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Federal Register / Vol. 86, No. 147 / Wednesday, August 4, 2021 / Rules and Regulations
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complications. An important part of identifying and addressing inequities in health care is improving data collection to allow us to better measure and report on equity across our programs and policies. We are considering stratification of quality measure results in the IPFQR Program by race and ethnicity and are considering which measures would be most appropriate for stratification.
As outlined in the 1997 Office of Management and Budget OMB
Revisions to the Standards for the Collection of Federal Data on Race and Ethnicity, the racial and ethnic categories, which may be used for reporting the disparity methods are considered to be social and cultural, not biological or genetic.43 The 1997 OMB
Standard lists five minimum categories of race: 1 American Indian or Alaska Native; 2 Asian; 3 Black or African American; 4 Native Hawaiian or Other Pacific Islander; 5 and White. In the OMB standards, Hispanic or Latino is the only ethnicity category included, and since race and ethnicity are two separate and distinct concepts, persons who report themselves as Hispanic or Latino can be of any race.44 Another example, the Race & EthnicityCDC
code system in Public Health Information Network PHIN Vocabulary Access and Distribution System VADS 45 permits a much more granular structured recording of a patients race and ethnicity with its inclusion of over 900 concepts for race and ethnicity. The recording and exchange of patient race and ethnicity at such a granular level can facilitate the accurate identification and analysis of health disparities based on race and ethnicity. Further, the Race & EthnicityCDC code system has a hierarchy that rolls up to the OMB
minimum categories for race and ethnicity and, thus, supports aggregation and reporting using the OMB standard. ONC includes both the CDC and OMB standards in its criterion for certified health IT products.46 For race and ethnicity, a certified health IT
product must be able to express both detailed races and ethnicities using any of the 900 plus concepts in the Race &
43 Executive Office of the President Office of Management and Budget, Office of Information and Regulatory Affairs. Revisions to the standards for the classification of Federal data on race and ethnicity. Vol 62. Federal Register. 1997:58782
58790
44 https www.census.gov/topics/population/
hispanic-origin/about.html.
45 https phinvads.cdc.gov/vads/
ViewValueSet.action?id=67D34BBC-617F-DD11B38D-00188B398520.
46 ONC criteria for certified health IT products:
https www.healthit.gov/isa/representing-patientrace-and-ethnicity.

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EthnicityCDC code system in the PHIN VADS, as well as aggregate each one of a patients races and ethnicities to the categories in the OMB standard for race and ethnicity. This approach can reduce burden on providers recording demographics using certified products.
Self-reported race and ethnicity data remain the gold standard for classifying an individual according to race or ethnicity. However, CMS does not consistently collect self-reported race and ethnicity for the Medicare program, but instead gets the data from the Social Security Administration SSA and the data accuracy and comprehensiveness have proven challenging despite capabilities in the marketplace via certified health IT products. Historical inaccuracies in Federal data systems and limited collection classifications have contributed to the limited quality of race and ethnicity information in Medicares administrative data systems.47 In recent decades, to address these data quality issues, we have undertaken numerous initiatives, including updating data taxonomies and conducting direct mailings to some beneficiaries to enable more comprehensive race and ethnic identification.48 49 Despite those efforts, studies reveal varying data accuracy in identification of racial and ethnic groups in Medicare administrative data, with higher sensitivity for correctly identifying White and Black individuals, and lower sensitivity for correctly identifying individuals of Hispanic ethnicity or of Asian/Pacific Islander and American Indian/Alaskan Native race.50 Incorrectly classified race or ethnicity may result in overestimation or underestimation in the quality of care received by certain groups of beneficiaries.
We continue to work with Federal and private partners to better collect and leverage data on social risk to improve our understanding of how these factors can be better measured in order to close 47 Eicheldinger, C., & Bonito, A. 2008. More accurate racial and ethnic codes for Medicare administrative data. Health Care Financing Review, 293, 2742.
48 Filice CE, Joynt KE. Examining Race and Ethnicity Information in Medicare Administrative Data. Med Care. 2017;5512:e170e176.
doi:10.1097/MLR.0000000000000608.
49 Eicheldinger, C., & Bonito, A. 2008. More accurate racial and ethnic codes for Medicare administrative data. Health Care Financing Review, 293, 2742.
50 Centers for Medicare and Medicaid Services.
Building an Organizational Response to Health Disparities Inventory of Resources for Standardized Demographic and Language Data Collection. 2020.
https www.cms.gov/About-CMS/AgencyInformation/OMH/Downloads/Data-CollectionResources.pdf.

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the health equity gap. Among other things, we have developed an Inventory of Resources for Standardized Demographic and Language Data Collection 51 and supported collection of specialized International Classification of Disease, 10th Revision, Clinical Modification ICD10CM
codes for describing the socioeconomic, cultural, and environmental determinants of health, and sponsored several initiatives to statistically estimate race and ethnicity information when it is absent.52 The Office of the National Coordinator for Health Information Technology ONC included social, psychological, and behavioral standards in the 2015 Edition health information technology IT certification criteria 2015 Edition, providing interoperability standards LOINC
Logical Observation Identifiers Names and Codes and SNOMED CT
Systematized Nomenclature of MedicineClinical Terms for financial strain, education, social connection and isolation, and others. Additional stakeholder efforts underway to expand capabilities to capture additional social determinants of health data elements include the Gravity Project to identify and harmonize social risk factor data for interoperable electronic health information exchange for EHR fields, as well as proposals to expand the ICD10
International Classification of Diseases, Tenth Revision Z codes, the alphanumeric codes used worldwide to represent diagnoses.53
While development of sustainable and consistent programs to collect data on social determinants of health can be considerable undertakings, we recognize that another method to identify better race and ethnicity data is needed in the short term to address the need for reporting on health equity. In working with our contractors, two algorithms have been developed to indirectly estimate the race and ethnicity of Medicare beneficiaries as described further in the following paragraphs. We feel that using indirect estimation can 51 Centers for Medicare and Medicaid Services.
Building an Organizational Response to Health Disparities Inventory of Resources for Standardized Demographic and Language Data Collection. 2020.
https www.cms.gov/About-CMS/AgencyInformation/OMH/Downloads/Data-CollectionResources.pdf.
52 https pubmed.ncbi.nlm.nih.gov/18567241/, https pubmed.ncbi.nlm.nih.gov/30506674/, Eicheldinger C, Bonito A. More accurate racial and ethnic codes for Medicare administrative data.
Health Care Finance Rev. 2008;293:2742. Haas A, Elliott MN, Dembosky JW, et al. Imputation of race/
ethnicity to enable measurement of HEDIS
performance by race/ethnicity. Health Serv Res.
2019;541:1323. doi:10.1111/14756773.13099.
53 https aspe.hhs.gov/pdf-report/second-impactreport-to-congress.

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