From the Cover: Why the proportion of transmission during early-stage HIV infection does not predict the long-term impact of treatment on HIV incidence

Antiretroviral therapy (ART) reduces the infectiousness of HIV-infected persons, but only after testing, linkage to care, and successful viral suppression. Thus, a large proportion of HIV transmission during a period of high infectiousness in the first few months after infection (“early transmission”) is perceived as a threat to the impact of HIV “treatment-as-prevention” strategies. We created a mathematical model of a heterosexual HIV epidemic to investigate how the proportion of early transmission affects the impact of ART on reducing HIV incidence. The model includes stages of HIV infection, flexible sexual mixing, and changes in risk behavior over the epidemic. The model was calibrated to HIV prevalence data from South Africa using a Bayesian framework. Immediately after ART was introduced, more early transmission was associated with a smaller reduction in HIV incidence rate—consistent with the concern that a large amount of early transmission reduces the impact of treatment on incidence. However, the proportion of early transmission was not strongly related to the long-term reduction in incidence. This was because more early transmission resulted in a shorter generation time, in which case lower values for the basic reproductive number (R₀) are consistent with observed epidemic growth, and R₀ was negatively correlated with long-term intervention impact. The fraction of early transmission depends on biological factors, behavioral patterns, and epidemic stage and alone does not predict long-term intervention impacts. However, early transmission may be an important determinant in the outcome of short-term trials and evaluation of programs.Recent studies have confirmed that effective antiretroviral therapy (ART) reduces the transmission of HIV among stable heterosexual couples (1–3). This finding has generated interest in understanding the population-level impact of HIV treatment on reducing the rate of new HIV infections in generalized epidemic settings (4). Research, including mathematical modeling (5–10), implementation research (11), and major randomized controlled trials (12–14), are focused on how ART provision might be expanded strategically to maximize its public health benefits (15, 16).One concern is that if a large fraction of HIV transmission occurs shortly after a person becomes infected, before the person can be diagnosed and initiated on ART, this will limit the potential impact of HIV treatment on reducing HIV incidence (9, 17, 18). Data suggest that persons are more infectious during a short period of “early infection” after becoming infected with HIV (19–22), although there is debate about the extent, duration, and determinants of elevated infectiousness (18, 23). The amount of transmission that occurs also will depend on patterns of sexual behavior and sexual networks (17, 24–27). There have been estimates for the contribution of early infection to transmission from mathematical models (7, 17, 21, 24–26) and phylogenetic analyses (28–31), but these vary widely, from 5% to above 50% (23).In this study, we use a mathematical model to quantify how the proportion of transmission that comes from persons who have been infected recently affects the impact of treatment scale-up on HIV incidence. The model is calibrated to longitudinal HIV prevalence data from South Africa using a Bayesian framework. Thus, the model accounts for not only the early epidemic growth rate highlighted in previous research (5, 9, 18), but also the heterogeneity and sexual behavior change to explain the peak and decline in HIV incidence observed in sub-Saharan African HIV epidemics (32, 33).The model calibration allows uncertainty about factors that determine the amount of early transmission, including the relative infectiousness during early infection, heterogeneity in propensity for sexual risk behavior, assortativity in sexual partner selection, reduction in risk propensity over the life course, and population-wide reductions in risk behavior in response to the epidemic (32, 33). This results in multiple combinations of parameter values that are consistent with the observed epidemic and variation in the amount of early transmission. We simulated the impact of a treatment intervention and report how the proportion of early transmission correlates with the reduction in HIV incidence from the intervention over the short- and long-term.