Correlations between prescription opioid analgesic dispensing levels and related mortality and morbidity in <scp>O</scp>ntario, <scp>C</scp>anada, 2005–2011
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Prescription opioid analgesic (POA)-related harms constitute a major public health problem in North America. Ontario features above-average POA use levels in Canada and has seen consistent increases in related mortality and morbidity. Recent studies documented strong correlations between POA dispensing levels and related harm outcomes on population levels. We examined correlations between POA dispensing and key POA-related mortality and morbidity indicators in Ontario, 2005-2011. DESIGN AND METHODS: Correlations between (i) annual dispensing levels of four strong POA formulations (fentanyl, hydromorphone, morphine and oxycodone; from IMS Brogan's Compuscript converted to defined daily doses) and POA-related mortality (based on provincial coroner's data) and (ii) annual total POA dispensing and POA-related treatment caseload (from the Drug and Alcohol Treatment Information System) were examined for the study context. RESULTS: Strong and significant correlations were observed between POA dispensing and mortality for three formulations, namely hydromorphone: 0.98 [95% confidence interval (CI) 0.89-1.00; P<0.001], fentanyl: 0.93 (95% CI 0.58-0.99; P=0.003) and oxycodone: 0.93 (95% CI 0.57-0.99; P=0.003), but not morphine (-0.29; 95% CI-0.86-0.59; P=0.523), as well as for treatment when examining congruent years [0.99 (95% CI 0.92-1.00); P<0.001] and when using a 1-year offset (1.00; 95% CI 0.96-1.00; P<0.001). DISCUSSION AND CONCLUSIONS: POA dispensing levels were found to be strongly correlated with mortality and morbidity (treatment) indicators. Targeted and sensible reductions of POA use level would likely constitute a primary measure to reduce POA-related harms on a population level, especially in a jurisdiction with high POA consumption levels.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".