Geographical-level contributions of risk factors for HIV infections using generalized additive models: results from a cohort of South African women

Abstract

ABSTRACT South Africa has the highest burden of human immunodeficiency virus (HIV) infections in the world with significant geographical variations. We identified the predictors of HIV infections and their sub-geographical-level contributions to the epidemic using a decade long data (2002–2012) from 6484 South African women. Generalized additive models were used to uncover the most significant features of these estimates across the region. In the overall analysis, younger age, not married or cohabiting with a partner, partner has another partner(s) and null/prim parity, using injectable contraceptives and presence of other sexually transmitted infections (STIs) were identified as independent predictors of HIV seroconversions. Overall, the top three highest contributors to infections were women’s marital status (PAR%\u2009=\u200973%, 95% CI: 68%, 77%), parity (PAR%\u2009=\u200947%, 95% CI: 42%, 53%) and partnership factors (PAR%\u2009=\u200937%, 95% CI: 30%, 44%). However, their contributions varied remarkably at sub-geographical level. This was mainly due to the substantial localized variations in their prevalence and hazard ratios across the region. Our results will guide policy makers to develop tailored prevention strategies in order to allocate scarce resources by targeting the most significant contributors of HIV infection at sub-geographical level.