Evidence map›Paper›PMID 31770373›Full record

ArticlePLoS medicine2019

Opioid agonist treatment scale-up and the initiation of injection drug use: A dynamic modeling analysis.

Charles Marks, Annick Borquez, Sonia Jain, Xiaoying Sun, Steffanie A Strathdee, Richard S Garfein, M-J Milloy, Kora DeBeck, Javier A Cepeda, Dan Werb and 1 more

Abstract read
In one paragraph

Article in PLoS medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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, 1 synthesis or guideline pooled it.

  1. Pooled it
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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

11 authors.

Charles MarksSDSU-UCSD Joint Doctoral Program in Interdisciplinary Research on Substance Use, San Diego, California, United States of America.ORCID 0000-0002-3893-1914
Annick BorquezDivision of Infectious Diseases and Global Public Health, University of California San Diego, La Jolla, California, United States of America.ORCID 0000-0002-6033-9837
Sonia JainBiostatistics Research Center, Department of Family Medicine and Public Health, University of California San Diego, La Jolla, California, United States of America.
Xiaoying SunBiostatistics Research Center, Department of Family Medicine and Public Health, University of California San Diego, La Jolla, California, United States of America.
Steffanie A StrathdeeDivision of Infectious Diseases and Global Public Health, University of California San Diego, La Jolla, California, United States of America.ORCID 0000-0002-7724-691X
Richard S GarfeinDivision of Infectious Diseases and Global Public Health, University of California San Diego, La Jolla, California, United States of America.ORCID 0000-0003-3663-7153
M-J MilloyBritish Columbia Centre on Substance Use, Vancouver, Canada.ORCID 0000-0003-3821-6221
Kora DeBeckBritish Columbia Centre on Substance Use, Vancouver, Canada.ORCID 0000-0003-2045-1653
Javier A CepedaDivision of Infectious Diseases and Global Public Health, University of California San Diego, La Jolla, California, United States of America.ORCID 0000-0003-0304-3172
Dan WerbDivision of Infectious Diseases and Global Public Health, University of California San Diego, La Jolla, California, United States of America.ORCID 0000-0003-0614-9386
Natasha K MartinDivision of Infectious Diseases and Global Public Health, University of California San Diego, La Jolla, California, United States of America.ORCID 0000-0001-8344-1810

Funding

UC San Diego Clinical and Translational Research InstituteUL1TR001442 · NCATS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI FIRESTEIN, GARY S, HOGARTH, MICHAEL · 2015 to 2024
$88.3M
VirologyP30AI036214 · NIAID · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI SONIA JAIN · 1994 to 2026
$78.4M
Impact of Drug Policy Reform on the HIV Risk Environment among IDUs in TijuanaR37DA019829 · NIDA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI STRATHDEE, STEFFANIE A. · 2010 to 2019
$6.7M
Impacts of universal access to HIV/AIDS care among HIV+ injection drug usersU01DA021525 · NIDA · UNIVERSITY OF BRITISH COLUMBIA · PI MILLOY, M-J · 2017 to 2024
$4.3M
Drug tourism to Mexico: Impact of Mexicos new drug law on HIV-HCV-TB in US IDUsR01DA031074 · NIDA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI GARFEIN, RICHARD S · 2011 to 2015
$3.2M
Real-time prediction of emerging drug use epidemics in the United StatesDP2DA049295 · NIDA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI BORQUEZ, ANNICK · 2019 to 2019
$2.4M
Preventing HIV by modifying the context for injecting initiationDP2DA040256 · NIDA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI WERB, DANIEL M · 2015 to 2015
$2.3M
Optimizing HIV prevention portfolios targeting people who inject drugs using dynamic economic modelingR01AI147490 · NIAID · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI MARTIN, NATASHA · 2019 to 2022
$2.3M
MODELING THE IMPACT OF A POLICE EDUCATION PROGRAM ON HIV INCIDENCE AMONG PEOPLE WHO INJECT DRUGSK01DA043421 · NIDA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI CEPEDA, JAVIER · 2017 to 2021
$941k
NCATS NIH HHS UL1 TR001442NIAID NIH HHS P30 AI036214NIAID NIH HHS R01 AI147490NIDA NIH HHS DP2 DA040256NIDA NIH HHS DP2 DA049295NIDA NIH HHS K01 DA043421NIDA NIH HHS R01 DA031074NIDA NIH HHS R37 DA019829NIDA NIH HHS U01 DA021525
6 · The paper itself

Abstract

backgroundInjection drug use (IDU) is associated with multiple health harms. The vast majority of IDU initiation events (in which injection-naïve persons first adopt IDU) are assisted by a person who injects drugs (PWID), and as such, IDU could be considered as a dynamic behavioral transmission process. Data suggest that opioid agonist treatment (OAT) enrollment is associated with a reduced likelihood of assisting with IDU initiation. We assessed the association between recent OAT enrollment and assisting IDU initiation across several North American settings and used dynamic modeling to project the potential population-level impact of OAT scale-up within the PWID population on IDU initiation. METHODS AND

findingsWe employed data from a prospective multicohort study of PWID in 3 settings (Vancouver, Canada [n = 1,737]; San Diego, United States [n = 346]; and Tijuana, Mexico [n = 532]) from 2014 to 2017. Site-specific modified Poisson regression models were constructed to assess the association between recent (past 6 month) OAT enrollment and history of ever having assisted an IDU initiation with recently assisting IDU initiation. Findings were then pooled using linear mixed-effects techniques. A dynamic transmission model of IDU among the general population was developed, stratified by known factors associated with assisting IDU initiation and relevant drug use behaviors. The model was parameterized to a generic North American setting (approximately 1% PWID) and used to estimate the impact of increasing OAT coverage among PWID from baseline (approximately 21%) to 40%, 50%, and 60% on annual IDU initiation incidence and corresponding PWID population size across a decade. From Vancouver, San Diego, and Tijuana, respectively, 4.5%, 5.2%, and 4.3% of participants reported recently assisting an IDU initiation, and 49.4%, 19.7%, and 2.1% reported recent enrollment in OAT. Recent OAT enrollment was significantly associated with a 45% lower likelihood of providing recent IDU initiation assistance among PWID (relative risk [RR] 0.55 [95% CI 0.36-0.84], p = 0.006) compared to those not recently on OAT. Our dynamic model predicts a baseline mean of 1,067 (2.5%-97.5% interval [95% I 490-2,082]) annual IDU initiations per 1,000,000 individuals, of which 886 (95% I 406-1,750) are assisted by PWID. Based on our observed statistical associations, our dynamic model predicts that increasing OAT coverage from approximately 21% to 40%, 50%, or 60% among PWID could reduce annual IDU initiations by 11.5% (95% I 2.4-21.7), 17.3% (95% I 5.6-29.4), and 22.8% (95% I 8.1-36.8) and reduce the PWID population size by 5.4% (95% I 0.1-12.0), 8.2% (95% I 2.2-16.9), and 10.9% (95% I 3.2-21.8) relative to baseline, respectively, in a decade. Less impact occurs when the protective effect of OAT is diminished, when a greater proportion of IDU initiations are unassisted by PWID, and when average IDU career length is longer. The study's main limitations are uncertainty in the causal pathway between OAT enrollment and assisting with IDU initiation and the use of a simplified model of IDU initiation.

conclusionsIn addition to its known benefits on preventing HIV, hepatitis C virus (HCV), and overdose among PWID, our modeling suggests that OAT scale-up may also reduce the number of IDU initiations and PWID population size.

Indexed as

AdultAnalgesics, OpioidCaliforniaCanadaCohort StudiesDrug-Seeking BehaviorFemaleForecastingHumansIncidenceMaleMexicoMiddle AgedModels, TheoreticalProspective StudiesRegression AnalysisAnalgesics, Opioid

Identifiers

PMID31770373
PMCPMC6879119

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Registered trials

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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.