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Record W4410085235 · doi:10.1377/hlthaff.2024.00984

Medicaid Patients With ED Visits For Overdose: Disparities In Initiation Of Medications For Opioid Use Disorder

2025· article· en· W4410085235 on OpenAlexaff
Thủy Nguyễn, Yang Jiao, Stephanie S. Lee, Pooja Lagisetty, Amy S. B. Bohnert, Keith E. Kocher, Kao‐Ping Chua

Bibliographic record

VenueHealth Affairs · 2025
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsSt. Stephen's University
FundersNational Institute on Drug Abuse
KeywordsMedicaidOpioid use disorderOpioid overdoseMedicinePsychiatryMedicare Part DOpioidMedical emergencyFamily medicineEmergency medicineHealth careNursingMedical prescriptionInternal medicinePrescription drug

Abstract

fetched live from OpenAlex

Medications for opioid use disorder (MOUD) after emergency department (ED) visits for overdose can reduce subsequent overdose deaths, but disparities in receiving MOUD persist in the US. Using national Medicaid claims data from the period 2016-20, we examined racial and ethnic disparities in MOUD initiation after ED visits for opioid overdose. Overall, 6.4 percent of Medicaid ED visits were associated with a claim for MOUD within thirty days. This rate was highest among non-Hispanic White (7.3 percent) patients and lowest among non-Hispanic Black (4.3 percent) and Hispanic (4.9 percent) patients. The adjusted rate of MOUD initiation was 2.5 percentage points lower among Black patients compared with White patients, and this disparity increased nearly twofold between 2016 and 2020. Although policy and clinical efforts to increase MOUD use in the Medicaid population are important, findings suggest that efforts targeting patients from racial and ethnic minority groups may be warranted.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score0.484

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.312
Teacher spread0.301 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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