The impact of proposed regulatory changes and rescheduling on low-dose codeine purchasing in Canada: a time-series analysis
Bibliographic record
Abstract
Background: Low-dose codeine products can be purchased without a prescription in most of Canada. We explored trends in the purchasing of these products across the Canadian provinces from 2014 to 2019, evaluating the impact of Health Canada’s 2016 announcement of a proposed regulatory change and the 2017 opening of a 60-day public comment period, as well as the impact of Manitoba’s 2016 policy change requiring a prescription for the purchase of all codeine products in that province. Methods: We evaluated population-adjusted monthly purchasing of codeine products from January 2014 to October 2019 using the IQVIA Canadian Drug Store and Hospital Purchases Audit database, stratified by province and over-the-counter (OTC) status. The primary outcomes were change in the monthly volume of low-dose codeine purchased after the 2016 federal regulatory proposal and the 2017 period of public comment across the provinces. Our secondary analysis was the impact of Manitoba’s policy change in February 2016 requiring a prescription for low-dose codeine. We conducted a time-series analysis using interventional autoregressive integrated moving average models. Results: Over the study period, 24 120 kg of codeine (3.025 billion units) and 937 867 kg of acetaminophen were sold as OTC, low-dose codeine products across the Canadian provinces. Health Canada’s 2016 announcement did not significantly affect OTC codeine purchasing (p = 0.57). The initiation of a 60-day public comment period was associated with a roughly 44% decrease in OTC codeine purchasing (p = 0.03). In Manitoba, purchasing of the same codeine formulations decreased after rescheduling in February 2016 (p < 0.001). We observed no significant change in the rate of purchasing of higher dose codeine formulations in response to scheduling changes in Manitoba (p = 0.22). Interpretation: Although Health Canada’s 2016 announcement of a proposed regulatory change did not appear to have an effect on OTC codeine purchasing nationally, the 60-day comment period was associated with a decrease in purchasing. Further, Manitoba’s 2016 policy change was associated with a significant and sustained decrease in the overall volume of codeine purchased. Given the potential risks of codeine dependence and acetaminophen toxicity with these products, a national rescheduling strategy should be considered.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".