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

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.

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.

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.

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.

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.