Evaluating the early impacts of delisting high-strength opioids on patterns of prescribing in Ontario
Bibliographic record
Abstract
INTRODUCTION: Ontario delisted high-strength fentanyl, hydromorphone and morphine from the public drug formulary for non-palliative care prescribers on 31 January, 2017. Our aim is to assess the early impact of this policy on prescribing patterns and to examine whether this impact varied by prescriber type, opioid type and opioid strength. METHODS: We conducted a population-based, cross-sectional study on palliative and non-palliative care patients dispensed fentanyl, hydromorphone or morphine through the Ontario public drug program between 1 January, 2014, and 31 July, 2017. For each month during the study period, we reported the total number of high-strength opioid recipients stratified by prescriber type, and the total volume of each drug dispensed, stratified by strength. We used interventional autoregressive integrated moving average (ARIMA) models to assess the policy's impact on prescribing patterns. RESULTS: We observed a 98% decrease in the total number of publicly funded recipients of high-strength opioids between December 2016 and July 2017 (5930 to 133 recipients) for all prescribers. The policy led to a significant decline in the total volume of all three opioids dispensed: hydromorphone from 20 374 621 to 16 952 097 mg (p < .01); morphine from 40 644 190 to 33 555 480 mg (p < .03); and fentanyl from 9 604 913 to 5 842 405 mcg/h (p < .01). For both fentanyl and hydromorphone, this reduction generally corresponded to an increase in the number of low-strength opioids dispensed. CONCLUSION: Delisting high-strength opioids substantially reduced the number of highstrength opioid recipients and reduced the overall volume of long-acting opioids dispensed in Ontario through the public drug program. Future studies should examine its impact on patient 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".