Addressing Population Heterogeneity for HIV Incidence Estimation Based on Recency Test

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 scenarios: one when the target population is a subset of the population with cross-sectional recency testing data and the other with an external target population. In addition, we propose estimators to incorporate HIV subtypes, a special type of covariate that modifies the properties of recency tests, into our framework. Through simulation studies and a data application, we demonstrate the performance of the proposed methods. We conclude with a discussion on sensitivity analysis and future work to improve our framework.