Evidence map›Paper›PMID 27282146›Full record

ArticleGlobal health action2016

Prospects for HIV control in South Africa: a model-based analysis.

Leigh F Johnson, Calvin Chiu, Landon Myer, Mary-Ann Davies, Rob E Dorrington, Linda-Gail Bekker, Andrew Boulle, Gesine Meyer-Rath

Open access · goldAbstract read
In one paragraph

Article in Global health action, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed
6.9field-weighted citation impact, top 3% 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

36 citing papers in PubMed, 53 citations in OpenAlex.

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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

8 authors at 4 institutions in 2 countries.

Leigh F JohnsonCentre for Infectious Disease Epidemiology and Research, School of Public Health and Family Medicine, University of Cape Town, Cape Town, South Africa; Leigh.Johnson@uct.ac.za.
Calvin ChiuHealth Economics and Epidemiology Research Office, Department of Internal Medicine, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
Landon MyerCentre for Infectious Disease Epidemiology and Research, School of Public Health and Family Medicine, University of Cape Town, Cape Town, South Africa.
Mary-Ann DaviesCentre for Infectious Disease Epidemiology and Research, School of Public Health and Family Medicine, University of Cape Town, Cape Town, South Africa.
Rob E DorringtonCentre for Actuarial Research, University of Cape Town, Cape Town, South Africa.
Linda-Gail BekkerDesmond Tutu HIV Centre, University of Cape Town, Cape Town, South Africa.
Andrew BoulleCentre for Infectious Disease Epidemiology and Research, School of Public Health and Family Medicine, University of Cape Town, Cape Town, South Africa.
Gesine Meyer-RathHealth Economics and Epidemiology Research Office, Department of Internal Medicine, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
University of Cape Town · ZACenter for Global Health · USDesmond Tutu HIV Foundation · ZAUniversity of the Witwatersrand · ZA

Funding

CHAMPS: Choices for Adolescent Methods of Prevention in South AfricaR01AI094586 · NIAID · DESMOND TUTU HIV FOUNDATION · PI BEKKER, LINDA-GAIL GAIL · 2011 to 2014
$2.5M
NIAID NIH HHS 1R01AI094586-01NIAID NIH HHS R01 AI094586
6 · The paper itself

Abstract

backgroundThe goal of virtual elimination of horizontal and mother-to-child HIV transmission in South Africa (SA) has been proposed, but there have been few systematic investigations of which interventions are likely to be most critical to reducing HIV incidence.

objectiveThis study aims to evaluate SA's potential to achieve virtual elimination targets and to identify which interventions will be most critical to achieving HIV incidence reductions.

designA mathematical model was developed to simulate the population-level impact of different HIV interventions in SA. Probability distributions were specified to represent uncertainty around 32 epidemiological parameters that could be influenced by interventions, and correlation coefficients (r) were calculated to assess the sensitivity of the adult HIV incidence rates and mother-to-child transmission rates (2015-2035) to each epidemiological parameter.

resultsHIV incidence in SA adults (ages 15-49) is expected to decline from 1.4% in 2011-2012 to 0.29% by 2035 (95% CI: 0.10-0.62%). The parameters most strongly correlated with future adult HIV incidence are the rate of viral suppression after initiating antiretroviral treatment (ART) (r=-0.56), the level of condom use in non-marital relationships (r=-0.40), the phase-in of intensified risk-reduction counselling for HIV-positive adults (r=0.29), the uptake of medical male circumcision (r=-0.24) and the phase-in of universal ART eligibility (r=0.22). The paediatric HIV parameters most strongly associated with mother-to-child transmission rates are the relative risk of transmission through breastfeeding when the mother is receiving ART (r=0.70) and the rate of ART initiation during pregnancy (r=-0.16).

conclusionsThe virtual elimination target of a 0.1% incidence rate in adults will be difficult to achieve. Interventions that address the infectiousness of patients after ART initiation will be particularly critical to achieving long-term HIV incidence declines in South Africa.

Indexed as

Models, TheoreticalAdultAnti-HIV AgentsChildFemaleHIV InfectionsHumansIncidenceInfectious Disease Transmission, VerticalMalePregnancyPregnancy Complications, InfectiousSouth AfricaAnti-HIV AgentsHIV/AIDSmathematical modelSouth Africa

Identifiers

PMID27282146
PMCPMC4901512
OpenAlexW2417083559

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.