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NORC at the University Chicago

Race, Ethnicity, and Language Data Collection in Medicaid

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Best practices in measuring race, ethnicity, and language of Medicaid beneficiaries by state
  • Funder
    NORC
  • Dates
    December 2021 – February 2022

Problem

What cannot be measured cannot be improved.

Health equity is of critical importance for the federal government and states. The first step toward ensuring that equity is front and center in the Medicaid program is ensuring there is comprehensive data on Medicaid beneficiaries’ race, ethnicity, and language. How can Medicaid programs identify disparities in health care access or outcomes without good data? How can Medicaid programs hold their Managed Care Organizations accountable for improving health equity if they do not possess accurate data on their beneficiaries’ race, ethnicity, or language?

Solution

NORC helps Medicaid improve the quality of its race, ethnicity, and language data.

NORC at the University of Chicago has analyzed the process states follow when obtaining information on race, ethnicity, and language spoken at home for their Medicaid beneficiaries. NORC used Transformed Medicaid Statistical Information System (T-MSIS) data from the Data Quality Atlas to identify 15 states that have collected race and ethnicity (RE) data on their beneficiaries over the years. We then summarized state efforts to improve such data collection, including profiles of California, Washington state, Michigan, and Pennsylvania, and identify lessons learned and recommendations for states to improve data collection that supports health equity initiatives.

Result

Medicaid beneficiaries are encouraged to self-report race, ethnicity, and language. 

The comprehensiveness of state Medicaid data on race, ethnicity, and language (REL) can be assessed using the T-MSIS Data Quality Atlas, based on T-MSIS Analytic Files (TAF). Our review of data completeness and quality for three years (2017 - 2019) finds that most states were consistent in their level of concern.

We identified four states (California, Washington, Michigan, and Pennsylvania) as low concern for the collection of RE data in the DQ Atlas. Key features across the states include multiple formats for applications, extensive response options for REL (beyond the OMB categories), and state use of REL data on Medicaid beneficiaries to analyze health disparities and support programs to reduce such disparities. Other lessons learned included: 

  • Ensuring alignment in questions and options across applications
  • Encouraging applicants to self-report REL data
  • Linking collection of REL data to performance monitoring, contract requirements, and value-based payment

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