Improving Biomarker-based HIV Incidence Estimation in the Treatment Era

Background: 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 a recent infection testing algorithm (RITA). Methods: This article shows how testing and diagnosis reduce both FRR and mean duration of recent infection compared to a treatment-naive population. A new method is proposed for calculating appropriate context-specific estimates of FRR and mean duration of recent infection. The result of this is a new formula for incidence that depends only on reference FRR and mean duration of recent infection parameters derived in an undiagnosed, treatment-naive, nonelite controller, non-AIDS-progressed population. Results: Applying the methodology to eleven cross-sectional surveys in Africa results in good agreement with previous incidence estimates, except in 2 countries with very high reported testing rates. Conclusions: Incidence estimation equations can be adapted to account for the dynamics of treatment and recent infection testing algorithms. This provides a rigorous mathematical foundation for the application of HIV recency assays in cross-sectional surveys.

Automated summary

Estimating HIV-1 incidence using biomarker assays in cross-sectional surveys is important but limited by uncertainty about input parameters. This article proposes a new method for calculating appropriate estimates and provides a new formula for incidence. Applying this method to eleven surveys shows good agreement with previous estimates, except in countries with high testing rates.