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Incidence estimation of HIV infection can be performed using recent infectiontesting algorithm (RITA) results from a cross-sectional sample. This allowspractitioners to understand population trends in the HIV epidemic withouthaving to perform longitudinal follow-up on a cohort of individuals. Theutility of the approach is limited by its precision, driven by the (low)sensitivity of the RITA at identifying recent infection. By utilizing resultsof previous HIV tests that individuals may have taken, we consider an enhancedRITA with increased sensitivity (and specificity). We use it to propose anenhanced estimator for incidence estimation. We prove the theoreticalproperties of the enhanced estimator and illustrate its numerical performancein simulation studies. We apply the estimator to data from a cluster-randomizedtrial to study the effect of community-level HIV interventions on HIVincidence. We demonstrate that the enhanced estimator provides a more preciseestimate of HIV incidence compared to the standard estimator.

An Enhanced Cross-Sectional HIV Incidence Estimator that Incorporates Prior HIV Test Results