High correlations between levels of consumption and mortality related to strong prescription opioid analgesics in British Columbia and Ontario, 2005 – 2009
Bibliographic record
Abstract
PURPOSE: Prescription opioid analgesic (POA)-related burden of disease - including mortality - is high and constitutes a major public health problem in the US and Canada. Associations between the overall levels of POA consumption and key related morbidity indicators in the population have been demonstrated. We examined potential correlations between levels of consumption of four commonly used POAs and related mortality in British Columbia (BC) and Ontario. METHODS: We investigated the correlation between annual population standardized rates of fentanyl, hydromorphone, morphine and oxycodone-related mortality (based on provincial coroners' data) and the annual Defined Daily Doses per 1000 population/day for each of the drugs dispensed (based on representative retail pharmacy sales data) in the two provinces, 2005-2009. RESULTS: Death rates increased for three (Ontario) and two (BC) of the four POA drugs; the rate of deaths for each POA drug was consistently higher in the jurisdiction with higher use levels. For each drug, strong correlations (range 0.83 to 0.97; p < 0.003) were found between POA use and mortality levels; consistent within-province correlations were found for two drugs (hydromorphone and oxycodone). CONCLUSIONS: Our findings of strong correlations between select POA use and mortality levels reflect similar evidence from elsewhere on correlations between POA consumption and morbidity or mortality indicators. In the context of high and increasing levels of POA consumption in Canada, efforts to reduce POA-related mortality may require a comprehensively revised approach towards more appropriate and safer prescribing to reduce POA use volumes together with more effective monitoring of POA medications.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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".