Impact of the COVID-19 Controlled Drugs and Substances Act exemption on pharmacist prescribing of opioids, benzodiazepines and stimulants in Ontario: A cross-sectional time-series analysis
Bibliographic record
Abstract
Background: Due to the coronavirus disease 2019 (COVID-19) pandemic, Health Canada issued an exemption to the Controlled Drugs and Substances Act (CDSA) on March 19, 2020, enabling pharmacists to act as prescribers of controlled substances to support continuity of care. Our study investigates utilization of the CDSA exemption by Ontario pharmacists with the intent to inform policy on pharmacist scope of practice and to improve future patient outcomes. Methods: We conducted a time-series analysis of pharmacist-prescribed opioid, benzodiazepine and stimulant claims data using Ontario Narcotics Monitoring System (NMS) data between January 2019 and December 2021. We used ARIMA modelling to measure the change to these classes of claims and to opioid claims containing quantities greater than a 30-day supply. Results: Postexemption, the average weekly number of pharmacist-prescribed opioid, benzodiazepine and stimulant claims rose by 146% (160 to 393 claims/week), 960% (49 to 515 claims/week) and 2150% (8 to 177 claims/week), respectively. There was a 2-week lag period between the time of announcement and the statistically significant increase in claims on April 5, 2020( p < 0.0001). The total number of claims for opioid quantities exceeding a 30-day supply decreased by 60%. Cumulative pharmacist-prescribed claims accounted for under 2% of the total NMS claims. Interpretation: Ontario pharmacists used the CDSA exemption but were prescribing at low rates. These findings suggest an effective change to pharmacy practice as the low rates show pharmacists used the exemption as a last line of defense. This may lead to further studies exploring treatment breaks during the COVID-19 pandemic and future changes to pharmacist scope to benefit patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".