title: A model-based cluster analysis approach to adolescent problem behaviors and young adult outcomes.
abstract: Data from a community-based sample of 1,126 10th- and 11th-grade adolescents were analyzed using a model-based cluster analysis approach to empirically identify heterogeneous adolescent subpopulations from the person-oriented and pattern-oriented perspectives. The model-based cluster analysis is a new clustering procedure to investigate population heterogeneity utilizing finite mixture multivariate normal densities and accordingly to classify subpopulations using more rigorous statistical procedures for the comparison of alternative models. Four cluster groups were identified and labeled multiproblem high-risk, smoking high-risk, normative, and low-risk groups. The multiproblem high risk exhibited a constellation of high levels of problem behaviors, including delinquent and sexual behaviors, multiple illicit substance use, and depressive symptoms at age 16. They had risky temperamental attributes and lower academic functioning and educational expectations at age 15.5 and, subsequently, at age 24 completed fewer years of education, and reported lower levels of physical health and higher levels of continued involvement in substance use and abuse. The smoking high-risk group was also found to be at risk for poorer functioning in young adulthood, compared to the low-risk group. The normative and the low risk groups were, by and large, similar in their adolescent and young adult functioning. The continuity and comorbidity path from middle adolescence to young adulthood may be aided and abetted by chronic as well as episodic substance use by adolescents.
authors.affiliation: Rutgers University, Piscataway, NJ 08854, USAeymun@rci.rutgers.edu.
authors.forename: Eun Young
authors.initials: EY
authors.lastname: Mun
authors.forename: Michael
authors.initials: M
authors.lastname: Windle
authors.forename: Lisa M
authors.initials: LM
authors.lastname: Schainker
mesh_terms: D000293:Adolescent; D000328:Adult; D000437:Alcoholism / Q000453:epidemiology* / Q000523:psychology; D016000:Cluster Analysis; D015897:Comorbidity; D019955:Conduct Disorder / Q000175:diagnosis / Q000453:epidemiology* / Q000523:psychology; D003865:Major Depressive Disorder / Q000175:diagnosis / Q000453:epidemiology* / Q000523:psychology; D005260:Female; D006304:Health Status; D006306:Health Surveys; D006801:Humans; D013287:Illicit Drugs; D007604:Juvenile Delinquency / Q000523:psychology / Q000706:statistics & numerical data*; D008137:Longitudinal Studies; D008297:Male; D015999:Multivariate Analysis; D009518:New York; D012307:Risk Factors; D012725:Sexual Behavior; D012907:Smoking / Q000453:epidemiology* / Q000523:psychology; D019966:Substance-Related Disorders / Q000175:diagnosis / Q000453:epidemiology* / Q000523:psychology; D013694:Temperament; D014477:Underachievement
chemical_list: D013287:Illicit Drugs