Evidence map›Paper›PMID 36807874›Full record

ArticleJournal of the International AIDS Society2023

Impact and cost-effectiveness of the national scale-up of HIV pre-exposure prophylaxis among female sex workers in South Africa: a modelling analysis.

Jack Stone, Rutendo Bothma, Gabriela B Gomez, Robyn Eakle, Christinah Mukandavire, Hasina Subedar, Hannah Fraser, Marie-Claude Boily, Sheree Schwartz, Jenny Coetzee and 7 more

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in Journal of the International AIDS Society, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 12 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 2 pooled it
2.9field-weighted citation impact, top 9% 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

12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 15 citations in OpenAlex.

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  10. ERRATUM.Journal of the International AIDS Society · 2023
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

17 authors at 8 institutions in 4 countries.

Jack StonePopulation Health Sciences, University of Bristol, Bristol, UK.ORCID 0000-0002-4584-0068
Rutendo BothmaWits RHI, University of the Witwatersrand, Johannesburg, South Africa.ORCID 0000-0003-2967-4562
Gabriela B GomezDepartment of Global Health and Development, London School of Hygiene and Tropical Medicine, London, UK.
Robyn EakleWits RHI, University of the Witwatersrand, Johannesburg, South Africa.
Christinah MukandavirePopulation Health Sciences, University of Bristol, Bristol, UK.
Hasina SubedarNational Department of Health, Pretoria, South Africa.
Hannah FraserPopulation Health Sciences, University of Bristol, Bristol, UK.ORCID 0000-0002-4340-9995
Marie-Claude BoilyDepartment of Infectious Disease Epidemiology, Imperial College London, London, UK.
Sheree SchwartzDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
Jenny CoetzeePerinatal HIV Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
Kennedy OtwombePerinatal HIV Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
Minja MilovanovicPerinatal HIV Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.ORCID 0000-0002-4654-8755
Stefan BaralDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.ORCID 0000-0002-5482-2419
Leigh F JohnsonCentre for Infectious Disease Epidemiology and Research, University of Cape Town, Cape Town, South Africa.ORCID 0000-0002-2717-011X
Willem Daniel Francois VenterEzintsha, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
Helen ReesWits RHI, University of the Witwatersrand, Johannesburg, South Africa.
Peter VickermanPopulation Health Sciences, University of Bristol, Bristol, UK.ORCID 0000-0002-8291-5890
University of the Witwatersrand · ZAUniversity of Bristol · GBJohns Hopkins University · USDepartment of Health · ZAImperial College London · GBSanofi (France) · FRSouth African Medical Research Council · ZAUniversity of Cape Town · ZA

Funding

Vaccine Response and Immunotherapeutics SWGP30AI094189 · NIAID · JOHNS HOPKINS UNIVERSITY · PI Anna Palmer Durbin · 2012 to 2026
$67.0M
Examining PrEP use, implementation preferences and the potential for social support to improve PrEP delivery and persistence among female sex workers in Durban, South AfricaR01NR016650 · NINR · JOHNS HOPKINS UNIVERSITY · PI BARAL, STEFAN DAVID · 2016 to 2020
$3.2M
Optimizing PrEP implementation and effectiveness among women at high risk for HIV acquisition in South AfricaR01MH121161 · NIMH · JOHNS HOPKINS UNIVERSITY · PI SCHWARTZ, SHEREE RENAE · 2020 to 2024
$3.1M
Bill & Melinda Gates Foundation OPP1084416Department of Health NIHR200877Medical Research Council MR/R015600/1NIAID NIH HHS P30 AI094189NIH HHS P30AI094189NIMH NIH HHS R01 MH121161NINR NIH HHS R01 NR016650Wellcome TrustWellcome Trust 214204/A/18/Z
6 · The paper itself

Abstract

introductionIn 2016, South Africa (SA) initiated a national programme to scale-up pre-exposure prophylaxis (PrEP) among female sex workers (FSWs), with ∼20,000 PrEP initiations among FSWs (∼14% of FSW) by 2020. We evaluated the impact and cost-effectiveness of this programme, including future scale-up scenarios and the potential detrimental impact of the COVID-19 pandemic.

methodsA compartmental HIV transmission model for SA was adapted to include PrEP. Using estimates on self-reported PrEP adherence from a national study of FSW (67.7%) and the Treatment and Prevention for FSWs (TAPS) PrEP demonstration study in SA (80.8%), we down-adjusted TAPS estimates for the proportion of FSWs with detectable drug levels (adjusted range: 38.0-70.4%). The model stratified FSW by low (undetectable drug; 0% efficacy) and high adherence (detectable drug; 79.9%; 95% CI: 67.2-87.6% efficacy). FSWs can transition between adherence levels, with lower loss-to-follow-up among highly adherent FSWs (aHR: 0.58; 95% CI: 0.40-0.85; TAPS data). The model was calibrated to monthly data on the national scale-up of PrEP among FSWs over 2016-2020, including reductions in PrEP initiations during 2020. The model projected the impact of the current programme (2016-2020) and the future impact (2021-2040) at current coverage or if initiation and/or retention are doubled. Using published cost data, we assessed the cost-effectiveness (healthcare provider perspective; 3% discount rate; time horizon 2016-2040) of the current PrEP provision.

resultsCalibrated to national data, model projections suggest that 2.1% of HIV-negative FSWs were currently on PrEP in 2020, with PrEP preventing 0.45% (95% credibility interval, 0.35-0.57%) of HIV infections among FSWs over 2016-2020 or 605 (444-840) infections overall. Reductions in PrEP initiations in 2020 possibly reduced infections averted by 18.57% (13.99-23.29). PrEP is cost-saving, with $1.42 (1.03-1.99) of ART costs saved per dollar spent on PrEP. Going forward, existing coverage of PrEP will avert 5,635 (3,572-9,036) infections by 2040. However, if PrEP initiation and retention doubles, then PrEP coverage increases to 9.9% (8.7-11.6%) and impact increases 4.3 times with 24,114 (15,308-38,107) infections averted by 2040.

conclusionsOur findings advocate for the expansion of PrEP to FSWs throughout SA to maximize its impact. This should include strategies to optimize retention and should target women in contact with FSW services.

Indexed as

Anti-HIV AgentsCOVID-19HIV InfectionsPre-Exposure ProphylaxisSex WorkersCost-Benefit AnalysisFemaleHumansPandemicsSouth AfricaAnti-HIV Agentsfemale sex workersHIVmathematical modellingpre-exposure prophylaxispreventionSouth Africa

Identifiers

PMID36807874
PMCPMC9939943
OpenAlexW4321379113

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.