that reported use of lighting Primary reliance on clean fuels and techno- logies for cooking, space heating, and lighting5 Number of persons Mombasa 43.3 44.8 3,399 2.9 204 93.6 3,466 42.2 3,480 Kwale 7.4 91.5 2,353 0.0 77 84.0 2,335 7.5 2,359 Kilifi 8.6 90.5 4,259 0.0 436 81.1 4,278 9.0 4,293 Tana River 1.7 98.3 859 0.0 14 97.4 864 2.1 864 Lamu 10.0 89.9 467 0.0 8 99.0 470 10.6 470 Taita/Taveta 11.9 87.1 1,120 1.6 124 91.5 1,128 11.5 1,128 Garissa 4.1 94.9 1,505 - 0 97.4 1,512 4.6 1,516 Wajir 0.9 99.1 913 0.0 23 98.6 910 1.7 920 Mandera 1.0 99.0 1,296 0.0 136 97.0 1,300 1.3 1,302 Marsabit 2.0 98.0 792 3.4 24 96.5 697 2.1 795 Isiolo 13.2 86.1 678 33.6 40 96.5 679 13.1 680 Meru 9.6 89.7 4,500 7.3 869 86.9 4,529 9.4 4,568 Tharaka-Nithi 9.0 90.4 1,340 12.5 80 91.4 1,345 9.0 1,345 Embu 14.7 84.2 1,678 10.1 106 89.0 1,684 14.6 1,685 Kitui 6.6 93.1 3,464 0.0 107 91.5 3,470 6.3 3,479 Machakos 31.5 66.7 4,217 6.6 873 91.0 4,248 29.4 4,250 Makueni 8.0 90.7 2,893 0.9 604 88.8 2,895 7.4 2,903 Nyandarua 13.2 86.3 1,838 2.0 1,433 91.1 1,844 7.1 1,846 Nyeri 31.0 67.9 2,126 13.7 522 91.4 2,128 28.4 2,138 Kirinyaga 30.6 67.5 1,930 7.9 71 88.4 1,939 30.6 1,940 Murang’a 16.6 82.0 3,135 6.8 829 85.8 3,155 15.0 3,155 Kiambu 62.4 33.1 7,855 13.8 1,464 96.1 7,889 56.6 7,889 Turkana 1.8 98.2 1,842 0.0 476 82.8 1,466 2.4 1,854 West Pokot 4.2 95.8 2,265 0.3 1,647 87.2 2,250 2.3 2,266 Samburu 3.7 96.3 862 0.1 639 88.0 851 2.4 863 Trans Nzoia 9.8 89.8 3,211 0.7 1,577 93.3 3,217 6.7 3,219 Uasin Gishu 26.9 72.1 4,062 3.4 2,352 96.9 4,086 18.5 4,090 Elgeyo/Marakwet 4.3 95.6 1,277 0.3 767 97.1 1,279 2.4 1,279 Nandi 9.7 89.8 2,668 0.4 830 94.0 2,672 7.7 2,681 Baringo 2.6 97.3 1,955 0.7 901 93.6 1,893 2.1 1,967 Laikipia 22.3 77.3 1,458 5.4 711 94.3 1,462 15.5 1,467 Nakuru 28.2 71.1 6,820 5.5 3,474 97.4 6,848 19.8 6,850 Narok 7.9 92.0 3,715 0.4 1,617 97.0 3,726 5.2 3,740 Kajiado 54.8 43.3 3,733 9.6 828 95.5 3,754 49.3 3,761 Kericho 9.6 90.2 3,119 1.1 1,587 97.7 3,135 5.5 3,135 Bomet 4.6 95.3 2,833 0.0 828 98.5 2,869 4.0 2,869 Kakamega 8.7 91.1 6,033 0.4 1,731 88.8 6,047 7.0 6,047 Vihiga 6.6 93.3 1,761 0.0 284 82.7 1,761 5.2 1,762 Bungoma 7.6 92.0 5,196 0.5 2,248 89.8 5,211 5.5 5,226 Busia 7.0 92.9 3,038 2.2 203 86.2 3,041 6.7 3,042 Siaya 5.5 94.4 2,686 0.0 142 95.2 2,703 5.9 2,703 Kisumu 15.0 82.3 3,467 0.0 246 91.2 3,477 14.7 3,477 Homa Bay 5.5 94.5 3,387 0.7 603 93.0 3,391 5.1 3,393 Migori 6.7 93.1 3,330 0.2 1,014 88.7 3,341 5.5 3,341 Kisii 12.7 86.8 3,721 0.4 1,173 82.7 3,715 9.9 3,722 Nyamira 6.9 93.0 1,644 0.4 943 82.2 1,650 3.4 1,653 Nairobi City 81.7 5.3 14,566 51.8 1,814 96.7 14,614 76.4 14,614 Total 23.8 73.8 141,265 5.3 36,680 92.0 141,221 21.2 142,026 1 Includes stoves/cookers using electricity, LPG/natural gas/biogas, solar, and alcohol/ethanol 2 Includes coal/lignite, charcoal, wood, straw/shrubs/grass, agricultural crops, and animal dung/waste, processed biomass (pellets) or woodchips, garbage/plastic, and sawdust 3 Includes electricity, LPG/natural gas/biogas, solar air heater, and alcohol/ethanol 4 Includes electricity, solar lantern, rechargeable flashlight/torch/lantern, battery powered flashlight/torch/lantern, and biogas lamp 5 In order to calculate SDG indicator 7.1.2, persons living in households that report no cooking, no space heating, or no lighting are not excluded from the numerator. Housing Characteristics and Household Population • 31 Table 2.5 Household possessions Percentage of households possessing various household effects, means of transportation, agricultural land, and livestock/farm animals by residence, Kenya DHS 2022 Residence Total Possession Urban Rural Household effects Radio 71.2 62.1 65.8 Television 67.6 38.2 50.1 Mobile phone 97.4 90.9 93.5 Computer 20.6 4.1 10.7 Non-mobile telephone 2.4 1.8 2.0 Refrigerator 22.0 3.5 10.9 Watch 43.0 21.3 30.0 Solar panel 10.4 46.5 32.0 Table 85.8 86.3 86.1 Chair 83.0 88.6 86.4 Sofa 71.7 56.2 62.4 Bed 93.4 93.0 93.1 Cupboard 50.1 45.0 47.0 Clock 23.6 10.0 15.5 Microwave oven 14.5 2.1 7.1 DVD player 25.5 8.8 15.6 Cassette or CD player 13.1 4.5 8.0 Means of transportation Bicycle 16.6 16.2 16.3 Animal drawn cart 1.4 3.1 2.4 Motorcycle/scooter 10.8 15.3 13.5 Car/truck 13.1 5.2 8.4 Boat with a motor 0.6 0.3 0.4 Ownership of agricultural land 33.3 70.7 55.6 Ownership of farm animals1 40.8 77.6 62.8 Ownership of dwelling 21.1 84.5 58.9 Ownership of land on which dwelling is built 20.2 80.6 56.3 Number of households 15,277 22,634 37,911 1 Local cattle (indigenous), exotic/grade cattle, horses, donkeys, camels, goats, sheep, chickens/other poultry, or pigs 32 • Housing Characteristics and Household Population Table 2.6 Wealth quintiles Percent distribution of the de jure population by wealth quintiles, and the Gini coefficient, according to residence and county, Kenya DHS 2022 Wealth quintile Total Number of persons Gini coefficient1 Residence/county Lowest Second Middle Fourth Highest Residence Urban 1.9 3.7 9.0 32.0 53.5 100.0 47,730 0.10 Rural 29.2 28.3 25.6 14.0 3.1 100.0 94,296 0.24 County Mombasa 2.5 4.7 12.4 40.5 39.9 100.0 3,480 0.13 Kwale 51.8 12.9 15.2 13.4 6.7 100.0 2,359 0.40 Kilifi 53.0 10.0 15.7 13.1 8.2 100.0 4,293 0.43 Tana River 68.4 8.2 11.8 9.1 2.5 100.0 864 0.47 Lamu 30.6 19.6 18.0 18.2 13.6 100.0 470 0.31 Taita/Taveta 13.6 21.1 27.1 24.4 13.8 100.0 1,128 0.26 Garissa 32.5 8.5 15.0 32.9 11.0 100.0 1,516 0.28 Wajir 63.5 10.3 11.6 11.9 2.8 100.0 920 0.36 Mandera 71.6 9.3 10.6 6.8 1.5 100.0 1,302 0.28 Marsabit 61.7 11.1 13.2 12.2 1.9 100.0 795 0.42 Isiolo 37.4 7.9 14.1 22.7 17.9 100.0 680 0.37 Meru 23.9 28.0 21.4 20.0 6.7 100.0 4,568 0.28 Tharaka-Nithi 25.1 26.9 22.3 18.0 7.7 100.0 1,345 0.36 Embu 14.4 20.7 28.0 23.9 13.0 100.0 1,685 0.30 Kitui 40.0 31.1 16.5 8.6 3.8 100.0 3,479 0.35 Machakos 7.7 24.7 24.6 16.5 26.5 100.0 4,250 0.31 Makueni 19.8 29.1 28.1 17.3 5.7 100.0 2,903 0.30 Nyandarua 5.6 21.9 36.0 27.5 8.9 100.0 1,846 0.24 Nyeri 2.7 9.5 32.4 34.6 20.8 100.0 2,138 0.21 Kirinyaga 4.2 12.8 29.8 32.6 20.5 100.0 1,940 0.24 Murang’a 6.1 15.8 32.8 33.8 11.4 100.0 3,155 0.19 Kiambu 0.9 3.0 12.1 35.8 48.2 100.0 7,889 0.15 Turkana 75.2 5.7 6.6 8.5 4.1 100.0 1,854 0.48 West Pokot 63.9 12.9 10.9 9.0 3.3 100.0 2,266 0.42 Samburu 67.7 6.5 9.1 10.4 6.4 100.0 863 0.48 Trans Nzoia 9.9 34.7 26.2 18.7 10.5 100.0 3,219 0.33 Uasin Gishu 3.2 16.6 22.1 30.7 27.5 100.0 4,090 0.31 Elgeyo/Marakwet 30.0 24.4 26.9 16.1 2.6 100.0 1,279 0.28 Nandi 17.5 26.5 34.6 17.8 3.5 100.0 2,681 0.30 Baringo 41.9 17.5 21.1 15.4 4.2 100.0 1,967 0.35 Laikipia 12.8 18.6 30.3 22.2 16.2 100.0 1,467 0.24 Nakuru 12.3 13.4 17.3 28.5 28.5 100.0 6,850 0.28 Narok 37.0 27.8 19.7 10.5 5.0 100.0 3,740 0.35 Kajiado 19.9 6.6 10.8 19.8 42.9 100.0 3,761 0.25 Kericho 13.5 31.4 30.9 18.6 5.7 100.0 3,135 0.29 Bomet 24.4 40.9 22.9 10.2 1.6 100.0 2,869 0.29 Kakamega 17.5 36.2 26.0 14.6 5.7 100.0 6,047 0.28 Vihiga 15.5 40.7 26.2 13.1 4.5 100.0 1,762 0.22 Bungoma 18.3 35.0 26.7 13.3 6.6 100.0 5,226 0.32 Busia 19.5 34.6 25.4 13.5 7.0 100.0 3,042 0.28 Siaya 16.6 37.9 30.6 9.8 5.0 100.0 2,703 0.31 Kisumu 15.0 25.3 22.1 24.0 13.5 100.0 3,477 0.32 Homa Bay 26.6 34.5 23.5 9.3 6.2 100.0 3,393 0.29 Migori 31.8 31.7 19.5 11.1 5.8 100.0 3,341 0.33 Kisii 19.6 31.6 26.6 15.4 6.8 100.0 3,722 0.33 Nyamira 19.2 31.1 30.7 15.7 3.4 100.0 1,653 0.23 Nairobi City 0.0 1.4 4.4 23.8 70.5 100.0 14,614 0.09 Total 20.0 20.0 20.0 20.0 20.0 100.0 142,026 0.28 1 The Gini coefficient indicates the level of concentration of wealth, with 0 representing an equal wealth distribution and 1 representing a totally unequal distribution. Housing Characteristics and Household Population • 33 Table 2.7 Household population by age, sex, and residence Percent distribution of the de facto household population by various age groups and percentage of the de facto household population age 10–19, according to sex and residence, Kenya DHS 2022 Urban Rural Total Total Age Male Female Total Male Female Total Male Female <5 13.5 12.5 13.0 13.5 12.3 12.9 13.5 12.4 12.9 5–9 11.5 11.0 11.2 14.3 13.0 13.6 13.4 12.4 12.8 10–14 10.6 10.3 10.5 15.9 15.4 15.6 14.1 13.7 13.9 15–19 7.6 7.8 7.7 12.1 9.6 10.8 10.6 9.0 9.8 20–24 9.8 12.6 11.3 7.1 7.2 7.2 8.0 9.0 8.5 25–29 11.9 12.7 12.3 5.4 6.2 5.8 7.6 8.4 8.0 30–34 9.5 8.4 9.0 5.1 5.7 5.4 6.5 6.7 6.6 35–39 7.3 7.7 7.5 4.9 5.7 5.3 5.7 6.4 6.1 40–44 5.6 5.1 5.4 4.4 4.4 4.4 4.8 4.7 4.7 45–49 3.8 3.2 3.5 3.7 3.7 3.7 3.7 3.5 3.6 50–54 2.7 2.8 2.8 2.9 4.3 3.6 2.8 3.8 3.3 55–59 2.4 1.8 2.1 2.7 2.7 2.7 2.6 2.4 2.5 60–64 1.6 1.4 1.5 2.5 2.9 2.7 2.2 2.4 2.3 65–69 0.8 0.7 0.7 1.7 2.0 1.9 1.4 1.6 1.5 70–74 0.5 0.8 0.7 1.9 2.0 2.0 1.5 1.6 1.5 75–79 0.4 0.3 0.4 0.8 1.1 0.9 0.7 0.8 0.8 80 + 0.3 0.6 0.4 1.1 1.6 1.4 0.8 1.3 1.0 Don’t know/missing 0.1 0.0 0.0 0.1 0.1 0.1 0.1 0.0 0.1 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Dependency age groups 0–14 35.6 33.9 34.7 43.6 40.7 42.1 41.0 38.4 39.7 15–64 62.3 63.6 63.0 50.8 52.5 51.6 54.6 56.2 55.4 65+ 2.0 2.5 2.3 5.5 6.7 6.1 4.4 5.3 4.8 Don’t know/missing 0.1 0.0 0.0 0.1 0.1 0.1 0.1 0.0 0.1 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Child and adult populations 0–17 40.0 37.9 38.9 51.2 47.0 49.1 47.5 43.9 45.7 18+ 59.9 62.1 61.1 48.7 52.9 50.9 52.4 56.0 54.3 Don’t know/missing 0.1 0.0 0.0 0.1 0.1 0.1 0.1 0.0 0.1 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Adolescents age 10–19 18.2 18.1 18.2 27.9 25.0 26.4 24.7 22.7 23.7 Number of persons 21,807 23,841 45,648 44,222 46,612 90,834 66,029 70,454 136,483 34 • Housing Characteristics and Household Population Table 2.8 Household composition Percent distribution of households by sex of head of household and by household size; mean size of households; and percentage of households with orphans and children under age 18 not living with a biological parent, according to residence, Kenya DHS 2022 Residence Total Characteristic Urban Rural Household headship Male 69.2 64.1 66.1 Female 30.8 35.9 33.9 Total 100.0 100.0 100.0 Number of usual members 1 27.9 15.5 20.5 2 17.0 11.9 14.0 3 17.1 14.8 15.7 4 16.1 17.0 16.6 5 10.1 14.4 12.7 6 5.9 10.7 8.8 7 2.6 6.8 5.1 8 1.4 3.9 2.9 9+ 1.8 4.9 3.7 Total 100.0 100.0 100.0 Mean size of households 3.1 4.2 3.7 Percentage of households with children under age 18 who are orphans or not living with a biological parent Double orphans 0.6 1.4 1.1 Single orphans1 4.4 9.7 7.6 Children not living with a biological parent2 7.9 21.2 15.8 Orphans and/or children not living with a biological parent 10.2 25.8 19.5 Number of households 15,277 22,634 37,911 Note: Table is based on de jure household members, i.e., usual residents. 1 Includes children with one dead parent and an unknown survival status of the other parent 2 Children not living with a biological parent are those under age 18 living in households with neither their mother nor their father present. Housing Characteristics and Household Population • 35 Table 2.9 Children’s living arrangements and orphanhood Percent distribution of de jure children under age 18 by living arrangements and survival status of parents, percentage of children not living with a biological parent, and percentage of children with one or both parents dead, according to background characteristics, Kenya DHS 2022 Living with both parents Living with mother but not with father Living with father but not with mother Not living with either parent Total Percent- age not living with a biological parent Percent- age with one or both parents dead1 Number of children Background characteristic Father alive Father dead Mother alive Mother dead Both alive Only mother alive Only father alive Both dead Missing infor- mation on father/ mother Age 0–4 60.9 26.1 2.2 1.2 0.1 6.6 0.4 0.3 0.1 2.3 100.0 7.3 3.1 17,661 <2 64.2 28.7 1.5 0.3 0.0 2.9 0.1 0.1 0.0 2.2 100.0 3.1 1.7 6,910 2–4 58.7 24.4 2.7 1.7 0.1 8.9 0.5 0.3 0.2 2.4 100.0 10.0 4.0 10,751 5–9 53.9 22.1 3.8 3.3 0.5 12.1 1.1 0.8 0.5 2.0 100.0 14.5 6.8 17,806 10–14 47.8 21.0 6.3 4.1 0.9 12.7 2.2 1.3 1.2 2.5 100.0 17.4 12.1 20,042 15–17 44.8 19.7 8.4 4.4 1.5 12.6 2.9 1.3 2.0 2.4 100.0 18.8 16.5 9,811 Sex Male 52.9 21.9 4.9 3.6 0.7 10.6 1.4 0.8 0.9 2.3 100.0 13.8 8.9 32,799 Female 52.2 23.1 4.7 2.7 0.6 11.1 1.6 0.9 0.8 2.3 100.0 14.4 8.8 32,521 Residence Urban 58.9 23.8 3.5 3.2 0.4 6.2 1.1 0.7 0.6 1.6 100.0 8.7 6.5 18,862 Rural 50.0 22.0 5.4 3.1 0.7 12.7 1.7 0.9 0.9 2.5 100.0 16.3 9.9 46,458 Wealth quintile Lowest 50.4 23.2 7.5 3.0 0.9 10.6 1.5 0.9 0.8 1.5 100.0 13.7 11.6 15,862 Second 48.9 20.2 5.6 3.4 0.7 14.1 1.8 1.0 1.0 3.1 100.0 18.0 10.4 14,251 Middle 49.2 22.4 4.3 3.1 0.7 13.4 1.7 1.1 1.0 3.0 100.0 17.3 9.1 12,954 Fourth 53.5 24.4 3.7 2.9 0.6 9.8 1.4 0.6 0.7 2.5 100.0 12.5 7.2 11,447 Highest 63.4 22.5 1.8 3.3 0.2 5.1 1.1 0.6 0.6 1.3 100.0 7.4 4.4 10,807 Total <15 53.9 23.0 4.2 2.9 0.5 10.6 1.3 0.8 0.6 2.3 100.0 13.3 7.5 55,510 Total <18 52.5 22.5 4.8 3.1 0.6 10.9 1.5 0.9 0.8 2.3 100.0 14.1 8.9 65,321 Note: Table is based on de jure members, i.e., usual residents. 1 Includes children with father dead, mother dead, both dead and one parent dead but missing information on survival status of the other parent 36 • Housing Characteristics and Household Population Table 2.9C Children’s living arrangements and orphanhood by county Percent distribution of de jure children under age 18 by living arrangements and survival status of parents, percentage of children not living with a biological parent, and percentage of children with one or both parents dead, according to county, Kenya DHS 2022 Living with both parents Living with mother but not with father Living with father but not with mother Not living with either parent Total Percent- age not living with a biological parent Percent- age with one or both parents dead1 Number of children County Father alive Father dead Mother alive Mother dead Both alive Only mother alive Only father alive Both dead Missing infor- mation on father/ mother Mombasa 62.6 21.3 2.7 3.9 0.2 6.4 0.6 0.9 0.6 0.8 100.0 8.6 5.2 1,350 Kwale 52.4 22.3 3.3 6.5 1.1 12.0 0.7 1.0 0.4 0.3 100.0 14.1 6.5 1,213 Kilifi 47.5 28.2 4.3 5.0 0.9 11.5 0.9 0.8 0.3 0.6 100.0 13.4 7.3 2,132 Tana River 70.1 11.8 5.7 2.7 0.6 6.3 0.6 1.6 0.4 0.1 100.0 8.9 8.9 498 Lamu 58.6 21.7 4.8 2.5 0.3 7.8 1.2 1.6 0.5 0.9 100.0 11.2 8.5 225 Taita/Taveta 48.2 26.9 4.5 3.3 0.7 11.1 0.9 1.0 0.9 2.7 100.0 13.8 7.9 479 Garissa 62.8 15.2 6.0 1.5 0.7 10.0 2.1 1.4 0.3 0.0 100.0 13.8 10.5 872 Wajir 60.7 17.9 4.0 1.8 1.4 10.6 1.3 1.3 0.7 0.2 100.0 13.9 8.7 560 Mandera 66.4 17.9 4.6 1.7 1.0 6.4 0.8 0.8 0.3 0.1 100.0 8.3 7.5 830 Marsabit 56.2 22.5 9.0 0.9 0.4 7.5 1.3 0.7 1.4 0.2 100.0 10.9 12.8 459 Isiolo 53.6 24.5 6.5 2.2 1.1 8.2 1.1 0.9 1.4 0.6 100.0 11.6 10.9 373 Meru 50.4 19.6 3.8 5.1 0.5 12.1 2.2 1.2 0.8 4.5 100.0 16.1 8.9 1,895 Tharaka-Nithi 53.9 20.9 2.9 3.1 0.3 11.2 0.6 1.1 0.9 5.2 100.0 13.8 6.9 559 Embu 53.0 22.8 3.1 4.0 0.6 9.7 1.7 0.4 0.9 3.8 100.0 12.7 7.3 664 Kitui 44.4 34.5 3.8 1.9 0.5 10.0 0.9 1.3 1.3 1.6 100.0 13.4 7.9 1,566 Machakos 49.8 25.0 2.3 2.2 1.0 9.4 1.9 0.9 0.7 6.7 100.0 12.9 7.0 1,681 Makueni 38.8 33.4 3.5 2.6 0.4 12.8 1.5 0.3 0.1 6.6 100.0 14.7 6.3 1,198 Nyandarua 62.1 20.9 2.4 1.5 0.7 6.7 1.2 0.1 0.9 3.6 100.0 8.8 5.7 800 Nyeri 46.9 29.8 4.2 3.7 0.9 9.0 1.6 0.2 0.7 2.9 100.0 11.6 7.7 777 Kirinyaga 54.9 21.1 6.5 2.4 0.5 8.1 0.7 0.3 0.5 4.9 100.0 9.6 8.5 704 Murang’a 42.0 32.1 5.6 3.5 0.7 8.8 1.0 1.2 0.4 4.8 100.0 11.4 9.6 1,203 Kiambu 58.7 24.9 5.1 3.4 0.2 3.0 0.9 0.5 0.4 2.9 100.0 4.8 7.2 2,994 Turkana 44.9 22.0 9.2 2.3 1.2 13.8 2.4 1.4 2.3 0.4 100.0 19.9 16.7 1,082 West Pokot 54.6 24.7 4.5 1.5 0.7 11.4 0.6 1.0 0.4 0.7 100.0 13.4 7.2 1,365 Samburu 51.2 19.5 9.5 1.9 0.8 13.1 1.6 1.1 1.0 0.3 100.0 16.8 13.9 506 Trans Nzoia 42.0 26.9 3.3 4.2 0.6 17.0 1.5 0.6 1.0 2.8 100.0 20.1 7.4 1,670 Uasin Gishu 59.5 20.0 3.4 2.2 0.5 10.0 1.2 0.5 0.8 1.9 100.0 12.5 6.5 1,724 Elgeyo/Marakwet 53.9 26.2 2.1 2.6 0.6 11.2 0.8 0.6 0.4 1.6 100.0 13.1 4.6 637 Nandi 52.5 20.7 5.3 2.5 1.0 13.2 1.2 0.6 1.2 1.9 100.0 16.2 9.2 1,226 Baringo 46.7 28.8 6.7 2.8 0.2 10.6 1.1 1.1 0.5 1.6 100.0 13.3 9.5 1,058 Laikipia 50.3 28.4 7.3 1.1 0.4 7.7 1.2 0.7 0.4 2.5 100.0 10.1 10.6 635 Nakuru 51.9 26.4 4.7 2.2 0.5 7.1 1.4 0.5 0.7 4.5 100.0 9.7 8.1 2,970 Narok 63.8 17.6 6.1 2.0 0.4 7.6 0.6 0.5 0.5 0.9 100.0 9.2 8.1 2,063 Kajiado 58.0 21.9 4.4 2.0 0.6 10.5 0.8 0.6 0.1 1.0 100.0 12.1 6.5 1,693 Kericho 58.7 19.1 5.3 1.6 0.8 10.1 0.6 0.8 0.7 2.2 100.0 12.2 8.2 1,407 Bomet 56.5 23.0 5.1 1.6 0.4 10.5 0.8 0.6 0.4 1.2 100.0 12.2 7.3 1,410 Kakamega 43.2 21.3 3.2 5.1 1.6 19.0 2.4 0.7 1.1 2.2 100.0 23.2 9.4 3,113 Vihiga 34.8 22.2 6.9 4.5 0.5 21.2 2.7 1.9 1.0 4.3 100.0 26.8 13.1 834 Bungoma 47.2 18.6 4.6 3.9 0.2 19.6 2.5 1.3 0.8 1.4 100.0 24.2 9.4 2,734 Busia 45.0 19.5 2.9 6.1 0.1 22.1 2.3 0.9 0.6 0.4 100.0 25.9 6.9 1,583 Siaya 43.7 20.0 8.4 5.0 0.9 13.9 3.7 1.0 1.6 1.8 100.0 20.2 15.7 1,338 Kisumu 53.1 17.8 8.8 4.1 0.8 8.5 2.6 1.9 1.3 1.1 100.0 14.3 15.5 1,649 Homa Bay 47.3 14.8 8.4 3.0 1.3 13.8 3.8 1.5 3.1 3.0 100.0 22.2 18.5 1,779 Migori 53.0 16.6 11.5 2.1 1.6 8.0 2.3 1.1 1.5 2.3 100.0 12.9 18.3 1,868 Kisii 44.3 23.6 4.8 2.3 0.9 14.4 2.5 1.3 1.5 4.3 100.0 19.8 11.3 1,833 Nyamira 42.7 22.2 5.3 3.9 0.5 16.2 1.7 0.8 1.0 5.7 100.0 19.7 10.0 793 Nairobi City 66.6 21.3 1.7 2.9 0.1 4.3 0.8 0.5 0.5 1.2 100.0 6.2 3.8 5,320 Total <18 52.5 22.5 4.8 3.1 0.6 10.9 1.5 0.9 0.8 2.3 100.0 14.1 8.9 65,321 Note: Table is based on de jure members (usual residents). 1 Includes children with father dead, mother dead, both dead and one parent dead but missing information on survival status of the other parent Housing Characteristics and Household Population • 37 Table 2.10 Birth registration of children under age 5 Percentage of de jure children under age 5 whose births are registered with the civil registration authority, according to background characteristics, Kenya DHS 2022 Percentage of children whose births are registered and who: Total percentage of children whose births are registered Number of children Background characteristic Had a birth certificate Did not have birth certificate Age <1 15.3 58.4 73.7 3,555 1–4 38.5 38.1 76.5 14,107 Sex Male 34.0 42.3 76.3 8,975 Female 33.6 42.0 75.6 8,686 Residence Urban 46.6 34.6 81.3 6,053 Rural 27.1 46.1 73.2 11,608 Wealth quintile Lowest 14.2 48.4 62.6 4,182 Second 25.0 47.6 72.6 3,381 Middle 34.0 44.3 78.4 3,174 Fourth 41.1 39.9 81.0 3,405 Highest 58.2 29.8 88.0 3,519 Total 33.8 42.2 76.0 17,661 38 • Housing Characteristics and Household Population Table 2.10C Birth registration of children under age 5 by county Percentage of de jure children under age 5 whose births are registered with the civil registration authority, according to county, Kenya DHS 2022 Percentage of children whose births are registered and who: Total percentage of children whose births are registered Number of children County Had a birth certificate Did not have birth certificate Mombasa 46.3 44.3 90.6 429 Kwale 20.9 54.1 75.1 326 Kilifi 23.3 58.0 81.3 540 Tana River 18.7 39.2 57.8 147 Lamu 48.8 32.0 80.8 65 Taita/Taveta 45.0 45.0 90.0 131 Garissa 46.2 18.9 65.2 238 Wajir 34.2 15.4 49.6 143 Mandera 34.0 19.6 53.6 260 Marsabit 31.2 21.0 52.3 138 Isiolo 33.8 22.6 56.4 101 Meru 21.0 64.6 85.6 490 Tharaka-Nithi 41.8 51.4 93.2 141 Embu 46.7 47.6 94.2 170 Kitui 32.8 55.7 88.6 381 Machakos 35.1 58.3 93.4 417 Makueni 24.0 46.5 70.5 315 Nyandarua 51.8 38.7 90.6 205 Nyeri 58.2 38.1 96.2 226 Kirinyaga 50.0 37.1 87.1 198 Murang’a 39.5 47.7 87.2 319 Kiambu 46.7 46.2 92.9 961 Turkana 8.1 48.6 56.7 319 West Pokot 10.6 64.6 75.2 436 Samburu 17.0 59.3 76.3 150 Trans Nzoia 25.1 49.5 74.6 383 Uasin Gishu 43.3 43.5 86.8 509 Elgeyo/Marakwet 30.4 44.9 75.3 170 Nandi 29.2 39.5 68.7 304 Baringo 26.7 33.6 60.3 272 Laikipia 48.0 32.4 80.4 163 Nakuru 39.6 17.7 57.3 893 Narok 22.3 51.6 74.0 567 Kajiado 31.0 50.0 81.0 541 Kericho 26.8 37.4 64.2 347 Bomet 29.3 46.6 75.9 347 Kakamega 37.5 36.4 73.9 703 Vihiga 32.4 54.1 86.5 175 Bungoma 28.1 36.5 64.5 661 Busia 28.1 41.7 69.8 391 Siaya 28.9 40.1 69.0 330 Kisumu 24.6 48.3 72.8 428 Homa Bay 24.9 42.7 67.6 408 Migori 20.9 59.7 80.6 451 Kisii 36.6 52.2 88.8 412 Nyamira 36.0 31.5 67.6 155 Nairobi City 48.0 25.5 73.6 1,807 Total 33.8 42.2 76.0 17,661 Housing Characteristics and Household Population • 39 Table 2.11.1 Educational attainment of the female household population Percent distribution of the de facto female household population age 6 and over by highest level of schooling attended or completed and median years completed, according to background characteristics, Kenya DHS 2022 Background characteristic No education1 Some primary Completed primary2 Some secondary Completed secondary3 More than secondary4 Total Number Median years completed Age 6–9 36.5 63.4 0.0 0.1 0.0 0.0 100.0 7,128 0.4 10–14 3.4 87.3 6.3 3.0 0.0 0.0 100.0 9,649 4.5 15–19 2.9 19.6 12.9 49.8 12.0 2.7 100.0 6,294 7.3 20–24 3.4 9.7 12.3 15.8 31.3 27.5 100.0 6,258 9.3 25–29 5.1 12.2 17.1 10.7 25.9 29.1 100.0 5,830 9.2 30–34 8.6 16.8 24.4 10.2 18.3 21.7 100.0 4,633 7.8 35–39 8.6 21.6 27.4 8.5 16.3 17.5 100.0 4,433 7.6 40–44 7.9 22.0 27.3 8.3 17.6 17.0 100.0 3,251 7.6 45–49 8.8 25.4 27.7 8.1 16.6 13.5 100.0 2,477 7.4 50–54 13.6 27.4 24.1 10.4 15.1 9.4 100.0 2,670 7.0 55–59 17.4 21.8 28.2 7.9 15.4 9.2 100.0 1,688 6.4 60–64 28.1 27.8 18.6 8.6 10.5 6.4 100.0 1,669 5.0 65+ 51.9 27.0 12.4 2.9 2.5 3.3 100.0 3,722 0.0 Residence Urban 8.7 25.3 12.9 11.2 19.4 22.4 100.0 20,247 7.7 Rural 15.7 40.5 16.1 12.0 9.6 6.0 100.0 39,486 6.1 Wealth quintile Lowest 32.0 45.8 11.2 7.4 3.2 0.5 100.0 11,441 3.1 Second 12.5 46.6 17.9 13.3 7.6 2.0 100.0 12,058 6.0 Middle 10.1 37.7 19.3 14.6 12.4 5.9 100.0 11,954 6.6 Fourth 7.5 27.6 17.0 13.1 19.6 15.1 100.0 11,767 7.4 Highest 5.6 20.2 9.9 10.3 21.1 32.9 100.0 12,512 9.2 Total 13.3 35.4 15.1 11.8 12.9 11.5 100.0 59,733 6.6 Note: Respondents who reported vocational training as their highest current or previous level of education have been excluded from this table. Total includes 46 women for whom information on age is missing. 1 No education includes informal education (madrassa/duksi/adult education). 2 Completed grade 8 at the primary level, for those under age 53; because of the change in the school system in the 1980s, those age 53 and above are considered to have completed primary if they completed grade 7. 3 Completed 4 grades at the secondary level 4 More than secondary includes middle level colleges and university. 40 • Housing Characteristics and Household Population Table 2.11.1C Educational attainment of the female household population by county Percent distribution of the de facto female household population age 6 and over by highest level of schooling attended or completed and median years completed, according to county, Kenya DHS 2022 Background characteristic No education1 Some primary Completed primary2 Some secondary Completed secondary3 More than secondary4 Total Number Median years completed Mombasa 11.3 29.9 17.5 9.9 17.3 14.2 100.0 1,435 7.2 Kwale 25.9 42.5 14.3 6.7 6.9 3.7 100.0 1,012 4.0 Kilifi 23.3 46.1 13.1 6.1 7.1 4.1 100.0 1,816 4.1 Tana River 49.5 33.2 8.3 3.0 4.2 1.8 100.0 331 0.0 Lamu 21.0 44.2 13.8 10.4 5.9 4.6 100.0 187 4.8 Taita/Taveta 11.4 32.3 23.6 10.9 14.2 7.6 100.0 448 7.0 Garissa 57.9 24.3 2.7 7.7 4.3 3.2 100.0 577 0.0 Wajir 59.6 25.8 3.3 5.8 4.3 1.2 100.0 361 0.0 Mandera 65.2 25.2 2.9 2.6 3.2 0.9 100.0 484 0.0 Marsabit 54.3 28.8 7.7 4.3 3.1 1.8 100.0 317 0.0 Isiolo 32.6 33.9 10.2 7.4 8.4 7.5 100.0 285 3.6 Meru 13.5 39.9 16.0 12.2 10.2 8.2 100.0 1,986 6.3 Tharaka-Nithi 9.5 41.6 16.1 9.7 11.0 12.0 100.0 532 6.4 Embu 8.6 31.8 20.3 13.0 15.4 10.8 100.0 716 7.1 Kitui 15.1 36.8 21.9 9.5 8.5 8.2 100.0 1,496 6.5 Machakos 5.9 30.5 18.6 14.6 15.5 14.9 100.0 1,759 7.2 Makueni 10.8 33.0 20.9 12.5 13.8 9.0 100.0 1,244 6.9 Nyandarua 8.0 29.2 26.0 13.8 15.7 7.3 100.0 793 7.2 Nyeri 6.5 23.4 18.4 13.5 21.7 16.4 100.0 918 7.6 Kirinyaga 8.6 30.4 20.9 12.2 16.8 11.1 100.0 873 7.2 Murang’a 9.0 29.8 23.9 15.3 14.4 7.6 100.0 1,448 7.0 Kiambu 5.9 26.7 14.7 11.4 19.4 22.0 100.0 3,461 7.7 Turkana 62.7 24.7 3.3 3.1 3.2 3.0 100.0 745 0.0 West Pokot 38.9 39.4 5.2 7.3 4.6 4.7 100.0 854 2.2 Samburu 51.3 28.6 5.3 4.9 5.1 4.7 100.0 322 0.0 Trans Nzoia 9.6 43.6 14.2 14.1 10.1 8.4 100.0 1,366 6.3 Uasin Gishu 6.5 31.7 12.3 11.4 16.8 21.3 100.0 1,596 7.4 Elgeyo/Marakwet 8.9 39.2 15.8 13.2 12.7 10.1 100.0 494 6.6 Nandi 7.4 41.3 17.9 12.6 10.1 10.7 100.0 1,164 6.7 Baringo 17.5 39.7 13.6 8.6 11.6 9.0 100.0 778 6.0 Laikipia 10.9 32.7 17.0 12.8 17.0 9.5 100.0 626 6.8 Nakuru 8.5 32.9 17.4 13.5 15.7 12.0 100.0 2,994 7.1 Narok 23.8 42.7 11.2 9.1 6.0 7.2 100.0 1,428 5.0 Kajiado 17.5 27.6 10.2 8.8 16.4 19.5 100.0 1,513 7.1 Kericho 7.3 37.6 17.4 14.7 12.0 11.2 100.0 1,347 6.9 Bomet 8.9 43.7 16.4 14.1 9.6 7.3 100.0 1,177 6.5 Kakamega 10.4 43.5 14.5 12.6 10.8 8.3 100.0 2,584 6.3 Vihiga 7.2 44.1 16.7 16.5 10.7 4.8 100.0 801 6.4 Bungoma 7.1 43.9 12.9 16.5 10.5 9.1 100.0 2,201 6.5 Busia 12.0 49.9 13.0 13.4 7.2 4.5 100.0 1,269 5.7 Siaya 10.7 41.4 22.5 12.5 8.0 4.9 100.0 1,145 6.4 Kisumu 7.0 39.5 18.7 16.3 9.9 8.5 100.0 1,437 6.8 Homa Bay 11.5 45.6 16.1 14.1 7.5 5.1 100.0 1,459 6.0 Migori 12.5 52.9 14.5 10.3 6.4 3.4 100.0 1,409 5.7 Kisii 8.4 39.7 13.9 16.1 13.5 8.4 100.0 1,644 6.6 Nyamira 8.9 40.4 13.2 17.3 14.5 5.6 100.0 745 6.6 Nairobi City 5.6 21.9 12.1 10.2 22.9 27.3 100.0 6,154 9.0 Total 13.3 35.4 15.1 11.8 12.9 11.5 100.0 59,733 6.6 Note: Respondents who reported vocational training as their highest current or previous level of education have been excluded from this table. 1 No education includes informal education (madrassa/duksi/adult education). 2 Completed grade 8 at the primary level, for those under age 53; because of the change in the school system in the 1980s, those age 53 and above are considered to have completed primary if they completed grade 7. 3 Completed 4 grades at the secondary level 4 More than secondary includes middle level colleges and university. Housing Characteristics and Household Population • 41 Table 2.11.2 Educational attainment of the male household population Percent distribution of the de facto male household population age 6 and over by highest level of schooling attended or completed and median years completed, according to background characteristics, Kenya DHS 2022 Background characteristic No education1 Some primary Completed primary2 Some secondary Completed secondary3 More than secondary4 Total Number Median years completed Age 6–9 40.1 59.9 0.1 0.0 0.0 0.0 100.0 7,129 0.2 10–14 4.0 89.9 4.1 2.1 0.0 0.0 100.0 9,326 4.1 15–19 2.7 27.3 14.5 44.3 9.1 2.1 100.0 6,940 7.1 20–24 3.0 9.5 11.0 17.7 34.0 24.8 100.0 5,151 9.3 25–29 2.6 11.1 15.0 8.0 30.6 32.7 100.0 4,916 9.4 30–34 4.2 15.5 19.2 6.7 24.7 29.7 100.0 4,259 9.2 35–39 5.1 19.8 26.3 6.8 21.4 20.6 100.0 3,713 7.8 40–44 5.5 21.3 26.1 7.4 21.2 18.5 100.0 3,125 7.7 45–49 6.6 22.9 28.0 7.1 19.8 15.5 100.0 2,439 7.6 50–54 5.2 20.4 24.4 8.4 25.3 16.3 100.0 1,840 7.7 55–59 8.0 14.2 31.9 8.4 23.4 14.0 100.0 1,678 7.3 60–64 14.4 19.9 23.9 7.7 21.0 13.1 100.0 1,438 6.7 65+ 22.7 25.9 24.4 6.4 11.4 9.2 100.0 2,859 6.0 Residence Urban 7.2 24.5 12.1 9.6 22.4 24.2 100.0 18,156 8.0 Rural 11.5 41.9 15.9 11.9 11.7 7.1 100.0 36,726 6.3 Wealth quintile Lowest 23.1 49.9 12.5 7.7 5.5 1.2 100.0 10,687 3.9 Second 9.1 46.0 17.7 13.3 10.6 3.3 100.0 11,144 6.2 Middle 7.1 36.9 19.0 14.6 15.3 7.2 100.0 11,410 6.9 Fourth 6.1 26.6 15.5 11.9 23.8 16.0 100.0 11,281 7.6 Highest 5.0 21.0 7.6 7.7 21.1 37.5 100.0 10,360 9.4 Total 10.0 36.2 14.6 11.1 15.3 12.8 100.0 54,882 6.8 Note: Respondents who reported vocational training as their highest current or previous level of education have been excluded from this table. Total includes 87 men for whom information on age is missing. 1 No education includes informal education (madrassa/duksi/adult education). 2 Completed grade 8 at the primary level, for those under age 53; because of the change in the school system in the 1980s, those age 53 and above are considered to have completed primary if they completed grade 7. 3 Completed 4 grades at the secondary level 4 More than secondary includes middle level colleges and university. 42 • Housing Characteristics and Household Population Table 2.11.2C Educational attainment of the male household population by county Percent distribution of the de facto male household population age 6 and over by highest level of schooling attended or completed and median years completed, according to county, Kenya DHS 2022 County No education1 Some primary Completed primary2 Some secondary Completed secondary3 More than secondary4 Total Number Median years completed Mombasa 8.0 25.7 16.6 9.5 23.7 16.5 100.0 1,428 7.6 Kwale 15.5 44.0 16.9 8.1 10.4 5.1 100.0 893 5.6 Kilifi 9.9 45.0 18.7 10.0 10.8 5.7 100.0 1,665 6.0 Tana River 41.3 34.4 9.3 5.3 7.2 2.5 100.0 317 1.7 Lamu 15.0 41.1 18.6 8.8 9.4 7.2 100.0 180 5.7 Taita/Taveta 7.1 34.6 23.3 10.7 14.4 9.9 100.0 449 7.0 Garissa 42.2 26.6 7.7 8.2 9.1 6.2 100.0 592 1.4 Wajir 45.0 30.1 5.4 9.0 6.8 3.6 100.0 330 0.9 Mandera 47.1 33.5 5.8 6.0 5.3 2.3 100.0 446 0.3 Marsabit 36.3 34.4 11.2 6.9 6.9 4.2 100.0 264 2.7 Isiolo 24.2 34.8 11.6 8.9 10.6 10.0 100.0 241 5.0 Meru 8.8 43.6 16.8 10.5 11.8 8.6 100.0 1,858 6.3 Tharaka-Nithi 5.6 45.6 14.2 12.0 10.7 11.9 100.0 521 6.4 Embu 4.9 35.1 19.1 11.5 17.3 12.1 100.0 679 7.1 Kitui 9.7 40.9 20.2 9.8 11.0 8.4 100.0 1,344 6.5 Machakos 3.3 31.7 17.7 14.6 17.4 15.4 100.0 1,761 7.3 Makueni 6.5 38.7 20.9 11.7 13.0 9.2 100.0 1,088 6.7 Nyandarua 5.3 34.3 23.6 14.3 14.8 7.7 100.0 770 7.0 Nyeri 3.5 27.6 18.6 11.4 23.6 15.4 100.0 893 7.6 Kirinyaga 4.9 30.8 17.7 14.5 19.0 13.1 100.0 751 7.3 Murang’a 4.8 34.6 22.3 14.2 15.4 8.7 100.0 1,294 7.1 Kiambu 5.1 26.7 14.7 11.6 20.8 21.2 100.0 2,981 7.7 Turkana 51.4 27.1 5.0 5.4 6.2 5.0 100.0 625 0.0 West Pokot 32.5 40.7 5.8 9.7 5.9 5.4 100.0 793 3.1 Samburu 43.4 29.0 5.4 6.7 8.4 7.1 100.0 273 1.1 Trans Nzoia 9.0 44.5 12.0 13.0 12.2 9.3 100.0 1,221 6.3 Uasin Gishu 6.7 31.0 13.1 8.8 19.0 21.4 100.0 1,527 7.5 Elgeyo/Marakwet 7.9 38.6 15.4 13.7 14.1 10.3 100.0 500 6.7 Nandi 6.4 42.7 15.8 12.2 12.1 10.9 100.0 1,108 6.6 Baringo 13.6 40.5 14.1 10.3 12.1 9.3 100.0 771 6.2 Laikipia 7.9 29.7 18.9 13.9 18.1 11.4 100.0 590 7.1 Nakuru 6.4 34.5 16.6 13.4 17.3 11.9 100.0 2,559 7.1 Narok 20.2 45.6 10.7 7.4 9.2 6.9 100.0 1,444 4.8 Kajiado 15.8 27.5 7.7 9.7 18.7 20.6 100.0 1,396 7.3 Kericho 6.1 38.7 14.4 12.5 16.1 12.1 100.0 1,334 7.0 Bomet 6.5 43.6 16.3 11.9 12.4 9.3 100.0 1,133 6.6 Kakamega 8.3 45.5 12.8 14.7 10.5 8.1 100.0 2,460 6.2 Vihiga 7.0 41.6 18.5 13.3 12.8 6.7 100.0 690 6.6 Bungoma 8.5 42.0 12.1 13.8 13.3 10.4 100.0 1,972 6.6 Busia 7.3 50.9 14.0 12.4 9.5 6.0 100.0 1,214 6.0 Siaya 7.2 44.2 21.6 11.7 9.3 6.0 100.0 1,111 6.4 Kisumu 7.1 34.3 17.4 13.0 17.0 11.2 100.0 1,358 7.1 Homa Bay 10.2 44.4 14.8 12.3 10.4 8.0 100.0 1,218 6.2 Migori 10.1 51.1 14.8 9.3 9.1 5.5 100.0 1,215 5.7 Kisii 6.2 41.7 12.9 12.2 14.9 12.1 100.0 1,385 6.7 Nyamira 6.8 42.1 13.0 13.5 17.0 7.6 100.0 637 6.6 Nairobi City 4.7 20.2 9.9 7.9 26.6 30.7 100.0 5,606 9.3 Total 10.0 36.2 14.6 11.1 15.3 12.8 100.0 54,882 6.8 Note: Respondents who reported vocational training as their highest current or previous level of education have been excluded from this table. 1 No education includes informal education (madrassa/duksi/adult education). 2 Completed grade 8 at the primary level, for those under age 53; because of the change in the school system in the 1980s, those age 53 and above are considered to have completed primary if they completed grade 7. 3 Completed 4 grades at the secondary level 4 More than secondary includes middle level colleges and university. Housing Characteristics and Household Population • 43 Table 2.12 School attendance ratios Net attendance ratios (NAR) and gross attendance ratios (GAR) for the de facto household population by sex and level of schooling; and the Gender Parity Index (GPI), according to background characteristics, Kenya DHS 2022 Net attendance ratio 1 Gross attendance ratio2 Background characteristic Male Female Total Gender Parity Index3 Male Female Total Gender Parity Index3 PRIMARY SCHOOL Residence Urban 87.6 90.1 88.9 1.03 104.2 103.6 103.9 0.99 Rural 84.7 85.8 85.2 1.01 110.3 105.3 107.8 0.95 Wealth quintile Lowest 74.5 75.0 74.8 1.01 102.2 96.9 99.6 0.95 Second 88.7 89.6 89.2 1.01 116.1 110.8 113.4 0.95 Middle 89.5 90.9 90.2 1.02 114.8 108.0 111.4 0.94 Fourth 89.0 91.1 90.1 1.02 106.8 105.9 106.4 0.99 Highest 90.2 92.6 91.5 1.03 102.2 102.9 102.6 1.01 Total 85.5 87.0 86.2 1.02 108.7 104.8 106.7 0.96 SECONDARY SCHOOL Residence Urban 56.5 59.7 58.2 1.06 94.6 95.0 94.8 1.00 Rural 42.2 50.2 45.9 1.19 76.5 80.6 78.4 1.05 Wealth quintile Lowest 23.4 30.6 26.6 1.31 48.3 52.5 50.2 1.09 Second 42.3 50.4 46.2 1.19 79.2 80.1 79.7 1.01 Middle 49.7 59.6 54.1 1.20 88.4 94.6 91.2 1.07 Fourth 59.3 62.4 60.8 1.05 98.3 99.9 99.0 1.02 Highest 71.0 68.1 69.4 0.96 111.1 105.9 108.2 0.95 Total 45.4 52.7 48.8 1.16 80.5 84.3 82.3 1.05 Note: Respondents whose current or previous level of education was vocational training have been excluded from this table. 1 The NAR for primary school is the percentage of the primary-school age (6–13) population that is attending primary school. The NAR for secondary school is the percentage of the secondary-school age (14–17) population that is attending secondary school. By definition, the NAR cannot exceed 100.0. 2 The GAR for primary school is the total number of primary school students, expressed as a percentage of the official primary-school- age population. The GAR for secondary school is the total number of secondary school students, expressed as a percentage of the official secondary-school-age population. If there are significant numbers of overage and underage students at a given level of schooling, the GAR can exceed 100.0. 3 The Gender Parity Index for primary school is the ratio of the primary school NAR (GAR) for females to the NAR (GAR) for males. The Gender Parity Index for secondary school is the ratio of the secondary school NAR (GAR) for females to the NAR (GAR) for males. 44 • Housing Characteristics and Household Population Table 2.12C School attendance ratios by county Net attendance ratios (NAR) and gross attendance ratios (GAR) for the de facto household population by sex and level of schooling; and the Gender Parity Index (GPI), according to county, Kenya DHS 2022 Net attendance ratio 1 Gross attendance ratio2 County Male Female Total Gender Parity Index3 Male Female Total Gender Parity Index3 PRIMARY SCHOOL Mombasa 88.5 87.7 88.0 0.99 115.2 107.1 111.0 0.93 Kwale 78.9 79.0 78.9 1.00 109.6 104.5 106.9 0.95 Kilifi 86.0 89.3 87.6 1.04 122.0 121.5 121.8 1.00 Tana River 57.2 57.1 57.1 1.00 72.5 71.6 72.1 0.99 Lamu 84.4 86.0 85.2 1.02 107.4 106.7 107.1 0.99 Taita/Taveta 93.7 89.1 91.6 0.95 108.3 103.1 105.9 0.95 Garissa 49.0 48.6 48.8 0.99 62.6 66.0 64.1 1.05 Wajir 56.4 58.5 57.4 1.04 74.7 74.6 74.6 1.00 Mandera 58.3 51.6 55.1 0.89 78.9 65.9 72.5 0.84 Marsabit 73.4 66.8 69.9 0.91 88.7 78.6 83.4 0.89 Isiolo 74.8 78.7 76.9 1.05 92.1 93.6 92.9 1.02 Meru 85.5 89.1 87.5 1.04 112.8 105.1 108.5 0.93 Tharaka-Nithi 88.1 88.1 88.1 1.00 114.6 104.9 110.1 0.92 Embu 92.5 92.3 92.4 1.00 112.5 105.1 108.8 0.93 Kitui 92.1 92.9 92.5 1.01 118.3 117.3 117.8 0.99 Machakos 93.5 91.8 92.6 0.98 115.4 103.9 109.7 0.90 Makueni 92.9 89.9 91.5 0.97 114.4 116.1 115.2 1.01 Nyandarua 95.3 95.4 95.3 1.00 118.4 112.1 115.5 0.95 Nyeri 92.5 91.4 92.0 0.99 118.2 104.7 111.9 0.89 Kirinyaga 91.1 91.9 91.5 1.01 104.7 106.4 105.6 1.02 Murang’a 93.7 92.7 93.2 0.99 108.9 102.3 105.6 0.94 Kiambu 92.2 95.0 93.7 1.03 106.1 104.2 105.1 0.98 Turkana 44.6 43.4 44.0 0.97 60.1 54.4 57.2 0.91 West Pokot 63.3 70.9 67.1 1.12 77.9 86.7 82.3 1.11 Samburu 57.2 60.0 58.6 1.05 70.1 70.6 70.4 1.01 Trans Nzoia 89.9 90.5 90.2 1.01 112.0 108.1 110.0 0.97 Uasin Gishu 84.9 92.0 88.5 1.08 107.0 109.5 108.3 1.02 Elgeyo/Marakwet 87.7 90.3 88.9 1.03 112.4 108.8 110.7 0.97 Nandi 90.7 88.1 89.4 0.97 116.7 112.6 114.6 0.97 Baringo 81.2 87.5 84.0 1.08 100.4 113.3 106.2 1.13 Laikipia 89.8 91.6 90.7 1.02 106.4 106.1 106.2 1.00 Nakuru 88.3 90.5 89.4 1.02 111.1 106.4 108.6 0.96 Narok 80.6 81.7 81.1 1.01 107.9 102.5 105.3 0.95 Kajiado 83.8 80.8 82.2 0.96 104.4 99.2 101.7 0.95 Kericho 91.0 89.9 90.5 0.99 108.7 104.7 106.7 0.96 Bomet 91.7 92.7 92.2 1.01 125.3 116.8 121.2 0.93 Kakamega 90.8 91.7 91.2 1.01 123.3 115.1 119.4 0.93 Vihiga 91.8 92.4 92.2 1.01 120.7 117.8 119.1 0.98 Bungoma 86.5 92.0 89.3 1.06 116.1 114.5 115.3 0.99 Busia 90.9 90.4 90.7 0.99 124.0 111.5 117.9 0.90 Siaya 91.0 91.1 91.1 1.00 117.2 108.4 113.1 0.92 Kisumu 89.9 91.8 90.9 1.02 107.3 110.8 109.0 1.03 Homa Bay 85.7 86.6 86.2 1.01 111.5 100.7 106.0 0.90 Migori 85.7 85.4 85.5 1.00 115.7 106.5 111.0 0.92 Kisii 91.2 92.6 92.0 1.01 117.5 108.8 112.9 0.93 Nyamira 90.1 90.6 90.3 1.01 117.4 109.7 113.4 0.93 Nairobi City 87.8 93.1 90.6 1.06 100.1 102.7 101.5 1.03 Total 85.5 87.0 86.2 1.02 108.7 104.8 106.7 0.96 Continued… Housing Characteristics and Household Population • 45 Table 2.12C—Continued Net attendance ratio 1 Gross attendance ratio2 County Male Female Total Gender Parity Index3 Male Female Total Gender Parity Index3 SECONDARY SCHOOL Mombasa 44.1 45.7 44.9 1.04 82.7 75.8 79.2 0.92 Kwale 22.0 26.2 24.0 1.19 48.6 43.4 46.0 0.89 Kilifi 23.2 14.7 19.1 0.63 69.0 46.6 58.3 0.68 Tana River 20.0 13.7 17.1 0.69 41.6 25.3 34.1 0.61 Lamu 31.5 35.6 33.8 1.13 62.4 58.7 60.4 0.94 Taita/Taveta 57.3 64.4 60.5 1.12 81.7 103.9 91.8 1.27 Garissa 22.5 19.6 20.9 0.87 51.8 34.7 42.4 0.67 Wajir 41.1 35.3 38.4 0.86 76.4 60.7 69.1 0.79 Mandera 28.7 19.9 24.8 0.69 51.5 36.6 44.9 0.71 Marsabit 39.6 30.6 35.7 0.77 62.9 71.2 66.5 1.13 Isiolo 42.2 47.8 44.9 1.13 70.5 81.6 75.9 1.16 Meru 45.8 50.3 47.8 1.10 77.8 78.7 78.2 1.01 Tharaka-Nithi 38.4 50.1 43.2 1.31 80.4 82.5 81.3 1.03 Embu 58.0 64.8 60.6 1.12 85.8 127.3 101.7 1.48 Kitui 40.8 51.6 45.8 1.27 72.8 93.7 82.4 1.29 Machakos 62.6 76.8 68.3 1.23 101.8 115.9 107.4 1.14 Makueni 52.0 56.7 54.5 1.09 97.2 90.1 93.5 0.93 Nyandarua 60.1 67.5 63.4 1.12 75.6 86.1 80.4 1.14 Nyeri 60.8 78.9 69.3 1.30 88.6 99.0 93.5 1.12 Kirinyaga 75.6 70.1 73.0 0.93 123.1 101.2 112.6 0.82 Murang’a 62.4 78.7 70.8 1.26 100.5 105.7 103.2 1.05 Kiambu 69.3 72.6 70.8 1.05 92.4 106.2 98.7 1.15 Turkana 23.7 17.1 20.6 0.72 45.9 38.0 42.2 0.83 West Pokot 39.9 38.1 39.0 0.96 78.3 63.4 70.9 0.81 Samburu 36.7 27.8 32.5 0.76 60.7 35.7 49.1 0.59 Trans Nzoia 45.6 54.8 50.0 1.20 78.4 86.8 82.4 1.11 Uasin Gishu 44.4 58.3 52.2 1.31 80.9 84.7 83.0 1.05 Elgeyo/Marakwet 46.3 55.6 50.0 1.20 90.8 98.5 93.8 1.08 Nandi 44.4 42.2 43.4 0.95 86.4 91.0 88.5 1.05 Baringo 37.7 44.4 41.0 1.18 84.5 66.6 75.8 0.79 Laikipia 62.5 68.1 65.3 1.09 91.5 88.6 90.1 0.97 Nakuru 51.5 58.7 54.8 1.14 92.9 103.8 97.9 1.12 Narok 16.6 36.0 25.9 2.17 42.0 51.1 46.4 1.22 Kajiado 50.4 47.7 49.2 0.95 85.4 68.9 77.7 0.81 Kericho 59.4 71.8 65.8 1.21 110.4 107.7 109.0 0.98 Bomet 35.7 52.5 44.4 1.47 90.7 87.7 89.2 0.97 Kakamega 43.5 50.5 46.4 1.16 75.5 79.8 77.2 1.06 Vihiga 45.0 48.9 47.1 1.09 75.1 79.4 77.4 1.06 Bungoma 35.4 51.4 43.5 1.45 81.6 78.5 80.0 0.96 Busia 35.0 45.7 39.6 1.30 74.9 89.3 81.1 1.19 Siaya 47.0 67.3 55.4 1.43 78.4 102.3 88.4 1.30 Kisumu 52.7 61.5 57.2 1.17 93.9 91.7 92.8 0.98 Homa Bay 40.7 58.9 48.9 1.45 76.9 94.1 84.7 1.22 Migori 33.5 40.1 36.6 1.20 48.4 62.2 55.0 1.28 Kisii 44.8 59.3 51.9 1.32 76.1 81.6 78.8 1.07 Nyamira 43.4 58.9 50.4 1.36 72.8 98.8 84.5 1.36 Nairobi City 60.4 62.6 61.5 1.04 98.7 112.3 105.5 1.14 Total 45.4 52.7 48.8 1.16 80.5 84.3 82.3 1.05 Note: Respondents whose current or previous level of education was vocational training have been excluded from this table. 1 The NAR for primary school is the percentage of the primary-school age (6–13) population that is attending primary school. The NAR for secondary school is the percentage of the secondary-school age (14–17) population that is attending secondary school. By definition, the NAR cannot exceed 100.0. 2 The GAR for primary school is the total number of primary school students, expressed as a percentage of the official primary-school- age population. The GAR for secondary school is the total number of secondary school students, expressed as a percentage of the official secondary-school-age population. If there are significant numbers of overage and underage students at a given level of schooling, the GAR can exceed 100.0. 3 The Gender Parity Index for primary school is the ratio of the primary school NAR (GAR) for females to the NAR (GAR) for males. The Gender Parity Index for secondary school is the ratio of the secondary school NAR (GAR) for females to the NAR (GAR) for males. 46 • Housing Characteristics and Household Population Table 2.13 Participation rate in organised learning Percent distribution of children age one year younger than the official primary school entry age at the beginning of the school year by attendance at an early childhood education programme or primary school, and the adjusted net attendance ratio (NAR), according to background characteristics, Kenya DHS 2022 Percent distribution of children attending Adjusted NAR1 Number of children age 5 years at beginning of the school year Background characteristic An early childhood education programme Primary school Neither an early childhood education programme nor primary school Total Sex Male 66.5 20.4 13.1 100.0 86.9 1,887 Female 64.5 24.4 11.2 100.0 88.8 1,856 Residence Urban 67.7 23.5 8.8 100.0 91.2 1,132 Rural 64.6 21.9 13.6 100.0 86.4 2,611 Wealth quintile Lowest 55.3 17.2 27.5 100.0 72.5 1,019 Second 69.7 22.6 7.7 100.0 92.3 744 Middle 66.1 27.9 6.0 100.0 94.0 646 Fourth 72.9 20.8 6.3 100.0 93.7 678 Highest 68.5 26.3 5.3 100.0 94.7 657 Total 65.5 22.4 12.1 100.0 87.9 3,744 1 The adjusted net attendance ratio (NAR) to organised learning is the percentage of children of age one year younger than official primary school entry age (at the beginning of school year) who are attending early childhood education or primary school. Housing Characteristics and Household Population • 47 Table 2.13C Participation rate in organised learning by county Percent distribution of children age one year younger than the official primary school entry age at the beginning of the school year by attendance at an early childhood education programme or primary school, and the adjusted net attendance ratio (NAR), according to county, Kenya DHS 2022 Percent distribution of children attending Adjusted NAR1 Number of children age 5 years at beginning of the school year County An early childhood education programme Primary school Neither an early childhood education programme nor primary school Total Mombasa 74.3 18.3 7.4 100.0 92.6 93 Kwale 65.0 11.3 23.7 100.0 76.3 59 Kilifi 68.8 17.6 13.6 100.0 86.4 131 Tana River 44.5 5.6 49.8 100.0 50.2 31 Lamu 63.8 20.0 16.2 100.0 83.8 14 Taita/Taveta 77.2 14.4 8.3 100.0 91.7 30 Garissa 13.3 5.3 81.4 100.0 18.6 59 Wajir 20.9 8.6 70.5 100.0 29.5 37 Mandera 7.0 9.7 83.3 100.0 16.7 58 Marsabit 38.0 21.1 40.9 100.0 59.1 28 Isiolo 52.0 28.0 20.0 100.0 80.0 23 Meru 81.2 17.6 1.2 100.0 98.8 132 Tharaka-Nithi (62.4) (29.8) (7.8) 100.0 (92.2) 24 Embu (73.8) (23.7) (2.5) 100.0 (97.5) 31 Kitui 57.3 36.6 6.1 100.0 93.9 96 Machakos (52.1) (45.8) (2.1) 100.0 (97.9) 66 Makueni 66.2 30.8 3.0 100.0 97.0 47 Nyandarua 83.2 13.5 3.2 100.0 96.8 42 Nyeri (63.3) (30.9) (5.8) 100.0 (94.2) 42 Kirinyaga 69.4 30.6 0.0 100.0 100.0 42 Murang’a 84.5 11.7 3.8 100.0 96.2 73 Kiambu 68.1 28.3 3.6 100.0 96

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