Xiang Gao1, Federico E Vaca2,3, Denise L Haynie4, Bruce Simons-Morton4, Kaigang Li1,2,3,5. 1. Department of Health & Exercise Science, Colorado State University, Fort Collins, Colorado. 2. Department of Emergency Medicine, Yale University School of Medicine, New Haven, Connecticut. 3. Yale Developmental Neurocognitive Driving Simulation Research Center (DrivSim Lab), New Haven, Connecticut. 4. Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health & Human Development, Bethesda, Maryland. 5. Colorado School of Public Health, Denver, Colorado.
Abstract
OBJECTIVE: To identify trajectory classes of risky driving among emerging adults and examine predictive associations of depressive and psychosomatic symptoms in the 12th grade with the identified trajectory classes. METHODS: Data were from the last year in high school (12th-Grade - Wave 3 [W3]) and years 1-4 after high school (Waves 4-7 [W4-7]) of the NEXT Generation Health Study, a nationally representative study starting with 10th grade (2009-2010). We measured risky driving with the 21-item Checkpoints Self-Reported Risky Driving Scale (C-RDS). Using C-RDS data from W3-7, the latent class growth modeling (LCGM) was used to identify risky driving trajectory classes. Independent variables were W3 depressive symptoms and W3 psychosomatic symptoms. Covariates included family affluence and urbanicity. The LCGM was conducted with SAS PROC Traj. The multinomial logistic regressions were used to examine the associations between the trajectory classes and independent variables, taking complex survey sampling features into account. RESULTS: Three risky driving trajectories were identified: low (N = 583, 21.43%, weighted and hereafter), medium (N = 1423, 59.22%), and high (N = 389, 19.35%) risky driving classes. Compared to the low risky driving class, one unit increase in W3 depressive symptoms was significantly associated with a higher likelihood of belonging to the medium (adjusted odds ratio [AOR] = 1.04, 95% CI 1.01, 1.07) and the high (AOR = 1.05, 95% CI 1.02, 1.08) risky driving classes, respectfully, when controlling for the covariates. Likewise, compared to the low risky driving class, one unit increase in W3 psychosomatic symptoms was significantly associated with a higher likelihood of belonging to the medium (AOR = 1.06, 95% CI 1.00, 1.13) and the high (AOR = 1.10, 95% CI 1.04, 1.16) risky driving classes, respectively, when controlling for the covariates. CONCLUSIONS: High school students with depressive and psychosomatic symptoms were at higher risk of engaging in risky driving in the immediate years after leaving high school. These findings suggest that prevention programs that incorporate screening, referral to treatment, and treatment of mental and psychosomatic symptoms in high school may be important opportunities to reduce risky driving among youth as they transition from adolescence to emerging adulthood.
OBJECTIVE: To identify trajectory classes of risky driving among emerging adults and examine predictive associations of depressive and psychosomatic symptoms in the 12th grade with the identified trajectory classes. METHODS: Data were from the last year in high school (12th-Grade - Wave 3 [W3]) and years 1-4 after high school (Waves 4-7 [W4-7]) of the NEXT Generation Health Study, a nationally representative study starting with 10th grade (2009-2010). We measured risky driving with the 21-item Checkpoints Self-Reported Risky Driving Scale (C-RDS). Using C-RDS data from W3-7, the latent class growth modeling (LCGM) was used to identify risky driving trajectory classes. Independent variables were W3 depressive symptoms and W3 psychosomatic symptoms. Covariates included family affluence and urbanicity. The LCGM was conducted with SAS PROC Traj. The multinomial logistic regressions were used to examine the associations between the trajectory classes and independent variables, taking complex survey sampling features into account. RESULTS: Three risky driving trajectories were identified: low (N = 583, 21.43%, weighted and hereafter), medium (N = 1423, 59.22%), and high (N = 389, 19.35%) risky driving classes. Compared to the low risky driving class, one unit increase in W3 depressive symptoms was significantly associated with a higher likelihood of belonging to the medium (adjusted odds ratio [AOR] = 1.04, 95% CI 1.01, 1.07) and the high (AOR = 1.05, 95% CI 1.02, 1.08) risky driving classes, respectfully, when controlling for the covariates. Likewise, compared to the low risky driving class, one unit increase in W3 psychosomatic symptoms was significantly associated with a higher likelihood of belonging to the medium (AOR = 1.06, 95% CI 1.00, 1.13) and the high (AOR = 1.10, 95% CI 1.04, 1.16) risky driving classes, respectively, when controlling for the covariates. CONCLUSIONS: High school students with depressive and psychosomatic symptoms were at higher risk of engaging in risky driving in the immediate years after leaving high school. These findings suggest that prevention programs that incorporate screening, referral to treatment, and treatment of mental and psychosomatic symptoms in high school may be important opportunities to reduce risky driving among youth as they transition from adolescence to emerging adulthood.
Authors: Xiao Li; Jacob T Borodovsky; Erin M Kasson; Andrea Fentem; Patricia A Cavazos-Rehg Journal: Drug Alcohol Depend Date: 2020-10-27 Impact factor: 4.492
Authors: Kaigang Li; Federico E Vaca; Jimikaye B Courtney; Denise L Haynie; Bruce G Simons-Morton Journal: Traffic Inj Prev Date: 2021-01-26 Impact factor: 1.491