Probabilistic HIV Recency Classification -- A Logistic Regression without Labeled Individual Level Training Data

TL;DR

This paper introduces a semi-supervised logistic regression model for estimating individual HIV recency status, leveraging multiple data sources and national incidence estimates to improve accuracy over existing methods.

Contribution

The paper presents a novel semi-supervised logistic regression approach that combines survey data, cohort studies, and epidemiological models for HIV recency classification.

Findings

More accurate individual recency estimation in Malawi PHIA data

Improved HIV recency rate estimation at aggregate levels

Outperforms binary classification tree (BCT) in accuracy

Abstract

Accurate HIV incidence estimation based on individual recent infection status (recent vs long-term infection) is important for monitoring the epidemic, targeting interventions to those at greatest risk of new infection, and evaluating existing programs of prevention and treatment. Starting from 2015, the Population-based HIV Impact Assessment (PHIA) individual-level surveys are implemented in the most-affected countries in sub-Saharan Africa. PHIA is a nationally-representative HIV-focused survey that combines household visits with key questions and cutting-edge technologies such as biomarker tests for HIV antibody and HIV viral load which offer the unique opportunity of distinguishing between recent infection and long-term infection, and providing relevant HIV information by age, gender, and location. In this article, we propose a semi-supervised logistic regression model for estimating individual level HIV recency status. It incorporates information from multiple data sources -- the PHIA survey where the true HIV recency status is unknown, and the cohort studies provided in the literature where the relationship between HIV recency status and the covariates are presented in the form of a contingency table. It also utilizes the national level HIV incidence estimates from the epidemiology model. Applying the proposed model to Malawi PHIA data, we demonstrate that our approach is more accurate for the individual level estimation and more appropriate for estimating HIV recency rates at aggregated levels than the current practice -- the binary classification tree (BCT).