Evidence map›Paper›PMID 30212513›Full record

ArticlePloS one2018

Population-level HIV incidence estimates using a combination of synthetic cohort and recency biomarker approaches in KwaZulu-Natal, South Africa.

Eduard Grebe, Alex Welte, Leigh F Johnson, Gilles van Cutsem, Adrian Puren, Tom Ellman, Jean-François Etard, Consortium for the Evaluation and Performance of HIV Incidence Assays (CEPHIA), Helena Huerga

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
1.2field-weighted citation impact, top 22% 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

14 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Review
  6. The HIV Epidemic in South Africa: Key Findings from 2017 National Population-Based Survey.International journal of environmental research and public health · 2022
    Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Modeling methods for estimating HIV incidence: a mathematical review.Theoretical biology & medical modelling · 2020
    Review
  13. Article
  14. 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

9 authors at 5 institutions in 2 countries.

Eduard GrebeDST-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa.ORCID 0000-0001-7046-7245
Alex WelteDST-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa.
Leigh F JohnsonCentre for Infectious Diseases Epidemiology and Research (CIDER), University of Cape Town, Cape Town, South Africa.
Gilles van CutsemCentre for Infectious Diseases Epidemiology and Research (CIDER), University of Cape Town, Cape Town, South Africa.ORCID 0000-0002-8972-4480
Adrian PurenNational Institute for Communicable Diseases (NICD), National Health Laboratory Service, Johannesburg, South Africa.
Tom EllmanMédecins Sans Frontières, Cape Town, South Africa.
Jean-François EtardTransVIHMI, Institut de Recherche pour le Développement (IRD), Institut National de la Santé et de la Recherche Médicale (INSERM), Montpellier University, Montpellier, France.
Consortium for the Evaluation and Performance of HIV Incidence Assays (CEPHIA)
Helena HuergaEpicentre, Paris, France.ORCID 0000-0003-0302-9063
Stellenbosch University · ZAUniversity of Cape Town · ZAInserm · FRMédecins Sans Frontières · ZANational Health Laboratory Service · ZA

Funding

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
Unsolicited R24 for the CFAR-Network of Integrated Clinical Sciences, CNICSR24AI067039 · NIAID · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI Michael S. Saag · 2006 to 2026
$99.4M
Virology CoreP30AI027763 · NIAID · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI PETER W HUNT · 1988 to 2026
$93.6M
Transmission ProbabilityP01AI074621 · NIAID · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI RICHMAN, DOUGLAS D · 2008 to 2012
$23.1M
Primary Infection Resource Consortium (PIRC)R24AI106039 · NIAID · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI LITTLE, SUSAN JANET · 2013 to 2021
$23.1M
Southern California Primary Infection ProgramU01AI043638 · NIAID · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI RICHMAN, DOUGLAS D · 1998 to 2010
$21.3M
Transmission and Persistence of CTL and Drug Resistance MutationsP01AI071713 · NIAID · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI HECHT, FREDERICK M · 2007 to 2011
$14.3M
Laboratory and Statistical Development of Cross-Sectional HIV Incidence AssaysR01AI095068 · NIAID · JOHNS HOPKINS UNIVERSITY · PI ESHLEMAN, SUSAN H · 2011 to 2021
$6.6M
Host Factors Influencing HIV Viral Load and Infectivity in SemenR01HD074511 · NICHD · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI PILCHER, CHRISTOPHER D · 2012 to 2016
$3.1M
A Pilot of Intervention to Promote Acute HIV Testing by Ambulatory Care ProvidersR34MH096606 · NIMH · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI PILCHER, CHRISTOPHER D · 2012 to 2014
$693k
NIAID NIH HHS P01 AI071713NIAID NIH HHS P01 AI074621NIAID NIH HHS P30 AI027763NIAID NIH HHS R01 AI095068NIAID NIH HHS R24 AI067039NIAID NIH HHS R24 AI106039NIAID NIH HHS U01 AI043638NIAID NIH HHS UM1 AI068613NICHD NIH HHS R01 HD074511NIMH NIH HHS R34 MH096606
6 · The paper itself

Abstract

introductionThere is a notable absence of consensus on how to generate estimates of population-level incidence. Incidence is a considerably more sensitive indicator of epidemiological trends than prevalence, but is harder to estimate. We used a novel hybrid method to estimate HIV incidence by age and sex in a rural district of KwaZulu-Natal, South Africa.

methodsOur novel method uses an 'optimal weighting' of estimates based on an implementation of a particular 'synthetic cohort' approach (interpreting the age/time structure of prevalence, in conjunction with estimates of excess mortality) and biomarkers of 'recent infection' (combining Lag-Avidity, Bio-Rad Avidity and viral load results to define recent infection, and adapting the method for age-specific incidence estimation). Data were obtained from a population-based cross-sectional HIV survey conducted in Mbongolwane and Eshowe health service areas in 2013.

resultsUsing the combined method, we find that age-specific HIV incidence in females rose rapidly during adolescence, from 1.33 cases/100 person-years (95% CI: 0.98,1.67) at age 15 to a peak of 5.01/100PY (4.14,5.87) at age 23. In males, incidence was lower, 0.34/100PY (0.00-0.74) at age 15, and rose later, peaking at 3.86/100PY (2.52-5.20) at age 30. Susceptible population-weighted average incidence in females aged 15-29 was estimated at 3.84/100PY (3.36-4.40), in males aged 15-29 at 1.28/100PY (0.68-1.50) and in all individuals aged 15-29 at 2.55/100PY (2.09-2.76). Using the conventional recency biomarker approach, we estimated HIV incidence among females aged 15-29 at 2.99/100PY (1.79-4.36), among males aged 15-29 at 0.87/100PY (0.22-1.60) and among all individuals aged 15-59 at 1.66/100PY (1.13-2.27). DISCUSSION: HIV incidence was very high in women aged 15-30, peaking in the early 20s. Men had lower incidence, which peaked at age 30. The estimates obtained from the hybrid method are more informative than those produced by conventional analysis of biomarker data, and represents a more optimal use of available data than either the age-continuous biomarker or synthetic cohort methods alone. The method is mainly useful at younger ages, where excess mortality is low and uncertainty in the synthetic cohort estimates is reasonably small.

conclusionApplication of this method to large-scale population-based HIV prevalence surveys is likely to result in improved incidence surveillance over methods currently in wide use. Reasonably accurate and precise age-specific estimates of incidence are important to target better prevention, diagnosis and care strategies.

Indexed as

AdolescentAdultBiomarkersCross-Sectional StudiesFemaleHIV InfectionsHumansIncidenceMaleModels, TheoreticalSouth AfricaYoung AdultBiomarkers

Identifiers

PMID30212513
PMCPMC6136757
OpenAlexW2951989761

What OpenQuestion holds

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LicenceCC BY
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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.