Recreational and Medical Cannabis Legalization and Opioid Prescriptions and Mortality
Bibliographic record
Abstract
Importance: While some have argued that cannabis legalization has helped to reduce opioid-related morbidity and mortality in the US, evidence has been mixed. Moreover, existing studies did not account for biases that could arise when policy effects vary over time or across states or when multiple policies are assessed at the same time, as in the case of recreational and medical cannabis legalization. Objective: To quantify changes in opioid prescriptions and opioid overdose deaths associated with recreational and medical cannabis legalization in the US. Design, Setting, and Participants: This quasiexperimental, generalized difference-in-differences analysis used annual state-level data between January 2006 and December 2020 to compare states that legalized recreational or medical cannabis vs those that did not. Intervention: Recreational and medical cannabis law implementation (proxied by recreational and medical cannabis dispensary openings) between 2006 and 2020 across US states. Main Outcomes and Measures: Opioid prescription rates per 100 persons and opioid overdose deaths per 100 000 population based on data from the US Centers for Disease Control and Prevention. Results: Between 2006 and 2020, 13 states legalized recreational cannabis and 23 states legalized medical cannabis. There was no statistically significant association of recreational or medical cannabis laws with opioid prescriptions or overall opioid overdose mortality across the 15-year study period, although the results also suggested a potential reduction in synthetic opioid deaths associated with recreational cannabis laws (4.9 fewer deaths per 100 000 population; 95% CI, -9.49 to -0.30; P = .04). Sensitivity analyses excluding state economic indicators, accounting for additional opioid laws and using alternative ways to code treatment dates yielded substantively similar results, suggesting the absence of statistically significant associations between cannabis laws and the outcomes of interest during the full study period. Conclusions and Relevance: The results of this study suggest that, after accounting for biases due to possible heterogeneous effects and simultaneous assessment of recreational and medical cannabis legalization, the implementation of recreational or medical cannabis laws was not associated with opioid prescriptions or opioid mortality, with the exception of a possible reduction in synthetic opioid deaths associated with recreational cannabis law implementation.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".