Evidence map›Paper›PMID 28252526›Full record

ArticleAIDS (London, England)2017

Determinants of time from HIV infection to linkage-to-care in rural KwaZulu-Natal, South Africa.

Mathieu Maheu-Giroux, Frank Tanser, Marie-Claude Boily, Deenan Pillay, Serene A Joseph, Till Bärnighausen

Abstract read
In one paragraph

Article in AIDS (London, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
–field-weighted citation impact
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

23 citing papers in PubMed.

  1. Trial
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  5. Knowledge of HIV and/or AIDS and HIV testing services among young men in South Africa.African journal of primary health care & family medicine · 2023
    Article
  6. Article
  7. Built Environment and HIV Linkage to Care in Rural South Africa.Community health equity research & policy · 2023
    Article
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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

6 authors.

Mathieu Maheu-GirouxaDepartment of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montréal, Québec, Canada bAfrica Health Research Institute (AHRI), Somkhele cSchool of Nursing and Public Health dCentre for the AIDS Programme Research in South Africa - CAPRISA, University of KwaZulu-Natal, Durban, South Africa eDepartment of Infectious Disease Epidemiology, Imperial College London, St Mary's Hospital fDivision of Infection and Immunity, University College London, London, UK gDepartment of Epidemiology and Public Health, Swiss Tropical and Public Health Institute hUniversity of Basel, Basel, Switzerland iDepartment of Global Health & Population, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA jInstitute of Public Health, University of Heidelberg, Heidelberg, Germany.
Frank Tanser
Marie-Claude Boily
Deenan Pillay
Serene A Joseph
Till Bärnighausen

Funding

New Methods for the design and evaluation of large HIV prevention interventionsR01AI112339 · NIAID · YALE UNIVERSITY · PI SPIEGELMAN, DONNA L · 2014 to 2017
$2.7M
Causal Pathways to population health impact of HIV antiretroviral treatmentR01HD084233 · NICHD · UNIVERSITY OF KWAZULU-NATAL · PI BARNIGHAUSEN, TILL, TANSER, FRANK COURTENEY · 2015 to 2021
$2.3M
Can HIV Hot-Spots be eradicated? An intervention to decrease HIV transmission to young women in rural KwaZulu-Natal South AfricaR01AI124389 · NIAID · UNIVERSITY OF KWAZULU-NATAL · PI BARNIGHAUSEN, TILL, TANSER, FRANK COURTENEY · 2016 to 2020
$2.1M
CIHRNIAID NIH HHS R01 AI112339NIAID NIH HHS R01 AI124389NICHD NIH HHS R01 HD084233Wellcome Trust
6 · The paper itself

Abstract

objectiveTo estimate time from HIV infection to linkage-to-care and its determinants. Linkage-to-care is usually assessed using the date of HIV diagnosis as the starting point for exposure time. However, timing of diagnosis is likely endogenous to linkage, leading to bias in linkage estimation.

designWe used longitudinal HIV serosurvey data from a large population-based HIV incidence cohort in KwaZulu-Natal (2004-2013) to estimate time of HIV infection. We linked these data to patient records from a public-sector HIV treatment and care program to determine time from infection to linkage (defined using the date of the first CD4 cell count).

methodsWe used Cox proportional hazards models to estimate time from infection to linkage and the effects of the following covariates on this time: sex, age, education, food security, socioeconomic status, area of residence, distance to clinics, knowledge of HIV status, and whether other household members have initiated antiretroviral therapy.

resultsWe estimated that it would take an average of 4.9 years for 50% of HIV seroconverters to be linked to care (95% confidence intervals: 4.2-5.7). Among all cohort members who were linked to care, the median CD4 cell count at linkage was 350 cells/μl (95% confidence interval: 330-380). Men and participants aged less than 30 years were found to have the slowest rates of linkage-to-care. Time to linkage became shorter over calendar time.

conclusionAverage time from HIV infection to linkage-to-care is long and needs to be reduced to ensure that HIV treatment-as-prevention policies are effective. Targeted interventions for men and young individuals have the largest potential to improve linkage rates.

Indexed as

Delayed DiagnosisAdolescentAdultFemaleHIV InfectionsHumansLongitudinal StudiesMaleMiddle AgedRisk FactorsRural PopulationSeroepidemiologic StudiesSouth AfricaTime FactorsYoung Adult

Identifiers

PMID28252526
PMCPMC5859256

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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.