Skip to main content
PMC home page
JAMA Network logoLink to JAMA Network
. 2020 May 27;5(8):899–908. doi: 10.1001/jamacardio.2020.1458

Association of Low Socioeconomic Status With Premature Coronary Heart Disease in US Adults

Rita Hamad 1,2, Joanne Penko 3,4, Dhruv S Kazi 5,6, Pamela Coxson 3,4, David Guzman 3,7, Pengxiao C Wei 4, Antoinette Mason 8, Emily A Wang 9, Lee Goldman 10, Kevin Fiscella 11, Kirsten Bibbins-Domingo 3,4,7,✉
PMCID: PMC7254448  PMID: 32459344

This study uses a computer simulation to estimate the excess burden of a cardiovascular event and its downstream and upstream risk factors among adults with a low socioeconomic status.

Key Points

Question

Is low socioeconomic status associated with early myocardial infarction and coronary heart disease mortality, and what is the proportion attributable to traditional risk factors vs other factors?

Findings

In this computer simulation study of the US population aged 35 to 64 years, traditional risk factors for coronary heart disease explained 40% of excess events among those with low socioeconomic status, with the remaining 60% attributable to other risk factors. A simulation involving 1.3 million 35-year-olds with low socioeconomic status projected that 250 000 of these people will develop coronary heart disease by age 65 years, which is nearly double the rate projected for individuals with higher socioeconomic status.

Meaning

These findings suggest that, although addressing traditional risk factors may decrease coronary heart disease, the disparities in disease burden will likely remain unless upstream factors associated with low socioeconomic status are addressed.

Abstract

Importance

Individuals with low socioeconomic status (SES) bear a disproportionate share of the coronary heart disease (CHD) burden, and CHD remains the leading cause of mortality in low-income US counties.

Objective

To estimate the excess CHD burden among individuals in the United States with low SES and the proportions attributable to traditional risk factors and to other factors associated with low SES.

Design, Setting, and Participants

This computer simulation study used the Cardiovascular Disease Policy Model, a model of CHD and stroke incidence, prevalence, and mortality among adults in the United States, to project the excess burden of early CHD. The proportion of this excess burden attributable to traditional CHD risk factors (smoking, high blood pressure, high low-density lipoprotein cholesterol, low high-density lipoprotein cholesterol, type 2 diabetes, and high body mass index) compared with the proportion attributable to other risk factors associated with low SES was estimated. Model inputs were derived from nationally representative US data and cohort studies of incident CHD. All US adults aged 35 to 64 years, stratified by SES, were included in the simulations.

Exposures

Low SES was defined as income below 150% of the federal poverty level or educational level less than a high school diploma.

Main Outcomes and Measures

Premature (before age 65 years) myocardial infarction (MI) rates and CHD deaths.

Results

Approximately 31.2 million US adults aged 35 to 64 years had low SES, of whom approximately 16 million (51.3%) were women. Compared with individuals with higher SES, both men and women in the low-SES group had double the rate of MIs (men: 34.8 [95% uncertainty interval (UI), 31.0-38.8] vs 17.6 [95% UI, 16.0-18.6]; women: 15.1 [95% UI, 13.4-16.9] vs 6.8 [95% UI, 6.3-7.4]) and CHD deaths (men: 14.3 [95% UI, 13.0-15.7] vs 7.6 [95% UI, 7.3-7.9]; women: 5.6 [95% UI, 5.0-6.2] vs 2.5 [95% UI, 2.3-2.6]) per 10 000 person-years. A higher burden of traditional CHD risk factors in adults with low SES explained 40% of these excess events; the remaining 60% of these events were attributable to other factors associated with low SES. Among a simulated cohort of 1.3 million adults with low SES who were 35 years old in 2015, the model projected that 250 000 individuals (19%) will develop CHD by age 65 years, with 119 000 (48%) of these CHD cases occurring in excess of those expected for individuals with higher SES.

Conclusions and Relevance

This study suggested that, for approximately one-quarter of US adults aged 35 to 64 years, low SES was substantially associated with early CHD burden. Although biomedical interventions to modify traditional risk factors may decrease the disease burden, disparities by SES may remain without addressing SES itself.

Introduction

Coronary heart disease (CHD) is the leading cause of death in the United States.1 Mortality from CHD has been declining for more than 4 decades, in part because of reduced smoking, control of hyperlipidemia and hypertension, and improved treatment of acute CHD events.2 Unfortunately, improvements have been less marked for individuals with low socioeconomic status (SES), who bear a disproportionate share of the CHD burden.2 Coronary heart disease remains the leading cause of mortality in low-income US counties, whereas cancer has the top distinction in high-income counties.3 Numerous studies have demonstrated an inverse association between SES and CHD mortality.4,5,6 Although some of this excess risk is attributable to a higher burden of traditional risk factors among individuals with low SES, risk factor profiles may not fully account for the observed differences, suggesting that low SES itself and other upstream characteristics may be independent risk factors.7

Understanding what proportion of CHD disparities are attributable to traditional risk factors vs other aspects of SES has important implications for whether future interventions should focus on traditional risk factor management or policies aimed at upstream factors, such as expanding economic or educational opportunities. In this computer simulation study, we quantified the contribution of low SES to the burden of premature (before age 65 years) CHD occurring in US adults aged 35 to 64 years. This quantification provides insight into the magnitude of disparity between adults with low and those with higher SES that would remain even if traditional CHD risk factors were adequately addressed.

Methods

This computer simulation used input parameters drawn from several publicly available data sets, secondary analyses of cohort data, and published literature. The individual studies that provided inputs for the present study obtained informed consent from their participants. Ethics approval for the present study was granted by the institutional review board of the University of California, San Francisco.

We used national data to describe the disparity in CHD risk factors and mortality between adults with low SES and those with higher SES. Next, we used the Cardiovascular Disease Policy Model, an established computer simulation of US adults, to estimate excess myocardial infarction (MI) rates and excess CHD deaths experienced by individuals with low SES. We quantified the proportion of these excess events attributable to traditional risk factors compared with the proportion attributable to other upstream factors associated with low SES itself.

Structure of the Model

The Cardiovascular Disease Policy Model is a computer simulation, state-transition (Markov) model of CHD and stroke incidence, prevalence, mortality, and health care costs in the US population aged 35 years or older (eFigure in the Supplement).8,9,10,11,12 Using inputs from nationally representative databases, longitudinal cohort studies, and natural history studies, the model projected the annual incidence of CHD, stroke, and noncardiovascular death in individuals without cardiovascular disease on the basis of age, sex, and cardiovascular risk factors. Traditional risk factors included exposure to tobacco smoke, systolic blood pressure (SBP), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), body mass index (BMI) (calculated as weight in kilograms divided by height in meters squared), and the presence of type 2 diabetes. For the present study, we also included a variable representing the elevated risk associated with low SES compared with higher SES independent of traditional risk factors. After the initial cardiovascular event, the model characterized the event (angina, myocardial infarction [MI], arrest, or stroke) along with its sequelae in the first 30 days, a period with a heightened probability of repeat events, revascularization procedures, and mortality. The model then projected the number of subsequent MIs, strokes, and cause-specific deaths as a function of age, sex, and cardiovascular history. The risk of new and repeat cardiovascular events was elevated during the year of the incident event. The current version of the model was calibrated to within 1% of events and deaths reported in the 2010 National Hospital Discharge Survey13 and the Vital Statistics of the United States14 (eTable 1 in the Supplement), aligning with the 2010 US Census15 data. Additional details are provided in the eMethods in the Supplement.

Model Inputs

We defined low SES as household income below 150% of the federal poverty level or educational level less than a high school diploma, similar to the definitions used in previous studies.4,5,6,16 The remaining population was described as higher SES. Theoretical work suggests that using multiple measures of SES helps to capture the different individual and societal forces associated with heart disease.17 We identified the size of the populations with low and higher SES using the 2015 American Community Survey.18 We used data from the 2011 to 2016 National Health and Nutrition Examination Survey19 to define SES-stratified distributions of traditional risk factors by age and sex.

The analysis used the model’s risk function, calculated from Framingham data, to model specifications with a Cox proportional hazards regression model for quantifying the association between traditional risk factors and incident CHD, stroke, diabetes, and death from causes other than CHD or stroke.20,21 We used a relative risk of 1.58 (95% CI, 1.31-1.90) for the association of low SES with incident CHD (independent of the traditional risk factors) on the basis of a published analysis of Atherosclerotic Risk in Communities Study data,16 in which the definition of SES matched that in the present study and controlled for all factors included in the model’s risk function. A similar magnitude of association between SES and incident CHD was reported in individuals younger than 65 years in the Reasons for Geographic and Racial Differences in Stroke cohort study.22

Simulations

We developed separate versions of the model to represent low-SES and higher-SES populations. We restricted the analysis to individuals aged 35 to 64 years because we were interested in describing the role of SES in the development of CHD at younger ages and because SES after age 65 years is complicated by retirement and receipt of Social Security benefits.22 To estimate the current burden of CHD in populations with low SES and higher SES, we conducted simulations from 2015 to 2024, with those who reached age 35 years entering and those who turned age 65 years exiting the simulations each annual cycle. We also estimated the cumulative incidence of CHD in individuals with low SES aged 35 years who were free of cardiovascular disease in 2015, projecting through 2044 when the cohort will reach age 65 years.

We conducted a series of simulations that isolated individual inputs to apportion events in the low-SES population to 1 of the following: (1) the difference in traditional risk factor distributions observed in adults with low SES compared with those with higher SES; (2) the risk observed in individuals with low SES compared with those with higher SES independent of traditional risk factors; (3) the events that could be prevented if risk factors were improved to ideal levels; and (4) the underlying age- and sex-based risk not explained by traditional risk factors or SES. The ideal levels for traditional risk factors were defined as SBP of 110 mm Hg or lower, LDL-C of 70 mg/dL or lower (to convert milligrams per deciliter to millimoles per liter, multiply by 0.0259) for those with previous diabetes or cardiovascular disease and LDL-C of 100 mg/dL or lower for others, HDL-C of 50 mg/dL or higher, BMI of 25 or lower, and no cigarette smoking and diabetes.23,24 Treating to reach ideal levels may not deliver the same outcomes as if these risk factor thresholds were never exceeded in the first place. To compare the potential outcomes of theoretical interventions to eliminate the risk of low SES independent of traditional risk factors with interventions to eliminate traditional risks, we conducted simulations that first exchanged low-SES measures for higher-SES measures of the given risk factor as observed in the National Health and Nutrition Examination Survey. Then, we further improved the risk factor to its ideal level.

Statistical Analysis

We used Monte Carlo simulations to generate 95% uncertainty intervals (UIs) for model estimates. For each simulation, we conducted 1000 iterations, selecting inputs randomly (with replacement) from standard normal distributions scaled to the mean and CI for each varied parameter (eTable 2 in the Supplement). We used the SE estimated from the National Health and Nutrition Examination Survey to vary values for all risk factor inputs for individuals with low SES and those with higher SES. β Coefficients for the association of CHD events with smoking, SBP, LDL-C, HDL-C, and diabetes were varied based on the SE generated from fitted regressions. We assumed log normality for the relative risk of CHD among adults with low SES compared with those with higher SES, deriving the SE from published 95% CIs. Data were analyzed starting in 2015.

Results

Approximately 31.2 million US adults aged 35 to 64 years had low SES in 2015, of whom approximately 16 million (51.3%) were women. Compared with individuals with higher SES, those with low SES experienced 2-fold higher rates of CHD death per 10 000 person-years (men: 8.9 [95% CI, 8.9-9.0] vs 16.6 [95% CI, 16.5-16.8]; women: 3.4 [95% CI, 3.4-3.4] vs 7.5 [95% CI, 7.4-7.6]) (Table 1). Adults with low SES also had a higher burden of traditional risk factors vs those with higher SES, with higher proportions of smoking among men (0.37 vs 0.17) and women (0.29 vs 0.15) with low SES and worse metabolic indicators, particularly among women with low SES (mean [SE] BMI: 30.8 [0.3] vs 29.6 [0.2]; LDL-C: 122.0 [1.6] mg/dL vs 116.8 [1.8] mg/dL; HDL-C, 54.2 [0.6] mg/dL vs 60.4 [1.4] mg/dL; and proportion of diabetes, 0.19 vs 0.10) (Table 1).

Table 1. Population Totals, Coronary Heart Disease Death Rates, and Coronary Heart Disease Risk Factors .

Variable Men Women
Low SESa Higher SESa Low SESa Higher SESa
Population size and CHD death ratesb,c
Population total, No., millionsb 15.2 46.2 16.0 47.7
CHD death rate
Per 10 000, No. (95% CI)c 16.6 (16.5-16.8) 8.9 (8.9-9.0) 7.5 (7.4-7.6) 3.4 (3.4-3.4)
Ratio (95% CI)c 1.9 (1.8-1.9) 1 [Reference] 2.2 (2.2-2.2) 1 [Reference]
CHD risk factorsd
Active smoking prevalence, proportion 0.37 0.17 0.29 0.15
SBP, mm Hg
Mean (SE) 125.4 (0.8) 123.9 (0.4) 122.4 (0.6) 119.2 (0.5)
Proportion ≥110 0.83 0.84 0.73 0.70
LDL-C, mg/dL
Mean (SE) 119.9 (2.5) 119.0 (1.7) 122.0 (1.6) 116.8 (1.8)
Proportion ≥70 or ≥100e 0.74 0.71 0.70 0.72
HDL-C, mg/dL
Mean (SE) 46.4 (0.7) 47.5 (0.6) 54.2 (0.6) 60.4 (1.4)
Proportion <50 0.61 0.58 0.37 0.25
Diabetes prevalence, proportion 0.18 0.13 0.19 0.10
BMI
Mean (SE) 29.0 (0.2) 29.6 (0.2) 30.8 (0.3) 29.6 (0.2)
Proportion ≥25 0.74 0.81 0.75 0.68
Open in a new tab

Abbreviations: BMI, body mass index (calculated as weight in kilograms divided by height in meters squared); CHD, coronary heart disease; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; SBP, systolic blood pressure; SES, socioeconomic status.

SI conversion factors: To convert mg/dL to mmol/L, multiply by 0.0259.

a

For stratification of CHD risk factor values and other model inputs, low SES was defined as household income below 150% of the federal poverty level (FPL) or educational level less than high school; higher SES was defined as income at or above 150% of the FPL or a high school diploma or GED (General Education Development) certificate. For the CHD death rates and rate ratios, SES was based on education alone because the Vital Statistics of the United States report does not collect income data. SES-stratified CHD mortality rates demonstrated the increased burden of CHD in adults with low SES and that the model projections were in the range of the best data available at the national level.

b

Population totals were calculated with data from the 2015 American Community Survey.18

c

CHD death rates and rate ratios were calculated using the 2015 Vital Statistics of the United States14 report data as the numerator and the 2015 American Community Survey18 data as the denominator, and rates were standardized to the 2015 age structure for each sex stratum.

d

National Health and Nutrition Examination Survey19 data were used to calculate risk factor values, with data from 2011 to 2016 used for SBP, HDL-C, and BMI and data from 2011 to 2014 used for smoking, diabetes, and LDL-C (reflecting data available at time of analysis).

e

Ideal levels of LDL-C were 70 mg/dL or higher for those with previous diabetes or cardiovascular disease and 100 mg/dL or higher for everyone else.

Simulations using the Cardiovascular Disease Policy Model accurately reproduced the observed 2-fold increase in rates of early MI per 10 000 person-years (men: 34.8 [95% UI, 31.0-38.8] vs 17.6 [95% UI, 16.0-18.6]; women: 15.1 [95% UI, 13.4-16.9] vs 6.8 [95% UI, 6.3-7.4]) and CHD death per 10 000 person-years (men: 14.3 [95% UI, 13.0-15.7] vs 7.6 [95% UI, 7.3-7.9]; women: 5.6 [95% UI, 5.0-6.2] vs 2.5 [95% UI, 2.3-2.6]) among individuals with low SES compared with sex-matched individuals with higher SES. After accounting for higher levels of traditional risk factors, the simulations estimated that approximately 60% of the 2-fold excess in MIs and CHD deaths in populations with low SES was attributable to the independent risk of SES and other upstream risk factors (Table 2; Figure 1).

Table 2. Simulated Age-Standardized Rates of Myocardial Infarction and Coronary Heart Disease Death in Adults With Low or Higher Socioeconomic Statusa.

Group Rate, per 10 000 person-years (95% UI) Excess rate in low SES, per 10 000 person-years (95% UI)b
Low SESb Higher SESb Overall excess Attributable to excess risk factor burdenc Additional risk linked to SESd
Myocardial infarctions
Men 34.8 (31.0-38.8) 17.6 (16.0-18.6) 17.2 (12.8-22.0) 6.6 (4.7-8.6) 10.6 (5.9-15.7)
Women 15.1 (13.4-16.9) 6.8 (6.3-7.4) 8.3 (6.2-10.4) 3.6 (2.8-4.5) 4.7 (2.5-6.7)
CHD deaths
Men 14.3 (13.0-15.7) 7.6 (7.3-7.9) 6.8 (5.2-8.4) 2.6 (2.0-3.2) 4.2 (2.5-5.9)
Women 5.6 (5.0-6.2) 2.5 (2.3-2.6) 3.1 (2.4-3.8) 1.3 (1.0-1.5) 1.8 (1.1-2.5)
Open in a new tab

Abbreviations: CHD, coronary heart disease; SES, socioeconomic status; UI, uncertainty interval.

a

Rates were standardized to the 2015 US population age structure for each sex group.

b

Low SES was defined as household income below 150% of the federal poverty level or educational level less than high school.

c

Risk factors included in model simulations were systolic blood pressure, current smoking status, low-density and high-density lipoprotein cholesterol levels, diabetes status, and body mass index, measured by National Health and Nutrition Examination Survey waves from 2011-2016.

d

Additional excess risk reflects the projected excess rate of CHD outcomes associated with low SES that is independent of traditional CHD risk factors. For CHD death, the excess also captured model inputs reflecting heightened myocardial infarction case fatality among adults with low SES, which contributed to the excess of a total of 0.5 CHD deaths per 10 000 person-years for men and 0.4 for women of low SES.

Figure 1. Simulated Myocardial Infarction (MI) Rates and Coronary Heart Disease (CHD) Deaths per 10 000 Person-Years in Adults With Low Socioeconomic Status (SES).

Figure 1.

Open in a new tab

Traditional CHD risk factors were systolic blood pressure (ideal level: ≤110 mm Hg), low-density lipoprotein cholesterol (ideal: ≤70 mg/dL for those with a history of diabetes or cardiovascular disease; ≤100 mg/dL for all others), high-density lipoprotein cholesterol (ideal: ≥50 mg/dL), body mass index (ideal: ≤25), cigarette smoking (ideal: no exposure), and diabetes (ideal: none). The risk associated with low SES was independent of age, sex, and traditional factors.

Among adults with low SES, 31% of CHD events were attributable to the risk of low SES independent of traditional risk factors (Figure 1). This finding represents the disparity that would remain even if ideal levels of traditional risk factors were achieved. Meanwhile, 53% of all CHD events were attributable to traditional CHD risk factors (Figure 1).

A simulation of theoretical interventions for each individual risk factor revealed that the potential improvement associated with addressing the independent risk of low SES was greater than that for addressing any traditional risk factor alone (Figure 2). Smoking cessation, along with diabetes prevention in women, represented a high-yield target for decreasing the excess burden of CHD in adults with low SES compared with those with higher SES. Reducing BMI in all individuals with low SES to at least 25 would prevent 6.6 MIs and 2.9 CHD deaths per 10 000 person-years, but this decrease would not change the gap in CHD events between individuals with low SES and those with higher SES because of the high rates of obesity in both populations.

Figure 2. Projected Improvement in Myocardial Infarction (MI) Rates and Coronary Heart Disease (CHD) Deaths Associated With Simulated Interventions .

Figure 2.

Open in a new tab

Each risk factor was simulated in isolation (1) by exchanging the distributions between adults with low socioeconomic status (SES) and their age- and sex-matched counterparts with higher SES and then (2) by improving each factor to its ideal level. Traditional CHD risk factors were systolic blood pressure (SBP; ideal level: ≤110 mm Hg), low-density lipoprotein cholesterol (LDL-C; ideal: ≤70 mg/dL for those with a history of diabetes or cardiovascular disease; ≤100 mg/dL for all others), high-density lipoprotein cholesterol (ideal: ≥50 mg/dL), body mass index (ideal: ≤25), cigarette smoking (ideal: no exposure), and diabetes (ideal: none).

Among the simulated closed cohort of 1.3 million adults with low SES aged 35 years in 2015, we estimated that 250 000 (19%) will develop CHD by age 65 years in 2045. Of the cumulative cases, 119 000 (48%) were in excess of what would be expected if the cohort were composed of individuals with higher SES, with 84 000 (70%) of this excess attributable to risk factors associated with low SES independent of traditional risk factors (Figure 3).

Figure 3. Projected Cumulative Incidence of Coronary Heart Disease (CHD) Among Adults With Low Socioeconomic Status (SES).

Figure 3.

Open in a new tab

Incident CHD was defined as angina, myocardial infarction, or cardiac arrest as the index event in a population without preexisting cardiovascular disease. Traditional CHD risk factors were systolic blood pressure (ideal level: ≤110 mm Hg), low-density lipoprotein cholesterol (ideal: ≤70 mg/dL for those with a history of diabetes or cardiovascular disease; ≤100 mg/dL for all others), high-density lipoprotein cholesterol (ideal: ≥50 mg/dL), body mass index (ideal: ≤25), cigarette smoking (ideal: no exposure), and diabetes (ideal: none). The risk associated with low SES was independent of age, sex, and traditional factors. The projections assumed that the traditional and low-SES risk factors remained constant over 30 years, along with age- or sex-stratified event and death rates.

Discussion

One-quarter of the US population aged 35 to 64 years had low SES in 2015 (31.2 million), with rates of MIs and CHD deaths that were twice as high as those among individuals with higher SES. Although a substantial portion of this excess risk can be explained by a higher prevalence of traditional risk factors among adults with low SES, we found that 60% of the excess risk would remain if these factors were brought to the same level as that of the higher-SES population.

Findings of this study suggest that clinical interventions addressing traditional risk factors may decrease the burden of CHD, but they are unlikely to eliminate the socioeconomic disparities in CHD without interventions that directly address other upstream risk factors associated with low SES, such as poverty and education.25 These social conditions have been termed the fundamental causes of disease,26 and at least 3 interrelated pathways have been theorized to explain the association of SES with CHD.27 The first pathway is greater psychosocial stressors. A growing body of literature suggests that the cumulative stress from chronic poverty may be associated with biological changes that are atherogenic, perhaps because of elevated levels of catecholamines and cortisol.28 Allostatic load, a cumulative measure of the body’s response to stress, is also believed to be associated with weathering (premature deterioration of health) and an increased risk of CHD and mortality.29,30 In the United States, in which race/ethnicity and SES are highly connected, discrimination and structural racism may be factors in psychosocial stress, which in turn is associated with increased risk of CHD for black individuals and other non-white groups.31,32,33

The second pathway is limited economic and educational opportunity, which may reduce access to nutritious food, health care, and safe neighborhoods for physical activity.34,35,36,37,38 Poverty is also believed to play a role in decreased cognitive bandwidth and risky decision-making concerning health behaviors such as smoking, alcohol consumption, and drug use.39,40,41 The third pathway is social norms; peer influence among individuals with higher SES is associated with healthier lifestyle decisions.42,43 Low SES may also have intergenerational implications: one-third of US children live in poverty, and numerous studies have documented worsened CHD risk factors during childhood among children with low SES,44,45 likely associated with all 3 pathways described.46

For each of these pathways, part of the association of low SES with cardiovascular outcomes may be mediated by traditional risk factors. For instance, individuals with low SES have reduced access to health care, which may be a factor in undertreatment of traditional risk factors. Limited health care access may be linked to the high cost of medical care itself as well as difficulties with less transportation and childcare and poor health literacy.47,48,49 Nevertheless, as early as the 1950s in the first Framingham study, SES was recognized as an important independent factor in individual risk.50 The Whitehall study of British civil servants, all of whom were employed and had health care access, found an inverse association between occupational status and CHD.51 Even those in the middle of the hierarchy demonstrated increased CHD risk, with stressful work environments and social support implicated as potential mechanisms. Most recently, a mendelian randomization study of more than 500 000 adults demonstrated that traditional risk factors mediated only about one-third of the association between genetic variants for educational attainment and CHD.52 Although few studies have directly examined the possible approaches for addressing the upstream determinants of CHD,53,54 such interventions are supported by this study’s finding that low SES adds to the persistent disparities in CHD beyond its association with traditional risk factors.

Clinical Implications

Addressing traditional risk factors remains an important target for improving the health of individuals with low SES because these factors account for 53% of overall burden and 40% of excess burden. For example, although tobacco use is declining overall, improvements are less pronounced for adults with low SES, making tobacco control a high-yield target for reducing CHD disparities.55 Targeting traditional risk factors may have a meaningful outcome of narrowing the gap in CHD burden among women given that adverse risk factor profiles explain a larger proportion of excess events in women than in men. Yet socioeconomic factors may be the root of the higher burden of some traditional risk factors; for example, the obesity epidemic disproportionately affects women with low SES,56 and income-related factors, such as food insecurity, are associated with increased metabolic risk.57,58 These observations suggest that efforts to promote smoking cessation, lower LDL-C and SBP, and prevent and treat diabetes may decrease SES disparities, although these efforts should be coupled with interventions targeting upstream social determinants.

Approaches that solely target clinical risk factors may paradoxically increase disparities because groups with higher SES are more likely to reap the rewards of interventions in health care settings.59 Current prevention guidelines from the American College of Cardiology and American Heart Association highlight the importance of screening people for socioeconomic disadvantages that may hamper their ability to afford nutritious food or to engage in physical activity, although no recommendations are given for how to help those with a concerning screen.60,61 High-quality studies are needed to test downstream clinical interventions for minimizing traditional risk factors in low-SES populations (eg, tailored, accessible lifestyle programs or affordable medication regimens) and interventions for improving SES itself (eg, social service referrals facilitated in the clinical encounter).62 The latter could be based on existing experimental and quasiexperimental evidence on programs and policies that address socioeconomic risk factors to improve health outcomes.

Findings of this study also have clinical implications for cardiovascular risk assessment. Current approaches to cardiovascular disease prevention are tiered according to estimated cardiovascular risk.63 Although cardiovascular risk scores in some countries include some measure of SES,64 the risk scores of the American College of Cardiology and American Heart Association do not include SES. However, these guidelines note that the score may underestimate the risk in individuals with low SES.65,66 Race/ethnicity is included in the score, highlighting a challenge of assessing cardiovascular risk in the United States, where race/ethnicity is associated with SES. Without a more complete understanding of the mechanisms that underlie higher risk among black individuals, including race/ethnicity, but not SES in cardiovascular risk assessment may lead to undertreatment of non-black individuals with low SES and overtreatment of black individuals with higher SES. Because of the lack of available inputs in the literature, we were not able to model subgroup associations with SES in different racial/ethnic groups. Future studies could use modeling to estimate this association and disentangle the competing pathways. Similarly, failure to consider neighborhood-level SES may underestimate cardiovascular risk and diminish the success of health interventions.67,68 Internationally, several cardiovascular risk scores include individual- or neighborhood-level socioeconomic deprivation69,70; future studies should develop and test such scores for the United States.

Implications Beyond the Clinical Setting

The large size of the low-SES population and the large proportion of excess risk unexplained by traditional risk factors suggest that meeting the public health goals of cardiovascular disease prevention will require a focus on upstream factors associated with SES. Addressing the overall burden of CHD and the factors that explain the disparities in CHD will require research into the 60% excess risk not attributable to traditional risk factors among individuals with low SES and factors that are modifiable, particularly among the broader socioeconomic determinants of health across the life course. Previous experimental and quasiexperimental studies have demonstrated that early childhood educational interventions and policies to increase educational attainment have powerful long-lasting implications for CHD and associated risk factors.53,71,72 Similar experimental and quasiexperimental work has suggested the advantages of socioeconomic interventions to address income and neighborhood deprivation.36,37,73

In addition, policies that directly improve downstream pathways, such as diet and physical activity at the population level, specifically among individuals with low SES, should be evaluated. These interventions may include nutrition assistance programs, such as the Supplemental Nutrition Assistance Program and the Special Supplemental Nutrition Program for Women, Infants, and Children,74,75 or green-space initiatives to promote neighborhood walkability.76

Limitations

This study has several limitations. The analyses were limited by the uncertainty in model inputs. The association of SES with CHD was estimated from studies of community-based cohorts; although these cohorts were diverse, the generalizability of these findings to the entire US population is uncertain. The association of SES and CHD has been observed in a number of US cohorts, including the Framingham study.50 In contrast, estimates in this study were derived from the Atherosclerosis Risk in Communities study,16 which may have a higher risk of CHD than other cohorts. Previous studies defined SES in different ways, often including combinations of measures of income, education, wealth, and occupation.51,77 Given the data constraints, we included only income and education in this study’s definition of SES, thereby limiting our ability to explore the implications of other aspects of SES. In addition, much of the literature that provided the input for the simulations placed SES into 2 categories, limiting our ability to use a continuous SES exposure to model a more granular dose-response association with CHD. Nevertheless, previous studies have also suggested that the association between SES and CHD is not linear78,79 and that, for education in particular, the attainment of high school and college credentials serves as an important threshold.80 Furthermore, the true implications of risk factors over the lifespan and the variance based on degree of control may not be fully captured. Similarly, the implications of diet and physical activity for CHD, independent of their association with hypertension, lipids, and diabetes, were not captured. We simulated individuals aged 35 to 64 years given our interest in capturing the role of SES in early CHD; the magnitude of these results, however, may not extrapolate to the role of SES in older populations.

Conclusions

We believe that the findings from this study underscore the need to broaden the understanding of CHD risk and to develop interventions that go beyond traditional clinical risk factors. To reduce the socioeconomic disparities in CHD, we must focus future research on the implementation and evaluation of clinical, community-based, and population-level interventions that target upstream risk factors associated with low SES, such as poverty and education.

Supplement.

eMethods. Overview of CVD Policy Model Structure and Analytic Approach

eTable 1. Comparisons of Selected Cardiovascular Disease Policy Model Simulation Outputs for 2010 (Model Base Year) With National Targets for 2010

eTable 2. Key Input Values for the Low- and Higher-SES CVD Policy Models, With Measures of Variability Indicated for Inputs That Were Varied in Monte Carlo Simulations

eFigure. Schematic of the Cardiovascular Disease Policy Model

eReferences

Click here for additional data file. (521.4KB, pdf)

References

  • 1.Heron M. Deaths: leading causes for 2016. Natl Vital Stat Rep. 2018;67(6):1-77. [PubMed] [Google Scholar]
  • 2.Mensah GA, Wei GS, Sorlie PD, et al. . Decline in cardiovascular mortality: possible causes and implications. Circ Res. 2017;120(2):366-380. doi: 10.1161/CIRCRESAHA.116.309115 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Hastings KG, Boothroyd DB, Kapphahn K, et al. . Socioeconomic differences in the epidemiologic transition from heart disease to cancer as the leading cause of death in the United States, 2003 to 2015: an observational study. Ann Intern Med. 2018;169(12):836-844. doi: 10.7326/M17-0796 [DOI] [PubMed] [Google Scholar]
  • 4.Mozaffarian D, Benjamin EJ, Go AS, et al. ; Writing Group Members; American Heart Association Statistics Committee; Stroke Statistics Subcommittee . Heart disease and stroke statistics-2016 update: a report from the American Heart Association. Circulation. 2016;133(4):e38-e360. doi: 10.1161/CIR.0000000000000350 [DOI] [PubMed] [Google Scholar]
  • 5.Mensah GA, Mokdad AH, Ford ES, Greenlund KJ, Croft JB. State of disparities in cardiovascular health in the United States. Circulation. 2005;111(10):1233-1241. doi: 10.1161/01.CIR.0000158136.76824.04 [DOI] [PubMed] [Google Scholar]
  • 6.Hozawa A, Folsom AR, Sharrett AR, Chambless LE. Absolute and attributable risks of cardiovascular disease incidence in relation to optimal and borderline risk factors: comparison of African American with white subjects—Atherosclerosis Risk in Communities Study. Arch Intern Med. 2007;167(6):573-579. doi: 10.1001/archinte.167.6.573 [DOI] [PubMed] [Google Scholar]
  • 7.de Mestral C, Stringhini S. Socioeconomic status and cardiovascular disease: an update. Curr Cardiol Rep. 2017;19(11):115. doi: 10.1007/s11886-017-0917-z [DOI] [PubMed] [Google Scholar]
  • 8.Weinstein MC, Siegel JE, Gold MR, Kamlet MS, Russell LB. Recommendations of the Panel on Cost-Effectiveness in Health and Medicine. JAMA. 1996;276(15):1253-1258. doi: 10.1001/jama.1996.03540150055031 [DOI] [PubMed] [Google Scholar]
  • 9.Moran AE, Odden MC, Thanataveerat A, et al. . Cost-effectiveness of hypertension therapy according to 2014 guidelines. N Engl J Med. 2015;372(5):447-455. doi: 10.1056/NEJMsa1406751 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Kazi DS, Moran AE, Coxson PG, et al. . Cost-effectiveness of PCSK9 inhibitor therapy in patients with heterozygous familial hypercholesterolemia or atherosclerotic cardiovascular disease. JAMA. 2016;316(7):743-753. doi: 10.1001/jama.2016.11004 [DOI] [PubMed] [Google Scholar]
  • 11.Hunink MGM, Goldman L, Tosteson ANA, et al. . The recent decline in mortality from coronary heart disease, 1980-1990: the effect of secular trends in risk factors and treatment. JAMA. 1997;277(7):535-542. doi: 10.1001/jama.1997.03540310033031 [DOI] [PubMed] [Google Scholar]
  • 12.Bibbins-Domingo K, Coxson P, Pletcher MJ, Lightwood J, Goldman L. Adolescent overweight and future adult coronary heart disease. N Engl J Med. 2007;357(23):2371-2379. doi: 10.1056/NEJMsa073166 [DOI] [PubMed] [Google Scholar]
  • 13.Centers for Disease Control and Prevention National Hospital Discharge Survey. 2010. Updated November 6, 2015. Accessed March 29, 2012. https://www.cdc.gov/nchs/nhds/index.htm
  • 14.Centers for Disease Control and Prevention, National Center for Health Statistics Underlying cause of death 1999-2010. CDC WONDER database. Accessed January 15, 2013. https://wonder.cdc.gov/ucd-icd10.html
  • 15.US Census Bureau Population Division 2010. Census summary file 1 sex by age table. Issued September 2012. Accessed December 11, 2018. https://www.census.gov/data/datasets/2010/dec/summary-file-1.html
  • 16.Fiscella K, Tancredi D, Franks P. Adding socioeconomic status to Framingham scoring to reduce disparities in coronary risk assessment. Am Heart J. 2009;157(6):988-994. doi: 10.1016/j.ahj.2009.03.019 [DOI] [PubMed] [Google Scholar]
  • 17.Winkleby MA, Jatulis DE, Frank E, Fortmann SP. Socioeconomic status and health: how education, income, and occupation contribute to risk factors for cardiovascular disease. Am J Public Health. 1992;82(6):816-820. doi: 10.2105/AJPH.82.6.816 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.US Census Bureau American Community Survey 2015. sample. Accessed November 30, 2018. https://usa.ipums.org/usa-action/samples
  • 19.Centers for Disease Control and Prevention, National Center for Health Statistics National Health and Nutrition Examination Survey, 2011-2016. Accessed October 9, 2018. https://wwwn.cdc.gov/nchs/nhanes/Default.aspx
  • 20.Dawber TR. The Framingham Study: The Epidemiology of Atherosclerotic Disease. Harvard University Press; 1980. doi: 10.4159/harvard.9780674492097 [DOI] [Google Scholar]
  • 21.Feinleib M, Kannel WB, Garrison RJ, McNamara PM, Castelli WP. The Framingham Offspring Study. Design and preliminary data. Prev Med. 1975;4(4):518-525. doi: 10.1016/0091-7435(75)90037-7 [DOI] [PubMed] [Google Scholar]
  • 22.Lewis MW, Khodneva Y, Redmond N, et al. . The impact of the combination of income and education on the incidence of coronary heart disease in the prospective Reasons for Geographic and Racial Differences in Stroke (REGARDS) cohort study. BMC Public Health. 2015;15:1312. doi: 10.1186/s12889-015-2630-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Lewington S, Clarke R, Qizilbash N, Peto R, Collins R; Prospective Studies Collaboration . Age-specific relevance of usual blood pressure to vascular mortality: a meta-analysis of individual data for one million adults in 61 prospective studies. Lancet. 2002;360(9349):1903-1913. doi: 10.1016/S0140-6736(02)11911-8 [DOI] [PubMed] [Google Scholar]
  • 24.Stone NJ, Robinson JG, Lichtenstein AH, et al. ; American College of Cardiology/American Heart Association Task Force on Practice Guidelines . 2013 ACC/AHA guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2014;63(25, pt B):2889-2934. doi: 10.1016/j.jacc.2013.11.002 [DOI] [PubMed] [Google Scholar]
  • 25.Williams DR, Costa MV, Odunlami AO, Mohammed SA. Moving upstream: how interventions that address the social determinants of health can improve health and reduce disparities. J Public Health Manag Pract. 2008;14(suppl):S8-S17. doi: 10.1097/01.PHH.0000338382.36695.42 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Link BG, Phelan J. Social conditions as fundamental causes of disease. J Health Soc Behav. 1995;(Spec No):80-94. doi: 10.2307/2626958 [DOI] [PubMed] [Google Scholar]
  • 27.Rossen LM, Khan D, Schoendorf KC. Mapping geographic variation in infant mortality and related black-white disparities in the US. Epidemiology. 2016;27(5):690-696. doi: 10.1097/EDE.0000000000000509 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Dimsdale JE. Psychological stress and cardiovascular disease. J Am Coll Cardiol. 2008;51(13):1237-1246. doi: 10.1016/j.jacc.2007.12.024 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Seeman TE, McEwen BS, Rowe JW, Singer BH. Allostatic load as a marker of cumulative biological risk: MacArthur studies of successful aging. Proc Natl Acad Sci U S A. 2001;98(8):4770-4775. doi: 10.1073/pnas.081072698 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Geronimus AT, Hicken M, Keene D, Bound J. “Weathering” and age patterns of allostatic load scores among blacks and whites in the United States. Am J Public Health. 2006;96(5):826-833. doi: 10.2105/AJPH.2004.060749 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Williams DR. Race, socioeconomic status, and health. The added effects of racism and discrimination. Ann N Y Acad Sci. 1999;896(1):173-188. doi: 10.1111/j.1749-6632.1999.tb08114.x [DOI] [PubMed] [Google Scholar]
  • 32.Williams DR, Mohammed SA. Discrimination and racial disparities in health: evidence and needed research. J Behav Med. 2009;32(1):20-47. doi: 10.1007/s10865-008-9185-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Hicken MT, Lee H, Morenoff J, House JS, Williams DR. Racial/ethnic disparities in hypertension prevalence: reconsidering the role of chronic stress. Am J Public Health. 2014;104(1):117-123. doi: 10.2105/AJPH.2013.301395 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Adler NE, Ostrove JM. Socioeconomic status and health: what we know and what we don’t. Ann N Y Acad Sci. 1999;896:3-15. doi: 10.1111/j.1749-6632.1999.tb08101.x [DOI] [PubMed] [Google Scholar]
  • 35.Institute of Medicine Committee on Understanding and Eliminating Racial and Ethnic Disparities in Health Care Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care. National Academies Press; 2003. [PubMed] [Google Scholar]
  • 36.Ludwig J, Sanbonmatsu L, Gennetian L, et al. . Neighborhoods, obesity, and diabetes—a randomized social experiment. N Engl J Med. 2011;365(16):1509-1519. doi: 10.1056/NEJMsa1103216 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.White JS, Hamad R, Li X, et al. . Long-term effects of neighbourhood deprivation on diabetes risk: quasi-experimental evidence from a refugee dispersal policy in Sweden. Lancet Diabetes Endocrinol. 2016;4(6):517-524. doi: 10.1016/S2213-8587(16)30009-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Bauman AE, Reis RS, Sallis JF, Wells JC, Loos RJF, Martin BW; Lancet Physical Activity Series Working Group . Correlates of physical activity: why are some people physically active and others not? Lancet. 2012;380(9838):258-271. doi: 10.1016/S0140-6736(12)60735-1 [DOI] [PubMed] [Google Scholar]
  • 39.Shah AK, Mullainathan S, Shafir E. Some consequences of having too little. Science. 2012;338(6107):682-685. doi: 10.1126/science.1222426 [DOI] [PubMed] [Google Scholar]
  • 40.Mani A, Mullainathan S, Shafir E, Zhao J. Poverty impedes cognitive function. Science. 2013;341(6149):976-980. doi: 10.1126/science.1238041 [DOI] [PubMed] [Google Scholar]
  • 41.Schilbach F, Schofield H, Mullainathan S. The psychological lives of the poor. Am Econ Rev. 2016;106(5):435-440. doi: 10.1257/aer.p20161101 [DOI] [PubMed] [Google Scholar]
  • 42.Berkman LF, Glass T. Social integration, social networks, social support, and health In: Berkman L, Kawachi I, eds. Social Epidemiology. Oxford University Press; 2000:137-173. [Google Scholar]
  • 43.Lakon CM, Hipp JR, Wang C, Butts CT, Jose R. Simulating dynamic network models and adolescent smoking: the impact of varying peer influence and peer selection. Am J Public Health. 2015;105(12):2438-2448. doi: 10.2105/AJPH.2015.302789 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.McGavock J, Wicklow B, Dart AB. Type 2 diabetes in youth is a disease of poverty. Lancet. 2017;390(10105):1829. doi: 10.1016/S0140-6736(17)32461-3 [DOI] [PubMed] [Google Scholar]
  • 45.Galobardes B, Smith GD, Lynch JW. Systematic review of the influence of childhood socioeconomic circumstances on risk for cardiovascular disease in adulthood. Ann Epidemiol. 2006;16(2):91-104. doi: 10.1016/j.annepidem.2005.06.053 [DOI] [PubMed] [Google Scholar]
  • 46.Shonkoff JP, Garner AS; Committee on Psychosocial Aspects of Child and Family Health; Committee on Early Childhood, Adoption, and Dependent Care; Section on Developmental and Behavioral Pediatrics . The lifelong effects of early childhood adversity and toxic stress. Pediatrics. 2012;129(1):e232-e246. doi: 10.1542/peds.2011-2663 [DOI] [PubMed] [Google Scholar]
  • 47.Ahmed SM, Lemkau JP, Nealeigh N, Mann B. Barriers to healthcare access in a non-elderly urban poor American population. Health Soc Care Community. 2001;9(6):445-453. doi: 10.1046/j.1365-2524.2001.00318.x [DOI] [PubMed] [Google Scholar]
  • 48.Syed ST, Gerber BS, Sharp LK. Traveling towards disease: transportation barriers to health care access. J Community Health. 2013;38(5):976-993. doi: 10.1007/s10900-013-9681-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Batterham RW, Hawkins M, Collins PA, Buchbinder R, Osborne RH. Health literacy: applying current concepts to improve health services and reduce health inequalities. Public Health. 2016;132:3-12. doi: 10.1016/j.puhe.2016.01.001 [DOI] [PubMed] [Google Scholar]
  • 50.Dawber TR, Kannel WB, Revotskie N, Stokes J III, Kagan A, Gordon T. Some factors associated with the development of coronary heart disease: six years’ follow-up experience in the Framingham Study. Am J Public Health Nations Health. 1959;49(10):1349-1356. doi: 10.2105/AJPH.49.10.1349 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Marmot MG, Smith GD, Stansfeld S, et al. . Health inequalities among British civil servants: the Whitehall II Study. Lancet. 1991;337(8754):1387-1393. doi: 10.1016/0140-6736(91)93068-K [DOI] [PubMed] [Google Scholar]
  • 52.Carter AR, Gill D, Davies NM, et al. . Understanding the consequences of education inequality on cardiovascular disease: Mendelian Randomisation Study. BMJ. 2019;365:l1855. doi: 10.1136/bmj.l1855 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Campbell F, Conti G, Heckman JJ, et al. . Early childhood investments substantially boost adult health. Science. 2014;343(6178):1478-1485. doi: 10.1126/science.1248429 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Lumeng JC, Kaciroti N, Sturza J, et al. . Changes in body mass index associated with Head Start participation. Pediatrics. 2015;135(2):e449-e456. doi: 10.1542/peds.2014-1725 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Centers for Disease Control and Prevention Burden of cigarette use in the U.S. Page last reviewed March 23, 2020. Accessed October 15, 2019. https://www.cdc.gov/tobacco/campaign/tips/resources/data/cigarette-smoking-in-united-states.html
  • 56.Centers for Disease Control and Prevention Nutrition, physical activity, and obesity: data, trends and maps. Accessed October 15, 2019. https://www.cdc.gov/nccdphp/dnpao/data-trends-maps/index.html
  • 57.Dhurandhar EJ. The food-insecurity obesity paradox: a resource scarcity hypothesis. Physiol Behav. 2016;162:88-92. doi: 10.1016/j.physbeh.2016.04.025 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Kaur J, Lamb MM, Ogden CL. The association between food insecurity and obesity in children—the National Health and Nutrition Examination Survey. J Acad Nutr Diet. 2015;115(5):751-758. doi: 10.1016/j.jand.2015.01.003 [DOI] [PubMed] [Google Scholar]
  • 59.Frohlich KL, Potvin L. Transcending the known in public health practice: the inequality paradox: the population approach and vulnerable populations. Am J Public Health. 2008;98(2):216-221. doi: 10.2105/AJPH.2007.114777 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.Arnett DK, Blumenthal RS, Albert MA, et al. . 2019 ACC/AHA guideline on the primary prevention of cardiovascular disease: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. J Am Coll Cardiol. 2019;74(10):e177-e232. doi: 10.1016/j.jacc.2019.03.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61.Bibbins-Domingo K. Integrating social care into the delivery of health care. JAMA. 2019;322(18):1763-1764. doi: 10.1001/jama.2019.15603 [DOI] [PubMed] [Google Scholar]
  • 62.Gottlieb L, Sandel M, Adler NE. Collecting and applying data on social determinants of health in health care settings. JAMA Intern Med. 2013;173(11):1017-1020. doi: 10.1001/jamainternmed.2013.560 [DOI] [PubMed] [Google Scholar]
  • 63.Goff DC Jr, Lloyd-Jones DM, Bennett G, et al. ; American College of Cardiology/American Heart Association Task Force on Practice Guidelines . 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation. 2014;129(25)(suppl 2):S49-S73. doi: 10.1161/01.cir.0000437741.48606.98 [DOI] [PubMed] [Google Scholar]
  • 64.Collins GS, Altman DG. An independent external validation and evaluation of QRISK cardiovascular risk prediction: a prospective open cohort study. BMJ. 2009;339:b2584. doi: 10.1136/bmj.b2584 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 65.Lloyd-Jones DM, Braun LT, Ndumele CE, et al. . Use of risk assessment tools to guide decision-making in the primary prevention of atherosclerotic cardiovascular disease: a special report from the American Heart Association and American College of Cardiology. Circulation. 2019;139(25):e1162-e1177. doi: 10.1161/CIR.0000000000000638 [DOI] [PubMed] [Google Scholar]
  • 66.Schultz WM, Kelli HM, Lisko JC, et al. . Socioeconomic status and cardiovascular outcomes: challenges and interventions. Circulation. 2018;137(20):2166-2178. doi: 10.1161/CIRCULATIONAHA.117.029652 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 67.Dalton JE, Perzynski AT, Zidar DA, et al. . Accuracy of cardiovascular risk prediction varies by neighborhood socioeconomic position: a retrospective cohort study. Ann Intern Med. 2017;167(7):456-464. doi: 10.7326/M16-2543 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 68.Kind AJH, Buckingham WR. Making neighborhood-disadvantage metrics accessible—the neighborhood atlas. N Engl J Med. 2018;378(26):2456-2458. doi: 10.1056/NEJMp1802313 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69.Hippisley-Cox J, Coupland C, Vinogradova Y, Robson J, Brindle P. Performance of the QRISK cardiovascular risk prediction algorithm in an independent UK sample of patients from general practice: a validation study. Heart. 2008;94(1):34-39. doi: 10.1136/hrt.2007.134890 [DOI] [PubMed] [Google Scholar]
  • 70.Woodward M, Brindle P, Tunstall-Pedoe H; SIGN Group on Risk Estimation . Adding social deprivation and family history to cardiovascular risk assessment: the ASSIGN score from the Scottish Heart Health Extended Cohort (SHHEC). Heart. 2007;93(2):172-176. doi: 10.1136/hrt.2006.108167 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 71.Hamad R, Elser H, Tran DC, Rehkopf DH, Goodman SN. How and why studies disagree about the effects of education on health: a systematic review and meta-analysis of studies of compulsory schooling laws. Soc Sci Med. 2018;212:168-178. doi: 10.1016/j.socscimed.2018.07.016 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 72.Hamad R, Nguyen TT, Bhattacharya J, Glymour MM, Rehkopf DH. Educational attainment and cardiovascular disease in the United States: a quasi-experimental instrumental variables analysis. PLoS Med. 2019;16(6):e1002834. doi: 10.1371/journal.pmed.1002834 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 73.Rehkopf DH, Strully KW, Dow WH. The short-term impacts of earned income tax credit disbursement on health. Int J Epidemiol. 2014;43(6):1884-1894. doi: 10.1093/ije/dyu172 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 74.Hamad R, Batra A, Karasek D, et al. . The impact of the revised WIC food package on maternal nutrition during pregnancy and postpartum. Am J Epidemiol. 2019;188(8):1493-1502. doi: 10.1093/aje/kwz098 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 75.Basu S, Seligman H, Bhattacharya J. Nutritional policy changes in the supplemental nutrition assistance program: a microsimulation and cost-effectiveness analysis. Med Decis Making. 2013;33(7):937-948. doi: 10.1177/0272989X13493971 [DOI] [PubMed] [Google Scholar]
  • 76.Lovasi GS, Grady S, Rundle A. Steps forward: review and recommendations for research on walkability, physical activity and cardiovascular health. Public Health Rev. 2012;33(4):484-506. doi: 10.1007/bf03391647 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 77.Loucks EB, Lynch JW, Pilote L, et al. . Life-course socioeconomic position and incidence of coronary heart disease: the Framingham Offspring Study. Am J Epidemiol. 2009;169(7):829-836. doi: 10.1093/aje/kwn403 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 78.Liu SY, Kawachi I, Glymour MM. Education and inequalities in risk scores for coronary heart disease and body mass index: evidence for a population strategy. Epidemiology. 2012;23(5):657-664. doi: 10.1097/EDE.0b013e318261c7cc [DOI] [PubMed] [Google Scholar]
  • 79.Rehkopf DH. Quantile regression for hypothesis testing and hypothesis screening at the dawn of big data. Epidemiology. 2012;23(5):665-667. doi: 10.1097/EDE.0b013e318261f7be [DOI] [PubMed] [Google Scholar]
  • 80.Ross CE, Mirowsky J. Refining the association between education and health: the effects of quantity, credential, and selectivity. Demography. 1999;36(4):445-460. doi: 10.2307/2648083 [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement.

eMethods. Overview of CVD Policy Model Structure and Analytic Approach

eTable 1. Comparisons of Selected Cardiovascular Disease Policy Model Simulation Outputs for 2010 (Model Base Year) With National Targets for 2010

eTable 2. Key Input Values for the Low- and Higher-SES CVD Policy Models, With Measures of Variability Indicated for Inputs That Were Varied in Monte Carlo Simulations

eFigure. Schematic of the Cardiovascular Disease Policy Model

eReferences

Click here for additional data file. (521.4KB, pdf)

Articles from JAMA Cardiology are provided here courtesy of American Medical Association

RESOURCES