Summary
Introduction:
This text provides an analysis of the relationship between recreational marijuana legalization (RML) and marijuana vaping among young adults in the United States. The study employs a machine learning approach to identify risk profiles for marijuana vaping and investigates whether these risk profiles differ by state-level RML status. The analysis is based on data from the Population assessment of Tobacco and health Study and includes predictors such as sociodemographic characteristics, psychosocial factors, digital media use behaviors, and substance use experiences.
Key Points:
* The study found that marijuana vaping initiation was significantly higher among young adults living in states with RML compared to those living in states without RML.
* Substance-use-related predictors were rarely found as leading predictors for marijuana vaping in the states with RML, but were more common in the states without RML.
* Externalizing symptoms, such as having a hard time listening to instructions, emerged as a leading predictor of marijuana vaping in the states without RML.
* The study suggests that externalizing symptoms may be a behavioral endophenotype of marijuana vaping, indicating a possible genetic predisposition for marijuana vaping.
* The machine learning approach used in the study revealed nonlinear interactions between predictors, highlighting the complexity of marijuana vaping behavior.
* The LaSSO models retained fewer predictors for marijuana vaping in the states with RML compared to the states without RML.
* The study did not include potential predictors such as risk perception and attitudes towards marijuana use, and future studies are needed to explore these factors.
Main Message:
The study highlights the importance of accounting for RML status in developing risk profiles of marijuana vaping to inform effective and tailored prevention programs. The findings suggest that externalizing symptoms may be a behavioral endophenotype of marijuana vaping, indicating a possible genetic predisposition for marijuana vaping. additionally, the machine learning approach used in the study revealed nonlinear interactions between predictors, emphasizing the complexity of marijuana vaping behavior. Overall, the study provides valuable insights into the relationship between RML and marijuana vaping among young adults in the United States.
Citation
han Dh, Seo DC. Identifying risk profiles for marijuana vaping among U.S. young adults by recreational marijuana legalization status: a machine learning approach. Drug and alcohol dependence. 2022;232:109330. doi:10.1016/j.drugalcdep.2022.109330
