Evidence map›Paper›PMID 42624957›Full record

Articlenpj health systems2026

Differential associations between social determinants of health and the initiation of medications for opioid use disorder across care settings.

Zhen Luo, Mackenzie Hofford, Ruochong Fan, Wenyu Song, Thomas G Kannampallil, Adam B Wilcox, Linying Zhang

Abstract read
In one paragraph

Article in npj health systems, 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

7 authors.

Zhen LuoInstitute for Informatics, Data Science and Biostatistics, Washington University School of Medicine in St. Louis, St. Louis, MO, USA.
Mackenzie HoffordInstitute for Informatics, Data Science and Biostatistics, Washington University School of Medicine in St. Louis, St. Louis, MO, USA.
Ruochong FanInstitute for Informatics, Data Science and Biostatistics, Washington University School of Medicine in St. Louis, St. Louis, MO, USA.
Wenyu SongDepartment of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
Thomas G KannampallilInstitute for Informatics, Data Science and Biostatistics, Washington University School of Medicine in St. Louis, St. Louis, MO, USA.
Adam B WilcoxInstitute for Informatics, Data Science and Biostatistics, Washington University School of Medicine in St. Louis, St. Louis, MO, USA.
Linying ZhangInstitute for Informatics, Data Science and Biostatistics, Washington University School of Medicine in St. Louis, St. Louis, MO, USA. linyingz@wustl.edu.

Funding

Linking Genetic and Clinical Data to Optimize Surgical Opioid Analgesic Prescribing and Predict Risks of Opioid-Related Adverse Drug EventsK01DA059572 · NIDA · BRIGHAM AND WOMEN'S HOSPITAL · PI Wenyu Song · 2024 to 2026
$581k
NIDA NIH HHS K01 DA059572NIDA NIH HHS K01DA059572Washington University Here and Next Research Grant PJ000030799Washington University Transdisciplinary Institute in Applied Data Sciences (TRIADS) Seed Grant Program PJ000030883
6 · The paper itself

Abstract

Timely initiation of medications for opioid use disorder (MOUD) remains a critical gap in the health-system response to the opioid crisis. Although social determinants of health (SDoH) influence treatment access, their impact may vary across care settings owing to differences in clinical workflows, regulatory constraints, and system-level processes. In this retrospective cohort study, we linked electronic health records from an integrated midwestern U.S. health system with neighborhood-level SDoH and area deprivation data to examine time to MOUD initiation among patients diagnosed with opioid use disorder between 2020 and 2024. Using inverse-probability-of-censoring-weighted Cox proportional hazards models with care-setting-specific effects, we found that SDoH associations with MOUD initiation were concentrated in the outpatient setting and differed significantly across settings: a greater neighborhood share of racial or ethnic minority residents was associated with slower outpatient initiation, whereas inpatient initiation was driven primarily by clinical complexity and emergency-department initiation showed limited sensitivity to neighborhood context. These patterns were robust to adjustment for informative censoring and to follow-up length. By linking geospatial social context to routine EHR data, this approach enables scalable, care-setting-specific surveillance of treatment inequities and can inform resource allocation to accelerate equitable delivery of addiction treatment.

Identifiers

PMID42624957
PMCPMC13493884

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.