VISIT STATE HEALTH COMPARE
Blog & News
Race/Ethnicity Data in CMS Medicaid (T-MSIS) Analytic Files: 2022 Data Assessment
November 14, 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 encourage 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
Survey Data Season Essentials: ACS vs CPS: What Is the Difference Between These Two Federal Surveys?
October 03, 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 covers the data releases of multiple federal surveys from a variety of agencies, beginning with the National Health Interview Survey (NHIS) in June continuing through the release of Current Population Survey Annual Social and Economic Supplement (CPS ASEC) and American Community Survey (ACS) data products in September through January.
Recently, survey data from both the 2024 Current Population Survey Annual Social and Economic Supplement and the 2023 American Community Survey were released. While these two surveys overlap in a number of topics and similarities, they are also distinct in what they measure and how.
In this blog, we will discuss the similarities and differences between two of the biggest federal survey resources available: the ACS and the CPS ASEC.
Which Federal Agencies Conduct the CPS and the ACS?
The United States Census Bureau has conducted the Current Population Survey Annual Social and Economic Supplement (CPS ASEC) since 1947 and the current iteration of the American Community Survey (ACS) since 2005 (though Census has conducted similar, prototype ACS surveys since 2000).
The Current Population Survey is jointly sponsored by the Census Bureau and the U.S. Bureau of Labor Statistics (BLS).
When Are CPS and ACS Data Released?
Single-year data from the two surveys is released annually in the second week of September, with supplemental materials for the ACS - such as the Public Use Microdata Sample or “PUMS” files, and the 5-year combined estimates - released later in the year.
Find the survey data release schedules for these and other survey data resources on our Survey Data Season page.
Can You Combine or Compare Multiple Years of ACS or CPS Data?
Yes - you can combine and/or compare multiple years of ACS data*. Along with 1-year ACS data files released in September, the Census Bureau has also released 5-year ACS data files annually in December since 2009.
Yes - you can generally combine and/or compare multiple years of CPS data. Care should be taken in combining or comparing across CPS data years, as the survey methodology has been revised numerous times throughout its history. Additionally, because some of the same individuals are surveyed across two CPS data years, researchers should think carefully about how to treat those repeated observations.
On SHADAC’s online data tool, State Health Compare, multiple measures utilize ACS or CPS data that allow users to compare years, trends, and more. We will go over some specific examples of how we use ACS and CPS data later in this blog.
*Except 2020 data, for which the COVID-19 pandemic majorly impacted data collection and distribution efforts. These data are considered “experimental only” and should not be compared to or combined with other years of data. However, the Census Bureau notes that the 5-year files that have the 2020 data year within them are okay for normal use.
Design Differences Between the CPS and the ACS
While both of these surveys are conducted by the Census Bureau, they differ in design, methods, measures, and more. The table below provides an overview of some of the most major differences.
| Current Population Survey (CPS) | American Community Survey (ACS) | |
|---|---|---|
| Data Collection Period | February through April of survey year | January through December of survey year |
| Method | Survey of civilian non-institutionalized U.S. population | Survey of U.S. population (including group quarters) |
| Annual Housing Units Interviewed | About 60,000 | About 2.15 million |
| Geography* | Nation, region, states | Nation, states, sub-state |
| Mode | Phone and in-person^ | Mail, in-person, and internet^ |
| Uninsurance: Measure |
Uninsured measured by: - All year - Part of year (since 2018) - Point-in-time (since 2014) |
Uninsurance measured by: - Point-in-time |
| Health Insurance Coverage: Years Available |
1979 to 2024 | 2008 to 2023✝ |
*Geographic level available for data breakdowns - i.e., CPS data is available for the U.S., by region (Midwest, South, Northeast, West) and for states.
^In-person (ACS & CPS) and mailing activities (ACS) were halted in March 2020. Both resumed in limited capacity in July 2020, and in-person activities resumed fully in September 2020 while mailing activities resumed fully in April 2021.
✝ 2020 data are experimental release only and should not be compared to other years of data.
What Do the ACS vs CPS Measure?
Both the ACS and the CPS gather information on a wide variety of measures.
According to its page on the Census Bureau website, the Current Population Survey, “[provides] information on many of the things that define us as individuals and as a society – our work, our earnings, and our education. In addition to being the primary source of monthly labor force statistics, the CPS is used to collect data for a variety of other studies that keep the nation informed of the economic and social well-being of its people.”
Specifically, the CPS collects data on measures like:
- Health insurance coverage
- Unemployment
- Labor force participation rate
- Employment data (occupation, industry, class of worker)
- Employment-population ratio
- Child support
- School enrollment
- Demographic data collection (age, race, sex, gender, etc.)
According to its page on the Census Bureau website, the American Community Survey, “helps local officials, community leaders, and businesses understand the changes taking place in their communities. It is the premier source for detailed population and housing information about our nation.”
Specifically, the ACS collects data on measures like:
- Health insurance coverage
- Jobs and occupations
- Educational attainment
- Information on veterans
- Whether people own or rent their homes
- Employment status
- Disability information
- Housing costs
- Demographic data collection (age, race, sex, gender, etc.)
You can learn more about the specific questions & measures on the American Community Survey, and why they are asked, on the “Why We Ask Each Question” page on the Census Bureau website.
As you can see, both surveys collect data on similar topics: housing, income, insurance coverage, demographic information, employment, education, and more. A key difference between these two sources is that the ACS provides us with both national and state-level data and estimates for these measures (in addition to lower levels of geography), while the CPS mainly focuses on national-level data.
ACS vs CPS: Guidance on When to Use Each Source
Knowing what survey data to use depends on what you’re looking at, what measures you’re interested in, the years you’re looking at, and more. The table below guides you through common uses of ACS and CPS survey data, and when to use which source.
| Current Population Survey (CPS) | American Community Survey (ACS) | |
|---|---|---|
| Trends by Year | 1979 to 1986 1987 to 2012 2013 to 2017 2017 forward |
2008 forward*✝ |
| State Estimates | Yes - can be used for state-level estimates | Yes - can be used for state-level estimates |
| Sub-state Estimates | N/A: CPS does not collect sub-state data |
Yes: 1-year for geographies with populations > 65,000 5-year for all geographic areas including all counties and ZIP code tabulation areas (ZCTA) |
| Small Populations | Sample size does not support estimates for small populations | Yes |
* While the ACS began in 2005, health insurance coverage questions were not added until 2008.
✝ Except 2020 data, which were “experimental” and should not be compared to other data years.
How Does SHADAC Use ACS and CPS data?
One of the main ways that SHADAC uses the data from these surveys is for our health insurance coverage estimates. These two sources are essential for how SHADAC estimates uninsurance, public insurance, and private insurance coverage rates, including providing information on health insurance coverage nationally, by state, and by demographic categories like race and ethnicity, income, age, and more.
Using the latest 2023 data released in September 2024 from each of these two surveys, SHADAC researchers created two resources explaining overall health insurance coverage estimates. Some of our main findings from these data sources this year include:
- a stable national uninsurance rate for the total U.S. population in 2023
- at 7.9% according to the ACS (compared to 8.0% in 2022)
- at 8.0% according to the CPS (compared to 7.9% in 2022)
- the rate of uninsurance among children (age 0-18) rose significantly
- to 5.8% in 2023 from 5.4% in 2022 according to CPS data
- to 5.4% in 2023 from 5.1% in 2022 according to ACS data
Read the details on our findings from these two surveys at the links below:
SHADAC also uses the wide variety of measures and data available from these surveys on our State Health Compare tool. This online and interactive data tool allows users to create customized data sets and visualizations of state-level health estimates for a number of measures under many categories, including measures on:
- Health Insurance Coverage
- Access to Care
- Cost of Care
- And more
Take a look at the measures available on our State Health Compare (SHC) site that use data from the ACS and CPS!
Measures that Use ACS Data on SHC
Click on any of these measures to explore the data on State Health Compare.
Health Insurance Coverage Type
Percent of adults with fair or poor health status
Percent of households with a broadband internet subscription
Percent of children considered to be poor (<100% FPG)
Income inequality (Gini Coefficient)
Percent of cost-burdened rental households
Measures That Use CPS Data on SHC
Click on any of these measures to explore the data on State Health Compare.
Health Insurance Coverage Type
Percent of people with a high medical cost burden
Median Medical Out-of-Pocket Spending
Percent of adults with fair or poor health status
Have you used State Health Compare to explore data and delve into health care topics? Share your work and tag us on LinkedIn - we love to see how people use SHC to make connections, identify gaps, and work towards making health care accessible & affordable for all people. You can also e-mail us at shadac@umn.edu - we would love to connect!
Stay Updated with SHADAC’s Survey Data Season Series
Stay up to date on our Survey Data Season series, with more Essentials blogs like this one along with other products analyzing newly released data, by signing up for our newsletter and following us on LinkedIn.
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.
Related Survey Data Season Materials
Publication
WEBINAR: U.S. Census Bureau Data Explained: Breaking Down 2023 Health Insurance Coverage Estimates from the ACS & CPS - featuring a Q&A with a Census Bureau Expert
This presentation 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.
On Thursday, September 26th at 1:00 PM CST, SHADAC hosted a webinar covering the release of new Census data on health insurance coverage estimates for 2023. The estimates come from two key federal surveys conducted by the U.S. Census Bureau: The American Community Survey (ACS) and the Current Population Survey (CPS).
SHADAC researchers and presenters Robert Hest and Andrea Stewart discussed the 2023 health insurance data at national and state levels, as well as by coverage type, and a range of other demographic categories (age, geography, poverty level, and more). In addition, SHADAC walked through how to access the data and examples of how to use it to answer research questions. We were also pleased to once again welcome a special guest from the Census Bureau, Sharon Stern, who joined the end of the webinar to participate in a Q&A, answering questions from attendees.
Webinar attendees came away knowing more about:
- The new 2023 health insurance coverage estimates
- How to access the estimates via Census reports and the data.census.gov website
- How to access state-level estimates from the ACS using SHADAC’s State Health Compare web tool
- When and how to use 2023 Census data to understand health insurance coverage trends
Find a recording of this webinar, along with presentation slides and a transcript of webinar audio, below.
Find the slides from this presentation here. Find a transcript of this webinar here.
Speakers
Elizabeth Lukanen, Moderator
Deputy Director, SHADAC
Ms. Lukanen serves as the Deputy Director for SHADAC, overseeing center research and providing strategic direction and oversight. With 20 years of experience in the health policy arena, Ms. Lukanen specializes in managing complex projects and translating quantitative findings into actionable, policy-relevant information. Ms. Lukanen currently oversees SHADAC’s technical assistance to states in the areas of data use, analysis, and evaluation. Ms. Lukanen is vocal about the importance of collecting and reporting disaggregated data to explore issues related to health equity, while actively working with policymakers to improve data on race, ethnicity, sexual orientation, and gender identity.
Robert Hest, Speaker
Senior Research Fellow, SHADAC
Robert Hest joined SHADAC in 2017, providing expertise in survey data, quantitative data analysis, data visualization, and health coverage policy. Mr. Hest leads SHADAC’s work monitoring and investigating developments in the measurement of health insurance coverage and health care access, affordability and utilization in federal survey data. He also manages SHADAC’s State Health Compare website, coordinating data processing, quality assurance, dissemination, and documentation of data used on the site and leads SHADAC’s work in the Minnesota Research Data Center applying restricted-use data to analyze health care affordability, access, and utilization at the state level.
Andrea Stewart, Speaker
Research Fellow, SHADAC
Andrea Stewart joined SHADAC in 2018 as a Marketing and Communications Specialist, transitioning to a Research Fellow in 2022. Currently, Ms. Stewart leads SHADAC’s efforts to track and report health insurance coverage data from major federal surveys, and is involved in several other ongoing projects, including an effort to explore the potential for the creation of a Medicaid Equity Monitoring Tool. Portions of her other research work range in topic from health equity measurement to understanding the impacts of COVID-19 on federal survey data.
Sharon Stern, Speaker
Assistant Division Chief, U.S. Census Bureau
Sharon Stern is the Assistant Division Chief for employment characteristics in the U.S. Census Bureau’s Social, Economic and Housing Statistics Division. In her position, Ms. Stern oversees statistics on the labor force, health insurance and disability from several Census Bureau surveys. She has authored a wide variety of Census Bureau reports and papers on topics related to poverty, disability, and health insurance.
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