OBJECTIVES: In Medicaid applications, accurate race and ethnicity data collection is paramount, guided by the 1997 Office of Management and Budget's (OMB) minimum standards required by federal law and the 2011 Department of Health and Human Services' (HHS) detailed guidance under the Affordable Care Act (ACA). This study evaluates the consistency of state-level race and ethnicity applicant questions and assesses adherence to these federal directives to reinforce data integrity in healthcare policymaking.

METHODS: This scoping review employed a targeted search of 50 state-specific Medicaid websites to manually aggregate the race and ethnicity categories enumerated in Medicaid applications (paper or online). We conducted a comparative analysis between the various states' race and ethnicity categories as defined by OMB and HHS.

RESULTS: Fifty Medicaid applications were reviewed, and the median number of race and ethnicity categories offered for patients was 13 (range: 5-56) and 6 (range: 2-37), respectively. Maryland (n=56) offered the most race categories, whereas Oregon (n=37) and Massachusetts (n=35) provided the most extensive ethnicity category selections. All states met OMB standards for race and ethnicity data, with seven (14%) only collecting minimum race categories (White, Black/African American, Asian, American Indian/Alaska Native, Native Hawaiian/Other Pacific Islander) and 16 states (32%) only collecting minimum ethnicity categories (Hispanic/Latino vs. Non-Hispanic/Latino). Fewer states met HHS guidance: 31 states (62%) provided the recommended 14 or more race categories, and 34 states (68%) included the five expanded ethnicity categories. Contrary to recommended guidelines, both Colorado and Oregon merged their race and ethnicity sections. Oregon, however, separately categorized Hispanic/Latino identity.

CONCLUSIONS: HHS's broader categories offer patients more precise identity options, reducing potential misclassification. While all states comply with OMB's standards, further incentives to adopt HHS's detailed classifications could amplify this progress. Comprehensive, standardized data is crucial for identifying and addressing health disparities and inequities, thereby informing more effective healthcare policies.