Medicare Beneficiaries’ Race and Ethnicity Data Inaccurate

Significant inaccuracies exist in Medicare beneficiaries’ race and ethnicity data, which could exacerbate disparities in healthcare access and quality, according to a study published in Medical Care.

Monitoring and reducing healthcare disparities requires accurate data on race and ethnicity that are not always consistently available, the research team noted. The country’s elderly population is not only rapidly growing, but also becoming more ethnically and racially diverse. To identify disparities and improve minority health outcomes, it is increasingly critical for researchers to collect and use accurate data on this population’s race and ethnicity.

Administrative data, including insurance plan enrollment and demographic information, are contained in the Medicare Beneficiary Summary File (MBSF), the team stated. In contrast to administrative data sources, national surveys of Medicare beneficiaries include self-reported race and ethnicity.

Researchers set out to compare Medicare beneficiaries’ race and ethnicity data from the two most widely-used administrative data sources to data sources that include beneficiaries’ self-reported race and ethnicity information.

The results showed that in 19 states, the administrative data sources significantly undercounted the proportion of people who are Hispanic. The findings uncovered even more widespread undercounting of Asian American, Native Hawaiian, Pacific Islander, and American Indian populations.

“The inaccuracy of state-level data on Medicare beneficiaries’ race and ethnicity is staggering,” said Irina Grafova, assistant professor at Rutgers School of Public Health.

“We found that, in 19 states, about 20 percent of Hispanic Medicare beneficiaries were misclassified as belonging to another ethnic group. In 24 states, more than 80 percent of American and Alaskan Native beneficiaries of Medicare were misclassified. And in the majority of states, at least one-fourth of Asian American and Pacific Islander beneficiaries were misclassified.”

Medicare requires the collection of self-reported race and ethnicity data during standardized assessments in home healthcare and other care settings. Researchers who are documenting racial disparities and the impact of racism on healthcare use and outcomes should use this self-reported data, the team noted.

Self-reported data is also crucial for researchers to better understand the prevalence of conditions among certain populations. For example, after examining self-reported data, the team found that dementia is particularly widespread in Hispanic populations.

“From a methodological standpoint, the choice of race/ethnicity data source is essential at the study design stage for health disparities research. The impact of race/ethnicity variable selection on estimates of disease prevalence is of special concern, as we found in the case of dementia prevalence among Hispanics,” researchers stated.

The results of the study mirror findings from past research on care disparities among Medicare beneficiaries. In 2017, a team from the University of Rochester Medical Center (URMC) showed that there are significant racial disparities for 30-day hospital readmission rates between black and white Medicare and Medicare Advantage patients.

“Our findings of persistent racial disparities in surgical readmissions despite the national trend are troubling, and they may suggest that recent readmission reduction efforts were broadly targeted— incorporating few incentives for reducing disparities beyond overall reduced readmissions,” the team said at the time.

“Furthermore, concerns have been raised that the Hospital Readmissions Reduction Program and the value-based purchasing of health care currently being implemented in traditional Medicare might have the unintended effect of exacerbating disparities in quality and outcomes of care.”

Using self-reported data could help leaders and researchers across the healthcare industry identify and reduce racial and ethnic disparities in Medicare.

“Our study highlights the potential for bias and error introduced during the selection of race/ethnicity data source,” researchers said.

“We also show that further reductions in error and bias can be gained by using self-reported race/ethnicity contained in assessment datasets. These findings have important implications for the design of future studies and the interpretation of prior published research on minority health and health disparities.”

The team noted that the study did include several limitations. The study population consisted only of Medicare beneficiaries who utilized home healthcare in 2015, and blacks are slightly overrepresented in the home healthcare population compared with the Medicare population.

Despite these limitations, the group expects that their findings could inform research on minority populations within Medicare, leading to better quality care and increased care access.

“The Centers for Medicare and Medicaid can incorporate our findings to improve the accuracy of racial and ethnic data used in the future to estimate minority health and health disparities within the US Medicare population,” said Olga Jarrín Montaner, assistant professor at Rutgers School of Nursing and Institute for Health, Health Care Policy, and Aging Research.

“By creating more accurate estimates of the demographic profile of older adults, we can inform future public health and policy, and better understand the magnitude of disparities in population health outcomes, such as those we are currently seeing with COVID-19.”