A Likelihood Approach to Incorporating Self-Report Data in HIV Recency Classification

TL;DR

This paper introduces a likelihood-based probabilistic model that combines self-report testing history and biomarkers to improve the classification of recent versus long-term HIV infections, addressing challenges in estimating new HIV cases.

Contribution

The paper presents a novel likelihood approach that effectively incorporates both labeled and unlabeled data for HIV recency classification, outperforming existing methods.

Findings

Model achieves more efficient parameter estimates.

Method is less biased and robust to reporting errors.

Outperforms logistic regression and classification trees.

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 v.s. long-term) could be determined from the combination of 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 one 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 both labeled and unlabeled data 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, via a set of logistic regression models. We compare our method to logistic regression and the binary classification tree (current practice) on Malawi, Zimbabwe, and Zambia 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.