Artificial Neural Network Analysis Examining Substance Use Problems Co-Occurring with Anxiety and Depressive Disorders Among Adults Receiving Mental Health Treatment.

Objective: The co-occurrence of anxiety disorders, depressive disorders, and substance use problems was examined. Methods: The Mental Health Client-Level Data dataset was used to conduct logistic regression models and an artificial neural network analysis. Logistic regression analyses were conducted among adults with anxiety (n = 547,473) or depressive disorders (n = 1,610,601) as their primary diagnosis who received treatment in a community mental health center. The artificial neural network analysis was conducted with the entire sample (N = 2,158,074). Results: Approximately 30% of the sample had co-occurring high-risk substance use or substance use disorder. Characteristics including region of treatment receipt, age, education, gender, race and ethnicity, and the presence of co-occurring anxiety and depressive disorders were associated with the co-occurring high-risk substance use or a substance use disorder. Conclusions: Findings from this study highlight the importance of mental health facilities to screen for and provide integrated treatment for co-occurring disorders.

Building Equitable Community-Academic Partnerships for Opioid Recovery Research: Lessons Learned from Stakeholder Engagement With Peer and Provider Organizations.

Forming equity-based community-academic partnerships focused on recovery research is a time- consuming and challenging endeavor, but one well worth the care and effort required. Through building trusting relationships, vital research collaborations emerge, which are driven by expressed community needs and supported with university resources. This article describes the stakeholder engagement process utilized by a university-based and opioid-focused initiative entitled Innovations in Recovery through Infrastructure Support (IRIS). IRIS developed a diverse and representative network of clinical providers, peer recovery workers, academics, and other behavioral health leaders. The process was informed by community-based participatory research (CBPR) practices and principles aimed at creating equitable partnerships. Lessons learned include the need to reshape the relationship between research and the community through an acknowledgment of harms committed by academia, as well as the importance of maintaining an approach of humility, accountability, and patience with the partnership process. Concrete benefits that go beyond the long-term promise of change, including compensating partners financially for their time, help ensure equity. A commitment to always asking "Who's missing?" and then filling those gaps builds a broad network inclusive of the various constituencies that make up the recovery support system. As IRIS builds on these lessons learned and plans next steps, we share our experience to support others engaged in forming community-academic partnerships through deep stakeholder engagement and use of participatory approaches within and outside of recovery research.