Evidence map›Paper›PMID 29985949›Full record

ArticlePloS one2018

Improving the impact of HIV pre-exposure prophylaxis implementation in small urban centers among men who have sex with men: An agent-based modelling study.

Jason R Gantenberg, Maximilian King, Madeline C Montgomery, Omar Galárraga, Mattia Prosperi, Philip A Chan, Brandon D L Marshall

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in PloS one, 2018. 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 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 pooled it
1.6field-weighted citation impact, top 18% 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

16 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors at 2 institutions in 1 country.

Jason R GantenbergDepartment of Epidemiology, Brown University School of Public Health, Providence, RI, United States of America.ORCID 0000-0002-8144-4273
Maximilian KingDepartment of Epidemiology, Brown University School of Public Health, Providence, RI, United States of America.
Madeline C MontgomeryDepartment of Medicine, Brown University, Providence, RI, United States of America.
Omar GalárragaDepartment of Health Services, Policy, and Practice, Brown University School of Public Health, Providence, RI, United States of America.
Mattia ProsperiDepartment of Epidemiology, College of Public Health and Health Professions & College of Medicine, University of Florida, Gainesville, FL, United States of America.
Philip A ChanDepartment of Medicine, Brown University, Providence, RI, United States of America.
Brandon D L MarshallDepartment of Epidemiology, Brown University School of Public Health, Providence, RI, United States of America.
Brown University · USUniversity of Florida · US

Funding

Translational ScienceP30AI042853 · NIAID · MIRIAM HOSPITAL · PI CURT G BECKWITH · 1998 to 2026
$51.7M
Science/Technical CoreP2CHD041020 · NICHD · BROWN UNIVERSITY · PI Margot Jackson · 2016 to 2026
$7.6M
Highly Adaptive Epidemic Control Strategies for HIV Prevention in Drug UsersDP2DA040236 · NIDA · BROWN UNIVERSITY · PI MARSHALL, BRANDON DAVID LEWIS · 2015 to 2015
$2.4M
Enhancing Population Impact of HIV Pre-Exposure Prophylaxis ImplementationR21MH109360 · NIMH · BROWN UNIVERSITY · PI CHAN, PHILIP ANDREW, MARSHALL, BRANDON DAVID LEWIS · 2016 to 2017
$459k
NIAID NIH HHS P30 AI042853NICHD NIH HHS P2C HD041020NIDA NIH HHS DP2 DA040236NIMH NIH HHS R21 MH109360
6 · The paper itself

Abstract

objectivesIdentifying prescribing strategies that improve the efficiency of PrEP should increase its impact at the population level. This study identifies PrEP allocation criteria that most effectively reduce 10-year HIV incidence by 25%, in accordance with the US National HIV/AIDS Strategy's goal for the proportionate reduction in new diagnoses.

methodsWe used a discrete-time stochastic agent-based model to simulate several PrEP engagement strategies. The model represented MSM aged 15-74 in Rhode Island and was calibrated to statewide prevalence from 2009-2014. We simulated HIV transmission in the absence of PrEP and compared the following PrEP engagement scenarios: 1) allocation to the current patient population; 2) random allocation; 3) allocation to MSM with greater than 5 sexual partners in one year; 4) allocation to MSM with greater than 10 sexual partners in one year. For each scenario and coverage level we estimated the number and proportion of infections averted and the person-years on PrEP per averted infection.

resultsIn 2014, HIV prevalence before PrEP implementation was between 4% and 5%. In the No PrEP scenario 826 new infections (95% simulation limits [SL]: 711, 955) occurred over 10 years, with an incidence rate of 3.51 per 1000 person-years (95% SL: 3.00, 4.08). Prevalence rose to 7.4% (95% SL: 6.7, 8.1). None of the PrEP scenarios reduced new HIV infections by 25% while covering less than 15% of the HIV-uninfected population. At 15% coverage, allocating PrEP to the current patient population, MSM with greater than 5 sexual partners in a year, and MSM with greater than 10 partners reduced new infections by at least 25%, requiring 161 (95% SL: 115, 289), 150 (95% SL: 107, 252), and 128 (95% SL: 100, 184) person-years on PrEP per averted infection, respectively.

conclusionsEngaging MSM with high numbers of sexual partners would improve the population-level impact and efficiency of PrEP in settings where PrEP coverage remains low. However, the sustained population-level PrEP coverage needed to reduce new infections by 25% is substantially higher than current levels of PrEP uptake.

Indexed as

Homosexuality, MaleModels, StatisticalAdolescentAdultAgedHIV InfectionsHumansMaleMiddle AgedPre-Exposure ProphylaxisUrban Population

Identifiers

PMID29985949
PMCPMC6037355
OpenAlexW2831384185

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.