Examining correlations between opioid dispensing and opioid-related hospitalizations in Canada, 2007–2016
Bibliographic record
Abstract
BACKGROUND: High levels of opioid-related mortality, as well as morbidity, contribute to the excessive opioid-related disease burden in North America, induced by high availability of opioids. While correlations between opioid dispensing levels and mortality outcomes are well-established, fewer evidence exists on correlations with morbidity (e.g., hospitalizations). METHODS: We examined possible overtime correlations between medical opioid dispensing and opioid-related hospitalizations in Canada, by province, 2007-2016. For dispensing, we examined annual volumes of medical opioid dispensing derived from a representative, stratified sample of retail pharmacies across Canada. Raw dispensing information for 'strong opioids' was converted into Defined Daily Doses per 1000 population per day (DDD/1000/day). Opioid-related hospitalization rates referred to opioid poisoning-related admissions by province, for fiscal years 2007-08 to 2016-17, drawn from the national Hospital Morbidity Database. We assessed possible correlations between opioid dispensing and hospitalizations by province using the Pearson product moment correlation; correlation values (r) and confidence intervals were reported. RESULTS: Significant correlations for overtime correlations between population-levels of opioid dispensing and opioid-related hospitalizations were observed for three provinces: Quebec (r = 0.87, CI: 0.49-0.97; p = 0.002); New Brunswick (r = 0.85;CI: 0.43-0.97; p = 0.004) and Nova Scotia (r = 0.78; CI:0.25-0.95; p = 0.012), with an additional province, Saskatchewan, (r = 0.073; CI:-0.07-0.91;p = 0.073) featuring borderline significance. CONCLUSIONS: The correlations observed further add to evidence on opioid dispensing levels as a systemic driver of population-level harms. Notably, correlations were not identified principally in provinces with reported high contribution levels (> 50%) of illicit opioids to mortality, which are not captured by dispensing data and so may have distorted or concealed potential correlation effects due to contamination.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".