Tatiana "Annie" Gowlovech
Research Assistant

Adult Excessive Alcohol Consumption Rate Trends in the United States

From pre-pandemic to during the pandemic, heavy alcohol use among Americans rose nearly 20%. This increase in drinking was sustained through 2022. Excessive alcohol consumption has been linked to adverse health outcomes, such as increased risk of cancer, heart disease, stroke, and mental health conditions, such as anxiety and depression.

To understand the current trends of excessive alcohol consumption at the national and state-levels in the United States, we used SHADAC’s State Health Compare tool to analyze data on the measure, “Percent of Adults Reporting Excessive Alcohol Consumption in the Past 30 Days”.

Continue reading to learn more about these general trends, along with drinking trends amongst various subpopulations.

A Note on Methods

In May of 2024, SHADAC changed the way we measure risky drinking behaviors, pivoting from measuring only "binge drinking" to measuring "excessive alcohol consumption", which encompasses both binge drinking and heavy drinking. Below, you can see the definitions between these drinking terms are similar, but the 'excessive alcohol consumption' definition is the most comprehensive.

To identify any significant changes in excessive alcohol consumption patterns or trends, SHADAC researchers conducted statistical testing to compare 2023 rates to 2022 rates of excessive alcohol consumption at both the national and state level, as well as an analysis by race and ethnicity.

National Excessive Alcohol Consumption Rates 2019-2023

In 2023, the United States experienced its lowest rate of excessive alcohol consumption in the last five years. 16.4% of U.S. adults reported binge and/or heavy drinking in 2023, significantly lower than the 2022 rate of 17.9%.

Figure 1. National Adult Excessive Alcohol Consumption Rate Over Time, 2019-2023

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

Underlying Factors of Medicaid Inequities: Conversations with Experts on Racism and Medicaid

Medicaid serves as a vital public health safety net for over 79 million people, including many from historically excluded or marginalized communities. There is a great opportunity to improve Medicaid programs’ accountability for persistent health inequities by confronting the historical and structural racism perpetuated in the administration, policies, and practices that undergird the program.

The Medicaid Equity Monitoring Tool (MET) project is a collaborative effort from the State Health Access Data Assistance Center (SHADAC) with support from the Robert Wood Johnson Foundation (RWJF) and partner organizations working to assess whether a data tool could increase accountability for state Medicaid programs to advance health equity while also improving population health.

During the first phase of this project, a wireframe was created to organize the different sections of a potential tool, including a section on "Underlying Factors" that lead to and perpetuate health inequities for people of color and other historically marginalized communities.

In order to inform the Medicaid Equity Monitoring Tool (MET) project and the Underlying Factors section of the tool, SHADAC produced an annotated bibliography of resources to better understand the available academic and gray literature on those underlying factors of health inequities in Medicaid.

While the bibliography covers a number of structural and systemic underlying factors of health inequities (e.g., ableism, sexual orientation and gender identity discrimination), most of the resources compiled in the bibliography address structural racism specifically. These resources discuss the history, policy context, and impacts of systemic racism on Medicaid recipients.

As a follow-up to the creation of the annotated bibliography, SHADAC’s Health Equity Fellow held consulting conversations with authors of select resources cited in the structural racism section. Through these conversations, our goal was to:

1. Connect with experts in order to elicit feedback on key insights from SHADAC’s annotated bibliography

2. Ask experts questions about what topics related to systemic racism need to be discussed within the tool

3. Discuss strategies on how best to convey and disseminate this important information

This brief summarizes these conversations, including specific examples and quotes from experts, for audiences interested in communicating about the effects of structural racism with the aim to dismantle it.

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/ 

“American Indian 101”: Understanding the history and contemporary experiences of Native people in a United States health policy context

American Indian and Alaska Native (AI/AN) is a racial/ethnic category that describes people with ancestry indigenous to North America prior to colonization in 1492. As of the 2020 U.S. Census, there were approximately 9.7 million Americans who identified as Native American alone or as Native American in combination with another race. 

Despite millions of Americans identifying as AI/AN, Quin Mudry Nelson, MPH, SHADAC’s inaugural Health Equity Fellow, noticed a lack of attention on Indigenous and American Indian health and health care systems throughout their education. With their lens on health equity combined with their own experience as a Native person, they knew that Indigenous people in the U.S. face a number of health disparities compared to other groups, including, among others, the highest uninsured rate, lowest life expectancy at birth, and adverse impacts to health outcomes.

They also saw that it wasn’t only American Indian health services and systems being overlooked, it’s the people as well: data collection on AI/AN individuals is, and has been, a known challenge in the U.S. One report from the Department of Labor describes “substantial” criticism received “from tribes and other stakeholders regarding population undercounts, the accuracy and timeliness of the data, and the burden for tribes, due to lack of sufficient resources and trained personnel, in reporting the data to the Federal Government.”

After seeing these gaps between her education and her knowledge & experiences “as a two-spirit, Native person who is tribally affiliated with the Oneida Nation, I wanted to write a piece about AI/AN people’s history and identity in the context of health policy,” says Quin. 

Quin’s in-depth research combined with their experience and understanding of the AI/AN community has culminated in this important brief: “American Indian 101”: Understanding the history and contemporary experiences of Native people in a United States health policy context.

Focused on health care and health insurance access among American Indian and Alaska Native people, this brief provides readers with essential context and commentary on United States and American Indian health policy, health disparities, and AI/AN identity as it relates to health care and policy. 

The brief is divided into four sections, organized and grounded in the indigenous ways of knowing – outlining the present, reflecting upon the past, and using that context to chart a path for the future:

• Section 1 describes information about AI/AN people within the United States and the health care system presently, providing demographic data, a brief explanation on contemporary data collection issues, and a breakdown of the unique care delivery structure of the Indian Health Service
• Section 2 provides an overview of early U.S. history and American Indian health and social policies, including pivotal legal cases
• Section 3 contextualizes how this history constructed a unique identity for AI/AN people in comparison to other racial groups that impacts not only their access to care, but also to their “dedicated” health services
• Section 4 concludes with an overview of recent progress in AI/AN health and social policy, highlighting both potential future directions and limitations

This brief provides important historical context and actions that have shaped the landscape of American Indian health care today. Not only that, but this brief also provides a distinct perspective from an individual who has the lived experience of AI/AN people along with experience and education in public health and health equity. 

Quin’s voice and ideas found in this brief are as intriguing as they are important. Publications with a foundation of personal perspective & experience such as this one are essential for uplifting voices from historically marginalized groups, identifying & spotlighting the structural issues that individuals face, and learning from the stakeholders who are impacted directly and indirectly. 

Read the brief in full here or click on the image of the cover page above. We welcome any questions or comments – you can send them to Quin directly at nels9793@umn.edu. 

Are you curious about American Indian health statistics, data collection, health care, health disparities, and more? You can start by delving into the references that informed this brief - these resources are rich with information. You can continue learning with some of the following SHADAC products and resources:

• Explore State Health Compare: Health Insurance Coverage by Race/Ethnicity (Includes AI/AN Breakdown)
• SHVS Brief: Collection of Race, Ethnicity, Language Data on Medicaid Applications: New and Updated Information on Medicaid Data Collection Practices in the States, Territories, and D.C.
• SHADAC Brief: The Kids Aren't Alright. Adverse Childhood Experiences and Implications for Health Equity