Substance use & misuse · 2025
Characterizing Profiles of Substance Use and Addictive Behaviors Among High School Students: A Latent Class Analysis.
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Rostam-Abadi Yasna, Stefanovics Elina A, Gueorguieva Ralitza, Potenza Marc N
Аннотация
BACKGROUND: Adolescence is a critical period for experimenting with substances and addictive behaviors. Early initiation and co-occurrence of these behaviors are associated with adverse mental health, academic, and social outcomes/measures, underscoring the importance of identifying distinct patterns. We investigated correlates of substance use, gambling, and high-frequency screen time from a representative school-based survey.
METHODS: Using 2019 Youth Risk Behavior Survey Connecticut data (N = 2,015), we conducted a latent class analysis using 17 behavioral and substance use indicators. Chi-square test and logistic regressions explored associations between class membership and demographics, health, academic grades, perceived support, physical fights, and other high-risk behaviors.
RESULTS: Four classes emerged: minimal substance use and addictive behaviors (Low Class: 63.9%), frequent screen time, gambling, e-vaping, alcohol and marijuana use (MEG (Marijuana/E-vapor/Gambling) Class: 22.0%), frequent screen time, gambling, e-vaping, alcohol use, binge drinking, marijuana and non-prescribed pain medication use (BigMEG (Binge-drinking/Marijuana/E-vapor/Gambling) Class: 11.8%), and high probability across all features (High Class: 2.3%). Compared to Low Class, males had lower odds of BigMEG Class membership; higher grades with lower odds of MEG, BigMEG, and High Classes membership; higher perceived family/teacher support with lower odds of High Class membership; good mental health with lower odds of MEG and BigMEG membership; and, involvement in physical fights and high-risk behaviors with higher odds of MEG, BigMEG, and High Classes membership.
CONCLUSION: Findings highlight the need for targeted prevention strategies addressing specific behavioral patterns. Future studies with more specific assessments are needed to better understand the patterns of gambling and high-frequency screen time.
METHODS: Using 2019 Youth Risk Behavior Survey Connecticut data (N = 2,015), we conducted a latent class analysis using 17 behavioral and substance use indicators. Chi-square test and logistic regressions explored associations between class membership and demographics, health, academic grades, perceived support, physical fights, and other high-risk behaviors.
RESULTS: Four classes emerged: minimal substance use and addictive behaviors (Low Class: 63.9%), frequent screen time, gambling, e-vaping, alcohol and marijuana use (MEG (Marijuana/E-vapor/Gambling) Class: 22.0%), frequent screen time, gambling, e-vaping, alcohol use, binge drinking, marijuana and non-prescribed pain medication use (BigMEG (Binge-drinking/Marijuana/E-vapor/Gambling) Class: 11.8%), and high probability across all features (High Class: 2.3%). Compared to Low Class, males had lower odds of BigMEG Class membership; higher grades with lower odds of MEG, BigMEG, and High Classes membership; higher perceived family/teacher support with lower odds of High Class membership; good mental health with lower odds of MEG and BigMEG membership; and, involvement in physical fights and high-risk behaviors with higher odds of MEG, BigMEG, and High Classes membership.
CONCLUSION: Findings highlight the need for targeted prevention strategies addressing specific behavioral patterns. Future studies with more specific assessments are needed to better understand the patterns of gambling and high-frequency screen time.