Evidence map›Paper›PMID 42060640›Full record

ArticlePloS one2026

Characterizing complex opioid use disorder care trajectories and outcomes following acute service utilization: A protocol for a population-based data linkage study.

Noa Krawczyk, Ignacio Bórquez, Megan Miller, Sung Woo Lim, Teena Cherian, Daniel Schatz, Alex Harocopos, Emily Carter, Marc Scott, Brandy F Henry and 3 more

Abstract read
In one paragraph

Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

13 authors.

Noa KrawczykDepartment of Population Health, Center for Opioid Epidemiology and Policy, NYU Grossman School of Medicine, New York, New York, United States of America.ORCID https://orcid.org/0000-0002-7396-3938
Ignacio BórquezDepartment of Population Health, Center for Opioid Epidemiology and Policy, NYU Grossman School of Medicine, New York, New York, United States of America.ORCID https://orcid.org/0000-0002-3746-578X
Megan MillerDepartment of Population Health, Center for Opioid Epidemiology and Policy, NYU Grossman School of Medicine, New York, New York, United States of America.
Sung Woo LimNew York City Department of Health and Mental Hygiene, Center for Population Health Data Science, Queens, New York, United States of America.
Teena CherianNew York City Department of Health and Mental Hygiene, Center for Population Health Data Science, Queens, New York, United States of America.ORCID https://orcid.org/0000-0002-7766-6076
Daniel SchatzNYC Health + Hospitals Office of Behavioral Health, New York, New York, United States of America.
Alex HarocoposDepartment of Population Health, Center for Opioid Epidemiology and Policy, NYU Grossman School of Medicine, New York, New York, United States of America.
Emily CarterNYC Health + Hospitals Office of Behavioral Health, New York, New York, United States of America.
Marc ScottDepartment of Applied Statistics, Social Science, and Humanities, New York University, New York, New York, United States of America.
Brandy F HenryDepartment of Educational Psychology, Counseling, and Special Education, College of Education, Social Science Research Institute, Consortium on Substance Use and Addiction, Pennsylvania State University, University Park, Pennsylvania, United States of America.ORCID https://orcid.org/0000-0002-0667-9283
David FrankDepartment of Social and Behavioral Sciences, School of Global Public Health, New York University, New York, New York, United States of America.
Magdalena CerdáDepartment of Population Health, Center for Opioid Epidemiology and Policy, NYU Grossman School of Medicine, New York, New York, United States of America.
Arthur Robin WilliamsColumbia University Department of Psychiatry, New York New York, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Despite robust evidence that medications for opioid use disorder (MOUD) reduce overdose and mortality, substantial care gaps remain following opioid-related hospital encounters. The opioid use disorder (OUD) Cascade of Care framework conceptualizes progression from identification to treatment initiation and retention, yet limited research has examined how real-world OUD treatment trajectories unfold, particularly across treatment episodes and multiple care settings. This paper describes an NIH-funded study protocol (1R01DA061367-01A1) to conduct a longitudinal observational study using linked administrative data across New York City to characterize OUD treatment trajectories following opioid-related hospital encounters. Using the OUD Cascade of Care framework, we will apply state sequence analysis to identify common patterns of OUD treatment engagement in the year following hospitalization, including transitions between treatment modalities and periods in and out of care. We will examine how care trajectories vary by individual and neighborhood characteristics, and assess associations between trajectories and key outcomes, including rehospitalization, overdose, and mortality. By applying novel data-driven longitudinal methods, this study will advance understanding of the complex, non-linear nature of OUD treatment engagement. Findings will inform health system and policy efforts to identify populations at elevated risk, hospital-based interventions, and opportunities to address gaps in care to reduce overdose-related harms.

Indexed as

Opioid-Related DisordersHospitalizationHumansInformation Storage and RetrievalLongitudinal StudiesNew York CityOpiate Substitution Treatment

Identifiers

PMID42060640
PMCPMC13132183

What OpenQuestion holds

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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.