U.S. Department of Health and Human Services 
Office of Inspector General 
 
A Resource Guide for Using 
Medicares Enrollment Race 
and Ethnicity Data
 
 
 
Ann Maxwell  
Deputy Inspector General 
   for Evaluation and Inspections 
June 2023, OEI-02-21-00101  U. S. Department of Health and Human Services 
OBAffCicKe GoRf OInUspNeDct or General 
June 2023, OEI-02-21-00101 
 
A Resource Guide for Using Medicares Enrollment Race and Ethnicity 
Data 
Why did OIG create this resource guide? 
Key Takeaway 
Medicare is an essential part of the Nations health care system, with 
The race and 
66 million people enrolled.  The COVID-19 pandemic brought persistent 
ethnicity data in 
disparities in health care access and outcomes to the forefront, including in 
Medicares 
the Medicare program.  The Office of Inspector General (OIG) and the Centers 
enrollment data have 
for Medicare & Medicaid Services (CMS) have made advancing health equity 
limitations, but can 
a top priority.  In order to address health disparities, it is important to assess 
still be used in 
them using accurate, complete, and comprehensive data.  The results of these 
important health 
analyses can be used to tailor interventions aimed at improving disparities.  
equity work if those 
The data can then be used to evaluate the efficacy of these interventions.  
limitations are 
Ultimately, success in advancing health equity hinges on a thorough 
recognized and 
understanding of the underlying data. 
accounted for during 
analysis.    In June 2022, OIG issued a data brief, Inaccuracies in Medicares Race and 
  Ethnicity Data Hinder the Ability To Assess Health Disparities (OEI-02-21-
  00100), analyzing the quality of the race and ethnicity data for people 
enrolled in Medicare.  That data brief makes constructive recommendations 
to CMS for improving the data.1   
As CMS works to implement these recommendations, improvements are 
expected to take several years to yield better data for health disparities 
research.2  Until then, the existing data remain a vital source for 
understanding one of the largest Federal programs and conducting health 
equity work.  
What is the resource guide? 
This resource guide is a tool that provides practical information about the 
race and ethnicity data for people enrolled in Medicare.  Specifically, using 
information derived from the prior data brief, it provides a concise, user-
friendly explanation of the origins and limitations of Medicares enrollment 
race and ethnicity data and offers considerations for the use of these data.   
This guide is for public and private sector users of Medicare data, such as 
researchers, managed care organizations, Congress, and others, involved in 
health equity work.
   
  RESOURCE GUIDE 
 
 
 
 
This guide includes the following sections: (1) overview of Medicares race and 
ethnicity data; (2) origins of Medicares enrollment race and ethnicity data; 
(3) understanding the limitations of the data; (4) developing findings based on these 
data; and (5) available self-reported race and ethnicity data for some people enrolled 
in Medicare. 
Overview of Medicares race and ethnicity data 
Race, ethnicity, and other demographic information for people enrolled in Medicare 
are included in Medicares enrollment data.  The enrollment data are the only source 
of race and ethnicity data for all people enrolled in Medicare.  
  These enrollment data are featured in the Master Beneficiary Summary File 
(MBSF), the Chronic Condition Warehouse (CCW), and the Integrated Data 
Repository (IDR). 
Origins of Medicares enrollment race and ethnicity data 
Knowing more about where Medicares data come from may be useful for developing 
effective analyses and findings; understanding their limitations; and communicating 
them.  There are two race and ethnicity variables available from Medicares enrollment 
data: 
BENE_RACE_CD: This is the original variable for race and ethnicity, and its primary 
source is the Social Security Administration (SSA).   
  Prior to 1980, people applying for Social Security cards could select from only 
three categoriesWhite, Black, or Otherwhen voluntarily providing their 
race and ethnicity information.  
  In 1980, the categories were expanded to American Indian/Alaska Native, 
Asian/Pacific Islander, Black, Hispanic, White, and Other.   
  SSA stopped routinely collecting these data in 1989, though information for 
most current Medicare beneficiaries was collected prior to this date.3 
RTI_RACE_CD: To improve information for the Asian/Pacific Islander and Hispanic 
groups, who were not captured in the original response categories, CMS applies an 
algorithm developed by the Research Triangle Institute (RTI) to the BENE_RACE_CD 
data.4  This creates the RTI_RACE_CD variable. 
A Resource Guide for Using Medicares Enrollment Race and Ethnicity Data 
OEI-02-21-00101  Guide | 1 
    The RTI_RACE_CD variable includes all of the BENE_RACE_CD data.  CMS then 
updates these data for certain individuals by using the RTI algorithm, which 
categorizes individuals as Asian/Pacific Islander or Hispanic on the basis of 
name, geography, and requests for SSA and Medicare materials in Spanish.   
Of the two variables, the RTI_RACE_CD data are more accurate and complete than 
the BENE_RACE_CD data and are what we use in our analyses.5 
Understanding the limitations of the data  
Although the RTI_RACE_CD data are more accurate and complete than the 
BENE_RACE_CD data for the Asian/Pacific Islander and Hispanic groups, there are 
some limitations associated with the RTI_RACE_CD data that warrant consideration 
when conducting analyses. 
Accuracy 
The RTI_RACE_CD data are not as accurate as self-reported data, which are considered 
the gold standard.6  When the RTI_RACE_CD data were compared to self-reported 
data for a subset of Medicare enrollees with nursing home assessments, the 
RTI_RACE_CD data were less accurate for people identified as American 
Indian/Alaska Native, Asian/Pacific Islander, or Hispanic.7  For example, 
28 percent of the people identified as Hispanic in the RTI_RACE_CD data do not 
identify themselves as Hispanic on their nursing home assessments.  Also note that, as 
designed, the RTI algorithm does not address accuracy concerns related to the 
American Indian/Alaska Native group.  See Exhibit 1. 
Exhibit 1: Percentage of Medicare Enrollees Identified in Medicares RTI_RACE_CD 
Data as a Race and Ethnicity With Which They Do Not Identify Themselves on the 
Nursing Home Assessment 
 
Source: OIG analysis of CMS data, 2021. 
A Resource Guide for Using Medicares Enrollment Race and Ethnicity Data 
OEI-02-21-00101  Guide | 2 
  Completeness 
Of approximately 66 million people enrolled in Medicare in 2020, a total of 2 million 
were lacking race and ethnicity information in the RTI_RACE_CD data, with 
1.3 million categorized as Unknown or missing, and 600,000 categorized as Other.  
Because of the way the SSA source data were collected, these people are more likely 
to be American Indian/Alaska Native, Asian/Pacific Islander, and Hispanic. 
Collection methods 
The RTI_RACE_CD data are inconsistent with Federal standards for the collection of 
race and ethnicity data, which are designed to ensure consistent data collection and 
appropriately detailed data.8  For example, Federal standards recommend requesting 
race and ethnicity via two separate questions to capture both racial and ethnic 
identities and multi-racial individuals.  However, the RTI_RACE_CD data combine race 
and ethnicity and limit each person to only one race or ethnicity.  See Exhibit 2. 
Exhibit 2: Notable Differences Between Federal Standards for the 
Collection of Race and Ethnicity Data and Medicares 
RTI_RACE_CD Data 
 
 
A Resource Guide for Using Medicares Enrollment Race and Ethnicity Data 
OEI-02-21-00101  Guide | 3 
  Developing findings based on these data 
Important conclusions can still be drawn using the RTI_RACE_CD data despite their 
limitations.   
Users of the RTI_RACE_CD data should consider the limitations of the data when 
conducting their analyses and communicate decisions they have made based on these 
limitations.  For example, users of the data may want to include statements about the 
overall accuracy of the data; their decisions about combining data or not reporting on 
certain data; or background information about how the data were collected.   
Taking these considerations into account, OIG has used the RTI_RACE_CD data in 
studies assessing health disparities.  For examples of OIGs findings using these data 
in our reports, see Exhibit 3 below.9  Note that OIG chose not to report findings based 
on the RTI_RACE_CD data for the American Indian/Alaska Native group in these three 
instances because of concerns about accuracy.  CMS has also used the RTI_RACE_CD 
data in its own analyses of COVID-19 among Medicare enrollees.10   
Exhibit 3: Select OIG Findings on Health Disparities Using the RTI_RACE_CD Data 
 Asian/Pacific Islander, Hispanic, and Black Medicare enrollees were less 
likely to receive medication than White enrollees to treat their opioid use 
disorder. 
 Hispanic people enrolled in Medicare were more likely than others to use 
telehealth during the COVID-19 pandemic.  
 Black and Hispanic people enrolled in Medicare were disproportionately 
hospitalized with COVID-19. 
 
In addition, below is an example of a statement about its race and ethnicity data 
analysis that OIG included in one of its studies. 
Note that Medicare data combine race and ethnicity and allow for only one 
response.  Furthermore, we did not include information on American 
Indian/Alaska Native [enrollees] because of limitations.  This analysis uses the race 
and ethnicity information from Medicares enrollment data, which is based on 
data collected from the Social Security Administration and an algorithm.  
Although this information is currently the best available for the entire Medicare 
[enrollee] population, comparisons to self-reported data (available in certain, 
limited circumstances) show that race and ethnicity is still misclassified for some 
[enrollees].  In particular, Medicare [enrollees] with a race and ethnicity of 
American Indian/Alaska Native, Asian/Pacific Islander, or Hispanic are more likely 
to be misclassified.11   
A Resource Guide for Using Medicares Enrollment Race and Ethnicity Data 
OEI-02-21-00101  Guide | 4 
  Available self-reported race and ethnicity data for some people 
enrolled in Medicare  
Self-reported race and ethnicity data are available for subsets of the Medicare 
population and should be considered for use if appropriate to the study topic.  As 
noted earlier, self-reported race and ethnicity data are considered the gold standard.  
  Self-reported race and ethnicity data are collected on post-acute care 
assessments such as the Minimum Data Set (MDS) used in certified nursing 
facilities and the Outcome and Assessment Information Set (OASIS) used in 
home health care.  These assessments collect and report data on quality of 
care for individuals receiving post-acute care services such as nursing home 
care and home health care.  
  OIG has used these data to look at health disparities.  For example, an OIG 
study of COVID-19 in nursing homes used MDS self-reported data.  The study 
found that about half of Black, Hispanic, and Asian residents in nursing homes 
had or likely had COVID-19 in 2020 while 41 percent of White residents did.12   
  The Medicare Current Beneficiary Survey is another source of self-reported 
race and ethnicity data from a random sample of enrollees. 
A Resource Guide for Using Medicares Enrollment Race and Ethnicity Data 
OEI-02-21-00101  Guide | 5 
   
METHODOLOGY 
This resource guide is based on our recent data brief, Inaccuracies in Medicares Race 
and Ethnicity Data Hinder the Ability To Assess Health Disparities (OEI-02-21-00100).13  
The resource guide highlights aspects of our findings and discusses the implications 
of those findings that are most relevant to users of Medicares race and ethnicity data.  
See the methodology section of the recent data brief for information about how we 
assessed the quality of the race and ethnicity data.  
A Note About Terms 
Some of the race and ethnicity categories have been described with different labels 
historically.  Below is a complete list of the terms that have been used along with the 
one we use to refer to each category in this resource guide. 
  American Indian, North American Native, North American Indian, or Alaska 
Native (American Indian/Alaska Native); 
  Asian, Asian American, Pacific Islander, or Native Hawaiian (Asian/Pacific 
Islander); 
  Black or African American (Black); and 
  Hispanic or Latino (Hispanic). 
Limitations 
This guide is meant to serve as a practical resource for those who may use the 
Medicare enrollment race and ethnicity data; it is not meant to be a prescriptive 
document about the way the data must be used to conduct analyses. 
Our analysis of the accuracy of the Medicare enrollment data is based on the subset 
of Medicare enrollees with a nursing home assessment.  The racial and ethnic makeup 
of this subset differs from that of the overall Medicare population.   
The subset has a larger proportion of White and Black beneficiaries than the overall 
population.14 
Standards 
We conducted this study in accordance with the Quality Standards for Inspection and 
Evaluation issued by the Council of the Inspectors General on Integrity and Efficiency.  
 
A Resource Guide for Using Medicares Enrollment Race and Ethnicity Data 
OEI-02-21-00101  Methodology | 6    
Notice  
This resource guide was prepared as a technical resource and is not intended to be 
used to determine compliance with any laws, regulations, or other guidance.  In no 
event shall OIG or its employees, agents, or staff be liable for any claim, damages, or 
liability, whether in an action of contract, tort, or otherwise, and including direct, 
indirect, incidental, special, exemplary, or consequential damages, however caused, and 
on any theory of liability, arising in any way out of the use of this resource guide, even 
if advised of the possibility of such damage.  
 
A Resource Guide for Using Medicares Enrollment Race and Ethnicity Data 
OEI-02-21-00101  Methodology | 7    
ACKNOWLEDGMENTS AND CONTACT 
Acknowledgments 
Rachel Bryan served as the team leader for this study.  Others in the Office of 
Evaluation and Inspections who conducted the study include Heather Koenig.  Office 
of Evaluation and Inspections headquarters staff who provided support include 
Robert Gibbons and Michael Novello.  
This report was prepared under the direction of Jodi Nudelman, Regional Inspector 
General for Evaluation and Inspections in the New York Regional Office, and Nancy 
Harrison and Meridith Seife, Deputy Regional Inspectors General. 
Contact 
To obtain additional information concerning this report, contact the Office of Public 
Affairs at Public.Affairs@oig.hhs.gov.  OIG reports and other information can be found 
on the OIG website at oig.hhs.gov. 
Office of Inspector General 
U.S. Department of Health and Human Services 
330 Independence Avenue, SW 
Washington, DC 20201
A Resource Guide for Using Medicares Enrollment Race and Ethnicity Data 
OEI-02-21-00101  Acknowledgements and Contact | 8    
ABOUT THE OFFICE OF INSPECTOR GENERAL 
The mission of the Office of Inspector General (OIG), as mandated by Public Law 
95-452, as amended, is to protect the integrity of the Department of Health and 
Human Services (HHS) programs, as well as the health and welfare of beneficiaries 
served by those programs.  This statutory mission is carried out through a nationwide 
network of audits, investigations, and inspections conducted by the following 
operating components: 
The Office of Audit Services (OAS) provides auditing services for HHS, 
either by conducting audits with its own audit resources or by overseeing audit work 
done by others.  Audits examine the performance of HHS programs and/or its 
grantees and contractors in carrying out their respective responsibilities and are 
intended to provide independent assessments of HHS programs and operations.  
These audits help reduce waste, abuse, and mismanagement and promote economy 
and efficiency throughout HHS. 
The Office of Evaluation and Inspections (OEI) conducts national 
evaluations to provide HHS, Congress, and the public with timely, useful, and reliable 
information on significant issues.  These evaluations focus on preventing fraud, waste, 
or abuse and promoting economy, efficiency, and effectiveness of departmental 
programs.  To promote impact, OEI reports also present practical recommendations 
for improving program operations. 
The Office of Investigations (OI) conducts criminal, civil, and administrative 
investigations of fraud and misconduct related to HHS programs, operations, and 
beneficiaries.  With investigators working in all 50 States and the District of Columbia, 
OI utilizes its resources by actively coordinating with the Department of Justice and 
other Federal, State, and local law enforcement authorities.  The investigative efforts 
of OI often lead to criminal convictions, administrative sanctions, and/or civil 
monetary penalties. 
The Office of Counsel to the Inspector General (OCIG) provides 
general legal services to OIG, rendering advice and opinions on HHS programs and 
operations and providing all legal support for OIGs internal operations.  OCIG 
represents OIG in all civil and administrative fraud and abuse cases involving HHS 
programs, including False Claims Act, program exclusion, and civil monetary penalty 
cases.  In connection with these cases, OCIG also negotiates and monitors corporate 
integrity agreements.  OCIG renders advisory opinions, issues compliance program 
guidance, publishes fraud alerts, and provides other guidance to the health care 
industry concerning the anti-kickback statute and other OIG enforcement authorities.
A Resource Guide for Using Medicares Enrollment Race and Ethnicity Data 
OEI-02-21-00101  About OIG | 9    
ENDNOTES 
 
1 In the data brief, OIG recommended that 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 the data are as 
standardized as possible; and (4) educate beneficiaries about CMSs efforts to improve the race and ethnicity information.   
CMS did not explicitly concur with the first recommendation and concurred with the other three recommendations. 
2 CMS has not yet implemented the data briefs four recommendations, but CMS has added race and ethnicity questions to the 
2023 Medicare Part C/D enrollment forms and plans to collaborate with the Social Security Administration about collecting 
these data for Medicare Part A/B enrollees.  
CMS, The Path Forward: Improving Data to Advance Health Equity Solutions, November 2022.  
https://www.cms.gov/files/document/path-forwardhe-data-paper.pdf. 
3 In 1989, individuals started receiving Social Security numbers when their birth certificates were filed rather than applying for 
them separately.  Now, race and ethnicity information are only collected in certain limited circumstances.   
4 CMS has taken two other actions to add to the SSA source data in BENE_RACE_CD.  In 1997, CMS sent a postcard survey to 
individuals with Hispanic surnames and geographical locations to more accurately identify Hispanic enrollees, an 
underrepresented category in the SSA data.  Eicheldinger, Celia; Bonito, Arthur, More Accurate Racial and Ethnic Codes for 
Medicare Administrative Data, Health Care Financing Review, Vol. 29, No. 3, Spring 2008, p. 2.  
https://pubmed.ncbi.nlm.nih.gov/18567241/. 
Also, in 1999, CMS began collaborating with the Indian Health Service to better capture American Indian and Alaska Native 
individuals in the enrollment data.  CMS, Report to Congress: Improving Medicare Post-Acute Care Transformation (IMPACT) 
Act of 2014 Strategic Plan for Accessing Race and Ethnicity Data, January 5, 2017, p. 9.  https://www.cms.gov/About-
CMS/Agency-Information/OMH/Downloads/Research-Reports-2017-Report-to-Congress-IMPACT-ACT-of-2014.pdf.   
According to our analysis, the postcard survey and Indian Health Service adjustments improved race and ethnicity data for 0.6 
percent of Medicare enrollees in 2020. 
5 CMS has developed an alternative algorithm called Medicare Bayesian Improved Surname Geocoding or MBISG that is not 
yet widely available for CMSs enrollment data.  This algorithm relies on similar inputs as the RTI algorithm, as well as some 
additional data, such as Census-linked geocoding.  We note that this alternative algorithm also relies on the SSA source data 
and proxy information.  Moreover, the alternative algorithm continues to use limited categories with the addition of Multiracial 
(American Indian/Alaska Native, Asian/Pacific Islander, Black, Hispanic, White, and Multiracial) and to combine race and 
ethnicity. 
6 Eicheldinger, Celia; Bonito, Arthur, More Accurate Racial and Ethnic Codes for Medicare Administrative Data, Health Care 
Financing Review, Vol. 29, No. 3, Spring 2008, p. 2.  https://pubmed.ncbi.nlm.nih.gov/18567241/. 
7 OIG, Inaccuracies in Medicares Race and Ethnicity Data Hinder the Ability To Assess Health Disparities (OEI-02-21-00100), June 
2022.  https://oig.hhs.gov/oei/reports/OEI-02-21-00100.pdf. 
8 Federal standards include those published by the Office of Management and Budget in 1997 and by the Department of 
Health and Human Services, per the Patient Protection and Affordable Care Act, in 2011.   
Office of Management and Budget, Federal Register Vol. 62, No. 210, Revisions to the Standards for the Classification of 
Federal Data on Race and Ethnicity, October 30, 1997.  https://www.govinfo.gov/content/pkg/FR-1997-10-30/pdf/97-
28653.pdf. 
Department of Health and Human Services, Implementation Guidance on Data Collection Standards for Race, Ethnicity, Sex, 
Primary Language, and Disability Status, October 2011.  https://aspe.hhs.gov/system/files/pdf/76331/index.pdf. 
 
A Resource Guide for Using Medicares Enrollment Race and Ethnicity Data 
OEI-02-21-00101  Endnotes | 10    
 
9 OIG, Many Medicare Beneficiaries Are Not Receiving Medication to Treat Their Opioid Use Disorder (OEI-02-20-00390), 
December 2021.  https://oig.hhs.gov/oei/reports/OEI-02-20-00390.pdf. 
OIG, Certain Medicare Beneficiaries, Such as Urban and Hispanic Beneficiaries, Were More Likely Than Others To Use Telehealth 
During the First Year of the COVID-19 Pandemic (OEI-02-20-00522), September 2022.  https://oig.hhs.gov/oei/reports/OEI-02-
20-00522.pdf. 
OIG, Medicare Beneficiaries Hospitalized With COVID-19 Experienced a Wide Range of Serious, Complex Conditions (OEI-02-20-
00410), September 2021.  https://oig.hhs.gov/oei/reports/OEI-02-20-00410.pdf. 
10 CMS, Preliminary Medicare COVID-19 Data Snapshot, December 17, 2021, p. 6.  
https://www.cms.gov/files/document/medicare-covid-19-data-snapshot-fact-sheet.pdf. 
11  OIG, Many Medicare Beneficiaries Are Not Receiving Medication to Treat Their Opioid Use Disorder (OEI-02-20-00390), 
December 2021.  https://oig.hhs.gov/oei/reports/OEI-02-20-00390.pdf. 
12 OIG, COVID-19 Had a Devastating Impact on Medicare Beneficiaries in Nursing Homes During 2020 (OEI-02-20-00490), June 
2021.  https://oig.hhs.gov/oei/reports/OEI-02-20-00490.pdf. 
13 OIG, Inaccuracies in Medicares Race and Ethnicity Data Hinder the Ability To Assess Health Disparities (OEI-02-21-00100), 
June 2022.  https://oig.hhs.gov/oei/reports/OEI-02-21-00100.pdf. 
14 The overall Medicare population is 74 percent White and 11 percent Black, while the subset with a nursing home assessment 
is 78 percent White and 12 percent Black, according to the RTI variable. 
A Resource Guide for Using Medicares Enrollment Race and Ethnicity Data 
OEI-02-21-00101  Endnotes | 11