Opioid Prescribing in Canada following the Legalization of Cannabis: A Clinical and Economic Time Series Analysis
Bibliographic record
Abstract
Rationale, aims and objectives: Between January 2016 and March 2019, an estimated 12,800 Canadians died from an opioid-related overdose. A contributing factor has been the abuse of legally obtained prescription opioids. The use of plant derived cannabinoids for chronic pain has been growing in recent years. In October 2018, recreational cannabis became legal in Canada, which resulted in increased access and a reduction in the stigma associated with usage. The purpose of this study was to assess trends in the amount and total cost of opioid prescribing in Canada prior to and following cannabis legalization. Methods: National monthly prescription claims data for public and private payers were obtained from January 2016 to June 2019. The drugs evaluated consisted of morphine, codeine, fentanyl, hydrocodone, hydromorphone, meperidine, oxycodone, tramadol and the non-opioids gabapentin and pregabalin. All opioid volumes were converted to a mean morphine equivalent dose (MED)/claim. Gabapentin and pregabalin claims data were analyzed separately from the opioids. Time series regression modelling was undertaken with dependent variables being mean MED/claim and total monthly spending. The slopes of the time series curves were then compared pre vs. post cannabis legalization. Results: Over the 42-month period, the mean MED/claim declined within public plans (p < 0.001). However, the decline in MED/claim was 5.4 times greater in the period following legalization (4.1 vs. 22.3 mg/claim). Total monthly opioid spending by public payers was also reduced to a greater extent post legalization ($95,000 vs. $267,000 per month). The findings were similar for private drug plans; however, the absolute drop in opioid use was more pronounced (30.8 mg/claim pre vs. 76.9 mg/claim post). Over the 42-month period, gabapentin and pregabalin usage also declined. Conclusions: Our findings support the hypothesis that easier access to cannabis for pain may reduce opioid use for both public and private drug plans.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.001 | 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".