Advancing Health Justice Using Medicaid Data: Key Lessons from Minnesota for the Nation AcademyHealth in partnership with the Disability Policy Consortium (DPC) Authors: Ellen Breslin, M.P.P. (HMA); Dennis Heaphy, M.Div. M.Ed. M.P.H. (DPC); Tony Dreyfus, M.C.P. (Independent); Anissa Lambertino, Ph.D. (HMA); Jeff Schiff, M.D., M.B.A. (AcademyHealth) Contributors: Susan Kennedy, M.P.P., M.S.W. (AcademyHealth); Sunita Krishnan, M.P.H. (AcademyHealth); Kelsi Jackson, M.P.H. (HMA) January 2021 Authors and Contributors About AcademyHealth national health equity committees, and is engaged in action AcademyHealth is a leading national organization serving research projects led by persons with disabilities. the fields of health services and policy research and the Tony Dreyfus, M.C.P., is a health care consultant, with professionals who produce and use this important work. a focus on Medicaid, chronic illness, risk adjustment and Together with our members, we offer programs and health disparities. Dreyfus has collaborated with Health services that support the development and use of rigorous, Management Associates on several projects including relevant and timely evidence to increase the quality, helping to design the analysis of health disparities in Medicaid accessibility, and value of health care, to reduce disparities, populations. Dreyfus was the lead analyst for the Chronic and to improve health. A trusted broker of information, Illness and Disability Payment system, a diagnosis-based risk AcademyHealth brings stakeholders together to address adjustment system used by many state Medicaid programs. the current and future needs of an evolving health system, inform health policy, and translate evidence into action. Anissa Lambertino, Ph.D., is a senior consultant with Learn more at www.academyhealth.org and follow us on Health Management Associates, Chicago office. She is a Twitter @AcademyHealth. former National Institute of Occupational Safety and Health Trainee and served as a data analyst at the University About Disability Policy Consortium of Illinois at Chicago's Department of Epidemiology and The Disability Policy Consortium (DPC) is a Biostatistics. At HMA, Anissa has served as the data and Massachusetts-based cross-disability advocacy and action geographic information system (GIS) analyst for projects research organization whose mission is to advance the civil focused on demographic projections, chronic disease rights of all people with disabilities. prevalence, mortality rates, social determinants of health, health equity, and health care access among Medicaid About the Authors populations. Ellen Breslin, M.P.P., is a principal with Health Jeff Schiff, M.D., M.B.A., is a Senior Scholar at Management Associates, Boston office. Her work focuses AcademyHealth and a health services policy consultant on Medicaid policies, payments, and integrated care focusing on improving the outcomes for those with limited programs to address the needs of all populations including resources. His work includes the use of policy and the dually eligible individuals. Ellen has over 30 years of implementation of quality improvement to fundamentally experience in health care at the state and national levels. change health care systems. He served as the Chief Ellen is a former principal analyst with the Congressional Medical Officer for the Minnesota Department of Human Budget Office, a former director of managed care Services (including the state's Medicaid program) from payment and analysis for the MassHealth program, and 2006 to 2019. He sponsored the work in Minnesota former health and human services budget analyst for the highlighted in this report. He is a past chair of the Medicaid Massachusetts House Ways and Means. Prior to HMA, Medical Directors national network and a past president Ellen was an independent health care consultant. Ellen is of the Minnesota Chapter of the American Academy of a Board member of the Massachusetts Disability Policy Pediatrics. He is a pediatrician with a subspecialty in Consortium. pediatric emergency medicine. Dennis Heaphy, M.Div. M.Ed. M.P.H., is a health justice policy analyst and researcher with the Massachusetts About the Contributors Disability Policy Consortium. His work centers on promoting Kelsi Jackson, M.P.H., is a consultant with Health meaningful community integration and Long-Term Service Management Associates, Atlanta office. Kelsi uses her and Supports policies that advance the dignity and civil public health training to apply an equity lens to myriad rights of persons with disabilities. Dennis is committed projects, ranging from evaluating small programs run by to ensuring health equity policies address intersectional community-based organizations to analyzing Medicaid and barriers to health and wellness rooted in discrimination and Medicare data for large health plans. She was previously a bias experienced by persons within BIPOC populations. research assistant at a small consulting firm in Atlanta and Dennis serves on several Massachusetts health policy and the American Cancer Society. 2 Acknowledgments We would like to thank everyone who provided support KEY REPORTS ON MINNESOTA for this report. We are grateful for the support of MEDICAID'S APPROACH TO AcademyHealth. We would like to thank Justine Nelson MEASURING HEALTH DISPARITIES PhD and the team at the Minnesota Department of Human Medicaid and Social Determinants of Health: Services (DHS) for its leadership in measuring health Adjusting Payment and Measuring Health disparities for Medicaid populations. Outcomes. State Health Value Strategies. Issue Brief, July 2017. Breslin, Lambertino, Dreyfus, We are also very grateful to our reviewers for their Heaphy. Available at: valuable input and comments. We wish to thank the https://www.shvs.org/wp- following people: Dr. Nathan Chomilo, the state's medical content/uploads/2017/07/SHVS_ director of Medicaid, Minnesota Department of Human SocialDeterminants_HMA_July2017.pdf Services; Justine Nelson, Ph.D., research scientist, Population Health Innovation, Minnesota Department of A Report to the Minnesota Department of Human Services; Vayong Moua, Director of Health Equity Human Services (DHS). An Account of Health Advocacy, Blue Cross and Blue Shield of Minnesota; and, Disparities in Minnesota's Medicaid Population: Leanne Berge, Esq., chief executive officer, Community Which Populations Within the Medicaid Program Health Plan of Washington, Community Health Network of Experience the Greatest Health Disparities Washington. and Poorest Health Outcomes? Report to the Minnesota State Legislature, June 2018. Breslin, Heaphy, Dreyfus, Lambertino. Available at: About the Cover Photo Disability is complex. The photo on the cover of this report https://www.healthmanagement.com/ does not include a person in a wheelchair. This does wp-content/uploads/MN-Summary- not mean the picture is absent a person with a physical Report-to-Legislature_DHS_HMA_ disability. People cannot be labeled by appearance DPC_08.01.17_6.11.18.pdf whether it be a visual impairment, mental health diagnosis or substance use disorder. Each person is unique. An Account of Health Disparities in Minnesota's Medicaid Population: Which Populations Within the Medicaid Program Experience the Contact Information for Authors and Greatest Health Disparities and Poorest Health Contributors Outcomes? Ellen Breslin: ebreslin@healthmanagement.com Dennis Heaphy: dheaphy@dpcma.org White Paper, June 2018. Breslin, Lambertino, Tony Dreyfus: tdreyfus-omega@comcast.net Dreyfus, Heaphy. Available at: Anissa Lambertino: alambertino@healthmanagement.com https://www.healthmanagement.com/wp- Jeff Schiff: jeff.schiff@academyhealth.org content/uploads/MN-White Paper_DHS_ Kelsi Jackson: kelsijackson@healthmanagement.com HMA_DPC_08.01.17_6.11.18.pdf Susan Kennedy: susan.kennedy@academyhealth.org Sunita Krishnan: sunita.krishnan@academyhealth.org Accounting for Social Risk Factors in Minnesota Health Care Program Payment. Legislative report supplement, December 2018. Minnesota MINNESOTA DEPARTMENT OF HUMAN Department of Human Services. Available at: SERVICES To learn more about how Minnesota is using results on https://edocs.dhs.state.mn.us/lfserver/ health disparities, contact: Public/DHS-7834-ENG Justine Nelson: justine.nelson@state.mn.us 3 Table of Contents Report The Importance of Language . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Section 1. Medicaid's Mission to Reduce Health Disparities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Section 2. National and State Attention to Health Disparities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Section 3. Minnesota Medicaid's Examination of Health Disparities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 Section 4. Key Findings and Policy Implications for States . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 Section 5. Medicaid's Opportunity to Achieve Health Equity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 Boxes Box 1. The Disproportionate Impact of COVID-19 Due to Structural Inequity . . . . . . . . . . . . . . . . . . . . . . . 9 Box 2. What is Racism? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Box 3. State Medicaid Directors Express Commitment to Health Equity . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Box 4. Governors Speak Up on Need for Long-Term Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Box 5. Health Equity for Dually Eligible Populations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 Box 6. Civil Rights Leaders Call for a Just Health System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Box 7. Key Data Collection Challenges Facing Medicaid Programs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 Box 8. The Potential of Using Z Codes to Capture SDOH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 Box 9. Minnesota DHS Uses Results on Health Disparities to Inform VBP Model . . . . . . . . . . . . . . . . . . . 22 Box 10. Two States That Spotlight the Use of Data to Advance Health Equity . . . . . . . . . . . . . . . . . . . . . . 23 Box 11. Disability Is a Social Risk Factor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 Box 12. Raising the Bar for Medicaid Populations with an Intersectional Approach . . . . . . . . . . . . . . . . . . . 28 Box 13. Braveman on the Intersection of Health Disparities and Justice . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 Box 14. Healthy People's Disability Health Goals and Identified Barriers . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 Tables Table 1. Medicaid's Essential Role in Providing Health Care Coverage . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Table 2. Key Steps Taken to Measure Health Disparities in Medicaid Populations . . . . . . . . . . . . . . . . . . . 23 Table 3. Tables with Bivariate Results for Adults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Table 4. An Account of Health Disparities for Adults Covered Under Minnesota Medicaid . . . . . . . . . . . . . 27 Table 5. An Account of Health Disparities for Adults in Category 1: Very low Income . . . . . . . . . . . . . . . . . 29 Table 6. An Account of Health Disparities for Adults in Category 2: Race and Ethnicity . . . . . . . . . . . . . . . 30 Table 7. An Account of Health Disparities for Adults in Category 3: Disability . . . . . . . . . . . . . . . . . . . . . . . 31 Table 8. An Account of Health Disparities for Adults in Category 3: Disability-SPMI and SUD . . . . . . . . . . 32 Table 9. A Summary of Poor Health Outcomes Among Adult Population Groups . . . . . . . . . . . . . . . . . . . 33 Table 10.The Odds of Health Disparities for Medicaid Adults by Income, Race, and Disability . . . . . . . . . . 37 Table 11.Children Covered Under Minnesota's Medicaid Program . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 Table 12.The Odds of Health Disparities for Medicaid Children by Income, Race, and Disability . . . . . . . . . 40 Figures Figure 1. Mortality Rates Among Medicaid Adults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 Figure 2. Cost Ratios Among Medicaid Adults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 Appendices Appendix A.Key Terms Used in This Report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 Appendix B.Laws of Minnesota 2015, Chapter 71, Article 11, Section 63 . . . . . . . . . . . . . . . . . . . . . . . . . 49 Appendix C.Steps Taken to Measure Health Disparities in Medicaid Populations . . . . . . . . . . . . . . . . . . . . 49 Appendix D.Data Sources to Measure Health Disparities in Medicaid Populations . . . . . . . . . . . . . . . . . . . 51 Appendix E.Adults Covered Under Minnesota Medicaid by Category . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 Appendix F. Guide to Reading Health Disparity Results Shown in Table 4 . . . . . . . . . . . . . . . . . . . . . . . . . 53 Appendix G.The Odds of Health Disparities for Children with Child Protection Involvement (CPI) . . . . . . . . 54 ENDNOTES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 4 The Importance of Language Writing this report on health disparities, our team DISABILITY TYPES worked to use language that is in keeping with "DISABILITY" remains problematic as it perpetuates language emerging in public health practice. In doing the idea that a person is lacking and or somehow so, we took care to: (1) name the specific populations defective. The word disability is used due to the lack affected by inequities instead of relying entirely on of alternative language in public health practice. monolithic categories; and (2) use alternatives to When possible, we adhered to person-first language common public health euphemisms. State Medicaid and avoided grouping all persons with a disability into programs, other government entities, and policy one large group. The term "disability" literally means leaders must be attuned to shifts in language. These a loss of ability. This is an inadequate term, because shifts can provide insight into the priorities of different people with disabilities represent a heterogeneous populations and potential opportunities for change in population. The population includes persons with research, policy and practice. lived experience of mental illness, substance use disorder, and a range of intellectual, cognitive and EXAMPLES OF EVOLVING LANGUAGE physical disabilities. People within the disability "RACISM" and other terms such as ableism and community experience discrimination and stigma. homophobia are used when possible. Solely using The degree of discrimination may vary based on "race" or "disability" can perpetuate discrimination, factors including the type of disability, a person's implicit bias, and stigma because the locus of the race, gender identity, etc. For example, some BIPOC problem becomes the person, not the external subpopulations are more likely to have disabilities determinants that impact the person. It is critical than their white counterparts. In addition, some to avoid erasure of race, ethnicity or disability via BIPOC subpopulations with disabilities are more likely language. In this work, it is important to keep at the to experience higher incidences of health disparities forefront, that these "isms" are the barriers to health, when compared to their white counterparts with not the person's race, identity or disability status. disabilities. "UNDER-RESOURCED" is slowly replacing DATA NOTE: DATA LIMITATIONS the term "vulnerable." Under-resourced refers to CREATE CHALLENGES IN REPORTING populations and communities disproportionately HEALTH DISPARITIES impacted by social constructs that increase poverty In reporting our health disparity results for levels (low-income wages), inadequate access to populations covered under Minnesota Medicaid, social services, and educational supports. In contrast we were limited to the Medicaid data descriptors to "vulnerable," under-resourced focuses on the for race and ethnicity and disability. (1) Race and causes of poverty and poor health outcomes rather ethnicity: We were not able to use current terms such than on the idea that specific populations may be as BIPOC and Latinx. We were limited to American inherently vulnerable. Indian, Black or African American, Hispanic, and Asian. (2) Disability: We were not able to present "BLACK, INDIGENOUS, (AND) PEOPLE OF health disparities by disability type. We deferred COLOR" (BIPOC) is a term that is used as an to Medicaid eligibility status and claims data to alternative to "people of color." BIPOC is a term distinguish persons with a mental health diagnosis recognizing the multiple dimensions of racial and and/or substance use disorder from populations with ethnic identity and that not all people of color face any disability. equal levels of injustice. 5 Executive Summary In partnership with AcademyHealth, we wrote this report, advanced data collection systems may want to build out Advancing Health Justice Using Medicaid Data: an intersectional framework, one that incorporates multiple Key Lessons from Minnesota for the Nation. factors to capture the impact of social factors on health outcomes. The approach introduced in this report for This report was created in response to the disproportionate Medicaid populations should also be modified for dually impact of COVID-19 sickness and deaths on Americans eligible populations. who are Black, African American, Latinx, Native American, Asian, and other people of color; people with disabilities; Underscores racial injustice, discrimination, bias, and people subsisting on poverty-level income. Along and stigma in our health care system. The authors with many others, including state Medicaid directors provide a robust account of health disparity results by and governors, the authors of this paper believe that income, race, ethnicity, and disability for adults and children state Medicaid programs must address health inequities covered under Minnesota Medicaid's program. Results stemming from racism and discrimination. by race indicate that adults who are American Indian and adults who are U.S. born Black or African American face THIS REPORT: greater health disparities than White adults. Adults who Provides information to support state Medicaid are American Indian had poor outcomes in 89 percent programs to measure and address health disparities. of the measures that we examined. Adults who are U.S. Section 1 addresses Medicaid's essential role in providing born Black or African American had poor outcomes in 68 coverage to populations who have historically experienced percent of the measures that we examined. Adults who are racism and discrimination. Section 2 discusses our nation's White had poor outcomes in 26 percent of the measures progress in documenting health disparities and its ongoing that we examined. (See Table 9 in this report.) For example, challenges in collecting data on the Medicaid population by adults who are American Indian are 150 percent more race and ethnicity and by disability type. Section 3 provides likely to have diabetes than White adults. Adults who are a robust account of health disparities in Minnesota's U.S. born Black or African American are 100 percent Medicaid population by income, race and ethnicity, and more likely to have a disability than White adults. Adults disability. Section 4 provides a straightforward translation of who are American Indian or U.S. born Black or African Minnesota's results into key findings and policy implications American were more likely to have preventable emergency for all state Medicaid programs. Section 5 shares seven department visits and hospitalizations than White adults, a opportunities for consideration by Medicaid in achieving result that is also true for adults with disabilities compared health equity. to adults without disabilities. Highlights the essential contribution to the evidence Emphasizes the importance of using an intersectional base by one state's Medicaid program. The approach approach to disparity measurement. Minnesota used by Minnesota's Medicaid program to measure health Medicaid's program data confirms the intersectional disparities serves as an example of the initial steps that connection between poverty, race, and disability. The state Medicaid programs could take to identify disparities intersectional nature of health disparities requires states to through data and to address inequities through action. develop interventions that address these complex barriers The results from our work with Minnesota highlight the to health. Adults with income at or below 50 percent of the relationship between health disparities and racism, Federal Poverty Level (FPL) are seven times more likely to discrimination, bias, and stigma. State Medicaid programs have a disability than adults with income above the FPL; in a nascent stage of health disparity measurement might adults who are U.S. born Black or African American are find the framework especially helpful. Other states with 100 percent more likely to have a disability than White 6 adults. Health equity cannot be achieved for all racial and The authors wish to emphasize that the ethnic groups without addressing disability, due to the analytical work presented in this report, disproportionate level of disability in these populations. although important, should only be This is an ethical imperative. considered an initial step in a long process to prioritize health equity. State Medicaid Urges state Medicaid programs to invest in data and analysis to measure health disparities. An evidence programs must move beyond measurement base is needed to establish priorities, tailor interventions, and take action to reduce health disparities, set appropriate goals, measure improvement, and to which will require significant effort and make a public case to elected officials that resources commitment. are required. States and state Medicaid programs need additional federal support to achieve these aims. 7 Introduction In partnership with AcademyHealth, we have written this circumstances." Health inequities refer to the underlying report, Advancing Health Justice Using Medicaid systems that disproportionately advantage some Data: Key Lessons from Minnesota for the Nation, populations and disproportionally disadvantage other in response to the disproportionate impact of COVID-19 populations in achieving their full health potential. sickness and deaths on people who are Black, African For a description of key terms used in this report, American, Latinx, Native American, Asian, and other see Appendix A. people of color, people with disabilities; and people subsisting on poverty-level income. See Box 1 to learn The evidence shows that "health disparities, including more about the impact of COVID-19 on people by race disparities related to COVID-19, are symptoms of broader and ethnicity, and disability. underlying social and economic inequities that reflect structural and systemic barriers and biases across sectors." Equity also refers to the goal of advancing public This report is about the importance of policy to reduce or eliminate systemic injustices. investing in data and analysis to measure and reduce health disparities. Health Finally, equity also refers to the means of achieving the disparities data is essential to identifying goal. As articulated by the disability rights movement differences in health outcomes. However, statement, "nothing about us without us." The achievement of equity requires proactive inclusion and the voice of measuring health disparities is only the populations for whom systemic racism and ableism lead initial step in a process that will be long and to health disparities. Reducing health disparities requires challenging. Addressing inequities that lead a commitment to addressing racism and other forms of to health disparities requires engagement discrimination that may lead to health inequalities. See with communities most impacted by Box 2 for a definition of racism. injustices that cause inequities in health care outcomes. State Medicaid programs play an essential role in address- ing social inequities. In the coming years, we can expect this role to increase significantly. In addition to the people already Health disparity, health inequity, and health equity describe covered under Medicaid, including people directly affected the context, the problem, and the opportunity for the by COVID-19, we anticipate an increase in the number of nation. Health "disparities" mean differences, such as the people who will seek Medicaid coverage due to the second- prevalence of disease in one group relative to another. ary effects of COVID-19 on the economy and job market. Health disparities are closely related to health inequity, This report provides information to help state Medicaid pro- which refers to differences related to injustice. grams undertake the initial work to measure health disparities around which they must design and implement strategies Health equity is what we achieve when everyone reaches to advance health equity. We appreciate that state Medicaid their full potential for health and wellness. With health equity, directors are committed to health equity. See Box 3 to read no one is "disadvantaged from achieving this potential the statement of commitment from state Medicaid directors. because of social position or other socially determined 8 BOX 1. THE DISPROPORTIONATE IMPACT OF COVID-19 DUE TO STRUCTURAL INEQUITY In 2020, the coronavirus pandemic in the U.S. magnified pre-existing racial health inequities. As a nation, we are witnessing the disproportionate impact of COVID-19 on Black and African Americans, Native Americans, Latinx, Asians, persons with disabilities, and low-income populations. The coronavirus pandemic impact on African Americans, Black Americans, and Native Americans was in part due to a combination of higher pre-existing conditions, structural racism, and discrimination.12 Access to medical care and the multiple systematic barriers that under-resourced populations face further impact individuals' ability to achieve optimal health with these conditions. Many pre-existing chronic conditions can be traced to structural racism. Marginal housing, food insecurity, and inadequate or inconsistent employment are examples of social drivers of health that directly add specific health challenges (e.g., lead and mold risk in housing) and increase the overall stress on families. Regarding data collection as noted in Section 2 of this report, health statistics for adults by income and race are available, but significant improvements are needed in the gathering of data around documentation of specific race and ethnic subgroups to create an actionable evidence base.13 People with disabilities were also disproportionately impacted by COVID-19 due to underlying chronic conditions, discrimination, and the lesser value that society places on the lives of persons with disabilities.14 "Reports about COVID-19 trends among people with disabilities are scarce. Although disability status should be considered a piece of important demographic information (given its prevalence and known risk factors for poorer outcomes), data is not systematically collected. To date, disability has not been included in authoritative reports."15 Persons with disabilities, older adults, and others living in congregate settings have experienced high levels of morbidity and mortality due to COVID-19 for a range of reasons, including inequities in state Medicaid program coverage for home-and-community- based services (HCBS). Historical differences in Medicaid HCBS regulations across states, as well as changes made in response to the pandemic, have led to inequitable access to community-based living opportunities for people with disabilities. As a result, a significant portion of persons with disabilities died. Due to a lack of consistent data collection and reporting on persons with disabilities, the full impact of COVID-19 is unknown.16 Finally, we underscore that COVID-19 also had a devastating effect on older people, especially those living in nursing homes. A large proportion of COVID deaths occurred among nursing home residents, including many who had little contact with family before they died. Although nursing home care lies outside the scope of this report, this issue shows again that COVID draws attention to deficits that demand change: the high toll of mortality among elders living in nursing homes calls out for large-scale change in how we provide care for elders. BOX 2. WHAT IS RACISM? In the words of Camara Phyllis Jones, MD, MPH, PhD, racism is a "system of structuring opportunity and assigning value based on the social interpretation of how one looks (which is what we call "race"), that unfairly disadvantages some individuals and communities, unfairly advantages other individuals and communities, and saps the strength of the whole society through the waste of human resources."17 9 BOX 3. STATE MEDICAID DIRECTORS EXPRESS COMMITMENT TO HEALTH EQUITY In June 2020, state Medicaid directors expressed a unified commitment to health equity: "The National Association of Medicaid Directors (NAMD) is committed to health equity and working with our members and partners to improve the health and well-being of the 70 million people served by Medicaid and the Children's Health Insurance Program (CHIP). Racism and racial injustices are barriers to health and to the ability for Black, Indigenous and People of Color [BIPOC] to access resources that support health, including access to health care services, stable housing, safe communities, nutritious food, and employment. As a non-partisan, consensus- based organization we commit to working with our members, our federal partners, and other health care organizations to pursue policies and programmatic innovations that seek to erase inequity and create meaningful opportunities for good health and well-being for Marginalized Communities and all individuals served by Medicaid and CHIP. We also recognize the need to ensure that Medicaid leadership is more representative of those served by Medicaid, and we will add this focus to NAMD's leadership development curriculum."18 PURPOSE OF THE REPORT or below 50 percent of the FPL; (2) children by race and This report's primary purpose is to provide state ethnicity; and (3) children with parents with a disability of Medicaid programs with a framework to identify, mental illness. track, and address health disparities impacting their covered members. Because of the defined scope of our prior work for the Minnesota DHS, our findings on the intersectionality of The report's authors created the framework used in income, race, ethnicity, and disability are limited. As data partnership with staff of the Minnesota Department collection and analysis develops, the work must advance of Human Services (DHS). DHS operates the state's an intersectional framework. Having an intersectional Medicaid program. The framework describes the data framework will enable states to analyze the impact relationship and impact of a range of social and family of crosscutting determinants of health, such as income, health risk factors on health outcome measures to race and ethnicity, and disability on health disparities. address health disparities among populations covered under Medicaid. As reported to the Minnesota Legislature STATE MEDICAID PROGRAMS MUST in 2018, the results of applying this framework in MOVE FROM DATA TO ANALYSIS TO Minnesota expand our understanding of the relationship ACTION between social risk factors and health disparities. These State Medicaid programs must collect data, analyze results also support state action to reduce disparities. data, and act on data to achieve health justice. Today, Medicaid programs are well-positioned to take the Based upon our work with Minnesota Medicaid, this necessary steps to advance health justice, given their report presents health disparities for several populations role and power as a health care payer. We identify the covered under its Medicaid program. In the adult following three fundamental steps. First, they must invest population, we present health disparities for three in improved capacity to collect, analyze, and measure categories: (1) adults with very low income such as adults health disparities among covered populations. Second, with income at or below 50 percent of the federal poverty we recommend state Medicaid programs engage level (FPL) and adults experiencing homelessness; (2) people with lived experience of inequity, racism, and adults by race and ethnicity such as adults who are discrimination in developing the data collection strategy, American Indian, U.S. born Black or African American, analyzing the data, defining the scope of interventions, Hispanic, and White; and, (3) adults with disabilities such and implementing those interventions. Third, we as those who qualify for Medicaid based on disability recommend that federal and state policymakers invest in status, adults with serious and persistent mental illness, Medicaid program capacity to collect and analyze data and adults with a substance use disorder. and provide resources required to address health and wellness barriers for under-resourced communities. In In presenting health disparities for children, we also used 2020, the National Governor's Association demonstrated three categories: (1) children with parents with income at its support for this point. See Box 4. 10 BOX 4. GOVERNORS SPEAK UP ON NEED FOR LONG-TERM PLANNING As the National Governor's Association (NGA) outlined in a 2020 memorandum about "the policy approaches to address the disproportionate impact of COVID-19 on communities of color": "The most effective strategies are not limited to the period of the immediate crisis. Long-term planning that addresses equity will allow governors to alleviate the economic and health impacts on the most vulnerable communities as states begin to reopen and recover."19 STRUCTURE OF THE REPORT of health disparities in the Minnesota Medicaid program The report has five major sections and is structured among Medicaid populations by income, race, ethnicity, and as follows. disability. This section contains findings from the research we conducted with and for the Minnesota Department of Section 1 provides an overview of Medicaid's essential role Human Services (DHS). The purpose of the research was in providing health care coverage to people who experience to measure health disparities among Medicaid population health disparities and racism, discrimination, bias and groups. This report's findings highlight the relationship stigma. This section focuses on people with very low between health disparities and racism, discrimination, income, Black and African Americans, Native Americans, bias, and stigma. Section 4 provides a straightforward Latinx, and people with disabilities. Section 2 provides translation of Minnesota's results into key findings and policy a brief account of the nation's progress in documenting implications for all state Medicaid programs. Section 5 health disparities and ongoing challenges to collect data by shares seven opportunities for consideration by Medicaid in race and ethnicity and disability type needed to document achieving health equity. health disparities. Section 3 provides a robust account 11 Section 1. Medicaid's Mission To Address Health Disparities MEDICAID'S ROLE TO PROVIDE COVERAGE population increased.28 29 The correlation between access TO POPULATIONS WITH LOW INCOME to Medicaid and health disparities provides clear evidence In 1965, the U.S. Congress created the Medicaid program that states need to take action and build Medicaid under Title XIX of the Social Security Act to provide health programs' capacity to address the social needs of low- care services to "low-income children deprived of parental income populations, particularly BIPOC populations. support, their caretaker relatives, the elderly, the blind, and individuals with disabilities."20 In 2021, Medicaid is MEDICAID'S HEIGHTENED IMPORTANCE the single largest purchaser of health care in the U.S., TO PEOPLE WITH LONG-STANDING providing coverage to about 75.1 million people with HEALTH DISPARITIES low income.21 This number includes enrollment in the As previously stated, the Medicaid program covers Medicaid program and the Children's Health Insurance about 20 percent of the people in this country, or 1 in 5 Program (CHIP). Together, Medicaid and CHIP cover people. Medicaid's covered populations include families, children, parents, pregnant women, other adults with children, non-elderly adults, and older adults. Medicaid's low income, people with disabilities, and older adults to role is critical to people with low income as well as varying degrees across the states.22 23 African American, Black, Hispanic, and Native American populations, and people with disabilities. MEDICAID'S POSITIVE IMPACT ON HEALTH DISPARITIES Medicaid's role also lies at the intersection of income, Over the last five decades, Medicaid has more than doubled race, ethnicity, and disability.18 This is a crucial point to its role in providing health care coverage as a share of the emphasize for two reasons. First, Medicaid populations U.S. population.24 Between 1978 and 2020, Medicaid have a long history of health disparities such as higher enrollment increased from 9 percent to 20 percent of the burdens of illness, disability and mortality than non- U.S. population. Medicaid's coverage ratio increased to 1 Medicaid populations.30 31 32 33 Second, in this country, in 5 people, from 1 in 10 people.25 Several factors account poverty and disability rates are higher among many racial for Medicaid's enrollment growth. The mandated eligibility and ethnic communities than in White communities.34 expansions in the 1980s for pregnant women and children Some researchers suggest that this country has two and the 2010 passage of the Affordable Care Act (ACA) Americas.35 Blacks and Native Americans, followed have made significant contributions.26 The ACA expanded by Hispanics, have the highest poverty rates. High health care options for non-elderly adults with income up poverty rates in communities result from long-standing to 138 percent of the federal poverty level (FPL). government practices and policies that lead to under- investment in these communities, a form of structural A credit to the ACA, the Medicaid program has expanded racism, and a lack of opportunity.36 Over 20 percent access to comprehensive health care for millions of of the Black population and roughly 20 percent of the Americans. It has also significantly narrowed the nation's Hispanic population have incomes below the poverty disparities in coverage and access to health care for many level compared to less than 10 percent of the White racial and ethnic groups, including African American, population.37 The poverty rate is nearly 25 percent among Black, Hispanic, and Native American.27 To date, 39 the American Indian and Alaska Native populations. The states, including the District of Columbia, have adopted disability rate is also higher among communities of color the ACA's Medicaid coverage expansions. Twelve than in White communities. Among adults 18 years of states have not adopted the ACA's Medicaid coverage age and older covered under Medicaid, the disability expansions as of November 2020. In the 12 states that rate is 24 percent.38 In contrast, about 30 percent of have not adopted that Medicaid coverage expansion, the non-Hispanic Black population, 31 percent among low-income black Americans' health and wellness have the Hispanic population, and about 40 percent of the been negatively impacted, with health disparities in this American Indian or Alaska Native have "any disability." 12 MEDICAID'S ESSENTIAL ROLE IN Medicaid's Coverage of People by Race and Ethnicity. PROVIDING HEALTH CARE COVERAGE BY Medicaid provides coverage to more than 1 in 3 people who POPULATION GROUP are Black, 1 in 3 people who are Hispanic, and 1 in 3 people Medicaid plays an essential role in providing health care who are Native American. In contrast, only 15 percent of the coverage to people with low income, Black, African White population are covered under the Medicaid program.43 American, Native American and Hispanic, and people with 44 Over the last decade, Medicaid's expansion under the ACA disabilities. See Table 1 for a summary of these facts. has contributed positively to Black communities' increased coverage. Yet, many people representing our nation's diversity Medicaid's Coverage of People with Low Income. have been left out of this expansion because they reside in Among people with income below 100 percent of the FPL states that have not adopted the ACA's Medicaid expansion. and under the age of 65, Medicaid covers 3 in 5 people or 60 percent of this income group, and an even higher share Medicaid's Coverage of People with a Disability. More of people with income at or below 50 percent of the FPL.41 than 1 in 3 people under age 65 covered under Medicaid Eight in 10 children from families with income below 100 have a disability, based upon Medicaid eligibility standards.45 46 percent of the FPL are covered under Medicaid. Medicaid is also critical to children and youth with disabilities. More than 1 in 2 children with special health care needs are covered under Medicaid. Other children with disabilities are In 2020, the FPL was $12,760, or $1,063 per offered Medicaid at the option of the state, which means that month, for an individual; and $21,720, or the share of children with special health care needs covered by $1,810 per month for a family of three.42 Medicaid/CHIP varies by state from 15 percent to 67 percent.47 Table 1. Medicaid's Essential Role In Providing Health Care Coverage Populations Medicaid Coverage by Income, Race and Ethnicity, and Disability Medicaid covers: People with Low Income •60 percent of non-elderly persons with income below 100 percent of the FPL •80 percent of children from families with income below 100 percent of the FPL Medicaid covers: •15 percent of adults who are White •15 percent of adults and 30 percent of children who are White People by Race and •More than 30 percent of adults who are Black or African American Ethnicity •More than 30 percent of adults who are Hispanic •More than 30 percent of adults who are Native American • More than 50 percent of children who identify as American Indian or Alaska Native (AI/AN), Black, other or multi-racial, or Latino Medicaid covers: •More than 30 percent of people under 65 years of age with a disability People with •42 percent of non-institutionalized adults 21-64 years of age, with wide variation across the Disabilities nation ranging from 26 percent in North Dakota to 57 percent in Massachusetts and Rhode Island to 66 percent in D.C.39 40 •About 50 percent of children with special health care needs 13 Medicaid's critical role in the lives of people with disabilities intersectional impact of racism and ethnic bias on persons also extends to the dually eligible population. This population with disabilities within these populations. We must also note group is covered under Medicaid and Medicare. Persons who the absence of data by disability type. The Disability and are dually eligible experience high morbidity and mortality Health Data System (DHDS), created by the Centers for rates for several reasons including racism, discrimination, Disease Control and Prevention (CDC), provides national bias, and stigma. These reasons include racism, statistics for disability rates and disability types. As classified discrimination, bias, and stigma. See Box 5 for a description by DHDS, disability types include cognitive disability, of the dually eligible population. Offering a variety of integrated hearing disability, mobility disability, vision disability, self- care options to individuals who are dually eligible is essential care disability, and independent living disability, based upon to advancing health equity in this population. definitions of disability used by the Behavioral Health Risk Factor Surveillance System (BRFSS). As we summarize this focus on disability, we must note that disability rates are higher among Black or Native Americans than their White counterparts. These rates underscore the BOX 5. HEALTH EQUITY FOR DUALLY ELIGIBLE POPULATIONS The Medicare-Medicaid Coordination Office (MMCO) of the Centers for Medicare and Medicaid Services (CMS) reports that 12.2 million people are enrolled in the Medicare and Medicaid programs.48 These individuals are "dually eligible." The dually eligible population includes persons with a range of disabilities and chronic conditions. Dually eligible persons often have high medical, behavioral health, and social needs. This population group is more racially and ethnically diverse than the overall Medicare population.49 For example, 20 percent of the dually eligible population is Black or African American, and about 15 percent is Latinx.50 In total, more than 40 percent of dually eligible individuals are either Black, African American, Latinx, or from another community of color, as compared to 17 percent of the total Medicare-only population. Under the current Medicaid and Medicare programs, most dually eligible individuals receive health care through a fragmented health care system, from multiple providers in various care settings with little to no care coordination across delivery systems. As a result of barriers to quality, the dually eligible population experiences numerous health disparities. In 2015, the "CMS Equity Plan for Improving Medicare" issued by CMS specifically identified the dually eligible population as "vulnerable" to disparities in access to quality care.51 Several states and CMS have partnered to transform the care delivery for dually eligible individuals through integrated programs of care, such as the capitated Financial Alignment Initiative (FAI) demonstration.52 Yet, as of 2020, only one in ten dually eligible individuals is enrolled in a truly integrated care program.53 This issue has received much attention nationally. At least five reports were released in 2020 on this topic, pointing to the need for federal and state Medicaid policymakers to create the next generation of integrated programs for dually eligible individuals and to optimize the use of current programs.54 As federal and state initiatives continue to emerge and take shape around improving the quality of life for dually eligible individuals, we hope that they are grounded in health equity and designed around an intersectional understanding of health disparities. Federal and state partners should work together to create integrated Medicare and Medicaid datasets to identify health inequities and track health equity progress against an accurate and transparent baseline for all population subgroups. 14 Section 2. National And State Attention To Health Disparities The United States has a long history of documenting to include "Black, Hispanics, Native Americans including health disparities by race and ethnicity to support sound Alaskan Natives and Hawaiian Natives, and Asian Pacific scientific approaches to reducing health care disparities.55 Islanders."59 Despite this investment into documentation, health inequities continue to exist. The lack of a single, and Experts wrote that such disparities had been in accurate data source on race and ethnicity in the United existence "for as long as federal health statistics States has not helped the cause. All state Medicaid were routinely collected."60 programs must find a way to collect detailed, accurate, and complete data on its population to: (1) establish The Task Force found that health disparities accounted an evidence base; and (2) inform the development and for 60,000 excess deaths each year. Six causes of death implementation of interventions. The data must be accounted for more than 80 percent of mortality among available at the individual level by race and ethnicity, Blacks and other minority populations.61 These six disability type, and social risk factors and linkable to conditions (causes of death) became priority issue areas health care data to advance and respond to our nation's for the Task Force study: cancer; cardiovascular disease ethical imperative to achieve health equity. See Box 6 for and stroke; chemical dependency, as measured by deaths statements from civil rights leaders. due to cirrhosis; diabetes; homicide and accidents; and infant mortality. This landmark report also led to the creation of the Office of Minority Health (OMH) in 1986 and EFFORTS TO DOCUMENT HEALTH its reauthorization in 2010 under the ACA. DISPARITIES More than 30 years ago, in 1985, the U.S. Department "The Office of Minority Health is dedicated to of Health and Human Services (HHS) issued its landmark improving the health of racial and ethnic minority "Report of the Secretary's Task Force on Black and populations through the development of health Minority Health."57 58 This report details health disparities policies and programs that will help eliminate health experienced by "minority groups" as compared with the disparities."62 U.S. population. The Task Force defined minority groups BOX 6. CIVIL RIGHTS LEADERS CALL FOR A JUST HEALTH CARE SYSTEM For more than 50 years, civil rights leaders from the Reverend Dr. Martin Luther King Jr. to Ed Roberts, the father of the disability rights movement, have long advocated for a more just health care system for low-income communities, disability communities, and communities of color. Reverend Dr. Martin Luther King Jr. was the civil rights movement leader from 1955 until his assassination in 1968. Citing the discriminatory practices in our health care system, in 1966, Reverend Dr. Martin Luther King Jr. spoke at the Second National Convention of the Medical Committee for Human Rights, Chicago: "Of all the forms of inequality, injustice in health is the most shocking and inhuman." It has been more than 50 years since Dr. King spoke at this convention, and the nation's civil rights leaders are again calling for the end of racist practices in our health care system.56 Ed Roberts was the first individual with severe disabilities to attend the University of California Berkeley; he became Director of California Vocational Rehabilitation in 1976. In his words: "We will not tolerate another generation of young people with disabilities going through segregated education, segregated society, being dependent upon their parents and public aid. We can make a difference in their future. If people with disabilities have a future, then everyone will have a future." 15 Within HHS, many agencies have contributed to the DATA IS CENTRAL TO UNDERSTANDING evidence base on health disparities spanning decades.63 HEALTH DISPARITIES These agencies include OMH, the Agency for Healthcare Data is considered central to measuring and monitoring Research and Quality (AHRQ), and the CDC. For nearly two progress in eliminating disparities in the Healthy People decades, AHRQ has published national health care quality goals and objectives at the national and state levels.74 and disparities reports that measure quality by race and ethnicity.64 65 In its 2018 National Healthcare Quality and As the IOM writes in one report: "standardized data Disparities Report, AHRQ reported that Black Americans, collection is also critically important in efforts to American Indians and Alaska Natives (AI/ANs), and Native understand and eliminate racial and ethnic disparities Hawaiians/Pacific Islanders (NHPIs) received worse care in health care. Data on patient and provider race than White Americans in approximately 40 percent of and ethnicity would allow researchers to better quality measures. Hispanic Americans received worse care disentangle factors that are associated with health than Whites for about 35 percent of quality measures. Asian care disparities, help health plans to monitor Americans received worse care than White Americans for performance, ensure accountability to enrolled 27 percent of quality measures but better care than White members and payors, improve patient choice, allow Americans for 28 percent of quality measures.66 CDC for evaluation of intervention programs, and help reports on health disparities and inequalities in 2011 and identify discriminatory practices."75 2013, along with strategies for reducing health disparities in 2014 and 2016, have also demonstrated our nation's As required under the ACA, HHS developed an concerns about health disparities.67 68 evidence-based set of data-collection standards for five According to the CDC: "reducing health disparities demographic categories: race, ethnicity, sex, primary brings us closer to health equity."69 language, and disability status.76 The standards are informed by many sources, including the Office of Management and Budget, the American Community Survey, and the EFFORTS TO UNDERSTAND HEALTH International Classification of Functioning, Disability, and DISPARITIES Health.77 The standards apply to national population health Among the many efforts to understand health disparities, surveys.78 The standards for race, for example, expand the in 1980, HHS began establishing national public health basic OMB categories into 14 categories. These categories "Healthy People" goals and objectives.70 The "Healthy enable people to check more than one category.79 People" goals and objectives help raise the bar on health and wellness nationally and make prevention and health Disability status is essential to addressing health disparities. equity a public health priority.71 These goals and objectives The standards for disability status are based upon six are updated every 10 years and increasingly emphasize questions to collect data on disability type. The six required reducing health disparities and advancing health equity. questions on disability status cover the following domains of functioning: seeing, hearing, mobility, cognition, self- The National Academies of Sciences, Engineering and care, and independence. The purpose of the standardized Medicine (NASEM) has also helped to understand health questions about disability is to "help facilitate more accurate disparities through the work of the Institute of Medicine and nuanced disability data and better inform federal, tribal, (IOM), (now a division within NASEM). The IOM wrote state, and local initiatives."80 about the role that bias and prejudice play in leading to health disparities and the need for racial and ethnicity At the federal and the state population levels, the CDC's data to eliminate these disparities. In "Unequal Treatment: DHDS makes available state population-level data on Confronting Racial and Ethnic Disparities in Healthcare," the adults with disabilities from the BRFSS. The data from IOM reported on the existence of health disparities among DHDS provides disability estimates and disability types racial and ethnic population groups in the U.S.72 In this including, cognitive, mobility, vision, self-care, independent report, the IOM concluded that "(al)though myriad sources living, and hearing, stratified by age, sex, race/ethnicity, contribute to these disparities, some evidence suggests and veteran status. DHDS data can easily support an that bias, prejudice, and stereotyping on the part of health intersectional approach at the population level by allowing care providers may contribute to differences in care." users to examine disability and race and ethnicity together. As such, DHDS is an important data source for examining Finally, it must also be noted that the Centers for Medicare the intersectionality of disability and race at the state level. and Medicaid Services (CMS) has made significant On the other hand, DHDS data has one major shortcoming. progress in reporting health disparities by race and ethnicity The data does not link to health care data needed to for the Medicare program.73 understand the impact of disability status on health utilization or health outcomes. 16 MEDICAID'S CHALLENGE: INCOMPLETE GETTING DATA COLLECTION RIGHT DATA ON RACE AND ETHNICITY The disproportionate impact of the coronavirus pandemic To the detriment of Medicaid populations, the ACA's goal on BIPOC persons and protests against racism, many to improve detailed data on race and ethnicity data has state public health leaders have stepped up their efforts to not been achieved.81 As of 2021, there is no single source address disparities in health care access and outcomes by of data on race and ethnicity for the Medicaid program. improving data reporting by race and ethnicity.94 Medicaid's This said, since 2011, CMS has been working with state programs' capacity and competency to collect data and Medicaid programs to transform its Medicaid Statistical reduce disparities impacting BIPOC populations must Information System (MSIS) system to improve data collection also improve.95 Federal and state partners must commit to by implementing the Transformed Medicaid Statistical implementing data integrity, data collection strategies, and Information System (T-MSIS). The goal of T-MSIS is to data reporting. improve the completeness, accuracy and timeliness of Medicaid data. Most importantly, T-MSIS introduces new State Medicaid programs have been and should continue data elements to previously unavailable race and ethnicity to work with HHS to create a sustainable data infrastructure and disability types on people covered under Medicaid. The with the capacity to collect standardized data and support reports indicate that implementation of T-MSIS has been local and state decision-makers.96 It is essential that data very slow, however.82 is collected by race and ethnicity and by disability type. It is equally important that data is collected in a culturally National implementation of T-MSIS began in 2013. competent way and in an ethical manner to protect privacy. Despite efforts, more than 10 percent of race and For purposes of interoperability, there should be increased ethnicity data is missing for most states, with some standardization of health risk factors and indicators. states missing as much as 50 percent of data.83 84 85 Finally, data on SDOH must be available at the individual and The lack of data on race and ethnicity for persons covered population levels. HHS should invest in linking data sets at under Medicaid has been an ongoing challenge, according the individual and community level to give policymakers a full to many researchers and analysts. Back in 2015, for sense of the impact of disparities on health and well-being. example, the National Committee for Quality Assurance Z codes, for example, are a potentially important and too (NCQA) found that race, ethnicity, and language was missing often overlooked source of data. These codes, a subset of in the Healthcare Effectiveness Data and Information Set ICD-10-CM codes, are essential to capturing SDOH data. (HEDIS). Only half of Medicaid plans reported complete Only by harnessing the linked information can planning, and partially complete data on race. Data on ethnicity and intervention, and monitoring of outcomes be truly effective. language were also poor. Fewer than half of Medicaid plans See Box 8 to read more about Z codes. reported complete or partially complete data on spoken language, and even fewer reported complete or partially complete written language or other language needs data.86 See Box 7 for an overview of the challenges facing state Medicaid programs. 17 BOX 7. KEY DATA CHALLENGES FACING MEDICAID PROGRAMS In 2021, the U.S. lacks a single source of data for race and ethnicity or a systematic method of collecting data reported by individuals or at regular intervals. COVID-19 has made evident the deficiencies and variation in reporting data on race and ethnicity. For example, Louisiana reports ethnicity data as either Hispanic/Latino or non-Hispanic/Latino. In contrast, Connecticut reports Hispanic, non-Hispanic white, non-Hispanic black, non- Hispanic Asian, non-Hispanic other, and non-Hispanic unknown. Many states report data on race and ethnicity as "unknown" or missing.87 The lack of disaggregation of the data on race and ethnicity is another important challenge. Addressing the disparate impact of COVID-19 on Latinx, Black, Indigenous, and Southeast Asians (Hmong, Lao, Vietnamese, Cambodian, Karen) refugee populations requires data collection to shift away from an aggregated model to one that is disaggregated by racial, ethnic and other inequalities. Aggregated data masks health disparities in different populations, a problem that has affected the Asian American population. For example, Asian American populations are often excluded from data findings because of a small population size (small N) in comparison to other populations. The result, Asian populations are often underrepresented or not represented in data samples providing information on health disparities. Disaggregated data is needed to understand the nuances in health care access and outcomes experienced by race and income and to create the right interventions.88 As reported in the American Journal of Public Health, the "failure to disaggregate health data for individual Asian subgroups disguises disparities and leads to inaccurate conclusions about needs for interventions and research."89 Medicaid programs do not have the exact racial and ethnic and disability type necessary to meet HHS data collection standards.90 Today, state Medicaid programs lack detailed, accurate and, complete data for their members. CMS is making progress but remains limited in its capacity to measure large-scale inequities because the system still lacks racial and ethnic data for a large portion of the Medicaid population.91 According to experts, state Medicaid programs do not require people to provide race data during the enrollment process, out of concern for potential discrimination. Race and ethnicity are optional enrollment fields. After the enrollment process, the opportunity to collect this data diminishes. Methods and mechanisms to collect this data after enrollment are inconsistent and/or not implemented. Managed care organizations or care providers sometimes collect data, but this data is underutilized in data analysis. Methods, moreover, to collect data are not necessarily adequate, appropriate, and even sophisticated. Collecting data on race and ethnicity is complex. AHRQ provides information on steps to improve data collection.92 AHRQ data collection methods also outline considerations important to data collection methods such as how to ask individuals about race, ethnicity, language, and communication needs and how to train staff to elicit this information respectfully and efficiently. AHRQ also outlines the need for increased integration of different entities to reduce redundancy in data collection of race and ethnicity to increase data stratification for comparison purposes. State Medicaid programs also need data on social risk factors (often called social determinants of health or SDOH). These data must be gathered from multiple sources and linked to health data to be effective. Social risk factor measurement is complex. However, non-Medicaid agencies such as the Supplemental Nutrition Assistance Program (SNAP) or Temporary Assistance for Needy Families (TANF) records risk factors related to economic status. Risk factors related to family functioning, included explicitly in Adverse Childhood Experiences (ACEs) for children (i.e., child welfare involvement, criminal justice involvement, parental mental health status) are also available, but from more diverse sources. State Medicaid programs have many opportunities to improve data on social risk factors to improve consistency across states and state programs by leveraging multiple data sources. For instance, data from Medicaid enrollment applications could be augmented from other data sources to improve the quality of the data.93 18 BOX 8. THE POTENTIAL OF USING Z CODES TO CAPTURE SDOH In 2014, the National Academies of Medicine (NAM) encouraged the collection of SDOH data in an electronic health record (EHR) to help providers address health disparities and support research into the health effects of SDOH. Z codes are a subset of ICD-10-CM codes, used as reason codes to capture "factors that influence health status and contact with health services."97 98 Z codes can and have been used as a health care strategy for making and tracking referrals.99 Z codes can provide a rich source of data on social determinants that could be used in advancing health equity. The U.S. Department of Health and Human Services requires the use of ICD 10 codes in electronic health record documentation. There are nine Z codes related to SDOH and several sub-codes, comprising a total of 97 granular codes. For example, Z55 is used to capture problems related to education and literacy; Z57 is used to capture occupational exposure to risk factors; Z59 is used to capture problems related to housing and economic circumstances; and Z60 is used to capture problems related to the social environment.100 A recent study, the first of its kind by CMS, analyzes Z codes' use in 2017 among Medicare beneficiaries. The study finds that Z code claims in 2017 represented only 1.4 percent of the total FFS population. The authors of this study reported that the five most utilized Z codes are homelessness, problems related to living alone, disappearance and death of a family member, other specified problems related to psychosocial circumstances, and problems in relationship with spouse or partner. Unfortunately, Z codes are not routinely used for coding and billing purposes on SDOH.101 As a result, important information on nonmedical causal factors is lost. State Medicaid offices should consider developing a standardized approach across provider groups to screen and collect data on SDOH using Z codes. State Medicaid data collection efforts could focus on how to increase the use of Z codes.102 19 Section 3. Minnesota Medicaid's Examination Of Health Disparities Across the country, states report on health disparities in a Medicaid programs, it also signals an imperative to report variety of ways. Increasingly, state Medicaid programs are on health disparities that address the needs of multiple documenting health disparities in Medicaid populations stakeholders and high-risk groups. and raising awareness to address implicit bias in health care. We consider many state Medicaid programs to be leaders in designing approaches to measure health THE STATE'S COMMITMENT TO disparities, from Massachusetts to Michigan to Ohio to HEALTH EQUITY Oregon to Pennsylvania to Washington.103 Minnesota's history and efforts to document, address, and eliminate health disparities have evolved. In 1987, Minnesota is a leader in its longstanding public reporting the Minnesota Department of Health (MDH) began of health disparities for the state and the Medicaid documenting and reporting health disparities results by program. It is also a leader in its commitment to health race and ethnicity.105 More than 25 years later, in 2014, equity. This section highlights how Minnesota's Medicaid MDH released Advancing Health Equity in Minnesota, in program measured health disparities in Medicaid response to a 2013 Minnesota law that directed MDH to populations and demonstrates how health disparities prepare this report because "disparities in health status vary across populations, based upon critical factors outcomes for certain populations continued unabated, such as income, race and ethnicity, and disability. Key including disparities based on race or ethnicity."106 results are presented for adults and children by income, Advancing Health Equity in Minnesota provides an account race and ethnicity, and disability to demonstrate each of the state's health disparities and recommends best factor's importance to health outcomes. Minnesota's practices, policies, processes, data strategies, and other measurement of health disparities also included identifying steps to promote health equity for all Minnesotans. The social risk factors (often called social determinants final recommendation identifies the need to "strengthen the of health) and their disproportionate impact on collection and analysis of data to advance health equity." racial and ethnic communities. Minnesota's work on This report led to the creation of the Center for Health measuring health disparities in Medicaid populations is Equity, established to advance health equity within the comprehensive and informative, offering a roadmap to Minnesota Department of Health and across the state. other states on conducting this work. Despite a continuous focus on health equity, Minnesota At the same time, Minnesota's work on measuring struggles with reducing racial health disparities. In 2019, health disparities reveals Medicaid's nascent stage of the state described these challenges in a report to development. Key results by race distinguish between the legislature on eliminating health disparities: "While people who are Black, born and not born in the United Minnesota ranks high in terms of general health status States. The results do not capture the diversity of the compared to other states, the health disparities that Black immigrant population, however.104 Key results by exist in Minnesota are among the worst in the nation. disability do not stratify health disparities by disability type. Such disparities have meant that, compared to Whites, Data on disability type using HHS categories for disability Minnesota's populations of color and American Indians type was not available for our work. Finally, none of the experience shorter life spans; higher rates of infant results fully capture the intersectionality of an individual's mortality; higher incidences of diabetes, heart disease, race, ethnicity, and disability type, along with economic cancer, and other diseases and conditions; and poorer status and family structure. While Minnesota's work general health. When such disparities persist, they have a represents an important step in the evolution of state negative effect on the quality of life, the cost of health care, and the overall health of all Minnesotans."107 20 THE STATE'S REPORTING ON HEALTH language. By 2016, DHS submitted its first report to DISPARITIES CONTINUES TO EVOLVE the Minnesota Legislature to update the Legislature In 2004, Minnesota Community Measurement, an initiative on its progress in developing a payment methodology spurred on by commercial health plans, released its first incorporating social risk factors.113 In this report, DHS report on provider performance on select quality metrics named six social risk factors as strongly associated for every clinic in the state, a first for the nation108 In with poor health in children including factors related to 2007, MN Community Measurement (MNCM) began a economic status and family functioning. The report also collaboration with DHS to produce Health Care Disparities explained the association of these factors with poor Report, in response to a legislative directive109 These health and suggested how DHS might identify people with reports provide an evaluation of health plan performance these risk factors. One social factor that was found to be and identify improvement opportunities. Health disparities associated with better health was immigration status, or are displayed by race, ethnicity, language, and country being born outside of the United States. of origin, an advancement over the common federal categories. In 2017, MNCM started to report disparities By 2018, DHS submitted another report to the Legislature: by race and ethnicity and insurance type, including "Accounting for Social Risk Factors in Minnesota Health Medicaid.110 Care Program Payments."114 In this report, DHS provides: (1) recommendations to reduce health disparities In May 2020, MNCM released its latest report for nine among Medicaid populations and other DHS program quality indicators for people covered through managed participants; and (2) summarizes progress toward care plans under the Minnesota Health Care Programs reducing differences in health outcomes among the (MHCP), representing Minnesota's Medicaid program, state's various populations. This report also provides a and other purchasers.111 Minnesota's reporting on health quantitative answer to the legislature's central question: care disparities provides clear evidence of disparities Which Medicaid populations within the Medicaid program between Medicaid and non-Medicaid populations across experience the most significant health disparities and poor nine performance indicators; six of the measures apply health outcomes? For more than a year, DHS worked to adults and three apply to children. All rates for adults with the authors of this report from Health Management covered under Medicaid were lower than the rates for Associates and the Disability Policy Consortium. Together, other payers across the board. they co-created a conceptual framework for measuring health disparities, prepared an analytic plan, developed For example, the breast cancer screening rate for MHCP analytic files, conducted a comprehensive analysis of (Medicaid) was 60 percent. Medicaid's rate was 18 health disparities, and prepared findings for the final report. percentage points below the rate for other purchasers. Populations within Medicaid fared even worse when The final report identifies a strong association between reported by race and ethnicity. The breast cancer medical and social risk factors and poor health outcomes screening rate was 48.3 percent for members who are for several population groups. The populations with American Indian/Alaskan Native and 52.7 percent for the most significant health disparities and poor health Black/African American. The rate was higher for White outcomes were identified as: (1) adults and children at members at 62 percent. or below 50 percent of the federal poverty level (FPL); (2) adults with substance use disorder and their children; (3) adults with serious and persistent mental illness (SPMI) LEGISLATIVE DIRECTIVE TO EXAMINE and their children; (4) people experiencing homelessness; HEALTH DISPARITIES IN THE MEDICAID (5) adults with previous prison incarceration and their POPULATION children; (6) people who are Native American; and (7) In 2015, the Minnesota State Legislature added one children with child protection involvement (CPI). more component to the state's agenda to understand health disparities by directing the Medicaid program to The report links parental social risk factors with child identify Medicaid populations with the greatest health health outcomes. It is important to remind readers that disparities to advance health equity.112 In response to population groups – such as people at or below 50 this directive, the Department of Human Services (DHS) percent of the FPL and people who are Native American began its work. DHS is responsible for administering the – are not mutually exclusive. Many people covered under Medicaid program. See Appendix B to read the legislative Medicaid have more than one medical or social risk factor. 21 All reports with results for adults and children are publicly Other state Medicaid programs have also made available.115 See Box 9 to learn more about the way in contributions to the cause of health equity by measuring which Minnesota is using the results. health disparities. See Box 10 for a brief description of Oregon's and Washington's accomplishments. AN ACCOUNT OF HEALTH DISPARITIES IN POPULATIONS COVERED UNDER STEPS TAKEN TO MEASURE HEALTH MEDICAID DISPARITIES IN THE MEDICAID We begin our account of health disparities in populations POPULATION covered under Medicaid by presenting the framework In Minnesota, the researchers from Minnesota DHS, HMA used to examine health disparities in Medicaid populations. and DPC took several steps to perform the quantitative We then present the key results for adults covered by work. The work was shared across a multi-disciplinary Minnesota's Medicaid program, followed by key results for team comprised of experts in data science, epidemiology, children. public policy, public health and medicine. It is highly likely that this work would benefit from an even more expansive Minnesota Medicaid's account of health disparities in set of disciplines, skills and experiences. To advance Medicaid populations represents a major contribution health equity, for example, the creation of an analytical towards understanding health disparities and advancing dataset not only requires technical skills but also needs health equity for the 75 million people covered under input from the lived experience of racism, discrimination, Medicaid and CHIP in this country. It is our hope that bias and stigma. It is important to note that the Minnesota's work will lead to improved health outcomes quantitative work conducted by the HMA and DPC team for all Medicaid populations including all persons with was not performed in equal collaboration with community very low income, all racial and ethnic populations, and all members. persons with disabilities. Table 2 provides an overview of the steps taken to Through extensive research and analyses including input measure health disparities in Minnesota's Medicaid from the diverse populations served by the program, population. A more detailed description of these steps Minnesota DHS identified several population groups that is also described in Appendix C. Finally, a detailed experience poor health outcomes associated with medical description of the available data (step 1 on Table 2) is and social risk factors found commonly among Medicaid provided in Appendix D. adults and children. Minnesota DHS is both the Medicaid agency and a primary social service agency (providing benefits such as SNAP and TANF, as well as child welfare services), and has much of the information needed to mobilize efforts to eliminate health disparities. BOX 9. MINNESOTA DHS USES RESULTS ON HEALTH DISPARITIES TO INFORM VBP MODEL Minnesota DHS has incorporated the health disparity results in the Medicaid value-based payment model for the Integrated Health Partnership (IHP) initiative. Providers enrolled in this model are paid a per member per month (PMPM) amount based on social and medical risk with the expectation that this funding will be used to address these risks. The details of this model are publicly available.116 IHPs are required to propose a health equity measure tied to interventions intended to reduce health disparities.117 Several other states are making health disparities a key part of quality performance measurement among its providers and accountable care organizations. 22 BOX 10. TWO STATES THAT USE DATA TO ADVANCE HEALTH EQUITY To measure health disparities, the Minnesota Medicaid program created an analytical dataset for the Medicaid populations by integrating several sources of data. The dataset included Medicaid enrollment application data and Medicaid claims data. In addition to Minnesota, many other state Medicaid programs are using data to support health and wellness. Oregon and Washington have created integrated datasets and dashboards to advance health equity. Interestingly, Oregon is a state Medicaid program that is "free standing," meaning that the program is not part of a larger health and human services administration.118 Washington's Medicaid program, on the other hand, is more like Minnesota in structure. Washington operates within a larger health and health services administration.119 Most importantly, both state Medicaid types have created integrated datasets. OREGON. In October 2020, the Governor launched the "Oregon Child Integrated Dataset (OCID) Project, a vital online resource to provide policymakers with a more complete picture of how state programs interact with Oregon children and families over time. OCID reaches across state data systems to combine data from five agencies to ensure accountability for the well-being of the children who touch state services."120 The project is led by the Center for Evidence-based Policy at Oregon Health & Science University. From another source: the integrated dataset provides "a powerful cross-program, longitudinal view of the well-being of children in Oregon. OCID serves as an objective, nonpartisan data resource for answering questions, generating ideas, and advancing collective accountability for the well-being of Oregon's children."121 WASHINGTON. Since the early 1990s, the Washington State Department of Social and Health Services has been a leader in data analytics to inform policy and better serve the state's clients.122 The State of Washington uses data to improve outcomes for its members and to make improvements to programs, and has integrated its data from several state agencies. The state uses this linked data to understand health disparities in access to services, quality of care, utilization, and health outcomes.123 Beyond technology needs, the state's research and data analysis leader would also encourage states to invest in highly-skilled analytical staff to build organizational analytic capacity.124 Table 2. Key Steps Taken To Measure Health Disparities In Medicaid Populations Step Description of Steps (see Appendix C) Step 1 Identify the available data (see Appendix D) Step 2 Establish a framework for examining health disparities Step 3 Define the population groups Step 4 Select measures of health disparities Step 5 Prepare an analytic plan Step 6 Develop the analytical dataset Step 7 Conduct the analyses and interpret the results Step 8 Report results and communicate results 23 A NEW LENS ON HEALTH DISPARITY Category 3: Disability. This category includes three adult RESULTS populations defined by disability status and type. These In view of the COVID-19 pandemic's disproportionate three categories are: adults with a disability, because they impact on people who are Black and African American, qualify for disability-based Medicaid (Group 7); adults with other racial and ethnic populations, low-income a diagnosis of serious and persistent mental illness (SPMI) populations, and persons with disabilities, we were (Group 8); and adults with a diagnosis of substance use motivated to group the health disparity results from our disorder (SUD) (Group 9). It should be noted that Group pre-pandemic work with Minnesota Medicaid into three 7 represents adults with all types of disabilities. While we population categories: income, race and ethnicity, and hope that this grouping brings attention to the level of health disability. Populations are often categorized by income, care disparities experienced by people with disabilities, race and ethnicity, and disability by policymakers, we also know that a more refined approach is needed. researchers, and advocates to discuss and address health Analyses of health disparities for people with disabilities disparities. In our original work with Minnesota Medicaid, should be stratified by disability type based upon the we had many more population categories. For the types of disabilities used in the Disability and Health Data purpose of this report, however, we focused on Medicaid System developed by the Centers for Disease Control and populations that we could be grouped by income, race Prevention. For more information about how disability was and ethnicity, and disability categories. See Appendix D used as a social risk factor in this report, see Box 11. for more detail about the three population categories. THE RESULTS FROM MINNESOTA PROVIDE USEFUL INSIGHTS TO OTHER STATES ADULTS Prior to reviewing the key results on measures of health disparity for Medicaid adults in Minnesota, we wish to Three Categories for Adults note that these results are consistent with the national The results from our work in Minnesota Medicaid are literature that provides evidence of health disparities. For presented for the following three adult categories including this reason, Minnesota's results may be helpful to other nine population groups. These nine groups are not distinct. states in their efforts to better understand the importance Adults may fall into more than one group. See Appendix of medical and social risk factors to health disparities. E for more information about each population group. We do wish to add the caveat that while other state Category 1: Very Low Income. This category includes Medicaid programs may identify health disparities by two adult population groups that experience poor health income, race and ethnicity, and disability, we expect that outcomes compared to other populations covered under there will be much variation across the state Medicaid Medicaid. This first category includes adults with very programs as to the level of health disparities. Health low income, as defined by having income at or below 50 disparities may be worse for Medicaid members residing in percent of the federal poverty level (Group 1); and, adults some states than in other states. experiencing homelessness (Group 2). We appreciate that states and state Medicaid programs Category 2: Race and Ethnicity. This category includes differ in ways that impact health disparities. They differ four adult populations defined by race or ethnicity. Race in demographics, health care coverage, health care and ethnicity are social risk factors associated with health access, use and costs. Each state Medicaid program outcomes, related to many factors, including structural would do well to focus on risk factors that may be most racism and discrimination. This second category includes relevant to the populations they serve. In Minnesota, for adults who are American Indian (Group 3); adults who example, American Indians are an important population are U.S. born Black or African American (Group 4); adults group experiencing significant health disparities. Outside who are Hispanic (Group 5); and adults who are White of Minnesota, other states may have additional groups (Group 6). The terms used to describe populations by race upon which to focus. In the end, each state must conduct and ethnicity are consistent with the categories used by its own analysis of health disparities based upon the Minnesota DHS on its enrollment forms. American Indian, available data. instead of Native American, is the term used in Minnesota. Minnesota uses 11 categories altogether, including Asian, and "Other/Unknown." More detailed results for all adults U.S. born are available in documents listed in the front of this report. 24 BOX 11. DISABILITY IS A SOCIAL RISK FACTOR IN THE CONTEXT OF OTHER RISK FACTORS In this report, we have used disability to describe both a population group and a health outcome. This makes disability a risk factor (used as an independent variable in the analysis), and an outcome measure (used as a dependent variable in the analysis). As a social risk factor, disability, must be placed in the context of discrimination and bias. The extent to which disability has a negative impact on a person is dependent upon factors outside of the person. These factors include racism, homophobia, inadequate access to accessible environments, barriers to education, and more. As readers will learn, we present health disparity results for people with disabilities as a population group. We also present health disparity results on the rate of disability in population groups with very low income and for racial and ethnic groups. Our understanding of disability as a social risk factor is evolving in public health practice and in delivery of medical services. Fortunately, this is also leading to an increasing understanding of disability. The use of disability as a risk factor has been questioned by some authorities, and supported by others. Important to note that the National Academies of Sciences, Engineering, and Medicine (NASEM), for example, did not include disability as a social risk factor in its conceptual framework to guide its approach to accounting for social risk factors in Medicare payments. A prominent disability researcher involved in NASEM's work, Dr. Lisa Iezzoni, argues strongly for disability as a social risk factor in measuring health disparities.125 The 2016 HHS "Report to Congress: Social Risk Factors and Performance Under Medicare's Value-Based Purchasing Programs" identified disability as an important risk factor in Medicare payment programs but not as a social risk factor. However, it identified disability as closely linked to numerous social risk factors and health outcomes. In 2017, the Board on Population Health and Public Health Practice and the Board on Health Care Services of the NASEM's Health and Medicine Division held an event titled "Dissemination Meeting on the Report Series Accounting for Social Risk Factors in Medicare Payment." At the event, Dr. Iezzoni argued for inclusion of disability as a social factor noting that disability shares attributes of other social risk factors identified, namely race and ethnicity, gender, and sexual minority status. "For example, she explained, people with disabilities continue to experience significant discrimination and stress-inducing barriers and challenges on a daily basis."126 These included high rates of violence, smoking, obesity and stigma associated with disability and access to care and treatment. To note, the 2017 National Quality Forum final report "Disparities in Healthcare and Health Outcomes in Selected Conditions" included disability as a social risk factor. The Committee also expressed a desire to look beyond social risk factors to include behavioral risk factors, environmental exposures and access to green space, health care access, and cultural considerations. AN ANALYTICAL APPROACH FOR population. Second, we present the results on health MEASURING HEALTH DISPARITIES disparities for all nine population groups that constitute To produce the analytical results in our work with the three population categories. Readers may compare Minnesota Medicaid, we conducted two common types the results for the overall adult Medicaid population to of analyses to measure health disparities. The results the results for nine adult populations across the three presented in this section are based upon bivariate analysis categories. Bivariate analysis is used to highlight the and regression analysis. See Appendix C for more detail relationships between income, race and ethnicity, and on these analyses. disability and health disparities. Using this approach, we can examine the relationship between adults with Bivariate results. Bivariate analyses provide a very low income and mortality, (where income is the straightforward account of health disparities for a diverse social risk factor and mortality is the measure of health audience. The results of the analysis are easy to interpret. disparity). Finally, comparisons among population groups First, we present the results on health disparities for all can highlight disparities and create benchmarks for adults covered under Medicaid. These results establish a improvements. baseline picture of health outcomes in the adult Medicaid 25 Regression results. We also present the results from Readers should note that Table 4 serves as the our regression analyses, summarized for the reader. model for presenting the bivariate results for very Regression analysis is used to identify the statistical low income, race and ethnicity, and disability. association between social risk factors and outcomes. The regression analyses' key results provide evidence of the unique contribution of each medical and social risk Key Results for All Adults Covered Under factor to a range of outcomes. The results can answer Medicaid a range of questions for Medicaid populations about the Table 4 provides a detailed set of results for all adults association between income, race and ethnicity, disability, covered under Medicaid. Results highlighted in red and outcomes. For example, regression analysis can help font indicate that the outcome for the population under answer such questions as: do adults with very low-income examination is worse than the outcome for the comparison experience higher mortality rates than adults with relatively group. For all adults, we compared outcomes for males to higher incomes? females covered under Medicaid. Non-dichotomous data on gender was not available. BIVARIATE RESULTS FOR ADULTS The lines are numbered in Table 4 to assist readers in COVERED UNDER MEDICAID understanding the results. Appendix F provides a detailed The bivariate results for 550,341 adults covered under "walk through" of the results across all measures shown Medicaid for all measures of health disparity examined in in Table 4. For example, see Table 4, Line 4: The mortality Minnesota are presented in this section. This presentation rate for male adults is worse than the rate for female of "all adults" includes all ages 18-64, all genders, all adults. This rate for male adults is highlighted in red. The Medicaid-eligible income levels, all races and ethnicities, mortality measure refers to the proportion of individuals all disabilities, and those who are U.S. born and non- who died in the 2.5 years that we examined. The results U.S. born. The data is restricted to persons who are indicate that the mortality rate was 1.1 percent for male only eligible for Medicaid, which excludes persons with adults, 0.6 percent for female adults, and 0.8 percent for disabilities who are eligible for Medicaid and Medicare. all adults in the Medicaid program. Overall, male adults Table 3 provides a list of the tables containing bivariate had poorer outcomes than female adults for 11 of 19 results for adults. measures. For more information on the diseases and conditions, see the list provided by the CDC.127 For costs, The key results for adults covered under Medicaid, as calendar year expenditures were slightly lower for male shown in Table 4, provide a comprehensive account of adults than female adults. On the other hand, the rate of health outcomes across 20 measures. The 20 measures emergency department (ED) visits for female adults was include mortality, morbidity (burden of illness), disability, nearly double the male adult rate. and health care use, quality and cost. Excluding the cost measure, the count is 19. It is important to note that there are many ways to analyze health disparities among Medicaid populations. See Box 12 for three additional ways to raise the bar for Medicaid populations. Table 3. Tables With Bivariate Results For Adults Table Table Name Table 4 An Account of Health Disparities for Adults Covered Under Minnesota Medicaid Table 5 An Account of Health Disparities for Adults in Category 1: Very Low Income Table 6 An Account of Health Disparities for Adults in Category 2: Race and Ethnicity Table 7 An Account of Health Disparities for Adults in Category 3: Disability (D) Table 8 An Account of Health Disparities for Adults in Category 3: D-SPMI and SUD 26 Table 4. An Account Of Health Disparities For Adults Covered Under Minnesota Medicaid An Account of Health Disparities for All Adults Covered Under Minnesota's Medicaid Program, 18-64 Years Line Description Male Female Total # % # % # % 1 Population 244,730 44% 305,611 56% 550,341 100% 2 Average age 37.8 36.8 37.2 3 HEALTH DISPARITY MEASURES 4 Mortality 2,572 1.1% 1,725 0.6% 4,297 0.8% 5 Morbidity 6 Type 2 diabetes 17,623 7.2% 20,652 6.8% 38,275 7.0% 7 Asthma 16,217 6.6% 35,491 11.6% 51,708 9.4% 8 Human Immunodeficiency Virus (HIV)/Hepatitis C Virus 5,051 2.1% 3,776 1.2% 8,827 1.6% 9 Hypertension 5,238 2.1% 23,028 7.5% 28,266 5.1% 10 Cardiovascular 4,314 1.8% 3,224 1.1% 7,538 1.4% 11 Chronic Obstructive Pulmonary Disease (COPD) 18,961 7.8% 27,987 9.2% 46,948 8.5% 12 Injury 14,200 5.8% 16,561 5.4% 30,761 5.6% 13 Lung/Laryngeal Cancer 644 0.3% 561 0.18% 1,205 0.22% 14 Substance Use Disorder (SUD) 45,474 18.6% 33,875 11.1% 79,349 14.4% 15 Post-Traumatic Stress Disorder (PTSD) 9,492 3.9% 23,001 7.5% 32,493 5.9% 16 Depression 36,993 15.1% 68,774 22.5% 105,767 19.2% 17 Serious and Persistent Mental Illness (SPMI) 11,999 4.9% 18,530 6.1% 30,529 5.6% 18 Disability 22,460 9.2% 22,590 7.4% 45,050 8.2% 19 Health Care Access, Use, Quality - 20 Potentially preventable emergency department visits 18,242 7.5% 39,700 13.0% 57,942 10.5% 21 Potentially preventable hospital admissions 1,539 0.63% 1,790 0.59% 3,329 0.60% 22 HEDIS measures - 23 Annual preventive visit 60,908 24.9% 121,971 39.9% 182,879 33.2% 24 Comprehensive diabetes care - A1c test1 9,420 92.9% 12,211 91.4% 21,631 92.0% 25 Annual dental visit (ADV) for adults2 49,149 43.5% 84,060 51.9% 133,209 48.4% 26 Health Care Costs 27 Total Calendar Year (CY) expenditures per individual $7,074 $7,128 $7,104 Notes: (1) Denominator: n = 10,145 (M); n = 13,366 (F); n = 23,511 (Total) (2) Denominator: n = 113,003 (M); n = 162,042 (F); n = 275,045 (Total) 27 BOX 12. RAISING THE BAR FOR MEDICAID POPULATIONS State Medicaid programs have many opportunities to raise the bar for all Medicaid populations. Medicaid can compare outcomes across payers. Medicaid can expand the work to dually eligible populations. Medicaid can adopt an intersectional approach. First, state Medicaid programs could raise the bar by comparing outcomes for populations with Medicaid and other insurance coverage types. This suggestion would require Medicaid to integrate data with a commercial database. For adults covered under Medicaid, the prevalence of asthma is 9.4 percent, and the prevalence of chronic obstructive pulmonary disease (COPD) is 8.5 percent. By contrast, for adults across all forms of coverage in Minnesota, the prevalence of asthma is lower at 7.6 percent, and the prevalence of COPD is lower at 3 percent. Medicaid leaders could consider the opportunities to eliminate this disparity across payers. Second, state Medicaid programs could measure health disparities among dually eligible individuals by disability type. It is critical to measure health disparities among dually eligible individuals. This population has a range of complex chronic conditions, disabilities and needs, and often lacks access to coordinated, integrated care models. Medicaid would be required to integrate data for individuals from Medicaid and Medicare. Medicaid leaders could develop a plan to track and measure improvement. Finally, state Medicaid programs could collect more detailed and expansive data to ensure that health disparity analyses are grounded in an intersectional approach. Key Results for Category 1: Adults by Very Low groups. Results highlighted in purple font indicate that Income the result is worse than the results for the two remaining Key results for adults in category 1 shown in Table 5 comparison groups. are based upon the bivariate analyses for 20 measures, including mortality rates, burdens of illness, disability rates, Key Results for Category 3: Adults by Disability and health care use, quality and costs. The results are two Category 3 focuses on three population groups: adults with adult population groups: those with very low income and disabilities, adults with serious and persistent mental illness those experiencing homelessness. Key results are shown in (SPMI), and adults with substance use disorder (SUD). The Table 5. This comparative structure helps us to understand results for adults in category 3 shown in Table 7 and Table the impact of income and homelessness on health across 8 are based upon the bivariate analyses for all measures all measures. The red font results indicate that the results including mortality rates, burdens of illness, disability are worse than the comparison group. Health outcomes rates, and health care use, quality and costs. Results for adults with very low income (at or below 50 percent highlighted in red font reflect that the result is worse than of the FPL) are compared to outcomes for adults with the comparison group. For example, the mortality rate income higher than the FPL. Health outcomes for adults for adults with a disability is worse (higher rate) than the experiencing homelessness are compared to outcomes for rate for adults without a disability (3.9 percent versus 0.5 adults who are not experiencing homelessness. percent). Key Results for Category 2: Adults by Race and Table 7 includes results for adults with a disability based Ethnicity on eligibility status. It is important to note that this account Key results for adults in category 2 shown in Table 6 of health disparities among adults with disabilities does are based upon the bivariate analyses for 20 measures, not present results by disability type. Without results by including mortality rates, burdens of illness, disability disability type, it is impossible to address health disparities rates, and health care use, quality and costs. The results between different disability types or address underlying are for four populations, American Indian, adults who are inequities, which may lead to these disparities. U.S. born Black or African American, Hispanic, and White adults. The results or average for all adults (n = 550,441) Table 8 includes results for adults with a diagnosis of are also shown. This comparative structure helps us to SPMI and/or SUD. This comparative structure helps us understand the impact of race and ethnicity on health to understand the impact of disability, SPMI, and SUD on across all measures. Results highlighted in red font indicate health across all measures. that the result is worse than the result for the comparison 28 Table 5. An Account Of Health Disparities For Adults In Category 1: Very Low Income An Account of Health Disparities for Category 1 (Groups 1, 2) Line Description Group 1. Income Group 2. Homelessness Income at or Income > Experiencing Not Experiencing < 50% FPL 100% FPL Homelessness Homelessness 1 Population 240,350 116,938 38,721 511,620 2 Average age 37.3 38.1 35.6 37.4 3 HEALTH DISPARITY MEASURES 4 Mortality 1.3% 0.3% 1.2% 0.8% 5 Morbidity 6 Type 2 diabetes 8.7% 5.5% 6.9% 7.0% 7 Asthma 11.7% 7.0% 14.6% 9.0% 8 Human Immunodeficiency Virus (HIV)/Hepatitis C Virus 2.6% 0.6% 4.3% 1.4% 9 Hypertension 6.1% 3.5% 7.7% 4.9% 10 Cardiovascular 2.0% 0.8% 1.6% 1.4% 11 Chronic Obstructive Pulmonary Disease (COPD) 11.2% 6.2% 11.5% 8.3% 12 Injury 7.2% 3.4% 13.0% 5.0% 13 Lung/Laryngeal Cancer 0.3% 0.1% 0.24% 0.22% 14 Substance Use Disorder (SUD) 20.3% 6.3% 37.9% 12.6% 15 Post-Traumatic Stress Disorder (PTSD) 8.4% 2.7% 13.0% 5.4% 16 Depression 25.2% 12.8% 32.2% 18.2% 17 Serious and Persistent Mental Illness (SPMI) 8.4% 2.3% 12.1% 5.1% 18 Disability 15.7% 1.1% 10.8% 8.0% 19 Health Care Access, Use, Quality 20 Potentially preventable emergency department visits 13.5% 6.1% 22.5% 9.6% 21 Potentially preventable hospital admissions 0.84% 0.3% 1.1% 0.6% 22 HEDIS measures 23 Annual preventive visit 34.8% 31.9% 34.9% 33.1% 24 Comprehensive diabetes care - A1c test 91.4% 94.2% 87.8% 92.3% 25 Annual dental visit (ADV) for adults 48.3% 49.2% 41.9% 48.8% 26 Health Care Costs 27 Total Calendar Year (CY) expenditures per individual $10,447 $3,694 $9,833 $6,898 Notes: (1) Group 1. Adults with very low income (income at or below 50 percent of the FPL), where measures of health disparities for this population are compared to adults with higher relative income than the FPL. This comparative structure helps us to understand the impact of very low income on disparities. Adults with very low income had poorer outcomes than adults with relative higher income across 18 of 19 measures. Expenditures were also much higher for adults with very low income than for the comparison group. (2) Group 2. Adults experiencing homelessness, where measures of health disparities for this population are compared to adults who are not experiencing homelessness had poorer outcomes than adults not experience homelessness for 17 of 19 measures, although Type 2 diabetes is nearly the same. Expenditures were also much higher for adults experiencing homelessness than the comparison group. 29 Table 6. An Account Of Health Disparities For Adults In Category 2: Race And Ethnicity An Account of Health Disparities for Category 2 (Groups 3, 4, 5, 6) Line Description Group 3. Group 4. Group 5. Group 6. Black or African American Hispanic White All American (U.S. Indian (U.S. born) (U.S. born) Adults born) 1 Population 23,464 66,093 16,907 296,992 550,341 2 Average age 35.1 35 31.2 38.7 37.2 3 HEALTH DISPARITY MEASURES 13 4 2 4 Mortality 1.4% 0.8% 0.5% 1.0% 0.8% 5 Morbidity 6 Type 2 diabetes 12.4% 8.3% 7.6% 6.2% 7.0% 7 Asthma 12.5% 16.5% 10.0% 9.6% 9.4% Human Immunodeficiency Virus (HIV)/Hepatitis C 8 Virus 4.5% 2.7% 1.7% 1.5% 1.6% 9 Hypertension 7.7% 9.6% 5.6% 3.9% 5.1% 10 Cardiovascular 2.1% 2.0% 0.7% 1.5% 1.4% 11 Chronic Obstructive Pulmonary Disease (COPD) 11.9% 8.4% 6.7% 10.2% 8.5% 12 Injury 10.5% 7.0% 6.6% 6.0% 5.6% 13 Lung/Laryngeal Cancer 0.3% 0.2% .07% 0.3% 0.2% 14 Substance Use Disorder (SUD) 35.4% 20.1% 14.1% 15.6% 14.4% 15 Post-Traumatic Stress Disorder (PTSD) 10.5% 8.6% 6.1% 5.6% 5.9% 16 Depression 30.3% 20.6% 19.2% 22.4% 19.2% 17 Serious and Persistent Mental Illness (SPMI) 7.4% 7.1% 4.8% 6.2% 5.6% 18 Disability 10.5% 14.8% 6.6% 8.1% 8.2% 19 Health Care Access, Use, Quality 20 Potentially preventable emergency department visits 21.6% 19.3% 12.7% 9.3% 10.5% 21 Potentially preventable hospital admissions 1.1% 1.0% 0.5% 0.6% 0.6% 22 HEDIS measures 23 Annual preventive visit 35.0% 35.9% 31.4% 33.8% 33.2% 24 Comprehensive diabetes care - A1c test 87.5% 90.8% 90.2% 92.5% 92.0% 25 Annual dental visit (ADV) for adults 47.0% 45.5% 46.8% 48.6% 48.4% 26 Health Care Costs 27 Total Calendar Year (CY) expenditures per individual $11,578 $8,211 $6,159 $7,597 $7,104 Notes: (1) Group 3. Adults who are American Indian, where measures of health disparities are compared to adults of other races and ethnicities including adults who are U.S. born Black or African American, Hispanic, White, and the average for all 550,441 adults. Adults who designate as American Indian have the worst health outcomes for 13 of 19 measures, and second worst for 4 of 19 measures. Expenditures per individual, as a measure, is excluded from this count. (2) Group 4. Adults who are Black or African American, where measures of health disparities are compared to other populations. Adults who are U.S. born Black or African American have the worst health outcomes for 4 of 19 measures, and second worst for 9 of 19 measures. Expenditures per individual, as a measure, is excluded from this count. (3) Group 5. Adults whose ethnicity is Hispanic, where measures of health disparities are compared to other populations. Adults whose ethnicity is Hispanic have the worst health outcomes for 1 of 19 measures, and second worst for 2 of 19 measures. Expenditures per individual, as a measure, is excluded from this count. (4) Group 6. Adults who are White, where measures of health disparities are compared to other populations. Adults who are White have the worst health outcomes for 1 of 19 measures. Adults who are White have a range of chronic conditions; however, they compare favorably to other comparison groups with one of the lowest rates of disability and lowest rates of ED visits. Expenditures per individual, as a measure, is excluded from this count. 30 Table 7. An Account Of Health Disparities For Adults In Category 3: Disability An Account of Health Disparities for Category 3 (Group 7: Disability Status) Line Description Group 7. Disability Adults with Adults without a Disability a Disability 1 Population 45,050 505,291 2 Average age 43.7 36.7 3 HEALTH DISPARITY MEASURES 4 Mortality 3.9% 0.5% 5 Morbidity 6 Type 2 diabetes 20.7% 5.7% 7 Asthma 20.1% 8.4% 8 Human Immunodeficiency Virus (HIV)/Hepatitis C Virus 6.4% 1.2% 9 Hypertension 8.9% 4.8% 10 Cardiovascular 6.3% 0.9% 11 Chronic Obstructive Pulmonary Disease (COPD) 24.0% 7.2% 12 Injury 9.6% 5.2% 13 Lung/Laryngeal Cancer 1.2% 0.1% 14 Substance Use Disorder (SUD) 28.2% 13.2% 15 Post-Traumatic Stress Disorder (PTSD) 17.5% 4.9% 16 Depression 42.8% 17.1% 17 Serious and Persistent Mental Illness (SPMI) 22.8% 4.0% 18 Disability n.a n.a. 19 Health Care Access, Use, Quality 20 Potentially preventable emergency department visits 16.6% 10.0% 21 Potentially preventable hospital admissions 2.5% 0.44% 22 HEDIS measures 23 Annual preventive visit 47.6% 32.0% 24 Comprehensive diabetes care - A1c test 92.3% 91.9% 25 Annual dental visit (ADV) for adults 51.9% 47.9% 26 Health Care Costs 27 Total Calendar Year (CY) expenditures per individual $32,594 $4,832 Notes: (1) Group 7. Adults with a disability, where measures of health disparities are compared to adults without a disability. Adults with a disability have the worst health outcomes for 16 out of 18 measures. Adults with a disability have higher rates across all measures as compared to adults without a disability. (Note that one measure is excluded: disability, which explains why there are 19 measures instead of 20 measures.) Expenditures per individual, as a measure, is excluded from this count. 31 Table 8. An Account Of Health Disparities For Adults In Category 3: D-SPMI and SUD Population Groups An Account of Health Disparities for Category 3 (Group 8: SPMI, Group 9: SUD) Line Description Group 8. SPMI Group 9. SUD With Without With Without 1 Population 30,529 519,812 79,349 470,992 2 Average age 39 37.1 37.8 37.1 3 HEALTH DISPARITY MEASURES 4 Mortality 1.7% 0.7% 2.5% 0.5% 5 Morbidity 6 Type 2 diabetes 14.1% 6.5% 9.3% 6.6% 7 Asthma 22.6% 8.6% 16.5% 8.2% Human Immunodeficiency Virus (HIV)/Hepatitis C 8 Virus 5.1% 1.4% 6.4% 0.8% 9 Hypertension 10.2% 4.8% 8.9% 4.5% 10 Cardiovascular 2.6% 1.3% 3.3% 1.1% 11 Chronic Obstructive Pulmonary Disease (COPD) 19.7% 7.9% 18.2% 6.9% 12 Injury 24.1% 4.5% 17.3% 3.6% 13 Lung/Laryngeal Cancer 0.3% 0.2% 0.5% 0.2% 14 Substance Use Disorder (SUD) 50.4% 12.3% n.a. n.a. 15 Post-Traumatic Stress Disorder (PTSD) 39.7% 3.9% 18.0% 3.9% 16 Depression n.a n.a. 49.7% 14.1% 17 Serious and Persistent Mental Illness (SPMI) n.a n.a. 19.4% 3.2% 18 Disability 33.7% 6.7% 16.0% 6.9% 19 Health Care Access, Use, Quality 20 Potentially preventable emergency department visits 21.5% 9.9% 20.8% 8.8% 21 Potentially preventable hospital admissions 1.4% 0.6% 1.8% 0.4% 22 HEDIS measures 23 Annual preventive visit 49.4% 32.3% 41.0% 31.9% 24 Comprehensive diabetes care - A1c test 91.6% 92.1% 89.0% 92.7% 25 Annual dental visit (ADV) for adults 57.2% 47.7% 49.4% 48.3% 26 Health Care Costs 27 Total Calendar Year (CY) expenditures per individual $26,816 $5,947 $17,761 $5,309 Notes: (1) Group 8. Adults with SPMI, where measures of health disparities are compared to adults without SPMI. Adults with a diagnosis of SPMI have the worst health outcomes for 15 out of 17 measures. (Note that two measures are excluded: Depression and SPMI.) Expenditures per individual, as a measure, is excluded from this count. (2) Group 9. Adults with SUD, where measures of health disparities are compared to adults without SUD. Adults with a diagnosis of SUD have the worst health outcomes for 16 out of 18 measures. (Note that one measure is excluded: SUD.) Expenditures per individual, as a measure, is excluded from this count. 32 TAKING STOCK: A SIMPLE SUMMARY OF Indian had a total of 17 poor health outcomes out of a total of THE BIVARIATE RESULTS FOR ADULTS 19 outcome measures. To take stock of the many bivariate results, we prepared a simple summary of the results in Table 9. The total number As the data in Table 9 shows, adults subsisting on a poverty- of poor health outcomes for each population group is based level income have much poorer health outcomes than adults on the sum of "worst" and "second worst" outcomes for with more income, adults who are American Indian and adults each population group. In this context, worst means highest who are Black or African American have much poorer health prevalence. outcomes than adults who are White, and adults with a disability have much poorer health outcomes than adults who For example, adults who are American Indian (Group 3): This are not disabled. Readers will want to review all population- population group had 13 of the worst outcomes and four of specific tables (Tables 4, 5, 6, 7, and 8) carefully to the second-worst outcomes compared to the results for three appreciate the variation in health disparities among population other adult groups (U.S. born adults who are Black/African groups across measures of mortality, morbidity, and disability. American, Hispanic, or White). Adults who are American Table 9. A Summary Of Poor Health Outcomes Among Four Adult Population Groups The Total Number of Poor Health Outcomes for Adults, Based Upon Bivariate Results The Number of Poor Outcomes by Group Category Adult Groups Worst Second- Poor Total Poor as Out- Worst Outcomes Measures a % of comes Out- Total comes A B C D E Baseline All Adults Male 11 n.a. 11 19 58% Female 8 n.a. 8 19 42% Category 1 Adults with Very Low Income Group 1 Income at or below 50% of FPL 18 n.a. 18 19 95% Group 2 Experiencing Homelessness 17 n.a. 17 19 89% Category 2 Adults by Race and Ethnicity Group 3 American Indian 13 4 17 19 89% Group 4 Black/African American (U.S. born) 4 9 13 19 68% Group 5 Hispanic (U.S. born) 1 2 3 19 16% Group 6 White (U.S. born) 1 4 5 19 26% Category 3 Adults with Disabilities Group 7 Disability: Eligibility Status 16 n.a. 16 18 89% Group 8 SPMI 15 n.a. 15 17 88% Group 9 SUD 16 n.a. 16 18 89% 33 MORTALITY RATES AMONG ADULT Figure 1 shows mortality rates for all population groups by POPULATION GROUPS category. (The size of each Medicaid group is also shown The mortality rate is one of the most important measures in this figure.) The mortality rates range from a low of 0.5 of health disparity. As defined in this analysis, the mortality percent to a high of 3.9 percent. As the figure shows, the rate refers to the proportion of people who died within the mortality rates for both category 1 populations are higher 2.5 years in our data period. In the 2.5 years that mortality than the average for all Medicaid adults. The mortality rate was measured for all adults, the rate was 0.8 percent, or is 1.3 percent for adults with very low income, and 1.2 less than 1 percent of all adults in the Medicaid program. percent for adults experiencing homelessness. This mortality rate reflects the average for all adults covered under the Medicaid program, including adults Most strikingly, however, are the mortality rates for adults who are U.S. born and adults who are not U.S. born. in category 3. Adults with a disability based upon eligibility status had a mortality rate of 3.9 percent, adults with a diagnosis of SPMI had a mortality rate of 1.7 percent, and adults with SUD had a mortality rate of 2.5 percent. Figure 1. Mortality Rates Among Medicaid Adults Mortality Rates for Adults Covered Under Medicaid in Minnesota by Category and by Population Group Category 3 4.5% 3.9% Disability 4.0% 3.5% Category 1 Category 2 3.0% Very Low Income Race and Ethnicity 2.5% 2.5% 2.0% 1.7% 1.3% 1.2% 1.4% 1.5% 1.0% 1.0% 0.8% 0.8% 0.5% 0.5% 0.0% 3) ) 9) ) ) 7) 0) ) 41 29 92 4) 1) 09 50 34 90 05 46 72 ,3 ,5 ,9 ,3 6, 9, 6, 5, 50 30 96 3, 8, 40 (6 (7 (1 (4 (2 (3 (5 I( (2 (2 an D c y M an s lts te ni ilit SU L ic es SP FP pa hi di du er ab el W In 3. Am is lA is 3. om % H n D 2. at Al 50 at a H an 2. C 3. ic at C 1. er < ric at C at 1. Am C at C Af C at k/ 2. C ac at Bl C 2. at C 34 COST DIFFERENCES AMONG ADULT use of medical services such as potentially preventable ED POPULATION GROUPS visits or institutional services such as nursing homes. Cost differences are essential to consider because they A comprehensive analysis of costs and savings can help us to appreciate the relative variation in costs focused on policy and practice changes would be among groups and they are an important outcome to a worthwhile pursuit. Such a model should capture state Medicaid programs. There is extreme variation assumptions around costs and savings related to in the average annual cost per adult across the nine interventions, services to address SDOH needs, expanded population groups. Such variation in costs is the subject home and community-based services, and other efforts of significant study among actuaries, researchers, and to close the gaps in health outcomes. A commitment to policymakers interested in understanding the key factors implementing these changes and a framework for data driving costs relating to health risk, social determinants of feedback and iterative modification is essential. health, functional status, benefit coverage, prices, service use, and the care delivery approach. This report does not State Medicaid programs could begin this work by attempt to analyze the cost variation among Medicaid building a model focused on children and teens. This populations. would provide policymakers with an understanding of the potential to alter the cost trajectory from investing Readers should not assume that cost differences are in younger generations. Investing in children served by attributable to health disparities and that efforts to close Medicaid could lead to higher tax revenue and lower these gaps would yield savings. While it is true that health care use as adults. Studies have shown that today's costs under Medicaid programs reflect poor health Medicaid coverage leads to positive long-term impacts in outcomes for many populations, we have not attempted adulthood.128 to quantify the potential savings from reducing the high Figure 2. Cost Ratios Among Medicaid Adults Cost Ratios Among Adults Covered under Medicaid in Minnesota by Category and by Population Group Category 3 5.0 4.6 Disability 4.5 4.0 3.8 Category 1 Category 2 3.5 Very Low Income Race and Ethnicity 3.0 2.5 2.5 2.0 1.5 1.6 1.4 1.5 1.0 1.2 1.1 0.9 1.0 0.5 - 3) ) 9) ) ) 7) 0) ) 41 4) 29 92 1) 50 09 34 90 05 46 72 ,3 ,5 ,9 ,3 6, 9, 6, 5, 50 30 96 3, 8, 40 (6 (7 (1 (4 (2 (3 (5 I( (2 (2 an D c y M n s lts te ni ilit SU L ia ic es SP FP pa hi du ab er nd el W 3. is Am lA is 3. om I % H an D 2. at 50 Al at H 2. an C 3. c at C i 1. er < at C ric at 1. Am C at C Af C at k/ 2. C ac at Bl C 2. at C 35 Figure 2 provides a picture of the variation in costs across CHILDREN AND TEENS the three categories and populations by comparing the average annual cost per population group to the average AN ACCOUNT OF HEALTH DISPARITIES IN for all adults ($7,104). Cost ratios for each population are CHILDREN COVERED UNDER MEDICAID presented in Figure 2. These cost ratios were calculated by Understanding health disparities in younger people dividing the average cost for each population group by the including children and teens has lifelong importance. average cost for Medicaid adults. For example, the average Minnesota's comprehensive study included 303,140 cost per adult for those with very low income is 1.5 times persons under the age of 18. Our analysis included only higher, or 50 percent higher, than the average cost for the children who had a parent enrolled in Medicaid. Children total adult population group (n = 550,341). The ratios range who did not have a Medicaid-covered parent were from 0.9 (i.e. lower than average) for Hispanic adults to 4.6 excluded from the dataset, given our goal was to examine times higher than average for adults with disabilities. the relationship between social risk factors for parents and health outcomes for children. REGRESSION RESULTS FOR ADULTS COVERED UNDER MEDICAID CHILDREN COVERED UNDER MEDICAID Although much of this report focuses on the results from Table 11 provides a picture of Medicaid-covered children the bivariate analyses, the analytical plan also included and teenagers under the age of 18 by providing counts of multiple regression analyses. Regression analysis helps the number of younger people by income, by households us to determine the statistical association between risk that experience homelessness, race and ethnicity, factors and outcomes. Using regression analysis, for and disability status. Table 11 provides the numbers example, we could examine the relationship between of children by risk factor. Types of risk factors include income and mortality. It is important to note that all social, medical, and disability. Several of the risk factors regressions were adjusted for demographics, geography, include risks for children, which are based upon parental and other social risk factors. conditions. For example, 12.6 percent of children have a parent with chemical dependency and 6.1 percent have a Table 10 highlights the impact of poverty, structural parent with a mental health diagnosis. racism, and disability on health outcomes. From the wealth of regression results, we selected 12 health disparity Health disparities among children covered under measures to report in Table 10. Only statistically significant Minnesota Medicaid were measured using mortality rates results are reported. In many cases, the regression results and morbidity measures. Morbidity measures included the confirm that specific comparisons among population prevalence of several diagnoses and conditions including groups are essential to understanding the intersectional asthma, injury, teenage substance use disorder, ADHD, social factors that may lead to health disparities. and PTSD. Health care use and quality measures such as well-child visits and annual dental visits were also From the bivariate results, we identified strikingly high examined. As Table 11 indicates, the prevalence of injury rates of disability among adults with income at or below among children is 4.8 percent; the prevalence of asthma 50 percent of the FPL (15.7 percent) and for adults who among children is 11.7 percent. are Black or African American (14.8 percent). The rates of disability for these two population groups are nearly double It is important to note that these data points for children the overall rate for all adults (8.2 percent). covered under Medicaid can also be compared to data for the state and the nation too. When those comparisons In the same way that the bivariate results were striking, the are performed, an 11.7 percent prevalence of asthma regression results were too. The odds of having a disability is concerning for children covered under Medicaid. The are much higher for two population groups. As indicated prevalence of asthma is higher for children covered in Table 10, adults who are Black or African American under Medicaid than children in the state of Minnesota are nearly 100 percent more likely to have a disability than and in the nation. In 2016, for example, the Minnesota White adults. Adults living on an income less than half Department of Health reported the prevalence of asthma of the poverty level are 7.2 times more likely to have a to be 7.1 percent for all children in Minnesota.129 The CDC disability than adults with income above the FPL. All in all, also reported a child lifetime prevalence of 9 percent in adults' results underscore a great injustice in our society Minnesota in 2018. This is also lower than the prevalence that state Medicaid programs can begin to address. For for children covered under Medicaid (11.7 percent).130 more regression results by category, see Table 10. 36 Table 10. The Odds Of Health Disparities For Medicaid Adults By Income, Race, Disability Category 1. Category 2. Race or Ethnicity Category 3. (As designated by the individual on the Very Low Income Medicaid enrollment application) Disability Health Disparity Group 1. # Group 3. Group 4. Measures Adults with income at Group 7. Adults who are Adults who are Black or below 50% of the Adults with Disabilities American Indian or African American FPL Comparison Adults with income Adults who are White Adults without disabilities Group above the FPL Adults with income at or Adults who are Adults who are Black below 50% of the FPL American Indian are Adults with a disability are or African American are are 6% more likely to 150% or 1.5 times 77% more likely to have 1 Type 2 diabetes 56% more likely to have Type 2 diabetes more likely to have Type 2 diabetes than have Type 2 diabetes than adults with income Type 2 diabetes than adults without a disability than White adults above the FPL White adults 4% more likely to The regression result 39% more likely to 66% more likely to 2 Asthma have asthma than the is not statistically have asthma than the have asthma than the comparison group significant comparison group comparison group 43% more likely to 87% more likely to The regression result is 86% more likely to have 3 HIV/Hep-C have HIV or Hep-C than have HIV or Hep-C than not statistically HIV or Hep-C than the the comparison group the comparison group significant comparison group 10% more likely to 37% more likely to 80% more likely to 36% more likely to have 4 Hypertension have hypertension than have hypertension than have hypertension than hypertension than the the comparison group the comparison group the comparison group comparison group 180% or 1.8 times 32.6% more likely to 33% more likely to 32% more likely to more likely to have a have a cardiovascular have a cardiovascular have a cardiovascular 5 Cardiovascular cardiovascular condition condition than the condition than the condition than the than the comparison comparison group comparison group comparison group group 12% more likely to Less likely to have Less likely to have 95% more likely to 6 COPD have COPD than the COPD than the COPD than the have COPD than the comparison group comparison group comparison group comparison group Lung or 44% more likely to Less likely to have lung 3.5 times more The regression result have lung or laryngeal or laryngeal cancer likely to have lung or 7 laryngeal is not statistically cancer than the than the comparison laryngeal cancer than the cancer significant comparison group group comparison group Nearly 100% more Nearly 100% more Less likely to have SUD 27% more likely to likely to have a SUD likely to have a SUD 8 SUD than the comparison have a SUD than the than the comparison than the comparison group comparison group group group 120% or 1.2 times 21% more likely to Less likely to have Less likely to have more likely to have 9 Depression have depression than depression than the depression than the depression than the the comparison group comparison group comparison group comparison group Close to 100% 7.2 times more likely The regression result Health disparity measure more likely to have 10 Disability to have a disability than is not statistically does not apply to adults a disability than the the comparison group significant with disabilities comparison group About 40% more likely 53% more likely to 68% more likely to Over 32% more likely Potentially to have a preventable have a preventable have a preventable to have a preventable ED 11 preventable ED ED visit than the ED visit than the ED visit than the visit than the comparison visits comparison group comparison group comparison group group 160% or 1.6 times 23% more likely to 26% more likely to 30% more likely to more likely to Preventable have a preventable have a preventable have a preventable 12 have a preventable hospitalization hospitalization than the hospitalization than the hospitalization than the hospitalization than the comparison group comparison group comparison group comparison group 37 Table 11. Children Covered Under Minnesota Medicaid's Program Younger People Covered Under Minnesota's Medicaid Program Under 18 years of age Line # % 1 Population 303,140 2 Age 0-3 81,292 26.8% 3 Age 4-5 38,394 12.7% 4 Age 6-8 56,795 18.7% 5 Age 9-12 62,216 20.5% 6 Age 13-17 64,443 21.3% 7 Very low income: at or below 50 percent of the FPL 155,131 51.2% 8 Income between 50 and 100 percent of the FPL 92,265 30.4% 9 Income above the FPL (100 percent of the FPL or above) 41,456 13.7% 10 Family homelessness 12,866 4.2% 11 American Indian (U.S. born) 15,224 5.0% 12 Black or African American (U.S. born) 48,746 16.1% 13 Hispanic ethnicity (U.S. born) 15,651 5.2% 14 White (U.S. & Non-U.S. born) 118,641 39.1% 15 Total for Lines 11, 12, 13, 14 198,262 65.4% 16 All Other Children 104,878 34.6% 17 SOCIAL, MEDICAL, AND DISABILITY RISK FACTORS 18 Child protection involvement 32,648 10.8% 19 Parental chemical dependency 38,323 12.6% 20 Parental mental illness 18,557 6.1% 21 Parent: disability/medical condition 11,498 3.8% 22 Parents married 117,159 38.6% 23 Child in household with 4+ children 76,377 25.2% 24 Parent: language is English 251,468 83.0% 25 Parent: language is other than English 51,672 17.0% 26 Parent immigrated 82,519 27.2% 27 Likely parental incarceration 6,580 2.2% 28 HEALTH DISPARITY MEASURES 29 Mortality 344 0.1% 30 Morbidity 31 Injury 14,601 4.8% 32 Asthma 35,368 11.7% 33 Substance Use Disorder (SUD) (denominator: n = 36,657) 2,041 5.6% 34 Attention Deficit Hyperactivity Disorder (ADHD) 24,830 8.2% 35 Post-Traumatic Stress Order (PTSD) 5,546 1.8% 36 Depression 11,225 3.7% 37 Disability 10,243 3.4% 38 Health Care Measures 39 Well-child visits for all children (denominator: n = 131,057) 82,253 62.8% 40 Annual dental visit (denominator = 177,685) 114,183 64.3% 38 We grouped children into three categories, although the Readers should also note that key results for children with children's categories are somewhat different than those for child protection involvement (CPI) from the child protection adults. system are presented in Appendix F.131 Children with CPI, representing 10.8 percent of all children in this study, Category 1: Very Low income. This category includes all does not fit neatly into the structure of this report's three children from families with income at or below 50 percent categories. However, there is an urgent need to develop of the FPL. The data show that many children covered data systems to understand the unique barriers to health under Medicaid are growing up in poverty. More than 1 of 2 that children face. children or 51.2 percent of children come from families with income at or below 50 percent of the FPL. Key Results Category 1: Very Low Income. One in two children Category 2: Race. This category includes children by covered under Medicaid in Minnesota is growing up in race and ethnicity. The data show that children who are families where the income is at or below 50 percent of White, based on all children covered under Medicaid, are the FPL (see Table 11). As we learn from our regression the largest group within this category. Children who are analyses, children from families with income at or below White represent 39.1 percent of all children in this analysis. 50 percent of the FPL are two times more likely to die than Children who are Black or African American are the second children from families with income above the FPL, in the largest population group in this category, representing 16.1 2.5 years it was measured. This group of children is also percent of all children in this analysis. more likely to have PTSD than the comparison group. Category 3: Parental Disability: Diagnosis of Mental Category 2: Race. About 5 percent of children are Illness. This category focuses only on children with American Indian and 16.1 percent of children are Black or parents with mental illness, which corresponds to line 20 in African American. The regression results provided evidence Table 11. Children with parents with a diagnosis of mental that children who are American Indian have worse health illness represent 6.1 percent of all children. outcomes than White children. Children who are American Indian are 1.3 times more likely to have a SUD condition as a teen than White children covered under Medicaid. REGRESSION RESULTS FOR CHILDREN This group of children is also more likely to have a higher COVERED UNDER MEDICAID prevalence of PTSD. Key results for five measures of health disparities for children are shown in Table 12. These results show the Category 3: Parental Disability: Mental Illness. Based statistical associations generated through the regression on the definition of mental illness used for this study, 6.1 analysis for category 1 (very low income), category 2 (race), percent of children have a parent with lived experience of and category 3 (disability). All results shown are statistically mental illness. Children with a parent with a disability of significant, unless indicated otherwise. The select results mental illness are more likely to have asthma, SUD, ADHD, shown in Table 12 highlight the impact of poverty, race, and PTSD than children with parents who do not have a and disability on health outcomes. mental health diagnosis. Children in this population group are 1.25 times more likely to have PTSD than children with a parent without mental illness. 39 Table 12. The Odds Of Health Disparities For Medicaid Children Category 3. Category 1. Category 2. Parental Disability: Health Very Low Income Race Mental Illness Disparity # Children from families Children who are Children with a parent Measures Children who are with income at or below Black or African with a diagnosis of American Indian 50% of the FPL (U.S. born) mental illness Comparison Children with a parent Group Children from families with Children who are White without a mental health income above the FPL diagnosis 1 Mortality Children from families The regression result The regression result This regression result at or below 50% of FPL is not statistically is not statistically is not statistically are more than 2 times significant significant significant more likely to die in the study period than children from families with income above the FPL 2 Asthma Less likely to have asthma Children who are Children who are Children with a parent than the comparison American Indian are Black or African with mental illness group 24% more likely to American are 74% are 22% more likely have asthma than more likely to have to have asthma than White children asthma than White children with parents children who do not have a mental health diagnosis 3 SUD The regression result is Children who are The regression result Children with a parent not statistically significant American Indian are is not statistically with mental illness are 130% or 1.3 times significant 31% more likely to more likely to have SUD than the have SUD than the comparison group comparison group 4 ADHD The regression result is Less likely to have a Less likely to have a Children with a parent not statistically significant diagnosis of ADHD diagnosis of ADHD with mental illness than the comparison than the comparison are 84% more likely group group to have a diagnosis of ADHD than the comparison group 5 PTSD Children from families Children who are The regression result Children with a parent at or below 50% of FPL American Indian are is not statistically with mental illness are 10% more likely to 39% more likely to significant are 125% or 1.25 have PTSD than the have PTSD than the times more likely to comparison group comparison group have PTSD than the comparison group 40 Section 4. Key Findings & Policy Implications For States Minnesota's examination of health disparities in Medicaid African American have worse health outcomes than adults populations is informative for the nation. Minnesota's who are White. The population of adults who are American account of health disparities by income, race, ethnicity, and Indian also has the highest mortality rate among all adults. disability demonstrates how state Medicaid programs might Adults who are African American are also impacted by move from conceptual support for health equity to action. health disparities, with many poor health outcomes. These adults have the highest rate of disability among all race and Medicaid programs can begin by investing in data collection ethnicity groups. Finally, adults with disabilities (category 3) and analytics and move to implementation. Minnesota has experienced disparities across all outcomes. Adults with a also taken steps to act on its analytics. As mentioned in disability have the highest rate of mortality among all adults. Section 3, DHS required its contracted care delivery entities to launch interventions to address health equity and infused Policy Implication. Data shows the relationship between health equity goals into its value-based payment models. health disparities and inequities experienced by adults covered by Medicaid in Minnesota. The data also State Medicaid programs have a long and challenging establishes an important baseline against which the state's process ahead of them to reduce health disparities. Data Medicaid program can monitor progress in reducing these collection and analytics are only the starting point. Reducing health disparities. The results confirm that every state disparities and achieving measurable improvement in Medicaid program can advance health equity by creating health equity requires a concerted effort across sectors and a state-specific baseline to catalyze action. State Medicaid communities to create a shared vision and sustain programs programs must take the lead in addressing inequities that to achieve health equity. The federal and state governments lead to health disparities. These efforts require robust data should be ready to make a significant level of investment. systems that advance the capacity of Medicaid programs, health care providers, and community-based organizations State Medicaid offices need to collaborate to address the intersection of race, ethnicity, disability, with federal partners, other state poverty, and factors not analyzed in the making of this report departments, and diverse partners to address (e.g., sexual orientation and gender identity).132 communities' basic social needs to achieve improved and equitable health outcomes. KEY FINDING 2. High rates of disability intersect with They must invest in upstream prevention poverty and race. Over 15 percent of adults with very low initiatives to address community inequities income have a disability, and nearly 15 percent of Black that drive poor health. These inequities adults have a disability. When controlling for all factors, include inadequate housing, food insecurity, including age, gender, and income, adults who are Black limited transportation, other immediate or African American are 100 percent more likely to have a problems, and more underlying inequities in disability than White adults. Adults with income at or below education, employment, and income. the FPL are 7.2 times more likely to have a disability than adults with income above the FPL. Key findings from this examination of Minnesota's Medicaid program and the policy implications for state Policy Implication. Poverty, race, and disability appear to Medicaid programs are as follows. go hand in hand, a relationship that must end. The results underscore the strong relationship between income, KEY FINDING 1. Health disparities in Medicaid exposure to structural racism, disability, and poor health population groups reflect health injustice. In adults, outcomes among Medicaid populations. The data suggest lower-income levels are associated with worse health that disability is a social risk factor disproportionately outcomes across nearly all measures. Most measures impacting Black and African Americans with income at or show that adults who identify as either American Indian or below 50 percent of the FPL. Taking this approach would 41 advance our understanding of disability as a social risk Policy Implication. The results for adults who are factor alongside other risk factors and inequities, including American Indian are consistent with the findings on a structural racism, reduced educational and economic national level. Reducing disparities among adults who opportunity, lower-income and wealth, and toxic stress. are American Indian requires innovative models to promote better health equity and outcomes by engaging KEY FINDING 3. Adults with very low income have communities and empowering them to lead in finding poorer health outcomes than adults with higher solutions. Generations of structural racism and inequity incomes. Many adults covered under Medicaid have have led to American Indians dying at higher rates than very low income, as defined by an income at or below 50 other Americans in many categories. According to the percent of the FPL. Health disparities are worse for adults Indian Health Services of the U.S. Department of Health with very low income than adults with relatively higher and Human Services, "lower life expectancy and the income across all measures. Those who are poorest have disproportionate disease burden exist perhaps because higher mortality rates, burdens of illness, disability, and of inadequate education, disproportionate poverty, ED use. Adults with very low income are 33 percent more discrimination in health services delivery, and cultural likely to have a cardiovascular condition and 100 percent differences. These are broad quality of life issues rooted in more likely to have a substance use disorder than the economic adversity and poor social conditions."133 comparison group. Costs to Medicaid are higher for adults with incomes at or below 50 percent of the FPL. The cost KEY FINDING 5. Adults with a disability have the to Medicaid was $10,447 (2014), or 2.5 times higher than highest mortality rates of all population groups. Adults the cost for adults with relatively higher incomes. The cost eligible for Medicaid based on disability status, adults with to Medicaid for adults with income above the FPL was a diagnosis of SPMI, and adults with a SUD had higher $3,694 (2014). Adults experiencing homelessness have mortality rates than any other population group examined in higher rates of health disparities than adults who are not the analysis. From lowest to the highest rate, the mortality experiencing homelessness. rate was 1.7 percent for adults with SPMI, 2.5 percent for adults with SUD, and 3.9 percent for adults with a disability, Policy Implication. The results highlight the strong link in the 2.5 years it was measured for adults. between the social conditions of poverty and illness and mortality rates. Adults with the lowest level of income Policy Implication. The adults' results underscore the have the worst health disparities. These results reinforce need to advance comprehensive disability health initiatives, the influence that social drivers related to poverty have on informed by much-improved data collection efforts. State health and well-being. Low-income adults need increased Medicaid programs must implement HHS data collection access to broader social supports. The results also standards to collect disability types. The data should be provide evidence for increased investment in data-driven used in conjunction with the CDC's Disability and Health policies that address social supports, most often, housing. Data System source-level data on adults with disabilities. KEY FINDING 4. Adults who are American Indian Reducing health disparities impacting persons with experience more significant health disparities than disabilities requires Medicaid programs to address the other races. Adults who are American Indian experience anti-disability bias that leads to policies that disadvantage a disproportionate degree of health disparities compared persons with disabilities compared with the general with other populations. These disparities include higher population, as evident from state efforts to put in place mortality rates than other races. The mortality rate is 1.4 policies rationing care to persons with disabilities amidst the percent for adults who are American Indian, compared 2020 COVID-19 pandemic.134 The results also underscore to the lower rates for adults who are White, Black or the need for increased commitment to the following: African American, and Hispanic. Adults who are American compliance with the Americans with Disabilities Act; Indian experienced poor health outcomes for 17 of the 19 implementation of Olmstead plans; investment in home and interest measures (see Table 4). Adults who are American community-based services; and reduced percentage of Indian had a higher prevalence of many conditions such as persons residing in skilled nursing facilities through funding Type 2 diabetes (12.4 percent), COPD (11.9 percent), SUD available through federal programs such as Money Follows (35.4 percent), depression (30.3 percent) than other adults. the Person. For persons living with SUD and/or a mental Finally, adults who are American Indian had a higher rate of health diagnosis, states must invest in care integration and potentially preventable ED visits and higher costs than any peer-driven resources endorsed by the Substance Abuse other race or ethnicity examined in this study. and Mental Health Services Administration (SAMHSA). 42 KEY FINDING 6. Children covered under Medicaid achievement and employment, and they are more likely experience health disparities. Children's results to have a nonmarital teenage birth and some involvement provide evidence that children who are American Indian with the criminal justice system."137 Unless addressed, and Black or African American have worse health these poor outcomes will continue to disproportionately outcomes than White children for certain measures. affect Black and African American children. Black and Children who are Black or African American are 74 African American children are more likely to live in poverty percent more likely to have asthma than White children; than White children. According to the Children's Defense and children who are American Indian are 24 percent Fund, "73 percent of children living in poverty are children more likely to have asthma than White children. of color." Nearly 1 in 3 children who are Black or African American and Native American/Alaska Native are poor. Policy Implication. Minnesota results underscore the Among Hispanic children, 25 percent live in poverty. impact of race and income on health outcomes, where These statistics stand in stark contrast to children who American Indian and Black or African American children are White. Less than 10 percent White children are living are worse off than White children. Most importantly, in poverty.138 The results reinforce the need for Medicaid children covered under Minnesota's Medicaid program programs to end inequities that lead to extreme poverty included in this analysis are growing up in families where among children who identify as BIPOC. the income is at or below 50 percent of the FPL. Eight in 10 children are from families where the income is under In the national context, 80 percent of this nation's 100 percent of the FPL. children from families with income below 100 percent of the FPL have Medicaid coverage.139 Children covered To be reminded, in 2020, the FPL was $21,720, or under Medicaid are more diverse than adults covered $1,810 per month for a family of three.135 An income of under Medicaid. The results strengthen the argument 50 percent of the FPL is 50% of $21,720. This provides a that state Medicaid programs invest in interventions family of three only $10,860 annually. that address children's health disparities to achieve the long-term goal of health and wellness. We must build According to the American Academy of Pediatrics, healthy communities for children, which includes robust "poverty is an important social determinant of health and educational systems and high graduation rates. In the contributes to child health disparities," with implications long run, we all benefit by eliminating health disparities. for "adverse health outcomes in childhood and across the Children gain a higher quality of life, while the country life course."136 Poor health outcomes represent only one and the health care system benefit from lower health care aspect of the disparities that come from poverty, however. costs.140 We must redesign how care is delivered and Children who experience poverty "are less successful improve the conditions in communities. than their never-poor counterparts in their educational 43 Section 5. Medicaid's Opportunity To Achieve Health Equity KEY MESSAGES Many state Medicaid programs have been leaning into efforts to advance health equity by addressing the adverse This report emphasizes state Medicaid programs' health outcomes and associated economic costs resulting critical importance to millions of people who from systemic racism and discrimination.143 Some state experience structural racism, discrimination, Medicaid programs have launched initiatives to target implicit bias, and stigma. These millions include health disparities through quality measurement and value- American Indian, Black, African American, Latinx, Asian, based payment models.144 Other states have added health other racial and ethnic populations, and people with equity as a goal to their transformation efforts and funded disabilities.141 Historically, our federal and state laws and health-related services.145 Several other state Medicaid their implementation have perpetuated system racism programs have created opportunities to address long- and discrimination. Despite changes in laws to address standing health disparities among people with disabilities systems-level discrimination, bias still exists, resulting through Financial Alignment Initiative demonstrations for in health and wellness barriers for these populations. dually eligible populations.146 There are many examples of discriminatory policies that exist. A recent change in federal law in 2020, for We are firm in our belief that we cannot example, reduced food assistance benefits for people achieve health equity without making racial struggling to find or sustain work. This type of policy and health justice the cornerstone of all disproportionately hurts people who identify as BIPOC, efforts.147 We must also be ready to address given their higher unemployment rates. The lack of equitable access to education, housing, food security, the disproportionately high disability rate etc., also disproportionately impacts populations that among Black, Indigenous and People of have experienced discrimination. These social supports Color (BIPOC), Latinx, Asian, other racial are endemic drivers of poor health outcomes that are well and ethnic populations. It is an ethical documented and supported by health disparity data. imperative. This report highlights the essential contribution to COVID-19: A CALL TO ACTION the evidence base by one state's Medicaid program As we endure the second wave of the COVID-19 and strengthens the case for action. Minnesota's pandemic, we must take direct action to prevent the level Medicaid program identifies people who have very low of harm experienced by racial and ethnic populations income, people who are American Indian, people who are during the first wave of COVID-19 during the winter and Black and African American, and people with disabilities spring of 2020. COVID-19 continues to shine a spotlight with poorer health outcomes. on inequities in our health care delivery system. These inequities result from federal and state failure to invest The real benefit of measurement is to in a robust public health infrastructure and to address provide an evidence base against which social structural inequities stemming from racism and state Medicaid programs can establish discrimination against populations, including people priorities, tailor interventions, set with disabilities.148 During the first wave, nursing homes appropriate goals, measure improvement, were epicenters of COVID-19 infections and residents and make a public case to elected officials suffered a disproportionate number of deaths.149 Many that resources are required. people were infected by COVID-19 and will experience on-going medical adverse effects of COVID-19 in the As a publicly funded program, Medicaid should advance coming years.150 In this second wave of COVID-19, federal health equity through partnerships with Medicare, and state policymakers' inaction has caused hospitals, public health, housing, economic development, income health care workers, and other frontline workers such assistance, and multiple sectors of our society.142 as home health workers and personal care assistants to 44 be stretched thin and put at increased risk. During this understanding of health drivers by holding the health care time, we can only project a disproportionate morbidity system responsible for taking active steps to address and mortality rate among African Americans and other direct and indirect bias in their policies, practices, and historically under-resourced populations.151 procedures. Data systems must have the capacity to track bias at the individual as well as the systems-level. THE IMPERATIVE COVID-19 has made clear that federal and state States in partnership with the federal government need to policymakers have not taken the necessary steps to invest in comprehensive data collection and data analytic reduce health disparities or advance health equity among systems to track health disparities across populations ethnic minority populations, older adults and persons at the local, state and regional levels. This approach with disabilities. Policymakers should now confront the promises to improve the trajectory of health outcomes moral, ethical and financial need to address COVID-19's across the country. Significant data improvements are disproportionate impact on these populations. States must needed to collect race, ethnicity and disability status. take immediate steps to address disparities and advance Efforts should embrace a system of authentic co-creation health equity. See Box 13 for Braveman's discussion on with the communities served to bring shared knowledge, health disparities and justice. accountability, and experiences to the table and power- sharing with CBOs. This report argues that states begin this process by developing robust data systems in collaboration with States should strengthen their data analytic capacities to other state public health offices, state agencies that address high morbidity and mortality rates among racial provide services to Medicaid populations, and community- and ethnic groups of people and among people with based organizations (CBOs). State Medicaid programs disabilities covered by Medicaid. Two sets of strategies in partnership with public health – with support from the can be used, corresponding to two distinctive risk groups. federal government – must rebalance health care priorities The most immediate results could come from focusing on to address structural racism, inequity, and upstream people with serious chronic illness, where changes to help health drivers. At the same time, we also must shift our them could be made by extending and intensifying existing models within the health care system. BOX 13. BRAVEMAN ON HEALTH DISPARITIES AND JUSTICE In 2011, Paula Braveman wrote a seminal article on the intersection of health disparities and justice, Health Disparities and Health Equity: The Issue Is Justice.152 In this article, Braveman proposed a causal relationship between health disparities resulting from social disadvantage "need not be established." Instead, attention needs to be on establishing public policies with clear definitions that are contextually relevant and grounded in human rights principles. The elimination of health disparities is achievable only when rooted in health equity and the principle of human dignity. Dignity includes the opportunity to participate in society fully. Almost 10 years since that article was written, policymakers continue to struggle with ways to address health disparities, too often seeking to do so, absent efforts to address injustice and human rights. It is not surprising that the populations most negatively impacted by COVID 19 include African Americans, other racial and ethnic populations, and people with disabilities. The Supreme Court recognizes these populations as being subject to structural and systemic level discrimination that has led to their being under-resourced and lacking the opportunity to participate in American society fully. This full participation includes the right to achieve health and wellness. It is specifically because of Supreme Court rulings such as the 1964 Civil Rights Act and the 1990 Americans with Disabilities Act that policymakers must increase investment in data analytics and create policies that can directly impact disparities resulting from historical and ongoing injustices. 45 More broadly, however, states will need to invest in data- bolster and equalize the role of CBOs in health care delivery driven initiatives to advance health equity for adults and and advance data-driven service delivery and care. children living in poverty. The solutions are much more likely to focus on housing, economic support, and safety More broadly, addressing poverty requires a commitment rather than traditional medical interventions. Improved to developing alternatives to building services within outcomes and government savings are much more likely the health care sector. This can include advocacy to be realized by these efforts. by health care providers with CBOs to propose both evidence-based and evidence-informed solutions. These We recognize that not all states have the same capacity organizations can be instrumental in developing new or understanding of options available to them to improve approaches. In the end, states have an opportunity to populations' health and wellness. We also understand partner with CBOs or align incentives to encourage CBOs that states still have not seen or may not yet recognize the to address health disparities and advance equity. direct connection between addressing the needs of high- cost populations and improving the overall health of the Opportunity 4. Rebalance Long-Term Services and residents of their states. Supports (LTSS) to Advance Health Equity. State Medicaid programs have an opportunity to expand long- Recognizing variations in state capacity term services and supports (LTSS) in the community. For and the need for immediate action, we have persons with disabilities, access to LTSS in the community outlined several opportunities for states to and health equity go hand in hand. See Box 14 for more consider as they develop their strategies information about the identified barriers facing people with disabilities that impede health equity. to reduce health disparities and advance health equity actively. The options provided In 2020, Health Affairs reported, "People living in nursing are examples of what policymakers can homes make up less than 1 percent of the U.S. population do to advance health and wellness in yet account for approximately 40 percent of all COVID-19 Medicaid populations. deaths to date."154 In recognition of these disparities and the associated costs, state contracting requirements and data collection must partner with health care to SEVEN OPPORTUNITIES FOR STATE rebalance away from institutional care to community- MEDICAID POLICYMAKERS focused settings, in compliance with Olmstead and other Opportunity 1. Commit to a Multi-State Effort to provisions of the Americans with Disabilities Act. Measure Health Disparities. State Medicaid programs can support each other to advance health equity, while Opportunity 5. Invest in Person-Centered Care. appreciating the differences in state Medicaid programs' States have many other types of opportunities, including capacity to perform data analytics. This requires those that support person-centered care for high-cost developing a standardized and comprehensive data populations in the community. States can invest in the collection process for race and ethnicity, disability types, Community Health Worker (CHW) workforce to address and social determinants of health.153 SDOH in a culturally competent manner. States can increase flexibility in spending on services to enable people Opportunity 2. Launch State-Level Interagency and to live in the community, support consumer-controlled Cross-Sector Collaboration to Collect Data. State personal care attendant services, assistive technology (AT), Medicaid programs can support enhanced data collection and nonmedical transportation. Finally, states can expand across state agencies. A successful effort would increase the use of certified peer specialists and certified recovery uniformity in data collection methods and refinement of coaches, and other innovative peer-led interventions to intersectional analyses to understand health disparities in support overall health and wellbeing. smaller populations. Opportunity 6. Invest in Prevention Strategies for Opportunity 3. Partner with Communities and Ongoing Sustainability. State Medicaid programs Community Based Organizations (CBOs). States can can invest in the future. Medicaid has already made a partner with CBOs to shape data collection strategies to positive and long-term difference in the lives of millions identify populations. States can work with CBOs to better of children.155 156 States need to develop systems that understand and clarify the needs of the communities they can advance ongoing opportunities to prevent the long- serve. Data collection efforts in these communities must term consequences of poverty, racism and discrimination commit to implementing effective programming based on health and well-being. Strategies must also consider on the data. A collaborative approach like this would help Medicaid's role in assisting states and U.S. territories in 46 BOX 14. HEALTHY PEOPLE'S DISABILITY HEALTH GOALS AND IDENTIFIED BARRIERS The ACA requires HHS to "report on barriers to health care or public health programs, accessible facilities, and the number of trained providers."74 A Healthy People 2020 report identified barriers to health and wellness for people with disabilities that include: economic barriers to Long-Term Service and Supports (LTSS) and accommodations that enable persons with disabilities to more fully participate in the community. LTSS includes home modifications, equipment (wheelchairs, hearing aids, grab bars) and personal assistant services (PAS).159 High rates of morbidity and mortality among nursing home residents resulting from COVID-19 has increased the urgency of state investment in programs such as Money Follows the Person (MFP). MFP incentivizes state investment in community-based options for persons in nursing home settings. Other programs in which states should invest include those that support tribal communities with federal matching dollars to assist people with disabilities to live in their choice of settings. In addition to addressing barriers to health and wellness for persons with disabilities in general, states need to do more to address disparities in access to community-based services between ethnic minority populations and Whites. There is plenty of evidence: (1) A recent study of Home and Community Based Services (HCBS) used by persons with Multiple Sclerosis (MS) found extensive disparities in utilization of key services needed for persons with disabilities to reside in the community. (2) A comparison of HCBS use between Blacks and Whites revealed that Blacks were less likely to "use case management, equipment, technology, and modifications and nursing services."160 White men had the highest HCBS expenditures, while Black men had the lowest Medicaid HCBS expenditures. (3) The self‐management of health by people with intellectual and developmental disabilities: The definition of self-management includes the person playing a central role in their health management and collaboration with health care professionals. Many other studies might be cited in this context, including studies that show significant disparities in support needed by Blacks, Asians and Pacific Islanders with developmental disabilities to self-manage their health compared to Whites. providing timely medical care for populations impacted As a nation, we must accept our call to action and by emergencies or disasters as possible.157 Data systems advance a national agenda to achieve health equity and must be used to understand these strategies. to prevent another such disaster from taking a similar toll. Counting the number of deaths by race or ethnicity is not Opportunity 7. Secure Federal Investment and enough. Federal and state policymakers must invest in Incentivize Value. Finally, state Medicaid programs and activities that will reduce barriers to health and wellness in public health must have the resources to address health populations disproportionately harmed by COVID-19. disparities. Federal investments are needed to effectively address inequity in access to housing, food, and economic The authors of this document and our collaborators assistance. State Medicaid programs can also focus on believe that change can only come about if policymakers other types of actions such as implementing Medicaid address racism and other social and civil rights injustices payment reforms to advance value-based models that that are a significant underlying cause of health disparities. promote health equity. For example, Minnesota's Medicaid Only by addressing the social and civil inequities that program has implemented Integrated Health Partnerships harm BIPOC and persons with disabilities will change (IHP), a value-based payment system. Some IHP systems come about. have interacted with alternative care delivery systems intending to improve the Medicaid population and individual As set forth in this report, data is essential to any effort to members' health.158 Small investments in data should be address health disparities. This data must be intersectional made as a part of this effort, to measure and track results and its elements determined based upon a dialogue with and demonstrate long-term health outcomes, reductions in stakeholders across the care delivery system. We must inequity, and return on investment. break down the silos between policy and populations with direct experience of racism, xenophobia, ableism and other social determinants that have reduced their CONCLUDING REMARKS opportunity to achieve health and wellness. The devastation wrought on historically under-resourced communities by the coronavirus pandemic has severely struck the nation.161 The morbidity and mortality rates are highest among African Americans. 47 Appendices APPENDIX A: KEY TERMS USED IN Important effects on health and mortality are also found for race and ethnicity. In this report, we use the term "social risk THIS REPORT factors" and selected factors for which Minnesota was able to provide individual data and for which strong argument Health Equity and Health Disparities or evidence suggests a role in affecting health status. Key Health Equity. According to the Centers for Disease social risk factors for adults include economic factors such Control and Prevention (CDC), "Health equity is achieved as very low income and homelessness, race and ethnicity, when every person has the opportunity to "attain his or her having a disability, having serious mental illness, and having full health potential" and no one is "disadvantaged from a substance use disorder. The use of economic factors and achieving this potential, because of social position or other of race and ethnicity is very common in research on social socially determined circumstances." Health inequities are risk factors. The use of disability status, serious mental reflected in differences in length of life; quality of life; rates illness and substance use disorder as social risk factors is of disease, disability, and death; severity of disease; and less common, in part because they can also be seen as access to treatment." health outcomes rather than factors affecting health. For people covered under Medicaid, however, disability status, Health Disparities. "Health disparities refer to a higher serious mental illness and substance use disorder are key burden of illness, injury, disability, or mortality experienced by characteristics and may affect their access to care and are one group relative to another. Health care disparities typically associated with significantly elevated rates of mortality and refer to differences between groups in health insurance prevalence of morbidities. coverage, access to and use of care, and quality of care."162 A health disparity is "a particular type of health difference Intersectionality. The term intersectionality is used to that is closely linked with economic, social, or environmental describe a framework for capturing two or more social risk disadvantage. Put another way, health disparities adversely factors such as income, race, and disability.165 Taking an affect groups of people who have systematically experienced intersectional approach requires the researcher to ground greater social or economic obstacles to health based on the examination of health disparities in an intersectional their racial or ethnic group, religion, socioeconomic status, approach to take into account the whole person in gender, age, or mental health; cognitive, sensory, or physical recognition of the heterogeneous nature of the population disability; sexual orientation or gender identity; geographic in the context of society and its systemic racism and location; or other characteristics historically linked to discriminatory practices.166 An intersectional approach discrimination or exclusion."163 offers great value. A social risk factor does not act in isolation to affect a person's health status but joins with Excess Deaths. "Excess deaths" were defined as the other social risk factors and social circumstances. One difference between the number of deaths observed in way to describe the way in which multiple factors can act the minority populations and the number that would have together is "intersectionality," a term that has been used been expected if the minority population had the same to describe, for example, how race and gender together age- and sex-specific death rates as the non-minority create particular challenges for Black women.163 Similar population. This method quantified the number of deaths thinking has led those who study disability to consider the that would not have occurred had mortality rates for joint effects of disability, race and ethnicity.167 Among people minorities equaled those of non-minorities." 164 covered under Medicaid, many experience the compound challenges of low income, poor neighborhoods, mental Social Risk Factor. Efforts to understand how illness, chronic physical illness and physical disability. Being circumstances beyond health care can affect health homeless, leaving incarcer­ation, or experiencing domestic outcomes have considered a very wide array of factors, violence or neglect can combine with illness to form other sometimes referred to as social determinants of health and compound challenges with poverty or racial discrimination. sometimes as social risk factors. Social factors such as Their short-term risks of illness, injury and mortality are often income, education and community conditions greatly affect high due to such combinations of challenges. how much health and how much life each American enjoys. 48 APPENDIX B. LAWS OF MINNESOTA Step 3. Define the population groups. Based on the research conducted by DHS and known associations 2015, CHAPTER 71, ARTICLE 11, between risk factors and health outcomes, several SECTION 63 population groups were defined based on the social and This section of Chapter 71 led to the examination of health medical risk factors found in the Medicaid data or created disparities in Medicaid populations. from the data.168 These medical and social factors define and compare population groups. For example, data Health Disparities Payment Enhancement: "(a) The on income, a social risk factor, was used to stratify the commissioner of human services shall develop a Medicaid population by income categories. Serious and methodology to pay a higher payment rate for health care persistent mental illness (SPMI) is a medical risk factor and providers and services that takes into consideration the disability. Health outcomes for persons with SPMI were higher cost, complexity, and resources needed to serve compared to persons without SPMI. To note, in an analysis patients and populations who experience the greatest that examines health disparities for adults and children, health disparities in order to achieve the same health medical and social risk factors will vary by age group. and quality outcomes that are achieved for other patients Certain medical and social risk factors such as SPMI or and populations …" prior history of incarceration would only apply to adults. These factors for adult population groups were used to define social risk factors for children. For children, the APPENDIX C. STEPS TAKEN TO relevant factor translates into parental risk factors, such as MEASURE HEALTH DISPARITIES IN a child having a parent with SPMI or a parent with a prior MEDICAID POPULATIONS history of incarceration. Step 4. Select measures of health disparities. Step 1. Identify the available data. Minnesota Informed by NASEM's conceptual framework and data identified several data sources for this project, including availability, we developed health disparity measures for Medicaid enrollment data, medical claims, and other DHS adults and children. Adult measures included mortality administrative data. This data was integrated with DHS data rates, the prevalence of selected illnesses, rates of at the individual level. Data from calendar years 2013 and disability, health care access, and health equity. Because 2014 for children and adults covered under Minnesota's of the project's limited scope, we had to limit the number Medicaid program (called Medical Assistance) and the of age-appropriate measures for children's health MinnesotaCare program, referred to as "Medicaid." The disparities. The average annual calendar year cost per data files included individuals age 0-64 who had at least individual was also used as an outcome measure. one month of enrollment in 2014. Our research included children if they had at least one parent enrolled in the Step 5. Prepare an analytical plan. An analytical plan Medicaid program. (People with Medicaid and Medicare must be grounded in the project's goals and detailed to were excluded due to limited access to Medicare data.) support the development of datasets. The plan must also include clear analytical specifications and limits around the The total number of Medicaid enrollees included in the number and range of analyses to perform. The plan must data set was 853,000. Of this number, 550,341 were include algorithms for defining all Medicaid populations adults and 303,140, children. We had access to cost, and all health disparity measures. In brief, the plan: utilization, and medical diagnosis at the individual level as well as geographic and demographic data. Importantly, the (1) defined several Medicaid populations around a range data also included social risk factors. of medical or social risk factors. People with a diagnosis of serious and persistent mental illness (SPMI), were defined Step 2. Establish a framework for examining health based upon a combination of diagnoses and utilization disparities. The framework relied heavily upon the measures from the claims data. By contrast, certain conceptual model developed by the National Academies population groups such as persons experiencing very of Sciences, Engineering, and Medicine (NASEM) to low income were defined based upon social risk factors account for social risk factors in Medicare payments. including income data from the enrollment form plus the NASEM's conceptual framework expresses relationships value of the Supplemental Nutrition Assistance Program between social risk factors and health outcomes, health (SNAP) benefit. (2) defined health disparity measures. For care use, and costs. The development of the framework example, the mortality rate was defined based the number also included analysis of qualitative interviews conducted of deaths occurring over a 2.5-year period. The prevalence by the Disability Policy Consortium. 49 of a health condition was defined based on the numbers as cross-tabulations. For example, for adults, health of people with that condition recorded in medical claims disparity measures are not only examined for those adults data. (3) outlined the role of two important methods of experiencing homelessness, but they are also compared analysis: bivariate analysis to examine the relationship to other adults who are not experiencing homelessness between two variables, e.g. between having very low and to all adults. This work included an examination for all income and prevalence of chronic illness, and regression measures from mortality rates to health care use measures analysis for examining the relationships among many such as potentially preventable emergency department variables. A bivariate analysis shows directly how the rates visits. Results from multivariate regression analyses and prevalence indicators vary across population groups, identify which relationships between social and medical without any statistical adjustment for other risk factors. risk factors and outcomes are statistically significant. (The This approach provides easily understood results and is output from these regressions including coefficients, odds also helpful for the design of the regression analysis. ratios, and p-values or probability values are not presented in this report.) Again, fewer health disparity measures were Regression analysis is useful in assessing the relative used for children, than for adults. importance of population characteristics and outcomes. We used a set of regressions inclusive of age, gender, While the results generated from the data's bivariate diagnostic risk score and social risk factors to predict analysis are not adjusted for differences in demographic outcomes of interest (e.g. mortality, morbidity, disability, or other medical or social risk factors, they remain valid health care access and quality measures).169 These indicators of health disparities. It is important to note that analyses help us show each risk factor's contribution to a the regression analyses' results are adjusted (or controlled) person's health while controlling other social risk factors. for demographic, geographic, and other social risk factors. (Cost regression results, not included in this report, were Step 6. Develop an analytical dataset. Skilled staff are also adjusted for diagnostic conditions.)165 170 Regression needed to extract and integrate data, read analytical plans, analysis isolates the unique contribution of a specific and build datasets. Appendix D provides a description demographic or social risk factor. These findings, while of the data sources and algorithms used to develop the significant, must be interpreted in a clinical and social population groups. service context. Control factors for adults, for example, include age, gender, diagnostic risk, race and ethnicity, Step 7. Conduct the analyses and interpret the geographic area, income relative to the FPL, education, results. Given the number of medical and social risk homelessness, SMI, SUD, development or intellectual factors and measures of health disparities selected disability, and disability status. and the two types of analyses performed (bivariate and regression), the analytical plan's execution generated a Step 8. Report results and communicate results. substantial volume of results. Finally, Minnesota reported its results to the legislature, discussed the implications of the results with state Results from the bivariate analyses provide a simple agencies, and made the results available to the public to way to identify disparities within and across population facilitate consideration and action towards the development groups. Bivariate analyses are also more familiarly known of targeted interventions to reduce health disparities. 50 APPENDIX D. DATA SOURCES TO MEASURE HEALTH DISPARITIES IN MEDICAID POPULATIONS Category 1: Very Low Income Group 1. Persons at Data Source: Medicaid enrollment application. or below 50 percent of the FPL Description: Raw income data was collected from the Medicaid enrollment forms. Supplemental Nutrition Assistance Program (SNAP) benefits were also considered as income. Income was then measured relative to the federal poverty level (FPL), to calculate income below and above the FPL. Many researchers consider SNAP like income. Households receive an Electronic Benefit Transfer (EBT) card. SNAP provides nutrition benefits to supplement the food budget of households based on the income of the household.171 The benefit amount is based on income, expenses and the number of people in the household. SNAP benefits are a significant proportion of income for persons covered under Medicaid. Group 2. Data Source: Medicaid enrollment application. Homelessness Description: Homelessness was identified based on person as coded as being homeless sometime during 2014, if person: (1) checked the "check if homeless" box on an enrollment application in 2014; and (2) gave a known homeless shelter as their address; (unfortunately, this method is likely to underestimate the rate of homelessness). Category 2: Race and Ethnicity Group 3. American Data Source: Medicaid enrollment application. Indian Description: Race and ethnicity data were collected from the Medicaid enrollment forms. Persons have Group 4. Black or several options including "Other" on the enrollment form. The data on race and ethnicity is considered African American complete for the data year used for the health disparity analysis. This is not always the case for states; and furthermore, there are some reports that data on race has declined since 2014 because people are not required to complete the question on race on enrollment forms.172 Group 5. Hispanic The 11 categories used by Minnesota DHS are as follows: (1) those who are born in the U.S. and that Group 6. White includes American Indian, Black/African American, White, Hispanic, Asian, Other/Unknown; and, (2) those who immigrated to the U.S. and that includes: Black/African American, White, Hispanic, Asian, Other/Unknown. Note: American Indian is the term that Minnesota DHS uses, as designated by the individual on the enrollment form.173 Category 3: Disability Group 7: Disability Data Source: Medicaid eligibility status. Description: Disability status was based on their eligibility for Medicaid, if the person qualified for disability-based Medical Assistance.174 This only includes persons who have been able to navigate the disability determination process; and, would therefore exclude persons who have not been able to navigate this process. This category also includes a range of disabilities. Health disparities were measured combining all persons with disabilities into one group. As a result, health disparities by disability type could not be measured. Group 8: SPMI Data Source: Medicaid claims data. Description: Persons with a diagnosis of SPMI were identified in the claims data based on certain diagnoses, and a high level of service use, using Medicaid claim forms for the 18 months ending in 2014. Persons identified as SPMI based on Medicaid claims and meeting criteria of having Schizoaffective Disorder, Borderline Personality Disorder, Major Depression Disorder or Bipolar disorder; and had to have received a high level of mental health care, often inpatient or residential treatment. Given that studies consistently show a delay in diagnosis, the use of this algorithm may produce an underestimate of the prevalence of this condition. Group 9. SUD Data Source: Medicaid claims data. Description: Persons with a diagnosis of SUD were identified by using the Medicaid claims and having certain diagnoses. 51 APPENDIX E. ADULTS COVERED UNDER MINNESOTA MEDICAID BY CATEGORY Adult Population Covered under Minnesota Medicaid by Category and Group Adult Population All Other Adults All Adults Category Group # % of All # % of All # Adults with Very Low Income Category 1 Group 1. At or below 50% FPL 240,350 43.7% 309,991 56.3% 550,341 Group 2. Homelessness 38,721 7.0% 511,620 93.0% 550,341 Adults by Race and Ethnicity Group 3. American Indian (U.S. born) 23,464 4.3% 526,877 95.7% 550,341 Category 2 Group 4. Black/African American (U.S. born) 66,093 12.0% 484,248 88.0% 550,341 Group 5. Hispanic (U.S. born) 16,907 3.1% 533,434 96.9% 550,341 Group 6. White (U.S. born) 296,992 54.0% 253,349 46.0% 550,341 Adults with Disabilities Group 7. Disability 45,050 8.2% 505,291 91.8% 550,341 Category 3 Group 8. SPMI 30,529 5.5% 519,812 94.5% 550,341 Group 9. SUD 79,349 14.4% 470,992 85.6% 550,341 Note: There are 550,341 adults in the total dataset. Category 2 also includes certain population groups such as adults who are Asian American and all adults who were not born in the United States for whom population-specific data is not shown in this table and in this report. 52 APPENDIX F. GUIDE TO READING HEALTH DISPARITY RESULTS SHOWN IN TABLE 4 OF THE REPORT Line # Measure Guide for Adults to Accompany the Report's Table 4 (T.4) on T.4 1 Population For this analysis, the Medicaid population included 550,341 adults. This number reflects all adults who are U.S. born and not U.S. born, across all income levels, all races and ethnicities, and all types of disabilities. 4 Mortality This measure refers to the proportion of deaths in the 2.5 years it was measured. The mortality rate was 0.8 percent in the 2.5 years it was measured for all adults. The mortality rate is less than 1 percent of all adults in this Medicaid study population. 6 Type 2 Diabetes This measure refers to the proportion of adults with Type 2 Diabetes. For all adults, the prevalence is 7 percent. 7 Asthma This measure refers to the proportion of adults with Asthma. For all adults, the prevalence is 9.4 percent. 8 Human Immunodeficiency This measure refers to the proportion of adults with either the HIV or the hepatitis C virus. For all adults, the Virus (HIV)/Hepatitis C Virus prevalence is 1.6 percent. 9 Hypertension This measure refers to the proportion of adults with hypertension. For all adults, the prevalence is 5.1 percent. 10 Cardiovascular (Heart This measure refers to the proportion of adults with heart failure, or a heart attack/heart disease which condition) requires hospitalization. For all adults, the prevalence is 1.4 percent. 11 Chronic Obstructive This measure refers to the proportion of adults with COPD. For all adults, the prevalence is 8.5 percent. Pulmonary Disease (COPD) 12 Injury This measure refers to the proportion of adults with an injury, due to accident or violence. For all adults, the prevalence is 5.6 percent. 13 Lung or Laryngeal Cancer This measure refers to the proportion of adults with cancer of this type. For all adults, the prevalence is 0.22 percent. 14 Substance Use Disorder This measure refers to the proportion of adults with a diagnosis of SUD. For all adults, the prevalence is (SUD) 14.4 percent. 15 Post-Traumatic Stress Disorder This measure refers to the proportion of adults with PTSD. For all adults, the prevalence is 5.9 percent. (PTSD) 16 Depression This measure refers to the proportion of adults with depression. For all adults, the prevalence is 19.2 percent. 17 Serious and Persistent This measure refers to the proportion of adults with a diagnosis of SPMI. For all adults, the prevalence is Mental Illness (SPMI) 5.6 percent. 18 Disability Status This measure refers to the proportion of adults with a disability, based upon eligibility requirements. For all adults, the rate of disability is 8.2 percent. 20 Potentially Preventable This measure refers to the proportion of adults that have a potentially preventable ED visit: 10.5 percent of Emergency Department all adults covered under the Medicaid program had at least one ED visit. (ED) Visits 21 Potentially Preventable This measure refers to the proportion of adults that have a potentially preventable hospital admission: 0.6 Hospital Admissions percent of all adults covered under the Medicaid program had at least one admission. 23 Annual Preventive Visit This measure refers to the proportion of adults that had an annual preventive visit: 33.2 percent of all adults covered under the Medicaid program had this visit. Note: Higher rate is a better outcome. 24 Comprehensive Diabetes This measure refers to the proportion of adults that had a hemoglobin A1c test: 92 percent of all adults Care - A1c Test covered under Medicaid had this test. (This calculation was made using a denominator that was smaller than the total population.) Note: Higher rate is a better outcome. 25 Annual Dental Visit (ADV) for This measure refers to the proportion of adults that had an ADV: 48.4 percent of all adults covered under Adults Medicaid had an ADV. (This calculation was made using a denominator that was smaller than the total population.) Note: Higher rate is a better outcome. 27 Health Care Costs The average cost per adult per calendar year was $7,104. 53 APPENDIX G. THE ODDS OF HEALTH DISPARITIES FOR CHILDREN WITH CPI In Minnesota's Medicaid program, we examine children's data with child protection involvement (CPI). There were 32,648 children in the data, representing about 10.8 percent of the children in the analytic data files. Through our analytical work, we found that CPI proved to be a strong predictor of poor health outcomes among children.167 The following table provides regression results for children with CPI. Children with CPI are compared with children without CPI. The Odds Of Health Disparities For Medicaid Children with Child Protection Involvement # 1 Comparison Group Children who do not have child protection involvement (CPI) Children with CPI are 100% more likely to die in the study period than 2 Mortality children who do not have CPI 3 Morbidity Children with CPI are 5% more likely to have asthma than the comparison 4 Asthma group Children with CPI are 62% more likely to have an injury than the 5 Injury comparison group Children with CPI are 1.6 x more likely to have a SUD condition than the 6 Substance Use Disorder (SUD) comparison group Children with CPI are 85% more likely to have a diagnosis of ADHD than 7 Attention Deficit Hyperactivity Disorder (ADHD) the comparison group Children with CPI are 2 x more likely to have PTSD than the comparison 8 Post-Traumatic Stress Order (PTSD) group Children with CPI are 47% more likely to have a disability than the 9 Disability comparison group 54 15 Population Reference Bureau, "Majority of People Covered by Endnotes Medicaid and Similar Programs Are Children, Older Adults, or 1 Chomilo, Nathan T. Health Affairs, "Building Racial Equity Into The Disabled," June 2017, available at https://www.prb.org/majority-of- Walls Of Health Policy," December 1, 2020, available at https:// people-covered-by-medicaid-and-similar-programs/ www.healthaffairs.org/do/10.1377/hblog20201119.508776/ full/?utm_medium=social&utm_source=twitter&utm_ 16 Kaiser Family Foundation (KFF), "Medicare And Medicaid at 50," campaign=blog&utm_content=chomilo June 2015, available at https://www.kff.org/medicaid/poll-finding/ medicare-and-medicaid-at-50/ 2 Davis, L. J. (Ed.). (2013). The disability studies reader. ProQuest Ebook Central https://ebookcentral.proquest.com 17 Provost, C., & Hughes, P., "Medicaid: 35 Years of Service," Health care financing review, 22(1), 141–174, Fall 2000, available at https:// 3 COVID-Racial Trackers, (examples of): Kaiser Family Foundation, www.ncbi.nlm.nih.gov/pmc/articles/PMC4194689/ COVID-19 Coronavirus Tracker – Updated as of December 11, available at https://www.kff.org/coronavirus-covid-19/fact-sheet/ 18 The Commonwealth Fund, "How the Affordable Care Act Has coronavirus-tracker/Boston University, The COVID Racial Data Narrowed Racial and Ethnic Disparities in Access to Health Care," Tracker, available at https://covidtracking.com/race January 2020, available at https://www.commonwealthfund.org/ publications/2020/jan/how-ACA-narrowed-racial-ethnic-disparities- 4 National Association of Medicaid Director (NAMD), "NAMD access Statement on Inequities in Health and Health Care," June 2020. https://medicaiddirectors.org/press-release/2020/06/namd- 19 Kaiser Family Foundation (KFF), "Status of State Medicaid statement-on-inequities-in-health-and-health-care/ Expansion Decisions: Interactive Map," October 2020, available at https://www.kff.org/medicaid/issue-brief/status-of-state-medicaid- 5 Statements from State Medicaid directors and governors: National expansion-decisions-interactive-map/ Association of Medicaid Director (NAMD), "NAMD Statement on Inequities in Health and Health Care," June 2020, available 20 The Commonwealth Fund, "Status of Medicaid Expansion and at https://medicaiddirectors.org/press-release/2020/06/namd- Work Requirement Waivers," Accessed July 20, 2020, available statement-on-inequities-in-health-and-health-care/National at https://www.commonwealthfund.org/publications/maps- Governor's Association (NGA), "Reducing the Disproportionate and-interactives/2020/jul/status-medicaid-expansion-and-work- Impact of COVID-19 Among Communities of Color," June 25, 2020, requirement-waivers available at https://www.nga.org/wp-content/uploads/2020/06/ 21 Kaiser Family Foundation (KFF), "The Impact of the Coverage Gap COVID-19-Health-Equity-Memo.pdf for Adults in States not Expanding Medicaid by Race and Ethnicity," 6 COVID-Racial Trackers, (examples of): Kaiser Family Foundation, October 2015, available at https://www.kff.org/racial-equity-and- COVID-19 Coronavirus Tracker – Updated as of December 11, health-policy/issue-brief/the-impact-of-the-coverage-gap-in-states- available at https://www.kff.org/coronavirus-covid-19/fact-sheet/ not-expanding-medicaid-by-race-and-ethnicity/ coronavirus-tracker/; Boston University, The COVID Racial Data 22 MN Community Measurement, Minnesota Department of Tracker, available at https://covidtracking.com/race Human Services, "2019 Minnesota Health Care Disparities by 7 Kaiser Family Foundation (KFF), "Health Disparities are a Symptom Insurance Type," May 2020, available at https://mncmsecure.org/ of Broader Social and Economic Inequities," June 2020, available website/Reports/Community%20Reports/Disparities%20by%20 at https://www.kff.org/policy-watch/health-disparities-symptom- Insurance%20Type/2019%20Disparities%20by%20Insurance%20 broader-social-economic-inequities/ Type.pdf 8 Georgetown Health Policy Institute Center for Children and Families, 23 U.S. Department of Health and Human Services, "Report to "We Need to Name it: Racism is a Public Health Crisis," June 2020, Congress, Improving the Identification of Health Care Disparities available at https://ccf.georgetown.edu/2020/06/02/we-need-to- in Medicaid and CHIP," November 2014, available at https://www. name-it-racism-is-a-public-health-crisis/ medicaid.gov/medicaid/quality-of-care/downloads/4302b-rtc-2014. pdf 9 Charlton, James I. Nothing About Us Without Us: Disability Oppression and Empowerment. 1st ed., University of California 24 Medicaid.gov, "Quality of Care Health Disparities," available at Press, 1998. JSTOR, Accessed 13 Dec. 2020, available at www. https://www.medicaid.gov/medicaid/quality-of-care/quality- jstor.org/stable/10.1525/j.ctt1pnqn9 improvement-initiatives/quality-of-care-health-disparities/index.html 10 National Academy for State Health Policy (NASHP), "Early Evidence 25 Note: National Healthcare Quality and Disparities Reports from the Suggests Increased Medicaid Enrollment Due to COVID-19," June Agency for Healthcare Research and Quality (AHRQ) are available at 2020, available at https://www.nashp.org/early-evidence-suggests- https://nhqrnet.ahrq.gov/inhqrdr/resources/info increased-medicaid-enrollment-due-to-covid-19/ 26 Centers for Disease Control and Prevention (CDC), "Disability & 11 American Academy of Actuaries. FAQs on COVID-19s Potential Health Data at Your Fingertips," available at https://www.cdc.gov/ Impact on Medicaid and Medicaid Managed Care Organizations, April ncbddd/disabilityandhealth/features/disability-health-data.html 2020, available at https://www.actuary.org/COVID-Medicaid-FAQs Note: Information about the Disability Health Data System (DHDS) 12 Berkowitz E., "Medicare and Medicaid: the past as prologue," can be found at this link. "Respondents were defined as having Health care financing review, 27(2), 11–23, Winter 2005, available at any disability if they reported serious difficulty concentrating, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4194925/ remembering or making decisions (cognitive disability); serious difficulty hearing or deafness (hearing disability), serious difficulty 13 Medicaid.gov, "Medicaid & CHIP Enrollment Data Highlights," July walking or climbing stairs (mobility disability); serious difficulty seeing 2020, available at https://www.medicaid.gov/medicaid/program- or blindness (vision disability); difficulty dressing or bathing (self- information/medicaid-and-chip-enrollment-data/report-highlights/ care disability); or difficulty doing errands alone (independent living index.html disability)." 14 Kaiser Family Foundation (KFF), "10 Things to Know about 27 The Stanford Center on Poverty and Inequality, "State of the Union Medicaid: Setting the Facts Straight," March 2019, available at 2017, POVERTY," available at https://inequality.stanford.edu/sites/ https://www.kff.org/medicaid/issue-brief/10-things-to-know-about- default/files/Pathways_SOTU_2017_poverty.pdf medicaid-setting-the-facts-straight/ 55 28 Ross, T., and Solomon, D., "Lessons from Flint: The Case for 41 Journal of the National Medical Association, NMA Activities, Investing in the Building Blocks of Communities of Color," Center Legislative Forum, Report of the Secretary's Task Force on Black for American Progress, March 2016, available at https://www. and Minority Health: A Summary and A Presentation of Health Data americanprogress.org/issues/race/reports/2016/03/03/132341/ With Regard to Blacks," 1986, available at https://www.ncbi.nlm. lessons-from-flint-the-case-for-investing-in-the-building-blocks-of- nih.gov/pmc/articles/PMC2571303/pdf/jnma00257-0117.pdf communities-of-color/ 42 Riley W. J., "Health disparities: gaps in access, quality and 29 Kaiser Family Foundation (KFF), "Poverty Rate by Race/Ethnicity" affordability of medical care," Transactions of the American Clinical 2019 data, available at https://www.kff.org/other/state-indicator/ and Climatological Association, 123, 167–174, 2012, available at poverty-rate-by-raceethnicity/?currentTimeframe=0&sortModel=%7 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3540621/ B%22colId%22:%22Location%22,%22sort%22:%22asc%22%7D 43 U.S. Department of Health and Human Services, Heckler, Margaret, 30 Centers for Disease Control and Prevention (CDC), "Disability & "Report of the Secretary's Task Force, Black & Minority Health, Health Data at Your Fingertips," available at https://www.cdc.gov/ Volume 1, Executive Summary," 1985, available at https://www. ncbddd/disabilityandhealth/features/disability-health-data.html minorityhealth.hhs.gov/assets/pdf/checked/1/ANDERSON.pdf 31 Kaiser Family Foundation (KFF), "Health Insurance Coverage of the 44 U.S. Department of Health and Human Services, Office of Minority Nonelderly (0-64) with Incomes below 100% Federal Poverty Level Health, available at https://minorityhealth.hhs.gov/Default.aspx (FPL)," 2019 data, available at https://www.kff.org/other/state- 45 U.S. Department of Health and Human Services, Office of Minority indicator/nonelderly-up-to-100-fpl/?currentTimeframe=0&sortMode Health, Office of Minority Health Resource Center, available at l=%7B%22colId%22:%22Location%22,%22sort%22:%22asc%22 https://www.minorityhealth.hhs.gov/omh/browse.aspx?lvl=1&lvlid=3 %7D 46 Agency for Healthcare Quality and Research, National Healthcare 32 U.S. Department of Health and Human Services, Office of the Quality and Disparities Reports, available at https://www.ahrq.gov/ Assistant Secretary for Planning and Evaluation, "HHS Poverty research/findings/nhqrdr/index.html Guidelines for 2020," January 2020, available at https://aspe.hhs. gov/poverty-guidelines 47 Agency for Healthcare Quality and Research, National Healthcare Quality and Disparities Reports, available at https://www.ahrq.gov/ 33 Kaiser Family Foundation (KFF), "Health Coverage by Race and research/findings/nhqrdr/index.html Ethnicity: The Potential Impact of the Affordable Care Act," March 2013, available at https://www.kff.org/disparities-policy/issue-brief/ 48 Agency for Healthcare Research and Quality, 2018 National health-coverage-by-race-and-ethnicity-the-potential-impact-of-the- Healthcare Quality and Disparities Report, (Content last reviewed affordable-care-act/ April 2020), available at https://www.ahrq.gov/research/findings/ nhqrdr/nhqdr18/index.html 34 Kaiser Family Foundation (KFF), "Medicaid Coverage Rates for the Nonelderly by Race/Ethnicity" 2019 data, available at https://www. 49 Centers for Disease Control and Prevention, "CDC Health Disparities kff.org/medicaid/state-indicator/rate-by-raceethnicity-3/?currentTim & Inequalities Report (CHDIR)," 2013, available at https://www.cdc. eframe=0&sortModel=%7B%22colId%22:%22Location%22,%22so gov/minorityhealth/CHDIReport.html rt%22:%22asc%22%7D 50 Centers for Disease Control and Prevention, "Mission, Role and Pledge," 35 Center for Budget and Policy Priorities, "Medicaid Works for People available at https://www.cdc.gov/about/organization/mission.htm with Disabilities," August 2017, available at https://www.cbpp.org/ 51 Centers for Disease Control and Prevention. Health Disparities and research/health/medicaid-works-for-people-with-disabilities Strategies Reports, available at https://www.cdc.gov/minorityhealth/ 36 Note: According to the Medicaid and CHIP Payment and Access chdir/index.html Commission: "More than one-third of persons who are eligible 52 U.S. Department of Health and Human Services, Healthy People for coverage under Medicaid due to disability find their pathway 2020, available at https://www.healthypeople.gov/2020/About- to Medicaid as a result of meeting the criteria for Supplemental Healthy-People Security Income (SSI). SSI is the federal cash assistance program for the elderly and people with disabilities who have low levels 53 Centers for Disease Control and Prevention, "Health Objectives of income and assets. People with disabilities. Source report: for the Nation," MMWR Weekly, September 22, 1989, available at MACPAC, 2017, "Federal Requirements and State Options: https://www.cdc.gov/mmwr/preview/mmwrhtml/00001462.htm Eligibility," available at https://www.macpac.gov/subtopic/people- 54 National Conference of State Legislatures, "State Approaches to with-disabilities/ Reducing Health Disparities," 2014, available at https://www.ncsl. 37 Kaiser Family Foundation (KFF), "Medicaid's Role for Children with org/Portals/1/HTML_LargeReports/HealthDisparity_1.htm Special Health Care Needs: A Look at Eligibility, Services, and 55 To learn more about how Medicare is using data on race and Spending," June 2019, available at https://www.kff.org/medicaid/ ethnicity data to improve health equity: See interview: https:// issue-brief/medicaids-role-for-children-with-special-health-care- atlanticquality.org/health-equity-qa-with-madeline-shea/ needs-a-look-at-eligibility-services-and-spending/ See webinar: https://www.healthmanagement.com/knowledge- 38 Institute of Medicine of the National Academies, "Race, Ethnicity, share/webinars/the-importance-of-race-and-ethnicity-in- and Language data. Standardization for Health Care Quality accounting-for-social-risks-in-medicare-value-based-payments/ Improvement Subcommittee," 2009, available at https://www.ahrq. See Guide to Reducing Disparities in Readmissions: https://www. gov/sites/default/files/publications/files/iomracereport.pdf cms.gov/About-CMS/Agency-Information/OMH/Downloads/OMH_ 39 Health, "Remembering Margaret Heckler's Commitment to Readmissions_Guide.pdf Advancing Minority Health, November 2018, available at https:// See Mapping Medicare Disparities (MMD) Tool: https://data.cms. www.healthaffairs.org/do/10.1377/hblog20181115.296624/full/ gov/mapping-medicare-disparities 40 Note: This report was a landmark report as the first convening of CMS created the Mapping Medicare Disparities (MMD) Tool. This health experts by the U.S. government to conduct a comprehensive tool contains health outcome measures including hospitalizations study of racial and ethnic minority health. for many chronic conditions and enables the examination of disparities in chronic conditions by county and state across population groups. 56 56 Note: the U.S. Department of Health and Human Services 66 State Health Access Data Assistance Center (SHADAC), "Race/ released two reports in 2011 and 2014 on health disparities in Ethnicity Data in CMS Medicaid (T-MSIS) Analytic Files," August Medicaid populations, in compliance with ACA requirements. In 2020, available at https://www.shadac.org/news/raceethnicity-data- 2011, HHS released "Approaches for Identifying, Collecting, and cms-medicaid-t-msis-analytic-files Evaluating Data on Health Care Disparities in Medicaid and CHIP." 67 Centers for Medicare and Medicaid Services (CMS), "Quantifying In 2014, HHS released "Improving the Identification of Health Care the Distribution and Completeness of Select Demographic Variables Disparities in Medicaid and CHIP." HHS Action Plan to Reduce in 2016," November 2019, available at https://www.resdac.org/ Health Disparities" was released in 2011, available at https://www. sites/resdac.umn.edu/files/4121_Demographic_Variables.pdf minorityhealth.hhs.gov/npa/files/Plans/HHS/HHS_Plan_complete. pdf 68 Health Affairs, "Data On Race, Ethnicity, And Language Largely Incomplete For Managed Care Plan Members," March 2017, 57 Institute of Medicine, "Unequal Treatment: What Healthcare DOI:10.1377/hlthaff.2016.1044, https://www.healthaffairs.org/doi/ Providers Need to Know About Racial And Ethnic Disparities in full/10.1377/hlthaff.2016.1044 Health-Care," March 2002, available at https://www.nap.edu/ resource/10260/disparities_providers.pdf 69 Association of State and Territorial Health Officials (ASTHO). Why We Need Race and Ethnicity Data to Beat COVID-19. April 2020. 58 U.S. Department of Health and Human Services, Secretary of the https://www.astho.org/StatePublicHealth/Why-We-Need-Race-and- Department of Health and Human Services Report to Congress, Ethnicity-Data-to-Beat-COVID-19/04-22-20/ "Improving the Identification of Health Care Disparities in Medicaid and CHIP," November 2014, available at https://www.medicaid.gov/ 70 State Health Value Strategies, Advances in States' Reporting of medicaid/quality-of-care/downloads/4302b-rtc-2014.pdf COVID-19 Health Equity Data, November 2020, available at https:// www.shvs.org/advances-in-states-reporting-of-covid-19-health- 59 U.S. Department of Health and Human Services, "Improving Data equity-data/ Collection to Reduce Health Disparities," available at https:// minorityhealth.hhs.gov/assets/pdf/checked/1/Fact_Sheet_ 71 U.S. Department of Health and Human Services. Roundtable Section_4302.pdf Report: Leveraging Data on the Social Determinants of Health. October 2019. http://reports.opendataenterprise.org/Leveraging- 60 U.S. Department of Health and Human Services, Office of Minority Data-on-SDOH-Summary-Report-FINAL.pdf Health, "Data Collection Standards for Race, Ethnicity, Primary Language, Sex, and Disability Status," available at https:// 72 Many states are documenting health disparities in Medicaid minorityhealth.hhs.gov/omh/browse.aspx?lvl=3&lvlid=53 populations and raising awareness of implicit bias in health care. States view Medicaid as a key program to close and to reduce 61 U.S. Department of Health and Human Services, Office of Minority racial health disparities. California: https://www.dhcs.ca.gov/ Health, "Data Collection Standards for Race, Ethnicity, Primary dataandstats/reports/Pages/HealthDisparities.aspx Language, Sex, and Disability Status," available at https:// minorityhealth.hhs.gov/omh/browse.aspx?lvl=3&lvlid=53 Massachusetts: https://www.mass.gov/doc/building-toward-racial- justice-and-equity-in-health-a-call-to-action/download 62 Office of Disease Prevention and Health Promotion, Healthy People 2020, "Using Law and Policy to Promote Health for People with Michigan: https://www.michigan.gov/ Disabilities in the United States," Health and Law Policy, 2020, mdhhs/0,5885,7-339-71547_4860-489167--,00.html available at https://www.healthypeople.gov/sites/default/files/DHG_ Ohio: https://medicaid.ohio.gov/Portals/0/Resources/Workgroups/ ExecutiveSummary_2020-02-20_508c.pdf AdvisoryCommittee/2020/MCAC-Health-Equity-Presentation-11- 63 U.S. Department of Health and Human Services, Report to 19-2020-Applegate.pdf Congress, "Approaches for Identifying, Collecting, and Evaluating 73Washington: https://www.hca.wa.gov/assets/hwdashboard/ Data on Health Care Disparities in Medicaid and CHIP," September healthier_wa_dashboard_documentation.html 2011, available at https://www.medicaid.gov/medicaid/quality-of- care/downloads/4302b-rtc.pdf Pew Research Center, "Understanding and Addressing Racial Disparities in Health Care," April 2015, available at https://www. 64 Note: In 2017, the U.S. Department of Health and Human Services, pewsocialtrends.org/2015/04/09/chapter-1-statistical-portrait-of- Office of the Inspector General prepared this report describing key the-u-s-black-immigrant-population/ implementation challenges for CMS: "Status Update: T-MSIS Data Not Yet Available For Overseeing Medicaid," available at https://oig. 74 Minnesota Department of Health, Eliminating Health Disparities hhs.gov/oei/reports/oei-05-15-00050.pdf Initiative, "Report to the Minnesota Legislature 2013," January 2013, available at https://www.health.state.mn.us/communities/equity/ 65 CMS.gov. The Medicaid Analytic Extract 2012 Chartbook. reports/legislativerpt2013.pdf According to the chartbook, "White beneficiaries comprised 44 percent of the Medicaid population and were the largest racial/ 75 Note: By way of background, in 2013, the Minnesota State ethnic group enrolled in Medicaid in 2012 (Table 2.2). An additional Legislature passed a law to direct MDH to prepare this report 22 percent of beneficiaries were African American. Smaller because "disparities in health status outcomes for certain populations percentages were Asian (4 percent), Native American (2 percent), continued unabated, including disparities based on race or ethnicity." and Pacific Islander (1 percent). Twenty four percent of beneficiaries Session Laws 2013, Chapter 108, Article 12, Section 102. were Hispanic or Latino. The trend for states to identify beneficiaries 76 Minnesota Department of Health, "Eliminating Health Disparities as "unknown race" continued, with about 28 percent of beneficiaries Initiative: Fiscal Years 2015 to 2018, Report to The Minnesota thus identified in 2012. The trend in "unknown race" is the result of Legislature 2019," March 2019, available at https://www.health. fewer states requiring applicants to self-report race in their Medicaid state.mn.us/communities/equity/reports/legislativerpt2019.pdf applications; also, in many states, individuals with Hispanic ethnicity are not asked to report their race separately, available 77 Minnesota Business Partnership, "Minnesota's Health Care at https://www.cms.gov/Research-Statistics-Data-and-Systems/ Performance Scorecard: Putting the state's health care system in Computer-Data-and-Systems/MedicaidDataSourcesGenInfo/ national perspective," January 2015, available at https://mnbp.com/ MAXGeneralInformation wp-content/uploads/2015/02/MBP_HealthScorecard.pdf 57 78 MN Community Measurement and Minnesota Department of 92 Center for Health Care Strategies (CHCS), "Stemming the Risk Human Services, Minnesota Health Care Disparities By Race, of Disability Bias During the COVID-19 Pandemic," April 2020, Hispanic Ethnicity, Language and Country Of Origin 2019, Revised available at https://www.chcs.org/stemming-the-risk-of-disability- June 2020, available at https://mncmsecure.org/website/Reports/ bias-during-the-covid-19-pandemic/ Community%20Reports/Disparities%20by%20RELC/2019%20 93 U.S. Department of Health and Human Services, Office of the Disparities%20by%20RELC%20Chartbook%20-%20FINAL.pdf Assistant Secretary for Planning and Evaluation, "HHS Poverty 79 MN Community Measurement, "Community reports," available at Guidelines for 2020," January 2020, available at https://aspe.hhs. https://mncm.org/reports/#community-reports gov/poverty-guidelines 80 Two important sources from Minnesota Community Measurement: 94 Pediatrics, "Poverty and Child Health in the United States," April 2016, available at https://pediatrics.aappublications.org/ MN Community Measurement, "New Reports Show Continued content/137/4/e20160339 Disparities in Minnesota Health Care," May 2020, available at https://mncm.org/new-reports-show-continued-disparities-in- 95 Urban Institute, "Child Poverty and Adult Success," September minnesota-health-care/ 2015, available at https://www.urban.org/sites/default/files/ publication/65766/2000369-Child-Poverty-and-Adult-Success.pdf MN Community Measurement, Minnesota Department of Human Services, "2019 Minnesota Health Care Disparities by 96 Children's Defense Fund, available at https://www.childrensdefense. Insurance Type," May 2020, available at https://mncmsecure.org/ org/wp-content/uploads/2020/02/The-State-Of-Americas- website/Reports/Community%20Reports/Disparities%20by%20 Children-2020.pdf Insurance%20Type/2019%20Disparities%20by%20Insurance%20 97 Kaiser Family Foundation (KFF), "Health Insurance Coverage of Type.pdf Children 0-18 Living in Poverty (under 100% FPL)," 2019 data, 81 The Office of the Revisor of Statutes. 2015 Minnesota Session available at https://www.kff.org/other/state-indicator/poor-children/? Laws. Chapter 71, Article 11, "Section 63. Health Disparities currentTimeframe=0&sortModel=%7B%22colId%22:%22Location% Payment Enhancement," available at https://www.revisor.mn.gov/ 22,%22sort%22:%22asc%22%7D laws/2015/0/Session+Law/Chapter/71/ 98 Minnesota Department of Health, "Blue Ribbon Commission, Health 82 Minnesota Department of Human Services, Legislative Report, Equity Webinar," December 2019, available at https://mn.gov/dhs/ Accounting for Social Risk Factors in Minnesota Health Care assets/BRC-121319-deck_tcm1053-412853.pdf Program Payments. Phase I Initial Findings. April 2016. https:// 99 Health Affairs Blog, "How Foundational Moments In Medicaid's www.leg.mn.gov/docs/2016/mandated/160992.pdf History Reinforced Rather Than Eliminated Racial Health 83 Accounting for Social Risk Factors in Minnesota Health Care Disparities," September 2020, available at https://www.healthaffairs. Program Payments. Legislative report supplement. December 2018. org/do/10.1377/hblog20200828.661111/full/ https://edocs.dhs.state.mn.us/lfserver/Public/DHS-7834-ENG 100 Note: Many foundations and organizations are focused on what 84 HMA Experts Contribute to Report on Health Disparities in the heath care sector can do to advance health equity. A report Minnesota's Medicaid Population, January 2019, available at funded by the Robert Wood Johnson Foundation "aims to assist https://www.healthmanagement.com/blog/hma-experts-contribute- those working in health care, public health, and other fields that report-health-disparities-minnesotas-medicaid-population/ powerfully shape health-such as education, childcare, housing, and community development-to build a world in which everyone 85 Centers for Disease Control and Prevention (CDC), "Diseases has a fair and just opportunity to be as healthy as possible." The and Conditions A-Z Index," available at https://www.cdc.gov/ report name is: "What Can the Health Care Sector Do to Advance diseasesconditions/az/a.html Health Equity?," November 2019, a, , , , and . "What Can the Health 86 Brown, D; Kowalski, A; and Lurie, I., "Long-Term Impacts of Care Sector Do to Advance Health Equity?" November 12, 2019. Childhood Medicaid Expansions on Outcomes in Adulthood," U.S. vailable at https://www.rwjf.org/en/library/research/2019/11/what- Department of the Treasury, Office of Tax Analysis, May 2019, can-the-health-care-sector-do-to-advance-health-equity.html available at http://www.restud.com/wp-content/uploads/2019/07/ 101 Center for Health Care Strategies, Inc (CHCS). Advancing Health MS25585manuscript.pdf Equity in Medicaid: Emerging Value-Based Payment Innovations. 87 Minnesota Post, "Childhood asthma rates are declining, but March 2019. https://www.chcs.org/advancing-health-equity-in- disparities persist," February 2018, available at https://www. medicaid-emerging-value-based-payment-innovations/ minnpost.com/second-opinion/2018/02/childhood-asthma-rates- 102 Advancing Health Equity in Medicaid: Emerging Value-Based are-declining-disparities-persist/ Payment Innovations https://www.chcs.org/advancing-health- 88 Centers for Disease Control and Prevention (CDC), National equity-in-medicaid-emerging-value-based-payment-innovations/ Asthma Control Program, Accessed November 2020, "Asthma in 103 Breslin, E, Arthur, H, Heaphy, D, "Achieving Health Equity and Minnesota," available at https://www.cdc.gov/asthma/stateprofiles/ Wellness for Medicaid Populations: A Case Study of Community Asthma_in_MN.pdf Based Organization (CBO) Engagement in the Delivery System 89 Minnesota Department of Human Services, Child Protection Reform Incentive Payment (DSRIP) Program," 2019, available at webpage, available at https://mn.gov/dhs/people-we-serve/ https://www.academyhealth.org/sites/default/files/achieving_health_ children-and-families/services/child-protection/ equity_medicaid_cbos_april2019.pdf 90 Minnesota Department of Health, "Blue Ribbon Commission, Health 104 Health Management Associates. Issue Brief #1. Medicare-Medicaid Equity Webinar," December 2019, available at https://mn.gov/dhs/ Integration: Integrated Model Enrollment Rates Show Majority of assets/BRC-121319-deck_tcm1053-412853.pdf Medicare-Medicaid Dual Eligible Population Not Enrolled. April 2020. 91 U.S. Department of Health and Human Services, U.S. Indian Health https://www.healthmanagement.com/wp-content/uploads/04-20- Service, "Disparities Factsheet," October 2019, available at https:// 2020-Issue-Brief-1-final.pdf www.ihs.gov/newsroom/factsheets/disparities/ 58 105 Attribution of idea to: https://www.healthaffairs.org/do/10.1377/ 120 Bowleg L., "The problem with the phrase women and minorities: hblog20200831.419320/full/ intersectionality-an important theoretical framework for public health," American Journal of Public Health, 102(7), 1267–1273, 106 Bilinski, A., & Emanuel, E. J. (2020), "Covid-19 and excess all- 2012, available at https://doi.org/10.2105/AJPH.2012.300750. cause mortality in the US and 18 comparison countries," available at https://jamanetwork.com/journals/jama/fullarticle/2771841?gu 121 Caiola, C., Docherty, S., Relf, M., & Barroso, J., "Using an estAccessKey=0b2df654-b775-4691-bf01-b6e762f46c6c&utm_ intersectional approach to study the impact of social determinants source=For_The_Media&utm_medium=referral&utm_ of health for African-American mothers living with HIV," ANS. campaign=ftm_links&utm_content=tfl&utm_term=101220 Advances in nursing science, 37(4), 287, 2014, available at https:// www.ncbi.nlm.nih.gov/pmc/articles/PMC4221802/ 107 AARP Nursing Home COVID-19 Dashboard, November 2020, available at https://www.aarp.org/ppi/issues/caregiving/info-2020/ 122 Yee, Iezzoni and others describe an approach to using a continuum nursing-home-covid-dashboard.html of disability in "Compounded Disparities: Health Equity at the Intersection of Disability, Race, and Ethnicity," a paper for The 108 Centers for Disease Control and Prevention (CDC), "Long- National Academies of Sciences, Engineering, and Medicine, Term Effects of COVID-19," available at https://www.cdc.gov/ presented June 14, 2016, pp. 10-11. They also provide much useful coronavirus/2019-ncov/long-term-effects.html data on associations between disability, race and ethnicity. Available 109 The Commonwealth Fund, "Beyond the Case Count: The at https://dredf.org/wp-content/uploads/2018/01/Compounded- Wide-Ranging Disparities of COVID-19 in the United States," Disparities-Intersection-of-Disabilities-Race-and-Ethnicity.pdf September 2020, available at https://www.commonwealthfund.org/ 123 Minnesota Department of Human Services (DHS), "Accounting for publications/2020/sep/beyond-case-count-disparities-covid-19- Social Risk Factors in Minnesota Health Care Program Payments," united-states?gclid=EAIaIQobChMIs7-h2u3_7AIVCaGzCh1Apg- Legislative report supplement, December 2018, available at https:// pEAMYASAAEgJmq_D_BwE edocs.dhs.state.mn.us/lfserver/Public/DHS-7834-ENG 110 U.S. Department of Health and Human Services. Office of Minority 124 "Medicaid and Social Determinants of Health: Adjusting Payment Health, "Data Collection Standards for Race, Ethnicity, Primary and Measuring Health Outcomes," July 2017, available at Language, Sex, and Disability Status," available at https:// https://www.shvs.org/wp-content/uploads/2017/07/SHVS_ minorityhealth.hhs.gov/omh/browse.aspx?lvl=2&lvlid=23 SocialDeterminants_HMA_July2017.pdf 111 Health Affairs, "Building the Long-Term Care System Of The Future: 125 Health Management Associates (HMA), "A Report to The Will The COVID-19 Nursing Home Tragedies Lead To Real Reform?" Minnesota Department of Human Services (DHS). An Account July 2020, available at https://www.healthaffairs.org/do/10.1377/ of Health Disparities in Minnesota's Medicaid Population: Which hblog20200729.267815/full/ Populations Within the Medicaid Program Experience the Greatest 112 Review of Economic Studies, "Long-Term Impacts of Health Disparities and Poorest Health Outcomes?" (2018), Childhood Medicaid Expansions on Outcomes in Adulthood," available at https://www.healthmanagement.com/wp-content/ July 2019, available at https://academic.oup.com/restud/ uploads/MN-Summary-Report-to-Legislature_DHS_HMA_ article/87/2/792/5538992 DPC_08.01.17_6.11.18.pdf 113 Georgetown University Health Policy Institute, Center for Children 126 Minnesota Department of Human Services (DHS), Supplemental and Families, "Medicaid Provides an Excellent Long-Term Return Nutrition Assistance Program (SNAP), available at https://mn.gov/ on Investment," July 2015, available at https://ccf.georgetown. dhs/people-we-serve/adults/economic-assistance/food-nutrition/ edu/2015/07/28/medicaid-provides-excellent-long-term-return- programs-and-services/supplemental-nutrition-assistance-program. investment/ jsp 114 Centers for Medicare and Medicaid Services, "Hurricanes & tropical 127 State Health Access Data Assistance Center, "Availability and Use storms," available at https://www.cms.gov/About-CMS/Agency- of Enrollment Data from the ACA Health Insurance Marketplace," Information/Emergency/EPRO/Past-Emergencies/Hurricanes-and- September 2014, available at https://www.shadac.org/sites/ tropical-storms default/files/publications/ACADataAnalytics_Paper_%233_ 115 Note: Integrated Health Partnerships, see: https://mn.gov/dhs/ Availability_and_Use_of_Enrollment_Data_for_web.pdf partners-and-providers/news-initiatives-reports-workgroups/ 128 Minnesota Department of Human Services (DHS), "Accounting for minnesota-health-care-programs/integrated-health-partnerships/ Social Risk Factors in Minnesota Health Care Program Payments," 116 Karaye, I. M., & Horney, J. A. (2020), "The impact of social Legislative report supplement, December 2018, available at https:// vulnerability on COVID-19 in the US: an analysis of spatially varying edocs.dhs.state.mn.us/lfserver/Public/DHS-7834-ENG. Note: DHS relationships," American journal of preventive medicine, 59(3), uses several categories to describe the race and ethnicity based 317-325, available at https://www.ajpmonline.org/article/S0749- upon the categories used on its enrollment application. 3797(20)30259-2/pdf 129 Minnesota Department of Human Services (DHS), "Applying for 117 Kaiser Family Foundation (KFF), "Disparities in Health and Health Medical Assistance (MA)," available at https://mn.gov/dhs/people- Care: Five Key Questions and Answers," March 2020, available we-serve/people-with-disabilities/health-care/health-care-programs/ at https://www.kff.org/disparities-policy/issue-brief/disparities-in- programs-and-services/disabilities-apply.jsp health-and-health-care-five-key-questions-and-answers/ 130 National Association of Medicaid Director (NAMD), "NAMD 118 Healthy People.gov., available at https://www.healthypeople. Statement on Inequities in Health and Health Care," June 2020, gov/2020/ available at https://medicaiddirectors.org/press-release/2020/06/ namd-statement-on-inequities-in-health-and-health-care/ 119 U.S. Department of Health and Human Services, Heckler, Margaret, "Report of the Secretary's Task Force, Black & Minority Health, 131 Evans, M. K., Rosenbaum, L., Malina, D., Morrissey, S., & Rubin, Volume 1, Executive Summary," 1985, available at https://www. E. J., "Diagnosing and Treating Systemic Racism," New England minorityhealth.hhs.gov/assets/pdf/checked/1/ANDERSON.pdf Journal of Medicine, July 16, 2020, DOI: 10.1056/NEJMe2021693, available at https://www.nejm.org/doi/full/10.1056/NEJMe2021693 59 132 Centers for Disease Control and Prevention (CDC, "Summary Health https://www.macpac.gov/wp-content/uploads/2020/06/Chapter-2- Statistics for U.S. Adults: National Health Interview Survey," 2012, Integrating-Care-for-Dually-Eligible-Beneficiaries-Policy-Issues-and- available at https://www.cdc.gov/nchs/data/series/sr_10/sr10_260. Options.pdf; pdf https://bipartisanpolicy.org/wp-content/uploads/2020/07/BPC_ 133 Solomon, M. Z., Wynia, M. K., & Gostin, L. O., Perspective: Health_Integration_of_Care_V3.pdf "Covid-19 crisis triage-optimizing health outcomes and disability 146 The Undefeated, "Ibram Kendi, one of the nation's leading scholars rights," New England Journal of Medicine, July 30, 2020, available of racism, says education and love are not the answer," at https://www.nejm.org/doi/full/10.1056/NEJMp2008300 September 2017, available at https://theundefeated.com/features/ 134 Kaiser Family Foundation (KFF), "Low-Income and Communities of ibram-kendi-leading-scholar-of-racism-says-education-and-love- color at Higher Risk of Serious Illness if Infected with Coronavirus, are-not-the-answer/ May 2020, available at https://www.kff.org/coronavirus-covid-19/ issue-brief/low-income-and-communities-of-color-at-higher-risk-of- 147 National Academy for State Health Policy (NASHP), "States Use serious-illness-if-infected-with-coronavirus/ Race and Ethnicity Data to Identify Disparities and Inform their COVID-19 Responses," April 2020, available at https://www.nashp. 135 Sabatello, M., Landes, S. D., & McDonald, K. E., "People With org/states-use-race-and-ethnicity-data-to-identify-disparities-and- Disabilities in COVID-19: Fixing Our Priorities," The American Journal inform-their-covid-19-responses/ of Bioethics, 20(7), 187-190, available at https://www.tandfonline. com/doi/full/10.1080/15265161.2020.1779396 148 Pew Research Center, "Income Inequality in the U.S. Is Rising Most Rapidly Among Asians," July 2018, available at https://www. 136 Jones CP, "Confronting Institutionalized Racism," Phylon 2003;50(1- pewsocialtrends.org/2018/07/12/income-inequality-in-the-u-s-is- 2):7-22, available at https://www.apha.org/~/media/files/pdf/ rising-most-rapidly-among-asians/ webinars/naming_racism_jones3.ashx 149 Adia, A., Nazareno, J., Operario D., and Ponce,N.A., "Health 137 Cornell University, Disability Statistics, available at https:// Conditions, Outcomes, and Service Access Among Filipino, disabilitystatistics.org/reports/acs.cfm?statistic=11 Vietnamese, Chinese, Japanese, and Korean Adults in 138 Congressional Research Service (CRS)," Medicaid Eligibility: Older California, 2011–2017," March 2020, available at https://ajph. Adults and Individuals with Disabilities," December 2019, available aphapublications.org/doi/abs/10.2105/AJPH.2019.305523 at https://crsreports.congress.gov/product/pdf/R/R46111 150 Center for Healthcare Strategies (CHCS), "Learning from Each 139 Centers for Medicare and Medicaid Services (CMS), Medicare- Other: Assessing How Medicaid Agencies Collect Racial/Ethnic Medicaid Coordination Office, Fact Sheet-March 2020, "People Data," June 2006, available at https://www.chcs.org/resource/ Dually Eligible for Medicare and Medicaid," available at https://www. learning-from-each-other-assessing-how-medicaid-agencies- cms.gov/Medicare-Medicaid-Coordination/Medicare-and-Medicaid- collect-racialethnic-data/ Coordination/Medicare-Medicaid-Coordination-Office/Downloads/ 151 State Health Access Data Assistance Center (SHADAC), "Race/ MMCO_Factsheet.pdf Ethnicity Data in CMS Medicaid (T-MSIS) Analytic Files," August 140 U.S. Department of Health and Human Services, Office of the 2020, available at https://www.shadac.org/news/raceethnicity-data- Assistant Secretary for Planning and Evaluation, "Analysis of cms-medicaid-t-msis-analytic-files Pathways to Dual Eligible Status: Final Report," May 2019, available 152 Agency for Healthcare Research and Quality (AHRQ), "Race, at https://aspe.hhs.gov/basic-report/analysis-pathways-dual- Ethnicity, and Language Data: Standardization for Health Care eligible-status-final-report Quality Improvement," available at https://www.ahrq.gov/research/ 141 Community Catalyst, "Miles to Go: Progress on Addressing Racial findings/final-reports/iomracereport/reldata5.htm and Ethnic Health Disparities in the Dual Eligible Demonstration 153 Data strategy for collecting data on homelessness proposed by Projects," November 2014, available at https://www. experts from Minnesota. communitycatalyst.org/resources/publications/document/Miles-to- Go-Health-Disparities-in-the-Dual-Eligible-DemonstrationsFINAL.pdf 154 ICD-10.com. Factors influencing health status and contact with health services. https://www.icd10data.com/ICD10CM/Codes/Z00-Z99 142 Centers for Medicare and Medicaid Services (CMS), Office of Minority Health, "CMS Equity Plan for Medicare," September 2015, 155 Mathew, J, Hodge, C, and Khau, M. Z Codes Utilization among available at https://www.cms.gov/About-CMS/Agency-Information/ Medicare Fee-for-Service (FFS) Beneficiaries in 2017. CMS OMH OMH/OMH_Dwnld-CMS_EquityPlanforMedicare_090615.pdf Data Highlight No. 17. Baltimore, MD: CMS Office of Minority Health. 2019. https://www.cms.gov/files/document/cms-omh- 143 For more information about the Financial Alignment january2020-zcode-data-highlightpdf.pdf Initiative (FAI): https://www.cms.gov/Medicare-Medicaid- Coordination/Medicare-and-Medicaid-Coordination/Medicare- 156 Friedman, N, "Toward Addressing Social Determinants of Health: A Medicaid-Coordination-Office/FinancialAlignmentInitiative/ Health Care System Strategy," The Permanente Journal, October FinancialModelstoSupportStatesEffortsinCareCoordination 2018, available at https://www.ncbi.nlm.nih.gov/pmc/articles/ PMC6207437/ 144 Health Management Associates (HMA), "Issue Brief #1. Medicare- Medicaid Integration: Integrated Model Enrollment Rates Show 157 ICD10 Data.com, "Persons with potential health hazards related Majority of Medicare-Medicaid Dual Eligible Population Not to socioeconomic and psychosocial circumstances, Z55-Z65," Enrolled," April 2020, available at https://www.healthmanagement. available at https://www.icd10data.com/ICD10CM/Codes/Z00-Z99/ com/wp-content/uploads/04-20-2020-Issue-Brief-1-final.pdf Z55-Z65 145 In 2020, several national organizations released reports about 158 Health Affairs, "Integrating Social and Medical Data To Improve integrated programs for dually eligible individuals. Reports Population Health: Opportunities And Barriers," November are listed in chronological order of release: https://www.chcs. 2016, available at https://www.healthaffairs.org/doi/full/10.1377/ org/media/State-Efforts-to-Integrate-Care-for-Dually-Eligible- hlthaff.2016.0723 Beneficiaries_022720.pdf; https://bipartisanpolicy.org/wp-content/ 159 Centers for Medicare and Medicaid Services, "Z Codes Utilization uploads/2020/04/BPC_Health_WhitePaperPt2_FInal1-1.pdf; among Medicare Fee-for-Service (FFS) Beneficiaries in 2017," https://www.healthmanagement.com/wp-content/uploads/04-20- January 2020, available at https://www.cms.gov/files/document/ 2020-Issue-Brief-1-final.pdf; cms-omh-january2020-zcode-data-highlightpdf.pdf 60 160 Minnesota Department of Human Services, Integrated Health 176 U.S. Department of Health and Human Services, U.S. Indian Health Partnerships (IHP), Updated August 2020, available at https:// Service, "Disparities Factsheet," October 2019, available at https:// mn.gov/dhs/partners-and-providers/news-initiatives-reports- www.ihs.gov/newsroom/factsheets/disparities/ workgroups/minnesota-health-care-programs/integrated-health- 177 Center for Health Care Strategies (CHCS), "Stemming the Risk partnerships/ of Disability Bias During the COVID-19 Pandemic," April 2020, 161 "Advancing Health Equity in Medicaid: Emerging Value-Based available at https://www.chcs.org/stemming-the-risk-of-disability- Payment Innovations," Center for Health Care Strategies, March bias-during-the-covid-19-pandemic/ 2019, available at: 178 U.S. Department of Health and Human Services, Office of the https://www.chcs.org/advancing-health-equity-in-medicaid- Assistant Secretary for Planning and Evaluation, "HHS Poverty emerging-value-based-payment-innovations/ Guidelines for 2020," January 2020, available at https://aspe.hhs. gov/poverty-guidelines 162 Oregon Health Authority: https://www.oregon.gov/oha/HSD/ Medicaid-Policy/Pages/index.aspx 179 Pediatrics, "Poverty and Child Health in the United States," April 2016, available at https://pediatrics.aappublications.org/ 163 Washington Department of Social and Health Services: https:// content/137/4/e20160339 www.dshs.wa.gov/ 180 Urban Institute, "Child Poverty and Adult Success," September 164 Oregon Child Integrated Dataset, Governor announces OCID official 2015, available at https://www.urban.org/sites/default/files/ launch, October 2020, available at https://www.ocid-cebp.org/ publication/65766/2000369-Child-Poverty-and-Adult-Success.pdf governor-announces-ocid-official-launch/ 181 Children's Defense Fund, available at https://www.childrensdefense. 165 Oregon Child Integrated Dataset, Governor announces OCID official org/wp-content/uploads/2020/02/The-State-Of-Americas- launch, October 2020, available at https://www.ocid-cebp.org/ Children-2020.pdf governor-announces-ocid-official-launch/ 182 Kaiser Family Foundation (KFF), "Health Insurance Coverage of 166 State agencies use data to drive policies, help Washingtonians, May Children 0-18 Living in Poverty (under 100% FPL)," 2019 data, 2018, available at https://medium.com/wagovernor/state-agencies- available at https://www.kff.org/other/state-indicator/poor-children/? use-data-to-drive-policies-help-washingtonians-a572568b3a60 currentTimeframe=0&sortModel=%7B%22colId%22:%22Location% 167 Center for Health Care Strategies, "The Exponential Value of 22,%22sort%22:%22asc%22%7D Integrating Cross-Agency Data: Lessons from Washington State's 183 Minnesota Department of Health, "Blue Ribbon Commission, Health David Mancuso," March 2020, available at https://www.chcs.org/ Equity Webinar," December 2019, available at https://mn.gov/dhs/ the-exponential-value-of-integrating-cross-agency-data-lessons- assets/BRC-121319-deck_tcm1053-412853.pdf from-washington-states-david-mancuso/ 184 Health Affairs Blog, "How Foundational Moments In Medicaid's 168 Center for Health Care Strategies, "The Exponential Value of History Reinforced Rather Than Eliminated Racial Health Integrating Cross-Agency Data: Lessons from Washington State's Disparities," September 2020, available at https://www.healthaffairs. David Mancuso," March 2020, available at https://www.chcs.org/ org/do/10.1377/hblog20200828.661111/full/ the-exponential-value-of-integrating-cross-agency-data-lessons- from-washington-states-david-mancuso/ 185 Note: Many foundations and organizations are focused on what the heath care sector can do to advance health equity. A report 169 The National Academies of Sciences, Engineering, and Medicine, funded by the Robert Wood Johnson Foundation "aims to assist "Accounting for Social Risk Factors in Medicare Payment those working in health care, public health, and other fields that Dissemination Meeting," July 2017, available at https://www.nap. powerfully shape health-such as education, childcare, housing, edu/resource/23635/SES-and-Medicare-Dissemination-Meeting.pdf and community development-to build a world in which everyone 170 The National Academies of Sciences, Engineering, and Medicine, has a fair and just opportunity to be as healthy as possible." The "Accounting for Social Risk Factors in Medicare Payment report name is: "What Can the Health Care Sector Do to Advance Dissemination Meeting," July 2017, available at https://www.nap. Health Equity?," November 2019, a, , , , and . "What Can the Health edu/resource/23635/SES-and-Medicare-Dissemination-Meeting.pdf Care Sector Do to Advance Health Equity?" November 12, 2019. 171 Brown, D; Kowalski, A; and Lurie, I., "Long-Term Impacts of vailable at https://www.rwjf.org/en/library/research/2019/11/what- Childhood Medicaid Expansions on Outcomes in Adulthood," U.S. can-the-health-care-sector-do-to-advance-health-equity.html Department of the Treasury, Office of Tax Analysis, May 2019, 186 Center for Health Care Strategies, Inc (CHCS). Advancing Health available at http://www.restud.com/wp-content/uploads/2019/07/ Equity in Medicaid: Emerging Value-Based Payment Innovations. MS25585manuscript.pdf March 2019. https://www.chcs.org/advancing-health-equity-in- 172 Minnesota Post, "Childhood asthma rates are declining, but medicaid-emerging-value-based-payment-innovations/ disparities persist," February 2018, available at https://www. 187 Advancing Health Equity in Medicaid: Emerging Value-Based minnpost.com/second-opinion/2018/02/childhood-asthma-rates- Payment Innovations are-declining-disparities-persist/ https://www.chcs.org/advancing-health-equity-in-medicaid- 173 Centers for Disease Control and Prevention (CDC), National emerging-value-based-payment-innovations/ Asthma Control Program, Accessed November 2020, "Asthma in 188 Breslin, E, Arthur, H, Heaphy, D, "Achieving Health Equity and Minnesota," available at https://www.cdc.gov/asthma/stateprofiles/ Wellness for Medicaid Populations: A Case Study of Community Asthma_in_MN.pdf Based Organization (CBO) Engagement in the Delivery System 174 Minnesota Department of Human Services, Child Protection Reform Incentive Payment (DSRIP) Program," 2019, available at webpage, available at https://mn.gov/dhs/people-we-serve/ https://www.academyhealth.org/sites/default/files/achieving_health_ children-and-families/services/child-protection/ equity_medicaid_cbos_april2019.pdf 175 Minnesota Department of Health, "Blue Ribbon Commission, Health 189 Health Management Associates. Issue Brief #1. Medicare-Medicaid Equity Webinar," December 2019, available at https://mn.gov/dhs/ Integration: Integrated Model Enrollment Rates Show Majority of assets/BRC-121319-deck_tcm1053-412853.pdf Medicare-Medicaid Dual Eligible Population Not Enrolled. April 2020. https://www.healthmanagement.com/wp-content/uploads/04-20- 2020-Issue-Brief-1-final.pdf 61 190 Attribution of idea to: https://www.healthaffairs.org/do/10.1377/ 204 Karaye, I. M., & Horney, J. A. (2020), "The impact of social hblog20200831.419320/full/ vulnerability on COVID-19 in the US: an analysis of spatially varying relationships," American journal of preventive medicine, 59(3), 191 Bilinski, A., & Emanuel, E. J. (2020), "Covid-19 and excess all- 317-325, available at https://www.ajpmonline.org/article/S0749- cause mortality in the US and 18 comparison countries," available 3797(20)30259-2/pdf at https://jamanetwork.com/journals/jama/fullarticle/2771841?gu estAccessKey=0b2df654-b775-4691-bf01-b6e762f46c6c&utm_ 205 Kaiser Family Foundation (KFF), "Disparities in Health and Health source=For_The_Media&utm_medium=referral&utm_ Care: Five Key Questions and Answers," March 2020, available campaign=ftm_links&utm_content=tfl&utm_term=101220 at https://www.kff.org/disparities-policy/issue-brief/disparities-in- health-and-health-care-five-key-questions-and-answers/ 192 AARP Nursing Home COVID-19 Dashboard, November 2020, available at https://www.aarp.org/ppi/issues/caregiving/info-2020/ 206 Healthy People.gov., available at https://www.healthypeople. nursing-home-covid-dashboard.html gov/2020/ 193 Centers for Disease Control and Prevention (CDC), "Long- 207 U.S. Department of Health and Human Services, Heckler, Margaret, Term Effects of COVID-19," available at https://www.cdc.gov/ "Report of the Secretary's Task Force, Black & Minority Health, coronavirus/2019-ncov/long-term-effects.html Volume 1, Executive Summary," 1985, available at https://www. minorityhealth.hhs.gov/assets/pdf/checked/1/ANDERSON.pdf 194 The Commonwealth Fund, "Beyond the Case Count: The Wide- Ranging Disparities of COVID-19 in the United States," 208 Bowleg L., "The problem with the phrase women and minorities: intersectionality-an important theoretical framework for public September 2020, available at https://www.commonwealthfund.org/ health," American Journal of Public Health, 102(7), 1267–1273, publications/2020/sep/beyond-case-count-disparities-covid-19- 2012, available at https://doi.org/10.2105/AJPH.2012.300750. united-states?gclid=EAIaIQobChMIs7-h2u3_7AIVCaGzCh1Apg- pEAMYASAAEgJmq_D_BwE 209 Caiola, C., Docherty, S., Relf, M., & Barroso, J., "Using an intersectional approach to study the impact of social determinants 195 Braveman, P. and others, "Health Disparities and Health Equity: of health for African-American mothers living with HIV," ANS. The Issue Is Justice," American Journal of Public Health, December Advances in nursing science, 37(4), 287, 2014, available at https:// 2011, available at https://www.ncbi.nlm.nih.gov/pmc/articles/ www.ncbi.nlm.nih.gov/pmc/articles/PMC4221802/ PMC3222512/ 210 Yee, Iezzoni and others describe an approach to using a continuum 196 U.S. Department of Health and Human Services. Office of Minority of disability in "Compounded Disparities: Health Equity at the Health, "Data Collection Standards for Race, Ethnicity, Primary Intersection of Disability, Race, and Ethnicity," a paper for The Language, Sex, and Disability Status," available at https:// National Academies of Sciences, Engineering, and Medicine, minorityhealth.hhs.gov/omh/browse.aspx?lvl=2&lvlid=23 presented June 14, 2016, pp. 10-11. They also provide much useful 197 Health Affairs, "Building the Long-Term Care System Of The Future: data on associations between disability, race and ethnicity. Available Will The COVID-19 Nursing Home Tragedies Lead To Real Reform?" at https://dredf.org/wp-content/uploads/2018/01/Compounded- July 2020, available at https://www.healthaffairs.org/do/10.1377/ Disparities-Intersection-of-Disabilities-Race-and-Ethnicity.pdf hblog20200729.267815/full/ 211 Minnesota Department of Human Services (DHS), "Accounting for 198 Review of Economic Studies, "Long-Term Impacts of Social Risk Factors in Minnesota Health Care Program Payments," Childhood Medicaid Expansions on Outcomes in Adulthood," Legislative report supplement, December 2018, available at https:// July 2019, available at https://academic.oup.com/restud/ edocs.dhs.state.mn.us/lfserver/Public/DHS-7834-ENG article/87/2/792/5538992 212 "Medicaid and Social Determinants of Health: Adjusting Payment 199 Georgetown University Health Policy Institute, Center for Children and Measuring Health Outcomes," July 2017, available at and Families, "Medicaid Provides an Excellent Long-Term Return https://www.shvs.org/wp-content/uploads/2017/07/SHVS_ on Investment," July 2015, available at https://ccf.georgetown. SocialDeterminants_HMA_July2017.pdf edu/2015/07/28/medicaid-provides-excellent-long-term-return- 213 Health Management Associates (HMA), "A Report to The investment/ Minnesota Department of Human Services (DHS). An Account 200 Centers for Medicare and Medicaid Services, "Hurricanes & tropical of Health Disparities in Minnesota's Medicaid Population: Which storms," available at https://www.cms.gov/About-CMS/Agency- Populations Within the Medicaid Program Experience the Greatest Information/Emergency/EPRO/Past-Emergencies/Hurricanes-and- Health Disparities and Poorest Health Outcomes?" (2018), tropical-storms available at https://www.healthmanagement.com/wp-content/ 201 Note: Integrated Health Partnerships, see: https://mn.gov/dhs/ uploads/MN-Summary-Report-to-Legislature_DHS_HMA_ partners-and-providers/news-initiatives-reports-workgroups/ DPC_08.01.17_6.11.18.pdf minnesota-health-care-programs/integrated-health-partnerships/ 214 Minnesota Department of Human Services (DHS), Supplemental 202 Healthy People, "The Role of Law and Policy in Achieving Healthy Nutrition Assistance Program (SNAP), available at https://mn.gov/ People's Disability and Health Goals around Access to Health Care, dhs/people-we-serve/adults/economic-assistance/food-nutrition/ Activities Promoting Health and Wellness, Independent Living and programs-and-services/supplemental-nutrition-assistance-program. Participation, and Collecting Data in the United States," available at jsp https://www.healthypeople.gov/sites/default/files/LHP_Disability- 215 State Health Access Data Assistance Center, "Availability and Use Health-Policy_2020.03.12_508_0.pdf of Enrollment Data from the ACA Health Insurance Marketplace," 203 Fabius, Chanee D., et al. "Racial disparities in Medicaid home September 2014, available at https://www.shadac.org/sites/ and community-based service utilization and expenditures default/files/publications/ACADataAnalytics_Paper_%233_ among persons with multiple sclerosis." BMC health services Availability_and_Use_of_Enrollment_Data_for_web.pdf research 18.1 (2018): 1-9, available at https://pubmed.ncbi.nlm.nih. gov/30314479/ 62 216 Minnesota Department of Human Services (DHS), "Accounting for 218 Braveman, P. and others, "Health Disparities and Health Equity: Social Risk Factors in Minnesota Health Care Program Payments," The Issue Is Justice," American Journal of Public Health, December Legislative report supplement, December 2018, available at https:// 2011, available at https://www.ncbi.nlm.nih.gov/pmc/articles/ edocs.dhs.state.mn.us/lfserver/Public/DHS-7834-ENG. Note: DHS PMC3222512/ uses several categories to describe the race and ethnicity based 219 Fabius, Chanee D., et al. "Racial disparities in Medicaid home upon the categories used on its enrollment application. and community-based service utilization and expenditures 217 Minnesota Department of Human Services (DHS), "Applying for among persons with multiple sclerosis." BMC health services Medical Assistance (MA)," available at https://mn.gov/dhs/people- research 18.1 (2018): 1-9, available at https://pubmed.ncbi.nlm.nih. we-serve/people-with-disabilities/health-care/health-care-programs/ gov/30314479/ programs-and-services/disabilities-apply.jsp 63