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.

翻译：利用横断面样本中的近期感染检测算法（RITA）结果，可进行HIV感染发病率的估计。这一方法使研究者无需对个体队列进行纵向追踪，即可了解人群中HIV疫情的演变趋势。然而，该方法的应用受限于其精确度，而精确度又取决于RITA识别近期感染的（低）灵敏度。通过利用个体可能已接受的既往HIV检测结果，我们提出了一种灵敏度（及特异度）更高的增强型RITA，并以此构建了改进的发病率估计方法。我们证明了该增强型估计量的理论性质，并通过模拟研究展示了其数值表现。将该估计量应用于一项整群随机试验数据，以分析社区级HIV干预措施对发病率的影响，结果证实相较于标准估计量，增强型估计量能提供更精确的HIV发病率估计。