Reporting Rates of Opioid-Related Adverse Events Since 1965 in Canada: A Descriptive Retrospective Study
Bibliographic record
Abstract
BACKGROUND: Patients with chronic or acute/postoperative pain frequently use opioids. However, opioids may cause considerable adverse reactions (ARs), such as respiratory depression, which could be lethal. Unfortunately, only 5% of drug-related ARs (including those to opioids) are reported to health authorities. Therefore, little is known regarding the occurrence of opioid-related ARs at the population level. OBJECTIVE: The aim of this study was to investigate how the rates of reported opioid-related ARs have changed in Canada since 1965. METHODS: Our retrospective study examined trends of reported opioid-related ARs occurring in hospitalized and outpatients. Data on opioid-related ARs and mortality between 1965 and 2019 were obtained from the Canada Vigilance and Statistics Canada databases. Descriptive and Joinpoint regression analyses were performed. RESULTS: Oxycodone and normethadone were the most and least involved opioid agents, respectively, among the 18,407 reported ARs. The highest rate of reported opioid ARs (3.8 per 100,000 person-years) was recorded in 2012, whereas the lowest was recorded in 1965 (0.1 per 100,000 person-years). Between 1965 and 2019, annual rates climbed by 4.2% (95% confidence interval [CI] 3.1-5.2), and many fluctuations were observed: 1965-1974: +22.3% (95% CI 12.0-33.6); 1974-2000: - 4.1% (95% CI - 5.3 to - 2.9); 2000-2008: +30.3% (95% CI 22.6-38.4); 2008-2014: +4.1% (95% CI - 1.5 to 10.1); 2014-2017: -26.0% (95% CI - 44.7 to - 0.9); and, finally, 2017-2019: +35.4% (95% CI 3.8-76.7). CONCLUSION: Reported opioid-related ARs have increased since 1965, although fluctuations were observed in recent decades. The absolute number of opioid-related ARs might be seriously underestimated. Future studies should look into how to close this gap.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| 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".