Evidence map›Paper›PMID 33382803›Full record

ArticlePloS one2020

Individual and community-level benefits of PrEP in western Kenya and South Africa: Implications for population prioritization of PrEP provision.

Edinah Mudimu, Kathryn Peebles, Zindoga Mukandavire, Emily Nightingale, Monisha Sharma, Graham F Medley, Daniel J Klein, Katharine Kripke, Anna Bershteyn

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
0.4field-weighted citation impact, top 26% 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

18 citing papers in PubMed, 23 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. 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

9 authors at 6 institutions in 3 countries.

Edinah MudimuDepartment of Decision Sciences, University of South Africa, Pretoria, South Africa.ORCID 0000-0002-5479-9255
Kathryn PeeblesDepartment of Global Health, University of Washington, Seattle, WA, United States of America.
Zindoga MukandavireCentre for Data Science, Coventry University, Coventry, United Kingdom.
Emily NightingaleDepartment of Global Health and Development, London School of Hygiene and Tropical Medicine, London, United Kingdom.ORCID 0000-0002-9265-1841
Monisha SharmaDepartment of Global Health, University of Washington, Seattle, WA, United States of America.
Graham F MedleyDepartment of Global Health and Development, London School of Hygiene and Tropical Medicine, London, United Kingdom.ORCID 0000-0002-0030-7278
Daniel J KleinInstitute for Disease Modeling, Seattle, WA, United States of America.
Katharine KripkeAvenir Health, Washington, District of Columbia, United States of America.ORCID 0000-0001-8228-4577
Anna BershteynInstitute for Disease Modeling, Seattle, WA, United States of America.
Institute for Disease Modeling · USLondon School of Hygiene & Tropical Medicine · GBUniversity of Washington · USAvenir Health · USCoventry University · GBUniversity of South Africa · ZA

Funding

PEPFAR
6 · The paper itself

Abstract

backgroundPre-exposure prophylaxis (PrEP) is highly effective in preventing HIV and has the potential to significantly impact the HIV epidemic. Given limited resources for HIV prevention, identifying PrEP provision strategies that maximize impact is critical.

methodsWe used a stochastic individual-based network model to evaluate the direct (infections prevented among PrEP users) and indirect (infections prevented among non-PrEP users as a result of PrEP) benefits of PrEP, the person-years of PrEP required to prevent one HIV infection, and the community-level impact of providing PrEP to populations defined by gender and age in western Kenya and South Africa. We examined sensitivity of results to scale-up of antiretroviral therapy (ART) and voluntary medical male circumcision (VMMC) by comparing two scenarios: maintaining current coverage ("status quo") and rapid scale-up to meet programmatic targets ("fast-track").

resultsThe community-level impact of PrEP was greatest among women aged 15-24 due to high incidence, while PrEP use among men aged 15-24 yielded the highest proportion of indirect infections prevented in the community. These indirect infections prevented continue to increase over time (western Kenya: 0.4-5.5 (status quo); 0.4-4.9 (fast-track); South Africa: 0.5-1.8 (status quo); 0.5-3.0 (fast-track)) relative to direct infections prevented among PrEP users. The number of person-years of PrEP needed to prevent one HIV infection was lower (59 western Kenya and 69 in South Africa in the status quo scenario; 201 western Kenya and 87 in South Africa in the fast-track scenario) when PrEP was provided only to women compared with only to men over time horizons of up to 5 years, as the indirect benefits of providing PrEP to men accrue in later years.

conclusionsProviding PrEP to women aged 15-24 prevents the greatest number of HIV infections per person-year of PrEP, but PrEP provision for young men also provides indirect benefits to women and to the community overall. This finding supports existing policies that prioritize PrEP use for young women, while also illuminating the community-level benefits of PrEP availability for men when resources permit.

Indexed as

Circumcision, MaleAdolescentAdultAnti-HIV AgentsFemaleHIV InfectionsHumansIncidenceKenyaMalePre-Exposure ProphylaxisPublic HealthResidence CharacteristicsSouth AfricaYoung AdultAnti-HIV Agents

Identifiers

PMID33382803
PMCPMC7775042
OpenAlexW3115655966

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.