VISIT STATE HEALTH COMPARE
Publication
State Estimates of the Low-Income Uninsured Not Eligible for the ACA Medicaid Expansion
The Patient Protection and Affordable Care Act (ACA) will expand access to affordable health insurance for millions of Americans. In states that choose to implement the Medicaid expansion for low-income adults, Medicaid will provide an important new pathway to coverage. Yet, even in states that choose to expand Medicaid a significant proportion of the low-income nonelderly adult population will be excluded from the Medicaid expansion due to their immigration status. Legal permanent residents, in most circumstances, are ineligible for Medicaid benefits for the first five years during which they reside legally in the U.S. and unauthorized immigrants are excluded from Medicaid coverage. SHADAC Issue Brief #35 provides the first state-specific estimates of the number of uninsured low-income adults that will potentially be excluded from the Medicaid expansion because of their immigration status.
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:
-
7.6% among all ages
-
9.1% among the nonelderly (age 0–64)
-
4.6% among children (age 0–17), and
-
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
Publication
“American Indian 101”: Understanding the history and contemporary experiences of Native people in a United States health policy context
American Indian and Alaska Native (AI/AN) is a racial/ethnic category that describes people with ancestry indigenous to North America prior to colonization in 1492. As of the 2020 U.S. Census, there were approximately 9.7 million Americans who identified as Native American alone or as Native American in combination with another race.
Despite millions of Americans identifying as AI/AN, Quin Mudry Nelson, MPH, SHADAC’s inaugural Health Equity Fellow, noticed a lack of attention on Indigenous and American Indian health and health care systems throughout their education. With their lens on health equity combined with their own experience as a Native person, they knew that Indigenous people in the U.S. face a number of health disparities compared to other groups, including, among others, the highest uninsured rate, lowest life expectancy at birth, and adverse impacts to health outcomes.
They also saw that it wasn’t only American Indian health services and systems being overlooked, it’s the people as well: data collection on AI/AN individuals is, and has been, a known challenge in the U.S. One report from the Department of Labor describes “substantial” criticism received “from tribes and other stakeholders regarding population undercounts, the accuracy and timeliness of the data, and the burden for tribes, due to lack of sufficient resources and trained personnel, in reporting the data to the Federal Government.”
After seeing these gaps between her education and her knowledge & experiences “as a two-spirit, Native person who is tribally affiliated with the Oneida Nation, I wanted to write a piece about AI/AN people’s history and identity in the context of health policy,” says Quin.
Quin’s in-depth research combined with their experience and understanding of the AI/AN community has culminated in this important brief: “American Indian 101”: Understanding the history and contemporary experiences of Native people in a United States health policy context.
Focused on health care and health insurance access among American Indian and Alaska Native people, this brief provides readers with essential context and commentary on United States and American Indian health policy, health disparities, and AI/AN identity as it relates to health care and policy.
The brief is divided into four sections, organized and grounded in the indigenous ways of knowing – outlining the present, reflecting upon the past, and using that context to chart a path for the future:
- Section 1 describes information about AI/AN people within the United States and the health care system presently, providing demographic data, a brief explanation on contemporary data collection issues, and a breakdown of the unique care delivery structure of the Indian Health Service
- Section 2 provides an overview of early U.S. history and American Indian health and social policies, including pivotal legal cases
- Section 3 contextualizes how this history constructed a unique identity for AI/AN people in comparison to other racial groups that impacts not only their access to care, but also to their “dedicated” health services
- Section 4 concludes with an overview of recent progress in AI/AN health and social policy, highlighting both potential future directions and limitations
This brief provides important historical context and actions that have shaped the landscape of American Indian health care today. Not only that, but this brief also provides a distinct perspective from an individual who has the lived experience of AI/AN people along with experience and education in public health and health equity.
Quin’s voice and ideas found in this brief are as intriguing as they are important. Publications with a foundation of personal perspective & experience such as this one are essential for uplifting voices from historically marginalized groups, identifying & spotlighting the structural issues that individuals face, and learning from the stakeholders who are impacted directly and indirectly.
Read the brief in full here or click on the image of the cover page above. We welcome any questions or comments – you can send them to Quin directly at nels9793@umn.edu.
Are you curious about American Indian health statistics, data collection, health care, health disparities, and more? You can start by delving into the references that informed this brief - these resources are rich with information. You can continue learning with some of the following SHADAC products and resources:
- Explore State Health Compare: Health Insurance Coverage by Race/Ethnicity (Includes AI/AN Breakdown)
- SHVS Brief: Collection of Race, Ethnicity, Language Data on Medicaid Applications: New and Updated Information on Medicaid Data Collection Practices in the States, Territories, and D.C.
- SHADAC Brief: The Kids Aren't Alright. Adverse Childhood Experiences and Implications for Health Equity
Blog & News
Walsh, Elliot
Hest, Robert
Lukanen, Elizabeth
What Do the New Census Data Say About Insurance Coverage for Children and Adults? A Look at Both the National and State Level (SHVS Cross-Post)
November 01, 2024:
The following Expert Perspective (EP) is cross-posted from State Health & Value Strategies.
Authors: Elizabeth Lukanen, Elliot Walsh, Robert Hest, SHADAC
Original posting date October 30, 2024. Find the original post here on the SHVS website.
Background on Census Data
Every fall, the U.S. Census Bureau releases detailed data on the population, including data on health insurance coverage. The American Community Survey (ACS) is the premier source for detailed state and substate data on income, poverty, disability, marital status, education, occupation, travel to work, disability, and health insurance coverage, among other topics. An important feature of the ACS is that it includes a large enough sample for estimates for all 50 states and the District of Columbia and, in most states, there is sample to explore data disaggregated by insurance coverage type, age, race/ethnicity, and more.
2023 Data Do Not Capture the Full Impact of Coverage Changes Related to Medicaid Unwinding
The new, full-year 2023 insurance estimates from the ACS reflect data collected from January 1, 2023 to December 31, 2023. This health insurance coverage data provide new national- and state-level insurance coverage estimates, but do not fully reflect the Medicaid unwinding.
According to administrative data from the Centers for Medicare & Medicaid Services, Medicaid and Children’s Health Insurance Program (CHIP) enrollment declined by 13.9 million between March 2023 and June 2024. Some of those who disenrolled likely transitioned to other coverage, and others may have become uninsured. However, coverage transitions that happened in 2024 are not captured in the 2023 estimates from the Census Bureau. Because of the large changes in Medicaid and CHIP coverage during the unwinding, compared to the current, on-the-ground reality in 2024, these 2023 coverage estimates likely overestimate rates of public coverage and potentially underestimate rates of uninsurance.
Despite this limitation, the data are still instructive about changes to coverage during the early months of the unwinding period and generally for the year 2023. This expert perspective looks at health insurance coverage estimates at the national and state levels, and examines children’s health insurance coverage, looking at children’s uninsurance rates by state and by income level. As more data are released SHADAC will conduct additional analyses, presenting coverage estimates across key demographic categories, including by race/ethnicity. Disaggregating data is critical to identifying inequities and gaps in coverage, informing policies to improve equity and to prevent new disparities. It is also vital for identifying state policies and practices effective in maintaining coverage for specific populations so that they might be adopted in other states.
Overall Uninsurance Rates Were Fairly Stable
The uninsurance rate in the U.S. remained unchanged from the previous year, sitting at 7.9% in 2023. This was mirrored by stability in uninsurance in the states. From 2022 to 2023, the uninsured rate fell in 11 states and increased in three states – Iowa, New Jersey, and New Mexico (these changes were statistically significant). Table 1 below highlights the states with the highest and lowest rates of uninsurance in 2023.
Table 1. States with the Highest and Lowest Rates of Uninsurance, 2023
|
Highest Rates of Uninsurance |
Lowest Rates of Uninsurance |
||
|---|---|---|---|
|
Texas |
16.4% |
Massachusetts |
2.6% |
|
Georgia and Oklahoma |
11.4% |
District of Columbia |
2.7% |
|
Nevada |
10.8% |
Hawaii |
3.2% |
|
Wyoming and Florida |
10.7% |
Vermont |
3.4% |
|
Alaska |
10.4% |
Minnesota |
4.2% |
Source: SHADAC analysis of U.S. Census Bureau 2022 and 2023 American Community Surveys. Click here for data from all states.
Children’s Uninsurance Rates Increased
Notably, while the overall uninsurance rate remained stable between 2022 and 2023, the rate for children went up slightly (rising from 5.1% in 2022 to 5.4% in 2023). This comes after two years of falling rates of uninsurance for kids.
Five states saw an increase in their uninsured rate for children (Alabama, Louisiana, New Mexico, South Carolina, and Texas). This wasn’t clearly driven by changes in private or public coverage, though, as both remained stable nationally and went up and down in a variety of states.
Texas is the only state where the data tell a clear story — there was a decline in public coverage for children (1.9 percentage point (PP) decrease to 36.8% in 2023) and an increase in uninsurance for children (1.0 PP increase to 11.9%).
Insurance Coverage Changes for Children (Birth Through Age 18), 2022 to 2023
Source: SHADAC analysis of U.S. Census Bureau 2022 and 2023 American Community Surveys.
Low-Income Children Saw Declines in Public Coverage
For U.S. children below 200% of the Census poverty threshold, uninsured rates rose by 0.4 PP to 7.3% in 2023 (see Figure 1 below). This significant increase was likely driven by a 0.6PP decrease in public coverage (bringing that national rate down to 72.6% in 2023) and a statistically unchanged rate of private coverage (27.0%).
Note: Low income children are defined as those below 200% of the Census poverty threshold. Source: SHADAC analysis of U.S. Census Bureau 2022 and 2023 American Community Surveys.
Among the states, Louisiana (+1.5PP), Michigan (+1.2PP), South Carolina (+1.6PP), and Texas (+1.5PP) all saw significant increases in uninsurance for low-income children, rising to 5.5%, 4.3%, 8.0%, and 15.4%, respectively. No states saw decreases in uninsurance rates for low-income children.
These increases in children without coverage are likely tied to declines in public coverage for low-income children. 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%.
Health Equity Implications
Rising uninsurance rates for children, particularly for low-income children, has a number of implications for health equity. First, it is important to address the likely reason that uninsurance rates for low-income children rose. While the ACS data doesn’t collect information on the reasons for coverage transitions, the data suggests this was driven by declines in public coverage that correspond with the end of the Medicaid continuous coverage requirement (this is supported by administrative data showing that more than 4.5 million children lost Medicaid or CHIP between March 2023 and June 2024, a reduction of almost 11%).
Second, the essential role Medicaid plays in providing health insurance to low-income children, the majority of whom are racial and ethnic minorities, must be acknowledged. Over 60% of children enrolled in Medicaid and CHIP identify as African American or Black, Hispanic, Asian American, Native Hawaiian and Other Pacific Islander, American Indian or Alaska Native, or multi-racial (State Health Compare, SHADAC, University of Minnesota, Accessed 10/10/2024). Declines in public coverage therefore disproportionately impact children of color. This is supported by early evidence that enrollees who identified as Hispanic or Black were twice as likely to report losing coverage because they could not complete the renewal process. SHVS will explore differences in coverage losses by race and ethnicity as more data are released.
Finally, Medicaid plays a critical role in children’s ability to access care – children with Medicaid or CHIP coverage report high rates of having a usual source of care and access to routine care. Further, evidence shows that they were as likely in the past 12 months to have seen a doctor, had a well child visit, and have had a dental exam as privately insured children. While having Medicaid does not erase the health inequity propagated by systemic racism it does improve access to important healthcare services and losing that coverage risks widening gaps in equity. As stated earlier, the full impact of the unwinding isn’t reflected in the latest data as it only includes the beginning months of the unwinding period. It will be important to revisit this once data from the entire unwinding period is released in order to get the full picture of coverage changes for this and other groups.
