Cross-sectional HIV incidence estimation leverages recency test results to determine the HIV incidence of a population of interest, where recency test uses biomarker profiles to infer whether an HIV-positive individual was "recently" infected. This approach possesses an obvious advantage over the conventional cohort follow-up method since it avoids longitudinal follow-up and repeated HIV testing. In this manuscript, we consider the extension of cross-sectional incidence estimation to estimate the incidence of a different target population addressing potential population heterogeneity. We propose a general framework that incorporates two settings: one with the target population that is a subset of the population with cross-sectional recency testing data, e.g., leveraging recency testing data from screening in active-arm trial design, and the other with an external target population. We also propose a method to incorporate HIV subtype, a special covariate that modifies the properties of recency test, into our framework. Through extensive simulation studies and a data application, we demonstrate the excellent performance of the proposed methods. We conclude with a discussion of sensitivity analysis and future work to improve our framework.

翻译：横断面HIV发病率估计利用近期检测结果确定目标人群的HIV发病率，其中近期检测通过生物标志物特征推断HIV阳性个体是否为“近期”感染。该方法相较于传统队列随访法具有明显优势，因其无需纵向追踪与重复HIV检测。本文探讨如何扩展横断面发病率估计方法，在解决潜在人口异质性的前提下，估算不同目标人群的发病率。我们提出一个通用框架，涵盖两种情境：其一，目标人群为具有横断面近期检测数据人群的子集（例如利用主动干预试验设计中的筛查近期检测数据）；其二，目标人群为外部人群。我们同时提出一种方法，将HIV亚型（一种可改变近期检测特性的特殊协变量）纳入该框架。通过大规模模拟研究与实际数据应用，我们验证了所提方法的优异性能。最后，我们围绕敏感性分析与未来框架改进方向展开讨论。