U.S. Department of Health and Human Services 
U.S. Department of Health and Human Services 
Office of Inspector General 
 Office of Inspector General 
 
Data Brief 
Draft Data Brief 
 
J Muanrech 2 2002222, ,O OEIE-0I-20-221--20011-0000 100 
Inaccuracies in Medicares Race and Ethnicity Data Hinder the Ability 
To Assess Health Disparities 
Why OIG Did This Review 
Key Results 
The disparate impacts of the COVID-19 pandemic 
o Medicares enrollment data on race and on various racial and ethnic groups have brought 
health disparities to the forefront.  Health disparities 
ethnicity have inaccuracies and other
are differences in health that adversely affect certain 
limitations, particularly for those
groups.  People of color have been found to 
identified as American Indian/Alaska
experience disparities in areas such as access to 
Native, Asian/Pacific Islander, or Hispanic.
health care and quality of health care.1  Such 
o The inaccuracies stem from deficiencies disparities have profound implications for the health 
in the source data.  The use of an and well-being of these individuals.   
algorithm also has drawbacks. Medicare is an essential part of the Nations health 
care system, with 66 million beneficiaries enrolled.  
o Inaccuracies in Medicares enrollment
The Centers for Medicare & Medicaid Services 
data limit the ability to assess and
(CMS) has made advancing health equity a top 
address health disparities.
priority.  Ensuring that Medicare is able to assess 
o Medicare should develop its own source disparities is key to this goal.  The ability to assess 
of race and ethnicity data. health disparities hinges on the quality of the 
underlying race and ethnicity data.  
How OIG Did This Review 
We analyzed the race and ethnicity data in Medicares enrollment database, the only source of this 
information for all enrolled beneficiaries.  These race and ethnicity data are derived from source data 
from the Social Security Administration (SSA) and the results of an algorithm that CMS applies to the 
source data.  We assessed the accuracy of Medicares enrollment race and ethnicity data for different 
groups by comparing them to self-reported data for a subset of beneficiaries who reside in nursing 
homes.  Race and ethnicity data that are self-reported are considered the most accurate.  We also 
assessed the adequacy of Medicares data using the Federal standards for collecting race and ethnicity 
data as a benchmark.   
What OIG Found 
Medicares enrollment race and ethnicity data are less accurate for some groups, particularly for 
beneficiaries identified as American Indian/Alaska Native, Asian/Pacific Islander, or Hispanic.  Data that 
are not accurate limit the ability to assess health disparities.  Limited race and ethnicity categories and 
missing information contribute to inaccuracies in the enrollment data.  Although the use of an 
algorithm improves the existing data to some extent, it falls short of self-reported data.  Finally, 
Medicares enrollment data on race and ethnicity are inconsistent with Federal data collection 
standards; these inconsistencies inhibit the work of identifying and improving health disparities within 
the Medicare population.  What OIG Recommends 
Advancing health equity is a priority for CMS and the Department.  Race and ethnicity data are 
foundational to identifying and understanding health disparities among Medicare beneficiaries and to 
assessing the effectiveness of efforts to reduce such disparities.  It is critical that these data are accurate, 
complete, and comprehensive.  Therefore, CMS must improve its race and ethnicity data; though a 
significant undertaking, the need for better data is pressing.  Accordingly, we recommend 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.      
  Primer: Medicares Race and Ethnicity Data 
  Race, ethnicity, and other demographic information for Medicare beneficiaries are included in 
Medicares enrollment data.  
 
  Medicares primary source of these race and ethnicity data is SSA.  Historically, SSA collected 
race and ethnicity data on a voluntary basis when an individual applied for a Social Security card.  
SSA stopped routinely collecting these data in 1989 when individuals started receiving Social 
Security numbers when filing a birth certificate rather than applying for them separately, and 
now collects them only in certain, limited circumstances. 
 
  Medicare improves the accuracy of the race and ethnicity data from SSA by imputing race and 
ethnicity data for some beneficiaries using the Research Triangle Institute (RTI) algorithm.2  
 
Medicares Race and Ethnicity Data 
 
 
  Medicare also collects race and ethnicity data for beneficiaries in nursing homes.  This 
information is self-reported and gathered during required assessments of beneficiaries 
functional capabilities.  It is included in the 
nursing home assessment data.3  Whereas the    Self-reported race and ethnicity 
information is considered the gold 
enrollment data include all Medicare 
standard because it is the most 
beneficiaries, the nursing home assessment data 
accurate.* 
include just Medicare beneficiaries in nursing 
 
homes.    Federal standards recommend self-
  reporting as the preferred method of 
  Current Federal standards for the collection of  collecting race and ethnicity data. 
 
race and ethnicity data were developed in 1997  *Jarrin, Olga F.; Nyandege, Abner N.; et al., Validity of Race 
and 2011.  Both standards recommend two  and Ethnicity Codes in Medicare Administrative Data 
Compared With Gold-Standard Self-Reported Race Collected 
questions, one for race and one for ethnicity; a  During Routine Home Health Care Visits, Medical Care, Vol. 
58, No. 1, January 2020, pp. e1e8. 
comprehensive set of categories from which to 
choose; and allowing individuals to select all     
categories that apply.  (See Appendix A for more information about the standardized 
categories.)  Though these standards were developed more recently than Medicares data were 
collected, they serve as a helpful benchmark for assessing the adequacy of Medicares data.
Data Brief: Inaccuracies in Medicares Race and Ethnicity Data Hinder the Ability To Assess Health Disparities 
OEI-02-21-00100  Primer | 3   
RESULTS 
 
Medicares enrollment data on race and ethnicity are less 
accurate for some groups, limiting the ability to assess and 
address health disparities 
Medicares enrollment data contain race and ethnicity information for about 
66 million beneficiaries who were enrolled in Medicare at any point in 2020.  However, 
this information is less accurate for some groups than for others when compared to 
self-reported data.  Specifically, inaccuracies are more common for beneficiaries 
identified as American Indian/Alaska Native, Asian/Pacific Islander, or Hispanic.  
Inaccuracies in race and ethnicity data have far-reaching consequences, affecting 
understanding of disparities in the prevalence, severity, and outcomes of diseases and 
conditionsincluding COVID-19and in health care quality and access.  As such, 
these inaccuracies limit the ability to effectively design and target efforts to address 
disparities in these areas and to measure the results of such efforts. 
Nursing home assessment data contain 
self-reported race and ethnicity    The nursing home assessment 
information for about 5 million  includes self-reported 
beneficiaries who have been in nursing  demographic data.  Self-reported 
homes.  Self-reported race and ethnicity  data are considered the most 
data are considered the most reliable.   accurate.   
When Medicares enrollment data are   
  Residents report their race and 
compared to the self-reported race and 
ethnicity information on the 
ethnicity data, the enrollment data are 
assessment, but if the resident is 
much less accurate for some groups than 
not able to do so, a caregiver or 
for others.   
clinician may respond. 
Inaccuracies in the enrollment data reflect 
two errors.  First, the enrollment data 
sometimes identify beneficiaries as a race and ethnicity with which they do not 
identify themselves on the nursing home assessment.  For example, 28 percent of the 
beneficiaries identified as Hispanic in the enrollment data do not identify themselves 
as Hispanic on their nursing home assessments.  The same error occurs for 46 percent 
of beneficiaries identified in the enrollment data as American Indian/Alaska Native 
and 17 percent of beneficiaries identified as Asian/Pacific Islander.  (See Exhibit 1.) 
 
 
Data Brief: Inaccuracies in Medicares Race and Ethnicity Data Hinder the Ability To Assess Health Disparities 
OEI-02-21-00100  Results | 4  Exhibit 1: Percentage of Medicare Beneficiaries Identified in Medicares 
Enrollment 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. 
Second, the enrollment data do not capture the race and ethnicity with which these 
beneficiaries do identify.  For example, 13 percent of beneficiaries who self-identified 
as Hispanic on their nursing home assessments are not identified that way in the 
enrollment data.  The same error occurs for 35 percent of beneficiaries who self-
identified as American Indian/Alaska Native on their nursing home assessments and 
for 24 percent of the beneficiaries who self-identified as Asian/Pacific Islander.  In 
other words, almost one-quarter of the Asian/Pacific Islander population, as self-
reported on nursing home assessments, are identified with another race and ethnicity 
or are missing race and ethnicity information in the enrollment data.  (See Exhibit 2.) 
Exhibit 2: Percentage of Medicare Beneficiaries Who Self-Identified as a Race 
and Ethnicity on Their Nursing Home Assessments but Are Not Identified 
That Way in Medicares Enrollment Data 
 
Source: OIG analysis of CMS data, 2021. 
Data Brief: Inaccuracies in Medicares Race and Ethnicity Data Hinder the Ability To Assess Health Disparities 
OEI-02-21-00100  Results | 5  Therefore, analyses to understand the unique health needs or outcomes of a racial or 
ethnic group will be based on faulty data if Medicare enrollment data are used.  The 
data for each group will include some beneficiaries who are not in the group and will 
be missing other beneficiaries who should be in the group.  The actual population of 
each group is different from the one identified by Medicares enrollment data.   
As a result, the usefulness of Medicares enrollment data is diminished.  The 
enrollment data can be a powerful source of information because they include all 
Medicare-enrolled beneficiaries.  These data are used to evaluate the health needs 
and outcomes of the Medicare population and assess important issues.4  These issues 
could include racial and ethnic disparities in the long-term impacts of COVID-19, 
access to health care providers, or the quality of services provided.  However, 
inaccurate race and ethnicity data can raise concerns that efforts to address 
disparities, to some extent, would be targeted at the wrong beneficiaries.  Further, 
using inaccurate data to try to measure the effects of these efforts could produce 
misleading results. 
Deficiencies in the source data contribute to inaccuracies in 
Medicares enrollment race and ethnicity data 
The primary source for Medicares race and ethnicity information in the enrollment 
data is SSA.  These source data have several limitations.  Specifically, the data are 
limited to only a few race and ethnicity categories and are missing for more than 
3 million beneficiaries. 
The race and ethnicity categories are not comprehensive 
The race and ethnicity categories used by 
Key Limitations of the Source Data 
SSA have historically been limited.  Prior 
to 1980, beneficiaries could select from    Historically, the categories for 
only three categoriesWhite, Black, or  race were limited to White, Black, 
Otherwhen applying for a Social  or Other. 
Security card (individuals who did not   
complete the question were categorized    The categories of American 
as Unknown).5  The SSA information for  Indian/Alaska Native, 
most beneficiaries enrolled in Medicare is  Asian/Pacific Islander, and 
limited to these categories.   Hispanic were subsequently 
added as options in 1980. 
Though SSA subsequently revised its 
 
categories, they remain limited in the data 
  SSA stopped routinely collecting 
Medicare uses.  Of note, they conflate 
race and ethnicity data in 1989. 
race and ethnicity and beneficiaries are 
limited to just one category.  These 
limitations mean that Medicares data do not capture separate racial and ethnic 
identities (e.g., Black and Hispanic) or multiracial beneficiaries.  Ultimately, these 
Data Brief: Inaccuracies in Medicares Race and Ethnicity Data Hinder the Ability To Assess Health Disparities 
OEI-02-21-00100  Results | 6  limitations impact Medicares ability to accurately and meaningfully understand the 
health needs and outcomes of different populations. 
Further, the limited categories contribute to the inaccuracies described above for the 
American Indian/Alaska Native, Asian/Pacific Islander, and Hispanic groups.  They also 
likely explain the relatively low overall percentages of beneficiaries who are identified 
as Asian/Pacific Islander or Hispanic in the data from SSA.  Only 2.5 percent are 
identified as Asian/Pacific Islander and 3 percent as Hispanic.  For reference, a 2019 
Profile of Older Americans estimates the over-65 population to be 5.1 percent 
Asian/Pacific Islander and 8 percent Hispanic.6   
Data on race and ethnicity are lacking for more than 3 million 
beneficiaries and the completeness of the data will decrease over 
time 
The data collected by SSA are lacking for 3.3 million Medicare beneficiaries.  That is, 
1.5 million beneficiaries are categorized as Unknown or have no race and ethnicity 
information in the SSA data.  Another 1.8 million beneficiaries are included in the race 
and ethnicity category of Other.   
The completeness of the data from SSA upon which Medicare relies will only decrease 
with time.  SSA stopped routinely collecting race and ethnicity information in 1989.7  
As such, the SSA data will increasingly have missing or Unknown data.  In fact, SSA 
no longer uses its data for race-related research because of its diminishing reliability.8  
Using an algorithm improves the accuracy of Medicares 
enrollment race and ethnicity data but falls short of self-
reported data 
To improve the quality of the information from SSA for the Asian/Pacific Islander and 
Hispanic groups, CMS applies an algorithm to that information in the Medicare 
enrollment data.9  This algorithm attempts to amend the race data from SSA by 
categorizing individuals based on name, geography, and requests for SSA and 
Medicare materials in Spanish.  Though the RTI algorithm appears to improve the 
accuracy of race and ethnicity information for beneficiaries identified as Asian/Pacific 
Islander and Hispanic, the use of an algorithm has drawbacks.   
The use of the algorithm increases the percentage of all beneficiaries identified as 
Asian/Pacific Islander from 2.5 percent in the SSA source data alone to 3.6 percent 
after the algorithm is applied and of beneficiaries identified as Hispanic from 
3 percent to 8.7 percent.  This means that Medicare is relying on the algorithm to 
supply race and ethnicity data for one-third of beneficiaries identified as Asian/Pacific 
Islander and two-thirds of beneficiaries identified as Hispanic.  (See Appendix B for 
the distribution of the race and ethnicity data for all Medicare beneficiaries.) 
Data Brief: Inaccuracies in Medicares Race and Ethnicity Data Hinder the Ability To Assess Health Disparities 
OEI-02-21-00100  Results | 7  Using the RTI algorithm improves the accuracy of the enrollment data for these two 
groups over using the data from SSA alone.  For the subset of beneficiaries with 
nursing home assessments, the RTI algorithm improves upon the accuracy of the SSA 
data for those who self-identified as Asian/Pacific Islander by 18 percentage points.  
For those who self-identified as Hispanic, the RTI algorithm increases the accuracy by 
55 percentage points.  (See Exhibit 3.) 
Exhibit 3: The RTI algorithm improves the accuracy of Medicares enrollment 
data for beneficiaries who self-identified as Hispanic or Asian/Pacific 
Islander on their nursing home assessments. 
 
Source: OIG analysis of CMS data, 2021. 
Despite improvements in accuracy, data derived by an algorithm relying on proxy 
information have more errors and are more inaccurate than self-reported data.10  (See 
the text box for more information about the RTI algorithm.)    
Imputation of Race and Ethnicity Is Less Accurate Than Self-Reported Data 
  The RTI algorithm relies on a list of last names that are frequently associated with 
Asian/Pacific Islander or Hispanic individuals. 
o  As researchers and others have found, this practice does not accurately account for 
individuals who change their name after marriage, who are from colonized countries 
with names that closely align with the nationality of the colonizing nation, or who 
changed their name upon immigrating to the United States.* 
  The RTI algorithm also uses geography for individuals living in Puerto Rico (Hispanic) or 
Hawaii (Asian/Pacific Islander). 
  The RTI algorithm also factors in whether individuals requested SSA and Medicare 
materials in Spanish. 
  Imputation may be appropriate for estimating race and ethnicity at the population level 
but is problematic when predicting an individuals race and ethnicity. 
*Jarrin, Olga F.; Nyandege, Abner N.; et al., Validity of Race and Ethnicity Codes in Medicare Administrative Data 
Compared With Gold-Standard Self-Reported Race Collected During Routine Home Health Care Visits, Medical Care, 
Vol. 58, No. 1, January 2020, pp. e1e8.  Roberts, Sam, New Life in U.S. No Longer Means New Name, New York 
Times, August 25, 2010.  Accessed at https://www.nytimes.com/2010/08/26/nyregion/26names.html on May 10, 2022. 
Data Brief: Inaccuracies in Medicares Race and Ethnicity Data Hinder the Ability To Assess Health Disparities 
OEI-02-21-00100  Results | 8  Finally, even after applying the algorithm, Medicare lacks race and ethnicity 
information for 2 million beneficiaries.  Specifically, 1.3 million beneficiaries still have 
Unknown or missing information and another 600,000 remain categorized as 
Other.  It is also important to note that the RTI algorithm 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.  
Medicares enrollment race and ethnicity data are inconsistent 
with Federal data collection standards 
Federal standardsthe 1997 Office of Management and Budget (OMB) standards and 
the 2011 standards developed by the Department of Health and Human Services 
(HHS) per the Patient Protection and Affordable Care Actrecommend that race and 
ethnicity data be collected in a comprehensive way.  Comprehensive data collection is 
necessary to sufficiently understand health disparities and inform appropriate 
interventions and solutions.  Medicares race and ethnicity data are inconsistent with 
key standards for the collection of such data, thereby limiting the usefulness of the 
data. 
As noted above, Medicares enrollment data  Key Federal Standards 
conflate race and ethnicity in one question and    Separate questions for race 
beneficiaries may select only one category.  This 
and ethnicity 
conflicts with OMB and HHS standards, which 
 
recommend including two questionsone for    Allowing respondents to 
race and one for ethnicityand allowing  select all categories that apply 
individuals to select all categories that apply.    
  Comprehensive race and 
The structure allows individuals to most 
ethnicity categories 
accurately and fully represent both their racial 
 
and ethnic identities.   
Further, the categories provided by Medicares enrollment data are not 
comprehensive.  This conflicts with the HHS standards that expanded the categories 
of race and ethnicity data collection by adding granularity for the Hispanic and 
Asian/Pacific Islander groups.  (See Appendix A for the categories.)  Collecting these 
more granular data is crucial to knowledge about health disparities because a diverse 
racial or ethnic group may have very different health outcomes between its 
subgroups.11  This type of specificity aids in more accurate analysis and more targeted 
interventions. 
By expanding the categories on a large dataset like Medicares enrollment data to be 
consistent with the HHS standards, more robust analysis of race and ethnicity 
subcategories can be conducted.  These categories are already employed on HHS 
surveys (e.g., Medicare Current Beneficiary Survey) and on other large-scale surveys 
(e.g., U.S. Census).  Lack of consistency with the Federal standards in the Medicare 
enrollment data inhibits the work of identifying and reducing health disparities within 
the Medicare population. 
Data Brief: Inaccuracies in Medicares Race and Ethnicity Data Hinder the Ability To Assess Health Disparities 
OEI-02-21-00100  Results | 9   
CONCLUSION AND RECOMMENDATIONS 
 
 
Comparisons to self-reported data show that Medicares enrollment data on race and 
ethnicity are less accurate for some groups, particularly for beneficiaries identified as 
American Indian/Alaska Native, Asian/Pacific Islander, or Hispanic.  Data that are not 
accurate limit the ability to assess and address health disparities. 
Deficiencies in the source data, such as categories that are not comprehensive and 
missing information, contribute to inaccuracies in Medicares enrollment race and 
ethnicity data.  In addition, using the RTI algorithm improves the accuracy of 
Medicares enrollment race and ethnicity data but falls short of self-reported data.  
Finally, Medicares enrollment race and ethnicity data are inconsistent with Federal 
data collection standards. 
CMS has some work underway to improve the quality of the race and ethnicity data it 
collects.  For example, it will incorporate race and ethnicity questions that are 
consistent with the HHS standards on the next versions of the post-acute 
assessments, such as the nursing home assessments.  CMS has also presented 
strategies for improving the enrollment data in a Report to Congress mandated by 
the Improving Medicare Post-Acute Care Transformation (IMPACT) Act of 2014.12   
In the meantime, CMS seeks to expand the use of an alternative algorithm (Medicare 
Bayesian Improved Surname Geocoding) that it has found to be highly accurate.13  
This algorithm relies on similar inputs as the RTI algorithm, as well as some additional 
data, such as Census-linked geocoding.  We recognize that resolving the inaccuracies 
in the data identified in this report cannot happen overnight and we appreciate CMSs 
attention to this important issue.  However, we note that this alternative algorithm 
also relies on the SSA source data and proxy information.  Moreover, the alternative 
algorithm continues to use the same categories (limited to American Indian/Alaska 
Native, Asian/Pacific Islander, Black, Hispanic, and White) and to combine race and 
ethnicity. 
Given the inaccuracies in Medicares enrollment data and the priority of advancing 
health equity, CMS needs to improve its race and ethnicity data.  Though this will be a 
significant undertaking, the need for better data is pressing.  Accordingly, we 
recommend that CMS: 
Develop its own source of race and ethnicity data 
Given the limitations associated with the data from SSA and with imputation, CMS 
must transition away from the current process for obtaining race and ethnicity data 
and, instead, develop its own source of this information.  This information should be 
voluntarily self-reported by beneficiaries and otherwise consistent with Federal 
standards. 
Data Brief: Inaccuracies in Medicares Race and Ethnicity Data Hinder the Ability To Assess Health Disparities 
OEI-02-21-00100  Recommendations | 10  For example, CMS could develop a way to collect race and ethnicity information from 
beneficiaries at Medicare enrollment, capturing information for newly enrolled 
beneficiaries, in coordination with SSA, as needed.  CMS could also pursue alternative 
mechanisms for collection, such as online, by mail, and/or via the Parts C and D 
enrollment forms.  Once collected, CMS should reconsider its need for imputation. 
Use self-reported race and ethnicity information to improve 
data for current beneficiaries  
As a more immediate step to improve the race and ethnicity data for current 
beneficiaries, CMS should incorporate the existing data from the nursing home and 
other post-acute care assessments, like the Outcome and Assessment Information Set 
(OASIS) assessment for home health care, into the enrollment data for current 
beneficiaries where available.  These assessments allow beneficiaries to select more 
than one race or ethnicity, are ideally self-reported, and should be consistent with the 
HHS standards soon.  Once established, new data from post-acute care assessments 
could be incorporated into the enrollment data on an ongoing basis.   
Next, CMS should explore other ways of collecting self-reported data from current 
beneficiaries who do not have post-acute care assessments.  There are many options 
for this.  CMS could add race and ethnicity demographic questions to beneficiaries 
online Medicare accounts.  CMS could further collaborate with the Indian Health 
Service to improve the data for American Indian/Alaska Native beneficiaries.  CMS 
could also conduct a targeted survey, similar to the postcard survey it used in the 
past, to reach beneficiaries not captured through other mechanisms.14  In choosing 
among these and other options, CMS should assess the number of beneficiaries likely 
impacted, the potential response rates, and the burdens on and benefits to 
beneficiaries and the Medicare program.       
Develop a process to ensure that the data are as standardized as 
possible 
CMS should develop a process to make the race and ethnicity data it collects as 
standardized as possible and able to be incorporated into the enrollment database.  
For example, CMS should standardize the phrasing of questions and response options 
pertaining to race and ethnicity and should use the Federal standards as a benchmark.  
Consistent and centralized data will allow for more comprehensive analyses and 
assessments of health disparities. 
CMS should also develop a process for incorporating existing and new race and 
ethnicity data from different sources into the centralized enrollment database.  It 
could start with the data from the nursing home and other post-acute care 
assessments.  This process will need to address multiracial beneficiaries, which the 
enrollment database currently cannot accommodate, and beneficiaries who may 
report their information differently on multiple assessments. 
Data Brief: Inaccuracies in Medicares Race and Ethnicity Data Hinder the Ability To Assess Health Disparities 
OEI-02-21-00100  Recommendations | 11  Educate beneficiaries about CMSs efforts to improve the race 
and ethnicity information  
CMS should educate beneficiaries about its efforts to improve the race and ethnicity 
data.  Beneficiaries may be reluctant to provide this information to CMS if they do not 
understand how it will be used.  CMS should develop and implement a plan to 
address these concerns that includes education and communication strategies.  CMS 
should consider its own experiences and consult with others in determining 
appropriate and effective strategies for communicating with beneficiaries.  These 
strategies could emphasize, for example, that responses do not affect Medicare 
coverage and include explanations for why the data are being collected at the point 
of collection (e.g., on an enrollment form, assessment, or Medicare account).   
Data Brief: Inaccuracies in Medicares Race and Ethnicity Data Hinder the Ability To Assess Health Disparities 
OEI-02-21-00100  Recommendations | 12  AGENCY COMMENTS AND OIG RESPONSE 
 
 
CMS concurred with three of our recommendations and did not explicitly indicate 
whether it concurred with one recommendation.  CMS emphasized its shared interest 
in self-reported and granular race and ethnicity data to better identify and address 
health disparities.  
In response to our first recommendation to develop its own source of race and 
ethnicity data, CMS stated that it is committed to obtaining more accurate and 
comprehensive race and ethnicity data, consistent with its authorities and 
appropriation of funds under the law.  CMS further noted that the Presidents Fiscal 
Year 2023 Budget includes a proposal to increase SSA sharing and collection of race 
and ethnicity data for individuals with Medicare.  In the meantime, CMS stated that it 
is exploring opportunities to obtain more accurate and comprehensive race and 
ethnicity data.  For example, CMS solicited comments on collecting race and ethnicity 
data on the Medicare Part C and D enrollment forms.  In response, OIG emphasizes 
that the need for better data is pressing.  We encourage CMS to continue pursuing 
these and other options and to seek statutory authority or request appropriations, as 
appropriate, to obtain more accurate and comprehensive race and ethnicity data.  
CMS concurred with our second recommendation to use self-reported race and 
ethnicity information to improve data for current beneficiaries.  CMS stated that it will 
continue exploring ways to obtain more accurate and comprehensive data for current 
beneficiaries and consider whether existing data, such as data obtained from nursing 
home or other post-acute care assessments, should be incorporated into the 
enrollment database.  
CMS concurred with our third recommendation to develop a process to ensure that 
the data are as standardized as possible.  CMS stated that it is exploring ways to 
standardize collected race and ethnicity data in a way that is compliant with up-to-
date Federal standards.  
CMS concurred with our fourth recommendation to educate beneficiaries about its 
efforts to improve the race and ethnicity information.  CMS stated that it will explore 
opportunities to educate beneficiaries about its efforts to improve the collection of 
race and ethnicity data and how it relates to the agencys commitment to advancing 
health equity, expanding coverage, and improving health outcomes.  CMS also noted 
it developed the Inventory of Resources for Standardized Demographic and Language 
Data Collection (updated March 2022) for stakeholders to improve the collection of 
standardized data, which can help them identify and address health disparities. 
For the full text of CMSs comments, see Appendix C.  
Data Brief: Inaccuracies in Medicares Race and Ethnicity Data Hinder the Ability To Assess Health Disparities 
OEI-02-21-00100  Agency Comments and OIG Response | 13   
METHODOLOGY 
 
 
Data Sources 
We based this data brief on Medicares race and ethnicity data from the Master 
Beneficiary Summary Filereferred to as the enrollment data.  We included all 
beneficiaries enrolled in Medicare at any point in 2020.  We also used data for any 
beneficiary who had at least one assessment in the Minimum Dataset (MDS)
referred to as the nursing home assessment datawith completed race and ethnicity 
data between January 1, 2018, and December 31, 2020.  Lastly, we conducted 
interviews with CMS staff and other selected stakeholders to gain a better 
understanding of current data collection methods and strategies for improving the 
data. 
Analysis of Accuracy of Medicare Race and Ethnicity Enrollment Data 
Compared to Self-Reported Nursing Home Assessment Data 
To assess the accuracy of Medicares race and ethnicity data, we compared the RTI 
variable in the enrollment data with the self-reported nursing home assessment data 
in the MDS.  For the purposes of this report, when this information did not match, we 
considered the enrollment data to be inaccurate, as self-reported data are considered 
the gold standard.15  First, we calculated the percentage of beneficiaries identified as 
a race and ethnicity in the enrollment data who did not self-identify themselves the 
same way on their nursing home assessments.  Second, we calculated the percentage 
of beneficiaries who self-identified themselves as a race and ethnicity on their nursing 
home assessments but were not identified the same way in the enrollment data.  For 
both measures, we calculated accuracy rates for each race and ethnicity category.16 
To assess the relative accuracy of the RTI variable and the SSA source data, we 
identified beneficiaries who self-identified in the nursing home assessment data as 
either Asian/Pacific Islander or Hispanic.  We then calculated the percentage that were 
identified as the same group in Medicares enrollment data for: (1) the SSA source 
data alone and (2) the data after the RTI algorithm was applied. 
Analysis of Completeness of Medicare Race and Ethnicity Data 
To assess the extent to which Medicares race and ethnicity data were missing, we 
calculated the number and percentage of beneficiaries by race and ethnicity category.  
We calculated the extent to which the information has values of Other, Unknown, 
or missing.  We performed this calculation on both the data from SSA and on the data 
after CMS applies the RTI algorithm.  We calculated the number and percentage of 
beneficiaries with incomplete race and ethnicity data for each variable by totaling the 
amount marked Other and the amount marked Unknown or missing.   
Data Brief: Inaccuracies in Medicares Race and Ethnicity Data Hinder the Ability To Assess Health Disparities 
OEI-02-21-00100  Methodology | 14  Analysis of Federal Standards for Collection of Race and Ethnicity Data 
We evaluated Medicares enrollment data based on the 1997 OMB revised standards 
as well as the 2011 HHS standards per the Patient Protection and Affordable Care Act.  
Areas of analysis included race and ethnicity categories, separation or combination of 
race and ethnicity questions, and category selection process, such as single selection 
versus multiple selection.  We also assessed the ways in which further alignment with 
these standards would improve Medicares data.  We used the OMB and HHS 
standards as a benchmark for assessing the adequacy and usefulness of Medicares 
enrollment race and ethnicity data; this was not a compliance review.  
Analysis of Interview Data 
We conducted interviews with CMS staff and other stakeholders to further assess the 
history of the data collection process, as well as current and future efforts to adapt 
and improve race and ethnicity data collection from Medicare beneficiaries.  
Interviews covered strategies for and barriers to improving the 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 report. 
  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 
Our analysis of the accuracy of the Medicare enrollment data is based on the subset 
of Medicare beneficiaries 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.17 
We did not assess all potential costs, barriers, or benefits related to collecting data 
directly from Medicare beneficiaries.   
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.  
Data Brief: Inaccuracies in Medicares Race and Ethnicity Data Hinder the Ability To Assess Health Disparities 
OEI-02-21-00100  Methodology | 15   
APPENDIX A 
 
Federal Race and Ethnicity Data Collection Standards 
Office of Management and Budget,  HHS Implementation Guidance on Data 
1997  Collection Standards, 2011 
Race  
White  White 
Black  Black or African American 
American Indian or Alaska Native  American Indian or Alaska Native 
Asian  Asian Indian 
Native Hawaiian or Other Pacific Islander  Chinese 
Filipino 
Japanese 
Korean 
Vietnamese 
Other Asian 
Native Hawaiian 
Guamanian or Chamorro 
Samoan 
Other Pacific Islander 
Ethnicity 
Not Hispanic or Latino  No, not of Hispanic, Latino/a, or Spanish origin 
Hispanic or Latino  Yes, Mexican, Mexican American, Chicano/a 
Yes, Puerto Rican 
Yes, Cuban 
Yes, another Hispanic, Latino/a, or Spanish origin 
Sources: 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 10, 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.
Data Brief: Inaccuracies in Medicares Race and Ethnicity Data Hinder the Ability To Assess Health Disparities 
OEI-02-21-00100  Appendix A | 16   
APPENDIX B 
 
Distribution of Medicare Beneficiaries by Race and Ethnicity in 
Medicares Enrollment Data  
Percentage of  Percentage of  Number of 
Beneficiaries  Beneficiaries  Beneficiaries 
Race and Ethnicity  (SSA)  (RTI)  (RTI) 
American Indian/Alaska Native  0.4%  0.4%  275,366 
Asian/Pacific Islander  2.5%  3.6%  2,376,832 
Black  10.8%  10.6%  6,992,296 
Hispanic  3.0%  8.7%  5,730,381 
White  78.3%  73.7%  48,583,154 
Other  2.7%  1.0%  641,033 
Unknown/Missing  2.3%  2.0%  1,340,795 
     Total  100%  100%  65,939,857 
Source: OIG analysis of CMS data, 2021.
Data Brief: Inaccuracies in Medicares Race and Ethnicity Data Hinder the Ability To Assess Health Disparities 
OEI-02-21-00100  Appendix B | 17  DATE:  May 3, 2022 
TO:  Gregory E. Demske 
Acting Principal Deputy Inspector General 
Office of Inspector General 
FROM:  Chiquita Brooks-LaSure 
Administrator 
Centers for Medicare & Medicaid Services 
SUBJECT:  Office of Inspector General (OIG) Draft Report: Inaccuracies in Medicares Race 
and Ethnicity Data Hinder the Ability To Assess Health Disparities (OEI-02-21-
00100) 
The Centers for Medicare & Medicaid Services (CMS) appreciates the opportunity to review and 
comment on the Department of Health and Human Services (HHS) Office of Inspector Generals 
(OIG) above-titled draft report.  
CMS serves the public as a trusted partner and steward, dedicated to advancing health equity, 
expanding coverage, and improving health outcomes. CMS defines health equity as the 
attainment of the highest level of health for all people, where everyone has a fair and just 
opportunity to attain their optimal health regardless of race, ethnicity, disability, sexual 
orientation, gender identity, socioeconomic status, geography, preferred language, or other 
factors that affect access to care and health outcomes. CMS is working to advance health equity 
by designing, implementing, and operationalizing policies and programs that support health for 
all the people served by our programs, eliminating avoidable differences in health outcomes 
experienced by people who are disadvantaged or underserved, and providing the care and 
support that our enrollees need to thrive. 
Central to these efforts is improving the collection, reporting, and analysis of CMSs health 
equity-related data. The Social Security Administration (SSA) is responsible for enrolling 
Medicare-eligible individuals into Medicare, and as such, is the primary source of race and 
ethnicity data for people with Medicare. However, SSA discontinued collecting these data for 
most people with Medicare born after 1989. The Presidents Fiscal Year (FY) 2023 Budget 
includes a proposal to increase SSA sharing and collection of race and ethnicity data for 
individuals with Medicare.1 This administrative proposal would have SSA increase sharing of 
race and ethnicity data with CMS for current and prospective Medicare enrollees, and consider 
expanding collection of detailed data, e.g. at 2011 HHS Data Standards2 or, when available, 
newer data standards. CMS would assist SSA by conducting appropriate research and user 
testing for collection of these data to ensure it is useful for the purposes of identifying and 
tracking disparities in healthcare treatment and outcomes by race and ethnicity.  
1 https://www.hhs.gov/sites/default/files/fy-2023-budget-in-brief.pdf 
2 https://aspe.hhs.gov/reports/hhs-implementation-guidance-data-collection-standards-race-ethnicity-sex-primary-
language-disability-0 
Data Brief: Inaccuracies in Medicares Race and Ethnicity Data Hinder the Ability To Assess Health Disparities 
OEI-02-21-00100                                       Appendix C | 18 To augment the race and ethnicity data received from SSA, CMS has taken a variety of 
approaches to obtain or improve race and ethnicity information for people with Medicare 
including the use of indirect estimation methods (statistical imputation), conducting a postcard 
survey, and collaborating with the Indian Health Service. Most recently, in January 2022, CMS 
solicited comments regarding CMS intention to pilot the collection of race and ethnicity data on 
the Medicare Part C and D enrollment forms.3 
As part of our work to advance health equity, CMS has interest in identifying patterns of 
differences across many key process and care outcomes by sociodemographic characteristics, 
including race and ethnicity. To best characterize these differences, we agree that self-reported 
and granular race and ethnicity data are needed. Efforts to improve the collection of such data 
will continue to strengthen our work to advance health equity, expand coverage, and improve 
health outcomes. In addition, new data will allow us to validate the existing indirect estimation 
methods that CMS uses for race and ethnicity to ensure that we do not rely on methodologies 
that unintentionally create or exacerbate disparities.  
We appreciate the OIG's work on this area and look forward to working collaboratively on this 
and other issues in the future. 
The OIGs recommendations and CMS responses are below. 
OIG Recommendation  
The OIG recommends that CMS develop its own source of race and ethnicity data. 
CMS Response  
CMS is committed to obtaining more accurate and comprehensive race and ethnicity data for 
people with Medicare, consistent with our authorities and appropriation of funds under the law. 
As stated above, the Presidents FY 2023 Budget includes a proposal to increase SSA sharing 
and collection of race and ethnicity data for individuals with Medicare. As SSA enrolls 
Medicare-eligible individuals into Medicare and shares that information with CMS, there is an 
opportunity to improve data collection through that mechanism.  
In the meantime, CMS will continue to explore opportunities to obtain more accurate and 
comprehensive race and ethnicity data for people with Medicare consistent with our current 
authorities. For example, in January 2022, CMS solicited comments regarding our intention to 
pilot the collection of race and ethnicity data on the Medicare Part C and D enrollment forms. 
With the long-term goal of collecting race and ethnicity data from all people with Medicare, 
CMS plans to focus initial efforts on those who newly elect or change coverage in the Medicare 
Part C and D programs. The detailed race and ethnicity categories collected through the pilot 
demographic questions on the Medicare Part C and Part D enrollment forms would be compliant 
with the 2011 HHS Data Standards to provide granular information for plans and for CMS to 
understand the diversity of the Medicare population. If finalized, the data will be used to: (1) 
Explore the response rate to race and ethnicity questions as a whole and how it intersects with 
enrollee income and other demographics; (2) Conduct focus groups to understand how people 
who elect to not respond to the race and ethnicity questions perceive the addition of those 
questions on the form; (3) Continue to test CMS race and ethnicity indirect estimation methods 
by adding additional race and ethnicity data to the data CMS already has; and (4) Determine the 
data necessary for sufficient samples sizes to conduct analyses of disaggregated race and 
ethnicity categories. In addition to the aforementioned uses, CMS will ultimately use this 
3 https://www.govinfo.gov/content/pkg/FR-2022-01-12/pdf/2022-00375.pdf 
Data Brief: Inaccuracies in Medicares Race and Ethnicity Data Hinder the Ability To Assess Health Disparities 
OEI-02-21-00100                                       Appendix C | 19 information to: (1) Track enrollment, including tracking patterns in enrollment by race and 
ethnicity over time; (2) To identify, monitor, and develop effective and efficient strategies and 
incentives to reduce and eliminate health and health care inequities; (3) Validate existing race 
and ethnicity statistical imputation methods; and (4) Ensure that clinically appropriate and 
equitable care (in terms of payment, access, and quality) is consistently provided to all enrollees. 
The comment period for the proposal has ended, however, CMS is currently reviewing 
comments received.  
OIG Recommendation  
The OIG recommends that CMS use self-reported race and ethnicity information to improve data 
for current beneficiaries.  
CMS Response 
CMS concurs with this recommendation. CMS will continue to explore opportunities to obtain 
more accurate and comprehensive race and ethnicity data for people with Medicare consistent 
with the priorities and objectives of the Administration. CMS will consider whether any existing 
data, such as data obtained from nursing home or other post-acute care assessments, should be 
incorporated into the enrollment database.  
OIG Recommendation  
The OIG recommends that CMS develop a process to ensure that the data are as standardized as 
possible.  
CMS Response 
CMS concurs with this recommendation. CMS is exploring ways to standardize race and 
ethnicity data collected. For example, any new CMS data collection will be compliant with the 
2011 HHS Data Standards or, when available, newer data standards. CMS will take the OIGs 
findings and recommendations in to account when determining any additional next steps.  
OIG Recommendation 
The OIG recommends that CMS educate beneficiaries about CMSs efforts to improve the race 
and ethnicity information.  
CMS Response 
CMS concurs with this recommendation. It is important that enrollees understand the value of 
this type of data and how the data will be utilized. It is equally important that enrollees 
understand that providing this type of data is voluntary and that providing such data is not 
required to get care or coverage and will not affect any payments. CMS will explore 
opportunities to educate its enrollees about CMS efforts to improve the collection of race and 
ethnicity data and how it relates to the agencys commitment to advancing health equity, 
expanding coverage, and improving health outcomes through a variety of approaches and 
channels. 
In addition, CMS believes that it is important to provide educational resources on this topic for 
other parties that may be looking for recommendations on how to best collect and use race and 
ethnicity data. For this purpose, CMS developed the Inventory of Resources for Standardized 
Demographic and Language Data Collection (updated March 2022).4 The inventory combines data 
collection best practices from reports, toolkits, webinars, and training tools into a single resource 
that can be used by health care organizations of all types. Stakeholders can use these resources to 
4 https://www.cms.gov/About-CMS/Agency-Information/OMH/Downloads/Data-Collection-Resources.pdf 
Data Brief: Inaccuracies in Medicares Race and Ethnicity Data Hinder the Ability To Assess Health Disparities 
OEI-02-21-00100                                       Appendix C | 20 improve the collection of standardized data, which can help them identify and address health 
disparities. 
Data Brief: Inaccuracies in Medicares Race and Ethnicity Data Hinder the Ability To Assess Health Disparities 
OEI-02-21-00100                                       Appendix C | 21  
ACKNOWLEDGMENTS AND CONTACT 
 
Acknowledgments 
Rachel Bryan served as the team leader for this study.  Other Office of Evaluation and 
Inspections staff from the New York regional office who conducted the study include 
Heather Koenig.  Office of Evaluation and Inspections 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 
Department of Health and Human Services 
330 Independence Avenue, SW 
Washington, DC 20201 
Data Brief: Inaccuracies in Medicares Race and Ethnicity Data Hinder the Ability To Assess Health Disparities 
OEI-02-21-00100  Acknowledgments and Contact | 22  DNOTES 
ENDNOTES 
 
 
 
1 The Centers for Disease Control and Prevention, Health Equity Considerations and Racial and Ethnic Minority Groups, July 
  24, 2020.  https://www.cdc.gov/coronavirus/2019-ncov/community/health-equity/race-ethnicity.html. 
  2 CMS contracted with RTI to create an algorithm that could be applied to the existing enrollment data from SSA to improve 
their accuracy.  The RTI algorithm attempts to amend the race data for Hispanic and Asian/Pacific Islander beneficiaries by 
  categorizing them based on name, geography, and requests made for SSA or Medicare materials in Spanish.  
  3 For beneficiaries in nursing homes, these data are collected on Minimum Dataset (MDS) assessments.  Medicare also collects 
race and ethnicity data in other post-acute care settings, using different assessments, such as the Outcome and Assessment 
 
Information Set (OASIS) for home health care patients.  Although the assessments are different, they all include the same race 
and ethnicity question.  Beneficiaries report their race and ethnicity information on the assessments, but if the beneficiary is 
 
not able to do so, a caregiver or clinician may respond. 
 
4 See, for example, 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. 
 
5 Eicheldinger, Celia; Bonito, Arthur, More Accurate Racial and Ethnic Codes for Medicare Administrative Data, Health Care 
Financing Review, Vol. 29, No. 3, Spring 2008.  https://pubmed.ncbi.nlm.nih.gov/18567241/. 
 
6 The Administration for Community Living, 2019 Profile of Older Americans, May 2020.  
https://acl.gov/sites/default/files/Aging%20and%20Disability%20in%20America/2019ProfileOlderAmericans508.pdf.  This 
  report relied on self-reported race and ethnicity data from sources such as the U.S. Census Bureau. 
7 Scott, Charles G., Identifying the Race or Ethnicity of SSI Recipients, Social Security Bulletin, Vol. 62, No. 4, 1999. 
 
8 SSA instead uses survey data.  See Martin, Patricia P., Why Researchers Now Rely on Surveys for Race Data on OASDI and SSI 
Programs: A Comparison of Four Major Surveys, Social Security Office of Retirement and Disability Policy, Research, and 
Statistics Note No. 2016-01.  https://www.ssa.gov/policy/docs/rsnotes/rsn2016-01.html. 
 
9 In addition to the algorithm, CMS has taken two other actions to improve the SSA source data.  In 1997, CMS sent a postcard 
survey to individuals with Hispanic surnames and geographical locations to more accurately identify Hispanic beneficiaries, 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/. 
And, 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 current Medicare beneficiaries. 
10 Jarrin, Olga F.; Nyandege, Abner N.; et al., Validity of Race and Ethnicity Codes in Medicare Administrative Data Compared 
With Gold-Standard Self-Reported Race Collected During Routine Home Health Care Visits, Medical Care, Vol. 58, No. 1, 
January 2020, pp. e1e8. 
11 Gordan, Nancy, et al., Aggregation of Asian-American subgroups masks meaningful differences in health and health risks 
among Asian ethnicities: an electronic health record based cohort study, November, 25, 2019.  
https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-019-7683-3.  
 
Data Brief: Inaccuracies in Medicares Race and Ethnicity Data Hinder the Ability To Assess Health Disparities 
OEI-02-21-00100  Endnotes | 23  DNOTES   
12 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.  https://www.cms.gov/About-CMS/Agency-
Information/OMH/Downloads/Research-Reports-2017-Report-to-Congress-IMPACT-ACT-of-2014.pdf. 
 
13 CMS, Medicare Program; Hospital Inpatient Prospective Payment Systems for Acute Care Hospitals and the Long-Term Care 
  Hospital Prospective Payment System and Policy Changes and Fiscal Year 2022 Rates; Quality Programs and Medicare 
Promoting Interoperability Program Requirements for Eligible Hospitals and Critical Access Hospitals; Changes to Medicaid 
 
Provider Enrollment; and Changes to the Medicare Shared Savings Program, 86 Fed. Reg. 44774, 45264 (Aug. 13, 2021).  
https://www.govinfo.gov/content/pkg/FR-2021-08-13/pdf/2021-16519.pdf. 
 
14 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/.   
 
15 Self-reported data are used as the gold-standard to assess the accuracy of other data sources.  Eicheldinger, Celia; Bonito, 
  Arthur, More Accurate Racial and Ethnic Codes for Medicare Administrative Data, Health Care Financing Review, Vol. 29, No. 
3, Spring 2008.  https://pubmed.ncbi.nlm.nih.gov/18567241/.  
 
16 For beneficiaries with more than one nursing home assessment during our timeframe, we used the race and ethnicity data 
from the most recent one.   
 
17 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. 
 
 
 
 
 
Data Brief: Inaccuracies in Medicares Race and Ethnicity Data Hinder the Ability To Assess Health Disparities 
OEI-02-21-00100  Endnotes | 24