Predictors of active and remission substance use disorder: A decision tree analysis



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Substance use disorders (SUD) are a major public health crisis in the United States. Despite much attention, there are problems in the way that SUD has been researched and addressed in service delivery systems. Additionally, there are gaps in understanding the complexity of how individuals find resolution to their problems with substances. Recovery-oriented systems of care is a set of organizing principles to guide an overhaul of the field of addiction recovery. This study (n = 937) explored the complexity of predictors associated with SUD, identified some important predictors of SUD remission, and analyzed trends among subgroups identified in the analysis. Machine learning analyses were used to examine the exploratory research questions, confirming the complexity of predictors associated with Active SUD and SUD remission. Results identified life meaning and purpose, motivations for using, quality of life, and family acceptance as some of the important predictors. Some of the relationships between the predictors in this study fit with previous research inquiries and findings, while others had not previously been examined or demonstrated contrary or more complex findings. Overall, this study provides support for the need for a shift from the problem-focused nature of treatment to a recovery-oriented model that incorporates important principles associated with recovery-oriented systems of care.



Substance use disorder, Multiple pathways to recovery, Decision tree