VISIT STATE HEALTH COMPARE
Publication
Children's Uninsurance by Race and Ethnicity Using American Community Survey (ACS) Data
The table below provides data for children's uninsurance by race and ethnicity from 2017 through 2022. This data was obtained from SHADAC analysis of American Community Survey (ACS) Public Use Microdata Sample (PUMS) files.
| Children's Uninsurance by Race and Ethnicity, 2017 - 2022 | |||||
|---|---|---|---|---|---|
| Race/Ethnicity | 2017 | 2018 | 2019 | 2021 | 2022 |
| African-American / Black | 4.6% | 4.4% | 4.6% | 4.7% | 4.4% |
| American Indian / Alaska Native | 14.2% | 14.0% | 14.3% | 13.0% | 11.8% |
| Asian | 4.0% | 3.8% | 4.1% | 3.6% | 3.6% |
| Hispanic / Latino | 7.8% | 8.1% | 9.3% | 8.7% | 8.1% |
| Native Hawaiian and Other Pacific Islander | 5.1% | 5.0% | 8.4% | 6.9% | 9.4% |
| Other / Multiple Races | 3.6% | 3.7% | 3.8% | 4.0% | 3.9% |
| White | 3.9% | 4.1% | 4.3% | 4.1% | 3.9% |
Source: SHADAC analysis of the American Community Survey (ACS) Public Use Microdata Sample (PUMS) files
Notes
Respondents are asked about their health insurance coverage at the time of the interview and may select multiple types of coverage.
Data for 2020 were not officially released by the U.S. Census Bureau due to pandemic-related disruptions to data collection and resulting problems with data quality.
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
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.
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.
