The Impact of Preexposure Prophylaxis Among Men Who Have Sex With Men: An Individual-Based Model

Abstract

Objectives: Preexposure prophylaxis (PrEP) is recommended for preventing HIV infection among individuals at high risk, including men who have sex with men (MSM). Although its individual-level efficacy is proven, questions remain regarding population-level impact of PrEP implementation.

Design: We developed an agent-based simulation of HIV transmission among MSM, accounting for demographics, sexual contact network, HIV disease stage, and use of antiretroviral therapy. We use this framework to compare PrEP delivery strategies in terms of impact on HIV incidence and prevalence.

Results: The projected reduction in HIV incidence achievable with PrEP reflects both population-level coverage and individual-level adherence (as a proportion of days protected against HIV transmission). For example, provision of PrEP to 40% of HIV-negative MSM reporting more than one sexual partner in the last 12 months, taken with sufficient adherence to provide protection on 40% of days, can reduce HIV incidence by 9.5% (95% uncertainty range: 8%-11%) within 5 years. However, if this could be increased to 80% coverage on 80% of days (eg, through mass campaigns with a long-acting injectable formulation), a 43% (42%-44%) reduction in HIV incidence could be achieved. Delivering PrEP to MSM at high risk for HIV acquisition can augment population-level impact up to 1.8-fold.

Conclusions: If highly ambitious targets for coverage and adherence can be achieved, PrEP can substantially reduce HIV incidence in the short-term. Although the reduction in HIV incidence largely reflects the proportion of person-years protected, the efficiency of PrEP delivery can be enhanced by targeting high-risk populations.

Figure 1. Overview of simulation modules

This figure illustrates the schematic simulation logic for modeling the demographic and partnership networks among MSM (panel A), as well as HIV natural history and the cascade of care (panel B). Panel A: The population is structured into a collection of Community Statistical Areas (CSAs) and CSA neighborhood groups, which are in turn based on geographical proximity, and the level of similarity in income and racial structure (represented schematically by same colors). Partnership domains are determined via discrete choice within an individual’s own CSA of residence, a random neighboring CSA, or a non-neighbor CSA. Once the partnership domain is established, individuals follow a search mechanism based on a combination of race- and age-dependent mixing patterns to select their future partner from the pool of eligible people in that domain. Panel B: HIV infection is modeled as a gradual decline in CD4 count and via three main states in the absence of HIV treatment (acute/chronic/late infection). The cascade of care models the processes for screening infected individuals, linking to care, retaining in care and starting ART.

Figure 2. Model Calibration to Epidemiological Data

Shown are the mean values of 200 simulations (in green) compared against empirical data (in orange). The black arrows represent the range of observations around each simulated measure. The data bars represent available point estimates on each measure including measures of partnership frequency (Panels A and B) reported through unpublished data from the latest survey of Baltimore’s MSM in 2014 [14], as well as available estimates of HIV prevalence, racial disparities and the HIV continuum of care among Baltimore’s MSM in year 2012 (Figure 2-Panels C through E) [15].

Figure 3. Percent reduction in HIV incidence after delivering PrEP to MSM reporting more than one partner in a year (scenario 2) at different individual-level HIV protection and population-level coverage

Contour lines show the percent reduction in HIV incidence at various levels of population coverage (on the x-axis, with the percent of the eligible population covered on top and the numbers of MSM on PrEP in parentheses) and individual-level protection (on the y-axis). Panel A shows impact after one year of PrEP delivery, Panel B shows the same impact after sustaining the same level of PrEP for five years, and Panel C shows the impact one year after stopping PrEP (at the end of year five). All impact estimates are shown relative to a baseline of no PrEP delivery.

Figure 4. Comparative impact of targeted PrEP scenarios on HIV incidence at various level of HIV protection

Each panel maps the comparative impact of PrEP scenarios on HIV incidence at a different level of achievable protection under PrEP, ranging from 20% (top left) to 100% (bottom left). The y-axis of each panel shows the percent reduction in incidence under each PrEP scenario at the end of the 5^th year of implementation, relative to a baseline with no PrEP. The x-axis represents the number of MSM receiving PrEP in each of five scenarios (described in the inset and the manuscript text). Shaded areas represent the 95% confidence interval around the simulated means. The end of each line represents the total eligible population in each scenario except for scenario 1 (e.g., S3 is continued until the estimated 2000 MSM in Baltimore with more than 5 partners are all covered). For a given number of MSM on PrEP, all targeted scenarios (Scenarios 2–4) show modest improvements over random selection (Scenario 1), and Scenario 5 shows the greatest impact of all strategies evaluated.