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Abstract

Health care in the United States has seen many great innovations and successes in the past decades. However, to this day, the color of a person’s skin determines—to a considerable degree—his/her prospects of wellness; risk of disease, and death; and the quality of care received. Disparities in cardiovascular disease (CVD)—the leading cause of morbidity and mortality globally—are one of the starkest reminders of social injustices, and racial inequities, which continue to plague our society. People of color—including Black, Hispanic, American Indian, Asian, and others—experience varying degrees of social disadvantage that puts these groups at increased risk of CVD and poor disease outcomes, including mortality. Racial/ethnic disparities in CVD, while documented extensively, have not been examined from a broad, upstream, social determinants of health lens. In this review, we apply a comprehensive social determinants of health framework to better understand how structural racism increases individual and cumulative social determinants of health burden for historically underserved racial and ethnic groups, and increases their risk of CVD. We analyze the link between race, racism, and CVD, including major pathways and structural barriers to cardiovascular health, using 5 distinct social determinants of health domains: economic stability; neighborhood and physical environment; education; community and social context; and healthcare system. We conclude with a set of research and policy recommendations to inform future work in the field, and move a step closer to health equity.
Out of the many “–isms” that drive the actions, inactions, and narratives around life, work, and personal/group identity in the United States, the effects of racism on health are perhaps the most pervasive, widespread and long-lasting. Race and ethnicity are woven deep into the fabric of the American society; yet, rarely have these constructs been as pivotal in shaping the ideals of equity, equality and justice, as the days and weeks leading up to this work. People of color—including Black, Hispanic, American Indian, Asian, and others—experience adverse social and environmental conditions such as barriers to accessing health care and living in safe physical environments, receiving quality education, and getting equal employment opportunities.1 These barriers predispose these groups to poor health outcomes, including cardiovascular disease (CVD)—the leading cause of morbidity and mortality globally.2 Collectively, these conditions are known as social determinants of health (SDOH).
In the United States alone, CVD claims over 650 000 lives annually, and puts a huge strain on the health care system and the economy, both in terms of cost of care, and lost productivity.3 Unfortunately, under-represented racial/ethnic groups—which collectively form nearly 40% of the total US population—continue to be victimized by deep-rooted structural racism; entrenched in, and perpetrated by historical policies and institutional practices. As a result, the benefits of groundbreaking advancements in cardiovascular care, and associated decline in CVD mortality in the United States in recent decades, have not been reaped equitably across racial and ethnic boundaries. Black adults experience higher burden of CV risk factors such as hypertension and obesity, and are more than twice as likely to die of CVD, relative to White adults.4 Similarly, American Indian individuals are 1.5 times as likely to be diagnosed with coronary heart disease, compared with the White population.5 Increasing evidence points to structural racism as the root cause of racial/ethnic disparities in the United States, including 4 recent scientific statements and Presidential Advisory from the American Heart Association.1,6–8
Racism takes a huge toll on health and wellbeing, with documented physical, behavioral, and emotional effects.9 However, literature on the association between racism and CVD is scant; rarely has structural racism been examined as a fundamental driver of disparities in CVD. To the best of our knowledge, racial/ethnic disparities in CVD, and the complex interplay between racism and CVD have not been studied using a comprehensive SDOH framework. In this narrative review, we apply a unique racial equity lens to a comprehensive SDOH framework, to (1) examine the role of deep-rooted structural racism and discrimination as a key driver of racial/ethnic disparities in CVD—in the context of SDOH-mediated inequities in CV care and outcomes and (2) propose a set of research and policy recommendations to inform future efforts to address systemic racism, reduce racial disparities, and advance health equity.

Social Determinants of Health Framework

Our SDOH framework is structured around models put forth by the Kaiser Family Foundation and Healthy People 202010,11 and examines race and ethnicity as purely social constructs, rather than biologic; as shaped by various societal, financial, geographic, and political forces. Overall, deliberation of individual SDOH through the prism of race/ethnicity and racism, and impact on CVD disparities via upstream, midstream and downstream pathways is highlighted in Figure 1. We classified race/ethnicity into 5 categories: White, Black, Hispanic, Asian, and American Indian/Alaskan Native. While we acknowledge the heterogeneity in the Asian population, the term “Asian” in this review refers to individuals from both South and East Asia.
Figure 1. Race as Social Determinants of Health (SDOH): upstream, midstream, downstream pathways. Individual racism: discriminatory words/actions or biases/stereotypes against under-represented racial/ethnic individuals and/or groups. Institutional racism: policies/practices that perpetuate and/or exacerbate racial/ethnic disparities via established societal institutions/systems (education, employment, housing, etc); often shaped by decades of social injustices toward marginalized populations. CVD indicates cardiovascular disease.

Economic Stability

Socioeconomic status SES is one of the strongest determinants of CVD, and underlies major disparities in outcomes by race and ethnicity in the United States, and globally. Economic stability indicators such as income, wealth, employment status, and occupational category are important determinants of access to care, safe housing, and many other factors that directly or indirectly affect CVD. The value of social mobility in determining life expectancy has been demonstrated in prior studies,12 including findings from a recent study that reported a longitudinal association between upward wealth mobility and lower risk of CVD events and death.13
Despite the abolition of slavery over a century ago, and the notorious Jim Crow laws that legalized educational and occupational segregation in the United States from the post-Civil War to as recent as 1964—with the passage of the 1964 Civil Rights Act—people of color continue to face major obstacles in employment opportunities.14 While such abhorrent laws no longer exist in policy, racial/ethnic discrimination continues to live on as an unfortunate legacy of the injustices of the past including ongoing discriminatory hiring practices and exclusion from social welfare benefits.15 In addition, jobs in the service industry—a major occupation for Black, Hispanic and Asian individuals16—are more prone to economic shocks such as from the current pandemic, and increase the likelihood of unemployment.
These structural barriers continue to predispose historically underserved racial/ethnic communities (Black, Hispanic, American Indian, Asian, others) to unstable socioeconomic conditions, as evidenced by the facts that Black, Hispanic and American Indian individuals have lower median incomes, are more likely to be unemployed, work low paying jobs, and often earn less for the same level of expertise, relative to their White counterparts.15,17 In turn, this may limit health care access either directly via limited financial resources to pay for health care, or indirectly via restricted access to employer-sponsored insurance coverage.
Differences in income, wealth, and other socioeconomic determinants are consistently linked to poor CV health and quality of life; and are major drivers of racial/ethnic disparities in CVD.18 Additionally, employment status and occupational category are important predictors of physical and psychosocial wellbeing, including depression, hypertension, and diabetes, all of which may affect risk of CVD.19,20 It has been previously documented that Black and Hispanic employees are 48% to 52% more likely to experience insecurity in their jobs and be exposed to various psychosocial occupational stressors such as low job control and high demands. In turn, these exposures are strong predictors of CVD risk factors, including diabetes and hypertension.21 Other barriers, such as immigration-related challenges and English language proficiency may further restrict prospects of economic prosperity in immigrant communities.
Effects of economic instability on health have mostly been studied in White, Black, and Hispanic individuals, with limited evidence for other underserved groups, such as the American Indian population, which merits greater study. Despite the fact that the Asian population in the United States has similar or better economic profile overall, relative to the White population, recent data suggest widening socioeconomic inequalities within this population subgroup; the health implications of which are not yet fully understood, and must be investigated further.22 Given the unique heterogeneity of the Asian population, coupled with under-representation in national databases, additional study is needed to elucidate the socioeconomic variation among different Asian subgroups.
Future research should investigate the effects of economic instability over the life course, that is, the effects of low income/wealth and overall financial insecurity on CVD cumulatively over time. On a policy level, programs to achieve socioeconomic equity, such as the homebuyer tax credit program, individual development accounts (IDA) and child development accounts (CDA) exist but have fallen short.23 Recently proposed legislative actions, including the Child Wealth Building Act of 2021 and expanded home tax credit for first-time buyers are important steps toward reducing the aforementioned disparities.24,25 Deliberate efforts for equitable distribution of resources are critical to ensure that the benefits of such programs uplift the truly disadvantaged segments of the population and narrow the racial divide in economic wellbeing.

Neighborhood and Physical Environment

Ever since their inception via the National Housing Act of 1934, residential segregation and redlining laws have continued to haunt the modern-day American society.26 The effects of historical discriminatory policies—such as by the Federal Housing Administration to facilitate home construction and subsequent ownership for the White population, and discourage ownership for the Black population—continue to perpetuate to this day.26 The glaring racism—legalized by the federal government—is exemplified by the fact that the Home Owner’s Loan Corporation and Federal Housing Administration created maps for every major metropolitan area in the United States, which were color coded to depict mortgage insurance-worthiness; areas with high Black population were coded red to signal high-risk areas for insurance, essentially preventing Black families the right to own a house, and creating a legal barrier that created prime conditions for concentrated poverty—the effects of which continue to reverberate to this day27 (Figure 2). Such injustices have historically condemned populations of color to under-resourced and unsafe neighborhoods—predisposing them to poor CVD outcomes.
Figure 2. Residential Redlining: An Unfortunate History. Figure shows the Baltimore “Redlining” map that organized neighborhoods by social grade. (Retrieved from: http://jhir.library.jhu.edu/handle/1774.2/61867). National Archives and Records Administration. Image courtesy of the Map Collection, Sheridan Libraries & Museums, Johns Hopkins University. Different colors depict “potential risk” for mortgage lenders; red represents the highest risk areas.
Neighborhood disadvantage and unhealthy/unsafe physical environment have been documented to increase the risk of CVD and/or worsen CV outcomes. For example, findings from a longitudinal study of over 5000 middle aged and older Black, White, and Hispanic adults documented that over a 10-year follow-up period, neighborhood-level racial/ethnic segregation was associated with a 12% increased risk of CVD in Black, compared with White patients.28
Despite reports of narrowing neighborhood poverty gap, Black, American Indian and Hispanic individuals are more likely—among all racial/ethnic groups—to live in high-poverty neighborhoods.29 Socioeconomically disadvantaged neighborhoods present unsafe and unhealthy living conditions, including poor walkability and limited availability of green spaces to encourage physical activity, which may lead to increased risk of CV risk factors and CVD.30 Poor neighborhoods may also pose significant mobility barriers via poor access to transportation, which may in turn restrict access to supermarkets, grocery stores, and hospitals, all of which contribute collectively to CVD.31 Similarly, neighborhood violence and disorder are associated with increased risk of cardiometabolic risk factors including insulin resistance, deranged glucose and blood pressure, and metabolic syndrome,32 with some evidence suggesting stronger effects for Black, compared with White individuals.33 In addition, detrimental neighborhood conditions may modulate CVD risk via psychological mechanisms, including stress-mediated pathways.34
Living/working in socioeconomically disadvantaged neighborhoods may increase exposure to a variety of air pollutants, including particulate matter, which may increase the risk of CVD. Prior evidence suggests neighborhoods with >60% Hispanic population are 8% to 30% more likely to be exposed to air pollutants, including PM2.5 and NOx, relative to areas with <25% Hispanic population.35 Similarly, it has been reported previously that particulate matter pollution may mediate nearly 25% of the higher risk of incident CVD and all-cause mortality in Black versus White individuals.36
Neighborhood disadvantage may restrict access to health care such as lack of nearby hospitals or regular primary care provider,37 thereby increasing the risk of delayed and/or missed care, a poor prognostic factor for CVD.38 Current evidence suggests that Black race is a strong, independent predictor of long delay times (symptom onset to hospital presentation) for patients with myocardial infarction (MI).39 Low SES, low education, and Black race have all been implicated as risk factors for delayed care.40
Neighborhoods are important determinants of access to affordable, healthy food; socioeconomically disadvantaged neighborhoods are less likely to have healthy food outlets and more likely to have small grocery and convenience stores.41 Residence in a food desert has been linked to poor CV health, particularly among individuals experiencing additional adverse SDOH, such as economic instability.42 Related, neighborhoods comprising predominantly communities of color are more likely to experience pharmacy deserts, which are in turn linked to poor medication adherence, and raises concerns for CVD treatment/prognosis.43
Widespread efforts—including informed state and federal policy-making, and strong community partnerships—are needed to improve neighborhood/physical environmental conditions for Black, Hispanic, and other under-represented racial/ethnic groups. The Affirmatively Furthering Fair Housing, part of the Fair Housing Act and reaffirmed as recently as 2015, requires recipients of the Housing and Urban Development funds to take substantive actions toward reducing housing discrimination by desegregating neighborhoods and creating more inclusive communities.44 The 2021 Restoring Affirmatively Furthering Fair Housing Definitions and Certifications rule requires Housing and Urban Development program participants to certify inclusion of such actions as part of their short and long-term plans.44
Existing programs, such as the first-time homebuyer down payment assistance (American Dream Downpayment Initiative) are critical toward narrowing the racial gap in homeownership and quality of living.45 Recent legislative actions such as the First-Time Homebuyer Act, 2021 and Downpayment Toward Equity Act, 2021, respectively propose up to $15 000 in tax credits and $25 000 in direct downpayment assistance for first-time home buyers.46 Strong advocacy efforts, led by key stakeholders and informed by local community partners are needed for successful implementation of these and similar policies, and advance housing equity in the United States.

Education

Level of educational attainment is considered one of the most reliable social risk factors for CVD.47 In this context, one of the most staggering aspects of the history of racism in the United States is the education divide. Less than 70 years ago, Heman Marion Sweatt was rejected admission to the University of Texas Law school because of the color of his skin.48 Sweatt was a young Black man; the university proposed the creation of a new school for Black students instead of allowing a Black man to study in a White school (Sweatt v. Painter, 1950). Soon after, in a landmark judgement (Brown v Board, 1954) the Supreme Court struck down the legality of racial segregation in public schools.49 However, to this day, individuals and communities of color face multiple barriers to education, deep-rooted in structural racism, and perpetuated by discriminatory policies that have historically marginalized the Black, Hispanic, and American Indian communities. The ripple effects of poor education on CVD manifest both directly and indirectly via other SDOH.
Despite generally increasing college enrollment rates in the past 2 decades, Black, Hispanic, and American Indian populations have lower educational attainment, as evidenced by lower college participation and completion rates, relative to the White population.50 Nearly 90% of American children attend public schools, which are governed by zoning laws that prioritize enrollment for students within the school zone.51 In fact, 73% of Black children and 40% of children of color attended a high-poverty school in 2017, that is, schools in which at least 75% of enrolled children belong to low income families, as per established federal poverty guidelines that impact higher education opportunities.52 The concentrated poverty dwindles prospects of academic and professional excellence later in life, including risk of high school/college drop-out. Overall, Black, Hispanic, and American Indian students are 20%, 65%, and over 300% more likely to dropout, compared with White students.53
The education challenges for under-represented communities extend far beyond the issue of access; multiple reports point to significant racial/ethnic disparities in education debt, with higher overall student debt, and greater delinquency rate for Black student, relative to their White counterparts.53,54 Such disparities persist even after accounting for family income/wealth, which may be explained at least partly by discriminatory practices limiting employability for candidates from under-represented racial and ethnic groups.55,56
Poor educational attainment predicts poor employment prospects, and economic instability, which in turn act as barriers to healthy living, food security, and health care—all strong determinants of CVD.57 The impact of low education on CVD manifests via poor health literacy, lack of access to relevant information on CVD risk factors, and provider-patient communication barriers.47,58 In addition, education and literacy are important factors in assisting with adoption of healthy behaviors, and ensuring medication adherence in patients with CVD.58 Conversely, higher education is posited to be associated with positive psychosocial states, including greater sense of emotional and personal control, rewarding jobs, and other facilitators of health such as access to health care (eg, employer-sponsored coverage), with resultant positive effects on overall and CV health.59
Holistic public health interventions are needed to narrow the racial educational achievement gap in the United States, beginning from childhood. Federal, state, and community level educational policies and programs should take a multi-pronged approach toward creating a more just and equitable education system. Such efforts should target sustainable funding for historically underfunded schools (such as for historically Black colleges and universities/HBCUs); teacher training and curriculum development to ensure instructional quality; school location, culture and diversity; parent involvement and other resources such as counseling and support services for students of all ages to help achieve academic success. Programs such as Head Start and Early Childhood Education and Assistance Program offer unique opportunities to achieve greater education equity in early life60; similar programs should be developed and implemented to ensure equitable access to college and higher education. A strong political will and community engagement— particularly, voices from disadvantaged racial/ethnic groups—are needed to ensure such programs benefit the most underprivileged segments of the society and truly achieve their long-term goals.
Evidence from large-scale, prospective studies is needed to better understand the intergenerational effects of poor access to educational resources and overall low educational attainment, on CV health in underserved racial/ethnic groups.

Community and Social Context

Three recent reports from the American Heart Association highlighted the state of CVD in Black, Hispanic, and American Indian individuals and acknowledged the role of social, cultural and community environment in determining the burden of CVD in these underserved populations.7,8,61 Collectively, these conditions offer emotional and material support, facilitate access to employment opportunities, provide role models, and shape attitudes and practices toward health and health care.62 Social support is associated with positive CV outcomes; whereas lack of one is associated with increased risk of CVD, and CV mortality.63,64 Structural barriers that create poverty-concentrated neighborhoods and inhibit upward socioeconomic mobility for underserved racial/ethnic groups may also restrict positive community engagement and creation of supportive social networks,65 thereby increasing the risk of CVD. Prior research has documented a strong link between socioeconomic depravity and small/poor social networks.66,67 Conversely, mixed-income neighborhoods have been shown to increase positive community-level interaction, expand social networks, and benefit health overall.68
The disadvantage experienced by Black, Hispanic, American Indian, and other under-represented racial/ethnic groups is explained by the social capital theory.69 Social capital has a bidirectional relationship with SES; people of color generally experience socioeconomic disadvantage, which precludes the development of social capital. In their systematic review, Uphoff et al69 found that people with low SES have less social capital, and the amount of available capital cannot be used effectively for health benefits. Others have reported a strong effect of social support in explaining the Black-White differences in hypertension prevalence.70 It has been documented previously that increased social support may buffer the negative emotional effects of stress exposure in patients with acute coronary syndrome71 and offer similar protection against other stressful experiences such as racism/discrimination, offering additional pathways for protection against CVD.72 Similarly, social networks that offer positive social support have been documented to increase health promoting (healthier diet, physical activity, etc) and health seeking (seeing a doctor, seeking preventive health care) behaviors, and reduce deleterious behaviors.73
In general, literature on racial/ethnic disparities in community engagement is scant and merits greater research. Prior studies have reported that Black individuals are more likely to be involved in church support networks, relative to Hispanic or White individuals.74 In contrast, other studies have reported either smaller networks for this group,75 or higher prevalence of negative community interactions—despite overall greater social ties.74 Poor social cohesion among middle class Black adults has been reported previously76 and supports the need for evidence-based community-level interventions to create healthy community environments, and promote CV health in this population. Despite the hypothesized buffering role of social support and social networks in preserving Hispanic health,77 several reports in recent years suggest that while the Hispanic community may have larger social networks, Hispanic communities may not be as socially cohesive as originally posited, with resulting health implications.78 A population-based study reported that Hispanic individuals generally experienced larger social networks with increasing proportion of fellow Hispanic people in the community; however, after controlling for a variety of sociodemographic factors, an inverse relationship was observed between Hispanic concentration and neighborhood social cohesion.78
Given the chronic trauma experienced by the American Indian population, coupled with the highest CVD rates of any racial/ethnic group,79 the significance of social support in this population subgroup cannot be overstated. While social support in the American Indian community has been posited as a strength, with possible beneficial effects on health, the dynamics of social support/networks and social cohesion are possibly different from the other racial/ethnic groups and center around aging, marriage and sex.80 Efforts to better understand these relationships in this high-risk population should include greater data collection to ensure reliability and generalizability.
Current understanding of the impact and pathways of social support for CV health among other racial/ethnic groups, such as Asian Americans is limited, at best. Community/social context in this population should be analyzed in light of relevant issues such as acculturation and nativity. Some evidence suggests that Asian/Asian American individuals may be less willing to seek social support, compared with European Americans, and that seeking such support may be associated with stress.81 For example, findings from a study of Chinese immigrants suggest that social support was not a buffer for CV response to stress; in fact, higher levels of structural, emotional and instrumental support seeking were associated with higher blood pressure reactivity.81
CVD affects the incarcerated population disproportionately, with up to 3-fold higher CVD rates compared with the general US population.82 It is known that Black individuals are 5 to 6 times more likely to be incarcerated compared with the White population.81 Findings from the recent CARDIA cohort study suggest that over 29 years of follow-up, CVD incidence rates were 1.7-fold higher in participants with a history of incarceration, compared with those without.83 Similarly, incident CVD rates were 2.5-fold higher in Black women with history of incarceration, compared with their White counterparts.83 Perhaps, the challenges of discrimination and injustice marring the nation’s law enforcement reached its peak with the death of George Floyd; however, racism continues to affect people of color adversely, as evidenced by the alarmingly higher rates of arrests, incarceration and long sentencing.84
Greater partnerships among clinicians, population health scientists, policy-makers, and community members/leaders are invaluable for successful efforts to design and implement community-level interventions, and create healthy, equitable living conditions for all. The cause for racial equity may also benefit from greater representation of historically under-represented populations in community voices to inform local policy-making, as well as greater representation of Black, Hispanic, American Indian, and Asian populations in the legal justice system, which is severely lacking currently.

Health Care System

Health care is the largest industry in the United States, and as such, plays a major role in shaping the landscape of racial justice.85 In this section, we discuss various health system factors that facilitate, or contribute to racial/ethnic disparities in CVD on a structural level and examine such disparities from both the provider (eg, implicit bias) and patient (eg, barriers to access, variation in quality of care) perspective.
It is well established that communities of color are disproportionately affected by disparities in access, and quality of CVD care.86 Access to health care, including insurance coverage, continues to be a major barrier among individuals with established CVD. In 2018, nearly 7.3 million nonelderly adults with CVD in the United States were uninsured.1,87 In spite of the improvements seen with the Affordable Care Act, uninsured rates are ≈2 to 4 times higher among Hispanic (28.7%) and Black (12.9%) individuals, compared with White individuals (7.4%).88 Data from the Behavioral Risk Factor Surveillance System suggest that among adults with self-reported hypertension, 27% and 20% of Hispanic and Black adults, respectively lacked a personal health care provider compared with 15% for the White population.89 Further, 31% Hispanic and 28% Black individuals had to forego care because of cost, versus 21% for White individuals.89 Lack of health coverage severely restricts access to prevention and treatment interventions, thus increasing risk for delayed/missed diagnosis and poor CVD outcomes in the long term.90 In addition, other health care access barriers such as transportation disproportionately impact underserved and under-represented racial/ethnic groups and may delay much-needed care.31
The 2018 National Healthcare Quality and Disparities Report documented that Black, American Indian, and Alaskan Native patients receive worse care for nearly 40% of all quality of care measures, relative to White patients; whereas Hispanic patients receive worse care for about 35% of such measures.86 In contrast, Asian patients receive worse care for 27%, and better care for 28% of quality measures, relative to their White counterparts. A national study of over 1100 hospitals across the United States reported that Black patients from socially disadvantaged neighborhoods were 24% less likely to receive coronary artery bypass grafting treatment at top tier cardiac hospitals, relative to White patients from the same sociodemographic background.91
Similarly, hospitals providing care to predominantly underserved communities are reported to have higher readmission and mortality rates from CVD than those catering to mostly White population, even after adjusting for the proportion of Medicaid patients and the patient Disproportionate Share Index.92 These disparities may be attributed to poor quality of care, the extent of which should be assessed in the context of additional factors such as lower resource availability, site of care and comorbidity burden.93,94 Data from the Centers for Medicare and Medicaid Services suggest that despite a narrowing gap, Black patients are less likely to receive timely fibrinolytic medication after heart attack, compared with White patients.85
Racial inequities in health outcomes are perpetuated by discriminatory state and federal policies that are deeply entrenched within the health care system. For example, increasing evidence points to higher financial penalties for hospitals serving underserved communities, for scoring lower on quality of care metrics (eg, readmission rates), which do not fully account for the underlying social risk factors that are strong predictors of adverse population health outcomes.95 In a recent national study, Aggarwal et al showed that hospitals caring for predominantly Black patients were penalized disproportionately by the 3 major Centers for Medicare and Medicaid Services value-based programs, including Hospital Value-Based Purchasing Program, Hospital Readmission Reduction Program, or Hospital-Acquired Condition Reduction Program.95 Value-based health care must therefore value equity as an important benchmark of performance, define health equity as a distinct performance measure, and hold organizations financially accountable for failing to meet defined goals.
Implicit provider attitudes may result in bias toward patient care and contribute to observed disparities. For example, health care professionals may have implicit feelings about medication use/adherence in certain racial/ethnic groups, which may affect quality of delivered care, including screening, prescribing, monitoring, etc The interplay between different forms of discrimination, and major SDOH domains, is depicted in Figure 3. A systematic review found evidence of bias in 14 of the 15 studies included in the review.96 The study concluded that implicit bias was associated with patient–provider interactions, treatment decisions, treatment adherence, and patient outcomes. The authors performed a meta-analysis on 13 reviewed studies and reported a statistically significant overall implicit bias effect (d=0.34, P<0.001: a positive score represents provider preference for White patients versus people of color), highlighting the extent of implicit among health care providers in the United States. Breathett et al found that Black race influenced treatment allocation decision, including bias resulting from believing that the Black man was sicker compared with the White man; as well as concerns for trust, and treatment adherence. Ultimately, such implicit bias lead to offering transplantation to the White man and ventricular assisted device implantation to the Black man.97
Figure 3. Social determinants of health (SDOH) and cardiovascular disease (CVD): the role of racial/ethnic discrimination. Figure depicts interactions between SDOH and individual and institutional racism. Right-most column: historical context of structural racism in the United States. EEO indicates Equal Employment Opportunity; SES, socioeconomic status.
Dominant communication styles, fewer demonstrated positive emotions, infrequent requests for input about treatment decisions, and less patient-centered care have been reported to characterize patient-provider interactions involving people of color.98 Addressing the issue of provider bias and discrimination requires widespread, vigorous education/training efforts to help students, clinicians and researchers understand the historical context of racial/ethnic discrimination in the United States, as well as interpersonal and structural barriers that contribute to disparities in health outcomes. Such interventions during medical school and residency training may limit development of implicit attitudes and prepare the health care workforce to provide informed, culturally competent care.

Pathways From Racism to CVD

Behavioral Pathways

Racism can affect CVD, and overall health, via both behavioral and physiological pathways. For example, perceived racial/ethnic discrimination has been linked to poor health (coping) behaviors such as smoking, excessive alcohol consumption, and illicit drug use, which may increase risk of CVD.99 Consistent evidence from the literature suggests a strong link between discrimination and blood pressure reactivity.100 In their meta-analysis of existing evidence on perceived racial discrimination and hypertension, Dolezsar et al101 reported a strong correlation between the two. Perceived discrimination has been associated with poor preventive health practices, such as low rates of disease screening.102 Other studies have linked discrimination to medication nonadherence and missed appointments for CVD.103,104

Physiological Pathways

Various physiological pathways link racism to poor CV outcomes, including abnormal activation of sympathetic nervous system from internalized stress, and resulting release of cortisol.105 Perceived discrimination has been associated with inflammatory markers such as high C-reactive protein levels as well as more distal outcomes such as coronary artery calcification.106 Findings from the MESA (Multi-Ethnic Study of Atherosclerosis) study of over 6000 middle aged and older adults suggest that self-reported discrimination is associated with high IL-6 (interleukin-6) levels.107 Similarly, social isolation, depression, and mental distress, which may result from experiences of discrimination, are associated with high risk of MI, as well as CVD mortality.108 Additional study is needed to understand the pathways between implicit bias and CVD.

Conclusions and Recommendations

Racial/ethnic disparities in CVD are a product of deeply entrenched policies, practices and views on racial equality and social justice in the United States. In this review, we applied a comprehensive SDOH framework to examine various societal conditions that contribute to such disparities, and predispose under-represented and historically underserved racial/ethnic groups to CVD. In particular, Black, Hispanic, and American Indian populations experience greater social disadvantage across all SDOH domains compared with the White population, with implications for CVD development, progression, and mortality.
As is often observed in health care, resources and energies are traditionally directed by outcomes, or the downstream effects of social adversity—an upstream construct (Figure 1, including definitions of upstream, midstream, downstream). Pioneering work in health disparities, including the Heckler, and Institute of Medicine Unequal Treatment reports, attracted much needed attention toward the state of disparities in CV health, and lead to calls for equity in health care in the United States.109 However, it is unfortunate that many decades after such injustices were acknowledged, Black, Hispanic, Asian, American Indian, and other disadvantaged populations continue to face disadvantage in all major SDOH—income, education, neighborhood/built environment, community and social context, and health care system.
The ongoing SARS-CoV-2 pandemic serves as a grim reminder of the fact that even in today’s America, a multitude of adverse social, economic, and environmental conditions continue to predispose Black, Hispanic, Asian and American Indian populations to higher risk of both all-cause and CV mortality.110 In spite of the gains in clinical care, we continue to ignore SDOH and perhaps more importantly, the reality of structural racism in policy-making and practice, without due attention to which, true health equity cannot be achieved. Therefore, we present a comprehensive set of research and policy recommendations, with the aim of improving current understanding of major racial/ethnic disparities in CVD, and informing policies and practices to address such inequities (Table).
Table. Research and Policy Recommendations
Research recommendationsPolicy recommendations
1. Improve reliability and generalizability of race/ethnicity data1. Create robust SDOH data collection platforms on local (hospital), state (health department) and national (HHS, CDC) levels
a. Improve racial/ethnic and other SDOH data collection and documentation in existing population health and clinical databases, eg, national survey databases, claims data, and EHR databases.a. Use existing SDOH frameworks/models (KFF, WHO) as templates to collect, analyze, and monitor patterns of racial/ethnic disparities in CVD.
b. Real-world frameworks have recently been developed by agencies across the country, eg, BARHII111 and INPH112
b. Ensure routine monitoring of accuracy and reliability of race/ethnicity, and other SDOH data, using inbuilt quality checks.
c. Develop, validate and apply such frameworks to further the understanding of interactions between race and other SDOH, and help build SDOH-informed models of CV care to address racial disparities.
2. Generate greater evidence from large-scale, longitudinal studies to improve current understanding of the race-CVD link2. Improve SDOH data interoperability
a. Greater longitudinal evidence must complement existing knowledge from cross-sectional studies to better understand causal links among race and CVD, in the context of other SDOH.a. Create opportunities for interoperability between ‘social’ (ie, SDOH) and clinical (ie, EHR, clinical registries) data, with the aim of creating robust, SDOH-informed ‘patient-centered care’ models.
b. Frameworks for real-world SDOH data integration, and improved interoperability with existing databases have recently been developed, and must be adapted for use in diverse demographic settings.113
b. Appropriate statistical tools (eg, mediation/moderation) must be used to elucidate interlinkages between race, other SDOH, and CVD.
3. Develop a race-inclusive polysocial risk score for predicting CVD risk, and outcomes3. Improve state-level race and ethnicity data collection and reporting
a. Improve race/ethnicity data collection/reporting for Medicaid, and other state-sponsored public benefit programs. In 2018, 5 states reported >50% missing race/ethnicity data whereas 14 states had >20% missing data.114
a. Develop a comprehensive SDOH risk score, which accounts for the effects of race.
b. Frameworks to build SDOH score for CVD have been developed117 and must be applied in diverse sociodemographic and racial/ethnic settings.
b. Standards to collect/report race data exist,115 however, greater enrollee and other stakeholder engagement should be ensured for improved reliability and generalizability of collected information—as recommended widely.116
c. Existing national databases (NHIS, HRS) offer wealth of SDOH information and should be used to develop such risk prediction tools.
4. Analyze racial/ethnic disparities using an ‘intersectional’ approach4. Develop digital tools and platforms to inform clinical CV care, and improve population CV health
a. Analyze possible variation in the race-CVD association, by age and sex separately, and for individual SDOH.a. Enhance utility of SDOH data by developing novel analytic and data visualization tools, which may enable researchers, care providers, and other stakeholders to ‘screen’ high-risk population subgroups.
b. Frameworks, such as PRAPARE118 should be modeled to assess CVD risk in underserved racial groups.
c. Future efforts must focus on seamless integration of race/ethnicity, and other relevant SDOH data into existing clinical data platforms, for easy use/access.
5. Avoid the use/interpretation of race/ethnicity as a biological constructa. Advocate for the use of race/ethnicity as a social construct (vs purely a biological construct, eg, for calculation of eGFR), to better contextualize the role of racism/discrimination in determining CVD disparities.
6. Well-designed prospective studies are needed to evaluate the impact of social interventions on CV, and overall healtha. Greater partnerships among social scientists, community partners and other relevant stakeholders are needed for successful design and implementation of such interventions.
5. Ensure provider training to reduce implicit bias and discrimination in health care
a. Improve medical students’/trainees’ understanding of historical context of racial/ethnic disparities in health in the United States
b. Equip—via curricula and real-world experiences—physicians, nurses, and other health care staff with an understanding of the unique challenges faced by communities of color, and the importance of their role in providing culturally competent care to reduce widespread disparities in CVD.
c. Improve provider cultural competence to facilitate shared decision-making, reduce implicit bias, and improve health outcomes for all; real-world training programs—such as the SHARE approach—exist and are documented effective interventions.119
6. Effective implementation of anti-discriminatory policies and “consequence-free” reporting of discriminatory practices is critical.
a. Ensure effective implementation of anti-discrimination laws (federal/local) at the workplace via a robust system of discrimination reporting and accountability at all levels of employment: hiring, day-to-day work, promotion.
b. Create an inclusive environment at workplace, encourage employees to report discriminatory attitudes/practice, without fear of “consequences.”
 
7. Greater reporting of quality of care and patient outcomes (CVD/CV risk factors) by race/ethnicity should be widely implemented.
a. Increase public reporting of quality of care on an organizational, and state level may improve patient outcomes, as documented previously.120
 b. Increase reporting of patient outcomes by race/ethnicity and other sociodemographic characteristics, as proposed/implemented by the Health and Human Services (HHS) commission as part of “approaches for identifying, collecting, and evaluating data on health care disparities in Medicaid and CHIP.”121
 8. Increase work-force diversity for better patient outcomes
a. Race-concordance is associated with better patient satisfaction and subjective health; efforts must be made to increase clinical workforce diversity.122
b. Data shows health care delivered via racially diverse provider workforce is associated with improved outcomes for both under-represented and majority racial and ethnic populations.123
9. Incentivize efforts to address racial disparities, with a focus on institutional and individual racism
a. System-wide interventions to reduce disparities should be accompanied by efforts to customize and personalize care to the individual patient; patient-centered care is key to ensuring equitable care delivery.124
b. Financially incentivize quality-improvement (QI) initiatives with a clear goal of addressing institutional racism in health care to reduce racial/ethnic disparities; coupled with workforce (provider) training to address individual racism.
c. Existing QI programs—such as value-based models—often lack a defined focus on racial disparities and/or racism/discrimination as a root-cause of such, with resulting poor outcomes.125
 10. Greater multisectoral collaboration among community stakeholders, ie, “voices from the community”; health care providers; and policy-makers is key to advancing the health equity agenda.
11. Real-world examples of successful community engagement programs exist and should be adapted for application in diverse healthcare settings.126 Patient experiences/perceptions are key to designing effective interventions to address systemic racism.
BARHII indicates Bay Area Regional Health Inequities Initiative; CDC, Centers for Disease Control; CHIP, Children’s Health Insurance Program; CV, cardiovascular; CVD, cardiovascular disease; EHR, electronic health record; eGFR, estimated glomerular filtration rate; HHS, Health and Human Services; HRS, Health and Retirement Study; INPH, Indiana Network for Population Health; KFF, Kaiser Family Foundation; NHIS, National Health Interview Survey; PREPARE, Protocol for Responding to and Assessing Patients’ Assets, Risks, and Experiences; SDOH, Social Determinants of Health; SHARE, Seek Help Assess Reach Evaluate; and WHO, World Health Organization.
Our recommendations were informed by a multi-pronged approach, including: (1) review of relevant literature for outstanding research, policy and practice gaps in the context of race, racism, and CVD; (2) drawing on evidence from novel local/community, state and federal programs that use SDOH frameworks to address structural racism and advance the cause of racial equity111–116; and (3) using the authors’ expertise in preventive cardiology and social epidemiology117 to recommend domain-specific interventions to address identified gaps—as supported by real-world evidence—such as enhanced SDOH data interoperability,111–113,118 improved race/ethnicity data collection standards,114–116 provider training to address implicit bias,119 increased workforce diversity and reporting of patient outcomes by race/ethnicity,120–123 incentivized quality improvement initiatives with a focus on racial equity,124,125 and greater community engagement for effective design and implementation of interventions to address systemic racism.126
We hope that we can heed to Dr Martin Luther King’s stark reminder from nearly half a century ago, “Of all the forms of inequality, injustice in health care is the most shocking and inhumane”, and overcome the challenges and barriers highlighted herein, with a common goal of achieving health equity.

Footnote

Nonstandard Abbreviations and Acronyms

CVD
cardiovascular disease
SDOH
social determinants of health

References

1.
Churchwell K, Elkind MSV, Benjamin RM, Carson AP, Chang EK, Lawrence W, Mills A, Odom TM, Rodriguez CJ, Rodriguez F, et al.; American Heart Association. Call to action: structural racism as a fundamental driver of health disparities: a presidential advisory from the American Heart Association. Circulation. 2020;142:e454–e468. doi: 10.1161/CIR.0000000000000936
2.
Virani SS, Alonso A, Aparicio HJ, Benjamin EJ, Bittencourt MS, Callaway CW, Carson AP, Chamberlain AM, Cheng S, Delling FN, et al. Heart disease and stroke statistics—2021 update. Circulation. 2021;143:E254–E743. doi: 10.1161/CIR.0000000000000950
3.
Centers for Disease Control and Prevention. Heart Disease Facts 2021. https://www.cdc.gov/heartdisease/facts.htm Accessed September 1, 2021.
4.
National Center for Health Statistics. Racial and Ethnic Disparities in Heart Disease 2019. Accessed September 1, 2021. Available at: https://www.cdc.gov/nchs/hus/spotlight/HeartDiseaseSpotlight_2019_0404.pdf
5.
US Department of Health and Human Services Office of Minority Health. Heart Disease and Hispanic Americans - The Office of Minority Health. 2021. Available at: https://minorityhealth.hhs.gov/omh/browse.aspx?lvl=4&lvlid=64. Accessed September 21, 2021.
6.
Havranek EP, Mujahid MS, Barr DA, Blair IV, Cohen MS, Cruz-Flores S, Davey-Smith G, Dennison-Himmelfarb CR, Lauer MS, Lockwood DW, et al.; American Heart Association Council on Quality of Care and Outcomes Research, Council on Epidemiology and Prevention, Council on Cardiovascular and Stroke Nursing, Council on Lifestyle and Cardiometabolic Health, and Stroke Council. Social determinants of risk and outcomes for cardiovascular disease: a scientific statement from the American Heart Association. Circulation. 2015;132:873–898. doi: 10.1161/CIR.0000000000000228
7.
Breathett K, Sims M, Gross M, Jackson EA, Jones EJ, Navas-Acien A, Taylor H, Thomas KL, Howard BV; American Heart Association Council on Epidemiology and Prevention; Council on Quality of Care and Outcomes Research; Council on Cardiovascular and Stroke Nursing; Council on Clinical Cardiology; and Council on Lifestyle and Cardiometabolic Health. Cardiovascular health in American Indians and Alaska Natives: a scientific statement from the American Heart Association. Circulation. 2020;141:e948–e959. doi: 10.1161/CIR.0000000000000773
8.
Rodriguez CJ, Allison M, Daviglus ML, Isasi CR, Keller C, Leira EC, Palaniappan L, Piña IL, Ramirez SM, Rodriguez B, et al.; American Heart Association Council on Epidemiology and Prevention; American Heart Association Council on Clinical Cardiology; American Heart Association Council on Cardiovascular and Stroke Nursing. Status of cardiovascular disease and stroke in Hispanics/Latinos in the United States: a science advisory from the American Heart Association. Circulation. 2014;130:593–625. doi: 10.1161/CIR.0000000000000071
9.
Paradies Y, Ben J, Denson N, Elias A, Priest N, Pieterse A, Gupta A, Kelaher M, Gee G. Racism as a determinant of health: a systematic review and meta-analysis. PLoS One. 2015;10:e0138511. doi: 10.1371/journal.pone.0138511
10.
Beyond Health Care: The Role of Social Determinants in Promoting Health and Health Equity | KFF. 2018. Accessed September 1, 2021. https://www.kff.org/racial-equity-and-health-policy/issue-brief/beyond-health-care-the-role-of-social-determinants-in-promoting-health-and-health-equity/.
11.
Social Determinants | Healthy People 2020. 2021. Available at: https://www.healthypeople.gov/2020/leading-health-indicators/2020-lhi-topics/Social-Determinants. Accessed September 21, 2021.
12.
Venkataramani A, Daza S, Emanuel E. Association of social mobility with the income-related longevity gap in the United States: a cross-sectional, county-level study. JAMA Intern Med. 2020;180:429–436. doi: 10.1001/jamainternmed.2019.6532
13.
Machado S, Sumarsono A, Vaduganathan M. Midlife wealth mobility and long-term cardiovascular health. JAMA Cardiol. 2021;6:1152–1160. doi: 10.1001/jamacardio.2021.2056
14.
Hiring Discrimination Against Black Americans Hasn’t Declined in 25 Years. 2017. Available at: https://hbr.org/2017/10/hiring-discrimination-against-black-americans-hasnt-declined-in-25-years. Accessed September 21, 2021.
15.
Omi, M and Winant, H. Racial Formation in the United States. 2014. Available at: https://routledgetextbooks.com/textbooks/9780415520317/default.php. Accessed September 21, 2021.
16.
BLS Reports: U.S. Bureau of Labor Statistics. Labor force characteristics by race and ethnicity, 2018. 2018. Available at: https://www.bls.gov/opub/reports/race-and-ethnicity/2018/home.htm. Accessed September 21, 2021.
17.
Economic Policy Institute. Black-white wage gaps expand with rising wage inequality. 2016. Available at: https://www.epi.org/publication/black-white-wage-gaps-expand-with-rising-wage-inequality/#epi-toc-7. Accessed September 21, 2021.
18.
Schultz WM, Kelli HM, Lisko JC, Varghese T, Shen J, Sandesara P, Quyyumi AA, Taylor HA, Gulati M, Harold JG, et al. Socioeconomic status and cardiovascular outcomes: challenges and interventions. Circulation. 2018;137:2166–2178. doi: 10.1161/CIRCULATIONAHA.117.029652
19.
Madsen IEH, Nyberg ST, Magnusson Hanson LL, Ferrie JE, Ahola K, Alfredsson L, Batty GD, Bjorner JB, Borritz M, Burr H, et al.; IPD-Work Consortium. Job strain as a risk factor for clinical depression: systematic review and meta-analysis with additional individual participant data. Psychol Med. 2017;47:1342–1356. doi: 10.1017/S003329171600355X
20.
Mutambudzi M, Javed Z. Job strain as a risk factor for incident diabetes mellitus in middle and older age U.S. workers. J Gerontol B Psychol Sci Soc Sci. 2016;71:1089–1096. doi: 10.1093/geronb/gbw091
21.
Fullerton AS, Anderson KF. The role of job insecurity in explanations of racial health inequalities. Sociol Forum. 2013;28:308–325. doi: 10.1111/socf.12020.
22.
Kochhar R and Cilluffo A. Pew Research Center. Income Inequality in the U.S. Is Rising Most Rapidly Among Asians. 2018. Available at: https://www.pewresearch.org/social-trends/2018/07/12/income-inequality-in-the-u-s-is-rising-most-rapidly-among-asians/.
23.
Rethinking homeownership incentives to improve household financial security and shrink the racial wealth gap. 2020. Available at: https://www.brookings.edu/research/rethinking-homeownership-incentives-to-improve-household-financial-security-and-shrink-the-racial-wealth-gap/. Accessed September 21, 2021.
24.
Press Release: McDuffie Secures Baby Bonds and Funding to Reduce Racial Wealth Gap, Facilitate an Equitable Recovery and Reimagine Public Safety in Budget – TheDCLine.org. 2021. Available at: https://thedcline.org/2021/08/03/press-release-mcduffie-secures-baby-bonds-and-funding-to-reduce-racial-wealth-gap-facilitate-an-equitable-recovery-and-reimagine-public-safety-in-budget/. Accessed September 21, 2021.
25.
Earl B. House, House Ways and Means Committee. HR 2863. First-Time Homebuyer Act of 2021. Accessed September 20, 2021. https://www.congress.gov/bill/117th-congress/house-bill/2863?s=1&r=59.
26.
Gross, T. A “Forgotten History” Of How The U.S. Government Segregated America: NPR. 2017. Available at: https://www.npr.org/2017/05/03/526655831/a-forgotten-history-of-how-the-u-s-government-segregated-america. Accessed September 22, 2021.
27.
Residential Security Map of Baltimore Md. Baltimore City Sheet Maps. National Archives and Records Administration. Image Courtesy of the Johns Hopkins Sheridan Libraries. 1937. Available at: http://jhir.library.jhu.edu/handle/1774.2/61867. Accessed November 27, 2021.
28.
Kershaw KN, Osypuk TL, Do DP, De Chavez PJ, Diez Roux AV. Neighborhood-level racial/ethnic residential segregation and incident cardiovascular disease: the multi-ethnic study of atherosclerosis. Circulation. 2015;131:141–148. doi: 10.1161/CIRCULATIONAHA.114.011345
29.
Neighborhood Poverty | National Equity Atlas. Neighborhood poverty: All neighborhoods should be communities of opportunity. 2021. Available at: https://nationalequityatlas.org/indicators/Neighborhood_poverty#/. Accessed September 22, 2021.
30.
Wen M, Zhang X, Harris CD, Holt JB, Croft JB. Spatial disparities in the distribution of parks and green spaces in the USA. Ann Behav Med. 2013;45(Suppl 1):S18–S27. doi: 10.1007/s12160-012-9426-x
31.
Syed ST, Gerber BS, Sharp LK. Traveling towards disease: transportation barriers to health care access. J Community Health. 2013;38:976–993. doi: 10.1007/s10900-013-9681-1
32.
Clark CR, Ommerborn MJ, Hickson DA, Grooms KN, Sims M, Taylor HA, Albert MA. Neighborhood disadvantage, neighborhood safety and cardiometabolic risk factors in African Americans: biosocial associations in the Jackson Heart study. PLoS One. 2013;8:e63254. doi: 10.1371/journal.pone.0063254
33.
Sprung MR, Faulkner LMD, Evans MK, Zonderman AB, Waldstein SR. Neighborhood crime is differentially associated with cardiovascular risk factors as a function of race and sex. J Public Health Res. 2019;8:1643. doi: 10.4081/jphr.2019.1643
34.
Ross CE, Mirowsky J. Neighborhood disadvantage, disorder, and health. J Health Soc Behav. 2001;42:258–276.
35.
Jones MR, Diez-Roux AV, Hajat A, Kershaw KN, O’Neill MS, Guallar E, Post WS, Kaufman JD, Navas-Acien A. Race/ethnicity, residential segregation, and exposure to ambient air pollution: the Multi-Ethnic Study of Atherosclerosis (MESA). Am J Public Health. 2014;104:2130–2137. doi: 10.2105/AJPH.2014.302135
36.
Erqou S, Clougherty JE, Olafiranye O, Magnani JW, Aiyer A, Tripathy S, Kinnee E, Kip KE, Reis SE. Particulate matter air pollution and racial differences in cardiovascular disease risk. Arterioscler Thromb Vasc Biol. 2018;38:935–942. doi: 10.1161/ATVBAHA.117.310305
37.
Kullgren JT, McLaughlin CG, Mitra N, Armstrong K. Nonfinancial barriers and access to care for U.S. adults. Health Serv Res. 2012;47(1 Pt 2):462–485. doi: 10.1111/j.1475-6773.2011.01308.x
38.
Walsh MN, Joynt KE. Delays in seeking care: a women’s problem? Circ Cardiovasc Qual Outcomes. 2016;9(2 Suppl 1):S97–S99. doi: 10.1161/CIRCOUTCOMES.116.002668
39.
Ting HH, Chen AY, Roe MT, Chan PS, Spertus JA, Nallamothu BK, Sullivan MD, DeLong ER, Bradley EH, Krumholz HM, et al. Delay from symptom onset to hospital presentation for patients with non-ST-segment elevation myocardial infarction. Arch Intern Med. 2010;170:1834–1841. doi: 10.1001/archinternmed.2010.385
40.
Ting HH, Bradley EH, Wang Y, Lichtman JH, Nallamothu BK, Sullivan MD, Gersh BJ, Roger VL, Curtis JP, Krumholz HM. Factors associated with longer time from symptom onset to hospital presentation for patients with ST-elevation myocardial infarction. Arch Intern Med. 2008;168:959–968. doi: 10.1001/archinte.168.9.959
41.
Testa A, Jackson DB, Semenza DC, Vaughn MG. Food deserts and cardiovascular health among young adults. Public Health Nutr. 2021;24:117–124. doi: 10.1017/S1368980020001536
42.
Kelli HM, Hammadah M, Ahmed H, Ko YA, Topel M, Samman-Tahhan A, Awad M, Patel K, Mohammed K, Sperling LS, et al. Association between living in food deserts and cardiovascular risk. Circ Cardiovasc Qual Outcomes. 2017;10:e003532. doi: 10.1161/CIRCOUTCOMES.116.003532
43.
Wisseh C, Hildreth K, Marshall J, Tanner A, Bazargan M, Robinson P. Social determinants of pharmacy deserts in Los Angeles County. J Racial Ethn Health Disparities. 2021;8:1424–1434. doi: 10.1007/s40615-020-00904-6
44.
Affirmatively Furthering Fair Housing. HUD.gov / U.S. Department of Housing and Urban Development (HUD). 2021. Available at: https://www.hud.gov/program_offices/fair_housing_equal_opp/affh#_What_is_AFFH. Accessed September 21, 2021.
45.
American Dream Downpayment Initiative. Housing and Urban Development. 2007. Accessed Sep 1, 2021. https://obamawhitehouse.archives.gov/sites/default/files/omb/assets/omb/expectmore/summary/10009022.2007.html.
46.
Green, D. A Guide to the $15,000 First-Time Homebuyer Act of 2021. 2021. Available at: https://homebuyer.com/learn/15000-first-time-home-buyer-tax-credit. Accessed September 21, 2021.
47.
Kelli HM, Mehta A, Tahhan AS, Liu C, Kim JH, Dong TA, Dhindsa DS, Ghazzal B, Choudhary MK, Sandesara PB, et al. Low educational attainment is a predictor of adverse outcomes in patients with coronary artery disease. J Am Heart Assoc. 2019;8:e013165. doi: 10.1161/JAHA.119.013165
48.
Roberts C. Flashback: History Made 70 Years Ago This Week: Heman Sweatt Enrolls. Texas Law News. 2020. Available at: https://law.utexas.edu/news/category/flashback/. Accessed September 21, 2021.
49.
Brown, v. Board of Education of Topeka (1). Oyez. Available at: www.oyez.org/cases/1940-1955/347us483. Accessed 26 October 2021.
50.
de Brey, C, Musu, L, McFarland, J, Wilkinson-Flicker, S, Diliberti, M, Zhang, A, Branstetter, C, Wang, X. Status and Trends in the Education of Racial and Ethnic Groups 2018. 2019. Accessed September 1, 2021. Available at: https://nces.ed.gov/pubs2019/2019038.pdf.
51.
United States Joint Economic Committee. Zoned Out: How School and Residential Zoning Limit Educational Opportunity. 2019. Available at: https://www.jec.senate.gov/public/index.cfm/republicans/2019/11/zoned-out-how-school-and-residential-zoning-limit-educational-opportunity. Accessed September 21, 2021.
52.
Boschma, J, Brownstein, R. Students of Color Are Much More Likely to Attend High-Poverty Schools - The Atlantic. 2016. https://www.theatlantic.com/education/archive/2016/02/concentration-poverty-american-schools/471414/. Accessed September 21, 2021.
53.
McFarland, J, Cui, J, Rathbun, A, Holmes, J. on behalf of the Department of Education. Trends in High School Dropout and Completion Rates in the United States: 2018 Compendium Report. Accessed September 1, 2021. https://nces.ed.gov/pubs2019/2019117.pdf
54.
Addo FR, Houle JN, Simon D. Young, black, and (still) in the red: parental wealth, race, and student loan debt. Race Soc Probl. 2016;8:64–76.
55.
Grinstein-Weiss M, Perantie DC, Taylor SH, Guo S, Raghavan R. Racial disparities in education debt burden among low- and moderate-income households. Child Youth Serv Rev. 2016;65:166–174. doi: 10.1016/j.childyouth.2016.04.010
56.
Bertrand M, Mullainathan S. “Are emily and greg more employable than Lakisha and Jamal? A field experiment on labor market discrimination.” Am Econ Rev. 2004;94: 991–1013. doi: 10.1257/0002828042002561.
57.
U.S. Bureau of Labor Statistics. Earnings and Unemployment Rates by Educational Attainment 2020. 2021. Available at: https://www.bls.gov/emp/chart-unemployment-earnings-education.htm. Accessed September 21, 2021.
58.
Magnani JW, Mujahid MS, Aronow HD, Cené CW, Dickson VV, Havranek E, Morgenstern LB, Paasche-Orlow MK, Pollak A, Willey JZ; American Heart Association Council on Epidemiology and Prevention; Council on Cardiovascular Disease in the Young; Council on Cardiovascular and Stroke Nursing; Council on Peripheral Vascular Disease; Council on Quality of Care and Outcomes Research; and Stroke Council. Health literacy and cardiovascular disease: fundamental relevance to primary and secondary prevention: a scientific statement from the American Heart Association. Circulation. 2018;138:e48–e74. doi: 10.1161/CIR.0000000000000579
59.
Hahn RA, Truman BI. Education improves public health and promotes health equity. Int J Health Serv. 2015;45:657–678. doi: 10.1177/0020731415585986
60.
Washington State Association of Head Start and ECEAP. 2021. Accessed December 20, 2021. https://wsaheadstarteceap.com/.
61.
Carnethon MR, Pu J, Howard G, Albert MA, Anderson CAM, Bertoni AG, Mujahid MS, Palaniappan L, Taylor HA, Willis M, Yancy CW; American Heart Association Council on Epidemiology and Prevention; Council on Cardiovascular Disease in the Young; Council on Cardiovascular and Stroke Nursing; Council on Clinical Cardiology; Council on Functional Genomics and Translational Biology; and Stroke Council. Cardiovascular health in African Americans: a scientific statement from the American Heart Association. Circulation. 2017;136:e393–e423. doi: 10.1161/CIR.0000000000000534
62.
Emmons KM, Barbeau EM, Gutheil C, Stryker JE, Stoddard AM. Social influences, social context, and health behaviors among working-class, multi-ethnic adults. Health Educ Behav. 2007;34:315–334. doi: 10.1177/1090198106288011
63.
Freeborne N, Simmens SJ, Manson JE, Howard BV, Cené CW, Allison MA, Corbie-Smith G, Bell CL, Denburg NL, Martin LW. Perceived social support and the risk of cardiovascular disease and all-cause mortality in the Women’s Health Initiative Observational Study. Menopause. 2019;26:698–707. doi: 10.1097/GME.0000000000001297
64.
Compare A, Zarbo C, Manzoni GM, Castelnuovo G, Baldassari E, Bonardi A, Callus E, Romagnoni C. Social support, depression, and heart disease: a ten year literature review. Front Psychol. 2013;4:384. doi: 10.3389/fpsyg.2013.00384
65.
Matthews, P, Besemer, K. Poverty and social networks evidence review A Report for the Joseph Rowntree Foundation Anti-Poverty Programme. 2014. Accessed September 1, 2021. https://core.ac.uk/download/pdf/42543158.pdf
66.
Emerson E, Hatton C. Socioeconomic disadvantage, social participation and networks and the self-rated health of English men and women with mild and moderate intellectual disabilities: cross sectional survey. Eur J Public Health. 2008;18:31–37. doi: 10.1093/eurpub/ckm041
67.
Algren MH, Ekholm O, Nielsen L, Ersbøll AK, Bak CK, Andersen PT. Social isolation, loneliness, socioeconomic status, and health-risk behaviour in deprived neighbourhoods in Denmark: a cross-sectional study. SSM Popul Health. 2020;10:100546. doi: 10.1016/j.ssmph.2020.100546
68.
Schwartz, H, Burkhauser, S, Griffin, BANN, Kennedy, D, Green, H, Kennedy-hendricks, A, Pollack, C. Mixed-income neighborhoods expand social networks and benefit health. 2014 Available at: https://www.macfound.org/media/files/hhm_brief_-_mixed-income_neighborhoods_expand_social_networks_benefit_health.pdf
69.
Uphoff EP, Pickett KE, Cabieses B, Small N, Wright J. A systematic review of the relationships between social capital and socioeconomic inequalities in health: a contribution to understanding the psychosocial pathway of health inequalities. Int J Equity Health. 2013;12:54. doi: 10.1186/1475-9276-12-54
70.
Bell CN, Thorpe RJ, Bowie JV, LaVeist TA. Race disparities in cardiovascular disease risk factors within socioeconomic status strata. Ann Epidemiol. 2018;28:147–152. doi: 10.1016/j.annepidem.2017.12.007
71.
Wiesmaierova S, Petrova D, Arrebola Moreno A, Catena A, Ramírez Hernández JA, Garcia-Retamero R. Social support buffers the negative effects of stress in cardiac patients: a cross-sectional study with acute coronary syndrome patients. J Behav Med. 2019;42:469–479. doi: 10.1007/s10865-018-9998-4
72.
Halpern D, Nazroo J. The ethnic density effect: results from a national community survey of England and Wales. Int J Soc Psychiatry. 2000;46:34–46. doi: 10.1177/002076400004600105
73.
Garcés IC, Scarinci IC, Harrison L. An examination of sociocultural factors associated with health and health care seeking among Latina immigrants. J Immigr Minor Health. 2006;8:377–385. doi: 10.1007/s10903-006-9008-8
74.
Krause N, Bastida E. Social relationships in the church during late life: assessing differences between African Americans, whites, and Mexican Americans. Rev Relig Res. 2011;53:41–63. doi: 10.1007/s13644-011-0008-3
75.
Antonucci TC, Ajrouch KJ, Birditt KS. The convoy model: explaining social relations from a multidisciplinary perspective. Gerontologist. 2014;54:82–92. doi: 10.1093/geront/gnt118
76.
Hobson-Prater T, Leech TGJ. The significance of race for neighborhood social cohesion: perceived difficulty of collective action in majority black neighborhoods. J Sociol Soc Welf. 2012;39:89-109. Available at: https://scholarworks.wmich.edu/jssw/vol39/iss1/6
77.
Medina-Inojosa J, Jean N, Cortes-Bergoderi M, Lopez-Jimenez F. The Hispanic paradox in cardiovascular disease and total mortality. Prog Cardiovasc Dis. 2014;57:286–292. doi: 10.1016/j.pcad.2014.09.001
78.
Almeida J, Kawachi I, Molnar BE, Subramanian SV. A multilevel analysis of social ties and social cohesion among Latinos and their neighborhoods: results from Chicago. J Urban Health. 2009;86:745–759. doi: 10.1007/s11524-009-9375-2
79.
The Office of Minority Health; US Department of Health and Human Services. Heart Disease and American Indians/Alaska Natives. 2017. Available at: https://www.minorityhealth.hhs.gov/omh/browse.aspx?lvl=4&lvlID=34. Accessed September 21, 2021.
80.
Conte KP, Schure MB, Goins RT. Correlates of social support in older American Indians: the Native Elder Care Study. Aging Ment Health. 2015;19:835–843. doi: 10.1080/13607863.2014.967171
81.
Kim HS, Sherman DK, Taylor SE. Culture and social support. Am Psychol. 2008;63:518–526. doi: 10.1037/0003-066X
82.
Wang EA, Redmond N, Dennison Himmelfarb CR, Pettit B, Stern M, Chen J, Shero S, Iturriaga E, Sorlie P, Diez Roux AV. Cardiovascular disease in incarcerated populations. J Am Coll Cardiol. 2017;69:2967–2976. doi: 10.1016/j.jacc.2017.04.040
83.
Coleman J, Lloyd-Jones DM, Ning H, Allen NB, Kiefe CI, Wang EA, Huffman MD. Association between incarceration and incident cardiovascular disease events: results from the CARDIA cohort study. BMC Public Health. 2021;21:214. doi: 10.1186/s12889-021-10237-6
84.
Criminal Justice Fact Sheet | >National Association for the Advancement of Colored People. 2021. Available at: https://naacp.org/resources/criminal-justice-fact-sheet. Accessed September 23, 2021.
85.
U.S. Census Bureau QuickFacts: United States. 2019. Available at: https://www.census.gov/quickfacts/fact/table/US/PST045219. Accessed September 21, 2021.
86.
2018 National Healthcare Quality and Disparities Report. 2019. Agency for Healthcare Research and Quality, Rockville, MD. Available at: https://www.ahrq.gov/research/findings/nhqrdr/nhqdr18/index.html. Accessed August 29, 2021.
87.
>Access to Care. American Heart Association. 2018. Available at: https://www.heart.org/en/get-involved/advocate/federal-priorities/access-to-care. Accessed September 21, 2021.
88.
Barghi A, Torres H, Kressin NR, McCormick D. Coverage and access for Americans with cardiovascular disease or risk factors after the ACA: a Quasi-experimental study. J Gen Intern Med. 2019;34:1797–1805. doi: 10.1007/s11606-019-05108-1
89.
Fang J, Yang Q, Ayala C, Loustalot F. Disparities in access to care among US adults with self-reported hypertension. Am J Hypertens. 2014;27:1377–1386. doi: 10.1093/ajh/hpu061
90.
Hannan EL, Racz MJ, Walford G, Jacobs AK, Stamato NJ, Gesten F, Berger PB, Sharma S, King SB. Disparities in the use of drug-eluting coronary stents by race, ethnicity, payer, and hospital. Can J Cardiol. 2016;32:987.e25–987.e31. doi: 10.1016/j.cjca.2016.01.012
91.
Popescu I, Nallamothu BK, Vaughan-Sarrazin MS, Cram P. Racial differences in admissions to high-quality hospitals for coronary heart disease. Arch Intern Med. 2010;170:1209–1215. doi: 10.1001/archinternmed.2010.227
92.
Joynt KE, Orav EJ, Jha AK. Thirty-day readmission rates for Medicare beneficiaries by race and site of care. JAMA. 2011;305:675–681. doi: 10.1001/jama.2011.123
93.
Joynt KE, Sarma N, Epstein AM, Jha AK, Weissman JS. Challenges in reducing readmissions: lessons from leadership and frontline personnel at eight minority-serving hospitals. Jt Comm J Qual Patient Saf. 2014;40:435–437. doi: 10.1016/s1553-7250(14)40056-4
94.
Danziger J, Ángel Armengol de la Hoz M, Li W, Komorowski M, Deliberato RO, Rush BNM, Mukamal KJ, Celi L, Badawi O. Temporal trends in critical care outcomes in U.S. minority-serving hospitals. Am J Respir Crit Care Med. 2020;201:681–687. doi: 10.1164/rccm.201903-0623OC
95.
Aggarwal R, Hammond JG, Joynt Maddox KE, Yeh RW, Wadhera RK. Association between the proportion of black patients cared for at hospitals and financial penalties under value-based payment programs. JAMA. 2021;325:1219–1221. doi: 10.1001/jama.2021.0026
96.
Hall WJ, Chapman MV, Lee KM, Merino YM, Thomas TW, Payne BK, Eng E, Day SH, Coyne-Beasley T. Implicit racial/ethnic bias among health care professionals and its influence on health care outcomes: a systematic review. Am J Public Health. 2015;105:e60–e76. doi: 10.2105/AJPH.2015.302903
97.
Breathett K, Yee E, Pool N, Hebdon M, Crist JD, Knapp S, Larsen A, Solola S, Luy L, Herrera-Theut K, et al. Does race influence decision making for advanced heart failure therapies? J Am Heart Assoc. 2019;8:e013592. doi: 10.1161/JAHA.119.013592
98.
Johnson RL, Roter D, Powe NR, Cooper LA. Patient race/ethnicity and quality of patient-physician communication during medical visits. Am J Public Health. 2004;94:2084–2090. doi: 10.2105/ajph.94.12.2084
99.
Davis BA. Discrimination: a social determinant of health inequities. Health Aff. 2020. doi: 10.1377/hblog20200220.518458
100.
Brondolo E, Rieppi R, Kelly KP, Gerin W. Perceived racism and blood pressure: a review of the literature and conceptual and methodological critique. Ann Behav Med. 2003;25:55–65. doi: 10.1207/S15324796ABM2501_08
101.
Dolezsar CM, McGrath JJ, Herzig AJM, Miller SB. Perceived racial discrimination and hypertension: a comprehensive systematic review. Health Psychol. 2014;33:20–34. doi: 10.1037/a0033718
102.
Mouton CP, Carter-Nolan PL, Makambi KH, Taylor TR, Palmer JR, Rosenberg L, Adams-Campbell LL. Impact of perceived racial discrimination on health screening in black women. J Health Care Poor Underserved. 2010;21:287–300. doi: 10.1353/hpu.0.0273
103.
Cuffee YL, Hargraves JL, Rosal M, Briesacher BA, Schoenthaler A, Person S, Hullett S, Allison J. Reported racial discrimination, trust in physicians, and medication adherence among inner-city African Americans with hypertension. Am J Public Health. 2013;103:e55–e62. doi: 10.2105/AJPH.2013.301554
104.
Brewer LC, Cooper LA. Race, discrimination, and cardiovascular disease. Virtual Mentor. 2014;16:455–460.
105.
Harrell CJ, Burford TI, Cage BN, Nelson TM, Shearon S, Thompson A, Green S. Multiple pathways linking racism to health outcomes. Du Bois Rev. 2011;8:143–157. doi: 10.1017/S1742058X11000178
106.
Lewis TT, Aiello AE, Leurgans S, Kelly J, Barnes LL. Self-reported experiences of everyday discrimination are associated with elevated C-reactive protein levels in older African-American adults. Brain Behav Immun. 2010;24:438–443. doi: 10.1016/j.bbi.2009.11.011
107.
Kershaw KN, Lewis TT, Diez Roux AV, Jenny NS, Liu K, Penedo FJ, Carnethon MR. Self-reported experiences of discrimination and inflammation among men and women: the multi-ethnic study of atherosclerosis. Health Psychol. 2016;35:343–350. doi: 10.1037/hea0000331
108.
Alcaraz KI, Eddens KS, Blase JL, Diver WR, Patel AV, Teras LR, Stevens VL, Jacobs EJ, Gapstur SM. Social isolation and mortality in US black and white men and women. Am J Epidemiol. 2019;188:102–109. doi: 10.1093/aje/kwy231
109.
Lavizzo-Mourey RJ, Besser RE, Williams DR. Understanding and mitigating health inequities - past, current, and future directions. N Engl J Med. 2021;384:1681–1684. doi: 10.1056/NEJMp2008628
110.
Wadhera RK, Figueroa JF, Rodriguez F, Liu M, Tian W, Kazi DS, Song Y, Yeh RW, Joynt Maddox KE. Racial and ethnic disparities in heart and cerebrovascular disease deaths during the COVID-19 pandemic in the United States. Circulation. 2021;143:2346–2354. doi: 10.1161/CIRCULATIONAHA.121.054378
111.
Bay Area Regional Health Inequities Initiative (BARHII). Applying Social Determinants of Health Indicator Data for Advancing Health Equity: A Guide for Local Health Department Epidemiologists and Public Health Professionals. 2020. Accessed September 9, 2021. Available at: https://www.barhii.org/sdoh-indicator-guide
112.
Indiana Leaders Launch SDOH Data-Sharing Initiative. Indiana Network for Population Health (INPH). 2020. Accessed September 9, 2021. Available at: https://www.hcinnovationgroup.com/population-health-management/social-determinants- of-health/article/21137984/indiana-leaders-launch-sdoh-datasharing-initiative
113.
HealthIT.gov. Advancing Interoperable Social Determinants of Health Data. 2019. Accessed August 28, 2021. Available at: https://www.healthit.gov/buzz-blog/interoperability/advancing-interoperable-social- determinants-of-health-data
114.
Lukanen, E, Zylla, E. Exploring Strategies to Fill Gaps in Medicaid Race, Ethnicity, and Language Data. 2020. Accessed August 30, 2021. Available at: https://www.shvs.org/exploring-strategies-to-fill-gaps-in-medicaid- race-ethnicity-and-language-data/
115.
US Department of Interior. Standards for Maintaining, Collecting, and Presenting Federal Data on Race and Ethnicity. 1997. Accessed August 30, 2021. Available at: https://www.doi.gov/pmb/eeo/directives/race-data
116.
Khodyakov, D, Bromley, E, Evans, SK, Sieck, K. Best Practices for Participant and Stakeholder Engagement in the All of Us Research Program. Santa Monica, CA: RAND Corporation, 2018. Accessed August 30, 2021. https://www.rand.org/pubs/research_reports/RR2578.html
117.
Javed Z, Valero-Elizondo J, Dudum R, Khan SU, Dubey P, Hyder AA, Xu J, Bilal U, Kash BA, Cainzos-Achirica M, et al. Development and validation of a polysocial risk score for atherosclerotic cardiovascular disease. Am J Prev Cardiol. 2021;8:100251. doi: 10.1016/j.ajpc.2021.100251
118.
The Protocol for Responding to and Assessing Patients’ Assets, Risks, and Experiences (PRAPARE). 2020. Accessed September 20, 2021. Available at: https://www.nachc.org/research-and-data/prapare/
119.
Agency for Healthcare Research and Quality. The SHARE Approach—Taking Steps Toward Cultural Competence: A Fact Sheet. 2020. Accessed September 1, 2021. Available at: https://www.ahrq.gov/health- literacy/professional-training/shared-decision/tool/resource-7.html
120.
County Health Rankings. Public reporting of health care quality performance. 2020. Accessed August 29, 2021. Available at: https://www.countyhealthrankings.org/take-action-to-improve-health/what-works-for- health/strategies/public-reporting-of-health-care-quality-performance
121.
Department of Health and Human Services. Improving the identification of health care disparities in medicaid and CHIP. 2014. Accessed August 29, 2021. Available at: https://www.medicaid.gov/medicaid/quality-of- care/downloads/4302b-rtc-2014.pdf
122.
Takeshita J, Wang S, Loren AW, Mitra N, Shults J, Shin DB, Sawinski DL. Association of racial/ethnic and gender concordance between patients and physicians with patient experience ratings. JAMA Netw Open. 2020;3:e2024583. doi: 10.1001/jamanetworkopen.2020.24583
123.
Johnson AE, Talabi MB, Bonifacino E, Culyba AJ, Davis EM, Davis PK, De Castro LM, Essien UR, Maria Gonzaga A, Hogan MV, et al. Racial diversity among American Cardiologists: implications for the past, present, and future. Circulation. 2021;143:2395–2405. doi: 10.1161/CIRCULATIONAHA.121.053566
124.
Atkins D, Kilbourne A, Lipson L. Health equity research in the Veterans Health Administration: we’ve come far but aren’t there yet. Am J Public Health. 2014;104(Suppl 4):S525–S526. doi: 10.2105/AJPH.2014.302216
125.
Chien AT, Chin MH, Davis AM, Casalino LP. Pay for performance, public reporting, and racial disparities in health care: how are programs being designed? Med Care Res Rev. 2007;64(Suppl 5):283S–304S. doi: 10.1177/1077558707305426
126.
Racial Equity Tools: Community Engagement. 2020. Available at: Accessed September 21, 2021. https://www.racialequitytools.org/resources/act/strategies/community-engagement

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Go to Circulation: Cardiovascular Quality and Outcomes
Go to Circulation: Cardiovascular Quality and Outcomes
Circulation: Cardiovascular Quality and Outcomes
Pages: e007917
PubMed: 35041484

History

Published in print: January 2022
Published online: 18 January 2022

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Keywords

  1. cardiovascular disease
  2. ethnicity
  3. morbidity
  4. quality of care
  5. racism

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Zulqarnain Javed, MD, MPH, PhD* https://orcid.org/0000-0002-4137-9198
Division of Health Equity & Disparities Research, Center for Outcomes Research, Houston Methodist, TX (Z.J., M.C.-A., K.N.).
Muhammad Haisum Maqsood, MD*
Department of Medicine, Lincoln Medical Center, New York (M.H.M.).
Tamer Yahya, MD
Center for Outcomes Research, Houston Methodist, TX (T.Y., I.A., J.V.-E., M.C.-A., K.N.).
Center for Outcomes Research, Houston Methodist, TX (T.Y., I.A., J.V.-E., M.C.-A., K.N.).
Javier Valero-Elizondo, MD, MPH
Center for Outcomes Research, Houston Methodist, TX (T.Y., I.A., J.V.-E., M.C.-A., K.N.).
Division of Cardiovascular Prevention and Wellness, Houston Methodist DeBakey Heart and Vascular Center, TX (J.V.-E., M.C.-A., K.N.).
Center for Cardiovascular Computational Health & Precision Medicine (C3-PH), Houston Methodist, TX (J.V.-E., M.C.-A., K.N.).
Population Health and Primary Care (J.A.), Houston Methodist Hospital, TX.
Prachi Dubey, MD, MPH
Houston Methodist Hospital, Houston Methodist Research Institute, TX (P.D.).
Ryane K. Jackson, BA, MLA
Office of Community Benefits (R.K.J.), Houston Methodist Hospital, TX.
Mary A. Daffin, JD
Barrett Daffin Frappier Turner & Engel, L.L.P., Houston, TX (M.A.D.).
Miguel Cainzos-Achirica, MD, MPH, PhD https://orcid.org/0000-0002-8073-2337
Division of Health Equity & Disparities Research, Center for Outcomes Research, Houston Methodist, TX (Z.J., M.C.-A., K.N.).
Center for Outcomes Research, Houston Methodist, TX (T.Y., I.A., J.V.-E., M.C.-A., K.N.).
Division of Cardiovascular Prevention and Wellness, Houston Methodist DeBakey Heart and Vascular Center, TX (J.V.-E., M.C.-A., K.N.).
Center for Cardiovascular Computational Health & Precision Medicine (C3-PH), Houston Methodist, TX (J.V.-E., M.C.-A., K.N.).
Adnan A. Hyder, MD, MPH, PhD
Milken Institute School of Public Health, George Washington University, DC (A.A.H.).
Division of Health Equity & Disparities Research, Center for Outcomes Research, Houston Methodist, TX (Z.J., M.C.-A., K.N.).
Division of Cardiovascular Prevention and Wellness, Houston Methodist DeBakey Heart and Vascular Center, TX (J.V.-E., M.C.-A., K.N.).
Center for Cardiovascular Computational Health & Precision Medicine (C3-PH), Houston Methodist, TX (J.V.-E., M.C.-A., K.N.).

Notes

*
Z.J. and M.H.M. contributed equally as co-first authors.
The opinions expressed in this article are not necessarily those of the editors or of the American Heart Association.
For Sources of Funding and Disclosures, see page 83.
Correspondence to: Khurram Nasir, MD, MPH, MSc, Division of Cardiovascular Prevention and Wellness, Department of Cardiology, Houston Methodist DeBakey Heart & Vascular Center, 6550 Fannin St Suite 1801, Houston, TX 77030, Email [email protected]

Disclosures

Disclosures Dr Nasir is on the advisory board of Amgen, Novartis, and his research is partly supported by the Jerold B. Katz Academy of Translational Research. The other authors report no conflicts.

Sources of Funding

None.

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Race, Racism, and Cardiovascular Health: Applying a Social Determinants of Health Framework to Racial/Ethnic Disparities in Cardiovascular Disease
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