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Table 3.77: Misperception that people with TB are always HIV-positive: breakdown by sex, age, race, locality type and province (among respondents aged 15 years and older), South Africa, 201)

Table 3 77: Misperception that people with TB are always HIV-positive: breakdown by sex, age, race, locality type and province (among respondents aged 15 years and older), South Africa, 201)

Related Figures (172)

Figure I: HIV prevalence by province, among people of all ages, South Africa, 2017
Figure I: HIV prevalence by province, among people of all ages, South Africa, 2017
Figure I: HIV prevalence by selected age groups and sex, South Africa, 2017 Figure Ill: HIV prevalence by age and sex, South Africa, 2017
Figure I: HIV prevalence by selected age groups and sex, South Africa, 2017 Figure Ill: HIV prevalence by age and sex, South Africa, 2017
Notes: ‘Female partner’ and ‘male partner’ refer to the primary sexual partners. Totals denote self-identifiec heterosexual couples. Results are shown as a percentage of this group, with 95% CI in brackets. Table I: HIV serodiscordance between couples, South Africa, 2017
Notes: ‘Female partner’ and ‘male partner’ refer to the primary sexual partners. Totals denote self-identifiec heterosexual couples. Results are shown as a percentage of this group, with 95% CI in brackets. Table I: HIV serodiscordance between couples, South Africa, 2017
HIV serodiscordance between heterosexual couples and in mother-child pairs Figure IV: HIV prevalence by race and among people of all ages, South Africa, 2012 and 2017
HIV serodiscordance between heterosexual couples and in mother-child pairs Figure IV: HIV prevalence by race and among people of all ages, South Africa, 2012 and 2017
Exposure to antiretroviral treatment Figure V: HIV prevalence by orphanhood status among children aged O-18 years, South Africa, 2017
Exposure to antiretroviral treatment Figure V: HIV prevalence by orphanhood status among children aged O-18 years, South Africa, 2017
Notes: a Totals include missing demographic data b These results reflect the survey sample, namely people living with HIV who provided blood specimens for ARV testins Table II: Exposure to antiretroviral treatment among people living with HIV who provided blood specimens for ARV testing, by sex, age, race and province, South Africa, 2017
Notes: a Totals include missing demographic data b These results reflect the survey sample, namely people living with HIV who provided blood specimens for ARV testins Table II: Exposure to antiretroviral treatment among people living with HIV who provided blood specimens for ARV testing, by sex, age, race and province, South Africa, 2017
a Sample was too small for reliable estimates by sex Table Ill: HIV incidence (%) and number of new infections by age and sex, South Africa, 2017
a Sample was too small for reliable estimates by sex Table Ill: HIV incidence (%) and number of new infections by age and sex, South Africa, 2017
Figure VII: Viral load suppression among all people living with HIV by province, South Africa, 2017 Figure VI: Viral load suppression by age and sex, South Africa, 2017
Figure VII: Viral load suppression among all people living with HIV by province, South Africa, 2017 Figure VI: Viral load suppression by age and sex, South Africa, 2017
Key: EC, Eastern Cape; KZN, KwaZulu-Natal; FS, Free State; LP, Limpopo; MP, Mpumalanga; NW, North West; GP, Gauteng; WC, Western Cape; NC, Northern Cape
Key: EC, Eastern Cape; KZN, KwaZulu-Natal; FS, Free State; LP, Limpopo; MP, Mpumalanga; NW, North West; GP, Gauteng; WC, Western Cape; NC, Northern Cape
Figure VIII: 90-90-90 cascade for people living with HIV aged 15-64 years, South Africa, 2017
Figure VIII: 90-90-90 cascade for people living with HIV aged 15-64 years, South Africa, 2017
Figure IX: Trends in adult male self-reported circumcision by type, South Africa, 2002, 2008, 201. and 2017 Behavioural determinants of HIV
Figure IX: Trends in adult male self-reported circumcision by type, South Africa, 2002, 2008, 201. and 2017 Behavioural determinants of HIV
Age-disparate relationships Figure X: Early sexual debut among young men and women aged 15-24 years, South Africa 2002, 2005, 2008, 2012 and 2017
Age-disparate relationships Figure X: Early sexual debut among young men and women aged 15-24 years, South Africa 2002, 2005, 2008, 2012 and 2017
Figure XI: Age-disparate sexual relationships in the 15—19-year age group by sex, South Africa 2002, 2005, 2008, 2012 and 2017
Figure XI: Age-disparate sexual relationships in the 15—19-year age group by sex, South Africa 2002, 2005, 2008, 2012 and 2017
Figure XII: Sexually active people aged 15 years and older, who had more than one sex partner ir the last 12 months, South Africa, 2002, 2005, 2008, 2012 and 2017
Figure XII: Sexually active people aged 15 years and older, who had more than one sex partner ir the last 12 months, South Africa, 2002, 2005, 2008, 2012 and 2017
Figure XIII: Most recent condom use by age and sex, South Africa, 2002, 2005, 2008, 2012 and 201)
Figure XIII: Most recent condom use by age and sex, South Africa, 2002, 2005, 2008, 2012 and 201)
a HIV-positive status is shown as a percentage of the relevant population group Table IV: Perceived risk of HIV infection among people aged 15 years and older, by sex ana HIV status, South Africa, 2017
a HIV-positive status is shown as a percentage of the relevant population group Table IV: Perceived risk of HIV infection among people aged 15 years and older, by sex ana HIV status, South Africa, 2017
Figure XIV: Awareness of HIV status in the past 12 months, males and females aged 15 years and older, South Africa, 2017
Figure XIV: Awareness of HIV status in the past 12 months, males and females aged 15 years and older, South Africa, 2017
being 3 032 000 in 2008 and 3 132 000 in 2012. In 2017 a decrease was noted, with the number of orphans declining to an estimated 2 135 000. Figure XV: Orphanhood status among children aged 18 years and younger, South Africa, 2008, 2012, 2017
being 3 032 000 in 2008 and 3 132 000 in 2012. In 2017 a decrease was noted, with the number of orphans declining to an estimated 2 135 000. Figure XV: Orphanhood status among children aged 18 years and younger, South Africa, 2008, 2012, 2017
Table V: Consistency of condom use during past 12 months, high-risk groups, South Africa, 2017
Table V: Consistency of condom use during past 12 months, high-risk groups, South Africa, 2017
Figure XVI: Sexually active people having more than one sexual partner in the past 12 months, among high-risk groups, South Africa, 2017
Figure XVI: Sexually active people having more than one sexual partner in the past 12 months, among high-risk groups, South Africa, 2017
Figure XVI: Condom use at last sex among high-risk groups, South Africa, 2017 Perceived risk of HIV infection
Figure XVI: Condom use at last sex among high-risk groups, South Africa, 2017 Perceived risk of HIV infection
Figure XVIII: Perceived low risk for HIV infection among high-risk groups (people aged 15 years and older), South Africa, 2017
Figure XVIII: Perceived low risk for HIV infection among high-risk groups (people aged 15 years and older), South Africa, 2017
Figure XIX: Accurate knowledge among high-risk groups about preventing sexual transmission oj HIV and rejecting myths about HIV transmission, South Africa, 2008, 2012 and 2017
Figure XIX: Accurate knowledge among high-risk groups about preventing sexual transmission oj HIV and rejecting myths about HIV transmission, South Africa, 2008, 2012 and 2017
Table VI: HIV prevalence in 16 selected districts, South Africa, 2017
Table VI: HIV prevalence in 16 selected districts, South Africa, 2017
Viral load suppression Table VII: Exposure to ART in individuals living with HIV in the 16 selected districts, South Africa, 2017
Viral load suppression Table VII: Exposure to ART in individuals living with HIV in the 16 selected districts, South Africa, 2017
Table IX: Percentage of condom use at last sex among people aged 15 years and older in 16 selected districts, South Africa, 2017 District-level multiple sexual partners
Table IX: Percentage of condom use at last sex among people aged 15 years and older in 16 selected districts, South Africa, 2017 District-level multiple sexual partners
Awareness of HIV status — district level Table X: Percentage of sexually active people aged 15 years and older who reported having multiple sexual partners in the past 12 months in 106 selected districts, South Africa, 2017 Table XI presents self-reported awareness of HIV status in the last 12 months, at district level, among people aged 15 years and older in the selected districts. Overall, there was no significant difference in the awareness of HIV status across the 16 selected districts (50.0%). The two districts with the highest awareness were Bojanala Platinum (59.6%) and West Rand (59.5%). The two districts with the lowest awareness of HIV status were the City of Cape Town (40.3%) and Uthukela (40.7%).
Awareness of HIV status — district level Table X: Percentage of sexually active people aged 15 years and older who reported having multiple sexual partners in the past 12 months in 106 selected districts, South Africa, 2017 Table XI presents self-reported awareness of HIV status in the last 12 months, at district level, among people aged 15 years and older in the selected districts. Overall, there was no significant difference in the awareness of HIV status across the 16 selected districts (50.0%). The two districts with the highest awareness were Bojanala Platinum (59.6%) and West Rand (59.5%). The two districts with the lowest awareness of HIV status were the City of Cape Town (40.3%) and Uthukela (40.7%).
Table XI: Self-reported awareness of HIV status among people aged 15 and older in 16 selected districts, South Africa, 2017
Table XI: Self-reported awareness of HIV status among people aged 15 and older in 16 selected districts, South Africa, 2017
Medical male circumcision
Medical male circumcision
Table XII: Medical male circumcision for males aged 15 years and older in 106 selected districts South Africa, 2017
Table XII: Medical male circumcision for males aged 15 years and older in 106 selected districts South Africa, 2017
District-level orphanhood Table XIII: Correct knowledge and rejection of myths of HIV among people aged 15 years and older in 16 selected districts, South Africa, 2017
District-level orphanhood Table XIII: Correct knowledge and rejection of myths of HIV among people aged 15 years and older in 16 selected districts, South Africa, 2017
Table XIV: Orphanhood status among children aged 18 years and younger, in 16 selected districts, South Africa, 2017
Table XIV: Orphanhood status among children aged 18 years and younger, in 16 selected districts, South Africa, 2017
VPs and households were used as secondary sampling units (SSUs). Within each household, all consenting members formed the ultimate sampling unit (USU). Figure 2.2 shows the sampling approach used in the field. A systematic random sample of 15 VPs was selected from each of the 1 000 SALs, yielding a total sample of 15 000 households or VPs. If more than one household existed in a VP, a Kish grid (Kish 1965) was used to randomly select a responding household in which all household members were eligible to participate. Therefore, in the final sample, one household was sampled in each VP. Figure 2.2 summarises the sampling procedure. Figure 2.1: Map showing SALs realised in the survey
VPs and households were used as secondary sampling units (SSUs). Within each household, all consenting members formed the ultimate sampling unit (USU). Figure 2.2 shows the sampling approach used in the field. A systematic random sample of 15 VPs was selected from each of the 1 000 SALs, yielding a total sample of 15 000 households or VPs. If more than one household existed in a VP, a Kish grid (Kish 1965) was used to randomly select a responding household in which all household members were eligible to participate. Therefore, in the final sample, one household was sampled in each VP. Figure 2.2 summarises the sampling procedure. Figure 2.1: Map showing SALs realised in the survey
Figure 2.2: Steps in sampling to obtain national estimates The preselected households for the study were identified using aerial maps, with the aid of GPS instruments if necessary. Up to four visits were made to each selected household to ensure maximum participation. This approach meant that an interviewer visited the household first, explained the purpose of the survey, sought informed consent from the head of the household or a representative, completed the VP questionnaire, and provided information about the study. The household was then informed that the same data collector (possibly together with other collectors) would conduct a survey with individual members of the household who were at home on the day of the first visit, or at a later visit at a pre-agreed time. Electronic tablets were used for data collection. Any household from which a member was absent at the first visit was revisited up to three more times. The revisits were conducted according to people’s availability on certain days of the week and at certain times of the day.
Figure 2.2: Steps in sampling to obtain national estimates The preselected households for the study were identified using aerial maps, with the aid of GPS instruments if necessary. Up to four visits were made to each selected household to ensure maximum participation. This approach meant that an interviewer visited the household first, explained the purpose of the survey, sought informed consent from the head of the household or a representative, completed the VP questionnaire, and provided information about the study. The household was then informed that the same data collector (possibly together with other collectors) would conduct a survey with individual members of the household who were at home on the day of the first visit, or at a later visit at a pre-agreed time. Electronic tablets were used for data collection. Any household from which a member was absent at the first visit was revisited up to three more times. The revisits were conducted according to people’s availability on certain days of the week and at certain times of the day.
Table 2.2: Household questionnaires used in the 2017 survey
Table 2.2: Household questionnaires used in the 2017 survey
Table 2.3: Questionnaires completed for each individual in the 2017 survey
Table 2.3: Questionnaires completed for each individual in the 2017 survey
Figure 2.3: Communication strategy and media coverage, South Africa, 2017
Figure 2.3: Communication strategy and media coverage, South Africa, 2017
Figure 2.4: HIV-antibody testing strategy used in this study As part of the external quality control, SAMRC did an on-site quality control at Global Clinical & Viral Laboratory. These samples were selected randomly after all collected specimens had been tested.
Figure 2.4: HIV-antibody testing strategy used in this study As part of the external quality control, SAMRC did an on-site quality control at Global Clinical & Viral Laboratory. These samples were selected randomly after all collected specimens had been tested.
Figure 2.5: Testing algorithm for recent infection
Figure 2.5: Testing algorithm for recent infection
2.10 Weighting of the sample Figure 2.6: HIV drug-resistance testing algorithm
2.10 Weighting of the sample Figure 2.6: HIV drug-resistance testing algorithm
Table 2.5: Selected districts and SALs sampled in the 2017 survey, South Africa, 2017 13 Three districts were in the original sampling frame, giving a total of 16 selected districts.enforcement (StatsSA 2004).
Table 2.5: Selected districts and SALs sampled in the 2017 survey, South Africa, 2017 13 Three districts were in the original sampling frame, giving a total of 16 selected districts.enforcement (StatsSA 2004).
Figure 2.7: Map showing 16 districts with district-level estimates, South Africa, 2017
Figure 2.7: Map showing 16 districts with district-level estimates, South Africa, 2017
Table 3.1: Comparison of demographic characteristics of survey saniple versus StatsSA population estimates for 2017
Table 3.1: Comparison of demographic characteristics of survey saniple versus StatsSA population estimates for 2017
3.1.2 Response-rate analysis
3.1.2 Response-rate analysis
a Column totals for ‘Province’ contain missing provincial information. Table 3.2: Household (visiting point) response rates, demographic characteristics, South Africa, 2017
a Column totals for ‘Province’ contain missing provincial information. Table 3.2: Household (visiting point) response rates, demographic characteristics, South Africa, 2017
a No recorded information about testing status. b Column totals contain missing demographic information. Table 3.3: HIV-testing coverage by demographic characteristics: percentage distribution amon; respondents by testing status, South Africa, 2017
a No recorded information about testing status. b Column totals contain missing demographic information. Table 3.3: HIV-testing coverage by demographic characteristics: percentage distribution amon; respondents by testing status, South Africa, 2017
Table 3.4: HIV-risk associated characteristics among respondents aged 15 years and older who were interviewed and either provided or did not provide a blood sample, South Africa, 2017
Table 3.4: HIV-risk associated characteristics among respondents aged 15 years and older who were interviewed and either provided or did not provide a blood sample, South Africa, 2017
*" significant at p<0.001; ** p<0.01
*" significant at p<0.001; ** p<0.01
Table 3.5: Overall HIV prevalence by sex, age, race and locality, South Africa, 2017
Table 3.5: Overall HIV prevalence by sex, age, race and locality, South Africa, 2017
Key: KZN, KwaZulu-Natal; MP, Mpumalanga; FS: Free State; EC: Eastern Cape; N, North West; GP, Gauteng; LP, Limpopo; WC, Western Cape; NC, Northern Cape Figure 3.2: HIV prevalence by province, among people of all ages, South Africa, 2017
Key: KZN, KwaZulu-Natal; MP, Mpumalanga; FS: Free State; EC: Eastern Cape; N, North West; GP, Gauteng; LP, Limpopo; WC, Western Cape; NC, Northern Cape Figure 3.2: HIV prevalence by province, among people of all ages, South Africa, 2017
Table 3.7: HIV prevalence by sex and age, South Africa, 2017 Table 3.6: HIV prevalence by province among people aged 2 years and older, South Africa, 2002, 2005, 2008, 2012 and 2017
Table 3.7: HIV prevalence by sex and age, South Africa, 2017 Table 3.6: HIV prevalence by province among people aged 2 years and older, South Africa, 2002, 2005, 2008, 2012 and 2017
Figure 3.3: HIV prevalence by sex and age, South Africa, 2017
Figure 3.3: HIV prevalence by sex and age, South Africa, 2017
Table 3.8 shows the HIV prevalence by sex for selected age groups. Among children 12 months and younger, where infections are likely to have been transmitted from mother to child, HIV prevalence was 2.7%. This figure marks a 2-fold increase from 2012 (1.3%). However, the difference was not statistically significant. HIV prevalence was not different among male and female children. HIV prevalence among children younger than 5 years was 3.1% and among children aged 5-14 years it was 2.5%. In both age groups, the prevalence was higher than in 2012 but the differences were not statistically significant. There was no statistical significant difference for HIV prevalence between male and female children aged 0-14 years. Table 3.8 HIV prevalence by sex for age groups, South Africa, 2017
Table 3.8 shows the HIV prevalence by sex for selected age groups. Among children 12 months and younger, where infections are likely to have been transmitted from mother to child, HIV prevalence was 2.7%. This figure marks a 2-fold increase from 2012 (1.3%). However, the difference was not statistically significant. HIV prevalence was not different among male and female children. HIV prevalence among children younger than 5 years was 3.1% and among children aged 5-14 years it was 2.5%. In both age groups, the prevalence was higher than in 2012 but the differences were not statistically significant. There was no statistical significant difference for HIV prevalence between male and female children aged 0-14 years. Table 3.8 HIV prevalence by sex for age groups, South Africa, 2017
Table 3.9: HIV prevalence by age (people aged 2 years and older), South Africa, 2002, 2005, 2008, 2012 and 2017 Table 3.10: Prevalence of HIV by province in the 2-14-year age group, South Africa, 2002, 2005, 2008, 2012 and 201;
Table 3.9: HIV prevalence by age (people aged 2 years and older), South Africa, 2002, 2005, 2008, 2012 and 2017 Table 3.10: Prevalence of HIV by province in the 2-14-year age group, South Africa, 2002, 2005, 2008, 2012 and 201;
Table 3.11: HIV prevalence among youth aged 15-24 years by race, province and locality type, South Africa, 2017
Table 3.11: HIV prevalence among youth aged 15-24 years by race, province and locality type, South Africa, 2017
Table 3.13: HIV prevalence among people aged 15-49 years by race, province and locality type South Africa, 2017
Table 3.13: HIV prevalence among people aged 15-49 years by race, province and locality type South Africa, 2017
Table 3.14: HIV prevalence by province among people aged 15-49 years, South Africa, 2002, 2005, 2008, 2012 and 201
Table 3.14: HIV prevalence by province among people aged 15-49 years, South Africa, 2002, 2005, 2008, 2012 and 201
Table 3.15: HIV prevalence among adults aged 25 years and older by race, province and localit type, South Africa, 2017
Table 3.15: HIV prevalence among adults aged 25 years and older by race, province and localit type, South Africa, 2017
Table 3.17: HIV prevalence among adults aged 50 years and older, South Africa, 2017
Table 3.17: HIV prevalence among adults aged 50 years and older, South Africa, 2017
Table 3.18: Trends in HIV prevalence by province among adults aged 50 years and older, South Africa, 2002, 2005, 2008, 2012 and 201 Table 3.19: HIV prevalence by race and locality type among people of all ages, South Africa, 2017
Table 3.18: Trends in HIV prevalence by province among adults aged 50 years and older, South Africa, 2002, 2005, 2008, 2012 and 201 Table 3.19: HIV prevalence by race and locality type among people of all ages, South Africa, 2017
1 693 couples provided the information for this analysis. ‘Male partner’ and ‘female partner’ refer to the respondent’ main sexual partner (that is, each other). Results are shown as a percentage of n=1 693, with 95% CI in brackets. Table 3.21: HIV serodiscordance and seroconcordance between couples, South Africa, 2017
1 693 couples provided the information for this analysis. ‘Male partner’ and ‘female partner’ refer to the respondent’ main sexual partner (that is, each other). Results are shown as a percentage of n=1 693, with 95% CI in brackets. Table 3.21: HIV serodiscordance and seroconcordance between couples, South Africa, 2017
Table: 3.22: HIV serodiscordance between mother-and-child pairs for children aged 2 years anc younger, South Africa, 2017
Table: 3.22: HIV serodiscordance between mother-and-child pairs for children aged 2 years anc younger, South Africa, 2017
Table 3.23: HIV serodiscordancy between mother-and-child pairs in which children were younger than 10 years, South Africa, 2017 3.3.12 Orphans and HIV prevalence
Table 3.23: HIV serodiscordancy between mother-and-child pairs in which children were younger than 10 years, South Africa, 2017 3.3.12 Orphans and HIV prevalence
Table 3:24: HIV prevalence by orphanhood status and type among children aged 18 years and younger, South Africa, 2017
Table 3:24: HIV prevalence by orphanhood status and type among children aged 18 years and younger, South Africa, 2017
Significantly more children aged 15-18 years who were orphans were living with HIV (9.0%) than non-orphans (2.9%) (p<0.001). Most of these teenagers living with HIV were maternal orphans (13.1%) or double orphans (12.4%), with fewer paternal orphans (6.3%) and non-orphans (2.9%) (p<0.001). Table 3.26 shows the results for the teenage group (aged 15-18 years). Table 3.25: HIV prevalence by orphanhood status and type among children aged 14 years and younger, South Africa, 2017
Significantly more children aged 15-18 years who were orphans were living with HIV (9.0%) than non-orphans (2.9%) (p<0.001). Most of these teenagers living with HIV were maternal orphans (13.1%) or double orphans (12.4%), with fewer paternal orphans (6.3%) and non-orphans (2.9%) (p<0.001). Table 3.26 shows the results for the teenage group (aged 15-18 years). Table 3.25: HIV prevalence by orphanhood status and type among children aged 14 years and younger, South Africa, 2017
Table 3.26: HIV prevalence by orphanhood status and type among teenagers aged 15-18 years, South Africa, 2017
Table 3.26: HIV prevalence by orphanhood status and type among teenagers aged 15-18 years, South Africa, 2017
3.4 Exposure to antiretroviral treatment Figure 3.4: HIV prevalence and orphanhood status among children aged 18 years and younger, South Africa, 2017 As in 2008 and 2012, the 2017 survey protocol included methodologies for directly estimating exposure to ART among those living with HIV. Measuring ART exposure in the HIV-positive survey population is critical for assessing progress made towards the UNAIDS 90-90-90 treatment targets. Such information also helps in assessing the impact of ART on HIV-prevalence levels and in interpreting HIV-prevalence trends.
3.4 Exposure to antiretroviral treatment Figure 3.4: HIV prevalence and orphanhood status among children aged 18 years and younger, South Africa, 2017 As in 2008 and 2012, the 2017 survey protocol included methodologies for directly estimating exposure to ART among those living with HIV. Measuring ART exposure in the HIV-positive survey population is critical for assessing progress made towards the UNAIDS 90-90-90 treatment targets. Such information also helps in assessing the impact of ART on HIV-prevalence levels and in interpreting HIV-prevalence trends.
Notes: Column totals contain missing demographic information. The results were obtained for people with HIV who provided blood specimens for ARV testing. Table 3.27: Exposure to antiretroviral treatment among people living with HIV, by sex, age, race and province, South Africa, 2017
Notes: Column totals contain missing demographic information. The results were obtained for people with HIV who provided blood specimens for ARV testing. Table 3.27: Exposure to antiretroviral treatment among people living with HIV, by sex, age, race and province, South Africa, 2017
a Sample size too small for reliable estimates by sex. b Rounded off to the nearest 100. Table 3.28: HIV incidence by age and sex, South Africa, 2017
a Sample size too small for reliable estimates by sex. b Rounded off to the nearest 100. Table 3.28: HIV incidence by age and sex, South Africa, 2017
a sample too small for reliable estimates for males Table 3.29: HIV incidence across age groups: comparison between 2012 and 2017, South Africa
a sample too small for reliable estimates for males Table 3.29: HIV incidence across age groups: comparison between 2012 and 2017, South Africa
a_ Includes farms and traditional (tribal) areas. Table 3.30: HIV incidence by race and locality type among people aged 2 years and older, South Africa, 2017
a_ Includes farms and traditional (tribal) areas. Table 3.30: HIV incidence by race and locality type among people aged 2 years and older, South Africa, 2017
Figure 3.5: Viral load suppression by age and sex, South Africa, 2017
Figure 3.5: Viral load suppression by age and sex, South Africa, 2017
Table 3.31: HIV incidence: behavioural and sociodemographic variables among people aged 15-49 years, South Africa, 2017
Table 3.31: HIV incidence: behavioural and sociodemographic variables among people aged 15-49 years, South Africa, 2017
Table 3.32: Viral load suppression by age, sex, race, locality type and province, South Africa, 2017
Table 3.32: Viral load suppression by age, sex, race, locality type and province, South Africa, 2017
Figure 3.6: 90-90-90 indicators for people living with HIV aged 15-64 years, South Africa, 201;
Figure 3.6: 90-90-90 indicators for people living with HIV aged 15-64 years, South Africa, 201;
a Column totals include missing demographic information Table 3.33: 90-90-90 indicators for people aged 15-64 yeras living with HIV, South Africa, 2017
a Column totals include missing demographic information Table 3.33: 90-90-90 indicators for people aged 15-64 yeras living with HIV, South Africa, 2017
Note: NNRTI: non-nucleoside reverse transcriptase inhibitor; NRTI: nucleoside reverse transcriptase inhibitor PI: protease inhibitor Table 3.34: HIV drug resistance by drug class, South Africa, 2017 (n=697)
Note: NNRTI: non-nucleoside reverse transcriptase inhibitor; NRTI: nucleoside reverse transcriptase inhibitor PI: protease inhibitor Table 3.34: HIV drug resistance by drug class, South Africa, 2017 (n=697)
Notes: DRM, drug resistant mutations, NNRTI, non-nucleoside reverse transcriptase inhibitors; NRTI, non-nucleoside reverse transcriptase inhibitors; PIs, protease inhibitors; ARV -ve, but on ART by self-report: respondents who reported taking ARVs daily but tested negative for ARVs (they are a subgroup of those who tested ARV -ve). Table 3.35: HIV drug resistance by ART exposure, age, sex and locality type, South Africa, 2017
Notes: DRM, drug resistant mutations, NNRTI, non-nucleoside reverse transcriptase inhibitors; NRTI, non-nucleoside reverse transcriptase inhibitors; PIs, protease inhibitors; ARV -ve, but on ART by self-report: respondents who reported taking ARVs daily but tested negative for ARVs (they are a subgroup of those who tested ARV -ve). Table 3.35: HIV drug resistance by ART exposure, age, sex and locality type, South Africa, 2017
Table 3.30: Males aged 15 years and older who reported having been circumcised, demographic characteristics, South Africa, 2017
Table 3.30: Males aged 15 years and older who reported having been circumcised, demographic characteristics, South Africa, 2017
Table 3.37: Self-reported circumcision settings and demographic breakdown among men aged 15 years and older, South Africa, 2017
Table 3.37: Self-reported circumcision settings and demographic breakdown among men aged 15 years and older, South Africa, 2017
1 In 2005, population-based data on male circumcision were not collected. Figure 3.7: Trends in male circumcision by type among males aged 15 years and older, South Africa, 2002, 2008, 2012 and 2017
1 In 2005, population-based data on male circumcision were not collected. Figure 3.7: Trends in male circumcision by type among males aged 15 years and older, South Africa, 2002, 2008, 2012 and 2017
Table 3.38: Percentage of circumcision among boys aged 14 years and younger, South Africa, 2017
Table 3.38: Percentage of circumcision among boys aged 14 years and younger, South Africa, 2017
Table 3.39: Settings of circumcision among boys aged 14 years and younger, South Africa, 2017 3.8.4 Demand for male circumcision among those not circumcised
Table 3.39: Settings of circumcision among boys aged 14 years and younger, South Africa, 2017 3.8.4 Demand for male circumcision among those not circumcised
Table 3.40: Uncircumcised males aged 15 years and older who indicated that they would like to be circumcised, demographic characteristics, South Africa, 2017
Table 3.40: Uncircumcised males aged 15 years and older who indicated that they would like to be circumcised, demographic characteristics, South Africa, 2017
Figure 3.8: Early sexual debut reported by youth aged 15-24 years by sex, South Africa, 2002, 2005, 2008, 2012 and 2017
Figure 3.8: Early sexual debut reported by youth aged 15-24 years by sex, South Africa, 2002, 2005, 2008, 2012 and 2017
Table 3.41: Early sexual debut reported by youth aged 15-24 years, demographic characteristics, South Africa, 2017 Table 3.42 indicates the provincial rates of early sexual debut across all five surveys. Gauteng had the highest proportion of early debut for 2017, at 16.5%, an increase of 7 percentage points over the 2012 rate and an overall increase of 10.2 percentage points since 2002. Eastern Cape reported the second highest proportion for 2017, at 15.6%, marking a slight decrease since 2012. Western Cape reported the third highest proportion at 14.4%, a marginal increase (0.2 percentage points) from 2012. In Free State, there has been a steady increase from 0.9% in 2002 to 13.8% in 2017, representing an overall increase of 13 percentage points since 2002. This is higher than the national average increase of 8.6 percentage points from 2002 to 2017. In Mpumalanga, early sexual debut almost doubled from 7.7% in 2012 to 13.7% in 2017.
Table 3.41: Early sexual debut reported by youth aged 15-24 years, demographic characteristics, South Africa, 2017 Table 3.42 indicates the provincial rates of early sexual debut across all five surveys. Gauteng had the highest proportion of early debut for 2017, at 16.5%, an increase of 7 percentage points over the 2012 rate and an overall increase of 10.2 percentage points since 2002. Eastern Cape reported the second highest proportion for 2017, at 15.6%, marking a slight decrease since 2012. Western Cape reported the third highest proportion at 14.4%, a marginal increase (0.2 percentage points) from 2012. In Free State, there has been a steady increase from 0.9% in 2002 to 13.8% in 2017, representing an overall increase of 13 percentage points since 2002. This is higher than the national average increase of 8.6 percentage points from 2002 to 2017. In Mpumalanga, early sexual debut almost doubled from 7.7% in 2012 to 13.7% in 2017.
Table 3.42: Sexual debut before the age of 15 years among youth aged 15-24 years, by province, South Africa, 2002, 2005, 2008, 2012 and 2017
Table 3.42: Sexual debut before the age of 15 years among youth aged 15-24 years, by province, South Africa, 2002, 2005, 2008, 2012 and 2017
Figure 3.9 Age-disparate relationships among adolescent males and females aged 15-19 years South Africa, 2002-2017
Figure 3.9 Age-disparate relationships among adolescent males and females aged 15-19 years South Africa, 2002-2017
Table 3.43: Age-disparate relationships among adolescent males and females aged 15-19 years, South Africa, 2017
Table 3.43: Age-disparate relationships among adolescent males and females aged 15-19 years, South Africa, 2017
a The sample for males was too small to report on. Table 3.44: HIV prevalence and age discrepancy in sexual relationships in the 15-19 age group, South Africa, 2017
a The sample for males was too small to report on. Table 3.44: HIV prevalence and age discrepancy in sexual relationships in the 15-19 age group, South Africa, 2017
Table 3.45: Percentage of people aged 15 years and older who reported having multiple sexual partners in the past year by age, sex, race, locality and province, South Africa, 2017
Table 3.45: Percentage of people aged 15 years and older who reported having multiple sexual partners in the past year by age, sex, race, locality and province, South Africa, 2017
High-risk groups at risk of HIV exposure Figure 3.10: Percentage of people in high-risk groups who reported more than one sexual partner in the past year, South Africa, 2017
High-risk groups at risk of HIV exposure Figure 3.10: Percentage of people in high-risk groups who reported more than one sexual partner in the past year, South Africa, 2017
Figure 3.10 shows the proportions of MSPs reported among high-risk groups for the past 12 months. The highest reported proportion was that of hazardous drinkers (22.4%), followed by recreational drug users (18.2%), black African men aged 25-49 years (16.3%) and disabled people (12.1%). Unmarried people living together aged 15-49 years (8.6%) anc black African women aged 20-34 years (7.1%) reported the lowest proportions of MSPs in the past year.
Figure 3.10 shows the proportions of MSPs reported among high-risk groups for the past 12 months. The highest reported proportion was that of hazardous drinkers (22.4%), followed by recreational drug users (18.2%), black African men aged 25-49 years (16.3%) and disabled people (12.1%). Unmarried people living together aged 15-49 years (8.6%) anc black African women aged 20-34 years (7.1%) reported the lowest proportions of MSPs in the past year.
3.9.4 Condom use Figure 3.11: Sexually active people aged 15 years and older who reported having more than one sexual partner in the last year, South Africa, 2002, 2005, 2008, 2012 and 2017 Laboratory and epidemiological studies have shown that consistent and correct usage of male and female latex condoms reduces the risk of acquiring sexually transmitted diseases, including HIV (Giannou et al. 2015; SANAC 2017). This section presents the findings regarding condom use at the most recent sexual encounter or with the most recent partner. The consistency of condom usage over the previous year is also discusse
3.9.4 Condom use Figure 3.11: Sexually active people aged 15 years and older who reported having more than one sexual partner in the last year, South Africa, 2002, 2005, 2008, 2012 and 2017 Laboratory and epidemiological studies have shown that consistent and correct usage of male and female latex condoms reduces the risk of acquiring sexually transmitted diseases, including HIV (Giannou et al. 2015; SANAC 2017). This section presents the findings regarding condom use at the most recent sexual encounter or with the most recent partner. The consistency of condom usage over the previous year is also discusse
a Column totals contain missing demographic information. Table 3.46: Condom use during most recent sexual encounter among people aged 15 years and older, demographic characteristics, South Africa, 2017
a Column totals contain missing demographic information. Table 3.46: Condom use during most recent sexual encounter among people aged 15 years and older, demographic characteristics, South Africa, 2017
Figure 3.12: Condom use at last sex by age and sex, South Africa, 2002, 2005, 2008, 2012 and 2017
Figure 3.12: Condom use at last sex by age and sex, South Africa, 2002, 2005, 2008, 2012 and 2017
Table 3.47: Consistency of recent condom use among people aged 15 years and older, by demographic characteristics, South Africa, 2017
Table 3.47: Consistency of recent condom use among people aged 15 years and older, by demographic characteristics, South Africa, 2017
a The sample was too small to report on. Table 3.48: Condom use at most recent sexual encounter by marital status and age, South Africa, 2017 Table 3.49 shows trends in condom use at the last sexual encounter among people aged 15 years and older, broken down by province, for the five surveys (2002-2017). In 2012, the results showed that condom-use rates at the last sexual encounter had declined across all provinces. The decrease was significant in eight of the nine provinces.
a The sample was too small to report on. Table 3.48: Condom use at most recent sexual encounter by marital status and age, South Africa, 2017 Table 3.49 shows trends in condom use at the last sexual encounter among people aged 15 years and older, broken down by province, for the five surveys (2002-2017). In 2012, the results showed that condom-use rates at the last sexual encounter had declined across all provinces. The decrease was significant in eight of the nine provinces.
Table 3.49: Condom use at last sexual encounter, by province among people aged 15 years and older, South Africa, 2002, 2005, 2008, 2012 and 2017
Table 3.49: Condom use at last sexual encounter, by province among people aged 15 years and older, South Africa, 2002, 2005, 2008, 2012 and 2017
Figure 3.13 Condom use at last sexual encounter among high-risk groups, South Africa, 2017
Figure 3.13 Condom use at last sexual encounter among high-risk groups, South Africa, 2017
Table 3.50: Consistency of condom use with most recent partner among high-risk groups, Soutk Africa, 2017
Table 3.50: Consistency of condom use with most recent partner among high-risk groups, Soutk Africa, 2017
Table 3.51: Awareness of HIV-testing sites among people aged 15 years and older, demographic variables, South Africa, 2017
Table 3.51: Awareness of HIV-testing sites among people aged 15 years and older, demographic variables, South Africa, 2017
3.10.2 History of HIV-testing
3.10.2 History of HIV-testing
Table 3.52: HIV-testing among people aged 15 years and older, demographic characteristics, South Africa, 2017
Table 3.52: HIV-testing among people aged 15 years and older, demographic characteristics, South Africa, 2017
Although effort has been made to promote HIV prevention and care programmes in the workplace, only 4.7% of the respondents reported having been tested in the workplace. As observed in 2012, males were more likely than females to have been tested in the workplace. Traditional healers were the least likely among healthcare providers to be consulted for HIV-testing. Special-purpose centres, such as youth centres, loveLife’s youth- friendly clinics, and HIV-testing centres not based at clinics or mobile clinics were utilised far less than the public health facilities. Table 3.54: HIV-testing sites accessed by people aged 15 years and older, South Africa, 2017
Although effort has been made to promote HIV prevention and care programmes in the workplace, only 4.7% of the respondents reported having been tested in the workplace. As observed in 2012, males were more likely than females to have been tested in the workplace. Traditional healers were the least likely among healthcare providers to be consulted for HIV-testing. Special-purpose centres, such as youth centres, loveLife’s youth- friendly clinics, and HIV-testing centres not based at clinics or mobile clinics were utilised far less than the public health facilities. Table 3.54: HIV-testing sites accessed by people aged 15 years and older, South Africa, 2017
Table 3.55: Perceived risk of becoming infected with HIV, people aged 15 years and older, by background characteristics, South Africa, 2017
Table 3.55: Perceived risk of becoming infected with HIV, people aged 15 years and older, by background characteristics, South Africa, 2017
No difference was found in the perception of risk between males and females. In terms of racial differences, black Africans (19.7%) had a higher risk perception than the other races. Regarding the age groups, more than a fifth of people aged 25-49 years (21.0%) believed they were likely to become infected with HIV. Regarding locality type, inhabitants in rural informal (tribal) areas (20.3%) had the highest proportion of risk perception, and urban dwellers had the lowest (15.1%). Mpumalanga (23.2%) had the highest proportion of HIV- risk perception whereas the Western Cape had the lowest (10.5%).
No difference was found in the perception of risk between males and females. In terms of racial differences, black Africans (19.7%) had a higher risk perception than the other races. Regarding the age groups, more than a fifth of people aged 25-49 years (21.0%) believed they were likely to become infected with HIV. Regarding locality type, inhabitants in rural informal (tribal) areas (20.3%) had the highest proportion of risk perception, and urban dwellers had the lowest (15.1%). Mpumalanga (23.2%) had the highest proportion of HIV- risk perception whereas the Western Cape had the lowest (10.5%).
Table 3.50: Reasons given for why they believed they would not become infected by HIV among people aged 15 years and older, South Africa, 2017
Table 3.50: Reasons given for why they believed they would not become infected by HIV among people aged 15 years and older, South Africa, 2017
Figure 3.15: Portion of high-risk groups that perceived themselves to be at low risk for HIV infection among people aged 15 years and older, South Africa, 2017
Figure 3.15: Portion of high-risk groups that perceived themselves to be at low risk for HIV infection among people aged 15 years and older, South Africa, 2017
Table 3.57: Reasons given by people aged 15 years and older who believed they would become infected with HIV, South Africa, 2017
Table 3.57: Reasons given by people aged 15 years and older who believed they would become infected with HIV, South Africa, 2017
Table 3.58: Reasons given by groups at high risk of HIV exposure for believing they would not become infected with HIV, South Africa, 2017 Table 3.59 shows the main reasons why some members of high-risk groups perceived themselves to be at risk for HIV infection. The main reasons were being sexually active (38.7%), not using a condom (17.1%) and inconsistent condom use (17.0%).
Table 3.58: Reasons given by groups at high risk of HIV exposure for believing they would not become infected with HIV, South Africa, 2017 Table 3.59 shows the main reasons why some members of high-risk groups perceived themselves to be at risk for HIV infection. The main reasons were being sexually active (38.7%), not using a condom (17.1%) and inconsistent condom use (17.0%).
Table 3.59: Reasons given by groups at high risk of HIV exposure for believing they would become infected with HIV, South Africa, 2017
Table 3.59: Reasons given by groups at high risk of HIV exposure for believing they would become infected with HIV, South Africa, 2017
Table 3.60: Perceived risk of HIV infection among people aged 15 years and older by sex and HIV status, South Africa, 2017
Table 3.60: Perceived risk of HIV infection among people aged 15 years and older by sex and HIV status, South Africa, 2017
Table 3.61: Accurate knowledge among people aged 15 years and older about the sexual transmission of HIV and rejection of major misconceptions about HIV, South Africa, 2017
Table 3.61: Accurate knowledge among people aged 15 years and older about the sexual transmission of HIV and rejection of major misconceptions about HIV, South Africa, 2017
Figure 3.16: Accurate knowledge of preventing the sexual transnuission of HIV and rejection of misconceptions about HIV transmission among high-risk groups, South Africa, 2008, 2012, 2017 3.13 Social and behaviour change communication programmes
Figure 3.16: Accurate knowledge of preventing the sexual transnuission of HIV and rejection of misconceptions about HIV transmission among high-risk groups, South Africa, 2008, 2012, 2017 3.13 Social and behaviour change communication programmes
a Social media includes Facebook, Twitter, WhatsApp and Instagram. Table 3.62: Respondent access to one of these media platforms at least once a week
a Social media includes Facebook, Twitter, WhatsApp and Instagram. Table 3.62: Respondent access to one of these media platforms at least once a week
Table 3.63: Individual exposure to progammes offered by CCI in the past 12 months, by sex among people aged 15-55 years
Table 3.63: Individual exposure to progammes offered by CCI in the past 12 months, by sex among people aged 15-55 years
Table 3.64: Individual exposure to programmes offered by CCI in the past 12 months, by sex, among respondents aged 15-55 years
Table 3.64: Individual exposure to programmes offered by CCI in the past 12 months, by sex, among respondents aged 15-55 years
The levels of exposure for this composite indicator were similar for males and females. Among respondents, 18.5% of males and 23.5% of females had participated in face-to- face SBCC programmes — such as community meetings, community dialogues or other meetings related to HIV — during the past year. Table 3.65: Individual exposure to progammes offered by Soul City in the past 12 months, by sex, people aged 15-55 years
The levels of exposure for this composite indicator were similar for males and females. Among respondents, 18.5% of males and 23.5% of females had participated in face-to- face SBCC programmes — such as community meetings, community dialogues or other meetings related to HIV — during the past year. Table 3.65: Individual exposure to progammes offered by Soul City in the past 12 months, by sex, people aged 15-55 years
Table 3.66: Behaviour-change communication exposure and selected behavioural and other outcomes
Table 3.66: Behaviour-change communication exposure and selected behavioural and other outcomes
Table 3.68: Percentage of people aged 15 years and older, by province, who agreed with individual statements about people living with HIV and AIDS, South Africa, 2017
Table 3.68: Percentage of people aged 15 years and older, by province, who agreed with individual statements about people living with HIV and AIDS, South Africa, 2017
Table 3.69: People aged 15 years and older who agreed with individual statements about people living with HIV and AIDS, by age group, South Africa, 2005, 2008, 2012 and 2017
Table 3.69: People aged 15 years and older who agreed with individual statements about people living with HIV and AIDS, by age group, South Africa, 2005, 2008, 2012 and 2017
Table 3.70: Experiences of intimate partner violence reported by both sexes, South Africa, 2017
Table 3.70: Experiences of intimate partner violence reported by both sexes, South Africa, 2017
Table 3.71: Injuries experienced as a result of intimate partner violence, South Africa, 2017 To determine the frequency of experiences of physical violence, we asked respondents to indicate how often this had happened in the last year. As shown in Table 3.72, most respondents (72.1%) indicated that they had not experienced any physical violence in the past year, whereas about a quarter (26.5%) had experienced violence ‘only sometimes’. Less than 2% indicated that physical violence occurred ‘often’, with a slightly higher proportion of females (1.6%) than males (1.1%) reporting this.
Table 3.71: Injuries experienced as a result of intimate partner violence, South Africa, 2017 To determine the frequency of experiences of physical violence, we asked respondents to indicate how often this had happened in the last year. As shown in Table 3.72, most respondents (72.1%) indicated that they had not experienced any physical violence in the past year, whereas about a quarter (26.5%) had experienced violence ‘only sometimes’. Less than 2% indicated that physical violence occurred ‘often’, with a slightly higher proportion of females (1.6%) than males (1.1%) reporting this.
Table 3.72: Frequency of physical violence in the past year, South Africa, 2017 Table 3.73 (page 116) shows the HIV status among people who experienced IPV. The results show that HIV prevalence was higher in both men and women who reported experiences of being pushed, shaken or having objects thrown at them or being slapped, compared to people who did not experience any IPV. Further analysis showed that there were no significant differences between males and females in this regard. However, men
Table 3.72: Frequency of physical violence in the past year, South Africa, 2017 Table 3.73 (page 116) shows the HIV status among people who experienced IPV. The results show that HIV prevalence was higher in both men and women who reported experiences of being pushed, shaken or having objects thrown at them or being slapped, compared to people who did not experience any IPV. Further analysis showed that there were no significant differences between males and females in this regard. However, men
Table 3.73: IPV among individuals living with HIV, South Africa, 2017 The analysis in Table 3.74 shows the results for people who reported that they had acted violently towards their intimate partners. The results are broken down by sex. More men and women who reported perpetrating IPV had an HIV-positive status, compared to people who were not violent towards their partners. Among males, the difference was significant (b=0.001).
Table 3.73: IPV among individuals living with HIV, South Africa, 2017 The analysis in Table 3.74 shows the results for people who reported that they had acted violently towards their intimate partners. The results are broken down by sex. More men and women who reported perpetrating IPV had an HIV-positive status, compared to people who were not violent towards their partners. Among males, the difference was significant (b=0.001).
Table 3.74: IPV by HIV status by sex, South Africa, 2017
Table 3.74: IPV by HIV status by sex, South Africa, 2017
Table 3.76: Estimated percentage of orphans aged 18 years and younger, by province, South Africa, 2012 and 2017 Figure 3.17: Comparison of orphanhood status, South Africa, 2008, 2012 and 2017
Table 3.76: Estimated percentage of orphans aged 18 years and younger, by province, South Africa, 2012 and 2017 Figure 3.17: Comparison of orphanhood status, South Africa, 2008, 2012 and 2017
3.17.3 History of TB diagnosis
3.17.3 History of TB diagnosis
Table 3.78: History of TB diagnosis by sex, race and locality type among respondents aged 15 year: and older, South Africa, 2017
Table 3.78: History of TB diagnosis by sex, race and locality type among respondents aged 15 year: and older, South Africa, 2017
3.17.4 Testing for HIV at time of TB diagnosis
3.17.4 Testing for HIV at time of TB diagnosis
a Some sample sizes were too small to report reliably. Table 3.79: Testing for HIV at the time of TB diagnosis: breakdown by sex, race, locality type an province among respondents aged 15 years and older, South Africa, 2017
a Some sample sizes were too small to report reliably. Table 3.79: Testing for HIV at the time of TB diagnosis: breakdown by sex, race, locality type an province among respondents aged 15 years and older, South Africa, 2017
Table 3.80: Stigma among respondents aged 15 years and older diagnosed with TB, South Africa, 2017
Table 3.80: Stigma among respondents aged 15 years and older diagnosed with TB, South Africa, 2017
Percentages in each row refer to the proportion of 7 in the final column. Table 3.81: Testing coverage in 16 selected districts by demographic characteristics: percentage distribution among people for HIV testing by testing status, South Africa, 2017
Percentages in each row refer to the proportion of 7 in the final column. Table 3.81: Testing coverage in 16 selected districts by demographic characteristics: percentage distribution among people for HIV testing by testing status, South Africa, 2017
Table 3.82: HIV prevalence in 16 selected districts, South Africa, 2017
Table 3.82: HIV prevalence in 16 selected districts, South Africa, 2017
Table 3.83: Exposure to ART among individuals living with HIV in 16 selected districts, South Africa, 2017 3.18.3 Viral load suppression in selected districts Table 3.84 (page 126) shows the results for VL suppression in the 16 selected districts. Among the 4 303 people living with HIV from these districts, 60.6% (95% CI: 56.9-64.2) were virally suppressed. VL suppression among all people living with HIV — not necessarily receiving ART — was highest in Uthungulu (71.2%) in KwaZulu-Natal and lowest in the City of Tshwane (52.8%), Ekurhuleni (54.2%) in Gauteng, and in Greater Sekhukhune in Limpopo (54.3%). By contrast, the percentage of HIV-positive people receiving ART who were virally suppressed was high. Only three districts recorded VL-suppression rates of less than 80%: Ehlanzeni in Mpumalanga (65.3%), Gert Sibande in Mpumalanga (77.4%) and eThekwini in KwaZulu-Natal (78.3%). Viral load suppression reached levels above 90% in the following districts: the City of Cape Town, Sedibeng, Umzinyathi, Uthungulu and West Rand. Among people living with HIV in the additional sample, who were interviewed and provided blood specimens for testing, 62.3% were on ART (see Table 3.83). ART coverage in Uthungulu was the highest (77.4%), whereas the lowest coverage was in Tshwane (49.3%). 3.18.2 District-level ART coverage among people living with HIV
Table 3.83: Exposure to ART among individuals living with HIV in 16 selected districts, South Africa, 2017 3.18.3 Viral load suppression in selected districts Table 3.84 (page 126) shows the results for VL suppression in the 16 selected districts. Among the 4 303 people living with HIV from these districts, 60.6% (95% CI: 56.9-64.2) were virally suppressed. VL suppression among all people living with HIV — not necessarily receiving ART — was highest in Uthungulu (71.2%) in KwaZulu-Natal and lowest in the City of Tshwane (52.8%), Ekurhuleni (54.2%) in Gauteng, and in Greater Sekhukhune in Limpopo (54.3%). By contrast, the percentage of HIV-positive people receiving ART who were virally suppressed was high. Only three districts recorded VL-suppression rates of less than 80%: Ehlanzeni in Mpumalanga (65.3%), Gert Sibande in Mpumalanga (77.4%) and eThekwini in KwaZulu-Natal (78.3%). Viral load suppression reached levels above 90% in the following districts: the City of Cape Town, Sedibeng, Umzinyathi, Uthungulu and West Rand. Among people living with HIV in the additional sample, who were interviewed and provided blood specimens for testing, 62.3% were on ART (see Table 3.83). ART coverage in Uthungulu was the highest (77.4%), whereas the lowest coverage was in Tshwane (49.3%). 3.18.2 District-level ART coverage among people living with HIV
Table 3.84: Viral load suppression in 106 selected districts, South Africa, 2017 3.18.4 90-90-90 indicators in selected districts
Table 3.84: Viral load suppression in 106 selected districts, South Africa, 2017 3.18.4 90-90-90 indicators in selected districts
Table 3.86: Medical circumcision among males aged 15 years and older in 16 selected district: South Africa, 2017 4.18.6 Behavioural determinants of HIV at district level
Table 3.86: Medical circumcision among males aged 15 years and older in 16 selected district: South Africa, 2017 4.18.6 Behavioural determinants of HIV at district level
Table 3.87: Percentage of people aged 15 years and older in 106 selected districts who used a condom at the last sexual encounter, South Africa, 2017 Multiple sexual partners
Table 3.87: Percentage of people aged 15 years and older in 106 selected districts who used a condom at the last sexual encounter, South Africa, 2017 Multiple sexual partners
Table 3.88: Percentage of people aged 15 years and older in 106 selected districts who reported having multiple sexual partners in the past year, South Africa, 2017
Table 3.88: Percentage of people aged 15 years and older in 106 selected districts who reported having multiple sexual partners in the past year, South Africa, 2017
Awareness of HIV status
Awareness of HIV status
Table 3.89: Self-reported awareness of HIV status among people aged 15 years and older in 16 selected districts, South Africa, 2017
Table 3.89: Self-reported awareness of HIV status among people aged 15 years and older in 16 selected districts, South Africa, 2017
Orphanhood Table 3.90: Accurate knowledge about HIV among people aged 15 years and older in 16 selectea districts, South Africa, 2017 Table 3.91 (page 132) shows the current status of orphanhood in the 16 districts in South Africa among children aged 18 years and younger. Across these districts, the highest percentage of orphans was paternal orphans (6.4%). The highest number of paternal orphans lived in OR Tambo (11.8%) and eThekwini (11.0%) while the lowest percentage was found in the City of Tshwane (3.9%). The proportion of maternal (3.0%) and double orphans (2.1%) remained low across the 16 districts. The highest percentage of maternal orphans was found in Sekhukhune (4.6%) and the lowest percentage in the City of Cape Town (0.7%). The highest proportion of double orphans lived in iLembe (3.8%) and the lowest in West Rand (0.4%). Table 3.90 presents the levels of accurate knowledge and rejection of myths about HIV among people aged 15 years or older in the 16 selected districts. Overall, 36.3% of people had accurate knowledge of HIV across the districts. Respondents in the City of Cape Town (47.2%), iLembe (40.0%) and eThekwini district (39.6%) displayed the highest levels of accurate HIV knowledge. Respondents in Sekhukhune (24.6%), West Rand (27.5%) and Bojanala Platinum (27.4%) had the lowest levels of accurate knowledge in the selected districts.
Orphanhood Table 3.90: Accurate knowledge about HIV among people aged 15 years and older in 16 selectea districts, South Africa, 2017 Table 3.91 (page 132) shows the current status of orphanhood in the 16 districts in South Africa among children aged 18 years and younger. Across these districts, the highest percentage of orphans was paternal orphans (6.4%). The highest number of paternal orphans lived in OR Tambo (11.8%) and eThekwini (11.0%) while the lowest percentage was found in the City of Tshwane (3.9%). The proportion of maternal (3.0%) and double orphans (2.1%) remained low across the 16 districts. The highest percentage of maternal orphans was found in Sekhukhune (4.6%) and the lowest percentage in the City of Cape Town (0.7%). The highest proportion of double orphans lived in iLembe (3.8%) and the lowest in West Rand (0.4%). Table 3.90 presents the levels of accurate knowledge and rejection of myths about HIV among people aged 15 years or older in the 16 selected districts. Overall, 36.3% of people had accurate knowledge of HIV across the districts. Respondents in the City of Cape Town (47.2%), iLembe (40.0%) and eThekwini district (39.6%) displayed the highest levels of accurate HIV knowledge. Respondents in Sekhukhune (24.6%), West Rand (27.5%) and Bojanala Platinum (27.4%) had the lowest levels of accurate knowledge in the selected districts.
Statistics pertaining to HIV-prevalence estimates The table below shows the results for HIV prevalence among children and adults and includes socio- demographic characteristics, the coefficient of variation (CV), and the design effect (DEFF). The results are based on StatsSA 2017 mid-year estimates.
Statistics pertaining to HIV-prevalence estimates The table below shows the results for HIV prevalence among children and adults and includes socio- demographic characteristics, the coefficient of variation (CV), and the design effect (DEFF). The results are based on StatsSA 2017 mid-year estimates.
Ap pw ximately 7.9 milion people ofallages (0+ years) were living with HIV (PIHIV) in South Affic a in 2017. HIV prevalence among adultsaged 15 to 49 yearsin South Africa is 20.6 percent; 26.3 percentamong femalesand 14.8 perrentamong males. The Fifth South African National HIV Prevalence, Incidence, Behaviourand Communic ation Survey, (SABSSM V), a population-based cwss-sec tional surve y of householdsin South Africa, wasdesigned to assess the prevalence and trendsofkey HlV-related indicators. The survey was conducted between January and December 2017 by the Human Sciences Research Council (HSRC) and provides nfo mation on national and sub-national pw gress toward HIV epidemic contili the country. This report summarizes the HIV epidemic and impactof South Afric a’s national HIV response. HSRC hasa long history of undertaking population-based surve ys on HIV, starting with the 2002 Nelson Mande la/HSRC survey on ‘the 2005, 2008, 2012 surveys. The 2017 survey is the fifth wave of the survey.
Ap pw ximately 7.9 milion people ofallages (0+ years) were living with HIV (PIHIV) in South Affic a in 2017. HIV prevalence among adultsaged 15 to 49 yearsin South Africa is 20.6 percent; 26.3 percentamong femalesand 14.8 perrentamong males. The Fifth South African National HIV Prevalence, Incidence, Behaviourand Communic ation Survey, (SABSSM V), a population-based cwss-sec tional surve y of householdsin South Africa, wasdesigned to assess the prevalence and trendsofkey HlV-related indicators. The survey was conducted between January and December 2017 by the Human Sciences Research Council (HSRC) and provides nfo mation on national and sub-national pw gress toward HIV epidemic contili the country. This report summarizes the HIV epidemic and impactof South Afric a’s national HIV response. HSRC hasa long history of undertaking population-based surve ys on HIV, starting with the 2002 Nelson Mande la/HSRC survey on ‘the 2005, 2008, 2012 surveys. The 2017 survey is the fifth wave of the survey.
Among PIHIWV aged 15 to 64 yearscunently on ART 87.5 percent are virally suppressed: 89.9 percentof HIV-positive femalesand 82.1 per ent of HIV-positive maleswho are on ARTare virally suppressed. 8 8 eee Among PIHIV aged 15 to 64 yearswho know their HIV sta tus, 70.6 percent are on ART 72.2 percent of HIV-positive femalesand 67.4 percent of HIV- positive males who know ther HIV status are on ARB. The VISgeographic pattem is quite similarto HIV prevalence with the lowest VISprevalence among PIHIV at 52.8 perc ent (95% CI 40.0-65.2) in Northem Cape and the highest VIS prevalence among PIHW at 68.0 per ent (95% CI 60.5-74.7) in EFastem Cape in the second lowest HIV prevalence and second highest HIV prevalence prvinces, respec tively.
Among PIHIWV aged 15 to 64 yearscunently on ART 87.5 percent are virally suppressed: 89.9 percentof HIV-positive femalesand 82.1 per ent of HIV-positive maleswho are on ARTare virally suppressed. 8 8 eee Among PIHIV aged 15 to 64 yearswho know their HIV sta tus, 70.6 percent are on ART 72.2 percent of HIV-positive femalesand 67.4 percent of HIV- positive males who know ther HIV status are on ARB. The VISgeographic pattem is quite similarto HIV prevalence with the lowest VISprevalence among PIHIV at 52.8 perc ent (95% CI 40.0-65.2) in Northem Cape and the highest VIS prevalence among PIHW at 68.0 per ent (95% CI 60.5-74.7) in EFastem Cape in the second lowest HIV prevalence and second highest HIV prevalence prvinces, respec tively.
Overall, 31.8 perrentofmalesaged 15-64 yearsold reported being medically cir umcised. Forty-three percentofmales 15-24 were medically cic umcised while males 0-14 and 55-64 yearshad the lowest prevalence of medicalcicumc isin at 12.0 and 21.8 percent, respectively. Of 13.4% ofcie umcised malesaged 0-14 years, 89.9% were medically cic umcised.
Overall, 31.8 perrentofmalesaged 15-64 yearsold reported being medically cir umcised. Forty-three percentofmales 15-24 were medically cic umcised while males 0-14 and 55-64 yearshad the lowest prevalence of medicalcicumc isin at 12.0 and 21.8 percent, respectively. Of 13.4% ofcie umcised malesaged 0-14 years, 89.9% were medically cic umcised.
Among individualsaged 15-64 years with two or more sexual partners mn the last year, 55.6% self- reported 25-49 ye condom use at last sex. Of those aged ars, both males (53.0%) and females (53.4%) had similar pw portionsofcondom use. Sixty-e ig with mu sexcom ht percentof young males (15-24 years) tiple partners reported condom use at last pared to 47.3% of femalesof the same age. Only 33.3 percent of olderadults (50-64) years with mu tiple partners used condomsat last sex.
Among individualsaged 15-64 years with two or more sexual partners mn the last year, 55.6% self- reported 25-49 ye condom use at last sex. Of those aged ars, both males (53.0%) and females (53.4%) had similar pw portionsofcondom use. Sixty-e ig with mu sexcom ht percentof young males (15-24 years) tiple partners reported condom use at last pared to 47.3% of femalesof the same age. Only 33.3 percent of olderadults (50-64) years with mu tiple partners used condomsat last sex.
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