Time trends in opioid prescribing among Ontario long-term care residents: a repeated cross-sectional study
Bibliographic record
Abstract
Background: Opioids are an important pain therapy, but their use may be associated with adverse events in frail and cognitively impaired long-term care residents. The objective of this study was to investigate trends in opioid prescribing among Ontario long-term care residents over time, given the paucity of data for this setting. Methods: We used linked clinical and health administrative databases to conduct a population-based, repeated cross-sectional study of opioid use among Ontario long-term care residents between Apr. 1, 2009, and Mar. 31, 2017. We identified prevalent opioid use by drug type, dosage and coprescription with benzodiazepines, and within certain vulnerable subgroups. We used log-binomial regression to quantify the percent change between 2009/10 and 2016/17. Results: Among an average of 76 147 long-term care residents per year, the prevalence of opioid use increased from 15.8% in 2009/10 to 19.6% in 2016/17 (p < 0.001). Over the study period, the use of hydromorphone increased by 233.2%, whereas the use of all other opioid agents decreased. The use of high-dose opioids (> 90 mg of morphine equivalents) and the coprescription of opioids with benzodiazepines decreased significantly, by 17.7% (p < 0.001) and 23.8% (p < 0.001), respectively. Increases in opioid prevalence were more notable among frail residents (37.6% v. 18.8% among nonfrail residents, p < 0.001) and those with dementia (38.6% v. 21.6% among those without dementia, p < 0.001). Interpretation: Within Ontario long-term care, trends suggest a shift toward increased use of hydromorphone but reduced prevalence of use of other opioid agents and potentially inappropriate opioid prescribing. Further investigation is needed on the impact of these trends on resident outcomes.
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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 | 0.002 |
| 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) | 0.001 | 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".