Evidence map›Paper›PMID 24968135›Full record

ArticlePloS one2014

A comparison of two measures of HIV diversity in multi-assay algorithms for HIV incidence estimation.

Matthew M Cousins, Jacob Konikoff, Devin Sabin, Leila Khaki, Andrew F Longosz, Oliver Laeyendecker, Connie Celum, Susan P Buchbinder, George R Seage, Gregory D Kirk and 12 more

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
2.7field-weighted citation impact, top 9% 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

10 citing papers in PubMed, 1 synthesis or guideline pooled it, 39 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. Diagnosis of Human Immunodeficiency Virus Infection.Clinical microbiology reviews · 2019
    Review
  5. Article
  6. Observational
  7. Article
  8. Observational
  9. Article
  10. Review
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

22 authors at 12 institutions in 1 country.

Matthew M CousinsDepartment of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.
Jacob KonikoffDepartment of Biostatistics, School of Public Health, University of California Los Angeles, Los Angeles, California, United States of America.
Devin SabinDepartment of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.
Leila KhakiDepartment of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.
Andrew F LongoszNational Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, United States of America.
Oliver LaeyendeckerNational Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, United States of America; Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.
Connie CelumDepartments of Global Health and Medicine, University of Washington, Seattle, Washington, United States of America.
Susan P BuchbinderBridge HIV, San Francisco Department of Health, San Francisco, California, United States of America; Departments of Epidemiology and Medicine, University of California San Francisco, San Francisco, California, United States of America.
George R SeageDepartment of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, United States of America.
Gregory D KirkDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America.
Richard D MooreDepartment of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.
Shruti H MehtaDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America.
Joseph B MargolickDepartment of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America.
Joelle BrownDepartment of Epidemiology, School of Public Health, University of California Los Angeles, Los Angeles, California, United States of America; Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, United States of America.
Kenneth H MayerThe Fenway Institute/Beth Israel Deaconess Medical Center/Harvard Medical School, Boston, Massachusetts, United States of America.
Beryl A KobinLaboratory of Infectious Disease Prevention, New York Blood Center, New York, New York, United States of America.
Darrell WheelerGraduate School of Social Work, Loyola University Chicago, Chicago, Illinois, United States of America.
Jessica E JustmanDepartments of Epidemiology and Medicine, Columbia University, New York, New York, United States of America.
Sally L HodderDepartment of Medicine, Division of Infectious Diseases, New Jersey Medical School, Newark, New Jersey, United States of America.
Thomas C QuinnNational Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, United States of America; Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.
Ron BrookmeyerDepartment of Biostatistics, School of Public Health, University of California Los Angeles, Los Angeles, California, United States of America.
Susan H EshlemanDepartment of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.
Johns Hopkins University · USJohns Hopkins Medicine · USNational Institute of Allergy and Infectious Diseases · USUniversity of California, Los Angeles · USUniversity of California, San Francisco · USColumbia University · USFenway Health · USHarvard University · USLoyola University Chicago · USNew York Blood Center · USRutgers New Jersey Medical SchoolUniversity of Washington · US

Funding

LOC: HIV Prevention Trials NetworkUM1AI068619 · NIAID · FAMILY HEALTH INTERNATIONAL · PI Sinead Delany-Moretlwe, RAPHAEL J LANDOVITZ · 2011 to 2026
$779.5M
SDMC: HPTN 084 Pregnancy SupplementUM1AI068617 · NIAID · FRED HUTCHINSON CANCER RESEARCH CENTER · PI Elizabeth Renata Brown, Deborah J Donnell · 2011 to 2026
$204.9M
LC: HIV Prevention Trials Network - Laboratory Support for the SARS-CoV-2 Seroprevalence Study (CoVPN 5002)UM1AI068613 · NIAID · JOHNS HOPKINS UNIVERSITY · PI SUSAN H ESHLEMAN, Mark A Marzinke · 2011 to 2026
$100.2M
Institute for Clinical and Translational Research (UL1)UL1RR025005 · NCRR · JOHNS HOPKINS UNIVERSITY · PI FORD, DANIEL ERNEST · 2007 to 2011
$75.8M
NATURAL HISTORY OF AIDS IN HOMOSEXUAL MENU01AI035040 · NIAID · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI DETELS, ROGER, MARTINEZ-MAZA, OTONIEL · 1993 to 2018
$75.2M
University of Pittsburgh Multicenter AIDS Cohort Study (MACS)U01AI035041 · NIAID · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI MARTINSON, JEREMY JAMES, RINALDO, CHARLES R · 1993 to 2018
$71.4M
MULTICENTER AIDS COHORT STUDYU01AI035039 · NIAID · NORTHWESTERN UNIVERSITY AT CHICAGO · PI WOLINSKY, STEVEN M · 1993 to 2018
$66.4M
Multicenter Aids Cohort Study- Part B (Baltimore Site)U01AI035042 · NIAID · JOHNS HOPKINS UNIVERSITY · PI BROWN, TODD T, MARGOLICK, JOSEPH B. · 1993 to 2018
$61.5M
Ujima Mentoring ProgramP30MH062246 · NIMH · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Sheri Ann Lippman · 2001 to 2026
$57.4M
Columbia Partnership for Prevention and Control of HIV/AIDS Clinical Trials UnitUM1AI069470 · NIAID · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI MAGDALENA ELZBIETA SOBIESZCZYK · 2012 to 2026
$44.8M
San Francisco Vaccine and Prevention UnitUM1AI069496 · NIAID · PUBLIC HEALTH FOUNDATION ENTERPRISES · PI Susan Buchbinder, Diane V Havlir · 2012 to 2026
$30.5M
The AIDS Linked to the Intravenous Experience (ALIVE) StudyU01DA036297 · NIDA · JOHNS HOPKINS UNIVERSITY · PI Gregory D Kirk, Shruti H Mehta · 2014 to 2026
$28.9M
Intramural NIH HHSNCRR NIH HHS UL1 RR025005NCRR NIH HHS UL1-RR025005NIAAA NIH HHS R01 AA016893NIAAA NIH HHS R01-AA016893NIAID NIH HHS AI-45202NIAID NIH HHS N01 AI045200NIAID NIH HHS N01-AI35176NIAID NIH HHS N01-AI-45200NIAID NIH HHS R01 AI095068NIAID NIH HHS R01-AI095068NIAID NIH HHS U01 AI035039NIAID NIH HHS U01 AI035040NIAID NIH HHS U01 AI035041NIAID NIH HHS U01 AI035042NIAID NIH HHS U01 AI035043NIAID NIH HHS U01-AI35039NIAID NIH HHS U01-AI35040NIAID NIH HHS U01-AI35041NIAID NIH HHS U01-AI35042NIAID NIH HHS U01-AI35043NIAID NIH HHS UM1 AI035043NIAID NIH HHS UM1 AI068613NIAID NIH HHS UM1-AI068613NIAID NIH HHS UM1 AI068617NIAID NIH HHS UM1-AI068617NIAID NIH HHS UM1 AI068619NIAID NIH HHS UM1-AI068619NIAID NIH HHS UM1 AI069470NIAID NIH HHS UM1 AI069496NICHD NIH HHS R25 HD045810NIDA NIH HHS R01 DA004334NIDA NIH HHS R01 DA011602NIDA NIH HHS R01-DA011602NIDA NIH HHS R01 DA012568NIDA NIH HHS R01-DA-04334NIDA NIH HHS R01-DA12568NIDA NIH HHS U01 DA036297NIDA NIH HHS U01 DA036935NIGMS NIH HHS T32 GM008752NIMH NIH HHS P30 MH062246
6 · The paper itself

Abstract

backgroundMulti-assay algorithms (MAAs) can be used to estimate HIV incidence in cross-sectional surveys. We compared the performance of two MAAs that use HIV diversity as one of four biomarkers for analysis of HIV incidence.

methodsBoth MAAs included two serologic assays (LAg-Avidity assay and BioRad-Avidity assay), HIV viral load, and an HIV diversity assay. HIV diversity was quantified using either a high resolution melting (HRM) diversity assay that does not require HIV sequencing (HRM score for a 239 base pair env region) or sequence ambiguity (the percentage of ambiguous bases in a 1,302 base pair pol region). Samples were classified as MAA positive (likely from individuals with recent HIV infection) if they met the criteria for all of the assays in the MAA. The following performance characteristics were assessed: (1) the proportion of samples classified as MAA positive as a function of duration of infection, (2) the mean window period, (3) the shadow (the time period before sample collection that is being assessed by the MAA), and (4) the accuracy of cross-sectional incidence estimates for three cohort studies.

resultsThe proportion of samples classified as MAA positive as a function of duration of infection was nearly identical for the two MAAs. The mean window period was 141 days for the HRM-based MAA and 131 days for the sequence ambiguity-based MAA. The shadows for both MAAs were <1 year. Both MAAs provided cross-sectional HIV incidence estimates that were very similar to longitudinal incidence estimates based on HIV seroconversion.

conclusionsMAAs that include the LAg-Avidity assay, the BioRad-Avidity assay, HIV viral load, and HIV diversity can provide accurate HIV incidence estimates. Sequence ambiguity measures obtained using a commercially-available HIV genotyping system can be used as an alternative to HRM scores in MAAs for cross-sectional HIV incidence estimation.

Indexed as

Genetic VariationCross-Sectional StudiesGenotypeGenotyping TechniquesHIV-1HIV InfectionsHumansImmunoenzyme TechniquesIncidenceReproducibility of ResultsViral Load

Identifiers

PMID24968135
PMCPMC4072769
OpenAlexW2150505558

What OpenQuestion holds

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Registered trials

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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 OpenQuestion graph.