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

ABSTRACT: 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 histo

SUBMITTER: Yang W 

PROVIDER: S-EPMC11647912 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature