Evidence map›Paper›PMID 31846456›Full record

ArticlePLoS computational biology2019

Large benefits to youth-focused HIV treatment-as-prevention efforts in generalized heterosexual populations: An agent-based simulation model.

John E Mittler, James T Murphy, Sarah E Stansfield, Kathryn Peebles, Geoffrey S Gottlieb, Neil F Abernethy, Molly C Reid, Steven M Goodreau, Joshua T Herbeck

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
0.6field-weighted citation impact, top 32% 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

6 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.

  1. A Review of Network Models for HIV Spread.Journal of acquired immune deficiency syndromes (1999) · 2025
    Pooled it
  2. Review
  3. Article
  4. Improving and Extending STERGM Approximations Based on Cross-Sectional Data and Tie Durations.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2024
    Article
  5. Article
  6. 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 1 institution in 1 country.

John E MittlerDepartment of Microbiology, University of Washington, Seattle, WA, United States of America.ORCID 0000-0002-2638-2700
James T MurphyDepartment of Microbiology, University of Washington, Seattle, WA, United States of America.ORCID 0000-0003-0910-8846
Sarah E StansfieldDepartment of Anthropology, University of Washington, Seattle, WA, United States of America.ORCID 0000-0003-3251-8501
Kathryn PeeblesDepartment of Epidemiology, University of Washington, Seattle, WA, United States of America.ORCID 0000-0002-2095-7446
Geoffrey S GottliebDepartment of Medicine, University of Washington, Seattle, WA, United States of America.
Neil F AbernethyDepartment of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States of America.ORCID 0000-0001-5453-0769
Molly C ReidDepartment of Global Health, University of Washington, Seattle, WA, United States of America.
Steven M GoodreauDepartment of Anthropology, University of Washington, Seattle, WA, United States of America.ORCID 0000-0003-1009-5763
Joshua T HerbeckDepartment of Global Health, University of Washington, Seattle, WA, United States of America.
University of Washington · US

Funding

Methods and Data CoreR24HD042828 · NICHD · UNIVERSITY OF WASHINGTON · PI CURRAN, SARA R. · 2002 to 2016
$9.3M
Integrated bio-social models for HIV epidemiologyR01AI108490 · NIAID · UNIVERSITY OF WASHINGTON · PI GOODREAU, STEVEN M., HERBECK, JOSHUA T · 2014 to 2018
$3.4M
Statistical Methods for Network EpidemiologyR01HD068395 · NICHD · UNIVERSITY OF WASHINGTON · PI MORRIS, WANDA MARTINA · 2011 to 2015
$3.0M
Modeling the Evolutionary and Public Health Impacts of HIV Adaptation in Response to VaccinationR01AI150467 · NIAID · UNIVERSITY OF WASHINGTON · PI MITTLER, JOHN E. · 2019 to 2020
$844k
Using Sexual Network Transmission Models to Explain HIV Disparities Between BlackR21HD075662 · NICHD · UNIVERSITY OF WASHINGTON · PI GOODREAU, STEVEN M. · 2013 to 2014
$418k
NIAID NIH HHS R01 AI108490NIAID NIH HHS R01 AI150467NICHD NIH HHS R01 HD068395NICHD NIH HHS R21 HD075662NICHD NIH HHS R24 HD042828
6 · The paper itself

Abstract

Predominantly heterosexual HIV-1 epidemics like those in sub-Saharan Africa continue to have high HIV incidence in young people. We used a stochastic, agent-based model for age-disparate networks to test the hypothesis that focusing uptake and retention of ART among youth could enhance the efficiency of treatment as prevention (TasP) campaigns. We used the model to identify strategies that reduce incidence to negligible levels (i.e., < 0.1 cases/100 person-years) 20-25 years after initiation of a targeted TasP campaign. The model was parameterized using behavioral, demographic, and clinical data from published papers and national reports. To keep a focus on the underlying age effects we model a generalized heterosexual population with average risks (i.e., no MSM, no PWIDs, no sex workers) and no entry of HIV+ people from other regions. The model assumes that most people (default 95%, range in variant simulations 60-95%) are "linkable"; i.e., could get linked to effective care given sufficient resources. To simplify the accounting, we assume a rapid jump in the number of people receiving treatment at the start of the TasP campaign, followed by a 2% annual increase that continues until all linkable HIV+ people have been treated. Under historical scenarios of CD4-based targeted ART allocation and current policies of untargeted (random) ART allocation, our model predicts that viral replication would need to be suppressed in 60-85% of infected people at the start of the TasP campaign to drive incidence to negligible levels. Under age-based strategies, by contrast, this percentage dropped by 18-54%, depending on the strength of the epidemic and the age target. For our baseline model, targeting those under age 30 halved the number of people who need to be treated. Age-based targeting also minimized total and time-discounted AIDS deaths over 25 years. Age-based targeting yielded benefits without being highly exclusive; in a model in which 60% of infected people were treated, ~87% and ~58% of those initiating therapy during a campaign targeting those <25 and <30 years, respectively, fell outside the target group. Sensitivity analyses revealed that youth-focused TasP is beneficial due to age-related risk factors (e.g. shorter relationship durations), and an age-specific herd immunity (ASHI) effect that protects uninfected adolescents entering the sexually active population. As testing rates increase in response to UNAIDS 90-90-90 goals, efforts to link all young people to care and treatment could contribute enormously to ending the HIV epidemic.

Indexed as

AdultAge FactorsCD4 Lymphocyte CountComputational BiologyComputer SimulationEpidemicsFemaleHeterosexualityHIV-1HIV InfectionsHumansMaleSex FactorsSoftwareSystems AnalysisYoung Adult

Identifiers

PMID31846456
PMCPMC6938382
OpenAlexW2995696847

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.