Impact of changes in opioid funding and clinical policies on rapid tapering of opioids in Ontario, Canada
Bibliographic record
Abstract
ABSTRACT: Reports have emerged of abrupt tapering among recipients of long-term prescription opioids to conform new prescribing guidelines. We conducted a population-based, repeated cross-sectional time-series study among very high-dose (≥200 MME) opioid recipients in Ontario, Canada, to examine changes in the monthly prevalence of rapid tapering from 2014 to 2018, defined as recipients experiencing either a ≥50% reduction in daily doses or abrupt discontinuation sustained for 30 days. Interventional autoregressive integrated moving average models were used to test for significant changes following key guidelines and drug policies and programs. A sensitivity analysis examined rapid tapering sustained for 90 days. The monthly prevalence of rapid tapering events was stable from January 2014 to September 2016 (average monthly prevalence: 1.4%) but increased from 1.4% in October 2016 to 1.8% in April 2017 (P = 0.001), coincident with Ontario's Fentanyl Patch-for-Patch Return Program implementation. Transient spikes in the prevalence of rapid tapering also occurred 2 months after Ontario's delisting of publicly funded high-strength opioids and the release of updated Canadian Opioid Prescribing Guideline for Chronic Pain, reaching 2.3% in March 2017 and July 2017, respectively. However, this prevalence decreased to 1.2% in December 2018 (P < 0.0001). Although the prevalence of abrupt opioid discontinuation was lower, similar trends were observed. Our sensitivity analysis examining long-lasting rapid tapering found similar trends but lower prevalence, with no changes in complete discontinuation. These temporary increases in rapid tapering events highlight the need for improved communication and evidence-based resources for prescribers to minimize negative consequences of evolving policies and guidelines.
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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 | 0.001 | 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".