Evidence map›Paper›PMID 33587136›Full record

ArticleJAMA network open2021

Projected Estimates of Opioid Mortality After Community-Level Interventions.

Benjamin P Linas, Alexandra Savinkina, R W M A Madushani, Jianing Wang, Golnaz Eftekhari Yazdi, Avik Chatterjee, Alexander Y Walley, Jake R Morgan, Rachel L Epstein, Sabrina A Assoumou and 5 more

Open access · goldAbstract read
In one paragraph

Article in JAMA network open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.

0numbers the graph read from it
0cells of the map it votes in
47citing papers in PubMed
11.0field-weighted citation impact, top 1% 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

47 citing papers in PubMed, 73 citations in OpenAlex.

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  4. Modeling Recurrent Events: A Tutorial Based on Relapse and Remitting Episodes during Medication-Assisted Treatment for Opioid Use Disorder.Medical decision making : an international journal of the Society for Medical Decision Making · 2026
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

15 authors at 4 institutions in 1 country.

Benjamin P LinasSection of Infectious Diseases, Boston Medical Center, Boston, Massachusetts.
Alexandra SavinkinaSection of Infectious Diseases, Boston Medical Center, Boston, Massachusetts.
R W M A MadushaniSection of Infectious Diseases, Boston Medical Center, Boston, Massachusetts.
Jianing WangSection of Infectious Diseases, Boston Medical Center, Boston, Massachusetts.
Golnaz Eftekhari YazdiSection of Infectious Diseases, Boston Medical Center, Boston, Massachusetts.
Avik ChatterjeeClinical Addiction Research and Education Unit, Section of General Internal Medicine, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts.
Alexander Y WalleyClinical Addiction Research and Education Unit, Section of General Internal Medicine, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts.
Jake R MorganBoston University School of Public Health, Boston, Massachusetts.
Rachel L EpsteinSection of Infectious Diseases, Boston Medical Center, Boston, Massachusetts.
Sabrina A AssoumouSection of Infectious Diseases, Boston Medical Center, Boston, Massachusetts.
Sean M MurphyBoston University School of Public Health, Boston, Massachusetts.
Bruce R SchackmanBoston University School of Public Health, Boston, Massachusetts.
Stavroula A ChrysanthopoulouSchool of Public Health, Brown University, Providence, Rhode Island.
Laura F WhiteBoston University School of Public Health, Boston, Massachusetts.
Joshua A BarocasSection of Infectious Diseases, Boston Medical Center, Boston, Massachusetts.
Boston Medical Center · USBoston University · USCornell University · USBrown University · US

Funding

Translational ScienceP30AI042853 · NIAID · MIRIAM HOSPITAL · PI LARRY K BROWN · 1998 to 2026
$51.7M
Utilization of health services after engagement with opioid use disorder (OUD) treatment provider by individuals with and without post-traumatic stress disorderP30DA040500 · NIDA · WEILL MEDICAL COLL OF CORNELL UNIV · PI Benjamin P. Linas · 2015 to 2026
$20.7M
Researching Effective Strategies to Prevent Opioid Death (RESPOND)R01DA046527 · NIDA · BOSTON MEDICAL CENTER · PI Benjamin P. Linas · 2018 to 2026
$7.2M
Development of a novel community-based high-performance surveillance network for drug useDP2DA051864 · NIDA · UNIVERSITY OF COLORADO DENVER · PI BAROCAS, JOSHUA ADAM · 2020 to 2020
$2.3M
Strategies to reduce serious bacterial infections and overdose among people who inject drugsK01DA051684 · NIDA · UNIVERSITY OF COLORADO DENVER · PI BAROCAS, JOSHUA ADAM · 2020 to 2024
$809k
NIAID NIH HHS P30 AI042853NIDA NIH HHS DP2 DA051864NIDA NIH HHS K01 DA051684NIDA NIH HHS P30 DA040500NIDA NIH HHS R01 DA046527
6 · The paper itself

Abstract

Importance: The United States is experiencing a crisis of opioid overdose. In response, the US Department of Health and Human Services has defined a goal to reduce overdose mortality by 40% by 2022. Objective: To identify specific combinations of 3 interventions (initiating more people to medications for opioid use disorder [MOUD], increasing 6-month retention with MOUD, and increasing naloxone distribution) associated with at least a 40% reduction in opioid overdose in simulated populations. Design, Setting, and Participants: This decision analytical model used a dynamic population-level state-transition model to project outcomes over a 2-year horizon. Each intervention scenario was compared with the counterfactual of no intervention in simulated urban and rural communities in Massachusetts. Simulation modeling was used to determine the associations of community-level interventions with opioid overdose rates. The 3 examined interventions were initiation of more people to MOUD, increasing individuals' retention with MOUD, and increasing distribution of naloxone. Data were analyzed from July to November 2020. Main Outcomes and Measures: Reduction in overdose mortality, medication treatment capacity needs, and naloxone needs. Results: No single intervention was associated with a 40% reduction in overdose mortality in the simulated communities. Reaching this goal required use of MOUD and naloxone. Achieving a 40% reduction required that 10% to 15% of the estimated OUD population not already receiving MOUD initiate MOUD every month, with 45% to 60%% retention for at least 6 months, and increased naloxone distribution. In all feasible settings and scenarios, attaining a 40% reduction in overdose mortality required that in every month, at least 10% of the population with OUD who were not currently receiving treatment initiate an MOUD. Conclusions and Relevance: In this modeling study, only communities with increased capacity for treating with MOUD and increased MOUD retention experienced a 40% decrease in overdose mortality. These findings could provide a framework for developing community-level interventions to reduce opioid overdose death.

Indexed as

Decision Support TechniquesComputer SimulationHumansMassachusettsNaloxoneNarcotic AntagonistsOpiate OverdoseOpiate Substitution TreatmentRetention in CareRural PopulationUrban PopulationNaloxoneNarcotic Antagonists

Identifiers

PMID33587136
PMCPMC7885041
OpenAlexW3132147424

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.