Identifying treatment response subgroups for adolescent cannabis use. (August 2016)

• Record Type: Journal Article
• Authors: Babbin, Steven F.; Stanger, Catherine; Scherer, Emily A.; Budney, Alan J.
• Is Part Of: Addictive behaviors. Volume 59(2016)
• Page Start: 72
• Page End: 79
• Publication Date: 2016-08
• Subjects: Cluster analysis -- Subgroup analysis -- Adolescents -- Cannabis use -- Substance abuse; Substance abuse -- Periodicals; Alcoholism -- Periodicals; Drug addiction -- Periodicals; Nicotine addiction -- Periodicals; Smoking -- Periodicals; Gambling -- Psychological aspects -- Periodicals; Electronic journals; 362.29
• DOI: 10.1016/j.addbeh.2016.03.033
• Languages: English
• ISSNs: 0306-4603

Abstract

Abstract: Introduction: Outpatient treatments for adolescent substance use demonstrate clinically meaningful reductions in substance use, but effect sizes are often low, relapse rates are high, and response to treatment is heterogeneous across participants. The present study utilized cluster analysis to identify subgroups of treatment response among adolescents from three randomized clinical trials evaluating behavioral treatments for substance use. Methods: Analyses were performed on a sample of 194 adolescents (average age = 15.8, 81.4% male) who reported cannabis use during the past 30 days or had a cannabis-positive urine test. Clustering was based on percent days cannabis use at 5 time periods (intake, end of treatment, 3, 6, and 9 months post-treatment). Participants in the identified subgroups were then compared across a number of variables not involved in the clustering (e.g., substance use, demographics, and psychopathology) to test for predictors of cluster membership. Results: Four clusters were identified based on statistical indices and visual inspection of the resulting cluster profiles: Low Use Responders (n = 109, low baseline level, sustained decrease); High Use Responders (n = 45, high baseline level, sustained decrease); Relapsers (n = 25, medium baseline level, decrease, rapid increase post-treatment); and Non-Responders (n = 15; consistently high level of use). Cannabis dependence, mean cannabis uses per day, and socioeconomic status were predictive of cluster membership. Conclusions: Cluster analysis empirically identified different patterns of treatment response over time for adolescent outpatients. Investigating homogenous subgroups of participants provides insight into study outcomes, and variables associated with clusters have potential utility to identify participants that may benefit from more intensive treatment. Highlights: Response to outpatient adolescent substance use treatment is heterogeneous. Cluster analysis was utilized to identify response subgroups for cannabis use. Low Use Responders, High Use Responders, Relapsers, and Non-Responders were found. Cannabis dependence, cannabis uses per day, and SES predicted cluster membership. These clusters provide insight into study outcomes.