Impact of delisting high‐strength opioid formulations from a public drug benefit formulary on opioid utilization in Ontario, Canada
Bibliographic record
Abstract
PURPOSE: High-strength opioid formulations were delisted (removed) from Ontario's public drug formulary in January 2017, except for palliative patients. We evaluated the impact of this policy on opioid utilization and dosing. METHODS: We conducted a longitudinal study among patients receiving publicly funded, high-strength opioids from August 2016 to July 2017. The primary outcome measure was weekly median daily opioid dose (in milligrams of morphine or equivalent; MME) of (1) publicly funded and (2) all opioid prescriptions irrespective of funding source, evaluated using interrupted time series analyses and stratified by palliative care status. RESULTS: Following policy implementation, the weekly median daily dose of publicly funded opioids decreased immediately among non-palliative patients by 10 MME (95% confidence limit [CL], -16.8 to -3.1) from a pre-intervention dose of 424.5 MME (95% CL, 417.8-431.2) and fell gradually among palliative patients by 3.9 MME per week (95% CL, -5.5 to -2.3) from a pre-intervention dose of 450.1 MME (95% CL, 432.5-467.7). In contrast, among all opioid prescriptions, gradual reductions in weekly median daily doses were observed only for non-palliative patients, which decreased by 0.7 MME per week (95% CL, -1.3 to -0.2) from a pre-intervention dose of 426.2 MME (95% CL, 420.9-431.5). CONCLUSION: The delisting of publicly-funded, high-strength opioids was accompanied by changes in funding source and small reductions in the weekly median daily doses dispensed. Although observed dose reductions of less than 1 MME weekly are likely not clinically relevant, safety implications of these changes require further monitoring.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".