A likelihood approach to incorporating self-report data in HIV recency classification.

Estimating new HIV infections is significant yet challenging due to the difficulty in distinguishing between recent and long-term infections. We demonstrate that HIV recency status (recent versus long-term) could be determined from self-report testing history and biomarkers, which are increasingly available in bio-behavioral surveys. HIV recency status is partially observed, given the self-report testing history. For example, people who tested positive for HIV over 1 year ago should have a long-term infection. Based on the nationally representative samples collected by the Population-based HIV Impact Assessment (PHIA) Project, we propose a likelihood-based probabilistic model for HIV recency classification. The model incorporates individuals with known recency status based on testing histories and individuals whose recency status could not be determined and integrates the mechanism of how HIV recency status depends on biomarkers and the mechanism of how HIV recency status, together with the self-report time of the most recent HIV test, impacts the test results. We compare our method to logistic regression and the binary classification tree (current practice) on Malawi PHIA data, as well as on simulated data. Our model obtains more efficient and less biased parameter estimates and is relatively robust to potential reporting error and model misspecification.