Patterns, trends and determinants of medical opioid utilization in Canada 2005–2020: characterizing an era of intensive rise and fall
Bibliographic record
Abstract
BACKGROUND: Into the 21st century, the conflation of high rates of chronic pain, systemic gaps in treatment availability and access, and the arrival of potent new opioid medications (e.g., slow-release oxycodone) facilitated strong increases in medical opioid dispensing in Canada. These persisted until post-2010 alongside rising opioid-related adverse (e.g., morbidity/mortality) outcomes. We examine patterns, trends and determinants of opioid dispensing in Canada, and specifically its 10 provinces, for the years 2005-2020. METHODS: Raw data on prescription opioid dispensing were obtained from a large national community-based pharmacy database (IQVIA/Compuscript), converted into Defined-Daily-Doses/1,000 population/day for 'strong' and 'weak' opioid categories per standard methods. Dispensing by opioid category and formulations by province/year was assessed descriptively; regression analysis was applied to examine possible segmentation of over-time strong opioid dispensing. RESULTS: All provinces reported starkly increasing strong opioid dispensing peaking 2011-2016, and subsequent marked declines. About half reported lower strong opioid dispensing in 2020 compared to 2005, with continuous inter-provincial differences of > 100 %; weak opioids also declined post-2011/12. Segmented regression suggests breakpoints for strong opioids in 2011/12 and 2015/16, coinciding with main interventions (e.g., selective opioid delisting, new prescribing guidelines) towards more restrictive opioid utilization control. CONCLUSIONS: We characterized an era of marked rise and fall, while featuring stark inter-provincial heterogeneity in opioid dispensing in Canada. While little evidence for improvements in pain care outcomes exists, the starkly inverting opioid utilization have been associated with extensive population-level harms (e.g., misuse, morbidity, mortality) over-time. This national case study raises fundamental questions for opioid-related health policy and practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".