Evidence map›Paper›PMID 29359533›Full record

ArticleJournal of the International AIDS Society2018

The City of Johannesburg can end AIDS by 2030: modelling the impact of achieving the Fast-Track targets and what it will take to get there.

Robyn M Stuart, Nicole Fraser-Hurt, Cliff C Kerr, Emily Mabusela, Vusi Madi, Fredrika Mkhwanazi, Yogan Pillay, Peter Barron, Batanayi Muzah, Thulani Matsebula and 2 more

Open access · goldAbstract read
In one paragraph

Article in Journal of the International AIDS Society, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
2.5field-weighted citation impact, top 11% 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

11 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 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

12 authors at 8 institutions in 4 countries.

Robyn M StuartDepartment of Mathematical Sciences, University of Copenhagen, Copenhagen, Denmark.ORCID 0000-0001-5482-9584
Nicole Fraser-HurtThe World Bank, Washington DC, USA.
Cliff C KerrBurnet Institute, Melbourne, Australia.
Emily MabuselaDepartment of Health, Gauteng Province, Johannesburg, South Africa.
Vusi MadiDepartment of Health, Gauteng Province, Johannesburg, South Africa.
Fredrika MkhwanaziDepartment of Health, Johannesburg Health District, Johannesburg, South Africa.
Yogan PillayNational Department of Health, Pretoria, South Africa.
Peter BarronSchool of Public Health, University of the Witwatersrand, Johannesburg, South Africa.
Batanayi MuzahNational Department of Health, Pretoria, South Africa.
Thulani MatsebulaThe World Bank, Washington DC, USA.
Marelize GorgensThe World Bank, Washington DC, USA.
David P WilsonBurnet Institute, Melbourne, Australia.
World Bank · USDepartment of Health · ZANational Institute for Occupational Health · ZAAnova Health Institute · ZABurnet Institute · AUThe University of Sydney · AUUniversity of Copenhagen · DKUniversity of the Witwatersrand · ZA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionIn 2014, city leaders from around the world endorsed the Paris Declaration on Fast-Track Cities, pledging to achieve the 2020 and 2030 HIV targets championed by UNAIDS. The City of Johannesburg - one of South Africa's metropolitan municipalities and also a health district - has over 600,000 people living with HIV (PLHIV), more than any other city worldwide. We estimate what it would take in terms of programmatic targets and costs for the City of Johannesburg to meet the Fast-Track targets, and demonstrate the impact that this would have.

methodsWe applied the Optima HIV epidemic and resource allocation model to demographic, epidemiological and behavioural data on 26 sub-populations in Johannesburg. We used data on programme costs and coverage to produce baseline projections. We calculated how many people must be diagnosed, put onto treatment and maintained with viral suppression to achieve the 2020 and 2030 targets. We also estimated how treatment needs - and therefore fiscal commitments - could be reduced if the treatment targets are combined with primary HIV prevention interventions (voluntary medical male circumcision (VMMC), an expanded condom programme, and comprehensive packages for female sex workers (FSW) and young females).

resultsIf current programmatic coverage were maintained, Johannesburg could expect 303,000 new infections and 96,000 AIDS-related deaths between 2017 and 2030 and 769,000 PLHIV by 2030. Achieving the Fast-Track targets would require an additional 135,000 diagnoses and 232,000 people on treatment by 2020 (an increase in around 80% over 2016 treatment numbers), but would avert 176,000 infections and 56,500 deaths by 2030. Assuming stable ART unit costs, this would require ZAR 29 billion (USD 2.15 billion) in cumulative treatment investments over the 14 years to 2030. Plausible scale-ups of other proven interventions (VMMC, condom distribution and FSW strategies) could yield additional reductions in new infections (between 4 and 15%), and in overall treatment investment needs. Scaling up VMMC in line with national targets is found to be cost-effective in the medium term.

conclusionsThe scale-up in testing and treatment programmes over this decade has been rapid, but these efforts must be doubled to reach 2020 targets. Strategic investments in proven interventions will help Johannesburg achieve the treatment targets and be on track to end AIDS by 2030.

Indexed as

Acquired Immunodeficiency SyndromeCircumcision, MaleCondomsFemaleHIV InfectionsHumansMaleResource AllocationSex WorkersSouth AfricaTime Factorsallocative efficiencyending AIDSFast-Track targetsHIV modellingJohannesburg

Identifiers

PMID29359533
PMCPMC5810342
OpenAlexW2792563222

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.