Sources of racial disparities in HIV prevalence in men who have sex with men in Atlanta, GA, USA: a modelling study

Abstract

Background: In the USA, men who have sex men (MSM) are at high risk for HIV, and black MSM have a substantially higher prevalence of infection than white MSM. We created a simulation model to assess the strength of existing hypotheses and data that account for these disparities.

Methods: We built a dynamic, stochastic, agent-based network model of black and white MSM aged 18-39 years in Atlanta, GA, USA, that incorporated race-specific individual and dyadic-level prevention and risk behaviours, network attributes, and care patterns. We estimated parameters from two Atlanta-based studies in this population (n=1117), supplemented by other published work. We modelled the ability for racial assortativity to generate or sustain disparities in the prevalence of HIV infection, alone or in conjunction with scenarios of observed racial patterns in behavioural, care, and susceptibility parameters.

Findings: Race-assortative mixing alone could not sustain a pre-existing disparity in prevalence of HIV between black and white MSM. Differences in care cascade, stigma-related behaviours, and CCR5 genotype each contributed substantially to the disparity (explaining 10·0%, 12·7%, and 19·1% of the disparity, respectively), but nearly half (44·5%) could not be explained by the factors investigated. A scenario assessing race-specific reporting differences in risk behaviour was the only one to yield a prevalence in black MSM (44·1%) similar to that observed (43·4%).

Interpretation: Racial assortativity is an inadequate explanation for observed disparities. Work to close the gap in the care cascade by race is imperative, as are efforts to increase serodiscussion and strengthen relationships among black MSM particularly. Further work is urgently needed to identify other sources of, and pathways for, this disparity, to integrate concomitant epidemics into models, and to understand reasons for racial differences in behavioural reporting.

Funding: The Eunice Kennedy Shriver National Institute of Child Health and Development, the National Institute of Allergy and Infectious Diseases, the National Institute of Minority Health and Health Disparities, and the National Institute of Mental Health.

Figure 1. Changes in race-specific HIV incidence and prevalence over time

These scenarios probe Question 1, the ability for pre-existing disparities that may have been generated early in the epidemic to be sustained, either by assortative mixing alone (Null model) or by the full set of race-specific behavioral, biological, demographic and clinical conditions as drawn from our studies and the literature (As-observed scenario). Results demonstrate that the disparities cannot be indefinitely sustained, but would begin to noticeably reduce within a few years. Prevalence plots are on left, incidence plots on right. (A-B) Null model. (C-D) As-observed scenario. Initial HIV prevalence by race is set to that observed in our source data. Individual lines represent each of 16 simulations; thick lines represent means.

Figure 2. Black vs. White HIV prevalence, by model scenario

This figure presents the race-specific HIV prevalence levels generated by each scenario, providing visual insight into whether the challenges in explaining observed disparities are due to systematic under-estimation of BMSM prevalence, over-estimation of WMSM prevalence, or both. BMSM prevalence is shown on the X-axis, WMSM prevalence on the Y-axis. Observed point estimates are shown by the small black square, surrounded by confidence limits shown by whiskers. For sake of legibility, individual runs are not shown; shapes represent the convex hull of 16 points reflecting individual runs. Plots are divided across two panels for legibility. (A) First seven scenarios; (B) final five scenarios. For scenario explanations, see Table 3. Many scenarios approximate WMSM prevalence; however, the first 12 scenarios all strongly underestimate Black prevalence. This suggests the value in considering a scenario incorporating misclassification of some risk behaviors by Black MSM, based potentially on stigma or mistrust of research. This final scenario captured BMSM prevalence, but over-estimated that for WMSM.