Disparities, Perceived Discrimination, and Patient-Clinician Communication in Alcohol Use Disorder Treatment: An All of Us Cohort Study

Background and AimsAlcohol use disorder (AUD) remains a major public health concern, with persistent disparities in access to evidence-based treatment. This study aimed to examine associations between perceived discrimination in healthcare settings (PDHS), patient-clinician communication (PCC), and receipt of treatment for AUD, and compared these with sociodemographic and insurance-related factors. DesignCross-sectional analysis using structural equation modeling (SEM), logistic and multinomial logistic regression, and machine learning approaches including SHapley Additive exPlanations (SHAP). SettingUnited States, using data from the National Institutes of Health All of Us Research Program. ParticipantsA total of 5,287 adults with AUD (mean age 61 years; 57% men), including 71.6% non-Hispanic White, 12.2% Black, and 8.6% Hispanic participants. Insurance coverage included 52% government (Medicaid/Medicare), 37% private, and 21% military with 19% reporting more than one type. MeasurementsPrimary outcomes were receipt of Food and Drug Administration-approved pharmacotherapy and/or psychotherapy for AUD, examined as binary and multinomial outcomes. The primary exposure was PDHS, measured using a 7-item scale (range 7-35), with higher scores indicating more frequent discrimination. PCC, assessed using a 2-item scale (range 2-8) with higher scores indicating poorer communication, was examined as a potential mediator. Models were adjusted for age group, sex at birth, race/ethnicity, insurance type (government, private, military), household income, and Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) scores (range 0-12). FindingsPDHS was associated with poorer PCC ({beta} = 0.209, p < 0.001), although communication was not independently associated with treatment receipt. The indirect pathway from discrimination to treatment via communication was not supported. Military insurance was the strongest predictor of treatment receipt, with 6-7 times higher odds compared with other insurance types. Higher AUDIT-C scores and greater PDHS were also associated with increased likelihood of treatment. In analyses restricted to civilian participants, PDHS showed a stronger association with treatment receipt, while PCC demonstrated more modest effects. Machine learning models identified PDHS, AUDIT-C, and PCC as strong contributors, with the impact of poor communication most pronounced among individuals with lower income. ConclusionsAccess to treatment for alcohol use disorder is most strongly associated with insurance coverage, particularly military insurance. PDHS and PCC also contribute to treatment engagement, with differential effects across socioeconomic groups. These findings highlight the importance of addressing structural and interpersonal barriers to improve equitable access to evidence-based AUD treatment.