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Preventable Health Disparities

Health disparities are preventable differences in the burden of disease, injury, violence, or opportunities to achieve optimal health that are experienced by populations that have been disadvantaged by their social or economic status, geographic location, and environment.[1] Many populations experience health disparities, including people from some racial and ethnic minority groups, people with disabilities, women, people who are LGBTQI+ (lesbian, gay, bisexual, transgender, queer, intersex, or other), people with limited English proficiency, and other groups.

Across the country, people in some racial and ethnic minority groups experience higher rates of poor health and disease for a range of health conditions, including diabetes, hypertension, obesity, asthma, heart disease, cancer, and preterm birth, when compared to their White counterparts. For example, the average life expectancy among Black or African American people in the United States is four years lower than that of White people.[3] These disparities sometimes persist even when accounting for other demographic and socioeconomic factors, such as age or income.

Communities can prevent health disparities when community- and faith-based organizations, employers, healthcare systems and providers, public health agencies, and policymakers work together to develop policies, programs, and systems based on a health equity framework and community needs.

CDC is transforming its public health research, surveillance, and implementation science efforts to expand beyond listing the markers of health inequities to identifying and addressing the drivers of these disparities. Through the CORE strategy, CDC is integrating health equity as a foundational element across our work – from science and research, to programs, partnerships, and workforce. As part of the initiative, OMHHE has adopted four CORE goals.

Cultivate comprehensive health equity science

OMHHE will facilitate and accelerate health equity principles’ adoption across CDC programs, policies, data systems, and funding structures.

OMHHE is working to:

Standardize health equity language and principles.

Establish standards in health equity data collection.

Provide guidance on analyzing and using data to assess health equity and manage public health programs.

Incorporate health equity principles and data collection standards into Notice of Funding Opportunities that support research and non-research public health programs at the state and local level.

Potential Impact: National, state, local, tribal, and territorial public health staff will have a better understanding of health equity, the increased capacity to use data to integrate health equity into public health systems and interventions, and ultimately eliminate health disparities in the communities they serve.

Goal in Action: CDC’s CORE Commitment to Health Equity Science and Intervention Strategy

Optimize interventions

OMHHE/Office of Women’s Health will collaborate with partners to address and reduce the impact of gender discrimination and gendered racism in the workplace.

OMHHE is collaborating with internal and external partners to:

Provide input to a national survey to assess the status and impact of gender discrimination and gendered racism in the U.S. population.

Compile and communicate strategies, policies, and best practices intended to reduce gender discrimination and gendered racism in the workplace.

Develop and implement strategies for strengthening organizational capacity to achieve and sustain systems changes that promote health equity in the workplace.

Potential Impact: Systems changes will occur in the workplace, including workplaces that set the standard for gender equity best practices, that decrease experiences of gender discrimination and gendered racism, and ultimately, improve mental and physical health among people of all gender identities.

Goal in Action: Evaluation of data on perceptions and experiences of gender discrimination and gendered racism in the workplace.

Reinforce and expand robust partnerships

OMHHE will mobilize partners to develop and implement strategies addressing health disparities and long-standing inequities including social determinants of health.

OMHHE is providing guidance and support to partners who respond to public health needs to:

Develop partners’ capacity to work with CDC to address health inequities, health disparities, and structural and social determinants of health in response to public health crises.

Design new, or evaluate and refine existing, evidence-based strategies that address health equity and long-standing health disparities and inequities; and develop guidance for implementation of these strategies in diverse communities.

Potential Impact: CDC partners will be engaged and ready to respond to public health emergencies and address long-standing health inequities, health disparities, and structural and social determinants of health.

Goal in Action: National Initiative to Address COVID-19 Health Disparities Among Populations at High-Risk and Underserved, Including Racial and Ethnic Minority Populations and Rural Communities

Enhance capacity and workforce engagement

OMHHE will transform the public health workforce to ensure diversity and health equity competencies in existing and future staff.

OMHHE collaborates with internal and external partners to:

Expand access to undergraduate student internships by linking CDC Undergraduate Public Health Scholars (CUPS) grantees with state, local, and community partners interested in hosting students or establishing pipeline programs.

Integrate competencies of health equity as well as racism and health into the CDC and public health workforce.

Potential Impact: National, state, local, tribal, and territorial public health agencies will have increased opportunity to support underserved undergraduate students and the current public health workforce with learning how to integrate health equity competencies into public health work. Ultimately, we will create a public health workforce that reflects the communities we serve and is responsive to the country’s changing demographics.

Goal in Action: CDC OMHHE Student Programs

You—as a community member or member of an organization—can join the effort to ensure that all people have equitable access to resources to maintain and manage their physical and mental health, including easy access to important information, goods and services, and affordable medical and mental health care. Community- and faith-based organizations, employers, healthcare systems and providers, public health agencies, policy makers, and others play a key part in promoting fair access to health, improving opportunity, and ensuring all communities can thrive.

Communities can promote health equity by adopting policies, programs, and practices that:

Support equitable access to quality and affordable health and other social services (e.g., education, housing, transportation, child care) and accessibility within these services.

Recognize, respect, and support the diversity of the community they serve.

Partner with trusted messengers and community health workers/promotores de salud to share clear and accurate information tailored to a community’s languages, literacy levels, and cultures.

Include community engagement efforts that can help strengthen partnerships between community members and public health entities, build trust, and promote social connection.

Engage trusted leaders known by the community and who share the same race and ethnicity, sexual orientation, and cultural or religious beliefs as the community to share information, collect input, or conduct outreach.[24]

Use clear, easy-to-read, accurate, transparent, and consistent information from a reputable source that is locally and culturally relevant in terms of language, messaging, tone, images, and format.[24] Information should be suitable for diverse audiences, including people with disabilities, limited English proficiency, low literacy, or people who face other challenges accessing health information.

Below are examples of additional actions that organizations and agencies can take to support health equity.

Community and faith-based organizations can:

Help connect people with healthcare providers, goods (e.g., healthy foods, temporary housing), and services to meet their physical, spiritual, and mental health needs.

Work with others to address misinformation, myths, and lack of access to appropriate resources. This might include working with trusted local media, local public health departments, or community members to share information or community insights that help connect individuals to resources and free or low-cost services.

Employers can:

Train employees at all levels of the organization to identify and interrupt all forms of discrimination; provide them with training in implicit bias.

Establish and maintain equitable leave policies that are fair and flexible to meet the needs of all employees.

Healthcare delivery systems can:

Deliver all health-related services in a culturally appropriate way and according to the needs of patients. This may include providing the necessary patient supports (e.g., translator, patient navigators).

Ensure providers show awareness of and respect for culture when providing care.

Collect and report race and ethnicity data on all patients and educate staff and patients on why this information is an important part of making sure populations are receiving equitable access to care.

Public health agencies can:

Build partnerships with different sectors (e.g., community- and faith-based organizations, racial and ethnic minority-serving organizations, tribal communities, school and transportation systems, scientific researchers, professional organizations) and community members to share information and collaborate to advance health equity in communities.

Address misunderstandings about why people are being asked for personal information, including race and ethnicity, and why this information is important to allocate resources and information sharing to people who need them most.

State, tribal, local, and territorial governments can:

Explore options to provide free or low-cost broadband Internet access so people can use telehealth and get information on health care and social services.

Reassess policies that create barriers for healthcare providers to collect and report data on race and ethnicity and social determinants of health.

Partner with public health agencies to evaluate current and proposed policies in transportation, housing, community development, and more for their impacts on health, using a Health in All Policies framework. Prioritize health for communities experiencing health disparities in all policy change.

Explore options to protect renters from evictions.

Work to expand childcare service options.

Increase public transportation services (e.g., free access to city bike programs).

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What is Health Equity? | Health Equity | CDC

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The Office of Minority Health and Health Equity (OMHHE) Mission: Advance health equity and women’s health issues across the nation through CDC’s science and programs, and increase CDC’s capacity to leverage its diverse workforce and engage stakeholders toward this end.

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Abstract

Health disparities are real. The evidence base is large and irrefutable. As such, the time is now to shift the research emphasis away from solely documenting the pervasiveness of the health disparities problem and begin focusing on health equity, the highest level of health possible. The focus on health equity research will require investigators to propose projects that develop and evaluate evidence-based solutions to health differences that are driven largely by social, economic, and environmental factors. This article highlights ongoing research and programmatic efforts underway at the National Institutes of Health that hold promise for advancing population health and improving health equity.

In the early 20th century, African American thought leaders and abolitionists first sounded the alarm that the United States was a nation divided-separate and unequal, with wide gaps in health and life expectancy between white and black people. William Edward Burghardt (W.E.B.) Du Bois,1 Booker T. Washington,2 the National Negro Business League,3 and others lamented the lack of access to quality health care experienced by black people during the early emancipation period. Several decades later, researchers and scientists began again, in earnest, to extensively document the unequal care and disparity in life expectancy between white and black people in the U.S. The data were compelling.4 Racial/ethnic minority groups experienced decreased access to health care, worse health outcomes, increased morbidity and mortality, and shorter life expectancy when compared with white people. Even worse, since that time, the racial/ethnic health disparity gaps have progressively widened.5,6 For example, while the breast cancer mortality rates have fallen steadily since 1990 for all racial/ethnic groups, Native Americans' rates have remained the same. Additionally, while black people have a lower incidence of breast cancer than white people, their mortality rate remains higher.7

FROM A FOCUS ON INDIVIDUAL TO SOCIAL DETERMINANTS

In 1985, with the publication of the "Report of the Secretary's Task Force on Black and Minority Health," research focured singularly on documenting the magnitude and persistence of health disparities. The predominantly descriptive methodology included tracking health outcomes by race/ethnicity. The Task Force documented health disparities as excess deaths in black and minority populations, compared with white people, that were directly linked to six health outcomes-cancer, cardiovascular disease, cirrhosis of the liver, diabetes, homicides/suicides, and infant mortality.4 However, most of the health disparity research in the 1980s and 1990s focused on individual-level factors that contributed to health disparities-such as cell mutations, individual health beliefs and behaviors, and, later in the 1990s, genetic susceptibility.

At the turn of the 21st century, there was growing recognition that the variation in health outcomes and widespread health disparities experienced by social groups in the U.S. could not be explained by individual-level determinants alone. As a consequence, there is increasing awareness that factors outside the individual's control, such as social, economic, and policy factors, contribute to persistent and disparate health outcomes.8 The aforementioned racial/ethnic differences in health outcomes are now attributed largely to and further exacerbated by socioeconomic status.9 This increased awareness of the role of socioeconomic factors has generated greater interest in social determinants and their effects on health outcomes, health promotion, and disease prevention.

Thus, social determinants of health (SDH) can be understood as the social conditions in which people are born, grow, live, work, and age (including the health system), and are shaped by the distribution of money, power, and resources at global, national, and local levels, which are themselves influenced by policy choices.10 Research on SDH and their contribution to population health emphasizes the complex role that overlapping social structures and economic systems play in the health of populations. The World Health Organization confirmed in its 2008 Commission on the Social Determinants of Health report that SDH are mostly responsible for health inequities-the unfair and avoidable factors in health status-within and between countries.11 However, in the U.S., health disparities continue to be framed as racial/ethnic disparities largely devoid of class differences or socioeconomic factors.

FROM A DISPARITY MODEL TO AN EQUITY MODEL

More recently, however, there has been some recognition in the U.S. that research focusing only on race/ethnicity does not provide a complete understanding of these health inequities. There is now a desire to shift the language and emphasis away from health disparities solely (i.e., a focus on problem identification) to a focus on health equity, the highest level of health possible (i.e., a focus on solutions).

The U.S. Department of Health and Human Services12 and Healthy People 20208 define health equity as the "attainment of the highest level of health for all people. Achieving health equity requires valuing everyone equally with focused and ongoing societal efforts to address avoidable inequalities, historical and contemporary injustices, and the elimination of health and health care disparities." The consensus in the field is that, while there is overwhelming evidence that health disparities are real, there is limited research that supports the development of effective and sustainable strategies to reduce or eliminate these disparities. This new emphasis on equity is not just a shift in language; rather, it involves shifting the research agenda toward population-level solutions. Until recently, studies of health disparities have been largely descriptive and focused on differences in population health that are closely linked with social advantage and disadvantage. The shift to health equity involves developing and implementing interventions at the neighborhood, local, community, state, and national levels.

IMPLICATIONS OF A SHIFT FROM A DISPARITY TO AN EQUITY MODEL

Several factors must be considered to shift the research agenda from a disparity model to an equity model, in which the central theme is achieving the highest level of health. The following, though not a comprehensive list, provides five initial steps to consider.

1. Consider the role of population health in research and interventions

Since SDH are inseparable from the health of populations, it is necessary to investigate factors operating not only at the individual level but also at the societal and structural levels.13 Population health strategies and interventions allow for such investigation of individual and environmental effects simultaneously. This research agenda integrates the science across the life-span and across multiple generations, while simultaneously accounting for multiple factors from biological, behavioral, social, and population levels. This approach is crucial to addressing the complex nature of public health challenges, as it simultaneously accounts for variables at the biological, behavioral, and social levels.

Specific components of integrative and multidisciplinary population-level research have seen some preliminary success in state and local studies and projects. One example is the Shape Up Somerville project14 in Massachusetts. This citywide program aims to increase daily physical activity and promote healthy eating through the collaborative efforts of all segments of the community-schools, community and civic organizations, city government, and other community residents who live and work in Somerville. By affecting change in the built environment; recreation; and environmental, public safety, and food policies, this innovative program has seen a reduction in childhood obesity. As Somerville Mayor Joseph Curtatone succinctly stated in an interview on National Public Radio, the project is "not about losing weight, it's about how we live."15 This research program exemplifies the kind of solutions-oriented, multidisciplinary research, accounting for individual and community levels simultaneously, that is critical to a sustainable, comprehensive health equity research agenda.

An effective approach to health equity research would utilize the current state-of-the-science to develop interventions that combine behavioral modifications with community-level interventions. An example of this type of individual-/community-level intervention is addressing the persistent rates and increased incidence of mortality from asthma experienced by impoverished children residing in inner-city public housing. We know that implementing individual-level strategies and interventions, such as asthma awareness and education, are important, but they have limited impact on families and populations.16 So, besides the individual components, asthma interventions should also promote intervention strategies at the societal level-for instance, providing cleaner air technologies in public housing units, incorporating improvements to the built environment to increase physical activity, and achieving improved environmental abatement and greater environmental justice.

One such program is the Seattle-King County Healthy Homes Project, which sought to reduce exposure to allergens and irritants in low-income households of children with asthma. The researchers noted that single-trigger and single-component interventions were generally not effective and employed a multi-trigger and multicomponent intervention with home visits and improved housing, including options for remediation and new housing.17 The project intervened minimally at two levels. First, it intervened at the behavioral level, providing individuals in public housing units materials to reduce exposures, such as bedding covers and low-emission vacuums, cleaning supplies, and provision of tools for roach and rodent eradication. Second, at the community level, the project built capacity in the community to advocate for improved housing conditions, in the process removing asthma triggers such as mold-contaminated wallboard and carpeting.18

Thus, an effective health equity research program should generate data that can address the broader structural factors affecting health, such as improving conditions in low-income housing, implementing strategies for better health education, eliminating environmental hazards, and improving the diversity and cultural sensitivity of health systems, while at the same time accounting for individual-level variables, such as genetic predisposition and health risks and behaviors.

2. Understand complex, multidisciplinary, multilevel, and multi-factorial interactions

A health equity research agenda requires multidisciplinary, multilevel, and multi-factorial research efforts that identify and account for the roles of multiple, complex, and interacting factors simultaneously. The difficulty in developing such an agenda stems from our limited understanding of the complex -interactions among various determinants, including genetic, biological, behavioral, socioeconomic, and environmental factors. Despite remarkable advances in our understanding of human biology and its interaction with behavioral and social factors, the impact of these discoveries will be constrained without an effective long-term strategy for linking the knowledge of disease biology and genomics with the knowledge of social and environmental factors that contribute to population-level health. Achieving these important research goals will require an integration of knowledge of the genetic, biological, behavioral, social, and population health sciences to create a more comprehensive understanding of disease pathways-from a molecular to a societal level. Such integration is necessary to identify effective measures to promote health, prevent disease, and enhance well-being in all populations, especially among those disadvantaged groups that experience the greatest burden of disease.

Specific components of integrative and multidisciplinary population-level research have seen some preliminary success in key National Institutes of Health (NIH) programs, such as the Transdisciplinary Research on Energetics and Cancer (TREC) Centers initiative. This kind of research fosters collaboration across multiple disciplines to look at impacts of nutrition, physical activity, weight, and energetics on cancer risk.19 Another example of a program that pursues this kind of research is the NIH Centers for Population Health and Health Disparities (CPHHD), which focuses on population-level cardiovascular and cancer outcomes.20 CPHHD comprises transdisciplinary research centers engaged in multilevel and multi-factorial social, behavioral, biological, and genetic research to better understand the causes of health disparities and to devise effective methods of promoting health by preventing, diagnosing, and treating disease. One of the studies showed that the level of neighborhood disadvantage was strongly associated with diagnosis of late-stage breast cancer. The researchers state, "Breast cancer patients residing in neighborhoods that became relatively more disadvantaged over the 1990-2000 decade experienced an additional risk of late-stage diagnosis." Furthermore, they noted that the benefits of immigrant enclaves are counterbalanced by the lack of structural capacity and, specifically, quality health-care resources to improve access to health care for breast cancer patients.21

3. Improve research methodologies and statistical analytical techniques

A robust health equity agenda would identify specific research measures and replicate promising models for reducing and eventually eliminating health disparities. Addressing persistent health challenges requires more concerted intervention research and implementation methods involving underserved communities that exist in small scattered groups and often in remote, isolated communities. Developing methodologies to address issues of power and quasi-experimental research designs that account for these small isolated groups are important steps in addressing challenges for health promotion and disease prevention. Methodological and statistical procedures-such as rank and propensity score methods-have been used in some studies to test, implement, and evaluate disparities-reducing interventions.22,23 Recently, the American Statistical Association held a meeting on developing distinctive survey methods for hard-to-reach populations.24 The Journal of Official Statistics is planning a special issue based on the proceedings at the conference, and additional submissions are being planned (Personal communication, Gordon Willis, PhD, Cognitive Psychologist, National Cancer Institute, and H2R Conference Committee Member, August 2013).

There have been pockets of isolated success in achieving local health equity (e.g., the Seattle-King County Healthy Homes Project18) that have achieved better health outcomes by removing environmental barriers to health promotion. Yet, there appear to be no active large-scale projects underway that aim to achieve health equity on a national scale. While large-scale health equity research projects are ambitious, one could conceivably start with developing funding streams for research in which smaller projects seek to identify common themes, metrics, and measures that could offer potential solutions to achieve health equity.

4. Build on community resiliency and partnerships

The shift from focusing on health disparities to health equity research must be paralleled by a move from a community deficit model to one of capitalizing on the community's strengths and resources. This shift allows for the research to be conducted with an eye toward sustainable change, which requires input and buy-in from the local community. Creating a community feedback loop, in which the community participates in developing and implementing health equity interventions and sustaining the health improvement as a result of the interventions, is essential. Even though these types of multilevel studies are costly and require more time and effort, it is possible to develop individual (i.e., biological and behavioral) and community interventions that focus on tangible health improvements as took place in the Seattle-King County Healthy Homes Project.18 Likewise, the Shape Up Somerville project14 integrated changes in physical activity, nutrition, and energetics with community and civic organizations, businesses, city government, and schools. This inclusion of several relevant organizations, together with the incorporation of multiple factors, is what makes these programs so unique, innovative, and effective. Thus, to achieve health equity, intervention research should be designed to inform policies that stimulate and involve community participation.

5. Develop the research and professional workforce

A shift to an SDH perspective and a look at the larger context in which people are born, live, work, and play will give rise to research and a health-care system that increasingly focuses on prevention. This shift to an SDH prevention agenda requires training and enhancing the skill sets of the health research and care delivery workforce. Multilevel and multi-factorial health equity research promotes the building of research teams that are transdisciplinary and multi-professional. Both the CPHHD and TREC initiatives have a training core that promotes the building of such diverse research teams. These NIH-supported centers recognize the challenges faced by underserved, resource-poor communities, understand the constraints and health-care barriers these populations face, and, at the same time, acknowledge that these communities may also have health-promoting assets. Thus, these research centers train students across disciplines and professions to work in large scientific and research teams to address the goal of health equity.

In addition, new partnerships will have to be formed across federal agencies as well as with universities and colleges, health-care, and community organizations to adequately prepare a health workforce with the capacity to understand the role of social determinants on the health and health outcomes of populations. One of NIH's sister agencies is the Health Resources and Services Administration (HRSA). HRSA's Bureau of Health Professions workforce training programs demonstrate the agency's commitment to reducing disparities and improving health equity by increasing the proportion of racial/ethnic minority groups and people with educational and economic disadvantages in the health professions. The rationale of this program is that a culturally and linguistically diverse workforce with their scope for sensitivity would facilitate increased access to quality health care. Partnerships with agencies such as HRSA are critical to ensure that a health workforce is trained to recognize and address health disparities and drive toward health equity from the context of an SDH perspective.

CONCLUSION

To advance a health equity research agenda that extends beyond documenting the problem of health disparities, it is necessary to commit to a complex, multidisciplinary, multilevel approach to research. Ideally, the research methods would be able to investigate the complex interplay among individual/behavioral, social, and structural factors and their impact on population health. In addition, communities and researchers should work collaboratively to incorporate community input to develop a set of robust measures and methodologies that will enable discoveries to be translated into effective interventions and public policies for health equity.

Footnotes

The views expressed in this article are those of the authors and do not necessarily represent those of the National Cancer Institute, the Health Resources and Services Administration, or the U.S. Department of Health and Human Services.

REFERENCES

1. DuBois WEB. The Philadelphia negro: a social study. Philadelphia: University of Pennsylvania Press; 1899. [ Google Scholar]

2. Quinn SC, Thomas SB. The National Negro Health Week, 1915 to 1951: a descriptive account. Minority Health Today. 2001;2:44-9. [ Google Scholar]

4. Department of Health and Human Services (US) Report of the Secretary's Task Force on Black and Minority Health. Washington: Government Printing Office (US); 1985. [ Google Scholar]

5. Smedley BD, Stith AY, Nelson AR, editors. Institute of Medicine, Committee on Understanding and Eliminating Racial and Ethnic Disparities in Health Care, Board on Health Sciences Policy. Unequal treatment: confronting racial and ethnic disparities in health care. Washington: National Academies Press; 2003. [ Google Scholar]

6. Department of Health and Human Services (US), Agency for Healthcare Research and Quality. National healthcare disparities report, 2011. Rockville (MD): AHRQ; 2012. [ Google Scholar]

11. World Health Organization, Commission on Social Determinants of Health. Geneva: WHO; 2008. Closing the gap in a generation: health equity through action on the social determinants of health. CSDH final report. [[ PubMed] Google Scholar]

14. Economos CD, Hyatt RR, Must A, Goldberg JP, Kuder J, Naumova EN, et al. Shape Up Somerville two-year results: a community-based environmental change intervention sustains weight reduction in children. Prev Med. 2013 Jun 10; [Epub ahead of print] [[ PubMed] Google Scholar]

23. National Cancer Institute, Surveillance, Epidemiology, and End Results Program. Health disparities calculator (HD*Calc) [cited 2013 Aug 21] Available from: URL: http://seer.cancer.gov/hdcalc.

Articles from Public Health Reports are provided here courtesy of SAGE Publications

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Transitioning from Health Disparities to a Health Equity Research Agenda: The Time Is Now

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Health disparities are real. The evidence base is large and irrefutable. As such, the time is now to shift the research emphasis away from solely documenting the pervasiveness of the health disparities problem and begin focusing on health equity, the ...

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Centers for Disease Control and Prevention

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Minority Health Determines the Health of the Nation - The United States has become increasingly diverse in the last century. According to the 2010 U.S. Census, approximately 36 percent of the population belongs to a racial or ethnic minority group. Though health indicators such as life expectancy and infant mortality have improved for most Americans, some minorities experience a disproportionate burden of preventable disease, death, and disability compared with non-minorities.

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Minority Health and Health Equity information from the CDC

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The Office of Minority Health and Health Equity (OMHHE) Mission: Advance health equity and women's health issues across the nation through CDC's science and programs, and increase CDC's capacity to leverage its diverse workforce and engage stakeholders toward this end.

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How does health disparities among ethnic minorities and migrants associated with COID-19 mortality in the US? What are ways to reduce and eventually eliminate health disparities to create a robust health equity agenda?

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By Bridgette J. Peteet (Associate Professor of Psychology) and Patricia M. Flynn (Assistant Professor of Psychology) - May 26, 2020

What are the health disparities associated with COVID-19?

Over the past several weeks public health officials and the media have brought to our attention the extensive racial, ethnic, and economic disparities surrounding COVID-19. Black people and American Indians are experiencing the most disproportionate mortality rates. Across the nation, Blacks are 2.4 times more likely to die from COVID-19 as compared to non-Latino Whites and in some states the mortality rate is up to seven times higher. The Navajo Nation has been particularly devastated, as evidenced by mortality rates 5-7 times higher for the indigenous populations in Arizona and New Mexico. A close look at California's statistics reveal dismal mortality rates for Latino Americans between 35-49 years of age, who account for 74% of all COVID-19 deaths in this age group. Furthermore, data from Los Angeles County found that residents from poor neighborhoods are three times more likely to die from the virus than residents from wealthier communities.

Health disparities experts argue that systemic inequalities and discriminatory policies contribute to the unequal distribution of resources needed to maintain optimal health and obtain quality healthcare. Public health data suggests that the elderly and individuals with chronic health conditions such as diabetes, hypertension, and cardiovascular disease experience the worst COVID-19 outcomes. Racial and ethnic minorities in the US suffer from some of the highest rates of these chronic conditions, which research suggests may be, in part, a function of chronic stress (e.g., poverty, discrimination). In the current pandemic, the added stressors associated with unclear health communication, the cost and quality of healthcare, obstacles to social distancing in multigenerational households and overcrowded communities, and the day-to-day risks our essential workers experience, may further exacerbate these already existing health disparities.

What are some of the systemic factors linked to disparities in COVID-19?

The rapid development of the pandemic has led to health communications, which have unfortunately been complex, contradictory, and in some cases untrue. For example, false rumors emerged that Black Americans were immune to COVID-19. The media also showed gatherings which violated social distancing guidelines and implied that Blacks were not taking the threat seriously. Yet, a recent study found that Blacks were nearly twice as likely as Whites to view the virus as a serious threat to their health. Further, the mislabeling of COVID-19 as the "Chinese virus" has caused an increase in stigma and discrimination towards individuals and communities of Asian descent and is a mischaracterization of the threat the virus poses to other groups. Stigmatization may extend to essential workers as well. Some individuals such as the elderly, those who struggle to understand English, and those with less education may experience difficulties comprehending and filtering complex or inaccurate health communication. While misinformation, unclear health communication, and at-risk job responsibilities can be a barrier to taking appropriate self-protection measures, it can also contribute to stigmatization and undue blame.

While state and regionally mandated stay-at-home orders were implemented to protect the health of our nation, for many, staying at home is a luxury. For example, Black and Latino Americans are less likely than Asian Americans and Whites to be able to telecommute for work. Jobs in fields like food service, retail sales, and personal/home health cannot be done remotely. These workers often do not have paid sick time or health insurance and have experienced disproportionate unemployment rates. Social distancing is particularly problematic for people who rely on public transportation and for those living in overpopulated communities and multigenerational households with no alternative locations to isolate sick relatives. Thus, high-risk, low resource communities are particularly vulnerable to the effects of the pandemic.

The economic burden of medical care and health provider bias may also be factors contributing to COVID-19 disparities. Depending on case complexity, the average cost of inpatient treatment for those with employer coverage ranges from $10-20,000 but for the uninsured it is $40-70,000. While the government plans to provide some financial support to hospitals for the uninsured, the remaining cost is likely to cause extreme financial distress for those with no insurance, potentially resulting in delayed medical care and worse outcomes. Existing health disparities literature also illuminates the implications of health professional bias on the quality of healthcare, which may contribute to medical mistrust or suspicion of healthcare providers, organizations, and systems. Collectively the limitations of our healthcare system may inhibit treatment seeking for members of marginalized groups and exacerbate both short and long-term psychological and behavioral outcomes associated with COVID-19.

What are the potential outcomes for marginalized populations during this pandemic?

The mounting psychological, behavioral, and economic outcomes of COVID-19 may have a disproportionate impact on racial/ethnic minorities and individuals from a low socioeconomic status. The weight of social distancing, altered school and work routines, health and financial stressors is new for everyone, but may fall hardest on those already reporting fewer physical and mental health resources. We have little knowledge about the mental health consequences of a global health epidemic, but outcomes of disasters like Hurricane Katrina and 9/11 suggest there will be an increase in the prevalence of mood disorders. In the absence of health resources, people often turn to alcohol, tobacco, and other drugs to cope with crises. Communities that experience the hardest economic strife tend to have higher rates of death due to drug overdose and suicide.

The mental, physical, and social consequences of COVID-19 also extend to children. During this crisis, most parents are juggling to provide homeschooling, adequate supervision, and social/emotional support for their children. It is likely even more difficult for single parents and essential workers. Unsupervised children may be at a higher risk for accidents, engagement in risky behaviors (e.g., alcohol/drugs), or in more frequent contact with abusers. For children who rely on school breakfast and lunch, the consequences of food insecurity are amplified due to school closures. Research suggests that aversive childhood experiences like low parental monitoring, poverty, and drug availability are all risk factors for future mental health and substance use disorders. Thus, COVID-19 may have short- and long-term implications on mental health and substance use behaviors that have yet to unfold.

What can mental health professionals do to address health disparities in the wake of this pandemic?

As this crisis persists, we need to be more circumspect about the mental, physical, emotional, behavioral, and economic hardships of our patients and work to build our capacity to better serve the needs of health disparate populations. We offer the following suggestions:

1. Communicate clearly about risks and strategies to keep safe from the virus

2. Connect your patients with community resources (e.g., food banks, health services)

3. Educate yourself and other health professionals about the historical and systemic contributions to health disparities

4. Check your biases andredress mislabeling and misinformation

5. Participate/train in telehealth to provide continuity of care and expand our reach

6. Consider offering pro bono mental health services

Resources:

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Health Disparities and the COVID-19 Pandemic | School of Behavioral Health

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By Bridgette J. Peteet (Associate Professor of Psychology) and Patricia M. Flynn (Assistant Professor of Psychology) - May 26, 2020 Over the past several weeks public health officials and the media have brought to our attention the extensive racial, ethnic, and economic disparities surrounding COVID-19.

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Data about Health care relating to health desparties

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Data about Health care relating to health desparties

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Technical Notes

Data Source

The National Center for Health Statistics' (NCHS) Division of Research and Methodology (DRM) contracted with NORC at the University of Chicago (NORC) to conduct the Research and Development Survey (RANDS) during COVID-19. RANDS during COVID-19 included three rounds of data collection (Round 1, Round 2, and Round 3). The RANDS survey for each round was conducted in English using web and telephone administration where survey administration mode was determined by the preference of the panelists. The samples for this study were drawn from NORC's AmeriSpeak® Panel ( amerispeak.norc.orgexternal icon ) using a stratified sample design to obtain a random, representative sample of U.S. adults aged 18 and over, where sampling strata were defined by race and Hispanic origin (grouped by 1) Hispanic, 2) black non-Hispanic, and 3) white non-Hispanic or other non-Hispanic), age group (18-34 years, 35-49 years, 50-64 years, and 65 years and over), sex (male or female), education (Associate's degree/some college or less and Bachelor's degree or above), and annual household income (less than $75,000 and more than $75,000). NORC performed sampling independently within each of the 96 strata using simple random sampling. Rounds 1 and 2 of RANDS during COVID-19 had a longitudinal design. Sampled panelists were invited to participate in both rounds of the study. For these two rounds, NORC excluded panelists recruited in 2019 from the sampling to improve the cumulative response rate of the study as a nonresponse follow up was not conducted that year. RANDS during COVID-19 Round 3 was designed to evaluate a telephone oversample (see completion rate by mode for each round below) and panelists were sampled independently from Rounds 1 and 2. To meet the target number of completed surveys, Round 3 included panelists recruited to the AmeriSpeak Panel in 2019 and 2020.

In Round 1, NORC invited 8,663 randomly selected panelists to participate in RANDS between June 9, 2020 and July 6, 2020. At the completion of Round 1, 6,800 interviews were completed for an overall completion rate of 78.5%. Of the 6,800 completed interviews, 6,390 (94.0%) were completed via web administration and 410 (6.0%) were completed via telephone. In Round 2, NORC invited 8,651 of the panelists from Round 1 to participate in Round 2 of RANDS during COVID-19. Responses were collected from August 3, 2020 to August 20, 2020. At the completion of Round 2, 5,981 interviews were completed for an overall completion rate of 69.1%. Of the 5,981 completed interviews, 5,559 (92.9%) were completed via web administration and 422 (7.1%) were completed via telephone. Overall, 5,452 panelists participated in both Rounds 1 and 2. In Round 3, NORC invited 7,852 panelists who were sampled independently from the previous two rounds to participate between May 17, 2021 and June 30, 2021. At the completion of Round 3, 5,458 interviews were completed for an overall completion rate of 69.5%. Of the 5,458 completed interviews, 4,181 (76.6%) were completed via web administration and 1,277 (23.4%) were completed via telephone.

Survey Questions

The questionnaire included questions to assess U.S. adults' loss of work due to illness with COVID-19, use of telemedicine, and access to healthcare during the COVID-19 pandemic. Questions related to reduced access to care evaluated the ability of U.S. adults to receive selected types of medical care for any reason and due to the Coronavirus pandemic in the past 2 months. Survey questions on RANDS related to this topic included the following:

Reduced Access to Care
In the last two months, were you unable to get any of the following types of care for any reason- urgent care for an accident or illness, a surgical procedure, diagnostic or medical screening test, treatment for an ongoing condition, a regular check-up, prescription drugs or medications, dental care, vision care, or hearing care?
The estimates reported are the percentage unable to receive any of the specified types of care for any reason, including the coronavirus pandemic, in the last 2 months among all adults. The percentages for reduced access to a specific type of care for any reason are compared to all other adults, including those who received the specified care or who did not seek the specified care.
Were you unable to get this because of the coronavirus pandemic?
The estimates reported are the percentage unable to receive any of the specified types of care in the last 2 months due to the pandemic among all adults. The percentages for reduced access for a specific type of care due to the pandemic are compared to all other adults, including those who received the specified care, did not seek the specified care, or did not receive the care for a reason other than the pandemic.

Weighting and Estimation

NORC provided sample weights for each round of RANDS during COVID-19 which account for the sample design and are calibrated to U.S. population counts to account for differential nonresponse and under coverage of some groups on the sample frame. The sample weights developed by NORC were based on the panel weights from the AmeriSpeak® Panel and the RANDS survey-specific sampling weights for each round. The panel weights account for the sampling probability from the AmeriSpeak® Panel, calculated as the inverse probability of selection from the NORC National Sample Frame, which are adjusted for nonresponse and for a subsample of housing units that have a nonresponse follow-up. These panel weights are adjusted using raking to align the panel weights with external population totals obtained from the U.S. Census Bureau Current Population Survey (CPS). From the panel weights, the RANDS survey-specific sampling weights are derived using the probability of selection into RANDS associated with a sampled panel member and an adjustment for nonresponse to the RANDS survey. The RANDS survey-specific sampling weights for each round of RANDS during COVID-19 are raked to adult population totals, trimmed for extreme weights, and raked again to the same population totals to form the final NORC-provided sample weights.

NCHS implemented an additional weighting step to calibrate the NORC-provided weights to the National Health Interview Survey (NHIS), an established core household survey conducted by NCHS. NORC-provided sample weights for each round were adjusted through an additional post-stratification step in which the marginal totals of RANDS from each round were raked to the marginal population totals from the NHIS. RANDS during COVID-19 Rounds 1 and 2 weights were raked using the 2018 NHIS sample adult file ( n = 25,417) on the following demographic and health characteristics: age, sex, race and Hispanic origin, education, household income, Census region, marital status, ever diagnosed high cholesterol, ever diagnosed asthma, ever diagnosed hypertension, and ever diagnosed diabetes. RANDS during COVID-19 Round 3 weights were raked using the 2019 NHIS sample adult file ( n = 31,997) on the following demographic and health characteristics: age, sex, race and Hispanic origin, education, household income, Census region, marital status, ever diagnosed high cholesterol, ever diagnosed asthma, ever diagnosed hypertension, ever diagnosed diabetes, metropolitan status, and phone service. Characteristics with missing values in RANDS and in NHIS were included in the raking procedure, while characteristics with missing values in one of the surveys were removed for the raking step with an adjustment on the weights for the non-missing values. The calibrated weights were proportionally adjusted to the total number of RANDS respondents for each round ( n = 6,800 for Round 1, n = 5,981 for Round 2, and n = 5,458 for Round 3).

All analyses reported are based on the NCHS calibrated sample weights for the respective round. It is important to note that RANDS during COVID-19 is a probability-sampled panel survey. Although the NORC-provided sample weights are designed to provide nationally representative estimates, the additional calibration further adjusts the RANDS data for differences in health and demographic factors between RANDS and NHIS due to possible differences in response propensities, coverage, and sample variability. The application of sample weights to the data is required to produce results with meaningful population representativeness and to accurately assess the sampling error of statistics based on the survey data.

For each outcome reported, the sample size, percentage estimate, and standard error estimate are shown by round. Standard error estimates were generated using a Taylor series linearization approach. SUDAAN's PROC CROSSTAB was used to calculate all estimates. PROC CROSSTAB accounts for the complex survey design variables, including the sampling strata and sampling weights. The MISSUNIT option in SUDAAN was used to account for singleton primary sampling units in the variance estimation for the reported standard error estimates and statistical testing. Missing values were excluded from the reported estimates. Estimates that did not meet the NCHS Data Presentation Standards for Proportions were suppressed ( https://www.cdc.gov/nchs/data/series/sr_02/sr02_175.pdfpdf icon ).

Each set of estimates is reported for the total overall (at the national level) and by selected factors including demographics and diagnosed chronic conditions. Selected factors included age group (18-44 years, 45-64 years, and 65 years and over), race and Hispanic origin (white non-Hispanic, black non-Hispanic, other non-Hispanic, Hispanic), sex (male or female), education (high school graduate or less, some college, Bachelor's degree or above), urbanization (metropolitan or non-metropolitan), and chronic conditions (one or more chronic conditions, ever diagnosed diabetes, ever diagnosed hypertension, or current diagnosed asthma). For subgroup analyses, sex is identified by the response to the survey item "Please tell us your gender." Urbanization is assigned by zip code, where metropolitan includes metropolitan and micropolitan areas and non-metropolitan includes all other designations. One or more chronic conditions is defined as a diagnosis of one or more of the following: hypertension, also called high blood pressure; high cholesterol; coronary heart disease; current asthma; chronic obstructive pulmonary disease (COPD), emphysema, or chronic bronchitis; cancer or a malignancy of any kind; or diabetes excluding pre-diabetes and borderline diabetes. Estimates are accompanied by bar charts displaying comparisons within the selected factors between the three rounds.

Comparisons between Rounds

Reported statistical testing results identify statistically significant differences in the experimental estimates between rounds (Rounds 1 and 2, Rounds 1 and 3, Rounds 2 and 3). Since the sampled panelists from Round 1 of RANDS during COVID-19 were also invited to participate in Round 2, the percentage estimates for the selected indicators are not independent. Of the 6,800 respondents to Round 1 and 5,981 respondents to Round 2, 5,452 panelists participated in both rounds. To account for the correlation in the repeated measurements, point estimates provided for Rounds 1 and 2 were statistically compared using an unpaired correlated two-sample t-test. The variance of the difference between point estimates was approximated using the variance of the estimates from Rounds 1 and 2 and a correction factor to account for the sample of panelists responding to both rounds. The correction factor incorporated the design effects from both rounds and estimated correlation using the weighted Pearson correlation coefficient. Correction factors were estimated separately for each indicator within subgroups. Correlations between rounds differed by indicator and for categories within subgroups. The resulting test statistic was assumed to be approximately normally distributed.

Statistical testing between Round 1 and 3 estimates and Round 2 and 3 estimates was performed using a survey regression approach. Data from the comparison rounds were parameterized using a reference cell model to estimate the mean difference and the standard error of the difference of the two estimates while accounting for the covariance of the estimates in clustered samples. Note that while the covariance of two estimates for distinct samples is zero under simple random sampling, the covariance of two estimates may be nonzero for clustered samples. The statistical testing for differences between Round 1 and 3 estimates and Round 2 and 3 estimates was also validated to verify that findings of statistically significant differences were not impacted by the change in the reference dataset (2018 NHIS for Rounds 1 and 2, 2019 NHIS for Round 3) for the raking step to produce the NCHS calibrated sample weights.

The reported significance for all three comparisons is based on a two-sided test with a significance level of 0.05 and does not account for multiple comparisons. Significance testing was not reported in cases where either of the estimates in the comparison rounds was suppressed.

Limitations and Data Use

The information presented here is based on data collected by commercial vendors that maintain groups of respondents, called panels, who agree to participate in surveys, typically in exchange for payment or prizes. Panel surveys can be implemented much quicker and can be less expensive to conduct than typical NCHS surveys that draw new samples from nationally representative frames and collect information by phone, online, or in person. Panel surveys also have more sample bias and less accuracy, than traditional survey methods.

RANDS is an ongoing NCHS research program designed to investigate whether and, if so, how panels with more biases can be used in conjunction with other, higher quality data collections to increase the scope of information collected, the timeliness of data collection and the sample size, and to expand the scope and granularity of NCHS statistical products. The RANDS was not designed to replace NCHS' higher quality, core data collections but to be used in conjunction with those surveys especially during periods when standard data collection methods face challenges.

The COVID-19 pandemic offers an opportunity to further explore the collection of new information more quickly than is possible using the Federal Statistical System's core data collection approaches. Building on past work, NCHS took advantage of the RANDS platform to collect information regarding aspects of the public health emergency not currently being captured in sufficient detail in government or nongovernment surveys. In addition, measurement research that may be instructive for interpreting other federal and non-federal surveys during the pandemic will be conducted and calibration research will continue. Experimental national estimates are only provided for selected variables. The other variables are being collected specifically for research purposes. The experimental national estimates for the inability to work due to illness with COVID-19, telemedicine access before and during the pandemic, telemedicine use during the pandemic, and reduced access to specific types of health care for any reason and due to the pandemic have been calibrated using information from NCHS' NHIS to correct for some portion of the potential bias in the panel relative to the NHIS, as described above. Information on the effect of calibration for past data collections and for this round of data collection will be presented on the RANDS website. As research is underway to both improve the calibration method and understand potential sources of measurement error, estimates may be updated based on the results of that research. These estimates should be considered experimental. On August 28, 2020, NORC provided a revised file for the Round 1 respondents and the calibrated weights were updated accordingly. The reported estimates reflect this update. In the event of future updates to the weights or the calibration approach, the change will be documented and the tables will be updated accordingly.

Statistically significant differences between experimental estimates from Rounds 1 and 2, Rounds 1 and 3, and Rounds 2 and 3 identified using the reported p-value approach should be interpreted with caution. Small estimated design effects for subgroups may impact the calculated test statistic and the normality assumption may be violated in the case of small sample sizes. The American Statistical Association (ASA) has released a statement on statistical significance based on p-values ( https://amstat.tandfonline.com/doi/full/10.1080/00031305.2016.1154108external icon ). The ASA's published guidance indicates that the p-value should not be the only factor used when making decisions about scientific findings. NCHS will release a future methodological report with additional details and results from the correlated two-sample t-test and the reference cell regression approach used for statistical testing on the RANDS during COVID-19 published indicators.

Note that a statistical test of the difference between the RANDS during COVID-19 estimates using the published estimates and their standard errors and assuming independence between rounds is conservative for positively correlated estimates and may not identify a statistically significant difference that would be identified if the dependence due to repeated measurements on subjects or due to clustered samples was considered. However, differences between rounds that are identified as statistically significant under the assumption of independence will be significant when accounting for the correlation. Statistical testing results in the published tables accounted for the correlation.

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Reduced Access to Care - Research and Development Survey - COVID-19

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Experimental estimates show the percentage of U.S. adults who were unable to receive selected types of medical care. Data are available by age, race and Hispanic origin, sex, education, urbanization, and chronic conditions.

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Centers for Disease Control and Prevention

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CDC's home for COVID-19 data. Visualizations, graphs, and data in one easy-to-use website.

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Minorities and economically poor communities are at a higher risk for then others due to the systemic health, social and economic disparities. Data shown by organizations such as WHO, and the CDC, has shown that African Americans or Blacks, Hispanics, and Native Americans are impacted more than any other racial and ethnic group in the United States. Historically, Blacks/African Americans have limited access to health care due to their inability to access health insurance because of low income and low employment rate.

Since health inequities contribute to overall poor health for the nation, health inequity has consequences for the U.S. economy, national security, business workforce, and public finances. Having delayed access to a caregiver can only affect our economy in a negative way. Throughout the Covid-19 pandemic many were unable to access the proper care to prevent longer lasting diseases. Eliminating health disparities for minorities would have reduced indirect costs associated with illness and premature death by more than $1 trillion which was only for the years between 2003 through 2006 which only shows how much more our economy can save in the long run.

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Inequities in the burden of Covid-19 have been uncovered among vulnerable populations across the world and are particularly striking in racial and ethnic minorities in the United States. In New York City, African Americans and Latinos diagnosed with Covid-19 had mortality rates that were 1.6 to 2 times higher than those of whites. Across the United States, Covid-19 infections are threefold higher and mortality rates are sixfold higher in predominantly Black counties compared with predominantly white counties. In Arizona, Native Americans make up 4.6% of the population but comprise 16% of the state's Covid-19-related deaths., Although these inequities are acute, they reflect long-standing disparities in health that many communities, clinicians, and researchers have striven to address, often with too few resources and inconsistent public support.

National and global media have drawn attention to these alarming Covid-19 health inequities, and, in response, communities, health care providers, and elected officials⁻ are demanding answers and action. The causes of these inequities are likely multifold and involve differences in exposure, susceptibility, testing, and treatment. Groups socially disadvantaged because of race/ethnicity, social position, and/or economic status may have greater exposure to the virus because of jobs that prevent work from home, dependence on childcare outside the home, reliance on public transportation, and household size. Poverty and experiences with discrimination and racism lead to chronic psychosocial stress, causing prolonged secretion of stress hormones, which leads to physiological dysregulation and inflammation.⁻ The resulting maladaptation increases susceptibility both acutely, through impaired immune response to the virus,, and chronically, through predisposition to diabetes, obesity, and cardiovascular disease, which are linked to worse Covid-19 outcomes. Disadvantaged groups are less likely to have a primary care provider, medical home, or regular access to care and may have limited access to Covid-19 testing and treatment.,

Despite widespread concern about the excess Covid-19 burden and deaths among racial and ethnic minorities, little attention has been paid to immediate solutions. Dismantling upstream factors such as racism, economic opportunity, housing inequality, and food insecurity is critical to addressing health inequities, although doing so can be challenging, particularly during a pandemic. Some state and local governments are taking mitigating steps, for example, by placing a moratorium on evictions or expanding access to food assistance programs. Such short-term policies are important for limiting economic burden but may not decrease Covid-19 infection rates or deaths. Directly addressing more proximal or downstream contributors such as lack of access to care and inequity in testing and treatment may be more feasible during this time and could immediately impact health outcomes.

Despite widespread concern about the excess Covid-19 burden and deaths among racial and ethnic minorities, little attention has been paid to immediate solutions.

Leaders must do more than set broad goals to lessen or eliminate health inequities; they must develop specific, measurable objectives and implement strategies that will successfully address those inequities.

African American health care leaders recently published recommendations to address Covid-19 disparities. In addition to addressing social determinants of health (SDOH) in the long term, they recommended immediate actions to record and report race and ethnicity data, ensure access to current and emerging treatment, provide mobile access to testing, and communicate via trusted community leaders. Health systems may have difficulty implementing these recommendations during a pandemic, particularly in the absence of an existing infrastructure to support health equity initiatives.

Interventions likely to be successful are often embedded in the community and are built on trusting relationships, which are developed over time.⁻ Without well-established, mutually beneficial relationships, it may be difficult to effectively mobilize resources and partner with trusted community leaders during a pandemic. The lack of accuracy and incompleteness of race, ethnicity, and language (REAL) data in health records are long-standing issues and limit progress toward eliminating health inequities., Similarly, ineffective recruitment and retention of racial and ethnic minorities into clinical trials is an ongoing challenge, and there has been minimal progress in the 25 years since the National Institutes of Health required reporting of race and ethnicity.

A Systems Approach to Health Equity

We used a systems approach that emphasizes interdependence and interaction across the health system to address the complex drivers of Covid-19 inequities and rapidly respond to data trends in real time. As part of our Vanderbilt University Medical Center (VUMC) Covid-19 Command Center, we created a health equity workstream to prevent, identify, and address Covid-19-related inequities. (The Command Center refers to the team that coordinates the health system's response to an emergency or disaster, as well as the designated work space for those involved.) Led by our Office of Health Equity (OHE) and Vice President for Health Equity, we identified five initial areas of concentration: four Covid-19-specific areas focused on prevention, testing, treatment, and clinical research; and the fifth area, telehealth, which, although not specific to Covid-19, is increasingly used because of the pandemic and could lead to inequities in access to care.

The work leverages our OHE, which was established in 2018 to catalyze initiatives that address and prevent health equities. Our OHE has dedicated full-time staff focused on program pillars that align with clinical, educational, and research missions. The OHE is home to VUMC's Community Health Needs Assessment office and maintains ongoing partnerships with public health agencies and more than 100 community-based organizations. Our initial Covid-19 work focuses on racial and ethnic minorities and individuals with limited English proficiency (LEP). We developed objectives, tactics, and workstreams to address the five areas of concentration ( Table 1).

Workstream (Team Expertise)

Objectives

Key Tactics

Effective risk communication (public health, health communications, interpreter services, patient education, and marketing)

Evaluate patient- and employee-facing materials (including website) for appropriate reading level, language access, usability, and ease of finding and making decisions

Create/revise materials to be 6th-grade reading level or lower, available in commonly spoken languages, and appropriate for individuals from diverse backgrounds

Optimize website for readability and multiple language options

Develop standard operating procedures for creating and distributing patient and employee materials

Monitor use of various platforms to ensure employees and patients with a range of backgrounds are being reached (e.g., employees without regular email access, patients without computers/Internet access)

Partner with public health and trusted community organizations/leaders to share via multiple channels including Web, print, radio, social media, and virtual town halls

Equitable testing (epidemiology, community health, clinical care, and quality improvement)

Provide/facilitate timely testing

Report aggregate test results by key demographics including age, sex, race/ethnicity, preferred language, and ZIP Code

Assess availability of Covid-19 testing on the basis of location, hours of operation, access via public transportation, access regardless of insurance, availability of interpreters, and surveillance process for opportunities to improve equity

Develop dashboard with Covid-19 testing data by race, ethnicity, language, and ZIP Code

Review percentages of Covid-19-positive within racial/ethnic groups, within language categories, and by ZIP Code

Compare Covid-19 testing utilization by race/ethnicity/language with demographics of VUMC patients and Nashville metro area population

Create plans to rapidly address any inequities in testing

Frequently communicate with local/state health departments to identify/address community-level inequities

Equitable care (hospital medicine, nursing, social work, population health, quality improvement, and clinical research)

Provide quality care that does not vary because of race, ethnicity, sex, or socioeconomic status

Effectively communicate postdischarge plans and facilitate transitions of care

Report aggregate outcomes by key demographics including age, sex, and race/ethnicity

Develop dashboard that includes Covid-19 treatment and hospitalization data by race, ethnicity, language, and ZIP Code

Compare hospitalization rates, use of specific treatments (including remdesivir and dexamethasone), discharge rates, and deaths by race, ethnicity, language, and ZIP Code

Assess availability of discharge information at appropriate reading level and in multiple languages

Assess transitions of care/follow-up care

Monitor enrollment in clinical trials

Inclusivity in clinical trials (research operations, clinical care, and minority recruitment)

Increase awareness of importance of clinical research for Covid-19, given there is no proven effective therapy

Engage and enroll racial and ethnic minorities and other socially disadvantaged groups in Covid-19 clinical trials

Develop and disseminate general messaging about the importance of clinical research for Covid-19 and reasons minorities should consider participating

Create a checklist for Covid-19 clinical trials to avoid inclusion and exclusion criteria that limit eligibility among minorities

Identify research champions from racial/ethnic minority groups

Develop recruitment materials with diverse imagery and culturally appropriate language

Assist with translation of consent forms and recruitment documents

Broad adoption of telehealth (social work, patient experience, quality improvement, clinical care, and IT)

Effectively use telehealth to provide care for patients including those with limited health literacy, LEP, and limited Internet access

Increase adoption of telehealth among racial and ethnic minorities, patients with LEP, and people living in underserved rural communities

Develop dashboard that includes telehealth visits by race/ethnicity/language for all ambulatory clinics

Assess telehealth utilization by race/ethnicity/language/ZIP Code

Create telehealth awareness and "how-to" information for individuals with varying health literacy, English proficiency, access to Internet, and availability of telehealth-capable device

Create training materials to support use of interpreters in telehealth visits

Develop specific plans to address any identified inequities

We created an interprofessional Covid Health Equity Team, comprising physicians, nurses, social workers, and operational leaders with expertise in minority health, SDOH, quality improvement, public health, health communication, clinical research, diversity and inclusion, and telehealth. In addition to the primary team, each area of concentration has a work group that includes other VUMC stakeholders with content area expertise (such as interpreter services, patient access, communications, and health IT) and decision-making authority.

The lack of accuracy and incompleteness of race, ethnicity, and language (REAL) data in health records are long-standing issues and limit progress toward eliminating health inequities. Similarly, ineffective recruitment and retention of racial and ethnic minorities into clinical trials is an ongoing challenge.

We Need REAL Data and SDOH Metrics To Make Decisions

The importance of REAL data to understanding Covid-19 cannot be overstated. Without these data, we are unable to identify disparities in Covid-19 testing, care, and outcomes. Unfortunately, race and ethnicity are missing in 20% to 30% of health records and, when available, can be insufficient for mitigating health inequities, which also requires SDOH data. Recognizing the issues with missing data and that there is no standardized collection of social determinants, a principal strategy in our work is to use the most reliable information currently available in the health record to disaggregate data, while simultaneously working to improve the quality and accuracy of the data. We have created visualization dashboards for all patients tested for SARS-CoV-2 that can be filtered by race, ethnicity, primary language, and ZIP Code, which can be linked to community-level socioeconomic data ().

The dashboards are updated daily and reviewed by our team to assess differences in testing, Covid-19-positive rates, hospitalizations, types of Covid-19 treatments provided, and deaths.

To facilitate collection of missing REAL data, we are creating phone scripts for the team who currently conducts 14-day follow-up calls to patients with Covid-19 ( Appendix). We are also reviewing charts to determine whether REAL data are documented in other areas of the health record, such as in the social or family history contained in clinicians' notes. These strategies are intended to improve the accuracy and completeness of REAL data among patients with Covid-19 and will not replace our long-term approaches, which include training staff on data collection, informing patients about self-reporting, and providing opportunities for self-reporting outside the context of a clinical encounter.

We Must Communicate Effectively to All Audiences

Effectively communicating disease risk is vital during a pandemic, and messaging must be clear and easy to understand. Because Covid-19 is novel, evidence is emerging continuously, and health systems may struggle to quickly create and revise public-facing materials at the appropriate reading level and in multiple languages. The evolving information landscape makes it difficult to acquire and apply knowledge, especially for individuals with limited health literacy and LEP. Individuals from groups who have been marginalized and historically underserved may be less trusting of new information and less likely to apply new knowledge or change behavior. Our health equity goals for prevention of Covid-19 are to effectively communicate disease risks and preventive strategies to patients and families from diverse backgrounds as well as to our employees, including those in roles not directly involved in patient care but vital to our health system, such as dietary, maintenance, and environmental services staff.

The importance of race, ethnicity, and language (REAL) data to understanding Covid-19 cannot be overstated. Without these data, we are unable to identify disparities in Covid-19 testing, care, and outcomes.

Key strategies involve creating and revising patient- and employee-facing messaging to be at a reading level appropriate for a general audience, making Covid-19 information available in the three most common languages spoken among VUMC patients (English, 95%; Spanish, 2.6%; and Arabic, 1.1%), and creating standard operating procedures for the rapid review of new Covid-19 materials for both understandability and culturally appropriate language. In partnership with trusted community-based organizations and leaders, we are convening virtual town halls in Spanish and Arabic and disseminating Covid-19-related information using multiple channels, including social media. For example, in conjunction with a local community center, we held an Arabic-language livestream event on the topic of Covid-19 and the implications for pregnancy and newborns that received 3,200 views, 28 shares, and 46 comments in June.

Early on, we found the need to clarify which personnel were considered health care workers, a phrase being used frequently in the media and often presumed to be direct patient care roles such as physicians and nurses. To address this, we revised messaging to employees to explicitly include roles such as environmental services and patient transport, positions often likely to be held by racial and ethnic minorities and individuals without a college degree. To support different learning styles and avoid reliance on written educational modules, we provided in-person education on donning and doffing of personal protective equipment for our environmental services team. We also considered methods such as sharing information in huddles and posting in break rooms to ensure messaging reaches patients and employees who do not use or regularly access email and those without computers and Internet access at home.

Dashboards Provide Greater Transparency in Testing, Care, and Research

Timely access to testing is a critical component of identifying, preventing, and addressing inequities, particularly given the mounting evidence of Covid-19 disparities among communities of color. VUMC, a Nashville-based academic health center with more than 26,000 employees and 1,000 residents, has worked closely with the Nashville/Davidson County Metro Public Health Department and the Tennessee Department of Health to facilitate broad access to SARS-CoV-2 testing. As of July 1, 2020, VUMC had tested 45,954 patients for SARS-CoV-2, and 3,171 tested positive. When disaggregated, we found higher percentages of Covid-19 within all racial/ethnic minority groups and among speakers of languages other than English ( Table 2).

Population Demographics* (Percent of the Total Population in Nashville Metropolitan Statistical Area: 1,932,000)

SARS-CoV-2 Tests, n (% of 45,954)

Positive SARS-CoV-2, n (% of 3,171)

Within-group Positive SARS-CoV-2 Tests (Percent Positive Tests Within Racial/Ethnic or Linguistic Group)

The most striking differences were among people who spoke a primary language other than English. Forty-eight different primary languages were spoken, and 607 (26%) of the 2,310 people with LEP had positive results.

To better understand the social and environmental context, we mapped the ZIP Codes of those who tested positive and found the highest number of cases in two adjacent ZIP Codes in southeast Nashville, where more than 30% of residents' primary language is not English and adjusted gross income is $36,384 compared with the county average of $56,507 median ().

In addition to sharing our findings with the city/county and state health departments, which currently are collecting language data episodically but not routinely, we created additional Covid-19 resources in the languages with the highest number of cases (Arabic, Nepali, and Spanish), catalogued multilingual patient-facing Covid resources from national organizations, and shared these resources with provider teams internally and with community organizations connected with these communities. We also informed our interpreter services department and assessed its capacity to meet a possible increase in demand.

Dashboards are reviewed daily by our Covid Health Equity Team to assess differences in Covid-19 hospitalizations, types of treatments received, and deaths. As of July 1, 2020, there had been 342 hospitalizations, and 209 (61%) were patients with LEP. There had been 25 Covid-19-related deaths at VUMC, including 18 white patients and three patients with LEP. Because there are no proven effective treatments for Covid-19, we are monitoring Covid-19 clinical trial enrollment and working with the Vanderbilt Institute for Clinical and Translational Research and Recruitment Innovation Center to support minority enrollment in trials. Our strategies include developing culturally appropriate messaging, using diverse images in recruitment materials, and making consent forms available in multiple languages.

Mitigating Health Inequities Indirectly Related to Covid-19

In addition to the excess morbidity and mortality related directly to Covid-19, the loss of income, disruption of education, and other indirect effects of Covid-19 will likely disproportionately impact racial and ethnic minorities., As VUMC and many other health systems quickly pivoted to provide more telehealth, we wanted to mitigate potential inequities in access among racial and ethnic minorities who might be less trusting of telehealth, have differential access to high-speed Internet and electronic devices, or speak other languages. To assess adoption of telehealth, we created a dashboard with telehealth visits by REAL for each of the 21 VUMC ambulatory patient care centers (PCCs).

At VUMC, where making diversity and inclusion is intentional with values and objectives that guide strategic direction, we integrated health equity into our health system's Covid-19 operations to make it a priority, not an isolated stream of work.

At two PCCs, for example, we found that telehealth visits were 24% lower than the prepandemic in-person visits for African American patients, 54% lower for Latino patients, 47% lower for Asian patients, and 71% lower among people who speak languages other than English.

We identified limited prior use of patient portals, lack of detail in instructions for telehealth use, and challenges to using interpreters in virtual visits as specific barriers to patients with LEP. To address these, we expedited release of a Spanish version of our patient portal and have provided additional instructions to providers and staff to allow interpreters to join telehealth visits. Because some patients have limited technical proficiency, medical students have been deployed to help patients prepare for telehealth visits. To supplement telehealth, we have provided home visits and performed in-home testing for some patients who live in subsidized housing and have limited transportation.

Key Lessons

During a pandemic, health systems face many demands and must rapidly respond in ways to ensure everyone has the best opportunity to be healthy. Tools enable health systems to implement a comprehensive approach to Covid-19; however, systematic ways to address health inequities may be limited. At VUMC, where making diversity and inclusion is intentional with values and objectives that guide strategic direction, we integrated health equity into our health system's Covid-19 operations to make it a priority, not an isolated stream of work. This approach has allowed us to identify and work to mitigate inequities. Our key lessons so far are:

1. Executive leaders should clearly state that achieving health equity is a priority and allocate resources, including people, to do this important work; in the case of VUMC, institutional funds totaling more than $1.5 million annually were committed to the OHE prior to Covid-19, facilitating our ability to pivot and rapidly respond;

2. Health equity-related goals and programs should be integrated into the health system's organizational readiness and response with clear expectations for accountability and action;

3. REAL data must be available in real time, and new processes may be needed to collect and aggregate data;

4. The Covid-19 Command Center includes a wide range of clinical, administrative, and operations leaders, some of whom have limited knowledge of health equity; this exposure could facilitate culture change and different ways of advancing health equity in the long term; and

5. Health systems must work closely with public health departments and trusted organizations that are closely connected to communities.

The impacts of the Covid-19 pandemic will be felt for a long time, and, without intervention, racial and ethnic minorities will likely bear a higher burden of the disease and greater socioeconomic loss. It is not too late for health systems to take systematic and intentional steps to prioritize health equity. By setting clear health equity objectives, disaggregating data by REAL, and implementing strategies informed by social context, we may prevent or lessen health inequities and be better positioned to address the underlying contributors to health that require more equitable infrastructure and broad changes in policies.

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A Systems Approach to Addressing Covid-19 Health Inequities

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Inequities in the burden of Covid-19 have been uncovered among vulnerable populations across the world and are particularly striking in racial and ethnic minorities in the United States. In New York City, African Americans and Latinos diagnosed with Covid-19 had mortality rates that were 1.6 to 2 times higher than those of whites.

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Abstract

Aim

To analyze racial disparities in Coronavirus disease (COVID-19) cases in the United States of America and discuss possible reasons behind this inequality.

Subject and methods

We obtained estimated case counts of African-American, Caucasian, Native American, Asian and Hispanic individuals with coronavirus disease (COVID-19)infection through May 5, 2020, from publicly available data on state departments of health websites. We calculated race-specific fractions as the percentage of the total population and analyzed the reasons behind this disparity.

Results

The incident rates of COVID-19 were higher among African Americans and among Latinos disproportionately higher than their representation in 14 states and 9 states, respectively. A similar observation was also reported for New York city. The percentage of deaths reported among African Americans was disproportionately higher than their represented share in the population in 23 out of 35 states. It was reported that 22.4% of COVID-19 deaths in the USA were African American, even though black people make up 13.4% of the USA population.

Conclusions

The analysis shows the disparity of coronavirus disease outcomes by ethnicity and race. Additional research is needed to determine the factors behind this inequality.

Keywords: Coronavirus, COVID-19, SARS-CoV-2, African-American, Hispanic, Asian, Native-American, Minorities, Racial disparities

1. Introduction

The first case of Coronavirus disease (COVID-19) was detected in Wuhan Province of China in December 2019 [ 1]. The disease has since then spread rapidly across the globe and was declared a pandemic on March 11, 2020, by the WHO [ 2]. While many recover without the need for any specialized care, the disease is known to severely impact the elderly and those with co-morbidities like obesity, hypertension, diabetes, and other chronic illnesses. As of June 4, 2020, the global disease burden stands at 6.4 million cases and 382 867 deaths worldwide with gradually escalating numbers [ 3]. As the world puts its resources together in fighting this pandemic, it has become increasingly clear that the disease has not affected everyone equally. Publicly shared information on the racial and ethnic patterns of COVID-19 so far paints an alarming portrait. An article published in the New England Journal of Medicine (NEJM) on May 6, 2020, showed that in the US, African-Americans, Hispanics, and Native American minorities have a disproportionately higher number of cases and deaths as a direct consequence of COVID-19 [ 4]. In this paper, we consolidate the available data, calculate the statistics on racial and ethnic minorities and subsequently highlight the racial disparities in 16 States and New York City by analyzing publicly available information from their respective health departments, and Centers for Disease Control and Prevention (CDC) as of May 5, 2020. We also discuss the possible reasons for this inequity.

2. Methods

We used publicly available data for COVID-19 incidence rates in the US among sixteen states and New York City to evaluate racial and ethnic variation in COVID-19 incidence ( Table 1 ).

Table 1

State

Caucasian

African American

Cases, No. (%)

Population, No. (%)

Observed Prevalence Rateb

Cases, No. (%)a

Population, No. (%)

Observed Prevalence Rateb

Colorado

c (48)

3 867 288 (67.9)
c
c (7.6)

261 996 (4.6)
c

Asian

Hispanic

State

Cases, No. (%) a

Population, No. (%)

Observed Prevalence Rate b

Cases, No. (%) a

Population, No. (%)

Observed Prevalence Rate b

Michigan

879 (2)

339 861 (3.4)

259
c
519 788 (5.2)
c

Florida
c
638 980 (3)
c
5640 (41.5)

5 559 124 (26.1)

101

Indiana

308 (2)

167 297 (2.5)

184
c
475 123 (7.1)
c

Colorado

c (2.7)

199 345 (3.5)
c
c(38.3)

1 235 937 (21.7)
c

Rhode Island
c
38 063 (3.6)
c
4247 (44)

168 113 (15.9)

2526

We obtained actual case counts of African-American, Caucasian, Asian, and Hispanic populations with COVID-19 infection from the COVID-19 surveillance data reported by state departments of health through May 5, 2020. Denominators used here are the 2018 US Census Bureau race-specific population estimates for the sixteen states and New York City [ 5]. New York state bears the brunt of COVID-19 infections in the US. However, all pertinent metrics of New York state data were not publicly available at the time of authoring this article and hence, publicly available New York City data was instead used in our study as the majority of cases from NY state was from the city. It was difficult to obtain specific latest data due to the ongoing census in 2020. We acknowledge that this is a possible limitation of the current study. In our analysis, we measured state-level race-specific fractions as the percentage of the given race and ethnicity ( Table 1 .)

To evaluate death rates among African American and Latino populations, the dataset was downloaded from Data. CDC.gov [ 6]. The dataset reported the percentage of deaths from COVID-19 infection for each race or ethnicity. Death rate by race and ethnicity was compared with racial and ethnic composition of the general population for 39 states, reported through May 13, 2020.

3. Results

We observed racial/ethnic disparities in COVID-19 incidence ( Table 1 ). The differences were highly significant in the Z-test ( p < 0.00001) for African American and Latino populations. The most profound disparities are the following: Black Michiganders are five times more likely to be infected with COVID-19 (998 per 100 000 population) than white Michiganders (200 per 100 000 population). Latino population is 5.1 times more likely to be infected (2526 per 100 000 population) than whites in Rhode Island (494 per 100 000 population). Meanwhile, the COVID-19 incidence rate is lower among Asian and Caucasian populations.

The incident rates of COVID-19 were higher among African Americans than their representation in 14 states and New York City out of 16 states and New York City ( Fig. 1 ). The incidence rate among Latinos was disproportionately higher than their share in the population in 10 out of 11 states and New York City ( Fig. 2 ). The data was available for 35 states for the death rate among the African-American population ( Fig. 3 ). The number of COVID-19 related deaths was available for the Native American population in three states only ( Fig. 4 ), and the death rate in the Hispanic population is presented in Fig. 5 for 24 states where data was reported.

Provisional Death Count for COVID-19 among African Americans, Native Americans, and Hispanics is shown in Fig. 3 , Fig. 4 , Fig. 5 , respectively. For 23 out of 35 states, we observed a significant discrepancy between the expected number of deaths associated with COVID-19 and the actual percentage of the deaths among African-Americans.

In the District of Columbia, 77% of deaths associated with COVID-19 were among African Americans, whereas they comprise only 45% of the population. The data shows that African-American people are also disproportionately affected in Delaware, Louisiana, South Carolina, Michigan.

Only three states report data for Native Americans. New Mexico has about 8.9% of the total population as Native Americans, but they accounted for 38.1% of deaths due to COVID-19 ( Fig. 4 ).

According to CDC.gov, the data weighted distribution of the Hispanic population was 26.8% in the USA, the distribution of COVID-19 deaths among Hispanics was 16.6%, as shown in Fig. 5 . The weighted distribution of the Asian population was 11.5% in the USA, and the distribution of COVID-19 deaths among Asians was 5.8%. So, the death rate among Hispanics and Asians are lower than their representation in the overall national population.

4. Discussion

Racial and ethnic information is available for only about 35% of the total deaths in the US [ 7]. Yet, it is clear from the data that historically disadvantaged groups like African-Americans and Latinos are represented disproportionately higher, not only in the total number of cases but also total deaths. One of the reasons behind this is the higher incidence of co-morbidities like - diabetes, cardiovascular disease, asthma, HIV, morbid obesity, liver disease, and kidney disease - among racial and ethnic minorities [ 8]. In a retrospective cohort study, in Louisiana between March 1 and April 11, researchers found that African-American patients had a higher prevalence of obesity, diabetes, hypertension, and chronic kidney disease compared to patients of Caucasian descent [9]. They also report that 76.9% of the total COVID-19 cases were African-American. African-Americans also accounted for 70.6% of the total deaths, which is an alarming figure because the African-American community comprises only 31% of the total population. There is also a growing concern about the African American community being exposed to constant mental stress due to various social and environmental determinants leading to a process of "weathering" in turn leading to an increase in co-morbid conditions. However, we believe that focusing only on this higher incidence of co-morbidities does not give the complete picture. A study found that in large cities of Texas, access to coronavirus testing depends on which zip code an individual lives in, and is hence not equitable [ 10]. In Dallas, recent research by the University of Texas, School of Public Health identified parts of South Dallas as being higher for COVID-19 hospitalization. These were predominantly minority neighborhoods that also coincided with having a lack of testing sites.

Data from the CDC shows there is an overrepresentation of African-American persons among hospitalized patients [ 11]. It also emphasizes that racial and ethnic minorities are more likely to live in densely populated areas. People living in densely populated areas may find it more difficult to practice prevention measures such as social distancing with little choice to do so. Racial and ethnic minorities also often live in neighborhoods that have fewer well-stocked grocery stores and medical facilities. This makes it more difficult to receive care when they fall sick and impairs their ability to stock up on supplies that would allow them to stay home. In all age groups, African-Americans are more likely than Caucasians to report not being able to see a doctor in the past year because of healthcare costs and as a result, have a higher prevalence of chronic conditions. Racial minorities in the US are more likely to be uninsured, and have notably fewer visits to the hospital compared to Whites [ 19]. Institutional racism and systemic inequities undermine the prevention efforts taken by public health agencies. According to an analysis by National Equity Atlas, only 6.5% of Caucasian households do not have access to a car [ 12]. This is compared to 19.7% of Black households, 12% Latino households, and 13.6% Native American households who do not own one. Overall people of color are much less likely to be able to drive to grocery stores, medical centers, testing facilities, etc. Many forms of public transportation, due to low utilization in the pandemic, have faced funding cuts, and their use invariably leads to a higher risk of contracting COVID-19 than driving a private car.

CDC also reports that racial and ethnic minority families are more likely to live in multi-generational households, thus increasing the odds of contracting the highly infectious disease for vulnerable age groups. Racial and ethnic minority groups are also overrepresented in jails, prisons, and detention centers [ 20] - where close proximity between inmates can lead to the rapid spread of the virus. Compared to Caucasians, African Americans are almost twice as likely to be uninsured & Hispanics are almost three times as likely. In a recent piece published by the American Medical Association [ 13], it was reported that more than 28% of people diagnosed with COVID-19 in the US are Hispanic. Hispanics only account for 16.7% of the total US population. Many Hispanic communities usually congregate in Church where they discuss recent events. Now with stay-at-home orders in multiple states, this community is at a disadvantage with respect to the dissemination of information [ 13]. This has especially affected the elderly in the Latino community, as they not only lost a vital source of acquiring new information but are also harder hit by COVID-19, whose mortality increases with age.

Racial disparities in health outcomes are not limited to African-Americans and Hispanics. American Indian reservations have also reported higher COVID-19 cases compared to the general population [ 14]. Another far less talked about problem is the categorization of data. American Indian and Alaska Native (AIAN) individuals are being incorrectly categorized as African-American or Hispanic based on surnames or appearance in certain state counties. In places, they are simply classified as "other".

Hand hygiene is vital to prevent the spread of infection. Racial and ethnic minorities continue to face a lack of indoor plumbing [ 15]. Indigenous peoples, African-American, and Hispanics are much more likely than Caucasian households to lack access to complete plumbing, electricity, & sanitation among other essential amenities. These differences exist even when taking into account other characteristics like household size, age, and income. They are also more likely to be employed in occupations requiring public contact that impairs their ability to exercise social distancing.8 Many of them serve as essential workers and their jobs do not easily translate to a "work from home" model (meat processing, assembly lines, cleaning, delivery personnel, etc) [ 13]. This has led to confusion and reservations about the ongoing stay-at-home orders across the country. Many have continued to show up at their jobs for fear of layoffs, and to financially support their families, at great expense and risk to personal health.

Another group that has continuously showed up for work is frontline healthcare workers, many of whom are immigrants. Over 2.6 million health care workers in the US are foreign born. They have experienced a sharp increase in racist verbal abuse and physical attacks during the pandemic. This has especially affected the immigrants from China and other South-East Asian countries [16], [17]. The FBI's warning of a potential surge in hate crimes against immigrants sends a chilling message to those on the frontlines [ 21]. The impact of such discrimination on our health system and health care workers needs further investigation and study in the future.

The racial and ethnic minorities in the U.S. are at a higher risk for COVID-19 infection than the general population. It is becoming increasingly apparent that COVID-19 has not created, but only widened the existing gaps in our current public health system. We encourage the U.S. Department of Health and Human Services, its sub agencies, state and local health departments, health care institutions, and laboratories to standardize, collect and immediately make publicly available, existing race and ethnicity data so that adequate planning to prioritize equity and measures to effectively manage this pandemic can be initiated. Data needs to be disaggregated to a granular level with a breakdown of who receives tests, who tests positive, who is hospitalized, who dies, and, once developed, who receives treatment and a vaccine, along with who participated in clinical trials. This is as important, if not more, as having data stratified by age, which has been readily available from the CDC and Johns Hopkins Coronavirus Resource Center. Additionally, an effort should be made to ensure public service announcements (PSAs) are provided in multiple languages [ 18]. Only when these communities fully understand how to correctly implement social distancing, access testing, and health care services, and are made aware of harms from misinformation, can we effectively bring the pandemic under control.

5. Conclusions

A strong case can be made for the collection and reporting of COVID-19 cases and outcomes stratified by race/ethnicity, sex, socioeconomic status, and community health status in all states [ 18]. The granular racial and ethnic data will help us better understand the full extent of the pandemic, ensure equitable access to testing, treatment, as well as other resources in the present. It will also inform future emergency preparedness and response. A better understanding of the facts and greater public awareness will mount pressure on public authorities to mobilize more resources to underserved communities. We urge public health agencies to be cognizant of these findings during policymaking. More research is required to understand and document all the reasons why minorities have been affected at a disproportionately higher rate. Any effective plan to contain the spread of COVID-19 must factor in the impact amongst colored, and marginalized communities in society. If we ignore these factual inequities, we will ultimately be unsuccessful in quelling the burden of disease not only for minorities but for everyone.

Ethical approval

This study did not involve human, animal subjects or tissue.

Other relationships

We declare that there are no other relationships or activities that could appear to have influenced the submitted work.

CRediT authorship contribution statement

Raghavendra Tirupathi: Conceptualization, Methodology, Writing - original draft, Writing - review & editing, Visualization, Supervision. Valeriia Muradova: Software, Validation, Formal analysis, Resources. Raj Shekhar: Writing - original draft, Resources, Data curation. Sohail Abdul Salim: Investigation, Writing - review & editing. Jaffar A. Al-Tawfiq: Formal analysis, Data curation, Supervision. Venkataraman Palabindala: Writing - review & editing, Supervision, Project administration.

Declaration of competing interest

The authors declare no conflict of interest.

References

9. Price-Haywood E.G., Burton J., Fort D., Seoane L. Hospitalization and mortality among black patients and white patients with covid-19. N Engl J Med. 2020 Jun 25;382(26):2534-2543. doi: 10.1056/NEJMsa2011686. [[ PMC free article] [ PubMed] [ CrossRef] Google Scholar]

19. T A Lieu, P W Newacheck, M A McManus: Race, ethnicity, and access to ambulatory care among US adolescents. Am J Publ Health 83, 960_965, 10.2105/AJPH.83.7.960. [ PMC free article] [ PubMed] [ CrossRef]

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COVID-19 disparity among racial and ethnic minorities in the US: A cross sectional analysis

# data[].attributes.attachment_link.description
To analyze racial disparities in Coronavirus disease (COVID-19) cases in the United States of America and discuss possible reasons behind this inequality. We obtained estimated case counts of African-American, Caucasian, Native American, Asian and Hispanic individuals with coronavirus disease (COVID-19)infection through May 5, 2020, from publicly available data on state departments of health websites.

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Viewpoint

Lester Darryl Geneviève1, MBChB, MSc, Dr sc med des ;

Andrea Martani1, ML, Dr sc med des ;

Tenzin Wangmo1, PhD ;

Bernice Simone Elger1, 2, MD

¹Institute for Biomedical Ethics, University of Basel, Basel, Switzerland

²University Center of Legal Medicine, University of Geneva, Geneva, Switzerland

Corresponding Author:

Lester Darryl Geneviève, MBChB, MSc, Dr sc med des

Institute for Biomedical Ethics

University of Basel

Bernoullistrasse 28

Basel, 4056

Switzerland

Phone: 41 612071782

Fax:41 612671780

Email: lester.genevieve@unibas.ch

Abstract

The COVID-19 pandemic has revealed deeply entrenched structural inequalities that resulted in an excess of mortality and morbidity in certain racial and ethnic groups in the United States. Therefore, this paper examines from the US perspective how structural racism and defective data collection on racial and ethnic minorities can negatively influence the development of precision public health (PPH) approaches to tackle the ongoing COVID-19 pandemic. Importantly, the effects of structural and data racism on the development of fair and inclusive data-driven components of PPH interventions are discussed, such as with the use of machine learning algorithms to predict public health risks. The objective of this viewpoint is thus to inform public health policymaking with regard to the development of ethically sound PPH interventions against COVID-19. Particular attention is given to components of structural racism (eg, hospital segregation, implicit and organizational bias, digital divide, and sociopolitical influences) that are likely to hinder such approaches from achieving their social justice and health equity goals.
JMIR Public Health Surveill 2022;8(3):e33277
doi:10.2196/33277

Keywords

Structural Racism and Defective Data: Health Equity Threats for the Public Health Response Against COVID-19

Structural racism refers to “[a] system in which public policies, institutional practices, cultural representations, and other norms work in various, often reinforcing ways to perpetuate racial group inequity” []. Structural racism affects all social determinants of health. Its impact was accentuated and made more visible with the COVID-19 pandemic. Indeed, the US Centers for Disease Control and Prevention recognized the centrality of the social determinants of health in the disproportionate impact of the pandemic on racial and ethnic minority groups. In addition, they acknowledged the pervasive contributing influences of (structural) racism across each one of these determinants []. Undeniably, since the start of the pandemic, a cornucopia of examples suggesting the impact of structural racism has surfaced []. This was particularly evident in countries from which statistics are presently available, such as the United States, showing devastating consequences for the health of racial and ethnic minorities []. For instance, as of November 2021 in the United States, the risk of dying from COVID-19 for non-White groups (except for the Asian non-Hispanic community) was about twice that of White people []. Such inequities can be partly explained by structural vulnerabilities influencing access to and quality of care offered to these racial/ethnic minority groups.

The structural vulnerabilities of some racial and ethnic minority groups to COVID-19 stem from political and social influences, which were observed, for instance, to have a greater impact on their health than individual choices made []. These influences led to the normalization of discrimination, stereotyping, and prejudices, and ultimately impacted racial minorities’ health and access to quality care []. Indeed, studies have demonstrated the existence of implicit racial/ethnic biases among health care professionals, and their presumed impact on the quality of care due to suboptimal patient-provider interactions with racial/ethnic minorities (see review []). For instance, emerging evidence based on billing data started indicating disparities in COVID-19 testing among racial and ethnic groups, with African Americans being less likely to be offered COVID-19 testing than Whites, even when presenting with similar symptoms such as fever and cough []. Aside from individual health care professionals, health care institutions and organizations can also harbor implicit biases regarding their approach toward minority groups. It was highlighted that testing centers for COVID-19 in the United States were predominantly located in wealthy and White neighborhoods, which further limits access to health care for people located in poor neighborhoods []. These implicit provider and institutional/organizational biases could be an additional source of structural health disparities for ethnic and racial groups during the COVID-19 pandemic []. Indeed, average threshold metrics for public health interventions (eg, those used as indicators for closing or opening schools/businesses) are biased if COVID-19 testing is carried out mostly in wealthy neighborhoods—where viral transmission rates are actually lower—and deprived neighborhoods (where residents are predominantly minorities) are insufficiently tested [].

Additionally, public health recommendations for containing the spread of COVID-19 have been particularly difficult for racial and ethnic minority groups to implement, partly due to the downstream influences of structural racism [,]. As rightly noted by Krieger [], racial and ethnic minority groups constitute a good percentage of low-wage workers, making them more vulnerable to the effects of the pandemic. They often live in crowded multigenerational houses (often a consequence of racial residential segregation and redlining policies—two historical examples of structural racism [,]), with neither the possibility of working remotely from the safety of their homes nor adequate access to COVID-19 testing and treatment (eg, because minorities are less likely to be insured) [,].

Other indicators of how structural elements determine health inequity for racial/ethnic minorities during the COVID-19 pandemic have been highlighted by some seroprevalence studies. For instance, a large-scale nationwide study carried out in the United States showed higher seropositivity rates (2- to 3-fold higher) for SARS-CoV-2 antibodies in dialysis patients residing in Hispanic and non-Hispanic Black neighborhoods in comparison to those residing in White (non-Hispanic) neighborhoods, and a 2-fold higher seropositivity for those living in poorer neighborhoods []. This study and others (eg, []) challenge the underlying assumption made elsewhere that “the risk of infection is homogeneous within the population” []. Such implicit assumptions are not only detrimental to the efforts made to contain the virus but they may also be a manifestation of structural racism itself. Indeed, one of the defining characteristics of structural racism is its invisibility to the dominant racial/ethnic group []. The situation also highlights the need for more comprehensive data on the impact of the COVID-19 pandemic on racial and ethnic minorities, in particular for those living in disadvantaged neighborhoods.

In that regard, eminent public health researchers have raised the alarm regarding missing or incomplete data on racial/ethnic minorities [,]. Indeed, data on the impact of COVID-19 on racial and ethnic minorities were not systemically and uniformly collected across the United States []. This resulted in the skewing of our understanding of the deadly evolution of the virus within these communities, while hampering the ability to provide them with timely and adapted public health interventions and care [,]. Although US state and local public health departments were required—at latest by August 1, 2020—to report demographic data for COVID-19 cases, Krieger and colleagues [] have shown that, despite the new reporting requirements, compliance was far from being achieved and much work still needs to be done in this regard.

In the COVID-19 era, the underreporting and inadequate reporting of racial and ethnic information create data gaps that hamper the proper functioning of public health institutions in initiating culturally appropriate measures to prevent the spread of the virus in these communities []. Given the fast pace at which the pandemic is evolving and the continuous need for updated public health responses, a paramount question is how these data gaps and structural racism could influence public health practice, in particular when innovative data-driven approaches are being considered as complementary to traditional public health interventions. In this paper, we thus reflect on how structural racism in the health care/public health domain and the defective collection of data on racial and ethnic minorities could undermine the health equity and social justice goals of precision public health (PPH) interventions.

Structural Racism and Defective Data: Potential Impact on PPH

To respond to this worrying public health situation while making use of existing and emerging data sources, Rasmussen and colleagues [] argue for the need to implement PPH interventions as an additional tool to fight the pandemic. PPH means “the application and combination of new and existing technologies […] to tailor preventive interventions for at-risk groups and improve the overall health of the population” []. Horton [] is even more explicit in highlighting the importance of data in PPH and characterizes it as being “about using the best available data to target more effectively and efficiently interventions of all kinds to those most in need.” At least two distinct approaches to PPH exist. The first one is a reductionist version, where PPH focuses solely on the use of genetic information to tailor interventions to specific subgroups of the population (with the risk of neglecting foundational considerations of public health such as the impact of the social determinants of health). The second (wider and more encompassing) version does not limit itself to genetic information, but also considers other sources of data (eg, big data, granular population surveillance data, and data from mobile apps) to guide public health practice [,]. Given the limits of focusing solely on genetic data to guide interventions (eg, risk of exacerbating existing health inequalities []), we find the second version of PPH more promising, as it uses a plethora of data sources [], in particular if it will ally both high-risk strategies and population-based approaches to maximize the impact of public health interventions [].

Although still in its infancy [], PPH has already shown promise in the fight against COVID-19. For instance, at the start of the pandemic, pathogen genomics such as whole genome sequencing analysis, coupled with epidemiological data, was successfully used in the Netherlands to not only monitor the emergence of local or regional clusters of SARS-CoV-2, but also to help in understanding transmission patterns while guiding public health interventions in breaking the chain of transmission of SARS-CoV-2 []. Additionally, some innovative sources of digital data are also being used to reduce the transmission of SARS-CoV-2. The usefulness of participatory disease surveillance systems has already been demonstrated for the early detection of other transmissible diseases such as influenza-like illness []. In such systems, citizens can actively get involved in the public health response by directly reporting COVID-19–related symptoms via mobile apps or digital platforms []. Furthermore, the use of COVID-19 contact trackers, whereby cellphone tracking data are used to alert people who might have been in contact with an active case of COVID-19 [], is another example of how PPH can be helpful in a pandemic context.

Given the disproportionate burden of the pandemic on racial/ethnic minorities, as previously argued, it would then be legitimate to consider them as part of those “most in need” [] and thus principal supposed beneficiaries of PPH interventions during the COVID-19 pandemic. However, given the aforementioned issues related to structural racism and the skewed collection of data on minorities, it is important to consider the following two issues before the widespread deployment of PPH interventions in connection to COVID-19. First, one must reflect on how structural racism can influence the generation and use of data sets in public health practice (eg, data racism) []. Second, it is critical to explore how the use of certain data-driven technologies—which are becoming essential components of PPH—could lead to novel racial/ethnic discrimination in public health interventions for those “most in need” [].

One set of technologies that has the capacity to both improve or worsen health inequities between ethnic and racial groups if employed in clinical care and PPH interventions is machine learning []. Machine learning can be defined as “a branch of artificial intelligence (AI) focused on building applications that learn from data and improve their accuracy over time without being programmed to do so” []. There are 3 main classifications of machine learning approaches, namely supervised, semisupervised, and unsupervised learning, depending on whether the machine learning algorithms are trained on labeled, semilabeled, or unlabeled data sets, respectively []. If such technologies are used in PPH, one of the important aspects to consider is the appropriateness of data sets used to train machine learning algorithms, in particular if they are to be used in a population that is either underrepresented in the training data sets or has long been systemically disadvantaged and marginalized [,]. Indeed, individual and societal biases can be encoded in big data and other training data sets destined for public health practice and medical care [,]. This phenomenon is sometimes described as data racism, a term that refers to “the multiple systems and technologies - deployed in a range of fields - that either primarily target or disproportionately impact migrants and people of [color]” []. To better explain how data racism—combined with structural racism—could impact PPH interventions through machine learning techniques in the future, one can look at the unfortunate experiences emerging in the field of data-driven predictive policing [].

PPH and predictive policing can be compared since they function according to analogous principles. Indeed, one of the foreseeable goals of PPH is to forecast disease outbreaks and identify hotspots or subgroups of the population for tailored interventions based on big data predictive analytics []. Similarly, predictive policing aims at forecasting the likelihood of a crime being committed in a specific location, to then prioritize focused police interventions in certain at-risk areas (eg, by having more frequent police patrols) or on people having some prespecified characteristics deemed relevant by the used software. Existing predictive policing software companies, such as PredPol, aspire to also be fair, since the “starting point is data: objective, agreed-upon facts that can be used to guide the discussion” [,]. PredPol claimed to provide fairer and more objective risk evaluation and predictions regarding crimes than subjective police assessment []. However, Richardson and colleagues [] argue that, although PredPol took significant actions to reduce bias in their data sets when training their machine learning algorithms (eg, by excluding traffic citation data and data on drug-related crimes), such measures still do not capture the whole complexity and diversity of police interactions where bias can be introduced into the data. They also highlighted the methodological difficulties for vendors of such technologies to identify “these problematic practices and policies in real-time; therefore, any system that includes recent or live data may be subject to additional undocumented biases” []. Consequently, the alleged promise of being fair cannot always be kept, since data sets on which predictive policing is based often present relevant fallacies.

We can reasonably expect that PPH interventions might encounter an analogous set of problems, in particular considering the previously discussed COVID-19–related data crisis for racial and ethnic minority groups. Indeed, within the precincts of structural/data racism, machine learning algorithms will likely replicate some degree of discrimination unless appropriate measures are taken to address the situation []. Therefore, it is important for machine learning developers to have an adequate understanding of structural racism and its potential real-world ramifications through their software []. This could help ensure that the developed machine learning algorithms both advance public health utility and promote a fair distribution of resources along racial and ethnic lines, while minimizing the risks of worsening health inequities []. However, it is also important to note that addressing the technical flaws of machine learning algorithms is catering only to the downstream consequences of structural racism and therefore this cannot be the silver bullet to reduce health inequities between racial and ethnic groups. To bridge the disparities between racial and ethnic groups will likely require changes at the societal, institutional, and individual levels, so that the upstream influences of structural racism are mitigated []. A few additional potential solutions that can help in tackling algorithmic biases have been discussed in a previous publication [].

Aside from technological considerations, it is also paramount to tackle the low representation of racial/ethnic minorities (in particular those of African descent) in AI and machine learning communities. This can also be considered a consequence of structural racism in industrial and academic settings []. Racial and ethnic diversity in these communities could help safeguard against blatant and implicit discrimination toward minorities, even if these minority researchers could themselves be subsequently exposed to consequences from the power structures in place. Indeed, it is documented that some of the effects of structural racism in the workplace also involve microaggressions toward these minorities (eg, harassment, disrespect, racial slurs) by their White colleagues, which undermine their capacity to work, while operationalizing the corporate culture of maintaining racial hierarchy [,].

There are also other means by which structural racism could impact the generation of data for PPH during the COVID-19 pandemic. One of them is through the racial and ethnic digital divide, whereby minorities, as a consequence of their often-lower socioeconomic status, would be less likely to engage in PPH activities due to poorer internet access []. According to the Pew Research Center, in 2019, White individuals still have better access to the internet in comparison to other racial/ethnic groups (92% versus 86% in Hispanic individuals and 85% in African Americans) []. Therefore, public health surveillance systems relying on internet-collected data (eg, social media mining to guide interventions against COVID-19) may be particularly vulnerable to the underrepresentation of minority groups in the gathered data sets. The problem might also be that of overrepresentation of minorities in internet-collected data. For example, a recent study has found that—despite worse internet access—ethnic/racial minority groups are more likely to post COVID-19–related information on social media, possibly due to a “reversal of digital divides” or the fact that the pandemic has disproportionately impacted racial and ethnic minority groups, and that social media is often perceived as a coping strategy for stress and for obtaining community support []. Either way, the issue remains that proper public health surveillance should ensure the ethnic and racial representativeness of the collected data before initiating any public health intervention [], or at least perform some sort of data adjustment to account for the selection bias.

Lastly, it cannot be ignored that structural racism could lead to more biased data sets for ethnic and racial minority groups because of the influence of sociopolitical factors on the functioning of public health institutions []. It is important to note that institutional (or structural) racism can influence the functioning of institutions in a number of ways (ie, at individual and organizational levels). For instance, institutions can either become increasingly racialized and adopt racist motivations (eg, excluding or limiting employment of qualified minorities in decision-making positions, or employing a few of them only, as part of racial capitalism [], to showcase that they are dedicated to diversity) or they can be burdened by other organizational structures that impede their achievements in terms of racial and health equity (eg, lack of resources and low care given to people of color) []. Indeed, it is well known that hospitals in marginalized neighborhoods (eg, those with a high percentage of people of color) are underfunded, understaffed, and sometimes filled with less skilled health care professionals, which then limit their ability to offer services and treatment comparable to those offered in other neighborhoods [,].

Therefore, such public health institutions could be hampered in generating high quality data on racial and ethnic minority groups due to (1) the limited resources dedicated to these groups, (2) the unrepresentative racial and ethnic composition of their decision-making teams and health care professionals, leading to viewing racial disparities from a “White framing,” (3) the lack of adequately trained professionals to fulfill these duties, and (4) hospital segregation—that is, the refusal of some of the most resourceful hospitals to treat racial/ethnic minority groups suffering from COVID-19 due to their lower socioeconomic conditions (eg, inferior health insurance plans, which is also a known consequence of structural racism) [,,]. The unfortunate consequence could be that of contributing to the generation of biased data sets that do not reflect the lived reality of racial and ethnic minorities in facing COVID-19. Therefore, PPH interventions relying on these data sets would likely be limited in their effectiveness and health equity could never be achieved for all racial and ethnic groups, unless these different parameters are given due consideration.

Conclusions

In this article, we have highlighted the role of structural racism and the presence of defective data for racial and ethnic minorities as important factors to consider in designing ethically acceptable public health policies during and after the COVID-19 pandemic. Moreover, we have discussed PPH, an innovative approach to tackle public health issues such as COVID-19, which can however encounter several ethical challenges if the aforementioned issues of structural racism and defective data collection are not tackled. Our aim is not that of discouraging the use of PPH, which we consider as an important new element in the public health toolbox that deserves to be fully implemented to fight the COVID-19 pandemic; rather, we want to highlight a few issues that need to be considered by policy makers and scientists developing PPH measures to make sure that their efforts to improve public health do not ignore the danger posed by structural racism and defective data on minorities. In this regard, we join our voices to those cited in this article on the importance of having improved, harmonized, and nationwide data collection systems that are as free as possible from the influences of structural racism and inclusive of all racial and ethnic groups. Although insufficient on their own to promote health equity among racial and ethnic groups, these measures could be an important contribution to the effective and nondiscriminatory use of PPH approaches that are inclusive of the racial and ethnic composition of the societies in which they are deployed. PPH approaches deserve to be better planned and their effectiveness critically assessed, and this will not be achieved unless their data and technological foundations are deep-rooted in health equity and social justice.

Acknowledgments

This research was funded by the Swiss National Science Foundation (NRP 74 “Smarter health care,” project “Advancing SMart solutions and eliminating barriers for the Acquisition, Analysis, and Sharing of Health data in Switzerland [SMAASH],” grant 407440_167356). The publication fee was also funded by the Swiss National Science Foundation. The funder had no role in the writing of the manuscript or in the decision to publish the results.

Authors' Contributions

LDG was involved in the conceptualization of the manuscript, wrote the original draft and subsequently reviewed and edited the manuscript following other coauthors’ suggestions and modifications made to the manuscript. AM was involved in the conceptualization of the manuscript, and in reviewing and editing of the original draft and subsequent versions. TW was involved in the conceptualization of the manuscript, supervised LDG during the writing of the original draft, and reviewed and edited the original draft and subsequent versions. BSE was involved in the conceptualization of the manuscript, and reviewed and edited the original draft and subsequent versions. BSE was also responsible for project administration and acquired funding from the Swiss National Science Foundation for this publication. All authors have read and agreed to the published version of the manuscript.

Conflicts of Interest

None declared.

‎
Abbreviations

AI: artificial intelligence
PPH: precision public health

Edited by T Sanchez; submitted 01.09.21; peer-reviewed by C Campos-Castillo, B Loring; comments to author 28.10.21; revised version received 31.12.21; accepted 27.01.22; published 04.03.22
Copyright
©Lester Darryl Geneviève, Andrea Martani, Tenzin Wangmo, Bernice Simone Elger. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 04.03.2022.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on https://publichealth.jmir.org, as well as this copyright and license information must be included.

# data[].attributes.attachment_link.authors[].name
Lester Darryl Geneviève1, MBChB, MSc, Dr sc med des

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Precision Public Health and Structural Racism in the United States: Promoting Health Equity in the COVID-19 Pandemic Response

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The COVID-19 pandemic has revealed deeply entrenched structural inequalities that resulted in an excess of mortality and morbidity in certain racial and ethnic groups in the United States. Therefore, this paper examines from the US perspective how structural racism and defective data collection on r…

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Department of Geography, Texas State University, San Marcos, TX 78666, USA

School of Biological and Population Health Sciences, Oregon State University, Corvallis, OR 97331, USA

Author to whom correspondence should be addressed.

Academic Editor: Stephen Bustin

Received: 11 June 2021 / Revised: 16 July 2021 / Accepted: 2 August 2021 / Published: 5 August 2021

Health disparity is an unacceptable, unjust, or inequitable difference in health outcomes among different groups of people that affects access to optimal health care, as well as deterring it. Health disparity adversely affects disadvantaged subpopulations due to a higher incidence and prevalence of a particular disease or ill health. Existing health disparity determines whether a disease outbreak such as coronavirus disease 2019, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), will significantly impact a group or a region. Hence, health disparity assessment has become one of the focuses of many agencies, public health practitioners, and other social scientists. Successful elimination of health disparity at all levels requires pragmatic approaches through an intersectionality framework and robust data science.

1. Introduction

The coronavirus disease 2019 (COVID-19) outbreak caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was declared a pandemic by the World Health Organization on 11 March 2020 due to its growing prevalence, particularly its increase in morbidity and mortality over a wide geographic area [ 1], and has since generated considerable attention. The infection rate varies across geographic areas, indicating spatial and regional disparities. Evidence of regional and within-country variations in coronavirus infection and mortality has been well documented [ 1, 2, 3, 4, 5]. Shortly after the first outbreak in Wuhan, China, several other American and European countries exhibited clusters of COVID-19 infections. Not long after, the United States became a significant hotspot of COVID-19 morbidity and mortality. Figure 1 highlights the 7-day average cumulative cases of COVID-19 in five countries: the United States, Canada, Germany, the Netherlands, and Nigeria. Recently, when the distribution of COVID-19 vaccination became more accessible in most communities in the United States, the daily hospitalization cases due to infection and related death decreased significantly [ 6, 7, 8, 9]. However, overwhelming evidence indicates differences in infection and associated death rates across different populations [ 2, 3].

The recent COVID-19 pandemic revealed many things ranging from the possibilities of remote learning/working, virtual recruitment, and service delivery such as telemedicine. The pandemic has further shown additional evidence of health disparity in our society, particularly in health outcomes and access to health care systems. As a background for this paper, Figure 2 shows the intersection between structural inequality as the root cause of health disparity, public health, and data science. We believe that the adoption of intersectionality and data science approaches would help foster data-driven interventions in addressing health disparities.

Health disparity is an important theme that has generated much interest from different domains as well as a significant public health debate that must not be overlooked in any society and in chronicling the health outcomes of COVID-19, particularly in the United States. This entry paper examines the concept of health disparity within the intersection of public policy, public health, and data science ( Figure 2). Furthermore, this contribution explores the definition of health disparity and determinants of health regarding COVID-19 from different countries, with specific emphasis on high-income countries with diverse populations such as the United States and the United Kingdom. For example, the United States has the most extensive published papers on health disparity, mainly from racial and ethnic, social class, gender, and sexual orientation angles, and serves as a reference point for research on racial and health disparity.

In addition, this paper provides a general overview of the implication(s) of health disparities for achieving viable public health policies. Considering different approaches to measuring health disparity, this study presents how the field of public health and other allied (health) disciplines could leverage the power of data science such as geographic information science/systems (GIS), machine learning, and artificial intelligence to help solve some of the societal issues including the current COVID-19 pandemic. In addition, this paper contributes to the field of spatial epidemiology and public health by showing how data science and intersectionality frameworks can be fused to improve general/public health. Finally, this paper concludes with some expert-based recommendations toward reducing health disparity.

2. Defining Health Disparity

Health disparity is a ubiquitous phrase in most health writeups found on the pages of printed and digital media, journals, and reports from governmental and non-governmental organizations, as well as in discussions among health professionals, policymakers, and the general public. However, the conceptualization and determination of health disparity vary by geographic location. Table 1 presents the various but similar definitions of health disparity and health inequality as used within and outside the United States. For instance, in the United States, health disparity determines whether health, broadly used, differs by comparing different health outcomes among various population groups and subpopulations of interest. In contrast, the terms "health inequality" and "health inequity" are frequently used outside the United States. For example, the World Health Organization consistently uses the term "inequity," which connotes the differences in health that are unnecessary and avoidable but unfair and unjust [ 11].

Generally, health disparity refers to a higher burden of illness, injury, disability, or mortality experienced by one group relative to another. Determining which health situations could be considered unfair or unjust depends on the place and time of examination. However, the widely used measure hinges on the degree of choice involved. Essentially, any strive toward attaining equity and health is "not to eliminate all health differences so that everyone has the same level and quality of health, but to reduce or eliminate those which result from factors considered to be both avoidable and unfair" [ 12]. This prompts the question, what is avoidable and/or unavoidable health inequality? However, the answer to this question is beyond the scope of this article (for additional readings, see: Braveman [ 11, 13]; also Whitehead [ 12]).

The National Institute of Minority Health Disparities' (NIMHD) definition [ 14] of health disparity highlights health differences among different populations, emphasizing disadvantaged populations. According to the legislation that birthed the NIMHD, what defines health disparity includes several factors characterized by poorer health outcomes, indicated by the overall disease incidence, prevalence, morbidity, mortality, or survival in the population compared with the general population. The NIMHD recognizes health disparity populations as racial/ethnic minorities, socioeconomically disadvantaged populations, underserved rural populations, and sexual and gender minorities.

Similarly, the United Kingdom's National Health Service describes adversely affected populations within the "Inclusion Health" framework. Inclusion health refers to "a number of groups of people who are not usually well provided for by health care services and have poorer access, experiences and health outcomes. The definition covers people who are homeless and rough sleepers, vulnerable migrants (e.g., refugees and asylum seekers), sex workers, and those from the Gypsy, Roma and Traveller communities" [ 15]. Nevertheless, inclusion health or each of the compositions of people affected by health disparity could be examined in more detail based on how they have been impacted by COVID-19 if and when detailed health data are available. The related findings could help inform better intervention policies to reduce health disparity among different population groups.

Table 1. Different definitions of health disparity and health inequality.

Organization, Country

Definition

National Institute of
Minority Health
Disparities [ 14]

A health difference, determined on the basis of one or more health outcomes that adversely affect disadvantaged populations.

Healthy People
2020 [ 16]

A particular type of health difference closely linked with social, economic, and
environmental disadvantages.

Centers for Disease
Control and
Prevention [ 17]

Preventable differences in the burden of disease, injury, violence, or opportunities to achieve optimal health experienced by socially disadvantaged racial, ethnic, and other
population groups and communities.

Institute of Medicine [ 18]

A health service disparity between population groups is determined as differences in treatment or access not justified by differences in the health status or preferences of the groups.

National Health
Service [ 15]

Unfair and avoidable differences in health across the population and between different groups within society.

Compared to the NIMHD's definition of health disparity that centers on disadvantaged populations, Healthy People 2020's definition is more expansive. Healthy People 2020's definition of health disparities underscores social, economic, and environmental resources differentiation. Hence, health disparities adversely affect groups of people who have systematically experienced greater obstacles to health based on their racial or ethnic group, religion, socioeconomic status, gender, age, mental health, cognitive, sensory, or physical disability, sexual orientation or gender identity, geographic location, or other characteristics historically linked to discrimination or exclusion [ 19]. Similarly, England's National Health Service defines health inequality as unfair and avoidable differences in health across the population and between different groups within society ( Table 1). Carter-Porkras and Baquet [ 20] presented other definitions of health disparity within the United States context. For example, the Taskforce on Black and Minority Health, Department of Health and Human Services, in 1985, used excess deaths (excess death is referred to as the difference between the number of deaths observed in minority populations and the number of deaths which would have been expected if the minority population had the same age- and sex-specific death rate as the non-minority population) of six key health indicators. These health indicators are cancer, cardiovascular disease and stroke, cirrhosis, diabetes, homicide and unintentional injuries, and infant mortality.

The Institute of Medicine differentiates health disparity between minority and non-minority groups based on clinical appropriateness, needs, and patient preferences; discrimination at the individual patient-provider level that results from biases, prejudices, stereotyping, and uncertainty in clinical communication and decision making; and, lastly, the operation of health care system functions. The American Medical Association (AMA) also recognizes health disparities in health care systems.

In 2000, the United States Department of Health and Human Services launched Healthy People 2010, which had two goals: (1) to improve the overall health status of Americans and (2) to eliminate racial and ethnic health care disparities. The AMA was deemed fit to promote this agenda. In 2004, the association established the Commission to End Health Care Disparities, while the National Medical Association was established to address gaps in health care. As part of this agenda to eliminate racial and ethnic health care disparity, the AMA strives to promote diversity by increasing the share of physicians through its policies and advocacy work and therefore proposed immigration reform as a means to eliminate health care disparities, prioritized access to care for patients with disabilities, and proposed its intent to tackle unequal treatment [ 21]. Hence, closing health disparities requires an intersectional approach and bold and unapologetic political willingness.

3. Intersectionality Framework and Health Disparity

Although several studies [ 22, 23, 24, 25] have underscored the importance of intersectionality in adverse health outcomes, such as the current COVID-19 pandemic, its application is scarce. The adoption of intersectionality in health analysis, intervention, and policy could reduce health disparity. According to Kimberly Crenshaw [ 26], "intersectionality is a lens through which one can see where power comes and collides, where it locks and intersect." Intersectionality is accepting that different people have unique experiences of discrimination and privilege at different stages in their lifetime. Scholars have employed an intersectionality framework to examine the experiences of individuals belonging to different marginalized groups. Some of the indicators that should be considered in order to achieve intersectionality include race, ethnicity, gender identity, class, languages, skin color, neurodiversity, religions, (dis)ability, sexuality, mental health, age, ecological systems (neighborhood, school, office, rural-urban, local, and federal policies), education, citizenship and immigration status, housing, and body size (this list could go on). The intersectionality framework could be helpful in transformative health disparity research, especially in understanding disparity in COVID-19 health outcomes.

Furthermore, the intersectionality framework posits that inequality is experienced through multiple contexts, and it should not be examined from a single standpoint. For example, the intersectionality framework could help us understand why Black women are more likely to die through childbirth than White women (maternal mortality disparity) or why there is a high rate of incarceration or racially profiled crimes among Black men or Black adolescents/youth. Within the current COVID-19 pandemic context, we could continue to ask questions about whether more older Black, Hispanic, and Asian women were infected or died of COVID-19 and why? These are the questions intersectionality will help shed some light on if applied.

Using the intersectionality approach, Sekalala et al. [ 24] analyzed the intersection between age and sex to determine vaccination prioritization based on human rights needs. In addition, Eaves and Falconer Al-Hindi [ 23], in a short article published in Dialogues in Human Geography, claimed that "geographical research that is designed, conducted, and analyzed within an intersectional framework generates better geographical scholarship". Hence, researchers should consider how the concept of intersectionality could reveal more disparity by intersecting some of these indicators. Unfortunately, as seen in Table 2, this concept has received limited attention in the study of COVID-19.

3.1. COVID-19 Disparity

Regarding disparity in COVID-19 outcomes, it is evident that the disease has exacerbated health inequality. The health impacts of COVID-19 are unevenly distributed, particularly for underrepresented racial and ethnic minorities and migrants [ 27, 28, 29]. Figure 3 shows the pattern and distribution of COVID-19 deaths based on the 2017 population density per square kilometer for some selected countries. It is worth emphasizing that the pandemic did not cause health disparity in that some minority ethnic groups died at a significantly higher proportion; it only shed more light on the longstanding health disparity in various countries [ 30, 31, 32, 33]. Existing data on infections, hospitalizations, and deaths reveal significant variation among and within regions and communities [ 27, 28, 34, 35, 36], prompting questions about health inequality and disparity; they have also stirred curiosity to know which populations are at higher risk and why the risk is higher for one group than others [ 27]. The within-country health determinants may explain this variation among the countries of the world. This type of question is what proponents of social, health and environmental justice have been pursuing before the outbreak of the COVID-19 pandemic.

3.2. COVID-19 and Environmental Injustice

The advent of COVID-19 adds "icing on top of the cake" of environmental justice research that has been in progress for several decades, particularly in the United States. For example, in a roundtable discussion [ 37], Robert Billard referred to the COVID-19 pandemic as "a heat-seeking missile that is targeting the most vulnerable populations, and the bull's-eye is actually the environmental justice communities, the communities that are the poorest, that are the most polluted, that are the sickest when it comes to comorbidity, and the result of this heat-seeking missile called COVID-19 is a death bomb." In addition, many studies situate research on COVID-19 health disparity within the broader framework of environmental injustice [ 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48]. These studies examine environmental variables such as air pollution in different geographic locations from countries in North America through Africa to Europe (e.g., Germany, Italy, and Britain).

Several studies allude to the reduction in air pollutants (e.g., PM 2.5, NO ₂, O ₃) from the reduced human mobility due to lockdowns [ 38, 40, 49], and several others indicate a correlation between the COVID-19 burden and environmental pollution [ 50, 51], particularly in economically disadvantaged communities and areas with a high concentration of people of color [ 35, 47, 52, 53, 54, 55, 56]. For example, Chakraborty [ 54], using the local indicator of spatial association, showed convergence between the COVID-19 incidence rate and the respiratory risk from hazardous air pollutants across counties in the United States. Additionally, his study indicated that being Black and living in socioeconomically deprived neighborhoods increased the COVID-19 incidence, supporting a similar study from the United Kingdom [ 34].

Mackey et al.'s [ 28] systematic review of 37 cohort and cross-sectional studies and 15 ecological studies, for example, showed that African American/Black and Hispanic populations experience disproportionately higher rates of COVID-19 infection, hospitalization, and coronavirus disease-related mortality compared with non-Hispanic White populations, but not higher case fatality rates. In addition, the Atlantic, a COVID-19 dashboard that tracks infection and deaths by race/ethnicity across the United States, shows evidence of health disparity [ 57]. For example, Alabama, where 27% of the population is Black or African American, had a higher infection rate (29%) and the number of deaths (31%) for its Black people compared to its White population.

Chaudhuri et al.'s [ 34] work on the interaction of ethnicity and socioeconomic deprivation on COVID-19 mortality in the United Kingdom showed that Black, Asian, and Minority Ethnic (BAME) populations are at higher risk of developing COVID-19 and likely to experience severe health-related outcomes compared to White British populations. Relying on disaggregated ethnicity data, the England study [ 34] reported that the age-adjusted COVID-19 mortality rate was higher in minority neighborhoods in the highest deprivation quartile, with a high concentration of Black-African (regression coefficient, β 2.86; 95% CI 1.08-4.64), Black-Caribbean (β 9.66: 95% CI 5.25-14.06), and Bangladeshi (β 1.95: 95% CI 1.14-2.76) populations.

Using a combination of statistical and geographic information system techniques, Iyanda and colleagues [ 1] used 356 days of COVID-19 cases and deaths data to examine disease fatality in the United States ( Table 2). They showed that African American/Black and Hispanic populations experienced disproportionately higher case fatality rates in the rural counties with a higher share of minority populations. Their findings corroborate earlier data by the Centers for Disease Control and Prevention that show that Black and Hispanic communities are three and two times more likely to be infected and die from COVID-19, and other sources have shown similar racial/ethnic disparities [ 35, 36, 53, 54, 57, 58]. These results reinforce how structural racism may have shaped the distribution of social determinants of health and promotes social risk factors. More interestingly, action within the health care system has been hampered by a lack of understanding of how to abate the impact of institutional and structural racism on influencing minority health [ 59].

3.3. COVID-19 and Incidence of Violence

Table 2 presents ten studies examining different forms of violence (e.g., police brutality, riots, shooting), mostly during COVID-19. In addition to the institutional violence, environmental injustice, and segregationist and discriminatory laws that faced, majorly, the minorities and immigrants in most countries is the COVID-19 pandemic which stretches structural racism in many countries [ 30, 37, 60, 61, 62, 63, 64, 65]. It has become common knowledge that "pandemics always follow the fault line of society" [ 33]-structural or institutional racism. Research indicates that racism and xenophobia increased during the coronavirus outbreak due to the increasing nationalism and populism [ 30]. For example, Gibson et al. [ 66] chronicled institutional responses to protests and outbreaks of violence due to the deaths of several African Americans, including Ahmaud Arbery, Breonna Taylor, and George Floyd, amidst the global COVID-19 pandemic in 2020. Similar to the experience of the violence of the #BlackLivesMatter movement in the United States, there were violence outbreaks in the United Kingdom due to longtime racism. Within the Philippine context, Corpuz [ 62] called to end the police brutality of Filipinos, which was exacerbated during the coronavirus outbreak. Biana [ 67] argued that the outcome in health disparity could be related to a matter of class. For example, being Black in the United States is considered a risk factor for disease vulnerability, while being poor in the Philippines means "to be neglected and be reduced to nothing." This assertion might as well explain the excess infections and deaths from COVID-19 in different geographies.

4. Determinants of Health Disparities

According to Healthy People 2020, several powerful and complex relationships exist between biology or genetic compositions, individual behavior, socioeconomic status, the physical environment, discrimination, racism, literacy levels [ 16], and non-pharmaceutical policies that influence the susceptibility, exposure, transmission, infection, recovery, and fatality rate of COVID-19 and the probability of receiving timely as well as adequate health care.

Common health determinant factors in developed and developing countries include the availability of or lack of access to high-quality education, nutritious food, decent and safe housing, affordable and dependable public transportation, culturally and socially sensitive health care providers, health insurance, and clean water and air. Several studies have examined how the COVID-19 pandemic affects some of these factors, including air quality, business, nutrition, and health care services [ 68, 69, 70, 71]. In addition, available data indicate that a more significant proportion of racial and ethnic minorities depend on government assistance programs such as SNAP. Moreover, most of them have no health insurance, a critical barrier to access adequate health care, particularly during a pandemic. Research such as the work of Chakraborty [ 54] in the United States and Iyanda et al. [ 1] global ecological study highlight the importance of health insurance during the COVID-19 pandemic. Thus, it is not surprising that COVID-19 only shows the evidenced health disparity that has existed for decades in most countries.

4.1. Social and Structural Determinants of Health

Social determinants of health are the non-medical factors that influence health outcomes, and they are generally the conditions in which people are born, grow, work, live, and age, and the broader set of forces and systems shaping the conditions of daily life [ 72]. COVID-19 has exhibited various characteristics based on some of the previously highlighted conditions. As evidenced by the existing data, the disparities in COVID-19 morbidity and mortality in the United States were deepened mainly by structural factors such as racism [ 73, 74, 75, 76, 77]. There are different nomenclatures of structural racism. Online dictionaries describe it as societal racism, state racism, covert racism, systemic racism, and institutional racism. Irrespective of the adopted nomenclature of racism, it has significant implications on health and health outcomes.

Structural, institutional, and systemic racism synonymously refer to structures with procedures or processes that disadvantage people of color, particularly African Americans. An excellent example of structural racism is the "redlining" policies that discriminated against Black communities regarding the opportunity to secure loans [ 78]. Since housing is a critical determinant of health, discriminatory housing policies play a critical role in the health conditions of the group of people being discriminated against. For example, breast, colorectal, cervical, and lung cancers, as well as birth outcomes and asthma, have been shown to correlate with redlining policy [ 79, 80, 81, 82, 83, 84]. In addition, housing congestion and overcrowding are major underlying factors for the diffusion of infectious diseases such as COVID-19. Therefore, the authors adopt structural racism in this article, which is more prevalent and favored in health research.

4.2. Structural Racism

Structural racism is how societies foster discrimination through mutually reinforcing inequitable systems. It is usually embedded through laws within society or different levels of an organization. For example, during the peak of the COVID-19 outbreak in the United States in 2020, more racial and ethnic minorities were infected and died more than their White counterparts. COVID-19 impacted more Blacks or African Americans and Hispanics than any other racial and ethnic group in the United States. Studies on COVID-19 focusing on geographic disparity in the infection, death, and case fatality rates show that Black/African Americans living in inner cities with a high population density were at high risk [ 85]. In addition, research on the rural-urban disparity of COVID-19 case fatality ratio in the United States indicates that Blacks in rural counties were disproportionately burdened [ 27]. These disparities are inherently linked with racial and housing segregation, intergenerational poverty, and structural racism [ 74].

Historically, African Americans had the least access to health care in the United States due to their inability to access health insurance, mainly because of the low income and low employment rate among this subpopulation [ 73, 75, 76]. In addition, African Americans were more impacted because they have limited access to single-family homes due to historical institutional discriminatory policies (e.g., redlining and FHA policies) that force them to converge in mostly the poorest, densely populated inner-city spaces [ 78]. Similar experiences exist for Black, Asian, and Minority Ethnic (BAME) groups in the United Kingdom [ 34, 86, 87, 88, 89] and indigenous populations around the world [ 90, 91, 92, 93]. Generally, minorities are disproportionately overrepresented in neighborhoods of concentrated poverty [ 94], which is detrimental to mental and physical health and wellbeing. Consequently, during the lockdown, the recommendations to maintain social distancing were less realistic for them and their communities.

4.3. Age and Gender Disparity

Globally, senior adults aged 65 and above are more likely to die from COVID-19 complications than other age groups, and more males than females were infected and died from the disease. What research has not readily shown is age disparity in minority groups. In addition, the observed sex and gender differences in COVID-19-related health outcomes could be explained by the sex- and gender-based medicine (SGBM) model that incorporates how biological sex and the socio-cultural aspects of gender affect health and illness [ 95]. Previous epidemiological research on similar respiratory infectious diseases such as the 2003 severe acute respiratory syndrome (SARS) and the 2012 Middle East respiratory syndrome (MERS) outbreaks show evidence of sex and gender disparities. Age-based research on COVID-19 also demonstrates age group disparity of COVID-19 infection and death rates within the United States context, and several examples are available globally. Sadly, despite the United States having some of the highest coronavirus cases and deaths, comprehensive age- and sex-specific data concerning patients in the United States are not currently available to the community of researchers to effectively inform health policy.

4.4. Case Study of COVID-19

COVID-19, as a peculiar case study, could help us better understand racism, structural racism, inter-racism, and equity. A deep understanding of equity matters because it helps us better understand how resources are distributed according to the underlying structure. For example, in the case of COVID-19, we could go back and analyze the distribution of PPE, hospital beds, access to care and services, and, now, the distribution of COVID-19 vaccines by race and ethnicity, age groups, and gender and sexual orientation. We can then ask whether minority communities that are most impacted receive the required care and can access vaccines as needed. Existing data on vaccine distribution at the county level indicate that counties identified as the most vulnerable and most impacted received fewer vaccines [ 96], indicating the existing health disparity by geography, social class, and racial and ethnic groups.

5. Implication for Achieving Global and Sustainable Health

The variances in the definition of health disparity have significant policy implications in obtaining the desired health outcomes, particularly for reaching global health goals such as the United Nations' Sustainable Development Goals and other multilateral agencies (e.g., World Health Organization and World Bank). Figure 4 presents a snapshot of global disparity in the distribution of coronavirus vaccination. A stark difference exists between developed and less developed regions. For example, the dose per 100 people varies between 4.2 in Africa and 78 in North America. Most of the prominent multilateral organizations are all part of the United Nations, intending to improve the health and wellbeing of all global citizens. Since its creation in 1945, the United Nations has provided a common platform where all the world's nations can gather together, discuss common problems such as historical pandemics and epidemics, and find shared solutions that benefit humanity.

Although some multilateral agencies do not directly seek to improve global health, they are essential mechanisms to promote healthy living. A good reference, in this case, is the World Bank, whose goal is to alleviate poverty at all levels by providing loans, credits, and grants to developing/emerging countries toward improving the standard of living of citizens in developing nations. Additional goals include implementing various development projects such as education, health care, agriculture, environmental and natural resource management, infrastructure, and other relevant projects toward general improved wellbeing. For example, at the onset of the COVID-19 pandemic, the World Bank highlighted 34 health indicators referred to as the "World Bank Indicators of Interest to the COVID-19 Outbreak," partly to investigate the global disparities and drivers of COVID-19 (see [ 1]). Despite the fact that the United States is an active player and frontline contributor in helping to fight global health issues and other socioeconomic matters, the COVID-19 pandemic hit hard on the "World's Police" country [ 97] and makes it the global hotspot of COVID-19 infection and mortality.

Figure 4. The global disparity in the COVID-19 vaccination doses per 100 people (data source: The New York Times [ 98]).

The United States Centers for Disease Control and Prevention (CDC) developed a social vulnerability index (SVI) for determining the impact of the COVID-19 community infection rate and morbidity and mortality rates. Briefly, social vulnerability refers to the potential adverse effects on communities caused by external stress on human health, including natural/human-caused disasters and disease outbreaks. Therefore, a pragmatic effort towards reducing social vulnerability can have a longtime effect on decreasing human exposure, suffering, and economic loss. Based on the CDC's SVI, counties in the United States were categorized into low to high social vulnerability [ 96].

Typically, neighborhoods in the highest social vulnerability tertile should be prioritized for distributing COVID-19 vaccines to quickly curtail the excess COVID-19-related morbidity and mortality, which will eventually address two common health issues-equality and equity. To address inequality, a similar allocation of health resources such as the supply of COVID-19 vaccines should be supplied proportionately based on the population across different jurisdictions or health regions. Likewise, the issue of equity can be addressed by prioritizing communities most affected by the pandemic. Nevertheless, the two issues of equity and inequality should be judiciously managed to avoid inter-racial and political conflicts.

6. Measuring Health Disparity

To monitor trends in health disparities in federally funded programs, Section 4302 of the Patient Protection and Affordable Care Act (ACA) suggests the need to improve data collection to reveal any evidence of health disparity [ 99]. However, a comprehensive and accurate measure of health disparity has become a significant question among public health researchers. Compared to the measure of inequities, which are relatively easy to characterize, assessing health disparities or disparities in health conditions is not easy. Generally, the effort toward measuring health disparity has one main goal: to help inform transformative policies and provide justice. Health disparity assessment requires a sufficient understanding of health disparities and consistent measurement and variables that shape them. Consequently, several approaches have been used to measure health disparities. For example, disparities can be measured relative to a standard such as a Healthy People target [ 100].

Researchers have measured health disparities in absolute and relative terms for better comparisons across populations, geographic areas, and indicators [ 101, 102]. Pairwise comparisons measure health disparity when the goal is to improve the health status of a particular group or population [ 103]. More specifically, the absolute measure of health disparity can determine the arithmetic difference between a group rate and a reference category. In contrast, a relative measure expresses the simple difference from the reference point as a percentage of the reference point [ 102]. Health disparities can also be measured relative to the total population represented by the domain of groups, using graphical displays over time and finding the ratio between the highest and lowest rates [ 104, 105].

Nevertheless, these measures vary in terms of magnitude and the opposite direction in which these magnitudes can occur due to temporal changes [ 101]. In addition to measuring disparity in absolute or relative terms, several issues have been associated with measuring health disparities [ 102]. For example, researchers could choose to measure health disparity between groups in a pairwise manner or in a summary fashion, weighting groups according to size or in terms of favorable or adverse events [ 102]. The most common issue with measuring health disparity is the selection of a reference point from which to measure disparity. Selecting the best measure depends on the research question at hand, as this can affect the magnitude and direction of disparities measured at a point in time [ 102].

7. The Role of Geospatial and Machine Learning Techniques in Health Disparity

Since the global outbreak of COVID-19, several attempts have been made to investigate factors driving the variability of COVID-19 infection, mortality rates, and case-fatality rates/ratios at different geographic scales using different approaches. One well-known technique that has gained substantial popularity in public health and epidemiology research of infectious and noninfectious diseases is the use of geographic information systems (GIS). GIS has become an essential tool for revealing spatial disparity and distribution and understanding the change in disease patterns. Furthermore, to complement medical interventions, epidemiologists are keen on discovering the origin and diffusion rate of diseases using geospatial techniques for appropriate health intervention and preventing a future outbreak. Of the 40 selected studies in Table 2, 9 highlighted the application of machine and deep learning and artificial intelligence (ML/DL/AI), and 11 used spatial and aspatial techniques to study COVID-19, reinforcing the importance of innovative technologies in disease epidemiology.

7.1. GIS and Health Data Linkage

The integration of spatial and nonspatial data, as well as linking census data with electronic health data to create a database that can be queried for meaningful insight in supporting health policies, is possible through the application of GISs. Geographers, spatial epidemiologists, and other spatial scientists are at the forefront in marshaling the power of GISs using spatially encoded data such as mobility data to estimate and explain COVID-19 transmission patterns in the United States and beyond. Another contribution of the use of GISs to COVID-19 studies and health disparity investigation, in general, is its capacity to empower the linkage of health outcomes (morbidity or mortality) with socioeconomic, cultural, infrastructure, and environmental data at the aggregate level (different scales) to support the ecological investigation of the possible correlation between health disparity and socioecological factors (see, for example, [ 1]).

Undeniably, we are currently in the era of "big data," and the use of GISs has helped governmental and non-governmental agencies handle these enormous data that keep pouring near real-time. Based on the insights gained from manipulating and analyzing the available data, policymakers, and health agencies developed non-pharmaceutical interventions such as total or partial lockdown, travel bans for nonessential workers within a country, and international travel restrictions. What we have found is that the degree of implementation of various non-pharmaceutical measures and the level of the effective reproduction number (R t) at the time of the intervention correspond to the suppression rate of COVID-19 in the United States, that is, the average number of people each infected person can spread the virus to [ 27].

Moving forward, geographically focusing on policies that will eliminate the most critical determinants of health, structural racism and poverty, is crucial to reducing health disparity among racial/ethnic groups. In this regard, persistent recognition of spatial data and scientists/geographers and their tools, such as GISs, would continue to be relevant in measuring and untangling health disparities.

7.2. Disease Epidemiology, Machine Learning, and Artificial Intelligence

Apart from GIS techniques, other sophisticated data analytics techniques such as machine learning (ML), deep learning (DL), and artificial intelligence (AI) are available to help mitigate several impacts of COVID-19. The field of machine learning is both broad and deep and is constantly evolving. ML is an innovative technique that has extensive applications in prediction. The adoption of machine learning in disease epidemiology provides more accurate and useful features than a traditional explicitly calculation-based method. Furthermore, ML algorithms can analyze the risk factors considering age, social habits, location, and environmental factors. ML is essentially used for predicting future events based on available data.

Interestingly, some GIS interfaces have built-in ML algorithms that can smoothly carry out some health-based analyses. For example, ArcGIS Pro, a proprietary GIS software by ESRI [ 17], has built-in ML techniques and DL algorithms, e.g., a forest-based classification and regression embedded in ArcGIS Pro based on the traditional random forest (RF) technique. It is important to note that RF is a supervised learning and classification algorithm that consists of many decision trees. In a technical term, the RF techniques use bagging and feature randomness when building each tree to create an uncorrelated forest of trees whose prediction is more accurate than any individual tree. In other words, RF makes a prediction based on random samples drawn from different chunks of data (forest) to obtain optimal outcomes compared to relying on a single sample (single tree). An excellent example of how ML and AI have been used in disease surveillance is the contact tracing of COVID-19 infection. In addition, using machine learning, data scientists interlink several forms of data such as environmental, population, and mobility data with health data to predict infection, death, or fatality rates [ 106, 107, 108].

Punn et al. [ 109] used a series of ML and DL techniques such as support vector regression (SVR), polynomial regression (PR), deep neural networks (DNNs), and recurrent neural networks (RNNs) to predict the total number of confirmed, recovered, and death cases worldwide. Similarly, Pinter et al. [ 110] used hybrid machine learning techniques, an adaptive network-based fuzzy inference system (ANFIS), and a multi-layered perceptron-imperialist competitive algorithm (i.e., MLP-ICA) to predict time series of individuals infected with COVID-19 and mortality rates in Hungary. The authors opine that ML techniques could be more useful in epidemiological modeling than the standard susceptible-infected-resistant (SIR)-based models.

Finally, coupled with geospatial techniques, ML, DL, and AI are powerful tools to identify health disparities, especially in the era of "big data." Hence, these techniques need to be constantly applied to identify populations at high risk and transmission patterns among communities and predict death rates, other possible abnormalities, and second-order impacts emanating from COVID-19 [ 111]. In addition, the accrued benefits of these innovative technologies would help public health experts avert unnecessary excesses in hospitalization and deaths from preventable diseases and asking the right questions [ 109, 110, 111, 112, 113, 114].

8. Conclusions and Prospects

Health disparity is not a new challenge, nor is it going away easily without systematic and intersectional treatment. The COVID-19 pandemic provides a stage to reveal and re-examine the health disparities we have been facing in all aspects of public health, including health outcomes (infection, hospitalization, fatality, and various health complications), health care, and disease prevention (vaccine). Evidence of health disparities across different population groups manifested through all critical stages of the COVID-19 pandemic, from susceptibility to exposure, infection, and recovery or death.

The intersectionality approach can be used as a lens to understand the power structure and dynamics in a society that are central to the various social, political, and infrastructural issues leading to or sustaining health disparity. Treatment for health disparities calls for systematic tackling of these issues that place minority groups in disadvantaged positions for health and health services. Identifying the disadvantaged populations and communities and developing and implementing community-centered and place-based policies and practices to elevate the disadvantaged is core to reducing and eventually eliminating health disparity.

The continuous development of GIS and data science has greatly enabled our capabilities to promptly integrate and process unprecedently voluminous data to reveal health issues and disease patterns of particular challenges to specific populations or communities. As a result, community-centered and population-targeting health prevention and health service initiatives can be designed accordingly. We have seen some of these technologies being adopted to guide and optimize the distribution and administration of COVID-19 vaccines [ 115]. For example, a study in Flint and Genesee County, Michigan, investigated disparities in COVID-19 death using a GIS-based approach. Racial and location disparities detection resulted in public health interventions leading to prevention, testing, treatment, and vaccination rollout. With the world being interconnected and knitted together more than ever before, there is no room to leave any communities or population groups out to suffer from health disparity. As it sings in the song We're All in This Together, "Together together, come on let's do this right."

Author Contributions

Conceptualization, A.I.; investigation, A.I.; resources, A.I.; writing-original draft preparation, A.I., K.B., and Y.L.; writing-review and editing, A.I., K.B., and Y.L.; visualization, A.I.; and supervision, Y.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Informed Consent Statement

Data Availability Statement

Data and literature were sourced from Google Scholar. The list of works is available in the References section.

Acknowledgments

Conflicts of Interest

The authors declare no conflict of interest.

Entry Link on the Encyclopedia Platform

References

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Figure 1. Cumulative confirmed global cases of COVID-19 (data source: www.Ourworldindata.org accessed 13 July 2021, Ritchie et al. [ 10]).

Figure 2. The intersection between structural inequality, public health, and data science.

Figure 3. COVID-19 deaths versus population density based on data from 22 January to 14 July; time 20:03 London time. (data source: www.ourworldindata.org/Coronavirus-death accessed 13 July 2021; Ritchie et al., [ 10]). Note: countries with a < 1 million population are excluded from the visualization.

Table 2. Common variable of interest, intersectionality framework, and data analytical techniques in COVID-19 research.

Study

Location

Health Outcomes

Intersectionality Framework

Common Variables of Interest

Data Analytical Techniques

Violence

Health Care

Public Policies

Mobility

Racial/Ethnic Heterogeneity

Sociodemographic

Environmental Injustice

Health Disparity

Geographic/Temporal Disparity

GIS/Spatial Statistics

AI, Machine, and Deep Learning

Aspatial

Iyanda et al., 2021

USA

COVID-19
case fatal ratio

x

x

x

x

GWR

Poisson

Bashir et al., 2020

Germany

COVID-19
confirmed cases,
deaths,
recoveries

x

x

wavelet transform coherence; correlation

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Health disparity is an unacceptable, unjust, or inequitable difference in health outcomes among different groups of people that affects access to optimal health care, as well as deterring it. Health disparity adversely affects disadvantaged subpopulations due to a higher incidence and prevalence of a particular disease or ill health.

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To rapidly monitor recent changes in mental health, the National Center for Health Statistics (NCHS) partnered with the Census Bureau on an experimental data system called the Household Pulse Survey. This 20-minute online survey was designed to complement the ability of the federal statistical system to rapidly respond and provide relevant information about the impact of the coronavirus pandemic in the U.S. Data collection began on April 23, 2020.

NCHS included questions to obtain information on health insurance coverage. These questions are similar to, but not exactly the same as, questions included on the National Health Interview Survey (NHIS). The NHIS, conducted by NCHS, is the source for high-quality data to monitor health insurance coverage in the U.S. Estimates of health insurance coverage based on the 2019 NHIS may be useful benchmarks for comparison with estimates from the Household Pulse Survey. In 2019, 14.5% of adults aged 18–64 were uninsured at the time of interview, 20.4% had public coverage, and 67.5% had private health insurance coverage.

Estimates on this page are derived from the Household Pulse Survey and show the percentage of adults aged 18-64 years who were uninsured at the time of the interview or had public or private health insurance coverage.

Use the drop-down menus to show data for selected indicators or categories. Select the buttons at the bottom of the dashboard to view national and state estimates. The data table may be scrolled horizontally and vertically to view additional estimates.

Technical Notes

Survey Questions

Respondents were asked, “Are you currently covered by any of the following types of health insurance or health coverage plans?”  They were then provided with a list of 8 possible sources of health care coverage and asked to mark yes or no to each.  The list consisted of:

Insurance through a current or former employer or union (through yourself or another family member)

Insurance purchased directly from an insurance company, including marketplace coverage (through yourself or another family member)

Medicare, for people 65 and older, or people with certain disabilities

Medicaid, Medical Assistance, or any kind of government-assistance plan for those with low incomes or a disability

TRICARE or other military health care

VA (including those who have ever used or enrolled for VA health care)

Indian Health Service

Other

Definitions

Health insurance coverage is categorized into three categories: private, public, and uninsured.  The private and public categories are not mutually exclusive.  Adults can be enrolled in both.

Private health insurance coverage includes adults with a) insurance through a current or former employer or union; b) insurance purchased directly from an insurance company, including marketplace coverage; or c) TRICARE or other military health care.

Public health plan coverage includes adults with a) Medicare; b) Medicaid; or c) VA health care.

Adults were classified as uninsured if they did not report any private health insurance coverage or public health plan coverage at the time of interview.  Adults were also classified as uninsured if they had only Indian Health Service coverage.

Estimation

Estimates on this page are based on responses from adults aged 18-64 years. Information about adults of all ages are available from the Census Bureau website: https://www.census.gov/householdpulsedata.

Adults with missing responses to all eight questions are not shown separately in the tables and are not included in the calculation of percentages. Adults with missing responses to some, but not all, of the questions were included in the calculation of percentages; if the response was missing, the adult was classified as not having that source of health care coverage.

Data Source

The U.S. Census Bureau, in collaboration with five federal agencies, launched the Household Pulse Survey to produce data on the social and economic impacts of Covid-19 on American households.  The Household Pulse Survey was designed to gauge the impact of the pandemic on employment status, consumer spending, food security, housing, education disruptions, and dimensions of physical and mental wellness.

The survey was designed to meet the goal of accurate and timely weekly estimates. It was conducted by an internet questionnaire, with invitations to participate sent by email and text message. The sample frame is the Census Bureau Master Address File Data. Housing units linked to one or more email addresses or cell phone numbers were randomly selected to participate, and one respondent from each housing unit was selected to respond for him or herself. Estimates are weighted to adjust for nonresponse and to match Census Bureau estimates of the population by age, sex, race and ethnicity, and educational attainment. All estimates shown meet the NCHS Data Presentation Standards for Proportions.

Response Rate and Sample Size

Limitations

The Household Pulse Survey is different from other surveys. NCHS, the Census Bureau, and other federal statistical agencies are considered the preeminent source of the nation’s most important benchmark surveys.  Many of these surveys have been in production for decades and provide valuable insight on health, social, and economic trends. However, the production of benchmark data requires a relatively long lead time, and personal interviews (face-to-face or telephone) require additional time.  While efforts are underway to introduce COVID-19 questions into these surveys, that process can take months, sometimes years, before data are made available.

The Household Pulse Survey is different:  It was designed to go into the field quickly, to be administered via the web, and to disseminate data in near real-time, providing data users with information they can use now to help ease the burden on American households and expedite post-pandemic recovery.  The Census Bureau is fielding the Household Pulse Survey as a demonstration project, with data released as part of its Experimental Statistical Products Series.

Confidence intervals included in the tables on this page only reflect the potential for sampling error.  Nonsampling errors can also occur and are more likely for surveys that are implemented quickly, achieve low response rates, and rely on online response.  Nonsampling errors for the Household Pulse Survey may include:

Measurement error: The respondent provides incorrect information, or an unclear survey question is misunderstood by the respondent. The Household Pulse Survey schedule offered only limited time for testing questions.

Coverage error: Individuals who otherwise would have been included in the survey frame were missed. The Household Pulse Survey only recruited households for which an email address or cell phone number could be identified.

Nonresponse error: Responses are not collected from all those in the sample or the respondent is unwilling to provide information. The response rate for the Household Pulse Survey was substantially lower than most federally sponsored surveys.

Processing error: Forms may be lost, data may be incorrectly keyed, coded, or recoded. The real-time dissemination of the Household Pulse Survey provided limited time to identify and fix processing errors.

For more information on nonresponse bias for the 2020 Household Pulse Survey, please visit https://www2.census.gov/programs-surveys/demo/technical-documentation/hhp/2020_HPS_NR_Bias_Report-final.pdf.

For more information on the Household Pulse Survey, please visit https://www.census.gov/householdpulsedata.

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Health Insurance Coverage - Household Pulse Survey - COVID-19

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To rapidly monitor recent changes in mental health, the National Center for Health Statistics (NCHS) partnered with the Census Bureau on an experimental data system called the Household Pulse Survey. This 20-minute online survey was designed to complement the ability of the federal statistical syste…

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Highlights

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C19VI was developed using machine learning on CDC socio-demographic and COVID-19 themes.

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'COVID-19 Impact Assessment' algorithm was developed using homogeneity and trend assessment.

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C19VI provides an accurate and reliable way to measure county level vulnerabilities in the US.

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Racial minorities and economically poor are disproportionately impacted by the pandemic.

Abstract

COVID-19 is now one of the most leading causes of death in the United States (US). Systemic health, social and economic disparities have put the minorities and economically poor communities at a higher risk than others. There is an immediate requirement to develop a reliable measure of county-level vulnerabilities that can capture the heterogeneity of vulnerable communities. This study reports a COVID-19 Vulnerability Index (C19VI) for identifying and mapping vulnerable counties. We proposed a Random Forest machine learning-based vulnerability model using CDC's sociodemographic and COVID-19-specific themes. An innovative 'COVID-19 Impact Assessment' algorithm was also developed for evaluating severity of the pandemic and to train the vulnerability model. Developed C19VI was statistically validated and compared with the CDC COVID-19 Community Vulnerability Index (CCVI). Finally, using C19VI and the census data, we explored racial inequalities and economic disparities in COVID-19 health outcomes. Our index indicates that 575 counties (45 million people) fall into the 'very high' vulnerability class, 765 counties (66 million people) in the 'high' vulnerability class, and 1435 counties (204 million people) in the 'moderate' or 'low' vulnerability class. Only 367 counties (20 million people) were found as 'very low' vulnerable areas. Furthermore, C19VI reveals that 524 counties with a racial minority population higher than 13% and 420 counties with poverty higher than 20% are in the 'very high' or 'high' vulnerability classes. The C19VI aims at helping public health officials and disaster management agencies to develop effective mitigation strategies especially for the disproportionately impacted communities.

View Abstract

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Using machine learning to develop a novel COVID-19 Vulnerability Index (C19VI)

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C19VI was developed using machine learning on CDC socio-demographic and COVID-19 themes. * 'COVID-19 Impact Assessment' algorithm was developed using homogeneity and trend assessment. * C19VI provides an accurate and reliable way to measure county level vulnerabilities in the US. * Racial minorities and economically poor are disproportionately impacted by the pandemic.

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Introduction

We all have been affected by the current COVID-19 pandemic. However, the impact of the pandemic and its consequences are felt differently depending on our status as individuals and as members of society. While some try to adapt to working online, homeschooling their children and ordering food via Instacart, others have no choice but to be exposed to the virus while keeping society functioning. Our different social identities and the social groups we belong to determine our inclusion within society and, by extension, our vulnerability to epidemics.

COVID-19 is killing people on a large scale. As of October 10, 2020, more than 7.7 million people across every state in the United States and its four territories had tested positive for COVID-19. According to the New York Times database, at least 213,876 people with the virus have died in the United States.1 However, these alarming numbers give us only half of the picture; a closer look at data by different social identities (such as class, gender, age, race, and medical history) shows that minorities have been disproportionally affected by the pandemic. These minorities in the United States are not having their right to health fulfilled.

According to the World Health Organization's report Closing the Gap in a Generation: Health Equity through Action on the Social Determinants of Health, "poor and unequal living conditions are the consequences of deeper structural conditions that together fashion the way societies are organized-poor social policies and programs, unfair economic arrangements, and bad politics."2 This toxic combination of factors as they play out during this time of crisis, and as early news on the effect of the COVID-19 pandemic pointed out, is disproportionately affecting African American communities in the United States. I recognize that the pandemic has had and is having devastating effects on other minorities as well, but space does not permit this essay to explore the impact on other minority groups.

Employing a human rights lens in this analysis helps us translate needs and social problems into rights, focusing our attention on the broader sociopolitical structural context as the cause of the social problems. Human rights highlight the inherent dignity and worth of all people, who are the primary rights-holders.3 Governments (and other social actors, such as corporations) are the duty-bearers, and as such have the obligation to respect, protect, and fulfill human rights.4 Human rights cannot be separated from the societal contexts in which they are recognized, claimed, enforced, and fulfilled. Specifically, social rights, which include the right to health, can become important tools for advancing people's citizenship and enhancing their ability to participate as active members of society.5 Such an understanding of social rights calls our attention to the concept of equality, which requires that we place a greater emphasis on "solidarity" and the "collective."6 Furthermore, in order to generate equality, solidarity, and social integration, the fulfillment of social rights is not optional.7 In order to fulfill social integration, social policies need to reflect a commitment to respect and protect the most vulnerable individuals and to create the conditions for the fulfillment of economic and social rights for all.

Disproportional impact of COVID-19 on African Americans

As noted by Samuel Dickman et al.:

economic inequality in the US has been increasing for decades and is now among the highest in developed countries ... As economic inequality in the US has deepened, so too has inequality in health. Both overall and government health spending are higher in the US than in other countries, yet inadequate insurance coverage, high-cost sharing by patients, and geographical barriers restrict access to care for many.8

For instance, according to the Kaiser Family Foundation, in 2018, 11.7% of African Americans in the United States had no health insurance, compared to 7.5% of whites.9

Prior to the Affordable Care Act-enacted into law in 2010-about 20% of African Americans were uninsured. This act helped lower the uninsured rate among nonelderly African Americans by more than one-third between 2013 and 2016, from 18.9% to 11.7%. However, even after the law's passage, African Americans have higher uninsured rates than whites (7.5%) and Asian Americans (6.3%).10 The uninsured are far more likely than the insured to forgo needed medical visits, tests, treatments, and medications because of cost.

As the COVID-19 virus made its way throughout the United States, testing kits were distributed equally among labs across the 50 states, without consideration of population density or actual needs for testing in those states. An opportunity to stop the spread of the virus during its early stages was missed, with serious consequences for many Americans. Although there is a dearth of race-disaggregated data on the number of people tested, the data that are available highlight African Americans' overall lack of access to testing. For example, in Kansas, as of June 27, according to the COVID Racial Data Tracker, out of 94,780 tests, only 4,854 were from black Americans and 50,070 were from whites. However, blacks make up almost a third of the state's COVID-19 deaths (59 of 208). And while in Illinois the total numbers of confirmed cases among blacks and whites were almost even, the test numbers show a different picture: 220,968 whites were tested, compared to only 78,650 blacks.11

Similarly, American Public Media reported on the COVID-19 mortality rate by race/ethnicity through July 21, 2020, including Washington, DC, and 45 states (see figure 1 ). These data, while showing an alarming death rate for all races, demonstrate how minorities are hit harder and how, among minority groups, the African American population in many states bears the brunt of the pandemic's health impact.

Approximately 97.9 out of every 100,000 African Americans have died from COVID-19, a mortality rate that is a third higher than that for Latinos (64.7 per 100,000), and more than double than that for whites (46.6 per 100,000) and Asians (40.4 per 100,000). The overrepresentation of African Americans among confirmed COVID-19 cases and number of deaths underscores the fact that the coronavirus pandemic, far from being an equalizer, is amplifying or even worsening existing social inequalities tied to race, class, and access to the health care system.

Considering how African Americans and other minorities are overrepresented among those getting infected and dying from COVID-19, experts recommend that more testing be done in minority communities and that more medical services be provided.12 Although the law requires insurers to cover testing for patients who go to their doctor's office or who visit urgent care or emergency rooms, patients are fearful of ending up with a bill if their visit does not result in a COVID test. Furthermore, minority patients who lack insurance or are underinsured are less likely to be tested for COVID-19, even when experiencing alarming symptoms. These inequitable outcomes suggest the importance of increasing the number of testing centers and contact tracing in communities where African Americans and other minorities reside; providing testing beyond symptomatic individuals; ensuring that high-risk communities receive more health care workers; strengthening social provision programs to address the immediate needs of this population (such as food security, housing, and access to medicines); and providing financial protection for currently uninsured workers.

Social determinants of health and the pandemic's impact on African Americans' health outcomes

In international human rights law, the right to health is a claim to a set of social arrangements-norms, institutions, laws, and enabling environment-that can best secure the enjoyment of this right. The International Covenant on Economic, Social and Cultural Rights sets out the core provision relating to the right to health under international law (article 12).13 The United Nations Committee on Economic, Social and Cultural Rights is the body responsible for interpreting the covenant.14 In 2000, the committee adopted a general comment on the right to health recognizing that the right to health is closely related to and dependent on the realization of other human rights.15 In addition, this general comment interprets the right to health as an inclusive right extending not only to timely and appropriate health care but also to the determinants of health.16 I will reflect on four determinants of health-racism and discrimination, poverty, residential segregation, and underlying medical conditions-that have a significant impact on the health outcomes of African Americans.

Racism and discrimination

In spite of growing interest in understanding the association between the social determinants of health and health outcomes, for a long time many academics, policy makers, elected officials, and others were reluctant to identify racism as one of the root causes of racial health inequities.17 To date, many of the studies conducted to investigate the effect of racism on health have focused mainly on interpersonal racial and ethnic discrimination, with comparatively less emphasis on investigating the health outcomes of structural racism.18 The latter involves interconnected institutions whose linkages are historically rooted and culturally reinforced.19 In the context of the COVID-19 pandemic, acts of discrimination are taking place in a variety of contexts (for example, social, political, and historical). In some ways, the pandemic has exposed existing racism and discrimination.

Poverty (low-wage jobs, insurance coverage, homelessness, and jails and prisons)

Data drawn from the 2018 Current Population Survey to assess the characteristics of low-income families by race and ethnicity shows that of the 7.5 million low-income families with children in the United States, 20.8% were black or African American (while their percentage of the population in 2018 was only 13.4%).20 Low-income racial and ethnic minorities tend to live in densely populated areas and multigenerational households. These living conditions make it difficult for low-income families to take necessary precautions for their safety and the safety of their loved ones on a regular basis.21 This fact becomes even more crucial during a pandemic.

Low-wage jobs. The types of work where people in some racial and ethnic groups are overrepresented can also contribute to their risk of getting sick with COVID-19. Nearly 40% of African American workers, more than seven million, are low-wage workers and have jobs that deny them even a single paid sick day. Workers without paid sick leave might be more likely to continue to work even when they are sick.22 This can increase workers' exposure to other workers who may be infected with the COVID-19 virus.

Similarly, the Centers for Disease Control has noted that many African Americans who hold low-wage but essential jobs (such as food service, public transit, and health care) are required to continue to interact with the public, despite outbreaks in their communities, which exposes them to higher risks of COVID-19 infection. According to the Centers for Disease Control, nearly a quarter of employed Hispanic and black or African American workers are employed in service industry jobs, compared to 16% of non-Hispanic whites. Blacks or African Americans make up 12% of all employed workers but account for 30% of licensed practical and licensed vocational nurses, who face significant exposure to the coronavirus.23

In 2018, 45% of low-wage workers relied on an employer for health insurance. This situation forces low-wage workers to continue to go to work even when they are not feeling well. Some employers allow their workers to be absent only when they test positive for COVID-19. Given the way the virus spreads, by the time a person knows they are infected, they have likely already infected many others in close contact with them both at home and at work.24

Homelessness. Staying home is not an option for the homeless. African Americans, despite making up just 13% of the US population, account for about 40% of the nation's homeless population, according to the Annual Homeless Assessment Report to Congress.25 Given that people experiencing homelessness often live in close quarters, have compromised immune systems, and are aging, they are exceptionally vulnerable to communicable diseases-including the coronavirus that causes COVID-19.

Jails and prisons. Nearly 2.2 million people are in US jails and prisons, the highest rate in the world. According to the US Bureau of Justice, in 2018, the imprisonment rate among black men was 5.8 times that of white men, while the imprisonment rate among black women was 1.8 times the rate among white women.26 This overrepresentation of African Americans in US jails and prisons is another indicator of the social and economic inequality affecting this population.

According to the Committee on Economic, Social and Cultural Rights' General Comment 14, "states are under the obligation to respect the right to health by, inter alia, refraining from denying or limiting equal access for all persons-including prisoners or detainees, minorities, asylum seekers and illegal immigrants-to preventive, curative, and palliative health services."27 Moreover, "states have an obligation to ensure medical care for prisoners at least equivalent to that available to the general population."28 However, there has been a very limited response to preventing transmission of the virus within detention facilities, which cannot achieve the physical distancing needed to effectively prevent the spread of COVID-19.29

Residential segregation

Segregation affects people's access to healthy foods and green space. It can also increase excess exposure to pollution and environmental hazards, which in turn increases the risk for diabetes and heart and kidney diseases.30 African Americans living in impoverished, segregated neighborhoods may live farther away from grocery stores, hospitals, and other medical facilities.31 These and other social and economic inequalities, more so than any genetic or biological predisposition, have also led to higher rates of African Americans contracting the coronavirus. To this effect, sociologist Robert Sampson states that the coronavirus is exposing class and race-based vulnerabilities. He refers to this factor as "toxic inequality," especially the clustering of COVID-19 cases by community, and reminds us that African Americans, even if they are at the same level of income or poverty as white Americans or Latino Americans, are much more likely to live in neighborhoods that have concentrated poverty, polluted environments, lead exposure, higher rates of incarceration, and higher rates of violence.32

Many of these factors lead to long-term health consequences. The pandemic is concentrating in urban areas with high population density, which are, for the most part, neighborhoods where marginalized and minority individuals live. In times of COVID-19, these concentrations place a high burden on the residents and on already stressed hospitals in these regions. Strategies most recommended to control the spread of COVID-19-social distancing and frequent hand washing-are not always practical for those who are incarcerated or for the millions who live in highly dense communities with precarious or insecure housing, poor sanitation, and limited access to clean water.

Underlying health conditions

African Americans have historically been disproportionately diagnosed with chronic diseases such as asthma, hypertension and diabetes-underlying conditions that may make COVID-19 more lethal. Perhaps there has never been a pandemic that has brought these disparities so vividly into focus.

Doctor Anthony Fauci, an immunologist who has been the director of the National Institute of Allergy and Infectious Diseases since 1984, has noted that "it is not that [African Americans] are getting infected more often. It's that when they do get infected, their underlying medical conditions ... wind them up in the ICU and ultimately give them a higher death rate."33

One of the highest risk factors for COVID-19-related death among African Americans is hypertension. A recent study by Khansa Ahmad et al. analyzed the correlation between poverty and cardiovascular diseases, an indicator of why so many black lives are lost in the current health crisis. The authors note that the American health care system has not yet been able to address the higher propensity of lower socioeconomic classes to suffer from cardiovascular disease.34 Besides having higher prevalence of chronic conditions compared to whites, African Americans experience higher death rates. These trends existed prior to COVID-19, but this pandemic has made them more visible and worrisome.

Addressing the impact of COVID-19 on African Americans: A human rights-based approach

The racially disparate death rate and socioeconomic impact of the COVID-19 pandemic and the discriminatory enforcement of pandemic-related restrictions stand in stark contrast to the United States' commitment to eliminate all forms of racial discrimination. In 1965, the United States signed the International Convention on the Elimination of All Forms of Racial Discrimination, which it ratified in 1994. Article 2 of the convention contains fundamental obligations of state parties, which are further elaborated in articles 5, 6, and 7.35 Article 2 of the convention stipulates that "each State Party shall take effective measures to review governmental, national and local policies, and to amend, rescind or nullify any laws and regulations which have the effect of creating or perpetuating racial discrimination wherever it exists" and that "each State Party shall prohibit and bring to an end, by all appropriate means, including legislation as required by circumstances, racial discrimination by any persons, group or organization."36

Perhaps this crisis will not only greatly affect the health of our most vulnerable community members but also focus public attention on their rights and safety-or lack thereof. Disparate COVID-19 mortality rates among the African American population reflect longstanding inequalities rooted in systemic and pervasive problems in the United States (for example, racism and the inadequacy of the country's health care system). As noted by Audrey Chapman, "the purpose of a human right is to frame public policies and private behaviors so as to protect and promote the human dignity and welfare of all members and groups within society, particularly those who are vulnerable and poor, and to effectively implement them."37 A deeper awareness of inequity and the role of social determinants demonstrates the importance of using right to health paradigms in response to the pandemic.

The Committee on Economic, Social and Cultural Rights has proposed some guidelines regarding states' obligation to fulfill economic and social rights: availability, accessibility, acceptability, and quality. These four interrelated elements are essential to the right to health. They serve as a framework to evaluate states' performance in relation to their obligation to fulfill these rights. In the context of this pandemic, it is worthwhile to raise the following questions: What can governments and nonstate actors do to avoid further marginalizing or stigmatizing this and other vulnerable populations? How can health justice and human rights-based approaches ground an effective response to the pandemic now and build a better world afterward? What can be done to ensure that responses to COVID-19 are respectful of the rights of African Americans? These questions demand targeted responses not just in treatment but also in prevention. The following are just some initial reflections:

First, we need to keep in mind that treating people with respect and human dignity is a fundamental obligation, and the first step in a health crisis. This includes the recognition of the inherent dignity of people, the right to self-determination, and equality for all individuals. A commitment to cure and prevent COVID-19 infections must be accompanied by a renewed commitment to restore justice and equity.

Second, we need to strike a balance between mitigation strategies and the protection of civil liberties, without destroying the economy and material supports of society, especially as they relate to minorities and vulnerable populations. As stated in the Siracusa Principles, "[state restrictions] are only justified when they support a legitimate aim and are: provided for by law, strictly necessary, proportionate, of limited duration, and subject to review against abusive applications."38 Therefore, decisions about individual and collective isolation and quarantine must follow standards of fair and equal treatment and avoid stigma and discrimination against individuals or groups. Vulnerable populations require direct consideration with regard to the development of policies that can also protect and secure their inalienable rights.

Third, long-term solutions require properly identifying and addressing the underlying obstacles to the fulfillment of the right to health, particularly as they affect the most vulnerable. For example, we need to design policies aimed at providing universal health coverage, paid family leave, and sick leave. We need to reduce food insecurity, provide housing, and ensure that our actions protect the climate. Moreover, we need to strengthen mental health and substance abuse services, since this pandemic is affecting people's mental health and exacerbating ongoing issues with mental health and chemical dependency. As noted earlier, violations of the human rights principles of equality and nondiscrimination were already present in US society prior to the pandemic. However, the pandemic has caused "an unprecedented combination of adversities which presents a serious threat to the mental health of entire populations, and especially to groups in vulnerable situations."39 As Dainius Pūras has noted, "the best way to promote good mental health is to invest in protective environments in all settings."40 These actions should take place as we engage in thoughtful conversations that allow us to assess the situation, to plan and implement necessary interventions, and to evaluate their effectiveness.

Finally, it is important that we collect meaningful, systematic, and disaggregated data by race, age, gender, and class. Such data are useful not only for promoting public trust but for understanding the full impact of this pandemic and how different systems of inequality intersect, affecting the lived experiences of minority groups and beyond. It is also important that such data be made widely available, so as to enhance public awareness of the problem and inform interventions and public policies.

Conclusion

In 1966, Dr. Martin Luther King Jr. said, "Of all forms of inequality, injustice in health is the most shocking and inhuman."41 More than 54 years later, African Americans still suffer from injustices that are at the basis of income and health disparities. We know from previous experiences that epidemics place increased demands on scarce resources and enormous stress on social and economic systems.

A deeper understanding of the social determinants of health in the context of the current crisis, and of the role that these factors play in mediating the impact of the COVID-19 pandemic on African Americans' health outcomes, increases our awareness of the indivisibility of all human rights and the collective dimension of the right to health. We need a more explicit equity agenda that encompasses both formal and substantive equality.42 Besides nondiscrimination and equality, participation and accountability are equally crucial.

Unfortunately, as suggested by the limited available data, African American communities and other minorities in the United States are bearing the brunt of the current pandemic. The COVID-19 crisis has served to unmask higher vulnerabilities and exposure among people of color. A thorough reflection on how to close this gap needs to start immediately. Given that the COVID-19 pandemic is more than just a health crisis-it is disrupting and affecting every aspect of life (including family life, education, finances, and agricultural production)-it requires a multisectoral approach. We need to build stronger partnerships among the health care sector and other social and economic sectors. Working collaboratively to address the many interconnected issues that have emerged or become visible during this pandemic-particularly as they affect marginalized and vulnerable populations-offers a more effective strategy.

Moreover, as Delan Devakumar et al. have noted:

the strength of a healthcare system is inseparable from broader social systems that surround it. Health protection relies not only on a well-functioning health system with universal coverage, which the US could highly benefit from, but also on social inclusion, justice, and solidarity. In the absence of these factors, inequalities are magnified and scapegoating persists, with discrimination remaining long after.43

This current public health crisis demonstrates that we are all interconnected and that our well-being is contingent on that of others. A renewed and healthy society is possible only if governments and public authorities commit to reducing vulnerability and the impact of ill-health by taking steps to respect, protect, and fulfill the right to health.44 It requires that government and nongovernment actors establish policies and programs that promote the right to health in practice.45 It calls for a shared commitment to justice and equality for all.

References

6. Blau. J. R. "Human rights: What the United States might learn from the rest of the world and, yes, from American sociology," Sociological Forum. 2016;31(4):1126-1139. [ Google Scholar]

7. Young and Sen (see note 6).

8. Dickman S., Himmelstein D., Woolhandler S. "Inequality and the health-care system in the USA," Lancet. 2017;389(10077):1431. [[ PubMed] Google Scholar]

14. Ibid; Hertel and Libal (see note 3).

17. Bailey Z., Krieger N., Agénor M. et al. "Structural racism and health inequities in the USA: Evidence and interventions," Lancet. 2017;389(10077):1453-1463. [[ PubMed] Google Scholar]

24. Artega et al. (see note 9).

27. United Nations Committee on Economic. 2000. Social and Cultural Rights, General Comment No. 14, The Right to the Highest Attainable Standard of Health, UN Doc. E/C.12/2000/4.

31. Sampson R. "The neighborhood context of well-being," Perspectives in Biology and Medicine. 2003;46(3):S53-S64. Ibid. [[ PubMed] Google Scholar]

36. International Convention on the Elimination of All Forms of Racial Discrimination, G. A. Res. 2106 (XX) (1965), art. 2.

38. Sun N. "Applying Siracusa: A call for a general comment on public health emergencies," Health and Human Rights Journal. (April 23, 2020). [ PMC free article] [ PubMed]

39. Pūras D. "COVID-19 and mental health: Challenges ahead demand changes," Health and Human Rights Journal. (May 14, 2020).

42. Chapman (see note 35).

44. World Health Organization (see note 12).

Articles from Health and Human Rights are provided here courtesy of Harvard University Press & François-Xavier Bagnoud Center for Health and Human Rights

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The Disproportional Impact of COVID-19 on African Americans

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We all have been affected by the current COVID-19 pandemic. However, the impact of the pandemic and its consequences are felt differently depending on our status as individuals and as members of society. While some try to adapt to working online, homeschooling their children and ordering food via Instacart, others have no choice but to be exposed to the virus while keeping society functioning.

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INTRODUCTION

In the United States, health equity and equal opportunity are inextricably linked, and the burdens of disease and poor health and the benefits of wellness and good health are inequitably distributed among groups of people.

Although biology, genetics, and individual behaviors play a role in these differences, many health outcomes are more substantially affected by social, economic, and environmental factors. Understanding the social determinants of health requires a shift toward a more "upstream" perspective-that is, the conditions that constitute the context in which an individual's behaviors are shaped. To put this more simply, Keyes and Galea (2016) describe the relationship between an individual and the conditions in which one lives using the metaphor of a fishbowl. If the bowl in which a fish lives is dirty, or the glass is cracked and the water is leaking, the fish will never reach its full health potential, despite any individual effort. Although the life of a person is clearly more complex than that of a fish, this metaphor illustrates the futility of only addressing individual behaviors without considering the context. People inhabit environments shaped by policies, forces, and actions that influence their individual choices and behaviors over a lifetime and over generations. Community-wide and national problems like poverty, unemployment, poor education, inadequate housing, poor public transportation, exposure to violence, and neighborhood deterioration (social or physical) are among the factors that shape people's health, and they do so in unequal ways, thus contributing to health inequities. The historic and ongoing interplay of structures, policies, norms, and demographic/geographic patterns shapes the life of every individual across the country. These factors are not intractable, and inequities in these factors can be mitigated by policies and community action in powerful ways (see Chapter 3 for a discussion of the evidence). Community assets can be built, leveraged, and modified to create a context to achieve health equity.

People are heavily influenced by the communities they work and live in, and the diverse actors that make up the community ecosystem can be powerful producers of health and well-being. Therefore, this report focuses on the promise of communities to create opportunities for their members to achieve their full health potential. By showcasing many creative, forward-looking, and bold community-led solutions for achieving health equity, this report aims to provide a new narrative about health in the United States. In addition to actors in communities, the report examines other elements that address the structures, policies, and norms needed to promote health equity.

DEFINING HEALTH EQUITY

This report makes frequent reference to a number of terms with meanings that vary depending on the context and the community of users. Such terms include "disparities," "inequities," "equity," "racism," and "bias," and they are defined in the glossary of key terms and when first introduced in the report.

It is difficult to fully separate the concepts of equity and equality because they are intertwined. Different fields have used varying terminology in legal, public health, government, and other contexts. This report uses the term "health equity" by applying the term equity to the field of public health. Health equity is the state in which everyone has the opportunity to attain full health potential and no one is disadvantaged from achieving this potential because of social position or any other socially defined circumstance. In this report promoting health equity means creating the conditions where individuals and communities have what they need to enjoy full, healthy lives. Health equity requires focused and sustained societal efforts to confront historical and contemporary injustices and eliminate health disparities ( Brennan Ramirez et al., 2008; HHS, n.d.). Health disparities are differences that exist among specific population groups in the attainment of full health potential and in incidence, prevalence, mortality, and burden of disease and other adverse health conditions ( NIH, 2010), and they stem from systematic differences-that are preventable and unjust-among groups and communities occupying unequal positions in society ( Graham, 2004).

As discussed later in this chapter, studies of health inequities have focused largely on health disparities across racial and ethnic populations. Although such studies have uncovered patterns of discrimination and inequitable health outcomes, enlarging this work to assess the effects of poverty, unemployment, toxic stress, and the many secondary unintended consequences (e.g., drug use and violence) for minority and other disproportionately impacted populations is needed. It is well documented that low socioeconomic status (SES) hampers an individual's ability to achieve optimal health by limiting access to health-preserving resources ( Williams and Purdie-Vaughns, 2015; Woolf and Braveman, 2011). However, SES does not fully explain health disparities based on race and ethnicity, sexual orientation and gender identity ( Williams and Purdie-Vaughns, 2015).

In the following sections, the nature and implications of disparities on three key health indicators and for health care are discussed. This discussion is followed by a brief introduction to the social determinants of health and the impacts of health inequities on society. Next, the changing social and environmental context and the role of communities in addressing health inequity are described. Finally, this chapter highlights the ongoing support for accelerating the progress to achieve health equity before providing an overview of the rest of the report.

DISPARITIES IN HEALTH OUTCOMES

The existence of racial and ethnic disparities in morbidity, mortality, and many indicators of health for African Americans, Native Americans, Hispanics, and Asians/Pacific Islanders was first acknowledged by the federal government in the 1985 Report of the Secretary's Task Force on Black and Minority Health ( Heckler, 1985). Since then, research has sought to identify additional disparities and explain the mechanisms by which these disparities occur.

Three indicators provide summary information about the overall health of a population or subpopulation: infant mortality, age-adjusted death rates, and life expectancy. The United States ranks lower than most peer nations on these indicators; moreover, racial and ethnic disparities exist in quality and length of life among U.S. residents. The failure to address growing income inequality, along with health inequities by race and ethnicity, contributes to the United States' low health ranking among peer nations ( Davis et al., 2014).

Infant mortality rates reflect the number of infants in a population who die before their first birthday per 1,000 live births. U.S. infant mortality rates have decreased since 2005 for the overall population and within each racial and ethnic group; however, sharp racial and ethnic disparities persist. In 2013, as in previous years, the infant mortality rate among African Americans (11.1 per 1,000 live births) was double the rate among whites (5.06 per 1,000 live births) ( Mathews et al., 2015). American Indians/Alaska Natives and Puerto Ricans also experienced higher infant mortality rates (of 7.61 and 5.93 per 1,000 live births, respectively) than whites ( Mathews et al., 2015). Infant mortality rates among Asians/Pacific Islanders and non-Puerto Rican Hispanics were lower than those of whites. If white America and black America were two separate nations, white America's infant mortality rate would rank 49th in the world, while black America's would be ranked 95th out of 224 nations listed by the U.S. Central Intelligence Agency's World Factbook, following Botswana, Sri Lanka, the United Arab Emirates, and Turks and Caicos Islands ( WHO, 2015).

Life expectancy, the average number of years a person is expected to live based on current mortality rates (typically reported as life expectancy at birth or average number of years a newborn would be expected to live), captures the degree to which all of the individual-level socioeconomic, environmental, and health care-related resources in a society enable members of that society to achieve a long and healthy life. Better living conditions and better access to health care-related resources throughout the lifespan extend longevity. From 1980 to 2014, U.S. life expectancy (at birth) increased by approximately 6 years for males, reaching 76.4 years, and increased 3 years for females, reaching 81.2 years. Racial and ethnic disparities decreased, but they were not eliminated. In 2014, the life expectancy for African American males was 72.0 years, while that for white males was 76.5 years and that for Latino males was 79.2 years. In the same year, life expectancy was 78.1 years for African American females, 81.1 years for white females, and 84.0 years for Latina females ( Arias, 2016). Childhood obesity, which disproportionately affects Hispanic and African American youth ( Asieba, 2016; Taveras et al., 2013), has been projected to reduce the steady increase in overall life expectancy in this century ( Olshansky et al., 2005).

Age-adjusted mortality rates capture population deaths due to all causes, and especially those not due to old age. High death rates suggest that a population not only faces serious threats to health but also lacks the resources needed to address them. The 2012 to 2014 U.S. age-adjusted rates ranged considerably. By race and ethnicity, they ranged from 399.8 per 100,000 people among Asian/Pacific Islanders to 858.1 among African Americans. From 2007 to 2009, the rate was even higher (943.0 per 100,000) among American Indian/Alaska Natives ( IHS, 2016). Although the overall death rate among whites (729.1 per 100,000) was substantially lower than the rate among African Americans, it exceeded that of Asian/Pacific Islanders and American Indian/Alaska Natives ( NCHS, 2014) for causes including liver disease, suicide, and unintentional injury ( Kochanek et al., 2016). Looking at more distal causes, research indicates that age-adjusted death rates among whites are higher for those who live in rural settings ( Caldwell et al., 2016) and have lower incomes ( HRSA, 2015).

The patterns of health disparities among immigrants and their children that emerge from available data are not straightforward. More than half of U.S. citizens of Asian/Pacific Islander and Hispanic background come from families that emigrated to the United States since 1965. Considerable socioeconomic and cultural heterogeneity exists within these groups, and some subpopulations (e.g., the Hmong population of Asian descent) experience particularly severe health disparities ( Cho and Hummer, 2001; de Souza and Anand, 2014; Vang et al., 2015). However, recent immigrant status has also shown positive health impact in some populations ( Hummer et al., 2007; Lee et al., 2013; Markides and Coreil, 1986).

Along with race and ethnicity, sexual orientation and gender identity have emerged as important factors in the study of health disparities. Recent epidemiologic surveys have attempted to comprehensively assess the physical and mental health of lesbian, gay, bisexual, and transgender (LGBT) persons ( Hsieh and Ruther, 2016; IOM, 2011). The available evidence shows that the LGBT population does experience health disparities and that the disparities are exacerbated for those who hold multiple minority statuses: this "intersectional" perspective describes the recognition that when multiple identities intersect, they represent overlapping inequalities or types of disadvantage ( IOM, 2011). Thus, LGBT persons who are also racial/ethnic minorities have worse outcomes than do white LGBT individuals ( Hsieh and Ruther, 2016).

Health Care

It is becoming clearer that health insurance coverage alone will not address health disparities associated with race, ethnicity, SES, and geography ( Kenney and Huntress, 2012; Ubri and Artiga, 2016). The Patient Protection and Affordable Care Act (ACA), passed in 2010, has accelerated progress toward improved health equity by expanding health insurance coverage to about 20 million Americans ( Uberoi et al., 2016). However, challenges remain in fully addressing health care inequity, including policy hurdles affecting subgroups of the population (e.g., lack of coverage for some immigrants and asylum seekers, or those subject to Deferred Action for Childhood Arrivals) ( HealthCare.gov, n.d.); limited system capacity or competence to care for some populations, such as LGBT persons (e.g., newly covered partners of insured LGBT individuals); and the lack of health data to monitor the health needs of some populations (e.g., for American Indians, of whom approximately 20 percent live on rural reservations) ( Kruse et al., 2016).

Merely increasing the availability of health care services does not necessarily reduce health care disparities. Consider how the availability of effective antiretroviral therapies has not reduced the rate of acquired immune deficiency syndrome (AIDS) equally across groups. In the U.S. context, the progression to AIDS may signal a failure to access treatment in a timely and appropriate manner as indicated by racial and ethnic trends that have been followed since the beginning of the epidemic, as shown in Figure 1-1.

Though AIDS diagnoses have decreased over time for all groups since the introduction of antiretroviral therapy, the proportion of diagnoses among whites has decreased substantially, while the percentage has increased for other groups, and most substantially for African Americans. Another striking example is found in the domain of clinical research, where clinical trials of drugs and devices are not always carried out in diverse populations; therefore, the outcomes of trials may be biased toward the populations studied and fail to account for cultural or other factors that may influence effectiveness ( George et al., 2014).

From a policy standpoint, the ACA has achieved its primary goal: the proportion of people who lack health insurance is lower than it has ever been. In the 29 states that have to date elected to participate in the ACA, Medicaid expansion has resulted in more than 10 million low-income individuals now being insured; furthermore, the associated reduction of cost-shifting for uncompensated care has benefited hospital budgets, particularly for disproportionate share hospitals ( CMS, 2016; Cunningham et al., 2015). However, the costs of care for these patients are often greater than expected, and Medicaid's reimbursement rates are a small fraction of the reimbursement rates from commercial payers; therefore, cost-shifting persists. The ACA has increased the availability of outpatient care for low-income persons through increased funding for the operation, expansion, and construction of community health centers.

Challenges remain, however. As noted above, access to insurance does not directly translate into health equity. The traditional fee-for-service system persists and influences utilization patterns more heavily than patient need or evidence-based practice guidelines ( The National Commission on Physician Payment Reform, 2013; Page, 2013; Robinson, 2001). Patients need only to receive the care they want and need. Also, there is still a subset of people without adequate insurance, and some commercial payers are leaving the exchanges, premiums are on the rise again, and pharmaceuticals and specialty drugs are increasing in complexity and pricing, untouched by the ACA.

A significant group of workers earn too much to qualify for Medicaid, but too little to afford health insurance. Also, the law does not address the needs of undocumented residents who are among the poorest people in the United States. Nearly one-third of all noncitizen immigrants lack insurance ( Barry-Jester and Casselman, 2015). Underutilization of health care services among subpopulations of Hispanic and Asian immigrants has been documented ( Alegria et al., 2006). The reasons for underutilization are complex and include an inability to speak the language, differences in the circumstances of immigration (e.g., refugees versus recruited professionals), and the fear of inadvertently outing family members who are in the country without documentation ( Ortega et al., 2015).

Because the law is still relatively new, researchers are investigating its outcomes, but there are concerns that some lower-income working class individuals remain underinsured but have had more cost of health care shifted to them ( Saloner et al., 2014), and the variations in use and cost persist.

SOCIAL DETERMINANTS OF HEALTH

Although most of the research being conducted at the time of the Heckler Report sought to explain how behavioral and other individual-level factors contribute to health and health care disparities, the evidence accrued since 1985 has led the field toward examining the social, environmental, economic, and cultural determinants of health. These determinants are the conditions in which one lives, learns, works, plays, worships, and ages, and these conditions are shaped by historical and contemporary policies, law, governance, investments, culture, and norms. Addressing the root causes of health inequities, such as the social determinants of health, is important in part to help enable sustainable interventions by engaging multiple sectors and addressing multiple health outcomes simultaneously. The solutions highlighted in this report recognize that national and state leadership are important to effect change in these determinants, but the report specifically addresses these interrelated determinants at the community level (see Chapter 3 for a detailed discussion of the root causes of health disparities, including the social determinants of health).

IMPACTS OF HEALTH INEQUITY IN THE UNITED STATES

Although moral arguments to promote health equity exist advancing progress toward health equity could produce economic, national security, and other benefits for the nation. The premise that social mobility, opportunity to succeed with hard work, and opportunity to achieve prosperity exist is fundamental to the American Dream ( Carr and Wiemers, 2016). However, recent literature demonstrates that worsening social, economic, and environmental factors are affecting health in serious ways that compromise opportunity for all ( Chetty et al., 2016; Rudolph et al., 2015; Woolf et al., 2015). Health is more than life expectancy, infant health, and fitness and nutrition-it is the ability to lead a full and productive life. Additionally, an opportunity to achieve good health is crucial to U.S. democracy, national security, and economic vitality, as described below. The burden of disparities lowers the nation's overall health status and its ranking relative to other nations.

Political and Economic Impacts of Health Disparities

In addition to the dollar cost of health care, because health inequities contribute to overall poor health for the nation, health inequity has consequences for the U.S. economy, national security, business workforce, and public finances.

CHANGING SOCIAL AND ENVIRONMENTAL CONTEXT

There are significant changes in the sociocultural (including demographic, economic, and political) and environmental landscapes affecting health disparities and the determinants of health.

The changing economic context is characterized by growing income inequality. According to an analysis performed by the Institute for Policy Studies, the income gap between higher- and lower-income individuals has increased substantially over the past 30 years, to the point that those with incomes in the top 10 percent average nine times the income of those in the bottom 90 percent, and those with incomes in the top 0.1 percent have incomes that are more than 184 times that of the bottom 90 percent ( Asante-Muhammad et al., 2016). This income inequality has a remarkable impact on individual health, as higher-income earners have longer life expectancies than lower-income earners in every region of the United States. There are significant economic changes that affect other social determinants as well. For instance, urban centers across the country are dealing with shifting demographics that can result in the displacement of long-term residents. The economic advantages of changing land value due to these shifts largely benefit those who are already in higher-income brackets. In contrast, dislocated low-income households face overwhelming challenges in efforts to find new housing with access to high-quality schools, jobs, and other essential social services that are vital to optimal health. The lasting effects of the 2008 recession and the resulting displacement of vulnerable populations exacerbated the impact on both their health and their economic well-being-resulting in greater income inequality and wealth inequality ( Smeeding, 2012).

Recent changes in U.S. demographics underscore the urgency of finding ways to attain health equity. For example, from 2000 to 2010 the African American population increased by 11 percent ( Rastogi et al., 2010), and the Hispanic population increased by 43 percent ( Ennis et al., 2011), while the white population increased by only 1.2 percent ( Hixson et al., 2011). By 2040 the number of U.S. counties in which the majority of the population is comprised of people of color is expected to more than double; those counties will then represent about one-third of the United States ( Frey, 2015). Without significant and fundamental policy changes, these changes in the racial and ethnic composition of U.S. communities can be expected to further widen health inequities associated with race and class. Disparities in health, income, and education have also all been increasing over time (see Chapters 2 and 3 for more information).

Climate change will increasingly affect health. In 2015, at the United Nations Climate Change Conference-also known as COP 21-multiple nations, including the United States, came together to create an agreement to combat climate change and attempt to prevent the global temperature from rising more than 2 degrees Celsius. Health representatives played an integral role in the conference, as health is and will continue to be significantly affected by climate change. Climate change is happening in all areas, but its impacts are not distributed equally. It exacerbates vulnerabilities in communities that are already disproportionately affected by preexisting social, economic, and environmental factors. Extreme weather events are one of the many examples of the ways in which climate change will impact health. Hurricane Katrina was not necessarily the direct result of climate change. However, it offers many important lessons on mitigating risk and increasing resiliency in making plans to help the entire population, especially the most vulnerable. Although there is a risk that climate change could worsen health inequities, there is also great opportunity to integrate efforts to promote health into mitigation and adaptation efforts to support more resilient, healthy, and equitable communities ( Rudolph et al., 2015).

Finally, recent events involving race and law enforcement relations have elucidated systematically unequal treatment in the criminal justice system ( The President's Task Force on 21st Century Policing, 2015). Moreover, there is a growing and bipartisan recognition that mass incarceration, which affects individuals of color disproportionately, plays a major role in the breakdown of families and communities, constitutes an unsustainable use of taxpayer dollars, and leads, in connection with the larger policy milieu in both the private and the public sector, to poor employment prospects and voter disenfranchisement ( Clear, 2008; NRC, 2014). These current realities serve as reminders that the vision of an equitable society will be challenging to reach, but community-driven solutions, such as those this report highlights, can help move in that direction on a local scale.

WHY COMMUNITIES?

Individuals and families are part of communities, and the role of communities is crucial to promoting health equity for several reasons. First, as discussed earlier, medical interventions are insufficient to address health equity, and behavioral health promotion continues to show little success in reducing disparities ( Baum and Fisher, 2014). Community-based and -driven efforts are needed to alter environmental, socioeconomic, and cultural conditions in ways that promote health equity. Community health refers to the overall well-being of a community at all levels (including the individuals within the community and the physical setting), which may involve multi-sector and multidisciplinary collaborative approaches to optimizing the health and quality of life of all persons who live, work, or are otherwise active in a defined community ( Goodman et al., 2014). A healthy community is the foundation for achieving all other goals, as it is essential for a productive society. (For example, a community with a healthy workforce has a good base upon which to build its economy, and healthier students are more equipped to learn and be successful academically.) Furthermore, communities differ in the local quality and availability of health care providers, the affordability and quality of housing, employment opportunities, transportation systems, the availability of parks, green space, and other aspects of the physical environment. Communities are uniquely positioned to drive solutions tailored to their needs that target the multiple determinants of health.

MOMENTUM FOR ACHIEVING HEALTH EQUITY

There is a clear urgency for the nation to fully address health inequity. An analysis of current trends provides evidence of persistent health inequity, but there are reasons for optimism. Turning the tide is not only possible, it is imperative; many organizations in the public and private sectors have recognized this, making health equity an explicit or implicit priority. These organizations span the sectors of finance, philanthropy, public health, community development, academia, and beyond. Local, regional, and state governments have also taken on issues essential to achieving health equity. For example, the Federal Reserve Bank and community development financial institutions are engaging in improving community development, employment, and housing-which drive health improvement-and they are making investments that expand access to healthy and affordable foods and neighborhoods with open space to promote physical activity and community safety ( Andrews and Erickson, 2012). Health equity is a guiding priority for the American Public Health Association in its initiative to make the United States the healthiest nation in a generation, with 2030 as a goalpost ( APHA, n.d.). In 2016 the Association of State and Territorial Health Officials' President's Challenge is to "Advance health equity and optimal health for all," and "Cultivating a culture of health equity" was the theme for the National Association of County and City Health Officials' annual meeting. Numerous states, including California, Colorado, Massachusetts, Pennsylvania, and Wyoming, have created statewide offices of health equity that work in collaboration with other agencies and departments to inform policies that promote health equity. Health equity has become central to the goals of some of the nation's largest philanthropic organizations, including The California Endowment, Ford Foundation, Kresge, and Kellogg. Advancing health equity is at the core of the Robert Wood Johnson Foundation's (RWJF's) new push for a culture of health ( RWJF, 2015). The federal government is investing heavily in health equity as well, and it recently established a National Institutes of Health research program to address health disparities in chronic disease as well as the National Partnership for Action to End Health Disparities. These investments seek to "transform lives and places for disinvested people" ( The Housing Fund, 2015), and at their core they are investments to create opportunity for all to achieve optimal health.

Conclusion 1-1: The persistent state of health disparities and health inequity in the United States has profound implications for the country's overall health standing, economic vitality, and national security. Thus, addressing health inequities is a critical need that requires this issue to be among our nation's foremost priorities.

ABOUT THIS REPORT

RWJF, as part of its Culture of Health Initiative, asked the Health and Medicine Division of the National Academies of Sciences, Engineering, and Medicine to help delineate the causes of and the solutions to health inequities in the United States. The charge to the committee is provided below (see Box 1-1 for the full statement of task). To respond to the charge, the Committee on Community-Based Solutions to Promote Health Equity in the United States was formed.

The focus of this report is on what communities can do to promote health equity and on the broader policy context and contributions of stakeholders that can support communities. In addition to the root causes and structural barriers that need to be overcome, the committee also examined levers and policies to support change, some of which span national, state, regional, and other contexts for the work of communities. To address its charge, the committee reviewed examples of community efforts across the country and was inspired by how these communities are rising to the challenge to address the difficult challenges and barriers to health and well-being.

Culture of Health Lens

RWJF defines a culture of health broadly "as one in which good health and well-being flourish across geographic, demographic, and social sectors; fostering healthy equitable communities guides public and private decision making; and everyone has the opportunity to make choices that lead to healthy lifestyles" ( RWJF, n.d.). RWJF also says that "the exact definition of a culture of health can look very different to different people. A national culture of health must embrace a wide variety of beliefs, customs and values. Ultimately it will be as diverse and multifaceted as the population it serves" ( RWJF, n.d.). The culture of health framework was developed by the foundation in collaboration with RAND Corporation through a combination of literature review and structured discussions with stakeholders ( Chandra et al., 2016). The framework includes four action areas that are interdependent-none can be achieved alone ( Plough and Chandra, 2016). The four action areas are

1.

Making health a shared value

2.

Fostering cross-sector collaboration to improve well-being

3.

Creating healthier, more equitable communities

4.

Strengthening the integration of health systems and services

The committee used the framework as a guide for this report and adapted it to apply specifically to its statement of task and at the community level.

The committee also referred to the ecological model illustrated in the 2003 Institute of Medicine (IOM) report The Future of the Public's Health in the 21st Century. This figure shows the multiple determinants of health, beginning with an individual's biology (and the biology of diseases) at the center, followed by individual behavior, and in the outermost layer, the highest level of social, economic, cultural, health, and environmental conditions and policy ( IOM, 2003). The committee was charged with examining community-based solutions, and it developed a simple model to show what it concluded are three important elements of community-based efforts to promote health equity. The community-based level of intervention is situated in the second and third outermost circles of Figure 1-2 (i.e., social, family, and community networks; living and working conditions). As Figure 1-2 clearly indicates, community effort is necessary, but it is not a sufficient contributor to population health and, by extension, health equity. The outermost ring-the broad milieu of social, economic, and environmental conditions and policies-is crucial to support community-level efforts.

Report Conceptual Model

Figure 1-3 is a conceptual model that grounds the committee's report. The model adapts elements of the Culture of Health Action Framework ( RWJF, 2015) and the Prevention Institute's Systems Framework to Achieve an Equitable Culture of Health ( Prevention Institute, 2016). The model applies the culture of health lens to the committee's understanding of the underlying causes and conditions of health inequity in addition to the community-based solutions that promote health equity.

The model begins with the outer circle and background as the context in which health inequities and community-driven solutions exist. The "socioeconomic and political context" was adapted from the World Health Organization Conceptual Framework for Action on the Social Determinants of Health ( WHO, 2010) and encompasses policies, law, governance, and culture. In the report conceptual model, this socioeconomic and political context includes structural inequities and biases that are produced along the axes of race, gender, class, sexual orientation, and other social domains. These inequities are manifested in systematic disadvantages that lead to inequitable access to or experience of the determinants of health. The committee adapted the determinants of health identified by the Achieving Health Equity Team at the RWJF, separating the social and physical environments, and adding transportation. Although the framework incorporates transportation as part of the "physical and social environment," transportation is vital to many areas of health (e.g., the ability to travel to health care facilities, community events, accessing jobs) and, alternatively, can have detrimental impacts on health (e.g., air pollution, unintentional injuries), and thus it has been highlighted in the model. (See Chapter 3 for a more detailed description of each determinant of health and the ways in which they affect health and well-being.)

The committee adapted two of the Culture of Health Action Framework Action Areas for community-level solutions: "Making health equity a shared vision and value" and "Fostering multi-sector collaboration." Based on the committee's information-gathering sessions, relevant literature, and committee deliberations, the committee also identified a third action area of importance for the framework when proposing solutions at the community level: "Increasing community capacity to shape outcomes." This is a process that has emerged as essential for communities to have the power to address inequities and to sustain their efforts. (These three elements are discussed in more detail in Chapter 4.) To align with its statement of task, the committee incorporated equity at the community level in its conceptual model. The Culture of Health action area "Creating healthier, more equitable communities" has been incorporated into the conceptual model as the outcome of the community-driven solutions in the center of the diagram.

The community examples featured in this report will highlight solutions that have been implemented at the community level to target one or more of the nine determinants of health using the processes identified in the conceptual model (see Chapter 5). By making health equity a shared vision and value, increasing community capacity to shape outcomes, and fostering multi-sector collaboration, these solutions foster equal opportunity for health, which is the foundation for a vibrant, healthy community.

Community Health

This report does not focus on interventions that target a single health condition, but on community-level changes and impacts on health through a holistic lens. The development of a community-based solution is a community-driven process that includes fair participation by the community in the decision-making process, in which all people have access to the information necessary to understand the matter and the process and which produces outcomes that the people accept as fair, equitable, and nondiscriminatory in the context of addressing health disparities. Therefore, the terms "community-driven solutions" and "community-based solutions" will be used interchangeably for the remainder of this report (see Box 1-2 for definitions). The importance of communities and their role and potential to promote health equity is discussed in Chapter 4.

Overview of the Study Process

To address its charge, the committee gathered information through a variety of means. It held three information-gathering meetings that were open to the public and webcast live. The first, held in January 2016, focused on obtaining information on health disparities and their root causes, including an overview from the report sponsor. The second, held in March 2016, focused on many of the social determinants of health and included presentations on how transportation, planning, environmental justice, and civil rights law affect health. The third meeting was held in April 2016, and presentation topics included faith-based community organizing, community-based participatory research, place-based factors and policy at the community level, and the economics of community development (meeting agendas are in Appendix C). The committee met in executive sessions for deliberative discussion throughout the study process. The committee received public submissions of materials for its consideration at the meetings and by e-mail throughout the course of the study. A website was created to provide information to the public about the committee's work and to facilitate communication between the public and the committee. The process used to identify the community examples highlighted is outlined in the Chapter 5 Annex.

Overview of Report

Chapter 2 begins with a description of the state of health disparities in the United States by geography, income, race and ethnicity, and other categories. Chapter 3 discusses how structural and institutional inequities have led to disparate health outcomes and highlights historical issues that continue to affect health outcomes today, as well as the ways in which current and emerging issues ultimately affect communities. This is followed by a discussion on the multiple determinants of health and how they affect health equity. Chapter 4 discusses the role and capacity of communities to promote health equity and explains the larger context in which communities are situated, as well as the types of evidence needed to support communities. Chapter 5 provides nine examples of communities that are tackling health inequity and the lessons learned from these efforts. Chapter 6 addresses the policies that ultimately affect communities and that could either hinder or promote solutions at the level of individual communities. Chapter 7 discusses the roles of various stakeholders and the actions that these actors could undertake in their communities, with an emphasis on multi-sector collaboration. Chapter 8 is geared toward communities and provides an array of strategies, tools, and activities available to communities to help them promote health equity. Chapter 9 provides brief summarizing thoughts.

CONCLUDING OBSERVATIONS

There are systemic root causes of health inequities in this country that can be overwhelming and that will take considerable time to address. It will require system-level changes to eliminate structural racism, reduce poverty, improve income equality, increase educational opportunity, and fix the laws and policies that perpetuate structural inequities. Until these root causes are addressed nationally, health equity will not be fully realized. However, actors at the community level-policy makers, businesses, state and local governments, anchor institutions, and community residents-are agents of local change who have the power to change the narrative and take action that will promote health equity. The latter is what this report will focus on, although, where possible, it will provide promising strategies to address these hard-to-tackle root causes at higher levels.

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1

Hispanic/Latino identification with country of origin: Four decades after the U.S. government mandated the use of Hispanic or Latino for data collection (e.g., in the decennial census), most Americans with roots in Spanish-speaking countries prefer to be identified by their country (51 percent versus 24 percent who prefer a pan-ethnic term). Also, 69 percent respond that they believe there are multiple cultures, not one monolithic "Hispanic" or "Latino" culture ( Taylor et al., 2012).

2

For example, Jones and colleagues cite valuing all people equally as foundational to the concept of equity, noting that the equal worth of all people is at the core of the human rights principle that all human beings equally possess certain rights ( Jones, 2009). Braveman and colleagues point out that health differences adversely affecting socially disadvantaged groups are particularly unacceptable because ill health can be an obstacle to overcoming social disadvantage. They further note that this "consideration resonates with common sense notions of fairness, as well as with ethical concepts of justice" ( Braveman et al., 2011). Daniels argues for the moral importance of health by exploring the necessities for justice as it relates to health care and the social determinants of health ( Daniels, 2008).

3

4

As of March 15, 2016, the Health and Medicine Division (HMD) carries out the work previously undertaken by the IOM.

5

The committee chose the term "community-driven solutions" because the community will be the driving force behind the solutions in this report.

6

Public access materials can be requested from (accessed December 23, 2016).

7

# data[].attributes.attachment_link.description
In the United States, health equity and equal opportunity are inextricably linked, and the burdens of disease and poor health and the benefits of wellness and good health are inequitably distributed among groups of people. Although biology, genetics, and individual behaviors play a role in these differences, many health outcomes are more substantially affected by social, economic, and environmental factors.

# data[].attributes.attachment_link.readable_text
Health disparities are gaps in the quality of health and health care that mirror differences in socioeconomic status, racial and ethnic background, and education level. These disparities may stem from many factors, including accessibility of health care, increased risk of disease from occupational exposure, and increased risk of disease from underlying genetic, ethnic, or familial factors. NIH-designated U.S. health disparity populations include American Indians/Alaska Natives, Asian Americans, Blacks/African Americans, Hispanics/Latinos, Native Hawaiians and other Pacific Islanders, sexual and gender minorities, socioeconomically disadvantaged populations, and underserved rural populations.

Why Is Minority Health a Priority for NIAID?

NIAID has long recognized that racial and ethnic differences affect susceptibility to infection and disease. For example, African Americans account for about 13 percent of the U.S. population, yet represent almost half of new AIDS diagnoses. Native Americans experience higher rates of meningitis and invasive bacterial disease from Haemophilus influenzae type B (Hib) than do other groups. Year after year, asthma has a disproportionate affect on inner-city populations, particularly among African American and Hispanic/Latino children.

How Is NIAID Addressing This Critical Topic?

NIAID is committed to research that helps reduce these and other health disparities. Its efforts have led to the development of better drugs for HIV/AIDS, vaccines that have almost eliminated Hib-related disease, and educational programs and other interventions to improve asthma control among inner-city children.

NIAID also works to attract minorities to careers in biomedical research through programs such as Intramural NIAID Research Opportunities, which provides training in NIAID labs for undergraduate, graduate, and medical students from underrepresented groups, and Research Centers in Minority Institutions, which is partly funded by NIAID and aims to enhance research infrastructure at minority colleges and universities that offer doctorates in health sciences.

# data[].attributes.attachment_link.description
Health disparities are gaps in the quality of health and health care that mirror differences in socioeconomic status, racial and ethnic background, and education level. These disparities may stem from many factors, including accessibility of health care, increased risk of disease from occupational exposure, and increased risk of disease from underlying genetic, ethnic, or familial factors.