Predicting HIV Incidence in the SEARCH Trial: A Mathematical Modeling Study

Background: The SEARCH study provided community-based HIV and multidisease testing and antiretroviral therapy (ART) to 32 communities in East Africa and reported no statistically significant difference in 3-year HIV incidence. We used mathematical modeling to estimate the effect of control arm viral suppression and community mixing on SEARCH trial outcomes. Setting: Uganda and Kenya. Methods: Using the individual-based HIV modeling software EMOD-HIV, we configured a new model of SEARCH communities. The model was parameterized using demographic, HIV prevalence, male circumcision, and viral suppression data and calibrated to HIV prevalence, ART coverage, and population size. Using assumptions about ART scale-up in the control arm, degree of community mixing, and effect of baseline testing, we estimated comparative HIV incidence under multiple scenarios. Results: Before the trial results, we predicted that SEARCH would report a 4%–40% reduction between arms, depending on control arm ART linkage rates and community mixing. With universal baseline testing followed by rapidly expanded ART eligibility and uptake, modeled effect sizes were smaller than the study was powered to detect. Using interim viral suppression data, we estimated 3-year cumulative incidence would have been reduced by up to 27% in the control arm and 43% in the intervention arm compared with a counterfactual without universal baseline testing. Conclusions: Our model suggests that the active control arm substantially reduced expected effect size and power of the SEARCH study. However, compared with a counterfactual “true control” without increased ART linkage because of baseline testing, SEARCH reduced HIV incidence by up to 43%.