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Cannabis-Involved Traffic Injury Emergency Department Visits After Cannabis Legalization and Commercialization

2023· article· en· W4386467548 on OpenAlexafffundabout
Daniel T. Myran, Adrienne Gaudreault, Michael Pugliese, Douglas G. Manuel, Peter Tanuseputro

Bibliographic record

VenueJAMA Network Open · 2023
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsOttawa HospitalBruyèreUniversity of Ottawa
FundersCanadian Institutes of Health ResearchUniversity of Ottawa
KeywordsCannabisLegalizationMedicineEmergency departmentInjury preventionPoison controlOccupational safety and healthEnvironmental healthPsychiatry

Abstract

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Importance: The impact of nonmedical cannabis legalization on traffic injuries and cannabis involvement in traffic injuries is unclear. Objective: To examine changes in the number and characteristics of cannabis-involved traffic injury emergency department (ED) visits from before to after legalization and subsequent commercialization (ie, increased retail store and product availability) of cannabis in Ontario, Canada. Design, Setting, and Participants: This repeated cross-sectional study examined changes in cannabis- and alcohol-involved traffic injury ED visits in Ontario, Canada, during 3 time periods: prelegalization (January 2010-September 2018), legalization with product and retail store restrictions (October 2018-February 2020), and commercialization with new products and expanded number of stores, which coincided with the COVID-19 pandemic (March 2020-December 2021). All individuals aged 16 years and older eligible for Ontario's Universal Health Coverage were included. Season- and time-adjusted quasi-Poisson models were used to generate rate ratios with 95% CIs. Data were analyzed from March to April 2023. Main Outcomes and Measures: Quarterly counts of cannabis-involved ED visits for traffic injury. Results: There were 947 604 traffic injury ED visits, of which 426 (0.04%) had documented cannabis involvement and 7564 (0.8%) had documented alcohol involvement. Of the 418 individuals with documented cannabis involvement, 330 (78.9%) were male, 109 (25.6%) were aged 16 to 21 years (mean [SD] age at visit, 30.6 [12.0] years), and 113 (27.0%) had an ED visit or hospitalization for substance use in the 2 years before their traffic injury ED visit. Annual rates of cannabis-involved traffic injury ED visits increased 475.3% over the study period (0.18 visits per 1000 total motor vehicle collisions in 2010 to 1.01 in 2021). Over the same period, alcohol-involved traffic injury ED visits increased by 9.4% (8.03 in 2010 to 8.79 per 1000 traffic injury ED visits in 2021). Legalization with restrictions was associated with a 94% increase in the quarterly rate of cannabis involvement in traffic injury ED visits relative to prelegalization (adjusted rate ratio [aRR], 1.94; 95% CI, 1.37-2.75). Commercialization/COVID-19 was associated with a greater increase of 223% in rates (aRR, 3.23; 95% CI, 2.42-4.33). After adjusting for time trends before legalization, only commercialization/COVID-19 was associated with increased rates. Male sex (adjusted odds ratio [aOR], 3.38; 95% CI, 2.66-4.29), living in the lowest-income neighborhood (aOR, 1.92; 95% CI, 1.39-2.67), being aged 19 to 21 years (aOR, 4.67; 95% CI, 3.27-6.67), and having a prior cannabis-related ED visit (aOR, 8.03; 95% CI, 5.85-11.02) were all positively associated with cannabis involvement during a traffic injury ED visit. Conclusions and Relevance: This cross-sectional study found large increases in cannabis involvement in ED visits for traffic injury over time, which may have accelerated following nonmedical cannabis commercialization. Although the frequency of visits was rare, they may reflect broader changes in cannabis-impaired driving. Greater prevention efforts, including targeted education and policy measures, in regions with legal cannabis are indicated.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.374
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.022
GPT teacher head0.325
Teacher spread0.303 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations39
Published2023
Admission routes3
Has abstractyes

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