Planalp, Colin
Research Fellow

p 612-624-4850
e cplanalp@umn.edu

2020 U.S. alcohol-involved deaths climbed by 26.6%, and drug overdose deaths by 30.6%

Size of alcohol, drug overdose death increases in first pandemic year were unparalleled

With the recent release of 2020 mortality data from the U.S. Centers for Disease Control and Prevention (CDC), we now know that fears that the pandemic could result in increased drug and alcohol deaths were well-founded. In just a single year, the U.S. alcohol-involved death rate increased 26.6%, and drug overdose deaths grew by 30.6% (Figure 1).

Figure 1. U.S. alcohol-involved and drug overdose death rates, 2000-2020

Overdose death rates increased significantly across most drug types (declining only for heroin), and they were led by a 55.6% increase in deaths from fentanyl and similar synthetic opioids, and a 48.2% increase in deaths from methamphetamine and other psychostimulant drugs (Figure 2).

Figure 2. Changes in U.S. drug overdose death rates by type, 2019-2020

The story was similarly grim across the states. From 2019 to 2020, forty states experienced statistically significant increases in their drug overdose death rates. Those ranged from the smallest increase of 12.9 percent in Connecticut (from 34.7 deaths per 100,000 people in 2019 to 39.1 in 2020) to the largest increase of 54.8 percent in Mississippi (from 13.6 deaths per 100,000 people in 2019 to 21.2 in 2020) (Figure 3).

Figure 3. Statistically significant state-level increases in drug overdose death rates, 2019-2020

During the same period, 44 states recorded statistically significant increases in their rates of alcohol-involved deaths, ranging from the smallest increase of 15.2 percent in Oklahoma (15.1 deaths per 100,00 people in 2019 to 17.4 in 2020) to the largest increase of 67.2 percent in Mississippi (7.0 deaths per 100,00 people in 2019 to 11.8 in 2020) (Figure 4).

Figure 4. Statistically significant state-level increases in alcohol-involved death rates, 2019-2020

While these increases in alcohol-involved and drug overdose death rates follow patterns a decade or more in the making, the twin crises of high-risk substance use clearly reached a crescendo during the pandemic, as evidenced by the unparalleled heights of substance-related death rates reported in recent decades.^1,2 And though data for 2021 are not yet fully available, provisional reports indicate that drug overdose deaths continued at historically elevated levels.³ Mortality data clearly show that the fallout of the pandemic has included the exacerbation of dangerous drug and alcohol use patterns in the U.S., and it is an issue that deserves redoubled focus as the COVID-19 emergency eventually begins to ebb.

The U.S. and state-level data analyzed in this blog post are all available on SHADAC’s State Health Compare data website: http://statehealthcompare.shadac.org/.

Stewart, Andrea
Research Fellow

p 612-624-4850
e astewart@umn.edu

State Health Compare: State-Level Data Resources on Measures of Health Equity (Part Two - Health Behaviors, Health Outcomes, Social and Economic Factors)

Health equity and social determinants of health (SDOH) are rapidly growing fields of public health research. SHADAC researchers believe that making strides toward achieving health equity depends equally on better understanding health disparities as well as on making concerted, measurable efforts toward reducing avoidable differences in populations’ health outcomes.

This blog, the second in a two-part series, provides a high-level overview of a range of state-level measures currently housed on SHADAC’s online data tool, State Health Compare, which may help states understand and track trends across indicators of health equity, such as health behaviors, health outcomes, and social and economic factors.

Health Behaviors
Achieving health equity across the nation means ensuring equal opportunities for individuals to live healthy lives, and part of this goal includes addressing disparities in social determinants of health such as drinking or smoking, as well as guaranteeing access to care and treatment for these behaviors.

Adult Smoking (2005-2010, 2011-2020)*
This measure shows the percent of adults who reported smoking, defined as adults who have smoked 100 or more cigarettes in their lifetime and who currently smoke some days or every day. The measure is broken down by race/ethnicity and educational attainment. Related Resource: SHADAC researchers used data from the Behavioral Risk Factor Surveillance System (BRFSS) to produce analyses focused on several different health behaviors, including Adult Smoking and E-cigarette Use in the United States. This blog, part of a spotlight series including binge drinking and obesity, looks at national and state-level rates at which adults with different racial/ethnic backgrounds smoke and vape in 2018 and 2017.

Adult E-Cigarette Use (2016-2020)
This measure shows the percent of adults who reported currently using e-cigarettes some days or every day. The measure is broken down by age, race/ethnicity and educational attainment. Related Resource: SHADAC researchers used data from the Behavioral Risk Factor Surveillance System (BRFSS) to produce analyses focused on several different health behaviors, including Adult Smoking and E-cigarette Use in the United States. This blog, part of a spotlight series including binge drinking and obesity, looks at national and state-level rates at which adults with different racial/ethnic backgrounds smoke and vape in 2018 and 2017.

Adult Binge Drinking (2005-2010, 2011-2020)*
This measure shows the percent of adults who reported binge drinking, defined as having on one occasion, four or more drinks for women or five or more drinks for men. The measure is broken down by race/ethnicity and educational attainment. Related Resource: SHADAC researchers used data from the Behavioral Risk Factor Surveillance System (BRFSS) to produce analyses focused on several different health behaviors, including Adult Smoking and E-cigarette Use in the United States. This blog, part of a spotlight series including smoking/e-cigarette use and obesity, looks at national and state-level rates at which adults with different racial/ethnic backgrounds smoke and vape in 2018 and 2017.

Health Outcomes
Closing the persistent gaps in health outcomes (e.g., higher rates of preventable conditions, disease prevalence, or premature death) between certain populations or geographic regions is an important step toward health equity.

Health Status (2005-2017, 2017-2020)*
Specifically, this measure on State Health Compare shows the percent of the adult population who report being in either poor or fair health. Estimate breakdowns are available by education level and race and ethnicity.

Adult Unhealthy Days (2011-2020)
Data users can examine this measure in a variety of ways. Overall, the measure captures the average composite number of days in the past 30 days that an adult’s mental or physical health was not good. Users can also individually examine the average number of mentally and physically unhealthy days in the past 30 days. Additionally, for each variation of this measure, users can make comparisons by age, health insurance coverage type, disability status, educational attainment, household income categories, and race and ethnicity are all available. Related Resource: Combining several years of data (2018-2020) from the Behavioral Risk Factor Surveillance System (BRFSS), SHADAC researchers produced one-page infographics that show how all 50 states and D.C. compare to the U.S. average in measures of people’s self-reported physical and mental health, and how people’s physical and mental health varies depending on their race and ethnicity, level of income, and age within each state.

Activities Limited due to Health Difficulty (2005-2010, 2011-2020)*
Similar to the “Adult Unhealthy Days” measure, estimates for “Activities Limited due to Health Difficulty” show the average number of days in the previous 30 days when a person indicates their activities are limited due to mental or physical health difficulties.

Chronic Disease Prevalence (2005-2010, 2011-2018)*
This measure captures the percent of adults who reported having one or more common chronic conditions such as diabetes, cardiovascular disease, heart attack, stroke, or asthma. Comparisons across rates can be made by race/ethnicity and educational attainment.

Cancer Incidence (2000-2018)
Estimates for this measure provide an age-adjusted rate per 100,000 people of breast, cervical, lung and colorectal cancer occurrence, and can be viewed by race/ethnicity subcategories.

Premature Death (2000-2019)
This measure provides a composite estimate of the average number of years lost due to premature death (deaths that occur before the average age of death within a given population) prior to age 75. Estimates are available by race/ethnicity breakdowns.

Suicide Deaths (1999-2020)
Estimates for this measure provide the age-adjusted rate of suicide deaths per 100,000 people. The data are available to view by age, race/ethnicity, sex, method, and metropolitan status subcategories. Related Resource: For the past several years, SHADAC researchers have produced an annual report looking at the alarming trend of rising suicide deaths in the U.S. Particularly concerning is the accelerated rate at which suicide deaths have increased across the nation in recent years, as well as the steep increase in deaths for children age 10-14. SHADAC also held a webinar discussing this growing public health issue, highlighting concerning trends and variations in suicide deaths by a variety of breakdowns.

Social and Economic Factors
A number of factors, from social to structural and economic to educational, can significantly impact goals of health equity, as well as present serious roadblocks in achieving more equitable and ideal health outcomes.

Children Considered to be Poor (2008-2019)
This measure details the percent of children considered to be poor, as defined by falling below 100% of the Federal Poverty Guidelines (FPG) set to measure the minimum amount of total income that a family needs for food, clothing, transportation, shelter and other necessities. This measure can be broken down by race/ethnicity.

Unemployment Rate (2000-2020)
Estimates for this measure represent the percent of the civilian labor force (age 16 and older) that was unemployed in the past year. A person is considered “unemployed” if they do not have a job, have actively looked for work in the prior 4 weeks, and/or are currently available for work. Breakdowns for this measure are available by race/ethnicity. Related Resource: SHADAC researchers recently produced a new analysis examining unemployment by racial/ethnic breakdowns—an important lens for analyzing unemployment as a social determinant of health, with unemployment often varying widely across racial/ethnic categories.

Unaffordable Rents (2012-2019)
The Unaffordable Rents measure is a state- and national-level indicator of housing affordability that measures the percent of rental households that spent more than 30 percent of their household’s monthly income on rent. This measure is available to be broken down by a significant number of categories, including disability status, household income, Medicaid enrollment, metropolitan status, and race (white/non-white). Related Resource: An analysis by SHADAC looked at the percent of cost-burdened rental households in each state in 2017, examining unaffordable rents overall while also analyzing unaffordable rents among rental households. SHADAC researchers also created state infographics for the five states with the highest percentages of unaffordable rents among rental households that had a Medicaid enrollee.

Adverse Childhood Experiences (2016-2020)
A more recent measure on State Health Compare, adverse childhood experiences, or ACEs, provides an estimation of the percent of children who experience events between the ages of 0-17 that have the potential to leave lasting traumatic impressions, such as violence, abuse, or neglect; witnessing violence in the home or community; or having a family member attempt or die by suicide. ACEs also include experiencing pervasive conditions that undermine a child’s sense of safety and stability, such as growing up in a household with substance misuse, mental health problems, and instability due to parental separation or household members being in jail or prison. Users can make comparisons in rates by subcategories of age, health insurance coverage type, education levels, poverty levels, and race/ethnicity.

Notes
For a full overview of all available state-level measures, please visit the “Explore Data” page on State Health Compare, or take a look at our one-page guide to State Health Compare measures and their data sources.

All measures marked with an “*”: This indicates a break in series due to survey changes in either data processing or implementation methodology.

Stewart, Andrea
Research Fellow

p 612-624-4850
e astewart@umn.edu

NHIS: National Rates of Health Insurance Coverage for Third Quarter of 2021 Statistically Unchanged from 2020

On Wednesday, January 19, the National Center for Health Statistics (NCHS) announced that health insurance coverage estimates from the National Health Interview Survey (NHIS) Early Release Program are now available for Quarter 3 of 2021 (July-September)

At a high level, the new estimates show no significant changes in coverage type (public, private) or uninsured rate across all ages and income groups when compared to the same time period in 2020, as discussed below.

Age
Among nonelderly adults (ages 18 to 64) surveyed between July and September of 2021, 13.0% were uninsured at the time of interview, 21.1% had public coverage, and 67.3% had private coverage. Comparatively, these rates measured at 14.1%, 20.3%, and 67.4% from July to September in 2020, as shown in Figure 1.

Figure 1. Type of Health Insurance Coverage Nonelderly Adults (18-64 years), Q3 2020 and Q3 2021

Poverty Level
Again among nonelderly adults at three differing thresholds of family income as a percentage of the federal poverty level (less than 100% FPL, 100%-199% FPL, and 200%+ FPL), rates of uninsurance as well as public and private coverage remained statistically unchanged in Q3 2021 from Q3 2020.

Looking at the below than 100% FPL category for nonelderly adults from July-September 2021, 21.7% were uninsured, 52.3% had public coverage, and 27.2% had private coverage. These rates were 28.6%, 51.3%, and 22.3%, respectively in 2020 (Figure 2).

Figure 2. Type of Health Insurance Coverage (<100% FPL) Nonelderly Adults (18-64 years), Q3 2020 and Q3 2021

COVID-19 and Cautions for 2020 NHIS Estimates
As has been extensively documented in reports from both SHADAC and NCHS, COVID-19 caused numerous disruptions to federal survey data collection and production efforts. For the NHIS in particular, personal visits were suspended beginning on March 19, 2020, and data collection in late Q1 and for all of Q2 in 2020 switched to a telephone-only mode. Personal visits (with telephone attempts first) resumed in all areas in September 2020.

Data collection methodologies were not the only casualty of the pandemic, however. While the initial NCHS report examined preliminary nonresponse bias in Q1 and Q2 of 2020, the same team published a follow-up report in September 2021 looking at effects of the pandemic on estimates for the entire year, including July to December 2020. Though in-person operations resumed, lingering concerns about low response rates and possible loss of coverage caused survey conductors to replace approximately half of the usual sample for the last 5 months of 2020 with a longitudinal component where a subset of the 2019 sample adults were re-interviewed over the telephone using the 2020 NHIS questionnaire. This process change means that comparisons between estimates from July–December 2020 and other time periods may be impacted by these differences in survey mode and methodology.

About the Numbers
The above estimates provide a point-in-time measure of health insurance coverage, indicating the percent of persons with that type of coverage at the time of the interview. The 2021 estimates discussed in this blog are only from Q3 (July-September) as well as the same period in 2020.

Differences described in this post are statistically significant at the 95% confidence level unless otherwise specified.

Citations
Cohen, R.A. & Cha, A.E. (2022, January 19). Health Insurance Coverage: Early Release of Quarterly Estimates from the National Health Interview Survey, July 2020–September 2021. National Center for Health Statistics (NCHS). https://www.cdc.gov/nchs/data/nhis/earlyrelease/Insur201902.pdf.

Dahlhamer, J.M., Bramlett, M.D., Maitland, A., & Blumberg, S.J. (February 2021). Preliminary evaluation of nonresponse bias due to the COVID-19 pandemic on National Health Interview Survey estimates, April-June 2020. National Center for Health Statistics (NCHS). https://www.cdc.gov/nchs/data/nhis/earlyrelease/nonresponse202102-508.pdf

Bramlett, M.D., Dahlhamer, J.M., & Bose, J. (September 2021). Weighting procedures and bias assessment for the 2020 National Health Interview Survey. National Center for Health Statistics (NCHS). https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/NHIS/2020/nonresponse-report-508.pdf

Theis, Lindsey
Planalp, Colin

Measuring State-level Disparities in Unhealthy Days (infographics)

Although health disparities in the United States have been common knowledge among public health professionals for years, the COVID-19 pandemic highlighted this problem with vivid urgency. The disproportionate impact of the pandemic on certain segments of the population—such as higher infection and death rates among Black people and American Indian and Alaska Native people—isn’t an aberration but rather a consequence of systems that fail many communities. Health inequities run wide and deep in the U.S., extending far beyond COVID into other areas of physical and mental health.

Another issue the pandemic has highlighted is the enormous power that states have to influence health policy, as shown recently by mask and vaccination requirements instituted by some states—and prohibited by others. As the pandemic wanes, states will have a new opening to exercise their powers to tackle health inequities.

To help policymakers and other stakeholders identify opportunities to improve health equity in their states, SHADAC has produced a set of data resources for the 50 states and the District of Columbia. Using the Behavioral Risk Factor Surveillance System (BRFSS) Survey—combining the three most recent years of data (2018-2020) to improve our ability to develop reliable state-level estimates for smaller population subgroups—we created both maps and charts that show how states compare to the U.S. average in measures of people’s self-reported physical and mental health, and how people’s physical and mental health varies depending on their race and ethnicity, level of income, and age within each state.

Click any state below to view its factsheet or click here to download a PDF of this blog and all state factsheets.

State physical and mental health
To assess how state residents’ physical and mental health matches up against the U.S. overall, SHADAC used statistical testing to compare the average number of days in the prior month that adults in each state report their physical or mental health was “not good” versus the average number for the same metric across the entire U.S.

Physical health
Among the states, 19 had an average number of physically unhealthy days that was better (i.e., lower) than the U.S. average of 3.8 days per month. Meanwhile, 20 states had an average number of physically unhealthy days that was worse (i.e., higher) than the U.S. average. The remaining 12 states had average numbers of physically unhealthy days that were not significantly different from the U.S. rate.

The District of Columbia reported the lowest average number of physically unhealthy days per month, at 3.0 days, while West Virginia reported the highest average number, at 5.5 days—a difference of two and a half extra days.

Mental health
For mental health, 17 states had an average number of unhealthy days that was better (i.e., lower) than the U.S. average of 4.2 days per month. Meanwhile, 19 states had an average number of mentally unhealthy days that was worse (i.e., higher) than the U.S. average. The remaining 15 states had average numbers of mentally unhealthy days that were not significantly different from the U.S. rate.

South Dakota reported the lowest average number of mentally unhealthy days per month, at 3.3 days, while West Virginia again reported the highest average number, at 5.7 days—a difference of almost two and a half extra days.

In addition to considering them separately, we also found substantial overlap in the states with mentally and physically unhealthy days that were significantly different from the U.S. average: 15 states had average numbers of unhealthy days that were better than the U.S. average for both physical and mental health, and 16 states had average numbers of unhealthy days that were worse than the U.S. average for both.

However, there were examples in which states demonstrated distinct differences. For instance, Utah and the District of Columbia both had physically healthy days that were significantly lower than the U.S. average, while their mentally unhealthy days were significantly higher than the U.S. average.

Physical and mental health inequities
While the dynamics vary state-to-state, physical and mental health data at the national level demonstrate clear inequities by demographics, including race and ethnicity, income, and age.

Race and ethnicity
Physical health
For the total U.S. population, the self-reported average of physically unhealthy days was 3.8 per month. This number varied across racial and ethnic population subgroups, with some clear health disparities—a finding that is consistent with other evidence of pervasive health inequities influenced by conditions such as discrimination and social risk factors, including lower incomes and limited access to health care.¹

Asian and Pacific Islander people reported the lowest number of physically unhealthy days, at 2.0 days per month, which was significantly lower than the total population. Hispanic people also reported physically unhealthy days that were significantly lower than the total population, at 3.6 days per month.

American Indian and Alaska Native people reported the highest number of physically unhealthy days, at 5.9 days per month, which was significantly higher than the total population rate. Black people and White people reported average physically unhealthy days that were only slightly higher than the total population, at 3.9 days and 3.8 days per month, though those small differences were still significantly different.² People reporting Any other race or multiple races also reported physically unhealthy days that were significantly higher than the total population, at 4.7 days per month.

Mental health
The pattern for mentally unhealthy days by race and ethnicity was similar to that for physically unhealthy days. For the total U.S. population, people reported an average of 4.2 mentally unhealthy days per month. Asian and Pacific Islander people reported the lowest number of mentally unhealthy days, at 2.8 days per month, which was significantly lower than the total population. Hispanic people also reported mentally unhealthy days that were significantly lower than the total population, at 4.0 days per month.

People reporting Any other race or multiple races reported the highest average number of mentally unhealthy days, at 5.9 days per month, which was significantly higher than the total population. American Indian and Alaska Native people reported the second-highest number of mentally unhealthy days, at 5.7 days per month, which again was significantly higher than the total population rate. Black people and White people reported average mentally unhealthy days that were only slightly higher than the total population, at 4.4 days and 4.3 days per month—seemingly small differences that were nevertheless statistically significant.

Income
Physical health
For the U.S. population, self-reported physical health was worse among people with lower incomes and better among people with higher incomes—an unsurprising finding, as income is associated with many factors related to health. For instance, people with lower incomes are more likely to live with poor air quality, as highways and industrial facilities that produce pollution tend to be found nearer to low-income housing.^3,4 And people with higher incomes are more likely to have both health insurance and easier access to health care.⁵

People with incomes of $75,000 or more (the highest category in our analysis), reported the lowest average number of physically unhealthy days, at 2.1 per month. Furthermore, the average number of physically unhealthy days reported by individuals increased as their incomes decreased, with those in the $50,000 to $74,999 income category reporting 3.0 days per month. Both of those were significantly lower than the total U.S. population rate of 3.8 physically unhealthy days per month.

People with the lowest incomes (below $25,000), reported the highest average number of physically unhealthy days at 6.4 days per month—a figure roughly two and a half days higher than the total U.S. population and a statistically significant difference. Those with incomes between $25,000 and $49,999 reported 3.9 physically unhealthy days per month, which was just slightly higher than the total U.S. population number of 3.8 days, though the difference was still statistically significant.

Mental health
The overall pattern for self-reported mentally unhealthy days by income was almost identical to that for physically unhealthy days. People with the highest ($75,000 and higher) and next-highest ($50,000 to $74,999) incomes reported the lowest average mentally unhealthy days, at 3.0 and 3.8 days per month, respectively. Both were significantly lower than the average number of mentally unhealthy days for the U.S. population, at 4.2 per month.

People with the lowest incomes (less than $25,000) reported the highest number of mentally unhealthy days, at 6.3 days per month. That was roughly two additionally mentally unhealthy days compared to the total population average, a statistically significant difference. People with the next-lowest incomes ($25,000 to $49,999), reported an average of 4.5 mentally unhealthy days per month, which also was significantly higher than the total population average.

Age
Physical health
For the U.S. population, the number of self-reported physically unhealthy days increased along with age, a finding that is consistent with the fact that many common chronic health issues—such as heart disease and diabetes—are more prevalent among the older population.

Adults age 65 and over (“older adults”) reported the highest average number of physically unhealthy days, at 5.1 days per month, which was more than one day over the total U.S. population average of 3.8 days—a statistically significant difference. Adults age 40-64 (“middle-aged adults”) also reported an average number of physically unhealthy days that were significantly higher than the total U.S. population average, at 4.3 days per month. Meanwhile, adults age 18-39 (“younger adults”) reported the lowest average number of physically unhealthy days, at 2.4 days per month, which was almost two and a half fewer days than the total U.S. average—a statistically significant difference.

Mental health
In contrast with physically unhealthy days, the pattern for mentally unhealthy days by age was reversed: Average mentally unhealthy days declined as age increased. Though this pattern may be surprising to those unfamiliar with issues of mental health, it is consistent with other evidence, such as data from the National Survey on Drug Use and Health (NSDUH), which finds that mental illness is roughly twice as common among adults 25 years and younger as compared to adults age 50 and older.⁶

Younger adults reported the highest average number of mentally unhealthy days per month, at 5.3 days. That number was roughly one day more than the total U.S. population rate of 4.2 days, a statistically significant difference. Meanwhile, older adults reported an average of 2.6 mentally unhealthy days per month, roughly one and a half fewer days than the overall U.S. population, and middle-aged adults reported an average of 4.1 mentally unhealthy days per month, which was only slightly lower than the overall population, but still a statistically significant difference.

Conclusion
Understanding how individuals’ self-reported mental and physical health vary across the states and by subpopulation at the national level offers one approach to identifying broad health inequities. Comparing the average number of physically and mentally unhealthy days for state residents against the U.S. average can allow states to identify widespread gaps. And within their populations, those same data offer states an opportunity to identify more specific health inequities. At the U.S. level, data show that certain demographic groups experience worse health. For instance, American Indian and Alaska Native people on average report significantly worse mental and physical health, as do people with lower incomes. Meanwhile, younger adults report significantly worse mental health, while older adults report significantly worse physical health. The state-level data SHADAC has published in this resource provides states with an ability to examine health inequities for their particular populations.

Click here to download this blog, data tables, and all state factsheets.

Stewart, Andrea
Research Fellow

p 612-624-4850
e astewart@umn.edu

State Health Compare: State-Level Data Resources on Measures of Health Equity (Part One - Coverage, Affordability and Cost of Care, Access to Care, and Quality of Care)

Health equity and social determinants of health (SDOH) are rapidly growing fields of public health research. SHADAC researchers believe that making strides toward achieving health equity depends equally on better understanding health disparities as well as on making concerted, measurable efforts toward reducing avoidable differences in populations’ health outcomes.

This blog, the first in a two-part series, provides a high-level overview of a range of state-level measures currently housed on SHADAC’s online data tool, State Health Compare, which may help states understand and track trends across indicators of health equity, such as health insurance coverage, health care access and affordability, and quality of care.

Insurance Coverage
One of the essential steps towards achieving health equity is providing comprehensive and affordable health insurance coverage in order to provide reasonable and increased access to health care services. State Health Compare provides annually updated state-level measures of health insurance coverage across a number of populations, including populations that have been historically marginalized.

Coverage Type (2020 only)*
This measure shows the rates of different types of health insurance coverage (Medicare, employer-sponsored insurance [ESI], Medicaid, individual, as well uninsurance) for 2020 only. Users can view this single-year measure by a limited set of breakdowns such as age, health status, and poverty level. Related Resource: In light of the data quality challenges noted for the 2020 American Community Survey (ACS), SHADAC instead analyzed estimates of national-level health insurance coverage for 2020 using the Current Population Survey (CPS). A recent blog looks at U.S. trends in rates of the uninsured, public and private coverage changes, and breakdowns by age, income level, race/ethnicity, and state Medicaid expansion status.

Coverage Type (2008-2019)
This measure shows the rates of different types of health insurance coverage, including Medicare, employer-sponsored insurance (ESI), Medicaid, and individual coverage, as well as no insurance coverage. Users can view this measure by a variety of breakdowns, including: age, citizenship, disability status, education, family income, health status, limited English proficiency, marital status, poverty level, race/ethnicity, sex, and work status. Related Resource: As part of our fall data release coverage, SHADAC produces an annual three-part blog series looking at health insurance coverage data from the American Community Survey (ACS). The first blog looks at state-level information about health insurance coverage by type, including uninsurance and private and public coverage for 2019.

Health Care Affordability
While having health insurance coverage is a critical step in eliminating health disparities, achieving equity also requires that everyone has sufficient resources to afford and access needed care or are adequately protected from health care costs by adequate and comprehensive health insurance coverage.

People with High Medical Care Cost Burden (2010-2012, 2013-2017, 2017-2020)*
This measure highlights the percent of individuals in families where out-of-pocket health care spending, including premiums, has exceeded 10 percent of annual income. Breakdowns by employer coverage, income, and race/ethnicity are available for each state.

Adults Who Forgo Needed Medical Care (2005-2010, 2011-2020)*
Data for this measure indicates the percent of adults in each state who could not get needed medical care due to cost. Breakdowns by education level and race/ethnicity are available for all states. Related Resource: SHADAC recently updated a blog focusing on racial/education inequities in access and ability to afford medical care, using the latest estimates (2019) for this measure along with estimates for Adults with No Personal Doctor. The original blog, Affordability and Access to Care in 2018: Examining Racial and Educational Inequities across the United States (Infographic), looked at the effects of rising health costs on delaying or skipping needed care.

Access to Health Care
Much like the ability to afford health care, individuals’ ability to access care has a number of associations with overall physical, social, and mental health status of populations. Individuals with no or weak connections to the health care system are less likely to get timely and adequate health care when needed.

Adults with No Personal Doctor (2005-2010, 2011-2020)*
This measure shows the percent of adults without a personal doctor and offers breakdowns by education level and race/ethnicity. Related Resource: SHADAC recently updated a blog focusing on racial/education inequities in access and ability to afford medical care, using the latest estimates (2019) for this measure along with estimates for Adults Who Forgo Needed Medical Care. The original blog, Affordability and Access to Care in 2018: Examining Racial and Educational Inequities across the United States (Infographic), looked at the effects of rising health costs on delaying or skipping needed care.

Broadband Internet Access (2016-2019)
Estimates for this measure show the percent of households that have a broadband internet subscription, which is an increasingly essential means of finding and accessing care and communicating with health care providers. Related Resource: SHADAC recently produced two new blogs aimed at measuring the impact of disparities in access to broadband internet across the states in light of coronavirus. The first blog looks at disparities in state-level broadband access by income, rurality, coverage, and disability status; the second examines the role broadband access plays in eliminating access barriers to healthcare services, such as enabling telehealth visits during the COVID pandemic.

Physicians Who Accept New Medicaid Patients (2014-2017)
State Health Compare’s newest measure, Physicians Who Accept New Medicaid Patients, provides a measure of the percent of physicians who reported accepting payments from Medicaid patients among the total number of physicians who accept new patients. Multiple years of data for this measure have been pooled together in order to provide reliable state-level estimates, and the measure can be broken down by the ratio of mid-level providers, setting, and share of Medicaid patients. Related Resource: In addition to producing the only state-level estimates available for this measure, SHADAC also published a factsheet for the Medicaid and CHIP Payment Access Commission (MACPAC) that analyzed physician acceptance of new Medicaid patients at the national and state levels, and found significant variations in rates of acceptance by state and by various patient, physician, and practice characteristics.

Quality of Care
Achieving health equity not only includes making strides toward increasing health insurance coverage and access to affordable health care but also ensuring that all populations receive high-quality care.

Adult Cancer Screenings (2005-2010, 2012-2020)
This measure shows the percentage of adults who have received the recommended cancer screenings, such as pap smears, colorectal cancer screenings, and mammograms, in the past year. Breakdowns for these estimates are available by education level and race/ethnicity categories.

Adult Flu Vaccinations (2017-2019)
One of the most recent additions to State Health Compare, this measure provides an estimate of the rate of adults (18+) who received a flu vaccine in the past twelve months, with breakdowns available by age, chronic disease status, coverage type, education, household income category, metropolitan status, personal doctor status, race/ethnicity, and sex. In order to provide reliable state-level estimates for smaller subpopulations, multiple years of data for this measure have been pooled together. Related Resource: Using this measure, SHADAC researchers produced a set of 50-state infographics showing State-level Flu Vaccination Rates among Key Population Subgroups as the best available proxy for predicting and understanding possible vaccination patterns for COVID-19. Each individual state infographic highlights vaccination rates for individual states compared with a number of demographic categories, including age, race/ethnicity, household income level, insurance status, chronic conditions, access to care, and educational attainment.

Notes
For a full overview of all available state-level measures, please visit the “Explore Data” page on State Health Compare, or take a look at our one-page guide to State Health Compare measures and their data sources.
All measures marked with an “*”: This indicates a break in series due to survey changes in either data processing or implementation methodology.