Improving Biomarker-based HIV Incidence Estimation in the Treatment Era

Area of Science:

• Epidemiology
• Biostatistics
• Infectious Disease Modeling

Background:

• Estimating HIV-1 incidence using biomarker assays in cross-sectional surveys is crucial for understanding the HIV pandemic.
• Uncertainty in input parameters for false recency rate (FRR) and mean duration of recent infection (MDRI) limits the utility of current HIV incidence estimates.
• Recent Infection Testing Algorithms (RITA) are employed, but parameter selection remains a challenge.

Purpose of the Study:

• To present a novel method for calculating context-specific FRR and MDRI estimates.
• To develop a new formula for HIV-1 incidence estimation.
• To address the limitations posed by testing and treatment dynamics in incidence estimation.

Main Methods:

• The study demonstrates how testing and diagnosis reduce FRR and MDRI compared to a treatment-naive population.
• A new formula for incidence is proposed, relying on reference FRR and MDRI parameters from an undiagnosed, treatment-naive population.
• Methodology is applied to eleven cross-sectional surveys in Africa.

Main Results:

• The proposed methodology shows good agreement with previous HIV-1 incidence estimates across multiple African surveys.
• Discrepancies were noted in two countries with exceptionally high reported testing rates.
• The findings highlight the impact of testing rates on incidence estimation.

Conclusions:

• Incidence estimation equations can be adapted to incorporate treatment dynamics and RITA.
• The study provides a robust mathematical framework for utilizing HIV recency assays in population-based surveys.
• This work enhances the accuracy and reliability of HIV-1 incidence estimations.