Walsh, Elliot
Hest, Robert
Stewart, Andrea

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

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

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

Recently, survey data from both the 2024 Current Population Survey Annual Social and Economic Supplement and the 2023 American Community Survey were released. While these two surveys overlap in a number of topics and similarities, they are also distinct in what they measure and how. 

In this blog, we will discuss the similarities and differences between two of the biggest federal survey resources available: the ACS and the CPS ASEC.

Which Federal Agencies Conduct the CPS and the ACS?

The United States Census Bureau has conducted the Current Population Survey Annual Social and Economic Supplement (CPS ASEC) since 1947 and the current iteration of the American Community Survey (ACS) since 2005 (though Census has conducted similar, prototype ACS surveys since 2000).

The Current Population Survey is jointly sponsored by the Census Bureau and the U.S. Bureau of Labor Statistics (BLS).

When Are CPS and ACS Data Released?

Single-year data from the two surveys is released annually in the second week of September, with supplemental materials for the ACS - such as the Public Use Microdata Sample or “PUMS” files, and the 5-year combined estimates - released later in the year. 

Find the survey data release schedules for these and other survey data resources on our Survey Data Season page. 

Can You Combine or Compare Multiple Years of ACS or CPS Data?

Yes - you can combine and/or compare multiple years of ACS data*. Along with 1-year ACS data files released in September, the Census Bureau has also released 5-year ACS data files annually in December since 2009.

Yes - you can generally combine and/or compare multiple years of CPS data. Care should be taken in combining or comparing across CPS data years, as the survey methodology has been revised numerous times throughout its history. Additionally, because some of the same individuals are surveyed across two CPS data years, researchers should think carefully about how to treat those repeated observations.

On SHADAC’s online data tool, State Health Compare, multiple measures utilize ACS or CPS data that allow users to compare years, trends, and more. We will go over some specific examples of how we use ACS and CPS data later in this blog.

*Except 2020 data, for which the COVID-19 pandemic majorly impacted data collection and distribution efforts. These data are considered “experimental only” and should not be compared to or combined with other years of data. However, the Census Bureau notes that the 5-year files that have the 2020 data year within them are okay for normal use.

Design Differences Between the CPS and the ACS

While both of these surveys are conducted by the Census Bureau, they differ in design, methods, measures, and more. The table below provides an overview of some of the most major differences.

*Geographic level available for data breakdowns - i.e., CPS data is available for the U.S., by region (Midwest, South, Northeast, West) and for states. 

^In-person (ACS & CPS) and mailing activities (ACS) were halted in March 2020. Both resumed in limited capacity in July 2020, and in-person activities resumed fully in September 2020 while mailing activities resumed fully in April 2021.

✝ 2020 data are experimental release only and should not be compared to other years of data.

What Do the ACS vs CPS Measure?

Both the ACS and the CPS gather information on a wide variety of measures.

According to its page on the Census Bureau website, the Current Population Survey, “[provides] information on many of the things that define us as individuals and as a society – our work, our earnings, and our education. In addition to being the primary source of monthly labor force statistics, the CPS is used to collect data for a variety of other studies that keep the nation informed of the economic and social well-being of its people.” 

Specifically, the CPS collects data on measures like:

• Health insurance coverage
• Unemployment
• Labor force participation rate
• Employment data (occupation, industry, class of worker)
• Employment-population ratio
• Child support
• School enrollment
• Demographic data collection (age, race, sex, gender, etc.)

According to its page on the Census Bureau website, the American Community Survey, “helps local officials, community leaders, and businesses understand the changes taking place in their communities. It is the premier source for detailed population and housing information about our nation.”

Specifically, the ACS collects data on measures like:

• Health insurance coverage
• Jobs and occupations
• Educational attainment
• Information on veterans
• Whether people own or rent their homes
• Employment status 
• Disability information
• Housing costs 
• Demographic data collection (age, race, sex, gender, etc.)

You can learn more about the specific questions & measures on the American Community Survey, and why they are asked, on the “Why We Ask Each Question” page on the Census Bureau website.

As you can see, both surveys collect data on similar topics: housing, income, insurance coverage, demographic information, employment, education, and more. A key difference between these two sources is that the ACS provides us with both national and state-level data and estimates for these measures (in addition to lower levels of geography), while the CPS mainly focuses on national-level data.

ACS vs CPS: Guidance on When to Use Each Source 

Knowing what survey data to use depends on what you’re looking at, what measures you’re interested in, the years you’re looking at, and more. The table below guides you through common uses of ACS and CPS survey data, and when to use which source.
 

* While the ACS began in 2005, health insurance coverage questions were not added until 2008.
✝ Except 2020 data, which were “experimental” and should not be compared to other data years.

How Does SHADAC Use ACS and CPS data?

One of the main ways that SHADAC uses the data from these surveys is for our health insurance coverage estimates. These two sources are essential for how SHADAC estimates uninsurance, public insurance, and private insurance coverage rates, including providing information on health insurance coverage nationally, by state, and by demographic categories like race and ethnicity, income, age, and more.

Using the latest 2023 data released in September 2024 from each of these two surveys, SHADAC researchers created two resources explaining overall health insurance coverage estimates. Some of our main findings from these data sources this year include:

• a stable national uninsurance rate for the total U.S. population in 2023 
  • at 7.9% according to the ACS (compared to 8.0% in 2022) 
  • at 8.0% according to the CPS (compared to 7.9% in 2022)
• the rate of uninsurance among children (age 0-18) rose significantly 
  • to 5.8% in 2023 from 5.4% in 2022 according to CPS data
  • to 5.4% in 2023 from 5.1% in 2022 according to ACS data

Read the details on our findings from these two surveys at the links below:

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

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

SHADAC also uses the wide variety of measures and data available from these surveys on our State Health Compare tool. This online and interactive data tool allows users to create customized data sets and visualizations of state-level health estimates for a number of measures under many categories, including measures on:

• Health Insurance Coverage
• Access to Care
• Cost of Care
• And more

Take a look at the measures available on our State Health Compare (SHC) site that use data from the ACS and CPS!

Measures that Use ACS Data on SHC

Click on any of these measures to explore the data on State Health Compare.

Health Insurance Coverage Type

Percent of adults with fair or poor health status

Percent of households with a broadband internet subscription

Percent of children considered to be poor (<100% FPG)

Income inequality (Gini Coefficient)

Percent of cost-burdened rental households

Measures That Use CPS Data on SHC

Click on any of these measures to explore the data on State Health Compare.

Health Insurance Coverage Type

Percent of people with a high medical cost burden

Median Medical Out-of-Pocket Spending

Percent of adults with fair or poor health status

Have you used State Health Compare to explore data and delve into health care topics? Share your work and tag us on LinkedIn - we love to see how people use SHC to make connections, identify gaps, and work towards making health care accessible & affordable for all people. You can also e-mail us at shadac@umn.edu - we would love to connect!

Stay Updated with SHADAC’s Survey Data Season Series

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.

Tatiana "Annie" Gowlovech
Research Assistant

What is Health Equity? — A SHADAC Basics Blog

Basics Blog Introduction

SHADAC has created a series of “Basics Blogs” to familiarize readers with common terms, concepts, and topics that are frequently covered.

This Basics Blog will focus on the concept of health equity. We will answer and provide explanations for questions like:

• What is health equity?
• What is the definition of health equity?
• How can we talk about health equity in public health work? 
• What are other supplemental terms and their definitions that are used in the health equity space?

With that foundation set, we then move into an example, using a study on provider discrimination data drawn from the Minnesota Health Access Survey (MNHA) to understand how to apply a health equity lens to public health work and analysis. Finally, we end with some further thoughts and considerations on our evolving understand of this complex concept. 

Keep on reading below to learn more about health equity.

Health Equity Definitions 

The Robert Wood Johnson Foundation, a philanthropic organization dedicated to advancing health equity and dismantling barriers in health care, defines health equity below:

It is important to note, though, that since health equity is conceptual, there is not a singular definition of it. This definition can also change and evolve, so it’s important to continue to educate yourself on health equity as time goes on.

What Is the Difference Between Equity and Equality? 

While these two concepts are similar, equity and equality are not one in the same. 

Equality is defined as giving the same treatment to everyone, regardless of individual needs or differences. 

Equity is defined as giving treatment that is specific to individual needs, which allows everyone a fair chance at being successful. 

The graphic below from the Robert Wood Johnson Foundation provides a visual representation of the difference between equality and equity. In the ‘Equality’ example, all people are given the same mode of transportation – they are given equal treatment. However, because the individual’s needs are not considered, the outcome is not necessarily equal as can be seen in the visual. In the ‘Equity’ example, each individual is given a mode of transportation that works for their needs, leading to a more equitable outcome as can be seen in the example. 

Source: Joan Barlow, “We Used Your Insights to Update Our Graphic on Equity” Robert Wood Johnson Foundation, November 2022, We Used Your Insights to Update Our Graphic on Equity (rwjf.org). Reproduced with permission of the Robert Wood Johnson Foundation, Princeton, N.J.

Additional Terminology Definitions

The topic of health equity is complex, with many factors feeding into it. To further understand health equity, it is important to define these additional factors and terms as well. While this list is not exhaustive, the terms below all refer to larger barriers and systems that must be addressed in order to reach equity in health and health care.

Health Disparities: Avoidable differences in health outcomes experienced by people with one characteristic (race, gender, sexual orientation, etc.) as compared to the socially dominant group (e.g., white, male, cis-gender, heterosexual, etc.). 

Measuring disparities can benchmark progress toward achieving health equity.

Social Determinants of Health (SDOH): The daily context in which people live, work, play, pray, and age that affect health. SDOH encompass multiple levels of experience from social risk factors (such as socioeconomic status, education, and employment) to structural and environmental factors (such as structural racism and poverty created by economic, political, and social policies). 

These factors are known quantities that contribute to social and health inequities.

Social Inequities: Differences between groups that are unfair, unjust, systemic, and avoidable. Social inequities can be characterized by race, ethnicity, gender, age, sexual orientation, income, etc. 

Social inequities can lead to poor health outcomes and further perpetuate systems and circumstances leading to health disparities (systemic racism, ableism, etc.). 

Structural Racism: A complex system rooted in historical and current realities of differential access to power and opportunity for different racial groups. This system is embedded within and across laws, structures, and institutions in a society or organization. This includes laws, inherited disadvantages (e.g., the intergenerational impact of trauma) and advantages (e.g., intergenerational transfers of wealth), and standards and norms rooted in racism. 

Structural racism contributes to a number of social determinants of health (SDOH). Public health systems must prioritize addressing structural racism as a primary barrier to health equity.

As you can see, many of these terms contribute to each other or are a result of another—truly showing how complex the topic of health equity is. 

Using a Health Equity Lens on Provider Discrimination—Challenges and Interventions

Now that we have gone over some helpful terminology to keep in mind, let’s look at how to apply a health equity lens when looking at a real-life public health example.

The example we will go over in this section is related to LGBTQ+ health equity and discrimination. With roughly 13.9 million adults in the United States identifying as LGBTQ+, this example is both relevant and important for conversations on equity. Individuals of minoritized sexual orientation and gender identity have been historically marginalized, underrepresented in data, and faced with many health disparities, compared to their heterosexual, cisgender counterparts. These disparities exist for LGBTQ+ people not only at the national level, but also at the community, organizational, and smaller societal levels. 

Thus, in June 2022, President Biden signed Executive Order 14075: Advancing Equality for Lesbian, Gay, Bisexual, Transgender, Queer, and Intersex Individuals. This executive order is a sign of progress toward breaking down systemic barriers and more relatedly for this blog, empowering the Secretary of Health and Human Services (HHS) to “promote the adoption of promising policies and practices to support health equity” for this population.  

Now we will look specifically at a relevant measure from the Minnesota Health Access Survey (MNHA) – provider discrimination by sexual orientation and gender identity (SOGI) in Minnesota – to demonstrate how we can perform studies, analyze results, and identify accessible solutions to health disparities by viewing them through a health equity lens. 

The Data  

Data coming from the 2021-2023 Minnesota Health Access Survey (MNHA) reveals that among all adults in Minnesota, over half of the transgender/non-binary population (56.3%) reported experiencing Sexual Orientation and/or Gender Identity (SOGI) based discrimination from health care providers – significantly higher compared with cisgender adults’ reported experiences of discrimination (6.7%). 

Nearly 9 in 10 transgender/non-binary adults (88.1%) and about one in four (24.1%) cisgender adults who identified as gay/lesbian reported discrimination. Two thirds of transgender/non-binary adults (66.1%) and about a quarter of cisgender adults (23.9%) that chose the ‘none of these’ option for sexual orientation also reported discrimination. Discrimination among people who identify as bisexual/pansexual is also high, and not statistically different for transgender/non-binary adults (40.5%) and cisgender adults (31.6%). 

Experiencing discrimination has been shown to negatively affect both mental and physical health, as individuals and members of historically marginalized communities report worse health status and may forgo or delay care to avoid further discrimination. 

Now, let’s look at potential ways to equitably address instances of provider discrimination by sexual orientation and gender identity at various levels. 

Community Level 

There are various ways to approach the issue of addressing provider discrimination. One potential solution is to make changes to education for the overall medical community, such as implementing cultural competency and communication training.

Cultural competency describes services, practices, and processes that are responsive to diverse practices, assets, needs, beliefs, and languages for an array of individuals and communities. Examples of incorporating cultural competence in health care include:

• Promoting awareness and knowledge
• Recognizing one’s biases
• Engaging in cultural competence & bias training
• Using accessible language
• Familiarizing oneself with the local community
• Recruiting diverse team members

Embracing these strategies can help immensely in understanding and reducing stigma as well as strengthening relationships between providers and the unique communities they serve.

Incorporating cultural competence into care can therefore offer meaningful ways to make strides toward reducing reported discrimination rates, such as those from individuals reporting SOGI-based provider discrimination in Minnesota. When cultural competence is practiced, better and more equitable health outcomes are observed for patients. Improved trust and communication between providers and patients can lead to more regular interactions with the health care system (like routine preventative visits), which can in turn lead to better treatments, health outcomes, and even better overall health status, another known issue for LGBTQ+ individuals.

Organizational Level 

Addressing provider discrimination at a higher organizational level can be done through changing health systems and how they operate. Structural issues, such as payment/reimbursement structures, lack of time for visits, and workforce diversity may also contribute to the discrimination felt by different individuals. 

Structural competency, which is the belief that inequalities in health must be conceptualized in relation to the institutions and social conditions that determine health related resources, can then play a crucial role in promoting equitable care alongside cultural competency by addressing issues at a structural level. This concept can be implemented in health systems through training in five core competencies: 

1. Recognizing the structures that shape clinical interactions
2. Developing an extra-clinical language of structure
3. Rearticulating “cultural” formations in structural terms
4. Observing and imagining structural interventions
5. Developing structural humility

Structural training and education helps medical professionals more effectively understand the economic and social determinants of health (SDOH) that occur for their patients before any interaction with the health care system occurs. In the instance of individuals belonging to the LGBTQ+ community, these can take the shape of lack of health insurance coverage or access barriers. Additionally, organizations can invest in structural changes to advance health equity through improved data systems that are able to track populations’ diverse makeup (race, ethnicity, sexual orientation, gender identity, primary language), alongside SDOH (income, education, employment) and health needs (utilization of care, treatment, and health outcomes), to better understand who they are serving and how to improve services. 

Societal Level

On a larger scale, the state of Minnesota has made it a goal to ensure all Minnesotans receive high quality health care, free of provider or system bias. 

Citing the 2021 Minnesota Health Access Survey (MNHA) data on responses to unfair treatment, the Minnesota Department of Health (DHS) recognized the disparity between the higher rates of unfair treatment by Black Minnesotans (39%) and Transgender Minnesotans (50%) compared to White Minnesotans and cis-gendered men (9% and 12%, respectively), and created a measurable goal to directly address this issue. 

By 2027, MN DHS states that it hopes to reduce the percentage of transgender and non-binary Minnesotans and Black Minnesotans reporting unfair treatment by their providers by 50%. This goal is a meaningful step toward health equity for individuals with different racial and ethnic backgrounds and different gender identities.

Shifting Definitions and Future Considerations

In the example above, examining reported rates of provider discrimination in Minnesota and potential solutions at different levels helps to understand not only a practical example of how better understanding of health equity and systemic issues can clarify what the major barriers to advancing equity look like, but also how they can be addressed. 

Even now as the definition of health equity is not set in stone and continues to evolve, we see opportunity for solutions to advance health equity--both in general, and for people with minoritized sexual and gender identities--to evolve as well. However, this does not apply only to health equity, as the definitions and understanding of many of the terms described above have evolved over time. As society and demographics continue to shift, so does our insight and understanding of these concepts. 

There is a need for more involvement at the community, organizational, and societal level in advancing health equity. While there is progress in the right direction, there are still structural barriers that need to be overcome and met with applicable solutions that lead to positive systemic change.

Interested in learning more about health equity? Now that you have the SHADAC Basics down, try some of the following resources to continue to expand your knowledge:

• Brief: Sexual Orientation and Gender Identity Data - New and Updated Information on Federal Guidance and Medicaid Data Collection Practices
• Brief: Minnesota’s COVID-19 vaccine campaign left vulnerable groups with lagging rates
• Annotated Bibliography: Underlying Factors of Medicaid Inequities

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.

Tatiana "Annie" Gowlovech
Research Assistant

Integrated Health Partnerships—Minnesota’s Medicaid Accountable Care Organization Model

Minnesota's Integrated Health Partnerships (IHPs) have pioneered a new approach to implementing accountable care organizations (ACOs) for the Medicaid population. Initially launched in 2013 by the Minnesota Department of Human Services (DHS), these partnerships are voluntary agreements between health provider groups and DHS. Health provider groups partner directly with DHS in multi-year contracts to both improve the quality of and reduce the total cost of care for specified patient populations using a shared savings/shared risk financing model. 

When IHPs were first established in Minnesota, there were six provider groups participating. A little more than 10 years later, the number of IHPs in Minnesota has more than tripled, with 25 total partnerships covering more than 505,000 beneficiaries, as of July 2024. 

DHS released the 2025 Request for Proposals (RFPs) (which recently closed) hoping to expand the program to more interested organizations and to allow current organizations to continue their partnership with the agency.  

In order to better understand potential expansions and changes that may be implemented through Minnesota’s IHPs, we must first understand the foundation, function, and purpose of these partnerships.

In this blog, SHADAC staff will explore:

• What is the purpose of an Accountable Care Organization (ACO)?
• How do IHPs relate to ACOs?
• How have IHPs been established in Minnesota and how have they evolved?
• How can IHPs advance health equity for included populations?
• What are some future considerations that could help in better understanding the effect of IHPs?

Keep reading to start learning about the IHP program and how it impacts communities in Minnesota.

ACOs in Medicaid

Accountable Care Organizations (ACOs) are groups of health care providers that agree to take financial responsibility for the quality and cost of care they deliver to a defined patient population. There are national, state, and regional ACO models, and they may support multiple insured populations, i.e., Medicare, Medicaid, commercial, and self-insured.

ACOs often operate alongside Managed Care Organizations (MCOs), offering additional avenues for health care reform and focusing increasingly on advancing health equity. ACOs are a pivotal tool in promoting access to care, investing in population health, and addressing social determinants of health that affect health equity.

Since the enactment of the Affordable Care Act (ACA) in 2010, the Center for Medicare and Medicaid Innovation (CMMI) has supported state initiatives like Minnesota's State Innovation Model (SIM) to test and expand ACO models within Medicaid, and Medicaid-specific ACOs have now been established in nearly a dozen states.*

These programs vary by state in terms of delivery system reform, payment expectations, and in the authorities needed to administer them, but all such Medicaid ACO alternative payment models (APMs) are standardized through classification under Categories 2, 3, and 4 of the Health Care Payment – Learning Access Network (HCP-LAN) APM framework.

IHPs are Minnesota’s Medicaid ACO model. In 2008, the Health Reform Law of Minnesota included recommendations for statewide quality reporting, payments for coordinated care services provided in health care homes (HCHs), and encouragement to participate in bundled payment demonstrations. The law was then amended in 2011 to require DHS to develop a program to “test alternative and innovative health care delivery systems, including ACOs that provide services to a specified patient population for an agreed-upon total cost of care or risk/gain sharing payment arrangement”, leading to the creation of IHPs.

In 2017, SHADAC had the opportunity to conduct an evaluation of early Minnesota IHP implementation as part of Minnesota’s SIM cooperative agreement with CMMI, which documented IHP expansion and evolution, helpful data analytics, and importance of ongoing monitoring of the effects of the IHP program on health care utilization, cost, and quality.

Minnesota's Integrated Health Partnerships Today

As noted earlier, Minnesota’s IHP program began over 10 years ago in 2013. In 2018, the program was updated with various enhancements and changes in order to place a stronger focus and effort on health equity, enhance the risk arrangement incentives, and provide a population-based payment. This led to the implementation of a new overall IHP model (Integrated Health Partnerships 2.0) with two possible tracks for Minnesota IHPs to operate under (see figure below). These two distinct organizational design tracks are tailored to both provider capabilities and population needs.

For full details and requirement on IHP design tracks, please reference the Minnesota Department of Human Services 2025 Request for Proposals for IHP Program

On top of improving care quality and reducing the total cost of care, IHPs on both tracks are also required to design interventions that address specific health disparities across their target population. IHPs in both tracks receive a quarterly risk-adjusted population-based payment (PBP) which is intended to contribute to care coordination and other investments for the population served. IHPs are also assessed based on quality, utilization, and health equity measures. This population-based payment is flexible, adjusting to reflect changing numbers of the included population specified by the IHP’s intervention plan, as well as changing risk factors due to medical and social complexities in the population makeup.**

IHPs are measured through quality and data metrics (e.g., comparing percent change between performance years), as well as a calculation of standards across five domains for those in the shared-risk model - Quality Core Set, Care for Children and Adolescents, Quality Improvement, Closing Gaps, and Equitable Care - a process which is described in further detail in the following section.

Ultimately, IHPs have proven quite successful in Minnesota, with significant savings and quality improvements. Since their inception, IHPs have yielded nearly $546 million in total savings through 2022.

Emphasis on Advancing Health Equity

In response to evolving health care needs and feedback suggesting that the timing was right to build on prior successes of the original IHP program, DHS introduced an improved program, IHP 2.0, enhancing its focus on health equity. The IHP 2.0 program, included modifications to allow the program to more directly address social determinants of health (SDOH) and incentivize partners to reduce racial, geographical, and/or other disparities.

The Minnesota Department of Health describes health equity as “addressing health disparities as part of a broad spectrum of public investments in housing, transportation, education, economic opportunity and criminal justice.” By implementing changes and integrating health equity goals into core operational frameworks, the hope with the IHP 2.0 program is to overcome the limitations of traditional health care approaches in addressing SDOH and health disparities that arise as a result. 

In fact, recent case studies on Medicaid payment reforms conducted by the Urban Institute directly referenced and cited Minnesota’s IHPs, describing their influence on health equity along with the overall changes made to the program in 2018 in order to continue to advance equity in Medicaid. 

The table below from the Urban Institute identifies and describes the core features of the IHP 2.0 program designed to both focus on and advance equity for beneficiaries.

Source: Allen & Willis, “Can Medicaid Payment and Purchasing Strategies Advance Health Equity?” The Urban Institute, December 2023, https://www.urban.org/sites/default/files/2023-12/Can%20Medicaid%20Payment%20and%20Purchasing%20Strategies%20Advance%20Health%20Equity_0.pdf

As described above, the success of IHPs is evaluated based on performance in health care quality, utilization, health equity, and total cost of care (TCOC). When monitoring quality for those in risk arrangements where quality has an impact on shared savings and losses, the quality-related assessments are organized into five domains.

As a reminder, those domains are as follows:

1. Quality Core Set
2. Care for Children and Adolescents
3. Quality Improvement
4. Closing Gaps
5. Equitable Care

Two of these domains, “Closing Gaps” and “Equitable Care,” specifically target health equity through tailored clinical and utilization measures aimed at reducing and eliminating disparities among specific Medicaid populations. IHPs can also receive ‘bonus points’ on their overall quality score for creating additional initiatives under the Quality Improvement, Closing Gaps, and Equitable Care domains.

Recent Health Equity Interventions

As a part of the enhanced focus on health equity, IHPs are encouraged to design interventions to address targeted populations’ health equity challenges. 

Target populations may differ among the IHPs, with some initiatives aimed at supporting the entire IHP patient population and some designed to serve a more specific sub-population. A summary list of common target populations of Minnesota’s 25 IHPs include:

• Children, adolescents, families, and new mothers
• Justice-involved individuals
• Patients with a mental illness or individuals living with a family member with a mental illness
• Patients that are food insecure
• Adults with substance use disorder
• Individuals experiencing challenges accessing care, including Black, Indigenous, and people of color

A summary list of common social risk factors across Minnesota’s 25 IHPs include:

• Housing instability
• Food insecurity
• Social isolation
• Transportation
• Difficulty paying bills
• Education
• Employment
• Mental health needs
• Access to care
• Language barriers
• Income
• Childcare

IHPs must first identify the population or populations that they are serving, and then they must design, develop, and implement targeted intervention efforts based on those populations, risk factors, and SDOH to advance equity for those groups. IHPs are encouraged to identify an intervention that will meet the needs of their specific population given their knowledge of their community. Examples of these efforts include community partnerships, screening initiatives, referrals to community resources or other needed programs, and care coordination for social needs. The following is a summary list of common interventions currently being used to address health equity across Minnesota’s 25 IHPs:

For a full list, the Health Equity Interventions Summary at this link provides the specific target population, social risk factors, interventions, and milestone components for each IHP in Minnesota.

Closing Thoughts

As DHS’ IHP Program continues to evolve, its focus on data analytics and population health management remains pivotal for achieving comprehensive care delivery and advancing health equity statewide. The IHP 2.0 Program exhibits the importance of translating equity-focused policy goals into actionable requirements and programs. DHS’ work to monitor and disseminate the outcomes of the health equity initiatives and interventions implemented by IHPs will continue to provide valuable insights into their effectiveness and impact on Minnesota's health care landscape. 
 
One potential dissemination method would be to make the Population Health Reports from IHPs publicly available. This would not only increase transparency around the data collected by IHPs and the lessons learned from putting interventions into practice, but also aid in our understanding of the effectiveness of IHPs in addressing SDOH, improving access to care, and eliminating health disparities. 
 
Continue learning about health equity in Medicaid and beyond with the following SHADAC products:

Notes

Walsh, Elliot
Research Dissemination Coordinator

e wals0421@umn.edu

Disability Health Care Data and Information: Resources from SHADAC

• Unfair treatment in health care settings, at work, or when applying for public benefits
• Adults with disabilities are more likely to live in poverty compared to adults with no disability
• People with a disability often have increased medical expenses, with a study from the National Disability Institute estimating that a U.S. household containing an adult with a disability must spend an estimated 28% more income to obtain the same standard of living as a household with no disability
• Those with disabilities have twice the risk of developing chronic health conditions like depression, diabetes, asthma, and poor oral health

Federal Survey Sample Size Analysis: Disability, Language, and Sexual Orientation and Gender Identity

• People who indicated sexual orientation or gender identity (SOGI)
• People with language access needs, and
• People with disabilities

Collection of Self-Reported Disability Data in Medicaid Applications: A Fifty-State Review of the Current Landscape (SHVS Brief)

States are likely undercounting the number of people with disabilities in their Medicaid programs. While some people with disabilities are covered by Medicaid as a result of disability- related eligibility, little is known about people with disabilities who are eligible on the basis of income or other factors. 

 

This State Health and Value Strategies (SHVS) Brief authored by SHADAC researchers Emily Zylla and Elizabeth Lukanen provides an overview of current disability data collection standards, including information on how states are collecting self-reported disability information on Medicaid applications. SHADAC researchers reviewed paper Medicaid applications for all 50 states and the District of Columbia alongside online applications for 36 states and the District of Columbia. 

 

Understanding and collecting better demographic data about the types of disabilities Medicaid enrollees experience can help us both identify any existing inequities and tailor possible solutions and resources appropriately. Read the brief in full here.

State Health Compare Disability Breakdowns

On SHADAC’s State Health Compare tool, users are able to examine a number of different measures related to health and health care at the state level. A number of these measures can be broken down by demographic categories, including by disability status, including:

• Health Insurance Coverage Type
• Broadband Internet Access
• Adult Unhealthy Days
• Unaffordable Rents

Data disaggregation such as this can help in identifying differences, trends, and similarities between different groups and demographics. See an example of what you can do with this data below, where we breakdown the measure ‘Adult Unhealthy Days’ by Disability Status.

Explore all of the measures including each of their available breakdowns at this link.

Housing Affordability Matters: Unaffordable Rents Infographics Updated with 2022 Data

Using data from the American Community Survey (ACS) in the ‘Unaffordable Rents’ measure on State Health Compare, we created a blog plus six accompanying infographics containing information on the connection between affordable housing and health. Within these publications, we included specific information on housing affordability for households with a person with a disability alongside all rental households to understand any differences in unaffordable rents between demographic groups.

 

For example, in the United States as a whole, 47.3% of rental households had unaffordable rents in 2022. In rental households that include a person that has a disability, that jumps to 54% had unaffordable rents in 2022. 

 

These resources also present data on other demographic groups including Medicaid enrollees, people of color, and household incomes less than 25,000 annually.

 

 

You can find each of the infographics created for the US and five individual states along with an explanatory blog on the topic here.

Minnesota Community and Uninsured Profile

This resource, originally funded by the Blue Cross Blue Shield of Minnesota Foundation, provides users with a tool that provides rates and counts of uninsured Minnesotans at various levels (ZIP code, county, MNSure rating area, state, etc.). The profile then can provide further breakdowns by a number of demographic factors, including by age, income, educational attainment, and disability status.  

 

Explore the profile here.

 

While this profile currently represents communities and uninsured populations from the single state of Minnesota, SHADAC researchers can provide additional technical assistance, profile customization, and analysis to other organizations and groups upon request.

 

If you or your organization is interested in working with this profile, or creating one relevant to you or your needs, don’t hesitate to reach out at shadac@umn.edu or on our contact page here.

Stay Informed on Disability Health Data Resources and Information

The resources and products above represent just some of the work SHADAC is doing that we hope can help advance health equity. Examining different demographic groups, identities, and experiences helps researchers, policymakers, and other stakeholders understand the challenges and barriers different people face – and, hopefully, use that information to tailor health equity work towards those differing needs in the future. 

 

You can stay up to date on our latest releases and updates by following us on LinkedIn, or by signing up for our newsletter.

 

Continue your journey to understand health disparities and health equity with the following products:

• Medicaid Equity Monitoring Tool Project
• LGBT Health Equity: Sexual Orientation and Gender Identity Data Resources from SHADAC
• Provider Discrimination by Sexual Orientation Among Cisgender and Transgender/Nonbinary Adults in Minnesota