Regine Haardörfer1, Michael Windle1, Robert T Fairman2, Carla J Berg3. 1. Department of Behavioral, Social, and Health Education Sciences, Rollins School of Public Health, Emory University, 1518 Clifton Rd NE, Atlanta, GA 30322, United States. 2. Department of Health Policy and Behavioral Sciences, School of Public Health, Georgia State University, 140 Decatur St SE, Atlanta, GA 30303, United States. 3. Department of Prevention and Community Health, Milken Institute School of Public Health, George Washington Cancer Center, George Washington University, 800 22nd Street NW, #7000C, Washington, DC 20052, United States. Electronic address: carlaberg@gwu.edu.
Abstract
BACKGROUND: Longitudinal research regarding young-adult college student alcohol use behaviors is needed to identify risk factors and inform interventions, particularly with regard to binge-drinking. METHODS: Data from 3,418 US college students (aged 18-25) in a two-year, six-wave panel study (64.6% female, 63.4% White) were used to examine alcohol use and binge-drinking trajectories, as well as predictors of differing trajectories across individual (sociodemographics, depressive symptoms, ADHD symptoms, early-onset substance use), interpersonal (adverse childhood events, social support, parental substance use), and community factors (college type, rural/urban). RESULTS: Baseline alcohol use was associated with being White, higher parental education, early-onset use of alcohol, cigarettes, and marijuana, greater social support, parental alcohol use, attending private institutions, and rurality (p's < 0.01). Greater alcohol use over time was predicted by being White and attending private institutions (p's < 0.01). Multivariable regression indicated that predictors of binge-drinking at any assessment included older age, sexual minority, greater ADHD symptoms, early-onset substance use, parental alcohol use, attending private institutions, and rurality (p's < 0.01). GMM indicated 4 binge-drinking trajectory classes: Dabblers (89.94% of the sample), Slow decelerators (7.35%), Accelerators (1.86%), and Fast decelerators (0.84%). Fast and Slow decelerators were older; Dabblers and Fast decelerators were more likely female; Accelerators reported more depressive symptoms; Dabblers were less likely early-onset substance users; and those from rural settings were more likely Slow decelerators vs. Dabblers (p's < 0.05). CONCLUSIONS: Intervention efforts should be informed by data regarding those most likely to drink, binge-drink, and escalate use (e.g., Whites, men, early-onset users, parental use, private college students, rural).
BACKGROUND: Longitudinal research regarding young-adult college student alcohol use behaviors is needed to identify risk factors and inform interventions, particularly with regard to binge-drinking. METHODS: Data from 3,418 US college students (aged 18-25) in a two-year, six-wave panel study (64.6% female, 63.4% White) were used to examine alcohol use and binge-drinking trajectories, as well as predictors of differing trajectories across individual (sociodemographics, depressive symptoms, ADHD symptoms, early-onset substance use), interpersonal (adverse childhood events, social support, parental substance use), and community factors (college type, rural/urban). RESULTS: Baseline alcohol use was associated with being White, higher parental education, early-onset use of alcohol, cigarettes, and marijuana, greater social support, parental alcohol use, attending private institutions, and rurality (p's < 0.01). Greater alcohol use over time was predicted by being White and attending private institutions (p's < 0.01). Multivariable regression indicated that predictors of binge-drinking at any assessment included older age, sexual minority, greater ADHD symptoms, early-onset substance use, parental alcohol use, attending private institutions, and rurality (p's < 0.01). GMM indicated 4 binge-drinking trajectory classes: Dabblers (89.94% of the sample), Slow decelerators (7.35%), Accelerators (1.86%), and Fast decelerators (0.84%). Fast and Slow decelerators were older; Dabblers and Fast decelerators were more likely female; Accelerators reported more depressive symptoms; Dabblers were less likely early-onset substance users; and those from rural settings were more likely Slow decelerators vs. Dabblers (p's < 0.05). CONCLUSIONS: Intervention efforts should be informed by data regarding those most likely to drink, binge-drink, and escalate use (e.g., Whites, men, early-onset users, parental use, private college students, rural).
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