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COMMUNITY: Research Reports

Race and Ethnicity and Cardiometabolic Risk Profile: Disparities Across Income and Health Insurance in a National Sample of US Adults

Javed, Zulqarnain PhD, MPH, MBBS; Maqsood, Muhammad Haisum MD; Amin, Zahir BS; Nasir, Khurram MD, MPH, MSc

Author Information
Journal of Public Health Management and Practice: January/February 2022 - Volume 28 - Issue - p S91-S100
doi: 10.1097/PHH.0000000000001441
  • Open

Abstract

Race/ethnicity is an important social determinant of health (SDOH). Racial/ethnic minorities, including non-Hispanic Blacks (NHB) and Hispanics, face considerable barriers to optimal cardiovascular (CV) health and are disproportionately affected by the burden of cardiovascular disease (CVD) in the United States.1 NHB experience higher CV mortality, whereas both NHB and Hispanics have worse cardiometabolic (CMB) risk profile, including hypertension, obesity, and diabetes, than non-Hispanic Whites (NHW).2

Income and insurance—key measures of socioeconomic status (SES) and access to care, respectively—are important determinants of CVD and may explain considerable variation in CVD, overall and by race and ethnicity.3,4 Both measures are often examined as upstream determinants of racial/ethnic inequities and statistically adjusted for in traditional analytic approaches, which precludes assessment of possible differences in the race-CVD association across varying levels of income and insurance.5,6 Relatively little is known about whether the disadvantage in cumulative CMB burden experienced by marginalized racial/ethnic populations persists at each level of income and insurance type.

Limited evidence suggests racial/ethnic disparities in CVD risk within specific insurance programs such as Medicare7; however, such evidence across the entire spectrum of insurance status (private, Medicare, Medicaid, uninsured) is lacking. Similarly, possible disparities in CMB burden across varying levels of income have not been studied. A greater understanding of such differences is critical toward informed conceptualization of racial/ethnic disparities in the United States via greater focus on structural barriers to racial equity in CV care and outcomes, including the role of systemic racism in health care delivery. The objectives of this article were to examine the association between race and ethnicity and cumulative CMB risk profile across income levels and insurance types on a population level. We hypothesized that race and ethnicity is an independent determinant of poor CMB risk profile overall and at each level of income and insurance.

Our findings may inform holistic approaches to the investigation of racial/ethnic inequities in CV care and outcomes, including the role of systemic and institutional barriers to optimal CV health in marginalized populations.

Methods

Data source

We used data from the National Health Interview Survey (NHIS), a database compiled by the National Center for Health Statistics. The NHIS is constructed from annual, cross-sectional national surveys that incorporate complex, multistage sampling to provide estimates on the noninstitutionalized US population.8

The NHIS is divided into 4 components: Household Composition; Family Core; Sample Child Core; and Sample Adult Core.9 The Household Composition file collects basic demographic information about all persons in a household; the Family Core file collects information on additional sociodemographic characteristics, indicators of health status, activity limitations, injuries, health insurance coverage, access to health care, and utilization of health services, additionally surveying individual families should more than 1 family member live in a specific household. Both the Household and Family components acquire information at the household and family levels, respectively. From each family, 1 adult and 1 child are randomly surveyed for additional information, more detailed information including (but not limited to) work characteristics, medical conditions, health status and activity limitations, health behaviors, and health care access and utilization.

The present study used data from the Sample Adult, Family, and Household Composition files. All NHIS data presented in this study are based on participant self-report. These data are publicly available and de-identified; hence, this study was exempt from the purview of Houston Methodist's institutional review board.

Research design and study population

This was a cross-sectional study of NHIS data for years 2013-2017. We included all participants 18 years and older, with complete information during the study period. We excluded participants with missing information on key variables and those who reported “other” for insurance coverage. Final study population included 134661 US adults.

Study variables

Race and ethnicity was the primary independent variable of interest, which was defined as a 3-level nominal variable: NHW, NHB, and Hispanics.

CMB risk profile was the primary dependent variable of interest, which included the following CV risk factors: diabetes, hypertension, hypercholesterolemia, and obesity. Each CMB factor was defined as binary, and given a value of “0” if absent and “1” if present. We calculated aggregate CMB burden by summing the individual risk factors, with a resulting score of 0 to 4. CMB risk profile was defined as a 3-level ordinal variable: optimum (score = 0), average (score = 1-2), and poor (score = 3-4). Despite considerable variation in cutoffs,10–12 CMB risk profile has been analyzed as an ordinal variable with 3 categories in prior studies.12

Insurance status was defined as a 4-level nominal variable, including uninsured, private, Medicare, and Medicaid.

Income level was categorized on the basis of established federal poverty level (FPL) cutoffs into lowest income (<125% of FPL), low (125% ≥ FPL <200%), middle (200% ≥ FPL <400%), and high income (400% of ≥FPL).

Covariates included were age (18-44, 45-64, ≥65 years), sex (male/female), smoking status (smoker/nonsmoker), physical activity (sufficient/insufficient), atherosclerotic cardiovascular disease (ASCVD) status (coronary artery disease and/or stroke: yes/no), and comorbidities (aggregate comorbidity index, including emphysema, chronic obstructive pulmonary disease, asthma, gastrointestinal ulcer, cancer [any], arthritis, and any liver conditions), categorized into 0, 1, and 2 or more comorbidities.

Statistical analyses

We reported descriptive characteristics, including demographics, health behaviors, and comorbidity status by race and ethnicity in the study population. We also reported CMB risk profile by race and ethnicity in the total population. Chi-squared tests were used to compare differences among the 3 (NHW, NHB, and Hispanic) groups. Age-adjusted weighted proportions were generated to report nationally representative estimates. Distribution (weighted proportions, 95% CI) of optimal, average, and poor CMB risk profile was reported separately for each racial/ethnic group, as well as for individual strata of insurance (uninsured, private, Medicare, and Medicaid) and income (lowest income, low, middle, and high income) within each racial/ethnic subpopulation.

Multivariable ordinal logistic regression models were used to examine the association between race and ethnicity and CMB risk profile, adjusted for demographic and clinical covariates. Multiple models were fitted. First, we examined the race-CMB association in the total population. Second, we examined the association across individual insurance types. Third, we analyzed the association between race and CMB at each level of income. For each analysis, we presented 2 models: model 1, adjusted for age and sex; and model 2, adjusted for model 1 + smoking status, physical activity, ASCVD status, and Elixhauser comorbidity index. For the total population, model 2 also included insurance status and income. Insurance-stratified analyses accounted for income, whereas income-stratified analyses were adjusted for insurance status.

Variance estimation for the entire pooled cohort was obtained from the Integrated Public Use Microdata Series.13 For all statistical analyses, P < .05 was considered statistically significant. All analyses were performed using Stata, version 16 (StataCorp, LP, College Station, Texas), and took into consideration the NHIS complex survey design.

Results

Final analytic sample comprised 134661 individuals, representing 197.8 million annualized US adults. NHW, NHB, and Hispanic populations comprised 70%, 13%, and 18%, respectively, of the total sample. NHB and Hispanics were more likely to have low/lowest income, be uninsured or receive government assistance for health care (Medicaid), and have insufficient physical activity, compared with NHW. NHB had the highest burden of ASCVD. Both NHB (14.7%) and Hispanic (11.1%) populations were more likely to experience poor CMB risk profile than their NHW counterparts (9.3%). Descriptive participant characteristics are presented in Table 1.

TABLE 1 - Sample Descriptive Characteristics by Race and Ethnicity, From the National Health Interview Survey 2013-2017
NHW NHB Hispanic P
Sample, N 94 168 18 648 21 845
Weighted sample (weighted %) 137 656 430 (69.6) 25 484 318 (12.9) 34 639 863 (17.5)
Sex <.001
Male 43 473 (49.0) 7 448 (45.0) 9 619 (49.3)
Female 50 695 (51.0) 11 200 (55.0) 12 226 (50.7)
Socioeconomic status <.001
Lowest income 14 139 (11.8) 6 458 (27.9) 7 537 (28.3)
Low income 12 425 (11.5) 3 551 (18.4) 4 739 (22.3)
Middle income 27 930 (29.3) 4 877 (28.9) 5 878 (28.9)
High income 39 674 (47.4) 3 762 (24.8) 3 691 (20.5)
Insurance <.001
Uninsured 7 594 (8.8) 2 588 (14.4) 6 242 (26.2)
Private 50 648 (63.8) 7 618 (47.1) 8 641 (41.4)
Medicare 23 322 (15.3) 3 239 (14.1) 2 030 (11.4)
Medicaid 7 714 (8.0) 4 245 (19.3) 4 135 (17.6)
Other 4 890 (4.0) 958 (5.1) 797 (3.4)
Smoking <.001
Nonsmoker 77 132 (81.6) 15 077 (82.6) 19 261 (89.5)
Smoker 17 036 (18.4) 3 571 (17.4) 2 584 (10.5)
Physical activity <.001
Sufficient 49 517 (55.7) 7 691 (44.1) 9 635 (43.1)
Insufficient 44 651 (44.3) 10 957 (55.9) 12 210 (56.9)
ASCVD <.001
No 84 580 (93.0) 16 725 (91.7) 20 542 (93.3)
Yes 9 588 (7.0) 1 923 (8.3) 1 303 (6.7)
Comorbidities <.001
0 48 646 (58.8) 10 650 (61.4) 15 057 (68.0)
1 27 869 (27.0) 5 277 (26.6) 4 873 (23.0)
≥2 17 653 (14.2) 2 721 (12.0) 1 915 (9.0)
CMB risk profilea <.001
Optimum 38 358 (46.3) 6 153 (36.1) 9 994 (43.1)
Average 44 494 (44.4) 9 327 (49.2) 9 683 (45.8)
Poor 11 316 (9.3) 3 168 (14.7) 2 168 (11.1)
Abbreviations: ASCVD, atherosclerotic cardiovascular disease; CMB, cardiometabolic; NHB, non-Hispanic Black; NHW, non-Hispanic White.
aCMB risk profile (risk factors = diabetes, hypertension, obesity, hypercholesterolemia): optimum = 0 risk factor; average = 1-2 risk factors; poor = 3-4 risk factors.

The age-adjusted prevalence of CMB risk profile overall and by race and ethnicity, insurance type, and income level is reported in Table 2. Approximately 44% of the population reported optimum CMB risk profile, whereas 46% and 10% reported average and poor profile, respectively. Overall, a higher proportion of NHB (15%) and Hispanics (11%) experienced poor CMB risk profile than NHW (9%). Across all racial/ethnic groups, the burden of poor CMB profile was higher among individuals with low/lowest income (relative to those with middle or high income) and those receiving public health benefits, that is, Medicaid or Medicare (compared with the uninsured or those receiving private insurance).

TABLE 2 - Age-Adjusted Prevalence of CMB Risk Profile,a By Race, Household Income, and Insurance Type, From the National Health Interview Survey 2013-2017
Optimum, % (95% CI) Average, % (95% CI) Poor, % (95% CI)
Total population 44.4 (44-44.8) 45.5 (45.1-45.6) 10.1 (9.9-10.3)
NHW
Total 46.3 (45.8-46.8) 44.4 (43.9-44.8) 9.3 (9.1-9.6)
Insurance
Uninsured 53 (51.4-54.5) 40.6 (39.1-42.2) 6.4 (5.5-7.5)
Private 49.3 (48.2-50.3) 42.9 (41.9-44) 7.8 (7.1-8.7)
Medicare 36.1 (32.9-39.5) 45.6 (42.1-49.2) 18.3 (16.4-20.3)
Medicaid 35.2 (33.8-36.6) 48.9 (47.1-50.2) 16.1 (15.1-17.2)
Socioeconomic status
Lowest income 39.2 (38.1-40.3) 46.2 (45-47.4) 14.6 (13.8-15.5)
Low income 41.8 (40.7-43.1) 45.8 (44.5-47) 12.3 (11.6-13.2)
Middle income 44 (43.2-44.9) 45.7 (44.9-46.6) 10.2 (9.8-10.7)
High income 50.1 (49.4-50.8) 42.6 (41.9,43.3) 7.3 (7-7.7)
NHB
Total 36.1 (35.1-37) 49.2 (48.3-50.1) 14.7 (14.1-15.3)
Insurance
Uninsured 45.6 (43.2-48.1) 46.1 (43.8-48.5) 8.3 (7.2-9.5)
Private 38.4 (36.9-40) 49.8 (48-51.6) 11.8 (10.5-13.3)
Medicare 23.4 (20-27.3) 51.1 (46.2-55.8) 25.5 (10.5-13.3)
Medicaid 28.7 (27-30.5) 51.4 (49.3-53.4) 20 (18.4-21.5)
Socioeconomic status
Lowest income 32.8 (31.3-34.3) 49.7 (48.1-51.4) 17.5 (16.3-18.7)
Low income 34.6 (32.5-36.8) 49.5 (47.3-51.6) 15.8 (14.5-17.3)
Middle income 37.7 (36-39.5) 47.8 (45.9-50) 14.5 (13.4-15.7)
High income 39 (37-41.1) 49.4 (47.3-51.6) 11.6 (10.4-12.8)
Hispanic
Total 43.1 (42.2-44) 45.8 (44.8-46.7) 11.1 (10.5-11.7)
Insurance
Uninsured 48.5 (46.3-50.6) 44.5 (42.5-46.5) 7 (5.8-8.5)
Private 45.6 (44.1-47.1) 46.6 (44.8-48.3) 7.8 (6.7-9.1)
Medicare 31.1 (27-35.6) 48.3 (43.5-53.1) 20.5 (17-24.6)
Medicaid 37 (34.8-38.3) 47 (45.1-49) 16.4 (15.1-17.8)
Socioeconomic status
Lowest income 39.8 (38.3-41.2) 46.6 (45.1-48) 13.7 (12.6-14.9)
Low income 41.9 (40.2-43.5) 46.0 (44.1-47.8) 12.2 (11-13.4)
Middle income 44.5 (42.8-46.2) 46.0 (42.3-47.1) 9.6 (8.6-10.6)
High income 46.7 (44.3-49.1) 44.7 (12.6-14.9) 8.6 (7.4-9.9)
Abbreviations: CMB, cardiometabolic; NHB, non-Hispanic Black; NHW, non-Hispanic White.
aCMB risk profile (risk factors = diabetes, hypertension, obesity, hypercholesterolemia): optimum = 0 risk factor; average = 1-2 risk factors; poor = 3-4 risk factors.

We also examined CMB profile across income and insurance for each racial/ethnic group. For both NHB and Hispanic populations, the age-adjusted prevalence of average and poor profile was higher at nearly each level of income and insurance than for NHW. Although persistent, these disparities were more marked for NHB versus NHW than for Hispanics versus NHW (Table 2). These findings are further highlighted in the Figure.

FIGURE
FIGURE:
Age-Adjusted Prevalence of Poor CMB Profile, by Household Income and Insurance StatusAbbreviation: CMB, cardiometabolic. This figure is available in color online (www.JPHMP.com).

Results from multivariable ordinal regression are presented in Table 3. In the total population, NHB and Hispanic individuals had more than 50% and 15% higher odds of poor CMB risk profile (vs optimum or average), respectively, compared with NHW. We further examined the race-CMB association by insurance type and income level. For each given type of health insurance, NHB and Hispanics had higher odds of poor CMB profile, with the greatest difference observed in the Medicare population, for which NHB experienced nearly 2-fold higher odds of poor profile (NHB: OR = 1.90; 95% CI, 1.73-2.08; Hispanics: OR = 1.31; 95% CI, 1.13-1.51). Similar patterns were observed in the income-stratified analyses. At nearly each level of income, NHB and Hispanics had a higher likelihood of being in the poor CMB profile category than NHW, with relatively wider disparities observed at the highest-income group for NHB (NHB: OR = 1.62; 95% CI, 1.47-1.79) and lowest income for Hispanics (Hispanics: OR = 1.27; 95% CI, 1.17-1.39).

TABLE 3 - Association Between Race and Ethnicity and CMB Risk Profilea
Race and Ethnicity Odds of Poor CMB Risk Profile
Model 1,b OR (95% CI) Model 2,c OR (95% CI)
Total
NHW Reference Reference
NHB 1.59 (1.52-1.67) 1.52 (1.45-1.60)
Hispanic 1.15 (1.10-1.20) 1.15 (1.10-1.21)
Insurance statusd
Private
NHW Reference Reference
NHB 1.55 (1.45-1.66) 1.57 (1.47-1.68)
Hispanic 1.11 (1.03-1.19) 1.12 (1.04-1.20)
Medicare
NHW Reference Reference
NHB 1.89 (1.73-2.07) 1.90 (1.73-2.08)
Hispanic 1.23 (1.08-1.41) 1.31 (1.13-1.51)
Medicaid
NHW Reference Reference
NHB 1.26 (1.14-1.39) 1. 33 (1.19-1.48)
Hispanic 0.91 (0.82-1.01) 1.03 (0.92-1.15)
Uninsured
NHW Reference Reference
NHB 1.20 (1.05-1.36) 1.25 (1.10-1.43)
Hispanic 1.10 (1.00-1.22) 1.17 (1.05-1.29)
Income levele
Lowest income
NHW Reference Reference
NHB 1.43 (1.32-1.55) 1.51 (1.38-1.64)
Hispanic 1.06 (0.98-1.15) 1.27 (1.17-1.39)
Low income
NHW Reference Reference
NHB 1.36 (1.24-1.50) 1.45 (1.32-1.61)
Hispanic 0.97 (0.89-1.07) 1.12 (1.02-1.24)
Middle income
NHW Reference Reference
NHB 1.37 (1.26-1.50) 1.48 (1.36-1.62)
Hispanic 0.93 (0.85-1.01) 1.07 (0.98-1.16)
High income
NHW Reference Reference
NHB 1.59 (1.44-1.75) 1.62 (1.47-1.79)
Hispanic 1.11 (1.00-1.24) 1.16 (1.04-1.30)
Abbreviations: ASCVD, atherosclerotic cardiovascular disease; CMB, cardiometabolic; NHB, non-Hispanic Black; NHW, non-Hispanic White.
aCMB risk profile (risk factors = diabetes, hypertension, obesity, hypercholesterolemia): optimum = 0 risk factor; average = 1-2 risk factors; poor = 3-4 risk factors.
bModel 1: Adjusted for age, sex.
cModel 2: Adjusted for age, sex, income, insurance status, ASCVD status, smoking, physical activity, comorbidity index.
dInsurance stratified analyses: model 2 excludes insurance status (ie, does not adjust for insurance status in the multivariable model).
eIncome-stratified analyses: model 2 excludes income (ie, does not adjust for income in the multivariable model).

Discussion

In this large, nationally representative study, we applied a unique lens to examine racial/ethnic disparities in CMB risk profile across 2 major social determinants of CV health—income and insurance. We found that race and ethnicity is a strong, independent determinant of poor CMB risk profile: at nearly each level of income and type of insurance, NHB and Hispanics were more likely to experience poor CMB risk profile than their NHW counterparts. Our findings highlight the pervasive disadvantage experienced by racial/ethnic minorities that persists across income and insurance strata, and merit greater attention to unmeasured barriers to care, including the role of systemic barriers such as structural racism, implicit bias, and discrimination that continue to affect quality of care and health outcomes for racial/ethnic minorities in the United States.

A recent presidential advisory from the American Heart Association declared structural racism as the fundamental driver of disparities in CVD in the United States,1 which is further corroborated by additional evidence documenting racist policies as the root cause of racial inequities in health.14 Structural racism impacts health via multiple pathways, including the closely interconnected relationships between upstream racist policies/practices (eg, structural barriers to health care, poor neighborhood environment due to redlining, and employment discrimination, among others) that expose marginalized groups to “adverse” SDOH, such as inability to receive timely health care, distrust in health care system, lack of access to healthy food options, and/or safe spaces for physical activity, thereby increasing downstream risk of CVD.1,14,15 In this context, modeling and interpretation of both the independent and dependent effects of racism on CVD merit considerable caution—both from conceptual and methodological standpoints. In particular, given the limitations of existing population health databases, which often offer limited granularity for measures of racism as well as individual SDOH, the true effects of racism may remain undetected or underappreciated. Our findings corroborate existing knowledge of racial inequities in CVD. Disparities in CVD by both race and ethnicity and income have been documented previously.4,6 Furthermore, the “residual” CVD risk that persists for NHB and Hispanics after accounting for SES and access to care (insurance status) has been reported in prior studies.5,16 In a recent study, Hackler et al5 studied racial differences in CV biomarkers in a diverse sample of 2635 participants and reported that in analyses accounting for income and health insurance, NHB men and women experienced worse CV biomarker profile than their NHW counterparts. Similarly, Rooks et al16 reported a higher risk of adverse CVD indicators in NHB participants than in NHW; the reported association persisted after accounting for education, income, and home ownership. Collectively, these results point to the possible role of additional, often unmeasured structural barriers to minority CV health in fully explaining the observed racial/ethnic disparities. Indeed, findings from the landmark Jackson Heart Study17 showed that even after accounting for age, gender, and SES, racial/ethnic discrimination was associated with nearly 10% increased risk of hypertension in the NHB population.

The role of income as an “explanatory” variable in the context of racial/ethnic disparities in health outcomes has been investigated broadly in prior studies3; however, few studies have investigated whether the disadvantage persists at different levels of income and insurance. Limited evidence from the literature supports our findings, in general. Bell et al18 used data from the National Health and Nutritional Examination Survey (NHANES) and demonstrated that NHB were more likely to experience obesity at each level of income and education, and diabetes at middle/high-income levels, compared with NHW. In addition, the authors found that the observed disparity was larger at higher SES—regardless of the CV risk factor/outcome. We found a similar trend of poor CMB risk profile for NHB at each level of income, as well as the widest disparity at the highest-income level. Our findings add considerably to the existing literature by addressing the following limitations: first, the aforementioned study only presented stratified models for obesity and diabetes and did not present associations for the overall/cumulative CMB risk—as was done in our study. Second, the study did not test for possible differential effects by insurance status. Third, the authors did not include the Hispanic population.

Racial/ethnic disparities in health insurance are well documented, with lower rates of health coverage for NHB, Hispanics, and other racial/ethnic minorities,19,20 which puts these marginalized populations at an increased risk of CVD.15 However, disparities in CVD within separate insurance types/programs are less well investigated. Some reports suggest that among Medicare enrollees, people of color are more likely to report poor overall health and experience worse CV outcomes such as diabetes and hypertension than the NHW beneficiaries.7 We reported similar findings of wider disparities in the Medicare population, versus other insurance programs, which suggests that racial/ethnic minority populations may face additional barriers to receiving CV care in public insurance programs.

In this context, the 2019 Agency for Healthcare Research and Quality National Healthcare Quality and Disparities Report found significant barriers to care for individuals with public insurance plans, including difficulty having a usual source of care, and quality of patient-provider interaction.21 However, knowledge of disparities in CVD across different insurance plans/programs (private, Medicare, Medicaid), as well as the uninsured population is limited and requires additional study.

We found that the incremental improvement in CMB risk profile (ie, a higher prevalence of optimum CMB profile and/or a lower prevalence of poor CMB profile) at high- versus low-income levels was smaller for NHB and Hispanics than for NHW. For example, NHW experienced a 50% lowering of poor CMB profile between lowest and highest-income levels, whereas the improvement for NHB and Hispanics was approximately 30% to 35%. These findings support prior reports of possibly smaller marginal health gains for racial/ethnic minorities than for NHW.18,22 Marginalized communities may experience fewer societal benefits of higher SES, including possibly lower social support, neighborhood cohesion, and physical and emotional resources for a healthy lifestyle.23,24 Further study is needed to fully comprehend the SES-health gradient across minority racial/ethnic groups.

Racial/ethnic minorities experience implicit bias and discrimination in health care in various forms, which may have a direct bearing on quality of care and health outcomes, including CVD.1,15 Findings from the 2018 National Healthcare Quality and Disparities Report suggest that NHB and American Indians/Alaskan Natives receive worse care for nearly 40% of all quality-of-care measures, whereas Hispanics receive worse care for approximately 35% of such measures.25 Implicit provider bias may affect CV health by influencing clinical decision making due to stereotypical assumptions/beliefs about medication compliance, health literacy, and outcomes in racial minority patients, as well as by directly affecting the quality of patient-provider interaction.26,27

A systematic review of the effects of implicit bias on quality of care in the US found evidence of bias in nearly all reviewed studies; implicit bias was associated with poor patient-provider interactions, treatment decisions, treatment adherence, and patient outcomes.28 Similarly, in a mixed-methods study, Breathett et al29 found that patients' race influenced treatment allocation decisions for heart failure therapies and found a greater provider concern for trust and treatment adherence associated for the NHB population. Multiple reports of implicit bias and racial discrimination exist in the literature and merit greater investigation for effects on health outcomes, including CVD, over the life course.30–32 Future research efforts to address racial inequities in CMB risk should focus on comprehensively capturing the effects of racism, including development of standardized tools to measure various forms of structural racism, and using established SDOH frameworks such as Healthy People 2030 and World Health Organization models to examine the mediating and/or moderating role of SDOH.33,34 Another challenge to address is the inadequate reporting of race and ethnicity data in existing population databases35 The Health and Human Services Commission provides detailed guidance on standardized reporting of outcomes by race and ethnicity,36 which should be widely adapted.

On a policy level, future work should advocate for greater encouragement for patient feedback in order to identify implicit or explicit discrimination and inform the development of interventions to address discriminatory practices, such as greater investment in provider training to address bias and discrimination. Real-world provider training programs and other resources exist and should be used to facilitate shared decision-making and advance the cause of equitable care.37,38

Strengths and limitations

Our work had several strengths, including large sample size, nationally representative estimates, and robust methodological approach. Our work adds considerably to existing literature by applying a novel approach to demonstrating persistent racial/ethnic inequities in cumulative CMB burden. Furthermore, we discuss various structural barriers to optimal CV health for NHB and Hispanics, with implications for clinical decision-making and system-wide policy interventions. Our findings are generalizable to the adult US population and may inform future work to investigate the “root causes” of observed racial/ethnic disparities in CVD.

Our results should be viewed in light of a few limitations. First, ours was a cross-sectional design; hence, causality cannot be inferred. However, these findings offer unique insights into pervasive racial/ethnic inequities in CV risk and are relevant from both a clinical and health equity standpoint. Our approach should be used to inform large-scale studies in the future, including the use of longitudinal designs. Second, we did not have data on self-reported discrimination and/or racism; hence, their association with CMB could not be assessed. A holistic understanding of the impact of racism on health merits analysis of its effects at both the individual and group/societal levels. However, analysis of such multilevel effects may often be difficult to conduct—such as in this study—especially given the shortcomings of available population-based data sources and lack of standardized frameworks to assess such effects. These challenges often prohibit comprehensive assessment of the effects of racism on health. While the unique relevance of both individual and group-level effects toward overall well-being has been documented previously,39 few studies to date have assessed such multilevel effects on discrete health outcomes, including CVD/CMB risk, which merits further study.

Third, we did not measure additional SDOH such as social support, neighborhood/physical environment, etc, which may explain the observed disparities, at least partially. However, income and insurance are widely used indicators of SES and access to health care, as well as established predictors of CVD; evidence of consistent disparities across individual levels of these 2 social determinants points to the possible role of structural barriers to minority CV health. Future studies should examine additional SDOH and develop and validate tools to measure perceived racism in order to better explain the disparities observed herein.

Conclusions

Racial/ethnic minorities, including NHB and Hispanics, experience considerable and persistent disparities in CMB risk—overall and at each level/type of income and insurance. Such inequities point to the possible role of structural barriers, including social, political, and environmental conditions that predispose racial/ethnic minorities to adverse SDOH and expose them to the detrimental effects of racism and discrimination. Future work should focus on measuring additional SDOH and developing robust polysocial risk scores to assess the role of a broad array of SDOH in observed racial/ethnic disparities in CVD. Further study is needed to quantify the possible mediating effects of racial/ethnic discrimination on CV health.

Implications for Policy & Practice

  • NHB and Hispanics experience poor CMB risk profile compared with NHW at each level of income and type of insurance. These findings suggest additional, unmeasured barriers to optimal CV health for underserved racial/ethnic minority groups.
  • Future work should focus on developing tools and approaches to quantify the effects of racism and discrimination on CVD.
  • Health systems should encourage greater patient feedback to improve quality of patient-provider interaction and overall satisfaction with care.
  • Policy interventions should include greater investment in provider training to address the issue of implicit bias and helping socially disadvantaged patients access available resources to address barriers to care such as possible financial assistance to help pay for health care, and transportation and requesting additional state/federal benefits, for example, food stamps.
  • Clinical/research interventions should target developing robust polysocial risk scores to assess the role of a broad array of SDOH in observed racial/ethnic disparities in CVD.

References

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2. National Center for Health Statistics. Health, United States, 2014: With Special Feature on Adults Aged 55-64. Hyattsville, MD: National Center for Health Statistics; 2014. Report No. 2015-1232.
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Keywords:

cardiometabolic risk profile; cardiovascular disease; racial/ethnic disparities

© 2022 The Authors. Published by Wolters Kluwer Health, Inc.