Socioeconomic factors impact the risk of HIV acquisition in the township population of South Africa: A Bayesian analysis.

With a prevalence almost twice as high as the national average, people living in South African townships are particularly impacted by the HIV epidemic. Yet, it remains unclear how socioeconomic factors impact the risk of HIV infection within township populations. Our objective was to estimate the extent to which socioeconomic factors (dwelling situation, education, employment status, and monthly income) explain the risk of HIV in South African township populations, after controlling for behavioural and individual risk factors. Using Bayesian logistic regression, we analysed secondary data from a quasi-randomised trial which recruited participants (N = 3095) from townships located across three subdistricts of Cape Town. We controlled for individual factors (age, sex, marital status, testing history, HIV exposure, comorbidities, and tuberculosis infection) and behavioural factors (unprotected sex, sex with multiple partners, with sex workers, with a partner living with HIV, under the influence of alcohol or drugs), and accounted for the uncertainty due to missing data through multiple imputation. We found that residing in informal dwellings and not having post-secondary education increased the odds of HIV (aOR, 89% CrI: 1.34, 1.07-1.68 and 1.82, 1.29-2.61, respectively), after controlling for subdistrict of residence, individual, and behavioural factors. Additionally, our results suggest different pathways for how socioeconomic status (SES) affect HIV infection in males and female participants: while socioeconomic factors associated with lower SES seem to be associated with a decreased likelihood of having recently sough HIV testing among male participants, they are associated with increased sexual risk taking which, among female participants, increase the risk of HIV. Our analyses demonstrate that social determinants of health are at the root of the HIV epidemic and affect the risk of HIV in multiple ways. These findings stress the need for the deployment of programs that specifically address social determinants of health.