Blog & News
Race/Ethnicity Data in CMS Medicaid (T-MSIS) Analytic Files: 2021 Data Assessment
December 6, 2023:The Transformed Medicaid Statistical Information System (T-MSIS) is the largest national database of current Medicaid and Children’s Health Insurance Program (CHIP) beneficiary information collected from U.S. states, territories, and the District of Columbia (DC).1 T-MSIS data are critical for monitoring and evaluating the utilization of Medicaid and CHIP, which together provide health insurance coverage to almost 90 million people.2
Due to their size and complexity, T-MSIS data files are challenging to use directly for research and analytic purposes. To optimize these files for health services research, Centers for Medicare and Medicaid Services (CMS) repackages them into a user-friendly, research-ready format called T-MSIS Analytic Files (TAF) Research Identifiable Files (RIF). One such file, the Annual Demographic and Eligibility (DE) file, contains race and ethnicity information for Medicaid and CHIP beneficiaries. This information is vital for assessing enrollment, access to services, and quality of care across racial and ethnic groups in the Medicaid/CHIP population, whose members are particularly vulnerable due to limited income, physical and cognitive disabilities, old age, complex medical conditions, housing insecurity, and other social, economic, behavioral, and health needs.
To guide researchers and other consumers in their use of T-MSIS data, CMS produces data quality assessments of the completeness of race and ethnicity data along with other data such as enrollment, claims, expenditures, and service use. The Data Quality (DQ) assessments for race and ethnicity data have been posted for data years 2014 through 2021 and indicate varying levels of “concern” regarding race and ethnicity data completeness. Some data years have multiple data versions (e.g., Preliminary, Release 1, Release 2), each with their own DQ assessment.
While completeness of race and ethnicity data reported to CMS has historically remained inconsistent among the states, territories, and DC, SHADAC has been monitoring the quality of these data over time. We are excited to discuss a noticeable improvement in quality as discussed below. This blog explores not only the 2021 Data Release 1, the most recent T-MSIS race and ethnicity data for which a DQ assessment is available, but also a brief analysis of data quality trends over time that we plan to follow in future T-MSIS file releases.
Evaluation of T-MSIS Race and Ethnicity Data
DQ assessments for each year and data version of T-MSIS data are housed in the Data Quality Atlas (DQ Atlas), an online evaluation tool developed as a companion to T-MSIS data.3 The DQ Atlas assesses T-MSIS race and ethnicity data using two criteria: the percentage of beneficiaries with missing race and/or ethnicity values in the TAF; and the number of race/ethnicity categories (out of five) that differ by more than ten percentage points between the TAF and American Community Survey (ACS) data. Taken together, these two criteria indicate the level of “concern” (i.e., reliability) for states’ T-MSIS race/ethnicity data. To construct the external ACS benchmark for evaluating T-MSIS data, creators of the DQ Atlas combine race and ethnicity categories in the ACS to mirror race and ethnicity categories reported in the TAF (see Table 1). More information about the evaluation of T-MSIS race and ethnicity data is available in the DQ Atlas’ Background and Methods Resource.
Five “concern” categories appear in the DQ Atlas: Low Concern, Medium Concern, High Concern, Unusable, and Unclassified. States with substantial missing race/ethnicity data or race/ethnicity data that are inconsistent with the ACS – a premier source of demographic data – are grouped into either the High Concern or Unusable categories, whereas states with relatively complete race/ethnicity data or race/ethnicity data that align with ACS estimates are grouped into either the Low Concern or Medium Concern categories. The Unclassified category includes states for which benchmark data are incomplete or unavailable for a given data year and version.
Table 1. Crosswalk of Race and Ethnicity Variables between the TAF and ACS
Race/Ethnicity Category |
Race/Ethnicity Flag Value in TAF |
Combination of Race and Hispanic Variables in ACS |
Hispanic, all races |
7=Hispanic, all races | Hispanic, all races |
Other races, non-Hispanic |
4= American Indian and Alaska Native, non-Hispanic 5=Hawaiian/Pacific Islander 6=Multiracial, non-Hispanic |
- American Indian alone - Alaska Native alone - American Indian and Alaska Native tribes specified; or American Indian or Alaska native, non-specified and no other race - Native Hawaiian and other Pacific Islander alone - Some other race alone - Two or more races |
Source: Medicaid.gov. (n.d.). DQ Atlas: Background and methods resource [PDF file]. Available from https://www.medicaid.gov/dq-atlas/downloads/background-and-methods/TAF-DQ-Race-Ethnicity.pdf Accessed December 1, 2023.
Quality Assessment by State
Table 2 shows the Race and Ethnicity DQ Assessments for the 2021 TAF (Data Version: Release 1). The categorization criteria used to determine the levels of concern for the 2021 TAF Release 1 data are the same as those used to assess T-MSIS data from previous years and versions. 16 states received a rating of “Low Concern.” There were 22 states (including Puerto Rico [PR]) that fell into the “Medium Concern” category.
Most of the “Medium Concern” states (19 of 22) fell into the subcategory denoting the higher percentage range of missing race/ethnicity data (from 10 percent up to 20 percent). A similar pattern can be seen among the “High Concern” states, most of which (8 of 11) fell into the subcategory denoting the highest percentage range of missing race/ethnicity data (from 20 percent up to 50 percent).
Finally, 11 states (including DC) received a rating of “High Concern.” Three states (Massachusetts, Tennessee, and Utah) received an “Unusable” rating, as each of these states was missing at least 50 percent of race/ethnicity data. The Virgin Islands (VI) is the only state/territory categorized as “Unclassified” in the 2021 TAF (Data Version: Release 1) due to insufficient or incomplete data, and does not appear in Table 2.
Table 2. Race and Ethnicity Data Quality Assessment, 2021 T-MSIS Analytic File (TAF) Data Release 1
Data quality assessment |
Percent of beneficiaries with missing race/ethnicity values | Number of race/ethnicity categories where TAF differs from ACS by more than 10% |
Number of states* |
States |
Low Concern | <10% | 0 | 16 | AK, DE, GA, KS, MI, MO, NE, NV, NM, NC, ND, OH, OK, PA, SD, WA |
Medium Concern | <10% | 1 or 2 | 3 | ID, IL, VA |
10% - <20% | 0 or 1 | 19 | AL, AR, CA, CO, FL, IN, KY, ME, MD, MN, MS, MT, NH, NJ, PR, TX, VT, WV, WI | |
High Concern | <10% | 3 or more | 1 | RI |
10% - <20% | 2 or more | 2 | AZ, LA | |
20% - <50% | Any value | 8 | CT, DC, HI, IA, NY, OR, SC, WY | |
Unusable | >50% | Any value | 3 | MA, TN, UT |
Notes: *T-MSIS includes all 50 states, the District of Columbia (DC), and the U.S. territories of Puerto Rico (PR) and the Virgin Islands (VI). However, a DQ assessment is not available for VI in the 2021 TAF (Data Version: Release 1) due to incomplete/unavailable data.
Despite ongoing variation in the completeness of race and ethnicity data reported to CMS, SHADAC researchers have noted a trend toward better quality data overall. Since beginning to track these quality assessments with the 2019 T-MSIS TAF release, a number of states have shifted up the quality assessment scale with noticeably fewer states seeing their data classified as “High Concern.” Specifically, 2021 race/ethnicity TAF data from 11 states received a rating of “High Concern” compared to 16 states’ data in 2020 and 17 states’ data in 2019. The number of states with “Unusable” data has also dropped each year – 3 states’ 2021 race/ethnicity TAF data was classified as “Unusable” compared to 4 states’ data in 2020 and 5 states’ data in 2019.
Visualizing T-MSIS Data in the DQ Atlas
The DQ Atlas enables users to generate maps and tables that compare the quality of T-MSIS data between states across different topics, such as race/ethnicity, age, income, and gender (see Figure 1). Visualizing T-MSIS data in this manner can help researchers quickly assess the completeness of a single variable as well as the relative completeness (or incompleteness) of certain variables compared to others. For example, in the 2021 TAF Data Release 1, all states and territories received a “Low Concern” rating for age data, whereas only 31 states and territories received a “Low Concern” rating for family income.
Figure 1. Data Quality Assessments of Beneficiary Information by U.S. State/Territory
Notes: Green = low concern; yellow = medium concern; orange = high concern; red = unusable; grey = unclassified.
Source: Medicaid.gov. (n.d.). DQ Atlas: Race and Ethnicity [2021 Data set: Version: Release 1]. Available from https://www.medicaid.gov/dq-atlas/landing/topics/single/map?topic=g3m16&tafVersionId=35 Accessed December 1, 2023.
Looking Ahead
Increasingly, a wide diversity of voices from non-profits, health insurers, state-based marketplaces, and policymakers have called for improving the collection of race, ethnicity, and language data, often with the goal of advancing health equity. CMS’s efforts to improve the quality and availability of T-MSIS data reflect this nationwide movement toward data collection practices that more accurately capture the diversity of the U.S. population.
SHADAC was excited to see the revised Office of Management and Budget (OMB) standards related to the collection of race and ethnicity data. The proposed revisions align with available evidence, are consistent with the changes made by leading states, and, most importantly, explicitly state that these standards should serve as a minimum baseline with a call to collect and provide more granular data. However, while these standards are specifically named as minimum reporting categories for data collection throughout the Federal Government, if adopted they are likely to shape data collection and reporting across all sectors, including the states that collect race/ethnicity data through the Medicaid application process.
Many states express difficulties reporting data, as there is misalignment in how state eligibility systems, Medicaid Management Information System (MMIS), and T-MSIS format race and ethnicity data. Before states submit data to T-MSIS, they must reformat and aggregate data, which may affect the quality of submitted data. One approach to improve the collection and reporting of data is providing states with an updated model application using evidence-based approaches to race and ethnicity questions that improve applicant response rate and data accuracy.
Sources
1 Medicaid.gov. Transformed Medicaid Statistical Information System (T-MSIS). Retrieved October 20, 2022, from https://www.medicaid.gov/medicaid/data-systems/macbis/transformed-medicaid-statistical-information-system-t-msis/index.html#
2 Medicaid.gov. August 2023 Medicaid & CHIP Enrollment Data Highlights. Retrieved on December 1, 2023, from https://www.medicaid.gov/medicaid/program-information/medicaid-and-chip-enrollment-data/report-highlights/index.html3 Saunders, H., & Chidambaram, P. (April 28, 2022). Medicaid Administrative Data: Challenges with Race, Ethnicity, and Other Demographic Variables. Kaiser Family Foundation. Retrieved October 31, 2022, from https://www.kff.org/medicaid/issue-brief/medicaid-administrative-data-challenges-with-race-ethnicity-and-other-demographic-variables/
4 Wang, H.L. (June 15, 2022). Biden officials may change how the U.S. defines racial and ethnic groups by 2024. NPR. Retrieved November 1, 2022, from https://www.npr.org/2022/06/15/1105104863/racial-ethnic-categories-omb-directive-15
5 Diaz, J. (August 16, 2022). California becomes the first state to break down Black employee data by lineage. NPR. Retrieved November 1, 2022, from https://www.npr.org/2022/08/16/1117631210/california-becomes-the-first-state-to-break-down-black-employee-data-by-lineage
6 The New York State Senate. (December 22, 2021). Assembly Bill A6896A. Retrieved November 2, 2022, from https://www.nysenate.gov/legislation/bills/2021/A689
Blog & News
SHADAC's Lynn Blewett Featured on 'Hospitals in Focus' Podcast
November 01, 2023:SHADAC Director Dr. Lynn Blewett was recently invited to join Federation of American Hospitals CEO and President Chip Kahn as a guest on his podcast Hospitals in Focus to share her thoughts on the impacts of the unwinding of the Medicaid continuous coverage requirement. The “unwinding” is a term used to describe the progressive ending of the provision included in the Families First Coronavirus Response Act (FFCRA), that allowed people to remain on Medicaid without needing to go through the re-enrollment process during the COVID-19 epidemic. With the end of this in sight, it’s been projected that millions are now facing either the need to quickly find other coverage or a total loss of coverage due to barriers for re-enrollment.
In the episode, Dr. Blewett discusses this very large change that’s occurring across the U.S. healthcare system. As of April 1, 2023, “States have started to redetermine all of the people on their programs to make sure they’re eligible,” she says, and “a new analysis shows that 500,000 people across eleven states have already dropped off Medicaid.” The podcast discusses specific cases and lessons that can be learned from states’ experiences - the good and the bad. Dr. Blewett dives into the different states’ methods for approaching the re-enrollment process, and how certain methods and diligence will affect any potential for procedural errors during this tumultuous period. There are also some states who are deciding to reinstate certain work requirements for people on Medicaid, which Blewett says “haven’t worked very well,” pointing out that: “There’s an estimate that 8 out of 10 Medicaid recipients who are not blind, disabled, or are able to work, already work. They either work part-time or full-time, and so you’re really targeting a very small number of people.”
In addition to the primary concern of people losing access to healthcare, there are many practical reasons that keeping populations on Medicaid actually helps state economies. To close out the discussion, Dr. Blewett highlights the financial impact that the unwinding could have on certain hospitals who were previously being compensated by the federal support that was coming from the increased number of Medicaid enrollees.
Blog & News
Race/Ethnicity Data in CMS Medicaid (T-MSIS) Analytic Files: 2020 Data Assessment
Updated January 2023:The Transformed Medicaid Statistical Information System (T-MSIS) is the largest national database of current Medicaid and Children’s Health Insurance Program (CHIP) beneficiary information collected from U.S. states, territories, and the District of Columbia (DC).1 T-MSIS data are critical for monitoring and evaluating the utilization of Medicaid and CHIP, which together provide health insurance coverage to more than 90 million people.2
T-MSIS data files are challenging to use directly for research and analytic purposes due to their size and complexity. To optimize these files for health services research, CMS repackages them into a user-friendly, research-ready format called T-MSIS Analytic Files (TAF) Research Identifiable Files (RIF). One such file, the Annual Demographic and Eligibility (DE) file, contains race and ethnicity information for Medicaid and CHIP beneficiaries. This information is vital for assessing enrollment, access to services, and quality of care across racial and ethnic subgroups in the Medicaid/CHIP population, whose members are particularly vulnerable due to limited income, physical and cognitive disabilities, old age, complex medical conditions, housing insecurity, and other social, economic, behavioral, and health needs.
Completeness of race and ethnicity data reported to CMS remains inconsistent among the states, territories, and DC. To guide researchers and other consumers in their use of T-MSIS data, CMS produces data quality assessments of the race and ethnicity data along with other data such as enrollment, claims, expenditures, and service use. The Data Quality (DQ) assessments for race and ethnicity data have been posted for data years 2014 through 2020 and indicate varying levels of “concern” regarding race and ethnicity data completeness. Some data years have multiple data versions (e.g., Preliminary, Release 1, Release 2), each with its own DQ assessment. This blog explores 2020 Data Release 1, the most recent T-MSIS race and ethnicity data for which a DQ assessment is available.
Evaluation of T-MSIS Race and Ethnicity Data
DQ assessments for each year and release of T-MSIS data are housed in the Data Quality Atlas (DQ Atlas), an online evaluation tool developed as a companion to T-MSIS data.3 The DQ Atlas assesses T-MSIS race and ethnicity data using two criteria: the percentage of beneficiaries with missing race and/or ethnicity values in the TAF; and the number of race/ethnicity categories (out of five) that differ by more than ten percentage points between the TAF and American Community Survey (ACS) data. Taken together, these two criteria indicate the level of “concern” (i.e., reliability) for states’ T-MSIS race/ethnicity data. Five “concern” categories appear in the DQ Atlas: Low Concern, Medium Concern, High Concern, Unusable, and Unclassified. States with substantial missing race/ethnicity data or race/ethnicity data that are inconsistent with the ACS – a premier source of demographic data – are grouped into either the High Concern or Unusable categories, whereas states with relatively complete race/ethnicity data or race/ethnicity data that align with ACS estimates are grouped into either the Low Concern or Medium Concern categories. The Unclassified category includes states for which benchmark data are incomplete or unavailable for a given data year and version.
To construct the external ACS benchmark for evaluating T-MSIS data, creators of the DQ Atlas combine race and ethnicity categories in the ACS to mirror race and ethnicity categories reported in the TAF (see Table 1). More information about the evaluation of T-MSIS race and ethnicity data is available in the DQ Atlas’ Background and Methods Resource.
Table 1. Crosswalk of Race and Ethnicity Variables between the TAF and ACS
Race/Ethnicity Category |
Race/Ethnicity Flag Value in TAF |
Combination of Race and Hispanic Variables in ACS |
Hispanic, all races |
7=Hispanic, all races | Hispanic, all races |
Other races, non-Hispanic |
4= American Indian and Alaska Native, non-Hispanic 5=Hawaiian/Pacific Islander 6=Multiracial, non-Hispanic |
- American Indian alone - Alaska Native alone - American Indian and Alaska Native tribes specified; or American Indian or Alaska native, non-specified and no other race - Native Hawaiian and other Pacific Islander alone - Some other race alone - Two or more races |
Source: Medicaid.gov. (n.d.). DQ Atlas: Background and methods resource [PDF file]. Available from https://www.medicaid.gov/dq-atlas/downloads/background_and_methods/TAF_DQ_Race_Ethnicity.pdf. Accessed January 5, 2023.
Quality Assessment by State
Table 2 shows the Race and Ethnicity DQ Assessments for the 2020 TAF (Data Version: Release 1). Approximately the same number of states received a rating of Low Concern (15 states), Medium Concern (17 states, including PR), and High Concern (16 states, including DC). Four states (Alabama, Kansas, Rhode Island, and Tennessee) received an “Unusable” rating, as each of these states was missing at least 50 percent of race/ethnicity data. Most of the Medium Concern states (14 of 17) fell into the subcategory denoting the higher percentage range of missing race/ethnicity data (from 10 percent up to 20 percent). A similar pattern can be seen among the High Concern states, most of which (15 of 16) fell into the subcategory denoting the highest percentage range of missing race/ethnicity data (from 20 percent up to 50 percent). The categorization criteria used to determine the levels of concern for the 2020 TAF Release 1 data are the same as those used to assess T-MSIS data from previous years and versions.
Table 2. Race and Ethnicity Data Quality Assessment, 2020 T-MSIS Analytic File (TAF) Data Release 1
Data quality assessment |
Percent of beneficiaries with missing race/ethnicity values | Number of race/ethnicity categories where TAF differs from ACS by more than 10% |
Number of states* |
States |
Low Concern | <10% | 0 | 15 | AK, CA, DE, MI, NE, NV, NM, NC, ND, OH, OK, PA, SD, VA, WA |
Medium Concern | <10% | 1 or 2 | 3 | GA, ID, IL |
10% - <20% | 0 or 1 | 14 | FL, IN, KY, ME, MN, MS, MT, NH, NJ, PR, TX, VT, WV, WI | |
High Concern | <10% | 3 or more | 0 | - |
10% - <20% | 2 or more | 1 | LA | |
20% - <50% | Any value | 15 | AZ, AR, CO, CT, DC, HI, IA, MD, MA, MO, NY, OR, SC, UT, WY | |
Unusable | >50% | Any value | 4 | AL, KS, RI, TN |
Notes: *T-MSIS includes all 50 states, the District of Columbia (DC), and the U.S. territories of Puerto Rico (PR) and the Virgin Islands (VI). A DQ assessment is not available for VI in the 2020 TAF (Data Version: Release 1) due to incomplete/unavailable data. VI is therefore the only state/territory categorized as “Unclassified” in the 2020 TAF (Data Version: Release 1), and does not appear in Table 2.
Visualizing T-MSIS Data in the DQ Atlas
The DQ Atlas enables users to generate maps that compare the quality of T-MSIS data between states across different topics, such as race/ethnicity, age, income, and gender (see Figure 1). Visualizing T-MSIS data in this manner can help researchers quickly assess the completeness of a single variable as well as the relative completeness (or incompleteness) of certain variables compared to others. For example, in the 2020 TAF Data Release 1, all states and territories received a “low concern” rating for age data, whereas only 29 states and territories received a “low concern” rating for family income.
Figure 1. Data Quality Assessments of Beneficiary Information by U.S. State/Territory
Notes: Green = low concern; yellow = medium concern; orange = high concern; red = unusable; grey = unclassified.
Source: Medicaid.gov. (n.d.). DQ Atlas: Race and Ethnicity [2020 Data set: Version: Release 1]. Available from https://www.medicaid.gov/dq-atlas/landing/topics/single/map?topic=g3m16&tafVersionId=25. Accessed January 5, 2023.
Looking Ahead
Increasingly, a wide diversity of voices from non-profits, health insurers, state-based marketplaces, and policymakers have called for improving the collection of race, ethnicity, and language data, often with the goal of advancing health equity. CMS’s efforts to improve the quality and availability of T-MSIS data reflect this nationwide movement toward data collection practices that more accurately capture the diversity of the U.S. population.
In June 2022, CMS released updated technical instructions for reporting beneficiary race, including clarification on how to report race information for beneficiaries who report multiple races. That same month, the Biden Administration announced its intent to revise the federal government’s standards for surveying race and ethnicity – a policy change that is expected to result in disaggregated categories for Hispanic individuals and people of Middle Eastern or North African descent.4
California and New York have enacted historic policies to disaggregate race/ethnicity data within the past year: California now requires state agencies to list a separate category for Black descendants of enslaved people when collecting state employee data; and New York now requires state agencies to disaggregate Asian, Native Hawaiian, and Pacific Islander data into more granular collection categories (e.g., Chinese, Filipino, Hawaiian, Samoan).5,6 It is likely that other states will follow these data disaggregation practices in the years ahead as public awareness of issues related to diversity, equity, inclusion, and racial justice continues to grow.
Sources
1 Medicaid.gov. Transformed Medicaid Statistical Information System (T-MSIS). Retrieved October 20, 2022, from https://www.medicaid.gov/medicaid/data-systems/macbis/transformed-medicaid-statistical-information-system-t-msis/index.html#
2 Medicaid.gov. September 2022 Medicaid & CHIP Enrollment Data Highlights. Retrieved on January 5, 2023, from https://www.medicaid.gov/medicaid/program-information/medicaid-and-chip-enrollment-data/report-highlights/index.html
3 Saunders, H., & Chidambaram, P. (April 28, 2022). Medicaid Administrative Data: Challenges with Race, Ethnicity, and Other Demographic Variables. Kaiser Family Foundation. Retrieved October 31, 2022, from https://www.kff.org/medicaid/issue-brief/medicaid-administrative-data-challenges-with-race-ethnicity-and-other-demographic-variables/
4 Wang, H.L. (June 15, 2022). Biden officials may change how the U.S. defines racial and ethnic groups by 2024. NPR. Retrieved November 1, 2022, from https://www.npr.org/2022/06/15/1105104863/racial-ethnic-categories-omb-directive-15
5 Diaz, J. (August 16, 2022). California becomes the first state to break down Black employee data by lineage. NPR. Retrieved November 1, 2022, from https://www.npr.org/2022/08/16/1117631210/california-becomes-the-first-state-to-break-down-black-employee-data-by-lineage
6 The New York State Senate. (December 22, 2021). Assembly Bill A6896A. Retrieved November 2, 2022, from https://www.nysenate.gov/legislation/bills/2021/A689
Publication
Demographic, Social, and Economic Characteristics of the General Population of Minnesotans aged 65 and Older
As part of a suite of studies titled: Own Your Future 3.0: Planning for Minnesotans' LTSS Needs, sponsored by Minnesota’s Department of Human Services and Board on Aging, this report summarizes the demographic, social, and economic characteristics of Minnesotans aged 65 and older using data from the 2019 U.S. American Community Survey (ACS). It compares the Medicaid population to the non-Medicaid Minnesota population; and compares the characteristics of Minnesota’s elderly population (both Medicaid and non-Medicaid) to the U.S. population.