Evidence map›Paper›PMID 37151596›Full record

ArticleFrontiers in public health2023

Barriers to opioid use disorder treatment: A comparison of self-reported information from social media with barriers found in literature.

Whitney Bremer, Karma Plaisance, Drew Walker, Matthew Bonn, Jennifer S Love, Jeanmarie Perrone, Abeed Sarker

Open access · goldAbstract read
In one paragraph

Article in Frontiers in public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
7.5field-weighted citation impact, top 2% 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

22 citing papers in PubMed, 26 citations in OpenAlex.

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  8. Exploring Preferences in Emergency Department Coordination of Opioid Use Disorder Treatment by Race and Ethnicity.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2026
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  16. Mining Social Media for Barriers to Opioid Recovery with LLMs.Proceedings of the ... Workshop on Patient-Oriented Language Processing · 2025
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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

7 authors at 3 institutions in 1 country.

Whitney BremerDepartment of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA, United States.
Karma PlaisanceDepartment of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, United States.
Drew WalkerDepartment of Behavioral, Social and Health Education Sciences, Rollins School of Public Health, Emory University, Atlanta, GA, United States.
Matthew BonnCanadian Association of People Who Use Drugs, Dartmouth, NS, Canada.
Jennifer S LoveDepartment of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States.
Jeanmarie PerroneDepartment of Emergency Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.
Abeed SarkerDepartment of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA, United States.
Emory University · USIcahn School of Medicine at Mount Sinai · USUniversity of Pennsylvania · US

Funding

Mining Social Media Big Data for Toxicovigilance: Studying Substance Use via Natural Language Processing and Machine Learning MethodsR01DA057599 · NIDA · EMORY UNIVERSITY · PI Abeed H Sarker · 2022 to 2026
$2.2M
Training in Advanced Data Analytics to End Drug-Related Harms (TADA)T32DA050552 · NIDA · EMORY UNIVERSITY · PI Howard H Chang, Hannah LF Cooper · 2020 to 2026
$1.6M
NIDA NIH HHS R01 DA057599NIDA NIH HHS T32 DA050552
6 · The paper itself

Abstract

Introduction: Medications such as buprenorphine and methadone are effective for treating opioid use disorder (OUD), but many patients face barriers related to treatment and access. We analyzed two sources of data-social media and published literature-to categorize and quantify such barriers. Methods: In this mixed methods study, we analyzed social media (Reddit) posts from three OUD-related forums (subreddits): r/suboxone, r/Methadone, and r/naltrexone. We applied natural language processing to identify posts relevant to treatment barriers, categorized them into insurance- and non-insurance-related, and manually subcategorized them into fine-grained topics. For comparison, we used substance use-, OUD- and barrier-related keywords to identify relevant articles from PubMed published between 2006 and 2022. We searched publications for language expressing fear of barriers, and hesitation or disinterest in medication treatment because of barriers, paying particular attention to the affected population groups described. Results: On social media, the top three insurance-related barriers included having no insurance (22.5%), insurance not covering OUD treatment (24.7%), and general difficulties of using insurance for OUD treatment (38.2%); while the top two non-insurance-related barriers included stigma (47.6%), and financial difficulties (26.2%). For published literature, stigma was the most prominently reported barrier, occurring in 78.9% of the publications reviewed, followed by financial and/or logistical issues to receiving medication treatment (73.7%), gender-specific barriers (36.8%), and fear (31.5%). Conclusion: The stigma associated with OUD and/or seeking treatment and insurance/cost are the two most common types of barriers reported in the two sources combined. Harm reduction efforts addressing barriers to recovery may benefit from leveraging multiple data sources.

Indexed as

Opioid-Related DisordersSocial MediaHumansMethadoneOpiate Substitution TreatmentSelf ReportMethadonehealth informaticsnatural language processingsocial mediasubstance usesubstance use disorder

Identifiers

PMID37151596
PMCPMC10158842
OpenAlexW4366548577

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.