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Introduction Substance use disorders (SUD) are chronic conditions that often warrant coordinated medical care throughout a relapsing and remitting course. However, SUD treatment is frequently measured as a binary outcome, where individuals either receive or do not receive care following the immediate treatment seeking event. This study aimed to describe longitudinal treatment seeking behaviors by assessing service use patterns among individuals with diagnosed SUDs in a safety net hospital that offers a “no wrong door” care model. This study also examined whether certain patient subgroups were more likely to transition to service use patterns that support recovery or treatment disengagement over time. Methods The team conducted a retrospective cohort study using electronic health record data from adult patients diagnosed with SUDs (n = 1157) who regularly accessed services at a safety net hospital over a five-year period. The study used latent class analysis (LCA) and latent profile analysis to empirically identify distinct treatment utilization profiles of individuals with SUDs. We used multinomial logistic regression to evaluate predictors of class membership and transitions over a five-year period. Results The research team identified five distinct service use classes, including patients who disengaged from services (42.4%), or those who predominantly used outpatient substance use services (7.0%), mental health services (13.0%), primary care services (24.7%), or other specialty care services (13.1%). Being female and an older adult were statistically significant predictors for membership in any service use–driven status. Black patients had increased odds for “substance use service” and “primary care” service statuses over time. Conclusion LCA and latent transition analysis (LTA) methods are novel approaches for identifying profiles of patients with higher risk for health services disengagement. SUD treatment engagement strategies are needed to reach males, young adults, and individuals with non-opioid SUDs.

INTRODUCTION: Opioid overdoses are a major public health emergency in the United States. Despite effective treatments that can save lives, access to and utilization of such treatments are limited. Community context plays an important role in addressing treatment barriers and increasing access. The HEALing Communities Study (HCS) is a multisite community-level cluster-randomized trial that will study implementation and outcomes of a community coalition-based intervention (Communities that HEAL [CTH]) that implements evidence-based practices (EBPs) to reduce opioid overdose deaths in four states. To examine contextual factors critical to understanding implementation, we assessed the perspectives of community members about their communities, current substance use–related services, and other important issues that could impact intervention implementation. METHODS: Researchers conducted 382 semi-structured qualitative interviews in the HCS communities. Interviews were audio-recorded and transcribed; researchers subsequently analyzed data using directed content analysis based on the constructs of the RE-AIM/PRISM implementation science framework to identify key themes within the external community context. RESULTS: Despite the diversity in states and communities, four similar themes related to the external community context emerged across communities: These themes included the importance of understanding: 1) community risk perceptions, 2) levels of stigma, 3) the health services environment and the availability of substance use services, and 4) funding for substance use services. CONCLUSION: Understanding and addressing the external community context in which the CTH intervention and EBPs are implemented are crucial for successful health services-related and community engaged interventions. While implementing EBPs is a challenging undertaking, doing so will help us to understand if and how a community-based intervention can successfully reduce opioid overdose deaths and influence both community beliefs and the community treatment landscape.