Are Temporal Trends Important Measures of Opioid-prescribing Risk?
Bibliographic record
Abstract
Objectives: The opioid toxicity crisis has disproportionately harmed First Nation Peoples, prompting efforts to expand access to opioid agonist therapy (OAT). We described trends in OAT use among this population in Ontario, Canada. Methods: We conducted a population-based repeated cross-sectional study of registered (“Status”) First Nation Peoples aged 15 years or older dispensed OAT in Ontario, Canada, between January 1, 2013 and December 31, 2023. We reported quarterly proportions (%) of First Nation Peoples who were dispensed OAT, including methadone, buprenorphine/naloxone, or buprenorphine extended-release (BUP-ER), overall and by OAT formulation. In addition, we examined the annual prevalence of OAT dispensing, overall and by formulation, stratified by age, sex, and residence within/outside of First Nation communities in 2023. Results: Between 2013 and 2023, quarterly OAT dispensed among First Nation Peoples doubled from 2.7% to 5.0%, plateauing at ∼5% in early 2020. Methadone dispensing remained steady, ranging from 2.0% to 2.6% of all First Nation Peoples, and was the most commonly prescribed OAT until mid-2018 when it was overtaken by buprenorphine-containing products (i.e., buprenorphine/naloxone, BUP-ER). Dispensing of buprenorphine-containing products among this population rose from 0.6% in Q1 of 2013 to 3.1% in Q4 of 2023; specifically, 2.8% buprenorphine/naloxone, 0.5% BUP-ER. In 2023, 6.1% (N = 8,518/140,615) First Nation Peoples accessed OAT, with higher rates among individuals aged 25–44 years and residing within First Nation communities. Conclusions: Information on changing trends in OAT use among First Nation Peoples may inform evolving approaches to delivering quality OUD treatment to this population that uphold their rights to dignity, autonomy, and culturally safe care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".