Evidence map›Paper›PMID 31455229›Full record

Trial reportBMC infectious diseases2019

Assessing rates and contextual predictors of 5-year mortality among HIV-infected and HIV-uninfected individuals following HIV testing in Durban, South Africa.

Ingrid V Bassett, Ai Xu, Janet Giddy, Laura M Bogart, Andrew Boulle, Lucia Millham, Elena Losina, Robert A Parker

Registry-linked trialAbstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in BMC infectious diseases, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01188941 (Linkage and Retention), which is not on this map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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.

NCT01188941 nacompletednot on this map

Linkage and Retention: A Randomized Trial to Optimize HIV/TB Care in South Africa

TypeinterventionalSponsorMassachusetts General HospitalRan2010 to 2015Enrolled1,899ConditionsHIVArmsHealth System Navigator
3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. 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

8 authors.

Ingrid V BassettDivision of Infectious Diseases, Massachusetts General Hospital, 100 Cambridge Street, 16th Floor, Boston, MA, 02114, USA. ibassett@mgh.harvard.edu.ORCID http://orcid.org/0000-0001-6920-6080
Ai XuBiostatistics Center, Massachusetts General Hospital, Boston, MA, USA.
Janet GiddyMcCord Hospital, Durban, South Africa.
Laura M BogartRAND Corporation, Santa Monica, CA, USA.
Andrew BoulleCentre for Infectious Diseases, Epidemiology and Research, School of Public Health and Family Medicine, University of Cape Town, Cape Town, South Africa.
Lucia MillhamMedical Practice Evaluation Center, Massachusetts General Hospital, Boston, MA, USA.
Elena LosinaMedical Practice Evaluation Center, Massachusetts General Hospital, Boston, MA, USA.
Robert A ParkerMedical Practice Evaluation Center, Massachusetts General Hospital, Boston, MA, USA.

Funding

UCLA Rapid, Relevant, Rigorous Implementation Science HubP30MH058107 · NIMH · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Nina Thawata Harawa · 1997 to 2026
$53.5M
Linkage & Retention: A Randomized Trial to Optimize HIV/TB Care in South AfricaR01MH090326 · NIMH · MASSACHUSETTS GENERAL HOSPITAL · PI BASSETT, INGRID VALERIE · 2010 to 2014
$2.9M
What happens after diagnosis: Characterizing long-term HIV care trajectories and mortality in South AfricaR01MH108427 · NIMH · MASSACHUSETTS GENERAL HOSPITAL · PI BASSETT, INGRID VALERIE · 2016 to 2019
$1.7M
NIMH NIH HHS P30 MH058107NIMH NIH HHS R01 MH090326NIMH NIH HHS R01 MH108427Weissman Family MGH Research Scholar Award N/A
6 · The paper itself

Abstract

backgroundLittle is known about contextual factors that predict long-term mortality following HIV testing in resource-limited settings. We evaluated the impact of contextual factors on 5-year mortality among HIV-infected and HIV-uninfected individuals in Durban, South Africa.

methodsWe used data from the Sizanani trial (NCT01188941) in which adults (≥18y) were enrolled prior to HIV testing at 4 outpatient sites. We ascertained vital status via the South African National Population Register. We used random survival forests to identify the most influential predictors of time to death and incorporated these into a Cox model that included age, gender, HIV status, CD4 count, healthcare usage, health facility type, mental health, and self-identified barriers to care (i.e., service delivery, financial, logistical, structural and perceived health).

resultsAmong 4816 participants, 39% were HIV-infected. Median age was 31y and 49% were female. 380 of 2508 with survival information (15%) died during median follow-up of 5.8y. For both HIV-infected and HIV-uninfected participants, each additional barrier domain increased the HR of dying by 11% (HR 1.11, 95% CI 1.05-1.18). Every 10-point increase in mental health score decreased the HR by 7% (HR 0.93, 95% CI 0.89-0.97). The hazard ratio (HR) for death of HIV-infected versus HIV-uninfected varied by age: HR of 6.59 (95% CI: 4.79-9.06) at age 20 dropping to a HR of 1.13 (95% CI: 0.86-1.48) at age 60.

conclusionsIndependent of serostatus, more self-identified barrier domains and poorer mental health increased mortality risk. Additionally, the impact of HIV on mortality was most pronounced in younger persons. These factors may be used to identify high-risk individuals requiring intensive follow up, regardless of serostatus.

trial registrationClinical Trials.gov Identifier NCT01188941. Registered 26 August 2010.

Indexed as

Health Knowledge, Attitudes, PracticeAdultCD4 Lymphocyte CountFemaleHIV InfectionsHumansMaleMortalityProportional Hazards ModelsRandom AllocationSocioeconomic FactorsSouth AfricaBarriers to careHIV infectionMortalityPredictors of mortality

Identifiers

PMID31455229
PMCPMC6712739

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Registered trials

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.