VISIT STATE HEALTH COMPARE
Publication
Underlying Factors of Medicaid Inequities: Conversations with Experts on Racism and Medicaid
Medicaid serves as a vital public health safety net for over 79 million people, including many from historically excluded or marginalized communities. There is a great opportunity to improve Medicaid programs’ accountability for persistent health inequities by confronting the historical and structural racism perpetuated in the administration, policies, and practices that undergird the program.
The Medicaid Equity Monitoring Tool (MET) project is a collaborative effort from the State Health Access Data Assistance Center (SHADAC) with support from the Robert Wood Johnson Foundation (RWJF) and partner organizations working to assess whether a data tool could increase accountability for state Medicaid programs to advance health equity while also improving population health.
During the first phase of this project, a wireframe was created to organize the different sections of a potential tool, including a section on "Underlying Factors" that lead to and perpetuate health inequities for people of color and other historically marginalized communities.
In order to inform the Medicaid Equity Monitoring Tool (MET) project and the Underlying Factors section of the tool, SHADAC produced an annotated bibliography of resources to better understand the available academic and gray literature on those underlying factors of health inequities in Medicaid.
While the bibliography covers a number of structural and systemic underlying factors of health inequities (e.g., ableism, sexual orientation and gender identity discrimination), most of the resources compiled in the bibliography address structural racism specifically. These resources discuss the history, policy context, and impacts of systemic racism on Medicaid recipients.
As a follow-up to the creation of the annotated bibliography, SHADAC’s Health Equity Fellow held consulting conversations with authors of select resources cited in the structural racism section. Through these conversations, our goal was to:
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Connect with experts in order to elicit feedback on key insights from SHADAC’s annotated bibliography
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Ask experts questions about what topics related to systemic racism need to be discussed within the tool
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Discuss strategies on how best to convey and disseminate this important information
This brief summarizes these conversations, including specific examples and quotes from experts, for audiences interested in communicating about the effects of structural racism with the aim to dismantle it.
Blog & News
2024 NHIS Data Early Release: Q2 (April – June) Estimates Show Uninsurance Remained Stable While Many Shifted from Public to Private Coverage
November 14, 2024:On November 8, 2024, The National Center for Health Statistics (NCHS) released quarterly estimates of health insurance coverage from the National Health Interview Survey (NHIS) covering the second quarter (April – June) of 2024.
This blog post covers this newly released Q2 NHIS data, including relevant changes in health insurance coverage compared to the same period the year prior, April – June 2023. We also delve into coverage changes by various breakdowns, including by age, race and ethnicity, and poverty level.
NHIS Data Begin to Show Medicaid Unwinding Impact on Coverage
These newest data from the NHIS are some of the first from this survey to show significant changes in coverage coincident with the Medicaid unwinding (the state-level resumption of normal Medicaid eligibility redetermination procedures in April 2022 that had been put on hold during the COVID-19 public health emergency). We will continue to monitor quarterly NHIS early release data to track changes in coverage during the unwinding.
Changes by Age
Between Q2 2023 and Q2 2024, rates of uninsurance remained stable across all measured age groups, at:
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7.6% among all ages
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9.1% among the nonelderly (age 0–64)
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4.6% among children (age 0–17), and
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10.8% among nonelderly adults (age 18–64)
There were concurrent and offsetting changes in public and private coverage over the same period. Among the nonelderly population, public coverage fell 3.6 percentage points (PP) to 26.6%, private coverage rose 2.8 PP to 66.3, and Exchange-based private coverage rose 1.7 PP to 6.0%.
Among nonelderly adults, public coverage fell 3.7 PP to 21.1%, private coverage rose 3.2 PP to 70.3%, and Exchange-based private coverage rose 2.1 PP to 7.1%.
Among persons of all ages, only public coverage had a statistically significant change, falling 2.8 PP to 39.0%.
There were no statistically significant changes in coverage among children, and coverage remained statistically unchanged at 42.0%, 55.2% and 2.9% for public, private, and Exchange-based private coverage, respectively.
Changes by Race and Ethnicity (nonelderly adults)
Rates of health insurance coverage and uninsurance were statistically unchanged among all measured groups by race and ethnicity except for nonelderly, non-Hispanic White adults. Among that group, the share with public coverage fell 5.0 PP to 17.2%, the share with private coverage rose 3.8 PP to 77.6%, and the share uninsured was statistically stable at 7.5%.
Changes by Poverty Level (nonelderly population)
There were fewer statistically significant changes by poverty level among the nonelderly population compared to changes by age group.
Among those with incomes in the range of 100%–200% of the federal poverty level (FPL), the share who were uninsured increased 4.1 PP to 15.4%.
Public coverage fell among those with incomes above 400% FPL, decreasing 2.0 PP to 7.0%.
Rates of uninsurance were statistically unchanged across measured poverty level groups, and coverage rates also held steady among the 0–100% FPL, 100–200% FPL, and 200–400% FPL groups.
Notes
All changes and differences described were statistically significant based on two-sided t-tests at the 95% level of confidence, indicating that these changes were likely to reflect true changes in the population given the available data. Lack of statistically significant changes does not indicate certainty that there was no true population change, but rather that any true population change was not detectable with the available data.
The presented estimates represent the U.S. civilian noninstitutionalized population.
Blog & News
Race/Ethnicity Data in CMS Medicaid (T-MSIS) Analytic Files: 2022 Data Assessment
November 26, 2024: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, unaffordable rents, 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 2022 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 encouraged by an improvement in quality as discussed below. This blog explores not only the 2022 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
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 2022 TAF (Data Version: Release 1). The categorization criteria used to determine the levels of concern for the 2022 TAF Release 1 data are the same as those used to assess T-MSIS data from previous years and versions. 15 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 (17 of 22) fell into the subcategory denoting the higher percentage range of missing race/ethnicity data (from 10% up to 20%). A similar pattern can be seen among the “High Concern” states, most of which (10 of 14) fell into the subcategory denoting the highest percentage range of missing race/ethnicity data (from 20% up to 50%).
Finally, 14 states (including DC) received a rating of “High Concern.” One state (Utah) received an “Unusable” rating, meaning it was missing at least 50% of race/ethnicity data. The Virgin Islands (VI) is the only state/territory categorized as “Unclassified” in the 2022 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, 2022 T-MSIS Analytic File (TAF) Data Release 1
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 2022 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, although the results in 2022 were somewhat more mixed.
Since beginning to track these quality assessments with the 2019 T-MSIS TAF release, several states have shifted up the quality assessment scale. The number of states with data of “High Concern” increased from 2021 to 2022. This primarily reflects two states (Massachusetts and Tennessee) moving from the “Unusable” category up to the “High Concern” category, which means they are reporting race and ethnicity data, even if it is of questionable quality.
Specifically, 2022 race/ethnicity TAF data from 14 states received a rating of “High Concern” compared to 11 states’ data in 2021 and 16 states’ data in 2020. The number of states with “Unusable” data has also dropped each year – 1 state’s 2022 race/ethnicity TAF data was classified as “Unusable” compared to 3 states’ data in 2021 and 4 states’ data in 2020.
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 2022 TAF Data Release 1, all states and territories received a “Low Concern” rating for age data, whereas only 30 states and territories received a “Low Concern” rating for income.
Figure 1. Data Quality Assessments of Beneficiary Race/Ethnicity 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 [2022 Data set: Version: Release 1]. Available from https://www.medicaid.gov/dq-atlas/landing/topics/single/map?topic=g3m16&tafVersionId=35 Accessed November 1, 2024.
Looking Ahead
Increasingly, a wide diversity of voices, from non-profits and health insurers to state-based marketplaces and policymakers, have called for improving data 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 November 8, 2024. https://www.medicaid.gov/medicaid/data-systems/macbis/transformed-medicaid-statistical-information-system-t-msis/index.html#
2 Medicaid.gov. July 2024 Medicaid & CHIP Enrollment Data Highlights. Retrieved November 8, 2024. 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. 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. 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. 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
Walsh, Elliot
Hest, Robert
Stewart, Andrea
Children’s Health Insurance in 2023: Exploring Rising Uninsured Rates for Low-Income Children
October 21, 2024:
This post is a part of our Survey Data Season series where we examine data from various surveys that are released annually from the summer through early fall. Find all of the Survey Data Season series posts on our Survey Data Season 2024 page here.
With the recent release of survey data from both the American Community Survey (ACS) and the Current Population Survey (CPS), SHADAC researchers have begun delving into what this data can tell us about health insurance coverage in 2023 for:
- The total U.S. population
- Nonelderly adults (age 19-64), and
- Children (age 0-18)
As seen in our full blog post on the 2023 ACS data release and our blog & infographic on the 2023 CPS data release, we found that overall rates of public coverage and private coverage remained unchanged in 2023. However, it is important to note that alongside that indicator of coverage stability at a national level, we also found that:
- Changes in uninsured rates for nonelderly adults' (age 19-64) varied depending on Medicaid expansion status and poverty level
- Rates of uninsured children (age 0-18) rose significantly
These findings sparked questions: After two years of falling rates, what could be contributing to rising uninsurance among children? Changes in nonelderly adults’ uninsured rates between 2022 and 2023 varied by poverty level and the status of Medicaid expansion in their state – could either of these factors also be impacting children’s uninsured rates in the newly released data?
While more research into causes behind these changes is needed, SHADAC began by looking at the ACS HI-11 tables, which include data specific to health insurance coverage rates for children below 200% of the Census poverty threshold.
Keep reading to see some of our findings on how the insurance coverage of low-income children (those with family incomes below 200% of the Census poverty threshold) changed between 2022 and 2023 both nationally and at the state level.
Nationally, Children’s Uninsured Rates Increased while Public Coverage Rates Decreased
Looking at 2023 ACS data for children below 200% of the Census poverty threshold, we found that uninsured rates for this group mirrored overall trends at a national level, rising by 0.4 percentage points (PP) to 7.3%. This significant increase was likely driven by a 0.6PP decrease in public coverage (bringing that national rate down to 72.6%) and a statistically unchanged rate of private coverage (27.0%).
Further, low-income children’s uninsured rate was 1.9PP higher than the 5.4% rate of uninsured children overall, a statistic which saw its own significant rise from 5.1% in 2022.
State-Level Coverage Changes for Low-Income Children
When we delve into state-level data for low-income children, we see that Louisiana (+1.5PP), Michigan (+1.2PP), South Carolina (+1.6PP), and Texas (+1.5PP) all saw significant increases in uninsurance, rising to 5.5%, 4.3%, 8.0%, and 15.4%, respectively.
As with the national uninsured rate, our analysis found that a couple of these states mirror a simultaneous decrease in public coverage that likely influenced the increase in uninsurance. For example, Michigan saw a 3.1PP decrease in public coverage, bringing it down to 73.2%, and Texas saw a 2.5PP decrease in public coverage, bringing it down to 65.0%.
Again, similar to trends at the national level, none of the states that saw increased uninsurance for low-income children saw any significant changes in private coverage among that group.
No states saw decreases in uninsurance.
Looking Closer at Public & Private Coverage Rates for Low-Income Children
Looking at public coverage, states saw a wide range of changes:
- Four states, Arkansas (-4.8PP ), Michigan (-3.1PP), Nebraska (-5.4PP), and Texas (2.5PP), saw significant decreases in public coverage.
- Kansas (+4.5PP), Tennessee (+3.2PP), and Wyoming (+15.8PP) saw significant increases in public coverage
Looking at private coverage, states also saw a wide range of changes:
- Alaska (-8.1PP), Kansas (-5.3PP), Tennessee (-3.8PP), and Wisconsin (-4.9PP) all saw decreases in private coverage
- Kentucky (+3.4PP), Nebraska (+6.5PP), and New York (+2.5PP) all saw increases in private coverage
One thing to note here is that Nebraska, the state with the largest significant decrease in public coverage for low-income children, did not appear on the list of states with significant increases in uninsurance for the same group. But it did also see the largest increase in private coverage for this group, which could have mitigated any change in uninsurance.
Conclusion & Discussion
By looking deeper into low-income children’s health insurance coverage in 2023, we hoped to find more information on why uninsured rates have increased both overall and specifically for this group.
At the national level and in a small number of states, we observed increases in uninsurance that coincided with decreases in public coverage. This could be a result of the unwinding of the Medicaid continuous coverage requirement that began during 2023. According to an issue brief on children’s poverty and health insurance coverage published by KFF in January 2024, Medicaid covers 8 in 10 children living in poverty. This brief goes on to report that based on reported unwinding data from 23 states, almost 4 in 10 of those disenrolled from Medicaid during the unwinding are children, likely impacting the many children living in poverty with this public coverage.
However, this story of falling public coverage and rising uninsurance was not consistent across the states. We did not observe significant changes in uninsurance in most states, either because decreases in one type of coverage were offset by increases in another type of coverage, or because there was little movement in coverage overall.
Coverage estimates for 2024 (to be released next fall) will give a more complete picture of coverage during the unwinding and may clarify how coverage evolved during this dynamic period.
Blog & News
2023 ACS Tables: State and County Uninsured Rates, with Comparison Year 2022
September 17, 2024:
This post is a part of our Survey Data Season series where we examine data from various surveys that are released annually from the summer through early fall. Find all of the Survey Data Season series posts on our Survey Data Season 2024 page here.
Each year, SHADAC uses data released from the American Community Survey (ACS) via the U.S. Census Bureau's data.census.gov tool to produce estimates of uninsurance at the state and county level.*
Click on a state below in the interactive map to see a PDF table of uninsured rates by state and sub-state geographies, but also by demographic characteristics (e.g., age, race/ethnicity, and poverty level) for 2023 and comparison year 2022.
Figure 1: Uninsured Rates by State for 2023, Comparison Year 2022
Click here to view uninsurance estimates for the United States.
Click here to view uninsurance estimates for Puerto Rico and its municipios.
Note: These tables present uninsured rates, which indicate the share of the population that is uninsured. For example, a 10 percent uninsured rate for adult women indicates that 10 percent of all adult women are uninsured.
Maps & Tables of Private, Public, & Uninsured Changes from 2022 to 2023
- Private Coverage Rates by State, Change from 2022 to 2023, for All People
- Public Coverage Rates by State, Change from 2022 to 2023, for All People
- Uninsurance Rates by State, Change from 2022 to 2023, for All People
About the American Community Survey (ACS)
The ACS is a household survey that began in 2005 and produces annually updated data on a variety of population characteristics, including health insurance coverage. In total, the ACS surveys approximately three million U.S. households each year. An important feature of the American Community Survey is that it includes a large enough sample for state‐level and sub‐state estimates.
The ACS began asking survey respondents about health insurance coverage during the 2008 calendar year. Specifically, the survey asks respondents about current coverage for each person in the respondent’s household. A person is categorized as “insured” if he or she has coverage at the point in time at which the survey is administered.
*Why Aren’t Estimates Provided for All Counties?
Due to sample size constraints, single-year ACS estimates are available at the county level only for counties with a population greater than 65,000.
How Are These Estimates Different from the Estimates that SHADAC Publishes Using Census Bureau Micro-Data Files?
Two definitions used by the Census Bureau to generate the tabulations above differ from those that SHADAC uses to generate tabulations for State Health Compare. The definitional differences are as follows:
Family
The Census Bureau defines a family as “all related people in a household.”
SHADAC defines a family using a measure called the “Health Insurance Unit” (HIU), which includes all individuals who would likely be considered a family unit in determining eligibility for either private or public coverage.
To learn more about the 2020 update of SHADAC's Health Insurance Unit (HIU) see our HIU resource page, which houses two issue briefs: The first brief describes the SHADAC HIU, its purpose, the most recent update, and improvements to HIU data inputs; the second brief outlines the impacts of using the SHADAC HIU in analysis so that researchers can assess whether the SHADAC HIU is suitable for their research and what the potential impacts of its use might be. The page also provides a link to STATA and SAS codes to aid in the use of the HIU variable.
Family Income
The Census Bureau determines family income as a percentage of the Federal Poverty Level (FPL), which is a definition of poverty used primarily for statistical purposes. For example, FPL is used to estimate the number of Americans living in poverty each year.
SHADAC determines family income as a percentage of the U.S. Department of Health and Human Services’ Federal Poverty Guidelines (FPG), which is a measure used for administrative purposes. For example, FPG is used to determine eligibility for federal programs such as Medicaid and the Supplemental Nutrition Assistance Program (SNAP).
To learn more about the difference between FPL and FPG, read our updated FPL and FPG blog post from April 2024.
