Cost-Effectiveness of Medical Cannabis Versus Opioids for Chronic Noncancer Pain
Bibliographic record
Abstract
Background: Chronic noncancer pain (CNCP) affects one in five adults and is commonly managed with long-term opioid therapy. Concerns regarding rare but catastrophic harms associated with opioids, including overdose and death, have generated interest in alternatives including cannabis; however, the comparative cost-effectiveness of these management options is uncertain. Methods: We used findings from a network meta-analysis of 90 randomized trials to develop a 1-year microsimulation model to compare costs and quality-adjusted life years (QALY) between oral medical cannabis and opioids for CNCP. We used a publicly funded health care payer perspective for our analyses and obtained cost and utility data from publicly available sources. All costs are reported in 2023 Canadian dollars. All analyses were probabilistic, and we conducted sensitivity and scenario analyses to assess robustness. Results: Total mean annual cost per patient was $1,980 for oral medical cannabis and $1,851 for opioids, a difference of $129 (95% confidence interval [CI]: −$723 to $525). Mean QALYs were 0.582 for both oral medical cannabis and opioids (95% CI: −0.007 to 0.015). Cost-effectiveness acceptability curves showed that oral medical cannabis was cost-effective in 31% of iterations at willingness-to-pay thresholds up to $50,000/QALY gained. Use of opioids is associated with nonfatal and fatal overdose, whereas medical cannabis is not. Discussion: Our findings suggest that medical cannabis as an alternative to opioids for chronic pain may confer similar, but modest, benefits to patients, and reduce the risk of opioid overdose without substantially increasing costs.
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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 | 0.013 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.009 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".