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Record W4409656928 · doi:10.1016/s2468-2667(25)00070-2

Comparing the effects of prescribed safer opioid supply and methadone in Ontario, Canada: a population-based matched cohort study

2025· article· en· W4409656928 on OpenAlexafffundabout
Tara Gomes, Daniel McCormack, Gillian Kolla, Samantha Young, Ahmed M. Bayoumi, Ashley Smoke, Ping Li, Tony Antoniou

Bibliographic record

VenueThe Lancet Public Health · 2025
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsSt. Michael's HospitalOntario Stroke NetworkSt. Paul's HospitalUniversity of TorontoBritish Columbia Centre on Substance UseMemorial University of NewfoundlandInstitute for Work & Health
FundersInstitut canadien d'information sur la santéMinistry of Long-Term CareCanadian Institutes of Health ResearchInstitute for Clinical Evaluative SciencesMinistry of Health, Ontario
KeywordsMethadoneMedicineOpioidSAFERCohort studyCohortEmergency medicinePopulationDemographyAnesthesiaEnvironmental healthInternal medicineStatistics

Abstract

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BACKGROUND: Prescribed safer opioid supply (SOS) programmes are novel harm reduction interventions. We examined health outcomes among people receiving SOS over time and relative to a similar group of people receiving methadone. METHODS: We conducted a population-based cohort study among new SOS and methadone recipients in Ontario, Canada, who commenced treatment between Jan 1, 2016 and Dec 31, 2021. People receiving SOS were matched (1:1) to new methadone recipients based on age (within 3 years), sex, location of residence (public health unit), and propensity score (within 0·2 SDs). Primary outcomes were hospital-treated opioid-related toxicities, emergency department visits and inpatient hospitalisations, incident infections, and health-care costs (in CA$, excluding costs related to primary care services and medications) over 1 year of follow-up. Outcome rates were calculated over the follow-up period, with censoring on death, discontinuation of SOS or methadone, or end of follow-up (360 days). Within-group changes in outcomes were assessed using interrupted time-series analysis, and Prentice-Williams-Peterson regression was used to assess between-group differences in recurrent events. FINDINGS: Of the 991 new recipients prescribed SOS and 25 116 new methadone recipients who met the eligibility criteria, 856 (86·4%) people receiving SOS were matched to 856 people receiving methadone. In the within-group analysis, matched SOS recipients had significant improvements in the monthly rate of opioid toxicities (step change -1·09 events per 100 individuals [95% CI -2·12 to -0·07]; p=0·037), all-cause emergency department visits (-8·85 per person-year [-13·5 to -4·20]; p=0·0002), all-cause inpatient hospitalisations (-2·08 per person-year [-3·41 to -0·75]; p=0·0022), incident infections (-0·68 per person-year [-1·22 to -0·14]; p=0·013), and non-primary-care-related health-care costs (-$91 699 per person-year [-112 749 to -70 650]; p<0·0001). Results were consistent for methadone recipients. In the between-group analysis, individuals commencing SOS had significantly higher hazards of opioid toxicity (hazard ratio 2·83 [95% CI 1·97 to 4·06]), emergency department visits (1·16 [1·05 to 1·29]), and inpatient admissions (1·50 [1·13 to 1·99]), no significant difference in the rate of incident infection (1·51 [0·87 to 2·61]), and were less likely to discontinue treatment than those commencing methadone (0·62 [0·55 to 0·70]). When treatment discontinuation was removed as a censoring criterion, we found no difference between groups in the hazard of any of the primary outcomes except opioid toxicity (1·65 [1·38 to 1·97]). INTERPRETATION: SOS and methadone were associated with improvements in health outcomes, including reduced opioid toxicities and health-care use, in the year after treatment initiation. The findings suggest SOS programmes play an important, complementary role to traditional opioid agonist treatment in expanding the options available to support people who use drugs. FUNDING: Canadian Institutes of Health Research and Ontario SPOR Support Unit.

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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)0.001
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 score0.035
Threshold uncertainty score0.687

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.029
GPT teacher head0.296
Teacher spread0.267 · 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

Citations9
Published2025
Admission routes3
Has abstractyes

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