Liu, Grace
Research Analyst

e gtliu@umn.edu

Food Insecurity in America: New Social Determinants State Health Compare Measure Tracks Percent of Households Experiencing Food Insecurity

Social determinants of health are social factors that impact an individual or community’s health. As explained by the Robert Wood Johnson Foundation, “Good health begins where we live, learn, work and play. Stable housing, quality schools, access to good jobs, and neighborhood safety are all important influences, as is culturally competent health care.”  

Access to food, nutritious and varied, is also considered a social determinant of health.¹ Numerous studies have shown that food insecurity, aka having limited or unstable access to food, is linked to poorer health outcomes, higher chronic disease prevalence, and overall financial hardship.

Newly added to SHADAC’s State Health Compare tool, our ‘Food Insecurity’ measure provides state-level estimates of the prevalence of household-level food insecurity by state and by a number of breakdowns. In this post, we will review a food insecurity definition, the details of SHADAC’s food insecurity measure, and some food insecurity statistics & data highlights from that measure.

What Is Food Insecurity?

As we stated earlier, a general food insecurity definition is when an individual or household has limited or unstable access to food. There are specific definitions, though, that the USDA Economic Research Service follows, which informed SHADAC’s Food Insecurity Measure on State Health Compare.

The USDA Economic Research Service defines a food insecure household as a household that, at times during the year, was “uncertain of having or unable to acquire enough food to meet the needs of all their members because they had insufficient money or other resources for food.”² 

Further, a household is designated as ‘low food security’ by the USDA if they reported reduced quality, variety, or desirability of diet with little or no indication of reduced food intake; the USDA designates a household as ‘very low food security’ if they reported multiple indications of disrupted eating patterns and reduced food intake.² 

The food insecurity estimates presented on State Health Compare indicate the percentage of households experiencing food insecurity defined as those experiencing either low or very low food security. 

SHADAC’s Food Insecurity Measure on State Health Compare

State Health Compare is SHADAC’s online, interactive data tool providing data visualizations, tables, and data sets of state-level health estimates on a variety of measures, including measures on:  

• Access to Care (e.g., Had usual source of medical care, broadband internet access, etc.)
• Cost of Care (e.g., People with high medical cost burden, forgone care, etc.)
• Health Outcomes (e.g., suicide deaths, chronic disease prevalence, etc.)
• Social and Economic Factors (e.g., adverse childhood experiences, etc.)

The new “Food Insecurity” measure falls under the ‘Social and Economic Factors’ category. Data for this measure is available for 2011 – 2022, produced using the Current Population Survey Food Security Supplement (CPS-FSS).

We have also added a number of breakdowns to this measure to allow for further analysis and disaggregation. The Food Insecurity measure is available by the following breakdowns, shown in Table 1:

Table 1. Available Breakdowns and Time Frames for Food Insecurity Measure on SHC

Accessible version of Table 1 found in "Accessible Tables" section in Conclusion.

Now that we have gone over the available data and breakdowns for this measure, let’s take a look at a few food insecurity statistics and data highlights straight from State Health Compare.

Food Insecurity in America: Data Highlights from State Health Compare

Nationwide, 11.2% of households experienced food insecurity in 2020-2022. During this same time frame, food insecurity ranged from as high as 16.6% of households in Arkansas, to as low as 6.2% of households in New Hampshire. 

Food Insecurity Over Time: Vast Majority of States Have Seen Decreased Food Insecurity Rates

When we examine food insecurity over time, we can see that the vast majority of states have experienced a statistically significant decrease in food insecurity between 2011 – 2013 and 2020 – 2022.  

Figure 1. Change in Percentage of Food Insecure Households by State Between 2011 and 2022  

Figure 1 shows the changes in food insecurity for the full range of time available on State Health Compare. When comparing the 2020 – 2022 time frame to the 2011 – 2013 time frame, the majority of states (40) saw statistically significant decreases in the percent of households experiencing food insecurity. 

The largest decrease was in North Carolina, which was down 6.6 Percentage Points (PP). The smallest statistically significant decrease was in Pennsylvania, which was down 1.8 PP. 

South Carolina was the only state to experience an increase in food insecurity between these two time frames (statistically significant or not) with an increase of 0.4 PP.  

Food Insecurity by Race / Ethnicity: African-American / Black Households Experienced the Highest Levels of Food Insecurity

As we saw in the previous section, most states had significant decreases in rates of food insecure households over time. However, when we break down this data by race/ethnicity, disparities in food insecurity levels are revealed.

Table 2. Five States and Racial/Ethnic Groups with the Highest and Lowest Percentages of Food Insecurity, 2020 - 2022

Accessible version of Table 2 found in "Accessible Tables" section in Conclusion.

Table 2 shows the five highest and lowest rates of insecurity for any race/ethnicity in the 2018 – 2022 time frame, which reveals that food insecurity varied greatly by race/ethnicity. 

Across the country and by race, the lowest rate of food insecurity in 2018-2022  was 1.3% for White individuals in the District of Columbia, while the highest rate was 29.0% for Black individuals in North Dakota. Food insecurity prevalence was generally highest for African American/Black households, with the lowest rate of household level food insecurity for African American/Black households in any state, in Massachusetts at 14.2%, still being higher than the highest rate for White households in any state, in West Virginia at 14.1%. 

Food Insecurity by Presence of Children in Household: Those with Children Experienced Higher Levels of Food Insecurity

Figure 2 presents the prevalence of household level food insecurity by presence of child in the household.

Figure 2. Change in Percentage of Food Insecure Households by State and by Presence of Children in Household Between 2020 and 2022  

During the 2020 – 2022 time frame, households in almost every state that included children experienced food insecurity at higher rates than households in the same state without children. 

The exceptions to this were Colorado, Connecticut, Vermont, and West Virginia, which saw more households without children experiencing food insecurity compared to those with children. 

The greatest difference in food insecurity between these two groups (households with and without children) was in Delaware (11.3 PP), while the smallest difference was in Connecticut (0.3 PP).

Conclusion 

As  we can see just from using State Health Compare, many factors can, and do, influence levels of food insecurity. Household makeup, the state you live in, and current events in time can all impact the stability and availability of food to different populations and families. Continued research into what impacts levels of food insecurity can hopefully help us identify where supports are needed, and what kind of actions would be most effective and efficient at providing people with stable and accessible food, care, and support.

You can get started on this important research yourself by exploring and using State Health Compare. Build data tables, visualizations, and more on a number of health care and public health related measures on our simple and easy to use site.

We’d love to hear what you discover on State Health Compare – tag us on LinkedIn, or send us an email with comments or questions at shadac@umn.edu.

Notes and Citations

1. NAMI, Social Determinants of Health: Food Security

2. USDA ERS - Key Statistics & Graphic

Accessible Tables

Table 1. Available Breakdowns and Time Frames for Food Insecurity Measure on SHC

Table 2. Five States and Racial/Ethnic Groups with the Highest and Lowest Percentages of Food Insecurity, 2020 - 2022

Highest Food Insecurity Prevalence by State and Race/Ethnicity

Lowest Food Insecurity Prevalence by State and Race/Ethnicity

Hest, Robert
Senior Research Fellow

p 612-624-5507
e hestx005@umn.edu

Seven Measures Have Been Retired from State Health Compare

To maintain State Health Compare (SHC) as a relevant and current resource for data users, SHADAC regularly evaluates our collection of SHC measures to ensure they inform current health care and health policy discussions. We also want to be able to update measures consistently with recent data.

As a part of this process, SHADAC both adds new measures to State Health Compare and retires existing measures if they can no longer be updated, are no longer relevant to the current moment, are little used by SHC users, or fall outside of SHADAC’s areas of focus.

To that end, SHADAC has retired seven measures from State Health Compare. The measures will continue to live at their same web addresses (URLs), but they will no longer be updated or displayed in the list of SHC measures on the Explore Data page. These seven measures that have been retired are as follows:

• Costs of Potentially Preventable Hospitalizations

• Adult Potentially Preventable Hospitalizations

• Child Potentially Preventable Hospitalizations

• Adult Binge Drinking

• Physicians Who Accept New Medicaid Patients

• Physicians Who Accept New Patients

• Child Poverty

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.

Find more data on SHADAC's State Health Compare

Walsh, Elliot
Research Dissemination Coordinator

e wals0421@umn.edu

Exploring Racial Disparities in Forgone Health Care Using State Health Compare

“Forgone care” describes when someone does not use or access health care despite a need for it. While there are a number of reasons why someone might choose to forgo care (e.g., fear of medical procedures or diagnosis, lack of health literacy and/or understanding, limited access to care, cultural beliefs), a very common reason for many is the cost of health care. 

Whatever the reason, forgoing or delaying care is correlated with poorer health outcomes, delayed diagnoses, and disruption in care for chronic conditions.^1,2 And, unfortunately, disrupted and/or forgone care can actually increase costs for some through further complications, missed preventative treatments, and later diagnoses.

Health care costs are rising in the United States³, and the prices of many other essentials have also gone up since the pandemic.⁴ As costs and overall pressures on family budgets continue to rise⁵, it is possible that people will also be more likely to forgo health care.

SHADAC’s State Health Compare tool tracks the percentage of adults who report forgoing care due to cost using data from the Behavioral Risk Factor Surveillance System (BRFSS). In 2023, it’s estimated that 11.6% of adults could not get medical care when they needed it due to cost.

While 11.6% represents how many of all adults reported forgoing care due to cost in 2023, we can look deeper at the phenomenon of forgone care by disaggregating the data and looking at how different factors, like insurance coverage type, sexual orientation, and race / ethnicity, impact rates of forgone care.   

Looking at how forgone care due to cost appears for different groups and communities can help us identify disparities that may not be clear from the aggregated data, understand how structural and/or systemic racism might impact health care decisions and outcomes, and help policymakers and others target efforts to make care more accessible. 

In this blog post, we are going to explore racial disparities in forgone health care due to cost. Using SHADAC’s State Health Compare tool, we will examine forgone care data broken down by race and ethnicity. Then, we will explore racial disparities in forgone care at the state level.  

National Levels of Forgone Care Higher Among Hispanic/Latino, Black, and Other/Multiple Race Adults Compared to White Adults

Using State Health Compare, we examined national levels of forgone care over time for all available race/ethnicity breakdowns: Hispanic/Latino, Black, White, and Other/Multiple Races between 2011 and 2023.

Figure 1. Percent of Adults Who Could Not Get Medical Care When Needed Due to Cost by Race/Ethnicity

Figure 1 shows that each racial / ethnic group’s forgone care follows a similar trendline. Despite similar trends, White adults consistently report the lowest rates of forgone care due to cost. While rates of forgone care due to cost have decreased for all groups compared to 2011, the racial disparities are clear and persistent.

In 2023, rates of foregone care for Black adults (12.7%) and Hispanic/Latino adults (20.1%) were statistically significantly higher than the overall rate (11.6%) and the rate for Whites (8.6%). Since 2021, the percentage of Hispanic / Latino adults who forgo care due to cost has been at least 10 percentage points (PP) higher than White adults. 

Forgone Care Due to Cost Differs by State, Ranges Widen for Hispanic/Latino and Black Adults

There is considerable variation across states in the rates of forgone care by race and ethnicity. As Figure 2 shows, state by state rates of forgone care due to cost range from 4.8% of White adults in Hawaii to 30.4% of Hispanic/Latino adults in Georgia . 

Figure 2 also highlights the extent to which the range of forgone care across states differs by race and ethnicity, with Hispanic/Latino adults having higher rates consistently across states. For example, the highest rate among Hispanic/Latino adults (Georgia, 30.4%), is more than double the highest rate for White adults (Tennessee, 13.6%). Similarly, the lowest rate among Hispanic/Latinos (Hawaii, 9.2%) is close to double the lowest rate for White adults (Hawaii, 4.8%).  

Figure 2. Forgone Care Due to Cost for Black, Hispanic, and White Adults by State

Low Rates of Forgone Care Overall Mask Racial Disparities

Figure 3 ranks the fifty states and D.C. by overall rates of forgone care. As shown, Hawaii, Vermont, Massachusetts, Iowa, and New Hampshire have some of the lowest rates of foregone care in the country.

Figure 3. Forgone Care Due to Cost for All Adults by State

What happens when we breakdown by race / ethnicity?

Figure 4. Forgone Care Due to Cost By State and By Race / Ethnicity

*Significant difference from White

When we disaggregate the data in states with low overall rates of forgone care, racial disparities are revealed. 

Let’s take Massachusetts as an example since that is a state where we have data for each group. In Massachusetts, the only group that has a rate equal to or less than the overall rate is White adults at 5.0%. All of the other racial / ethnic groups with available data have rates that are significantly higher than White adults, with Black adults having more than double (10.7%) and Hispanic/Latino adults having almost triple (14.8%) the rate of forgone care compared to White adults.

Health Equity Considerations & Conclusion

Racial disparities in health care can come in a number of forms; as seen in this blog, there are important disparities in forgone care among adults by race and ethnicity. These disparities appear in most states, even states with low overall rates of forgone care, and the disparities are even more stark in certain states. Low overall rates of forgone care can mask these racial disparities – disaggregating data and looking at differences between groups is crucial for identifying and closing gaps & health disparities.  

In some circumstances in this blog , we used White as the comparison group to test for statistically significant differences across racial and ethnic groups. This was because White adults had the lowest rates of forgone care, and are, generally, the most structurally advantaged compared to the other groups. 

However, we want to acknowledge that this method is not always appropriate; as Whitfield et al remarks in a study on comparing racial groups: “There is an assumption of differences, but different from what?” Continuously using White groups as the basis of comparison can perpetuate the false narrative that White is a “standard” that racial/ethnic minorities differ from, which serves to ‘other’ those minority groups instead of treating each as distinct, and varied, groups with different social, cultural, and systemic/structural influences.⁶ 

There are also equity issues related to data availability — states are much more likely to have insufficient data for Black, Hispanic/Latino, and people who identify as other/multiple races. Excluding PA and KY (both states did not have sufficient data to be included in the 2023 BRFSS public data file and are not included in the national data), data is not available for: 

• Other/Multiple Race adults in two states (NC and RI)
• Hispanic/Latino adults in three states (MS, WV, and VT) 
• Black adults in 11 states (HI, ID, MT, ND, NH, NM, OR, SD, WY, UT, and VT)

Improving data collection for those of minority groups on surveys like the BRFSS could help to highlight important differences between states and groups that are not visible with the current data.

Finally, we acknowledge that grouping responses in to “Other/Multiple Races” may obscure important disparities within this diverse group. Efforts are being made to improve demographic data collection, like the newly revised standards from the OMB on federal race/ethnicity data collection; improving sample size and data collection methodology are important steps towards centering health equity, making critical disparities visible to researchers, policymakers, and community members.

Continue to explore this and more data on our interactive online data tool, State Health Compare.

Notes

All statistically significant differences were 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.  

These results represent rates of adults that forgo needed medical care due to cost for the civilian non-institutionalized population who are 18 years and over.  

Citations

1. https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2775366 

2. https://pmc.ncbi.nlm.nih.gov/articles/PMC8683898/ 

3. https://www.cms.gov/data-research/statistics-trends-and-reports/national-health-expenditure-data/nhe-fact-sheet  

4. https://www.pewresearch.org/short-reads/2024/08/07/eggs-gasoline-and-car-insurance-where-inflation-has-hit-americans-hardest/ 

5. https://www.census.gov/newsroom/press-releases/2024/renter-households-cost-burdened-race.html 

6. https://pmc.ncbi.nlm.nih.gov/articles/PMC3663459/ 

Walsh, Elliot
Hest, Robert
Stewart, Andrea

Survey Data Season Essentials: ACS vs CPS: What Is the Difference Between These Two Federal Surveys?

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.

*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.
 

* 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:

CPS ASEC: 2023 National Health Insurance Coverage Estimates Remain Steady for Adults but Rise for Children

2023 ACS: After Two Years of Decline, Uninsured Rates Increased for Children; Public and Private Coverage Rates See Variations Across the States

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

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