A finite mixture model of growth trajectories of adolescent alcohol use: Predictors and consequences.

Abstract

The current study sought to identify classes of growth trajectories of adolescent alcohol use and to examine the predictors and outcomes associated with the classes. Alcohol use was assessed from Grades 7 to 12 in a school-based sample. Latent growth mixture modeling was used, and results indicated 5 discrete longitudinal drinking patterns. The 2 most common drinking patterns included occasional very light drinking from Grades 7 to 12 and moderate escalation in both quantity and frequency of alcohol use. One group drank infrequently but at high levels throughout the study period. Another group exhibited rapid escalation in both quantity and frequency. The final group started at high levels of frequency and quantity in Grade 7 and showed rapid de-escalation in frequency. Emotional distress and risk taking distinguished the classes, and all classes, particularly rapid escalators, showed elevated levels of alcohol-related problems relative to occasional very light drinkers.

Similarities and Differences of Longitudinal Phenotypes Across Alternate Indices of Alcohol Involvement: A Methodologic Comparison of Trajectory Approaches.

“…This is consistent with Bennett et al (1999), who found that escape and enhance reasons for use were greatest for the persistent and developmentally limited drinking groups. The effects for negative affect were less robust, supporting previous work showing this to be a weaker predictor than is behavioral undercontrol (e.g., Colder et al, 2002).Consistent with cross-sectional analyses of our own data (Sher et al, 1991), we expected to see the strongest effect of family history on more problematic measures of alcohol involvement. We did, in fact, find a difference between chronic and nondiagnosing groups for AUD and alcohol dependence.…”

“…Most of the correspondence was due to concordance between corresponding groups, although, at times, there was a tendency for the chronic group to be classified as developmentally limited or later onset. Bennett et al, 1999;Chassin et al, 2002;Colder et al, 2002;Hill et al, 2000;Stice, Myers, & Brown, 1998;Tucker et al, 2003;Wills et al, 1996). Substance use such as drinking may occur in the context of general problem behavior, whereby behavioral undercontrol and substance use are two of a number of behaviors associated with a deviant lifestyle (Farrell, Danish, & Howard, 1992;Jessor & Jessor, 1977).…”

“…Theoretical (Zucker, 1987(Zucker, , 1994Zucker et al, 1995) and empirical (e.g., Bennett, McCrady, Johnson, & Pandina, 1999;Chassin, Pitts, & Prost, 2002;Colder, Campbell, Ruel, Richardson, & Flay, 2002;Tucker, Orlando, & Ellickson, 2003) work has suggested that several prototypical courses can be identified. Although results with respect to the specific characterization of course and associated prevalences vary somewhat from study to study, there is consensus in findings revealing the presence of a nonuser/stable low-use course, a chronic or high-use course, a "developmentally limited" course (maturing out of drinking), and some evidence for a later onset or increasing course.Recent research on the developmental course of alcohol involvement has varied considerably with respect to the specific variables and domains assessed and includes indices of alcohol involvement such as heavy/binge drinking (e.g., Chassin et al, 2002;Tucker et al, 2003), problem drinking (e.g., Bennett et al, 1999), and quantity-frequency (e.g., Colder et al, 2002). Assuming moderate associations among various indices of alcohol involvement, it is reasonable to hypothesize that trajectories based on one type of measure may be similar to those based on others (e.g., quantity-frequency vs. binge drinking vs. problems).…”

Conjoint Developmental Trajectories of Young Adult Alcohol and Tobacco Use.

“…Other applications of GGMM have also distinguished classes based on growth factor means only (e.g., Colder et al, 2002;Tucker et al, 2003). Although Li et al (2002) were able to model growth factor variances across class, their model was limited to two classes, which is a relatively simple analytic model.…”

“…In addition, given the large sample size (which affects values of AIC and BIC), we considered three other criteria: class prevalence (we tended not to consider classes that included less than 5% of the sample as they were unlikely to be replicable), class interpretability (the extent to which an additional class provided unique information) and stability (the extent to which the nature and prevalence of the classes changed when demographic variables were controlled) See Colder et al (2002) for a more extended explanation of these criteria. We noted significant variability around the mean for the intercept and slope factors, suggesting individual differences and the likelihood of distinct classes of heavy drinkers and smokers over the observation period.…”