Evidence map›Paper›PMID 36368950›Full record

ArticleBMC infectious diseases2022

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 and 11 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC infectious diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.1field-weighted citation impact, top 58% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

21 authors at 11 institutions in 4 countries.

Wendy Grant-McAuleyDepartment of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Oliver LaeyendeckerLaboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Baltimore, MD, USA.
Daniel MonacoDepartment of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Athena ChenDepartment of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA.
Sarah E HudelsonDepartment of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Ethan KlockDepartment of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Ron BrookmeyerDepartment of Biostatistics, UCLA Fielding School of Public Health, Los Angeles, CA, USA.
Douglas MorrisonDepartment of Public Health Sciences, UC Davis School of Medicine, Davis, CA, USA.
Estelle Piwowar-ManningDepartment of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Charles S MorrisonBehavioral, Epidemiologic, and Clinical Sciences, Durham, NC, FHI 360, USA.
Richard HayesDepartment of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
Helen AylesZambart, University of Zambia School of Public Health, Lusaka, Zambia.
Peter BockDesmond Tutu TB Center, Department of Paediatrics and Child Health, Stellenbosch University, Stellenbosch, Western Cape, South Africa.
Barry KosloffZambart, University of Zambia School of Public Health, Lusaka, Zambia.
Kwame ShanaubeZambart, University of Zambia School of Public Health, Lusaka, Zambia.
Nomtha MandlaDesmond Tutu TB Center, Department of Paediatrics and Child Health, Stellenbosch University, Stellenbosch, Western Cape, South Africa.
Anneen van DeventerDesmond Tutu TB Center, Department of Paediatrics and Child Health, Stellenbosch University, Stellenbosch, Western Cape, South Africa.
Ingo RuczinskiDepartment of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA.
Kai KammersDivision of Biostatistics and Bioinformatics, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
H Benjamin LarmanDepartment of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Susan H EshlemanDepartment of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA. seshlem@jhmi.edu.
Johns Hopkins University · USLondon School of Hygiene & Tropical Medicine · GBStellenbosch University · ZAUniversity of Zambia · ZMDesmond Tutu HIV Foundation · ZAFamily Health International 360 · USJohns Hopkins Medicine · USNational Institute of Allergy and Infectious Diseases · USSidney Kimmel Comprehensive Cancer Center · USUCLA Health · USUniversity of California, Davis · US

Funding

Stellenbosch University Clinical Trial Unit, Cape Town, South AfricaUM1AI069521 · NIAID · STELLENBOSCH UNIVERSITY · PI Shaun Barnabas, Anneke Catharina Hesseling · 2012 to 2026
$21.5M
HIV Prevention Trials Network: Network LaboratoryU01AI068613 · NIAID · JOHNS HOPKINS UNIVERSITY · PI ESHLEMAN, SUSAN H · 2006 to 2010
$16.5M
Laboratory and Statistical Development of Cross-Sectional HIV Incidence AssaysR01AI095068 · NIAID · JOHNS HOPKINS UNIVERSITY · PI ESHLEMAN, SUSAN H · 2011 to 2021
$6.6M
Design and Analysis of Displayed PeptidomesR01GM136724 · NIGMS · JOHNS HOPKINS UNIVERSITY · PI LARMAN, HARRY BENJAMIN, RUCZINSKI, INGO · 2020 to 2023
$1.9M
Medical Research Council MR/R010161/1NIAID NIH HHS R01 AI095068NIAID NIH HHS U01 AI068613NIAID NIH HHS UM1 AI069521NIGMS NIH HHS R01 GM136724NIGMS NIH HHS R01-GM136724
6 · The paper itself

Abstract

backgroundMulti-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.

methodsSamples 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).

resultsThe 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.

conclusionsThe 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.

Indexed as

HIV InfectionsAlgorithmsAnti-Retroviral AgentsBiomarkersCross-Sectional StudiesHumansImmunoenzyme TechniquesIncidenceViral LoadAnti-Retroviral AgentsBiomarkersEarly antiretroviral treatmentHIV incidence estimationRecent infectionsUniversal antiretroviral treatmentViral suppression

Identifiers

PMID36368950
PMCPMC9652879
OpenAlexW4308915429

What Socratic holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.