Impact of cannabis mass gathering events on mental health and health service utilization
Bibliographic record
Abstract
Abstract Background The Canadian government has legalized and regulated access to cannabis as of October 2018. In this context, there is a need to analyze data that may provide insights on the effects of increased accessibility and tolerance for cannabis use. One source of data is the phenomenon known as “4/20”, a decades-old yearly mass gathering event supporting the legalization of cannabis. These events offer naturalistic epidemiologic data to ascertain specific impacts of cannabis consumption in a context of increased tolerance on health service utilization. Our study assessed the association between cannabis mass gathering events and health service utilization related to mental illness and substance use disorders at the nearest local emergency department. Methods Emergency department service utilization data (2005-2015) was used. The sample analyzed consists of emergency department visits due to mental and substance use disorders. A multiple linear regression model was used to predict the number of daily visits with year, month, day of the week, and day of income assistance distribution as independent variables. Daily residuals were averaged, and residuals for the days with the highest number of visits were compared with the mean residual number of visits. Also, correlation of number of visits with attendance to mass gathering events was explored. Results The residual number of visits for mental health and substance use disorder was the highest on April 20 th 2015 (n=51.0, z-score=11.0, p<0.001), and on days associated with subsequent cannabis mass gathering events. Moreover, this number of visits is positively correlated with the number of attendees at the “4/20” event (Pearson’s correlation coefficient: 0.76, 95% CI: 0.19 to 0.956, p=0.002), and increased over time. Conclusion Cannabis mass gathering events were associated with an increased number of emergency visits for patients with mental health and substance use diagnoses at the nearest local emergency department. In the context of legalization and regulation of cannabis use, these specific gatherings will not necessarily be discontinued. Indeed, as per news reports the recent post-legalization “4/20” drew tens of thousands of people in Vancouver. Also, in the new context other non-specific mass gatherings may also lead to foreseeable episodic surges in ER utilization. In light of this and from a public health perspective, services need to be prepared to care for predictably larger numbers of people suffering cannabis intoxication during mass gatherings, as well as to make provisions to provide all other services that are regularly needed for other emergency conditions. Also, educational campaigns about responsible use during these events will become particularly important, as well as offering on-site support, triage and basic services. This will allow for specific care to be provided in a non-stigmatizing manner, proportional to need, and without overcrowding general emergency services.
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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 | 0.003 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".