Uganda - Demographic and Health Survey - 2018

Publication date: 2018

Uganda Demographic and Health Survey 2016 U ganda 2016 D em ographic and H ealth S urvey GOVERNMENT OF UGANDA Uganda Demographic and Health Survey 2016 Uganda Bureau of Statistics Kampala, Uganda The DHS Program ICF Rockville, Maryland, USA January 2018 The 2016 Uganda Demographic and Health Survey (2016 UDHS) was implemented by the Uganda Bureau of Statistics. The funding for the 2016 UDHS was provided by the Government of Uganda, the United States Agency for International Development (USAID), the United Nations Children’s Fund (UNICEF), and the United Nations Population Fund (UNFPA). ICF provided technical assistance through The DHS Program, a USAID-funded project providing support and technical assistance in the implementation of population and health surveys in countries worldwide. Additional information about the 2016 UDHS may be obtained from the Directorate of Population and Social Statistics, Uganda Bureau of Statistics, Colville Street, P.O. Box 7186, Kampala, Uganda; Telephone +256-414-706000; E-mail: ubos@ubos.org; Internet: www.ubos.org 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 Cover photo “Uganda Kobs (Kobus thomasi)” ©2008 Bernard DuPont, used under Creative Commons licence (CC BY-SA 2.0). Recommended citation: Uganda Bureau of Statistics (UBOS) and ICF. 2018. Uganda Demographic and Health Survey 2016. Kampala, Uganda and Rockville, Maryland, USA: UBOS and ICF. Contents • iii CONTENTS TABLES AND FIGURES . ix FOREWORD . xix ABBREVIATIONS AND ACRONYMS . xxi READING AND UNDERSTANDING TABLES FROM THE 2016 UGANDA DEMOGRAPHIC AND HEALTH SURVEY (UDHS) . xxiii SUSTAINABLE DEVELOPMENT GOALS INDICATORS . xxxi MAP OF UGANDA . xxxii 1 INTRODUCTION AND SURVEY METHODOLOGY . 1 1.1 Survey Objectives . 1 1.2 Sample Design . 2 1.3 Questionnaires . 3 1.4 Anthropometry, Anaemia Testing, Malaria Testing, and Vitamin A Deficiency Testing . 5 1.5 Pretest . 8 1.6 Training of Field Staff . 8 1.7 Fieldwork . 9 1.8 Data Processing . 9 1.9 Community Mobilisation . 9 1.10 Response Rates . 9 2 HOUSING CHARACTERISTICS AND HOUSEHOLD POPULATION . 11 2.1 Drinking Water Sources and Treatment . 12 2.2 Sanitation . 12 2.3 Other Household Characteristics . 13 2.3.1 Housing Characteristics . 13 2.3.2 Exposure to Smoke inside the Home . 13 2.4 Household Wealth . 13 2.4.1 Household Durable Goods . 13 2.4.2 Wealth Index . 14 2.5 Hand Washing . 14 2.6 Household Population and Composition . 14 2.7 Children’s Living Arrangements and Parental Survival . 15 2.8 Birth Registration . 15 2.9 Education . 16 2.9.1 Educational Attainment . 16 2.9.2 School Attendance . 17 2.10 Disability . 17 2.10.1 Disability by Domain and Age . 17 2.10.2 Disability among Adults by Other Background Characteristics . 18 2.11 Child Discipline . 18 2.12 Deaths and Injuries . 19 3 CHARACTERISTICS OF RESPONDENTS . 45 3.1 Basic Characteristics of Survey Respondents . 45 3.2 Education and Literacy . 46 3.3 Mass Media Exposure and Internet Usage . 47 3.4 Employment . 48 iv • Contents 3.5 Occupation . 49 3.6 Health Insurance Knowledge and Coverage . 49 3.7 Tobacco Use . 50 4 MARRIAGE AND SEXUAL ACTIVITY . 71 4.1 Marital Status . 71 4.2 Polygyny . 72 4.3 Age at First Marriage . 72 4.4 Age at First Sexual Intercourse . 73 4.5 Recent Sexual Activity . 74 5 FERTILITY . 85 5.1 Current Fertility . 85 5.2 Children Ever Born and Living . 87 5.3 Birth Intervals . 87 5.4 Insusceptibility to Pregnancy . 88 5.5 Age at First Birth . 89 5.6 Teenage Childbearing . 89 5.7 Sexual and Reproductive Behaviours before Age 15 . 90 6 FERTILITY PREFERENCES . 99 6.1 Desire for Another Child . 99 6.2 Ideal Family Size . 100 6.3 Fertility Planning Status . 101 6.4 Wanted Fertility Rates . 102 7 FAMILY PLANNING . 111 7.1 Contraceptive Knowledge and Use . 111 7.2 Source of Modern Contraceptive Methods . 113 7.3 Informed Choice . 113 7.4 Discontinuation of Contraceptives . 114 7.5 Demand for Family Planning . 114 7.5.1 Decision Making about Family Planning . 115 7.5.2 Future Use of Contraception . 116 7.5.3 Exposure to Family Planning Messages in the Media . 116 7.6 Contact of Nonusers with Family Planning Providers . 116 8 INFANT AND CHILD MORTALITY . 133 8.1 Infant and Child Mortality . 134 8.2 Biodemographic Risk Factors . 135 8.3 Perinatal Mortality . 135 8.4 High-risk Fertility Behaviour . 136 9 MATERNAL HEALTH CARE . 141 9.1 Antenatal Care Coverage and Content . 142 9.1.1 Skilled Providers . 142 9.1.2 Timing and Number of ANC Visits . 142 9.2 Components of ANC Visits . 143 9.3 Protection against Neonatal Tetanus . 143 9.4 Delivery Services . 144 9.4.1 Institutional Deliveries . 144 9.4.2 Skilled Assistance during Delivery . 145 9.4.3 Delivery by Caesarean . 146 Contents • v 9.5 Postnatal Care . 146 9.5.1 Postnatal Health Check for Mothers . 146 9.5.2 Postnatal Health Check for Newborns . 147 9.6 Problems in Accessing Health Care . 147 9.7 Female Circumcision and Obstetric Fistula . 148 10 CHILD HEALTH AND DEVELOPMENT . 165 10.1 Birth Weight . 165 10.2 Vaccination of Children. 166 10.3 Symptoms of Acute Respiratory Infection . 168 10.4 Fever 168 10.5 Diarrhoeal Disease . 169 10.5.1 Prevalence of Diarrhoea . 169 10.5.2 Feeding Practices . 169 10.5.3 Treatment of Diarrhoea . 170 10.5.4 Knowledge of ORS Packets . 170 10.6 Treatment of Childhood Illness . 170 10.7 Disposal of Children’s Stools . 170 10.8 Early Childhood Development . 171 10.8.1 Early Childhood Education . 171 10.8.2 Support for Learning . 171 10.8.3 Children’s Books and Playthings . 172 10.8.4 Inadequate Care for Children . 172 10.8.5 Early Child Development Index . 172 11 NUTRITION OF CHILDREN AND ADULTS . 193 11.1 Nutritional Status of Children . 193 11.1.1 Measurement of Nutritional Status among Young Children . 193 11.1.2 Data Collection . 195 11.1.3 Levels of Child Malnutrition . 195 11.2 Infant and Young Child Feeding Practices . 195 11.2.1 Breastfeeding . 196 11.2.2 Complementary Feeding . 197 11.2.3 Minimum Acceptable Diet . 198 11.3 Anaemia Prevalence in Children . 200 11.4 Presence of Iodized Salt in Households . 201 11.5 Micronutrient Intake and Supplementation among Children. 201 11.6 Adults’ Nutritional Status . 202 11.6.1 Nutritional Status of Women . 202 11.6.2 Nutritional Status of Men . 203 11.7 Anaemia Prevalence in Adults. 203 11.8 Micronutrient Intake among Mothers . 204 11.9 Vitamin A Deficiency in Children . 204 12 MALARIA . 225 12.1 Ownership of Insecticide-Treated Nets . 225 12.2 Household Access to and Use of ITNs . 227 12.3 Use of ITNs by Children and Pregnant Women . 228 12.4 Malaria Prophylaxis in Pregnancy . 229 12.5 Case Management of Malaria in Children . 230 12.6 Prevalence of Low Haemoglobin in Children . 231 12.7 Prevalence of Malaria in Children . 231 vi • Contents 13 HIV/AIDS-RELATED KNOWLEDGE, ATTITUDES, AND BEHAVIOUR . 247 13.1 HIV/AIDS Knowledge, Transmission, and Prevention Methods . 248 13.1.1 Awareness of HIV/AIDS . 248 13.1.2 Knowledge of HIV/AIDS Prevention . 248 13.2 Knowledge about Mother-to-Child Transmission . 249 13.3 Discriminatory Attitudes towards People Living with HIV . 249 13.4 Multiple Sexual Partners . 250 13.5 Paid Sex . 251 13.6 Coverage of HIV Testing Services . 251 13.6.1 Awareness of HIV Testing Services and Experience with HIV Testing . 251 13.6.2 HIV Testing of Pregnant Women . 252 13.7 Male Circumcision . 253 13.8 Self-reporting of Sexually Transmitted Infections . 253 13.9 HIV/AIDS-related Knowledge and Behaviour among Young People . 254 13.9.1 Knowledge . 254 13.9.2 First Sex . 254 13.9.3 Premarital Sex . 255 13.9.4 Multiple Sexual Partners . 255 13.9.5 Coverage of HIV Testing Services . 255 14 WOMEN’S EMPOWERMENT . 275 14.1 Married Women’s and Men’s Employment . 276 14.2 Control over Women’s Earnings . 276 14.3 Control over Men’s Earnings . 277 14.4 Women’s and Men’s Ownership of Assets . 278 14.5 Bank Accounts and Mobile Phones . 278 14.6 Participation in Decision Making . 279 14.7 Attitudes toward Wife Beating . 280 14.8 Negotiating Sexual Relations . 281 15 ADULT AND MATERNAL MORTALITY . 305 15.1 Data . 305 15.2 Direct Estimates of Adult Mortality . 306 15.3 Trends in Adult Mortality . 307 15.4 Direct Estimates of Maternal Mortality . 307 15.5 Trends in Pregnancy-Related Mortality . 308 16 DOMESTIC VIOLENCE . 313 16.1 Measurement of Violence . 314 16.2 Experience of Physical Violence . 315 16.2.1 Perpetrators of Physical Violence . 316 16.3 Experience of Sexual Violence . 316 16.3.1 Prevalence of Sexual Violence . 317 16.3.2 Perpetrators of Sexual Violence . 317 16.4 Experience of Different Forms of Violence . 317 16.5 Marital Control by Spouse . 318 16.6 Forms of Spousal Violence . 319 16.6.1 Prevalence of Spousal Violence . 319 16.6.2 Injuries due to Spousal Violence . 321 16.6.3 Violence Initiated by Women and Men against Their Spouse . 322 16.7 Help Seeking among Those Who Have Experienced Violence . 322 REFERENCES. 365 Contents • vii APPENDIX A: SAMPLE DESIGN . 369 A.1 Introduction . 369 A.2 Sample Frame . 369 A.3 Sample Design and Implementation . 370 A.4 Sample Probabilities and Sampling Weights . 372 APPENDIX B: ESTIMATES OF SAMPLING ERRORS . 377 APPENDIX C: DATA QUALITY TABLES . 419 APPENDIX D: PERSONS INVOLVED IN THE 2016 UGANDA DEMOGRAPHIC AND HEALTH SURVEY . 425 APPENDIX E: QUESTIONNAIRES . 431 Household Questionnaire . 433 Biomarker Questionnaire . 463 Woman’s Questionnaire . 483 Man’s Questionnaire . 557 Fieldworker Questionnaire . 589 Tables and Figures • ix TABLES AND FIGURES 1 INTRODUCTION AND SURVEY METHODOLOGY . 1 Table 1.1 Results of the household and individual interviews . 10 2 HOUSING CHARACTERISTICS AND HOUSEHOLD POPULATION . 11 Table 2.1 Household drinking water . 21 Table 2.2 Availability of water . 22 Table 2.3 Household sanitation facilities . 22 Table 2.4 Household characteristics . 23 Table 2.5 Household possessions . 24 Table 2.6 Wealth quintiles . 24 Table 2.7 Hand washing . 25 Table 2.8 Household population by age, sex, and residence . 26 Table 2.9 Household composition . 27 Table 2.10 Children’s living arrangements and orphanhood . 28 Table 2.11 Birth registration of children under age 5 . 29 Table 2.12.1 Educational attainment of the female household population . 30 Table 2.12.2 Educational attainment of the male household population . 31 Table 2.13 School attendance ratios . 32 Table 2.14 Disability by domain and age . 33 Table 2.15 Disability among adults by background characteristics . 34 Table 2.16 Child discipline . 36 Table 2.17 Child discipline opinions and knowledge . 37 Table 2.18 Deaths and injuries from road traffic accidents . 38 Table 2.19 Types of road traffic accidents . 39 Table 2.20 Injuries due to road traffic accidents . 40 Table 2.21 Deaths and injuries from non-road traffic accidents . 41 Table 2.22 Types of non-road traffic accidents and injuries . 42 Table 2.23 Injuries due to non-road traffic accidents. 43 Table 2.24 Deaths from other causes . 44 Table 2.25 Death registration . 44 Figure 2.1 Household drinking water by residence . 12 Figure 2.2 Household toilet facilities by residence . 13 Figure 2.3 Household wealth by residence. 14 Figure 2.4 Population pyramid . 15 Figure 2.5 Birth registration by region . 16 Figure 2.6 Secondary school attendance by household wealth . 17 3 CHARACTERISTICS OF RESPONDENTS . 45 Table 3.1 Background characteristics of respondents . 51 Table 3.2.1 Educational attainment: Women . 53 Table 3.2.2 Educational attainment: Men . 54 Table 3.3.1 Literacy: Women . 55 Table 3.3.2 Literacy: Men . 56 Table 3.4.1 Exposure to mass media: Women . 57 Table 3.4.2 Exposure to mass media: Men . 58 Table 3.5.1 Internet usage: Women . 59 x • Tables and Figures Table 3.5.2 Internet usage: Men . 60 Table 3.6.1 Employment status: Women . 61 Table 3.6.2 Employment status: Men . 62 Table 3.7.1 Occupation: Women . 63 Table 3.7.2 Occupation: Men . 64 Table 3.8 Type of employment: Women . 65 Table 3.9.1 Health insurance coverage: Women . 66 Table 3.9.2 Health insurance coverage: Men . 67 Table 3.10.1 Tobacco smoking: Women . 68 Table 3.10.2 Tobacco smoking: Men . 69 Table 3.11 Average number of cigarettes smoked daily: Men . 70 Table 3.12 Smokeless tobacco use and any tobacco use . 70 Figure 3.1 Education of survey respondents . 46 Figure 3.2 Secondary education by region . 46 Figure 3.3 Secondary education by household wealth . 47 Figure 3.4 Exposure to mass media . 47 Figure 3.5 Employment status by wealth . 49 4 MARRIAGE AND SEXUAL ACTIVITY . 71 Table 4.1.1 Current marital status . 75 Table 4.1.2 Type of marriage . 75 Table 4.2.1 Number of women’s co-wives . 76 Table 4.2.2 Number of men’s wives . 77 Table 4.3 Age at first marriage . 78 Table 4.4 Median age at first marriage by background characteristics . 79 Table 4.5 Age at first sexual intercourse . 80 Table 4.6 Median age at first sexual intercourse by background characteristics . 81 Table 4.7.1 Recent sexual activity: Women . 82 Table 4.7.2 Recent sexual activity: Men . 83 Figure 4.1 Marital status . 71 Figure 4.2 Women’s median age at marriage by education . 73 Figure 4.3 Median age at first sex and first marriage . 74 5 FERTILITY . 85 Table 5.1 Current fertility . 91 Table 5.2 Fertility by background characteristics . 91 Table 5.3.1 Trends in age-specific fertility rates . 92 Table 5.3.2 Trends in age-specific and total fertility rates . 92 Table 5.4 Children ever born and living . 92 Table 5.5 Birth intervals . 93 Table 5.6 Postpartum amenorrhoea, abstinence, and insusceptibility . 94 Table 5.7 Median duration of amenorrhoea, postpartum abstinence, and postpartum insusceptibility . 95 Table 5.8 Menopause . 96 Table 5.9 Age at first birth . 96 Table 5.10 Median age at first birth . 97 Table 5.11 Teenage pregnancy and motherhood . 98 Table 5.12 Sexual and reproductive health behaviours before age 15 . 98 Figure 5.1 Trends in fertility by residence . 86 Figure 5.2 Trends in age-specific fertility . 86 Tables and Figures • xi Figure 5.3 Fertility by region . 86 Figure 5.4 Fertility by household wealth . 87 Figure 5.5 Median age at first birth by education . 89 6 FERTILITY PREFERENCES . 99 Table 6.1 Fertility preferences according to number of living children . 104 Table 6.2.1 Desire to limit childbearing: Women . 105 Table 6.2.2 Desire to limit childbearing: Men . 106 Table 6.3 Ideal number of children according to number of living children . 107 Table 6.4 Mean ideal number of children according to background characteristics . 108 Table 6.5 Fertility planning status . 109 Table 6.6 Wanted fertility rates . 109 Figure 6.1 Trends in desire to limit childbearing . 100 Figure 6.2 Desire to limit childbearing by number of living children . 100 Figure 6.3 Ideal family size . 101 Figure 6.4 Ideal family size by number of living children . 101 Figure 6.5 Trends in wanted and actual fertility . 103 7 FAMILY PLANNING . 111 Table 7.1 Knowledge of contraceptive methods . 118 Table 7.2 Knowledge of contraceptive methods according to background characteristics . 119 Table 7.3 Current use of contraception according to age . 120 Table 7.4.1 Current use of contraception according to background characteristics . 121 Table 7.4.2 Trends in the current use of contraception . 123 Table 7.5 Knowledge of fertile period . 123 Table 7.6 Knowledge of fertile period by age . 123 Table 7.7 Timing of sterilisation . 124 Table 7.8 Source of modern contraception methods . 124 Table 7.9 Use of social marketing brand pills and condoms . 124 Table 7.10 Informed choice . 125 Table 7.11 Twelve-month contraceptive discontinuation rates . 125 Table 7.12 Reasons for discontinuation . 126 Table 7.13.1 Need and demand for family planning among currently married women . 127 Table 7.13.2 Need and demand for family planning for all women and for sexually active unmarried women . 128 Table 7.14 Decisionmaking about family planning . 130 Table 7.15 Future use of contraception . 131 Table 7.16 Exposure to family planning messages . 131 Table 7.17 Contact of nonusers with family planning providers . 132 Figure 7.1 Contraceptive use . 112 Figure 7.2 Trends in contraceptive use . 112 Figure 7.3 Modern contraceptive use by region . 112 Figure 7.4 Use of modern contraceptive methods by education . 113 Figure 7.5 Contraceptive discontinuation rates . 114 Figure 7.6 Trends in demand for family planning . 115 Figure 7.7 Unmet need by wealth . 115 8 INFANT AND CHILD MORTALITY . 133 Table 8.1 Early childhood mortality rates . 137 Table 8.2 Five-year early childhood mortality rates according to background characteristics . 137 xii • Tables and Figures Table 8.3 Ten-year early childhood mortality rates according to additional characteristics . 138 Table 8.4 Perinatal mortality . 139 Table 8.5 High-risk fertility behaviour . 140 Table 8.6 Early childhood mortality rates by women’s status . 140 Figure 8.1 Trends in early childhood mortality rates . 134 Figure 8.2 Under-5 mortality by household wealth . 135 Figure 8.3 Perinatal mortality by mother’s education . 135 9 MATERNAL HEALTH CARE . 141 Table 9.1 Antenatal care . 149 Table 9.2 Number of antenatal care visits and timing of first visit . 150 Table 9.3 Components of antenatal care . 151 Table 9.4 Tetanus toxoid injections . 152 Table 9.5 Place of delivery . 153 Table 9.6 Assistance during delivery . 154 Table 9.7 Caesarean section . 155 Table 9.8 Duration of stay in health facility after birth . 156 Table 9.9 Timing of first postnatal check for the mother . 156 Table 9.10 Type of provider of first postnatal check for the mother . 157 Table 9.11 Timing of first postnatal check for the newborn . 158 Table 9.12 Type of provider of first postnatal check for the newborn . 159 Table 9.13 Content of postnatal care for newborns . 160 Table 9.14 Problems in accessing health care . 161 Table 9.15 Female circumcision . 162 Table 9.16 Fistula knowledge and experience . 163 Figure 9.1 Trends in antenatal care coverage . 142 Figure 9.2 Trends in place of birth . 144 Figure 9.3 Health facility births by region . 144 Figure 9.4 Health facility births by education . 145 Figure 9.5 Skilled assistance at delivery by birth order . 145 Figure 9.6 Postnatal care by place of delivery . 146 10 CHILD HEALTH AND DEVELOPMENT . 165 Table 10.1 Child’s size and weight at birth. 174 Table 10.2 Vaccinations by source of information . 175 Table 10.3 Vaccinations by background characteristics . 176 Table 10.4 Possession and observation of vaccination cards, according to background characteristics . 177 Table 10.5 Prevalence and treatment of symptoms of ARI . 178 Table 10.6 Source of advice or treatment for children with symptoms of ARI . 179 Table 10.7 Prevalence and treatment of fever . 180 Table 10.8 Prevalence and treatment of diarrhoea . 181 Table 10.9 Feeding practices during diarrhoea . 182 Table 10.10 Oral rehydration therapy, zinc, and other treatments for diarrhoea . 183 Table 10.11 Source of advice or treatment for children with diarrhoea . 184 Table 10.12 Knowledge of ORS packets or pre-packaged liquids. 185 Table 10.13 Disposal of children’s stools . 186 Table 10.14 Early childhood education . 187 Table 10.15 Support for learning . 188 Table 10.16 Learning materials . 189 Table 10.17 Inadequate care . 190 Table 10.18 Early child development index . 191 Tables and Figures • xiii Figure 10.1 Childhood vaccinations . 166 Figure 10.2 Trends in childhood vaccinations . 167 Figure 10.3 Vaccination coverage by region . 167 Figure 10.4 Diarrhoea prevalence by age . 169 Figure 10.5 Feeding practices during diarrhoea . 169 Figure 10.6 Prevalence and treatment of childhood illness . 170 11 NUTRITION OF CHILDREN AND ADULTS . 193 Table 11.1 Nutritional status of children . 206 Table 11.2 Initial breastfeeding . 208 Table 11.3 Breastfeeding status by age . 209 Table 11.4 Infant and young child feeding (IYCF) indicators on breastfeeding status . 209 Table 11.5 Median duration of breastfeeding . 210 Table 11.6 Foods and liquids consumed by children in the day or night preceding the interview . 211 Table 11.7 Minimum acceptable diet . 212 Table 11.8 Prevalence of anaemia in children . 213 Table 11.9 Presence of iodized salt in household . 214 Table 11.10 Micronutrient intake among children . 215 Table 11.11 Therapeutic and supplemental foods . 217 Table 11.12.1 Nutritional status of women . 218 Table 11.12.2 Nutritional status of men . 219 Table 11.13.1 Prevalence of anaemia in women . 220 Table 11.13.2 Prevalence of anaemia in men . 221 Table 11.14 Micronutrient intake among mothers . 222 Table 11.15 Prevalence of vitamin A deficiency in children . 223 Table 11.16 Prevalence of vitamin A deficiency in children by adjustment method . 224 Figure 11.1 Trends in nutritional status of children . 195 Figure 11.2 Stunting in children by mother’s education . 195 Figure 11.3 Breastfeeding practices by age . 197 Figure 11.4 IYCF indicators on Minimum Acceptable Diet (MAD) . 199 Figure 11.5 Trends in childhood anaemia . 200 Figure 11.6 Anaemia in children by region . 201 Figure 11.7 Nutritional status of women and men . 202 Figure 11.8 Trends in women’s nutritional status . 203 Figure 11.9 Trends in anaemia status among women . 204 12 MALARIA . 225 Table 12.1 Household possession of mosquito nets . 233 Table 12.2 Source of mosquito nets . 234 Table 12.3 Access to an insecticide-treated net (ITN) . 234 Table 12.4 Access to an ITN . 235 Table 12.5 Use of mosquito nets by persons in the household . 236 Table 12.6 Use of existing ITNs . 237 Table 12.7 Use of mosquito nets by children . 238 Table 12.8 Use of mosquito nets by pregnant women . 239 Table 12.9 Use of intermittent preventive treatment (IPTp) by women during pregnancy . 240 Table 12.10 Prevalence, diagnosis, and prompt treatment of children with fever . 241 Table 12.11 Source of advice or treatment for children with fever . 242 Table 12.12 Type of antimalarial drugs used . 243 Table 12.13 Coverage of testing for anaemia and malaria in children . 244 Table 12.14 Haemoglobin <8.0 g/dl in children . 245 Table 12.15 Prevalence of malaria in children . 246 xiv • Tables and Figures Figure 12.1 Household ownership of ITNs . 226 Figure 12.2 Trends in household ownership of ITNs . 226 Figure 12.3 ITN ownership by region . 227 Figure 12.4 Trends in ITN access and use . 228 Figure 12.5 ITN use . 228 Figure 12.6 Trends in IPTp use by pregnant women . 229 Figure 12.7 Trends in ACT use by children with fever . 230 Figure 12.8 Prevalence of malaria in children by region . 232 Figure 12.9 Prevalence of malaria in children by household wealth . 232 13 HIV/AIDS-RELATED KNOWLEDGE, ATTITUDES, AND BEHAVIOUR . 247 Table 13.1 Knowledge of HIV or AIDS . 257 Table 13.2 Knowledge of HIV prevention methods . 258 Table 13.3 Comprehensive knowledge about HIV . 259 Table 13.4 Knowledge of prevention of mother-to-child transmission of HIV . 259 Table 13.5 Discriminatory attitudes towards people living with HIV . 260 Table 13.6.1 Multiple sexual partners and higher-risk sexual intercourse in the past 12 months: Women . 261 Table 13.6.2 Multiple sexual partners and higher-risk sexual intercourse in the past 12 months: Men . 262 Table 13.7 Payment for sexual intercourse and condom use at last paid sexual intercourse . 263 Table 13.8.1 Coverage of prior HIV testing: Women . 264 Table 13.8.2 Coverage of prior HIV testing: Men . 265 Table 13.9 Pregnant women counselled and tested for HIV . 266 Table 13.10 Male circumcision . 267 Table 13.11 Self-reported prevalence of sexually transmitted infections (STIs) and STI symptoms . 268 Table 13.12 Women and men seeking treatment for STIs . 269 Table 13.13 Comprehensive knowledge about HIV among young people . 269 Table 13.14 Age at first sexual intercourse among young people . 270 Table 13.15 Premarital sexual intercourse among young people . 270 Table 13.16.1 Multiple sexual partners and higher-risk sexual intercourse in the past 12 months among young people: Women . 271 Table 13.16.2 Multiple sexual partners and higher-risk sexual intercourse in the past 12 months among young people: Men . 272 Table 13.17 Recent HIV tests among young people . 273 Figure 13.1 Trends in knowledge of mother-to-child transmission (MTCT) . 249 Figure 13.2 Discriminatory attitudes towards people living with HIV by education . 250 Figure 13.3 Sex and condom use with non-regular partners . 250 Figure 13.4 Trends in recent HIV testing . 251 Figure 13.5 Recent HIV testing among women by region . 252 Figure 13.6 Recent HIV testing by education . 252 Figure 13.7 Trends in male circumcision . 253 14 WOMEN’S EMPOWERMENT . 275 Table 14.1 Employment and cash earnings of currently married women and men . 283 Table 14.2.1 Control over women’s cash earnings and relative magnitude of women’s cash earnings . 284 Table 14.2.2 Control over men’s cash earnings . 285 Table 14.3 Women’s control over their earnings and over those of their husbands . 286 Table 14.4.1 Ownership of assets: Women . 287 Tables and Figures • xv Table 14.4.2 Ownership of assets: Men . 288 Table 14.5.1 Ownership of title or deed for house: Women . 289 Table 14.5.2 Ownership of title or deed for house: Men . 290 Table 14.6.1 Ownership of title or deed for land: Women . 291 Table 14.6.2 Ownership of title or deed for land: Men . 292 Table 14.7.1 Ownership and use of bank accounts and mobile phones: Women . 293 Table 14.7.2 Ownership and use of bank accounts and mobile phones: Men . 294 Table 14.8 Participation in decision making . 295 Table 14.9.1 Women’s participation in decision making by background characteristics . 296 Table 14.9.2 Men’s participation in decision making by background characteristics . 297 Table 14.10.1 Attitude toward wife beating: Women . 298 Table 14.10.2 Attitude toward wife beating: Men . 299 Table 14.11 Attitudes toward negotiating safer sexual relations with husband . 300 Table 14.12 Ability to negotiate sexual relations with husband . 301 Table 14.13 Indicators of women’s empowerment . 302 Table 14.14 Current use of contraception by women’s empowerment . 302 Table 14.15 Ideal number of children and unmet need for family planning by women’s empowerment . 303 Table 14.16 Reproductive health care by women’s empowerment . 303 Table 14.17 Early childhood mortality rates by women’s status . 304 Figure 14.1 Control over woman’s earnings . 277 Figure 14.2 Ownership of assets . 278 Figure 14.3 Women’s participation in decision making. 280 Figure 14.4 Attitudes towards wife beating . 281 15 ADULT AND MATERNAL MORTALITY . 305 Table 15.1 Adult mortality rates . 310 Table 15.2 Adult mortality probabilities . 310 Table 15.3 Maternal mortality . 310 Table 15.4 Maternal mortality ratio . 311 Figure 15.1 Adult mortality rates by age . 306 Figure 15.2 Trends in pregnancy-related mortality ratio (PRMR) with confidence intervals . 309 16 DOMESTIC VIOLENCE . 313 Table 16.1.1 Experience of physical violence: Women . 325 Table 16.1.2 Experience of physical violence: Men . 327 Table 16.2.1 Persons committing physical violence: Women . 329 Table 16.2.2 Persons committing physical violence: Men . 329 Table 16.3.1 Experience of sexual violence: Women . 330 Table 16.3.2 Experience of sexual violence: Men . 332 Table 16.4.1 Persons committing sexual violence: Women . 334 Table 16.4.2 Persons committing sexual violence: Men . 334 Table 16.5 Age at first experience of sexual violence . 335 Table 16.6 Experience of different forms of violence . 335 Table 16.7 Experience of violence during pregnancy . 336 Table 16.8.1 Marital control exercised by husbands . 338 Table 16.8.2 Marital control exercised by wives . 340 Table 16.9.1 Forms of spousal violence: Women . 342 Table 16.9.2 Forms of spousal violence: Men . 343 Table 16.10.1 Spousal violence by background characteristics: Women . 344 xvi • Tables and Figures Table 16.10.2 Spousal violence by background characteristics: Men . 346 Table 16.11.1 Spousal violence by husband’s characteristics and empowerment indicators . 348 Table 16.11.2 Spousal violence by wife’s characteristics and empowerment indicators . 349 Table 16.12.1 Violence by any husband/partner in the past 12 months: Women . 350 Table 16.12.2 Violence by any wife/partner in the past 12 months: Men . 351 Table 16.13.1 Experience of spousal violence by duration of marriage: Women . 352 Table 16.13.2 Experience of spousal violence by duration of marriage: Men . 352 Table 16.14 Injuries due to spousal violence . 353 Table 16.15.1 Violence by women against their husbands by women’s background characteristics . 354 Table 16.15.2 Violence by men against their wives by men’s background characteristics . 356 Table 16.16.1 Violence by women against their husband by husband’s characteristics and empowerment indicators . 358 Table 16.16.2 Violence by men against their wife by wife’s characteristics and empowerment indicators . 359 Table 16.17.1 Help seeking to stop violence: Women . 360 Table 16.17.2 Help seeking to stop violence: Men . 362 Table 16.18 Sources for help to stop the violence . 364 Figure 16.1 Women and men’s experience of violence by marital status . 316 Figure 16.2 Fear of current or most recent partner . 318 Figure 16.3 Types of spousal violence . 319 Figure 16.4 Spousal violence by husband’s alcohol consumption . 321 Figure 16.5 Help seeking by type of violence experienced . 323 APPENDIX A: SAMPLE DESIGN . 369 Table A.1 Distribution of residential households by region and type of residence . 370 Table A.2 Distribution of enumeration areas and their average size in number of households . 370 Table A.3 Sample allocation of clusters and households by region and type of residence. 371 Table A.4 Sample allocation of expected interviews with women and men by region and type of residence . 372 Table A.5 Sample implementation: Women . 374 Table A.6 Sample implementation: Men . 375 APPENDIX B: ESTIMATES OF SAMPLING ERRORS . 377 Table B.1 List of indicators for sampling errors, Uganda DHS 2016 . 379 Table B.2 Sampling errors: Total sample, Uganda DHS 2016 . 381 Table B.3 Sampling errors: Urban sample, Uganda DHS 2016 . 383 Table B.4 Sampling errors: Rural sample, Uganda DHS 2016 . 385 Table B.5 Sampling errors: Kampala sample, Uganda DHS 2016 . 387 Table B.6 Sampling errors: South Central sample, Uganda DHS 2016 . 389 Table B.7 Sampling errors: North Central sample, Uganda DHS 2016 . 391 Table B.8 Sampling errors: Busoga sample, Uganda DHS 2016 . 393 Table B.9 Sampling errors: Bukedi sample, Uganda DHS 2016 . 395 Table B.10 Sampling errors: Bugisu sample, Uganda DHS 2016 . 397 Table B.11 Sampling errors: Teso sample, Uganda DHS 2016 . 399 Table B.12 Sampling errors: Karamoja sample, Uganda DHS 2016 . 401 Table B.13 Sampling errors: Lango sample, Uganda DHS 2016 . 403 Table B.14 Sampling errors: Acholi sample, Uganda DHS 2016 . 405 Table B.15 Sampling errors: West Nile sample, Uganda DHS 2016 . 407 Table B.16 Sampling errors: Bunyoro sample, Uganda DHS 2016 . 409 Table B.17 Sampling errors: Tooro sample, Uganda DHS 2016 . 411 Table B.18 Sampling errors: Ankole sample, Uganda DHS 2016 . 413 Tables and Figures • xvii Table B.19 Sampling errors: Kigezi sample, Uganda DHS 2016 . 415 Table B.20 Sampling errors for adult, maternal, and pregnancy-related mortality rates, Uganda DHS 2016 . 417 APPENDIX C: DATA QUALITY TABLES . 419 Table C.1 Household age distribution . 419 Table C.2.1 Age distribution of eligible and interviewed women . 420 Table C.2.2 Age distribution of eligible and interviewed men . 420 Table C.3 Completeness of reporting . 421 Table C.4 Births by calendar years . 421 Table C.5 Reporting of age at death in days . 422 Table C.6 Reporting of age at death in months . 423 Table C.7 Completeness of information on siblings . 423 Table C.8 Sibship size and sex ratio of siblings . 423 Table C.9 Pregnancy-related mortality trends . 424 Foreword • xix FOREWORD he 2016 Uganda Demographic and Health Survey (2016 UDHS) was designed as a follow-up to the 1988-89, 1995, 2000-01, 2006, and 2011 Uganda DHS surveys. The data collection for the 2016 UDHS was implemented between 15 June and 18 December 2016 by the Uganda Bureau of Statistics (UBOS) in collaboration with the Ministry of Health (MOH). The Demographic and Health Surveys (DHS) Program is a global programme coordinated by ICF in Rockville, Maryland, USA. Technical and financial support for the 2016 UDHS was provided by the Government of Uganda, the United States Agency for International Development (USAID), the United Nations Children’s Fund (UNICEF), and the United Nations Population Fund (UNFPA). The main purpose of the 2016 UDHS is to provide the data needed to monitor and evaluate population, health, and nutrition programmes on a regular basis. Increasing emphasis by planners and policymakers on the utilisation of objective indicators for policy formulation, planning, and measuring progress has increased the reliance on regular household survey data, given the inadequate availability of appropriate information from administrative statistics and other routine data collection systems. The 2016 UDHS provides a comprehensive overview of population and maternal and child health issues, and the data are freely accessible to all stakeholders. The 2016 UDHS covers household and respondent characteristics, fertility and family planning, infant and child health and mortality, maternal health and maternal and adult mortality, child and adult nutrition, malaria, HIV/AIDS, disability, road traffic accidents, child discipline, early childhood development, and domestic violence. The survey also included measuring the height and weight of children and adults, testing children and adults for anaemia, and testing children for malaria and vitamin A deficiency; these measures will provide data for analysis of nutrition indicators throughout the country. The Uganda Bureau of Statistics would like to acknowledge the efforts of a number of organisations and individuals who contributed immensely to the success of the survey. All stakeholders have exerted themselves in the achievement of reliable, accurate, and up-to-date data. The Ministry of Health chaired both the Technical Working Committee, which offered guidance on the implementation of the survey, and the Steering Committee that oversaw the implementation of the 2016 UDHS. The Makerere University School of Public Health (MakSPH) undertook quality control for the overall survey. In addition, the Makerere University Department of Biochemistry and Sports Science, under the College of Natural Sciences, conducted laboratory testing for vitamin A deficiency, while external quality control was done by the Molecular Biology Laboratory (MoLab) of the Makerere University College of Health Sciences, with ICF providing technical support. The Bureau thus extends its appreciation to the stakeholders for providing important technical support. Finally, I would like to thank the management and staff of UBOS who were involved in the survey through coordination, implementation, or monitoring according to the UBOS Strategic Plan. I would also like to thank all of the participating respondents and communities for providing information during the survey fieldwork and hence making the 2016 UDHS a success. We urge the public to use the findings from this survey to make informed decisions and help guide policy development. Also, those in academia are encouraged to undertake further analytical work to provide an understanding of key topical areas. Ben Paul Mungyereza Executive Director Uganda Bureau of Statistics T Abbreviations and Acronyms • xxi ABBREVIATIONS AND ACRONYMS ACT artemisinin-based combination therapy AIDS acquired immune deficiency syndrome AIS AIDS Indicator Survey ANC antenatal care ARI acute respiratory infection BBSS Biological Behavioural Surveillance Survey BCG Bacille Calmette-Guérin BMI body mass index CAPI computer-assisted personal interviewing CBR crude birth rate CPR contraceptive prevalence rate CRP C-reactive protein CSPro Censuses and Surveys Processing DBS dried blood spot DHS Demographic and Health Survey DPT diphtheria, pertussis, and tetanus vaccine EA enumeration area ECDI Early Child Development Index GAR gross attendance ratio GBV gender-based violence GFR general fertility rate GPI Gender Parity Index HepB hepatitis B Hib Haemophilus influenzae type b HIV human immunodeficiency virus HRP-II histidine-rich protein II HSSP Health Sector Strategic Plan HTC HIV testing and counselling ICD-10 International Classification of Diseases-10 ICF ICF (originally, Inner City Fund) IFSS internet file streaming system IPTp intermittent preventive treatment during pregnancy IPV inactivated polio vaccine IRS indoor residual spraying ITN insecticide-treated net IUD intrauterine contraceptive device IYCF infant and young child feeding LAM lactational amenorrhoea method LLIN long-lasting insecticidal net LPG liquid petroleum gas MAD minimum acceptable diet MakSPH Makerere University School of Public Health xxii • Abbreviations and Acronyms MAM moderate acute malnutrition MICS Multiple Indicator Cluster Survey MMR maternal mortality ratio MoLab Molecular Biology Laboratory of the Makerere University College of Health Sciences MTCT mother-to-child transmission NAP National Action Plan NAR net attendance ratio NDP National Development Plan NGO nongovernmental organization NPHC National Population and Housing Census ORS oral rehydration salts ORT oral rehydration therapy PCV pneumococcal conjugate vaccine Pf Plasmodium falciparum PHIA Population-Based HIV Impact Assessment PMTCT prevention of mother-to-child transmission PRMR pregnancy-related mortality ratio Pv Plasmodium vivax RBP retinol binding protein RBP-EIA retinol binding protein enzyme immunoassay RDT rapid diagnostic test RHF recommended homemade fluids SAM severe acute malnutrition SD standard deviation SDGs Sustainable Development Goals SDM standard days method SE standard error SP sulfadoxine/pyrimethamine STI sexually transmitted infection TFR total fertility rate TOT training of trainers UAC Uganda AIDS Commission UBOS Uganda Bureau of Statistics UDHS Uganda Demographic and Health Survey UNAIDS Joint United Nations Programme on HIV/AIDS UNICEF United Nations Children’s Fund USAID United States Agency for International Development VAD vitamin A deficiency VIP ventilated improved pit VMMC voluntary medical male circumcision WG Washington Group on Disability Statistics WHO World Health Organization Reading and Understanding Tables from the 2016 UDHS • xxiii READING AND UNDERSTANDING TABLES FROM THE 2016 UGANDA DEMOGRAPHIC AND HEALTH SURVEY (UDHS) he new format of the 2016 UDHS final report is based on approximately 200 tables of data. They are located for quick reference through links in the text (electronic version) and at the end of each chapter. 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 regions in Uganda. 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, UDHS data users should be comfortable reading and interpreting tables. The following pages provide an introduction to the organization of UDHS 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 UDHS tables. T xxiv • Reading and Understanding Tables from the 2016 UDHS 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, by background characteristics, Uganda DHS 2016 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 10.8 20.9 54.5 4.2 37.6 4,264 20-24 10.8 24.9 61.8 5.6 31.1 3,822 25-29 11.7 25.3 60.0 6.4 32.3 3,051 30-34 8.7 20.5 59.3 5.0 35.2 2,543 35-39 8.3 18.8 59.1 4.8 36.3 2,011 40-44 8.5 14.6 58.4 4.6 37.9 1,608 45-49 7.2 13.0 58.0 3.3 38.1 1,207 Residence Urban 19.7 49.9 63.9 12.1 21.4 4,943 Rural 6.4 10.6 56.7 2.4 39.9 13,563 Region South Central 22.7 48.5 68.1 12.9 17.4 2,494 North Central 12.0 25.1 66.8 5.0 25.6 1,963 Kampala 25.6 76.2 64.9 18.6 12.7 1,025 Busoga 10.5 19.8 56.9 5.6 37.7 1,690 Bukedi 10.8 11.0 59.5 2.7 36.7 1,169 Bugisu 8.6 17.8 61.1 4.7 35.3 921 Teso 9.9 9.2 58.8 3.6 38.3 1,099 Karamoja 1.3 3.7 33.5 0.2 64.9 365 Lango 3.2 7.6 52.5 0.7 45.4 1,010 Acholi 3.3 4.3 36.3 0.5 61.5 924 West Nile 2.7 4.4 52.9 0.8 46.1 1,247 Bunyoro 2.5 7.3 44.0 0.8 52.6 1,014 Tooro 4.7 13.1 59.7 2.3 35.6 1,357 Kigezi 3.3 10.1 64.9 1.8 33.0 732 Ankole 4.7 12.5 62.0 1.9 33.4 1,498 Special area Island districts 6.8 22.7 57.6 2.8 36.2 203 Mountain districts 6.4 15.5 51.6 3.3 43.3 1,481 Greater Kampala 27.5 73.3 65.5 19.3 12.6 2,048 Education No education 0.3 6.3 41.3 0.2 56.6 1,781 Primary 4.0 11.4 55.7 1.0 40.3 10,630 Secondary 17.2 36.9 67.7 8.9 21.5 4,639 More than secondary 42.3 59.8 72.0 27.7 12.4 1,456 Wealth quintile Lowest 2.0 1.8 33.8 0.4 64.7 3,247 Second 3.5 3.6 51.8 0.6 46.6 3,397 Middle 4.6 5.1 62.8 0.6 35.2 3,460 Fourth 9.2 12.5 70.2 3.1 26.2 3,683 Highest 24.5 65.5 68.5 16.0 12.9 4,720 Total 15-49 9.9 21.1 58.6 5.0 35.0 18,506 Step 1: Read the title and subtitle—highlighted in orange in Example 1. They tell you the topic and the specific population group being described. In this case, the table is about women age 15-49 and the frequency of 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 categorized. 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 at least once a week. The last column lists the number of women age 15-49 interviewed in the survey. 1 2 3 4 5 Reading and Understanding Tables from the 2016 UDHS • xxv 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, region, special area, educational level, and wealth quintile. Most of the tables in the UDHS 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 access to different types of media. In this case, 9.9%* of women age 15-49 read a newspaper at least once a week, 21.1% watch television at least once a week, and 58.6% listen to the radio at least once a week. Step 5: To find out what percentage of women with more than secondary education access all three media at least once a week, draw two imaginary lines, as shown on the table. This shows that 27.7% of women age 15-49 with more than secondary education access all three types of media at least once a week. Step 6: By looking at patterns by background characteristics, we can see how exposure to mass media varies across Uganda. 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 Uganda do not access any of the three media at least once a week? b) Which age group of women are most likely to listen to the radio at least once a week? c) Compare women in urban areas to women in rural areas – which group is more likely to read a newspaper at least once a week? d) What are the lowest and highest percentages (range) of women who do not access any of the three media types at least once a week by region? e) Is there a clear pattern in exposure to television at least once a week by education level? f) Is there a clear pattern in exposure to newspapers at least once a week by wealth quintile? Answers: a) 35.0% b) Women age 20-24: 61.8% of women in this age group listen to the radio weekly c) Women in urban areas, 19.7% read a newspaper at least once a week, compared to 6.4% of women in rural areas d) Women with no exposure at least once a week to media ranges from a low of 12.7% in Kampala region to a high of 64.9% in Karamoja region. e) Yes. Exposure to television increases as a women’s level of education increases; 6.3% of women with no education watch television at least once a week, compared to 59.8% of women with more than secondary education. f) Yes. Exposure to newspapers increases as household wealth increases; 2.0% of women in the lowest wealth quintile read a newspaper at least once a week, compared to 24.5% of women in the highest wealth quintile. xxvi • Reading and Understanding Tables from the 2016 UDHS Example 2: Prevalence and Treatment of Symptoms of ARI A Question Asked of a Subgroup of Survey Respondents Table 10.5 Prevalence and treatment of symptoms of ARI Among children under age 5, percentage who had symptoms of acute respiratory infection (ARI) in the 2 weeks preceding the survey; and among children with symptoms of ARI in the 2 weeks preceding the survey, percentage for whom advice or treatment was sought, according to background characteristics, Uganda DHS 2016 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 sought from a health facility or provider2 Percentage for whom treatment was sought same or next day Number of children Age in months <6 8.2 1,480 69.0 38.0 122 6-11 12.4 1,582 76.8 36.8 196 12-23 11.6 2,859 84.7 38.8 331 24-35 9.8 2,890 79.1 39.7 283 36-47 7.4 2,819 87.1 37.0 209 48-59 7.4 2,863 80.6 40.7 213 Sex Male 9.7 7,252 79.9 38.7 703 Female 9.0 7,241 81.5 38.6 651 Mother's smoking status Smokes cigarettes/tobacco 8.1 105 * * 9 Does not smoke 9.4 14,388 80.6 38.5 1,345 Cooking fuel Electricity or gas (12.0) 46 * * 6 Kerosene * 10 * * 0 Charcoal 6.4 3,421 86.6 42.5 217 Wood/straw3 10.3 11,002 79.5 38.1 1,130 Other fuel * 3 * * 1 No food cooked in household * 11 * * 1 Residence Urban 7.1 3,094 83.6 46.7 219 Rural 10.0 11,398 80.1 37.1 1,135 Region South Central 8.1 1,808 80.4 35.3 147 North Central 8.6 1,537 84.8 38.9 131 Kampala 4.9 554 (88.4) (64.8) 27 Busoga 12.3 1,430 81.0 38.7 175 Bukedi 4.9 1,016 80.6 39.0 50 Bugisu 9.3 733 75.7 38.7 68 Teso 14.4 911 70.0 36.1 131 Karamoja 26.6 394 85.5 59.8 105 Lango 17.6 765 82.7 29.6 135 Acholi 9.1 713 94.6 48.2 65 West Nile 7.8 1,005 93.4 52.1 78 Bunyoro 0.9 845 * * 8 Tooro 13.2 1,140 70.3 22.0 150 Kigezi 6.4 484 (73.5) (33.0) 31 Ankole 4.6 1,157 (80.5) (38.4) 54 Special area Island districts 7.2 189 89.9 45.1 14 Mountain districts 11.1 1,198 76.1 30.3 133 Greater Kampala 4.2 1,197 (87.8) (59.7) 51 Mother's education No education 11.8 1,557 80.8 45.4 184 Primary 9.6 8,892 78.5 36.8 853 Secondary 8.4 3,113 85.5 38.3 263 More than secondary 5.8 931 (92.0) (46.2) 54 Wealth quintile Lowest 12.7 3,251 80.1 37.1 414 Second 10.5 3,038 78.0 37.3 318 Middle 9.0 2,799 78.2 40.1 252 Fourth 8.3 2,579 84.9 38.0 214 Highest 5.5 2,826 85.8 44.0 156 Total 9.3 14,493 80.7 38.6 1,354 Note: An asterisk indicates that a figure is based on fewer than 25 unweighted cases and has been suppressed. Figures in parentheses are based on 25-49 unweighted cases. 1 Symptoms of ARI include short rapid breathing which was chest-related and/or by difficult breathing which was chest-related 2 Includes advice or treatment from the following sources: public sector, private medical sector, shop, market, and itinerant drug seller. Excludes advice or treatment from a traditional practitioner 3 Includes grass, shrubs, crop residues 1 2 3 4 a b Reading and Understanding Tables from the 2016 UDHS • xxvii 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 two 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 those children under age 5 with symptoms of ARI in the two 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 two weeks before the survey? It’s 9.3%. Now look at the second panel. How many children under age 5 are there who had symptoms of ARI in the two weeks before the survey? It’s 1,354 children or 9.3% of the 14,493 children under age 5 (with rounding). The second panel is a subset of the first panel. Step 4: Only 9.3% of children under age 5 had symptoms of ARI in the two 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. • For what percentage of children under age 5 who had symptoms of ARI in the two weeks before the survey from Kampala region was advice or treatment sought from a health facility or provider? It’s 88.4%. 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 3.) • For what percentage of children under age 5 who had symptoms of ARI in the two weeks before the survey from Bunyoro region was advice or treatment sought from a health facility or provider? There is no number in this cell—only an asterisk. This is because there are fewer than 25 unweighted cases. 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. xxviii • Reading and Understanding Tables from the 2016 UDHS Example 3: Understanding Sampling Weights in UDHS Tables A sample is a group of people who have been selected for a survey. In the 2016 UDHS, 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 minimum sample size per area. For the 2016 UDHS, the survey sample is representative at the national and regional levels, and for urban and rural areas. To generate statistics that are representative of the country as a whole and the 15 regions, the number of women surveyed in each region should contribute to the size of the total (national) sample in proportion to size of the region. However, if some regions have small populations, then a sample allocated in proportion to each region’s population may not include sufficient women from each region for analysis. To solve this problem, regions with small populations are oversampled. For example, let’s say that you have enough money to interview 18,506 women and want to produce results that are representative of Uganda as a whole and its regions (as in Table 3.1). However, the total population of Uganda is not evenly distributed among the regions: some regions, such as South Central region, are heavily populated while others, such as Karamoja region, are not. Thus, Karamoja region must be oversampled. A sampling statistician determines how many women should be interviewed in each region in order to obtain reliable statistics. The blue column (1) in the table at the right shows the actual number of women interviewed in each region. Within the regions, the number of women interviewed ranges from 741 in Karamoja region to 1,615 in South Central region. The number of interviews is sufficient to get reliable results in each region. With this distribution of interviews, some regions are overrepresented and some regions are underrepresented. For example, the population in South Central region is about 14% of the population of Uganda, while Karamoja region contributes only 2% of the population of Uganda. But as the blue column shows, the number of women interviewed in South Central region accounts for only about 9% of the total sample of women interviewed (1,615 / 18,506) and the number of women interviewed in Karamoja region accounts for 4% of the total sample of women interviewed (741 / 18,506). This unweighted distribution of women does not accurately represent the population. In order to get statistics that are representative of Uganda, 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 region, like Karamoja, should only contribute a small amount to the national total. Women from a large region, like South Central, should contribute much more. Therefore, The DHS Program statisticians mathematically calculate a “weight” which is used to adjust the number of women from each region so that each region’s contribution to the total is proportional to the actual population of that region. 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 regional level. The total national sample size of 18,506 women has not changed after weighting, but the distribution of the women in the regions 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 Table 3.1 Background characteristics of respondents Percent distribution of women age 15-49 by selected background characteristics, Uganda DHS 2016 Women Background characteristic Weighted percent Weighted number Unweighted number Region South Central 13.5 2,494 1,615 North Central 10.6 1,963 1,410 Kampala 5.5 1,025 1,300 Busoga 9.1 1,690 1,530 Bukedi 6.3 1,169 1,205 Bugisu 5.0 921 957 Teso 5.9 1,099 1,347 Karamoja 2.0 365 741 Lango 5.5 1,010 1,236 Acholi 5.0 924 1,110 West Nile 6.7 1,247 1,281 Bunyoro 5.5 1,014 1,213 Tooro 7.3 1,357 1,301 Kigezi 4.0 732 959 Ankole 8.1 1,498 1,301 Total 15-49 100.0 18,506 18,506 12 3 Reading and Understanding Tables from the 2016 UDHS • xxix of Uganda, you would see that women in each region 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 South Central region and the proportion of women who live in Karamoja region. With sampling and weighting, it is possible to interview enough women to provide reliable statistics at national and regional levels. In general, only the weighted numbers are shown in each of the UDHS tables, so do not be surprised if these numbers seem low: they may actually represent a larger number of women interviewed. Sustainable Development Goals Indicators • xxxi Sustainable Development Goals Indicators—Uganda DHS 2016 Sex Total DHS table number(s) Indicator Male Female 2. Zero hunger 2.2.1 Prevalence of stunting among children under 5 years of age 30.9 26.9 28.9 11.1 2.2.2 Prevalence of malnutrition among children under 5 years of age 8.9 5.6 7.3a na a) Prevalence of wasting among children under 5 years of age 4.1 3.0 3.5a 11.1 b) Prevalence of overweight among children under 5 years of age 4.9 2.6 3.7a 11.1 3. Good health and well-being 3.1.1 Maternal mortality ratio1 na na 336 15.4 3.1.2 Proportion of births attended by skilled health personnel na na 74.2 9.6 3.2.1 Under-five mortality rate2 72 56 64 8.2 3.2.2 Neonatal mortality rate2 31 23 27 8.2 3.6.1 Death rate due to road traffic injuries3 46 7 53 2.18 3.7.1 Proportion of women of reproductive age (aged 15-49 years) who have their need for family planning satisfied with modern methods na 53.9 na 7.13 3.7.2 Adolescent birth rates per 1,000 women a) Girls aged 10-14 years4 na 2 na 5.1 b) Women aged 15-19 years5 na 132 na 5.1 3.a.1 Age-standardized prevalence of current tobacco use among persons aged 15 years and older6 9.4 0.8 5.1a 3.10.1, 3.10.2 3.b.1 Proportion of the target population covered by all vaccines included in their national programme7 36.5 35.0 35.8 10.3 4. Quality education 4.2.1 Proportion of children under 5 years of age who are developmentally on track in health, learning and psychosocial well-being, by sex8 62.0 64.6 63.3 10.18 5. Gender equality 5.2.1 Proportion of ever-partnered women and girls aged 15 years and older subjected to physical, sexual or psychological violence by a current or former intimate partner in the previous 12 months9,10 na 39.6 na 16.12.1 a) Physical violence na 22.5 na 16.12.1 b) Sexual violence na 16.6 na 16.12.1 c) Psychological violence na 29.3 na 16.12.1 5.3.1 Proportion of women aged 20-24 years who were married or in a union before age 15 and before age 18 a) before age 15 na 7.3 na 4.3 b) before age 18 na 34.0 na 4.3 5.6.1 Proportion of women aged 15-49 years who make their own informed decisions regarding sexual relations, contraceptive use and reproductive health care12 na 58.5 na na 5.b.1 Proportion of individuals who own a mobile telephone13 65.8 45.5 55.7a 14.7.1, 14.7.2 6. Clean water and sanitation 6.1.1 Proportion of the population using safely managed drinking water services14 90.8 74.2 77.9 2.1 6.2.1 Proportion of population using safely managed sanitation services, including a handwashing facility with soap and water15 31.7 17.7 20.8 2.3 7. Affordable clean energy 7.1.1 Proportion of population with access to electricity 57.5 18.0 26.7 2.4 7.1.2 Proportion of population with primary reliance on clean fuels and technology16 2.1 0.2 0.6 2.4 8. Decent work and economic growth 8.7.2 Proportion of adults (15 years and older) with an account at a bank or other financial institution or with a mobile-money-service provider13 21.9 12.9 17.4a 14.7.1, 14.7.2 16. Peace, justice, and strong institutions 16.2.1 Percentage of children aged 1-17 years who experienced any physical punishment and/or psychological aggression by caregivers in the past month17 85.2 84.6 84.9 2.16 16.9.1 Proportion of children under 5 years of age whose births have been registered with a civil authority 32.2 32.2 32.2 2.11 17. Partnerships for the goals 17.8.1 Proportion of individuals using the Internet18 22.5 8.6 15.6a 3.5.1, 3.5.2 na = Not applicable 1 Expressed in terms of maternal deaths per 100,000 live births in the 7-year period preceding the survey 2 Expressed in terms of deaths per 1,000 live births for the 5-year period preceding the survey 3 Calculated per 100,000 population 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-standardized and are available for women and men age 15-49 only. 7 Data are presented for children age 12-23 months receiving all vaccines included in their national programme appropriate for their age: BCG, three doses of DPT-HepB- Hib, four doses of oral polio vaccine, three doses of pneumococcal vaccine, and one dose of measles vaccine. 8 Measured for children age 36-59 months 9 Data are available for women age 15-49 who have ever been in union only. 10 In the DHS, psychological violence is termed emotional violence. 11 Data are available for women age 15-49 only. 12 Data are available for currently married women who are not pregnant only. 13 Data are available for women and men age 15-49 only. 14 Measured as the percentage of population using an improved water source: the percentage of de jure population whose main source of drinking water is a household connection (piped), public tap or standpipe, tube well or borehole, protected dug well, protected spring, or rainwater collection. Households using bottled water for drinking are classified as using an improved or unimproved source according to their water source for cooking and handwashing. 15 Measured as the percentage of population using an improved sanitation facility: the percentage of de jure population whose household has a flush or pour flush toilet to a piped water system, septic tank or pit latrine; ventilated improved pit latrine; pit latrine with a slab; or composting toilet and does not share this facility with other households. 16 Measured as the percentage of the population using clean fuel for cooking. 17 Data are available for children age 1-14 only. 18 Data are available for women and men age 15-49 who have used the internet in the past 12 months. a The total is calculated as the simple arithmetic mean of the percentages in the columns for males and females xxxii • Map of Uganda Introduction and Survey Methodology • 1 INTRODUCTION AND SURVEY METHODOLOGY 1 he 2016 Uganda Demographic and Health Survey (UDHS) was implemented by the Uganda Bureau of Statistics (UBOS). Data collection took place from 20 June to 16 December 2016. ICF provided technical assistance through The 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 were the Government of Uganda, the United Nations Children’s Fund (UNICEF), and the United Nations Population Fund (UNFPA). 1.1 SURVEY OBJECTIVES The primary objective of the 2016 UDHS project is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the 2016 UDHS collected information on:  Key demographic indicators, particularly fertility and under-5, adult, and maternal mortality rates  Direct and indirect factors that determine levels of and trends in fertility and child mortality  Contraceptive knowledge and practice  Key aspects of maternal and child health, including immunisation coverage among children, prevalence and treatment of diarrhoea and other diseases among children under age 5, and maternity care indicators such as antenatal visits and assistance at delivery  Child feeding practices, including breastfeeding, and anthropometric measures to assess the nutritional status of women, men, and children  Knowledge and attitudes of women and men about sexually transmitted infections (STIs) and HIV/AIDS, potential exposure to the risk of HIV infection (risk behaviours and condom use), and coverage of HIV testing and counselling (HTC) and other key HIV/AIDS programmes  Anaemia in women, men, and children  Malaria prevalence in children as a follow-up to the 2014-15 Uganda Malaria Indicator Survey  Vitamin A deficiency (VAD) in children  Key education indicators, including school attendance ratios, level of educational attainment, and literacy levels  The extent of disability  Early childhood development  The extent of gender-based violence The information collected through the 2016 UDHS is intended to assist policymakers and program managers in evaluating and designing programs and strategies for improving the health of the country’s population. T 2 • Introduction and Survey Methodology 1.2 SAMPLE DESIGN The sampling frame used for the 2016 UDHS is the frame of the Uganda National Population and Housing Census (NPHC), conducted in 2014; the sampling frame was provided by the Uganda Bureau of Statistics. The census frame is a complete list of all census enumeration areas (EAs) created for the 2014 NPHC. In Uganda, an EA is a geographic area that covers an average of 130 households. The sampling frame contains information about EA location, type of residence (urban or rural), and the estimated number of residential households. At the time of the NPHC, Uganda was divided administratively into 112 districts, which were grouped for this survey into 15 regions. The sample for the 2016 UDHS was designed to provide estimates of key indicators for the country as a whole, for urban and rural areas separately, and for each of the 15 regions. Estimates are also presented for three special areas: the Lake Victoria islands, the mountain districts, and greater Kampala. The 2016 UDHS regions include the following districts:  South Central: Butambala, Gomba, Mpigi, Bukomansimbi, Kalangala, Kalungu, Lwengo, Lyantonde, Masaka, Rakai, Sembabule, and Wakiso  North Central: Buikwe, Buvuma, Kayunga, Kiboga, Kyankwanzi, Luwero, Mityana, Mubende, Mukono, Nakaseke, and Nakasongola  Kampala: Kampala  Busoga: Bugiri, Namutumba, Buyende, Iganga, Jinja, Kaliro, Kamuli, Luuka, Mayuge, and Namayingo  Bukedi: Budaka, Butaleja, Kibuku, Pallisa, Tororo, and Busia  Bugisu: Bulambuli, Kapchorwa, Kween, Bududa, Manafwa, Mbale, Sironko, and Bukwo  Teso: Amuria, Bukedea, Katakwi, Kumi, Ngora, Soroti, Kaberamaido, and Serere  Karamoja: Abim, Amudat, Kaabong, Kotido, Moroto, Nakapiripirit, and Napak  Lango: Alebtong, Amolatar, Dokolo, Lira, Otuke, Apac, Kole, and Oyam  Acholi: Agago, Amuru, Gulu, Lamwo, Pader, Kitgum, and Nwoya  West Nile: Adjumani, Arua, Koboko, Maracha, Moyo, Nebbi, Yumbe, and Zombo  Bunyoro: Buliisa, Hoima, Kibaale, Kiryandongo, and Masindi  Tooro: Bundibugyo, Kabarole, Kasese, Ntoroko, Kyenjojo, Kamwenge, and Kyegegwa  Kigezi: Kabale, Kisoro, Kanungu, and Rukungiri  Ankole: Buhweju, Bushenyi, Ibanda, Isingiro, Kiruhura, Mbarara, Mitooma, Ntungamo, Rubirizi, and Sheema The 2016 UDHS special areas include the following:  Islands: islands and shoreline areas in Kalangala, Mayuge, Buvuma, Namayingo, Rakai, Mukono, and Wakiso districts Introduction and Survey Methodology • 3  Mountains: Bundibugyo, Kasese, Ntoroko, Bukwo, Bulambuli, Kapchorwa, Kween, Kisoro, Sironko, Mbale, and Kaabong districts  Greater Kampala: Kampala district and urban areas in Mukono and Wakiso districts The 2016 UDHS sample was stratified and selected in two stages. In the first stage, 697 EAs were selected from the 2014 Uganda NPHC: 162 EAs in urban areas and 535 in rural areas. One cluster from Acholi subregion was eliminated because of land disputes. Households constituted the second stage of sampling. A listing of households was compiled in each of the 696 accessible selected EAs from April to October 2016, with some listing overlapping with fieldwork. Maps were drawn for each of the sampled clusters and all of the listed households. The listing excluded institutional living arrangements such as army barracks, hospitals, police camps, and boarding schools. To minimise the task of household listing, each large EA (i.e., more than 300 households) selected for the 2016 UDHS was segmented. Only one segment was selected for the survey with probability proportional to segment size, and the household listing was conducted only in the selected segment. Thus, a 2016 UDHS cluster is either an EA or a segment of an EA. In total, a representative sample of 20,880 households (30 per EA or EA segment) was randomly selected for the 2016 UDHS. The allocation of the sample EAs featured a power allocation with a small adjustment because a proportional allocation would not have met the minimum number of clusters per survey domain required for a DHS survey. The sample EAs were selected independently from each stratum using probability proportional to size. The 20,880 selected households resulted in 18,506 women successfully interviewed, with an average of 1,200 complete interviews per domain. All women age 15-49 who were either permanent residents of the selected households or visitors who stayed in the household the night before the survey were eligible to be interviewed. In one-third of the sampled households, all men age 15-54, including both usual residents and visitors who stayed in the household the night before the interview, were eligible for individual interviews. In the subsample of households selected for the male survey, anaemia testing was performed among eligible women age 15-49 and men age 15-54 who consented to being tested and among children age 6-59 months whose parents or guardians consented. In the same subsample, blood samples were collected from children age 6-59 months whose parents or guardians consented to malaria testing with rapid diagnostic test (RDT) kits and laboratory testing of vitamin A deficiency. Height and weight information was also collected from eligible women and men, as well as children age 0-59 months. In addition, a subsample of one eligible woman in two-thirds of households (those households not selected for the male survey and biomarker collection) and one eligible man in one-third of households (those households selected for the male survey and biomarker collection) was randomly selected to be asked questions about domestic violence. 1.3 QUESTIONNAIRES Four questionnaires were used in the 2016 UDHS: the Household Questionnaire, the Woman’s Questionnaire, the 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 Uganda. In addition, information on the survey fieldworkers was collected through a self- administered Fieldworker Questionnaire. Input was solicited from various stakeholders representing government ministries and agencies, nongovernmental organisations, and development partners. After the preparation of the questionnaires in English, the questionnaires were then translated into eight major languages: Ateso, Ngakarimojong, Luganda, Lugbara, Luo, Runyankole-Rukiga, Runyoro-Rutoro, and Lusoga. The Household, Woman’s, and Man’s Questionnaires were programmed into tablet computers to facilitate computer-assisted personal interviewing (CAPI) for data collection purposes, with the capability to choose any of the nine languages for each questionnaire. The Biomarker Questionnaire was completed on paper during data collection and then entered into the CAPI system. 4 • Introduction and Survey Methodology The Household Questionnaire listed all members of and visitors to the selected households. Basic demographic information was collected on the characteristics of each person, including his or her age, sex, marital status, education, and relationship to the head of the household. Parents’ survival status was determined for children under age 18. The data on age and sex of household members obtained in the Household Questionnaire were used to identify women and men who were eligible for individual interviews, anthropometry measurements, and anaemia testing. The Household Questionnaire was also used to identify children for anthropometry measurements, anaemia and malaria testing, and blood sample collection for vitamin A testing. In addition, the questionnaire collected information on characteristics of the household’s dwelling unit, such as source of water, type of toilet facilities, and materials used for the floor of the dwelling unit, as well as ownership of various durable goods. The questionnaire further collected information on ownership and use of bed nets, child discipline, road traffic accidents and other causes of injury/death, and deaths in the households. An additional module based on the Short Set of questions developed by the Washington Group on Disability Statistics to estimate the prevalence of disabilities among persons age 5 or above was also included in the Household Questionnaire. The Woman’s Questionnaire collected information from all eligible women age 15-49. These women were asked questions on:  Background characteristics: age, education, and media exposure  Reproduction: children ever born, birth history, and current pregnancy  Family planning: knowledge and use of contraception, sources of contraceptive methods, and information on family planning  Maternal and child health, breastfeeding, and nutrition: prenatal care, delivery, postnatal care, breastfeeding and complementary feeding practices, vaccination coverage, prevalence and treatment of diarrhoea, symptoms of acute respiratory infection (ARI), fever, knowledge of oral rehydration salts (ORS), and use of oral rehydration therapy (ORT)  Marriage and sexual activity: marital status, age at first marriage, number of unions, age at first sexual intercourse, recent sexual activity, number and type of sexual partners, use of condoms, knowledge and experience of obstetric fistula, and female genital cutting  Fertility preferences: desire for more children, ideal number of children, gender preferences, and intention to use family planning  Husbands’ background characteristics and women’s work: husbands’ age, level of education, and occupation and women’s occupation and sources of earnings  STIs and HIV/AIDS: knowledge of STIs and AIDS and methods of transmission, sources of information, behaviours to avoid STIs and HIV, and stigma  Knowledge, attitudes, and behaviours related to other health issues such as injections and smoking  Adult and maternal mortality  Domestic violence (questions asked of one woman per household)  Early childhood development The Man’s Questionnaire was administered to all men age 15-54 in the subsample of households selected for the male survey. The Man’s Questionnaire collected much of the same information elicited with the Woman’s Questionnaire but was shorter because it did not contain a detailed reproductive history or questions on maternal and child health. Introduction and Survey Methodology • 5 The Biomarker Questionnaire recorded anthropometric measurements, anaemia and malaria testing results, and blood sample collection for vitamin A testing in the laboratory, as well as the signature of the fieldworker (health technician) who conducted the interview and obtained consent. For this survey, interviewers used tablet computers to record all questionnaire responses during the interviews. The tablet computers were equipped with Bluetooth® technology to enable remote electronic transfer of files, such as assignments from the team supervisor to the interviewers, individual questionnaires among survey team members, and completed questionnaires from interviewers to team supervisors. The CAPI data collection system employed in the 2016 UDHS was developed by The DHS Program with the mobile version of CSPro. The CSPro software was developed jointly by the U.S. Census Bureau, Serpro S.A., and The DHS Program. The purpose of the Fieldworker Questionnaire was to collect basic background information on the people who were collecting data in the field, including team leaders, field data managers, interviewers, and health technicians. The survey protocol, including biomarker collection, was reviewed and approved by the ICF Institutional Review Board. 1.4 ANTHROPOMETRY, ANAEMIA TESTING, MALARIA TESTING, AND VITAMIN A DEFICIENCY TESTING The 2016 UDHS incorporated four biomarkers: anthropometry, anaemia testing, malaria testing, and vitamin A testing. Biomarkers were collected in the one-third of households selected for the male survey. In contrast with the data collection procedure for the household and individual interviews, data related to biomarkers were initially recorded on a paper Biomarker Questionnaire and subsequently entered into interviewers’ tablet computers. Anthropometry. Height and weight measurements were recorded for children age 0-59 months, women age 15-49, and men age 15-54. Anaemia testing. Blood specimens for anaemia testing were collected from eligible women and men who voluntarily consented to be tested and from all children age 6-59 months for whom consent was obtained from their parents or the adult responsible for the children. Blood samples were obtained from a drop of blood taken from a finger prick (or a heel prick in the case of children age 6-11 months). A drop of blood from the prick site was drawn into a microcuvette, and haemoglobin analysis was carried out on site with a battery-operated portable HemoCue analyser. Results were provided verbally and in writing. Parents of children with a haemoglobin level below 8 g/dl were instructed to take the child to a health facility for follow-up care. Likewise, adults with haemoglobin levels below certain cut-off points (8 g/dl for nonpregnant women, 7 g/dl for pregnant women, and 8 g/dl for men) were referred for follow-up care. All households in which anaemia testing was conducted were given a brochure that explained the causes and prevention of anaemia. Malaria testing. Malaria testing was carried out only among children age 6-59 months; no adults were tested. With the same finger (or heel) prick used for anaemia testing, a drop of blood was tested immediately using the SD Bioline Pf/Pv RDT, which is a qualitative test for the detection of histidine-rich protein II (HRP-II) antigen of Plasmodium falciparum (Pf) and/or Plasmodium vivax (Pv) in human whole blood. Plasmodium falciparum is the predominant Plasmodium species found in Uganda. A tiny volume of blood is captured with a disposable sample applicator and placed in the well of the testing device. All health technicians were trained to perform RDTs in the field according to the manufacturers’ instructions. Technicians read, interpreted, and recorded the RDT results after 15 minutes, following the instructions in the kit insert. The RDT results were recorded as Pf positive, Pv positive, Pf/Pv positive, or negative, with faint test lines being considered positive. As with anaemia testing, malaria RDT results were provided to the child’s parent or guardian in oral and written form and were recorded on the Household Questionnaire. 6 • Introduction and Survey Methodology Children who tested positive for malaria were offered a full course of treatment according to the standard procedures for treating malaria in Uganda if they did not have a severe case of the disease (diagnosed by symptoms or the presence of severe anaemia), were not currently on treatment, and had not completed a full course of artemisinin-based combination therapy (ACT) during the preceding 2 weeks. Nurses on each field team were instructed to ask about signs of severe malaria and about any medications the child might be taking. The nurses then provided the age-appropriate dose of ACT and instructions for administering the medicine to the child.1,2 The anaemia brochure also contained information on malaria and was given to all households in which malaria testing was conducted. Vitamin A deficiency testing. Blood collection for vitamin A testing was carried out only among children age 6-59 months; no adults were tested. Using the same finger (or heel) prick used for anaemia and malaria testing, a drop of blood was collected on a filter paper card as a dry blood spot sample (DBS). The protocol for blood specimen collection and analysis was based on the anonymous linked protocol developed for the DHS Program. This protocol allows for merging of vitamin A test results with the sociodemographic data collected in the individual questionnaires after removal of all information that could potentially identify an individual. Interviewers explained the blood collection procedure, the confidentiality of the data, and the fact that the test results would not be made available to respondents. If a parent or guardian consented to the testing, up to five blood spots from the finger/heel prick were collected on a filter paper card to which a barcode label unique to the child was affixed. A duplicate label was attached to the Biomarker Questionnaire. A third copy of the same barcode was affixed to the Dried Blood Spot Transmittal Sheet to track the blood samples from the field to the laboratory. Children’s parents or guardians were asked if they would consent to the laboratory storing their child’s blood sample for future unspecified testing. If parents or guardians did not consent to additional testing on the sample, it was indicated on the Biomarker Questionnaire that they refused additional tests on their child’s specimen, and the words “no additional testing” were written on the filter paper card. Blood samples were dried overnight, and the filter paper cards were packaged for storage the following morning. Samples were periodically transported to the laboratory of the Department of Biochemistry at Makerere University in Kampala. Upon arrival at the laboratory, each blood sample was logged into the CSPro vitamin A Test Tracking System database, given a laboratory number, and stored at -20°C until tested. The vitamin A testing protocol stipulated that blood could be tested only after questionnaire data collection had been completed, the data had been verified and cleaned, and all unique identifiers other than the anonymous barcode number had been removed from the data file. After finalisation of testing, the vitamin A test results for the 2016 UDHS were entered into a spreadsheet with a barcode as the unique identifier for each result. The barcode was used to link the vitamin A test results with the data from the individual questionnaires. VAD was assessed using the retinol binding protein enzyme immunoassay (RPB-EIA) method. Rather than measuring retinol, this test measures retinol-binding protein (RPB), a surrogate marker for retinol that is more stable than retinol. The RBP-EIA has been rigorously evaluated on both venous blood and capillary blood in the form of a DBS sample (Hix et al. 2004; Hix et al. 2006). 1 The dosage of ACT was based on the age of the recipient. The proper dosage for a child age 4 months to age 3 years is one tablet of artemether-lumefantrine (co-formulated tablets containing 20 mg artemether and 120 mg lumefantrine) to be taken twice daily for 3 days, while the dosage for a child age 3-7 is two tablets of artemether-lumefantrine to be taken twice daily for 3 days. 2 Children who exhibited signs of severe malaria (based on symptoms or haemoglobin testing result of severe anaemia) were referred to the nearest medical facility for treatment. Introduction and Survey Methodology • 7 To run the RPB-EIA, two 6-mm (1/4-inch) discs punched out of the centre of two DBS drops on each card were first eluted by soaking overnight in a pre-prepared buffer. The following day, the concentration of RBP in the DBS eluates was determined using a commercial enzyme immunoassay kit manufactured by the Scimedx Corporation (Denville, New Jersey, USA). Because the elution does not remove 100% of the RPB that is in the dried blood spot on the filter paper card, it was necessary to use a correction factor that makes the concentration of RBP measured in the DBS sample equal to the concentration of RBP measured in a serum sample from the same individual. The Biochemistry Laboratory performed a validation comparing RPB from DBS and serum samples for 50 individuals and found that, on average, the concentration of RPB in the serum sample was 11% higher than the concentration of RBP in the eluted DBS sample. Therefore, a correction factor of 1.1 was applied to the RBP measurements of DBS samples for all individuals tested in the 2016 UDHS. This provides the unadjusted RBP measure. Because RBP levels decrease during infection/inflammation and, if not corrected for, may lead to overestimation of the prevalence of VAD, C-reactive protein (CRP) was used to correct the unadjusted RBP values for the influence of infection or inflammation. To obtain a correction factor to adjust RBP levels for the effects of infection and inflammation, 24% of the DBS samples were tested for CRP. To measure CRP, one 3.2-mm (1/8-inch) disc was punched from the centre of the DBS. The punched disc was placed into a micro-centrifuge tube, and 500 µL of CRP assay buffer was added. The tubes were vortexed for 15 seconds and centrifuged at 5,000 rpm for 2 minutes. Samples were incubated overnight at 4°C. The following day, samples were removed from the refrigerator and rotated at 350 rpm at room temperature for 1 hour. The eluted samples were then tested in duplicate using a commercial test kit (Bender MedSystems GmbH, Vienna, Austria). The cut-off used to define infection or inflammation was set at 3 mg/L of CRP: a CRP above 3 mg/L means that the person has infection/inflammation, and a CRP of 3 mg/L or below means that the person does not have infection/inflammation. In the subsample tested for CRP, children were classified into two groups: the healthy group (A; CRP 3 mg/L or below) and the group with infection or inflammation (B; CRP above 3 mg/L). Adjustment factors were then calculated as the ratio of the geometric mean of the RBP concentrations for the healthy group versus the group with raised CRP (the difference between the mean log RBP value for Group A and the mean log RBP value for Group B was back-transformed to provide the adjustment factor). RBP values for the group with raised CRP were then multiplied by the adjustment factor to provide adjusted values. The method suggested by Thurnham et al. (2003) was used to adjust the RBP values for infection/inflammation in the subsample that was not tested for CRP. To adjust the prevalence of VAD for all children—including those who were not tested for CRP—the VAD prevalence was determined after increasing their RBP values by the difference between the means of the RBP values for the CRP subsamples. First, the mean RBP values of the CRP subsample were calculated. Next, the RBP values for Group B were multiplied by 1.255 and added to the Group A RBP values, and a new mean RBP value for the subsample was calculated.3 Then all RBP values for the children who were not tested for CRP were adjusted by the difference between the new mean and the original mean as a percentage of the original mean. The corrected prevalence of VAD among all children was calculated using the newly adjusted RBP values. When vitamin A status is assessed using serum retinol, the concentration of retinol used to indicate VAD in children is 0.7 µmol/L. Current research suggests that a concentration of 0.7 µmol/L of retinol is equivalent to a concentration of 0.825 µmol/L of RBP (Engle-Stone et al. 2011; Gorstein et al. 2008). Thus, the cut-off to define VAD in children in the 2016 UDHS is 0.825 µmol/L of RBP. 3 The multiplication factor (1.255) is an estimate of the percentage reduction of RBP (and vitamin A) in the presence of infection, based on CRP results from previous studies (Thurnham et al. 2003). 8 • Introduction and Survey Methodology 1.5 PRETEST The UDHS technical team, composed of staff from UBOS and ICF, participated in a 2-day training of trainers (TOT) workshop conducted 17 and 18 March 2016. Immediately following the workshop, the pretest training took place between 21 March and 8 April 2016 at the Imperial Golf View Hotel in Entebbe Municipality. The UDHS technical team and ICF technical specialists trained 45 participants to administer the paper and electronic Household, Woman’s, and Man’s Questionnaires with tablet computers and eight participants to take anthropometric measurements; collect blood samples for haemoglobin, malaria, and vitamin A testing; and complete the paper Biomarker Questionnaire. All trainees had some experience with household surveys, either involvement in previous Uganda DHS surveys or involvement in other similar surveys such as the Uganda National Panel Survey. The pretest fieldwork, which took place 13-15 April 2016, was conducted in clusters surrounding the training venue in Entebbe Municipality that were not included in the 2016 UDHS sample area, which covered approximately 240 households. The UDHS technical team and ICF conducted debriefing sessions with the pretest field staff on 16 April 2016; modifications to the questionnaires were made based on lessons learned from the exercise. Teams then spent an additional week upcountry testing the translations. 1.6 TRAINING OF FIELD STAFF UBOS recruited and trained a total of 173 fieldworkers (108 women and 65 men) to serve as supervisors, CAPI managers, interviewers, health technicians, and reserve interviewers for the main fieldwork. Health technicians were trained separately from interviewers. The main training took place from 14 May to 14 June 2016 at the Imperial Golf View Hotel in Entebbe Municipality. The training course included instruction on interviewing techniques and field procedures, a detailed review of questionnaire content, instruction on administering the paper and electronic questionnaires, mock interviews between participants in the classroom, and practice interviews with actual respondents in areas outside the 2016 UDHS sample. Twenty-one individuals were recruited and trained on collecting biomarker data, including taking height and weight measurements, testing for anaemia by measuring haemoglobin levels, testing for malaria using RDTs, and preparing dried blood spots for subsequent vitamin A testing. The biomarker training was held from 21 May to 14 June 2016 at the same venue with interviewers. The training included lectures, demonstrations of biomarker measurement or testing procedures, field practice with children at a health clinic, and standardisation of height and weight measurements. To help place the importance of the 2016 UDHS into context for the trainees, the training also included presentations by staff from the Ministry of Health, UN Women, and UNICEF on Uganda-specific policies and programmes related to child immunisation, domestic violence, and early childhood development. A two-day field practice was organised on 11 and 13 June 2016 to provide trainees with additional hands- on practice before the actual fieldwork. Training participants were evaluated through classwork, in-class exercises, quizzes, and observations conducted during field practice. A total of 84 participants were selected to serve as interviewers, 21 as health technicians, 21 as field data managers, and 21 as team leaders. The selection of team leaders and field data managers was based on experience in leading survey teams and performance during the pretest and main training. Team leaders and field data managers received additional instructions and practice on performing supervisory activities with the CAPI system. Supervisory activities included assigning households and receiving completed interviews from interviewers, recognising and dealing with error messages, receiving system updates and distributing updates to interviewers, completing biomarker questionnaires and DBS transmittal sheets, resolving duplicated cases, closing clusters, and transferring interviews to the central office via a secure Internet file streaming system (IFSS). In addition to the CAPI material, team leaders and field data managers also received training on their roles and responsibilities. Introduction and Survey Methodology • 9 1.7 FIELDWORK Data collection was conducted by 21 field teams, each consisting of one team leader, one field data manager, three female interviewers, one male interviewer, one health technician, and one driver. The health technicians were responsible for anthropometric measurements, blood sample collection for haemoglobin and malaria testing, and DBS specimen collection for vitamin A testing. Electronic data files were transferred from each interviewer’s tablet computer to the team supervisor’s tablet computer every day. The field supervisors transferred data to the central data processing office via IFSS. Senior staff from the Makerere University School of Public Health, the Ministry of Health, and UBOS and a survey technical specialist from The DHS Program coordinated and supervised fieldwork activities. Data collection took place over a 6-month period, from 20 June 2016 through 16 December 2016. 1.8 DATA PROCESSING All electronic data files for the 2016 UDHS were transferred via IFSS to the UBOS central office in Kampala, where they were stored on a password-protected computer. The data processing operation included registering and checking for inconsistencies, incompleteness, and outliers. Data editing and cleaning included structure and consistency checks to ensure completeness of work in the field. The central office also conducted secondary editing, which required resolution of computer-identified inconsistencies and coding of open-ended questions. The data were processed by four staff (two programmers and two data editors) who took part in the main fieldwork training. They were supervised by three senior staff from UBOS. Data editing was accomplished with CSPro software. Secondary editing and data processing were initiated in August 2016 and completed in January 2017. 1.9 COMMUNITY MOBILISATION Prior to the onset of fieldwork, the UBOS Communication and Public Relations Team conducted advocacy and mobilisation activities that were designed to encourage promotion of the 2016 UDHS and encourage maximum community support and participation. Radio and television talk shows and community meetings were conducted to mobilise the general public and create public awareness. The advocacy also included field visits to the local communities before fieldwork began in a given area. During these visits, the advocacy teams discussed the survey objectives, implementation, content, and how the community would benefit from the exercise. 1.10 RESPONSE RATES Table 1.1 shows response rates for the 2016 UDHS. A total of 20,791 households were selected for the sample, of which 19,938 were occupied. Of the occupied households, 19,588 were successfully interviewed, which yielded a response rate of 98%. In the interviewed households, 19,088 eligible women were identified for individual interviews. Interviews were completed with 18,506 women, yielding a response rate of 97%. In the subsample of households selected for the male survey, 5,676 eligible men were identified and 5,336 were successfully interviewed, yielding a response rate of 94%. Response rates were higher in rural than in urban areas, with the rural- urban difference being more pronounced among men (95% and 90%, respectively) than among women (98% and 95%, respectively). 10 • 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), Uganda DHS 2016 Residence Total Result Urban Rural Household interviews Households selected 4,843 15,948 20,791 Households occupied 4,625 15,313 19,938 Households interviewed 4,469 15,119 19,588 Household response rate1 96.6 98.7 98.2 Interviews with women age 15-49 Number of eligible women 4,619 14,469 19,088 Number of eligible women interviewed 4,379 14,127 18,506 Eligible women response rate2 94.8 97.6 97.0 Interviews with men age 15-54 Number of eligible men 1,280 4,396 5,676 Number of eligible men interviewed 1,150 4,186 5,336 Eligible men response rate2 89.8 95.2 94.0 1 Households interviewed/households occupied 2 Respondents interviewed/eligible respondents Housing Characteristics and Household Population • 11 HOUSING CHARACTERISTICS AND HOUSEHOLD POPULATION 2 Key Findings  Drinking water: Seventy-eight percent of households in Uganda have access to an improved source of drinking water, an increase from 70% in 2011.  Sanitation: About 2 in 10 households (19%) in Uganda use improved toilet facilities.  Electricity: Twenty-nine percent of the households in Uganda have electricity. Electricity is more common among urban households (59%) than rural households (18%).  Household population and composition: The broad base of the population pyramid shows that the majority of Uganda’s population is young, which is characteristic of developing countries with high fertility and low life expectancy.  Orphans: Thirty-two percent of households in Uganda have foster or orphaned children. There are more households with single orphans (12%) than double orphans (2%).  Child discipline: Eighty-five percent of children age 1-14 experienced a violent discipline method within the previous month.  Death registration: Of deaths reported to have occurred in the previous year, only 24% were registered with the civil authority. nformation on the socioeconomic characteristics of the household population in the 2016 UDHS provides context to interpret demographic and health indicators and can furnish an approximate indication of the representativeness of the survey. In addition, this information sheds light on the living conditions of the population. The chapter presents information on sources of drinking water, sanitation, exposure to smoke inside the home, wealth, hand washing, household population and composition, birth registration, educational attainment, school attendance, family living arrangements, disability, child discipline, and persons injured or killed in accidents. I 12 • Housing Characteristics and Household Population 2.1 DRINKING WATER SOURCES AND TREATMENT Improved sources of drinking water Include piped water, public taps, standpipes, tube wells, boreholes, protected dug wells and springs, and rainwater. Households that use bottled water for drinking are classified as using an improved source only if the water they use for cooking and hand washing comes from an improved source. Sample: Households Use of unimproved water sources increases the prevalence of waterborne disease and the burden of service delivery through an increased demand for health care. Just over three quarters (78%) of households in Uganda have access to an improved source of drinking water (Table 2.1). Access to improved water sources is more predominant in urban (91%) than rural (74%) households. The 2016 UDHS asked all households whether they treat their water to ensure that it is safe for drinking. About half (52%) of households use an appropriate water treatment method. The most commonly used method is boiling (47% of households); more urban households (70%) than rural households (39%) reported boiling their water. More than half of households in rural areas (54%) do not treat their drinking water at all. More than half (55%) of rural households spend at least 30 minutes (round trip) to fetch drinking water, as compared with about a quarter (23%) of urban households. More than half of urban households (54%) use piped water for drinking: 23% have water piped into their dwelling/yard, 18% have water that is piped to a neighbour, and 13% use a public tap/standpipe. Rural households, on the other hand, rely mainly on tube wells or boreholes (45%) or on an unimproved source (26%) (Figure 2.1). Table 2.2 presents information on the availability of water in the last 2 weeks among households using piped water or water from a tube well or borehole. Sixty-seven percent of households in Uganda reported having water with no interruption of at least a single day in the 2 weeks preceding the survey. Urban households (50%) are more likely than rural households (24%) to report water being unavailable for at least 1 day. Trends: The proportion of households using an improved source of drinking water increased steadily from 1995 (49% of households) to 2000-01 (57%) to 2006 (68%) to 2011 (70%) to 2016 (78%). 2.2 SANITATION Improved toilet facilities Include any non-shared toilet of the following types: flush/pour flush toilets to piped sewer systems, septic tanks, and pit latrines; ventilated improved pit (VIP) latrines; pit latrines with slabs; and composting toilets. Sample: Households Figure 2.1 Household drinking water by residence 8 23 3 6 18 2 8 13 6 39 20 45 16 14 16 2 3 1 22 9 26 Total Urban Rural Percent distribution of households by source of drinking water Unimproved source Other improved sources* Protected well or spring Tube well or borehole Public tap/ standpipe Piped to neighbour Piped water into dwelling/yard/plot/ neighbour’s yard * Rain water, bottled/sachet water Housing Characteristics and Household Population • 13 About 2 in 10 households (19%) in Uganda use improved toilet facilities (Table 2.3). This is a slight improvement from 15% in 2011. Urban households are more prone to use shared facilities (46%) than rural households (11%). More than half of households in Uganda (55%) use unimproved toilet facilities, with nearly two-thirds (65%) of rural households and one quarter (25%) of urban households using such facilities (Figure 2.2). 2.3 OTHER HOUSEHOLD CHARACTERISTICS 2.3.1 Housing Characteristics Respondents were asked about access to electricity; the dwelling’s flooring materials were observed by the interviewer. Slightly less than a third (29%) of households in Uganda have electricity. Nearly 6 in 10 urban households (59%) have electricity, as compared with just under 2 in 10 rural households (18%) (Table 2.4). Urban and rural households use different flooring materials. Most urban households (59%) have floors made of cement screed, and most rural households (55%) have floors made of earth/sand. 2.3.2 Exposure to Smoke inside the Home Exposure to any type of smoke, for example resulting from cooking or smoking tobacco, can lead to diverse hazardous health effects. Ninety-five percent of the households in Uganda use a solid type of fuel for cooking, with wood being predominant (69%); 25% of households use charcoal. The health problems accruing from exposure to smoke can be aggravated if cooking takes place inside the dwelling rather than in a separate building or outdoors. Nearly 9 in 10 (86%) households do their cooking outside the house: 62% in a separate building and 24% outdoors (Table 2.4). 2.4 HOUSEHOLD WEALTH 2.4.1 Household Durable Goods Possessing durable consumer goods is an indicator of a household’s wealth. The survey collected information on household effects, ownership of means of transport, and ownership of agricultural land and farm animals (Table 2.5). Urban households are more likely to own various household effects other than bicycles; the difference is especially striking for televisions (44% of urban households versus 7% of rural households). Rural households are more likely to own agricultural land and farm animals. Figure 2.2 Household toilet facilities by residence 19 27 16 20 46 11 55 25 65 7 2 8 Total Urban Rural No facility/ bush/field Unimproved facility Shared facility Improved facility Percent distribution of households by type of toilet facilities 14 • Housing Characteristics and Household Population 2.4.2 Wealth Index Wealth index Households are given scores based on the number and kinds of consumer goods they own, ranging 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 using 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 her or his score, and then dividing the distribution into five equal categories, each comprising 20% of the population. Sample: Households Table 2.6 presents wealth quintiles according to urban-rural residence and region. The table also includes the Gini coefficient, a measure of disparity in wealth. The Gini coefficient ranges from 0-1, with 0 implying an equal distribution of wealth and 1 implying a totally unequal distribution. Nearly 6 in 10 (59%) households in urban areas are in the highest wealth quintile, in sharp contrast to about 1 in 10 (9%) in rural areas; close to half (48%) of households in rural areas are in the lowest or second lowest quintile (24% each) (Figure 2.3). 2.5 HAND WASHING Interviewers asked to observe the place where household members most often wash their hands; this place was observed in 59% of households. Among households in which the place for hand washing was observed, 44% had soap and water, 32% had water but no soap, and 21% had no water, no soap, and no other cleansing agent (Table 2.7). 2.6 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 otherwise specified. Figure 2.3 Household wealth by residence 8 246 24 8 23 19 20 59 9 Urban Rural Percent distribution of de jure population by wealth quintiles Wealthiest Fourth Middle Second Poorest Housing Characteristics and Household Population • 15 The 2016 UDHS included 19,588 households; 87,929 individuals slept in these households the night before the interview, among whom 45,532 were women and 42,397 were men (Table 2.8). The population pyramid in Figure 2.4 shows the de facto household population by 5-year age groups and sex. The broad base of the pyramid shows that a large proportion of Uganda’s population is young—children under age 15 constitute 50% of the total population. This kind of distribution is characteristic of developing countries with high fertility and low life expectancy. Table 2.9 shows that 3 in every 10 households (31%) are headed by women, similar to the proportions found in the 2000-01 (28%), 2006 (30%), and 2011 (30%) UDHS surveys. The average household size is 4.5 persons. Households are smaller in urban areas (3.9 persons) than in rural areas (4.8 persons). Single- member households are more common in urban (19%) than rural (12%) areas. 2.7 CHILDREN’S LIVING ARRANGEMENTS AND PARENTAL SURVIVAL Orphan A child with one or both parents who are dead. Sample: Children under age 18 One-third (32%) of households in Uganda include foster or orphaned children. Fourteen percent of households have orphans. There are more households with single orphans (12%) than double orphans (2%) (Table 2.9). Half of children under age 18 (52%) are living with both biological parents; the proportion of children living with both biological parents decreases with increasing child age (Table 2.10). 2.8 BIRTH REGISTRATION Registered birth Child has a birth certificate or child does not have a birth certificate, but his/her birth is registered with the civil authorities. Sample: De jure children under age 5 Apart from being the first legal acknowledgment of a child’s existence, birth registration is fundamental to the realisation of a number of rights and practical needs, including but not limited to access to health care and immunisation, education, and other social services. Figure 2.4 Population pyramid 10 5 0 5 10 <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+ Percent distribution of household population FemaleMale 16 • Housing Characteristics and Household Population About one-third (32%) of children under age 5 had their births registered with the civil authority (Table 2.11). There is regional variation in the proportion of births that are registered, ranging from 11% in Bugisu region to 57% in Kigezi region (Figure 2.5). The proportion of births that are registered is larger in the highest wealth quintile (39%) than in lower quintiles (30-32%). Birth registration increased from 21% in 2006 to 30% in 2011 and held stable at 32% in 2016. 2.9 EDUCATION 2.9.1 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 The majority of Ugandans have either no formal education or only some primary education (Tables 2.12.1 and 2.12.2). Nineteen percent of women and 13% of men age 6 and older have never had any formal education. Fifty-four percent of women and 54% of men have not completed primary education. Eight percent of women and 9% of men have completed primary school. A slightly higher percentage of both women (13%) and men (15%) have an incomplete secondary school education. Only 6% of women and 8% of men have completed secondary school or gone on to higher education. Women have completed a median of 3.4 years of school, while men have completed a median of 3.9 years. Trends: The proportion of women age 6 and older with no education decreased from 36% in 1995 to 19% in 2016; women’s median years of education increased from 0.9 years to 3.4 years in the same period. There has also been some improvement among men; the proportion of men with no education has decreased from 19% to 13%, and median number of years of schooling has increased from 2.7 to 3.9. Patterns by background characteristics  Urban women (5.6 years) and men (6.1 years) spend longer in school than rural women (2.9 years) and men (3.5 years).  Median number of years of education is lowest among women and men in Karamoja region (both 0.0) and highest among women and men in Kampala region (7.4 years and 8.7 years, respectively).  Among both women and men, median number of years of education increases with increasing wealth. Figure 2.5 Birth registration by region Percentage of de jure children under age 5 whose births are registered with the civil authorities Housing Characteristics and Household Population • 17 2.9.2 School Attendance Net attendance ratio (NAR) Percentage of the school-age population that attends primary or secondary school. Sample: Children age 6-12 for primary school NAR and children age 13-18 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 6-12 for primary school GAR and children age 13-18 for secondary school GAR Eighty-three percent of boys and 84% of girls age 6- 12 are attending primary school (Table 2.13). By region, the primary school net attendance ratio (NAR) ranges from 79% to 91% with the exception of Karamoja region (37%). The NAR drops to 20% in secondary school for both boys and girls. The secondary school NAR rises steadily with increasing household wealth among boys as well as girls (Figure 2.6). The gross attendance ratio (GAR) is similar for boys and girls at both the primary level (118% and 116%, respectively) and the secondary level (28% and 24%, respectively). 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. Sample: Primary school students and secondary school students The primary school Gender Parity Index (GPI) (0.98) implies that there is almost no gender gap. However, the secondary school GPI (0.86) indicates that more males attend secondary school than females. Patterns by background characteristics  The disparity in attendance between females and males at the primary level is minimal in all regions other than Karamoja region (0.67) and Ankole region (0.88). 2.10 DISABILITY 2.10.1 Disability by Domain and Age The 2016 UDHS included The DHS Program disability module, a series of questions based on the Washington Group on Disability Statistics (WG) Short Set that 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— Figure 2.6 Secondary school attendance by household wealth 5 9 19 27 35 20 8 11 17 26 42 20 Lowest Second Middle Fourth Highest Total Net attendance ratio for secondary school among children age 13-18 Girls Boys Poorest Wealthiest 18 • Housing Characteristics and Household Population and provide basic necessary information on disability comparable to that being collected worldwide via the WG disability tools. The respondent to the Household Questionnaire provided information for all household members and visitors age 5 and older on whether they had no difficulty, some difficulty, a lot of difficulty, or did not have the ability at all in each domain. This information was gathered for 72,143 people. Functional domains Seeing, hearing, communicating, remembering or concentrating, walking or climbing steps, and washing all over or dressing. Sample: De facto household population age 5 or above About three quarters (74%) of the de facto household population age 5 and older have no difficulty in any of the domains. Twenty percent have some difficulty in at least one domain, 6% have a lot of difficulty in at least one domain, and 0.6% cannot function at all in at least one domain. The proportion who have a lot of difficulty or cannot function at all in at least one domain ranges from 3% to 8% among those age 5-49 and then increases to 16% among those age 50-59 and 38% among those age 60 or above (Table 2.14). 2.10.2 Disability among Adults by Other Background Characteristics Functional domains Seeing, hearing, communicating, remembering or concentrating, walking or climbing steps, and washing all over or dressing. Sample: De facto household population age 15 or above Table 2.15 presents disability data among the de facto household population age 15 and older by additional background characteristics. Nine percent of women and 7% of men age 15 and older have a lot of difficulty or cannot function at all in at least one domain. 2.11 CHILD DISCIPLINE The 2016 UDHS Household Questionnaire included questions from the UNICEF Multiple Indicator Cluster Survey (MICS) module on Child Discipline. The questions were asked about one randomly selected de jure child age 1-14 per household. Non-violent disciplinary approaches Include one or more in the past 1 month:  taking away privileges, forbidding something the child liked, or not allowing the child to leave the house  explaining that the child’s behavior was wrong  giving the child something else to do Sample: De jure children age 1-14 Psychological aggression Includes one or both in the past 1 month:  shouting, yelling, or screaming at the child  calling the child dumb, lazy, or a similar term Sample: De jure children age 1-14 Housing Characteristics and Household Population • 19 Physical punishment Includes one or more in the past 1 month:  shaking the child  spanking, hitting, or slapping the child on the bottom with a bare hand  hitting the child on the bottom or other part of the body with a belt, hairbrush, stick, or other similar hard object  hitting or slapping the child on the face, head, or ears  hitting the child on the hand, arm, or leg  beating the child up, that is, hitting the child over and over as hard as one can Sample: De jure children age 1-14 Severe physical punishment Includes one or both in the past 1 month:  hitting or slapping the child on the face, head, or ears  beating the child up, that is, hitting the child over and over as hard as one can Sample: De jure children age 1-14 Eighty-five percent of children experienced at least one violent disciplinary action during the month before the interview. Only 10% of children experienced only non-violent forms of discipline. Children in households where the household head had more than a secondary education were more likely (18%) to experience only non-violent disciplinary methods than children in households where the head had less education (8-10%) (Table 2.16). Fifty percent of respondents believe that a child needs physical punishment in order to be raised or educated properly. Eighty-seven percent of respondents are aware that Uganda has a law that prohibits child abuse (Table 2.17). 2.12 DEATHS AND INJURIES Household respondents were asked if any member of the household had died or been seriously injured in a road traffic accident in the past 12 months. If a person was involved in more than one accident, only the most recent was discussed. Motorcycle accidents accounted for the greatest proportion (67%) of road traffic accidents leading to death or serious injury (Table 2.19). Respondents were also asked about deaths and serious injuries in the past year from causes other than road traffic accidents. For more information, see Tables 2.18 to 2.25. About a quarter (24%) of deaths among household members in the past year were registered with the civil authority (Table 2.25). 20 • Housing Characteristics and Household Population LIST OF TABLES For more information on household population and housing characteristics, see the following tables:  Table 2.1 Household drinking water  Table 2.2 Availability of water  Table 2.3 Household sanitation facilities  Table 2.4 Household characteristics  Table 2.5 Household possessions  Table 2.6 Wealth quintiles  Table 2.7 Hand washing  Table 2.8 Household population by age, sex, and residence  Table 2.9 Household composition  Table 2.10 Children’s living arrangements and orphanhood  Table 2.11 Birth registration of children under age 5  Table 2.12.1 Educational attainment of the female household population  Table 2.12.2 Educational attainment of the male household population  Table 2.13 School attendance ratios  Table 2.14 Disability by domain and age  Table 2.15 Disability among adults by background characteristics  Table 2.16 Child discipline  Table 2.17 Child discipline opinions and knowledge  Table 2.18 Deaths and injuries from road traffic accidents  Table 2.19 Types of road traffic accidents  Table 2.20 Injuries due to road traffic accidents  Table 2.21 Deaths and injuries from non-road traffic accidents  Table 2.22 Types of non-road traffic accidents and injuries  Table 2.23 Injuries due to non-road traffic accidents  Table 2.24 Deaths from other causes  Table 2.25 Death registration Housing Characteristics and Household Population • 21 Table 2.1 Household drinking water Percent distribution of households and de jure population by source of drinking water and by time to obtain drinking water, percentage of households and de jure population using various methods to treat drinking water, and percentage using an appropriate treatment method, according to residence, Uganda DHS 2016 Households Population Characteristic Urban Rural Total Urban Rural Total Source of drinking water Improved source 91.3 73.8 78.3 90.8 74.2 77.9 Piped into dwelling/yard/plot 23.1 2.9 8.1 23.7 2.6 7.3 Piped to neighbour 18.0 2.3 6.3 15.2 1.8 4.8 Public tap/standpipe 12.9 5.9 7.7 11.5 5.3 6.7 Tube well/borehole 20.0 45.3 38.8 23.0 47.1 41.9 Protected dug well 6.0 6.6 6.4 6.2 6.6 6.5 Protected spring 8.4 9.4 9.1 8.9 9.5 9.4 Rain water 1.2 1.3 1.3 1.4 1.2 1.2 Bottled/sachet water, improved source for cooking/hand washing1 1.8 0.1 0.6 0.8 0.0 0.2 Unimproved source 8.5 26.0 21.5 9.1 25.5 21.9 Unprotected dug well 4.2 10.2 8.7 4.4 10.1 8.9 Unprotected spring 1.5 4.2 3.5 1.7 4.3 3.8 Tanker truck/bicycle with jerrycans 0.8 0.7 0.7 0.6 0.4 0.5 Surface water 2.0 10.7 8.4 2.4 10.6 8.8 Bottled/sachet water, unimproved source for cooking/hand washing1 0.1 0.2 0.2 0.0 0.1 0.1 Other source 0.2 0.2 0.2 0.1 0.2 0.2 Total 100.0 100.0 100.0 100.0 100.0 100.0 Time to obtain drinking water (round trip) Water on premises2 47.5 8.8 18.7 44.9 8.0 16.1 Less than 30 minutes 29.3 35.7 34.0 28.7 33.8 32.7 30 minutes or longer 22.7 55.2 46.8 26.0 58.0 51.0 Don’t know/missing 0.5 0.4 0.4 0.5 0.2 0.3 Total 100.0 100.0 100.0 100.0 100.0 100.0 Water treatment prior to drinking3 Boiled 70.0 39.0 47.0 69.4 37.3 44.4 Bleach/chlorine added 5.2 6.2 5.9 5.7 6.7 6.5 Strained through cloth 1.7 3.0 2.7 1.7 3.2 2.9 Ceramic, sand, or other filter 0.6 0.7 0.7 0.7 0.7 0.7 Solar disinfection 0.1 0.1 0.1 0.1 0.1 0.1 Let it stand and settle 0.9 1.1 1.0 1.0 1.2 1.1 Other 0.4 0.1 0.2 0.3 0.1 0.2 No treatment 25.2 53.9 46.5 25.2 54.9 48.4 Percentage using an appropriate treatment method4 73.6 44.2 51.8 73.6 43.1 49.8 Number 5,027 14,561 19,588 19,459 69,360 88,819 1 Households using bottled water or sachet water for drinking are classified as using an improved or unimproved source according to their water source for cooking and hand washing. 2 Includes water piped to a neighbour 3 Respondents may report multiple treatment methods, so the sum of treatment may exceed 100%. 4 Appropriate water treatment methods include boiling, bleaching, filtering, and solar disinfecting. 22 • Housing Characteristics and Household Population Table 2.2 Availability of water Among households and de jure population using piped water or water from a tube well or borehole, percentage with lack of availability of water in the last 2 weeks, according to residence, Uganda DHS 2016 Availability of water in last 2 weeks Households Population Urban Rural Total Urban Rural Total Not available for at least 1 day 50.3 24.3 32.5 49.3 23.5 30.4 Available with no interruption of at least 1 day 48.2 75.2 66.7 49.8 76.3 69.2 Don’t know/missing 1.5 0.5 0.8 0.9 0.2 0.4 Total 100.0 100.0 100.0 100.0 100.0 100.0 Number using piped water or water from a tube well1 3,791 8,224 12,014 14,425 39,534 53,959 1 Includes households reporting piped water or water from a tube well or borehole as their main source of drinking water and households reporting bottled water as their main source of drinking water if their main source of water for cooking and hand washing is piped water or water from a tube well or borehole Table 2.3 Household sanitation facilities Percent distribution of households and de jure population by type of toilet/latrine facilities and percent distribution of households and de jure population with a toilet/latrine facility by location of the facility, according to residence, Uganda DHS 2016 Type and location of toilet/latrine facility Households Population Urban Rural Total Urban Rural Total Improved sanitation 26.5 16.0 18.7 31.7 17.7 20.8 Flush/pour flush to piped sewer system 1.9 0.1 0.6 2.3 0.1 0.6 Flush/pour flush to septic tank 5.2 0.4 1.6 5.6 0.4 1.5 Flush/pour flush to pit latrine 0.5 0.1 0.2 0.7 0.1 0.2 Ventilated improved pit (VIP) latrine 5.0 2.1 2.9 6.0 2.4 3.2 Pit latrine with slab 14.0 13.3 13.5 17.2 14.7 15.2 Composting toilet 0.0 0.1 0.0 0.0 0.1 0.1 Unimproved sanitation 73.5 84.0 81.3 68.3 82.3 79.2 Shared facility1 45.9 11.2 20.1 37.9 8.3 14.8 Flush/pour flush to piped sewer system 0.5 0.0 0.1 0.4 0.0 0.1 Flush/pour flush to septic tank 1.7 0.1 0.5 1.6 0.0 0.4 Flush/pour flush to pit latrine 1.0 0.0 0.3 0.9 0.0 0.2 Ventilated improved pit (VIP) latrine 9.0 1.9 3.7 7.0 1.3 2.6 Pit latrine with slab 33.6 9.0 15.3 27.9 6.8 11.4 Composting toilet 0.1 0.1 0.1 0.1 0.1 0.1 Unimproved facility 25.2 64.7 54.6 27.9 66.9 58.4 Flush/pour flush not to sewer/septic tank/pit latrine 0.3 0.0 0.1 0.3 0.0 0.1 Pit latrine without slab/open pit 24.6 63.9 53.8 27.3 66.2 57.7 Bucket 0.2 0.0 0.0 0.1 0.0 0.0 Hanging toilet/hanging latrine 0.1 0.4 0.3 0.1 0.4 0.3 Other 0.0 0.4 0.3 0.1 0.3 0.3 Open defecation (no facility/bush/ field) 2.3 8.1 6.6 2.5 7.1 6.1 Total 100.0 100.0 100.0 100.0 100.0 100.0 Number of households/population 5,027 14,561 19,588 19,459 69,360 88,819 Location of the facility In own dwelling 9.7 3.7 5.3 10.8 3.8 5.4 In own yard/plot 78.1 81.2 80.4 77.9 83.8 82.4 Elsewhere 12.2 15.1 14.3 11.3 12.4 12.1 Total 100.0 100.0 100.0 100.0 100.0 100.0 Number of households/population with a toilet/latrine facility 4,912 13,386 18,298 18,964 64,449 83,413 1 Facilities that would be considered improved if they were not shared by two or more households Housing Characteristics and Household Population • 23 Table 2.4 Household characteristics Percent distribution of households and de jure population by housing characteristics, percentage using solid fuel for cooking, percentage using clean fuel for cooking, and percent distribution by frequency of smoking in the home, according to residence, Uganda DHS 2016 Households Population Housing characteristic Urban Rural Total Urban Rural Total Electricity Yes 59.1 18.1 28.6 57.5 18.0 26.7 No 40.9 81.9 71.4 42.5 82.0 73.3 Total 100.0 100.0 100.0 100.0 100.0 100.0 Flooring material Earth/sand 19.5 54.9 45.9 21.3 54.6 47.3 Dung 5.4 18.7 15.3 6.2 20.3 17.2 Wood planks 0.4 0.4 0.4 0.3 0.5 0.4 Palm/bamboo 0.1 0.1 0.1 0.1 0.1 0.1 Parquet or polished wood 0.1 0.1 0.1 0.1 0.1 0.1 Concrete 2.5 2.1 2.2 2.5 2.2 2.3 Ceramic tiles 6.3 0.6 2.1 7.4 0.8 2.2 Cement screed 59.3 21.1 30.9 57.1 20.0 28.2 Carpet 5.8 1.4 2.5 4.3 1.0 1.7 Stones 0.2 0.1 0.2 0.2 0.1 0.2 Bricks 0.2 0.2 0.2 0.2 0.2 0.2 Other 0.2 0.2 0.2 0.1 0.2 0.2 Total 100.0 100.0 100.0 100.0 100.0 100.0 Rooms used for sleeping One 53.7 40.6 44.0 38.2 28.2 30.4 Two 24.4 31.1 29.4 29.8 33.5 32.7 Three or more 21.9 28.3 26.6 32.0 38.2 36.8 Total 100.0 100.0 100.0 100.0 100.0 100.0 Place for cooking In the house 20.6 7.7 11.0 17.4 5.2 7.9 In a separate building 36.4 70.2 61.5 45.8 77.7 70.7 Outdoors 38.0 19.4 24.2 35.0 16.4 20.5 No food cooked in household 5.0 2.6 3.2 1.7 0.7 0.9 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 Cooking fuel Electricity 1.1 0.1 0.4 0.8 0.1 0.2 LPG/cylinder gas/biogas 1.8 0.2 0.6 1.3 0.1 0.4 Kerosene 2.0 0.3 0.7 0.8 0.1 0.2 Charcoal 59.9 13.5 25.4 57.8 10.6 20.9 Wood 29.9 83.0 69.4 37.4 88.2 77.0 Straw/shrubs/grass 0.2 0.3 0.3 0.2 0.3 0.3 Agricultural crop 0.0 0.0 0.0 0.1 0.0 0.0 Other 0.0 0.0 0.0 0.0 0.0 0.0 No food cooked in household 5.0 2.6 3.2 1.7 0.7 0.9 Total 100.0 100.0 100.0 100.0 100.0 100.0 Percentage using solid fuel for cooking1 90.0 96.8 95.1 95.4 99.0 98.3 Percentage using clean fuel for cooking2 3.0 0.3 1.0 2.1 0.2 0.6 Frequency of smoking in the home Daily 4.8 9.9 8.6 5.2 10.5 9.4 Weekly 3.3 4.0 3.8 3.4 3.7 3.7 Monthly 0.4 0.6 0.6 0.4 0.6 0.6 Less than once a month 0.7 1.3 1.2 0.8 1.3 1.2 Never 90.8 84.1 85.8 90.3 83.8 85.2 Total 100.0 100.0 100.0 100.0 100.0 100.0 Number of households/population 5,027 14,561 19,588 19,459 69,360 88,819 LPG = Liquefied petroleum gas 1 Includes charcoal, wood, straw/shrubs/grass, and agricultural crops 2 Includes electricity and LPG/cylinder gas/biogas 24 • Housing Characteristics and Household Population Table 2.5 Household possessions Percentage of households possessing various household effects, means of transportation, agricultural land, and livestock/farm animals by residence, Uganda DHS 2016 Residence Total Possession Urban Rural Household effects Radio 66.9 55.6 58.5 Television 44.3 7.4 16.9 Mobile phone 89.9 68.8 74.3 Computer 10.8 1.4 3.8 Non-mobile telephone 2.8 0.6 1.2 Refrigerator 16.6 1.8 5.6 Cassette/CD/DVD player 29.5 5.7 11.8 Table 69.6 63.9 65.4 Chair 73.7 79.0 77.6 Sofa set 47.1 18.3 25.7 Bed 89.2 80.5 82.7 Cupboard 43.3 20.1 26.0 Clock 23.6 8.9 12.7 Means of transport Bicycle 21.2 37.0 32.9 Animal-drawn cart 0.6 0.4 0.4 Motorcycle/scooter 12.3 10.5 10.9 Car/truck 9.6 2.0 3.9 Boat with a motor 0.4 0.4 0.4 Boat without a motor 0.3 1.2 1.0 Ownership of agricultural land 44.2 76.3 68.1 Ownership of farm animals1 36.0 70.1 61.4 Number of households 5,027 14,561 19,588 1 Local cattle, exotic/cross-breed cattle, horses, donkeys, mules, goats, sheep, chickens, other poultry, or pigs Table 2.6 Wealth quintiles Percent distribution of the de jure population by wealth quintiles, and the Gini coefficient, according to residence and region, Uganda DHS 2016 Wealth quintile Total Number of persons Gini coefficient Residence/region Lowest Second Middle Fourth Highest Residence Urban 7.6 5.9 8.3 19.0 59.1 100.0 19,459 0.22 Rural 23.5 24.0 23.3 20.3 9.0 100.0 69,360 0.33 Region South Central 3.3 9.3 14.6 26.0 46.8 100.0 10,610 0.30 North Central 4.7 14.6 23.7 30.5 26.5 100.0 9,702 0.29 Kampala 0.0 0.1 0.0 4.2 95.7 100.0 3,454 0.08 Busoga 12.5 20.7 25.9 26.0 14.9 100.0 8,775 0.36 Bukedi 20.3 31.5 21.7 19.5 7.0 100.0 5,966 0.35 Bugisu 18.1 30.3 25.2 15.6 10.9 100.0 4,768 0.36 Teso 41.5 25.1 12.4 11.8 9.2 100.0 5,221 0.48 Karamoja 82.9 8.5 4.5 3.3 0.8 100.0 2,200 0.33 Lango 43.3 26.6 14.1 10.8 5.2 100.0 5,110 0.25 Acholi 61.2 17.8 7.1 6.3 7.6 100.0 4,583 0.38 West Nile 45.1 24.6 11.2 11.5 7.5 100.0 6,167 0.37 Bunyoro 21.9 25.7 23.0 16.7 12.8 100.0 4,853 0.32 Tooro 6.4 23.9 31.8 23.3 14.6 100.0 6,665 0.37 Kigezi 1.7 19.7 36.0 30.3 12.3 100.0 3,479 0.34 Ankole 6.3 20.6 29.8 28.4 14.9 100.0 7,265 0.31 Special area Island districts 16.3 23.9 25.5 26.0 8.3 100.0 1,000 0.30 Mountain districts 15.5 24.0 26.7 19.8 14.0 100.0 7,415 0.32 Greater Kampala 0.0 0.3 0.7 7.3 91.7 100.0 6,936 0.07 Total 20.0 20.0 20.0 20.0 20.0 100.0 88,819 0.31 Housing Characteristics and Household Population • 25 Table 2.7 Hand washing Percentage of households in which the place most often used for washing hands was observed by whether the location was fixed or mobile and total percentage of households in which the place for hand washing was observed, and among households in which the place for hand washing was observed, percent distribution by availability of water, soap, and other cleansing agents, according to background characteristics, Uganda DHS 2016 Percentage of households in which place for washing hands was observed: Number of households Among households in which place for hand washing was observed, percentage with: Number of households in which a place for hand washing was observed Background characteristic And place for hand washing was a fixed place And place for hand washing was mobile Total Soap and water1 Water and cleansing agent other than soap only2 Water only Soap but no water3 Cleansing agent other than soap only2 No water, no soap, no other cleansing agent Total Residence Urban 16.8 52.5 69.3 5,027 58.1 0.2 26.2 2.5 0.0 12.9 100.0 3,482 Rural 10.5 45.2 55.7 14,561 37.2 0.8 34.0 3.5 0.2 24.2 100.0 8,110 Region South Central 19.6 42.0 61.7 2,668 58.2 0.3 23.7 3.2 0.1 14.5 100.0 1,645 North Central 8.8 56.8 65.6 2,229 57.4 0.6 29.7 3.1 0.0 9.2 100.0 1,462 Kampala 17.9 61.2 79.0 979 70.8 0.0 23.6 1.4 0.0 4.2 100.0 774 Busoga 11.8 37.8 49.6 1,840 21.7 0.4 41.9 1.8 0.2 34.0 100.0 913 Bukedi 8.3 65.9 74.2 1,123 54.2 2.2 30.9 1.0 0.9 10.7 100.0 834 Bugisu 14.0 71.5 85.4 1,098 26.6 1.0 39.7 1.7 0.0 30.9 100.0 938 Teso 14.9 24.3 39.2 961 15.7 0.0 17.3 2.0 0.0 65.0 100.0 377 Karamoja 7.7 46.4 54.1 469 15.3 5.9 19.6 1.4 0.7 57.0 100.0 254 Lango 6.7 8.3 15.1 1,043 22.6 0.0 37.4 7.9 0.6 31.4 100.0 157 Acholi 10.4 22.4 32.8 955 22.0 1.3 16.4 26.5 0.6 33.2 100.0 313 West Nile 14.1 47.3 61.4 1,257 36.8 0.6 42.0 1.9 0.4 18.2 100.0 772 Bunyoro 8.3 34.2 42.6 1,089 38.4 0.5 31.1 1.4 0.0 28.5 100.0 464 Tooro 11.2 48.2 59.4 1,401 57.3 0.1 23.5 5.3 0.3 13.4 100.0 832 Kigezi 8.7 63.4 72.1 847 31.3 0.5 49.9 2.4 0.0 15.9 100.0 611 Ankole 10.5 66.0 76.5 1,630 37.1 0.0 36.3 3.2 0.0 23.4 100.0 1,247 Special area Island districts 4.4 46.9 51.3 266 66.0 0.1 17.8 3.5 0.2 12.5 100.0 136 Mountain districts 13.6 59.9 73.4 1,641 31.8 0.6 38.1 2.1 0.3 27.0 100.0 1,205 Greater Kampala 21.0 50.5 71.5 1,901 69.9 0.3 22.7 1.2 0.0 5.9 100.0 1,360 Wealth quintile Lowest 6.2 33.3 39.5 3,838 24.0 1.7 30.2 5.0 0.3 38.7 100.0 1,515 Second 8.6 45.3 53.9 3,753 29.7 1.2 38.1 4.1 0.3 26.6 100.0 2,024 Middle 8.9 52.2 61.1 3,616 36.5 0.6 35.4 3.9 0.3 23.4 100.0 2,210 Fourth 12.2 53.1 65.3 3,914 45.7 0.3 31.8 2.6 0.2 19.4 100.0 2,557 Highest 22.8 50.8 73.6 4,467 63.9 0.1 25.7 1.9 0.0 8.4 100.0 3,286 Total 12.1 47.0 59.2 19,588 43.5 0.6 31.6 3.2 0.2 20.8 100.0 11,592 1 Soap includes soap or detergent in bar, liquid, powder, or paste form. This column includes households with soap and water only as well as those that had soap and water and another cleansing agent. 2 Cleansing agents other than soap include locally available materials such as ash, mud, or sand. 3 Includes households with soap only as well as those with soap and another cleansing agent 26 • Housing Characteristics and Household Population Table 2.8 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, Uganda DHS 2016 Urban Rural Male Female Total Age Male Female Total Male Female Total <5 17.3 15.6 16.4 19.0 17.8 18.4 18.7 17.3 18.0 5-9 16.3 14.4 15.3 18.6 17.3 18.0 18.1 16.7 17.4 10-14 11.1 11.9 11.6 15.9 14.6 15.2 14.9 14.0 14.4 15-19 9.6 10.2 9.9 10.4 9.5 9.9 10.2 9.6 9.9 20-24 9.7 11.4 10.6 6.9 7.9 7.4 7.5 8.7 8.1 25-29 9.1 10.2 9.7 5.3 6.4 5.9 6.1 7.3 6.7 30-34 7.2 6.8 7.0 4.8 5.3 5.1 5.3 5.6 5.5 35-39 5.4 5.1 5.3 4.1 4.5 4.3 4.4 4.7 4.5 40-44 4.1 3.6 3.8 3.4 3.5 3.4 3.6 3.5 3.5 45-49 3.0 2.4 2.7 2.9 2.7 2.8 2.9 2.7 2.8 50-54 1.9 2.7 2.3 2.3 3.0 2.7 2.2 2.9 2.6 55-59 1.5 1.4 1.4 1.5 1.8 1.7 1.5 1.7 1.6 60-64 1.3 1.4 1.4 1.5 1.6 1.6 1.5 1.6 1.5 65-69 0.7 0.9 0.8 1.1 1.2 1.1 1.0 1.2 1.1 70-74 0.6 0.7 0.6 0.9 1.1 1.0 0.8 1.0 0.9 75-79 0.4 0.4 0.4 0.5 0.6 0.6 0.5 0.6 0.5 80+ 0.5 0.7 0.6 0.7 1.0 0.9 0.7 1.0 0.8 Don’t know/missing 0.2 0.0 0.1 0.1 0.0 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 44.7 42.0 43.3 53.6 49.7 51.6 51.7 48.0 49.8 15-64 53.0 55.2 54.2 43.2 46.3 44.8 45.3 48.3 46.9 65+ 2.1 2.8 2.5 3.2 4.0 3.6 2.9 3.7 3.3 Don’t know/missing 0.2 0.0 0.1 0.1 0.0 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 50.5 47.9 49.1 60.4 55.5 57.9 58.3 53.8 56.0 18+ 49.4 52.0 50.8 39.5 44.4 42.0 41.6 46.2 44.0 Don’t know/missing 0.2 0.0 0.1 0.1 0.0 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 10-19 20.8 22.1 21.5 26.3 24.1 25.1 25.1 23.7 24.3 Number of persons 9,009 10,285 19,294 33,388 35,247 68,635 42,397 45,532 87,929 Housing Characteristics and Household Population • 27 Table 2.9 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 foster children under age 18, according to residence, Uganda DHS 2016 Residence Total Characteristic Urban Rural Household headship Male 67.2 69.6 69.0 Female 32.8 30.4 31.0 Total 100.0 100.0 100.0 Number of usual members 1 19.0 11.8 13.7 2 14.0 10.5 11.4 3 16.6 13.0 13.9 4 15.6 14.8 15.0 5 12.6 14.1 13.7 6 8.5 11.6 10.8 7 6.1 8.9 8.2 8 3.4 6.2 5.5 9+ 4.2 9.0 7.8 Total 100.0 100.0 100.0 Mean size of households 3.9 4.8 4.5 Percentage of households with orphans and foster children under age 18 Double orphans 1.7 2.3 2.1 Single orphans1 10.2 12.2 11.7 Foster children2 24.6 29.1 27.9 Foster and/or orphan children 27.8 33.0 31.7 Number of households 5,027 14,561 19,588 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 Foster children are those under age 18 living in households with neither their mother nor their father present, and the mother and/or the father are alive. 28 • Housing Characteristics and Household Population Table 2.10 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 the percentage of children with one or both parents dead, according to background characteristics, Uganda DHS 2016 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 father alive Only mother alive Both dead Missing informa- tion on father/ mother Age 0-4 63.3 21.3 1.6 2.3 0.2 10.0 0.4 0.4 0.2 0.4 100.0 11.0 2.8 15,758 <2 69.3 24.8 1.1 0.8 0.1 3.3 0.3 0.0 0.0 0.3 100.0 3.6 1.5 5,940 2-4 59.6 19.1 1.9 3.2 0.2 14.0 0.5 0.7 0.3 0.4 100.0 15.5 3.6 9,818 5-9 51.8 16.9 3.3 6.1 0.5 17.2 0.9 2.0 0.7 0.6 100.0 20.8 7.5 15,318 10-14 45.1 14.7 5.8 7.6 1.2 17.8 1.6 3.6 1.7 0.8 100.0 24.8 14.1 12,739 15-17 38.3 12.4 8.6 6.6 1.4 21.5 2.3 4.9 3.2 0.7 100.0 32.0 20.5 5,402 Sex Male 52.9 17.2 3.9 5.9 0.8 14.6 0.9 2.2 1.1 0.5 100.0 18.8 9.0 24,744 Female 51.7 17.3 4.0 4.8 0.5 16.5 1.3 2.2 1.1 0.6 100.0 21.1 9.2 24,472 Residence Urban 47.8 19.3 3.7 5.7 0.6 17.0 1.4 2.4 1.2 0.8 100.0 22.0 9.4 9,457 Rural 53.4 16.7 4.1 5.2 0.7 15.2 1.0 2.2 1.0 0.5 100.0 19.4 9.1 39,759 Region South Central 48.7 17.4 3.4 4.7 0.4 19.5 1.6 2.4 1.3 0.6 100.0 24.7 9.2 5,325 North Central 45.5 18.8 3.9 5.9 0.7 19.6 1.2 2.5 1.1 0.7 100.0 24.4 9.6 5,309 Kampala 48.0 22.9 1.7 6.6 0.5 16.1 1.2 1.4 0.7 0.9 100.0 19.5 5.6 1,420 Busoga 53.0 14.0 3.3 7.0 0.5 18.5 0.9 1.6 0.7 0.5 100.0 21.8 7.2 5,182 Bukedi 59.7 14.1 2.8 5.0 0.5 14.3 1.1 1.6 0.6 0.3 100.0 17.6 6.8 3,505 Bugisu 51.7 14.8 3.2 7.2 0.2 18.2 1.5 2.2 0.7 0.3 100.0 22.6 8.0 2,675 Teso 55.7 19.0 4.2 5.1 0.8 11.1 0.4 1.7 0.9 1.0 100.0 14.1 8.0 2,961 Karamoja 57.3 18.6 6.5 1.9 0.6 10.2 0.8 2.1 1.9 0.0 100.0 15.0 11.9 1,293 Lango 58.2 13.2 5.8 6.0 0.7 11.0 0.6 2.8 1.2 0.6 100.0 15.6 11.1 2,927 Acholi 50.2 16.9 6.4 4.8 0.7 12.3 1.0 4.3 3.0 0.3 100.0 20.6 15.4 2,684 West Nile 45.6 21.8 5.2 5.5 0.6 15.5 1.2 3.5 0.9 0.2 100.0 21.1 11.5 3,607 Bunyoro 53.5 17.2 3.1 7.1 1.3 14.3 0.9 1.5 0.8 0.4 100.0 17.5 7.6 2,736 Tooro 55.1 17.1 2.6 6.0 1.0 13.0 1.3 2.2 1.1 0.6 100.0 17.6 8.2 3,735 Kigezi 54.2 20.2 3.9 1.2 0.7 14.5 0.8 2.1 1.0 1.5 100.0 18.4 8.6 1,809 Ankole 55.1 17.3 5.1 3.0 0.9 14.2 0.8 1.7 0.9 1.0 100.0 17.5 9.5 4,049 Special area Island districts 50.8 19.1 2.7 7.1 0.8 15.3 1.0 2.1 0.9 0.4 100.0 19.2 7.5 521 Mountain districts 53.2 16.6 3.5 5.8 0.6 15.3 1.4 2.2 1.0 0.4 100.0 19.9 8.8 4,178 Greater Kampala 49.1 21.5 2.6 5.7 0.5 15.6 1.5 1.5 1.0 0.9 100.0 19.7 7.4 2,984 Wealth quintile Lowest 53.8 19.2 6.0 4.1 0.6 11.3 0.8 2.5 1.4 0.4 100.0 16.0 11.3 10,540 Second 54.9 15.9 4.8 5.5 0.6 13.8 1.0 2.1 0.9 0.6 100.0 17.8 9.4 10,413 Middle 55.4 15.8 3.5 5.0 0.7 15.3 1.0 1.8 0.8 0.5 100.0 18.9 7.9 10,260 Fourth 47.2 17.9 3.1 6.0 0.7 19.4 1.3 2.3 1.3 0.7 100.0 24.3 8.9 9,819 Highest 49.1 17.2 2.0 6.4 0.7 18.7 1.5 2.5 0.9 0.8 100.0 23.7 7.8 8,184 Total <15 54.0 17.8 3.4 5.2 0.6 14.8 0.9 1.9 0.8 0.6 100.0 18.4 7.7 43,815 Total <18 52.3 17.2 4.0 5.3 0.7 15.5 1.1 2.2 1.1 0.6 100.0 19.9 9.1 49,217 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 Housing Characteristics and Household Population • 29 Table 2.11 Birth registration of children under age 5 Percentage of de jure children under age 5 whose births are registered with the civil authorities, according to background characteristics, Uganda DHS 2016 Percentage of children whose births are registered and who: Number of children Background characteristic Had a birth certificate Did not have a birth certificate Total percentage of children whose births are registered Age <2 16.6 11.7 28.3 5,940 2-4 20.8 13.8 34.5 9,818 Sex Male 19.2 12.9 32.2 7,903 Female 19.2 13.0 32.2 7,855 Residence Urban 21.9 14.3 36.2 3,176 Rural 18.5 12.6 31.2 12,582 Region South Central 25.1 11.1 36.3 1,930 North Central 14.3 14.7 29.0 1,686 Kampala 18.0 16.8 34.8 536 Busoga 9.6 6.4 16.0 1,603 Bukedi 19.5 4.3 23.9 1,093 Bugisu 5.4 5.1 10.5 809 Teso 32.0 11.6 43.5 950 Karamoja 16.5 25.5 42.0 432 Lango 27.0 27.5 54.5 852 Acholi 28.2 16.6 44.7 786 West Nile 16.8 12.6 29.4 1,091 Bunyoro 26.4 7.7 34.1 923 Tooro 21.7 18.2 39.9 1,248 Kigezi 15.5 41.8 57.3 539 Ankole 14.6 4.5 19.0 1,280 Special area Island districts 7.9 8.8 16.7 198 Mountain districts 16.6 14.5 31.1 1,302 Greater Kampala 22.8 16.7 39.5 1,136 Wealth quintile Lowest 16.8 13.8 30.5 3,504 Second 18.9 11.5 30.3 3,331 Middle 17.4 12.7 30.1 3,143 Fourth 19.9 12.3 32.2 2,978 Highest 24.0 14.8 38.8 2,802 Total 19.2 13.0 32.2 15,758 30 • Housing Characteristics and Household Population Table 2.12.1 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, Uganda DHS 2016 Background characteristic No education Some primary Completed primary1 Some secondary Completed secondary2 More than secondary Don’t know/ missing Total Number Median years completed Age 6-9 37.3 62.6 0.0 0.1 0.0 0.0 0.0 100.0 6,017 0.0 10-14 4.7 91.2 1.7 2.2 0.0 0.0 0.0 100.0 6,378 2.7 15-19 2.9 53.6 11.5 29.2 0.6 1.7 0.3 100.0 4,393 5.6 20-24 4.1 36.1 14.3 31.8 3.2 10.2 0.2 100.0 3,978 6.7 25-29 7.6 35.2 14.9 25.6 2.7 13.1 0.8 100.0 3,314 6.4 30-34 14.0 40.9 13.7 18.1 1.3 11.5 0.6 100.0 2,557 5.5 35-39 18.2 49.5 9.6 13.8 1.0 7.1 0.8 100.0 2,126 4.1 40-44 21.5 48.5 10.4 11.0 0.9 7.2 0.5 100.0 1,596 4.0 45-49 27.7 43.5 12.8 10.4 0.6 4.0 1.1 100.0 1,209 3.3 50-54 38.6 40.3 9.5 6.9 0.2 3.5 1.1 100.0 1,332 2.0 55-59 44.4 36.8 7.6 6.2 0.2 4.1 0.7 100.0 779 1.2 60-64 48.5 32.7 6.6 7.3 0.2 3.9 0.8 100.0 727 0.0 65+ 65.7 26.1 3.0 1.6 0.2 2.1 1.3 100.0 1,680 0.0 Don’t know/ missing * * * * * * * 100.0 9 * Residence Urban 12.1 40.3 8.9 23.4 2.6 11.7 0.9 100.0 8,377 5.6 Rural 21.0 58.0 7.6 10.3 0.4 2.5 0.3 100.0 27,716 2.9 Region South Central 13.0 40.4 9.7 22.1 2.4 10.9 1.5 100.0 4,332 5.5 North Central 15.8 51.0 9.6 16.6 1.4 4.8 0.8 100.0 3,853 4.2 Kampala 7.8 29.9 9.8 29.9 5.1 16.4 1.1 100.0 1,530 7.4 Busoga 17.9 54.1 7.2 17.2 0.4 3.0 0.2 100.0 3,514 3.5 Bukedi 15.5 64.5 6.3 10.8 0.3 2.5 0.1 100.0 2,432 3.1 Bugisu 16.5 59.0 7.2 14.3 0.5 2.6 0.0 100.0 1,939 3.6 Teso 14.8 63.7 7.2 9.7 0.1 4.1 0.5 100.0 2,189 3.4 Karamoja 70.6 25.2 1.4 2.1 0.2 0.5 0.0 100.0 844 0.0 Lango 21.3 64.0 7.3 4.9 0.1 2.3 0.1 100.0 2,001 2.7 Acholi 20.2 62.2 6.4 7.4 0.4 3.3 0.0 100.0 1,867 2.7 West Nile 21.5 65.2 4.2 6.7 0.2 2.2 0.1 100.0 2,540 2.1 Bunyoro 21.0 58.5 6.7 10.6 0.3 2.7 0.2 100.0 1,906 2.7 Tooro 23.0 53.9 8.8 11.5 0.5 2.3 0.0 100.0 2,639 3.0 Kigezi 19.6 55.7 9.3 10.5 0.9 3.6 0.4 100.0 1,503 3.2 Ankole 21.5 52.6 10.5 10.9 0.4 4.0 0.2 100.0 3,004 3.0 Special area Island districts 18.3 55.3 9.3 14.0 0.8 1.8 0.5 100.0 357 3.8 Mountain districts 22.5 55.3 6.5 12.5 0.4 2.8 0.0 100.0 3,035 2.9 Greater Kampala 8.3 28.8 9.5 30.1 4.7 16.6 2.0 100.0 3,096 7.4 Wealth quintile Lowest 31.1 60.4 4.5 3.4 0.1 0.4 0.1 100.0 6,986 1.4 Second 22.5 62.9 6.5 7.1 0.1 0.6 0.1 100.0 7,006 2.4 Middle 19.0 60.0 8.9 10.7 0.2 1.0 0.4 100.0 7,150 3.1 Fourth 14.9 52.7 10.4 17.7 0.5 3.5 0.3 100.0 7,237 4.1 Highest 8.4 35.2 8.9 26.5 3.5 16.5 1.1 100.0 7,713 6.6 Total 18.9 53.9 7.9 13.4 0.9 4.6 0.4 100.0 36,093 3.4 An asterisk indicates that a figure is based on fewer than 25 unweighted cases and has been suppressed. 1 Completed 7th grade at the primary level 2 Completed 6th grade at the secondary level Housing Characteristics and Household Population • 31 Table 2.12.2 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, Uganda DHS 2016 Background characteristic No education Some primary Completed primary1 Some secondary Completed secondary2 More than secondary Don’t know/ missing Total Number Median years completed Age 6-9 41.5 58.4 0.0 0.0 0.0 0.0 0.1 100.0 6,050 0.0 10-14 4.5 93.2 1.0 1.2 0.0 0.0 0.0 100.0 6,305 2.4 15-19 3.4 56.5 9.3 28.7 0.5 1.5 0.1 100.0 4,335 5.4 20-24 3.4 33.7 13.1 32.8 5.2 10.8 1.0 100.0 3,170 6.9 25-29 3.6 31.1 13.8 26.3 6.0 17.6 1.5 100.0 2,595 7.2 30-34 6.7 30.8 17.5 23.7 4.2 15.4 1.7 100.0 2,266 6.6 35-39 9.4 36.4 15.7 20.1 3.4 12.7 2.4 100.0 1,875 6.2 40-44 8.3 41.1 15.4 18.9 1.9 12.8 1.5 100.0 1,506 6.0 45-49 9.9 40.1 14.6 20.3 1.8 11.9 1.5 100.0 1,229 5.9 50-54 11.0 37.6 19.3 17.7 1.1 11.1 2.2 100.0 952 6.0 55-59 13.2 39.3 17.6 14.3 1.9 11.9 1.9 100.0 634 5.6 60-64 16.9 40.9 16.6 12.9 0.2 9.7 2.8 100.0 627 5.1 65+ 28.8 44.3 10.4 8.4 0.4 5.6 2.1 100.0 1,245 3.3 Don’t know/ missing (12.8) (25.2) (8.1) (12.3) (3.7) (0.0) (38.0) 100.0 50 (5.2) Residence Urban 9.8 38.2 8.6 22.5 4.8 14.3 1.8 100.0 7,123 6.1 Rural 14.3 58.1 8.8 13.0 0.9 4.2 0.7 100.0 25,716 3.5 Region South Central 12.8 41.0 8.1 19.5 3.7 12.3 2.7 100.0 3,818 5.3 North Central 15.5 47.2 11.7 16.9 1.9 5.0 1.8 100.0 3,651 4.1 Kampala 7.0 25.5 8.4 28.7 7.8 20.2 2.4 100.0 1,310 8.7 Busoga 12.5 56.3 7.7 17.1 1.8 3.9 0.7 100.0 3,168 3.8 Bukedi 10.0 62.9 7.7 14.6 0.7 3.7 0.4 100.0 2,217 3.7 Bugisu 9.8 61.1 7.5 15.9 1.2 4.1 0.3 100.0 1,805 4.0 Teso 8.3 60.9 7.6 14.4 1.2 6.9 0.7 100.0 1,921 4.0 Karamoja 53.7 33.6 3.0 6.5 0.8 2.3 0.0 100.0 718 0.0 Lango 12.6 61.1 11.6 7.9 0.5 6.2 0.1 100.0 2,027 3.7 Acholi 9.0 58.9 9.0 14.2 1.2 7.7 0.0 100.0 1,726 4.1 West Nile 10.9 63.8 6.8 12.1 0.7 5.6 0.1 100.0 2,227 3.3 Bunyoro 14.4 61.5 7.4 11.3 1.1 3.7 0.5 100.0 1,834 3.5 Tooro 17.0 54.7 8.2 15.3 1.1 2.9 0.7 100.0 2,470 3.4 Kigezi 11.0 55.5 11.1 13.0 2.1 6.7 0.5 100.0 1,279 3.9 Ankole 15.4 55.2 10.8 11.4 1.1 5.6 0.6 100.0 2,666 3.4 Special area Island districts 14.6 54.1 10.2 15.0 1.6 2.1 2.3 100.0 380 3.9 Mountain districts 14.1 56.4 7.2 16.8 1.3 3.9 0.4 100.0 2,710 3.7 Greater Kampala 7.9 25.6 8.3 27.6 7.7 19.7 3.1 100.0 2,486 8.5 Wealth quintile Lowest 19.4 63.7 7.4 7.5 0.3 1.3 0.3 100.0 6,330 2.6 Second 15.6 62.4 8.4 10.5 0.5 2.1 0.5 100.0 6,553 3.1 Middle 12.7 59.6 10.2 13.6 0.7 2.6 0.6 100.0 6,599 3.7 Fourth 10.9 51.6 10.0 19.0 1.6 5.7 1.1 100.0 6,783 4.4 Highest 8.5 31.9 7.7 24.1 5.7 20.0 2.1 100.0 6,574 7.0 Total 13.3 53.8 8.8 15.0 1.8 6.4 0.9 100.0 32,839 3.9 Figures in parentheses are based on 25-49 unweighted cases. 1 Completed 7th grade at the primary level 2 Completed 6th grade at the secondary level 32 • Housing Characteristics and Household Population Table 2.13 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, Uganda DHS 2016 Net attendance ratio1 Gross attendance ratio2 Background characteristic Male Female Total Gender Parity Index3 Male Female Total Gender Parity Index3 PRIMARY SCHOOL Residence Urban 85.1 87.1 86.1 1.02 114.5 113.3 113.8 0.99 Rural 82.9 83.4 83.2 1.01 118.9 116.6 117.8 0.98 Region South Central 81.6 86.7 84.2 1.06 107.9 112.4 110.1 1.04 North Central 81.1 85.4 83.2 1.05 109.4 112.5 110.9 1.03 Kampala 89.1 91.0 90.1 1.02 103.1 109.9 106.8 1.07 Busoga 88.5 92.0 90.2 1.04 121.2 123.4 122.3 1.02 Bukedi 88.8 90.5 89.7 1.02 138.9 128.1 133.4 0.92 Bugisu 87.9 88.8 88.4 1.01 130.3 130.0 130.1 1.00 Teso 89.0 91.9 90.4 1.03 131.5 136.7 134.1 1.04 Karamoja 43.9 30.8 37.2 0.70 65.5 43.4 54.2 0.66 Lango 80.8 81.4 81.1 1.01 119.9 117.1 118.6 0.98 Acholi 85.1 82.1 83.6 0.96 122.2 121.8 122.0 1.00 West Nile 83.4 79.6 81.6 0.95 126.4 121.8 124.2 0.96 Bunyoro 82.0 82.2 82.1 1.00 114.0 113.9 113.9 1.00 Tooro 80.5 79.6 80.1 0.99 110.4 107.8 109.1 0.98 Kigezi 91.0 90.9 90.9 1.00 125.1 123.8 124.4 0.99 Ankole 79.6 79.2 79.4 1.00 116.7 101.6 108.6 0.87 Special area Island districts 84.5 87.2 85.8 1.03 116.7 120.5 118.5 1.03 Mountain districts 84.6 82.8 83.7 0.98 120.8 116.9 118.7 0.97 Greater Kampala 86.2 89.7 88.1 1.04 104.4 108.1 106.4 1.04 Wealth quintile Lowest 77.5 73.9 75.7 0.95 112.6 104.6 108.7 0.93 Second 82.5 85.0 83.8 1.03 121.7 121.8 121.8 1.00 Middle 85.4 85.4 85.4 1.00 122.7 118.9 120.8 0.97 Fourth 84.1 87.1 85.6 1.04 117.6 118.2 117.9 1.00 Highest 88.8 90.7 89.8 1.02 115.1 116.4 115.8 1.01 Total 83.3 84.1 83.7 1.01 118.1 116.0 117.0 0.98 SECONDARY SCHOOL Residence Urban 35.8 32.2 33.8 0.90 48.6 38.2 42.9 0.79 Rural 16.2 15.8 16.0 0.97 22.7 19.3 21.0 0.85 Region South Central 30.2 30.7 30.5 1.01 36.1 34.2 35.1 0.95 North Central 27.4 24.4 26.0 0.89 34.3 30.6 32.5 0.89 Kampala 48.9 38.7 43.1 0.79 63.6 43.5 52.1 0.68 Busoga 25.6 28.7 27.2 1.12 33.0 33.0 33.0 1.00 Bukedi 18.8 16.7 17.7 0.89 32.2 20.3 26.3 0.63 Bugisu 20.2 22.0 21.1 1.09 31.5 27.3 29.3 0.87 Teso 18.9 14.5 16.6 0.77 29.5 19.7 24.3 0.67 Karamoja 5.7 1.1 3.4 0.20 8.2 2.7 5.5 0.32 Lango 4.5 2.8 3.7 0.62 6.4 5.1 5.8 0.80 Acholi 12.5 8.7 10.6 0.69 17.5 9.9 13.6 0.57 West Nile 10.1 7.5 8.8 0.75 18.2 11.3 14.6 0.62 Bunyoro 12.8 15.7 14.3 1.22 16.2 18.7 17.5 1.15 Tooro 20.7 20.6 20.6 1.00 31.0 25.9 28.5 0.84 Kigezi 20.8 24.9 22.8 1.20 29.5 28.5 29.0 0.97 Ankole 14.8 18.3 16.3 1.23 21.9 22.8 22.3 1.04 Special area Island districts 15.2 12.0 13.7 0.79 18.6 13.4 16.1 0.72 Mountain districts 21.9 21.2 21.5 0.97 35.2 26.0 30.4 0.74 Greater Kampala 46.4 38.7 41.7 0.83 61.2 43.1 50.2 0.70 Wealth quintile Lowest 7.6 4.9 6.3 0.64 11.3 6.5 8.9 0.58 Second 10.9 9.3 10.2 0.85 16.5 12.3 14.5 0.75 Middle 16.7 19.1 17.8 1.15 24.8 23.2 24.0 0.93 Fourth 26.1 27.1 26.6 1.04 35.9 31.6 33.8 0.88 Highest 41.7 35.2 38.0 0.84 53.5 42.0 47.0 0.79 Total 19.9 19.5 19.7 0.98 27.6 23.6 25.6 0.86 1 The NAR for primary school is the percentage of the primary school-age (6-12 years) population that is attending primary school. The NAR for secondary school is the percentage of the secondary school-age (13-18 years) population that is attending secondary school. By definition, the NAR cannot exceed 100%. 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%. 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. Housing Characteristics and Household Population • 33 Table 2.14 Disability by domain and age Percent distribution of the de facto household population age 5 and over by the degree of difficulty in functioning according to domain, and percent distribution by the highest degree of difficulty in at least one domain by age, Uganda DHS 2016 Degree of difficulty A lot of difficulty or cannot do at all Number of persons Domain and age No difficulty Some difficulty A lot of difficulty Cannot do at all Don’t know/ missing Total Domain Difficulty seeing 86.7 10.7 2.4 0.1 0.0 100.0 2.5 72,143 Difficulty hearing 93.6 5.3 1.0 0.1 0.0 100.0 1.1 72,143 Difficulty communicating 97.6 1.9 0.4 0.1 0.0 100.0 0.5 72,143 Difficulty remembering or concentrating 89.3 8.5 2.0 0.1 0.1 100.0 2.1 72,143 Difficulty walking or climbing steps 90.7 6.9 2.2 0.2 0.0 100.0 2.4 72,143 Difficulty washing all over or dressing 96.4 2.6 0.6 0.3 0.0 100.0 1.0 72,143 Difficulty in at least one domain1 5-9 84.6 12.0 2.6 0.9 0.0 100.0 3.5 15,279 10-14 83.7 13.1 2.9 0.4 0.0 100.0 3.2 12,683 15-19 83.8 13.2 2.7 0.3 0.0 100.0 3.0 8,728 20-29 81.5 15.6 2.6 0.3 0.0 100.0 2.9 13,056 30-39 72.0 23.0 4.7 0.3 0.0 100.0 5.0 8,823 40-49 56.0 36.5 6.9 0.5 0.0 100.0 7.5 5,540 50-59 38.3 45.7 15.4 0.6 0.0 100.0 16.0 3,697 60+ 18.0 44.1 34.6 3.3 0.0 100.0 38.0 4,279 Age 15 and over 67.1 24.5 7.8 0.6 0.0 100.0 8.4 44,123 Total 73.7 19.8 5.8 0.6 0.0 100.0 6.5 72,143 1 If a person was reported to have difficulty in more than one domain, only the highest level of difficulty is shown. 34 • Housing Characteristics and Household Population Table 2.15 Disability among adults by background characteristics Percentage of the de facto household population age 15 and over who have difficulty in functioning according to domain, and by the highest degree of difficulty in functioning in at least one domain, according to background characteristics, Uganda DHS 2016 No difficulty in any domain Some difficulty, a lot of difficulty, or cannot do at all Difficulty in at least one domain1 Number of persons Background characteristic Seeing Hearing Communi- cating Remem- bering or concen- trating Walking or climbing steps Washing all over or dressing Don’t know/ missing Some difficulty A lot of difficulty Cannot do at all A lot of difficulty or cannot do at all WOMEN Marital status Never married 83.2 6.4 4.0 2.1 6.9 4.1 1.3 0.0 13.0 3.2 0.5 3.7 5,144 Married 67.4 18.9 6.9 1.2 13.6 13.6 2.7 0.1 26.0 6.3 0.3 6.6 13,411 Widowed 21.3 61.3 27.9 4.6 40.6 53.2 18.9 0.1 44.9 30.5 3.2 33.8 2,463 Divorced 57.8 28.5 9.7 1.9 16.2 18.7 4.4 0.0 31.7 9.8 0.7 10.5 2,670 Missing * * * * * * * * * * * * 9 Residence Urban 74.5 16.1 4.8 1.2 9.3 11.4 3.2 0.1 19.1 5.9 0.5 6.3 5,968 Rural 61.7 23.5 10.1 2.1 17.3 17.8 4.6 0.0 28.0 9.5 0.8 10.2 17,729 Region South Central 70.5 20.2 6.2 1.4 10.2 13.6 3.8 0.1 21.6 7.0 0.7 7.8 3,111 North Central 66.8 22.4 6.9 1.6 10.6 15.6 3.7 0.0 23.8 8.3 1.1 9.3 2,527 Kampala 81.4 11.1 2.1 1.0 5.2 7.2 1.5 0.1 14.9 3.4 0.2 3.6 1,181 Busoga 64.5 23.0 8.7 3.4 16.7 15.3 3.6 0.0 26.6 8.1 0.9 8.9 2,148 Bukedi 64.1 22.3 10.1 1.8 14.1 16.0 5.1 0.1 27.8 7.6 0.5 8.0 1,525 Bugisu 69.9 18.8 8.1 2.3 14.5 14.8 1.6 0.1 21.8 7.6 0.6 8.3 1,240 Teso 69.8 18.9 8.0 1.2 10.5 13.5 2.5 0.0 23.3 6.6 0.3 6.9 1,423 Karamoja 74.6 14.5 8.2 2.0 9.0 13.0 5.6 0.0 19.6 4.6 1.2 5.9 526 Lango 52.7 29.3 14.1 2.3 21.8 22.1 4.0 0.0 36.1 10.7 0.5 11.2 1,287 Acholi 56.8 23.6 10.7 2.1 17.8 21.3 3.1 0.0 33.5 8.9 0.7 9.6 1,166 West Nile 67.2 20.0 6.9 1.7 11.3 16.6 2.8 0.0 25.3 6.9 0.6 7.5 1,629 Bunyoro 69.1 18.6 8.0 1.4 12.6 12.3 3.3 0.0 25.2 5.2 0.5 5.7 1,254 Tooro 56.7 22.4 13.1 2.5 24.7 20.4 8.8 0.2 28.8 13.1 1.1 14.2 1,718 Kigezi 49.5 30.3 14.6 2.6 32.9 26.1 9.4 0.2 29.7 19.8 0.7 20.5 1,033 Ankole 58.9 25.4 10.3 1.1 22.3 18.1 6.4 0.1 29.4 11.1 0.4 11.5 1,930 Special area Island districts 60.1 22.5 7.7 4.0 18.9 16.1 3.3 0.1 32.4 6.6 0.8 7.5 240 Mountain districts 64.8 19.1 10.0 1.9 19.0 16.8 4.0 0.1 24.4 9.8 0.9 10.7 1,957 Greater Kampala 81.7 11.8 2.4 0.7 4.4 7.1 2.0 0.0 14.4 3.7 0.2 3.8 2,371 Education No education 39.3 42.7 20.9 4.6 31.8 37.1 13.0 0.1 37.4 20.9 2.4 23.3 4,284 Primary 64.6 20.3 7.9 1.6 14.9 14.8 3.0 0.0 27.4 7.6 0.4 8.0 12,590 Secondary 82.0 10.4 2.6 0.6 6.1 5.6 1.1 0.0 15.5 2.4 0.1 2.5 5,006 More than secondary 81.4 12.2 3.1 0.7 4.4 5.9 1.3 0.2 15.6 2.5 0.2 2.7 1,665 Don’t know 75.0 17.3 9.2 1.0 7.9 11.9 4.7 0.5 13.1 9.5 2.0 11.5 152 Wealth quintile Lowest 60.6 24.2 11.8 2.6 17.2 19.4 4.5 0.0 29.3 9.3 0.7 10.0 4,423 Second 58.2 25.6 11.9 2.4 19.5 20.1 5.3 0.1 30.0 10.9 0.8 11.7 4,466 Middle 60.3 24.4 9.7 1.6 19.6 18.6 5.3 0.0 27.8 10.9 0.9 11.8 4,495 Fourth 64.2 22.0 8.2 1.9 15.1 16.0 4.0 0.0 26.7 8.3 0.8 9.1 4,673 Highest 77.9 14.2 3.7 1.0 7.1 9.0 2.6 0.1 17.2 4.5 0.4 4.9 5,641 Total 64.9 21.7 8.8 1.9 15.3 16.2 4.3 0.1 25.8 8.6 0.7 9.3 23,697 Continued… Housing Characteristics and Household Population • 35 Table 2.15—Continued No difficulty in any domain Some difficulty, a lot of difficulty, or cannot do at all Difficulty in at least one domain1 Number of persons Background characteristic Seeing Hearing Communi- cating Remem- bering or concen- trating Walking or climbing steps Washing all over or dressing Don’t know/ missing Some difficulty A lot of difficulty Cannot do at all A lot of difficulty or cannot do at all MEN Marital status Never married 82.0 5.6 4.4 2.8 7.1 4.1 1.2 0.1 13.8 3.5 0.5 4.0 7,313 Married 63.8 22.6 7.2 2.5 12.8 13.3 3.2 0.1 27.6 8.1 0.4 8.5 11,614 Widowed 29.2 55.0 28.6 7.9 34.5 44.6 20.4 0.0 38.9 26.8 5.0 31.8 321 Divorced 57.1 25.7 11.6 4.9 16.0 17.8 4.9 0.1 31.3 10.1 1.5 11.6 1,186 Missing (76.8) (14.0) (4.8) (2.4) (12.2) (10.6) (2.4) (2.9) (15.5) (4.8) (0.0) (4.8) 50 Residence Urban 77.4 12.4 4.1 2.0 7.4 6.9 1.9 0.2 17.6 4.4 0.4 4.8 4,978 Rural 66.8 18.7 7.6 3.1 12.6 12.0 3.2 0.1 24.8 7.6 0.6 8.3 15,506 Region South Central 74.9 14.2 4.9 2.4 6.9 8.7 2.2 0.1 19.1 5.4 0.5 5.9 2,616 North Central 72.3 16.1 5.0 2.8 9.5 9.5 2.4 0.1 20.5 6.8 0.3 7.1 2,333 Kampala 84.1 8.4 2.3 1.4 3.6 3.4 0.6 0.3 12.9 2.5 0.2 2.7 1,024 Busoga 68.2 18.8 7.8 3.7 11.8 10.5 1.8 0.2 24.2 6.9 0.4 7.3 1,802 Bukedi 66.9 18.1 9.0 3.7 12.5 10.7 3.0 0.1 26.7 5.6 0.6 6.2 1,318 Bugisu 70.9 17.0 7.8 3.7 11.2 11.8 2.3 0.0 22.6 6.0 0.5 6.5 1,128 Teso 74.9 14.8 6.2 2.4 7.7 9.1 2.2 0.0 18.4 6.3 0.4 6.8 1,182 Karamoja 79.1 9.8 7.3 2.1 6.2 8.9 3.8 0.0 15.6 4.0 1.3 5.3 419 Lango 56.9 25.2 9.5 5.9 15.7 13.7 3.3 0.0 32.9 9.4 0.9 10.2 1,203 Acholi 62.2 18.5 9.6 3.1 11.2 14.3 2.1 0.0 31.2 6.1 0.5 6.6 1,006 West Nile 69.3 18.5 6.3 2.3 9.0 12.6 3.5 0.0 22.1 7.0 1.6 8.6 1,276 Bunyoro 70.5 18.6 5.8 1.6 9.4 9.2 2.5 0.0 24.7 4.5 0.3 4.9 1,152 Tooro 60.1 20.0 9.0 2.6 20.5 14.4 4.8 0.3 28.3 10.3 1.0 11.3 1,537 Kigezi 59.7 21.5 8.2 3.4 22.3 16.9 6.4 0.2 25.4 14.5 0.2 14.6 819 Ankole 69.5 16.7 6.3 1.3 14.0 10.3 3.7 0.3 21.9 7.8 0.5 8.2 1,669 Special area Island districts 60.9 19.4 7.8 5.0 15.4 13.1 2.4 0.4 32.5 5.8 0.4 6.2 255 Mountain districts 68.4 16.8 8.0 2.6 14.1 12.1 2.9 0.0 23.2 7.8 0.7 8.4 1,678 Greater Kampala 83.1 10.1 2.0 1.0 3.6 4.2 1.0 0.3 14.1 2.4 0.1 2.5 1,917 Education No education 52.8 28.7 17.3 6.9 22.0 24.0 9.4 0.3 27.7 16.6 2.7 19.3 1,586 Primary 65.3 19.0 7.7 3.2 13.4 12.4 2.9 0.1 26.6 7.6 0.5 8.1 11,052 Secondary 79.3 11.5 3.2 1.6 6.4 5.3 1.5 0.2 16.7 3.7 0.1 3.8 5,448 More than secondary 77.8 13.8 3.1 1.0 5.5 5.5 1.1 0.1 18.2 3.6 0.3 3.9 2,100 Don’t know 70.4 14.4 7.1 2.1 8.0 13.1 3.1 0.9 18.5 9.5 0.8 10.2 299 Wealth quintile Lowest 64.3 20.4 9.9 4.0 12.4 13.5 3.5 0.0 27.0 7.7 1.0 8.7 3,636 Second 63.6 20.6 9.1 3.7 15.1 13.5 3.6 0.1 26.4 9.0 0.9 9.9 3,861 Middle 65.8 18.9 7.4 3.0 13.8 11.4 3.0 0.1 25.4 8.4 0.3 8.7 3,937 Fourth 70.2 16.2 5.7 2.5 10.9 11.0 3.2 0.3 22.6 6.5 0.4 6.9 4,277 Highest 80.1 11.4 3.0 1.3 5.6 5.7 1.3 0.2 15.8 3.6 0.4 4.0 4,773 Total 69.4 17.2 6.8 2.8 11.3 10.7 2.9 0.1 23.0 6.9 0.6 7.4 20,484 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. 1 If a person was reported to have difficulty in more than one domain, only the highest level of difficulty is shown. 36 • Housing Characteristics and Household Population Table 2.16 Child discipline Percentage of de facto children age 1-14 who, during the month before the interview, experienced child discipline of any kind, according to background characteristics, Uganda DHS 2016 Background characteristic Percentage of children age 1-14 who experienced: Number of children age 1-14 Only non-violent discipline Psychological aggression Any physical punishment Severe physical punishment Any violent discipline method1 Sex Male 9.3 74.9 67.3 15.6 85.2 7,271 Female 10.2 72.9 68.2 16.7 84.6 7,147 Residence Urban 11.1 70.8 60.0 13.8 82.5 2,757 Rural 9.4 74.7 69.6 16.7 85.5 11,661 Region South Central 13.9 70.3 49.0 9.4 78.1 1,561 North Central 13.4 70.1 48.1 7.2 78.5 1,567 Kampala 11.2 71.2 48.8 11.6 80.9 409 Busoga 3.1 74.5 85.5 24.8 90.1 1,543 Bukedi 10.0 77.1 71.7 19.6 86.6 1,028 Bugisu 8.2 78.0 64.7 10.1 86.8 777 Teso 5.5 82.8 83.1 20.7 91.1 853 Karamoja 4.2 74.2 78.2 19.2 85.2 380 Lango 22.3 58.5 58.0 12.0 70.5 860 Acholi 13.3 54.4 65.9 10.5 76.6 783 West Nile 4.5 88.5 73.6 15.8 94.0 1,057 Bunyoro 9.4 79.5 78.3 26.1 87.8 782 Tooro 10.5 69.9 66.6 22.9 85.2 1,096 Kigezi 5.6 81.9 79.8 17.6 93.4 534 Ankole 7.6 79.4 77.5 17.7 91.4 1,188 Special area Island districts 6.0 74.5 69.3 14.5 86.8 157 Mountain districts 10.3 75.2 67.5 17.8 86.4 1,218 Greater Kampala 11.5 70.5 47.3 8.3 80.1 864 Education of household head No education 8.2 75.4 70.4 18.4 85.5 2,202 Primary 9.1 75.0 69.7 16.5 85.9 8,233 Secondary 9.9 72.1 65.5 16.0 84.2 2,632 More than secondary 17.6 66.8 54.6 9.2 77.3 1,132 Don’t know 6.9 75.7 64.2 17.8 87.0 219 Wealth quintile Lowest 9.4 72.5 71.3 16.2 84.6 3,110 Second 8.2 75.3 72.2 18.0 86.2 3,042 Middle 8.9 76.6 71.8 19.9 86.4 3,017 Fourth 9.6 75.1 66.7 15.1 86.2 2,873 Highest 13.4 69.2 53.4 10.3 80.0 2,377 Total 9.7 73.9 67.7 16.2 84.9 14,418 1 MICS Indicator 8.3 - Violent Discipline Housing Characteristics and Household Population • 37 Table 2.17 Child discipline opinions and knowledge Percentage of respondents to the child discipline module who believe that physical punishment is needed to bring up, raise, or educate a child properly, and percentage who know that there is a government law in Uganda that prohibits child abuse, according to background characteristics, Uganda DHS 2016 Background characteristic Percentage who believe that a child needs to be physically punished Percentage who know that there is a law against child abuse Number of respondents Sex Male 49.4 87.1 7,271 Female 50.3 86.9 7,147 Residence Urban 38.0 88.5 2,757 Rural 52.6 86.6 11,661 Region South Central 6.6 84.9 1,561 North Central 5.6 87.9 1,567 Kampala 22.8 90.8 409 Busoga 86.8 92.4 1,543 Bukedi 48.8 82.6 1,028 Bugisu 46.8 91.3 777 Teso 74.3 91.9 853 Karamoja 60.5 81.9 380 Lango 52.7 86.8 860 Acholi 42.0 87.9 783 West Nile 42.4 80.6 1,057 Bunyoro 78.5 94.6 782 Tooro 67.7 89.4 1,096 Kigezi 79.4 80.6 534 Ankole 68.8 80.2 1,188 Special area Island districts 48.7 88.3 157 Mountain districts 54.7 87.9 1,218 Greater Kampala 13.9 89.2 864 Age of respondent <25 46.6 79.9 1,783 25-29 46.9 86.6 2,099 30-34 51.1 87.7 2,466 35-39 49.1 88.5 2,268 40-59 52.2 88.9 4,419 60+ 49.3 86.7 1,380 Missing * * 2 Relation to selected child Mother 50.1 85.3 6,411 Father 52.6 90.6 3,427 Other 47.4 86.7 4,580 Education No education 57.7 82.8 2,433 Primary 50.8 86.7 8,396 Secondary 43.8 89.8 2,656 More than secondary 37.4 92.7 928 Wealth quintile Lowest 56.3 84.0 3,110 Second 56.5 85.2 3,042 Middle 53.0 86.2 3,017 Fourth 46.8 90.1 2,873 Highest 32.3 90.3 2,377 Total 49.8 87.0 14,418 Note: An asterisk indicates that a figure is based on fewer than 25 unweighted cases and has been suppressed. 38 • Housing Characteristics and Household Population Table 2.18 Deaths and injuries from road traffic accidents Number of road traffic fatal injury deaths per 100,000 population, number of non-fatal road traffic injuries per 100,000 population, and number of road traffic accident injuries and deaths per 100,000 population, according to sex and background characteristics, Uganda DHS 2016 Background characteristic Deaths due to road traffic accidents per 100,000 population Persons with non-fatal road traffic injuries per 100,000 population Number of deaths and injuries due to road traffic accidents per 100,000 population De facto household population Women Men Total Women Men Total Women Men Total Age1 <15 4 11 15 198 371 570 202 382 584 43,748 15-24 12 73 85 820 2,155 2,974 832 2,228 3,059 15,876 25-34 12 131 143 1,158 4,534 5,692 1,170 4,664 5,835 10,731 35-44 11 78 89 1,117 4,264 5,381 1,129 4,342 5,470 7,102 45-59 0 17 17 1,194 2,323 3,517 1,194 2,340 3,534 6,135 60+ 21 41 62 787 1,142 1,929 808 1,183 1,991 4,279 Residence Urban 8 82 90 848 2,011 2,860 857 2,093 2,950 19,294 Rural 7 36 43 530 1,606 2,136 537 1,642 2,179 68,635 Region South Central 0 62 62 871 2,058 2,929 871 2,120 2,991 10,495 North Central 0 20 20 504 1,983 2,486 504 2,003 2,507 9,589 Kampala 23 62 85 922 2,085 3,007 945 2,147 3,091 3,461 Busoga 0 22 22 633 1,415 2,048 633 1,437 2,070 8,610 Bukedi 15 0 15 463 1,203 1,666 477 1,203 1,681 5,978 Bugisu 0 0 0 438 958 1,396 438 958 1,396 4,747 Teso 0 35 35 805 2,283 3,088 805 2,319 3,123 5,238 Karamoja 24 0 24 304 1,234 1,538 328 1,234 1,562 2,066 Lango 0 15 15 779 1,794 2,573 779 1,809 2,588 5,043 Acholi 45 69 114 663 1,293 1,956 708 1,362 2,070 4,530 West Nile 0 63 63 476 1,413 1,889 476 1,476 1,953 6,078 Bunyoro 15 11 26 234 1,060 1,293 248 1,071 1,320 4,841 Tooro 0 170 170 729 2,068 2,797 729 2,238 2,967 6,574 Kigezi 0 60 60 564 1,784 2,349 564 1,844 2,408 3,462 Ankole 22 61 83 380 1,996 2,376 402 2,057 2,459 7,218 Special area Island districts 0 51 51 701 1,699 2,400 701 1,750 2,451 970 Mountain districts 7 14 21 562 1,114 1,676 569 1,129 1,697 7,313 Greater Kampala 11 31 42 1,014 2,053 3,067 1,026 2,084 3,109 6,931 Wealth quintile Lowest 3 26 29 372 1,230 1,602 375 1,256 1,631 17,472 Second 0 50 50 479 1,304 1,783 479 1,354 1,833 17,570 Middle 5 55 60 518 1,625 2,143 523 1,680 2,203 17,569 Fourth 17 71 88 572 2,237 2,809 590 2,308 2,897 17,650 Highest 11 27 39 1,054 2,074 3,128 1,066 2,101 3,166 17,668 Total 7 46 53 600 1,695 2,295 607 1,741 2,348 87,929 1 For those who died, age is their age at death. Age is missing for 59 people. Housing Characteristics and Household Population • 39 Table 2.19 Types of road traffic accidents Among persons who were severely injured or killed in road traffic accidents in the past 12 months, percent distribution of type of road traffic accidents, according to background characteristics, Uganda DHS 2016 Background characteristic Type of road traffic accident Number killed or injured Car Truck Bus Motorcycle Bicycle Pedestrian Other Don’t know Total Age1 <15 4.6 1.2 0.9 44.4 38.3 9.2 1.4 0.0 100.0 256 15-24 7.2 3.1 0.4 71.6 15.6 1.9 0.0 0.0 100.0 486 25-34 10.3 2.5 0.5 75.9 7.5 2.5 0.7 0.0 100.0 626 35-44 13.0 4.7 0.6 67.6 10.5 2.4 1.1 0.0 100.0 389 45-59 12.8 3.8 1.5 64.3 12.7 5.0 0.0 0.0 100.0 217 60+ 8.0 2.6 1.2 53.5 25.9 3.4 4.4 1.0 100.0 85 Missing * * * * * * * * * 7 Sex Male 10.6 3.0 0.6 66.1 15.9 3.2 0.4 0.0 100.0 1,531 Female 6.6 3.3 0.8 70.6 12.6 4.1 1.7 0.2 100.0 534 Residence Urban 16.3 2.1 1.1 65.9 9.6 3.7 1.3 0.0 100.0 569 Rural 7.1 3.4 0.5 67.8 17.2 3.4 0.6 0.1 100.0 1,496 Region South Central 12.7 1.1 0.0 70.6 13.3 1.1 1.2 0.0 100.0 314 North Central 9.4 1.8 1.0 69.8 15.8 1.5 0.7 0.0 100.0 240 Kampala 20.9 2.0 1.0 65.7 3.8 4.9 1.7 0.0 100.0 107 Busoga 7.5 8.6 1.0 74.0 7.1 1.8 0.0 0.0 100.0 178 Bukedi 6.9 5.8 0.0 56.0 19.0 11.4 0.0 0.8 100.0 100 Bugisu 4.9 4.1 0.0 81.0 6.3 0.0 3.6 0.0 100.0 66 Teso 7.3 4.4 0.0 55.7 22.7 9.9 0.0 0.0 100.0 164 Karamoja 19.7 2.8 0.0 59.1 15.2 3.2 0.0 0.0 100.0 32 Lango 3.4 1.7 0.6 53.0 37.7 3.6 0.0 0.0 100.0 130 Acholi 4.8 2.7 1.9 72.1 17.5 1.0 0.0 0.0 100.0 94 West Nile 3.6 4.0 1.9 68.0 11.2 7.2 4.2 0.0 100.0 119 Bunyoro 9.6 1.5 0.0 68.9 13.9 4.6 1.4 0.0 100.0 64 Tooro 13.2 1.8 1.1 69.9 10.1 3.5 0.3 0.0 100.0 195 Kigezi 13.1 3.6 1.0 59.3 21.4 1.6 0.0 0.0 100.0 83 Ankole 8.8 2.3 0.5 73.3 13.9 1.1 0.0 0.0 100.0 178 Special area Island districts 6.6 0.0 0.5 84.1 7.0 1.8 0.0 0.0 100.0 24 Mountain districts 12.2 2.8 0.0 71.1 10.7 1.3 1.9 0.0 100.0 124 Greater Kampala 19.9 2.0 1.6 69.7 3.5 2.4 0.8 0.0 100.0 216 Survival status Alive 8.8 3.0 0.6 68.0 15.3 3.4 0.8 0.0 100.0 2,018 Dead (43.9) (6.4) (2.0) (37.6) (4.0) (6.1) (0.0) (0.0) (100.0) 47 Wealth quintile Lowest 5.3 4.3 0.7 52.8 31.0 5.9 0.0 0.0 100.0 285 Second 4.3 2.0 0.0 67.4 20.6 4.5 0.9 0.3 100.0 322 Middle 6.7 3.3 0.8 72.3 13.9 2.3 0.7 0.0 100.0 387 Fourth 9.5 3.4 0.7 70.0 11.7 3.5 1.2 0.0 100.0 511 Highest 16.9 2.5 1.0 68.7 7.7 2.5 0.8 0.0 100.0 559 Total 9.6 3.1 0.7 67.3 15.1 3.5 0.8 0.0 100.0 2,065 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. 1 For those who died, age is their age at death. 40 • Housing Characteristics and Household Population Table 2.20 Injuries due to road traffic accidents Among persons who were severely injured in a road traffic accident in the past 12 months, percentage with different types of injuries, according to background characteristics, Uganda DHS 2016 Background characteristic Type of injury Number injured Paralysis Brain damage Disfig- urement Loss of limb Loss of limb function Loss of eyesight Chronic pain Burn Cuts Broken bone Emo- tional trauma Bruising Other Age <15 1.1 0.5 7.9 0.8 5.6 1.4 15.9 2.5 67.5 14.4 5.1 8.3 2.1 249 15-24 1.2 1.5 7.2 0.0 5.4 0.4 22.7 3.6 66.2 13.6 6.0 5.9 2.1 472 25-34 0.7 1.1 10.2 1.2 7.1 0.7 22.8 3.4 63.8 15.3 5.1 5.0 3.1 610 35-44 2.6 1.1 8.9 0.4 8.4 1.0 27.7 2.5 56.6 19.6 7.1 5.2 0.6 380 45-59 1.0 0.4 7.4 0.3 7.0 2.3 31.6 2.8 53.6 25.7 11.4 3.9 2.5 215 60+ 3.0 1.2 4.3 1.3 10.7 2.1 30.6 1.3 49.9 20.5 7.7 3.0 2.2 83 Missing * * * * * * * * * * * * * 5 Sex Male 1.6 1.2 8.8 0.6 6.8 1.1 24.1 2.5 62.3 18.4 5.9 5.9 1.9 1,487 Female 0.8 0.7 7.3 0.5 7.0 0.8 24.3 4.6 61.0 13.0 8.0 4.1 2.9 528 Residence Urban 1.2 0.8 5.1 0.1 6.0 0.9 22.8 2.2 64.8 19.8 4.0 6.3 2.8 551 Rural 1.4 1.1 9.7 0.8 7.2 1.1 24.6 3.4 60.8 15.9 7.4 5.1 1.9 1,463 Region South Central 0.7 0.0 2.9 0.0 2.2 0.0 15.0 2.0 68.0 17.2 2.6 8.1 3.6 307 North Central 0.0 1.2 3.5 0.0 1.2 0.6 14.7 0.9 69.1 13.7 4.4 10.6 2.4 238 Kampala 0.7 0.5 3.7 0.0 1.2 1.0 27.3 4.5 56.8 16.8 1.5 8.1 3.0 104 Busoga 3.2 0.8 19.9 0.0 6.6 2.1 32.6 5.4 61.5 10.7 12.6 4.5 0.8 175 Bukedi 0.0 0.0 6.0 0.0 2.8 0.0 47.0 3.6 61.6 23.7 0.0 0.7 0.8 100 Bugisu 2.9 2.2 5.8 0.0 5.5 2.5 36.0 1.3 36.7 24.6 0.0 2.8 2.7 66 Teso 1.1 0.5 8.7 0.3 16.8 0.0 21.4 2.1 58.0 14.8 31.5 4.3 4.6 162 Karamoja 0.0 0.0 10.0 4.4 12.0 1.8 36.9 8.7 46.3 8.0 9.7 7.7 0.0 32 Lango 0.6 0.8 10.1 0.0 8.5 1.7 19.2 3.1 72.9 10.7 1.9 5.2 0.4 130 Acholi 0.0 0.0 9.7 0.0 15.6 0.0 26.0 3.3 71.4 16.2 3.9 5.1 1.6 89 West Nile 0.0 2.6 4.9 0.0 6.9 0.9 24.7 5.7 58.4 21.4 6.5 4.1 0.0 114 Bunyoro 1.8 3.5 25.9 0.0 6.8 3.4 23.7 1.5 56.1 9.9 12.9 10.6 2.2 63 Tooro 1.0 1.7 14.9 2.4 16.3 1.5 30.8 2.2 53.9 19.2 3.2 3.5 1.5 183 Kigezi 0.9 0.0 7.5 0.9 6.3 1.6 20.2 6.6 56.9 27.1 8.0 1.4 1.0 81 Ankole 6.4 2.8 5.8 3.1 3.9 1.4 22.8 2.9 63.4 22.0 0.0 0.4 3.2 170 Special area Island districts 2.6 1.9 7.6 0.0 2.4 0.0 33.1 1.0 65.5 9.3 4.7 1.9 2.0 23 Mountain districts 2.3 1.2 6.4 1.7 13.2 1.4 40.3 4.0 44.4 26.6 3.5 5.0 2.1 123 Greater Kampala 0.3 0.2 2.7 0.0 1.7 0.5 25.2 2.2 61.6 16.1 0.8 8.1 5.2 213 Wealth quintile Lowest 1.0 0.7 6.4 0.6 11.9 0.8 23.1 0.8 57.6 15.4 10.4 6.2 2.9 279 Second 1.2 1.7 15.9 0.3 9.1 1.3 24.5 3.9 57.8 15.6 6.9 4.7 1.8 313 Middle 1.4 2.1 10.3 0.7 5.9 1.3 25.5 2.9 58.3 17.7 4.5 4.3 2.3 375 Fourth 1.7 0.6 6.8 1.0 6.3 0.9 23.3 3.7 69.3 16.1 4.8 4.3 1.8 495 Highest 1.3 0.5 5.4 0.4 4.2 0.9 24.3 3.3 62.2 18.8 7.0 7.2 2.3 553 Total 1.4 1.0 8.4 0.6 6.9 1.0 24.1 3.1 61.9 17.0 6.5 5.4 2.2 2,014 Note: Percentages may sum to more than 100 because multiple responses were allowed. An asterisk indicates that a figure is based on fewer than 25 unweighted cases and has been suppressed. Housing Characteristics and Household Population • 41 Table 2.21 Deaths and injuries from non-road traffic accidents Number of non-road traffic fatal injury deaths per 100,000 population, number of non-fatal non-road traffic injuries per 100,000 population, and number of non-road traffic accident injuries and deaths per 100,000 population, according to background characteristics, Uganda DHS 2016 Background characteristic Deaths due to non-road traffic fatal injuries per 100,000 population Persons with non-fatal non-road traffic injuries per 100,000 population Number of deaths and injuries due to non-road traffic accidents per 100,000 population De facto household population Women Men Total Women Men Total Women Men Total Age1 <15 18 32 50 567 890 1,456 584 922 1,507 43,748 15-24 34 30 64 577 1,108 1,686 611 1,139 1,750 15,876 25-34 30 71 101 654 1,181 1,835 684 1,253 1,937 10,731 35-44 0 114 114 592 1,391 1,983 592 1,505 2,097 7,102 45-59 63 132 194 1,158 1,282 2,441 1,221 1,414 2,635 6,135 60+ 199 308 507 1,294 1,259 2,552 1,493 1,566 3,059 4,279 Residence Urban 30 94 124 670 1,138 1,807 700 1,232 1,932 19,294 Rural 33 55 88 656 1,025 1,681 689 1,081 1,770 68,635 Region South Central 45 34 79 631 1,103 1,734 676 1,137 1,813 10,495 North Central 17 5 22 551 949 1,499 568 954 1,521 9,589 Kampala 13 13 26 745 1,030 1,776 759 1,043 1,802 3,461 Busoga 39 24 63 250 624 874 289 648 937 8,610 Bukedi 0 40 40 659 932 1,591 659 972 1,632 5,978 Bugisu 19 43 62 491 776 1,267 510 819 1,329 4,747 Teso 116 103 219 1,407 1,783 3,190 1,524 1,885 3,409 5,238 Karamoja 44 262 306 713 1,211 1,924 756 1,473 2,230 2,066 Lango 31 123 154 922 1,157 2,079 953 1,280 2,232 5,043 Acholi 28 66 94 813 983 1,796 841 1,049 1,890 4,530 West Nile 37 30 68 627 1,012 1,639 665 1,042 1,707 6,078 Bunyoro 19 27 45 379 686 1,065 398 713 1,110 4,841 Tooro 45 166 211 709 1,224 1,933 754 1,390 2,144 6,574 Kigezi 20 158 178 810 1,443 2,253 830 1,601 2,431 3,462 Ankole 13 76 89 655 1,224 1,879 668 1,299 1,967 7,218 Special area Island districts 22 86 108 539 1,251 1,789 561 1,336 1,897 970 Mountain districts 26 87 113 636 920 1,556 662 1,006 1,669 7,313 Greater Kampala 7 15 22 657 1,155 1,812 663 1,170 1,834 6,931 Wealth quintile Lowest 37 78 115 738 1,162 1,900 775 1,240 2,015 17,472 Second 47 51 98 708 1,232 1,940 755 1,283 2,038 17,570 Middle 11 34 45 604 846 1,451 615 880 1,496 17,569 Fourth 37 101 138 617 1,047 1,664 654 1,148 1,802 17,650 Highest 32 54 85 628 963 1,591 660 1,017 1,677 17,668 Total 33 64 96 659 1,050 1,709 691 1,114 1,805 87,929 1 For those who died, age is their age at death. Age is missing for 59 people. 42 • Housing Characteristics and Household Population Table 2.22 Types of non-road traffic accidents and injuries Percent distribution of people injured or killed in the past 12 months in incidents other than road traffic accidents, by type of incident, according to background characteristics, Uganda DHS 2016 Type of incident Number killed or injured Background characteristic Violence/ assault Fire/ burning Animal bite Accidental fall Drowning Poisoning Accident while working Other Don’t know Total Age1 <15 3.5 43.2 9.4 33.5 0.4 0.4 5.4 3.4 1.0 100.0 643 15-24 12.4 14.5 18.0 32.7 2.4 0.7 10.1 9.3 0.0 100.0 269 25-34 25.4 7.9 15.0 30.3 1.7 2.2 9.9 5.9 1.8 100.0 201 35-44 28.3 6.0 16.6 31.8 0.3 0.2 10.5 5.4 0.8 100.0 143 45-59 16.0 6.9 17.7 35.1 1.6 2.1 13.6 7.1 0.0 100.0 157 60+ 6.5 3.9 16.4 63.1 1.0 0.0 5.6 1.3 2.2 100.0 114 Missing * * * * * * * * * * 1 Sex Male 14.6 19.7 11.7 36.6 1.3 0.8 9.5 5.3 0.6 100.0 943 Female 7.2 29.1 16.9 32.9 0.8 0.8 6.0 5.0 1.3 100.0 586 Residence Urban 11.9 25.0 7.1 35.3 1.3 1.1 7.5 8.0 2.7 100.0 358 Rural 11.7 22.8 15.7 35.2 1.0 0.7 8.3 4.3 0.3 100.0 1,171 Region South Central 10.0 27.0 13.7 27.6 1.6 0.0 7.7 7.6 4.8 100.0 184 North Central 10.5 30.8 8.0 32.9 1.0 2.5 5.9 8.2 0.2 100.0 144 Kampala 23.4 24.5 5.6 35.3 0.7 0.8 4.2 5.4 0.0 100.0 62 Busoga 11.6 43.3 7.2 25.2 0.4 0.0 7.9 4.5 0.0 100.0 76 Bukedi 11.9 21.8 20.3 35.8 0.0 2.7 4.8 0.9 1.8 100.0 96 Bugisu 6.7 10.1 15.0 48.8 4.3 0.0 9.7 5.3 0.0 100.0 62 Teso 16.2 16.3 12.2 47.0 0.5 0.9 3.6 3.2 0.0 100.0 171 Karamoja 8.6 45.5 18.0 18.5 2.2 1.3 0.0 5.9 0.0 100.0 41 Lango 13.3 26.3 18.8 29.6 0.0 0.0 8.0 2.6 1.5 100.0 106 Acholi 8.6 21.9 24.5 28.2 0.0 3.3 11.8 1.7 0.0 100.0 81 West Nile 12.4 29.6 15.6 29.4 1.0 0.9 3.7 7.3 0.0 100.0 102 Bunyoro 5.6 11.2 20.7 49.5 2.2 0.0 7.6 3.2 0.0 100.0 52 Tooro 13.1 18.7 16.2 33.0 2.2 0.0 10.7 5.3 0.9 100.0 134 Kigezi 11.7 14.7 9.2 45.9 1.9 0.0 9.9 6.6 0.0 100.0 80 Ankole 9.0 16.0 7.7 40.4 0.0 0.0 20.7 6.2 0.0 100.0 137 Special area Island districts 16.8 28.7 15.7 21.5 12.7 0.0 2.8 0.3 1.4 100.0 18 Mountain districts 8.9 15.7 13.3 45.1 2.3 0.0 9.9 4.8 0.0 100.0 117 Greater Kampala 14.8 22.7 4.4 27.0 2.3 0.4 9.2 12.1 7.0 100.0 126 Survival status Alive 11.5 23.6 13.7 35.5 0.8 0.8 8.3 5.1 0.7 100.0 1,503 Dead (28.4) (6.8) (12.8) (18.8) (14.3) (0.0) (0.0) (8.5) (10.4) (100.0) 26 Wealth quintile Lowest 11.0 23.9 18.8 33.8 0.3 1.8 7.1 2.6 0.7 100.0 338 Second 10.7 21.8 16.0 35.6 1.4 0.0 9.7 4.6 0.0 100.0 347 Middle 11.4 18.8 16.5 33.2 1.4 0.7 10.9 6.3 0.8 100.0 260 Fourth 14.5 22.7 8.8 39.5 0.6 0.2 6.6 7.1 0.1 100.0 298 Highest 11.4 29.2 7.2 33.6 1.7 1.4 6.5 5.9 3.1 100.0 285 Total 11.8 23.3 13.7 35.2 1.1 0.8 8.1 5.2 0.9 100.0 1,529 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. 1 For those who died, age is their age at death. Housing Characteristics and Household Population • 43 Table 2.23 Injuries due to non-road traffic accidents Among persons who were severely injured in incidents other than road traffic accidents in the past 12 months, percentage who had various types of injuries, according to background characteristics, Uganda DHS 2016 Type of injury Number injured Background characteristic Paralysis Brain damage Disfigure- ment Loss of limb Loss of limb function Loss of eyesight Chronic pain Burn Cuts Broken bone Emotional trauma Other Age <15 1.1 0.7 6.3 0.2 5.0 0.1 15.1 41.5 28.6 10.1 3.8 7.6 637 15-24 3.4 1.5 5.3 0.3 5.7 0.7 22.8 13.3 36.5 11.4 6.6 11.7 268 25-34 3.0 2.3 5.8 0.9 10.7 0.9 30.1 7.5 43.1 9.1 7.5 5.2 195 35-44 4.3 0.0 10.0 1.6 4.0 1.8 36.4 6.1 42.4 8.5 4.1 5.1 140 45-59 7.5 0.4 13.1 0.0 7.2 0.0 21.2 6.7 47.2 9.4 4.5 9.6 150 60+ 7.3 0.9 11.8 0.4 12.7 3.0 41.4 3.3 26.1 22.2 7.3 10.0 109 Missing * * * * * * * * * * * * 1 Sex Male 2.9 1.2 8.5 0.4 6.0 0.8 21.4 18.6 38.3 12.0 5.0 8.2 922 Female 3.6 0.5 5.9 0.4 7.4 0.5 25.4 28.6 29.6 9.0 5.4 8.1 579 Residence Urban 2.8 0.6 9.1 0.0 4.4 1.1 21.8 24.2 40.2 8.4 2.1 7.8 347 Rural 3.2 1.1 7.0 0.5 7.2 0.5 23.3 21.9 33.3 11.6 6.0 8.3 1,153 Region South Central 7.6 1.0 6.9 0.0 2.6 1.0 15.7 25.6 37.5 7.9 4.2 9.1 182 North Central 2.3 1.4 5.7 1.7 2.0 1.0 14.8 30.8 33.0 6.7 7.3 6.5 144 Kampala 2.3 2.0 6.4 0.0 3.1 0.0 6.1 25.9 51.9 9.1 4.6 8.1 61 Busoga 4.4 4.3 20.9 0.0 0.0 0.0 13.2 39.9 42.6 6.5 4.5 1.9 75 Bukedi 0.0 1.0 5.8 0.0 0.9 0.0 58.0 21.1 35.0 5.5 0.9 8.7 95 Bugisu 3.6 1.5 11.7 0.0 5.4 1.6 18.9 11.0 30.1 12.5 0.5 12.2 60 Teso 0.5 0.6 4.6 0.0 9.8 1.1 23.3 16.7 26.6 15.5 13.7 16.8 167 Karamoja 2.5 0.0 5.2 1.0 7.9 0.9 19.2 39.5 18.0 10.8 5.9 5.6 39 Lango 6.7 0.0 2.4 0.0 12.7 0.0 25.5 24.2 33.0 8.5 2.1 6.6 105 Acholi 6.3 2.7 4.4 1.0 13.5 0.0 39.1 20.4 39.0 6.1 4.3 3.7 81 West Nile 1.6 0.0 2.5 0.0 5.9 0.0 20.7 25.9 29.4 8.8 9.1 3.9 100 Bunyoro 3.8 1.9 3.9 0.0 8.0 1.9 35.1 11.6 28.2 11.7 3.7 11.6 52 Tooro 1.6 0.0 8.6 0.8 16.6 1.1 27.8 19.9 31.9 18.0 2.5 5.4 125 Kigezi 0.9 0.0 14.6 1.1 5.4 0.0 15.8 15.1 41.6 25.5 2.8 9.3 78 Ankole 2.0 0.0 12.3 0.6 4.4 1.0 17.1 14.1 43.5 11.0 3.4 7.6 136 Special area Island districts 5.7 0.0 2.2 0.0 0.0 0.0 19.2 29.4 43.1 8.8 7.6 5.3 17 Mountain districts 2.3 0.0 10.9 0.0 7.3 1.6 24.7 16.4 32.5 18.2 0.9 9.6 114 Greater Kampala 1.1 1.0 3.1 0.0 3.4 0.6 7.4 22.3 53.5 6.7 3.6 8.1 126 Wealth quintile Lowest 4.4 0.6 3.1 0.4 8.3 0.6 24.5 21.9 31.2 10.5 6.2 7.3 332 Second 4.1 0.8 8.7 0.6 8.9 1.0 23.8 20.7 32.2 9.5 8.3 8.3 341 Middle 3.8 1.1 6.5 0.6 4.4 0.4 25.0 18.3 37.8 12.8 3.4 10.8 255 Fourth 1.9 1.9 9.9 0.3 6.6 1.1 23.4 23.3 34.1 13.9 3.5 6.5 292 Highest 1.1 0.4 9.5 0.3 3.6 0.3 17.6 28.0 40.9 8.2 3.3 8.3 281 Total 3.1 1.0 7.5 0.4 6.5 0.7 22.9 22.4 34.9 10.9 5.1 8.1 1,501 Note: Percentages may sum to more than 100 because multiple responses were allowed. An asterisk indicates that a figure is based on fewer than 25 unweighted cases and has been suppressed. 44 • Housing Characteristics and Household Population Table 2.24 Deaths from other causes Number of deaths from other causes (not due to road traffic accidents or non-road traffic accidents) per 100,000 population, according to background characteristics, Uganda DHS 2016 Background characteristic Deaths from other causes1 per 100,000 population De facto household population Women Men Total Age2 <15 442 557 998 43,748 15-24 223 197 421 15,876 25-34 499 378 876 10,731 35-44 322 657 979 7,102 45-59 611 988 1,600 6,135 60+ 2,976 2,873 5,848 4,279 Residence Urban 436 549 985 19,294 Rural 587 660 1,247 68,635 Region South Central 352 603 955 10,495 North Central 734 631 1,364 9,589 Kampala 199 255 453 3,461 Busoga 746 717 1,463 8,610 Bukedi 353 629 981 5,978 Bugisu 349 201 551 4,747 Teso 450 597 1,047 5,238 Karamoja 1,016 924 1,941 2,066 Lango 579 652 1,232 5,043 Acholi 726 848 1,573 4,530 West Nile 400 806 1,206 6,078 Bunyoro 381 384 765 4,841 Tooro 971 1,236 2,207 6,574 Kigezi 317 533 850 3,462 Ankole 647 391 1,038 7,218 Special area Island districts 698 666 1,364 970 Mountain districts 664 610 1,274 7,313 Greater Kampala 183 264 447 6,931 Wealth quintile Lowest 530 826 1,356 17,472 Second 657 743 1,401 17,570 Middle 526 536 1,062 17,569 Fourth 659 651 1,310 17,650 Highest 396 423 819 17,668 Total 554 635 1,189 87,929 1 Other causes of death include illness, age, witchcraft, related to birth, and other/unknown. 2 Age at death. Age is missing for 59 people. Table 2.25 Death registration Percent distribution of deaths of household members in the 12 months preceding the survey by registration status, according to cause of death, Uganda DHS 2016 Cause of death Death registration with the civil authority Total Number of deaths Yes No Don’t know Road traffic accident (37.5) (47.8) (14.7) (100.0) 47 Non-road traffic accident 34.3 58.6 7.1 100.0 85 Other causes of death1 22.8 67.0 10.2 100.0 1,046 Total 24.2 65.7 10.1 100.0 1,177 Note: Figures in parentheses are based on 25-49 unweighted cases. 1 Other causes of death include illness, age, witchcraft, related to birth, and other/unknown. Characteristics of Respondents • 45 CHARACTERISTICS OF RESPONDENTS 3 Key Findings  Education: One-third (33%) of women and two-fifths (41%) of men age 15-49 have completed some secondary-level education or higher.  Literacy: Nearly 7 in 10 women (68%) and 8 in 10 men (79%) are literate.  Exposure to mass media: Only 5% of women and 10% of men have access to three specified types of mass media (newspaper, television, and radio) on a weekly basis.  Internet use: Overall, 9% of women and 23% of men age 15-49 have used the Internet in the past 12 months.  Employment: Seventy-three percent of women age 15- 49 are currently employed, as compared with 92% of men age 15-49. Half of working women (50%) and men (49%) work in agriculture.  Health insurance: Health insurance coverage is low, with 94% of women and men age 15-49 having no coverage.  Tobacco: Less than 1% of women and 9% of men age 15-49 smoke tobacco. his chapter presents information on the demographic and socioeconomic characteristics of the survey respondents such as age, education, place of residence, marital status, employment, and wealth status. This information is useful for understanding the factors that affect use of reproductive health services, contraceptive use, and other health behaviours. 3.1 BASIC CHARACTERISTICS OF SURVEY RESPONDENTS A total of 18,506 women age 15-49 and 5,336 men age 15-54 (5,043 men age 15-49) were interviewed in the 2016 UDHS. Forty-four percent of both women and men are in the 15-24 age group, while 30% of women and 29% of men are in the 25-34 age group (Table 3.1). The majority of respondents age 15-49 are Catholic (37% of women and 40% of men) or Anglican (31% of women and 33% of men). Fourteen percent of both women and men are Muslim, and 8% of women and 10% of men are Pentecostal. Among respondents age 15-49, women are more likely than men to be either married or living together with a partner (61% versus 54%), divorced or separated (11% versus 5%), or widowed (3% versus 0.3%). A higher proportion of men (41%) than women (26%) have never been married. Approximately three quarters of women (73%) and men (75%) age 15-49 live in rural areas. The most populous of the 15 regions in Uganda is South Central region (14% of women and 13% of men), and the least populous is Karamoja region (2% of both women and men). T 46 • Characteristics of Respondents 3.2 EDUCATION AND LITERACY Literacy Respondents who have attended higher than secondary school are assumed to be literate. All other respondents, shown a typed sentence to read aloud, are considered literate if they could read all or part of the sentence. Sample: Women and men age 15-49 One-third of women (33%) and two-fifths of men (41%) age 15-49 have attained some secondary-level education or above (Tables 3.2.1 and 3.2.2). Ten percent of women and 4% of men have no education. Advanced education is relatively rare; only 8% of women and 12% of men have more than a secondary education (Figure 3.1). Nearly 7 in 10 women (68%) and 8 in 10 men (79%) are literate (Tables 3.3.1 and 3.3.2). Trends: A comparison of median years of schooling between the 1995 and 2016 UDHS surveys indicates that educational attainment has increased among both women and men age 15-49; the improvement has been more dramatic among women, narrowing the gap between women and men. Median number of years of schooling completed in 1995 was 3.0 among women and 5.1 among men, as compared with 5.7 among women and 6.3 among men in 2016 (Tables 3.2.1 and 3.2.2). The proportion of women with no education decreased substantially during the same period from 31% to 10%. Among men, the proportion with no education decreased from 11% to 4%. Patterns by background characteristics  Urban women are more educated than their rural counterparts. Five percent of urban women have no education, as compared with 11% of rural women. Seventeen percent of urban women have more than a secondary education, compared with 5% of rural women.  There is considerable regional variation in educational attainment. The largest proportion of women and men with no education is found in Karamoja region (66% of women, 40% of men). Kampala region has the smallest proportion of women with no education (2%), and Acholi and West Nile regions have the smallest proportion of men with no education (0.3% each). The proportion of women who have completed secondary school or higher ranges from 2% in Karamoja region to 28% in Kampala region (Figure 3.2). Figure 3.1 Education of survey respondents Figure 3.2 Secondary education by region Percentage of women age 15-49 with a secondary education or higher 10 4 45 42 13 13 24 26 2 3 8 12 Women Men Percent distribution of women and men age 15-49 by highest level of schooling attended or completed More than secondary Completed secondary Some secondary Primary complete Primary incomplete No education Characteristics of Respondents • 47  The proportion of respondents who have completed secondary school or higher increases with increasing wealth. Twenty-eight percent of women and 39% of men in the highest wealth quintile have completed secondary school or higher, as compared with 1% of women and 4% of men in the lowest wealth quintile (Figure 3.3).  Literacy among women decreases with age, from 80% among those age 15-19 to 49% among those age 45-49.  Respondents living in urban areas are more likely to be literate than those living in rural areas, and the gap in literacy rates between women and men is higher in rural than in urban areas. Eighty-four percent of urban women and 86% of urban men are literate, as compared with 62% of rural women and 76% of rural men. 3.3 MASS MEDIA EXPOSURE AND INTERNET USAGE Exposure to mass media Respondents were asked how often they read a newspaper, listened to the radio, or watched television. Those who responded at least once a week are considered regularly exposed to that form of media. Sample: Women and men age 15-49 Access to information is essential in increasing people’s knowledge and awareness of what happens around them. Data on women’s and men’s exposure to mass media are especially important in the development of educational programmes and the dissemination of all types of information, particularly information on health, family planning, nutrition, HIV/AIDS, and other essential health topics. Radio is the dominant medium of information for both women and men across Uganda: 59% of women and 70% of men listen to the radio at least once a week (Tables 3.4.1 and 3.4.2). Men are more likely (10%) than women (5%) to access all three forms of media (newspaper, television, and radio) on a weekly basis (Figure 3.4). Slightly more than one- third (35%) of women and nearly one quarter (24%) of men do not access any of the three media on a weekly basis. The Internet is also a critical tool through which people access and share information. Internet use includes accessing web pages, email, and social media. Men are more than twice as likely as women (23% versus 9%) to have used the Internet in the past 12 months (Tables 3.5.1 and 3.5.2). Trends: There were no clear trends between 2000-01 and 2016 in women’s and men’s exposure to the three forms of mass media. For example, the percentage of women who did not access any of the forms of media at least once a week decreased from 45% in 2000-01 to 25% in 2006 to 21% in 2011 before Figure 3.3 Secondary education by household wealth Figure 3.4 Exposure to mass media 1 1 2 7 28 4 7 7 14 39 Lowest Second Middle Fourth Highest Percentage of women and men age 15-49 with secondary education complete or higher Women Men WealthiestPoorest 10 21 59 5 35 16 31 70 10 24 Reads news- paper Watches tele- vision Listens to radio All three media None of these media Percentage of women and men age 15-49 who are exposed to media on a weekly basis Women Men 48 • Characteristics of Respondents increasing to 35% in 2016. In 2000-01, 22% of men did not access any of the types of media at least once a week, as compared with 24% in 2016. Patterns by background characteristics  Rural women are more likely than their urban counterparts to have no regular exposure to any form of mass media (40% versus 21%). The same pattern holds true for men (28% versus 12%).  Exposure to the three forms of mass media increases with increasing education. The proportion of women with exposure to all three forms of media rises from 0.2% among those with no education to 28% among those with more than a secondary education. Among men, the corresponding increase is from 0% to 35%.  Only 13% of women and 10% of men in the highest wealth quintile lack regular exposure to any form of mass media, as compared with 65% of women and 49% of men in the lowest quintile.  Internet use in the past 12 months is more common in urban areas (21% of women and 47% of men) than in rural areas (4% of women and 14% of men).  Internet usage among women and men increases with increasing education and wealth quintile. Fifty- five percent of women and 68% of men with more than a secondary education used the Internet in the past 12 months, as compared with 0% of women and 2% of men with no education. Similarly, 28% of women and 54% of men in the highest wealth quintile used the Internet during the past 12 months, compared with 0.4% of women and 5% of men in the lowest wealth quintile. 3.4 EMPLOYMENT Currently employed Respondents who were employed in the 7 days before the survey. Sample: Women and men age 15-49 Men are more likely (92%) to be currently employed than women (73%) (Tables 3.6.1 and 3.6.2). Four percent of women and 3% of men reported that they were not currently employed but had worked in the past 12 months. Trends: The proportion of women who are currently employed has fluctuated, increasing from 73% in 2000-01 to 81% in 2006, decreasing to 69% in 2011, and then increasing slightly to 73% in 2016. The proportion of men who are currently employed increased from 63% in 2000-01 to 94% in 2006, decreased slightly to 91% in 2011, and remained stable at 92% in 2016. Characteristics of Respondents • 49 Patterns by background characteristics  The proportion of women currently employed increases steadily with age, doubling between age 15-19 (48%) and age 45-49 (98%). Employment among men increases sharply with age, from 77% among those age 15-19 to 94% to 98% among those in the older age groups.  Women and men in the highest wealth quintile are least likely to be currently employed (Figure 3.5). 3.5 OCCUPATION Occupation Categorised as professional/technical/managerial, clerical, sales and services, skilled agricultural/forestry/fishery, craft and trade, plant/machine operator, and elementary occupations Sample: Women and men age 15-49 who were currently employed or had worked in the 12 months before the survey Half of women (50%) and men (49%) in Uganda who are currently employed or worked in the 12 months before the survey work in skilled agriculture, forestry, and fishery (Tables 3.7.1 and 3.7.2). Seventeen percent of women are engaged in sales and services, and 15% work in elementary occupations. Among men, 13% work in elementary occupations or as craft and trade workers. Twenty-one percent of employed women in Uganda are not paid for the work they do. Women engaged in agricultural work are much more likely (33%) than women not working in agriculture (9%) to not be paid for their work. Six in 10 (62%) women who worked in the past year are self-employed (Table 3.8). Trends: The proportion of women employed as agriculture, forestry, and fishery workers fell from 68% in 2011 to 50% in 2016; the corresponding decrease among men was from 75% to 49%. The proportion of women and men who worked in professional, technical, and managerial occupations doubled between 2011 and 2016 (from 5% to 10% among women and from 5% to 11% among men). Patterns by background characteristics  Urban women are most likely to work in sales and services (32%) and in the elementary occupations (21%), while urban men are most likely to be engaged in professional, technical, and managerial occupations (22%) or as craft and trade workers (21%). In rural areas, however, the majority of both women and men work in agriculture (61% each).  The proportion of women and men working in professional, technical, and managerial occupations rises sharply with increasing education. 3.6 HEALTH INSURANCE KNOWLEDGE AND COVERAGE Only about one quarter (24%) of women and one-third (34%) of men age 15-49 have heard of health insurance. The vast majority of women and men (94% each) do not have health insurance (Tables 3.9.1 and 3.9.2). Trends: The percentage of women and men with health insurance increased slightly from 1% and 2%, respectively, in 2011 to 6% each in 2016. Figure 3.5 Employment status by wealth 79 78 74 72 66 94 94 94 90 90 Lowest Second Middle Fourth Highest Percentage of women and men age 15-49 who are currently employed Women Men WealthiestPoorest 50 • Characteristics of Respondents 3.7 TOBACCO USE Almost no women (0.8%) age 15-49 smoke any kind of tobacco (Table 3.10.1). Men are more likely (9%) to smoke tobacco (Table 3.10.2). Most of the men who use tobacco are regular smokers; 7% of all men say they are daily smokers, while 2% report they smoke occasionally. Nearly half (49%) of men who are daily smokers reported that they smoke on average less than five cigarettes per day (Table 3.11). Trends: The percentage of women and men age 15-49 who smoke tobacco decreased from 3% and 26%, respectively, in 2000-01 to 0.8% and 9%, respectively, in 2016. Patterns by background characteristics  The proportion of men who smoke tobacco generally increases with age; only 1% of men age 15-19 smoke tobacco, as compared with 22% of those age 40-44 and 20% of those age 45-49.  Men in West Nile region are most likely to smoke tobacco (24%), while those in Bukedi and Bugisu regions are least likely to do so (3% and 4%, respectively).  Tobacco smoking among men decreases with increasing education, from 24% among those with no education to 3% among those with more than a secondary education. LIST OF TABLES For more information on the characteristics of survey respondents, see the following tables:  Table 3.1 Background characteristics of respondents  Table 3.2.1 Educational attainment: Women  Table 3.2.2 Educational attainment: Men  Table 3.3.1 Literacy: Women  Table 3.3.2 Literacy: Men  Table 3.4.1 Exposure to mass media: Women  Table 3.4.2 Exposure to mass media: Men  Table 3.5.1 Internet usage: Women  Table 3.5.2 Internet usage: Men  Table 3.6.1 Employment status: Women  Table 3.6.2 Employment status: Men  Table 3.7.1 Occupation: Women  Table 3.7.2 Occupation: Men  Table 3.8 Type of employment: Women  Table 3.9.1 Health insurance coverage: Women  Table 3.9.2 Health insurance coverage: Men  Table 3.10.1 Tobacco smoking: Women  Table 3.10.2 Tobacco smoking: Men  Table 3.11 Average number of cigarettes smoked daily: Men  Table 3.12 Smokeless tobacco use and any tobacco use Characteristics of Respondents • 51 Table 3.1 Background characteristics of respondents Percent distribution of women and men age 15-49 by selected background characteristics, Uganda DHS 2016 Women Men Background characteristic Weighted percent Weighted number Unweighted number Weighted percent Weighted number Unweighted number Age 15-19 23.0 4,264 4,276 25.6 1,288 1,270 20-24 20.7 3,822 3,782 18.8 949 944 25-29 16.5 3,051 3,014 14.7 741 740 30-34 13.7 2,543 2,600 14.6 735 737 35-39 10.9 2,011 2,029 9.8 491 497 40-44 8.7 1,608 1,621 10.2 511 492 45-49 6.5 1,207 1,184 6.4 320 363 Disability status1 A lot of difficulty or unable to function in at least one domain 3.8 701 724 3.7 185 197 Some or no difficulty in all domains 96.2 17,805 17,782 96.3 4,852 4,846 Religion Catholic 37.1 6,863 7,170 40.4 2,035 2,074 Anglican 31.1 5,757 5,911 33.4 1,685 1,721 Muslim 13.7 2,541 2,173 13.5 681 617 Pentecostal 8.3 1,537 1,553 9.6 482 472 Seventh Day Adventist 1.6 289 265 1.4 72 66 Other 8.2 1,520 1,434 1.6 83 93 Ethnic group Acholi 4.8 891 1,069 5.5 276 336 Alur 2.7 498 514 2.7 138 148 Baganda 14.9 2,759 2,162 18.0 905 698 Bagisu 4.5 825 914 4.4 224 258 Bakiga 6.0 1,109 1,231 6.9 349 375 Bakonzo 2.3 420 372 2.3 118 106 Banyankore 9.7 1,796 1,562 10.6 533 443 Banyoro 2.5 465 538 2.4 120 138 Basoga 7.8 1,441 1,210 7.5 377 340 Batoro 2.4 448 435 3.1 156 153 Iteso 7.5 1,389 1,607 7.6 382 440 Lango 5.9 1,091 1,302 6.6 332 410 Lugbara 2.9 532 519 2.3 117 121 Other 26.2 4,841 5,071 20.1 1,012 1,077 Marital status Never married 25.8 4,783 4,738 41.3 2,080 2,027 Married 30.3 5,614 5,813 34.1 1,716 1,835 Living together 30.3 5,609 5,566 19.4 979 920 Divorced/separated 10.7 1,978 1,866 4.9 248 246 Widowed 2.8 522 523 0.3 14 15 Residen

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