Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

HIV-Prevalence in South Africa by settlement type: A repeat population-based cross-sectional analysis of men and women

  • Andrew Gibbs ,

    Roles Conceptualization, Project administration, Writing – original draft

    Andrew.gibbs@mrc.ac.za

    Affiliations Gender and Health Research Unit, South African Medical Research Council, Pretoria, South Africa, Centre for Rural Health, School of Nursing and Public Health, University of KwaZulu-Natal, Durban, South Africa

  • Tarylee Reddy,

    Roles Data curation, Formal analysis, Writing – review & editing

    Affiliation Biostatistics Unit, South African Medical Research Council, Durban, South Africa

  • Kristin Dunkle,

    Roles Conceptualization, Supervision, Writing – review & editing

    Affiliation Gender and Health Research Unit, South African Medical Research Council, Pretoria, South Africa

  • Rachel Jewkes

    Roles Conceptualization, Supervision, Writing – review & editing

    Affiliations Gender and Health Research Unit, South African Medical Research Council, Pretoria, South Africa, Office of the Executive Scientist, South African Medical Research Council, Pretoria, South Africa, School of Public Health, University of Witwatersrand, Johannesburg, South Africa

HIV-Prevalence in South Africa by settlement type: A repeat population-based cross-sectional analysis of men and women

  • Andrew Gibbs, 
  • Tarylee Reddy, 
  • Kristin Dunkle, 
  • Rachel Jewkes
PLOS
x

Abstract

To assess i) whether there is an independent association between HIV-prevalence and settlement types (urban formal, urban informal, rural formal, rural informal), and, ii) whether this changes over time, in South Africa. We draw on four (2002; 2005; 2008; 2012) cross-sectional South African household surveys. Data is analysed by sex (male/female), and for women by age categories (15–49; and 15–24; 25–49) at all-time points, for men in 2012 data is analysed by age categories (15–24; 25–49). By settlement type and sex/age combinations, we descriptively assess the association between socio-demographic and HIV-risk factors; HIV-prevalence; and trends in HIV-prevalence by time. Relative risk ratios assess unadjusted and adjusted risk for HIV-prevalence by settlement type. All estimates are weighted, and account for survey design. In all survey years, and combinations of sex/age categorisations, HIV-prevalence is highest in urban informal settlements. For men (15–49) an increasing HIV-prevalence over time in rural informal settlements was seen (p = 0.001). For women (15–49) HIV-prevalence increases over time for urban informal, rural informal, rural formal, and women (15–24) decreases in urban formal and urban informal, and women (25–49) increases urban informal and rural informal settlements. In analyses adjusting for potential socio-demographic and risk factors, compared to urban formal settlements, urban informal settlements had consistently higher relative risk of HIV for women, in all age categorisations, for instance in 2012 this was RR1.89 (1.50, 2.40) for all women (15–49), for 15–24 (RR1.79, 1.17–2.73), and women 25–49 (RR1.91, 1.47–2.48). For men, in the overall age categorization, urban informal settlements had a higher relative risk for HIV in all years. In 2012, when this was disaggregated by age, for men 15–24 rural informal (IRR2.69, 1.28–5.67), and rural formal (RR3.59, 1.49–8.64), and for men 25–49 it was urban informal settlements with the highest (RR1.68, 1.11–2.54). In 2012, rural informal settlements also had higher adjusted relative risk for HIV-prevalence for men (15–49) and women (15–49; 15–24; 25–49). In South Africa, HIV-prevalence is patterned geographically, with urban informal settlements having a particularly high burden. Geographical targeting of responses is critical for the HIV-response.

Introduction

HIV-incidence and HIV-prevalence is spatially distributed globally, nationally, and sub-nationally. The spatial patterning of HIV across settings reflects inequalities in access to resources, healthcare services, and power differentials, particularly along lines of poverty, sexuality, gender and race [13]. Across Africa (excluding South Africa) a limited body of work has looked at this by settlement type. In Kenya, HIV-prevalence was assessed comparing urban slums, with urban-non slum settlements, with the HIV-prevalence 12% and 5% respectively [4]. While in Namibia’s capital city, Windhoek, hotspot mapping identified particularly high HIV-incidence in informal settlement areas [5]. Understanding the spatial patterning of HIV-prevalence globally, and nationally, is critical to ensure limited resources are targeted most effectively, particularly as donor funding is declining [6].

In South Africa, a number of studies have explored the spatial patterning of HIV. Studies have, for instance, sought to highlight HIV ‘hot-spots’. For example, Tanser et al [7] found wide variation in HIV-prevalence (from 6–39%) in a rural demographic health surveillance area in KwaZulu-Natal, with clustering of HIV in homesteads [8], nearer national roads [7], and by transport corridors [9]. Amongst women screened for enrollment in clinical trials, also in KwaZulu-Natal, but in more urban settings, HIV-prevalence clustered in ‘hot spots’ with HIV-prevalence in those ranging from 56.0% to 39.0%; with distinguishing factors including being in peri-urban communities (typically including informal settlements), and reporting more lifetime sexual partners [10]. Finally, one nationally representative youth (15–24) survey, found that HIV-prevalence was pattern by rural/urban status, with higher HIV-prevalence in urban settings, and was highest for young women in urban informal settlements (but not men) [11].

In addition, in South Africa, there have been five nationally representative population based studies of HIV-prevalence and incidence, in 2002, 2005, 2008, 2012, and 2017 [1216]. In each of these data on settlement type is collected and analysed in the main report. The 2017 data is not yet publicly available, and in the report that has been produced, settlement types have been coded differently to previous studies, combing urban informal and urban formal to create one category of ‘urban’. All four previous (2002; 2005; 2008; 2012) nationally representative studies analysed data by urban formal, urban informal, rural formal and rural informal, and these analyses have described HIV-prevalence being substantially higher in urban informal settlements, compared to other settlement types [1315].

However these nationally representative data have not be disaggregated settlement type by age and sex, which are important factors in the patterning of the HIV-epidemic, with younger (15–24) women at greater risk of acquiring HIV than younger men, and women of all ages more likely to be living with HIV, compared to men [17]. Additionally, the prior descriptive work provided only prevalence estimates, without adjustment for the distribution of potential socio-demographic differences that might account these spatial differences. Nor have changes in HIV-prevalence and settlement-type been explored by time.

Currently, South Africa spends US$1.9 billion annually on HIV, with a quarter (U$0.5 billion) coming from international donors [18]. And while there are a number of large-scale HIV-prevention, and treatment programmes, including universal test-and-treat, and the Determined, Resilient, Empowered, AIDS-Free, Mentored, and Safe (DREAMS) programme for young women (15–24) [19], donors are reducing their funding [18]. Understanding the HIV-epidemic in more detail, by sex and age, will help focus targeting of limited resources.

Drawing on representative population-based data in South Africa of four surveys (2002; 2005; 2008 and 2012), this paper has three objectives. First, to describe HIV-risk acquisition factors by settlement type and age and sex. Second, to describe HIV-prevalence by settlement-type and assess trends over time by settlement-type. Third, to assess whether settlement-type independently predicts HIV-prevalence after accounting for risk factors.

Materials and methods

Data collection

We used four nationally representative population-based cross-sectional household surveys from 2002, 2005, 2008 and 2012 in South Africa. The National Prevalence Surveys employed a stratified multistage cluster sampling approach. In the first stage 1000 census enumeration areas (EAs) were selected proportional to size and stratified by province, geotype(settlement type) and race; after which a fixed number of households were selected per EA in the second stage. Sampling weights were calculated to correct for unequal probabilities of selection and non-response at the EA, household and individual level. The individual weights for each survey were benchmarked to relevant mid-year population estimates by age, race, sex, and province. This ensured that the sample for each survey was representative of the population distribution in South Africa for sex, age, race, settlement type, and province. Response rates for HIV-testing varied, with lower response rates consistently by men, compared to women (e.g. in 2012, for those older than 15, the testing response rate for females was 57.7%, compared to 42.3% in males) [14]. Detailed information about survey design, sampling methods, refusal rates, and survey administration is available in individual reports [1215]. The comparability of design makes these four surveys nationally representative data sets, enabling assessment of longitudinal change across them.

All studies received ethical approval from the Human and Social Research Council’s (HSRC) ethics committee before starting, and participants provided informed consent for questionnaires, and separately for HIV-testing. Information on this can be found in the original reports.

Outcome

The primary outcome for analysis is a binary HIV-status (positive or negative). Testing strategy varied by data collection year. In 2002 and 2005 oral saliva samples were obtained and tested using ELISA kits, with confirmatory testing on positive tests only in 2005 [12, 15]. The sensitivity and specificity of the ELISA test are 99% and 99% respectively [12, 15]. In 2008 and 2012, dried blood spots were collected and tested, and confirmatory tests conducted on all positive samples (and a 10% sub-set of negative samples) using a combination of three HIV-1 enzyme immunoassays, with 99% specificity and sensitivity [13, 14].

Classification of households into settlement type was done through coding of Enumeration Areas (EA) using census data and classifications provided by Statistics South Africa. Four options were available: urban formal, urban informal, rural formal (including commercial farms), and rural informal, which included tribal authority areas [1215]. According to Statistics South Africa, urban areas are continuously built-up areas, and either urban formal–defined as land proclaimed as residential where settlements are structured and organized. In addition, services are provided through local government and development is controlled. Or, urban informal, which are unplanned settlements on land not defined as residential, and comprising of informal dwellings. Rural areas are any area not deemed as urban, and comprises of rural informal, which areas are controlled by tribal authorities and informal settlements outside of urban areas. In earlier surveys this was just termed tribal authority areas. And rural formal settlements, which are formal settlements outside of urban areas, which do not fall under tribal authority areas, including commercial farms [20].

Covariates

In addition to age and sex, we identified comparable measures across all four surveys as potential co-variates, based on known risk factors for HIV-acquisition. All studies assessed current work status [employed (formal or informal), unemployed, or student]. Education level was assessed as either: none through to secondary not completed, else completed secondary or higher. For people reporting ever having had sex, age of sexual debut, and number of past year sexual partners were assessed.

Analysis

Analyses were done separately for men and women. For men, because of high refusal rates of HIV-testing and small sample sizes in the 2002, 2005 and 2008 surveys (for instance 31% of men refused to be tested for HIV in 2008) only one age categorization (15–49) was possible. In 2012, the larger sample size enabled age stratification by adolescents (15–24), and adult men (25–49), as well as overall (15–49). For women, we analysed all respondents (aged 15–49), and then stratified by adolescents (15–24), and adult women (25–49).

We first calculated the distribution of weighted proportions of socio-demographic and HIV-acquisition risk factors by settlement type, sex, age, and survey wave, presenting percentages and 95 percent confidence intervals (95%CI). We used chi-square tests to assess variation. We then calculated weighted HIV-prevalence by settlement type for each survey by sex, and age groups. Differences are assessed through chi-squared tests. We also assessed trends in HIV-prevalence by settlement type using chi-square tests.

To assess the potential impact of settlement type on prevalent HIV infection, we used individual level poisson regression reporting relative risk with robust standard errors, taking into account sampling weights, stratification by province, and clustering within EA. We then adjusted for education, employment status, age at first sex, number of partners in past year. We report both the unadjusted and adjusted relative risk ratios. All analyses were performed in STATA15, using the survey commands, and weighted estimations.

Results

Men aged 15–49

In total, 2430, 5047, 4291, and 8510 men participated in 2002, 2005, 2008 and 2012 surveys, respectively. Table 1 presents socio-demographic and HIV-acquisition risk factors for all four surveys for men aged 15–49. In 2002, a greater proportion of young men (15–24) were in rural informal settlements (58.0%), compared to other settlement types (37.4% urban formal; 37.2% urban informal; 27.6% urban formal; p<0.0001). In urban informal (43.2%) and rural informal (46.1%) settlements a significantly greater proportion reported being unemployed, compared to urban formal (24.0%) and rural formal (17.3%). The majority of men in urban informal, rural informal and rural formal had not completed secondary school (83.9%, 86.1%, 85.0% respectively), and this was significantly higher compared to urban formal (42.4%). A significantly greater proportion of men in urban informal settlements (58.1%) and rural formal (54.4%) reported sexual debut ≤18 years, compared to urban formal (47.4%), and rural informal (44.8%). In all other survey years (2005; 2008; 2012), a similar patterning of socio-demographic and sexual behaviours was seen, with little variation between years.

thumbnail
Table 1. Men 15–49 socio-demographic and risk factors by settlement types for all survey years.

https://doi.org/10.1371/journal.pone.0230105.t001

Table 2 reports the 2012 survey for men, disaggregated by age groups. Among men 15–24, the largest proportion reporting being unemployed (34.6%) were in urban informal settlement and this was higher than urban formal (25.3%) and rural informal (27.4%) and significantly higher than rural formal (17.0%, p<0.001). Over a third (37.5%) of 15–24 year olds in urban formal settlements reported completed secondary education, compared to a quarter (24.4%) in urban informal, and rural formal (27.3%) and a fifth (17.3%) in rural informal, and this proportion was significantly larger (p<0.001). Age of sexual debut was significantly younger in urban settlements, with 56.5% in urban informal and 52.7% urban formal reporting this, compared to 45.3% in rural informal (p = 0.03).

thumbnail
Table 2. 2012 Survey for men socio-demographic and risk factors, by settlement type and age disaggregation.

https://doi.org/10.1371/journal.pone.0230105.t002

For men aged 25–49 (Table 2B) the greatest proportion unemployed were in rural informal settlements (58.7%), and then urban informal settlements (36.1%), with significantly lower unemployment rates in urban formal (25.2%) and rural formal (12.3%, p<0.001). A significantly larger proportion of men in urban formal settlements (61.6%) had completed secondary education, compared to all other settlement types (31.6% urban informal; 32.2% rural informal; 41.3% rural formal, p<0.001). Finally, a greater proportion of men (25–49) in urban informal settlements (65.9%) reported sexual debut at 18 or less, compared to other settlement types (54.6% urban formal; 57.7% rural informal; 54.0% rural formal; p = 0.004).

HIV-prevalence by settlement type for men (15–49) showed consistent patterns (Table 3). HIV-prevalence in all surveys was highest in urban informal settlements, where it was almost twice that of urban formal settlements, and for 2005, 2008 and 2012, and these were significantly different. There was no difference between other settlement types, and urban formal settlements. Rural informal settlements showed an increasing HIV-prevalence over time (p = 0.001).

In the age disaggregated tables (Table 3B and 3C) HIV prevalence was highest among 15–24 year olds residing in rural forma areas, followed by urban informal areas and rural informal areas. Among men 25–49 years old, the highest HIV-prevalence was among those in urban informal settlements, and lowest among those in urban formal settlements, though 95%CI did overlap.

Table 4 presents the unadjusted and adjusted relative risk ratios for all men by settlement type and survey year. In multivariable regressions (Table 4A), for all men (15–49) compared to urban formal settlements, urban informal settlements had a significantly higher relative risk for HIV-prevalence for 2005 (RR1.68, 1.11–2.50), 2008 (RR1.51, 1.04–2.18) and 2012 (RR1.77, 1.19–2.64). In 2002, men in rural formal settlements had a lower relative risk of HIV-prevalence than urban formal settlement (RR0.43, 0.22–0.84), while in 2012 men (15–49) in rural informal settings had a higher relative risk for HIV-prevalence (RR1.44, 1.05–1.97).

thumbnail
Table 4. Unadjusted and adjusted relative risk ratios for men for all surveys, by settlement type and in 2012 by age.

https://doi.org/10.1371/journal.pone.0230105.t004

In 2012, where it was possible to disaggregate men by age (Table 4), for men aged 15–24, compared to urban informal settlements, the relative risk of HIV-prevalence was higher in rural informal (RR2.69, 1.28–5.67) and rural formal (RR3.59, 1.49–8.64), compared to urban formal settlements, however confidence intervals were wide, highlighting lack of precision. And for men 25–49 in 2012, those in urban informal settlements had a higher relative risk of HIV-prevalence (RR1.68, 1.11–2.54) compared to urban formal settlements.

Women

In total 3137, 7553, 6107 and 10454 women participated in the 2002, 2005, 2008, and 2012 surveys, respectively. In the 2002 survey, there were consistent patterns in distribution of socio-demographic and HIV-acquisition risk factors across both age groups (Table 5). Amongst all women (15–49; Table 5A) a significantly greater proportion of young women were in rural informal (46.6%) and rural formal (43.0%), compared to urban formal (31.0%) and urban informal (30.9%). For women in all age combinations (15–49; 15–24; 25–49) the highest proportion of unemployed were in urban informal settlements (Table 5A, 5B and 5C), and this was significantly higher than urban formal settlements (e.g. in 2002 for women 15–49 it was 68.9% compared to 47.4% for all women, respectively–Table 5A). The largest proportion who had not completed secondary school was in urban informal and rural informal for all age combinations, and the smallest proportion was always in urban formal settlements (Table 5A, 5B and 5C).

thumbnail
Table 5. Women (15–49) socio-demographic and risk factors, by settlement type for 2002.

https://doi.org/10.1371/journal.pone.0230105.t005

Consistently, over the three age categorization the largest proportion reporting sexual debut ≤18 years, was in urban informal settlements, and smallest in urban formal settlements. For the combined group (15–49) and older women (25–49) this was significantly different. For women aged 15–24 (Table 5C), a greater proportion in urban informal settlements reported ≥2 past year sexual partners (11.0%) and this was significantly different to other settlement types, where it was <5.0%.

The other three surveys showed very similar patterns in the distribution of socio-demographics and risk factors as the 2002 survey (Tables 6, 7 and 8). For example the 2012 survey showed very similar patterns for all women (15–49), adolescent women (15–24) and older women (25–49) (Table 8A, 8B and 8C respectively), as the 2002 survey. This included the largest proportion of young (15–24) women being in rural informal settlements (39.3%). The highest proportion of unemployed women was in urban informal and rural informal for all age combinations; for instance amongst 15–24 year olds, half (50.8%) in urban informal reported being unemployed, compared to 29.0% in urban formal settlements. Amongst all women, there were no differences with education levels, but among 15–24 nearly three-quarters in urban informal (73.7%), and rural informal (74.0%) had not completed secondary school, compared to half (53.5%) in urban formal.

thumbnail
Table 6. Women (15–49) socio-demographic and risk factors, by settlement type for 2005.

https://doi.org/10.1371/journal.pone.0230105.t006

thumbnail
Table 7. Women (15–49) socio-demographic and risk factors, by settlement type for 2008.

https://doi.org/10.1371/journal.pone.0230105.t007

thumbnail
Table 8. Women (15–49) socio-demographic and risk factors, by settlement type for 2012.

https://doi.org/10.1371/journal.pone.0230105.t008

For HIV-risk factors in the 2012 survey, amongst those 15–24 (Table 8B), the greatest proportion reporting two or more past year sexual partners were those in urban informal settlements (9.0%) and this was significantly different to other settlement types (4.8% urban formal; 3.3% rural informal; 1.2% rural formal; p = 0.008). For all age combinations, the greatest proportion reporting sexual debut of ≤18 years was in urban informal settlements, and lowest proportion in urban formal settlements, and these were all significantly different.

HIV-prevalence by settlement types, over surveys for all age categorisations for women (Table 9) showed consistent patterns. The highest HIV-prevalence was among urban informal settlements. For women 15–49 (Table 9A), compared to urban formal settlements these differences were significant at all-time points, and in 2008 and 2012, compared to urban formal settlements, rural informal settlements were also significantly higher. Similarly for women 15–24 (Table 9B), compared to urban formal settlements, urban informal settlements had a significantly higher HIV-prevalence for 2005, 2008 and 2012. And for women 25–49 (Table 9C), urban informal settlements had a higher proportion of HIV-positive women, in all years, and in 2008 and 2012, in rural informal areas also had a higher proportion of HIV-positive women, compared to urban formal areas.

thumbnail
Table 9. Weighted HIV-prevalence for women by settlement type over time.

https://doi.org/10.1371/journal.pone.0230105.t009

In the combined age group for women (15–49; Table 9A) urban informal, rural informal and rural formal, all saw significantly increasing HIV-prevalence trends. In younger (15–24; Table 9B) women, there were decreasing HIV-prevalence trends for urban formal and urban informal. And for older (25–49; Table 9C) women, increasing HIV-prevalence trends for urban informal, rural informal and rural formal settlement types.

In adjusted analyses for women (Table 10), for all survey years, and in all age categorisations (15–49; 15–24; 25–49), compared to urban formal settlements, women in urban informal settlements had significantly higher relative risks of living with HIV. In other settlement types, compared to urban formal settlements, there was no consistent patterning, but in 2012, women in rural informal settlements had a higher relative risk for HIV-prevalence, for all age combinations (15–49; 15–24; 25–49).

thumbnail
Table 10. Unadjusted and adjusted relative risk ratios for women by settlement type, and survey.

https://doi.org/10.1371/journal.pone.0230105.t010

Discussion

Our analysis of four representative household surveys in South Africa from 2002 to 2012, show consistent patterning around the distribution of HIV, by settlement types, with urban informal settlements showing the highest prevalence. This finding was consistent in multivariate models accounting for potential socio-demographic cofounders, and in multivariate models for all women and men aged 15–49, and for women across different age stratifications (15–24; 25–49). For men, stratification by age was only possible for the most recent survey (2012), and in adjusted models urban informal settlements had the highest relative risk of HIV-prevalence for older men (25–49), but for younger men (15–24) adjusted relative risk HIV-prevalence was highest among rural formal settlements.

The markedly high HIV-prevalence, and consistent association between urban informal settlements and HIV-prevalence, even in adjusted models may have a number of underlying explanations. HIV-risk behaviour showed clear patterning by settlement type, with early sexual debut more common amongst both women and men residing in urban informal settlements across all surveys (and age categorisations). Similarly, young women (15–24) in all survey waves more often reported two or more past year sexual partners in urban informal settlements compared to other settlement types. Early sexual debut and multiple sexual partners, both of which are often driven by poverty, inequitable gender norms and violence, are key risks for HIV-acquisition [2123]. Interventions targeting these factors may be important strategies.

Structural factors may also shape the disproportionate burden of HIV in urban informal settlements. Across surveys, women and men in urban informal settlements had lower educational attainment than those in formal settlements. Low education, since the early 2000s, has been recognised as a risk factor for HIV [24]. Similarly, urban informal settlements had high unemployment rates, and unemployment, via increased poverty, may be a risk factor for HIV-acquisition [1]. Structural interventions focused on strengthening educational attainment, and increasing employment/reducing poverty, are potentially important interventions.

Even after adjustment for potential co-founders, informal settlements consistently had higher HIV-prevalence than other settlement types, suggesting there remained unmeasured factors. Housing in informal settlements can be unstable, and studies from North America have highlighted how housing instability is an independent risk factor for HIV and STIs [25]. It may also be that settlement type is a proxy for sexual networks; studies in North America have highlighted how dense sexual networks, shaped by poverty and racism, are crucial for HIV-transmission patterns [26]. Finally, it may also be that people acquiring HIV are transitioning into urban informal settlements as they lose access to work and formal housing, and this explains the high HIV-prevalence. We could not, however, find a consistent measure of mobility across surveys to include this as a potential cofounder.

Our analysis also highlighted two trends in HIV-prevalence by settlement type. First, increasing HIV-prevalence amongst older (24–49) women in urban informal, rural informal and rural formal, and men (15–49) in rural informal. These changes may be indicative of the rollout of ARVs, with people surviving for longer, and HIV becoming a chronic disease [27]. The changing distribution of HIV also suggests that there may be important migration patterns as people age, with movement from urban to rural areas. The decreasing HIV-prevalence amongst younger (15–24) women in urban formal and urban informal settlements is likely indicative of the impact of improved prevention of mother-to-child transmission in South Africa [28], and also potentially the impact of HIV-prevention programming, reducing HIV-incidence [16]. The lack of similar trends in rural formal and informal settings highlights important gaps in impact for both programmes.

For men in 2012 the age stratified adjusted analysis showed that among young men those residing in rural formal areas had significantly higher HIV-prevalence than urban formal. Given that men typically acquire HIV at a later age [29], it may be the failure of prevention of mother-to-child transmission interventions in rural formal areas that leads to this patterning, paralleling what is seen among young women. For older men the sustained high HIV-prevalence in urban informal settlements, similarly parallels that seen among women of all ages, and is likely due to the rollout of ART. Importantly, given that one important pathway for HIV-transmission is often intergenerational [29], the high HIV-prevalence in older men in urban informal settlements could explain the continued high HIV-prevalence among young women in urban informal settings.

Limitations

This study has a number of limitations. In all surveys, there was relatively high refusal rates for the main survey and for HIV-testing, particularly amongst men, and while data were weighted to account for this, there may be bias. In addition, the low levels of HIV-testing prevented age-disaggregated analysis of the men’s data, apart from for the 2012 data, and even in 2012 the large confidence intervals in adjusted analyses highlight the lack of precision. South Africa has high levels of mobility and internal migration, and the assumption that all participants were regular residents of the settlement type where data were collected cannot be tested. Similarly, there was variation in the timing of studies, and seasonal migration may have played a greater, or lesser role in depending on the months of data collection. In addition, only a few potential cofounders were consistently assessed in all four surveys, and key risk factors such as intimate partner violence, condom use, and migration were not included. We were unable to include the 2017 survey data as the data set had not been made public, and the categorization of settlement type was changed, with urban formal and urban informal combined into one category, ‘urban’, limiting comparability across time. Finally, only HIV-prevalence rather than incidence was assessed, and as such, we cannot determine whether the pattern of HIV-incidence is the same as prevalence. Nonetheless, analysis is nationally generalizable, as the surveys were all population based samples, and analyses were weighted to census data and to account for response refusal.

Conclusions

The analyses presented have a number of implications for programming in South Africa. First, in terms of ensuring ARV coverage, and achieving 90-90-90, settlement types with the highest, and increasing HIV-prevalence (urban informal settlements, and rural informal and formal settlements) remain most poorly served. The historical legacies in South Africa remain around access to healthcare, despite attempts to address these via government. Spatially targeted access to ARVs is therefore an important priority. Second, given the concentration of urban informal settlements, in relatively geographically bounded, and dense situations, a significant effort is required around HIV-prevention programming. Current programmes such as DREAMS and She-Conquers, do recognize and target urban informal settlements [19], however, the specific challenges of interventions in these settings, and lack of well evaluated interventions delivered here, remains a major challenge to intervention programming.

More widely, this analysis suggests that settlement type may be important in understanding the spatial distribution of HIV in South Africa, and elsewhere. Such spatial patterning of HIV reflects patterns of wider marginalization of those who live there, including race, gender and poverty [1, 25, 26]. Further investigation into how these factors intersect to shape high HIV-incidence and prevalence is critical to understand how best to prevent HIV, and ensure HIV-treatment is available to all.

Acknowledgments

The four surveys were managed and made available by the HSRC team and we thank them and their staff for this, and recognize the huge investment in this process. We thank the participants who provided their time and information.

References

  1. 1. Magadi M. The Disproportionate High Risk of HIV Infection Among the Urban Poor in Sub-Saharan Africa. AIDS and Behaviour. 2013;17:1645–54.
  2. 2. Dunkle K, Jewkes R, Murdock D, Sikweyiya Y, Morrell R. Prevalence of Consensual Male–Male Sex and Sexual Violence, and Associations with HIV in South Africa: A Population-Based Cross-Sectional Study. PLoS Med. 2013;10(6):e1001472. pmid:23853554
  3. 3. Altman D. Globalization, political economy, and HIV/AIDS. Theory and Society. 1999;28(4):559–84.
  4. 4. Madise NJ, Ziraba AK, Inungu J, Khamadi SA, Ezeh A, Zulu EM, et al. Are slum dwellers at heightened risk of HIV infection than other urban residents? Evidence from population-based HIV prevalence surveys in Kenya. Health Place. 2012;18(5):1144–52. pmid:22591621
  5. 5. UNAIDS. Identifying Populations at Greatest Risk of Infection–Geographic Hotspots and Key Populations. Geneva: UNAIDS, 2013.
  6. 6. Kates J, Wexler A, Lief E, UNAIDS. Donor Government Funding for HIV in Low- and Middle-Income Countries in 2017. Geneva: Kaiser Foundation and UNAIDS, 2018.
  7. 7. Tanser F, Bärnighausen T, Cooke GS, Newell M-L. Localized spatial clustering of HIV infections in a widely disseminated rural South African epidemic. International journal of epidemiology. 2009;38(4):1008–16. pmid:19261659
  8. 8. Tanser F, LeSueur D, Solarsh G, Wilkinson D. HIV heterogeneity and proximity of homestead to roads in rural South Africa: an exploration using a geographical information system. Trop Med Int Health. 2000;5(1):40–6. pmid:10672204
  9. 9. Tanser F, Bärnighausen T, Dobra A, Sartorius B. Identifying ‘corridors of HIV transmission’in a severely affected rural South African population: a case for a shift toward targeted prevention strategies. International journal of epidemiology. 2017;47(2):537–49.
  10. 10. Wand H, Ramjee G. Targeting the hotspots: investigating spatial and demographic variations in HIV infection in small communities in South Africa. Journal of the International AIDS Society. 2010;13(1):41.
  11. 11. Kleinschmidt I, Pettifor A, Morris N, MacPhail C, Rees H. Geographic distribution of human immunodeficiency virus in South Africa. The American journal of tropical medicine and hygiene. 2007;77(6):1163–9. pmid:18165541
  12. 12. Rehle T, Shisana O, Pillay V, Zuma K, Puren A, Parker W. National HIV incidence measures—new insights into the South African epidemic. Samj S Afr Med J. 2007;97(3):194–9. pmid:17440667
  13. 13. Shisana O, Rehle T, Simbayi L, Zuma K. South African National HIV Prevalence, Incidence, Behaviour and Communication Survey, 2008. Cape Town: HSRC, 2009.
  14. 14. Shisana O, Rehle T, Simbayi LC, Zuma K, Jooste S, Zungu N, et al. South African National HIV Prevalence, Incidence and Behaviour Survey, 2012. Cape Town: HSRC Press, 2014.
  15. 15. Connolly C, Colvin M, Shishana O, Stoker D. Epidemiology of HIV in South Africa-results of a national, community-based survey. South African Medical Journal. 2004;94(9).
  16. 16. HSRC. South African National HIV Prevalence, Incidence, Behaviour and Communication Survey, 2017. Pretoria, South Africa: HSRC, 2018.
  17. 17. UNICEF. Opportunity in crisis: Preventing HIV from early adolescence to young adulthood. Geneva: UNICEF, 2011.
  18. 18. Editors L. Commitment needed from South Africa’s new president. Lancet HIV. 2018;5(4):PE155.
  19. 19. Saul J, Bachman G, Allen S, Toiv NF, Cooney C. The DREAMS core package of interventions: A comprehensive approach to preventing HIV among adolescent girls and young women. Plos One. 2018;13(12):e0208167. pmid:30532210
  20. 20. Statistics SA. Census 2011: Metadata. Pretoria: Statistics South Africa, 2012 Contract No.: Report No. 03-01-47
  21. 21. Jewkes R, Dunkle K, Nduna M, Shai N. Intimate partner violence, relationship power inequity, and incidence of HIV infection in young women in South Africa: a cohort study. Lancet. 2010;376(9734):41–8. pmid:20557928
  22. 22. Krishnan S, Dunbar MS, Minnis AM, Medlin CA, Gerdts CE, Padian NS. Poverty, gender inequities, and women's risk of human immunodeficiency virus/AIDS. Annals of the New York Academy of Sciences. 2008;1136(1):101–10.
  23. 23. Stöckl H, Kalra N, Jacobi J, Watts C. Is early sexual debut a risk factor for HIV infection among women in sub‐saharan Africa? A systematic review. American Journal of Reproductive Immunology. 2013;69:27–40. pmid:23176109
  24. 24. Hargreaves JR, Bonell CP, Boler T, Boccia D, Birdthistle I, Fletcher A, et al. Systematic review exploring time trends in the association between educational attainment and risk of HIV infection in sub-Saharan Africa. Aids. 2008;22(3):403–14. pmid:18195567
  25. 25. Linton SL, Cooper HL, Luo R, Karnes C, Renneker K, Haley DF, et al. Changing places and partners: associations of neighborhood conditions with sexual network turnover among African American adults relocated from public housing. Archives of sexual behavior. 2017;46(4):925–36. pmid:26927277
  26. 26. Adimora AA, Schoenbach VJ, Taylor EM, Khan MR, Schwartz RJ, Miller WC. Sex ratio, poverty, and concurrent partnerships among men and women in the United States: a multilevel analysis. Annals of epidemiology. 2013;23(11):716–9. pmid:24099690
  27. 27. Johnson LF, May MT, Dorrington RE, Cornell M, Boulle A, Egger M, et al. Estimating the impact of antiretroviral treatment on adult mortality trends in South Africa: A mathematical modelling study. PLoS medicine. 2017;14(12):e1002468. pmid:29232366
  28. 28. Barron P, Pillay Y, Doherty T, Sherman G, Jackson D, Bhardwaj S, et al. Eliminating mother-to-child HIV transmission in South Africa. B World Health Organ. 2013;91:70–4.
  29. 29. De Oliveira T, Kharsany AB, Gräf T, Cawood C, Khanyile D, Grobler A, et al. Transmission networks and risk of HIV infection in KwaZulu-Natal, South Africa: a community-wide phylogenetic study. The Lancet HIV. 2017;4(1):e41–e50. pmid:27914874