Comparison of opioid use among long-term care residents in Ontario and Alberta, Canada: A multi-jurisdictional, repeated cross-sectional study
Bibliographic record
Abstract
Background: Exploring regional variation in opioid use for pain among long-term care (LTC) residents may help identify modifiable factors associated with suboptimal prescribing practices. Aims: We aimed to compare recent trends in prevalent opioid use and higher risk prescribing among LTC residents in Ontario and Alberta, and to examine variation in opioid trends across resident subgroups within each province. Methods: Utilizing comparable linked clinical and health administrative databases for LTC residents (aged >65) in each province, we examined trends in monthly use of any opioid, specific drug types and formulations, high daily doses (≥90 Morphine Equivalents), and concurrent use with a benzodiazepine or gabapentinoid. Prevalence ratios comparing change in opioid measures, overall and across resident subgroups, from the first (March 2015) to last study (March 2022) months were estimated using age-sex adjusted log-binomial regression models. Results: Opioid prevalence (any, select types, long-acting formulations, high daily doses) was consistently higher among Ontario residents whereas concurrent use with a benzodiazepine or gabapentinoid was higher among Alberta residents. Overall use remained stable in Ontario but increased by 23% in Alberta LTC. In both provinces, there were significant decreases in higher risk opioid prescribing over time, including concurrent use with benzodiazepines, but also significant increases in the concurrent use with gabapentinoids and tramadol use (Alberta only). Conclusions: Although both provinces showed trends toward more appropriate opioid use in LTC, the factors driving observed provincial differences in opioid prescribing and the rise in concurrent opioid and gabapentinoid use among residents, warrant further investigation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".