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HIV-1 Recent Infection Testing Algorithm with Antiretroviral Drug Detection to Improve Accuracy of Incidence Estimates
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8 01 2021
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Source: J Acquir Immune Defic Syndr. 87(Suppl 1):S73-S80
Details:
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Alternative Title:J Acquir Immune Defic Syndr
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Personal Author:
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Description:Background:
HIV-1 incidence calculation currently includes recency classification by HIV-1 incidence assay and unsuppressed viral load (VL≥1000 copies/mL) in a recent infection testing algorithm (RITA). However, persons with recent classification not virally suppressed and taking antiretroviral medication (ARV) may be misclassified.
Setting:
We used data from 13 African household surveys to describe the impact of an ARV-adjusted RITA on HIV-1 incidence estimates.
Methods:
HIV-seropositive samples were tested for recency using the HIV-1 Limiting Antigen (LAg)-Avidity enzyme immunoassay, HIV-1 viral load, ARVs used in each country, and ARV drug resistance. LAg-recent result was defined as normalized optical density values ≤1.5. We compared HIV-1 incidence estimates using two RITA; RITA1: LAg-recent + VL ≥1000 copies/mL and RITA2: RITA1 + undetectable ARV. We explored RITA2 with self-reported ARV use and with clinical history.
Results:
Overall, 357 adult HIV-positive participants were classified as having recent infection with RITA1. RITA2 reclassified 55 (15.4%) persons with detectable ARV as having long-term infection. Those with detectable ARV were significantly more likely to be aware of their HIV-positive status (84% vs. 10%) and had higher levels of drug resistance (74% vs. 26%) than those without detectable ARV. RITA2 incidence was lower than RITA1 incidence (range, 0%–30% decrease), resulting in decreased estimated new infections from 390,000 to 341,000 across the 13 countries. Incidence estimates were similar using detectable or self-reported ARV (R2>0.995).
Conclusions:
Including ARV in RITA2 improved the accuracy of HIV-1 incidence estimates by removing participants with likely long-term HIV infection.
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Pubmed ID:34166315
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Pubmed Central ID:PMC8630595
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Funding:
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Volume:87
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[PDF-630.10 KB]
