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/ 

Hest, Robert
Stewart, Andrea

Survey Data Season Essentials: What Is the BRFSS and How Can Researchers Use It?

This post is a part of our Survey Data Season series where we examine data from various surveys that are released annually from the summer through early fall. Find all of the Survey Data Season series posts on our Survey Data Season 2024 page here.

Each year, SHADAC covers the data releases of multiple federal surveys from a variety of agencies, beginning with the National Health Interview Survey (NHIS) in June continuing through the release of American Community Survey (ACS) and Current Population Survey (CPS) data products in September through January. 

While our focus has traditionally been on the health insurance coverage data that found in these surveys, we have also looked at factors related to coverage, including ‘access to care’ via measures of adults without primary doctors, for example, and ‘cost of care’ via measures such as adults who forgo needed medical care (because of cost). Both of those measures come from the BRFSS, a survey that is both part of our overall Survey Data Season coverage and is used in our annual “Comparing Federal Surveys that Count the Uninsured” brief, but is not typically used as our main source of data when analyzing health insurance coverage.*

This blog post will provide an overview of the BRFSS, answer some common questions about this survey, walk through a few examples of how we at SHADAC use BRFSS data, and review how other researchers and analysts can use it, too.

What Does “BRFSS” Stand For?

BRFSS stands for Behavioral Risk Factor Surveillance System. “BRFSS” can be treated as either an acronym and pronounced “BUR-fiss” or an initialism with each letter read out individually.

Which Federal Agency Conducts the BRFSS?

Conducted since 1984, the BRFSS is a partnership between the Centers for Disease Control (CDC) and state health departments in U.S. states and territories, which are responsible for data collection in their area. 

How Are BRFSS Data Collected?

BRFSS is an annual, telephone-based survey of U.S. adults (18 years or older) that calls landlines and cell phones via random digit dialing. 

The survey questionnaire has three parts:

1. The core component, which includes demographic questions and asks about health-related perceptions, conditions, and behaviors. The core is composed of a “standard” core of questions that states ask every year, and also includes a “rotating” core of alternating and emerging content (e.g., questions related to COVID were added in 2021) that is asked in even and odd years. 
2. The optional modules, which focus on specific health conditions and additional risk factors and that states can optionally choose to ask.
3. The state-added questions, which are additional questions added by individual states to their own questionnaire that can be used to learn more about a specific topic, or can be used to oversample groups of interest.

All participating states and territories (50 states, District of Columbia, Guam, the Commonwealth of Puerto Rico, and the U.S. Virgin Islands) are required to ask the survey questions contained in the core component, but are not required to ask anything from the optional or state-added question sections. This can make it difficult to compare data across all states or analyze trends over time, depending on the health topic area of interest.

What Kind of Information Is Found in the BRFSS? 

BRFSS collects state-level data about adult “health-related risk behaviors and events, chronic health conditions, and use of preventive services.” 

When Does the BRFSS Release Data?

Data from the BRFSS is released annually, but the exact timeframe can vary. Unlike the American Community Survey (ACS) and Current Population Survey (CPS) - which are scheduled to come out on September 10 and September 12 this year, respectively - the BRFSS does not release data on a specific date. Rather, data usually come out in August, but can be released anytime from July to September. Researchers can check the BRFSS Annual Survey Data page to see when the data becomes available.

This year's data released on August 29th - you can find that here.

Can You Pool or Combine Multiple Years of BRFSS Data?

BRFSS data can generally be pooled (i.e., combined) across multiple years of data. This is usually done to increase both sample sizes and the statistical reliability of estimates for small populations or uncommon events.

Survey questions, measures, and variable names can change across data years, though, so care should be taken to appropriately harmonize variables when pooling multiple years of BRFSS data. For example, BRFSS’ usual source of care measure changed substantially in 2021, creating a “break in series” in that year. Survey weights can generally be divided by the number of pooled data years to produce accurate weighted counts.

Important to note for combining multiple years of BRFSS data specifically: There was a break in the BRFSS series in 2011 when the survey began including cell phones in addition to landlines. Thus, data before 2011 should not be pooled with data from 2011 forward. 

How Does SHADAC Use BRFSS Data?

SHADAC draws on data from the BRFSS to produce key resources like our Comparing Federal Government Surveys That Count the Uninsured, which looks at trends in rates of uninsurance across time and across five different federal surveys, helping researchers understand how and when to most appropriately use each one.

The BRFSS is a rich source of 50-state data on health outcomes and behaviors, and thus offers researchers and analysts a venue for tracking the effects of health reforms for persons of varying income levels. Since the BRFSS income categories do not always match those set by the federal government for calculating poverty levels, SHADAC produced a helpful brief detailing Four Methods for Calculating Income as a Percent of the Federal Poverty Guidelines (FPG) in the Behavioral Risk Factor Surveillance System (BRFSS) in order to assist with this analytic process.

Choosing Between Federal Surveys That Measure Rent Affordability is another resource produced by SHADAC researchers that uses data from multiple federal surveys, including the BRFSS, to help researchers better understand ways to measure the effect of a rising housing affordability crisis across varying populations and across states in the U.S. We also use a variety of data from BRFSS measures on our State Health Compare tool.

Take a look at the BRFSS measures that are available on our State Health Compare site!

Access, Cost, and Quality of Care

Adults Who Forgo Needed Medical Care contains estimates of adults who could not get needed medical care due to cost. Uniquely for this measure, our demographic subgroups are organized not only by racial and ethnic categories and educational attainment, but also by chronic disease status. See more on this measure in this recent resource.

Adults Who Have No Personal Doctor is a measure of adults who do not have a doctor or care provider that they regularly interact with or schedule visits with for routine health care. SHADAC pulls this measure from the BRFSS and has used it in several past analyses, including this blog. 

Adult Cancer Screenings provides users with the rate of adults receiving recommended cancer screenings, including pap smears (cervical cancer screening), colorectal cancer screenings, and mammograms (breast cancer screening). Check out this recent SHADAC blog for more detail on this measure and analysis by available demographics.

Adult Flu Vaccinations are a gauge for adults in the United States who received their annual vaccine to protect against influenza. Tracking flu vaccination rates can help in estimating broader vaccination trends - such as in this analysis - and help detect gaps in vaccination coverage across different demographic groups.

Health Behaviors and Outcomes

Adult Excessive Alcohol Consumption is newer measure on state health compare that provides the rate of adult excessive alcohol consumption, which in turn is defined as binge drinking (4 or more drinks for women or 5 or more drinks for men on one occasion) and/or heavy drinking (7 or more drinks per week for women or 15 or more drinks per week for men).

Adult Smoking and Adult E-Cigarette Use are two related measures that track annual state-level rates of adults who smoke traditional tobacco cigarettes and those who smoke e-cigarettes, respectively. SHADAC researchers used this data from the BRFSS to produce a series of blogs on health behaviors, including this one. 

Chronic Disease Prevalence looks at the percent of the adult population who report having one or more of the following specific chronic disease types: diabetes, cardiovascular disease (CVD), heart attack, stroke and asthma.

Adult Unhealthy Days is a self-reported measure of the number of days within a month (30 days) that an individual does not have good health, either mentally or physically. Recently, SHADAC produced a 50-state resource which takes a closer look at this measure.

Activities Limited due to Health Difficulty provides a look at rates on the average number of days in the past 30 days that a person reports limited activity due to mental or physical health difficulties.

Stay Updated on the BRFSS Data Release and More with SHADAC’s Survey Data Season Series

We hope that this blog helps you to better understand what the BRFSS is, what kinds of information we can get from the data, and how SHADAC and other researchers can use BRFSS data to understand health care use, cost, quality, and access in each of the states and the nation as a whole.

But Survey Data Season doesn’t stop with the BRFSS. Through September, SHADAC will be covering the release of various major survey data from important federal survey sources, including the NHIS, MEPS, ACS, CPS, and, of course, the BRFSS. Stay up to date on our Survey Data Season series, with more Essentials blogs like this one along with other products analyzing newly released data, by signing up for our newsletter and following us on LinkedIn.

Want to see what we’ve already made for our Survey Data Season series? Check out the Survey Data Season archive page for a full list of everything we’ve created so far, including a blog on recently released 2023 NHIS data. Check back often for updates and new additions.

Notes

*The BRFSS has included a question about current health insurance coverage within the standard core component since 1991. However, the question simply asked whether the respondent had coverage, and not about the type of health insurance coverage a respondent might have. 

Question: “Do you have any kind of health care coverage, including health insurance, prepaid plans such as HMOs, or government plans such as Medicare, or Indian Health Service?” 

Answers: Yes; No; Don’t Know/Not Sure; Refused

Recently, in 2021, the BRFSS added a primary source of coverage question, making it possible to understand what portions of the national and state populations have different types of coverage. 

Question: “What is the current primary source of your health insurance?” 

Answers: A plan purchased through an employer or union (including plans purchased through another person's employer); A private nongovernmental plan that you or another family member buys on your own; Medicare; Medigap; Medicaid; Children's Health Insurance Program (CHIP); Military related health care: TRICARE (CHAMPUS) / VA health care / CHAMP- VA; Indian Health Service; State sponsored health plan; Other government program; No coverage of any type; Don’t Know/Not Sure; Refused

BRFSS’ measure of health insurance coverage is substantially different from those found in other federal surveys (e.g., ACS, CPS or NHIS) that allow respondents to choose multiple sources of health insurance coverage, rather than requiring respondents to select one “primary” source of coverage. Though BRFSS coverage data are generally reliable, by preventing respondents from selecting multiple sources of coverage, they present a less nuanced picture of health insurance compared to other surveys. Further, because BRFSS only surveys adults and has lower response rates than other surveys, other surveys are typically better sources of information about health insurance coverage, per se.

Walsh, Elliot
Research Dissemination Coordinator

e wals0421@umn.edu

LGBT Health Equity: Sexual Orientation and Gender Identity Data Resources and Information from SHADAC

• Less likely to have health insurance coverage 
• Less likely to have a regular health care provider
• More likely to delay care
• More likely to report poor quality care and unfair treatment from providers

Sexual Orientation and Gender Identity Data: New and Updated Information on Federal Guidance and Medicaid Data Collection Practices (SHVS Brief) 

State Health Compare: Explore Health Data with SOGI Data Breakdowns

• Adults Who Forgo Needed Medical Care Due to Cost
• Adult Smoking
• Adult Excessive Alcohol Consumption
• Adult E-Cigarette Use
• Chronic Disease Prevalence
• Adult Unhealthy Days
• Activities Limited Due to Health Difficulty
• Adults with No Personal Doctor
• Adult Cancer Screenings
• Adult Flu Vaccinations

Gender Based Discrimination in Health Care by Gender Identity in Minnesota

• Over half (57.1%) of trans and non-binary people reported forgone care—more than double the overall average of 26.2%
• Nearly one-third of trans and non-binary adults had low confidence in getting necessary health care—compared to the overall average of 11.8%

Examining Discrimination and Health Care Access by Sexual Orientation in Minnesota

• Both lesbian/gay and bisexual/pansexual people were more likely to report barriers to health care access
• Bisexual/pansexual people were more likely to report having low confidence in the ability to get needed health care
• Both lesbian/gay or bisexual/pansexual people had significantly higher rates of forgone care

SHADAC Response to 2023 Request for Comments on American Community Survey SOGI Questions

Stay Up to Date on the Latest in SOGI and LGBT Health Data

• Medicaid Equity Monitoring Tool Project
• Affordable Housing and Health: Unaffordable Rents Infographics
• Collection of Self-Reported Disability Data in Medicaid Applications: A Fifty-State Review of the Current Landscape (SHVS Brief)

Comparing Federal Government Surveys That Count the Uninsured: 2023

How many Americans are uninsured? The exact answer is unclear, but general estimates can be obtained by comparing various federal government surveys that estimate uninsurance.

SHADAC has recently updated our annual “Comparing Federal Government Surveys that Count the Uninsured” brief following the release of new insurance coverage estimates from surveys conducted by the US Census Bureau, the Agency for Healthcare Research and Quality (AHRQ), and the Centers for Disease Control and Prevention (CDC).

Accurate estimates of the number of people that do not have insurance coverage (also referred to as uninsured or uninsurance) are important in understanding trends and the impacts of actions (policy changes), events (like public health emergencies), or shifts in the economic landscape (like periods of recession) that may affect health insurance coverage.

The brief provides an annual update to comparisons of uninsurance estimates from five federal surveys. As in prior years, we have included estimates from:

• The American Community Survey (ACS)
• The Current Population Survey (CPS)
• The Medical Expenditure Panel Survey - Household Component (MEPS-HC)
• The National Health Interview Survey (NHIS)

This year we have included data from an additional source:

• The Behavioral Risk Factor Surveillance System (BRFSS)

In this brief, SHADAC provides up-to-date and past national uninsurance estimates, as well as the latest state-level estimates from surveys (where feasible). 

 

We also analyze the primary factors contributing to variations in these estimates among surveys, along with potential reasons for their incomparability both within and across surveys.

View and download the Comparing Federal Government Surveys 2023 Brief to learn more about our findings and analysis.

Interested in insurance coverage trends? Learn more with the following SHADAC products:

• Comparing Federal Surveys That Count the Uninsured 2022 (last year’s version of this brief) 
• Tracking Health Insurance Coverage During the Unwinding: Monthly Data from the Household Pulse Survey
• Unemployment Rate Trends of the Past 5 Years: Pre-, Mid-, and Post-Pandemic

SHADAC Staff

p 612.624.4802
e shadac@umn.edu

Household Pulse Survey (HPS) Insurance Coverage Trend Analysis: How Does It Compare?

Introduction

The U.S. Census Bureau’s Household Pulse Survey (HPS) is an online survey that provides up-to-date monthly data on emergent issues and the social and economic wellbeing of U.S. adults. As one of the only sources of publicly available rapid response data, the HPS is a critical tool for monitoring how policies such as the “unwinding” of the Medicaid continuous coverage requirement are affecting the landscape of health insurance coverage in near real time, hopefully providing actionable information for those making and implementing policy.

However, data validation of the Household Pulse Survey is crucial, especially given its short track record and novel methods. Even while offering near-real-time data, the HPS also faces several other challenges, including a notably low response rate (6.8% as of September 2023); underrepresentation of harder-to-reach demographic groups, such as adults with lower levels of education and young adults; a lack of editing and imputation for most variables; and likely some degree of nonresponse bias.

As researchers at the State Health Access Data Assistance Center (SHADAC) utilize the subannual HPS data to monitor changes in insurance coverage, we wanted to compare the HPS’ ability to detect insurance coverage changes over time against other more established surveys that also provide subannual estimates.

To this end, we compared subannual estimates of Medicaid/CHIP coverage, private coverage, and uninsurance among U.S. civilian noninstitutionalized nonelderly adults (age 18–64) in the HPS to similar estimates in the National Health Interview Survey (NHIS) [quarterly estimates from 2021–2022] and the Behavioral Risk Factor Surveillance System (BRFSS) [monthly estimates from 2022]. Below you'll find our insurance trend analysis findings using survey data.

Key Findings

Our analysis reveals that HPS estimates of coverage among adults follow similar trends as found with BRFSS and NHIS estimates. However, the exact estimates tend to deviate from each other.

Medicaid/CHIP

HPS estimates of Medicaid coverage almost exactly follow the trend of NHIS estimates of Medicaid coverage. The HPS estimates were consistently greater than the NHIS estimates with the HPS estimating that around 18% of adults were covered by Medicaid in 2021 and around 19% in 2022. The NHIS estimates were approximately 4 percentage points lower at around 14% in 2021 and 15% in 2022.

Private

Similar to estimates of Medicaid coverage among adults, the HPS estimates of private insurance coverage among adults were consistently higher than NHIS estimates, but both followed the same overall trend. HPS estimates for private insurance coverage hovered around 75% in 2021 and 2022. The gap between HPS estimates and NHIS estimates widened slightly between 2021 and 2022, increasing from an approximately 6 percentage point difference to an approximately 8 percentage point difference – NHIS estimates were around 69% in 2021 and 67% in 2022.

Uninsured

Similarly to both Medicaid and private coverage estimates, HPS estimates of uninsurance among adults closely mirrored the trends observed in both NHIS and BRFSS data. In a change from Medicaid and private coverage estimates, though, HPS estimates of uninsurance consistently fall between the estimates from BRFSS and NHIS, with HPS figures consistently greater than those from BRFSS but lower than NHIS’. This pattern persists through the first quarter of 2022.

Following this point, HPS estimates of uninsurance among adults decrease while BRFSS estimates increase and NHIS estimates stay more or less consistent.

Data and Methods

This analysis utilizes publicly available data from three household surveys: the Household Pulse Survey (HPS), the National Health Interview Survey (NHIS), and the Behavioral Risk Factor Surveillance System (BRFSS). All three surveys provide national representation of the civilian, noninstitutionalized population, with data collected on either a quarterly basis (NHIS) or a monthly basis (HPS and BRFSS). In order to have comparable time periods, HPS data was pooled both at the quarterly level and at the monthly level prior to analysis.

In the Household Pulse Survey, coverage was defined as having any sort of coverage (e.g. Medicaid or private) at the time of the survey. Uninsured was allocated to those that did not answer yes to being currently covered by some sort of health insurance or health coverage plan.

In the National Health Interview Survey (NHIS), Medicaid coverage among adults was defined as currently having either Medicaid coverage or CHIP coverage. Private coverage was defined as currently having any private coverage. Uninsurance was designated to those who answered they did not currently have health insurance.

The Behavioral Risk Factor Surveillance System (BRFSS) has a unique method of ascertaining health insurance coverage. As opposed to asking which health insurance coverage an individual has, BRFSS only has asks for a primary source of insurance, and individuals are only allowed to select one type of coverage. So, while BRFSS does include data on Medicaid coverage and private coverage, these estimates were not provided in this analysis as this type of instrument is not sufficiently comparable with the questions used by the HPS and NHIS to include estimates of coverage. Uninsurance among adults in BRFSS was defined as not having coverage of any type at the time of survey or being covered by Indian Health Service.