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

Wendy Grant-McAuley; Oliver Laeyendecker; Daniel Monaco; Athena Chen; Sarah E Hudelson; Ethan Klock; Ron Brookmeyer; Douglas Morrison; Estelle Piwowar-Manning; Charles S Morrison; Richard Hayes; Helen Ayles; Peter Bock; Barry Kosloff; Kwame Shanaube; Nomtha Mandla; Anneen van Deventer; Ingo Ruczinski; Kai Kammers; H Benjamin Larman; Susan H Eshleman

doi:10.1186/s12879-022-07850-0

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

BMC Infect Dis. 2022 Nov 11;22(1):838. doi: 10.1186/s12879-022-07850-0.

Authors

Wendy Grant-McAuley¹, Oliver Laeyendecker^{2

3}, Daniel Monaco^{1

4}, Athena Chen⁵, Sarah E Hudelson¹, Ethan Klock³, Ron Brookmeyer⁶, Douglas Morrison⁷, Estelle Piwowar-Manning¹, Charles S Morrison⁸, Richard Hayes⁹, Helen Ayles^{10

11}, Peter Bock¹², Barry Kosloff^{10

11}, Kwame Shanaube¹⁰, Nomtha Mandla¹², Anneen van Deventer¹², Ingo Ruczinski⁵, Kai Kammers¹³, H Benjamin Larman^{1

4}, Susan H Eshleman¹⁴

Affiliations

¹ Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
² Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Baltimore, MD, USA.
³ Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
⁴ Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
⁵ Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA.
⁶ Department of Biostatistics, UCLA Fielding School of Public Health, Los Angeles, CA, USA.
⁷ Department of Public Health Sciences, UC Davis School of Medicine, Davis, CA, USA.
⁸ Behavioral, Epidemiologic, and Clinical Sciences, Durham, NC, FHI 360, USA.
⁹ Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
¹⁰ Zambart, University of Zambia School of Public Health, Lusaka, Zambia.
¹¹ Clinical Research Department, London School of Hygiene and Tropical Medicine, London, UK.
¹² Desmond Tutu TB Center, Department of Paediatrics and Child Health, Stellenbosch University, Stellenbosch, Western Cape, South Africa.
¹³ Division of Biostatistics and Bioinformatics, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
¹⁴ Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA. seshlem@jhmi.edu.

Abstract

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 ~ 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.

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

MeSH terms

Algorithms
Anti-Retroviral Agents / therapeutic use
Biomarkers
Cross-Sectional Studies
HIV Infections* / drug therapy
HIV Infections* / epidemiology
Humans
Immunoenzyme Techniques
Incidence
Viral Load

Substances

Anti-Retroviral Agents
Biomarkers

Abstract

MeSH terms

Substances

Grants and funding