Improving Biomarker Based HIV Incidence Estimation in the Treatment Era

TL;DR

This paper introduces a new method for more accurate HIV incidence estimation using biomarker assays by accounting for testing and diagnosis effects, providing context-specific parameters for better accuracy.

Contribution

It proposes a novel formula for HIV incidence estimation that adjusts for population testing and diagnosis, improving accuracy over previous methods.

Findings

Testing and diagnosis reduce FRR and MDRI compared to treatment-naive populations.

A new method calculates context-specific FRR and MDRI using self-reported testing history.

The derived formula improves HIV incidence estimates in the treatment era.

Abstract

Estimating HIV-1 incidence using biomarker assays in cross-sectional surveys is important for understanding the HIV pandemic. However, the utility of these estimates has been limited by uncertainty about what input parameters to use for False Recency Rate (FRR) and Mean Duration of Recent Infection (MDRI) after applying recent infection testing algorithm (RITA). This article shows how testing and diagnosis in a population reduce both FRR and MDRI compared to a treatment-naive population. Using self-reported testing history, a new method is proposed for calculating appropriate context-specific estimates of FRR and MDRI. The result of this is a new formula for incidence that depends only on reference FRR and MDRI parameters derived in an undiagnosed, treatment-naive, non-elite controller, non-AIDS-progressed population.