Challenges in estimating HIV prevalence trends and geographical variation in HIV prevalence using antenatal data: Insights from mathematical modelling

The Effect of HIV Programs in South Africa on National HIV Incidence Trends, 2000-2019

BACKGROUND

Recent studies have shown HIV incidence declines at a population level in several African countries. However, these studies have not directly quantified the extent to which incidence declines are attributable to different HIV programs.

METHODS

We calibrated a mathematical model of the South African HIV epidemic to age- and sex-specific data from antenatal surveys, household surveys, and death registration, using a Bayesian approach. The model was also parameterized using data on self-reported condom use, voluntary medical male circumcision (VMMC), HIV testing, and antiretroviral treatment (ART). Model estimates of HIV incidence were compared against the incidence rates that would have been expected had each program not been implemented.

RESULTS

The model estimated incidence in 15-49 year olds of 0.84% (95% CI: 0.75% to 0.96%) at the start of 2019. This represents a 62% reduction (95% CI: 55% to 66%) relative to 2000, a 47% reduction (95% CI: 42% to 51%) relative to 2010, and a 73% reduction (95% CI: 68% to 77%) relative to the incidence that would have been expected in 2019 in the absence of any interventions. The reduction in incidence in 2019 because of interventions was greatest for ART and condom promotion, with VMMC and behavior change after HIV testing having relatively modest impacts. HIV program impacts differed significantly by age and sex, with condoms and VMMC having greatest impact in youth, and overall incidence reductions being greater in men than in women.

CONCLUSIONS

HIV incidence in South Africa has declined substantially since 2000, with ART and condom promotion contributing most significantly to this decline.

Modeling the Impact of Social Determinants of Health on HIV

There is growing evidence for the key role of social determinants of health (SDOH) in understanding morbidity and mortality outcomes globally. Factors such as stigma, racism, poverty or access to health and social services represent complex constructs that affect population health via intricate relationships to individual characteristics, behaviors and disease prevention and treatment outcomes. Modeling the role of SDOH is both critically important and inherently complex. Here we describe different modeling approaches and their use in assessing the impact of SDOH on HIV/AIDS. The discussion is thematically divided into mechanistic models and statistical models, while recognizing the overlap between them. To illustrate mechanistic approaches, we use examples of compartmental models and agent-based models; to illustrate statistical approaches, we use regression and statistical causal models. We describe model structure, data sources required, and the scope of possible inferences, highlighting similarities and differences in formulation, implementation, and interpretation of different modeling approaches. We also indicate further needed research on representing and quantifying the effect of SDOH in the context of models for HIV and other health outcomes in recognition of the critical role of SDOH in achieving the goal of ending the HIV epidemic and improving overall population health.