Real-world complexities in opioid use disorder treatment: understanding family comorbidity, high-risk medication use, and costs related to treatment adherence and health outcomes

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PROJECT SUMMARY Medications for opioid use disorder (MOUD) have the potential to improve the health and well-being of more than 2.1 million Americans with OUD, however, long-term adherence particularly to buprenorphine is alarmingly poor. Pain, mental health, and substance use disorders are increasingly recognized as risk factors for inadequate treatment adherence and often co-occur in families due to shared genetic and environmental factors. Understanding comorbidities in patients with OUD and their family members, and the impact of these comorbidities on poor opioid use outcomes, can help identify patients at risk for inadequate treatment adherence and serious adverse events. Further, information on the costs associated with buprenorphine non-adherence and family comorbidities can inform health insurance reimbursement policies. The overall career goal of the recipient is to become a leading pharmacoepidemiologist focused on improving treatment for substance use disorders, particularly opioid use disorders. The goal of this K01 is to train the recipient to investigate associations between family comorbidities and/or prescription medications with a high risk of misuse and buprenorphine treatment adherence, opioid use outcomes, and costs to the family unit and health insurer. Research aims of this project are to: (1) develop a clinically relevant prediction model to identify patients prescribed buprenorphine at risk of inadequate adherence; (2) determine whether other prescriptions in the family are associated with poor buprenorphine adherence and opioid-related adverse events; and (3) compare overall healthcare costs to the family and health insurer across varying levels of buprenorphine adherence. The training aims of this project are to: (1) gain understanding of the clinical assessment and diagnosis of opioid use disorders and comorbid mental health conditions; (2) learn and apply innovative methods for dyadic data analyses; (3) learn and apply methods for conducting economic evaluations of substance use treatment; (4) hone professional skills in research, publishing, and project administration; and (5) responsible conduct of research. Training aims will be pursued through tutorials with world-renowned experts forming the recipient's mentorship team, graduate-level coursework, workshops and seminars, participation in scientific meetings, and mentored research. Research aims will be accomplished using the OptumLabs Data Warehouse, a large integrated commercial healthcare insurance claims database that tracks beneficiaries, spouses, and dependents across health plans and over time. This project will fill an important gap in our understanding of how family comorbidities and medication use by family members influence MOUD treatment adherence, outcomes, and costs, and will provide evidence to support interventions by clinicians and health insurers to improve MOUD adherence outcomes.