https://orcid.org/0000-0002-2717-011X
Figures
Abstract
Background
High incidence rates of HIV, sexually transmitted infections (STIs), and teenage pregnancy are major challenges facing South Africa. The role of socio-economic factors in driving these incidence rates is complex, with high socio-economic status protecting against some risk behaviours (condomless sex, early sexual debut, and casual/transactional sex in females) but increasing other risk behaviours (e.g., male engagement in casual and commercial sex). We aimed to model the effect of socio-economic status, and associated economic strengthening interventions, in South Africa.
Methods and findings
We extended a previously-developed agent-based model of HIV, STIs, and fertility in South Africa to assess effects of education, employment, and per capita household income on sexual behaviours. We estimated these effects from literature and from calibration of the model to African randomized controlled trials of economic strengthening interventions. Population attributable fractions (PAFs) were calculated. We considered three intervention types, all targeting households with log per capita income below the national average: school support to reduce school dropout; vocational training for unemployed adults; and unconditional cash transfers. We estimate that low socio-economic status accounted for 13% of new HIV infections, 7% of incident STIs (gonorrhoea, chlamydia, and trichomoniasis) and 31% of teenage births in South Africa, over 2000−2020. However, because of uncertainties regarding effect sizes, confidence intervals around these PAFs are wide (1,50% for HIV; −1,19% for STIs; and 10,76% for teenage births). Over 2025−2040, none of the interventions are estimated to reduce HIV, STIs, or teenage births significantly, due to limited impact on secondary economic outcomes. The greatest impact would be that of school support on teenage births (a 5% reduction, 95% CI: −1,12%). Key limitations include the assumption of uniform STI treatment access across socio-economic strata, and the exclusion of possible socio-economic effects at a community level.
Conclusions
Although poverty is likely to be a significant driver of HIV, STIs, and teenage pregnancy in South Africa, precise quantification is challenging. Recently trialled economic strengthening interventions have insufficient impact on socio-economic status to reduce HIV and STIs significantly at a population level.
Author summary
Why was this study done?
- There is a high incidence of HIV, sexually transmitted infections (STIs), and teenage pregnancy in many sub-Saharan African countries, which is often blamed on poverty.
- However, evidence of the relationship between socio-economic status and HIV and STIs is inconsistent, with studies suggesting that the relationship varies over time and between settings.
- Randomized controlled trials (RCTs) of economic strengthening interventions have been similarly inconsistent regarding the impact of these interventions on HIV, STIs, and teenage pregnancy. It remains unclear which economic strengthening interventions should be prioritised.
- South Africa has the largest HIV epidemic in the world as well as the highest index of income inequality, and is therefore an important setting in which to assess the potential impact of economic strengthening.
What did the researchers do and find?
- We developed a mathematical model of the South African population, simulating changes over time in education, employment, household income, sexual behaviour, HIV, STIs, and teenage births. We simulated three economic strengthening interventions: school support to prevent school dropout, vocational training to increase employment, and cash transfers (income support for low-income households).
- The model estimates that over the 2000–2020 period low socio-economic status accounted for 13% of new HIV infections, 7% of new STIs and 31% of teenage births in South Africa. However, because of uncertainty in many of the socio-economic effects, the 95% confidence intervals around these estimates were very wide.
- The model estimates that the economic strengthening interventions would have only modest effects on socio-economic indicators; for example, vocational training would only increase employment rates by 1% over the 2025–2040 period.
- Because these socio-economic impacts are small, none of the interventions are predicted to significantly reduce the incidence of HIV, STIs, or teenage births by 2040.
What do these findings mean?
- Although poverty has contributed to the high rates of HIV, STIs, and teenage pregnancy in South Africa, recently trialled economic strengthening interventions are unlikely to have much impact on these outcomes over the next 15 years.
- In a context of extreme income inequality, more radical economic interventions would be needed to reduce HIV and STI incidence. Our findings may be less applicable in other African settings, where there is typically more poverty but lower income inequality.
- Despite the disappointing results, there may still be a role for economic strengthening interventions. Future modelling studies should also evaluate their impact on outcomes such as mental health, tuberculosis, maternal and child health, and social well-being.
- A limitation is that some socio-economic effects were not represented in the model, e.g., effects of socio-economic status on access to STI treatment, and effects of community socio-economic status.
Citation: Johnson LF, Jamieson L, Kubjane M, Meyer-Rath G (2025) Evaluation of economic strengthening in South Africa and its impact on HIV, sexually transmitted infections, and teenage births: A modelling study. PLoS Med 22(12): e1004826. https://doi.org/10.1371/journal.pmed.1004826
Academic Editor: Aaloke Mody, Washington University School of Medicine, UNITED STATES OF AMERICA
Received: May 27, 2025; Accepted: November 11, 2025; Published: December 18, 2025
Copyright: © 2025 Johnson et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: This study relies only on publicly available data, which are referenced throughout the manuscript. The most important data, used in model calibration, are the summary estimates of intervention effectiveness from the various randomized controlled trials of economic strengthening interventions, which are shown in Table ZB of S1 Text. We have also made the C++ code available on a github repository: https://github.com/leighjohnson/MicroCOSM.
Funding: L.F.J. received funding from the Gates Foundation (awards 007145 and 064474): https://www.gatesfoundation.org/. The funder had no role in the study design, analysis, decision to publish, or manuscript preparation.
Competing interests: The authors have declared that no competing interests exist.
Abbreviations: APCHI, adjusted per capita household income; ART, antiretroviral treatment; OR, odds ratio; PAFs, population attributable fractions; PRCCs, partial rank correlation coefficients; RCTs, randomized controlled trials; STIs, sexually transmitted infections; VMMC, voluntary medical male circumcision
Introduction
Countries in southern and eastern Africa have the highest levels of HIV prevalence globally [1], as well as high incidence of curable sexually transmitted infections (STIs) [2] and teenage fertility [3]. High levels of poverty in the region are often blamed for these reproductive health challenges [4,5]. Teenage pregnancy in Africa is indeed associated with poverty, low female employment, and incomplete schooling [3,6–9]. However, the relationship between socio-economic status and the risk of HIV and STIs in Africa is more complex. For example, early reviews documented a positive relationship between socio-economic status and HIV in Africa [10–12], but later reviews have found a more nuanced picture, with suggestions of a change in the relationship between HIV and socio-economic status over time [13–16] and variation in the relationship across regions within Africa [15–21]. Similarly, STIs are often associated with employment [22,23] and wealth [23], but have also been found to be associated with low educational attainment [23,24].
The complexity of the relationship between socio-economic status and HIV/STI risk reflects a diversity of ways in which education, employment, and wealth can both mitigate and potentiate sexual risk behaviour. Higher educational attainment is strongly associated with greater condom use in nonspousal relationships [11,25–28], and schooling and household income both delay the timing of female sexual debut and entry into marriage [11,27,29–33]. In men, employment may be associated with engaging in commercial and transactional sex [34–36] as well as multiple partnerships [31,37]. In women, on the other hand, lower income is associated with transactional sex [38–40]. Factors other than sexual behaviour may also affect the observed association between HIV/STIs and socio-economic status. For example, voluntary medical male circumcision (VMMC), which protects against male HIV and STI acquisition, is more common in men with greater wealth [41,42]. A higher HIV prevalence in higher socio-economic strata might be a reflection of longer survival [43], due to higher rates of HIV testing [16,44] and better access to treatment in wealthier individuals. The relationship between wealth and HIV might also be partly confounded by urbanicity, with urban areas having both higher levels of wealth and higher HIV prevalence [14,18].
Economic strengthening interventions have been developed in an attempt to address some of the socio-economic drivers of HIV, STIs, and teenage pregnancy. The most commonly tested interventions include cash transfers (either unconditional or conditional upon achieving certain educational/health outcomes), educational support, vocational training, microcredit schemes, and food assistance, among others [45]. Although reviews of the impacts of these interventions have noted some reductions in self-reported risk behaviours and improved contraceptive knowledge, there is relatively little evidence of changes in clinical outcomes (HIV and STI incidence and unintended pregnancy) [45–49]. Very few studies evaluated whether intervention impacts were sustained over the longer-term [46] or whether the interventions themselves were sustainable [49]. Data from randomized controlled trials (RCTs) of these interventions have not been systematically synthesised using traditional meta-analytic techniques, a reflection of the heterogeneity in interventions and trial outcomes. There is a lack of consensus on which interventions are most appropriate and in what contexts.
Mathematical models are widely used to inform policy decisions around HIV and reproductive health [50–52], and play a particularly important role in evaluating the relative cost-effectiveness of different interventions and the optimal allocation of scarce healthcare resources. They also play a role in translating evidence from RCTs, which produce short-term estimates of impact at an individual or community level, into estimates of likely longer-term impact at a population level. Yet there is a notable lack of mathematical modelling of economic strengthening interventions [53]. This is partly a reflection of the challenges associated with representing the complex causal pathways described previously, a challenge common to the modelling of structural interventions generally [54]. It is also partly due to the lack of consistent evidence of the impact of economic strengthening interventions on clinical outcomes. Agent-based models, which represent individual-level variation in health risk exposure and outcomes, are well-suited to representing complex causal pathways and diverse outcomes, and are increasingly utilised in assessing socio-economic disparities in health [55].
In this study, we make a first attempt at modelling the impact of economic strengthening interventions in South Africa, the country with the world’s largest HIV epidemic. Although South Africa has a high gross domestic product per capita, by African standards ($6,253 in 2024 [56]), it also has the highest level of income inequality globally [57], and is therefore an important setting in which to assess the relationship between socio-economic status and reproductive health. Building upon recent advances in the use of agent-based models to represent socio-economic differences in health outcomes [55], we aim to assess which economic strengthening interventions are likely to have the greatest impact on HIV, STIs, and teenage births.
Methods
Model structure
We adapted a previously-developed model of HIV and reproductive health in South Africa (MicroCOSM, or Microsimulation for the Control of South African Morbidity and Mortality). This is an agent-based model, which has previously been used to evaluate structural drivers of HIV and STIs [58–60]. Briefly, the model simulates a nationally-representative sample of the South African population, with each individual (‘agent’) being randomly assigned a set of characteristics: demographic (age, sex, and race), socio-economic (education, urban/rural location, migration, and incarceration history), psychological (gender norms, conscientiousness), healthcare access (use of hormonal contraception, HIV prevention, and HIV testing/treatment), and behavioural (alcohol consumption, propensity for concurrent partners, sexual experience, sexual preference, and current relationship/marital status). Individuals are classified as ‘high-risk’ or ‘low-risk’ based on their propensity for concurrent partners, and four types of sexual relationship are modelled (Fig 1A): long-term marital/cohabiting, short-term noncohabiting, casual/once-off (characterised as ‘transactional sex’) and commercial sex (between sex workers and clients). When new partnerships are formed, individuals are linked to other individuals in the simulated population, and the transmission of HIV/STIs is modelled based on specified probabilities of transmission per condomless sex act (with multiplier adjustments to account for factors such as the HIV–positive partner’s HIV viral load and male circumcision). A woman’s monthly probability of conception is similarly based on the woman’s numbers of sexual partners, age, and contraceptive use. The simulation begins in 1985, with HIV being introduced into the simulated population in 1990.
In panel (a), high-risk women can move in and out of sex work and high-risk men can engage in sex with sex workers at any time (not shown); only high-risk individuals can have more than one partner at a time. In panel (b), all individuals currently in school can leave school at any time (not shown), and return to school is permissible only in “School support” intervention scenarios. Panel (c) shows how eligibility to receive different types of income varies in relation to the age of each household member (“School support” and “Cash transfers” only apply in the relevant intervention scenarios). In panel (d), dashed lines represent ‘feedback effects’; as described previously [59], girls who fall pregnant while in school are assumed to either drop out of school permanently or to repeat the grade they were in at the time of the pregnancy. LT, long-term (cohabiting/marital), ST, short-term, STI, sexually transmitted infection.
Socio-economic status has a number of dimensions, which can influence sexual behaviour in different ways, and socio-economic status is often measured in terms of household wealth/income [61]. We therefore chose to model socio-economic status based on a combination of household- and individual-level characteristics. We extended the model to identify family links between individuals (parents, children, and siblings), and we grouped individuals into households based on family and marital relationships. We modelled household formation and dissolution dynamically, calibrating the model to national survey data on household composition (see section 1.2 of S1 Text).
The modelling of educational attainment has been described previously [59], and is illustrated in Fig 1B. We further extended the model to simulate employment in individuals aged 15–64 who are not currently in school/studying or incarcerated. Rates of entering employment are assumed to depend on age, sex, educational attainment, race, urban/rural location, and parity (in women). In addition, we assumed people living with HIV experience a reduced odds of employment if they are untreated and have low CD4 counts [62–64]. We calibrated the model to national survey data on employment levels by age, sex, and race (see section 1.1 of S1 Text).
We modelled four sources of household income in the baseline scenario: salaries/wages (for each employed member in the household), child support grant payments, the state old age pension, and private pensions (Fig 1C). A more detailed description of these four income sources is provided in section 1.3 of S1 Text. The adjusted per capita household income (APCHI) is calculated as
where T is the total household income, and
are the numbers of adults (15+) and children respectively in the household,
is the relative cost of supporting children (relative to adults) and
represents the economies of scale in meeting the needs of larger households. In line with previous South African studies [65], we set
= 0.5 and
= 0.9; other equivalence scales are reviewed elsewhere [66].
Fig 1D illustrates the hypothesised causal pathways linking socio-economic status and HIV/reproductive health outcomes. We assumed economic strengthening interventions affect socio-economic variables (educational attainment, employment, and household income), and these in turn affect sexual behaviour; sexual behaviour then influences HIV, STIs, and pregnancy (which can in turn influence socio-economic status, e.g., pregnancy causing school dropout). Table 1 summarises the key parameters that relate socio-economic status to sexual risk behaviour and health seeking behaviour (described more fully in sections 1.4 and 1.5 of S1 Text). We modelled three intervention types, all targeting households with adjusted per capita income below the national average: (1) school support to reduce school dropout (the intervention is also assumed to increase the chance of re-enrolment in youth aged 13–22 who have dropped out of school); (2) vocational training (for ages 20–49); and (3) unconditional cash transfers. We selected these interventions from a more complete list of economic strengthening interventions [45], based on the availability of data regarding their impacts on HIV/STI/teenage pregnancy outcomes. The school support intervention is assumed to include both material support (e.g., school uniforms, cost of school transport) and nonmaterial support (e.g., attendance monitoring and counselling), both of which reduce the probability of school dropout. We modelled the effects of the two components separately, with half of the cash equivalent value of the material support being added to the household income, in the same way as for cash transfers. We assumed vocational training increases the odds of finding employment in currently unemployed individuals. Cash transfer amounts are added to total household income as another source of income.
Model calibration
We adopted a Bayesian approach to model calibration [91], similar to that in a previous application of our model [58]. Prior distributions were specified to represent the uncertainty in key model parameters (Table 1). We specified hurdle distributions to represent the uncertainty in the effect of socio-economic status on sexual risk behaviour, with the hurdle representing a nonzero probability of a null relationship (this was included to avoid excessive prior confidence in the purported causal relationship). We set the nonzero probabilities based on a review of the strength of evidence from RCTs; a more detailed description of the prior distributions is provided in section 2.1 of S1 Text.
We calibrated the model to the results of RCTs of economic strengthening interventions conducted in sub-Saharan Africa. RCTs were identified from recent reviews of economic strengthening interventions [45,46,48,49]. We classified included trials as being either ‘pure’ cash transfer interventions (including both conditional and unconditional cash transfers, but without any strong conditioning on school attendance), school support interventions (which typically aimed to promote school retention, often through the provision of financial support, or through cash transfers conditional on school attendance) and vocational training programmes (directed to individuals who were out of school, providing training to improve their employment prospects and/or credit to enable them to establish their own business). We excluded less frequently studied interventions (e.g., savings programmes, food assistance, and financial education) due to limited RCT data available on their effects [45]. Intervention effects were calculated on a natural log scale, with associated standard errors (Table ZB in S1 Text).
A sample of 5,000 parameter combinations was randomly drawn from the prior distributions in Table 1. For each parameter combination, we ran the model eight times: twice in the ‘baseline’ scenario, twice in the cash transfer scenario, twice in the school support scenario, and twice in the vocational training scenario (two simulations being necessary in order to quantify the extent of stochastic variation in model outputs; the average of the two results was calculated for the purpose of estimating intervention effects). For the purpose of calibration to historic RCT data, we assumed all interventions started in 2005 (close to the average start year of the included RCTs), and we set the annual value of the cash transfer in 2005 to R800 (equivalent to $117 in 2005), the average value of the cash transfers in the included RCTs. We assumed the interventions were directed to households with log APCHI below the national average (8.68 in 2005, equivalent to R5856 or $861 annually). The modelled intervention effect was calculated as the log of the odds ratio/relative risk for the outcome of interest (comparing the intervention and baseline scenarios). We calculated a likelihood value by comparing the modelled intervention effect and the reported intervention effect, assuming the log difference followed a normal distribution with zero mean. Log likelihood values were summed for all outcomes, across all RCTs, to calculate a total likelihood for each parameter combination; separate likelihoods were also calculated for each of the three intervention types.
We drew a posterior sample of 100 parameter combinations from the initial set of 5,000 parameter combinations, using the likelihood values as weights. This sample was used to calculate the posterior means and 95% confidence intervals (CIs), running the model five times for each parameter combination to reduce stochastic variation.
We validated the model by comparing the modelled odds ratios for the associations between socio-economic status and HIV/sexual risk behaviour, as observed in four nationally-representative household surveys in 2005, 2012, 2016, and 2017 [79,92].
In all scenarios, we set STI transmission probabilities at the median of the distribution of best-fitting parameters identified in previous model calibrations [93]. We adjusted HIV transmission probabilities per sex act to yield plausible estimates of HIV incidence and prevalence (see section 3.2 of S1 Text). We estimated teenage fertility rates, per year of sex with a single partner in the absence of contraception, from age- and race-specific fertility rates in the baseline scenario [94].
Population attributable fractions
We calculated the proportion of incident HIV cases/STIs/teenage births that were attributable to low socio-economic status, over the 2000–2020 period, by running a counterfactual scenario in which the sexual behaviour and health seeking behaviour of all individuals are the same as might be expected (a) if they had completed tertiary education, (b) if they remained in education at least to age 21, (c) if they were employed, and (d) if their adjusted per capita household income were no lower than the national mean in the baseline scenario (with the changes in behaviour occurring from mid-2000). We calculated the population attributable fraction (PAF) as the proportionate difference in cumulative HIV cases/STIs/teenage births, over the 2000–2020 period, between this counterfactual scenario and the baseline scenario. Partial rank correlation coefficients (PRCCs) were calculated to assess associations between PAFs and each of the parameters that were changed in the counterfactual scenario [95].
Future intervention scenarios
We modelled three future intervention scenarios: school support, vocational training, and cash transfers. Future intervention impacts were considered over the period from 2025 to 2040. We assumed interventions were limited to households with adjusted per capita household income below the national average in the baseline scenario. We set the annual value of the cash transfer to $227 per household in the case of cash transfer interventions and $114 per eligible youth in the case of school support (at 2023 exchange rates), increasing in line with inflation [96].
All model code and data inputs are available from https://github.com/leighjohnson/MicroCOSM.
Results
We present the results in two parts, the first describing the results of the model calibration and the role of socio-economic status in the period up to 2020, and the second describing possible future changes if economic strengthening interventions were to be introduced.
Calibration to economic and reproductive health data
Our model was in reasonable agreement with observed HIV prevalence and incidence trends in South Africa, external estimates of the incidence of gonorrhoea and chlamydia [97] and data on teenage fertility (Fig 2A–2D). The model was also in agreement with census and survey data showing rising levels of high school completion among youth, and stable employment levels (Fig 2E and 2F). The model also estimated rising income together with declining income inequality, the result of increasing levels of social welfare expenditure, and both trends were validated by estimates from Statistics South Africa (Fig 2G and 2H).
Model estimates of key reproductive health outcomes and economic indicators are compared against calibration targets. Reproductive health outcomes include HIV prevalence (panel a) and HIV incidence (panel b), both in 15−49 year olds, STI incidence per 100,000 population aged 15−49 (panel c), and total births to teenage mothers (panel d). Economic indicators include the proportion of youth who have completed high school (panel e), the proportion of 15−64 year olds who are employed (panel f), median monthly household income per household member (panel g) and Gini coefficient (panel h). Except in panel c, solid lines represent posterior mean model estimates and dashed lines represent 95% confidence intervals. Calibration data points in panels a and b are from national household surveys [79,92,98], while data in panel d are from the DHIS [99], censuses and surveys [79,100]. Data in panels e and f are from censuses, October Household Surveys, General Household Surveys and Labour Force Surveys. Data in panels g and h are derived from national surveys [101]. DHIS, District Health Information System; HSV-2 = herpes simplex virus type 2; STI, sexually transmitted infection.
Calibration to intervention effectiveness data
Posterior estimates of model parameters were largely consistent with prior distributions, although intervention effect parameters differed significantly: vocational training was estimated to be less effective in reducing unemployment (posterior odds ratio [OR] 0.93, prior OR 0.87), while posterior estimates of school support impacts were mixed, with material support being more effective, compared to prior assumptions about effects on school dropout (Table ZD in S1 Text). The posterior estimates of RCT effects were in good agreement with the data, although the model was unable to match the large reductions in HIV and herpes prevalence and pregnancy incidence in one trial [69] (Figs N–P in S1 Text). The model was also consistent with most of the validation data, although the model under-estimated the strength of the negative association between HIV prevalence and education in women (Fig Q in S1 Text).
Population attributable fractions
Low socio-economic status accounted for 13% of new HIV infections, 7% of incident STIs (gonorrhoea, chlamydia, and trichomoniasis) and 31% of teenage births in South Africa, over 2000−2020 (Table 2). However, because of uncertainties regarding effect sizes, CIs around these PAFs were wide (1,50% for HIV; −1,19% for STIs; and 10,76% for teenage births). PAF estimates were similar for men and women. Table 3 shows the socio-economic effect parameters that account for the most uncertainty. The effect of education on condom use was the most significant correlate of the HIV PAF (PRCC = 0.87) and the STI PAF (PRCC = 0.90), but was less significant as a correlate of the teenage birth PAF (PRCC = 0.57). The relative rate of female sexual debut while in school was the most significant correlate of the teenage birth PAF (PRCC = −0.95) and was also significantly correlated with the HIV and STI PAFs; the relative rate of male sexual debut while in school was also weakly associated with the teenage birth PAF. The effect of male employment on partnership formation was positively associated with all the PAFs, although this was only of borderline significance. Other parameters were not statistically significant, and fewer parameters were significant when standard correlation coefficients were calculated in place of PRCCs (Table ZG in S1 Text).
Intervention impacts
In the absence of any economic strengthening interventions, our model suggests there would be little change in income inequality and high school completion over the next 15 years, although employment is expected to increase modestly as a result of population ageing (Fig 3). The cash transfer intervention would reduce income inequality, with the Palma ratio (the ratio of income in the top decile to that in the four lowest deciles) decreasing from 8.5 to 7.8 by 2030; school support and vocational training would have more modest effects on income inequality (Fig 3A). The school support intervention would significantly increase the proportion of South African youth who have completed high school, from 30% to 38% by 2040, but the other interventions would have no effects on school completion (Fig 3B). Vocational training interventions would only slightly increase employment levels, while school support would slightly reduce the fraction of the population employed in the short-term, due to young adults remaining in school for longer (Fig 3C).
The Palma Ratio (panel A) is defined as the ratio of total income in the top income decile to the total income in the four lowest deciles. In panel (b), the high school completion fraction is the proportion of all 15–24 year old South Africans who have successfully completed grade 12. In panel (c) the denominator is all individuals aged 15–64 (including those in school and those not actively seeking work).
When considering intermediate behavioural outcomes, the effects of the cash transfer and vocational training interventions are close to zero (Table 4). However, the school support intervention is expected to significantly reduce the proportion of sexually active young females who are not using hormonal contraception, the proportion of adults in concurrent partnerships, and the proportion of men engaging in casual sex (the latter two effects being due to assumed effects of education on inequitable gender norms, and assumed effects of inequitable gender norms on male risk behaviour [58]). The school support intervention may also reduce the proportion of adolescents who are sexually experienced and the number of early marriages, although these reductions are not statistically significant (Table 4).
Our model suggests that economic strengthening interventions would have no significant impact on most reproductive health outcomes (Table 4). Over 2025–2040, schooling support is the intervention that would have the greatest impact, with an anticipated 5% reduction in teenage births (which is strongly correlated with the relative rate of sexual debut for girls who remain in school [PRCC = −0.84]) and a 1% reduction in HIV and STI incidence (which is strongly correlated with the relative rate of sexual debut among girls in school [PRCC = −0.58 for STIs and −0.27 for HIV] and the increase in condom use per year of education [PRCC = 0.58 for STIs and 0.25 for HIV]). For all other intervention/outcome combinations, the expected impact is 0%, with lower CI limits below zero (partly a reflection of residual stochastic variation). Vocational training is unlikely to have a significant impact if it is limited to females (for example, a 0% reduction [95% CI: −1,1] in STIs). Intervention impacts are similar in males and females. Proportionate reductions in HIV, STIs and teenage pregnancies are also similar over the 2025–2040 period, i.e., there is no suggestion of the intervention impact waning or improving at longer durations (Table ZH in S1 Text).
Discussion
Although our model suggests a potentially significant contribution of low socio-economic status to the incidence of HIV, STIs, and teenage pregnancy in South Africa, there is substantial uncertainty around the magnitude of this contribution. This uncertainty arises because socio-economic status affects sexual behaviour in diverse ways. On the one hand, our model identifies significant positive effects of the relationship between condom use and educational attainment and the relationship between schooling and sexual debut. On the other hand, increases in male socio-economic status could increase HIV and STI incidence because of the effect of male employment on entry into casual and commercial sex relationships. Because of these offsetting effects, the net effects of economic strengthening on sexual and reproductive health outcomes can be modest.
One possible response to this complexity would be to target economic strengthening to those groups that are most likely to be positively affected, for example, economically vulnerable adolescent girls and young women. While this may have merit, our simulations suggest that vocational training targeted to women (for example) would not significantly alter HIV and STI incidence. We also find that economic strengthening interventions have similar effects in men and women, despite very different socio-economic effects on sexual behaviour. This is largely because HIV and STI transmission in South Africa is predominantly heterosexual (i.e., any gain by one sex in the short-term is likely to result in reduced secondary transmission to the other sex in the longer-term).
The disappointing lack of impact of economic strengthening interventions on reproductive outcomes is to some extent a reflection of the difficulty in achieving significant changes in socio-economic status, against a background of extreme economic inequality. For example, vocational training interventions would increase employment levels by only 1%, and cash transfers to the poorest South African household would only reduce the Palma ratio of inequality by 8% (Fig 3). These modest effects on socio-economic indicators are further diluted at each subsequent step in the causal pathway, so that the final effects on reproductive health outcomes are negligible. More radical structural changes may be needed to achieve substantial gains in economic and reproductive health outcomes. When modelling the future rollout of economic strengthening interventions, we have optimistically assumed that all eligible individuals would receive the intervention and achieve levels of exposure similar to those in RCTs. This assumption is unrealistic, but the nonsignificance of the simulated intervention impacts, despite the optimistic assumptions, reinforces the point that they are unlikely to have substantial impacts in the South African setting.
A number of other factors may explain the nonsignificant intervention impacts. Firstly, we used hurdle distributions to represent prior uncertainty in key parameters, which is conservative because a substantial probability is assigned to a null relationship between each socio-economic variable and sexual risk behaviour. Secondly, and closely related, the RCT data to which we are calibrating the model are too heterogeneous and too imprecise (in most instances) to pull the posterior means away from the prior means (hence there are relatively few cases of significant differences between the prior and posterior estimates in Table ZD in S1 Text). Thirdly, the model is agent-based and therefore stochastic, and stochastic variation also contributes to the imprecision.
There have been few previous attempts to develop mathematical models of the structural drivers of sexual risk behaviour [53], and even fewer that have specifically assessed the role of socio-economic status. A few studies have modelled the effects of poverty and social protection in Brazil [102,103], but these are calibrated based on observed temporal associations between economic indicators and HIV indicators, not on the results of individual-level associations or RCTs of economic strengthening. A strength of our calibration approach is that we have used a Bayesian approach to combine both local observational data on the likely relationships between socio-economic status and sexual behaviour (through prior distributions) and RCT data on the impacts of economic strengthening interventions in African settings (through a likelihood function). It is perhaps disappointing that the resulting CIs around the posterior model estimates remain wide, despite the systematic approach to including different types of evidence, but we believe this reflects real uncertainty around socio-economic relationships. Due to the substantial computing time required for each simulation, it was not feasible to draw a larger posterior sample, which would be important in obtaining more accurate 95% CIs. The 95% CIs should therefore be interpreted with caution.
A limitation of this model is that we have focussed mainly on the effects of socio-economic status on sexual behaviour and have ignored some of the other effects that are relevant to sexual and reproductive health outcomes. For example, we have not modelled effects of socio-economic status on mortality in people living with HIV [43]. We have also not modelled an effect of socio-economic status on STI health seeking [104], although in the South African setting higher socio-economic status is not necessarily associated with better STI treatment [105,106]. We have nevertheless included socio-economic effects on hormonal contraceptive use, male circumcision and rates of HIV testing, all of which are important. We have not modelled some of the more detailed causal pathways that link socio-economic status and sexual risk behaviour. For example, there is evidence of a relationship between food insecurity and sexual risk behaviour, particularly in women [39,107–109]. In addition, poverty is associated with greater mental distress [110–112], which in turn may be linked to higher sexual risk behaviours [111,113,114]. Education is also associated with exposure to life skills programmes and HIV prevention messaging [115].
Another limitation is that we have not modelled the effects of COVID-related lockdowns on education [116], employment [117], antiretroviral treatment (ART) uptake [118], and contraceptive use [119]. The adverse social effects of the COVID pandemic were to some extent mitigated by the introduction of a Social Relief of Distress grant, which could be accessed by adults who were unemployed and not receiving any other income [120], and this grant has been continued into the post-COVID period. There have been calls to make this grant permanent, and to increase the amount of the grant [120]. A detailed analysis of the impact of COVID on sexual and reproductive health is beyond the scope of this study, as is the analysis of the potential impact of the Social Relief of Distress grant and its continuation. However, it is worth noting that in our cash transfer scenario we have considered a grant that is worth $227 per annum in 2023, which is similar to the $223 per annum currently under the Social Relief of Distress grant. Given the modest HIV and STI impacts estimated in our cash transfer scenario, it seems unlikely that the Social Relief of Distress grant has had much impact on sexual and reproductive health outcomes.
Other limitations should be considered. Our analysis relies only on publicly available data, but additional longitudinal individual-level data, such as collected in Health and Demographic Surveillance Systems [121], may be valuable in validating the model assumptions about effects of socio-economic status on sexual behaviour, fertility, and HIV. The model also does not represent the effect of social networks on sexual behaviour [122], and the formation of these networks is likely to be heavily influenced by education and employment. The model does not represent intergenerational transfers in education [123], and a 15-year projection term is likely to be insufficient for representing the longer-term benefits of investment in education. Different measures of socio-economic status can influence risk behaviour in different ways, but in the interests of model parsimony we have limited our model to one socio-economic effect on each risk behaviour. There is also uncertainty regarding the relative importance of absolute and relative measures of deprivation in driving risk behaviour [124,125]; in our model we assume a combination of both. In line with ‘Diffusion of innovations’ theory [126] and the ‘Inverse equity hypothesis’ [127], we have assumed increases in condom use in the early stages of the HIV epidemic follow a strong socio-economic gradient, but that this gradient reduces over time; further work is required to assess the validity of this assumption, and whether it applies to other HIV interventions. These complexities are discussed in further detail in section 1.4 of S1 Text.
This study considers the role of socio-economic factors at the individual and household level, but socio-economic characteristics of the community may also be important in driving sexual risk behaviour, even when controlling for individual socio-economic status [128,129]. In South Africa, income inequality at the municipal level significantly increases women’s risk of HIV [130], and income inequality has also been shown to explain much of the variation across countries in sexual risk behaviour [131], HIV prevalence [21,132,133], and teenage fertility [3]. Further work is required to understand (and potentially model) these community effects. Our results might not be generalisable to other African settings, where there is typically less income inequality but more absolute poverty. In a recent analysis of national household survey data, HIV prevalence was inversely associated with education in southern and East Africa, but not in West and Central Africa [16]. Unlike in southern Africa, ART coverage and socio-economic status were positively associated in other African regions [16].
In the short-term, and in the context of a dedicated but declining budget for HIV programmes, it would make sense to focus HIV spending on interventions that have been shown to be highly cost-effective in reaching HIV endpoints (e.g., condom distribution, ART, and pre-exposure prophylaxis for key populations [134]), rather than economic strengthening. However, economic interventions can have important positive impacts beyond sexual and reproductive health, including on mental health [76,135–137], tuberculosis [138], child health [139], and social well-being [140]. Ideally, all of these outcomes should be considered when evaluating the impact or cost-effectiveness of economic interventions, and we do not suggest that policy decisions should be based on sexual and reproductive health outcomes alone. There is a need for a more inter-sectoral ‘whole of government’ approach to addressing health challenges [141], and this is especially true when considering economic strengthening interventions. This necessitates more advanced analytic tools, both in cost-effectiveness evaluation [142,143] and in simulation of complex systems [55,144]. This study represents a first step towards capturing this inter-sectoral complexity in a model of a middle-income country with significant income inequality.
Acknowledgments
The authors thank Marie Stoner for helpful comments on an earlier draft.
The conclusions and opinions expressed in this work are those of the authors alone and shall not be attributed to the Gates Foundation.
References
- 1.
UNAIDS. The path that ends AIDS: 2023 global AIDS update. Geneva: UNAIDS; 2023. Available from: https://www.unaids.org/en/resources/documents/2023/global-aids-update-2023
- 2. Rowley J, Vander Hoorn S, Korenromp E, Low N, Unemo M, Abu-Raddad LJ, et al. Chlamydia, gonorrhoea, trichomoniasis and syphilis: global prevalence and incidence estimates, 2016. Bull World Health Organ. 2019;97(8):548-562P. pmid:31384073
- 3. Santelli JS, Song X, Garbers S, Sharma V, Viner RM. Global trends in adolescent fertility, 1990-2012, in relation to national wealth, income inequalities, and educational expenditures. J Adolesc Health. 2017;60(2):161–8. pmid:27863929
- 4. Fenton L. Preventing HIV/AIDS through poverty reduction: the only sustainable solution? The Lancet. 2004;364(9440):1186–7.
- 5. Stillwaggon E. HIV/AIDS in Africa: fertile terrain. J Dev Stud. 2002;38(6):1–22.
- 6. Kons K, Biney AAE, Sznajder K. Factors Associated with adolescent pregnancy in sub-Saharan Africa during the COVID-19 pandemic: a review of socioeconomic influences and essential interventions. Int J Sex Health. 2022;34(3):386–96.
- 7. Odimegwu C, Mkwananzi S. Factors associated with teen pregnancy in sub-Saharan africa: a multi-country cross-sectional study. Afr J Reprod Health. 2016;20(3):94–107. pmid:29553199
- 8. Yakubu I, Salisu WJ. Determinants of adolescent pregnancy in sub-Saharan Africa: a systematic review. Reprod Health. 2018;15(1):15. pmid:29374479
- 9. Ahinkorah BO, Kang M, Perry L, Brooks F, Hayen A. Prevalence of first adolescent pregnancy and its associated factors in sub-Saharan Africa: a multi-country analysis. PLoS One. 2021;16(2):e0246308. pmid:33539394
- 10. Hargreaves JR, Glynn JR. Educational attainment and HIV-1 infection in developing countries: a systematic review. Trop Med Int Health. 2002;7(6):489–98. pmid:12031070
- 11. Mishra V, Assche SB-V, Greener R, Vaessen M, Hong R, Ghys PD, et al. HIV infection does not disproportionately affect the poorer in sub-Saharan Africa. AIDS. 2007;21 Suppl 7:S17-28. pmid:18040161
- 12. Fortson JG. The gradient in sub-Saharan Africa: socioeconomic status and HIV/AIDS. Demography. 2008;45(2):303–22. pmid:18613483
- 13. 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
- 14. Andrus E, Mojola SA, Moran E, Eisenberg M, Zelner J. Has the relationship between wealth and HIV risk in Sub-Saharan Africa changed over time? A temporal, gendered and hierarchical analysis. SSM Popul Health. 2021;15:100833. pmid:34141854
- 15. Parkhurst JO. Understanding the correlations between wealth, poverty and human immunodeficiency virus infection in African countries. Bull World Health Organ. 2010;88(7):519–26. pmid:20616971
- 16. Allorant A, Kuchukhidze S, Stannah J, Xia Y, Masuku SS, Ekanmian GK, et al. Socio-demographic and geographic disparities in HIV prevalence, HIV testing and treatment coverage: an analysis of 108 national household surveys in 33 African countries. J Int AIDS Soc. 2025;28(8):e70024. pmid:40804796
- 17. Magadi MA. The disproportionate high risk of HIV infection among the urban poor in sub-Saharan Africa. AIDS Behav. 2013;17(5):1645–54. pmid:22660933
- 18. Hajizadeh M, Sia D, Heymann SJ, Nandi A. Socioeconomic inequalities in HIV/AIDS prevalence in sub-Saharan African countries: evidence from the Demographic Health Surveys. Int J Equity Health. 2014;13:18. pmid:24533509
- 19. Wojcicki JM. Socioeconomic status as a risk factor for HIV infection in women in East, Central and Southern Africa: a systematic review. J Biosoc Sci. 2005;37(1):1–36. pmid:15688569
- 20. Asiedu C, Asiedu E, Owusu F. The Socio‐economic determinants of HIV/AIDS infection rates in Lesotho, Malawi, Swaziland and Zimbabwe. Dev Policy Rev. 2012;30(3):305–26.
- 21.
Fox AM. The HIV-poverty thesis re-examined: poverty, wealth or inequality as a social determinant of HIV infection in sub-Saharan Africa? J Biosoc Sci. 2012;44(4):459–80. https://doi.org/10.1017/S0021932011000745 pmid:22273351
- 22. Seidu A-A, Ahinkorah BO, Dadzie LK, Tetteh JK, Agbaglo E, Okyere J, et al. A multi-country cross-sectional study of self-reported sexually transmitted infections among sexually active men in sub-Saharan Africa. BMC Public Health. 2020;20(1):1884. pmid:33287785
- 23. Dadzie LK, Agbaglo E, Okyere J, Aboagye RG, Arthur-Holmes F, Seidu A-A, et al. Self-reported sexually transmitted infections among adolescent girls and young women in sub-Saharan Africa. Int Health. 2022;14(6):545–53. pmid:35134172
- 24. Semwogerere M, Dear N, Tunnage J, Reed D, Kibuuka H, Kiweewa F, et al. Factors associated with sexually transmitted infections among care-seeking adults in the African Cohort Study. BMC Public Health. 2021;21(1):738. pmid:33863295
- 25. Cogneau D, Grimm M. Socioeconomic status, sexual behavior, and differential AIDS mortality: evidence from Côte d’Ivoire. Popul Res Policy Rev. 2006;25(4):393–407.
- 26. de Walque D. Does education affect HIV status? Evidence from five African countries. World Bank Econ Rev. 2009;23(2):209–33.
- 27. Glynn JR, Caraël M, Buvé A, Anagonou S, Zekeng L, Kahindo M, et al. Does increased general schooling protect against HIV infection? A study in four African cities. Trop Med Int Health. 2004;9(1):4–14. pmid:14728602
- 28.
Adair T. Men’s condom use in higher-risk sex: trends and determinants in five sub-Saharan countries [cited 2021 Oct 3]. 2008. Report no.: 34. Available from: https://www.dhsprogram.com/pubs/pdf/WP34/WP34.pdf
- 29. de Walque D, Nakiyingi-Miiro JS, Busingye J, Whitworth JA. Changing association between schooling levels and HIV-1 infection over 11 years in a rural population cohort in south-west Uganda. Trop Med Int Health. 2005;10(10):993–1001. pmid:16185233
- 30. Wand H, Ramjee G. The relationship between age of coital debut and HIV seroprevalence among women in Durban, South Africa: a cohort study. BMJ Open. 2012;2(1):e000285. pmid:22223838
- 31. Dinkelman T, Lam D, Leibbrandt M. Household and community income, economic shocks and risky sexual behavior of young adults: evidence from the Cape Area Panel Study 2002 and 2005. AIDS. 2007;21 Suppl 7:S49-56. pmid:18040164
- 32. Lopman B, Lewis J, Nyamukapa C, Mushati P, Chandiwana S, Gregson S. HIV incidence and poverty in Manicaland, Zimbabwe: is HIV becoming a disease of the poor? AIDS. 2007;21 Suppl 7:S57-66. pmid:18040166
- 33. Heinrich CJ, Hoddinott J, Samson M. Reducing Adolescent Risky Behaviors in a High-Risk Context: The Effects of Unconditional Cash Transfers in South Africa. Economic Development and Cultural Change. 2017;65(4):619–52.
- 34. Jewkes R, Morrell R, Sikweyiya Y, Dunkle K, Penn-Kekana L. Transactional relationships and sex with a woman in prostitution: prevalence and patterns in a representative sample of South African men. BMC Public Health. 2012;12:325. pmid:22551102
- 35. Dunkle KL, Jewkes R, Nduna M, Jama N, Levin J, Sikweyiya Y, et al. Transactional sex with casual and main partners among young South African men in the rural Eastern Cape: prevalence, predictors, and associations with gender-based violence. Soc Sci Med. 2007;65(6):1235–48. pmid:17560702
- 36. Seidu A-A, Darteh EKM, Kumi-Kyereme A, Dickson KS, Ahinkorah BO. Paid sex among men in sub-Saharan Africa: analysis of the demographic and health survey. SSM Popul Health. 2019;11:100459. pmid:32875050
- 37. Okafor CN, Christodoulou J, Bantjes J, Qondela T, Stewart J, Shoptaw S, et al. Understanding HIV risk behaviors among young men in South Africa: a syndemic approach. AIDS Behav. 2018;22(12):3962–70. pmid:30003507
- 38. Dunkle KL, Jewkes RK, Brown HC, Gray GE, McIntryre JA, Harlow SD. Transactional sex among women in Soweto, South Africa: prevalence, risk factors and association with HIV infection. Soc Sci Med. 2004;59(8):1581–92. pmid:15279917
- 39. Duby Z, Jonas K, McClinton Appollis T, Maruping K, Vanleeuw L, Kuo C, et al. From survival to glamour: motivations for engaging in transactional sex and relationships among adolescent girls and young women in South Africa. AIDS Behav. 2021;25(10):3238–54. pmid:33950338
- 40. Pitpitan EV, Kalichman SC, Eaton LA, Watt MH, Sikkema KJ, Skinner D, et al. Men (and women) as “sellers” of sex in alcohol-serving venues in Cape Town, South Africa. Prev Sci. 2014;15(3):296–308. pmid:23494405
- 41. Hamidouche M, Ante-Testard PA, Baggaley R, Temime L, Jean K. Monitoring socioeconomic inequalities across HIV knowledge, attitudes, behaviours and prevention in 18 sub-Saharan African countries. AIDS. 2022;36(6):871–9. pmid:35190511
- 42.
Lesotho Ministry of Health. Lesotho population-based HIV impact assessment 2020 (LePHIA 2020): final report. Maseru: Lesotho Ministry of Health; 2022.
- 43. Probst C, Parry CDH, Rehm J. Socio-economic differences in HIV/AIDS mortality in South Africa. Trop Med Int Health. 2016;21(7):846–55. pmid:27118253
- 44. Cremin I, Cauchemez S, Garnett GP, Gregson S. Patterns of uptake of HIV testing in sub-Saharan Africa in the pre-treatment era. Trop Med Int Health. 2012;17(8):e26-37. pmid:22943376
- 45. Swann M. Economic strengthening for HIV prevention and risk reduction: a review of the evidence. AIDS Care. 2018;30(sup3):37–84.
- 46. Stoner MCD, Kilburn K, Godfrey-Faussett P, Ghys P, Pettifor AE. Cash transfers for HIV prevention: a systematic review. PLoS Med. 2021;18(11):e1003866. pmid:34843468
- 47. Rogers K, Le Kirkegaard R, Wamoyi J, Grooms K, Essajee S, Palermo T. Systematic review of cash plus or bundled interventions targeting adolescents in Africa to reduce HIV risk. BMC Public Health. 2024;24(1):239. pmid:38245689
- 48. Pettifor A, MacPhail C, Nguyen N, Rosenberg M. Can money prevent the spread of HIV? A review of cash payments for HIV prevention. AIDS Behav. 2012;16(7):1729–38. pmid:22760738
- 49. Iwelunmor J, Nwaozuru U, Obiezu-Umeh C, Uzoaru F, Ehiri J, Curley J, et al. Is it time to RE-AIM? A systematic review of economic empowerment as HIV prevention intervention for adolescent girls and young women in sub-Saharan Africa using the RE-AIM framework. Implement Sci Commun. 2020;1:53. pmid:32885209
- 50. Anderson RM, Garnett GP. Mathematical models of the transmission and control of sexually transmitted diseases. Sex Transm Dis. 2000;27(10):636–43. pmid:11099079
- 51. Johnson LF, White PJ. A review of mathematical models of HIV/AIDS interventions and their implications for policy. Sex Transm Infect. 2011;87(7):629–34. pmid:21685191
- 52. Stover J. Influence of mathematical modeling of HIV and AIDS on policies and programs in the developing world. Sex Transm Dis. 2000;27(10):572–8. pmid:11099072
- 53. Stannah J, Flores Anato JL, Pickles M, Larmarange J, Mitchell KM, Artenie A, et al. From conceptualising to modelling structural determinants and interventions in HIV transmission dynamics models: a scoping review and methodological framework for evidence-based analyses. BMC Med. 2024;22(1):404. pmid:39300441
- 54. Hogan JW, Galai N, Davis WW. Modeling the impact of social determinants of health on HIV. AIDS Behav. 2021;25(Suppl 2):215–24. pmid:34478016
- 55. Boyd J, Wilson R, Elsenbroich C, Heppenstall A, Meier P. Agent-based modelling of health inequalities following the complexity turn in public health: a systematic review. Int J Environ Res Public Health. 2022;19(24):16807. pmid:36554687
- 56.
World Bank. World development indicators 2025 [cited 2025 Sep 1]. Available from: https://datatopics.worldbank.org/world-development-indicators/
- 57.
World Bank. Inequality in Southern Africa: an assessment of the Southern African Customs Union [cited 2024 Aug 14]. Washington DC, USA: World Bank; 2022. Available from: https://documents1.worldbank.org/curated/en/099125303072236903/pdf/P1649270c02a1f06b0a3ae02e57eadd7a82.pdf
- 58. Johnson LF, Kubjane M, de Voux A, Ohrnberger J, Tlali M. An agent-based model of binge drinking, inequitable gender norms and their contribution to HIV transmission, with application to South Africa. BMC Infect Dis. 2023;23(1):500. pmid:37516819
- 59.
Johnson LF, Kubjane M, Moolla H. MicroCOSM: a model of social and structural drivers of HIV and interventions to reduce HIV incidence in high-risk populations in South Africa. BioRxiv; 2018. https://doi.org/10.1101/310763
- 60. Rigby SW, Johnson LF. The relationship between intimate partner violence and HIV: a model-based evaluation. Infect Dis Model. 2017;2(1):71–89. pmid:29928730
- 61. Galobardes B, Shaw M, Lawlor DA, Lynch JW, Davey Smith G. Indicators of socioeconomic position (part 1). J Epidemiol Community Health. 2006;60(1):7–12. pmid:16361448
- 62. Levinsohn J, McLaren ZM, Shisana O, Zuma K. HIV status and labor market participation in South Africa. Rev Econ Stat. 2013;95(1):98–108.
- 63. Habyarimana J, Mbakile B, Pop-Eleches C. The impact of HIV/AIDS and ARV treatment on worker absenteeism: implications for African firms. J Hum Resour. 2010;45(4):809–39.
- 64. Rosen S, Larson B, Rohr J, Sanne I, Mongwenyana C, Brennan AT, et al. Effect of antiretroviral therapy on patients’ economic well being: five-year follow-up. AIDS. 2014;28(3):417–24. pmid:24076660
- 65.
Woolard I, Leibbrandt M. Measuring poverty in South Africa [cited 2021 Aug 20]. Document no.: 99/33. Development Policy Research Unit, University of Cape Town; 1999. Available from: https://open.uct.ac.za/bitstream/handle/11427/7237/DPRU_WP99-033.pdf?sequence=1
- 66. Karvonen S, Moisio P, Vepsäläinen K, Ollonqvist J. Assessing health gradient with different equivalence scales for household income–a sensitivity analysis. SSM Popul Health. 2021;15:100892. pmid:34430701
- 67. Handa S, Halpern CT, Pettifor A, Thirumurthy H. The government of Kenya’s cash transfer program reduces the risk of sexual debut among young people age 15-25. PLoS One. 2014;9(1):e85473. pmid:24454875
- 68. Pettifor A, MacPhail C, Hughes JP, Selin A, Wang J, Gómez-Olivé FX, et al. The effect of a conditional cash transfer on HIV incidence in young women in rural South Africa (HPTN 068): a phase 3, randomised controlled trial. Lancet Glob Health. 2016;4(12):e978–88. pmid:27815148
- 69. Baird SJ, Garfein RS, McIntosh CT, Ozler B. Effect of a cash transfer programme for schooling on prevalence of HIV and herpes simplex type 2 in Malawi: a cluster randomised trial. Lancet. 2012;379(9823):1320–9. pmid:22341825
- 70.
Abdoulayi S, Angeles G, Barrington C, Brugh K, Handa S, Kilburn K, et al. Malawi social cash transfer programme endline impact evaluation report [accessed 2023 April 8]. Carolina Population Center, University of North Carolina at Chapel Hill; 2016. Available fom: https://transfer.cpc.unc.edu/wp-content/uploads/2021/04/Malawi-SCTP-Endline-Report_Final.pdf
- 71. McGrath N, Nyirenda M, Hosegood V, Newell M-L. Age at first sex in rural South Africa. Sexually Transm Infect. 2009;85(Suppl 1):i49–55.
- 72. Marteleto L, Lam D, Ranchhod V. Sexual behavior, pregnancy, and schooling among young people in urban South Africa. Stud Fam Plann. 2008;39(4):351–68. pmid:19248720
- 73. Anderson KG, Beutel AM, Maughan-Brown B. HIV risk perceptions and first sexual intercourse among youth in Cape Town South Africa. Int Fam Plan Perspect. 2007;33(3):98–105. pmid:17938092
- 74.
Duflo E, Dupas P, Kremer M, Sinei S. Education and HIV/AIDS prevention: evidence from a randomized evaluation in Western Kenya [cited 2021 Aug 10]. Research working papaer no.: 4024. World Bank; 2006. Available from: https://openknowledge.worldbank.org/handle/10986/9007
- 75. Cho H, Hallfors DD, Mbai II, Itindi J, Milimo BW, Halpern CT, et al. Keeping adolescent orphans in school to prevent human immunodeficiency virus infection: evidence from a randomized controlled trial in Kenya. J Adolesc Health. 2011;48(5):523–6. pmid:21501814
- 76. Cho H, Mbai I, Luseno WK, Hobbs M, Halpern C, Hallfors DD. School support as structural HIV prevention for adolescent orphans in Western Kenya. J Adolesc Health. 2018;62(1):44–51. pmid:29107569
- 77. Hallfors DD, Cho H, Rusakaniko S, Mapfumo J, Iritani B, Zhang L, et al. The impact of school subsidies on HIV-related outcomes among adolescent female orphans. J Adolesc Health. 2015;56(1):79–84. pmid:25530603
- 78. Grant MJ, Hallman KK. Pregnancy-related school dropout and prior school performance in KwaZulu-Natal, South Africa. Stud Fam Plann. 2008;39(4):369–82. pmid:19248721
- 79.
Department of Health, Statistics South Africa, South African Medical Research Council, ICF. South Africa demographic and health survey 2016 [cited 2019 March 19]. Pretoria: Department of Health, Statistics South Africa, South African Medical Research Council, ICF; 2019. Available from: https://www.dhsprogram.com/pubs/pdf/FR337/FR337.pdf
- 80. Jewkes R, Morrell R, Sikweyiya Y, Dunkle K, Penn-Kekana L. Men, prostitution and the provider role: understanding the intersections of economic exchange, sex, crime and violence in South Africa. PLoS One. 2012;7(7):e40821. pmid:22911711
- 81. Leclerc PM, Garenne M. Clients of commercial sex workers in Zambia: prevalence, frequency, and risk factors. Open Demogr J. 2008;1(1):1–10.
- 82. Richter ML, Chersich M, Temmerman M, Luchters S. Characteristics, sexual behaviour and risk factors of female, male and transgender sex workers in South Africa. S Afr Med J. 2013;103(4):246–51. pmid:23547701
- 83.
Statistics South Africa. General household survey 2011 [cited 2024 May 22]. Report no.: P0318. Pretoria: Statistics South Africa; 2012. Available from: http://beta2.statssa.gov.za/publications/P0318/P03182011.pdf
- 84.
Mensch BS, Singh S, Casterline JB. Trends in the timing of first marriage among men and women in the developing world [cited 2024 June 4]. Report no.: 202. Population Council; 2005. Available from: https://knowledgecommons.popcouncil.org/cgi/viewcontent.cgi?article=1339&context=departments_sbsr-pgy
- 85.
Department of Health. South Africa demographic and health survey 1998: full report. 1999.
- 86.
Department of Health. South Africa demographic and health survey 2003: full report [cited 2023 July 24]. Pretoria: Department of Health; 2004. Available from: https://dhsprogram.com/pubs/pdf/fr206/fr206.pdf
- 87. Maughan-Brown B, Lloyd N, Bor J, Venkataramani AS. Changes in self-reported HIV testing during South Africa’s 2010/2011 national testing campaign: gains and shortfalls. J Int AIDS Soc. 2016;19(1):20658. pmid:27072532
- 88. Wilson SJ, Tanner‐Smith EE, Lipsey MW, Steinka‐Fry K, Morrison J. Dropout prevention and intervention programs: effects on school completion and dropout among school‐aged children and youth. Campbell Syst Rev. 2011;7(1):1–61.
- 89.
Garcia S, Saavedra J. Educational impacts and cost-effectiveness of conditional cash transfer programs in developing countries: a meta-analysis [cited 2023 April 30]. National Bureau of Economic Research, 2017. Working Paper 23594. Available from: https://www.nber.org/papers/w23594
- 90. Tripney JS, Hombrados JG. Technical and vocational education and training (TVET) for young people in low- and middle-income countries: a systematic review and meta-analysis. Empir Res Vocat Educ Train. 2013;5:3.
- 91. Smith AFM, Gelfand AE. Bayesian statistics without tears: a sampling-resampling perspective. Am Stat. 1992;46(2):84.
- 92.
Simbayi LC, Zuma K, Zungu N, Moyo S, Marinda E, Jooste S. South African National HIV prevalence, incidence, behaviour and communication survey, 2017 [cited 2019 Nov 6]. Cape Town: Human Sciences Research Council; 2019. Available from: https://www.hsrcpress.ac.za/books/south-african-national-hiv-prevalence-incidence-behaviour-and-communication-survey-2017
- 93. Johnson LF, Geffen N. A comparison of two mathematical modeling frameworks for evaluating sexually transmitted infection epidemiology. Sex Transm Dis. 2016;43(3):139–46. pmid:26859800
- 94.
Actuarial Society of South Africa. ASSA2008 AIDS and demographic model [cited 2011 April 5]. 2011. Available from: http://aids.actuarialsociety.org.za
- 95. Blower SM, Dowlatabadi H. Sensitivity and uncertainty analysis of complex models of disease transmission: an HIV model, as an example. Int Stat Rev. 1994;62(2):229–43.
- 96.
Statistics South Africa. Consumer price index: June 2024 [cited 2024 Aug 16]. Pretoria: Statistics South Africa; 2024. Available from: https://www.statssa.gov.za/publications/P0141/P0141June2024.pdf
- 97. Kularatne RS, Niit R, Rowley J, Kufa-Chakezha T, Peters RPH, Taylor MM, et al. Adult gonorrhea, chlamydia and syphilis prevalence, incidence, treatment and syndromic case reporting in South Africa: Estimates using the Spectrum-STI model, 1990-2017. PLoS One. 2018;13(10):e0205863. pmid:30321236
- 98. Rehle T, Johnson L, Hallett T, Mahy M, Kim A, Odido H, et al. A comparison of South African national HIV incidence estimates: a critical appraisal of different methods. PLoS One. 2015;10(7):e0133255. pmid:26230949
- 99. Barron P, Subedar H, Letsoko M, Makua M, Pillay Y. Teenage births and pregnancies in South Africa, 2017-2021–a reflection of a troubled country: analysis of public sector data. S Afr Med J. 2022;112(4):252–8.
- 100. Moultrie TA, McGrath N. Teenage fertility rates falling in South Africa. S Afr Med J. 2007;97(6):442–3. pmid:17691474
- 101.
Statistics South Africa. Inequality trends in South Africa: a multidimensional diagnostic of inequality [cited 2021 Jan 31]. Report no.: 03-10-19. Pretoria: Statistics South Africa; 2019. Available from: https://www.afd.fr/en/ressources/inequality-trends-south-africa-multidimensional-diagnostic-inequality
- 102. Rubio FA, Amad AAS, Aransiola TJ, de Oliveira RB, Naidoo M, Moya EMD, et al. Evaluating social protection mitigation effects on HIV/AIDS and tuberculosis through a mathematical modelling study. Sci Rep. 2024;14(1):11739. pmid:38778134
- 103. de Oliveira RB, Rubio FA, Anderle R, Sanchez M, de Souza LE, Macinko J, et al. Incorporating social determinants of health into the mathematical modeling of HIV/AIDS. Sci Rep. 2022;12(1):20541. pmid:36446878
- 104. Seidu A-A, Aboagye RG, Okyere J, Adu C, Aboagye-Mensah R, Ahinkorah BO. Towards the prevention of sexually transmitted infections (STIs): Healthcare-seeking behaviour of women with STIs or STI symptoms in sub-Saharan Africa. Sex Transm Infect. 2023;99(5):296–302. pmid:36202610
- 105. Bachmann MO, Colvin MSE, Nsibande D, Connolly C, Curtis B. Quality of primary care for sexually transmitted diseases in Durban, South Africa: influences of patient, nurse, organizational and socioeconomic characteristics. Int J STD AIDS. 2004;15(6):388–94. pmid:15186584
- 106. Schneider H, Blaauw D, Dartnall E, Coetzee DJ, Ballard RC. STD care in the South African private health sector. S Afr Med J. 2001;91(2):151–6. pmid:11288398
- 107. Eaton LA, Cain DN, Pitpitan EV, Carey KB, Carey MP, Mehlomakulu V, et al. Exploring the relationships among food insecurity, alcohol use, and sexual risk taking among men and women living in South African townships. J Prim Prev. 2014;35(4):255–65. pmid:24806889
- 108. Low A, Gummerson E, Schwitters A, Bonifacio R, Teferi M, Mutenda N, et al. Food insecurity and the risk of HIV acquisition: findings from population-based surveys in six sub-Saharan African countries (2016-2017). BMJ Open. 2022;12(7):e058704. pmid:35820770
- 109. Cluver LD, Orkin FM, Yakubovich AR, Sherr L. Combination social protection for reducing HIV-risk behavior among adolescents in South Africa. J Acquir Immune Defic Syndr. 2016;72(1):96–104. pmid:26825176
- 110. Myer L, Stein DJ, Grimsrud A, Seedat S, Williams DR. Social determinants of psychological distress in a nationally-representative sample of South African adults. Soc Sci Med. 2008;66(8):1828–40. pmid:18299167
- 111. Nduna M, Jewkes RK, Dunkle KL, Shai NPJ, Colman I. Associations between depressive symptoms, sexual behaviour and relationship characteristics: a prospective cohort study of young women and men in the Eastern Cape, South Africa. J Int AIDS Soc. 2010;13:44. pmid:21078150
- 112. Hamad R, Fernald LCH, Karlan DS, Zinman J. Social and economic correlates of depressive symptoms and perceived stress in South African adults. J Epidemiol Community Health. 2008;62(6):538–44. pmid:18477753
- 113. Cluver LD, Orkin FM, Meinck F, Boyes ME, Sherr L. Structural drivers and social protection: mechanisms of HIV risk and HIV prevention for South African adolescents. J Int AIDS Soc. 2016;19(1):20646. pmid:27086838
- 114. Smit J, Myer L, Middelkoop K, Seedat S, Wood R, Bekker L-G, et al. Mental health and sexual risk behaviours in a South African township: a community-based cross-sectional study. Public Health. 2006;120(6):534–42. pmid:16684549
- 115. Kincaid DL, Babalola S, Figueroa ME. HIV communication programs, condom use at sexual debut, and HIV infections averted in South Africa, 2005. J Acquir Immune Defic Syndr. 2014;66 Suppl 3:S278-84. pmid:25007197
- 116. Wills G, van der Berg S. COVID-19 disruptions and education in South Africa: two years of evidence. Dev South Afr. 2024;41(2):446–65.
- 117. Ranchhod V, Daniels RC. Labour market dynamics in South Africa at the onset of the COVID-19 pandemic. S Afr J Econ. 2021;89(1):44–62. pmid:33821045
- 118. Benade M, Long L, Rosen S, Meyer-Rath G, Tucker J-M, Miot J. Reduction in initiations of HIV treatment in South Africa during the COVID pandemic. BMC Health Serv Res. 2022;22(1):428. pmid:35361209
- 119. Pillay Y, Museriri H, Barron P, Zondi T. Recovering from COVID lockdowns: routine public sector PHC services in South Africa, 2019–2021. S Afr Med J. 2023;113(1):17–23.
- 120.
Senona E, Torkelson E, Zembe-Mkabile W. Social protection in a time of COVID: lessons for basic income support [cited 2024 June 30]. 2021. Available from: https://socialprotection.org/discover/publications/social-protection-time-covid-lessons-basic-income-support
- 121. Herbst K, Juvekar S, Jasseh M, Berhane Y, Chuc NTK, Seeley J, et al. Health and demographic surveillance systems in low- and middle-income countries: history, state of the art and future prospects. Glob Health Action. 2021;14(sup1):1974676. pmid:35377288
- 122. Yamanis TJ, Fisher JC, Moody JW, Kajula LJ. Young men’s social network characteristics and associations with sexual partnership concurrency in Tanzania. AIDS Behav. 2016;20(6):1244–55. pmid:26271813
- 123. Louw M, Van Der Berg S, Yu D. Convergence of a kind: educational attainment and intergenerational social mobility in South Africa. S Afr J Econ. 2007;75(3):548–71.
- 124. Eibner C, Evans WN. Relative deprivation, poor health habits, and mortality. J Hum Resour. 2005;40(3):591–620.
- 125. Mishra S, Novakowski D. Personal relative deprivation and risk: an examination of individual differences in personality, attitudes, and behavioral outcomes. Pers Indiv Dif. 2016;90:22–6.
- 126. Dearing JW, Cox JG. Diffusion of innovations theory, principles, and practice. Health Affairs. 2018;37(2):183–90.
- 127. Victora CG, Vaughan JP, Barros FC, Silva AC, Tomasi E. Explaining trends in inequities: evidence from Brazilian child health studies. Lancet. 2000;356(9235):1093–8. pmid:11009159
- 128. Kalichman SC, Simbayi LC, Jooste S, Cherry C, Cain D. Poverty-related stressors and HIV/AIDS transmission risks in two South African communities. J Urban Health. 2005;82(2):237–49. pmid:15888636
- 129. Burgard SA, Lee-Rife SM. Community characteristics, sexual initiation, and condom use among young Black South Africans. J Health Soc Behav. 2009;50(3):293–309. pmid:19711807
- 130.
Nordfors N. Economic inequality and HIV in South Africa [cited 2025 Sep 17]. Bachelor Thesis. University of Gothenburg; 2015. Available from: https://gupea.ub.gu.se/handle/2077/39184
- 131.
Holmqvist G. HIV and income inequality: if there is a link, what does it tell us? Brasilia: International Policy Centre for Inclusive Growth, United Nations Development Programme. 2009. Working paper 54.
- 132. Gillespie S, Kadiyala S, Greener R. Is poverty or wealth driving HIV transmission? AIDS. 2007;21 Suppl 7:S5–16. pmid:18040165
- 133. Drain PK, Smith JS, Hughes JP, Halperin DT, Holmes KK. Correlates of national HIV seroprevalence: an ecologic analysis of 122 developing countries. J Acquir Immune Defic Syndr. 2004;35(4):407–20. pmid:15097158
- 134. Meyer-Rath G, Jamieson L, Mudimu E, Imai-Eaton JW, Johnson LF. The cost of the plunge: the impact and cost of a cessation of PEPFAR-supported services in South Africa. AIDS. 2025;39:1476–80.
- 135. Kilburn K, Thirumurthy H, Halpern CT, Pettifor A, Handa S. Effects of a large-scale unconditional cash transfer program on mental health outcomes of young people in Kenya. J Adolesc Health. 2016;58(2):223–9. pmid:26576822
- 136. Baird S, De Hoop J, Ozler B. Income shocks and adolescent mental health. J Hum Resour. 2013;48(2):370–403.
- 137. Haushofer J, Shapiro J. The short-term impact of unconditional cash transfers to the poor: experimental evidence from Kenya. Q J Econ. 2016;131(4):1973–2042. pmid:33087990
- 138. J Carter D, Daniel R, Torrens AW, N Sanchez M, Maciel ELN, Bartholomay P, et al. The impact of a cash transfer programme on tuberculosis treatment success rate: a quasi-experimental study in Brazil. BMJ Glob Health. 2019;4(1):e001029. pmid:30740248
- 139. Ngamasana EL, Moxie J. Cash transfer, maternal and child health outcomes: a scoping review in sub-Saharan Africa. Glob Health Action. 2024;17(1):2309726. pmid:38333923
- 140. Keyes CL. Social well-being. Soc Psychol Q. 1998;61(2):121–40.
- 141. Ortenzi F, Marten R, Valentine NB, Kwamie A, Rasanathan K. Whole of government and whole of society approaches: call for further research to improve population health and health equity. BMJ Glob Health. 2022;7(7):e009972. pmid:35906017
- 142. Remme M, Martinez-Alvarez M, Vassall A. Cost-effectiveness thresholds in global health: taking a multisectoral perspective. Value Health. 2017;20(4):699–704. pmid:28408014
- 143. Stuart RM, Wilson DP. Sharing the costs of structural interventions: what can models tell us? Int J Drug Policy. 2021;88:102702. pmid:32173275
- 144. Hennessy DA, Flanagan WM, Tanuseputro P, Bennett C, Tuna M, Kopec J, et al. The Population Health Model (POHEM): an overview of rationale, methods and applications. Popul Health Metr. 2015;13:24. pmid:26339201