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

TL;DR

This paper introduces an improved HIV incidence estimator that leverages prior test results to increase accuracy and precision in cross-sectional studies, enhancing public health surveillance.

Contribution

It develops a novel estimator incorporating previous HIV test data, with proven theoretical properties and demonstrated improved performance over standard methods.

Findings

Enhanced estimator shows higher sensitivity and specificity.

Simulation studies confirm increased precision.

Application to real data demonstrates practical utility.

Abstract

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.