Trends in prescription opioid use for pain in Canada: a population-based repeated cross-sectional study of 6 provinces
Bibliographic record
Abstract
BACKGROUND: National information on prescription opioid use in Canada is lacking. We sought to examine trends in the incidence and prevalence of prescription opioid dispensing for pain in Canada. METHODS: We conducted a population-based repeated cross-sectional analysis of pharmacy dispensing data from 6 provinces, which captured data from around 80% of Canada's population, between Jan. 1, 2018, and Dec. 31, 2022. We reported monthly population-adjusted rates of new and prevalent recipients of prescription opioids by province and annual provincial population-adjusted rates overall and by age, sex, neighbourhood income quintile, and location of residence. We calculated the proportion of incident recipients receiving guideline-recommended initial opioid doses and prevalent recipients receiving specific opioids indicated for pain (i.e., codeine, hydromorphone, oxycodone, morphine, and fentanyl). RESULTS: Between 2018 and 2022, the annual incidence and prevalence of prescription opioid dispensing declined by 7.9% and 11.1%, respectively, across all provinces, with monthly trends showing that Manitoba consistently had the highest, and British Columbia the lowest, rates of prevalent opioid use. In 2022, we found 1 818 680 incident and 2 770 268 prevalent recipients, with incidence ranging from 55.2 (Ontario) to 63.0 (Alberta) per 1000 population and prevalence ranging from 85.1 (Saskatchewan) to 96.3 (Alberta) per 1000 population. Annual rates were higher among females, older adults, and people living in lower-income neighbourhoods and rural regions of Canada. Initial daily doses greater than 50 mg morphine equivalents declined over time, with provincial and temporal differences in the types of opioids prescribed. INTERPRETATION: Declines in initiation and overall dispensing of prescription opioids for pain between 2018 and 2022 aligned with national efforts to promote appropriate opioid prescribing for acute and chronic noncancer pain in Canada.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".