4.5 Article

Evaluation of multi-assay algorithms for cross-sectional HIV incidence estimation in settings with universal antiretroviral treatment

期刊

BMC INFECTIOUS DISEASES
卷 22, 期 1, 页码 -

出版社

BMC
DOI: 10.1186/s12879-022-07850-0

关键词

HIV incidence estimation; Recent infections; Universal antiretroviral treatment; Early antiretroviral treatment; Viral suppression

资金

  1. National Institute of Allergy and Infectious Diseases (NIAID) of the National Institutes of Health (NIH) [R01-AI095068]
  2. National Institute of General Medical Sciences (NIGMS) [R01-GM136724]
  3. Laboratory Center of HIV Prevention Trials Network (HPTN) - NIAID [UM1-AI068619, UM1-AI068617, UM1-AI068613]
  4. U.S. President's Emergency Plan for AIDS Relief (PEPFAR)
  5. Division of Intramural Research, NIAID
  6. International Initiative for Impact Evaluation (3ie)
  7. Bill & Melinda Gates Foundation
  8. NIAID of NIH
  9. National Institute on Drug Abuse (NIDA) of NIH
  10. National Institute of Mental Health (NIMH) of NIH
  11. UK Medical Research Council (MRC) under the MRC/DFID Concordat agreement, EDCTP2 programme - European Union [MR/R010161/1]
  12. UK Department for International Development (DFID) under the MRC/DFID Concordat agreement, EDCTP2 programme - European Union [MR/R010161/1]

向作者/读者索取更多资源

This study evaluated the performance of two multi-assay algorithms (MAAs) that do not include viral load (VL) in estimating HIV incidence. The results showed that the MAAs without VL overestimated the incidence and performed poorly in classifying some long-term infected individuals as recently infected.
Background: Multi-assay algorithms (MAAs) are used to estimate population-level HIV incidence and identify individuals with recent infection. Many MAAs use low viral load (VL) as a biomarker for long-term infection. This could impact incidence estimates in settings with high rates of early HIV treatment initiation. We evaluated the performance of two MAAs that do not include VL. Methods: Samples were collected from 219 seroconverters (infected < 1 year) and 4376 non-seroconverters (infected > 1 year) in the HPTN 071 (PopART) trial; 28.8% of seroconverter samples and 73.2% of non-seroconverter samples had VLs <= 400 copies/mL. Samples were tested with the Limiting Antigen Avidity assay (LAg) and JHU BioRad-Avidity assays. Antibody reactivity to two HIV peptides was measured using the MSD U-PLEX assay. Two MAAs were evaluated that do not include VL: a MAA that includes the LAg-Avidity assay and BioRad-Avidity assay (LAg + BR) and a MAA that includes the LAg-Avidity assay and two peptide biomarkers (LAg + PepPair). Performance of these MAAs was compared to a widely used MAA that includes LAg and VL (LAg + VL). Results: The incidence estimate for LAg + VL (1.29%, 95% CI: 0.97-1.62) was close to the observed longitudinal incidence (1.34% 95% CI: 1.17-1.53). The incidence estimates for the other two MAAs were higher (LAg + BR: 2.56%, 95% CI 2.01-3.11; LAg + PepPair: 2.84%, 95% CI: 1.36-4.32). LAg + BR and LAg + PepPair also misclassified more individuals infected > 2 years as recently infected than LAg + VL (1.2% [42/3483 and 1.5% [51/3483], respectively, vs. 0.2% [6/3483]). LAg + BR classified more seroconverters as recently infected than LAg + VL or LAg + PepPair (80 vs. 58 and 50, respectively) and identified similar to 25% of virally suppressed seroconverters as recently infected. Conclusions: The LAg + VL MAA produced a cross-sectional incidence estimate that was closer to the longitudinal estimate than two MAAs that did not include VL. The LAg + BR MAA classified the greatest number of individual seroconverters as recently infected but had a higher false recent rate.

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