Evidence map›Paper›PMID 29232366›Full record

ArticlePLoS medicine2017

Estimating the impact of antiretroviral treatment on adult mortality trends in South Africa: A mathematical modelling study.

Leigh F Johnson, Margaret T May, Rob E Dorrington, Morna Cornell, Andrew Boulle, Matthias Egger, Mary-Ann Davies

Open access · goldAbstract read
In one paragraph

Article in PLoS medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 87 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
87citing papers in PubMed, 1 pooled it
7.3field-weighted citation impact, top 2% 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

87 citing papers in PubMed, 1 synthesis or guideline pooled it, 154 citations in OpenAlex.

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27 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors at 3 institutions in 3 countries.

Leigh F JohnsonCentre for Infectious Disease Epidemiology and Research, University of Cape Town, Cape Town, South Africa.ORCID http://orcid.org/0000-0002-2717-011X
Margaret T MaySchool of Social and Community Medicine, University of Bristol, Bristol, United Kingdom.ORCID http://orcid.org/0000-0002-9733-1003
Rob E DorringtonCentre for Actuarial Research, University of Cape Town, Cape Town, South Africa.
Morna CornellCentre for Infectious Disease Epidemiology and Research, University of Cape Town, Cape Town, South Africa.ORCID http://orcid.org/0000-0001-7149-8799
Andrew BoulleCentre for Infectious Disease Epidemiology and Research, University of Cape Town, Cape Town, South Africa.
Matthias EggerCentre for Infectious Disease Epidemiology and Research, University of Cape Town, Cape Town, South Africa.
Mary-Ann DaviesCentre for Infectious Disease Epidemiology and Research, University of Cape Town, Cape Town, South Africa.
University of Cape Town · ZAUniversity of Bern · CHUniversity of Bristol · GB

Funding

Observational Antiretroviral Studies In Southern Africa (OASIS) CollaborationU01AI069924 · NIAID · UNIVERSITAT BERN · PI Cleophas Chimbetete, Mary-Ann Davies · 2006 to 2026
$55.9M
NIAID NIH HHS U01 AI069924
6 · The paper itself

Abstract

backgroundSubstantial reductions in adult mortality have been observed in South Africa since the mid-2000s, but there has been no formal evaluation of how much of this decline is attributable to the scale-up of antiretroviral treatment (ART), as previous models have not been calibrated to vital registration data. We developed a deterministic mathematical model to simulate the mortality trends that would have been expected in the absence of ART, and with earlier introduction of ART. METHODS AND

findingsModel estimates of mortality rates in ART patients were obtained from the International Epidemiology Databases to Evaluate AIDS-Southern Africa (IeDEA-SA) collaboration. The model was calibrated to HIV prevalence data (1997-2013) and mortality data from the South African vital registration system (1997-2014), using a Bayesian approach. In the 1985-2014 period, 2.70 million adult HIV-related deaths occurred in South Africa. Adult HIV deaths peaked at 231,000 per annum in 2006 and declined to 95,000 in 2014, a reduction of 74.7% (95% CI: 73.3%-76.1%) compared to the scenario without ART. However, HIV mortality in 2014 was estimated to be 69% (95% CI: 46%-97%) higher in 2014 (161,000) if the model was calibrated only to HIV prevalence data. In the 2000-2014 period, the South African ART programme is estimated to have reduced the cumulative number of HIV deaths in adults by 1.72 million (95% CI: 1.58 million-1.84 million) and to have saved 6.15 million life years in adults (95% CI: 5.52 million-6.69 million). This compares with a potential saving of 8.80 million (95% CI: 7.90 million-9.59 million) life years that might have been achieved if South Africa had moved swiftly to implement WHO guidelines (2004-2013) and had achieved high levels of ART uptake in HIV-diagnosed individuals from 2004 onwards. The model is limited by its reliance on all-cause mortality data, given the lack of reliable cause-of-death reporting, and also does not allow for changes over time in tuberculosis control programmes and ART effectiveness.

conclusionsART has had a dramatic impact on adult mortality in South Africa, but delays in the rollout of ART, especially in the early stages of the ART programme, have contributed to substantial loss of life. This is the first study to our knowledge to calibrate a model of ART impact to population-level recorded death data in Africa; models that are not calibrated to population-level death data may overestimate HIV-related mortality.

Indexed as

AdultAntiretroviral Therapy, Highly ActiveBayes TheoremFemaleHIV InfectionsHumansMaleMiddle AgedModels, TheoreticalMortalitySouth AfricaYoung Adult

Identifiers

PMID29232366
PMCPMC5726614
OpenAlexW2773346955

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.