Correlations between opioid mortality increases related to illicit/synthetic opioids and reductions of medical opioid dispensing - exploratory analyses from Canada
Bibliographic record
Abstract
BACKGROUND: North America has been experiencing a persistent epidemic of opioid-related overdose mortality, which has increasingly been driven by fatalities from illicit, toxic opioids in most recent years. Patterns of synthetic opioid availability and related mortality are heterogeneous across Canada, and differing explanations exist as to their differentiated proliferation. We examined the perspective that heterogeneous province-based variations in prescription opioid availability, facilitated by various control strategies, post-2010 may have created regionally differential supply gaps for non-medical opioid use substituted by synthetic opioid products with differential impacts on mortality risks and outcomes in Canada. METHODS: We examined annual, prescription opioid dispensing rates and changes in the ten Canadian provinces (for the periods of 1) 2011-2018, 2) 'peak-year'-to-2018) in Defined Daily Doses/1000 population/day, derived from data from a large representative, stratified sample of community pharmacies projected to a Canada total. Annual, provincial opioid-related mortality rates and changes for years 2016-2018 were calculated from federal data. We computed correlation values (Pearson's R) between respective province-based change rates for prescription opioid dispensing and opioid-related mortality for the two over-time scenarios. RESULTS: All but one province featured reductions in prescription opioid dispensing 2011-2018; seven of the ten provinces had increases in opioid mortality 2016-2018. The correlation between changes in opioid dispensing (2011-2018) and in opioid-mortality (2016-2018) was r = 0.63 (df = 8, p-value: 0.05); the correlation was r = 0.57 (df = 8, p-value: 0.09) for changes in opioid dispensing 'peak year'-to-2018, respectively. CONCLUSIONS: Quasi-significant results indicate that recent increases in opioid-related deaths driven by illicit, synthetic opioids tended to be larger in provinces where reductions in prescription opioid availability have been more extensive. It is a plausible explanation that these reductions created supply gaps for non-medical opioid use increasingly filled by illicit, synthetic opioids differentially contributing to opioid-related deaths, generating un-intended adverse effects for previous interventions. General prevention measures to reduce opioid availability, and targeted prevention for at-risk opioid users exposed to toxic drug supply may be include counteractive effects and require coordinated reconciliation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".