Medicare Hampered by Inaccurate Race, Ethnicity Data, OIG Says

Major inaccuracies and limitations have been found in Medicare’s enrollment data on race and ethnicity, according to a June 2022 report from the Office of Inspector General (OIG) of HHS.

The department’s federal watchdog recommended that CMS implement new strategies to improve their data.

“Inaccuracies in race and ethnicity data have far-reaching consequences, affecting understanding of disparities in the prevalence, severity, and outcomes of diseases and conditions—including COVID-19—and in health care quality and access,” wrote the OIG.

The OIG compared Medicare’s enrollment race and ethnicity data for different groups with self-reported data for a subset of beneficiaries who reside in nursing homes. Self-reported data are considered the most accurate.

Enrollment Data

Medicare’s enrollment data includes race, ethnicity, and other demographic information for beneficiaries sourced from the Social Security Administration (SSA). An algorithm is applied to the SSA data to improve accuracy.

Data on about 66 million beneficiaries enrolled in Medicare in 2020 was analyzed by the OIG, showing that data for some groups was less accurate than others when compared to self-reported data.

For example, Medicare data lists these the proportions of beneficiaries as identifiying with a particular race or ethnicity—but these do not align with the self-reported data collected for nursing homes:

• 46% were incorrectly identified as American Indian/Alaska Native
• 28% Hispanic
• 17% Asian/Pacific Islander
• 4% Black
• 1% White

Likewise, this proportion of beneficiaries self-identified as belonging to these groups, yet were not captured as such in the enrollment data:

1. 35% were not identified as American Indian/Alaska Native
2. 13% Hispanic
3. 24% Asian/Pacific Islander
4. 3% Black
5. 4% White

Deficiencies in Data From the SSA

The OIG reported that the race and ethnicity data collected by the SSA and used by Medicare are not comprehensive.

Race and ethnicity are considered one category, meaning that separate racial and ethnic identities and multiracial beneficiaries are not captured.

The SSA offered limited categories—White, Black, or Other—for identifying race and ethnicity, until American Indian/Alaska Native, Asian/Pacific Islander, and Hispanic were added in 1980.

Individuals who do not answer are categorized as “Unknown.”

The data collected are lacking for 3.3 million Medicare beneficiaries, with 1.5 million categorized as “Unknown” and 1.8 million categorized as “Other.”

As the SSA stopped routinely collecting this data in 1989, the reliability of the data used by Medicare will continue to diminish, the OIG states.

Issues With the Algorithm

The CMS applies an algorithm to the information collected from the SSA to improve the quality of information.

The OIG reports that data derived from the algorithm have more errors than self-reported data, relying on names frequently associated with specific races and ethnicities, geography for individuals in Hawaii and Puerto Rico, and requests for materials in Spanish.

Even using the algorithm, Medicare lacks accurate data for 2 million beneficiaries.

The OIG also notes that the algorithm used “will not be able to compensate for the increasingly missing data from SSA because it is designed for the Asian/Pacific Islander and Hispanic groups only.”

Federal Standards

The OIG found that Medicare’s race and ethnicity data are inconsistent with the 1997 Office of Management and Budget and 2011 Department of Health and Human services standards for collecting this data.

These standards recommend including 2 separate questions for race and ethnicity, allowing for responders to select all that apply.

The standards also suggest expanded categories of race and ethnicity to account for granularity for the Hispanic and Asian/Pacific Islander groups, which Medicare enrollment data does not account for.

Recommendations

The OIG provided the following recommendations to the CMS:

1. Develop its own source of race and ethnicity data
2. Use self-reported race and ethnicity information to improve data for current beneficiaries
3. Develop a process to ensure that data are as standardized as possible
4. Educate beneficiaries about CMS’s efforts to improve race and ethnicity information

Though the CMS did not explicitly concur with the first recommendation, they concurred with the other 3 recommendations.

“CMS ...must improve its race and ethnicity data; though a significant undertaking, the need for better data is pressing,” concluded the OIG.

Reference

Department of Health and Human Services. Office of the Inspector General. Data brief: inccuracies in Medicare’s race and ethnicity data hinder the ability to assess health disparities. June 2022. Accessed June 20, 2022. https://oig.hhs.gov/oei/reports/OEI-02-21-00100.pdf