Characterizing Profiles of Substance Use and Addictive Behaviors Among High School Students: A Latent Class Analysis.
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| Title: | Characterizing Profiles of Substance Use and Addictive Behaviors Among High School Students: A Latent Class Analysis. |
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| Authors: | Rostam-Abadi, Yasna (AUTHOR), Stefanovics, Elina A. (AUTHOR), Gueorguieva, Ralitza (AUTHOR), Potenza, Marc N. (AUTHOR) |
| Source: | Substance Use & Misuse. 2026, Vol. 61 Issue 1, p69-77. 9p. |
| Subjects: | Compulsive behavior -- Risk factors, Drug addiction risk factors, Risk assessment, Statistical models, Risk-taking behavior, Research funding, Mental health, Psychology of high school students, Multiple regression analysis, Smoking, Electronic cigarettes, Questionnaires, Gambling, Screen time, Structural equation modeling, Chi-squared test, Binge drinking, Age distribution, Odds ratio, Academic achievement, Cannabis (Genus), Alcoholism, Family support, Teacher-student relationships, Data analysis software, Confidence intervals, Educational attainment |
| Geographic Terms: | Connecticut |
| Abstract: | 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. [ABSTRACT FROM AUTHOR] |
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| Database: | Psychology and Behavioral Sciences Collection |
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| Abstract: | 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. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 10826084 |
| DOI: | 10.1080/10826084.2025.2544304 |
