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Blog & News
Housing Affordability Matters: Unaffordable Rents Infographics Updated with 2022 Data
June 06, 2024:- By 21% in the West
- By 20% in the South
- By 18% in the Midwest
- By 12% in the Northeast
Affordable Housing and Health: What Is the Connection?
- Access to health care services (or lack of access)
- Education
- Transportation
- Cost of services (healthcare, childcare, utilities, etc.)
- Community support
- Employment opportunities
- Food type, cost, and availability
Housing Affordability Infographics
| United States | California | Florida |
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| Nevada | New Jersey | Texas |
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Explore More Data on State Health Compare
[1] Office of Disease Prevention and Health Promotion. Social Determinants of Health: Housing Instability – Literature Summary. https://health.gov/healthypeople/priority-areas/social-determinants-health/literature-summaries/housing-instability
[2] Center on Budget and Policy Priorities. (2022, June 29). Chart Book: Housing and Health Problems Are Intertwined. So Are Their Solutions. https://www.cbpp.org/research/health/housing-and-health-problems-are-intertwined-so-are-their-solutions
Blog & News
Minnesota Community and Uninsured Profile Updated to Include 2022 American Community Survey Estimates
August 12, 2024:|
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- Provide information on Minnesota uninsured people and populations
- Assist policymakers in developing & evaluating enrollment and outreach strategies
- Understand uninsured community and population characteristics in Minnesota
- Create a tool that can be used to understand and evaluate equity work, inform strategic planning, assess community needs, and support grant writing for related and relevant programs
- How to Use - an overview on using the uninsured profile to both identify and gather information communities of interest
- Community and Uninsured Profile - the profile itself with breakdowns at the county, MNsure rating area, ZIP code, economic development region, and state levels
- ZIP Code Uninsured Rates - includes all reported Census-defined ZIP Codes, regions, and counties in Minnesota along with both the number and percent of uninsured within those communities
Interactive Map
Learn more about the profile and download the data here, where you can also find out how to use the profile, including a tutorial video.
Want to learn more about how we made this profile? Curious about its applications? Contact us here – we are always happy to discuss.
Related Products:
Blog & News
2020 Public Use Microdata Area (PUMA) Updates in the 2022 American Community Survey
March 26, 2024:A Public Use Microdata Area (PUMA) is a type of geographic unit created for statistical purposes. PUMAs represent geographic areas with a population size of 100,000–200,000 within a state (PUMAs cannot cross state lines). PUMAs are the smallest level of geography available in American Community Survey (ACS) microdata. They are designed to protect respondent confidentiality while simultaneously allowing analysts to produce estimates for small geographic areas.
Every ten years, the decennial census results are used to redefine ACS PUMA boundaries to account for shifts in population and continue to maintain respondent confidentiality. This process is intended to yield geographic definitions that are meaningful to many stakeholders.
Most recently, new PUMAs were created based on the 2020 Census; these 2020 PUMAs were implemented in the ACS starting in the 2022 data year. Although Public Use Microdata Area components remain consistent to the extent possible, they are updated based on census results and revised criteria. Therefore, they are not directly comparable with PUMAs from any previous ACS data years. For example, the 2020 PUMAs used in the 2022 data year are distinct from the 2010 PUMAs, which were used in the 2012–2021 ACS data years.
The 2020 PUMAs were created based on definitions that include two substantive changes relative to the 2010 PUMAs:
1) An increase in the minimum population threshold for the minimum size of partial counties from 2,400 to 10,000. Increasing the population minimum for a PUMA-county part aims to further protect the confidentiality of respondents. However, exceptions are allowed on a case-by-case basis in order to maintain the stability of PUMA definitions (that were based on the previous minimum of 2,400) and due to unique geography.
2) Allowing noncontiguous geographic areas. Allowing PUMAs to include noncontiguous geographic areas aims to avoid unnecessarily splitting up demographic groups in order to provide more meaningful data. This change is not intended to create highly fragmented PUMAs.
Other than the two changes listed above, PUMA criteria remained consistent, such as treating 100,000 as a strict minimum population size for PUMAs. The maximum population size for PUMAs can exceed a population of 200,000 in certain instances due to expected population declines or geographic constraints.
Generally, counties and census tracts are the building block geographies for PUMAs. This maximizes the stability of PUMA boundaries and therefore reduces disclosure risks. Counties may be combined or split as needed, depending on population size. Additionally, PUMAs are designed to avoid unnecessarily splitting metropolitan areas, minor civil divisions (MCDs) with a functional government, and areas of American Indian/Alaska Native (AIAN) and Native Hawaiian people. Carlton County in Minnesota, for example, illustrates that splitting a county was necessary to avoid splitting Fond du Lac reservation into multiple PUMAs (U.S. Census Bureau, 2022).
Figure 1: Minnesota PUMA 00400 Includes Fond du Lac Reservation in Its Entirety
Note: Minnesota PUMA 00400 featuring Fond Du Lac Reservation in its entirety. Reprinted from Final Criteria for Public Use Microdata Areas for the 2020 Census and the American Community Survey from https://www2.census.gov/geo/pdfs/reference/puma2020/2020PUMA_FinalCriteria.pdf.
Due to periodic changes in Public Use Microdata Area boundaries, states as a whole are the most granular geographic variable in the ACS microdata that can be compared consistently across all years. However, IPUMS USA offers harmonized geographic variables that greatly expand the options for analyzing ACS data across both time and geography. These variables are less granular than PUMAs, but more granular than states. For example, CONSPUMA and CPUMA0010 combine ACS PUMAs to create consistent geographies that can be compared across multiple decades of PUMA definitions; these have not yet been updated to include the new 2020 PUMAs. Currently, CITY, COUNTYFIP, and MET2013 allows users to compare cities, counties, and metropolitan areas, respectively, over years 2005-2022.
IPUMS USA also provides in-depth geographic resources for ACS data users, including interactive maps of the 2010 and 2020 PUMAs. For example, below is a map of the Twin Cities metropolitan area, Minnesota, showing 2020 PUMAs in blue and 2010 PUMAs in orange (IPUMS USA, 2020). This map illustrates that the updated PUMA criteria resulted in changes to PUMA boundaries in urban areas and some aggregation of PUMAs in more rural areas.
Figure 2: IPUMS USA Map of Twin Cities Metropolitan Area, Minnesota 2010 and 2020 PUMAs
Note: IPUMS USA Map of Twin Cities Metropolitan Area, Minnesota 2010 and 2020 PUMAs. Reprinted from 2020 PUMA DEFINITIONS: WEB MAP OF 2010 AND 2020 PUMAS from IPUMS USA, https://usa.ipums.org/usa/volii/pumas20.shtml.
Looking for other data and information from the American Community Survey? Learn more with the following SHADAC products:
2022 ACS Tables: State and County Uninsured Rates
American Indian/Alaska Native (AIAN) and Native Hawaiian and Other Pacific Islander (NHPI) data added to State Health Compare ACS estimates
Explore the Data on State Health Compare
Sources:
U.S. Census Bureau. (2022, July 25). Final Criteria for Public Use Microdata Areas for the 2020 Census and the American Community Survey. https://www2.census.gov/geo/pdfs/reference/puma2020/2020PUMA_FinalCriteria.pdf
IPUMS USA (2020). 2020 PUMA Definitions: Web Map of 2010 and 2020 PUMAs. https://usa.ipums.org/usa/volii/pumas20.shtml.
Blog & News
Now Available on State Health Compare: New 2022 Estimates from the ACS and CPS
March 04, 2024:
2022 estimates from the American Community Survey (ACS) and Current Population Survey (CPS) are now available for eight measures on SHADAC’s State Health Compare.
Updated measures using new ACS data include:
Coverage Type
Health insurance coverage by type of coverage (private, public, employer-sponsored, Medicare, Medicaid/CHIP, individual, and uninsured) available by a variety of breakdowns, including by income, disability status, race/ethnicity, and more.
Broadband Internet Access
This measure allows the user to view state-level percentages of people who have access to high-speed internet, which is an increasingly important tool for individuals to access health care (via telehealth), find employment, and connect with a range of other services. The measure has multiple layers of breakdowns by income, disability status, and metropolitan status, among others.
Child Poverty
Children who experience poverty can be more prone to many different negative health outcomes, often lacking in access to health care, education, shelter, sanitation, and more compared to children not considered to be in poverty. This is a vulnerable population that needs to be monitored continuously. This measure provides state-level data on children <100% Federal Poverty Guideline for both the total population and by race and ethnicity.
Income Inequality
Lower incomes are correlated to effects like housing instability, food insecurity, and more that can lead to negative health outcomes. This measure presents state-level data on income inequality as measured by the Gini coefficient.
Unaffordable Rents
Stable housing is a key component of health and wellbeing. Unaffordable Rents on State Health Compare measures the share of renters who may be struggling to afford their rents, thus contributing to unstable housing. This measure is available by a number of policy-relevant breakdowns, including race/ethnicity, Medicaid enrollment, and household income.
Updated measures using new CPS data include:
Percent of People with High Medical Cost Burden
Out-of-pocket spending on health care can make up a large share of income, creating burdens for those with large health care expenses. This measure can help us understand trends and disparities in healthcare affordability. These data are available both by insurance coverage type and by race/ethnicity.
Median Medical Out-of-Pocket (OOP) Spending
This measure shows the amount that the typical individual spends using their own money on health care costs, including health insurance coverage premiums, forms of insurance cost sharing (e.g., co-pays, deductibles), and services not covered by health insurance and/or paid by uninsured individuals. The 2022 estimates are available to be viewed by total and specifically by those with employer-sponsored insurance.
Health Status
Stable housing is a key component of health and wellbeing. Unaffordable Rents on State Health Compare measures the share of renters who may be struggling to afford their rents, thus contributing to unstable housing. This measure is available by a number of policy-relevant breakdowns, including race/ethnicity, Medicaid enrollment, and household income.
Be sure to check out a recent analysis of new data where we add context to the Health Insurance Coverage by Race/Ethnicity measure.
Other Related Reading:
Publication
ACS 5-year Estimates: State and County Uninsured
Following the release of 2022 single-year estimates of health insurance coverage, household income, and poverty levels, the U.S. Census Bureau has now made available 5-year American Community Survey data files. These ACS 5 Year estimates are generated by pooling together five years of American Community Survey (ACS) data to produce estimates for areas and subgroups with smaller populations.
The interactive map provided below offers users the opportunity to explore health insurance coverage estimates, specifically the percentage of uninsured individuals for each state and all counties for the pooled years 2018-2022.* These data can be accessed via the Census Bureau’s data.census.gov tool. Click on a county in the map below to view state/county data tables. Counties are easily searchable through bookmarks in each state file.
2018-2022 American Community Survey (ACS) 5-Year Estimates: Percent Uninsured, Total Civilian Noninstitutionalized Population by County
Click here to view estimates for Puerto Rico by municipio.
Click here to view a 50-state table of estimates.
About the American Community Survey and ACS Data
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 US households each year. An important feature of the ACS is that it includes a large enough sample for state‐level and sub‐state estimates. The Census Bureau provides ACS 1 year estimates and ACS 5 year estimates.
The Census Bureau publishes 1-year estimates for areas with populations of 65,000 or more and 5-year estimates (covering 60 months) for all statistical, legal, and administrative entities.
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.
*The U.S. Census Bureau has extensively cautioned against the use of single-year 2020 ACS estimates due to disruptions caused by the COVID-19 pandemic (e.g., limited means of data collection, such as shutdowns of mail operations, switches to telephone-first methodologies, etc., leading to low response rates and nonresponse bias). However, the Census Bureau believes that course corrections to address nonresponse bias, and the larger sample resulting from pooled data mean that the “data are fit for public release, government and business uses, and understanding the social and economic characteristics of the U.S. population and economy.”[1]
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 describes the SHADAC HIU, its purpose, the most recent update, and improvements to HIU data inputs; and the second 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 Guideline (FPG), which is a measure used for administrative purposes. For example, FPG is used to determine eligibility for federal programs such as Medicaid and CHIP, as well as the Supplemental Nutrition Assistance Program (SNAP).
Check out our blog post from April 2023 to learn more about the difference between FPL and FPG.
Related ACS Materials:
Blog: 2022 ACS Tables: State and County Uninsured Rates, with Comparison Year 2021
[1] U.S. Census Bureau. (2022, February 7). Census Bureau Update on 2016–2020 American Community Survey (ACS) 5-Year Estimates [Press Release CB22-RTQ.01]. https://www.census.gov/newsroom/press-releases/2022/acs-5-year-estimates-update.html
