Individual factors predict substance use treatment course patterns among patients in community-based substance use disorder treatment

by Evangelia Argyriou, Giorgos Bakoyannis, Wei Wu, Mary Jo Rattermann, Melissa A. Cyders

Background and objectives

Substance use disorders (SUDs) usually involve a complex natural trajectory of recovery alternating with symptom reoccurrence. This study examined treatment course patterns over time in a community SUD clinic. We examined depressive symptoms level, primary SUD assigned at each admission, and lifetime misuse of multiple substances as potential risk factors for premature treatment termination and subsequent treatment readmission.

Methods

De-identified longitudinal data were extracted from charts of 542 patients from an SUD treatment center. Survival analysis methods were applied to predict two time-to-event outcomes: premature treatment termination and treatment readmission.

Results

Primary opioid (vs alcohol) use disorder diagnosis at admission was associated with higher hazard of premature termination (HR = 1.91, p<0.001). The interaction between depressive symptoms level and substance use status (multiple vs single use) on treatment readmission was significant (p = 0.024), such that higher depressive symptoms level was predictive of readmission only among those with a history of single substance use (marginally significant effect). Lifetime use of multiple (vs single) substances (HR = 1.55, p = 0.002) and age (HR = 1.01, p = 0.019) predicted increased hazard of readmission.

Conclusions

Findings did not support a universal role for depressive symptoms level in treatment course patterns. Primary SUD diagnosis, age, and history of substance misuse can be easily assessed and incorporated into treatment planning to support SUD patients and families. This study is the first to our knowledge that afforded a stringent test of these relationships and their interactions in a time-dependent, recurrent event, competing risks survival analysis examining both termination and readmission patterns utilizing a real-world clinic-based sample.