Evidence map›Paper›PMID 36332654›Full record

Trial reportThe lancet. HIV2022

Projected outcomes of universal testing and treatment in a generalised HIV epidemic in Zambia and South Africa (the HPTN 071 [PopART] trial): a modelling study.

William J M Probert, Rafael Sauter, Michael Pickles, Anne Cori, Nomtha F Bell-Mandla, Justin Bwalya, Lucie Abeler-Dörner, Peter Bock, Deborah J Donnell, Sian Floyd and 11 more

Open access · hybridAbstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in The lancet. HIV, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.9field-weighted citation impact, top 25% 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

3 citing papers in PubMed, 10 citations in OpenAlex.

  1. Article
  2. Review
  3. 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

21 authors at 12 institutions in 4 countries.

William J M ProbertBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK. Electronic address: william.probert@bdi.ox.ac.uk.
Rafael SauterBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
Michael PicklesMedical Research Council Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK.
Anne CoriMedical Research Council Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK.
Nomtha F Bell-MandlaDesmond Tutu TB Centre, Department of Paediatrics and Child Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa.
Justin BwalyaZambart, University of Zambia, Lusaka, Zambia.
Lucie Abeler-DörnerBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
Peter BockDesmond Tutu TB Centre, Department of Paediatrics and Child Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa.
Deborah J DonnellFred Hutchinson Cancer Research Center, Seattle, WA, USA.
Sian FloydFaculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK.
David MacleodFaculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK.
Estelle Piwowar-ManningSchool of Medicine, Johns Hopkins University, Baltimore, MD, USA.
Timothy SkallandFred Hutchinson Cancer Research Center, Seattle, WA, USA.
Kwame ShanaubeZambart, University of Zambia, Lusaka, Zambia.
Ethan WilsonFred Hutchinson Cancer Research Center, Seattle, WA, USA.
Blia YangDesmond Tutu TB Centre, Department of Paediatrics and Child Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa.
Helen AylesZambart, University of Zambia, Lusaka, Zambia; Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK.
Sarah FidlerDepartment of Infectious Disease, Imperial College London, London, UK; NIHR Imperial Biomedical Research Centre, London, UK.
Richard J HayesFaculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK.
Christophe FraserBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
HPTN 071 (PopART) Study Team
University of Oxford · GBLondon School of Hygiene & Tropical Medicine · GBFred Hutch Cancer Center · USImperial College London · GBStellenbosch University · ZAZambart · ZMDesmond Tutu HIV Foundation · ZANIHR Imperial Biomedical Research Centre · GBUniversity of Zambia · ZMJohns Hopkins Medicine · USJohns Hopkins University · USMedical Research Council · GB

Funding

LOC: HIV Prevention Trials NetworkUM1AI068619 · NIAID · FAMILY HEALTH INTERNATIONAL · PI Sinead Delany-Moretlwe, RAPHAEL J LANDOVITZ · 2011 to 2026
$779.5M
SDMC: HPTN 084 Pregnancy SupplementUM1AI068617 · NIAID · FRED HUTCHINSON CANCER RESEARCH CENTER · PI Elizabeth Renata Brown, Deborah J Donnell · 2011 to 2026
$204.9M
LC: HIV Prevention Trials Network - Laboratory Support for the SARS-CoV-2 Seroprevalence Study (CoVPN 5002)UM1AI068613 · NIAID · JOHNS HOPKINS UNIVERSITY · PI SUSAN H ESHLEMAN, Mark A Marzinke · 2011 to 2026
$100.2M
Medical Research Council MR/R010161/1Medical Research Council MR/R015600/1NIAID NIH HHS UM1 AI068613NIAID NIH HHS UM1 AI068617NIAID NIH HHS UM1 AI068619PEPFAR
6 · The paper itself

Abstract

backgroundThe long-term impact of universal home-based testing and treatment as part of universal testing and treatment (UTT) on HIV incidence is unknown. We made projections using a detailed individual-based model of the effect of the intervention delivered in the HPTN 071 (PopART) cluster-randomised trial.

methodsIn this modelling study, we fitted an individual-based model to the HIV epidemic and HIV care cascade in 21 high prevalence communities in Zambia and South Africa that were part of the PopART cluster-randomised trial (intervention period Nov 1, 2013, to Dec 31, 2017). The model represents coverage of home-based testing and counselling by age and sex, delivered as part of the trial, antiretroviral therapy (ART) uptake, and any changes in national guidelines on ART eligibility. In PopART, communities were randomly assigned to one of three arms: arm A received the full PopART intervention for all individuals who tested positive for HIV, arm B received the intervention with ART provided in accordance with national guidelines, and arm C received standard of care. We fitted the model to trial data twice using Approximate Bayesian Computation, once before data unblinding and then again after data unblinding. We compared projections of intervention impact with observed effects, and for four different scenarios of UTT up to Jan 1, 2030 in the study communities.

findingsCompared with standard of care, a 51% (95% credible interval 40-60) reduction in HIV incidence is projected if the trial intervention (arms A and B combined) is continued from 2020 to 2030, over and above a declining trend in HIV incidence under standard of care.

interpretationA widespread and continued commitment to UTT via home-based testing and counselling can have a substantial effect on HIV incidence in high prevalence communities.

fundingNational Institute of Allergy and Infectious Diseases, US President's Emergency Plan for AIDS Relief, International Initiative for Impact Evaluation, Bill & Melinda Gates Foundation, National Institute on Drug Abuse, and National Institute of Mental Health.

Indexed as

EpidemicsHIV InfectionsBayes TheoremHumansSouth AfricaZambia

Identifiers

PMID36332654
PMCPMC9646978
OpenAlexW4307824566

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.