The COVID-19 pandemic has highlighted and exacerbated health care inequities in the United States. Calls to address health care disparities have intensified, and the Biden Administration has made equity a central component of its policy agenda. The confluence of these social and political forces has reinvigorated discussion about how to address health care inequities in public insurance programs, and refocused attention on Medicaid — which now covers more than 86 million Americans — as a lever for advancing health equity.
In 2021, the Center for Medicare and Medicaid Innovation (CMMI) explicitly added health care equity as one of its five core objectives, aspiring to “a health system that achieves equitable outcomes through high quality, affordable, patient-centered care.” CMMI has also specifically prioritized initiatives to improve care and outcomes for vulnerable and underserved populations in Medicaid. These efforts depend, however, on the ability to measure disparities in access to care, quality of care, and health outcomes by race and ethnicity. Due to lack of high-quality data, it remains impossible to fully evaluate the state of health equity in the Medicaid program.
Improving The Quality Of Existing Data
A legacy of underinvestment in Medicaid data quality and inconsistent data collection across states have historically impeded examination of racial and ethnic inequities in the program. The Centers for Medicare and Medicaid Services (CMS) has made efforts to improve Medicaid administrative data through the new Transformed Medicaid Statistical Information System (T-MSIS) Analytic Files (TAF). Yet, CMS recently reported that in a majority of states, race and ethnicity data in the TAF were missing for more than 10 percent of enrollees and that these data were “unusable” in five states and of “high concern” in 17 more. CMS only identified 15 states whose TAF race and ethnicity data were of “low concern.” Variation in race and ethnicity data quality across states can be attributed to the lack of federal guidance and standards for mandatory collection. Questions regarding enrollee race and ethnicity are optional fields on Medicaid enrollment forms; some enrollees may opt out of sharing this information because of distrust over how it will be used. The race and ethnicity categories included on Medicaid enrollment forms may also limit enrollees’ options to accurately self-identify — particularly if enrollees identify as a category that is not included.
Researchers at the University of Minnesota’s State Health Access Data Assistance Center have identified three strategies to improve administrative race and ethnicity data in Medicaid. First, since questions about race and ethnicity are optional on Medicaid enrollment forms, states, through their Medicaid application forms and intake processes, should take additional steps to explain the importance of the data and how they will be used. Second, states should partner with community organizations to proactively engage with enrollees, both to seek guidance on how to improve data collection and to communicate how the data will help ensure health equity in the Medicaid program. Third, states should explore augmenting race and ethnicity data obtained via self-report with data from other sources, including vital records, electronic health records, and data from other state-administered programs (such as the Supplemental Nutrition Assistance Program). Just as Medicaid programs are generally required to conduct so-called ex parte renewals — redetermining Medicaid eligibility by pulling eligibility information from all available sources, including other state agencies — they should similarly be encouraged to draw on secondary data sources to reduce the missingness of race and ethnicity data in Medicaid.
Seeking New Data: A National Medicaid Survey
Even if race and ethnicity data quality is improved, the ability to measure health equity in the Medicaid program requires the ability to accurately identify patients in need. Health care access is only considered equitable when the care provided aligns with social and clinical need. To measure need using existing data sources, analysts often rely on claims-based diagnosis codes that are only captured after individuals use care. Since racial and ethnic minority groups and other underserved communities face structural barriers to accessing care, claims data underestimate disease prevalence for these populations, and thus are a potentially poor proxy for need. Additionally, these data include little information about a patient’s health or social history, missing essential information for identifying the most vulnerable patient populations. This results in a data paradox: Medicaid enrollees with the greatest unmet need may be the most difficult to identify.
A national Medicaid survey could support more comprehensive monitoring of health care need relative to use, and if designed to oversample racial and ethnic minoritized populations, could also support more rigorous monitoring of quality of care and disparities within the program. While individual Medicaid managed care plans often collect patient experience data, a national survey of Medicaid enrollees has not occurred since 2014-2015, when CMS fielded a first-of-its-kind Nationwide Adult Medicaid Consumer Assessment of Healthcare Providers and Systems (NAM CAHPS), which obtained detailed information on enrollees’ sociodemographic characteristics (including race and ethnicity) in addition to need for, access to, and experiences with care. This stands in contrast to the Medicare program, where the Medicare CAHPS and Medicare Current Beneficiary Survey are fielded annually. While data from the latter surveys can be used to assess equity among Medicare enrollees dually eligible for Medicaid, they cannot inform assessments of health equity in the Medicaid population at large.
Population surveys provide a representative sample of individuals, regardless of whether they use care, and have the capacity to not only accurately capture race and ethnicity of respondents, but also identify unmet need. By expanding on existing survey infrastructure and investing in a national Medicaid survey, CMS has a clear path for evaluating and advancing health equity in the Medicaid program. A new data collection effort could strengthen monitoring of equitable care by both oversampling certain populations to obtain more precise estimates of patient experience and enhancing the set of questions asked about patient access to care.
The Path Forward
Ultimately, improving the quality of race and ethnicity data — paired with robust efforts to objectively measure unmet need — will facilitate monitoring of health equity in Medicaid to ensure that all patients receive the highest quality care necessary to meet their needs. CMS and many state Medicaid programs have recently indicated that health equity is a priority. Now, investment in the data infrastructure needed to continuously evaluate the state of health equity in the program must follow.
Dr. Schpero acknowledges funding from The Commonwealth Fund (Grant No. 20213316) and the Milbank Memorial Fund. Brittany L. Brown-Podgorski, PhD, MPH, is an assistant professor in the Department of Health Policy and Management at the University of Pittsburgh School of Public Health. Eric T. Roberts, PhD, is an assistant professor in the Department of Health Policy and Management at the University of Pittsburgh School of Public Health. William L. Schpero, Ph.D., is an assistant professor in the Division of Health Policy and Economics of the Department of Population Health Sciences at the Joan & Sanford I. Weill Medical College of Cornell University.