Evidence map›Paper›PMID 34261098›Full record

ArticleAIDS (London, England)2021

Inferring population HIV incidence trends from surveillance data of recent HIV infection among HIV testing clients.

Arnaud Godin, Jeffrey W Eaton, Katia Giguère, Kimberly Marsh, Leigh F Johnson, Andreas Jahn, Francisco Mbofana, Eboi Ehui, Mathieu Maheu-Giroux

Open access · greenAbstract read
In one paragraph

Article in AIDS (London, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
0.6field-weighted citation impact, top 30% 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

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.

  1. Pooled it
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  3. Article
  4. Article
  5. 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 7 institutions in 6 countries.

Arnaud GodinDepartment of Epidemiology, Biostatistics, and Occupational Health, School of Population and Global Health, Faculty of Medicine, McGill University, Montréal, Quebec, Canada.
Jeffrey W EatonMRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK.
Katia GiguèreCentre de recherche du CHUM, Université de Montréal, Montréal, Quebec, Canada.
Kimberly MarshHealth Protection Scotland, Glasgow, UK.
Leigh F JohnsonCentre for Infectious Disease Epidemiology and Research, University of Cape Town, Cape Town, South Africa.
Andreas JahnDepartment for HIV and AIDS, Ministry of Health and Population, Lilongwe, Malawi.
Francisco MbofanaConselho Nacional de Combate ao SIDA, Maputo, Mozambique.
Eboi EhuiProgramme National de lutte contre le SIDA, Abidjan, Côte d'Ivoire.
Mathieu Maheu-GirouxDepartment of Epidemiology, Biostatistics, and Occupational Health, School of Population and Global Health, Faculty of Medicine, McGill University, Montréal, Quebec, Canada.
McGill University · CACentre Hospitalier de l’Université de Montréal · CAHealth Protection Scotland · GBImperial College London · GBNational Directorate of Geology · MZUniversity of Washington · USWellcome Centre for Infectious Diseases Research in Africa · ZA

Funding

Statistical Models for Estimating and Projecting HIV/AIDS EpidemicsR01AI136664 · NIAID · PENNSYLVANIA STATE UNIVERSITY, THE · PI BAO, LE · 2017 to 2021
$3.6M
CIHRMedical Research Council MR/R015600/1NIAID NIH HHS R01 AI136664
6 · The paper itself

Abstract

backgroundMeasuring recent HIV infections from routine surveillance systems could allow timely and granular monitoring of HIV incidence patterns. We evaluated the relationship of two recent infection indicators with alternative denominators to true incidence patterns.

methodsWe used a mathematical model of HIV testing behaviours, calibrated to population-based surveys and HIV testing services programme data, to estimate the number of recent infections diagnosed annually from 2010 to 2019 in Côte d'Ivoire, Malawi, and Mozambique. We compared two different denominators to interpret recency data: those at risk of HIV acquisition (HIV-negative tests and recent infections) and all people testing HIV positive. Sex and age-specific longitudinal trends in both interpretations were then compared with modelled trends in HIV incidence, testing efforts and HIV positivity among HIV testing services clients.

resultsOver 2010-2019, the annual proportion of the eligible population tested increased in all countries, while positivity decreased. The proportion of recent infections among those at risk of HIV acquisition decreased, similar to declines in HIV incidence among adults (≥15 years old). Conversely, the proportion of recent infections among HIV-positive tests increased. The female-to-male ratio of the proportion testing recent among those at risk was closer to 1 than the true incidence sex ratio.

conclusionThe proportion of recent infections among those at risk of HIV acquisition is more indicative of HIV incidence than the proportion among HIV-positive tests. However, interpreting the observed patterns as surrogate measures for incidence patterns may still be confounded by different HIV testing rates between population groups or over time.

Indexed as

HIV InfectionsAdolescentAdultCote d'IvoireFemaleHIV TestingHumansIncidenceMaleModels, Theoretical

Identifiers

PMID34261098
PMCPMC8631145
OpenAlexW3182824832

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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 OpenQuestion graph.