Improving Analysis of Medicaid Enrollee Race and Ethnicity Using Data From a State-Based Marketplace and Regional Health Information Exchange

Vollständiger Abstract

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Background: Medicaid race and ethnicity data quality continues to pose challenges for patient-centered outcomes research. Collection practices and data systems vary; according to the Centers for Medicare & Medicaid Services Data Quality Atlas, many states have race and ethnicity data quality of concern (medium, high, or unusable), including Maryland. Objective: This study links data from 3 sources to reduce the percentage of Maryland Medicaid enrollees with an unknown race and ethnicity and improve the accuracy of population-level data. Research Design: People enrolled in Maryland Medicaid (MMIS2) at any point during calendar year 2023 ( N =1,898,041) were matched to data from Maryland’s state health insurance marketplace (MHBE) and designated health information exchange (CRISP). Enrollees were assigned a single race and ethnicity value from the data sources in the following order: MHBE; CRISP; historic MMIS2; current MMIS2. If a participant had an unknown race and ethnicity, the next source was used. Results: Most Medicaid enrollees (97.8%) were found in MHBE and/or CRISP. The study methodology allowed for greater disaggregation of the population by race and ethnicity and reduced the percentage with an unknown race and ethnicity from 23.0% to 1.0%. The distribution of enrollees by race and ethnicity after applying the study method was better aligned with American Community Survey benchmarks than the original data. Conclusions: These results will help stakeholders in Maryland better identify disparities in patient-centered outcomes and advance health equity goals. This methodology can assist researchers and policymakers in other states with similar data quality issues, as part of a larger effort to improve Medicaid race and ethnicity data.