Correlations between Changes in Medical Opioid Dispensing and Contributions of Fentanyl to Opioid-Related Overdose Fatalities: Exploratory Analyses from Canada
Bibliographic record
Abstract
Canada is experiencing an epidemic of opioid-related mortality, with increasing yet heterogeneous fatality patterns from illicit/synthetic (e.g., fentanyl) opioids. The present study examined whether differential provincial reductions in medical opioid dispensing following restrictive regulations (post-2010) were associated with differential contributions of fentanyl to opioid mortality. Annual provincial opioid dispensing totals in defined daily doses/1000 population/day, and change rates in opioid dispensing for the 10 provinces for (1) 2011–2018 and (2) “peak-year” to 2018 were derived from a pan-Canadian pharmacy-based dispensing panel. Provincial contribution rates of fentanyl to opioid-related mortality (2016–2019) were averaged. Correlation values (Pearson’s R) between provincial changes in opioid dispensing and the relative fentanyl contributions to mortality were computed for the two scenarios. The correlation between province-based changes in opioid dispensing (2011–2018) and the relative contribution of fentanyl to total opioid deaths (2016–2019) was −0.70 (t = 2.75; df = 8; p = 0.03); the corresponding correlation for opioid dispensing changes (“peak-year” to 2018) was −0.59 (t = −2.06; df = 8; p = 0.07). Provincial reductions in medical opioid dispensing indicated (near-)significant correlations with fentanyl contribution rates to opioid-related death totals. Differential reductions in pharmaceutical opioid availability may have created supply voids for nonmedical use, substituted with synthetic/toxic (e.g., fentanyl) opioids and leading to accelerated opioid mortality. Implications of these possible unintended adverse consequences warrant consideration for public health policy.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".