Correlates of driving under the influence of cannabis: A latent class analysis

As cannabis legislation evolves globally, concerns over driving under the influence of cannabis (DUIC) are increasing. The heterogeneity of DUIC risk among cannabis users remains poorly understood with most research originating from North America. In light of Germany's recent cannabis legalization and the raised legal tetrahydrocannabinol threshold for drivers, identifying high risk subpopulations is essential. This study aims to identify distinct DUIC risk profiles among German cannabis users.

We utilized Latent Class Analysis on 2023 pre-legalization survey data, involving 563 German drivers who use cannabis at least monthly. The analysis incorporated environmental and individual risk factors and risky traffic behaviors, like DUIC, as indicators. Sociodemographic characteristics were included as covariates to predict latent class membership.

Three distinct DUIC risk classes were identified. The majority (48%) fell into the "low risk" class, engaging minimally in DUIC and other risky behaviors. The "DUIC-specific risk" class (30%) demonstrated high engagement in DUIC, also among peers, and a low perceived risk associated with DUIC, but no other risky behaviors. The "global risk" class (22%) engaged in various risky behaviors and was burdened by multiple risk factors. Membership in the "global risk" class was associated with younger age, while the "DUIC-specific risk" class was linked to both younger age and male gender.

Our findings highlight the heterogeneity among individuals engaging in DUIC, suggesting tailored prevention strategies be developed based on these profiles, ranging from educational campaigns to mental health support and providing alternative transportation options.