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.

Minnesota Health Access Survey 2021 Technical Report

This report describes the Minnesota Health Access Survey (MNHA) data collection process and methodology, emphasizing the most recent administration of the survey completed in 2021. The 2021 MNHA represents the first time using a single address based (ABS) frame. 

Overview of MNHA

The Minnesota Health Access Survey (MNHA) is a biennial survey of non-institutionalized Minnesota residents. The survey collects detailed information on health insurance coverage options, access to coverage and health care services, and basic demographic data. The goal of the survey is to document trends in health insurance coverage, and access to insurance and health care at the state and regional level, as well as for select subpopulations (e.g., rural, low-income families, populations of color and American Indians). The MNHA represents a partnership between the Minnesota Department of Health (MDH) Health Economics Program and the University of Minnesota’s State Health Access Data Assistance Center (SHADAC). 

The MNHA data play an important role in monitoring trends in health insurance coverage, evaluating and informing health policy development in Minnesota on topics such as affordability of coverage, access to healthcare, and redesign of public program coverage. The MNHA provides precise and timely estimates on a range of coverage and access relevant questions, is adaptable and responsive to developing state health policy issues, and ensures the availability of micro-data for time sensitive research and policy analysis.

The MNHA has been conducted a number of times over the years: in 1990, 1995, 1999, 2001, 2004, and every two years beginning in 2007.¹ This technical report focuses primarily on the 2021 MNHA, providing some cumulative data in table form.²

 

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1 Beginning in 2007, MNHA funding is from a legislative appropriation to the Minnesota Department of Health and additional support from the Minnesota Department of Human Services since 2011.

2 For information about earlier versions of the MNHA contact Kathleen Thiede Call at callx001@umn.edu and the Health Economics Program at health.mnha@state.mn.us.

SHADAC Staff and External Authors

p 612-624-4802
e shadac@umn.edu

Examining Gender-Based Discrimination in Health Care Access by Gender Identity in Minnesota

Authors: Jeremy Duval, Natalie Mac Arthur, Kathleen Call

Introduction

Many barriers exist to creating an equitable health care experience for LGBTQ+ individuals. One critical barrier is gender-based discrimination from providers within health care systems. The biennial 2021 Minnesota Health Access Survey (MNHA) asked respondents how often their gender, sexual orientation, gender identity, or gender expression causes health care providers to treat them unfairly. We compared rates of gender-based discrimination and health care access in the Minnesota adult population and examined differences in access to care among cisgender (cis) and gender minorities who report gender-based discrimination (see Definition Box). We explored the impact of gender-based discrimination on health care access by comparing access rates among people who did and did not experience discrimination for cis men, cis women, transgender and non-binary populations in Minnesota. 

Results

The majority (58.9%) of transgender (trans) and non-binary respondents reported experiencing gender-based discrimination from health care providers in 2021—a stark contrast from the statewide average of 6.0% (Figure 1). Cis women also reported gender-based discrimination (7.7%) above the population average, while cis men were less likely to experience this form of discrimination (2.9%). Gender-based discrimination was especially high for both non-binary (63.9%) and trans (48.8%) respondents. Due to sample size limitations, these populations were combined in the remainder of our analyses.

Figure 1. Unfair treatment from health care providers based on gender in Minnesota


^ Rate significantly different from All Adults at the 95% confidence level.
Source: SHADAC analysis of the 2021 Minnesota Health Access Survey.

We also found differences in health care access among trans and non-binary people compared with the adult Minnesota population, particularly for confidence in getting care and forgoing needed care due to cost (Figure 2). We found that trans and non-binary respondents were similar to cis men in rates of having a usual source of care and having a medical visit (non-emergency) in the past year. Compared with the adult population in Minnesota, cis men were more likely to lack these forms of care, while cis women had better access to regular medical visits and a usual source of care. However, differences from the state average did not reach significance for trans and non-binary respondents, likely due to small sample size. Nearly a third (30.1%) of trans and non-binary adults had low confidence in getting necessary care compared to an average of 11.8% for adults in Minnesota. Over half (57.1%) of trans and non-binary people reported forgone care—more than double the average (26.2%).

Figure 2. Health care use and barriers to care

^ Rate significantly different from All Adults at the 95% confidence level.
† Estimate may be unreliable due to limited data (relative standard error greater than or equal to 30%).
Source: SHADAC analysis of the 2021 Minnesota Health Access Survey.

Figure 3. Experiences of gender-based discrimination associated with barriers to health care access

* Significant difference within a given subpopulation between rates of people who experienced unfair treatment and those who did not.
† Estimate may be unreliable due to limited data (relative standard error greater than or equal to 30%).
Source: SHADAC analysis of the 2021 Minnesota Health Access Survey.

Discussion

A worryingly high proportion of trans and non-binary adults reported gender-based discrimination and had forgone care or did not have confidence in getting needed care. This lack of confidence could be in part due to experienced or anticipated discrimination within a health care setting. Barriers to care were especially high among those who had experienced gender-based discrimination, which suggests that discrimination has a serious negative impact on health care access for trans and non-binary people.

We found higher rates of gender-based discrimination (58.9%) among trans and non-binary adults in Minnesota in 2021 compared to previously published literature on gender-based discrimination. National data indicate that between 20% and 40% of LGBTQ+ Americans experience discrimination while accessing health services (Kattari & Hasche, 2016), (Kachen & Pharr, 2020), (Penrose et al, 2020), (Rodriguez, Agardh & Asamoah, 2018), (Shires & Jaffee, 2015). Additionally, over 20% of the LGBTQ+ population avoided seeking health care due to anticipated discrimination (Kcomt et al, 2020). Notably, the majority of previously published estimates of trans peoples’ experiences of health care discrimination come from the National Transgender Discrimination Survey, which was conducted in 2016 and provides rich data, but for a specialized and non-probability sample. Such data are not considered generalizable. A strength of the MNHA survey is that it measures discrimination using a probability sample of adults reporting their gender identity.

Gender-based discrimination is just one factor affecting health care access. Gender minorities may additionally face disproportionate rates of other key barriers to access, such as lack of insurance coverage (Gonzales & Henning-Smith, 2017). Regardless, we found large gaps in health care access for trans people, non-binary people, and all people who experienced gender-based discrimination.

Conclusion

When the majority of a population is experiencing discrimination within health care systems, it is clear that change is necessary. Our data, based on a probability sample of Minnesotans, helps address gaps in knowledge about barriers transgender and non-binary adults face in accessing health care. The high rates of gender-based discrimination among gender minorities illustrate that gender-inclusive data collection is important for health equity.

However, quantifying rates of discrimination only scratches the surface of the true problem. Because of the limited sample size of gender minority adults, we were unable to explore the role of other social factors in gender-based discrimination and health care barriers by gender identity. Race, ethnicity, and class likely intersect to exacerbate experiences of discrimination and barriers to care for gender minorities. For example, Black and American Indian/Alaskan Native transgender women face disproportionate rates of victimization, and these experiences may impact their health care needs and intensify barriers to accessing care (Reisner, 2018). In this analysis, we only looked at gender identity; our future studies will look at sexual orientation with a similar lens and examine these two together.

One of the largest barriers to understanding discrimination and its effects on health access is data collection. Not all surveys collect and report inclusive data on gender identity, which makes it very hard to track access for trans and non-binary people. Even when gender-inclusive data are available, gender-based discrimination is rarely measured. Direct measurement of discrimination is essential for monitoring rates of discrimination in health care settings and associated barriers to care (Lett et al., 2022).

In Minnesota, state-level policies make this type of measurement possible. Historically, Minnesota has strong anti-discrimination laws in place to protect gender-diverse individuals. For example, Minnesota was one of the first states to allow an “X” option for gender on licenses (Walsh, 2018). Yet, we found alarmingly high reports of gender-based discrimination in health care among gender minorities in this state. The level of gender-based discrimination may be even higher in other states with less inclusive policies. Consequently, our results suggest that on a national level, gender-based discrimination in health care may affect a substantial number of Americans.

Understanding the full scope of gender-based discrimination in Minnesota and across the U.S. should be a priority in future research to support health equity. Our data contributes to the base of knowledge regarding gender-based discrimination in health care and its correlation with issues of health care access. Our findings highlight the need for more expansive research and policy changes in these areas.

Methods

Data are from the 2021 Minnesota Health Access (MNHA) survey, which is a biennial population-based survey on health insurance coverage and access conducted in collaboration with the Minnesota Department of Health. We limited the analysis to adults responding for themselves about experiences of discrimination and access (n=10,003); we excluded proxy reports (e.g., a household member answering for a spouse or roommate). Tests for statistical significance were conducted at the 95% confidence level.

Check out our companion blog "Examining Discrimination and Health Care Access by Sexual Orientation in Minnesota". 

References

Gonzales, G., & Henning-Smith, C. (2017). Barriers to Care Among Transgender and Gender Nonconforming Adults. The Milbank quarterly, 95(4), 726–748. https://doi.org/10.1111/1468-0009.12297

Kachen, A., & Pharr, J. R. (2020). Health Care Access and Utilization by Transgender Populations: A United States Transgender Survey Study. Transgender health, 5(3), 141–148. https://doi.org/10.1089/trgh.2020.0017

Kattari, S. K., & Hasche, L. (2016). Differences Across Age Groups in Transgender and Gender Non-Conforming People's Experiences of Health Care Discrimination, Harassment, and Victimization. Journal of aging and health, 28(2), 285–306. https://doi.org/10.1177/0898264315590228  

Lett E., Asabor E., Beltrán S., Cannon A.M., Arah O.A. (2022). Conceptualizing, Contextualizing, and Operationalizing Race in Quantitative Health Sciences Research. Ann Fam Med 20(2):157-163. https://doi.org/10.1370/afm.2792

Movement Advancement Project. "Equality Maps: Housing Nondiscrimination Laws." https://www.lgbtmap.org/equality-maps/non_discrimination_laws/housing. Accessed 11/07/2022.

Reisner, S. L., Bailey, Z., & Sevelius, J. (2014). Racial/ethnic disparities in history of incarceration, experiences of victimization, and associated health indicators among transgender women in the US. Women & health, 54(8), 750-767.

Shires, D. A., & Jaffee, K. (2015). Factors associated with health care discrimination experiences among a national sample of female-to-male transgender individuals. Health & social work, 40(2), 134–141. https://doi.org/10.1093/hsw/hlv025

Walsh, P. (2018, October 3). Minnesota Now Offers 'X' for Gender Option on Driver's Licenses. Star Tribune. Retrieved November 9, 2022, from https://www.startribune.com/minnesota-now-offers-x-for-gender-option-on-driver-s-licenses/494909961/.

Minnesota Health Access Survey 2019 Technical Report

June 2021:

This report describes the data collection process and methodology behind the Minnesota Health Access Survey (MNHA), emphasizing the most recent administration of the survey completed in 2019. The 2019 MNHA represents a break in series, involving for the first time the blending of two sample frames: the introduction of an address based frame and retention of a smaller dual landline/cell telephone frame used in all past MNHA surveys. This report describes this decision and the upshot for other aspects of the methodology.

Overview of MNHA

The Minnesota Health Access Survey (MNHA) is a biennial survey of non-institutionalized Minnesota residents. The survey collects detailed information on health insurance coverage options, access to coverage and health care services, and basic demographic data. The goal of the survey is to document trends in health insurance coverage, and access to insurance and health care at the state and regional level, as well as for select subpopulations (e.g., rural, low-income families, populations of color and American Indians). The MNHA represents a partnership between the Minnesota Department of Health (MDH) Health Economics Program and the University of Minnesota’s State Health Access Data Assistance Center (SHADAC). 

The MNHA data play an important role in monitoring trends in health insurance coverage, evaluating and informing health policy development in Minnesota on topics such as affordability of coverage, access to healthcare, and redesign of public program coverage. The MNHA provides precise and timely estimates on a range of coverage and access relevant questions, is adaptable and responsive to developing state health policy issues, and ensures the availability of micro-data for time sensitive research and policy analysis.

The MNHA has been conducted a number of times over the years: in 1990, 1995, 1999, 2001, 2004, and every two years beginning in 2007.¹ This technical report focuses primarily on the 2019 MNHA, providing some cumulative data in table form.²

 

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1 Beginning in 2007, MNHA funding is from a legislative appropriation to the Minnesota Department of Health and additional support from the Minnesota Department of Human Services since 2011.

2 For information about earlier versions of the MNHA contact Kathleen Thiede Call at callx001@umn.edu and the Health Economics Program at health.mnha@state.mn.us.

SHADAC Staff

p 612.624.4802
e shadac@umn.edu

Minnesota Health Access Survey (MNHA) Data Show Minimal Impact from COVID Pandemic on 2020 Insurance Coverage

A new report released by the Minnesota Department of Health (MDH) shows that Minnesota’s uninsured rate for the first half of 2020—as well as the first known period of the coronavirus pandemic—was relatively unchanged from the fall of 2019, at 4.6% and 4.7%, respectively. The report uses data from the biennial Minnesota Health Access Survey (MNHA), conducted collaboratively between the Minnesota Department of Health (MDH) and the State Health Access Data Assistance Center (SHADAC), to examine changes in health insurance coverage across the state from 2017 to 2019, as well as preliminary coverage estimates from 2020 to measure changes to the insurance landscapr from fall 2019 to spring and summer of 2020.

Pre-Pandemic Coverage Conditions

Prior to the COVID-19 pandemic, the state of Minnesota was enjoying historic levels of health insurance coverage among the overall population. From 2017, the last time the MNHA Survey was conducted, statewide coverage grew significantly to 95.3% in 2019, propelled by gains in employer-sponsored insurance coverage (51.9% in 2017 to 53.6% in 2019), relatively unchanging numbers of public health insurance coverage enrollment, and stabilization of the individual market.

Impacts on Health Insurance Coverage, 2019 to 2020

Using data from the 2019 Minnesota Health Access Survey and the 2020 Health Insurance Enrollment Survey, the report looks at the changes wrought by the COVID-19 pandemic on the insurance coverage landscape from October 2019 to April 2020 and October 2019 to July 2020.

From October 2019…

• …the number of uninsured individuals increased by 600 people in April 2020 and 5,800 people in July 2020.
• …enrollment in public coverage increased by 16,000 individuals in April 2020 and 60,500 individuals in July 2020.
• …enrollment in private insurance fell for both group (i.e., employer-sponsored) coverage—14,400 by April 2020 and 40,100 by July 2020—and individual coverage—15,600 by April 2020 and 13,000 by July 2020.

Health Insurance Coverage Costs, 2017 to 2019

Regardless of the pandemic, a frequently cited issue among Minnesotans with health insurance coverage was the high cost of care, which led to issues of both foregone care as well as reports of trouble paying medical bills, or trouble paying other basic bills (food, housing, etc.) due to high medical bills.

Estimates from the MNHA survey revealed that:

• 25.0% of Minnesotans overall reported having to forgo needed health care due to cost (up from 21.1% in 2017)
• 27.1% of Minnesotans with public insurance reported having to forgo needed health care due to cost (up from 23.7% in 2017)
• 21.0% of Minnesotans with group coverage reported having to forgo needed health care due to cost (up from 16.5% in 2017)

Estimates from the MNHA survey revealed that when it came to experiencing financial burden due to medical bills:

• 22.0% of Minnesotans overall reported experiencing high medical burden (up from 19.8% in 2017)
• 22.5% of Minnesotans with group coverage reported experiencing high medical burden (up from 19.9% in 2017)

Read the full brief detailing these findings, from SHADAC and the Minnesota Department of Health’s Health Economics Program.