Blog & News
NHIS: Early Release Estimates Show Unchanged Coverage Rates during First Half of 2020, although NCHS Reports Evidence of Nonresponse Bias due to COVID-19February 15, 2021:
The National Center for Health Statistics (NCHS) has just published health insurance coverage estimates for the first half of 2020 (January to June) from the National Health Interview Survey (NHIS) as part of the NHIS Early Release Program. These estimates are some of the first available coverage estimates for 2020 from a federal survey and among the first estimates to cover the beginning of the coronavirus (COVID-19) pandemic in the United States.
Topline Estimates from January-June 2020
At a high level, the new estimates show no significant changes in coverage type or uninsured rate when compared to the same period of time in 2019. Among nonelderly adults (ages 18 to 64) surveyed between January and June 2020, 13.4% were uninsured at the time of interview, 20.8% had public coverage, and 67.9% had private coverage. These rates were 14.7%, 20.4%, and 66.8%, respectively, from January to June 2019.
Nonresponse Bias Report for April-June, 2020 (Q2)
NCHS released a report to accompany the January-June 2020 estimates that presents a preliminary analysis of a drop in response rates in Q2 2020 (April-June).
NHIS data collection, which is typically conducted by making personal visits to respondents’ homes, was significantly impacted by the COVID-19 pandemic beginning in Q2 2020: Personal visits were suspended beginning on March 19, 2020, and data was collected by telephone instead. As a result of the shift to telephone-only interviewing, the sample household response rate declined from 60.0% in Q1 (January-March) to 42.7% in Q2; the adult response rate fell from 57.9% to 41.1%; and the child response rate dropped from 57.6% to 40.1%.
NCHS researchers compared the characteristics of Q2 respondents to the characteristics of respondents from Q1 to determine whether the smaller Q2 sample was systematically biased in any way and found that lower-income and renter households were underrepresented in the Q2 sample. This points to potential nonresponse bias and to the possibility that the actual uninsured rates for Q2 2020 were higher than the NHIS early release estimates indicate, as lower-income and renter households tend to have higher rates of uninsurance than do middle- and higher-income households and home-owning households.
SHADAC will continue to monitor all subsequent early data releases for 2020 from the NHIS survey, and will provide updates on any new methodology reports such as the investigation into nonresponse bias for Q2 2020 provided by NCHS.
Notes about the Estimates
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.
All changes described compare January-June 2020 to January-June 2019, and are statistically significant at the 95% confidence level unless otherwise specified.
Cohen, R.A., Terlizzi, E.P., Cha, A.E., & Martinez, M.E. (2021). Health insurance coverage: Early release of estimates from the National Health Interview Survey, January–June 2020. National Center for Health Statistics (NCHS). https://www.cdc.gov/nchs/data/nhis/earlyrelease/insur202102-508.pdf
Dahlhamer, J.M., Bramlett, M.D., Maitland, A., & Blumberg, S.J. 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?ACSTrackingID=USCDC_374-DM49266&ACSTrackingLabel=NHIS%20Early%20Release%3A%20Non-response%20Bias&deliveryName=USCDC_374-DM49266
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Spotlight on Health Behaviors: Adult Who Forgo Needed Medical Care and Adults Who Have No Personal DoctorDecember 21, 2020:
Prior to the arrival of the novel coronavirus, much of American consumer health care concerns surrounded rising costs of care. With health care spending rising a reported 4.6 percent in 2018 and the Centers for Medicare and Medicaid Services (CMS) Office of the Actuary projecting an average annual increase of 5.4 percent for 2019 to hit a record $3.82 trillion or around $11,559 per person—this issue will remain at the forefront of concern for the foreseeable future.1
Compounding these trends in spending, the continued rise in the share of Americans without health insurance coverage has left more individuals without a means of protecting themselves or their families from the financial burden of illness or injury and without strong ties to health care providers and the health care system to access care.
The effects of rising health care spending and rising rates of uninsurance can be seen in direct measures of actual dollars, such as Medical Out-of-Pocket Spending and Percent of Individuals with High Medical Care Cost Burden, but also in more indirect avenues, such as changes in health behaviors and access to care.
Two measures of such behaviors, Adults Who Forgo Needed Medical Care and Adults with No Personal Doctor, are housed on SHADAC’s State Health Compare and have been recently updated with 2019 data from the Center for Disease Control's Behavioral Risk Factor Surveillance System (BRFSS). This blog provides an analysis of these indirect costs of rising health care spending and uninsurance in the year prior to the COVID-19 pandemic and examines overall national and state-level trends as well as comparisons across race/ethnicity and educational attainment.
Adults Who Forgo Needed Care
Across the nation, progress was made in reducing the percentage of adults who forgo needed medical care in the years following the passage of the Affordable Care Act (ACA). However, that progress began to flatten out by 2016 and has now begun to reverse course and display a trend of smaller but significant increases in recent years, such as the growth from 12.9% in 2018 to 13.4% in 2019 at the national level.
Trends by Education and Race/Ethnicity
Examining forgone care by individual breakdowns showed that disparities by education level and race/ethnicity, found in a previous SHADAC analysis, have persisted from the year before.
Across the U.S., adults with less than a high school degree saw their rates of forgone care hit 22.2% in 2019 from 21.1% in 2018;i a figure nearly triple the rate among adults with a bachelor’s degree, who saw their rate of forgone care rise to 7.9% in 2019 (up from 7.4% in 2018).
Nationally, Hispanic/Latino adults experienced the largest increase in rates of forgone care, rising to 21.4% in 2019 from 20.2% in 2018. African-American/Black and Hispanic/Latino adults were also significantly more likely to report going without needed medical care than White adults, with the former being 1.5 times more likely (15.7% vs. 10.9%) and the latter nearly twice as likely (21.4% vs. 10.9%).
At the state level, the trends in forgone care are varied. Despite increasing national trends, some states, such as Florida and Michigan, have continued to make steady progress in reducing forgone care. Florida saw their overall rates drop by 5.9 percentage points,ii from 22.0% in 2011 to 16.0% in 2019, and Michigan saw a similarly steady drop in rates of forgone care from 16.5% in 2011 to 11.7% in 2019.
Unfortunately, progress in reducing the number of adults who report going without needed medical care has stalled in many states—California and Kentucky being two such examples. The former state has seen relatively unchanged rates of forgone care since 2016 (11.4%, 11.8% in 2017, and 11.9% in 2018 and 2019). The percentage of adults who have gone without needed medical care in Kentucky has likewise remained nearly unchanged from 2015 to 2019 (12.3% and 12.1%, respectively).
In other states, such as Kansas and Maine, rates of forgone care have followed the national trend and in 2015 begun reversing course on previous gains. The state of Kansas saw a 2.1 percentage-point increase from 2015 to 2019 (11.0% to 13.1%) and Maine saw a concerning increase of 2.9 percentage points during the same time period (9.4% in 2015 to 12.3% in 2019).
It is important to remember that these increases in forgone care occurred in the context of an economy that was growing steadily before the COVID recession. Though the release of 2020 data is at least another year away, early studies and surveys have given some indications as to the impact of the COVID-19 pandemic on health behaviors. SHADAC conducted a survey in April 2020 in which over half of U.S. adults (51.1 percent) said they had delayed or canceled health care appointments due to the pandemic.2
Adults With No Personal Doctor
As with the measure of forgone medical care, more adults reported having a usual source of care after the passage of the ACA. However, once again this promising trend reversed itself in 2015, after which the percent of adults with no personal doctor or health care provider has increased each year, nearly reaching its pre-ACA peak in 2019 at 23.4% (23.8% in 2013). Both of these increasing trends have paralleled an increase in the rate of the uninsured across the nation, from 8.6% in 2016 to 9.2% in 2019.3
Trends by Education and Race/Ethnicity
Significant disparities by education level and race/ethnicity were again present for this measure in 2019.
At the national level, adults with less than a high school education were more than twice as likely as adults with a bachelor’s degree to report not having a regular doctor (34.7% versus 16.0%). This pattern was consistent across more than half of states, as adults with less than a high school degree were more than twice as likely to report having no doctor as those with a bachelor’s degree in 26 states, and more than three times as likely in 5 states (Connecticut, Delaware, Maryland, Nebraska, and New Hampshire). There was no statistical difference between these educational groups in D.C. and 6 states (Kentucky, Mississippi, North Dakota, Tennessee, Vermont and West Virginia).
Nationally, Hispanic/Latino and Black adults were both significantly more likely to report not having a regular doctor as compared to White adults. Hispanic/Latino adults were more than twice as likely as White adults to report not having a personal doctor (40.5% vs. 18.7%), and African-American/Black adults were more than 1.2 times as likely as White adults to report not having a personal doctor (22.7% vs. 18.7%). Again this pattern persisted among over half of the nation, as Hispanic/Latino adults were more than twice as likely to report not having a regular doctor as White adults in 28 states, and more than three times as likely to report the same in 3 states (Delaware, Maryland, and Nebraska). African-American/Black adults were at least 1.2 times as likely to report not having a regular doctor as White adults in 17 states, and this gap measured 1.5 times or larger in 6 states (Nebraska, Iowa, Kansas, Massachusetts, Michigan and Utah).
Affordability and Access to Care in 2018: Examining Racial and Educational Inequities across the United States (Infographic)
Most U.S. Adults Report Reduced Access to Health Care due to Coronavirus Pandemic
Eleven Updated Measures are Now Available on State Health Compare
1 Hartman, M., Martin, A.B., Benson, J., & Catlin, A. (2019, December 5). National Health Care Spending in 2018: Growth Driven by Accelerations in Medicare and Private Insurance Spending. HealthAffairs, 39(1). https://doi.org/10.1377/hlthaff.2019.01451
Keehan, S.P., Cuckler, G.A., Poisal, J.A., Sisko, A.M., Smith, S.D., Madison, A.J., Rennie, K.E., Fiore, J.A., & Hardesty, J.C. (2020, March 24). National Health Expenditure Projections, 2019–28: Expected Rebound in Prices Drives Rising Spending Growth. HealthAffairs, 39(4). https://doi.org/10.1377/hlthaff.2020.00094
California Health Care Foundation (CHCF). (2019). Health Care Costs 101: Spending Keeps Growing. California Health Care Almanac. https://www.chcf.org/wp-content/uploads/2019/05/HealthCareCostsAlmanac2019.pdf
2 Planalp, C., Alarcon, G., & Blewett, L.A. (2020). Coronavirus pandemic caused more than 10 million U.S. adults to lose health insurance. https://shadac.org/news/SHADAC_COVID19_AmeriSpeak-Survey
3 State Health Access Data Assistance Center (SHADAC). (2020). 2019 ACS: Rising National Uninsured Rate Echoed Across 19 States; Virginia Only State to See Decrease (Infographics). https://www.shadac.org/sites/default/files/ACS_Estimates-2019-Infographic.pdf
Blog & News
State Health Compare Users Can Now Explore Unemployment by Race/EthnicityNovember 18th, 2020:
SHADAC has updated our State Health Compare online data tool to provide estimates of unemployment according to race and ethnicity. Previously, State Health Compare users could analyze unemployment nationwide and at the state level for years 2000 to 2019, but subgroup analyses were not possible. Now, users can explore State Health Compare’s unemployment estimates, which come from the federal Bureau of Labor Statistics, by four racial/ethnic breakdowns: Hispanic/Latino, African-American/Black, Asian and White.
Why This Change Matters
Racial/ethnic breakdowns provide an important lens for analyzing unemployment as a social determinant of health, with unemployment often varying widely across racial/ethnic categories in a way that can be masked by a consideration of population-wide unemployment numbers. We see this scenario at the national level, where the overall unemployment rate in 2019 was 3.7%, but an examination of unemployment for racial/ethnic subgroups reveals 2.7% unemployment among Asian Americans, 3.3% unemployment among Whites, 4.3% unemployment among Hispanics/Latinos, and 6.1% unemployment among African-Americans/Blacks.
Minnesota provides a state case that parallels the national picture on this point. If we look at statewide unemployment in 2019, we find that Minnesota’s unemployment rate was 3.2%, which is 0.5 percentage points below the national rate of 3.7%. However, if we look at the numbers sorted by race/ethnicity, a different picture emerges: The rate for Asians in Minnesota is 2.4%, Whites are at 3.0%, Hispanics/Latinos are at 5.0%, and African-Americans/Blacks are at 5.5%. Not only do Hispanics/Latinos and African-Americans/Blacks have higher unemployment rates than Whites and Hispanics/Latinos, but their rates are also above the broader Minnesota state average, as well as the national average. Thus, what looks at first glance like a state doing well on unemployment turns out to be a state with major disparities in unemployment for certain racial/ethnic subgroups.
An examination of trends in unemployment over time confirms that the 2019 data for Minnesota are not an anomaly. Though unemployment numbers for Asians in Minnesota were less stable from year to year, White Minnesotans consistently had unemployment rates below those of Hispanic/Latino Minnesotans each year during the 10-year period from 2009 to 2019, and African-American and Black Minnesotans consistently had the highest unemployment rates of any analyzed racial/ethnic group during this time (Figure 1.)
The Importance of Considering Individual States
Additionally, it is worth exploring both overall and racial/ethnic subgroup numbers within each individual state, as there is considerable variation both across states and between states and the nation. Washington state, for example, saw an overall unemployment rate of 4.3%, or 0.6 percentage points above the national rate. And among racial/ethnic subgroups, Washington’s numbers followed a different pattern than that seen nationally, with African-Americans/Blacks having the second lowest unemployment rate (4.2%), Whites having the third lowest rate (4.3%), and Hispanics/Latinos having the highest rate (5.7%).
Nevada is another state that had 2019 unemployment numbers that differed from the national story when looking at racial/ethnic breakdowns. Like Washington, Nevada’s state’s overall unemployment rate of 3.9% was above the national rate. However, unemployment in Nevada was second lowest among Hispanics/Latinos (3.8%), third lowest among Whites (3.9%), and highest among African-Americans/Blacks (5.9%).
Taking these state variations into consideration, it’s clear that efforts to analyze and/or address disparities in unemployment by race/ethnicity would potentially look different in Washington and Nevada, or even going to back our example in Minnesota, based on their respective divergences in overall and subgroup numbers.
Discussion: Unemployment as a Social Determinant of Health
Unemployment has important implications for health and health care, as employment affects access to stable housing, food, and health insurance coverage and care. At the same time, unemployment can also have a number of direct negative health consequences, including depression, anxiety, and stress-related illness such as high blood pressure, stroke, heart attack, heart disease, and arthritis.1-7
An examination of unemployment that takes into consideration important nuances in unemployment numbers among subgroups reveals the need for critical, targeted attention to unemployment, even in states where unemployment appears to be trending well overall. Consideration of interstate variation of unemployment by race/ethnicity reveals that policy levers to address unemployment that take race/ethnicity into consideration will require state-specific modifications as well.
Explore the Data
Visit State Health Compare to learn more about unemployment by race/ethnicity within and across the states.
About the Estimates
State Health Compare’s estimates of unemployment are produced using data from the federal Bureau of Labor Statistics and represent the percent of the civilian labor force (age 16 and older) that was unemployed. State refers to place of residence.
1 Avendano, M., Berkman, L. F. (2014). Labor markets, employment policies, and health. In L. F. Berkman, I. Kawachi, & M. Glymour (Eds.), Social Epidemiology (2nd ed., pp. 182-233). Open University Press.
2 Murray, L. R. (2003). Sick and tired of being sick and tired: scientific evidence, methods, and research implications for racial and ethnic disparities in occupational health. Am J Public Health, 93(2), 221-226.
3 Kasl, S. V., Cobb, S. (1970). Blood pressure changes in men undergoing job loss: a preliminary report. Psychosom Med, 32(1), 19-38.
4 Frumkin, H. E., Walker, D., Friedman-Jiménez, G. (1999). Minority workers and communities. Occup Med, 14(3), 495-517.
5 James, S. A., LaCroix, A. Z., Kleinbau, D. G., Strogatz, D. S. (1984). John Henryism and blood pressure differences among black men. II. The role of occupational stressors. J Behav Med, 7(3), 259-275.
6 Robert Wood Johnson Foundation. (2013). How does employment—or unemployment—affect health? Health policy snapshot. Available from http://www.rwjf.org/content/dam/farm/reports/issue_briefs/2013/rwjf403360
7 U.S. Department of Labor, Bureau of Labor Statistics. (2012). A profile of the working poor, 2010. News release. Available from https://www.bls.gov/opub/reports/working-poor/archive/workingpoor_2010.pdf
Blog & News
Update to SHADAC’s Minnesota Uninsured Profile Tool Features Customized Legislative District Infographics Aimed at Addressing DisparitiesNovember 12, 2020:
Our research team at SHADAC has continued its partnership with the Blue Cross Blue Shield Foundation of Minnesota to complete a new tool that provides critical information on the uninsured populations in Minnesota at the legislative district level. Using the most up-to-date estimates available from the United States Census Bureau, (2014-2018 American Community Survey, 5-year estimates) this unique set of infographics are designed to identify and categorize legislative districts with high uninsured rates using color-coded state maps and graphics that provide a deeper look into the socioeconomic data of each Minnesota House and Senate district.
In these new infographics, state legislative maps are color-coded according to four levels of need for coverage assistance: Needs Most Assistance (uninsured rate of 7.1% or more); Needs Significant Assistance (uninsured rates between 4.8-7.0%); Needs Some Assistance (uninsured rates between 2.4-4.7%); and Needs Less Assistance (under 2.3%).
Users are able to click on an individual district in the accompanying PDF to access a two-page infographic that contains detailed socioeconomic data about that respective district's uninsured population, such as race, ethnicity, income, and age. These infographics are designed to provide clear and concise snapshots of coverage needs to raise awareness and assist policymakers and health insurance navigators in developing strategies to reach the remaining uninsured in the state of Minnesota.
“While uninsured data for Minnesota are available elsewhere, the data we’ve compiled within these customized infographics are the most explicit in calling out levels of need and addressing disparities within the state,” Dr. Kathleen Call explains. “We felt that it was most important to highlight these categories of need at the legislative district level, because that is where policy is made and where our data would be most useful.”
In the coming months, Dr. Call and her team plan for another update to the tool as soon as the latest data from the Census Bureau (2015-2019 ACS 5-year estimates) are released. They also plan to produce a video tutorial to assist users in utilizing the profile tool as well as the newly released infographics.
For additional information on the original uninsured profile tool, users can access the Resource Page on our SHADAC website where the tool is available for download in Excel spreadsheet format. In addition to the just-released infographics, the profile tool is accompanied by an interactive map that shows users the geographic makeup of each specified region in relation to schools, hospitals, native reservations, and other important or distinctive landmarks. Users are also able to use the site to provide feedback on the current profile tool, and can also request that a similar tool be built specifically for their state.
Blog & News
Minnesota and U.S. Uninsurance Rates Grew in Years Leading up to PandemicOctober 27, 2020:
Children’s uninsurance rate held steady recently, while non-elderly adults’ rate increased in 2019
For the third consecutive year, the United States' uninsurance rate increased significantly, reaching 9.2 percent in 2019, or roughly 27.9 million people. The uninsurance rate in Minnesota also increased significantly to 4.9 percent in 2019—a total of more than 270,000 people. While those increases are troubling by themselves, uninsurance rates are likely to grow even further in 2020 due to the COVID-19 pandemic and ensuing recession, which triggered historic job losses and could further cause hundreds of thousands of Minnesotans—and tens of millions across the U.S.—to lose coverage.1
Successes and Reversals in the ACA’s Coverage Expansion
Following years of growing U.S. uninsurance rates, the passage of the Affordable Care Act (ACA) in 2010 was designed to expand health insurance coverage to millions of Americans through various reforms, including the expansion of Medicaid eligibility to low-income adults, and the creation of health insurance marketplaces and tax subsidies to make individual market coverage more affordable for people with moderate incomes.
The first year of the ACA’s coverage expansion provisions saw historic declines in U.S. uninsurance rates, dropping significantly from 14.5 percent in 2013 to 11.7 percent in 2014 (Figure 1). In the following years of 2015 and 2016, U.S. uninsurance rates declined further still, bottoming out at 8.6 percent in 2016. At the same time, Minnesota’s uninsurance rate was cut in half—declining from 8.2 percent in 2013 to 4.1 percent by 2016.
Beginning in 2017, however, uninsurance rates began to creep back up. In the U.S., uninsurance rates increased significantly in 2017, 2018, and 2019, when they reached 9.2 percent. Minnesota uninsurance rates also grew significantly in 2017 and 2019, when they reached 4.9 percent.
In Minnesota, the increases in uninsurance appear to be driven primarily by non-elderly adults. The uninsurance rate for adults age 19-64 increased significantly between 2018 and 2019, from 5.9 percent to 6.9 percent—the second-highest recorded rate since implementation of the ACA (Figure 2).2 However, the uninsurance rate for children in Minnesota remained statistically unchanged from 2018 to 2019. In fact, the 2019 uninsurance rate of 3.1 percent for Minnesota children was the lowest since implementation of the ACA (tied with the 3.1 percent rate in 2015).
Even with the overall increase in uninsurance, Minnesota continued to have one of the lowest rates in the U.S.—lower than all but four other states (Massachusetts, Rhode Island, Hawaii, and Vermont) and the District of Columbia. The lowest 2019 state uninsurance rate was 3.0 percent in Massachusetts, while the highest was 18.4 percent in Texas.
Generally, states such as Minnesota that have taken up the ACA’s option to expand their Medicaid programs tend to have lower uninsurance rates, while states that haven’t expanded their Medicaid programs tend to have higher uninsurance rates. For instance, each of the earlier mentioned states with the lowest uninsurance rates (DC, HI, MA, MN RI, VT) has expanded its Medicaid program, while none of the states with the five highest uninsurance rates (Florida, Georgia, Mississippi, Oklahoma, and Texas) has expanded its Medicaid program.
The Pandemic’s Uninsurance Perils
Though definitive estimates of the impact of the pandemic on health insurance coverage won’t be available until federal survey data are released in late 2021, it’s all but certain that 2020 will show a significant increase in uninsurance rates nationally and in many states. While the share of Americans who get health insurance through an employer has declined over time, employer-sponsored insurance (ESI) remains the backbone of the U.S. health insurance system, with a majority of the U.S. population getting their insurance through ESI (52.0 percent in 2018).3
The historically massive job losses caused by the pandemic and efforts to contain the outbreak are estimated to have caused millions of people to lose their ESI in 2020. Some of those people may regain ESI coverage, through a replacement job or as a dependent on a spouse or parent’s plan. Others may obtain individual market coverage through health insurance marketplaces, perhaps with the assistance of ACA premium subsidies.
In Minnesota and other states that took up the ACA’s Medicaid expansion provision, many low-income people who have lost ESI will be able to rely on that public insurance program as a safety net. However, Medicaid coverage will not be an option for most adults who lost ESI in the 12 states that have yet to expand their Medicaid programs to childless adults. For that reason, those states that have yet to expand Medicaid may see larger increases in uninsurance as a result of the pandemic.
1 Golberstein, E., Abraham, J.M., Blewett, L.A., Fried, B., Hest, R., & Lukanen, E. (2020). Estimates of the Impact of COVID-19 on Disruptions and Potential Loss of Employer-Sponsored Health Insurance (ESI) [PDF file]. https://www.shadac.org/publications/COVID-19-MNHealth-Insurance-Model
2 From 2013 to 2016, non-elderly adults were defined as age 18-64, but from 2017 to 2019, they are defined as 19-64, so statistical testing of annual changes in rates was not possible for the subcategories of children and non-elderly adults between 2016 and 2017.
3 State Health Compare. Health Insurance Coverage Type: 2008-2018 [Data set]. State Health Access Data Assistance Center. http://statehealthcompare.shadac.org/trend/11/health-insurance-coverage-type-by-total#0/1/86/1,2,3,4,5,6,7,8,15,24,25/21