Prescribing of opioid analgesics and related mortality before and after the introduction of long-acting oxycodone
Bibliographic record
Abstract
INTRODUCTION: Opioid-related mortality appears to be increasing in Canada. We examined the true extent of the problem and the impact of the introduction of long-acting oxycodone. METHODS: We examined trends in the prescribing of opioid analgesics in the province of Ontario from 1991 to 2007. We reviewed all deaths related to opioid use between 1991 and 2004. We linked 3271 of these deaths to administrative data to examine the patients' use of health care services before death. Using time-series analysis, we determined whether the addition of long-acting oxycodone to the provincial drug formulary in January 2000 was associated with an increase in opioid-related mortality. RESULTS: From 1991 to 2007, annual prescriptions for opioids increased from 458 to 591 per 1000 individuals. Opioid-related deaths doubled, from 13.7 per million in 1991 to 27.2 per million in 2004. Prescriptions of oxycodone increased by 850% between 1991 and 2007. The addition of long-acting oxycodone to the drug formulary was associated with a 5-fold increase in oxycodone-related mortality (p<0.01) and a 41% increase in overall opioid-related mortality (p=0.02). The manner of death was deemed unintentional by the coroner in 54.2% and undetermined in 21.9% of cases. Use of health care services in the month before death was common: for example, of the 3066 patients for whom data on physician visits were available, 66.4% had visited a physician in the month before death; of the 1095 patients for whom individual-level prescribing data were available, 56.1% had filled a prescription for an opioid in the month before death. INTERPRETATION: Opioid-related deaths in Ontario have increased markedly since 1991. A significant portion of the increase was associated with the addition of long-acting oxycodone to the provincial drug formulary. Most of the deaths were deemed unintentional. The frequency of visits to a physician and prescriptions for opioids in the month before death suggests a missed opportunity for prevention.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".