South Africa - Demographic and Health Survey - 2019

Publication date: 2019

South Africa Demographic and Health Survey 2016 January 2019 South Africa Demographic and Health Survey 2016: Report, National Department of Health (NDoH), Statistics South Africa (Stats SA), South African Medical Research Council (SAMRC), and ICF Published by National Department of Health, Civitas Building, Corner Struben and Thabo Sehume Streets, PRETORIA 0001 Financial support for the SADHS 2016 was provided by the government of South Africa through the NDoH and SAMRC, the Global Fund to Fight AIDS, Tuberculosis and Malaria (Global Fund), the European Union (EU), the United Nations Children’s Fund (UNICEF), the United Nations Population Fund (UNFPA). ICF provided technical assistance through The DHS Program, which is funded by the United States Agency for International Development (USAID), and offers support and technical assistance for the implementation of population and health surveys in countries worldwide. This report is available on the NDoH website: www.health.gov.za; Stats SA website: www.statssa.gov.za; SAMRC website: www.samrc.ac.za; and on The DHS Program website: www.DHSprogram.com. Copies are obtainable from: National Department of Health Tel.: 012 395 8000 012 395 8125 E-mail: tshilidzi.muthivhi@health.gov.za Additional information about the South Africa Demographic and Health Survey 2016 (SADHS 2016) may be obtained from the Cluster Manager: Health Information, Research, Monitoring & Evaluation, National Department of Health, Civitas Building, Pretoria, South Africa; +27 (012) 395 8411; e-mail: thulile.zondi@health.gov.za. 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; e-mail: info@DHSprogram.com; Internet: www.DHSprogram.com. Photo credits: Cover pictures obtained from NDoH photo library. Recommended citation: National Department of Health (NDoH), Statistics South Africa (Stats SA), South African Medical Research Council (SAMRC), and ICF. 2019. South Africa Demographic and Health Survey 2016. Pretoria, South Africa, and Rockville, Maryland, USA: NDoH, Stats SA, SAMRC, and ICF. Contents • iii CONTENTS TABLES AND FIGURES . ix ACKNOWLEDGEMENTS . xix ABBREVIATIONS . xxi READING AND UNDERSTANDING TABLES FROM THE SOUTH AFRICA DHS (SADHS) 2016 . xxiii SUSTAINABLE DEVELOPMENT GOAL INDICATORS . xxxi MAP OF SOUTH AFRICA . xxxii 1 INTRODUCTION AND SURVEY METHODOLOGY . 1 1.1 Survey Objectives . 1 1.2 Sample Design . 1 1.3 Questionnaires . 3 1.4 Measuring Iodine Content of Household Salt . 4 1.5 Anthropometry, Anaemia Testing, Blood Pressure Measurement, HbA1c Testing, and HIV Testing . 5 1.6 Pretest . 7 1.7 Training of Field Staff . 7 1.8 Fieldwork . 8 1.9 Data Processing and Analysis . 8 1.10 Response Rates . 9 2 HOUSING CHARACTERISTICS AND HOUSEHOLD POPULATION . 11 2.1 Drinking Water Sources and Treatment . 12 2.2 Sanitation . 13 2.3 Exposure to Smoke inside the Home . 13 2.4 Household Wealth . 14 2.5 Handwashing . 14 2.6 Household Population and Composition . 15 2.7 Children’s Living Arrangements and Parental Survival . 16 2.8 Education . 16 2.8.1 Educational Attainment . 16 2.8.2 School Attendance . 17 2.9 Disability . 18 2.9.1 Disability by Domain and Age . 18 2.9.2 Disability by Background Characteristics . 19 2.10 Social Grants and Food Security . 19 2.10.1 Social Grants by Background Characteristics . 19 2.10.2 Food Security by Background Characteristics . 20 3 CHARACTERISTICS OF RESPONDENTS . 35 3.1 Basic Characteristics of Survey Respondents . 35 3.2 Education and Literacy . 36 3.3 Mass Media Exposure and Internet Usage . 37 3.4 Employment . 38 3.5 Occupation . 39 3.6 Adult Health . 40 iv • Contents 4 MARRIAGE AND SEXUAL ACTIVITY . 57 4.1 Marital Status . 57 4.2 Polygyny . 58 4.3 Age at First Union . 59 4.4 Age at First Sexual Intercourse . 59 4.5 Recent Sexual Activity . 60 5 FERTILITY . 69 5.1 Current Fertility . 69 5.2 Children Ever Born and Living . 70 5.3 Birth Intervals . 71 5.4 Insusceptibility to Pregnancy . 71 5.5 Age at First Birth . 72 5.6 Teenage Childbearing . 73 6 FERTILITY PREFERENCES . 83 6.1 Desire for Another Child . 83 6.2 Ideal Family Size . 84 6.3 Fertility Planning Status . 85 6.4 Wanted Fertility Rates . 86 7 CONTRACEPTION . 93 7.1 Contraceptive Knowledge and Use . 94 7.2 Source of Modern Contraceptive Methods . 96 7.3 Informed Choice . 96 7.4 Discontinuation of Contraceptives . 96 7.5 Demand for Contraception . 97 7.6 Contact of Nonusers with Family Planning Providers . 99 8 INFANT AND CHILD MORTALITY . 117 8.1 Infant and Child Mortality . 118 8.2 Biodemographic Risk Factors . 119 8.3 Perinatal Mortality . 120 8.4 High-risk Fertility Behaviour . 120 9 MATERNAL HEALTH CARE . 127 9.1 Antenatal Care Coverage and Content . 128 9.1.1 Skilled Providers . 128 9.1.2 Timing and Number of ANC Visits . 129 9.2 Components of ANC Visits . 129 9.3 Protection against Neonatal Tetanus . 129 9.4 Delivery Services . 130 9.4.1 Institutional Deliveries . 130 9.4.2 Skilled Assistance during Delivery . 131 9.4.3 Delivery by Caesarean . 132 9.5 Postnatal Care . 133 9.5.1 Postnatal Health Check for Mothers . 133 9.5.2 Postnatal Health Check for Newborns . 134 9.5.3 Content of Postnatal Care for Newborns . 134 9.5.4 Discharge Timing of Newborns and Mothers . 135 10 CHILD HEALTH . 149 10.1 Birth Weight . 150 10.2 Vaccination of Children . 150 Contents • v 10.3 Symptoms of Acute Respiratory Infection . 154 10.4 Fever . 154 10.5 Diarrhoeal Disease . 155 10.5.1 Prevalence of Diarrhoea and Treatment-Seeking Behaviour . 155 10.5.2 Feeding Practices during an Episode of Diarrhoea . 156 10.5.3 Oral Rehydration Therapy and Other Treatments . 157 10.5.4 Knowledge of ORS Packets and Clinic-Recommended Homemade Fluids . 158 10.6 Prevalence and Treatment of Childhood Illness—Summary . 158 11 NUTRITION OF CHILDREN . 177 11.1 Nutritional Status of Children . 177 11.1.1 Measurement of Nutritional Status among Young Children . 177 11.1.2 Data Collection . 178 11.1.3 Levels of Child Malnutrition . 179 11.2 Infant and Young Child Feeding Practices . 180 11.2.1 Initiation of Breastfeeding . 180 11.2.2 Exclusive and Continued Breastfeeding . 180 11.2.3 Duration of Breastfeeding . 181 11.2.4 Complementary Feeding . 182 11.2.5 Minimum Acceptable Diet . 182 11.3 Anaemia Prevalence in Children . 184 11.4 Presence of Iodised Salt in Households . 185 11.5 Micronutrient Intake, Supplementation, and Deworming among Children . 186 11.6 Micronutrient Intake among Mothers . 187 12 HIV/AIDS-RELATED KNOWLEDGE AND BEHAVIOUR . 201 12.1 Knowledge about Mother-to-Child Transmission of HIV . 201 12.2 Multiple Sexual Partners . 202 12.3 Paid Sex . 203 12.4 Coverage of HIV Testing Services . 203 12.4.1 Awareness of HIV Testing Services and Experience with HIV Testing . 203 12.4.2 HIV Testing of Pregnant Women . 204 12.5 Male Circumcision . 204 12.6 Self-reporting of Sexually Transmitted Infections . 205 12.7 HIV/AIDS-related Behaviour among Young People . 205 12.7.1 First Sex . 205 12.7.2 Premarital Sex . 206 12.7.3 Multiple Sexual Partners . 206 12.7.4 Coverage of HIV Testing Services . 207 13 HIV PREVALENCE . 223 13.1 Coverage Rates for HIV Testing . 224 13.2 HIV Prevalence . 225 13.2.1 HIV Prevalence by Age and Sex . 225 13.2.2 HIV Prevalence by Sexual Risk Behaviour . 227 13.2.3 HIV Prevalence among Young People . 227 13.2.4 HIV Prevalence by Other Characteristics Related to HIV Risk . 228 13.2.5 HIV Prevalence among Couples . 229 14 ADULT AND PREGNANCY-RELATED MORTALITY . 241 14.1 Data . 241 14.2 Direct Estimates of Adult Mortality . 242 14.3 Trends in Adult Mortality . 242 vi • Contents 14.4 Direct Estimates of Maternal Mortality . 243 14.5 Direct Estimates and Trends in Pregnancy-related Mortality . 243 15 USE OF HEALTH SERVICES AND PRESCRIBED MEDICATIONS . 247 15.1 Health Insurance Coverage . 247 15.2 Use of Outpatient Health Care Services . 248 15.3 Women’s Access to Care . 249 15.3.1 Experience with a Pap Smear. 249 15.3.2 Problems in Accessing Health Care among Women . 250 15.4 Prescribed Medications. 251 15.4.1 Self-reported Use of Prescribed Medications . 251 15.4.2 Payment for Prescribed Medications . 251 15.5 Prescribed Medications for Chronic Conditions . 252 16 ADULT MORBIDITY . 263 16.1 Self-assessment of Health . 264 16.2 Self-reported Prevalence of Common Chronic Conditions . 265 16.3 Experience with Pain . 265 16.3.1 Prevalence of Pain. 266 16.3.2 Tooth and Mouth Pain . 267 16.4 Hypertension . 267 16.5 Chronic Respiratory Disease . 269 16.6 Diabetes . 271 16.7 Anaemia . 272 17 ADULT NUTRITION . 297 17.1 Body Mass Index of Adults and Short Stature of Women . 298 17.2 Waist Circumference and Waist-to-Height Ratio . 300 17.3 Consumption of Fruit, Vegetables, Sugar-sweetened Beverages, and Fruit Juice . 301 17.4 Consumption of Fried Foods, Fast Foods, Salty Snacks, and Processed Meats . 302 17.5 Interest in Lowering Salt Consumption . 302 18 TOBACCO, ALCOHOL, AND CODEINE USE AMONG ADULTS . 313 18.1 Tobacco Use among Adults . 313 18.1.1 Prevalence of Tobacco Smoking . 313 18.1.2 Smokeless Tobacco Use and E-cigarette Use . 315 18.1.3 Tobacco Use during Pregnancy . 315 18.2 Alcohol Use among Adults . 315 18.2.1 Alcohol Consumption, Risky Drinking, and Problem Drinking . 315 18.2.2 Alcohol Use during Pregnancy . 316 18.3 Use and Misuse of Codeine-containing Medications . 316 19 WOMEN’S EMPOWERMENT . 329 19.1 In-union Women’s and Men’s Employment . 330 19.2 Control over Women’s Earnings . 330 19.3 Control over Men’s Earnings . 331 19.4 Women’s and Men’s Ownership of Assets . 332 19.5 Participation in Decision Making . 333 19.6 Attitudes toward Wife Beating . 334 19.7 Negotiating Sexual Relations . 334 19.8 Child Discipline . 335 Contents • vii 20 DOMESTIC VIOLENCE . 355 20.1 Measurement of Intimate Partner Violence . 356 20.2 Women’s Experience of Physical, Sexual, or Emotional Violence by Any Partner . 356 20.2.1 Age at first experience of sexual violence by any partner . 357 20.3 Women’s Experience of Violence by Any Partner in the Past 12 Months . 358 20.4 Controlling Behaviours by Most Recent Partner . 358 20.5 Prevalence of Violence by Most Recent Partner . 359 20.6 Injuries to Women due to Partner Violence . 361 20.7 Violence Initiated by Women against Partners. 361 REFERENCES . 373 APPENDIX A SAMPLE DESIGN . 377 A.1 Introduction . 377 A.2 Sample Frame . 377 A.3 Sample Design and Implementation . 378 A.4 Sample Probabilities and Sampling Weights . 380 APPENDIX B ESTIMATES OF SAMPLING ERRORS . 389 APPENDIX C DATA QUALITY TABLES . 419 APPENDIX D PERSONS INVOLVED IN THE SOUTH AFRICA DEMOGRAPHIC AND HEALTH SURVEY 2016 . 427 APPENDIX E QUESTIONNAIRES . 431 Tables and Figures • ix TABLES AND FIGURES 1 INTRODUCTION AND SURVEY METHODOLOGY . 1 Table 1.1 Results of the household and individual interviews . 9 Figure 1.1 Subsampling scheme followed in the SADHS 2016 . 2 Figure 1.2 HIV testing algorithm . 7 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 refuse disposal . 24 Table 2.6 Household possessions . 24 Table 2.7 Wealth quintiles . 25 Table 2.8 Handwashing . 25 Table 2.9 Household population by age, sex, and residence . 26 Table 2.10 Household composition . 27 Table 2.11 Children’s living arrangements and orphanhood . 28 Table 2.12.1 Educational attainment of the female household population . 29 Table 2.12.2 Educational attainment of the male household population . 30 Table 2.13 School attendance ratios . 31 Table 2.14 Disability by domain and age . 32 Table 2.15 Disability among the household population according to background characteristics . 32 Table 2.16 Knowledge of where social grant forms may be obtained . 33 Table 2.17 Social grants . 33 Table 2.18 Food security: Adults . 34 Table 2.19 Food security: Children . 34 Figure 2.1 Improved source of drinking water by province . 12 Figure 2.2 Household drinking water by residence . 12 Figure 2.3 Household toilet facilities by residence . 13 Figure 2.4 Household wealth by residence. 14 Figure 2.5 Population pyramid . 15 Figure 2.6 Orphanhood by age . 16 Figure 2.7 Age-specific attendance rates . 18 Figure 2.8 Receipt of social grants . 19 3 CHARACTERISTICS OF RESPONDENTS . 35 Table 3.1 Background characteristics of respondents . 41 Table 3.2.1 Educational attainment: Women . 42 Table 3.2.2 Educational attainment: Men . 43 Table 3.3.1 Literacy: Women . 44 Table 3.3.2 Literacy: Men . 45 Table 3.4.1 Exposure to mass media: Women . 46 Table 3.4.2 Exposure to mass media: Men . 47 Table 3.5.1 Internet usage: Women . 48 x • Tables and Figures Table 3.5.2 Internet usage: Men . 49 Table 3.6.1 Employment status: Women . 50 Table 3.6.2 Employment status: Men . 51 Table 3.7.1 Occupation: Women . 52 Table 3.7.2 Occupation: Men . 53 Table 3.8 Type of employment: Women . 54 Table 3.9 Background characteristics of respondents who completed the adult health module . 55 Figure 3.1 Education of survey respondents . 36 Figure 3.2 Secondary education by province . 36 Figure 3.3 Secondary education by household wealth . 37 Figure 3.4 Exposure to mass media . 37 Figure 3.5 Employment status by residence . 38 Figure 3.6 Occupation . 39 4 MARRIAGE AND SEXUAL ACTIVITY . 57 Table 4.1 Current marital status . 61 Table 4.2.1 Number of women’s co-wives . 62 Table 4.2.2 Number of men’s wives . 63 Table 4.3 Age at first union . 64 Table 4.4 Age at first sexual intercourse . 65 Table 4.5 Median age at first sexual intercourse by background characteristics . 66 Table 4.6.1 Recent sexual activity: Women . 67 Table 4.6.2 Recent sexual activity: Men . 68 Figure 4.1 Marital status . 58 Figure 4.2 Polygyny by province . 58 Figure 4.3 Women’s median age at first sex by household wealth . 60 5 FERTILITY . 69 Table 5.1 Current fertility . 75 Table 5.2 Fertility by background characteristics . 75 Table 5.3.1 Trends in age-specific fertility rates . 76 Table 5.3.2 Trends in age-specific and total fertility rates . 76 Table 5.4 Children ever born and living . 77 Table 5.5 Birth intervals . 78 Table 5.6 Postpartum amenorrhoea, abstinence, and insusceptibility . 79 Table 5.7 Median duration of amenorrhoea, postpartum abstinence, and postpartum insusceptibility . 79 Table 5.8 Menopause . 80 Table 5.9 Age at first birth . 80 Table 5.10 Median age at first birth . 81 Table 5.11 Teenage pregnancy and motherhood . 82 Table 5.12 Sexual and reproductive health behaviours before age 15 . 82 Figure 5.1 Age-specific fertility rates . 70 Figure 5.2 Comparison in fertility between the SADHS 1998 and SADHS 2016 by residence . 70 Figure 5.3 Fertility by household wealth . 70 Figure 5.4 Birth intervals . 71 Figure 5.5 Median age at first birth by household wealth . 73 Tables and Figures • xi Figure 5.6 Teenage pregnancy and motherhood by province . 73 Figure 5.7 Teenage pregnancy and motherhood by household wealth . 74 6 FERTILITY PREFERENCES . 83 Table 6.1 Fertility preferences by number of living children . 88 Table 6.2 Desire to limit childbearing . 89 Table 6.3 Ideal number of children by number of living children . 90 Table 6.4 Mean ideal number of children . 91 Table 6.5 Fertility planning status . 91 Table 6.6 Wanted fertility rates . 92 Figure 6.1 Desire to limit childbearing by number of living children . 84 Figure 6.2 Ideal family size . 85 Figure 6.3 Ideal family size by number of living children . 85 Figure 6.4 Fertility planning status . 86 Figure 6.5 Comparison in wanted and actual fertility: 1998 and 2016 . 87 7 CONTRACEPTION . 93 Table 7.1 Knowledge of contraceptive methods . 101 Table 7.2 Current use of contraception by age . 102 Table 7.3.1 Trends in the current use of contraception . 103 Table 7.3.2 Current use of contraception according to background characteristics . 104 Table 7.4 Timing of sterilisation . 105 Table 7.5 Knowledge of fertile period . 105 Table 7.6 Knowledge of fertile period by age . 105 Table 7.7 Source of modern contraception methods . 106 Table 7.8 Informed choice . 107 Table 7.9 Twelve-month contraceptive discontinuation rates . 107 Table 7.10 Reasons for discontinuation . 108 Table 7.11.1 Need and demand for contraception among in-union women . 109 Table 7.11.2 Need and demand for contraception among all women and among sexually active women . 110 Table 7.12 Decision making about contraception . 112 Table 7.13 Future use of contraception . 113 Table 7.14 Exposure to family planning messages . 113 Table 7.15 Exposure to family planning messages at school . 114 Table 7.16 Contact of nonusers with family planning providers . 115 Figure 7.1 Contraceptive use . 94 Figure 7.2 Comparison in contraceptive use: 1998 and 2016 . 95 Figure 7.3 Modern contraceptive use by province . 95 Figure 7.4 Use of injectables by household wealth . 95 Figure 7.5 Contraceptive discontinuation rates . 97 Figure 7.6 Demand for contraception . 98 Figure 7.7 Unmet need by province . 98 8 INFANT AND CHILD MORTALITY . 117 Table 8.1 Early childhood mortality rates . 122 Table 8.2 Five-year early childhood mortality rates according to background characteristics . 122 Table 8.3 Ten-year early childhood mortality rates according to additional characteristics . 123 xii • Tables and Figures Table 8.4 Perinatal mortality . 124 Table 8.5 High-risk fertility behaviour . 125 Figure 8.1 Comparison in early childhood mortality rates between the SADHS 1998 and SADHS 2016 . 118 Figure 8.2 Childhood mortality by previous birth interval . 119 Figure 8.3 Under-5 mortality by province . 119 Figure 8.4 Under-5 mortality by household wealth . 119 Figure 8.5 Perinatal mortality by household wealth . 120 9 MATERNAL HEALTH CARE . 127 Table 9.1 Antenatal care . 136 Table 9.2 Number of antenatal care visits and timing of first visit . 137 Table 9.3 Components of antenatal care . 138 Table 9.4 Tetanus toxoid injections . 139 Table 9.5 Place of delivery . 140 Table 9.6 Assistance during delivery . 141 Table 9.7 Caesarean section . 142 Table 9.8 Duration of stay in health facility after birth . 142 Table 9.9 Timing of first postnatal check for the mother . 143 Table 9.10 Type of provider of first postnatal check for the mother . 144 Table 9.11 Timing of first postnatal check for the newborn . 145 Table 9.12 Type of provider of first postnatal check for the newborn . 146 Table 9.13 Content of postnatal care for newborns . 147 Table 9.14 Discharge timing . 148 Figure 9.1 Comparison of antenatal care coverage . 129 Figure 9.2 Components of antenatal care . 129 Figure 9.3 Comparison of place of birth . 130 Figure 9.4 Health facility births by education . 131 Figure 9.5 Assistance during delivery . 131 Figure 9.6 Skilled assistance at delivery by birth order . 132 Figure 9.7 Postnatal care by place of delivery . 133 10 CHILD HEALTH . 149 Table 10.1 Child’s size and weight at birth. 160 Table 10.2 Vaccinations by source of information . 161 Table 10.3 Possession and observation of vaccination cards, according to background characteristics . 162 Table 10.4 Vaccinations by background characteristics . 163 Table 10.5 Vaccinations by child’s residence with mother or other caregiver . 165 Table 10.6 Possession and observation of vaccination cards during interview with a caregiver . 166 Table 10.7 Reasons vaccinations were missed, late, or not given. 167 Table 10.8 Prevalence and treatment of symptoms of ARI . 168 Table 10.9 Source of advice or treatment for children with symptoms of ARI . 169 Table 10.10 Prevalence and treatment of fever . 170 Table 10.11 Source of advice or treatment for children with fever . 171 Table 10.12 Prevalence and treatment of diarrhoea . 172 Table 10.13 Feeding practices during diarrhoea . 173 Table 10.14 Oral rehydration therapy, zinc, and other treatments for diarrhoea . 174 Table 10.15 Source of advice or treatment for children with diarrhoea . 175 Table 10.16 Knowledge of ORS packets and clinic-recommended homemade fluids . 176 Tables and Figures • xiii Figure 10.1 All age-appropriate childhood vaccinations. 152 Figure 10.2 Vaccination coverage by whether vaccination card was seen . 153 Figure 10.3 Vaccination coverage by province . 153 Figure 10.4 Diarrhoea prevalence by age . 156 Figure 10.5 Feeding practices during diarrhoea . 157 Figure 10.6 Treatment of diarrhoea . 157 Figure 10.7 Prevalence and treatment of childhood illness . 158 11 NUTRITION OF CHILDREN . 177 Table 11.1 Nutritional status of children . 188 Table 11.2 Initial breastfeeding . 190 Table 11.3 Breastfeeding status by age . 191 Table 11.4 Infant and young child feeding (IYCF) indicators on breastfeeding status . 191 Table 11.5 Median duration of breastfeeding . 192 Table 11.6 Duration of past breastfeeding . 193 Table 11.7 Foods and liquids consumed by children in the day or night preceding the interview . 194 Table 11.8 Minimum acceptable diet . 195 Table 11.9 Consumption of sugary drinks and sugary or salty foods by children in the day or night preceding the interview . 196 Table 11.10 Prevalence of anaemia in children . 197 Table 11.11 Presence of iodised salt in household . 198 Table 11.12 Micronutrient intake and deworming medication among children . 199 Table 11.13 Consumption of liver . 200 Table 11.14 Micronutrient intake among mothers . 200 Figure 11.1 Nutritional status of children . 179 Figure 11.2 Stunting in children by province . 179 Figure 11.3 Stunting in children by household wealth . 179 Figure 11.4 Breastfeeding practices by age . 181 Figure 11.5 IYCF indicators on minimum acceptable diet . 183 Figure 11.6 Consumption of unhealthy foods and drinks . 184 12 HIV/AIDS-RELATED KNOWLEDGE AND BEHAVIOUR . 201 Table 12.1 Knowledge of prevention of mother-to-child transmission of HIV . 208 Table 12.2.1 Multiple sexual partners and higher-risk sexual intercourse in the past 12 months: Women . 209 Table 12.2.2 Multiple sexual partners and higher-risk sexual intercourse in the past 12 months: Men . 210 Table 12.3 Payment for sexual intercourse and condom use at last paid sexual intercourse . 211 Table 12.4.1 Coverage of prior HIV testing: Women . 212 Table 12.4.2 Coverage of prior HIV testing: Men . 213 Table 12.5 Pregnant women counselled and tested for HIV . 214 Table 12.6 Male circumcision . 215 Table 12.7 Self-reported prevalence of sexually transmitted infections (STIs) and STI symptoms . 216 Table 12.8 Women and men seeking treatment for STIs . 217 Table 12.9 Age at first sexual intercourse among young people . 217 Table 12.10 Premarital sexual intercourse among young people . 218 Table 12.11.1 Multiple sexual partners and higher-risk sexual intercourse in the past 12 months among young people: Women . 219 xiv • Tables and Figures Table 12.11.2 Multiple sexual partners and higher-risk sexual intercourse in the past 12 months among young people: Men . 220 Table 12.12 Recent HIV tests among young people . 221 Figure 12.1 Knowledge of mother-to-child transmission (MTCT) . 202 Figure 12.2 Multiple sexual partners and condom use . 202 Figure 12.3 HIV testing . 203 Figure 12.4 Male circumcision by age . 204 Figure 12.5 STI advice- or treatment-seeking behaviour . 205 13 HIV PREVALENCE . 223 Table 13.1 Coverage of HIV testing by residence and province . 230 Table 13.2 Coverage of HIV testing according to selected background characteristics . 231 Table 13.3 HIV prevalence by age . 232 Table 13.4 HIV prevalence by sociodemographic characteristics: Women and men age 15-49 . 232 Table 13.5 HIV prevalence by sociodemographic characteristics: Women and men age 50 and older . 233 Table 13.6 HIV prevalence by demographic characteristics . 234 Table 13.7 HIV prevalence by sexual behaviour . 235 Table 13.8 HIV prevalence among young people by background characteristics . 236 Table 13.9 HIV prevalence among young people by sexual behaviour . 237 Table 13.10 HIV prevalence by other characteristics . 237 Table 13.11 Prior HIV testing by current HIV status . 237 Table 13.12 HIV prevalence by male circumcision . 238 Table 13.13 HIV prevalence among couples . 239 Figure 13.1 HIV prevalence by age . 225 Figure 13.2 HIV prevalence by province . 226 Figure 13.3 HIV prevalence by education . 226 Figure 13.4 HIV prevalence by marital status . 226 Figure 13.5 HIV prevalence by number of lifetime partners . 227 Figure 13.6 HIV prevalence by circumcision status . 229 Figure 13.7 HIV prevalence among couples . 229 14 ADULT AND PREGNANCY-RELATED MORTALITY . 241 Table 14.1 Adult mortality rates . 245 Table 14.2 Adult mortality probabilities . 245 Table 14.3 Pregnancy-related mortality . 246 Table 14.4 Pregnancy-related mortality trends . 246 Figure 14.1 Adult mortality rates by age . 242 Figure 14.2 Comparison of the pregnancy-related mortality ratio (PRMR) with confidence intervals between the SADHS 1998 and the SADHS 2016 . 244 15 USE OF HEALTH SERVICES AND PRESCRIBED MEDICATIONS . 247 Table 15.1 Health insurance coverage . 254 Table 15.2 Outpatient health care services received . 255 Table 15.3 Source of outpatient health care services received . 256 Table 15.4 Experience with a Pap smear exam . 257 Table 15.5 Experience with a Pap smear exam by province . 258 Table 15.6 Problems in accessing health care . 259 Table 15.7 Prescribed medications . 260 Tables and Figures • xv Table 15.8 Source of payment for prescribed medications . 261 Table 15.9 Use of prescribed medication for common chronic conditions . 262 Figure 15.1 Source of outpatient health care services by household wealth . 248 Figure 15.2 Experience with a Pap smear by province . 249 Figure 15.3 Problems in accessing health care by province . 250 Figure 15.4 Problems in accessing health care by household wealth . 250 Figure 15.5 Prescribed medication by age . 251 Figure 15.6 Experience of medication stockouts by age . 252 16 ADULT MORBIDITY . 263 Table 16.1.1 Self-assessment of health: Women . 274 Table 16.1.2 Self-assessment of health: Men. 275 Table 16.2 Diagnosis and treatment of various health conditions and diseases . 276 Table 16.3 Experience with pain . 277 Table 16.4 Experience with pain by age and sex . 278 Table 16.5 Experience with tooth or mouth pain . 279 Table 16.6 Main reason treatment for tooth or mouth pain was not sought . 280 Table 16.7.1 Blood pressure status: Women . 281 Table 16.7.2 Blood pressure status: Men . 283 Table 16.8.1 Blood pressure status by health status measures: Women . 285 Table 16.8.2 Blood pressure status by health status measures: Men . 286 Table 16.9.1 Blood pressure status and treatment: Women . 287 Table 16.9.2 Blood pressure status and treatment: Men . 288 Table 16.10.1 Asthma and chronic obstructive pulmonary disease symptoms: Women . 289 Table 16.10.2 Asthma and chronic obstructive pulmonary disease symptoms: Men . 290 Table 16.11.1 Unadjusted glycated haemoglobin levels: Women . 291 Table 16.11.2 Unadjusted glycated haemoglobin levels: Men . 292 Table 16.12.1 Adjusted glycated haemoglobin levels: Women . 293 Table 16.12.2 Adjusted glycated haemoglobin levels: Men . 294 Table 16.13.1 Prevalence of anaemia in women . 295 Table 16.13.2 Prevalence of anaemia in men . 296 Figure 16.1 Self-reported health status . 264 Figure 16.2 Self-reported poor health by education . 264 Figure 16.3 Chronic conditions based on self-reports . 265 Figure 16.4 Site of chronic pain . 266 Figure 16.5 Chronic pain by age . 266 Figure 16.6 Blood pressure status . 268 Figure 16.7 Hypertension by age . 269 Figure 16.8 Uncontrolled hypertension by age . 269 Figure 16.9 Self-reported symptoms of respiratory disease . 270 Figure 16.10 Diabetes by age . 272 Figure 16.11 Diabetes by nutritional status . 272 Figure 16.12 Prevalence of anaemia in adults by age . 273 17 ADULT NUTRITION . 297 Table 17.1.1 Body mass index and short stature of women . 304 Table 17.1.2 Body mass index of men . 305 Table 17.2 Severe obesity among women and men . 306 Table 17.3.1 Waist circumference: Women . 307 Table 17.3.2 Waist circumference: Men . 308 Table 17.4 Consumption of fruit and vegetables . 309 xvi • Tables and Figures Table 17.5 Consumption of sugar-sweetened beverages and fruit juice . 310 Table 17.6 Consumption of fried and processed foods . 311 Table 17.7 Approach to salt consumption . 312 Figure 17.1 Nutritional status of women and men . 298 Figure 17.2 Overweight or obesity by province: Women . 299 Figure 17.3 Overweight or obesity by province: Men . 299 Figure 17.4 Severe obesity in adults by household wealth . 299 Figure 17.5 Waist-to-height ratio (WtHR) among adults by age . 300 Figure 17.6 Consumption of various foods and beverages by household wealth . 302 18 TOBACCO, ALCOHOL, AND CODEINE USE AMONG ADULTS . 313 Table 18.1.1 Tobacco smoking: Women . 318 Table 18.1.2 Tobacco smoking: Men . 319 Table 18.2.1 Average number of cigarettes smoked daily: Women . 320 Table 18.2.2 Average number of cigarettes smoked daily: Men . 321 Table 18.3 Smokeless tobacco use and any tobacco use . 322 Table 18.4 E-cigarette use . 322 Table 18.5 Tobacco use during pregnancy . 323 Table 18.6.1 Alcohol consumption and risky drinking: Women . 324 Table 18.6.2 Alcohol consumption and risky drinking: Men . 325 Table 18.7 Alcohol consumption during pregnancy . 326 Table 18.8.1 Use and misuse of codeine-containing medications: Women . 327 Table 18.8.2 Use and misuse of codeine-containing medications: Men . 328 Figure 18.1 Comparison of tobacco smoking in the SADHS 1998 and SADHS 2016 . 314 Figure 18.2.1 Prevalence of smoking by province: Women . 314 Figure 18.2.2 Prevalence of smoking by province: Men . 314 Figure 18.3.1 Prevalence of risky drinking by province: Women . 316 Figure 18.3.2 Prevalence of risky drinking by province: Men . 316 19 WOMEN’S EMPOWERMENT . 329 Table 19.1 Employment and cash earnings of in-union women and men . 337 Table 19.2.1 Control over women’s cash earnings and relative magnitude of women’s cash earnings . 338 Table 19.2.2 Control over men’s cash earnings . 339 Table 19.3 Women’s control over their own earnings and over those of their partners . 340 Table 19.4.1 Ownership of assets: Women . 341 Table 19.4.2 Ownership of assets: Men . 342 Table 19.5.1 Ownership of title or deed for house: Women . 343 Table 19.5.2 Ownership of title or deed for house: Men . 344 Table 19.6.1 Ownership and use of bank accounts and cellphones: Women . 345 Table 19.6.2 Ownership and use of bank accounts and cellphones: Men . 346 Table 19.7 Participation in decision making . 346 Table 19.8.1 Women’s participation in decision making by background characteristics . 347 Table 19.8.2 Men’s participation in decision making by background characteristics . 348 Table 19.9.1 Attitude toward wife beating: Women . 349 Table 19.9.2 Attitude toward wife beating: Men . 350 Table 19.10 Ability to negotiate sexual relations with partner . 351 Table 19.11.1 Child discipline: Women . 352 Table 19.11.2 Child discipline: Men . 353 Tables and Figures • xvii Figure 19.1 Employment by age . 330 Figure 19.2 Control over women’s earnings . 331 Figure 19.3 Ownership of assets . 332 Figure 19.4 Women’s participation in decision making. 333 20 DOMESTIC VIOLENCE . 355 Table 20.1 Experience of physical, sexual, or emotional violence by any partner . 363 Table 20.2 Experience of different forms of violence . 364 Table 20.3 Age at first experience of sexual violence . 364 Table 20.4 Violence by any partner in the last 12 months . 365 Table 20.5 Control exercised by partners . 366 Table 20.6 Forms of partner violence . 367 Table 20.7 Partner violence by background characteristics . 368 Table 20.8 Spousal violence by husband’s characteristics and empowerment indicators . 369 Table 20.9 Injuries to women due to violence by partner . 370 Table 20.10 Violence by women against their partner by women’s background characteristics . 371 Table 20.11 Violence by women against their husband/partner by husband’s/partner’s characteristics and empowerment indicators . 372 Figure 20.1 Experience of physical, sexual, or emotional violence among ever-partnered women age 18+ by marital status . 357 Figure 20.2 Partner violence by province. 358 Figure 20.3 Forms of violence . 359 Figure 20.4 Violence by partner’s alcohol consumption . 360 APPENDIX A SAMPLE DESIGN. 377 Table A.1 Household distribution . 378 Table A.2 PSUs and households . 378 Table A.3 Sample allocation of clusters and households . 379 Table A.4 Sample allocation of completed interviews with women and men . 380 Table A.5 Sample implementation: Women . 382 Table A.6 Sample implementation: Men . 383 Table A.7 Coverage of HIV testing by social and demographic characteristics: Women . 384 Table A.8 Coverage of HIV testing by social and demographic characteristics: Men . 385 Table A.9 Coverage of HIV testing by sexual behaviour characteristics: Women . 386 Table A.10 Coverage of HIV testing by sexual behaviour characteristics: Men . 387 APPENDIX B ESTIMATES OF SAMPLING ERRORS . 389 Table B.1 List of selected variables for sampling errors, South Africa DHS 2016 . 391 Table B.2 Sampling errors: National sample, South Africa DHS 2016 . 393 Table B.3 Sampling errors: Urban sample, South Africa DHS 2017 . 395 Table B.4 Sampling errors: Non-urban sample, South Africa DHS 2017 . 397 Table B.5 Sampling errors: Western Cape sample, South Africa DHS 2017 . 399 Table B.6 Sampling errors: Eastern Cape sample, South Africa DHS 2017 . 401 Table B.7 Sampling errors: Northern Cape sample, South Africa DHS 2017 . 403 Table B.8 Sampling errors: Free State sample, South Africa DHS 2017 . 405 Table B.9 Sampling errors: KwaZulu-Natal sample, South Africa DHS 2017 . 407 Table B.10 Sampling errors: North West sample, South Africa DHS 2017 . 409 Table B.11 Sampling errors: Gauteng sample, South Africa DHS 2017 . 411 Table B.12 Sampling errors: Mpumalanga sample, South Africa DHS 2017 . 413 xviii • Tables and Figures Table B.13 Sampling errors: Limpopo sample, South Africa DHS 2017 . 415 Table B.14 Sampling errors for adult and pregnancy-related mortality rates, South Africa 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 Height and weight data completeness and quality for children . 424 Table C.8 Completeness of information on siblings . 425 Table C.9 Sibship size and sex ratio of siblings . 425 Acknowledgements • xix ACKNOWLEDGEMENTS he need to implement a Demographic and Health Survey (DHS) was identified by the National Department of Health (NDoH) so as to obtain population-based data as a means of informing policy and management of strategic programmes. The survey was conducted as a collaboration between Statistics South Africa (Stats SA) and the South African Medical Research Council (SAMRC) with technical support from ICF through The DHS Program of the United States Agency for International Development (USAID). The DHS surveys are a standard series of national-level surveys focussing on demographic and primary health care indicators. By utilising DHS model questionnaires as the basis and following a standard implementation approach, internationally comparable national data are collected. Such data will contribute towards monitoring progress on the country’s National Development Plan (NDP) as well as the global Sustainable Development Goals (SDGs). Furthermore, the information will assist provinces in their planning. I sincerely acknowledge the efforts of a number of organisations and individuals who contributed substantially to the success of the survey. The survey could not have been carried out without the dedication of the staff from Stats SA and the SAMRC who planned, participated in, and oversaw the entire SADHS. I thank Professor Glenda Gray, the President of the SAMRC, and Mr Risenga Maluleka, the Statistician General, for their continued support in this project. The contributions of the sampling, field operations, data processing, communications, and content development teams are acknowledged, as well as the technical support of the Global Clinical and Viral Laboratory (GCVL). Professor Debbie Bradshaw, Director of the SAMRC Burden of Disease Research Unit, is thanked for her leadership of the project team. I would like to thank staff of the NDoH, specifically, Dr Gail Andrews (Chief Operations Officer); Dr Yogan Pillay (Deputy Director-General, HIV and AIDS, Tuberculosis, and Maternal, Neonatal, and Child Health); Ms Thulile Zondi (Chief Director); Dr Tshilidzi Muthivhi and Mr Jacques van der Westhuizen, in providing guidance and leadership. Furthermore, I would like to recognise the NDoH programme managers who contributed to the write-up and review of the report. I appreciate the support provided by the provincial departments of health in managing the medical waste. Ongoing input and guidance from ICF staff have been essential throughout. In addition to financial assistance from the government of South Africa through the NDoH and the SAMRC, support from the Global Fund to Fight AIDS, Tuberculosis and Malaria (Global Fund); the European Union (EU); the United Nations Children’s Fund (UNICEF); the United Nations Population Fund (UNFPA); and USAID is appreciated. Finally, I am grateful to the fieldwork teams who traversed the country to secure interviews with survey respondents, as well as the respondents themselves, who generously gave of their time to provide the information that forms the basis of this and future reports. MS. M.P. MATSOSO DIRECTOR-GENERAL HEALTH T Abbreviations • xxi ABBREVIATIONS ADA American Diabetes Association AIDS acquired immune deficiency syndrome ANC antenatal care ARI acute respiratory infection ASAR age-specific attendance rate ASFR age-specific fertility rate BCG Bacille Calmette-Guérin BMI body mass index CAGE Concern/Cut-down, Anger, Guilt, and Eye-Opener CAPI computer-assisted personal interviewing CARMMA Campaign on Accelerated Reduction of Maternal and Child Mortality in Africa CBR crude birth rate CDC Centers for Disease Control and Prevention CHW community health worker COPD chronic obstructive pulmonary disease CPR contraceptive prevalence rate CSPro Censuses and Surveys Processing DBE Department of Basic Education DSD Department of Social Development DBS dried blood spot DHS Demographic and Health Survey DTaP diphtheria, tetanus, and acellular pertussis vaccine DU dwelling unit DWCPD Department of Women, Children and People with Disabilities; now known as the Department of Women EA enumeration area ELISA enzyme-linked immunosorbent assay EU European Union GAR gross attendance ratio GBD global burden of disease GCVL Global Clinical and Viral Laboratory GFR general fertility rate Global Fund Global Fund to Fight AIDS, Tuberculosis and Malaria GPI gender parity index HbA1c glycated haemoglobin HepB hepatitis B Hib haemophilus influenzae type b HIV human immunodeficiency virus HCT HIV counselling and testing xxii • Abbreviations IASP International Association for the Study of Pain ICCIDD International Council for Control of Iodine Deficiency Disorders ICF ICF (originally, Inner City Fund) IFSS Internet file streaming system IPV inactivated polio vaccine IUD intrauterine contraceptive device IYCF infant and young child feeding MMR maternal mortality ratio MSF master sample frame MTCT mother-to-child transmission NAR net attendance ratio NCCEMD National Committee on Confidential Enquiries into Maternal Deaths NDoH National Department of Health NDP National Development Plan NGO nongovernmental organisation NHI National Health Insurance OPV oral polio vaccine ORS oral rehydration salts ORT oral rehydration therapy PCV pneumococcal conjugate vaccine PNC postnatal care PPM parts per million PRMR pregnancy-related mortality ratio PSU primary sampling unit RHF recommended homemade fluids RV rotavirus vaccine SADHS South Africa Demographic and Health Survey SAMRC South African Medical Research Council SASSA South African Social Security Agency SD standard deviation SDGs Sustainable Development Goals SSB sugar-sweetened beverage Stats SA Statistics South Africa TFR total fertility rate UNFPA United Nations Population Fund UNICEF United Nations Children’s Fund USAID United States Agency for International Development VIP ventilated improved pit latrine VMMC voluntary medical male circumcision WG Washington Group on Disability Statistics WHO World Health Organization WtHR waist-to-height ratio Reading and Understanding Tables from the South Africa DHS (SADHS) 2016 • xxiii READING AND UNDERSTANDING TABLES FROM THE SOUTH AFRICA DHS (SADHS) 2016 he SADHS 2016 final report is based on approximately 250 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 reader-friendly report features about 110 figures that clearly highlight background characteristics and changes over time. Colourful maps display breakdowns for provinces. The text highlights key points in bullets and clearly identifies 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, SADHS data users should be comfortable reading and interpreting tables. The following pages provide an introduction to the organisation of SADHS 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 practise their new skills in interpreting SADHS tables. T xxiv • Reading and Understanding Tables from the South Africa DHS (SADHS) 2016 Example 1: Women’s Exposure to Mass Media A Question Asked of All Survey Respondents Table 3.4.1 Exposure to mass media: Women Percentage of women age 15-49 who are exposed to specific media on a weekly basis, according to background characteristics, South Africa 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 37.1 71.8 47.3 25.1 20.2 1,427 20-24 40.2 72.1 53.6 27.7 18.0 1,415 25-29 41.3 73.8 55.4 30.2 16.7 1,444 30-34 43.8 75.8 57.8 34.0 16.7 1,333 35-39 40.3 75.1 59.1 31.2 16.4 1,072 40-44 40.4 73.9 55.8 30.6 18.2 941 45-49 38.7 72.9 58.2 28.9 17.7 883 Residence Urban 49.8 79.4 60.5 37.1 12.5 5,731 Non-urban 20.9 61.6 43.3 14.1 28.4 2,783 Province Western Cape 74.6 91.9 75.2 57.8 2.4 995 Eastern Cape 21.3 62.8 46.5 14.1 27.4 938 Northern Cape 46.1 80.9 53.4 31.8 12.2 173 Free State 46.7 78.1 68.0 37.3 12.1 442 KwaZulu-Natal 30.8 63.3 47.8 22.1 28.2 1,616 North West 33.2 80.3 59.8 25.2 12.3 570 Gauteng 52.2 77.3 60.1 39.5 13.0 2,284 Mpumalanga 27.0 68.0 42.9 15.1 20.3 671 Limpopo 17.4 69.5 39.5 10.4 23.5 824 Education No education 11.1 46.7 30.8 8.9 45.2 168 Primary incomplete 11.2 54.1 38.5 9.1 36.0 447 Primary complete 18.4 54.0 40.8 11.1 36.2 327 Secondary incomplete 34.4 71.1 50.6 24.3 20.1 4,195 Secondary complete 52.4 80.9 63.1 39.7 10.8 2,369 More than secondary 61.3 86.1 69.6 45.9 5.7 1,008 Wealth quintile Lowest 16.1 35.0 31.2 7.8 48.6 1,648 Second 26.2 70.4 48.8 16.7 19.1 1,715 Middle 43.3 83.9 57.2 31.8 9.9 1,805 Fourth 51.1 88.3 66.2 40.1 6.8 1,763 Highest 65.4 89.0 71.1 51.9 5.2 1,583 Total 40.3 73.6 54.9 29.6 17.7 8,514 Step 1: Read the title and subtitle. They tell you the topic and the specific population being described. In this case, the table is about women age 15-49 and their access to different types of media. All eligible female respondents age 15-49 were asked these questions. Step 2: Scan the column headings—highlighted in green in Example 1.They describe how the information is categorised. In this table, the first three columns of data show different types of media that women access at least once a week. The fourth column shows women who access all three media, while the fifth column is 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 interviewed in the survey. Step 3: Scan the row headings—the first vertical column highlighted in blue in Example 1. These show the different ways the data are divided into categories based on population characteristics. In this case, the table presents women’s exposure to media by age, urban/non-urban residence, province, educational level, and wealth quintile. Most of the tables in the SADHS 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 exposure to different types of media. In this case, 73.6%* of women watch television weekly and 54.9% listen to the radio weekly. 1 2 3 4 5 Reading and Understanding Tables from the South Africa DHS (SADHS) 2016 • xxv 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 45.9% of women age 15-49 with more than secondary education access three types of media weekly. Step 6: By looking at patterns by background characteristics, we can see how women’s access to media varies across South Africa. Mass media are often used to communicate health messages. Knowing how mass media exposure varies among different groups can help program 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 South Africa do not access any of the three media at least once a week? b) What age group of women are most likely to read a newspaper weekly? c) Compare women in urban and non-urban areas—which group is more likely to listen to the radio weekly? d) What are the lowest and highest percentages (range) of women who do not access any of the three media at least once a week by province? e) Is there a clear pattern in exposure to television on a weekly basis by education? f) Is there a clear pattern in exposure to radio by wealth quintile? Answers: a) 17.7% b) Women age 30-34—43.8% of women in this age group read the newspaper at least once a week. c) Women in urban areas—60.5% listen to the radio weekly compared with 43.3% of women in non-urban areas. d) 2.4% of women in Western Cape do not access any of the three media weekly, compared with 28.2% of women in KwaZulu-Natal. e) Exposure to television on a weekly basis increases as a woman’s level of education increases; 46.7% of women with no education watch television weekly compared with 86.1% of women with more than secondary education. f) Exposure to radio on a weekly basis increases as household wealth increases; 31.2% of women in the lowest wealth quintile listen to the radio weekly, compared with 71.1% of women in the highest wealth quintile. xxvi • Reading and Understanding Tables from the South Africa DHS (SADHS) 2016 Example 2: Prevalence and treatment of diarrhoea A Question Asked of a Subgroup of Survey Respondents Table 10.12 Prevalence and treatment of diarrhoea Percentage of children under age 5 who had diarrhoea in the 2 weeks preceding the survey; and among children with diarrhoea in the 2 weeks preceding the survey, percentage for whom advice or treatment was sought, according to background characteristics, South Africa DHS 2016 Percentage with diarrhoea Number of children Among children under age 5 with diarrhoea: Background characteristic Percentage for whom advice or treatment was sought1 Number of children with diarrhoea Age in months <6 7.0 363 (36.9) 25 6-11 16.3 325 66.2 53 12-23 16.8 677 62.4 114 24-35 8.2 660 55.9 54 36-47 8.5 688 79.6 58 48-59 7.1 730 (63.0) 52 Sex Male 11.2 1,783 59.9 200 Female 9.4 1,661 67.1 157 Source of drinking water2 Improved 10.2 3,111 62.9 318 Unimproved 11.4 333 (64.3) 38 Type of toilet facility3 Improved 10.4 2,523 64.3 263 Unimproved sanitation 10.2 920 59.6 94 Shared facility4 10.6 724 60.7 77 Unimproved facility 9.1 73 * 7 Open defecation 8.3 123 * 10 Handwashing place Observed, fixed place 8.6 1,727 71.2 148 Observed, mobile place 13.5 1,177 52.5 159 Not observed 9.2 539 (72.8) 49 Residence Urban 9.0 2,204 65.5 199 Non-urban 12.7 1,240 60.0 157 Province Western Cape 5.4 306 * 16 Eastern Cape 9.4 382 (63.8) 36 Northern Cape 8.1 67 * 5 Free State 5.8 156 * 9 KwaZulu-Natal 13.7 636 56.4 87 North West 16.4 269 57.9 44 Gauteng 8.6 980 (73.2) 85 Mpumalanga 10.7 309 (69.3) 33 Limpopo 12.0 338 57.1 41 Mother’s education No education 10.6 49 * 5 Primary incomplete 17.4 167 (61.6) 29 Primary complete 13.1 133 * 17 Secondary incomplete 10.3 1,680 58.5 173 Secondary complete 9.6 1,027 76.9 98 More than secondary 8.6 388 (49.1) 34 Wealth quintile Lowest 11.8 744 55.1 88 Second 13.8 822 53.7 114 Middle 8.7 766 80.3 67 Fourth 8.7 642 (65.6) 56 Highest 6.8 470 (77.8) 32 Total 10.3 3,444 63.0 356 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 Includes advice or treatment from the following sources: public sector, private medical sector, and supermarket. Excludes advice or treatment from a traditional health practitioner 2 See Table 2.1 for definition of categories 3 See Table 2.3 for definition of categories 4 Facilities that would be considered improved if they were not shared by two or more households 1 2 3 4 a b Reading and Understanding Tables from the South Africa DHS (SADHS) 2016 • xxvii Step 1: Read the title and subtitle. In this case, the table is about two separate groups of children: children under age 5 (a) and children under age 5 with diarrhoea 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 children under age 5 with diarrhoea in the two weeks before the survey (b). Step 3: Look at the first panel. What percentage of children under age 5 had diarrhoea in the two weeks before the survey? It’s 10.3%. Now look at the second panel. How many children under age 5 had diarrhoea in the two weeks before the survey? It’s 356 children or 10.3% of the 3,444 children under age 5 (with rounding). The second panel is a subset of the first panel. Step 4: Only 10.3% of children under age 5 had diarrhoea 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.  What percentage of children under age 5 with diarrhoea in the two weeks before the survey whose mothers have more than secondary education sought advice or treatment? 49.1%. This percentage is in parentheses because between 25 and 49 children under age 5 whose mothers have more than secondary education had diarrhoea in the two weeks before the survey (unweighted). Readers should use this number with caution—it may not be reliable. (For more information on weighted and unweighted numbers, see Example 3.)  What percentage of children under age 5 with diarrhoea in the two weeks before the survey whose mothers have no education sought advice or treatment? There is no number in this cell—only an asterisk. This is because fewer than 25 children under age 5 whose mothers have no education had diarrhoea in the two weeks before the survey (unweighted). 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, i.e., that the data are reliable. xxviii • Reading and Understanding Tables from the South Africa DHS (SADHS) 2016 Example 3: Understanding Sampling Weights in SADHS Tables A sample is a group of people who have been selected for a survey. In the SADHS, 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 SADHS 2016, the survey sample is representative at the national and provincial levels, and for urban and non-urban areas. To generate statistics that are representative of South Africa as a whole and the 9 provinces, the number of women surveyed in each province should contribute to the size of the total (national) sample in proportion to size of the province. However, if some provinces have small populations, then a sample allocated in proportion to each province’s population may not include sufficient women from each province for analysis. To solve this problem, provinces with small populations are oversampled. For example, let’s say that you have enough money to interview 8,514 women and want to produce results that are representative of South Africa as a whole and its provinces (as in Table 3.1). However, the total population of South Africa is not evenly distributed among the provinces: some provinces, such as Gauteng, are heavily populated while others, such as Northern Cape are not. Thus, Northern Cape must be oversampled. A sampling statistician determines how many women should be interviewed in each province in order to get reliable statistics. The blue column (1) at the right in the table above shows the actual number of women interviewed in each province. Within the provinces, the number of women interviewed ranges from 656 in Western Cape to 1,360 in KwaZulu-Natal. The number of interviews is sufficient to get reliable results in each province. With this distribution of interviews, some provinces are overrepresented and some provinces are underrepresented. For example, the population in Gauteng is about 27% of the population in South Africa, while Northern Cape’s population contributes only 2% of the population in South Africa. But as the blue column shows, the number of women interviewed in Gauteng accounts for only about 10% of the total sample of women interviewed (863/8,514) and the number of women interviewed in Northern Cape accounts for almost the same percentage of the total sample of women interviewed (8%, or 718/8,514). This unweighted distribution of women does not accurately represent the population. In order to get statistics that are representative of South Africa, the distribution of the women in the sample needs to be weighted (or mathematically adjusted) such that it resembles the true distribution in the South Africa. Women from a small province, like Northern Cape, should only contribute a small amount to the national total. Women from a large province, like Gauteng, should contribute much more. Therefore, DHS statisticians mathematically calculate a “weight” which is used to adjust the number of women from each province so that each province’s contribution to the total is proportional to the actual population of the province. The numbers in the purple column (2) represent the “weighted” values. The weighted values can be smaller or larger than the unweighted values at province level. The total national sample size of 8,514 women has not changed after weighting, but the distribution of the women in the provinces has been changed to represent their contribution to the total population size. How do statisticians weight each category? They take into account the probability that a woman was selected in the sample. If you were to compare the green column (3) to the actual population distribution of South Table 3.1 Background characteristics of respondents Percent distribution of women age 15-49 by selected background characteristics, South Africa DHS 2016 Women Background characteristic Weighted percent Weighted number Unweighted number Province Western Cape 11.7 995 656 Eastern Cape 11.0 938 1,041 Northern Cape 2.0 173 718 Free State 5.2 442 854 KwaZulu-Natal 19.0 1,616 1,360 North West 6.7 570 863 Gauteng 26.8 2,284 863 Mpumalanga 7.9 671 1,054 Limpopo 9.7 824 1,105 Total 15-49 100.0 8,514 8,514 1 2 3 Reading and Understanding Tables from the South Africa DHS (SADHS) 2016 • xxix Africa, you would see that women in each province are contributing to the total sample with the same weight that they contribute to the population of the South Africa. The weighted number of women in the survey now accurately represents the proportion of women who live in Gauteng and the proportion of women who live in Northern Cape. With sampling and weighting, it is possible to interview enough women to provide reliable statistics at national and province levels. In general, only the weighted numbers are shown in each of the SADHS tables, so don’t be surprised if these numbers seem low: they may actually represent a larger number of women interviewed. Sustainable Development Goal Indicators • xxxi SUSTAINABLE DEVELOPMENT GOAL INDICATORS South Africa DHS 2016 Sex Total SADHS table number Indicator Male Female 2. Zero hunger 2.2.1 Prevalence of stunting among children under 5 years of age 29.8 25.0 27.4 11.1 2.2.2 Prevalence of malnutrition among children under 5 years of age 17.6 13.8 15.7a na a) Prevalence of wasting among children under 5 years of age 2.1 2.8 2.5a 11.1 b) Prevalence of overweight among children under 5 years of age 15.5 11.0 13.3a 11.1 3. Good health and well-being 3.1.2 Proportion of births attended by skilled health personnel na na 96.7 9.6 3.2.1 Under-5 mortality rate1 49 35 42 8.2 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 79.7 na 7.11.2 3.7.2 Adolescent birth rates per 1,000 women a) Girls aged 10-14 years2 na 1 na 5.1 b) Women aged 15-19 years3 na 71 na 5.1 3.a.1 Age-standardized prevalence of current tobacco use among persons aged 15 years and older4 37.3 7.8 22.6a 18.1.1 and 18.2.2 3.b.1 Proportion of the target population covered by all vaccines included in their national programme5 53.2 52.3 52.7 10.4 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 months6,7 na 13.0 na 20.4 a) Physical violence na 7.7 na 20.4 b) Sexual violence na 2.3 na 20.4 c) Psychological violence na 9.1 na 20.4 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 0.9 na 4.3 b) Before age 18 na 3.6 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 care8 na 60.2 na na 5.b.1 Proportion of individuals who own a mobile telephone9 88.5 91.2 89.9a 19.6.1 and 19.6.2 Residence SADHS table number Urban Non-urban Total 7. Affordable and clean energy 7.1.1 Proportion of population with access to electricity 93.6 85.9 90.8 2.4 7.1.2 Proportion of population with primary reliance on clean fuels and technology10 91.3 56.1 78.5 2.4 Sex SADHS table number Male Female Total 8. Decent work and economic growth 8.10.2 Proportion of adults (15 years and older) with an account at a bank or other financial institution or with a mobile-money-service provider11 57.0 53.9 55.4 19.6.1 and 19.6.2 17. Partnerships for the goals 17.8.1 Proportion of individuals using the Internet12 52.0 47.4 49.7 3.5.1 and 3.5.2 na = Not applicable 1 Expressed in terms of deaths per 1,000 live births for the 5-year period preceding the survey 2 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 3 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 4 Data are not age-standardised 5 Data are presented for children age 12-23 months receiving all vaccines included in their national programme appropriate for their age: BCG, two doses of oral polio vaccine, three doses of DTaP-IPV-Hib, three doses of HepB, three doses of pneumococcal vaccine, two doses of rotavirus vaccine, and one dose of measles vaccine. 6 Data are available for women age 18 and older 7 In the DHS, psychological violence is termed emotional violence 8 Data are available only for currently married women who are not pregnant 9 Data are available only for women and men age 15-49 10 Measured as the percentage of the population using clean fuel for cooking 11 Data are available for women and men age 15-49 who have and use and account at bank or other financial institution; information on use of a mobile-money-service provider is not available 12 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 39 xxxii • Map of South Africa Introduction and Survey Methodology • 1 INTRODUCTION AND SURVEY METHODOLOGY 1 tastistics South Africa (Stats SA), in partnership with the South African Medical Research Council (SAMRC), conducted the South Africa Demographic and Health Survey 2016 (SADHS 2016) at the request of the National Department of Health (NDoH). Technical assistance was provided through The DHS Program. Timely information about the health of the nation is essential for monitoring and evaluation. Survey data collection took place from 27 June 2016 to 4 November 2016. 1.1 SURVEY OBJECTIVES The primary objective of the SADHS 2016 is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the SADHS 2016 collected information on fertility levels; marriage; sexual activity; fertility preferences; awareness and use of contraceptives; breastfeeding practices; nutrition; childhood and maternal mortality; maternal health, including antenatal and postnatal care; key aspects of child health, including immunisation coverage and prevalence and treatment of acute respiratory infection (ARI), fever, and diarrhoea; potential exposure to the risk of HIV infection; coverage of HIV counselling and testing (HCT); and physical and sexual violence against women. Another critical objective of the SADHS 2016 is to provide estimates of health and behaviour indicators for adults age 15 and older, including use of tobacco, alcohol, and codeine-containing medications. In addition, the SADHS 2016 provides estimates of the prevalence of anaemia among children age 6-59 months and adults age 15 and older, and the prevalence of hypertension, anaemia, high HbA1c levels (an indicator of diabetes), and HIV among adults age 15 and older. The information collected through the SADHS 2016 is intended to assist policymakers and programme managers in evaluating and designing programmes and strategies for improving the health of the country’s population. 1.2 SAMPLE DESIGN The sampling frame used for the SADHS 2016 is the Statistics South Africa Master Sample Frame (MSF), which was created using Census 2011 enumeration areas (EAs). In the MSF, EAs of manageable size were treated as primary sampling units (PSUs), whereas small neighbouring EAs were pooled together to form new PSUs, and large EAs were split into conceptual PSUs. The frame contains information about the geographic type (urban, traditional, or farm) and the estimated number of residential dwelling units (DUs) in each PSU. The sampling convention used by Stats SA is DUs. One or more households may be located in any given DU; recent surveys have found 1.03 households per DU on average. Administratively, South Africa is divided into nine provinces. The sample for the SADHS 2016 was designed to provide estimates of key indicators for the country as a whole, for urban and non-urban areas separately, and for each of the nine provinces in South Africa. To ensure that the survey precision is comparable across provinces, PSUs were allocated by a power allocation rather than a proportional allocation. Each province was stratified into urban, farm, and traditional areas, yielding 26 sampling strata.1 The SADHS 2016 followed a stratified two-stage sample design with a probability proportional to size sampling of PSUs at the first stage and systematic sampling of DUs at the second stage. The Census 2011 DU count was used as the PSU measure of size. A total of 750 PSUs were selected from the 26 sampling 1 Western Cape does not have traditional residential geotype PSUs, so only two substrata are applicable. S 2 • Introduction and Survey Methodology strata, yielding 468 selected PSUs in urban areas, 224 PSUs in traditional areas, and 58 PSUs in farm areas.2 A listing operation was carried out in all selected PSUs from January to March 2016, and the updated lists of DUs served as a sampling frame for the selection of DUs in the second stage. In the second stage of selection, a fixed number of 20 DUs per cluster were selected with systematic selection from the created listing. All households in a selected DU were eligible for interviews. Some of the selected PSUs were informal, unstructured settlements with no clear identifications of DUs. To ensure listing coverage within each informal, unstructured PSU selected,3 segmentation was carried out, with the PSU divided into multiple segments of about 20 DUs each. Only one segment was selected at random for the survey; in segments with 20 DUs or fewer, all DUs in the segment were eligible for the survey. In segments with more than 20 DUs, 20 DUs were randomly selected and were eligible for the survey. A cluster in the SADHS 2016 is therefore either a PSU or a segment of a PSU. Figure 1.1 diagrams the subsampling followed in the survey. In half of selected DUs, all households were eligible for interviews with the Household Questionnaire, and 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 for interviews with a standard individual questionnaire. Within this subsample, households in every other DU were eligible to have their salt tested for the presence of iodine. Figure 1.1 Subsampling scheme followed in the SADHS 2016 In the remaining half of DUs, all households were eligible for interviews with the Household Questionnaire, and all women and men age 15 and older who were either permanent residents of the selected households or visitors who stayed in the household the night before the survey were eligible for individual interviews and for biomarker collection. Women age 15-49 and men age 15-59 were eligible for the standard individual questionnaire, as well as a South Africa-specific module on adult health; women age 50 and older and men age 60 and older were eligible for a few sections of the individual questionnaire and the adult health module. In addition, children age 0-59 months were eligible for biomarker collection. Finally, in all households in selected DUs, one woman age 18 and older was selected for a module on domestic violence. In addition, for each child age 0-5 whose biological mother did not live in the household, a guardian was eligible to complete the Caregiver’s Questionnaire. 2 Four PSUs were dropped from the sample: one was vacant, two were non-accessible due to refusals, and one was an industrial area. 3 There were 26 informal, unstructured PSUs in the SADHS sample. Introduction and Survey Methodology • 3 1.3 QUESTIONNAIRES Five questionnaires were used in the SADHS 2016: the Household Questionnaire, the individual Woman’s Questionnaire, the individual Man’s Questionnaire, the Caregiver’s Questionnaire, and the Biomarker Questionnaire. These questionnaires, based on The DHS Program’s standard Demographic and Health Survey questionnaires, were adapted to reflect the population and health issues relevant to South Africa. Input was solicited from various stakeholders representing government ministries and agencies, nongovernmental organisations, and international donors. After the preparation of the questionnaires in English, the questionnaires were translated into South Africa’s 10 other official languages. In addition, information about the fieldworkers for the survey was collected through a self-administered Fieldworker Questionnaire. The Household Questionnaire was used to list all of the members of, and visitors to, selected households. Basic demographic information was collected on the characteristics of each person listed, including his or her age, sex, marital status, education, and relationship to the head of the household. For children under age 18, parents’ survival status was determined. The data obtained in the Household Questionnaire were used to identify women and men eligible to be interviewed with the relevant individual questionnaire, children whose caregiver was eligible for the Caregiver’s Questionnaire, and those persons eligible for the Biomarker Questionnaire. The Household Questionnaire also collected information on characteristics of the household’s dwelling unit, such as source of drinking water; type of sanitation facility; materials used for the floor, walls, and roof of the dwelling unit; and ownership of various durable goods. In addition, the questionnaire included a module based on questions developed by the Washington Group on Disability Statistics to estimate the prevalence of disabilities among individuals age 5 and older. The Woman’s Questionnaire was used to collect information from all eligible women age 15 and older. In all households, eligible women age 15-49 were asked questions on the following topics:  Background characteristics such as age, education, and media exposure  Birth history and child mortality  Knowledge and use of family planning methods  Antenatal, delivery, and postnatal care  Breastfeeding and infant feeding practices  Vaccinations and child illnesses  Marriage and sexual activity  Fertility preferences  Women’s work and partners’ background characteristics  Knowledge of HIV/AIDS and methods of HIV transmission  Adult and pregnancy-related mortality The Man’s Questionnaire was administered to all men age 15-59 in the subsample of households selected for the male survey. The Man’s Questionnaire collected much of the same information elicited by the Woman’s Questionnaire but was shorter because it did not contain a detailed reproductive history, questions on maternal and child health, or questions on adult and maternal mortality. Both the Woman’s and Man’s Questionnaires also included a module on adult health that captured information on use of tobacco, alcohol, and codeine-containing medications; consumption of fat, salt, sugar, fruit, and vegetables; health care-seeking behaviours; and self-reported prevalence of a variety of noncommunicable diseases. The module was administered to all men age 15 and older and to all women age 15 and older in the subsample of households selected for the male survey and biomarker collection. The Caregiver’s Questionnaire was used to collect information on children age 0-5 whose biological mother was deceased or did not live in the household. It gathered information on the child’s sociodemographic characteristics, vaccinations, and health in the 2 weeks prior to the survey. 4 • Introduction and Survey Methodology The Biomarker Questionnaire was used to record data on biomarkers (anthropometry, anaemia testing, blood pressure measurement, HbA1c testing, and HIV testing) collected from respondents by nurses. In addition, for adults age 15 and older, information on prescribed medications was recorded. The purpose of the Fieldworker Questionnaire was to collect basic background information on the people who were collecting data in the field, including the team supervisor, interviewers, and nurse. In this survey, interviewers used tablet computers to record responses during interviews. The tablets were equipped with Bluetooth technology to enable remote electronic transfer of files (transfer of assignment sheets from team supervisors to interviewers and transfer of completed questionnaires from interviewers to supervisors). The computer-assisted personal interviewing (CAPI) data collection system employed in the SADHS 2016 was developed by The DHS Program using the mobile version of CSPro. The CSPro software was developed jointly by the U.S. Census Bureau, The DHS Program, and Serpro S.A. The survey protocol was reviewed and approved by the SAMRC Ethics Committee and the ICF Institutional Review Board. 1.4 MEASURING IODINE CONTENT OF HOUSEHOLD SALT Salt collected from the subsample of households eligible for iodine testing was stored in 50-ml polypropylene tubes with screw-tops, away from direct light, and couriered to the SAMRC Iodine Laboratory in Cape Town in batches for testing. After the completion of the fieldwork, the iodine content of the salt was measured using the iodometric titration method recommended by the World Health Organization (WHO), globally recognised as the reference method (WHO/UNICEF/ICCIDD 2007). The titration method for salt fortified with potassium iodate (iodated salt) as described by DeMaeyer et al. (1979) has been slightly modified (Jooste and Strydom 2010). The principle of the method is that free iodine is liberated from the iodate in salt under acidic conditions. Potassium iodide solution is then added to keep the iodine in a dissolved state, and the dissolved free iodine (as triiodide) is titrated with standardised sodium thiosulfate solution, incorporating starch as an external (indirect) indicator. Specifically, 10 g salt was dissolved in 45 ml deionised water, followed by adding 2 ml of 2N sulfuric acid and 5 ml of 10% potassium iodide. The salt solution was kept in the dark for 10 minutes to reach optimal reaction time, and then titrated with sodium thiosulfate (0.005 N) with constant stirring until the reaction solution turned pale yellow. Next 2 ml starch solution (1% starch dissolved in 15% sodium chloride) was added, forming a blue starch-iodine complex, followed by continued titration with sodium thiosulfate, until the blue colour disappeared indicating that the equivalence point had been reached. As the amount of sodium thiosulfate used is proportional to the amount of free iodine liberated from the salt, the concentration of iodine in the salt sample was calculated based on the titrated volume (burette reading) of sodium thiosulfate using the formula below. The results are expressed as milligrams of iodine per kilogram (mg/kg) of salt or the equivalent parts of iodine per million parts of salt (ppm). Formula: mg/kg (ppm) iodine = titration volume in ml x normality of sodium thiosulfate (eq/l) x 21.15 (g/eq I) x 1,000 / salt sample weight in g The thiosulfate solution was standardised by using 5 ml of 0.005 N potassium iodate and following the same procedure for analysing a salt sample. The normality of the sodium thiosulfate solution is based on the law of equivalents: normality of sodium thiosulfate = normality potassium iodate x volume potassium iodate / volume sodium thiosulfate Introduction and Survey Methodology • 5 1.5 ANTHROPOMETRY, ANAEMIA TESTING, BLOOD PRESSURE MEASUREMENT, HBA1C TESTING, AND HIV TESTING In the subsample of households selected for the male survey and the adult health module, the SADHS 2016 incorporated the following biomarkers: anthropometry, anaemia testing, blood pressure measurement, HbA1c testing, and HIV testing. For each biomarker measurement or test for which an individual was eligible, the respondent or the child’s parent/guardian was required to provide written consent before the measurement or test could proceed. In the case of never-in-union respondents age 15-17, consent was required from both the respondent and the parent/guardian. All households in which children underwent anthropometry and/or were tested for anaemia were given a brochure on which the measurements were recorded. The brochure also explained the causes and prevention of anaemia. Similarly, each respondent age 15 and older received a different brochure on which relevant measurements were recorded. This brochure provided information about body mass index (BMI), anaemia, blood pressure, diabetes, and HIV. The brochure also included the national AIDS hotline number to enable respondents to locate nearby facilities that provide HIV testing and counselling. In contrast with the data collection procedure for the household and individual interviews, data related to all 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 for whom consent was obtained from their parents/guardians and for women and men age 15 and older who consented to measurement. Seca 878 digital scales, Seca 417 infantometers (for children under age 2), and Seca 213 portable stadiometers (for children age 2 and older and for adults) were used for these measurements. In addition, waist circumference was measured for women and men using a Seca 201 measuring tape. Anaemia testing. Blood specimens for anaemia testing were collected from women and men age 15 and older who consented to be tested and from children age 6-59 months for whom consent was obtained from their parents/guardians. Blood samples were drawn from a drop of blood taken from a finger prick (or a heel prick in the case of children age 6-11 months) and collected in a microcuvette. Haemoglobin analysis was carried out on-site using a battery-operated portable HemoCue 201+ analyser. Results were provided verbally and in writing. Parents/guardians of children with a haemoglobin level below 7 g/dl were instructed to take the child to a health facility for follow-up care. Likewise, nonpregnant women, pregnant women, and men were referred for follow-up care if their haemoglobin levels were below 7 g/dl, 9 g/dl, and 9 g/dl, respectively. Blood pressure. Three blood pressure measurements were taken from consenting women and men age 15 and older using Omron 1300 digital blood pressure monitors. Measurements were taken at intervals of 3 minutes or more. For the purpose of returning the result to the respondent, the third measurement was used to classify the respondent with respect to hypertension, according to internationally recommended categories (WHO 1999). Respondents who were informed that they had high blood pressure were provided with a written referral to a health facility for further management. HbA1c and HIV testing. Nurses collected finger-prick blood specimens for laboratory HbA1c and HIV testing of women and men age 15 and older who consented. The protocol for blood specimen collection and analysis was based on the anonymous linked protocol developed by The DHS Program. This protocol allows for merging of test results with the sociodemographic data collected in the individual questionnaires after removal of all information that could potentially identify an individual. Nurses explained the procedure, the confidentiality of the data, and the fact that the test results would not be made available to respondents. Blood for HbA1c and HIV testing was collected on a filter paper card. The card was preprinted with five circles, each of which could hold approximately 75 µl of blood and the 6 • Introduction and Survey Methodology first of which had been treated with a reagent required for HbA1c testing. If a respondent consented to both HbA1c and HIV testing, five blood spots from the finger prick were collected on the filter paper card, to which a barcode label unique to the respondent was affixed. Duplicate barcodes were attached to the Biomarker Questionnaire, one to indicate that the respondent had consented to HbA1c testing and another to indicate that the respondent had consented to HIV testing. A fourth copy of the same barcode was affixed to the dried blood spot (DBS) transmittal sheet to track the blood samples from the field to the laboratory. Respondents who consented to HIV testing were asked whether they would consent to having the laboratory store their blood sample for future unspecified testing. If respondents did not consent to additional testing using their sample, it was indicated on the Biomarker Questionnaire that they refused additional tests using their specimen, and the words “no additional testing” were written on the filter paper card. If the respondent consented only to HbA1c testing, a single blood drop was collected on the appropriate pretreated circle of the filter paper card to which the barcode label was affixed, and duplicate barcode labels were attached to the Biomarker Questionnaire and the DBS transmittal sheet. Blood samples were dried overnight and packaged for storage the following morning. Samples were periodically collected from the field and transported to the Global Clinical and Viral Laboratory (GCVL) in Durban. Upon arrival at GCVL, each blood sample was logged into the CSPro HIV Test Tracking System database, given a laboratory number, and stored at -20C until tested. The HbA1c and HIV testing protocols stipulated that blood could be tested only after questionnaire data collection had been completed, data had been verified and cleaned, and all unique identifiers other than the anonymous barcode number had been removed from the data file. To measure HbA1c, a common method was adapted for use with DBS specimens. Specifically, using a blood chemistry analyser, total haemoglobin concentration was measured by a colorimetric method monitoring the change in absorbance at 410 nm. HbA1c concentration was measured by a turbidimetric immunoinhibition method monitoring the change in absorbance at 340 nm. HbA1c concentration is expressed as a percentage of total haemoglobin. The HIV testing algorithm (Figure 1.2) called for testing all samples with an enzyme-linked immunosorbent assay (ELISA), the Genscreen HIV 1/2 Combi Assay (Bio-Rad). All samples that tested positive on the ELISA 1 were subjected to a second ELISA (ELISA 2), the E411 Cobas HIV 1/2 Combi Assay (Roche). Similar to samples that tested positive on the ELISA 1, 5% of the samples that tested negative on the ELISA 1 were also subjected to the ELISA 2 for internal quality control, while the other 95% were recorded as negative. Concordant negative results on the ELISA 1 and ELISA 2 were recorded as negative. If the results on the ELISA 1 and ELISA 2 were discordant, the two ELISAs were repeated. If the results remained discordant, the specimen was classified as inconclusive. Concordant positive results on the ELISA 1 and ELISA 2 were subjected to a third assay, the Geenius™ HIV1/2 confirmatory rapid test (Bio-Rad). When both the ELISA 1 and ELISA 2 were positive, the sample was classified as positive if the confirmatory rapid test was positive, and inconclusive if the confirmatory rapid test was negative or indeterminate. Introduction and Survey Methodology • 7 Figure 1.2 HIV testing algorithm 1.6 PRETEST The pretest for the SADHS 2016 consisted of classroom training and field practice. The classroom portion of the pretest was conducted 11-29 January 2016 at the Kopanong Hotel & Conference Centre in Benoni, Gauteng. The pretest fieldwork took place 1-5 February 2016 in five provinces: Eastern Cape, KwaZulu- Natal, Free State, Gauteng, and North West. Stats SA recruited three female interviewers, one male interviewer, one nurse, and one logistics officer from each of the five provinces selected for field practice, for a total of 30 fieldworkers. Coordinators from Stats SA’s provincial offices were trained as supervisors, for a total of five supervisors. Staff from Stats SA’s head office, SAMRC, and The DHS Program conducted training sessions. Nurses attended the first week of the main training of interviewers before breaking away for separate biomarker training. Following field practice, a daylong debriefing session was held with the pretest field staff at the Lakes Hotel & Conference Centre in Benoni. Modifications to the questionnaires, translations, and survey protocol were made based on lessons drawn from the exercise. 1.7 TRAINING OF FIELD STAFF Stats SA recruited and trained nearly 300 fieldworker candidates for the main training of field staff. This number made provision for male and female interviewers, supervisors, logistics officers/drivers, and nurses for 30 teams. Although only 210 fieldworkers were needed to conduct the survey, the number recruited and trained exceeded this figure so that (1) the top performers during the training could be selected for fieldwork and (2) there would be back-up fieldworkers in case anything happened to require replacing any of the appointed fieldworkers during the main data collection. The main training was conducted from 16 May 2016 to 17 June 2016 and took place at the Birchwood Hotel & Conference Centre in Kempton Park, Gauteng. For all fieldworker candidates except nurses and logistics officers, the training course consisted of instruction regarding interviewing techniques and field procedures, a detailed review of questionnaire 95% Inconclusive Indeterminate Inconclusive Inconclusive Positive PositiveNegative Negative Positive Negative Repeat Negative ELISA 1 & ELISA 2 NEGATIVE Geenius HIV 1/2 ELISA 1 & ELISA 2 POSITIVE ELISA 1 NEGATIVE & ELISA 2 POSITIVE ELISA 1 POSITIVE & ELISA 2 NEGATIVE Positive ELISA 1 & ELISA 2 Negative Negative Positive Internal Quality Control ELISA 2 5% ELISA 1 Genscreen HIV 1/2 Combi Assay ELISA 2 E411 Cobas HIV 1/2 Combi Assay E411 Cobas HIV 1/2 Combi Assay 8 • Introduction and Survey Methodology content, instruction on how to administer the paper and electronic questionnaires, and mock interviews between participants in the classroom. In addition, they were trained on map reading so that they could identify the sampled DUs in the field, and they received publicity training to ensure they were comfortable introducing themselves and explaining the purpose of the survey to respondents. A 1-day “on-site” field practice, held on 6 June 2016, paired trainees, and each had to complete a set of paper questionnaires. This provided them with an opportunity to familiarise themselves with the questionnaires in a closed environment. The completed questionnaires were later used during the CAPI training, when the data were entered into the electronic system. Nurses were trained to collect biomarker data, including taking height/length, weight, and waist measurements; testing for anaemia by measuring haemoglobin level; and preparing DBS specimens for subsequent HbA1c and HIV testing. The biomarker training was held 1-17 June 2016 and consisted of lectures, demonstrations of biomarker measurement or testing procedures, exercises aimed at standardisation of height and weight measurements, and practice with children at a health clinic. The logistics officers trained alongside the nurses to ensure that they would be able to support them. A 2-day field practice was organised on 14 and 15 June 2016 to provide trainees with hands-on practice before the actual fieldwork. A total of 30 teams were formed and participated in field practice. On the first day of field practice, each team consisted of a supervisor, a minimum of three female interviewers, one male interviewer, and one logistics officer (male). On the second day, each team was joined by one or more nurses. Training participants were evaluated through homework, in-class exercises, quizzes, and observations made during field practice. Ultimately, 120 (90 females and 30 males) were selected to serve as interviewers, 30 as nurses, 30 as field logistics officers/drivers, and 30 as team supervisors. The selection of team supervisors was based on their experience in leading survey teams and their performance during the main training. Following their selection, team supervisors received additional instruction and practice using the CAPI system to perform supervisory activities. These activities included assigning households for interviews and receiving completed interviews from interviewers, recognising and dealing with error messages, receiving system updates and distributing updates to interviewers, entering biomarker questionnaires and DBS transmittal sheets, dealing with duplicated cases, closing clusters, and transferring interviews to the Stats SA head office via a secure Internet file streaming system (IFSS). 1.8 FIELDWORK Data collection was carried out by 30 field teams, each consisting of one team supervisor, three female interviewers, one male interviewer, one nurse, and one logistics officer/driver. Electronic data files were transferred to the Stats SA head office in Pretoria every day via the secured IFSS. Senior staff from the Stats SA head office and provincial offices coordinated fieldwork activities. Stats SA also led fieldwork supervision, receiving support from SAMRC on the supervision of biomarker collection and from ICF on standard DHS supervision procedures. Field visits made by independent teams from Stats SA’s Survey Coordination, Monitoring and Evaluation chief directorate were an important aspect of supervision. At the midpoint of fieldwork, monitoring teams visited 84 completed clusters in four provinces (Gauteng, Free State, Western Cape, and KwaZulu-Natal) to confirm that the correct DUs had been visited, to ensure household members were correctly listed, and to verify nonresponse. Feedback was provided to provincial coordinators, and, where necessary, clusters were revisited. The survey data collection took place from 27 June 2016 to 4 November 2016. 1.9 DATA PROCESSING AND ANALYSIS All electronic data files for the SADHS 2016 were transferred via the IFSS to the Stats SA head office in Pretoria, where they were stored on a password-protected computer. The data processing operation included secondary editing, which required resolution of computer-identified inconsistencies and coding of Introduction and Survey Methodology • 9 open-ended questions. The data were processed by a core group of four people; secondary editing was completed by 11 people. All persons involved in data processing took part in the main fieldwork training, and they were supervised by senior staff from Stats SA with support from ICF. Data editing was accomplished using CSPro software. Secondary editing was initiated in October 2016 and completed in February 2017. Checking inconsistencies in dates of immunisations was aided by the digital images of the immunisation page of the Road-to-Health booklet that had been collected on the tablet by fieldworkers at the time of the interview for that purpose. Appropriate analysis weights were calculated, taking the design probabilities and the response rate into account. Standard methods of analysis (Rutstein and Rojas 2006) were applied involving conversion of all dates to century month codes to facilitate calculation of ages at the time of different life events. 1.10 RESPONSE RATES Table 1.1 shows response rates for the SADHS 2016. A total of 15,292 households were selected for the sample, of which 13,288 were occupied. Of the occupied households, 11,083 were successfully interviewed, yielding a response rate of 83%. In the interviewed households, 9,878 eligible women age 15-49 were identified for individual interviews; interviews were completed with 8,514 women, yielding a response rate of 86%. In the subsample of households selected for the male survey, 4,952 eligible men age 15-59 were identified and 3,618 were successfully interviewed, yielding a response rate of 73%. In this same subsample, 12,717 eligible adults age 15 and older were identified and 10,336 were successfully interviewed with the adult health module,4 yielding a response rate of 81%. Response rates were consistently lower in urban areas than in non- urban areas. Table 1.1 Results of the household and individual interviews Number of households, number of interviews, and response rates, according to residence (unweighted), South Africa DHS 2016 Residence Total Result Urban Non-urban Household interviews Households selected 9,547 5,745 15,292 Households occupied 8,397 4,891 13,288 Households interviewed 6,556 4,527 11,083 Household response rate1 78.1 92.6 83.4 Interviews with women age 15-49 Number of eligible women 5,858 4,020 9,878 Number of eligible women interviewed 4,805 3,709 8,514 Eligible women response rate2 82.0 92.3 86.2 Household interviews in subsample selected for male survey and adult health module Households selected 4,751 2,872 7,623 Households occupied 4,164 2,426 6,590 Households interviewed 3,240 2,237 5,477 Household response rate1 77.8 92.2 83.1 Interviews with men age 15-59 Number of eligible men 2,996 1,956 4,952 Number of eligible men interviewed 2,021 1,597 3,618 Eligible men response rate2 67.5 81.6 73.1 Interviews with adults age 15+ Number of eligible adults 7,463 5,254 12,717 Number of eligible adults interviewed 5,685 4,651 10,336 Eligible adults response rate2 76.2 88.5 81.3 1 Households interviewed/households occupied 2 Respondents interviewed/eligible respondents 4 The subsample of adults interviewed with the adult health module included all men age 15-59 and all women age 15-49 in those households selected for the male survey. Housing Characteristics and Household Population • 11 HOUSING CHARACTERISTICS AND HOUSEHOLD POPULATION 2 Key Findings  Drinking water: 92% of households use an improved source of water. Nearly all urban households (98%) use an improved source of water, as compared with only 80% of non-urban households.  Availability of water: 31% of households in South Africa using piped or borehole water reported having a water interruption of at least a single day in the last 2 weeks. Non-urban households are more likely than urban households to report an interruption (51% versus 23%).  Sanitation: 73% of households use an improved toilet facility, and 22% use a shared toilet facility of an otherwise acceptable type. Two percent of households use an unimproved facility, and 2% have no facility.  Electricity: Nine out of 10 households (90%) have electricity.  Household population and composition: 37% of the household population falls in a dependency age group: 30% are age 0-14, and 7% are age 65 or older. The average household consists of 3.4 members.  Orphans: Among children under age 18, 16% are orphans (one or both parents are dead), and almost one-quarter (23%) do not live with either biological parent.  School attendance: The net attendance ratio declines sharply from 88% in primary school to 77% in secondary school. Girls and boys are about equally likely to attend primary and secondary school.  Disability: Overall, 20% of the population age 5 and older was reported to have some level of difficulty in at least one functional domain. nformation on the socioeconomic characteristics of the household population in the SADHS 2016 provides a 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. This chapter presents information on source of drinking water, sanitation, exposure to smoke inside the home, wealth, handwashing, household population composition, educational attainment, school attendance, family living arrangements, disabilities, and food security among the household population. I 12 • Housing Characteristics and Household Population 2.1 DRINKING WATER SOURCES AND TREATMENT Improved sources of drinking water Include piped water, public taps, 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 Improved sources of water protect against outside contamination so that water is more likely to be safe to drink. In South Africa, 92% of households use an improved source of drinking water; almost all urban households (98%) report using an improved source of drinking water, as compared with only 80% of non- urban households (Table 2.1). By province, the proportion of the household population using an improved source of drinking water ranges from a low of 71% in Eastern Cape to a high of over 99% in Northern Cape (Figure 2.1). The most common source of drinking water for both urban and non-urban households is piped water, but the source of piped water differs (Figure 2.2). Among urban households, 62% use water piped into their dwelling, 24% use water piped into their yard/plot, and 2% use water piped to a neighbour. In contrast, only 9% of non-urban households have water piped into their dwelling, 29% have water piped into their yard/plot, and 5% use water piped to a neighbour. One in 10 (9%) urban households use a public or communal tap, as compared with one in four (25%) non-urban households. While 0.2% of urban households use surface water as their source of drinking water, 7% of non-urban households rely on it. Overall, 78% of South African households have water on the premises. Eight percent of households travel 30 minutes or longer to fetch water. Most households (92%) report that they do not treat their water prior to drinking. Table 2.2 presents information on the percentage of households using piped water or boreholes that reported availability of water in the last 2 weeks. Thirty-one percent of households in South Africa using piped or borehole water reported having a water interruption of at least a single day in the last 2 weeks. Non-urban households are more likely to report an interruption than urban households (51% versus 23%). Comparison with the SADHS 1998: The percentage of households obtaining water from improved sources increased from 85% in 1998 to 92% in 2016. Whereas in 1998 only 39% of households had water piped into their dwelling and 23% into their yard or plot, 45% reported having water piped into their dwelling and 26% Figure 2.1 Improved source of drinking water by province Percentage of the household population using an improved source of drinking water Figure 2.2 Household drinking water by residence 45 62 9 26 24 29 3 2 5 14 9 25 3 0.3 8 2 1 3 8 2 20 Total Urban Non-urban Unimproved source Other protected source Borehole Public/communal tap Piped to neighbour Piped into yard/plot Piped water into dwelling Percent distribution of households by source of drinking water Note: Percentages do not sum to 100% due to rounding. Housing Characteristics and Household Population • 13 into their yard or plot in 2016. Most of the observed gains in access to improved water sources came from an increase in the proportion of non-urban households using water piped into their yard/plot (17% in 1998 versus 29% in 2016). 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; composting toilets; and chemical toilets. Sample: Households As shown in Figure 2.3, nearly three-quarters (73%) of households in South Africa use improved toilet facilities, which are non-shared facilities that prevent people from coming into contact with human waste and can reduce the transmission of cholera, typhoid, and other diseases. Shared toilet facilities of an otherwise acceptable type are also common, especially in urban areas; 27% of urban households use a shared facility, as compared with 12% of non- urban households. Two percent of households in South Africa use unimproved facilities, with an additional 2% not using any facility (Table 2.3). Comparison with the SADHS 1998: The percentage of households not using any toilet facility decreased from 12% in 1998 to 2% in 2016. Most of this change is due to improvements in non-urban areas; 26% of non-urban households did not use a toilet facility in 1998, compared with 5% in 2016. 2.3 EXPOSURE TO SMOKE INSIDE THE HOME Exposure to smoke inside the home, either from cooking with solid fuels or smoking tobacco, has potentially harmful health effects. Thirteen percent of households in South Africa use solid fuels, consisting mostly of wood, for cooking (Table 2.4). Use of solid fuels for cooking is much more common in non-urban areas (33%) than urban areas (3%). Exposure to smoke from cooking is greater when cooking takes place inside the house rather than in a separate building or outdoors. In South Africa, the majority of households (87%) cook inside their house. Eight percent of households cook in a separate building, and 4% cook outside. Exposure to tobacco smoke is common in South Africa. In 20% of households, someone smokes inside the house on a daily basis, and in 2% of households, someone smokes inside on a weekly basis. Other Housing Characteristics The survey collected data on access to electricity, dwelling type, flooring materials, wall materials, and the number of rooms used for sleeping. Overall, 90% of households in South Africa have electricity, and 78% live in a formal dwelling type. The most common flooring materials are cement (39% of households) and ceramic tiles (33%), while the most common materials used for walls are bricks (32%) and cement (28%). Thirty-five percent of households use one room for sleeping. Figure 2.3 Household toilet facilities by residence 73 70 81 22 27 12 2 3 1 2 1 5 Total Urban Non-urban No facility/ bush/field Unimproved facility Shared facility Improved facility Percent distribution of households by type of toilet facilities Note: Percentages do not sum to 100% due to rounding. 14 • Housing Characteristics and Household Population Household Refuse Disposal As shown in Table 2.5, 57% of households in South Africa report that their refuse is removed at least once a week, 11% have their own refuse dump, and 20% burn their refuse. Households in non-urban areas are far more likely than those in urban areas to use their own refuse dump (25% versus 5%) or burn their refuse (54% versus 4%). Refuse burning is especially common in Limpopo and Mpumalanga (48% and 45%, respectively). 2.4 HOUSEHOLD WEALTH Household Durable Goods The survey collected information on household effects, means of transportation, and ownership of farm animals. As shown in Table 2.6, 96% of households own a cellphone, 84% an electric or gas stove, 77% a television, 61% a radio, and 22% a computer. Almost 3 in 10 households own an automobile (29%), and 8% own a bicycle. Non-urban households are much more likely to own farm animals than urban households (38% versus 5%). 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 2.7 presents the distribution of the de jure household population by wealth quintile according to residence and province. In South Africa, urban households are more likely than non-urban households to fall into the higher wealth quintiles, while non-urban households are more likely to fall into the lower wealth quintiles. Fifty-nine percent of the urban population is in the two highest wealth quintiles. By contrast, 73% of the non-urban population falls in the two lowest wealth quintiles (Figure 2.4). Wealth varies widely by province. Seventy-eight percent of the population in Western Cape is in the two highest wealth quintiles, as compared with only 14% of the population in Limpopo. Forty-two percent of the population in Eastern Cape is in the lowest wealth quintile, compared with only 3% in Western Cape. 2.5 HANDWASHING Handwashing is an important step in improving hygiene and preventing the spread of disease. Rather than asking direct questions on the practice of handwashing, which can be subject to overreporting, interviewers in the SADHS 2016 asked to see the place where members of the household most often wash their hands. A Figure 2.4 Household wealth by residence 10 38 12 35 20 21 29 5 31 1 Urban Non-urban Percent distribution of de jure population by wealth quintiles Wealthiest Fourth Middle Second Poorest Note: Percentages do not sum to 100% due to rounding. Housing Characteristics and Household Population • 15 place for washing hands was observed in 85% of households (Table 2.8). In half (50%) of the households where a place for handwashing was observed, interviewers observed soap and water. One-third of handwashing locations (34%) had water but no soap, 1% had soap but no water, and 14% did not have soap, water, or any other cleaning agents. The percentage of households with a place for handwashing in which no water, soap, or other cleansing agent was observed was markedly higher in non-urban areas than urban areas (26% and 9%, respectively). 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. A total of 37,128 individuals stayed overnight in the 11,083 sample households in the SADHS 2016. Fifty-two percent (19,407) were female, and 48% (17,721) were male (Table 2.9). The population pyramid in Figure 2.5 illustrates the distri- bution by 5-year age groups and sex. Children under age 15 account for 30% of the population, while individuals age 65 and older make up only 7%. As shown in Table 2.10, the majority of households in South Africa are male-headed (57%). The average household consists of 3.4 usual members. Non-urban house- holds are on average larger than urban households (3.8 and 3.1 persons per household, respectively). Overall, 22% of households in South Africa are caring for foster or orphaned children. Comparison with the SADHS 1998: The percentage of the population below age 15 decreased from 38% in 1998 to 30% in 2016, while the percentage of the population age 65 or older held relatively steady, increasing from 6% in 1998 to 7% in 2016. Over this same period, the average household size decreased from 4.2 persons to 3.4 persons, while there was essentially no change in the percentage of female-headed Figure 2.5 Population pyramid 8 6 4 2 0 2 4 6 8 <5 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80+ Age Percent distribution of the household population Male Female 24128 6 16 • Housing Characteristics and Household Population households (42% in 1998 versus 43% in 2016). The percentage of households with foster and/or orphan children dropped from 28% in 1998 to 22% in 2016. 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 Twenty-eight percent of children under age 18 are living with both parents, and 23% are not living with a biological parent (Table 2.11). Sixteen percent of children under age 18 are orphans, meaning that one or both parents have died. The percentage of children who are orphans rises rapidly with age, from 2% among children under age 2 to 13% among children age 5-9 and 32% among children age 15-17 (Figure 2.6). Free State has the highest percentage of children who are orphans (25%). Comparison with the SADHS 2016: The percentage of children under age 15 living with both parents decreased from 33% in 1998 to 29% in 2016, while the percentage not living with a biological parent decreased from 25% to 22%. The percentage of children under age 15 who are orphans increased from 10% to 14%. 2.8 EDUCATION 2.8.1 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 Tables 2.12.1 and 2.12.2 present information on educational attainment among the household population age 6 and over. Overall, 9% of women and girls age 6 and over have never been to school, 22% have attended some primary school, 6% have completed primary but advanced no further, 34% have attended some secondary school, 19% have completed secondary school but advanced no further, and 9% have attained some education after secondary school. Women and girls age 6 and over have completed a median of 8.8 years of schooling. Educational attainment among men and boys is similar to that among women and girls. Eight percent of men and boys age 6 and over have never attended school, 25% have attended some primary school, 6% have completed primary school, 33% have attended some secondary school, 19% have completed secondary school, and 8% have attained some education after secondary school. Men and boys age 6 and over have completed a median of 8.5 years of schooling. Comparison with the SADHS 1998: Educational attainment at the household level has increased since 1998. Among women and girls age 6 and over, the percentage who have never attended school has decreased from 14% to 9%, and the median number of years of schooling has increased from 6.4 to 8.8. Among men Figure 2.6 Orphanhood by age 2 6 13 25 32 <2 2-4 5-9 10-14 15-17 Percentage of children with one or both parents dead Age in years Housing Characteristics and Household Population • 17 and boys age 6 and over, the percentage who have never attended school has decreased from 11% to 8%, and the median number of years of schooling completed has increased from 6.4 to 8.5. Patterns by background characteristics  The median number of years of schooling is higher in urban areas than in non-urban areas among both females (9.6 years versus 7.0 years) and males (9.4 years versus 6.7 years).  The percentage of females and males with no education is higher in non-urban areas than urban areas (14% versus 7% for females and 10% versus 6% for males).  Educational attainment increases with increasing household wealth. Females in the lowest wealth quintile have completed a median of 6.3 years of schooling, as compared with a median of 11.2 years among females in the highest wealth quintile. The median number of years of schooling increases from 6.2 years among males in the lowest wealth quintile to 11.1 among those in the highest quintile.  Among both females and males, the median number of years of schooling is highest in Gauteng (10.1 years and 9.7 years, respectively) and lowest in Eastern Cape (7.4 years and 7.1 years, respectively). 2.8.2 School Attendance Net attendance ratio (NAR) Percentage of the school-age population that attends primary or secondary school Sample: Children age 7-13 for primary school NAR and children age 14-18 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 7-13 for primary school GAR and children age 14-18 for secondary school GAR School attendance ratios are shown in Table 2.13. Eighty-seven percent of girls age 7-13 attend primary school, as compared with 90% of boys. The net attendance ratio (NAR) drops in secondary school: 77% of boys and 76% of girls attend secondary school. The gross attendance ratio (GAR) for primary school is 109 for girls and 115 for boys; the GAR for secondary school is 110 for girls and 108 for boys. These figures indicate that a number of children outside the official school-age population for that level are attending primary or secondary school. Gender parity indices (GPI) The ratio of female to male students attending primary school and the ratio of female to male students attending secondary school. Each index reflects the magnitude of the gender gap. Sample: Primary school students and secondary school students The gender parity index (GPI) for the GAR at the primary school level is 0.95, indicating that in primary school there are slightly more male students than female students. However, at the secondary school level, the GPI for the GAR is 1.01, indicating that there is little disparity in secondary school attendance between boys and girls. 18 • Housing Characteristics and Household Population Age-specific attendance rate (ASAR) Children and young people attending school, irrespective of whether they are attending the appropriate grade for their age. Sample: De facto household population age 5-24 attending school ASARs for the population age 5 to 24 are presented in Figure 2.7 by age and sex. The ASAR indicates participation in schooling at any level, from primary to higher levels of education. The patterns are generally the same for males and females. By age 6, 7 in 10 children attend school. Between age 7 and age 16, more than 90% of children attend school. The attendance rate declines rapidly from age 17 to 24. Patterns by background characteristics  At the primary school level, there is little difference in the NAR between non-urban and urban areas (89% and 88%, respectively). However, at the secondary school level, the NAR is modestly higher in non-urban areas than in urban areas (79% versus 75%).  Among provinces, Limpopo has the highest NAR and GAR at the secondary school level (87% and 135%, respectively), and Western Cape has the lowest (71% and 92%, respectively). 2.9 DISABILITY The SADHS 2016 included The DHS Program’s disability module, a series of questions from the Washington Group on Disability Statistics (WG) 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—and provide basic necessary information on disability comparable to that being collected worldwide via the WG disability tools. 2.9.1 Disability by Domain and Age The respondent to the Household Questionnaire provided information for all household members and visitors on whether they had no difficulty, some difficulty, a lot of difficulty, or no ability at all in each of the functional domains. Results, based on 33,155 persons, are presented in Table 2.14 for the de facto household population age 5 and older. 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 Overall, 20% of the population age 5 and older was reported to have some level of difficulty in at least one functional domain. Six percent of the population was reported to either have a lot of difficulty functioning Figure 2.7 Age-specific attendance rates 0 10 20 30 40 50 60 70 80 90 100 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Percentage of the de facto household population age 5-24 attending school Female Male Age in years Housing Characteristics and Household Population • 19 in at least one domain or could not function in a domain at all. Those age 60 or older (23%) were much more likely than other age groups to have a lot of difficulty or not be able to function in at least one domain. The most common disability was having difficulty seeing (12%), followed by having difficulty walking or climbing steps (6%) and difficulty remembering or concentrating (6%). 2.9.2 Disability by Background Characteristics Table 2.15 presents disability data among the de facto household population age 5 and older by background characteristics. Women are slightly more likely than men to have difficulties in any functional domain; 22% of women have at least some level of disability in any domain, as compared with 18% of men. The percentage of the population in non-urban areas with one or more disability is greater than the percentage in urban areas (23% versus 19%). Surprisingly, among those receiving a disability grant, 43% are reported to have no difficulty in any domain. 2.10 SOCIAL GRANTS AND FOOD SECURITY 2.10.1 Social Grants by Background Characteristics A social grant is a sum of money typically paid monthly by the government to qualifying citizens. The types of grants available range from old age grants to social relief of distress. Seventy-one percent of South African households know at least one place where they can obtain social grant forms (Table 2.16). Among these households, 97% named the South African Social Security Agency (SASSA), the Department of Social Development (DSD), or a social development office as a place where grant forms can be obtained. Post offices, banks, magistrate’s courts, and pay points are among the other places where forms can be obtained, but less than 3% of households named any of these locations. The respondent to the Household Questionnaire was asked whether household members and visitors to the household received any social grants and, if so, which types. Overall, 35% of the household population receives at least one type of grant (Table 2.17). Child support is the most common type of grant, received by 24% of the household population. Patterns by background characteristics ▪ Non-urban households are more likely to know where to obtain forms for social grants than urban households (78% versus 68%) (Table 2.16). ▪ Children age 0-17 (67%-70%) and adults age 60 and older (77%) are much more likely than those in other age groups to receive a grant (Figure 2.8). ▪ Receipt of grants does not differ by sex; 35% of the male and female household population receives some type of social grant (Table 2.17). ▪ Receipt of a social grant is more common among persons living in non-urban than urban households (48% versus 27%). ▪ The proportion of the household population that receives a social grant is highest in Eastern Cape and Limpopo (45% each) and lowest in Gauteng (23%). Figure 2.8 Receipt of social grants 67 70 2 2 4 10 77 <5 5-17 18-29 30-39 40-49 50-59 ≥60 Percentage of household population that receives any social grant by age Age in years 20 • Housing Characteristics and Household Population  Receipt of social grants decreases with increasing household wealth. Forty-seven percent of the household population in the lowest wealth quintile receives social grants, as compared with 15% in the highest quintile. 2.10.2 Food Security by Background Characteristics Households were asked two questions related to food security. First, they were asked whether any adults in the household had gone hungry in the past 12 months because there was not enough food. Second, they were asked whether any child in the household had gone hungry in the past 12 months. Nearly all households included at least one adult, and among these households 82% had never experienced problems satisfying adult food needs in the past 12 months, 4% seldom experienced problems, 11% sometimes experienced problems, 2% often experienced problems, and 1% always experienced problems (Table 2.18). Among the households interviewed, only 53% had children age 0-17 as household members. Among these households, 80% reported never experiencing problems satisfying child food needs in the past 12 months, 4% seldom experienced problems, 11% sometimes experienced problems, 2% often experienced problems, and 1% always experienced problems (Table 2.19). 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 refuse disposal  Table 2.6 Household possessions  Table 2.7 Wealth quintiles  Table 2.8 Handwashing  Table 2.9 Household population by age, sex, and residence  Table 2.10 Household composition  Table 2.11 Children’s living arrangements and orphanhood  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 the household population according to background characteristics  Table 2.16 Knowledge of where social grant forms may be obtained  Table 2.17 Social grants  Table 2.18 Food security: Adults  Table 2.19 Food security: Children 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, South Africa DHS 2016 Households Population Characteristic Urban Non-urban Total Urban Non-urban Total Source of drinking water Improved source 98.1 79.6 92.2 98.5 76.3 90.4 Piped into dwelling 61.8 9.3 45.1 66.6 8.5 45.5 Piped into yard/plot 24.4 28.5 25.7 22.0 27.5 24.0 Piped to neighbour 1.6 5.4 2.8 1.4 5.2 2.8 Public/communal tap 8.9 25.1 14.1 7.3 24.7 13.6 Borehole 0.3 8.1 2.8 0.2 7.4 2.8 Protected well 0.0 0.5 0.2 0.0 0.5 0.2 Protected spring 0.1 0.8 0.3 0.1 0.9 0.4 Rainwater 0.0 1.6 0.5 0.0 1.3 0.5 Bottled water, improved source for cooking/handwashing1 1.1 0.3 0.8 0.9 0.2 0.6 Unimproved source 1.7 19.9 7.5 1.3 23.3 9.3 Unprotected well 0.0 3.9 1.3 0.0 5.0 1.8 Unprotected spring 0.1 2.6 0.9 0.1 3.1 1.2 Water carrier/tanker truck 1.4 3.8 2.1 1.0 3.7 2.0 Cart with small tank/water vendor 0.1 2.4 0.8 0.0 2.3 0.9 Surface water 0.2 7.0 2.3 0.2 9.1 3.4 Bottled water, unimproved source for cooking/handwashing1 0.1 0.2 0.1 0.0 0.2 0.1 Other source 0.2 0.5 0.3 0.1 0.4 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 90.0 51.8 77.8 91.8 48.7 76.1 Less than 30 minutes 8.3 25.9 13.9 6.6 26.4 13.8 30 minutes or longer 1.6 20.6 7.7 1.5 23.4 9.5 Don’t know 0.1 1.6 0.6 0.1 1.5 0.6 Total 100.0 100.0 100.0 100.0 100.0 100.0 Water treatment prior to drinking3 Boiled 5.4 3.6 4.8 5.3 3.4 4.6 Bleach/chlorine added 0.5 2.8 1.2 0.4 3.0 1.3 Strained through cloth 0.0 0.1 0.1 0.0 0.1 0.0 Ceramic, sand, or other filter 1.9 0.5 1.4 1.7 0.4 1.2 Solar disinfection 0.1 0.0 0.0 0.0 0.0 0.0 Let stand and settle 0.4 0.9 0.5 0.4 1.1 0.7 Other 0.2 0.1 0.2 0.2 0.1 0.2 No treatment 91.8 92.1 91.9 92.1 92.3 92.2 Percentage using an appropriate treatment method4 7.6 6.6 7.3 7.3 6.2 6.9 Number of households/population 7,542 3,541 11,083 23,656 13,549 37,205 1 Households using bottled water for drinking are classified as using an improved or unimproved source according to their water source for cooking and handwashing 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 Percent distribution of households and de jure population using piped water or water from a borehole, by availability of water in the last 2 weeks, according to residence, South Africa DHS 2016 Households Population Availability of water in last 2 weeks Urban Non-urban Total Urban Non-urban Total Not available for at least 1 day 22.8 51.4 30.5 24.0 54.9 33.3 Available with no interruption of at least 1 day 75.9 47.6 68.3 74.9 44.1 65.6 Don’t know 1.3 1.0 1.2 1.1 1.0 1.1 Total 100.0 100.0 100.0 100.0 100.0 100.0 Number using piped water or water from a borehole1 7,392 2,713 10,105 23,287 9,953 33,240 1 Includes households/population reporting piped water or water from a borehole as their main source of drinking water and households/population reporting bottled water as their main source of drinking water if their main source of water for cooking and handwashing is piped water or water from a 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, South Africa DHS 2016 Households Population Type and location of toilet/latrine facility Urban Non-urban Total Urban Non-urban Total Improved sanitation 69.5 81.4 73.3 77.8 86.6 81.0 Flush/pour flush to piped sewer system 60.0 7.1 43.1 67.6 6.1 45.2 Flush/pour flush to septic tank 0.9 1.7 1.2 1.0 1.2 1.0 Flush/pour flush to pit latrine 0.9 0.7 0.9 0.8 0.6 0.7 Ventilated improved pit (VIP) latrine 1.1 14.8 5.5 1.0 16.4 6.6 Pit latrine with ventilation pipe but no gauze mesh/netting 1.1 19.5 7.0 1.2 22.4 8.9 Pit latrine with a slab without ventilation pipe 5.2 36.2 15.1 5.9 38.5 17.8 Composting toilet/ecological sanitation system 0.3 1.4 0.6 0.3 1.4 0.7 Unimproved sanitation 30.5 18.6 26.7 22.2 13.4 19.0 Shared facility1 26.9 11.9 22.1 19.4 7.2 14.9 Flush/pour flush to piped sewer system 19.3 1.8 13.7 14.0 0.7 9.1 Flush/pour flush to septic tank 0.5 0.7 0.6 0.4 0.2 0.3 Flush/pour flush to pit latrine 0.5 0.1 0.4 0.3 0.1 0.2 Ventilated improved pit (VIP) latrine 0.4 1.1 0.7 0.3 0.7 0.5 Pit latrine with ventilation pipe but no gauze mesh/netting 0.5 2.0 1.0 0.6 1.6 1.0 Pit latrine with a slab without ventilation pipe 4.2 6.1 4.8 2.8 3.8 3.2 Composting toilet/ecological sanitation system 0.6 0.0 0.4 0.4 0.0 0.3 Chemical toilet 0.9 0.1 0.6 0.6 0.0 0.4 Unimproved facility 2.5 1.3 2.2 2.1 1.3 1.8 Flush/pour flush not to sewer/septic tank/pit latrine 0.6 0.4 0.6 0.5 0.3 0.4 Bucket 1.9 0.9 1.6 1.6 0.9 1.4 Open defecation (no facility/bush/field) 1.0 5.4 2.4 0.7 4.9 2.2 Total 100.0 100.0 100.0 100.0 100.0 100.0 Number of households/population 7,542 3,541 11,083 23,656 13,549 37,205 Location of toilet facility In own dwelling 55.9 12.4 42.4 59.0 11.2 42.1 In own yard/plot 37.7 84.6 52.2 36.1 86.5 54.0 Elsewhere 6.5 3.1 5.4 4.9 2.3 4.0 Total 100.0 100.0 100.0 100.0 100.0 100.0 Number of households/population with a toilet/latrine facility 7,468 3,351 10,819 23,486 12,885 36,371 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, South Africa DHS 2016 Households Population Housing characteristic Urban Non-urban Total Urban Non-urban Total Electricity Yes 91.6 86.6 90.0 93.6 85.9 90.8 No 8.4 13.4 10.0 6.4 14.1 9.2 Total 100.0 100.0 100.0 100.0 100.0 100.0 Dwelling type Formal 78.5 76.3 77.8 81.6 75.7 79.5 Traditional 0.3 16.3 5.4 0.3 18.7 7.0 Informal 20.3 6.5 15.9 17.2 5.0 12.8 Other 0.8 1.0 0.9 0.8 0.5 0.7 Total 100.0 100.0 100.0 100.0 100.0 100.0 Flooring material Earth, sand 0.8 2.0 1.2 0.5 2.3 1.2 Dung 0.4 7.0 2.5 0.3 7.9 3.1 Wood/planks 1.9 0.4 1.4 1.6 0.3 1.2 Laminated or polished wood 2.5 0.7 2.0 2.5 0.4 1.8 Vinyl or asphalt strips 8.6 5.5 7.6 8.2 5.1 7.1 Ceramic tiles 39.5 17.8 32.6 44.5 19.3 35.3 Cement 31.1 54.9 38.7 29.2 54.2 38.3 Carpet 14.6 11.2 13.5 12.6 10.1 11.7 Other 0.6 0.5 0.5 0.5 0.2 0.4 Total 100.0 100.0 100.0 100.0 100.0 100.0 Wall material No walls 1.8 2.0 1.9 1.5 1.9 1.6 Dirt/mud 0.7 9.8 3.6 0.6 11.1 4.4 Plastic 0.7 0.1 0.5 0.6 0.2 0.4 Wattle and daub 0.1 0.3 0.1 0.1 0.4 0.2 Stone with mud 0.2 1.0 0.4 0.1 1.4 0.6 Mud with cement mix 0.7 4.2 1.8 0.7 4.5 2.1 Cardboard 1.0 0.3 0.8 0.7 0.2 0.5 Reused wood 0.8 0.2 0.6 0.7 0.2 0.5 Cement 23.3 37.0 27.7 24.6 37.2 29.2 Stone with lime/cement 3.4 3.3 3.4 3.5 3.4 3.4 Bricks 36.1 22.0 31.6 37.5 21.1 31.5 Cement block/concrete 13.6 12.8 13.4 14.1 13.1 13.8 Wood planks 1.0 0.2 0.7 1.0 0.2 0.7 Corrugated iron/zinc 15.9 5.6 12.6 13.6 4.2 10.1 Other 0.8 1.1 0.9 0.8 1.1 1.0 Total 100.0 100.0 100.0 100.0 100.0 100.0 Rooms used for sleeping One 38.3 28.4 35.2 25.5 16.7 22.3 Two 33.8 27.4 31.7 37.2 26.5 33.3 Three or more 27.9 44.3 33.1 37.3 56.8 44.4 Total 100.0 100.0 100.0 100.0 100.0 100.0 Place for cooking In the house 97.3 64.7 86.9 97.1 57.8 82.8 In a separate building 1.6 22.6 8.3 2.0 28.8 11.7 Outdoors 0.7 11.9 4.3 0.8 13.2 5.3 No food cooked in household 0.3 0.7 0.4 0.1 0.2 0.2 Other 0.0 0.1 0.0 0.0 0.0 0.0 Total 100.0 100.0 100.0 100.0 100.0 100.0 Cooking fuel Electricity from mains 85.2 58.3 76.6 86.1 52.7 73.9 Electricity from other sources 0.6 0.2 0.5 0.6 0.2 0.4 Gas 4.4 3.6 4.1 4.6 3.3 4.1 Paraffin 6.8 3.8 5.9 5.3 2.4 4.3 Coal 0.7 1.4 0.9 0.7 2.2 1.3 Wood 1.6 31.7 11.2 2.0 38.8 15.4 Agricultural crop 0.0 0.0 0.0 0.0 0.0 0.0 Animal dung 0.4 0.2 0.3 0.5 0.2 0.4 Other 0.1 0.0 0.1 0.0 0.0 0.0 No food cooked in household 0.3 0.7 0.4 0.1 0.2 0.2 Total 100.0 100.0 100.0 100.0 100.0 100.0 Percentage using solid fuel for cooking1 2.6 33.4 12.5 3.2 41.2 17.0 Percentage using clean fuel for cooking2 90.2 62.0 81.2 91.3 56.1 78.5 Frequency of smoking in the home Daily 22.0 16.8 20.3 22.5 16.2 20.2 Weekly 2.3 2.5 2.3 2.2 2.5 2.3 Monthly 0.3 0.3 0.3 0.3 0.4 0.3 Less than once a month 0.8 0.9 0.9 0.8 1.0 0.9 Never 74.6 79.5 76.2 74.2 80.0 76.3 Total 100.0 100.0 100.0 100.0 100.0 100.0 Number of households/population 7,542 3,541 11,083 23,656 13,549 37,205 1 Includes coal, wood, straw/shrubs/grass, agricultural crops, and animal dung 2 Includes electricity and gas 24 • Housing Characteristics and Household Population Table 2.5 Household refuse disposal Percent distribution of households by household refuse removal, according to background characteristics, South Africa DHS 2016 Characteristic Removed at least once a week Removed less than once a week Communal refuse dump Communal container/ central collection point Own refuse dump Own refuse burned No rubbish disposal Other Total Number of households Residence Urban 78.3 4.9 4.6 2.2 4.5 4.4 0.9 0.2 100.0 7,542 Non-urban 12.1 1.1 4.0 0.9 25.1 53.7 2.7 0.4 100.0 3,541 Province Western Cape 89.0 1.1 2.0 4.8 1.6 1.0 0.4 0.1 100.0 1,206 Eastern Cape 35.5 2.2 3.3 2.3 18.8 34.7 3.1 0.1 100.0 1,301 Northern Cape 61.8 1.6 3.1 0.7 10.6 19.8 2.3 0.0 100.0 210 Free State 76.3 3.1 2.6 0.1 6.5 7.1 3.9 0.4 100.0 579 KwaZulu-Natal 50.5 2.5 1.4 1.5 14.0 28.6 0.9 0.5 100.0 1,968 North West 52.8 1.2 8.4 0.9 11.6 22.4 2.0 0.8 100.0 833 Gauteng 76.5 7.4 7.1 1.7 6.2 1.1 0.0 0.0 100.0 3,047 Mpumalanga 28.8 6.9 3.8 1.3 11.9 45.2 2.1 0.0 100.0 851 Limpopo 19.8 0.5 5.1 1.3 22.1 47.7 3.1 0.4 100.0 1,087 Total 57.1 3.7 4.4 1.8 11.1 20.1 1.4 0.3 100.0 11,083 Table 2.6 Household possessions Percentage of households possessing various household effects, means of transportation, and livestock/farm animals by residence, South Africa DHS 2016 Residence Total Possession Urban Non-urban Household effects Radio 63.1 55.9 60.8 Television 81.8 66.2 76.8 Cellphone 95.8 95.4 95.7 Computer 27.7 9.1 21.8 Non-mobile telephone 11.0 1.0 7.8 Refrigerator 77.9 67.8 74.7 Vacuum cleaner/floor polisher 17.4 4.2 13.2 Microwave oven 58.6 33.1 50.5 Electric/gas stove 88.2 74.0 83.7 Washing machine 44.0 17.2 35.4 A watch 52.5 30.2 45.4 Means of transport Bicycle 8.9 5.4 7.8 Animal-drawn cart 1.0 1.3 1.1 Motorcycle/scooter 3.1 0.9 2.4 Car/bakkie/van/truck 34.3 16.8 28.7 Boat with a motor 0.7 0.3 0.6 Ownership of farm animals1 4.5 38.2 15.2 Number 7,542 3,541 11,083 1 Cattle, horses, donkeys, goats, sheep, pigs, chickens, or other poultry Housing Characteristics and Household Population • 25 Table 2.7 Wealth quintiles Percent distribution of the de jure population by wealth quintiles, and the Gini coefficient, according to residence and province, South Africa DHS 2016 Wealth quintile Total Number of persons Gini coefficient1 Residence/province Lowest Second Middle Fourth Highest Residence Urban 9.6 11.5 19.5 28.7 30.7 100.0 23,656 0.14 Non-urban 38.2 34.8 20.9 4.7 1.3 100.0 13,549 0.23 Province Western Cape 2.7 7.5 11.8 32.1 45.8 100.0 4,071 0.14 Eastern Cape 42.4 16.8 14.8 17.0 9.0 100.0 4,728 0.31 Northern Cape 11.8 18.9 23.7 29.0 16.7 100.0 784 0.18 Free State 8.0 11.5 31.8 32.7 16.1 100.0 1,967 0.17 KwaZulu-Natal 25.5 22.6 20.6 15.8 15.4 100.0 6,939 0.28 North West 14.7 29.1 30.3 18.7 7.3 100.0 2,534 0.16 Gauteng 12.3 12.9 19.0 24.0 31.9 100.0 9,293 0.14 Mpumalanga 23.9 29.3 26.5 12.3 8.0 100.0 3,011 0.22 Limpopo 27.6 40.7 17.8 7.7 6.2 100.0 3,880 0.24 Total 20.0 20.0 20.0 20.0 20.0 100.0 37,205 0.20 1 The Gini coefficient presented is based on household wealth, not income Table 2.8 Handwashing 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 handwashing was observed, and among households in which the place for handwashing was observed, percent distribution by availability of water, soap, and other cleansing agents, according to background characteristics, South Africa DHS 2016 Percentage of households in which place for washing hands was observed: Number of households Among households in which place for handwashing 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 67.3 19.7 87.0 7,542 57.0 1.1 31.8 1.3 0.0 8.8 100.0 6,560 Non-urban 27.9 54.2 82.1 3,541 34.5 0.7 37.7 1.3 0.0 25.8 100.0 2,907 Province Western Cape 81.0 10.4 91.3 1,206 86.1 1.0 12.0 0.0 0.0 0.8 100.0 1,102 Eastern Cape 42.4 39.5 81.9 1,301 45.4 0.7 36.3 0.6 0.0 17.0 100.0 1,065 Northern Cape 67.5 22.9 90.3 210 54.2 0.6 33.5 1.2 0.0 10.6 100.0 190 Free State 64.5 22.1 86.6 579 38.1 0.3 47.9 0.9 0.0 12.7 100.0 502 KwaZulu-Natal 55.2 25.0 80.3 1,968 49.3 1.4 30.7 1.8 0.0 16.8 100.0 1,579 North West 47.3 48.2 95.5 833 35.5 0.1 45.7 1.4 0.1 17.1 100.0 796 Gauteng 61.2 18.6 79.8 3,047 49.2 2.0 36.3 1.6 0.0 10.9 100.0 2,431 Mpumalanga 47.9 42.6 90.5 851 35.2 0.1 39.4 1.8 0.0 23.5 100.0 771 Limpopo 24.6 70.2 94.8 1,087 47.0 0.1 31.4 1.5 0.0 19.9 100.0 1,031 Wealth quintile Lowest 17.0 51.4 68.4 2,187 23.0 0.7 37.4 2.0 0.0 36.9 100.0 1,495 Second 30.1 51.3 81.4 2,349 30.4 1.0 43.9 1.4 0.0 23.3 100.0 1,912 Middle 53.2 33.9 87.1 2,247 38.0 1.4 46.5 1.4 0.0 12.7 100.0 1,956 Fourth 82.5 11.9 94.4 2,043 61.6 1.1 32.1 1.5 0.0 3.7 100.0 1,929 Highest 93.2 3.1 96.4 2,257 86.7 0.8 11.7 0.4 0.0 0.4 100.0 2,175 Total 54.7 30.7 85.4 11,083 50.1 1.0 33.6 1.3 0.0 14.0 100.0 9,467 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 2.9 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, South Africa DHS 2016 Urban Non-urban Total Age Male Female Total Male Female Total Male Female Total <5 10.3 9.3 9.8 13.5 11.2 12.3 11.5 10.0 10.7 5-9 10.0 8.3 9.1 14.0 11.5 12.7 11.4 9.5 10.4 10-14 8.2 7.9 8.0 12.9 10.3 11.5 9.9 8.8 9.3 15-19 8.0 8.4 8.2 12.3 8.8 10.5 9.6 8.5 9.0 20-24 8.9 8.9 8.9 8.7 7.6 8.1 8.8 8.4 8.6 25-29 9.2 9.8 9.5 6.1 7.1 6.6 8.1 8.8 8.4 30-34 8.9 8.5 8.7 6.1 7.0 6.6 7.9 7.9 7.9 35-39 7.8 7.3 7.5 4.1 4.9 4.5 6.5 6.4 6.4 40-44 7.0 6.5 6.7 4.3 4.6 4.5 6.0 5.8 5.9 45-49 5.4 5.6 5.5 3.2 4.5 3.9 4.6 5.2 4.9 50-54 4.1 4.7 4.4 3.0 4.2 3.7 3.7 4.5 4.1 55-59 3.5 4.3 3.9 2.9 4.0 3.5 3.3 4.2 3.7 60-64 2.9 3.3 3.1 2.8 3.8 3.3 2.9 3.5 3.2 65-69 2.0 2.4 2.2 2.0 3.0 2.5 2.0 2.6 2.3 70-74 1.3 1.9 1.6 1.3 2.2 1.8 1.3 2.0 1.7 75-79 0.7 1.1 0.9 1.0 1.8 1.5 0.8 1.4 1.1 80+ 0.9 1.6 1.3 0.8 2.8 1.9 0.8 2.1 1.5 Don’t know 1.0 0.5 0.7 1.0 0.5 0.7 1.0 0.5 0.7 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Dependency age groups 0-14 28.5 25.4 26.9 40.5 33.0 36.5 32.8 28.2 30.4 15-64 65.7 67.1 66.4 53.4 56.7 55.2 61.3 63.2 62.3 65+ 4.9 7.0 6.0 5.1 9.8 7.6 5.0 8.1 6.6 Don’t know 1.0 0.5 0.7 1.0 0.5 0.7 1.0 0.5 0.7 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 33.2 30.2 31.6 48.1 38.5 43.0 38.5 33.3 35.8 18+ 65.9 69.3 67.6 50.9 61.0 56.3 60.5 66.2 63.5 Don’t know 1.0 0.5 0.7 1.0 0.5 0.7 1.0 0.5 0.7 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Adolescents 10-19 16.2 16.2 16.2 25.2 19.1 22.0 19.5 17.3 18.3 Number of persons 11,354 12,177 23,531 6,367 7,230 13,597 17,721 19,407 37,128 Housing Characteristics and Household Population • 27 Table 2.10 Household composition Percent distribution of households by sex of head of household and by household size, mean size of household, and percentage of households with orphans and foster children under age 18, according to residence, South Africa DHS 2016 Residence Total Characteristic Urban Non-urban Household headship Male 61.9 47.7 57.4 Female 38.1 52.3 42.6 Total 100.0 100.0 100.0 Number of usual members 0 0.2 0.1 0.2 1 24.9 21.5 23.8 2 21.2 16.3 19.6 3 17.1 15.6 16.7 4 15.7 13.2 14.9 5 9.4 10.4 9.7 6 5.0 8.2 6.0 7 2.8 5.4 3.6 8 1.5 3.7 2.2 9+ 2.3 5.6 3.3 Total 100.0 100.0 100.0 Mean size of households 3.1 3.8 3.4 Percentage of households with orphans and foster children under age 18 Double orphans 2.0 4.2 2.7 Single orphans1 8.5 16.2 10.9 Foster children2 11.3 31.2 17.7 Foster and/or orphan children 15.8 36.2 22.3 Number of households 7,542 3,541 11,083 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 2.11 Children’s living arrangements and orphanhood Percent distribution of de jure children under age 18 by living arrangements and survival status of parents, percentage of children not living with a biological parent, and percentage of children with one or both parents dead, according to background characteristics, South Africa 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 Missing infor- mation on father/ mother Total Percent- age not living with a biolo- gical parent Percent- age with one or both parents dead1 Percent- age maternal orphans Number of children Background characteristic Father alive Father dead Mother alive Mother dead Both alive Only father alive Only mother alive Both dead Age 0-4 33.1 45.1 2.5 2.0 0.1 12.8 0.5 0.7 0.4 2.8 100.0 14.3 4.4 1.2 3,966 <2 33.1 52.9 1.1 1.5 0.0 9.1 0.1 0.1 0.0 2.0 100.0 9.4 1.5 0.2 1,487 2-4 33.1 40.5 3.4 2.3 0.2 15.0 0.7 1.0 0.6 3.3 100.0 17.3 6.2 1.8 2,479 5-9 26.9 35.9 5.8 2.3 0.6 18.5 1.7 2.5 1.8 4.1 100.0 24.4 13.2 4.9 3,869 10-14 26.4 27.3 9.9 3.0 1.0 15.7 3.3 4.5 4.5 4.4 100.0 28.0 24.5 10.0 3,460 15-17 24.7 23.3 12.2 3.2 1.6 14.5 3.1 5.1 8.2 4.2 100.0 30.9 31.9 14.6 2,002 Sex Male 28.3 34.4 6.8 2.8 0.8 15.4 1.6 2.8 3.1 3.9 100.0 22.9 16.1 6.4 6,834 Female 28.2 34.6 6.9 2.2 0.6 15.6 2.3 2.9 2.9 3.7 100.0 23.7 16.6 6.7 6,463 Residence Urban 37.6 32.5 7.1 2.8 0.7 10.4 1.5 2.1 2.7 2.6 100.0 16.8 14.7 5.5 7,475 Non-urban 16.4 37.1 6.5 2.2 0.7 22.0 2.5 3.8 3.5 5.4 100.0 31.7 18.4 8.0 5,822 Province Western Cape 50.8 30.2 4.0 2.8 0.6 6.2 1.6 1.1 1.0 1.8 100.0 9.8 8.3 3.2 1,222 Eastern Cape 17.3 30.8 7.5 2.1 1.0 23.8 3.2 4.1 3.5 6.8 100.0 34.6 20.7 9.1 1,919 Northern Cape 32.0 35.5 5.3 1.7 1.1 14.7 1.7 1.9 2.5 3.8 100.0 20.7 13.6 6.2 291 Free State 25.6 31.7 10.3 2.4 1.2 12.5 3.6 3.4 5.2 4.0 100.0 24.7 25.0 11.1 734 KwaZulu-Natal 17.8 39.1 7.5 2.8 0.3 18.8 2.1 4.4 4.2 2.9 100.0 29.5 19.6 7.6 2,586 North West 26.6 40.2 5.2 2.5 1.7 12.3 1.6 1.6 3.8 4.5 100.0 19.3 15.8 9.1 909 Gauteng 42.5 30.8 7.8 3.0 0.5 8.0 1.0 2.2 2.1 1.9 100.0 13.3 14.1 4.0 2,821 Mpumalanga 27.5 36.1 7.4 3.5 0.3 15.9 2.1 2.3 2.3 2.5 100.0 22.6 15.4 5.6 1,174 Limpopo 18.5 37.8 4.8 1.0 0.8 23.3 1.5 2.5 2.7 7.0 100.0 30.1 13.7 6.3 1,641 Wealth quintile Lowest 18.5 35.3 8.0 1.8 0.6 19.8 2.7 4.5 3.6 5.2 100.0 30.5 20.4 7.9 3,131 Second 23.6 35.9 6.8 2.4 0.5 18.2 1.4 2.8 2.9 5.4 100.0 25.4 16.3 6.7 2,840 Middle 24.0 37.9 7.5 3.0 0.8 15.2 1.7 2.7 3.7 3.5 100.0 23.3 17.2 7.0 2,797 Fourth 31.8 34.2 6.7 2.7 1.1 13.3 2.2 2.4 3.1 2.5 100.0 21.0 16.0 6.9 2,510 Highest 51.7 27.0 4.5 2.9 0.4 8.0 1.5 1.3 1.4 1.3 100.0 12.2 9.2 3.5 2,020 Total <15 28.9 36.5 5.9 2.4 0.5 15.7 1.7 2.5 2.1 3.7 100.0 22.0 13.6 5.2 11,295 Total <18 28.3 34.5 6.9 2.5 0.7 15.5 1.9 2.9 3.0 3.8 100.0 23.3 16.4 6.6 13,297 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 2.12.1 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, South Africa DHS 2016 Background characteristic No education Some primary Completed primary1 Some secondary Completed secondary2 More than secondary Don’t know Total Number Median years completed Age 6-9 27.6 71.8 0.1 0.4 0.0 0.0 0.2 100.0 1,468 0.6 10-14 0.6 74.8 14.9 9.6 0.0 0.0 0.1 100.0 1,701 4.7 15-19 0.5 3.6 6.7 75.7 12.3 0.9 0.3 100.0 1,659 9.0 20-24 0.7 2.6 3.2 42.9 39.7 10.6 0.2 100.0 1,631 11.0 25-29 0.9 3.8 2.3 40.4 35.7 16.7 0.2 100.0 1,699 11.1 30-34 2.0 4.6 3.2 41.7 30.9 16.8 0.9 100.0 1,538 10.9 35-39 1.9 5.6 3.5 42.6 30.3 15.6 0.6 100.0 1,237 10.8 40-44 4.2 7.4 3.8 39.0 27.7 16.1 1.8 100.0 1,131 10.6 45-49 7.7 13.1 8.1 33.4 24.1 12.6 1.0 100.0 1,015 9.6 50-54 13.0 19.3 9.1 32.1 15.0 10.1 1.4 100.0 873 8.1 55-59 15.6 23.0 8.6 29.1 10.8 11.4 1.5 100.0 811 7.2 60-64 22.6 25.7 8.6 25.6 7.1 8.0 2.3 100.0 678 6.1 65+ 33.7 24.7 6.0 19.8 7.3 7.0 1.5 100.0 1,564 4.4 Don’t know 19.8 18.4 3.1 24.0 14.9 3.2 16.7 100.0 92 7.2 Residence Urban 6.6 17.9 5.4 34.3 23.1 11.9 0.8 100.0 10,837 9.6 Non-urban 13.6 29.5 6.3 32.9 12.1 4.6 1.0 100.0 6,262 7.0 Province Western Cape 6.1 17.4 4.7 35.4 20.5 14.8 1.1 100.0 1,996 9.5 Eastern Cape 9.9 29.0 8.0 33.3 12.0 7.2 0.7 100.0 2,176 7.4 Northern Cape 10.4 25.7 6.4 35.8 15.6 5.3 0.8 100.0 372 7.9 Free State 7.1 25.9 6.4 35.5 17.8 7.1 0.2 100.0 940 8.4 KwaZulu-Natal 11.0 23.7 5.4 31.2 20.5 7.3 0.9 100.0 3,317 8.7 North West 9.5 24.7 6.9 36.1 16.0 5.8 0.9 100.0 1,104 8.0 Gauteng 5.9 15.8 5.1 33.1 26.8 12.7 0.6 100.0 4,038 10.1 Mpumalanga 12.6 24.6 5.1 36.1 14.5 6.1 1.1 100.0 1,306 8.2 Limpopo 13.9 24.3 5.4 34.6 12.8 7.5 1.6 100.0 1,849 7.9 Wealth quintile Lowest 16.8 30.7 7.8 34.8 8.3 0.8 0.8 100.0 3,347 6.3 Second 11.0 26.8 5.8 38.3 14.3 2.9 1.0 100.0 3,283 7.9 Middle 8.1 22.6 6.7 36.7 19.9 5.4 0.6 100.0 3,303 8.8 Fourth 6.7 18.7 5.6 35.0 23.4 9.1 1.4 100.0 3,520 9.4 Highest 4.0 12.9 2.9 25.1 28.4 26.2 0.5 100.0 3,646 11.2 Total 9.2 22.1 5.7 33.8 19.1 9.2 0.9 100.0 17,099 8.8 1 Completed 7th grade/standard 5/AET 3 at the primary level 2 Completed 12th grade/standard 10/form 5/matric at the secondary level 30 • Housing Characteristics and Household Population 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, South Africa DHS 2016 Background characteristic No education Some primary Completed primary1 Some secondary Completed secondary2 More than secondary Don’t know Total Number Median years completed Age 6-9 28.5 71.1 0.0 0.1 0.0 0.0 0.3 100.0 1,633 0.4 10-14 1.0 81.2 11.4 5.9 0.0 0.0 0.5 100.0 1,754 4.2 15-19 0.6 8.1 10.7 72.9 6.2 0.9 0.6 100.0 1,695 8.4 20-24 1.5 4.6 3.1 47.7 34.0 8.7 0.4 100.0 1,560 10.5 25-29 1.8 5.2 3.5 41.3 33.1 13.8 1.2 100.0 1,434 10.8 30-34 2.7 4.2 5.0 43.5 30.4 13.4 0.8 100.0 1,398 10.7 35-39 3.1 7.5 3.8 39.5 30.9 13.5 1.8 100.0 1,143 10.7 40-44 4.3 10.6 5.2 34.1 30.6 13.4 1.9 100.0 1,062 10.6 45-49 5.4 13.7 7.1 30.0 27.8 14.4 1.6 100.0 813 10.4 50-54 9.6 17.5 6.7 33.1 18.4 11.4 3.3 100.0 661 9.0 55-59 11.9 22.4 6.1 25.2 19.3 11.7 3.4 100.0 580 8.1 60-64 15.3 25.0 6.0 27.8 10.8 12.9 2.1 100.0 510 7.2 65+ 27.0 18.9 6.1 21.1 10.5 13.3 3.1 100.0 880 6.4 Don’t know 22.6 19.6 2.4 23.8 15.1 4.1 12.4 100.0 173 6.7 Residence Urban 6.4 20.1 5.0 33.3 22.6 11.1 1.5 100.0 9,962 9.4 Non-urban 10.4 33.9 7.1 32.9 11.2 3.2 1.2 100.0 5,335 6.7 Province Western Cape 4.8 19.5 6.6 32.4 19.7 15.7 1.3 100.0 1,683 9.3 Eastern Cape 8.5 34.2 6.1 32.2 12.3 5.6 1.0 100.0 1,906 7.1 Northern Cape 12.0 27.7 6.0 32.1 16.7 3.9 1.7 100.0 312 7.4 Free State 9.0 28.3 5.9 31.8 18.4 5.9 0.7 100.0 755 7.9 KwaZulu-Natal 9.0 26.1 6.1 32.5 19.5 6.0 0.7 100.0 2,719 8.3 North West 10.0 28.1 6.4 31.5 18.0 4.0 2.1 100.0 1,117 7.6 Gauteng 5.7 18.7 4.4 34.6 23.3 11.7 1.7 100.0 4,060 9.7 Mpumalanga 9.0 25.0 5.5 33.0 19.5 6.1 1.9 100.0 1,246 8.3 Limpopo 9.5 29.3 6.7 35.1 11.4 6.5 1.5 100.0 1,499 7.5 Wealth quintile Lowest 13.6 34.6 7.1 34.3 8.8 0.6 1.1 100.0 2,950 6.2 Second 8.1 28.5 7.2 38.8 13.8 2.0 1.5 100.0 3,129 7.6 Middle 7.7 25.6 5.9 34.4 19.0 5.8 1.6 100.0 3,115 8.4 Fourth 6.1 22.4 4.8 33.9 22.5 8.4 1.9 100.0 3,002 9.1 Highest 3.7 13.9 3.6 24.4 28.7 24.8 0.9 100.0 3,101 11.1 Total 7.8 24.9 5.7 33.2 18.6 8.4 1.4 100.0 15,296 8.5 1 Completed 7th grade/standard 5/AET 3 at the primary level 2 Completed 12th grade/standard 10/form 5/matric at the secondary level Housing Characteristics and Household Population • 31 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, South Africa 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 89.5 86.7 88.2 0.97 115.0 108.4 111.8 0.94 Non-urban 89.8 87.1 88.5 0.97 115.8 109.9 113.0 0.95 Province Western Cape 84.4 88.0 86.2 1.04 102.1 105.8 103.9 1.04 Eastern Cape 91.9 87.8 89.9 0.96 116.3 103.4 109.9 0.89 Northern Cape 90.2 87.6 88.9 0.97 110.9 108.5 109.7 0.98 Free State 93.4 86.8 90.2 0.93 117.5 105.6 111.7 0.90 KwaZulu-Natal 87.6 83.3 85.6 0.95 113.6 111.9 112.8 0.98 North West 91.5 89.7 90.7 0.98 110.6 110.5 110.6 1.00 Gauteng 91.3 87.9 89.7 0.96 119.8 112.1 116.2 0.94 Mpumalanga 87.7 87.1 87.4 0.99 122.4 112.1 117.3 0.92 Limpopo 89.7 86.7 88.2 0.97 118.4 109.8 114.2 0.93 Wealth quintile Lowest 91.8 87.6 89.7 0.95 122.0 110.1 116.0 0.90 Second 89.3 88.3 88.8 0.99 115.6 111.6 113.7 0.96 Middle 89.9 86.5 88.4 0.96 117.3 111.0 114.4 0.95 Fourth 90.3 86.7 88.7 0.96 112.6 110.4 111.6 0.98 Highest 85.5 84.5 85.0 0.99 105.3 101.0 103.1 0.96 Total 89.6 86.9 88.3 0.97 115.3 109.1 112.3 0.95 SECONDARY SCHOOL Residence Urban 76.3 73.7 74.9 0.97 99.6 101.9 100.8 1.02 Non-urban 78.2 79.6 78.8 1.02 118.3 121.5 119.7 1.03 Province Western Cape 64.8 76.2 70.9 1.18 88.2 94.6 91.6 1.07 Eastern Cape 74.3 75.5 74.9 1.02 97.9 117.9 107.0 1.20 Northern Cape 77.3 77.2 77.3 1.00 105.9 102.9 104.3 0.97 Free State 73.2 75.1 74.1 1.03 102.6 109.3 105.9 1.07 KwaZulu-Natal 80.4 74.2 77.4 0.92 117.6 112.5 115.1 0.96 North West 66.8 78.3 72.3 1.17 100.3 104.9 102.5 1.05 Gauteng 81.7 69.8 75.5 0.86 100.1 96.0 97.9 0.96 Mpumalanga 70.8 80.7 75.7 1.14 111.2 115.1 113.1 1.03 Limpopo 88.4 86.3 87.4 0.98 136.5 133.2 134.9 0.98 Wealth quintile Lowest 68.6 70.0 69.3 1.02 100.2 101.7 101.0 1.02 Second 80.8 78.3 79.6 0.97 126.5 115.2 121.1 0.91 Middle 77.6 75.1 76.5 0.97 106.9 113.9 110.2 1.07 Fourth 76.1 78.1 77.1 1.03 100.0 106.1 103.1 1.06 Highest 85.0 80.2 82.6 0.94 107.6 113.2 110.4 1.05 Total 77.2 76.0 76.6 0.98 108.1 109.5 108.8 1.01 1 The NAR for primary school is the percentage of the primary school-age (7-13 years) population that is attending primary school. The NAR for secondary school is the percentage of the secondary school-age (14-18 years) population that is attending secondary school. By definition, the NAR cannot exceed 100.0. 2 The GAR for primary school is the total number of primary school students, expressed as a percentage of the official primary school-age population. The GAR for secondary school is the total number of secondary school students, expressed as a percentage of the official secondary school-age population. If there are significant numbers of overage and underage students at a given level of schooling, the GAR can exceed 100.0. 3 The gender parity index for primary school is the ratio of the primary school NAR (GAR) for females to the NAR (GAR) for males. The gender parity index for secondary school is the ratio of the secondary school NAR (GAR) for females to the NAR (GAR) for males. 32 • Housing Characteristics and Household Population 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 functioning in at least one domain by age, South Africa 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 Total Domain Difficulty seeing 87.9 9.7 2.2 0.2 0.1 100.0 2.3 33,155 Difficulty hearing 95.2 3.7 1.0 0.1 0.1 100.0 1.1 33,155 Difficulty communicating 98.7 0.7 0.3 0.2 0.0 100.0 0.6 33,155 Difficulty remembering or concentrating 94.1 4.3 1.3 0.3 0.1 100.0 1.5 33,155 Difficulty walking or climbing steps 93.5 4.1 1.9 0.4 0.0 100.0 2.3 33,155 Difficulty washing all over or dressing 97.6 1.6 0.4 0.4 0.0 100.0 0.8 33,155 Difficulty in at least one domain1 5-9 86.0 10.4 2.5 1.1 0.0 100.0 3.6 3,862 10-14 91.0 6.6 1.9 0.3 0.2 100.0 2.2 3,455 15-19 90.8 6.9 1.9 0.3 0.0 100.0 2.2 3,354 20-29 90.5 7.2 1.9 0.3 0.1 100.0 2.2 6,324 30-39 87.3 9.4 2.4 0.6 0.2 100.0 3.1 5,316 40-49 77.7 17.1 4.2 0.8 0.2 100.0 5.0 4,021 50-59 60.2 30.2 8.1 1.3 0.2 100.0 9.3 2,925 60+ 40.9 35.6 19.4 3.8 0.2 100.0 23.3 3,634 Don’t know 83.8 8.8 4.3 2.1 1.1 100.0 6.3 264 Age 15 and over 77.3 15.9 5.6 1.1 0.2 100.0 6.6 25,573 Total 79.8 14.2 4.8 1.0 0.2 100.0 5.8 33,155 1 If a person was reported to have difficulty in more than one domain, only the highest level of difficulty is shown Table 2.15 Disability among the household population according to background characteristics Percentage of the de facto household population age 5 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, South Africa DHS 2016 No difficulty in any domain Domain Difficulty in at least one domain1 A lot of difficulty or cannot do at all in more than one domain Number of persons Background characteristic Seeing Hearing Commu- nicating Remem- bering or concen- trating Walking or climbing steps Washing all over or dressing Some difficulty A lot of difficulty Cannot do at all A lot of difficulty or cannot do at all Sex Male 82.2 9.7 4.4 1.4 4.7 4.4 2.4 12.6 4.0 1.0 5.1 1.3 15,687 Female 77.7 14.1 5.1 1.1 6.8 8.2 2.4 15.6 5.5 1.0 6.5 1.9 17,468 Residence Urban 81.2 12.0 4.2 1.1 5.0 5.5 1.6 13.8 4.1 0.8 4.9 1.4 21,230 Non-urban 77.4 12.1 5.9 1.6 7.3 8.1 3.7 14.9 6.2 1.3 7.5 2.2 11,925 Province Western Cape 84.1 9.0 3.6 1.3 5.1 5.3 1.8 12.6 2.7 0.4 3.2 0.9 3,744 Eastern Cape 73.6 14.8 7.7 1.8 9.2 8.5 3.7 17.3 7.5 1.5 9.0 2.7 4,196 Northern Cape 74.9 14.3 6.2 1.5 9.7 9.8 2.0 19.1 4.5 1.2 5.7 1.9 699 Free State 77.9 14.0 4.7 1.1 6.6 6.9 1.9 13.2 7.6 1.1 8.8 2.6 1,731 KwaZulu-Natal 77.5 14.2 5.7 1.7 7.3 8.0 3.6 15.4 6.1 1.1 7.1 2.1 6,200 North West 80.6 10.7 4.6 1.5 7.8 6.1 2.9 14.7 3.7 0.8 4.5 1.5 2,269 Gauteng 81.9 12.6 3.8 0.9 3.4 4.0 0.9 13.9 3.3 0.8 4.0 1.0 8,269 Mpumalanga 84.6 8.5 3.1 0.7 4.0 5.6 1.0 10.2 3.7 1.3 5.0 1.4 2,618 Limpopo 79.9 8.9 4.2 1.1 4.7 7.9 3.8 12.9 5.5 1.2 6.8 1.8 3,430 Wealth quintile Lowest 77.1 11.8 6.2 1.9 7.2 8.0 3.8 14.9 6.4 1.3 7.8 2.1 6,495 Second 79.9 11.2 5.2 1.2 6.3 6.8 2.7 13.8 5.0 1.0 6.1 1.9 6,577 Middle 81.1 11.2 4.2 1.4 5.9 5.9 2.2 13.4 4.3 1.1 5.4 1.6 6,573 Fourth 80.1 12.9 4.0 1.1 5.4 5.8 1.6 14.0 4.7 0.9 5.7 1.5 6,644 Highest 80.9 13.1 4.3 0.8 4.4 5.6 1.5 14.7 3.7 0.6 4.3 1.2 6,865 Disability grant recipient2 Yes 42.7 25.4 13.3 10.5 23.8 25.2 9.2 27.2 19.2 10.8 30.0 9.4 763 No 84.2 10.3 2.6 0.6 3.6 3.4 0.4 12.6 2.7 0.2 3.0 0.4 19,176 Total 79.8 12.0 4.8 1.3 5.8 6.4 2.4 14.2 4.8 1.0 5.8 1.7 33,155 1 If a person was reported to have difficulty in more than one domain, only the highest level of difficulty is shown 2 Restricted to the household population age 18-59 Housing Characteristics and Household Population • 33 Table 2.16 Knowledge of where social grant forms may be obtained Percentage of households that know where forms to apply for government social grants may be obtained, and among households that know where forms may be obtained, percentage that cite specific places, according to background characteristics, South Africa DHS 2016 Percentage of households that know where forms to apply for government social grants may be obtained Number of households Among households that know where forms can be obtained, percentage that cite specific places: Number of households that know where forms may be obtained Background characteristic Post office Bank Magistrate’s court SASSA/ Department of Social Development/ social development office Pay point Other Residence Urban 67.6 7,542 1.2 0.2 0.8 96.6 3.0 2.1 5,101 Non-urban 78.0 3,541 0.4 0.0 1.0 96.6 2.6 3.3 2,762 Province Western Cape 69.7 1,206 0.7 0.5 1.6 95.0 7.6 3.7 841 Eastern Cape 73.3 1,301 0.4 0.0 0.5 94.2 2.0 6.2 953 Northern Cape 85.5 210 0.1 0.0 0.3 99.4 2.2 0.6 180 Free State 79.2 579 0.5 0.0 0.6 95.4 8.4 2.7 459 KwaZulu-Natal 79.9 1,968 0.6 0.2 1.0 98.5 2.4 1.3 1,573 North West 78.8 833 0.5 0.1 0.7 96.4 3.6 2.8 656 Gauteng 60.7 3,047 2.3 0.0 0.7 96.0 1.5 1.5 1,850 Mpumalanga 69.6 851 0.5 0.0 1.0 98.2 0.7 2.1 593 Limpopo 69.8 1,087 0.3 0.2 1.2 97.7 1.2 1.9 759 Wealth quintile Lowest 68.7 2,187 0.2 0.0 0.5 96.6 2.1 3.4 1,503 Second 69.8 2,349 0.9 0.1 0.5 97.2 2.4 2.3 1,638 Middle 75.5 2,247 1.2 0.0 1.3 96.3 3.0 2.4 1,697 Fourth 77.5 2,043 0.9 0.2 1.1 96.5 3.3 2.0 1,583 Highest 63.9 2,257 1.5 0.3 1.1 96.2 3.8 2.5 1,442 Total 70.9 11,083 0.9 0.1 0.9 96.6 2.9 2.5 7,863 SASSA = South African Social Security Agency Table 2.17 Social grants Percentage of the de facto household population by whether they are receiving a government social grant or pension, and percentage of the de facto population receiving specific grant types, according to background characteristics, South Africa DHS 2016 Percentage receiving any grant Percentage receiving specific grant types: Number of persons Background characteristic Old age Disability Child support Care dependency Foster child Other Don’t know Age 0-4 67.4 0.0 0.1 66.9 0.1 0.3 0.0 0.0 3,973 5-17 69.7 0.0 0.4 66.4 0.3 2.5 0.0 0.1 9,318 18-29 2.2 0.0 1.1 0.2 0.0 0.7 0.1 0.0 7,678 30-39 2.2 0.0 1.9 0.0 0.0 0.0 0.3 0.0 5,316 40-49 4.4 0.0 4.0 0.1 0.0 0.0 0.3 0.0 4,021 50-59 10.2 0.7 8.3 0.0 0.1 0.0 1.0 0.1 2,925 60+ 77.1 76.0 0.3 0.0 0.0 0.0 0.6 0.1 3,634 Don’t know 23.7 3.4 3.0 16.4 0.0 0.0 0.5 0.3 264 Sex Male 34.5 5.4 1.9 25.9 0.1 0.8 0.3 0.0 17,721 Female 34.5 9.5 1.6 22.2 0.1 0.8 0.2 0.1 19,407 Residence Urban 27.0 6.1 1.7 18.2 0.1 0.6 0.3 0.1 23,531 Non-urban 47.5 10.1 1.8 34.0 0.2 1.2 0.2 0.0 13,597 Province Western Cape 23.8 6.8 2.5 13.5 0.2 0.5 0.1 0.1 4,073 Eastern Cape 44.9 9.8 2.2 30.9 0.2 1.6 0.3 0.0 4,708 Northern Cape 43.4 10.3 3.6 27.7 0.3 0.9 0.4 0.0 774 Free State 38.9 7.3 2.3 26.2 0.3 1.9 0.9 0.0 1,921 KwaZulu-Natal 39.1 8.4 2.0 27.3 0.2 0.9 0.2 0.1 6,955 North West 34.6 7.5 1.3 24.3 0.1 1.1 0.2 0.1 2,563 Gauteng 23.1 5.3 1.0 16.3 0.0 0.2 0.2 0.1 9,235 Mpumalanga 38.0 5.8 1.6 29.8 0.0 0.7 0.1 0.0 2,993 Limpopo 44.9 10.2 1.5 32.3 0.1 0.8 0.1 0.0 3,906 Wealth quintile Lowest 46.5 8.6 1.8 34.5 0.2 1.1 0.2 0.0 7,424 Second 41.7 7.9 2.0 30.5 0.2 0.8 0.2 0.1 7,469 Middle 37.7 6.7 1.8 27.8 0.1 1.0 0.2 0.1 7,435 Fourth 31.5 7.6 2.0 20.7 0.0 0.8 0.3 0.0 7,377 Highest 14.9 6.7 1.1 6.5 0.1 0.3 0.2 0.0 7,422 Total 34.5 7.5 1.8 24.0 0.1 0.8 0.2 0.1 37,128 34 • Housing Characteristics and Household Population Table 2.18 Food security: Adults Percent distribution of households by the frequency of problems satisfying food needs of de jure adults in the past 12 months, according to background characteristics, South Africa DHS 2016 In the past 12 months, frequency of problems satisfying food needs of adults Number of households Background characteristic Never Seldom Sometimes Often Always Not applicable Total Residence Urban 84.5 3.3 8.7 1.6 0.8 1.1 100.0 7,507 Non-urban 77.0 4.3 14.6 2.8 0.9 0.4 100.0 3,510 Province Western Cape 87.1 3.3 6.8 0.6 0.4 1.8 100.0 1,204 Eastern Cape 75.3 6.1 13.5 2.7 2.0 0.3 100.0 1,287 Northern Cape 79.7 7.7 7.8 1.6 1.0 2.2 100.0 209 Free State 78.0 6.1 9.1 4.2 2.0 0.6 100.0 578 KwaZulu-Natal 75.5 3.2 16.7 2.8 1.0 0.7 100.0 1,956 North West 81.4 5.0 10.1 2.9 0.2 0.3 100.0 830 Gauteng 88.3 2.8 6.5 0.7 0.6 1.2 100.0 3,035 Mpumalanga 81.8 2.4 12.0 2.4 0.8 0.5 100.0 847 Limpopo 82.6 1.9 12.0 2.3 0.4 0.8 100.0 1,070 Wealth quintile Lowest 66.8 5.7 20.6 4.2 2.0 0.6 100.0 2,162 Second 79.4 4.6 11.9 2.5 0.8 0.8 100.0 2,329 Middle 81.9 3.4 11.2 1.4 0.7 1.3 100.0 2,234 Fourth 85.7 3.4 8.0 1.3 0.6 1.0 100.0 2,041 Highest 96.6 1.0 1.2 0.2 0.1 0.8 100.0 2,251 Total 82.1 3.6 10.6 2.0 0.9 0.9 100.0 11,016 Table 2.19 Food security: Children Percent distribution of households by the frequency of problems satisfying food needs of de jure children in the past 12 months, according to background characteristics, South Africa DHS 2016 In the past 12 months, frequency of problems satisfying food needs of children Number of households Background characteristic Never Seldom Sometimes Often Always Not applicable Total Residence Urban 83.4 3.0 8.5 1.7 0.8 2.7 100.0 3,688 Non-urban 74.4 4.3 15.2 3.2 0.9 2.0 100.0 2,236 Province Western Cape 85.0 4.0 9.0 0.4 0.3 1.4 100.0 657 Eastern Cape 74.3 6.4 12.1 2.2 1.5 3.5 100.0 777 Northern Cape 83.6 7.1 6.3 1.3 0.0 1.8 100.0 134 Free State 76.5 5.5 8.6 5.0 2.6 1.8 100.0 347 KwaZulu-Natal 68.9 3.2 18.8 4.1 1.1 3.8 100.0 1,011 North West 79.5 2.9 12.2 4.1 0.3 1.0 100.0 388 Gauteng 87.9 2.3 5.9 0.6 0.5 2.8 100.0 1,436 Mpumalanga 80.7 3.0 11.8 2.5 0.6 1.4 100.0 486 Limpopo 82.2 1.9 11.8 2.2 0.6 1.4 100.0 686 Wealth quintile Lowest 62.3 6.3 21.8 5.1 2.0 2.5 100.0 1,177 Second 77.0 3.4 13.1 3.1 0.9 2.5 100.0 1,208 Middle 81.1 3.8 10.9 1.5 0.7 2.0 100.0 1,230 Fourth 86.3 3.0 7.0 1.3 0.5 1.9 100.0 1,186 Highest 93.8 1.0 1.8 0.0 0.0 3.4 100.0 1,122 Total 80.0 3.5 11.0 2.2 0.8 2.4 100.0 5,923 Characteristics of Respondents • 35 CHARACTERISTICS OF RESPONDENTS 3 Key Findings  Education: About 1 in 4 respondents (28% of women and 24% of men) have completed secondary school, and an additional 1 in 10 respondents have attended more than secondary school (12% of women and 11% of men).  Literacy: 96% of women and 95% of men are literate.  Exposure to media: 30% of women and 27% of men are exposed to three specified types of media (newspapers, television, and radio) weekly.  Employment: Men are more likely to be currently employed than women (46% versus 34%). 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 8,514 women age 15-49 and 3,618 men age 15-59 were interviewed in the SADHS 2016 with the standard individual questionnaires. Among the respondents age 15-49, 33% of women and 39% of men were in the 15-24 age group, 33% of women and 30% of men were in the 25-34 age group, and 34% of women and 32% of men were in the 35-49 age group (Table 3.1). While men up to age 59 were interviewed, the body of tables in this report presents data for men age 15-49 so that this information is comparable with the data for women; data for men age 50-59 and age 15-59 are presented as separate rows in the tables. Black African is the largest self-reported population group, making up 87% of female and 88% of male respondents, followed by the Coloured population group, which accounts for 9% of women and 7% of men. The next-largest segment is the White population group (3% each of women and men), reflecting considerable underrepresentation of this group in the survey. Two percent of women and men each reported belonging to the Indian/Asian population group. Well over half of women (59%) and two-thirds of men (65%) have never been married. Women more often are married or living together with a partner (i.e., in a union) than men (36% and 31%, respectively). Women and men are equally likely to report that they are divorced or separated (3% and 4%, respectively). Two percent of women report that they are widowed, as compared with 1% of men. The majority of respondents live in urban areas (67% of women and 69% of men). By province, the largest percentage of women and men live in Gauteng (27% of women and 31% of men), followed by KwaZulu- Natal (19% of women and 16% of men). Only 2% of female and male respondents live in Northern Cape. T 36 • Characteristics of Respondents 3.2 EDUCATION AND LITERACY Educational attainment is high in South Africa. About 1 in 4 respondents (28% of women and 24% of men) have completed secondary school, and an additional 1 in 10 respondents have attended more than secondary school (12% of women and 11% of men) (Tables 3.2.1 and 3.2.2). Forty-nine percent of women report that they attended some secondary school but did not complete it, as compared with 51% of men. Only 2% of respondents reported having no formal education (Figure 3.1). The median number of years of schooling completed by women and men age 15-49 is 10.4 and 10.0, respectively. 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 Ninety-six percent of women and 95% of men are literate (Tables 3.3.1 and 3.3.2). Four percent of women and 5% of men cannot read at all. Comparison with the SADHS 1998: Among women, the median number of years of school completed has increased by 1.7 years, from 8.7 to 10.4. The percentage of women with no education decreased from 7% in 1998 to 2% in 2016. Patterns by background characteristics  Respondents from urban areas are more educated than their non-urban counterparts. For example, 14% of women in urban areas have attended more than secondary school, as compared with only 7% of women in non-urban areas. The median number of years of schooling completed is 10.8 years for urban women, compared with 9.7 years for non-urban women.  Educational attainment varies by province. Gauteng has the highest percentage of women who have completed secondary school or higher and Eastern Cape has the lowest (51% versus 28%) (Figure 3.2).  Educational attainment increases with increasing household wealth. Seventy-one percent of both women and men in the highest wealth quintile have completed secondary school or higher, as compared with 16% of women and 13% of men in the lowest wealth quintile (Figure 3.3). Figure 3.1 Education of survey respondents Figure 3.2 Secondary education by province Percentage of women age 15-49 with secondary education complete or higher 2 25 7 4 5 49 51 28 24 12 11 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 • 37  Literacy generally decreases with age. Among women, literacy decreases from 98% among those age 15-19 to 93% among those age 40-49. Among men, literacy decreases from 97% among those age 15-19 to 92% among those age 40-44 (Tables 3.3.1 and 3.3.2).  Literacy also increases with increasing wealth, rising from 91% among women in the lowest wealth quintile to 99% in the fourth quintile and from 88% among men in the lowest wealth quintile to nearly 100% in the highest quintile. 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 Data on women’s and men’s exposure to mass media are essential in the development of educational programmes and the dissemination of all types of information, particularly infor- mation about family planning, HIV/AIDS awareness, and other important health topics. Women are more likely than men to be exposed to newspapers and television, but not radio (Figure 3.4). Television is the most common form of media exposure for both women and men across nearly all subgroups (Table 3.4.1 and Table 3.4.2). Thirty percent of women and 27% of men are exposed to all three types of media. Eighteen percent of women and 16% of men are not regularly exposed to any form of media. The Internet is also a critical tool through which information is shared. Internet use includes accessing web pages, email, and social media. Approximately half of women and men accessed the Internet in the 12 months before the survey. Among those who had used the Internet in the past 12 months, 71% of women and 62% of men accessed it on a daily basis (Tables 3.5.1 and 3.5.2). Patterns by background characteristics  Non-urban women are twice as likely as their urban counterparts to have no regular exposure to any form of mass media (28% versus 13%). A similar pattern holds true for men (22% versus 13%). Figure 3.3 Secondary education by household wealth Figure 3.4 Exposure to mass media 16 27 39 47 71 13 21 32 44 71 Lowest Second Middle Fourth Highest Women Men WealthiestPoorest Percentage of women and men age 15-49 with secondary education complete or higher 40 74 55 30 18 39 71 60 27 16 Reads newspaper Watches television 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 38 • Characteristics of Respondents  Women in KwaZulu-Natal (28%) and men in Eastern Cape (31%) are most likely to report no regular exposure to any of the three media. Women and men in Western Cape are least likely to report that they do not access the three media at least weekly (2% of women and 7% of men).  Better-educated women and men have much greater exposure to mass media. Only 6% of both women and men with more than a secondary education lack regular exposure to any media, as compared with 45% of women and 40% of men with no education.  Exposure to mass media generally increases with increasing wealth. Fifty-two percent of women and 38% of men in the highest wealth quintile access all three forms of mass media, compared with 8% each of women and men in the lowest quintile.  Internet usage generally decreases with age among both women and men (Tables 3.5.1 and 3.5.2). For example, 61% of women age 20-24 used the Internet in the past 12 months, compared with 24% of women age 45-49.  Women and men in urban areas (56% and 58%, respectively) are more likely than their non-urban counterparts (30% and 39%, respectively) to have used the Internet in the past 12 months.  Internet usage increases with increasing wealth among both women and men; for example, 19% of women in the lowest wealth quintile accessed the Internet in the past 12 months, as compared with 81% in the highest quintile. 3.4 EMPLOYMENT Currently employed Respondents who were employed in the 7 days before the survey. Sample: Women and men age 15-49 Among respondents age 15-49, men are more likely to be employed than women; 34% of women are currently employed, as compared with 46% of men (Tables 3.6.1 and 3.6.2). An additional 4% of women and 6% of men reported that they were not currently employed but had worked in the past 12 months. Comparison with the SADHS 1998: The percentage of women who report being currently employed has remained relatively unchanged since 1998 (32% in 1998 versus 34% in 2016). Patterns by background characteristics  Women and men who are in a union (married or living together with a partner as though married) or divorced/separated/widowed women and men are more likely to be employed than those who have never been married or lived with a partner as though married.  A greater percentage of urban women and men are currently employed than their non-urban counterparts (Figure 3.5).  Employment status varies widely by province. Women and men in Western Cape were most likely to be currently employed at the time of the survey, while women in KwaZulu-Natal and men in Free State were least likely to be employed (Tables 3.6.1 and 3.6.2). Figure 3.5 Employment status by residence 34 46 39 51 26 36 Women Men Percentage of women and men age 15-49 who are currently employed Total Urban Non-urban Characteristics of Respondents • 39  For women, employment increases with increasing household wealth, rising from 22% in the lowest quintile to 53% in the highest quintile. For men, those in the highest quintile (60%) are more likely than those in other quintiles (41%-45%) to be currently employed. 3.5 OCCUPATION Occupation Categorised as professional/technical/managerial, clerical, sales and services, skilled manual, unskilled manual, domestic service, agriculture, and other Sample: Women and men age 15-49 who were currently employed or had worked in the 12 months before the survey Among those who are employed, 20% of women work in professional, technical, or managerial occupations; 19% work in unskilled manual occupations; 18% are engaged in sales and services; and 16% do clerical work (Table 3.7.1 and Figure 3.6). One-third (33%) of men who are employed are engaged in skilled manual occupations; 14% work in professional, technical, or managerial occupations; 14% work in sales and services; and 13% are engaged in unskilled manual occupations (Table 3.7.2 and Figure 3.6). Three percent of women and 5% of men work in agriculture. The vast majority of women earn only cash for their work (94%), are employed by a non-family member (85%), and work all year (78%) (Table 3.8). Patterns by background characteristics  Urban women are most likely to be employed in the professional, technical, or managerial sector (22%), while urban men are most likely to be employed in skilled manual occupations (32%). In non-urban areas, however, the largest percentage of women work in unskilled manual occupations (26%), while again the largest percentage of men work in the skilled manual sector (38%) (Table 3.7.1 and Table 3.7.2).  Women and men with more than a secondary education are much more likely to work in professional, technical, and managerial occupations (49% of women and 44% of men) than other occupations. Women with no education are most likely to work in domestic service (25%); men with an incomplete primary education most often work in skilled manual occupations (48%).  The proportion of women and men working in professional, technical, and managerial occupations increases with increasing wealth. For example, women in the highest quintile are 10 times more likely to work in a professional, technical, or managerial occupation than women in the lowest quintile (38% versus 3%). Figure 3.6 Occupation 20 16 18 5 19 12 3 14 4 14 33 13 <1 5 Professional/ technical/ managerial Clerical Sales and services Skilled manual Unskilled manual Domestic service Agriculture Percentage of women and men age 15-49 employed in the 12 months before the survey by occupation Women Men 40 • Characteristics of Respondents 3.6 ADULT HEALTH Table 3.9 shows the weighted and unweighted numbers and the weighted percent distributions of women and men age 15 and older who were interviewed using the SADHS adult health module. A total of 6,126 women and 4,210 men age 15 and older were interviewed in the SADHS 2016 with the adult health module. The characteristics of the respondents who completed the adult health module are generally similar to those presented in Table 3.1, except the age distribution differs because there was no upper age limit for eligibility. In addition, the percentages of women and men age 15 or older who had never been married or lived with a partner as though married (50% of women and 53% of men) were lower than the percentages for women and men age 15-49 (59% and 65%). Most strikingly, the percentage of respondents age 15 or older with no education is larger than for respondents age 15-49; 8% of women and 5% of men age 15 and older have no education, as compared with only 2% each of women and men age 15-49. All women and men who were administered the adult health module were eligible for biomarker collection including anthropometry, blood pressure measurement, and testing for anaemia, HIV, and HbA1c. The adult health module results are presented in Chapters 15, 16, 17, and 18. 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 Background characteristics of respondents who completed the adult health module Characteristics of Respondents • 41 Table 3.1 Background characteristics of respondents Percent distribution of women and men age 15-49 by selected background characteristics, South Africa DHS 2016 Women Men Background characteristic Weighted percent Weighted number Unweighted number Weighted percent Weighted number Unweighted number Age 15-19 16.8 1,427 1,505 20.2 647 705 20-24 16.6 1,415 1,408 18.4 588 602 25-29 17.0 1,444 1,397 15.8 506 492 30-34 15.7 1,333 1,295 14.1 450 436 35-39 12.6 1,072 1,032 12.3 395 339 40-44 11.0 941 964 10.8 345 335 45-49 10.4 883 913 8.5 271 270 Population group Black African 86.8 7,388 7,359 87.9 2,815 2,807 White 3.1 265 214 3.2 104 81 Coloured 8.6 730 848 7.3 232 251 Indian/Asian 1.5 126 88 1.5 48 38 Other 0.1 6 5 0.1 2 2 Marital status Never married 58.6 4,992 5,134 64.7 2,073 2,161 Married 23.3 1,983 1,825 19.5 624 547 Living together 12.5 1,066 1,016 11.4 364 343 Divorced/separated 3.4 287 337 3.5 113 99 Widowed 2.2 185 202 0.9 28 29 Residence Urban 67.3 5,731 4,805 68.8 2,203 1,768 Non-urban 32.7 2,783 3,709 31.2 999 1,411 Province Western Cape 11.7 995 656 10.2 328 186 Eastern Cape 11.0 938 1,041 11.3 362 411 Northern Cape 2.0 173 718 1.9 61 251 Free State 5.2 442 854 5.0 159 295 KwaZulu-Natal 19.0 1,616 1,360 16.3 521 471 North West 6.7 570 863 7.4 237 379 Gauteng 26.8 2,284 863 30.7 984 371 Mpumalanga 7.9 671 1,054 8.2 263 413 Limpopo 9.7 824 1,105 9.0 288 402 Education No education 2.0 168 190 1.9 62 76 Primary incomplete 5.3 447 524 6.8 219 274 Primary complete 3.8 327 338 5.2 166 189 Secondary incomplete 49.3 4,195 4,409 51.1 1,637 1,628 Secondary complete 27.8 2,369 2,172 24.1 773 722 More than secondary 11.8 1,008 881 10.8 345 290 Wealth quintile Lowest 19.4 1,648 1,763 19.3 618 664 Second 20.1 1,715 1,865 21.3 682 772 Middle 21.2 1,805 1,956 22.3 715 755 Fourth 20.7 1,763 1,733 20.4 653 597 Highest 18.6 1,583 1,197 16.7 534 391 Total 15-49 100.0 8,514 8,514 100.0 3,202 3,179 50-59 na na na na 416 439 Total 15-59 na na na na 3,618 3,618 Note: Education categories refer to the highest level of education attended, whether or not that level was completed. na = Not applicable 42 • Characteristics of Respondents Table 3.2.1 Educational attainment: Women Percent distribution of women age 15-49 by highest level of schooling attended or completed, and median years completed, according to background characteristics, South Africa DHS 2016 Highest level of schooling Total Median years completed Number of women Background characteristic No education Some primary Completed primary1 Some secondary Completed secondary2 More than secondary Age 15-24 0.3 2.5 4.1 63.1 22.5 7.4 100.0 9.9 2,842 15-19 0.2 2.8 4.9 81.0 9.6 1.4 100.0 9.1 1,427 20-24 0.4 2.3 3.2 45.0 35.6 13.5 100.0 11.0 1,415 25-29 0.4 3.9 2.5 41.6 36.6 14.9 100.0 11.0 1,444 30-34 1.8 3.9 3.0 45.1 31.8 14.4 100.0 10.8 1,333 35-39 2.2 5.7 3.4 44.5 29.9 14.4 100.0 10.7 1,072 40-44 4.3 8.5 3.4 42.3 27.4 14.1 100.0 10.4 941 45-49 7.5 14.2 7.6 36.8 22.4 11.5 100.0 9.4 883 Residence Urban 1.6 3.7 3.5 45.4 31.5 14.3 100.0 10.8 5,731 Non-urban 2.8 8.4 4.6 57.2 20.2 6.8 100.0 9.7 2,783 Province Western Cape 1.5 3.2 2.9 48.9 25.1 18.5 100.0 10.6 995 Eastern Cape 1.5 8.6 6.2 55.6 20.3 7.7 100.0 9.6 938 Northern Cape 2.7 7.0 4.5 52.8 24.6 8.4 100.0 9.8 173 Free State 1.1 5.4 3.5 50.6 29.9 9.6 100.0 10.4 442 KwaZulu-Natal 3.1 5.9 3.2 46.7 31.0 10.1 100.0 10.5 1,616 North West 2.7 8.5 4.3 53.3 23.6 7.6 100.0 9.9 570 Gauteng 1.6 3.0 4.2 40.5 36.0 14.7 100.0 11.0 2,284 Mpumalanga 3.0 6.2 4.1 57.0 20.1 9.6 100.0 10.0 671 Limpopo 1.2 5.4 2.1 61.3 19.4 10.6 100.0 10.1 824 Wealth quintile Lowest 4.7 11.7 7.7 59.6 14.8 1.5 100.0 9.0 1,648 Second 2.0 6.3 4.3 60.9 21.6 4.9 100.0 9.9 1,715 Middle 1.4 4.6 3.7 51.4 30.8 8.1 100.0 10.4 1,805 Fourth 1.1 3.0 3.0 45.8 35.0 12.1 100.0 10.8 1,763 Highest 0.9 0.6 0.3 27.4 36.7 34.1 100.0 11.5 1,583 Total 2.0 5.3 3.8 49.3 27.8 11.8 100.0 10.4 8,514 1 Completed 7th grade/standard 5/AET 3 at the primary level 2 Completed 12th grade/standard 10/form 5/matric at the secondary level Characteristics of Respondents • 43 Table 3.2.2 Educational attainment: Men Percent distribution of men age 15-49 by highest level of schooling attended or completed, and median years completed, according to background characteristics, South Africa DHS 2016 Highest level of schooling Total Median years completed Number of men Background characteristic No education Some primary Completed primary1 Some secondary Completed secondary2 More than secondary Age 15-24 0.5 5.8 4.8 65.4 18.5 5.1 100.0 9.2 1,235 15-19 0.0 6.6 7.8 78.3 7.0 0.3 100.0 8.5 647 20-24 1.0 4.9 1.5 51.1 31.1 10.4 100.0 10.4 588 25-29 0.5 6.1 4.8 42.3 29.1 17.3 100.0 10.8 506 30-34 1.8 5.1 4.8 45.4 26.9 16.0 100.0 10.6 450 35-39 3.0 8.0 5.6 43.5 28.1 11.8 100.0 10.4 395 40-44 6.2 9.9 6.2 41.0 25.3 11.4 100.0 10.2 345 45-49 4.8 10.3 6.2 36.2 28.8 13.6 100.0 10.4 271 Residence Urban 1.7 4.8 4.2 48.0 27.9 13.4 100.0 10.4 2,203 Non-urban 2.6 11.4 7.4 57.9 15.8 5.0 100.0 9.1 999 Province Western Cape 0.6 4.5 2.4 48.2 31.9 12.4 100.0 10.5 328 Eastern Cape 1.9 11.1 7.0 58.0 14.2 7.9 100.0 9.0 362 Northern Cape 3.8 9.3 6.4 43.7 31.5 5.3 100.0 9.7 61 Free State 1.2 9.6 5.4 49.8 24.9 9.1 100.0 9.5 159 KwaZulu-Natal 3.4 5.2 8.0 51.7 24.1 7.5 100.0 9.7 521 North West 2.7 11.8 7.4 51.2 23.6 3.4 100.0 9.7 237 Gauteng 1.3 4.7 3.8 47.2 26.9 16.0 100.0 10.6 984 Mpumalanga 4.1 8.4 4.0 51.4 24.8 7.3 100.0 9.7 263 Limpopo 0.7 6.8 4.6 59.9 16.2 11.7 100.0 9.5 288 Wealth quintile Lowest 5.3 15.4 9.1 57.3 12.2 0.8 100.0 8.3 618 Second 1.8 9.0 8.3 59.6 18.0 3.3 100.0 9.4 682 Middle 1.9 6.0 4.4 56.0 23.8 7.9 100.0 10.0 715 Fourth 0.5 2.6 2.8 49.8 33.2 11.1 100.0 10.6 653 Highest 0.1 0.5 0.8 28.1 35.3 35.3 100.0 11.5 534 Total 15-49 1.9 6.8 5.2 51.1 24.1 10.8 100.0 10.0 3,202 50-59 10.9 27.9 3.2 30.5 14.7 12.9 100.0 7.7 416 Total 15-59 3.0 9.3 5.0 48.7 23.0 11.0 100.0 9.8 3,618 1 Completed 7th grade/standard 5/AET 3 at the primary level 2 Completed 12th grade/standard 10/form 5/matric at the secondary level 44 • Characteristics of Respondents Table 3.3.1 Literacy: Women Percent distribution of women age 15-49 by level of schooling attended and level of literacy, and percentage literate, according to background characteristics, South Africa DHS 2016 Higher than secondary schooling No schooling, primary or secondary school Total Percentage literate1 Number of women Background characteristic Can read a whole sentence Can read part of a sentence Cannot read at all No card with required language Blind/visually impaired Age 15-24 7.4 83.4 6.8 2.3 0.0 0.0 100.0 97.7 2,842 15-19 1.4 90.0 6.7 1.9 0.0 0.0 100.0 98.1 1,427 20-24 13.5 76.8 6.9 2.7 0.1 0.0 100.0 97.2 1,415 25-29 14.9 75.1 7.7 2.1 0.1 0.0 100.0 97.8 1,444 30-34 14.4 75.0 7.3 2.8 0.2 0.3 100.0 96.7 1,333 35-39 14.4 73.6 8.3 3.4 0.0 0.3 100.0 96.2 1,072 40-44 14.1 67.2 11.9 6.7 0.1 0.0 100.0 93.2 941 45-49 11.5 69.6 11.3 7.4 0.0 0.2 100.0 92.5 883 Residence Urban 14.3 75.4 7.5 2.6 0.1 0.1 100.0 97.2 5,731 Non-urban 6.8 77.9 9.8 5.4 0.1 0.0 100.0 94.5 2,783 Province Western Cape 18.5 78.5 2.1 0.9 0.0 0.0 100.0 99.1 995 Eastern Cape 7.7 77.0 10.4 4.8 0.0 0.1 100.0 95.1 938 Northern Cape 8.4 77.0 11.0 3.3 0.0 0.3 100.0 96.4 173 Free State 9.6 83.9 5.1 1.4 0.0 0.0 100.0 98.6 442 KwaZulu-Natal 10.1 74.9 11.5 3.4 0.0 0.1 100.0 96.5 1,616 North West 7.6 79.4 6.8 5.7 0.4 0.0 100.0 93.8 570 Gauteng 14.7 72.1 9.2 3.7 0.1 0.2 100.0 96.0 2,284 Mpumalanga 9.6 80.2 5.0 5.0 0.2 0.0 100.0 94.8 671 Limpopo 10.6 77.2 8.9 2.9 0.0 0.3 100.0 96.8 824 Wealth quintile Lowest 1.5 75.0 14.6 8.9 0.0 0.0 100.0 91.0 1,648 Second 4.9 80.9 10.1 3.9 0.1 0.2 100.0 95.8 1,715 Middle 8.1 81.8 7.4 2.4 0.2 0.1 100.0 97.3 1,805 Fourth 12.1 82.0 5.0 0.8 0.1 0.0 100.0 99.1 1,763 Highest 34.1 59.8 4.3 1.6 0.0 0.3 100.0 98.2 1,583 Total 11.8 76.2 8.2 3.5 0.1 0.1 100.0 96.3 8,514 1 Refers to women who attended schooling higher than the secondary level and women who can read a whole sentence or part of a sentence Characteristics of Respondents • 45 Table 3.3.2 Literacy: Men Percent distribution of men age 15-49 by level of schooling attended and level of literacy, and percentage literate, according to background characteristics, South Africa DHS 2016 Higher than secondary schooling No schooling, primary or secondary school Total Percentage literate1 Number of men Background characteristic Can read a whole sentence Can read part of a sentence Cannot read at all No card with required language Blind/visually impaired Age 15-24 5.1 78.8 11.9 4.1 0.1 0.0 100.0 95.8 1,235 15-19 0.3 84.2 12.9 2.6 0.0 0.0 100.0 97.4 647 20-24 10.4 72.9 10.9 5.7 0.1 0.0 100.0 94.1 588 25-29 17.3 64.9 13.5 4.4 0.0 0.0 100.0 95.6 506 30-34 16.0 63.6 15.1 5.0 0.3 0.0 100.0 94.7 450 35-39 11.8 72.6 10.7 4.4 0.2 0.3 100.0 95.0 395 40-44 11.4 67.9 12.6 8.1 0.0 0.0 100.0 91.9 345 45-49 13.6 64.9 15.2 6.2 0.0 0.0 100.0 93.8 271 Residence Urban 13.4 71.0 12.0 3.5 0.0 0.1 100.0 96.4 2,203 Non-urban 5.0 72.2 14.5 8.1 0.2 0.0 100.0 91.6 999 Province Western Cape 12.4 72.2 12.3 2.7 0.0 0.4 100.0 96.9 328 Eastern Cape 7.9 74.9 11.1 5.5 0.6 0.0 100.0 93.9 362 Northern Cape 5.3 76.6 14.3 3.7 0.0 0.0 100.0 96.3 61 Free State 9.1 74.2 12.6 4.1 0.0 0.0 100.0 95.9 159 KwaZulu-Natal 7.5 71.0 18.5 3.0 0.0 0.0 100.0 97.0 521 North West 3.4 82.0 9.1 5.4 0.0 0.0 100.0 94.6 237 Gauteng 16.0 66.4 13.9 3.7 0.0 0.0 100.0 96.3 984 Mpumalanga 7.3 72.6 12.6 7.2 0.3 0.0 100.0 92.5 263 Limpopo 11.7 71.0 4.6 12.6 0.0 0.0 100.0 87.4 288 Wealth quintile Lowest 0.8 65.0 21.9 11.8 0.3 0.2 100.0 87.7 618 Second 3.3 75.3 14.9 6.3 0.1 0.0 100.0 93.6 682 Middle 7.9 73.7 14.0 4.3 0.0 0.0 100.0 95.7 715 Fourth 11.1 78.4 9.2 1.3 0.0 0.0 100.0 98.7 653 Highest 35.3 62.0 2.4 0.4 0.0 0.0 100.0 99.6 534 Total 15-49 10.8 71.4 12.8 4.9 0.1 0.0 100.0 94.9 3,202 50-59 12.9 48.0 22.3 16.4 0.0 0.4 100.0 83.2 416 Total 15-59 11.0 68.7 13.9 6.2 0.1 0.1 100.0 93.6 3,618 1 Refers to men who attended schooling higher than the secondary level and men who can read a whole sentence or part of a sentence 46 • Characteristics of Respondents Table 3.4.1 Exposure to mass media: Women Percentage of women age 15-49 who are exposed to specific media on a weekly basis, according to background characteristics, South Africa 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 37.1 71.8 47.3 25.1 20.2 1,427 20-24 40.2 72.1 53.6 27.7 18.0 1,415 25-29 41.3 73.8 55.4 30.2 16.7 1,444 30-34 43.8 75.8 57.8 34.0 16.7 1,333 35-39 40.3 75.1 59.1 31.2 16.4 1,072 40-44 40.4 73.9 55.8 30.6 18.2 941 45-49 38.7 72.9 58.2 28.9 17.7 883 Residence Urban 49.8 79.4 60.5 37.1 12.5 5,731 Non-urban 20.9 61.6 43.3 14.1 28.4 2,783 Province Western Cape 74.6 91.9 75.2 57.8 2.4 995 Eastern Cape 21.3 62.8 46.5 14.1 27.4 938 Northern Cape 46.1 80.9 53.4 31.8 12.2 173 Free State 46.7 78.1 68.0 37.3 12.1 442 KwaZulu-Natal 30.8 63.3 47.8 22.1 28.2 1,616 North West 33.2 80.3 59.8 25.2 12.3 570 Gauteng 52.2 77.3 60.1 39.5 13.0 2,284 Mpumalanga 27.0 68.0 42.9 15.1 20.3 671 Limpopo 17.4 69.5 39.5 10.4 23.5 824 Education No education 11.1 46.7 30.8 8.9 45.2 168 Primary incomplete 11.2 54.1 38.5 9.1 36.0 447 Primary complete 18.4 54.0 40.8 11.1 36.2 327 Secondary incomplete 34.4 71.1 50.6 24.3 20.1 4,195 Secondary complete 52.4 80.9 63.1 39.7 10.8 2,369 More than secondary 61.3 86.1 69.6 45.9 5.7 1,008 Wealth quintile Lowest 16.1 35.0 31.2 7.8 48.6 1,648 Second 26.2 70.4 48.8 16.7 19.1 1,715 Middle 43.3 83.9 57.2 31.8 9.9 1,805 Fourth 51.1 88.3 66.2 40.1 6.8 1,763 Highest 65.4 89.0 71.1 51.9 5.2 1,583 Total 40.3 73.6 54.9 29.6 17.7 8,514 Characteristics of Respondents • 47 Table 3.4.2 Exposure to mass media: Men Percentage of men age 15-49 who are exposed to specific media on a weekly basis, according to background characteristics, South Africa 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 men Age 15-19 27.1 72.8 45.7 17.6 18.9 647 20-24 35.0 69.0 57.2 22.8 15.9 588 25-29 39.3 65.3 62.0 28.8 19.1 506 30-34 39.7 74.3 63.3 29.2 13.9 450 35-39 49.1 70.7 65.2 32.7 13.4 395 40-44 47.4 68.5 68.1 35.9 18.0 345 45-49 46.9 74.4 71.7 34.3 9.7 271 Residence Urban 43.5 75.1 62.2 31.3 13.4 2,203 Non-urban 28.6 60.6 54.9 18.1 22.2 999 Province Western Cape 43.3 86.8 64.8 32.0 6.7 328 Eastern Cape 23.8 47.9 35.6 10.0 31.2 362 Northern Cape 40.1 72.1 37.9 19.4 16.5 61 Free State 35.9 71.3 57.9 23.7 15.0 159 KwaZulu-Natal 25.5 67.3 57.8 17.4 18.8 521 North West 33.5 71.2 66.7 24.7 14.7 237 Gauteng 47.9 75.2 64.1 36.2 14.2 984 Mpumalanga 53.9 64.9 70.1 40.2 16.7 263 Limpopo 37.9 74.5 65.1 24.3 10.7 288 Education No education 6.5 42.9 52.4 2.6 40.1 62 Primary incomplete 16.7 49.8 47.7 8.2 32.4 219 Primary complete 25.0 62.7 56.4 13.3 19.9 166 Secondary incomplete 34.1 69.5 55.5 23.2 17.6 1,637 Secondary complete 53.1 76.9 67.4 38.9 10.0 773 More than secondary 55.7 83.4 74.9 43.3 6.2 345 Wealth quintile Lowest 23.4 37.3 44.4 8.0 36.4 618 Second 31.1 64.4 57.6 20.5 19.3 682 Middle 42.8 79.3 63.4 31.9 11.2 715 Fourth 50.5 85.8 66.8 38.5 6.8 653 Highest 46.9 86.4 67.6 37.9 6.6 534 Total 15-49 38.8 70.6 59.9 27.2 16.1 3,202 50-59 40.0 71.2 64.9 26.8 13.3 416 Total 15-59 39.0 70.6 60.5 27.2 15.8 3,618 48 • Characteristics of Respondents Table 3.5.1 Internet usage: Women Percentage of women age 15-49 who have ever used the Internet, and percentage who have used the Internet in the past 12 months; and among women who have used the Internet in the past 12 months, percent distribution by frequency of Internet use in the past month, according to background characteristics, South Africa DHS 2016 Ever used the Internet Used the Internet in the past 12 months Number of women Among women who have used the Internet in the past 12 months, percentage who, in the past month, used the Internet: Background characteristic Almost every day At least once a week Less than once a week Not at all Total Number of women Age 15-19 61.0 58.4 1,427 63.6 25.1 9.2 2.1 100.0 833 20-24 63.4 60.7 1,415 73.0 18.4 7.0 1.6 100.0 858 25-29 58.1 55.7 1,444 75.7 15.5 7.7 1.1 100.0 803 30-34 49.9 47.5 1,333 73.4 17.5 8.5 0.6 100.0 634 35-39 40.1 38.0 1,072 71.7 18.2 9.4 0.7 100.0 407 40-44 32.4 31.0 941 71.3 22.7 4.8 1.2 100.0 291 45-49 24.9 24.0 883 70.0 20.6 7.6 1.8 100.0 212 Residence Urban 57.9 56.0 5,731 74.9 17.6 6.3 1.2 100.0 3,211 Non-urban 32.7 29.8 2,783 57.2 26.7 14.2 1.9 100.0 828 Province Western Cape 64.9 62.6 995 81.2 12.5 5.5 0.8 100.0 623 Eastern Cape 43.1 40.5 938 62.9 26.0 10.2 0.9 100.0 380 Northern Cape 43.9 40.9 173 61.9 21.5 10.2 6.4 100.0 71 Free State 47.0 45.3 442 61.8 21.8 15.6 0.8 100.0 200 KwaZulu-Natal 43.7 41.5 1,616 64.6 24.7 9.4 1.3 100.0 672 North West 43.5 41.4 570 62.6 19.1 17.8 0.6 100.0 236 Gauteng 59.1 57.1 2,284 79.8 15.9 3.1 1.3 100.0 1,305 Mpumalanga 42.4 39.1 671 62.4 25.9 7.8 3.9 100.0 263 Limpopo 37.1 35.2 824 61.6 22.4 14.8 1.2 100.0 290 Education No education 9.8 8.6 168 * * * * 100.0 14 Primary incomplete 4.5 4.3 447 (45.2) (36.1) (13.8) (4.9) 100.0 19 Primary complete 14.5 13.8 327 (59.2) (27.9) (8.2) (4.6) 100.0 45 Secondary incomplete 39.0 36.4 4,195 62.8 24.4 10.9 1.9 100.0 1,527 Secondary complete 67.2 64.8 2,369 74.3 17.9 6.5 1.3 100.0 1,534 More than secondary 90.8 89.2 1,008 81.7 12.8 5.2 0.3 100.0 899 Wealth quintile Lowest 20.3 18.5 1,648 53.7 31.1 13.0 2.2 100.0 304 Second 36.4 33.1 1,715 57.8 27.1 12.8 2.3 100.0 567 Middle 48.4 46.1 1,805 68.8 18.8 11.3 1.1 100.0 832 Fourth 62.1 59.7 1,763 71.7 19.7 7.2 1.4 100.0 1,052 Highest 82.1 81.1 1,583 82.6 13.6 3.0 0.9 100.0 1,284 Total 49.7 47.4 8,514 71.2 19.5 7.9 1.3 100.0 4,040 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. Characteristics of Respondents • 49 Table 3.5.2 Internet usage: Men Percentage of men age 15-49 who have ever used the Internet, and percentage who have used the Internet in the past 12 months; and among men who have used the Internet in the past 12 months, percent distribution by frequency of Internet use in the past month, according to background characteristics, South Africa DHS 2016 Ever used the Internet Used the Internet in the past 12 months Number of men Among men who have used the Internet in the past 12 months, percentage who, in the past month, used the Internet: Background characteristic Almost every day At least once a week Less than once a week Not at all Total Number of men Age 15-19 59.3 57.5 647 54.6 27.3 16.1 2.0 100.0 372 20-24 67.9 64.7 588 63.9 25.7 9.4 1.1 100.0 381 25-29 64.8 61.7 506 71.2 19.4 8.7 0.6 100.0 312 30-34 53.4 50.1 450 64.2 26.5 9.0 0.4 100.0 225 35-39 47.3 46.7 395 58.0 31.6 7.4 3.1 100.0 184 40-44 30.8 29.0 345 63.7 24.6 10.7 0.9 100.0 100 45-49 34.7 33.7 271 60.1 33.0 6.9 0.0 100.0 91 Residence Urban 59.7 58.0 2,203 67.1 23.1 8.8 1.0 100.0 1,279 Non-urban 42.5 38.7 999 46.8 35.5 15.6 2.1 100.0 387 Province Western Cape 62.8 61.7 328 67.0 25.9 5.4 1.6 100.0 202 Eastern Cape 40.4 37.6 362 65.6 25.2 7.8 1.4 100.0 136 Northern Cape 51.7 50.3 61 50.7 39.3 7.6 2.4 100.0 31 Free State 58.1 55.1 159 73.5 18.2 6.8 1.5 100.0 87 KwaZulu-Natal 44.6 40.8 521 53.7 24.8 19.6 1.9 100.0 213 North West 52.7 50.0 237 45.6 45.1 9.3 0.0 100.0 119 Gauteng 61.7 60.9 984 69.9 21.5 8.4 0.3 100.0 599 Mpumalanga 50.9 47.2 263 55.5 29.1 13.6 1.8 100.0 124 Limpopo 57.1 53.7 288 50.6 30.3 15.4 3.8 100.0 155 Education No education 2.9 2.9 62 * * * * 100.0 2 Primary incomplete 8.5 5.4 219 * * * * 100.0 12 Primary complete 20.3 19.2 166 (47.1) (35.4) (17.5) (0.0) 100.0 32 Secondary incomplete 47.9 45.4 1,637 48.5 33.3 16.2 2.1 100.0 743 Secondary complete 74.8 72.2 773 68.4 23.9 6.7 1.0 100.0 558 More than secondary 93.4 92.5 345 86.7 11.0 2.3 0.0 100.0 319 Wealth quintile Lowest 27.0 25.0 618 47.1 41.1 11.0 0.8 100.0 154 Second 43.6 39.6 682 51.4 32.0 13.7 3.0 100.0 270 Middle 54.1 51.9 715 54.6 30.9 12.8 1.8 100.0 371 Fourth 65.6 63.8 653 65.9 22.8 10.6 0.7 100.0 417 Highest 85.9 84.9 534 77.2 16.1 6.2 0.5 100.0 454 Total 15-49 54.3 52.0 3,202 62.4 26.0 10.4 1.3 100.0 1,665 50-59 23.0 21.8 416 74.6 16.7 7.3 1.4 100.0 91 Total 15-59 50.7 48.5 3,618 63.0 25.5 10.2 1.3 100.0 1,756 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. 50 • Characteristics of Respondents Table 3.6.1 Employment status: Women Percent distribution of women age 15-49 by employment status, according to background characteristics, South Africa DHS 2016 Employed in the 12 months preceding the survey Not employed in the 12 months preceding the survey Total Number of women Background characteristic Currently employed1 Not currently employed Age 15-19 2.5 0.9 96.6 100.0 1,427 20-24 19.2 5.3 75.5 100.0 1,415 25-29 37.0 6.2 56.9 100.0 1,444 30-34 45.7 5.2 49.2 100.0 1,333 35-39 47.8 4.7 47.5 100.0 1,072 40-44 52.4 2.2 45.4 100.0 941 45-49 54.0 3.6 42.4 100.0 883 Marital status Never married 27.2 3.8 69.0 100.0 4,992 Married or living together 42.8 4.4 52.7 100.0 3,050 Divorced/separated/widowed 56.6 4.9 38.5 100.0 472 Number of living children 0 18.1 2.5 79.4 100.0 2,436 1-2 41.0 5.1 53.9 100.0 4,155 3-4 43.3 3.9 52.8 100.0 1,629 5+ 28.6 4.2 67.2 100.0 294 Residence Urban 38.8 4.3 57.0 100.0 5,731 Non-urban 25.5 3.7 70.7 100.0 2,783 Province Western Cape 46.1 6.9 46.9 100.0 995 Eastern Cape 32.2 4.3 63.5 100.0 938 Northern Cape 29.2 5.9 64.9 100.0 173 Free State 29.0 4.4 66.6 100.0 442 KwaZulu-Natal 28.6 2.1 69.3 100.0 1,616 North West 34.2 5.1 60.7 100.0 570 Gauteng 39.0 3.6 57.4 100.0 2,284 Mpumalanga 31.4 4.1 64.5 100.0 671 Limpopo 28.3 4.5 67.2 100.0 824 Education No education 34.8 0.1 65.1 100.0 168 Primary incomplete 28.4 4.1 67.5 100.0 447 Primary complete 28.3 1.9 69.8 100.0 327 Secondary incomplete 23.6 3.4 73.0 100.0 4,195 Secondary complete 43.7 5.7 50.6 100.0 2,369 More than secondary 62.6 4.6 32.7 100.0 1,008 Wealth quintile Lowest 22.3 3.6 74.2 100.0 1,648 Second 28.4 4.3 67.2 100.0 1,715 Middle 33.6 3.4 63.0 100.0 1,805 Fourth 36.3 4.7 59.0 100.0 1,763 Highest 52.5 4.5 43.0 100.0 1,583 Total 34.4 4.1 61.5 100.0 8,514 1 “Currently employed” is defined as having done work in the past 7 days. Includes persons who did not work in the past 7 days but who are regularly employed and were absent from work for leave, illness, vacation, or any other such reason. Currently employed is not the same as the official government employment rate. Characteristics of Respondents • 51 Table 3.6.2 Employment status: Men Percent distribution of men age 15-49 by employment status, according to background characteristics, South Africa DHS 2016 Employed in the 12 months preceding the survey Not employed in the 12 months preceding the survey Total Number of men Background characteristic Currently employed1 Not currently employed Age 15-19 3.9 3.3 92.8 100.0 647 20-24 33.0 8.6 58.5 100.0 588 25-29 56.5 8.2 35.3 100.0 506 30-34 69.2 6.2 24.6 100.0 450 35-39 65.7 5.0 29.3 100.0 395 40-44 63.4 5.0 31.7 100.0 345 45-49 64.0 5.4 30.5 100.0 271 Marital status Never married 31.3 6.7 62.0 100.0 2,073 Married or living together 74.5 4.2 21.3 100.0 988 Divorced/separated/widowed 59.8 8.0 32.2 100.0 141 Number of living children 0 27.5 5.9 66.6 100.0 1,644 1-2 62.1 6.5 31.3 100.0 1,017 3-4 72.6 5.1 22.3 100.0 414 5+ 65.4 6.7 27.9 100.0 127 Residence Urban 50.5 5.6 44.0 100.0 2,203 Non-urban 35.6 7.0 57.4 100.0 999 Province Western Cape 59.6 9.2 31.2 100.0 328 Eastern Cape 36.2 8.4 55.5 100.0 362 Northern Cape 43.3 7.8 48.9 100.0 61 Free State 31.0 4.9 64.1 100.0 159 KwaZulu-Natal 41.9 5.5 52.7 100.0 521 North West 58.3 3.8 37.8 100.0 237 Gauteng 46.2 2.6 51.2 100.0 984 Mpumalanga 53.1 9.1 37.8 100.0 263 Limpopo 40.2 11.2 48.6 100.0 288 Education No education 48.3 6.0 45.6 100.0 62 Primary incomplete 39.4 6.2 54.4 100.0 219 Primary complete 40.3 6.2 53.6 100.0 166 Secondary incomplete 35.9 6.7 57.4 100.0 1,637 Secondary complete 57.5 6.2 36.3 100.0 773 More than secondary 73.3 1.9 24.8 100.0 345 Wealth quintile Lowest 41.2 6.6 52.2 100.0 618 Second 44.0 9.5 46.5 100.0 682 Middle 44.9 4.6 50.5 100.0 715 Fourth 41.

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