Walsh, Elliot
Research Dissemination Coordinator

e wals0421@umn.edu

Housing Affordability Matters: Unaffordable Rents Infographics Updated with 2022 Data

• By 21% in the West
• By 20% in the South
• By 18% in the Midwest
• By 12% in the Northeast

Affordable Housing and Health: What Is the Connection?

• Access to health care services (or lack of access)
• Education
• Transportation
• Cost of services (healthcare, childcare, utilities, etc.)
• Community support
• Employment opportunities
• Food type, cost, and availability

Housing Affordability Infographics

United States	California	Florida

Nevada	New Jersey	Texas

Explore More Data on State Health Compare

• Child Vaccinations
• Public Health Funding
• Adverse Childhood Experiences

State Medicaid Member Experience Qualitative Data Scan

As part of the ongoing work in Phase 2 of the Medicaid Equity Monitoring Tool project, SHADAC researchers have been working to understand what available data sources and measures currently exist that could be useful in populating a potential Medicaid Equity Monitoring Tool. 

In Phase 1, the project team conceptualized a high-level wireframe, displaying what such a tool could look like, and how measures could be included and categorized. Currently, five general categories have been proposed - State Medicaid Member Experience, Health Outcomes, Medicaid Program Actions, Demographics, and Underlying Factors - further stratified into various domains, under which individual measures could be housed.

In the case of State Medicaid Member Experience, the category of focus for SHADAC’s initial data scan, four domains have been proposed:

• Accessing Needed Services
• Member-Reported Health and Well Being
• Satisfaction with Services and Providers
• Seeking and Keeping Coverage

In starting the initial data scan process, SHADAC has already faced a number of critical decision points – first and foremost, where to begin.

With five main categories and a multiplicity of potential domains under each, there were numerous points of entry into a process we hoped would help to gain a better understanding of the potential measures currently available (as well as a process to identity gaps in such data collection) to populate a tool.

After deciding to start with the Medicaid Member Experience category, a secondary decision split the data scan process into two parts, quantitative and qualitative.¹ A resource documenting the quantitative data scan's process, findings, and further recommendations was published in January 2024. This new resource examines the qualitative aspects of Member Experience through identification of related research studies.

Top-level findings reveal that there are few sources documenting qualitative data collection methods regarding the experiences of Medicaid enrollees – our scan found just 21 – even fewer that focus specifically on our project’s 15 identified subpopulations of interest, and even fewer studies conducted over extended periods of time (2).

In addition to a summation of the scan results, the resource also contains an appendix table that crosswalks each source to all relevant measurement domains (e.g., Accessing Needed Services, Member-reported Health and Well Being, Satisfaction with Services and Providers, Seeking and Keeping Coverage, and Discrimination), provides relevant quotes from either Medicaid members themselves or their representatives, and includes population demographics such as race/ethnicity, disability status, and geographic location, among others.

Click on the image above to access the full report, or access it at this link.

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[1] For more on the decision-making process regarding the initial data scan, please see the State Medicaid Member Experience Quantitative Data Scan page.

State Medicaid Member Experience Quantitative Data Scan

As part of the ongoing work in Phase 2 of the Medicaid Equity Monitoring Tool project, SHADAC researchers have been working to understand what available data measures currently exist that could be useful in populating a potential Medicaid Equity Monitoring Tool.

In Phase 1, the project team conceptualized a high-level wireframe, displaying what such a tool could look like, and how measures could be included and categorized. Currently, five general categories have been proposed: State Medicaid Member Experience, Health Outcomes, Medicaid Program Actions, Demographics, and Underlying Factors. These would then be further stratified into varying numbers of domains, under which individual measures could be housed.

To begin a process as large in scope as populating such a data tool, SHADAC decided to narrow our focus and initially concentrate on one potential category area – Medicaid Member Experience. A variety of reasoning went into the decision to start with this category. 

• First, since centering the Medicaid member has been, and continues to be, a pivotal focus of all MET project work, so, too, should attempting to understand and represent their experiences be at the center of our measurement data scan and tool population work. We also believe that these data would be the most important in understanding how meaningful these measures are to the Medicaid community members themselves.
• Second, because of SHADAC’s extensive background and familiarity working with a variety of federal surveys and other data sources that support state estimates, we knew where to begin in examining data availability and completeness for existing quantitative data.

After an extensive data and measurement scan focused on Medicaid member experience, SHADAC researchers have compiled their findings thus far into a new resource. This resource highlights a number of key data sources that hold the greatest potential for measuring population in this area, including the Behavioral Risk Factor Surveillance System (BRFSS), the National Survey of Children’s Health (NSCH), the Survey of Income and Program Participation (SIPP), the Pregnancy Risk Assessment Monitoring System (PRAMS), and National Core Indicators – Aging and Disabilities and National Core Indicators – Intellectual and Developmental Disabilities (NCI-AD/NCI-IDD).

Not only that, but this resource also points to critical gaps in availability and existence of data sources and data measures themselves, including highlighting the lack of data stratification for key demographic groups. The resource also details potential measures that would benefit such a tool but are not currently part of any known data collection efforts. Additionally, SHADAC researchers outline several proposals for addressing these gaps with suggested improvements for current data collection efforts/programs alongside proposals for new data collection methods.

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

October 2023:

The Medicaid and CHIP Payment and Access Commission contracted with the State Health Access Data Assistance Center (SHADAC) to conduct a review and sample size analysis of 13 federal population health surveys to assess the quality and availability of data for certain populations facing health inequities, including people with disabilities, language access needs, and people who indicate their sexual orientation or gender identity (SOGI). This brief provides an overview of each federal survey and identifies which surveys included questions about disability, primary language, limited English proficiency (LEP), and sexual orientation and gender identity (SOGI). Below is an overview table of the Federal surveys included in this analysis. 

Federal Survey	Periodicity	Target Population	States, DC, and Territories Represented	Most Recent Data Year	Total Population Unweighted Sample Size	Total Unweighted Sample Size for Medicaid 1
ACS	Annual	Civilian, non-institutionalized U.S. population	50 states, D.C., Puerto Rico	2021	3,252,599	434,900
BRFSS	Annual	U.S. adults > 18 years	49 states (no Florida in 2021), D.C., Guam, Puerto Rico, Virgin Islands	2021	438,693	50,306
CPS	Annual	Civilian, non-institutionalized U.S. population	50 states, D.C.	2022	152,732	24,155
HPS	Panel	U.S. adults > 18 years	50 states, D.C.	2023	59,290	2,764
MEPS-HC2	Panel	Civilian, non-institutionalized U.S. population	No state estimates	2020	27,805	6,504
MCBS3	Annual	All individuals enrolled in Medicare	No state estimates	2020	13,916	2,593
NAMCAHPS4	Fielded once	Adults enrolled in Medicaid	50 states, D.C.	2014	272,679	272,679
NHANES	Periodic	Civilian, non-institutionalized U.S. population	No state estimates	20205	9,254	2,841
NHIS	Annual	Civilian, non-institutionalized U.S. population	50 states, D.C. when two years combined in the RDC	2022	35,115	4,918
NSCH6	Annual	U.S. children < 18 years	50 states, D.C. when two years combined	2021	50,892	11,259
NSDUH	Annual	Civilian, non-institutionalized U.S. population, age 12 +	No state estimates	2021	58,034	11,008
PRAMS	Annual	Pregnant women in 47 states, D.C., and Puerto Rico	See target population	2021	NA	NA
SIPP7	Panel	Civilian, non-institutionalized U.S. population	50 states, D.C.	2022	40,510	5,835

Notes: ACS is American Community Survey. BRFSS is Behavioral Risk Factor Surveillance System. CPS is Current Population Survey. HPS is Household Pulse Survey. MEPS-HC is Medical Expenditure Panel Survey-Household Component. MCBS is Medicare Current Beneficiary Survey. NAMCAHPS is Nationwide Adult Medicaid Consumer Assessment of Healthcare Providers & Systems. NHANES is National Health and Nutrition Examination Survey. NHIS is National Health Interview Survey. NSCH is National Survey of Children’s Health. NSDUH is National Survey on Drug Use and Health. PRAMS is Pregnancy Risk Assessment Monitoring System. SIPP is Survey of Income and Program Participation. DC is District of Columbia.  RDC is Research Data Center.  NA is not available.
1 Unless otherwise noted, where relevant, Medicaid was defined using the typical MACPAC hierarchy:
                (1) Medicare; (2) Private with no Medicare; (3) Medicaid with no Medicare or private; (4) Other type of insurance with no Medicare or private; (5) Uninsured all year
2 Medicaid coverage was defined based on the most recent date of data collection (e.g., December).
3 MCBS data uses “full year dual” to define Medicaid, meaning people who were enrolled in Medicare and Medicaid all year.
4 NAMCAHPS limited to adults enrolled in Medicaid.
5 Sample size analysis conducted using 2017-2018 file, because of pandemic impacts on data collection in 2020.
6 The NSCH includes three categories of children’s coverage (public, private, uninsured); we code children in public coverage as having Medicaid, because Medicare is much less common among children.
7 SIPP data uses the annual coverage variables to define Medicaid.
Source: SHADAC analysis of federal survey data.

The findings show that the majority of surveys include questions to identify individuals with a functional disability. Still, fewer than half of these surveys include questions about intellectual disability, serious mental illness, primary language, LEP, and SOGI. Of the surveys that include these questions, the majority have a sufficient sample for reporting about Medicaid-covered individuals for each demographic group.

SHADAC Staff

p 612.624.4802
e shadac@umn.edu

Examining Discrimination and Health Care Access by Sexual Orientation in Minnesota

Authors: Natalie Mac Arthur, Jeremy Duval, Kathleen Call

Introduction

Discrimination based on sexual orientation and gender identity (SOGI) from health care providers is a barrier to creating an equitable health care system. Nearly one in five lesbian, gay, bisexual, transgender, and queer (LBGTQ) adults reports avoiding health care due to anticipated discrimination (Casey et al., 2019). Compared with straight adults, lesbian/gay and bisexual adults are more likely to forgo or delay health care (Jackson et al., 2016, Nguyen et al., 2018). However, less is known about the association between reports of SOGI-based discrimination from health care providers and health care access.

We included three sexual orientation categories in this study: straight, lesbian/gay, and bisexual/pansexual. Survey respondents also had the option to select “none of these” and write in their own response. Due to sample size limitations, we excluded observations with responses that we could not recode to the existing categories. We tabulated SOGI-based discrimination and four measures of health care access by sexual orientation for adults in Minnesota. We also examined differences in health care access for respondents who did and did not report discrimination. 

Results

Reports of discrimination from health care providers based on SOGI were significantly higher among lesbian/gay (36.1%) and bisexual/pansexual (26.1%) populations compared with the state average of 6% (Figure 1). Sexual minorities were also more likely to report barriers to health care access when compared with all adults in Minnesota (Figure 2). Low confidence in getting needed health care was significantly above the state average (11.8%) for people who identify as bisexual/pansexual (30.6%). Statewide, over a quarter of adults reported forgone care due to costs (26.2%), which included routine medical care, prescription drugs, dental care, specialists, and mental health care. Rates of forgone care were significantly higher for people who identify as lesbian/gay (45.5%) or bisexual/pansexual (41.9%).

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

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

Figure 2. Health care access 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.

We found that Minnesotans who experienced SOGI-based discrimination were more likely to have low confidence in getting care and forgone care compared to those who did not experience discrimination (Figure 3). People who experienced discrimination had elevated barriers across all population groups including people identifying as straight, lesbian/gay, or bisexual/pansexual. However, low confidence in care was highest among bisexual/pansexual adults who reported SOGI-based discrimination (39.4%). Half of all adults with SOGI-based discrimination reported forgone care due to costs, while about two-thirds of bisexual/pansexual (69.0%) and lesbian/gay (66.1%) adults who reported SOGI-based discrimination had forgone care.

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

Within the health care setting, discrimination based on SOGI was prevalent among lesbian/gay and bisexual/pansexual adults. SOGI-based discrimination from health care providers was reported by over a third of lesbian/gay adults in Minnesota and over a quarter of bisexual/pansexual adults. Barriers to health care access, including low confidence in getting care and forgone care, were also high among lesbian/gay and bisexual/pansexual adults compared with the average rates seen among adults in Minnesota. Further, reports of SOGI-based discrimination correlated with even higher rates of barriers to access among lesbian/gay and bisexual/pansexual adults; a majority of these populations who reported discrimination also had forgone health care due to costs.

Discrimination by health care providers has substantial clinical implications. Across populations, discrimination negatively affects mental and physical health (Pascoe and Richman, 2009). Compared with straight adults, lesbian/gay and bisexual adults experience health disparities including mental and physical health, activity limitations, and chronic conditions (Gonzales and Henning-Smith, 2017). For LBGTQ adults, both discrimination and barriers to health care are associated with worse mental health, behavioral health, and health-related quality of life (Lee 2016 et al., Jung et al., 2023). One recent study suggests that delayed health care partially mediates the connection between discrimination and worse health status among LBGTQ women (Scott et al., 2022). Our work contributes evidence linking provider discrimination to forgone health care and lack of confidence in getting care.

The clinical impact of discrimination is likely to vary across the life course and across the spectrum of intersectional identities including LBGTQ and race/ethnicity. Compared with lesbians, bisexual women are more likely to report poor physical and mental health and disabilities; both groups of women face higher risks than straight women (Fredriksen-Goldsen 2023). Gay Black and Hispanic men face greater barriers to health care access than gay white men (Hsieh et al., 2017). Among older adults, one survey found that nearly four out of five LBGTQ people anticipate encountering discrimination in long-term care services (Dickson et al., 2022).

Differences in health care access and socioeconomic resources may exacerbate the influence of provider discrimination on health outcomes. Although studies have found that delays in health care among lesbian/gay and bisexual adults persist even with insurance coverage, their coverage may not provide comparable affordability of health care relative to straight adults (Jackson et al., 2016, Nguyen et al., 2018,Tabaac et al., 2020). Lesbian/gay and bisexual adults are less likely to have private coverage and more likely to have purchased a plan from the individual market, which may have higher premiums and deductibles. Furthermore, they are also more likely to experience lapses in coverage. These studies indicate that both cost concerns and previous bad health care experiences contribute to delays in care. Our results add to the growing body of literature documenting high rates of forgone care due to cost for lesbian/gay and bisexual/pansexual adults. Additionally, we document lack of confidence in getting health care among these populations and greater barriers to access among those who reported SOGI-based discrimination from a health care provider.

Conclusion

Reports of discrimination among lesbian/gay and bisexual/pansexual Minnesotans are troubling and require a response. The Affordable Care Act, which expanded Medicaid in willing states, also expanded non-discrimination protections based on sexual orientation and gender identity (KFF, 2014). However, these protections are limited in promoting health care access. Relative to other states, Minnesota offers a robust Medicaid program. Barriers to access may be even higher for LBGTQ people in states that did not expand Medicaid and states with fewer protective non-discrimination laws. Socioeconomic policies at the federal and state level are important for addressing gaps in health equity for many members of the LBGTQ community.

Greater availability of data including SOGI measures would strengthen efforts to better understand and address the health care needs of LBGTQ populations (SHADAC, 2021). Direct measures of discrimination are also important to monitor progress in providing equitable access to health care services (Lett et al., 2022). Ongoing research is needed to improve health equity and address barriers to health care for LBGTQ populations.

Check out our companion blog "Examining Gender-Based Discrimination in Health Care Access by Gender Identity in Minnesota". 

References