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Safer Opioid Supply, Subsequent Drug Decriminalization, and Opioid Overdoses

2025· article· en· W4408693964 on OpenAlexaffabout
Hai V. Nguyen, Shweta Mital, Shawn Bugden, Emma E. McGinty

Bibliographic record

VenueJAMA Health Forum · 2025
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsUniversity of ManitobaMemorial University of Newfoundland
Fundersnot available
KeywordsMedicineSAFERDecriminalizationOpioidPossession (linguistics)MethadoneOpioid overdosePopulationDrugEnvironmental healthPharmacologyComputer securityPsychologyInternal medicine(+)-NaloxoneCriminology

Abstract

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Importance: British Columbia, Canada, was the first and only jurisdiction globally to implement a province-wide safer supply policy, which offered pharmaceutical grade opioids to individuals at risk of opioid overdose, followed by the decriminalization of drug possession. Supporters of the safer supply policy argue that this policy could save lives by offering pharmaceutical-grade opioids to people who use toxic street drugs. Similarly, proponents of decriminalization suggest that decriminalizing drug possession could reduce drug overdoses by reducing stigma associated with drug use and enabling persons who use drugs to seek addictions treatment. However, critics of both policies believe that providing safer opioids and removing penalties for drug possession may worsen the crisis. Currently, there is limited evidence on the health impacts of these policies. Objective: To assess the association of British Columbia's adoption of the safer supply policy and subsequent decriminalization of drug possession with opioid overdose hospitalizations and deaths. Design, Setting, and Participants: This observational cohort study used synthetic difference-in-differences analysis with quarterly province-level data to compare prepolicy and postpolicy changes in British Columbia with those in other Canadian provinces that did not implement these policies. The study period spanned from quarter 1 of 2016 to quarter 4 of 2023. Exposure: Safer opioid supply policy implemented in March 2020 and decriminalization of drug possession implemented in January 2023. Main Outcomes and Measures: Opioid-poisoning hospitalizations and apparent opioid-related toxicity deaths, measured as number per 100 000 population. Results: The safer supply policy alone was associated with an increase of 1.66 opioid hospitalizations per 100 000 population (95% CI, 0.41-2.92; P = .009) or 33%. The addition of drug possession decriminalization was associated with a further increase of 1.27 opioid hospitalizations per 100 000 population (95% CI, 0.05-2.50; P = .046) for an overall 58% increase compared with the period before the safer supply policy was in effect. There was insufficient evidence to conclusively attribute an increase in opioid overdose deaths to these policy changes. Conclusions and Relevance: This cohort study found that neither the safer supply policy nor the subsequent decriminalization of drug possession appeared to alleviate the opioid crisis. Instead, both were associated with an increase in opioid overdose hospitalizations. The observed increase in opioid hospitalizations, without a corresponding increase in opioid deaths, may reflect greater willingness to seek medical assistance because decriminalization could reduce the stigma associated with drug use. However, it is also possible that reduced stigma and removal of criminal penalties facilitated the diversion of safer opioids, contributing to increased hospitalizations.

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)0.001
metaresearch head score (Gemma)0.007
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 score0.949
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.307
Teacher spread0.296 · 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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Citations8
Published2025
Admission routes2
Has abstractyes

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