The opioid overdose crisis emerged in predominantly White communities, but the opioid-related mortality rate is increasing most rapidly in the Black population. A key driver of the crisis is opioid use disorder, which affects over 2 million Americans. Despite their effectiveness, medications for opioid use disorder remain underused, especially among Black Americans. Compared to White Americans, Black Americans have lower access to medications for opioid use disorder, are one-third as likely to initiate treatment, and have lower retention in care. Black Americans face unique structural obstacles to care, such as mistrust of the health care system, lack of representation among medical providers, and racially-biased providers? perceptions. There is a critical gap in our understanding of the structural factors associated with treatment initiation and retention in care for Black patients with OUD. The scientific objective of this research plan is to identify modifiable structural factors at the community, provider, and facility levels that affect treatment initiation and retention in care for opioid use disorder in the Black population. This innovative project proposes to leverage machine learning-based causal inference methods with a combination of large national electronic medical records, corporate data warehouses, and publicly available data. By combining multiple data sources, this project will empirically evaluate modifiable factors such as provider characteristics (e.g., years of experience, patient satisfaction scores), facility characteristics (e.g., mental health staffing to patient ratios, number of buprenorphine-eligible prescribers), and patient-provider characteristics (e.g., number of previous visits or interactions). While focused on promoting equitable access to treatment for opioid use disorder in Black Americans, the public health implications of this proposal are expected to apply broadly to ameliorate the overall health burden of substance use disorders and reduce health disparities. This research plan is complemented by a career development plan that builds on the applicant?s background in epidemiology and biostatistics. Specifically, this career development plan outlines new training in three areas: (1) the clinical treatment of opioid use disorder, (2) analysis of the massive data of electronic medical records, and (3) machine learning-based causal inference methods. The combined research and training plan will prepare the applicant for a successful independent research career identifying, evaluating, and implementing multilevel interventions to reduce racial/ethnic inequalities in treatment for substance use disorders.
Opioid use disorder is a key driver of the U.S. opioid overdose crisis, yet treatment remains both underused and unequal. The proposed research is relevant to public health because it focuses on identifying modifiable structural predictors of treatment initiation and retention in care for opioid use disorder. The results of this project are expected to inform multilevel interventions equitably deliver treatment for opioid use disorder, ameliorate the overall health burden of substance use disorders, and reduce health disparities.
