  Incidence estimation of HIV infection can be performed using recent infection
testing algorithm (RITA) results from a cross-sectional sample. This allows
practitioners to understand population trends in the HIV epidemic without
having to perform longitudinal follow-up on a cohort of individuals. The
utility of the approach is limited by its precision, driven by the (low)
sensitivity of the RITA at identifying recent infection. By utilizing results
of previous HIV tests that individuals may have taken, we consider an enhanced
RITA with increased sensitivity (and specificity). We use it to propose an
enhanced estimator for incidence estimation. We prove the theoretical
properties of the enhanced estimator and illustrate its numerical performance
in simulation studies. We apply the estimator to data from a cluster-randomized
trial to study the effect of community-level HIV interventions on HIV
incidence. We demonstrate that the enhanced estimator provides a more precise
estimate of HIV incidence compared to the standard estimator.
