Identification of the geographical areas with low uptake of HIV testing could assist in spatial targeting of interventions to improve the uptake of HIV testing.
The objective of this research study was to map the uptake of HIV testing at the district level in South Africa.
The secondary analysis used data from the Human Sciences Research Council’s 2017 National HIV Prevalence, Incidence, Behaviour and Communication Survey, where data were collected using a multistage stratified random cluster sampling approach. Descriptive spatial methods were used to assess disparities in the proportion of those ever tested for HIV at the district level in South Africa.
The districts with the highest overall coverage of people ever having tested for HIV (> 85%) include West Rand in Gauteng, Lejweleputswa and Thabo Mofutsanyane in Free State, and Ngaka Modiri Molema in North-West. These provinces also had the least variation in HIV testing coverage between their districts. Districts in KwaZulu-Natal had the widest variation in coverage of HIV testing. The districts with the lowest uptake of HIV testing were uMkhanyakude (54.7%) and Ugu (61.4%) in KwaZulu-Natal and Vhembe (61.0%) in Limpopo. Most districts had a higher uptake of HIV testing amongst female than male participants.
The uptake of HIV testing across various districts in South Africa seems to be unequal. Intervention programmes must improve the overall uptake of HIV testing, especially in uMkhanyakude and Ugu in KwaZulu-Natal and Vhembe in Limpopo. Interventions must also focus on enhancing uptake of HIV testing amongst male participants in most districts. Strategies that would improve the uptake of HIV testing include HIV self-testing and community HIV testing, specifically home-based testing.
Eastern and Southern Africa is home to 53% of the 36.9 million people living with HIV globally,
Furthermore, there was a 42% reduction of AIDS-related illnesses, as a result of the increase in HIV testing and treatment coverage between 2010 and 2017.
South Africa has one of the largest HIV testing services (HTS), which is a crucial component of national HIV response.
South Africa has made progress towards the UNAIDS 90-90-90 targets, especially regarding HIV testing and viral load suppression.
Although South Africa has made steady progress towards reaching the UNAIDS targets, many people affected with HIV are still unaware of their HIV status.
In South Africa, gathering spatial data on HIV and mapping its distribution have been carried out in selected micro-geographical areas, limiting the generalisability of the findings to the country.
The data used in the secondary analysis were obtained from the National HIV Prevalence, Incidence, Behaviour and Communication Survey conducted in 2017.
The primary outcome measure ‘ever testing for HIV’ was obtained from individuals who responded to the original survey question ‘have you ever been tested for HIV?’ The response was dichotomised into a binary outcome (yes = 1 and no = 0).
The survey protocol was approved by the Human Sciences Research Council’s (HSRC) Research Ethics Committee (REC: 4/18/11/15), and the Associate Director for Science, Center for Global Health, Centres for Disease Control and Prevention (CDC). Ethical clearance was also obtained from the University of KwaZulu-Natal’s Biomedical Research Ethics Committee (BE 646/18). Verbal or written informed consent was sought before undertaking both the behavioural data and blood specimen collection.
Statistical analysis was carried out in STATA 15.0 (Stata Corporation, College Station, TX, United States [US]) software.
Descriptive statistics were used to summarise the sample characteristics. Multilevel mixed-effects logistic regression models were used to estimate the excess probability of prior testing for HIV after adjusting for the effect of age and sex. District-level random effects predicted from the model, including age and sex were used to estimate the excess probability of prior testing. Results are shown with 95% confidence intervals (CI), and p-values < 0.05 were reported for all statistically significant associations. The proportion of the population, aged 15 years and older, that have ever been tested for HIV were geo-located using the South African district-level boundaries. The maps were generated in QGIS, version 3.14.10. An adjusted weight, benchmarked to the general population by age and sex at the national level, was computed to facilitate this analysis.
Mean age and sex distribution of youth and adult 15 years and older by district, South Africa 2017.
Province | District name | Mean age (years) | Male ( |
Female ( |
|
---|---|---|---|---|---|
Eastern Cape | Alfred Nzo | 278 | 40.2 | 42.0 | 58.0 |
Eastern Cape | Amathole | 337 | 44.6 | 46.4 | 53.6 |
Eastern Cape | Buffalo City | 329 | 41.7 | 40.9 | 59.1 |
Eastern Cape | Chris Hani | 243 | 42.0 | 47.9 | 52.1 |
Eastern Cape | Joe Gqabi | 188 | 41.1 | 53.1 | 46.9 |
Eastern Cape | Nelson Mandela Bay | 1213 | 41.9 | 48.6 | 51.4 |
Eastern Cape | O.R. Tambo | 1369 | 39.9 | 45.3 | 54.7 |
Eastern Cape | Sarah Baartman | 712 | 39.8 | 49.0 | 51.0 |
Free State | Fezile Dabi | 263 | 44.2 | 55.8 | 44.2 |
Free State | Lejweleputswa | 365 | 39.0 | 49.0 | 51.0 |
Free State | Mangaung | 1068 | 39.7 | 48.9 | 51.1 |
Free State | Thabo Mofutsanyane | 776 | 39.2 | 48.7 | 51.3 |
Free State | Xhariep | 243 | 39.2 | 52.6 | 47.4 |
Gauteng | City of Johannesburg | 1754 | 40.0 | 49.4 | 50.6 |
Gauteng | City of Tshwane | 1718 | 38.9 | 50.3 | 49.7 |
Gauteng | Ekurhuleni | 2011 | 38.0 | 51.6 | 48.4 |
Gauteng | Sedibeng | 2894 | 39.1 | 50.9 | 49.1 |
Gauteng | West Rand | 1192 | 38.2 | 52.5 | 47.5 |
KwaZulu-Natal | Amajuba | 287 | 41.4 | 41.5 | 58.5 |
KwaZulu-Natal | eThekwini | 3583 | 41.7 | 47.3 | 52.7 |
KwaZulu-Natal | Harry Gwala | 427 | 37.7 | 38.6 | 61.4 |
KwaZulu-Natal | iLembe | 3605 | 36.0 | 44.2 | 55.8 |
KwaZulu-Natal | King Cetshwayo | 4003 | 34.3 | 43.9 | 56.1 |
KwaZulu-Natal | Ugu | 958 | 40.0 | 48.1 | 51.9 |
KwaZulu-Natal | uMgungundlovu | 601 | 41.2 | 54.6 | 45.4 |
KwaZulu-Natal | uMkhanyakude | 651 | 33.5 | 41.0 | 59.0 |
KwaZulu-Natal | uMzinyathi | 3227 | 37.5 | 40.6 | 59.4 |
KwaZulu-Natal | uThukela | 3770 | 36.4 | 40.1 | 59.9 |
KwaZulu-Natal | Zululand | 480 | 37.5 | 46.0 | 54.0 |
Limpopo | Capricorn | 659 | 40.0 | 42.2 | 57.8 |
Limpopo | Greater Sekhukhune | 1292 | 39.2 | 42.9 | 57.1 |
Limpopo | Mopani | 604 | 41.2 | 46.3 | 53.7 |
Limpopo | Vhembe | 705 | 37.8 | 47.7 | 52.3 |
Limpopo | Waterberg | 480 | 40.7 | 53.1 | 46.9 |
Mpumalanga | Ehlanzeni | 2731 | 35.3 | 47.5 | 52.5 |
Mpumalanga | Gert Sibande | 3585 | 34.4 | 52.2 | 47.8 |
Mpumalanga | Nkangala | 1247 | 36.7 | 54.9 | 45.1 |
North West | Bojanala | 2322 | 37.3 | 48.5 | 51.5 |
North West | Dr Kenneth Kaunda | 761 | 37.2 | 51.9 | 48.1 |
North West | Dr Ruth Segomotsi Mompati | 372 | 39.6 | 43.4 | 56.6 |
North West | Ngaka Modiri Molema | 447 | 39.1 | 48.0 | 52.0 |
Northern Cape | Frances Baard | 749 | 38.7 | 50.6 | 49.4 |
Northern Cape | John Taolo Gaetsewe | 262 | 36.7 | 50.3 | 49.7 |
Northern Cape | Namakwa | 200 | 43.3 | 50.5 | 49.5 |
Northern Cape | Pixley ka Seme | 1005 | 37.2 | 49.2 | 50.8 |
Northern Cape | Z F Mgcawu | 830 | 37.2 | 50.7 | 49.3 |
Western Cape | Cape Winelands | 750 | 40.9 | 45.7 | 54.3 |
Western Cape | Central Karoo | 108 | 42.3 | 44.0 | 56.0 |
Western Cape | City of Cape Town | 2362 | 38.5 | 49.9 | 50.1 |
Western Cape | Eden | 374 | 39.5 | 51.7 | 48.3 |
Western Cape | Overberg | 305 | 40.9 | 46.8 | 53.2 |
Western Cape | West Coast | 468 | 36.4 | 55.8 | 44.2 |
Geographical uptake of those aged 15 years and older who have ever been tested for HIV in the 52 districts of South Africa.
Overall, uMkhanyakude (54.7%), Vhembe (61.0%) and Ugu (61.4%) districts had the lowest coverage for HIV testing. Ngaka Modiri Molema district (86.1%) reported the highest coverage for testing, followed by Lejweleputswa (85.2%) and Thabo Mofutsanyane (84.8%) district.
In the Eastern Cape, Joe Gqabi district had the highest overall coverage (78.5%), while Sarah Baartman district had the lowest (66.2%) coverage for HIV testing.
In the Free State, Lejweleputswa district had the highest testing uptake, followed by Thabo Mofutsanyane district, while Xhariep district (73.0%) had the lowest. In Gauteng, West Rand district had the highest coverage (83.3%), and the City of Johannesburg had the lowest coverage for testing (78.2%).
In KwaZulu-Natal, Amajuba district (83.1%) had the highest coverage, followed by Ugu district (61.4%), while uMkhanyakude district (54.7%) had the lowest coverage in the country. KwaZulu-Natal was the only province with a significant difference in testing coverage between its districts (
In Limpopo, Waterberg district had the highest overall coverage (75.9%), while Vhembe district (61.1%) had the lowest coverage for testing. In Mpumalanga, Nkangala district had the highest overall coverage (80.4%), while Gert Sibande district (74.3%) had the lowest coverage for HIV testing. In North West, Ngaka Modiri Molema district (88.6%) had the highest coverage, while Dr Kenneth Kaunda district (76.9%) had the lowest coverage for testing. In the Northern Cape, Namakwa district (67.2%) had the lowest coverage, while Frances Baard district (81.4%) had the highest coverage.
In the Western Cape, Central Karoo district was the only district with over 80% coverage. In comparison, the West Coast and Cape Winelands district had the lowest coverage (< 70%), while the remaining districts’ coverage ranged from 70% to 79%.
Geographical coverage: proportion of people who have ever been tested for HIV amongst (a) male and (b) female participants aged 15 years and older in the 52 districts in South Africa.
The proportion of female participants who had ever been tested for HIV ranged from 59.0% to 88.6%. uMkhanyakude district had the lowest proportion of female participants who had ever been tested for HIV (59.0%), followed by Ugu district (63.3%). Districts with the highest coverage of female participants who had ever been tested for HIV included Ngaka Modiri Molema (88.6%), Frances Baard (88.4%) and Lejweleputswa (88.4%). The coverage range of male participants who have ever been tested for HIV was 46.6% – 89.9%. Vhembe and uMkhanyakude were the only districts with < 50% coverage, that is, at 46.6% and 48.5%, respectively. Amajuba had the highest coverage (89.9%) of male participants who have ever been tested for HIV.
Geographical coverage of excess probability of ever having tested for HIV after adjusting for age and sex in the 52 districts in South Africa.
HIV testing is a crucial component of the national HIV response in South Africa.
The mapping results revealed that the uptake of HIV testing varied across the various districts in South Africa. The age and sex distribution across the districts were different. Studies have revealed that age and sex are crucial factors in HIV testing.
The overall proportion of people who had ever tested for HIV at the district level in South Africa ranged from 54.7% to 86.1%. uMkhanyakude and Ugu districts in KwaZulu-Natal and Vhembe district in Limpopo had the lowest overall testing coverage of < 62%. Ngaka Modiri Molema district in North West, and Lejwelepuswa and Thabo Mofutsanyane districts both in Free State reported the highest coverage for HIV testing. None of the districts in the Eastern Cape or Limpopo had an overall coverage of higher than 80%. These districts are characterised as being predominately rural. Other studies have also found that people living in rural informal or tribal areas were significantly less likely to test for HIV when compared with those from urban areas.
Another factor playing a major role in the higher coverage districts included the epidemic control plans implemented by the President’s Emergency Plan for AIDS Relief (PEPFAR), which aims to achieve maximum impact and reach in areas with the highest burden of disease (COP19). This is informed by population-based surveillance. The PEPFAR country operational plan (COP) for 2017, in 27 districts with an estimated number of people living with HIV of 5.6 million, which account for 79% of number of people living with HIV in South Africa (COP19), identified 1969 sites for intensified support as part of the country’s district-level implementation plan (DIP).
Most districts had a higher coverage of ever having tested for HIV amongst female than male participants. uMkhanyakude and Ugu had the lowest coverage for female participants. Vhembe and uMkhanyakude had the lowest HIV testing coverage for male participants. Despite the countrywide scale-up, the observed geographic disparities in HIV testing are relevant from an epidemic control perspective, especially if the people who do not get tested are at higher risk of HIV infection.
There are various settings in which HTS can be provided to the public and expanded further, for instance in healthcare facilities, such as hospitals, clinics and mobile clinics, and at community sites, be these stand-alone or even home-based services, where testing services are provided within the community.
This research study has a few limitations. ‘Ever testing’ for HIV is self-reported, and therefore, prone to biases related to social desirability, recall and under-reporting. Nevertheless, the results of the nationally representative population-based survey can be generalised to adults aged 15 years and above who tested for HIV in South Africa. There may be a high degree of within-district heterogeneity. In future, work will include examining the sub-district level estimates applying the robust methodology of small area estimation, which involves using auxiliary predictors to improve the precision of imprecise district-level estimates.
This study demonstrated the utility of visually displaying spatial inequities in HIV testing using nationally representative data by presenting simple maps for targeted priority setting. The findings suggest that provinces and districts with low testing coverage, especially amongst male participants, should prioritise tailored interventions to improve uptake of HIV testing. The strategies for HTS should include scaling up of HIVST and community HIV testing, specifically home-based testing to improve the uptake of HIV testing in those districts that are lagging behind in order to ensure equity in the geographical coverage of HIV testing.
The authors thank study participants who allowed the survey fieldworkers into their households and provided the article information. They also thank the project team, especially the fieldwork teams who collected the survey data used in the analysis.
The authors declare that they have no financial or personal relationships that may have inappropriately influenced them in writing this article.
S.J. drafted the manuscript. S.J. and T.R. performed statistical analysis. L.M. and P.N. designed the maps. S.J., M.M., A.N., Y.S., M.T. and L.S. participated in the implementation of the survey that provided the data for the analysis. All authors contributed to the review of the draft manuscript and approved the final manuscript.
This research work was supported by the President’s Emergency Plan for AIDS Relief (PEPFAR) through the Centres for Disease Control and Prevention under the terms of Cooperative Agreement Number (NU2GGH001629), as well as the South African Department of Science and Technology, South African National AIDS Council (SANAC), Global Fund to Fight AIDS, Tuberculosis and Malaria, Right to Care, United Nations Children’s Fund (UNICEF), Centre for Communication Impact, Soul City, and LoveLife.
Data used in this analysis are available from HSRC’s public data repository (data set). SABSSM 2017 Combined. Version 1.0. Pretoria South Africa: Human Sciences Research Council [producer] 2017, Human Sciences Research Council [distributor] 2020.
The findings and conclusions of this research study are those of the authors and do not necessarily represent the official position of any affiliated agency of the authors or the funding agencies.
Uptake of those aged 15 years and older who have ever been tested for HIV in the 52 districts of South Africa.
District name | 95 |
||
---|---|---|---|
uMkhanyakude | 393 | 54.7 | 45.9–63.2 |
Vhembe | 447 | 61.0 | 56.5–65.3 |
Ugu | 606 | 61.4 | 54.6–67.7 |
Sarah Baartman | 450 | 66.2 | 60.7–71.4 |
Amathole | 207 | 66.3 | 59.5–72.5 |
Nelson Mandela Bay | 767 | 66.3 | 62.0–70.4 |
West Coast | 306 | 66.6 | 56.5–75.4 |
Cape Winelands | 463 | 67.1 | 60.8–72.8 |
Namakwa | 130 | 67.2 | 51.0–80.2 |
Z F Mgcawu | 541 | 67.7 | 61.6–73.3 |
iLembe | 2213 | 68.0 | 62.4–73.1 |
uMzinyathi | 1952 | 68.0 | 64.5–71.3 |
Zululand | 303 | 68.8 | 60.4–76.2 |
O.R. Tambo | 848 | 70.4 | 65.8–74.6 |
uMgungundlovu | 395 | 70.4 | 59.4–79.5 |
uThukela | 2291 | 70.9 | 67.3–74.2 |
Eden | 239 | 71.1 | 61.2–79.3 |
Mopani | 383 | 71.2 | 64.9–76.8 |
Greater Sekhukhune | 791 | 71.8 | 67.4–75.7 |
Buffalo City | 202 | 71.9 | 63.8–78.7 |
Xhariep | 155 | 73.0 | 58.9–83.6 |
King Cetshwayo | 2449 | 73.4 | 65.9–79.7 |
Alfred Nzo | 173 | 73.5 | 62.8–81.9 |
Gert Sibande | 2308 | 74.3 | 70.9–77.5 |
Capricorn | 400 | 74.4 | 68.0–79.9 |
Pixley ka Seme | 637 | 74.9 | 69.1–79.9 |
City of Cape Town | 1494 | 75.3 | 72.4–78.0 |
eThekwini | 2258 | 75.8 | 70.2–80.6 |
Waterberg | 313 | 75.9 | 66.5–83.3 |
Ehlanzeni | 1720 | 76.6 | 73.2–79.6 |
Harry Gwala | 258 | 76.6 | 67.9–83.5 |
Dr Kenneth Kaunda | 495 | 76.9 | 66.4–84.8 |
John Taolo Gaetsewe | 169 | 77.4 | 64.5–86.6 |
Overberg | 187 | 77.4 | 64.4–86.7 |
Chris Hani | 152 | 78.2 | 67.4–86.1 |
City of Johannesburg | 1123 | 78.2 | 74.8–81.3 |
Fezile Dabi | 178 | 78.5 | 68.5–86.0 |
Joe Gqabi | 121 | 78.5 | 75.5–81.3 |
Sedibeng | 1838 | 78.8 | 71.3–84.8 |
Ekurhuleni | 1289 | 79.0 | 74.9–82.7 |
Dr Ruth Segomotsi Mompati | 231 | 80.3 | 68.1–88.6 |
Nkangala | 813 | 80.4 | 77.0–83.4 |
City of Tshwane | 1096 | 81.2 | 78.1–83.9 |
Bojanala | 1458 | 81.3 | 78.6–83.7 |
Frances Baard | 490 | 81.4 | 75.5–86.1 |
Central Karoo | 66 | 81.7 | 70.8–89.2 |
Amajuba | 175 | 83.1 | 72.2–90.3 |
Mangaung | 680 | 83.1 | 78.6–86.8 |
West Rand | 780 | 83.3 | 78.2–87.3 |
Thabo Mofutsanyane | 491 | 84.8 | 81.3–87.8 |
Lejweleputswa | 234 | 85.2 | 81.7–88.2 |
Ngaka Modiri Molema | 284 | 86.1 | 79.4–90.9 |
CI, confidence interval.
Uptake of male and female participants aged 15 years and older who have ever been tested for HIV in the 52 districts of South Africa.
District name | Male participants |
Female participants |
||||
---|---|---|---|---|---|---|
95 |
95 |
|||||
Alfred Nzo | 68 | 58.9 | 43.8–72.5 | 105 | 85.4 | 78.7–90.2 |
Amajuba | 63 | 89.9 | 75.5–96.3 | 112 | 78.2 | 66.8–86.5 |
Amathole | 77 | 57.6 | 45.9–68.5 | 130 | 73.6 | 64.2–81.2 |
Bojanala | 594 | 78.6 | 74.4–82.4 | 864 | 83.8 | 80.8–86.3 |
Buffalo City | 75 | 64.8 | 55.1–73.4 | 127 | 77.3 | 68.0–84.5 |
Cape Winelands | 176 | 68.4 | 61.7–74.4 | 287 | 66.0 | 56.6–74.3 |
Capricorn | 141 | 64.6 | 54.0–74.0 | 259 | 81.4 | 77.1–85.1 |
Central Karoo | 24 | 87.7 | 83.1–91.2 | 42 | 76.8 | 58.8–88.4 |
Chris Hani | 61 | 75.7 | 57.6–87.7 | 91 | 80.4 | 67.1–89.2 |
City of Cape Town | 626 | 70.1 | 66.1–73.8 | 868 | 80.5 | 76.9–83.7 |
City of Johannesburg | 492 | 71.9 | 65.5–77.5 | 631 | 84.4 | 80.2–87.8 |
City of Tshwane | 474 | 80.9 | 75.4–85.5 | 622 | 81.4 | 77.5–84.8 |
Dr Kenneth Kaunda | 229 | 77.1 | 68.1–84.2 | 266 | 76.6 | 61.2–87.1 |
Dr Ruth Segomotsi Mompati | 90 | 71.2 | 48.3–86.8 | 141 | 87.0 | 77.5–92.8 |
Eden | 104 | 61.1 | 48.6–72.3 | 135 | 81.3 | 64.8–91.1 |
Ehlanzeni | 709 | 69.4 | 65.0–73.5 | 1011 | 83.0 | 79.6–85.9 |
Ekurhuleni | 567 | 74.4 | 68.6–79.5 | 722 | 84.0 | 80.3–87.1 |
eThekwini | 933 | 72.8 | 67.7–77.3 | 1325 | 78.4 | 70.7–84.6 |
Fezile Dabi | 93 | 73.9 | 61.2–83.6 | 85 | 84.3 | 71.5–92.0 |
Frances Baard | 231 | 74.7 | 67.9–80.5 | 259 | 88.4 | 81.5–93.0 |
Gert Sibande | 1031 | 69.0 | 64.5–73.2 | 1277 | 80.2 | 75.9–83.9 |
Greater Sekhukhune | 290 | 62.0 | 55.2–68.3 | 501 | 79.1 | 74.0–83.3 |
Harry Gwala | 89 | 69.9 | 60.0–78.2 | 169 | 80.9 | 69.0–89.0 |
iLembe | 821 | 64.2 | 56.6–71.2 | 1392 | 71.0 | 66.5–75.0 |
Joe Gqabi | 54 | 76.5 | 69.2–82.5 | 67 | 80.7 | 69.8–88.3 |
John Taolo Gaetsewe | 76 | 73.0 | 56.1–85.2 | 93 | 82.0 | 70.3–89.7 |
King Cetshwayo | 895 | 68.3 | 60.5–75.1 | 1554 | 77.4 | 69.0–84.1 |
Lejweleputswa | 103 | 81.9 | 73.7–87.9 | 131 | 88.4 | 84.6–91.4 |
Mangaung | 292 | 83.1 | 75.2–88.9 | 388 | 83.1 | 77.7–87.4 |
Mopani | 162 | 67.2 | 58.9–74.5 | 221 | 74.8 | 67.0–81.2 |
Namakwa | 60 | 68.3 | 45.7–84.7 | 70 | 66.1 | 51.7–78.0 |
Nelson Mandela Bay | 321 | 62.9 | 56.1–69.3 | 446 | 69.5 | 64.4–74.2 |
Ngaka Modiri Molema | 121 | 83.4 | 74.1–89.8 | 163 | 88.6 | 82.8–92.6 |
Nkangala | 379 | 76.7 | 70.9–81.7 | 434 | 84.9 | 81.0–88.0 |
O.R. Tambo | 327 | 59.2 | 53.1–65.1 | 521 | 79.6 | 74.9–83.7 |
Overberg | 69 | 79.8 | 63.2–90.2 | 118 | 75.4 | 64.3–83.9 |
Pixley ka Seme | 269 | 69.2 | 61.2–76.2 | 368 | 80.2 | 73.0–85.9 |
Sarah Baartman | 188 | 63.7 | 57.7–69.4 | 262 | 68.6 | 60.0–76.0 |
Sedibeng | 782 | 78.9 | 69.3–86.2 | 1056 | 78.7 | 71.2–84.7 |
Thabo Mofutsanyane | 206 | 83.1 | 77.5–87.6 | 285 | 86.4 | 82.7–89.4 |
Ugu | 254 | 59.3 | 50.4–67.7 | 352 | 63.3 | 54.6–71.1 |
uMgungundlovu | 189 | 66.5 | 47.6–81.2 | 206 | 75.4 | 59.3–86.5 |
uMkhanyakude | 135 | 48.5 | 37.8–59.3 | 258 | 59.0 | 46.6–70.3 |
uMzinyathi | 677 | 62.8 | 57.1–68.1 | 1275 | 71.6 | 67.4–75.5 |
uThukela | 812 | 61.8 | 56.2–67.1 | 1479 | 76.9 | 73.7–79.8 |
Vhembe | 189 | 46.6 | 39.3–53.9 | 258 | 73.7 | 68.6–78.3 |
Waterberg | 146 | 72.2 | 59.5–82.2 | 167 | 79.9 | 70.1–87.2 |
West Coast | 144 | 58.7 | 46.2–70.2 | 162 | 75.8 | 64.0–84.6 |
West Rand | 368 | 81.6 | 74.7–86.9 | 412 | 85.1 | 78.0–90.2 |
Xhariep | 67 | 66.8 | 60.4–72.6 | 88 | 79.9 | 33.7–96.9 |
Z F Mgcawu | 252 | 60.0 | 51.5–67.9 | 289 | 75.4 | 69.2–80.7 |
Zululand | 126 | 59.9 | 47.8–71.0 | 177 | 76.4 | 70.5–81.4 |
CI, confidence interval.
Excess probability of ever having tested for HIV after adjusting for age and sex in the 52 districts of South Africa.
District name | Excess probability |
---|---|
O.R. Tambo | 0.26 |
Amathole | 0.27 |
Chris Hani | 0.30 |
Buffalo City | 0.30 |
Sarah Baartman | 0.33 |
Alfred Nzo | 0.34 |
Nelson Mandela Bay | 0.36 |
Joe Gqabi | 0.41 |
Mangaung | 0.44 |
Fezile Dabi | 0.47 |
Lejweleputswa | 0.48 |
Thabo Mofutsanyane | 0.49 |
Xhariep | 0.54 |
City of Johannesburg | 0.36 |
City of Tshwane | 0.41 |
Ekurhuleni | 0.42 |
Sedibeng | 0.43 |
West Rand | 0.46 |
uMkhanyakude | 0.23 |
uMzinyathi | 0.31 |
Ugu | 0.31 |
iLembe | 0.32 |
uThukela | 0.32 |
Zululand | 0.33 |
Harry Gwala | 0.34 |
uMgungundlovu | 0.38 |
King Cetshwayo | 0.38 |
eThekwini | 0.39 |
Amajuba | 0.50 |
Vhembe | 0.26 |
Greater Sekhukhune | 0.30 |
Capricorn | 0.33 |
Mopani | 0.34 |
Waterberg | 0.37 |
Ehlanzeni | 0.40 |
Gert Sibande | 0.44 |
Nkangala | 0.50 |
Dr Ruth Segomotsi Mompati | 0.37 |
Dr Kenneth Kaunda | 0.39 |
Ngaka Modiri Molema | 0.39 |
Bojanala | 0.42 |
ZF Mgcawu | 0.33 |
John Taolo Gaetsewe | 0.37 |
Namakwa | 0.37 |
Pixley ka Seme | 0.38 |
Frances Baard | 0.47 |
Cape Winelands | 0.35 |
City of Cape Town | 0.38 |
West Coast | 0.38 |
Eden | 0.39 |
Overberg | 0.40 |
Central Karoo | 0.42 |
Multilevel mixed-effects logistic regression model.
Variable | OR | 95% CI | |
---|---|---|---|
Sex | 1.6 | 1.5–1.7 | < 0.001 |
Age | 1.0 | 1.0–1.1 | < 0.001 |
CI, confidence interval; OR, odds ratio.