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Retention in Opioid Agonist Therapy Among First Nations People

2025· article· en· W4411786213 on OpenAlexafffundabout
Alice Holton, Bisola Hamzat, Daniel McCormack, Sacha Bragg, Bernadette deGonzague, Graham Mecredy, Tonya Campbell, Tony Antoniou, Lorrilee McGregor, Jonathan Bertram, Tara Gomes

Bibliographic record

VenueJAMA Network Open · 2025
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental HealthInstitute for Clinical Evaluative SciencesNOSM UniversitySt. Michael's Hospital
FundersCanadian Institutes of Health Research
KeywordsDiscontinuationMethadoneMedicineBuprenorphine(+)-NaloxoneOpioidHazard ratioRetrospective cohort studyPopulationInternal medicineAnesthesiaEnvironmental healthConfidence interval

Abstract

fetched live from OpenAlex

Importance: First Nations people are disproportionately impacted by the opioid crisis in Canada. While many First Nation communities have expanded access to treatment, there is a need to better understand the factors associated with early discontinuation of opioid agonist therapies (OAT). Objective: To investigate factors associated with OAT retention within the first year of treatment among First Nations people in Ontario, Canada. Design, Setting, and Participants: This was a population-based retrospective cohort study including all registered (status) First Nations people aged 15 years or older initiating OAT between January 2013 and March 2021. Data were analyzed between October 2022 and June 2024. Exposure: Methadone and buprenorphine-naloxone initiation. Main Outcomes and Measures: The main outcome was duration of OAT treatment, with discontinuation defined as a gap in therapy of more than 14 days. Cox proportional hazards models followed up individuals until the first occurrence of OAT discontinuation, death, end of 1-year follow-up, or switching between OAT treatments. Results: A total of 17 880 OAT initiations among 7476 individuals (median [IQR] age, 31 [26-38] years; 8966 [50.1%] female) were identified, including 9074 new episodes of buprenorphine-naloxone and 8806 new episodes of methadone. Time to treatment discontinuation was shorter among buprenorphine-naloxone episodes (median [IQR], 42 [5-321] days) compared with methadone episodes (median [IQR], 71 [10-544] days) (P < .001). Several factors were associated with buprenorphine-naloxone and methadone retention, including living in moderately sized urban areas (buprenorphine-naloxone: adjusted hazard ratio [aHR], 0.81; 95% CI, 0.70-0.95; methadone: aHR, 0.79; 95% CI, 0.70-0.90) and being recently dispensed non-OAT opioids (buprenorphine-naloxone: aHR, 0.86; 95% CI, 0.80-0.94; methadone: aHR, 0.86; 95% CI, 0.79-0.93). In contrast, factors associated with higher rates of discontinuation included recent opioid toxic events (buprenorphine-naloxone: aHR, 1.36; 95% CI, 1.20-1.54; methadone: aHR, 1.24; 95% CI, 1.11-1.38), and recent methadone treatment (buprenorphine-naloxone: aHR, 1.09; 95% CI, 1.01-1.18; methadone: aHR, 1.67; 95% CI, 1.57-1.78). Methadone discontinuation increased over time; however this pattern was not observed for buprenorphine-naloxone. Conclusions and Relevance: This cohort study among First Nations people found low rates of OAT retention. Although retention was higher for methadone, it declined over time. These findings highlights important gaps in OAT provision for First Nations people that may be improved by investments into First Nations-led treatment programs that integrate traditional, land-based programs to better support people with opioid use disorder across Ontario.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.711
Threshold uncertainty score0.582

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.016
GPT teacher head0.291
Teacher spread0.275 · 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 source (direct Gemma or distilled Codex), 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".

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Citations6
Published2025
Admission routes3
Has abstractyes

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