The Society for Social Work and Research

2014 Annual Conference

January 15-19, 2014 I Grand Hyatt San Antonio I San Antonio, TX

Optimizing Prevention Targets Based On Behavioral Risk in Adolescence

Saturday, January 18, 2014: 11:00 AM
HBG Convention Center, Room 102B Street Level (San Antonio, TX)
* noted as presenting author
Michael G. Vaughn, PhD, Associate Professor, Saint Louis University, St. Louis, MO
Christopher P. Salas-Wright, PhD, Assistant Professor, University of Texas at Austin, Jamaica Plain, MA
Brandy R. Maynard, PhD, Assistant Professor, Saint Louis University, St. Louis, MO
Matt DeLisi, Professor, Iowa State University, Ames, IA
Background and purpose: Despite extensive research on problem behavior such as drug abuse and violence during adolescence, less is known about the characteristics that make adolescents most “at-risk”. This is important in order to ensure that prevention efforts are specified for and are reaching those adolescents most in need. The purpose of this study was to examine variation of behavioral risk in a large nationally representative sample of adolescents and model key relationships in order to identify optimal targets for prevention services and forestall negative consequences prior to adulthood.  

Methods: This study uses data from the 2010 National Survey on Drug Use and Health (NSDUH) (SAMHSA, 2011).  The NSDUH is designed to provide population estimates of substance use and health-related behaviors in the U.S. general population.  The NSDUH interview utilizes a computer-assisted interviewing (CAI) methodology to increase the likelihood of valid respondent reports of problem behaviors.  The current study restricted analyses to adolescents aged 12-17 years (N = 18,614).  Our analytic plan consisted of four steps. First, we employed finite mixture analysis to identify homogeneous subgroups in the data based on indicators on a number of externalizing behaviors (drug use, truancy, theft, selling drugs, carrying of handgun, fighting, and violent attacks). Second, we validated these subgroups using a range of external covariates using polytomous logistic regression to predict membership in the risk subgroups. Finally, an analysis of ratios was conducted to partition the proportions of each risk behavior attributable to each subgroup to create a risk immersion score.  

Results:  Finite mixture analysis identified four subgroups comprised of a normative class (72.59%, N = 13,512), a substance user class (13.33%, N = 2,482), a violent class (9.35%, N = 1,740), and a severe class (4.73%, N = 880). The severe subgroup was characterized by markedly and consistently elevated levels of substance use, delinquency, and violence and had the highest proportion of males (64.90%), and adolescents from families earning less than $50,000 per year (63.46%). Regression analysis indicated that risk propensity, lifetime depression, parental support and control, and school disengagement were significant predictors of severe subgroup membership. Finally, risk immersion scores indicated that the severe group accounted for between 17% and 71% of each externalizing behavior in the population. Females in the severe subgroup were comparatively similar to their male counterparts.

Implications for Policy and Practice: These results add greater precision to an emerging knowledge base on optimized prevention for adolescents who are most in need.  Findings suggest that social workers who work in school settings, family and mental health service settings, and youth development agencies can potentially identify factors that can be targeted for prevention services as early as possible before risk immersion leads to dropping out of school and formal contact with institutions such as the criminal justice system.