And the Monitoring the Future Study (MTF) (Johnston et al. 2006). Drug use items included assessment of last 30 days and lifetime use of alcohol, marijuana, cigarettes, inhalants, chewing tobacco, cigars, methamphetamine, Ritalin, steroids, ecstasy, LSD, heroin, cocaine,J Gambl Stud. Author manuscript; available in PMC 2013 June 01.Betancourt et al.Pageand any IV drug use. For each substance, use in the last 30 days was coded as “1” and nonuse was coded as “0”. These 14 individual scores were added together to create an overall Chloroquine (diphosphate) web recent drug use total score. Drug use trajectories used in analyses were based on this total score. Problem Behaviors Problem behaviors other than drug use were assessed with the Youth Self-Report (YSR) of the Achenbach System of Empirically Based Assessment (Achenbach and Rescorla 2001), which provides standardized ratings of Internalizing and Externalizing behavior problems. The Internalizing scale reflects problems with the self such as anxiety, depression, somatic complaints and social withdrawal. The Externalizing scale reflects problems with T0901317 supplier others such as aggressive and rule breaking behaviors. The YSR has very good reliability (Cronbach’s Alpha = 0.90) and is used extensively to identify clinically significant problem behaviors in youth. For analysis, scores collected at the last assessment were used and classified dichotomously as Normal (T score <60) or in the Borderline or Clinical range (T scores 60) based on YSR norms (Achenbach and Rescorla 2001). Statistical Analyses Group-based-trajectory modeling methods developed by Nagin et al. were used to group participants following similar developmental patterns for gambling (Jones et al. 1998; Nagin 1999; Nagin and Tremblay 2001). Trajectory modeling was also used to group participants following similar developmental patterns for executive cognitive functions, impulsivity, and drug use. Preliminary bivariate analyses, including Chi-square, t-tests, and logistic regression, were conducted to examine the relation between gambling group membership and important covariates. Finally, in our main analysis, a multivariable logistic regression model was fitted with gambling trajectory group as outcome and variables including all executive cognitive function and impulsivity trajectory groupings and other measured covariates that were significant at P 0.10 in preliminary bivariate analyses. A backward selection algorithm was applied.NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author ManuscriptResultsUsing the collapsed variable for self-reported gambling for money (no gambling, no recent gambling, and recent gambling in last 30 days) for each of the three assessments, two gambling trajectories were identified as shown in Fig. 1. The first trajectory group (n = 111), termed Early Gamblers, constituted 29 of the sample. By definition, 100 of Early Gamblers reported some gambling behavior. A second trajectory group (n = 276), termed Later Gamblers, constituted 71 of the sample. Of this group, 92 reported no gambling at the first two of the three assessments, and 65 reported no gambling behavior at any assessment. Early Gamblers had a higher probability of gambling at age 10 and by age 15 their probability of gambling was 1.0, with Later Gamblers' probability of gambling being 0.4 by age 15. The most frequently reported gambling behaviors were betting money on cards (27 ) and sports (24 ). Trajectories for executive cognitive function are shown in Fi.And the Monitoring the Future Study (MTF) (Johnston et al. 2006). Drug use items included assessment of last 30 days and lifetime use of alcohol, marijuana, cigarettes, inhalants, chewing tobacco, cigars, methamphetamine, Ritalin, steroids, ecstasy, LSD, heroin, cocaine,J Gambl Stud. Author manuscript; available in PMC 2013 June 01.Betancourt et al.Pageand any IV drug use. For each substance, use in the last 30 days was coded as "1" and nonuse was coded as "0". These 14 individual scores were added together to create an overall recent drug use total score. Drug use trajectories used in analyses were based on this total score. Problem Behaviors Problem behaviors other than drug use were assessed with the Youth Self-Report (YSR) of the Achenbach System of Empirically Based Assessment (Achenbach and Rescorla 2001), which provides standardized ratings of Internalizing and Externalizing behavior problems. The Internalizing scale reflects problems with the self such as anxiety, depression, somatic complaints and social withdrawal. The Externalizing scale reflects problems with others such as aggressive and rule breaking behaviors. The YSR has very good reliability (Cronbach's Alpha = 0.90) and is used extensively to identify clinically significant problem behaviors in youth. For analysis, scores collected at the last assessment were used and classified dichotomously as Normal (T score <60) or in the Borderline or Clinical range (T scores 60) based on YSR norms (Achenbach and Rescorla 2001). Statistical Analyses Group-based-trajectory modeling methods developed by Nagin et al. were used to group participants following similar developmental patterns for gambling (Jones et al. 1998; Nagin 1999; Nagin and Tremblay 2001). Trajectory modeling was also used to group participants following similar developmental patterns for executive cognitive functions, impulsivity, and drug use. Preliminary bivariate analyses, including Chi-square, t-tests, and logistic regression, were conducted to examine the relation between gambling group membership and important covariates. Finally, in our main analysis, a multivariable logistic regression model was fitted with gambling trajectory group as outcome and variables including all executive cognitive function and impulsivity trajectory groupings and other measured covariates that were significant at P 0.10 in preliminary bivariate analyses. A backward selection algorithm was applied.NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author ManuscriptResultsUsing the collapsed variable for self-reported gambling for money (no gambling, no recent gambling, and recent gambling in last 30 days) for each of the three assessments, two gambling trajectories were identified as shown in Fig. 1. The first trajectory group (n = 111), termed Early Gamblers, constituted 29 of the sample. By definition, 100 of Early Gamblers reported some gambling behavior. A second trajectory group (n = 276), termed Later Gamblers, constituted 71 of the sample. Of this group, 92 reported no gambling at the first two of the three assessments, and 65 reported no gambling behavior at any assessment. Early Gamblers had a higher probability of gambling at age 10 and by age 15 their probability of gambling was 1.0, with Later Gamblers' probability of gambling being 0.4 by age 15. The most frequently reported gambling behaviors were betting money on cards (27 ) and sports (24 ). Trajectories for executive cognitive function are shown in Fi.
