Evaluating Interpersonal Associations and Substance Use Treatment Using Clinical Data Mining

ABSTRACT

This study evaluates the influence of positive interpersonal associations on treatment length of stay for clients with a substance use disorder. We used clinical data mining (CDM) methodology for this study. The clinical data we used is from client records at three substance use day treatment locations (N = 444). These sites operate under the same agency. We analyzed four measures of positive interpersonal associations including outside support systems, substance-using housing environments, 12-Step involvement, and sponsorship in a 12-Step program. Using 10 imputed datasets to run the model, we found these variables to not significantly influence treatment length of stay. However, there was a significant relationship between number of children each client had and length of stay. Results suggest that other factors may better account for varying time clients spend in treatment. Further research on the effect of other influences, such as interpersonal associations specifically within treatment, on a client’s length of stay is important.

KEYWORDS:

• Substance use disorder
• addiction
• interpersonal associations
• treatment
• length of stay
• clinical data mining

Disclosure statement

During the time of data collection and for a short time afterwards, the second author was paid by the organization where the data came from to provide clinical supervision and to consult on occasion. Further, the owner and president at the time of data collection were family relatives. The organization is now under new ownership. There is no financial gain or other incentive tied to the study reported in this manuscript. The study is independent of the clinical support he was paid to provide..

Additional information

Funding

This work was supported by the Brigham Young University, Office of Research & Creative Activities, Mentoring Environment Grants.