Trends in dispensing of individual prescription opioid formulations, Canada 2005–2020
Bibliographic record
Abstract
BACKGROUND: Canada has experienced a distinctly bifurcated pattern of (strong) opioid utilization post-2000, with multifold increases rendering it one of the world's highest opioid consumption rates, followed by subsequent substantive declines since 2011/2012. Several interventions to control especially high-risk opioid use have been implemented post-2010 at different levels, yet with their effects assessed mostly for overall opioid utilization. Little knowledge exists for over-time patterns of individual opioid formulations. METHODS: Raw information on community-based prescription opioid dispensing for years 2005-2020 were obtained from a large national database based on a stratified sample of 6500 retail pharmacies across Canada (IQVIA/Compuscript), These data were converted into Defined-Daily-Doses/1000 population/day (DDD/1000/day) for individual (strong and weak) opioid formulations-specifically: fentanyl, hydromorphone, hydrocodone, morphine, oxycodone, codeine-per standard methods. Descriptive data on individual opioid dispensing were computed, and segmented regression (or 'broken-stick') analysis was applied to the overtime dispensing towards assessing potentially significant 'breakpoints' interrupting linear utilization trends. Akaike information criterion (AIC) values were computed to assess the resulting models' quality-of-fit. RESULTS: Five of the six opioid formulations featured a lower dispensing level in 2020 compared with 2005, but mostly with peak values in years between, contributing to the overall inversion pattern. For five of the six opioid formulations, a three-segmented model emerged as the best fit for the dispensing observed; only hydrocodone presented a linear (downward) dispensing trend. Among the five interrupted trend models for individual formulations, four (fentanyl, morphine, oxycodone, codeine but not hydromorphone) indicated their initial breakpoint during 2011-2014 introducing a downward dispensing trend. Inconsistently, morphine also featured a recent breakpoint (2018) towards a dispensing increase. CONCLUSIONS: While all opioids showed marked declines, we found heterogeneous patterns of dispensing for individual opioid formulations. While we cannot estimate direct causal effects, opioid control interventions appear to have had differential impacts on dispensing of individual formulations. The earliest breakpoint occurred towards substantive decreases for oxycodone dispensing in 2011; subsequently, there were increases in dispensing of hydromorphone and fentanyl likely due to substitution effects, followed by across-the-board declines post-2015/2016. Recent 'safer opioid' distribution programs to reduce illicit/toxic opioid exposure linked with high levels of poisoning fatalities seem to fuel resurgences in select opioid (e.g., morphine) dispensing.
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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 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".