Cardiovascular Risk Associated With Social Determinants of Health at Individual and Area Levels

Mengying Xia; Jaejin An; Monika M Safford; Lisandro D Colantonio; Mario Sims; Kristi Reynolds; Andrew E Moran; Yiyi Zhang

doi:10.1001/jamanetworkopen.2024.8584

. 2024 Apr 26;7(4):e248584. doi: 10.1001/jamanetworkopen.2024.8584

Cardiovascular Risk Associated With Social Determinants of Health at Individual and Area Levels

Mengying Xia ¹, Jaejin An ^2,³, Monika M Safford ⁴, Lisandro D Colantonio ⁵, Mario Sims ⁶, Kristi Reynolds ^2,³, Andrew E Moran ¹, Yiyi Zhang ^1,^✉

¹Division of General Medicine, Columbia University Irving Medical Center, New York, New York

²Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena

³Department of Health Systems Science, Kaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, California

⁴Division of General Internal Medicine, Department of Medicine, Weill Cornell Medicine, New York, New York

⁵Department of Epidemiology, University of Alabama at Birmingham, Birmingham

⁶Department of Social Medicine, Population, and Public Health, University of California, Riverside

Accepted for Publication: February 28, 2024.

Published: April 26, 2024. doi:10.1001/jamanetworkopen.2024.8584

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Corresponding Author: Yiyi Zhang, PhD, Columbia University Medical Center, Division of General Medicine, 622 W 168th St, PH9-109A, New York, NY 10032 (yz3160@cumc.columbia.edu).

Author Contributions: Dr Zhang had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Xia, An, Safford, Sims, Moran, Zhang.

Acquisition, analysis, or interpretation of data: Xia, An, Safford, Colantonio, Reynolds, Zhang.

Drafting of the manuscript: Xia, Zhang.

Critical review of the manuscript for important intellectual content: Xia, An, Safford, Colantonio, Sims, Reynolds, Moran.

Statistical analysis: Xia, Zhang.

Obtained funding: An, Safford, Zhang.

Administrative, technical, or material support: Xia, Safford, Colantonio, Moran.

Supervision: An, Safford, Zhang.

Conflict of Interest Disclosures: Dr An reported grants from AstraZeneca and Bayer outside the submitted work. Dr Safford reported owning stock from MedExplain outside the submitted work. Dr Colantonio reported grants from Amgen Inc outside the submitted work. Dr Reynolds reported receiving research support from Novartis and Merck Sharp & Dohme LLC outside the submitted work. Dr Moran reported grants from US National Heart, Lung, and Blood Institute (NHLBI) outside the submitted work. Dr Zhang reported grants from National Institutes of Health (NIH)/NHLBI during the conduct of the study. No other disclosures were reported.

Funding/Support: This work was supported by National Institutes of Health grants (Nos. R01HL155081 and R01HL168379 [Zhang, An]).

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Representatives of the National Institute of Neurological Disorders and Stroke, an NIH agency that funds Reasons for Geographic And Racial Differences in Stroke Study (REGARDS), were involved in the review of the manuscript but were not directly involved in the collection, management, analysis, or interpretation of the data.

Disclaimer: The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the NHLBI, the NIH, or the US Department of Health and Human Services.

Data Sharing Statement: See Supplement 2.

Additional Contributions: The authors thank the investigators, staff, and participants of all the cohorts for their valuable contributions. The Framingham Heart Study (FHS) was supported by National Heart, Lung, and Blood Institute (NHLBI; grant No. HHSN268201500001I) and the Boston University School of Medicine. The Jackson Heart Study (JHS) is supported and conducted in collaboration with Jackson State University (grant No. HHSN268201800013I), Tougaloo College (grant No. HHSN268201800014I), the Mississippi State Department of Health (grant No. HHSN268201800015I), and the University of Mississippi Medical Center (grant Nos. HHSN268201800010I, HHSN268201800011I, and HHSN268201800012I) contracts from the NHLBI and the National Institute on Minority Health and Health Disparities (NIMHD). The Multi-Ethnic Study of Atherosclerosis (MESA) was supported by contracts from the NHLBI (Nos. 75N92020D00001, HHSN268201500003I, N01-HC-95159, 75N92020D00005, N01-HC-95160, 75N92020D00002, N01-HC-95161, 75N92020D00003, N01-HC-95162, 75N92020D00006, N01-HC-95163, 75N92020D00004, N01-HC-95164, 75N92020D00007, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168 and N01-HC-95169), and by grants from the National Center for Advancing Translational Sciences (NCATS; grant Nos. UL1-TR-000040, UL1-TR-001079, and UL1-TR-001420). A full list of participating MESA investigators and institutions can be found at the study website.⁵⁸ This paper has been reviewed and approved by the MESA Publications and Presentations Committee. REGARDS was supported by cooperative agreement No. U01 NS041588 cofunded by the National Institute of Neurological Disorders and Stroke (NINDS) and the National Institute on Aging (NIA), National Institutes of Health, Department of Health and Human Service. Additional funding was provided by NHLBI grant No. R01HL165452 for the REGARDS-MI study. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NINDS, NIA, or the NHLBI. A full list of participating REGARDS investigators and institutions can be found at the study website.⁵⁹

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Corresponding author.

PMCID: PMC11053380 PMID: 38669015

Key Points

Question

Does adding individual- or area-level social determinants of health (SDOH) to the pooled cohort equations (PCEs) or the Predicting Risk of CVD Events (PREVENT) equations improve accuracy of risk estimates for atherosclerotic cardiovascular disease (ASCVD)?

Findings

In this cohort study of 26 316 participants from 4 large US studies, both individual- and area-level of low education, low income, and unemployment were associated with an increased risk of incident ASCVD events. Adding area-level SDOH alone to the PCEs did not improve model discrimination and modestly improved calibration, while adding both individual- and area-level SDOH to the PCEs modestly improved discrimination and calibration in non-Hispanic Black individuals; the addition of individual-level SDOH to the PREVENT plus social deprivation index (SDI) model also modestly improved calibration in non-Hispanic Black and White individuals.

Meaning

The findings suggest that both individual- and area-level SDOH may be considered in future development of ASCVD risk assessment tools, particularly among Black individuals.

This cohort study including participants from 4 large US studies examines the association of social determinants of health—including education, income, and employment status—with improvements in common tools for assessing risk of atherosclerotic cardiovascular disease.

Abstract

Importance

The benefit of adding social determinants of health (SDOH) when estimating atherosclerotic cardiovascular disease (ASCVD) risk is unclear.

Objective

To examine the association of SDOH at both individual and area levels with ASCVD risks, and to assess if adding individual- and area-level SDOH to the pooled cohort equations (PCEs) or the Predicting Risk of CVD Events (PREVENT) equations improves the accuracy of risk estimates.

Design, Setting, and Participants

This cohort study included participants data from 4 large US cohort studies. Eligible participants were aged 40 to 79 years without a history of ASCVD. Baseline data were collected from 1995 to 2007; median (IQR) follow-up was 13.0 (9.3-15.0) years. Data were analyzed from September 2023 to February 2024.

Exposures

Individual- and area-level education, income, and employment status.

Main outcomes and measures

ASCVD was defined as the composite outcome of nonfatal myocardial infarction, death from coronary heart disease, and fatal or nonfatal stroke.

Results

A total of 26 316 participants were included (mean [SD] age, 61.0 [9.1] years; 15 494 women [58.9%]; 11 365 Black [43.2%], 703 Chinese American [2.7%], 1278 Hispanic [4.9%], and 12 970 White [49.3%]); 11 764 individuals (44.7%) had at least 1 adverse individual-level SDOH and 10 908 (41.5%) had at least 1 adverse area-level SDOH. A total of 2673 ASCVD events occurred during follow-up. SDOH were associated with increased risk of ASCVD at both the individual and area levels, including for low education (individual: hazard ratio [HR], 1.39 [95% CI, 1.25-1.55]; area: HR, 1.31 [95% CI, 1.20-1.42]), low income (individual: 1.35 [95% CI, 1.25-1.47]; area: HR, 1.28 [95% CI, 1.17-1.40]), and unemployment (individual: HR, 1.61 [95% CI, 1.24-2.10]; area: HR, 1.25 [95% CI, 1.14-1.37]). Adding area-level SDOH alone to the PCEs did not change model discrimination but modestly improved calibration. Furthermore, adding both individual- and area-level SDOH to the PCEs led to a modest improvement in both discrimination and calibration in non-Hispanic Black individuals (change in C index, 0.0051 [95% CI, 0.0011 to 0.0126]; change in scaled integrated Brier score [IBS], 0.396% [95% CI, 0.221% to 0.802%]), and improvement in calibration in White individuals (change in scaled IBS, 0.274% [95% CI, 0.095% to 0.665%]). Adding individual-level SDOH to the PREVENT plus area-level social deprivation index (SDI) equations did not improve discrimination but modestly improved calibration in White participants (change in scaled IBS, 0.182% [95% CI, 0.040% to 0.496%]), Black participants (0.187% [95% CI, 0.039% to 0.501%]), and women (0.289% [95% CI, 0.115% to 0.574%]).

Conclusions and Relevance

In this cohort study, both individual- and area-level SDOH were associated with ASCVD risk; adding both individual- and area-level SDOH to the PCEs modestly improved discrimination and calibration for estimating ASCVD risk for Black individuals, and adding individual-level SDOH to PREVENT plus SDI also modestly improved calibration. These findings suggest that both individual- and area-level SDOH may be considered in future development of ASCVD risk assessment tools, particularly among Black individuals.

Introduction

Social determinants of health (SDOH) are the conditions in the environment where people are born, live, learn, work, play, worship, and age that affect a wide range of health, functioning, and quality of life outcomes and risks.¹ SDOH are important determinants of cardiovascular health.^2,3 Individual- and area-level SDOH may capture social factors in different ways and represent distinct influences on cardiovascular outcomes.⁴ For example, individual-level SDOH can capture granular data specific to each individual. However, these data may be subject to social desirability bias if collected by self-report, where individuals may provide socially desirable responses rather than truthful ones, and may also be harder to collect due to their sensitive nature.^5,6 Area-level SDOH are derived from geographic areas and capture characteristics of the neighborhood and built environment.⁷ They are more objective and can be easily collected and incorporated into a health care system setting when linked with census tract-level data.^8,9,10 Socioeconomic status including education, household income, and employment status are the most commonly collected and studied SDOH.^4,11 Previous studies have shown that both individual- and area-level socioeconomic status are associated with atherosclerotic cardiovascular disease (ASCVD).^12,13,14,15

Despite the importance of SDOH in determining ASCVD risk, they are not included in the pooled cohort equations (PCEs) when estimating 10-year ASCVD risk.^13,16 Studies have shown that PCEs systematically underestimate ASCVD risk for individuals with low socioeconomic status.^17,18 However, it is unclear if adding either or both individual- and area-level SDOH to the PCEs improves ASCVD risk estimates. Additionally, although area-level social deprivation index (SDI) was considered as an optional variable in the recently developed American Heart Association’s Predicting Risk of CVD Events (PREVENT) equations, the role of individual-level SDOH was not assessed in PREVENT.¹⁹ This study sought to (1) examine the association of SDOH drawn from the education, income, and employment status at both individual and area levels with the risk of incident ASCVD events, and (2) assess if adding individual- and area-level SDOH to the PCEs or to PREVENT plus SDI improves the accuracy of ASCVD risk estimation.

Methods

Study Design and Cohorts

This study analyzed data from 4 large, population-based prospective cohort studies in the US: (1) Framingham Heart Study Offspring Cohort (FHS Offspring)²⁰; (2) Jackson Heart Study (JHS)²¹; (3) Multi-Ethnic Study of Atherosclerosis (MESA)²²; and (4) Reasons for Geographic And Racial Differences in Stroke Study (REGARDS).²³ The design of each study is reported in the eMethods in Supplement 1. All study protocols were approved by the institutional review boards at participating institutions, and this study was approved by the Columbia University institutional review board. All participants provided written informed consent. The current analysis included participants aged 40 to 79 years in whom the PCEs were applicable.²⁴ We excluded participants with a history of ASCVD at baseline (6841 individuals), missing SDOH (8453 individuals), missing covariates included in the PCEs (1472 individuals), or missing follow-up for incident ASCVD events (297 individuals) (eFigure 1 in Supplement 1). The results are reported based on Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines.

Data Collection

Demographics and traditional ASCVD risk factors were measured using standardized protocols in each study.^20,21,22,23 Diabetes was defined as fasting blood glucose levels of 126 mg/dL or higher (to convert to millimoles per liter, multiply by 0.0555) or the use of antidiabetes medication. Information on individual-level education (less than high school, high school or higher education), income (less than $35 000, $35 000 or higher), employment status (unemployed, not unemployed including those who were retired), and race and ethnicity (Chinese American, Hispanic, non-Hispanic Black, non-Hispanic White) were self-reported by the participants using fixed categories drawn from the source studies. Area-level SDOH data were derived by linking each participants’ residential addresses at baseline via geocoding to US census tract 2000 data at the census tract level.^25,26,27 Specifically, we examined 3 area-level SDOH in the current analysis: (1) neighborhood education (percentage of residents aged 25 years or older with less than high school education), (2) neighborhood income (percentage of residents whose family income was below the federal poverty level), and (3) neighborhood unemployment (percentage of residents aged 16 years or older in the labor force who were unemployed). Low neighborhood income was defined as having 25% or more residents living below poverty level, as was done in a previous study.²⁸ Because there were no commonly used cutoffs for area-level education and unemployment, we used the upper quartile to define low education (ie, 33% or more residents with less than high school education) and high unemployment (11% or more residents unemployed).

Follow-Up and ASCVD Events

The primary outcome was time to the first incident ASCVD event. ASCVD events were defined as the composite outcome of nonfatal myocardial infarction (MI), death from coronary heart disease (CHD), and fatal or nonfatal stroke. Events were ascertained and adjudicated using the specific protocol of each cohort (eMethods in Supplement 1).^{20,21,22,23,29}

Statistical Analysis

Participant characteristics at the baseline visit were described for the overall pooled cohort, by individual studies, and by number of SDOH. The agreement between individual-level and area-level SDOH was assessed by Cohen κ statistics, which range between poor-to-fair agreement (below 0.40), moderate agreement (0.41 to 0.60), substantial agreement (0.61 to 0.80), and excellent agreement (0.81 to 1.00).^30,31

To examine the association between each individual- and area-level SDOH measure with incident ASCVD events, we used 3 Cox proportional hazards models with progressive adjustment for potential confounders. The base model was adjusted for sex and age at the baseline visit. The second model was further adjusted for race and ethnicity. In a third model, we further included covariates used in the PCEs (including smoking status, total cholesterol, high-density lipoprotein cholesterol, systolic blood pressure, use of antihypertension medication, and diabetes status) and use of lipid-lowering medication to assess if the associations between SDOH and ASCVD may be explained by these traditional ASCVD risk factors. All models were stratified by study cohort to account for potential cohort effect by allowing the baseline hazard function to vary across different cohorts. The proportional hazards assumption was checked by log(–log(survival)) vs log (survival time) plots and Schoenfeld residuals. In secondary analyses, we examined the association between the numbers of adverse SDOH and ASCVD, as well as included all individual- and area-level SDOH of interest simultaneously in the same Cox model. Since all Cohen κ results between individual- and area-level SDOH were below 0.62, collinearity was not an issue. Additionally, because previous studies showed that the association between SDOH and health outcomes may differ by sex and race,^32,33 we performed stratified analysis by sex and by race and ethnicity (Chinese American, Hispanic, non-Hispanic Black, and non-Hispanic White).

To assess if adding individual- and area-level SDOH to the PCEs improve the accuracy of ASCVD risk estimates, we fitted 4 separate Cox models: (1) a model only including the 10-year ASCVD risk estimated by the PCEs, (2) a model including the 10-year risk estimated by the PCEs plus individual-level SDOH measures, (3) a model including the 10-year risk estimated by the PCEs plus area-level SDOH measures, and (4) a model including the 10-year risk estimated by the PCEs plus both individual- and area-level SDOH measures. We assessed model discrimination by calculating Harrell C index at 10 years³⁴ and assessed model calibration by using calibration plots as well as calculating scaled integrated Brier score (scaled IBS, which takes values between 0% to 100% with a higher value representing better calibration).^35,36 We examined the changes in model performance in the overall population as well as by sex and by racial and ethnic groups. We used nonparametric bootstrapping with 500 iterations to calculate the 95% CIs for the change in Harrell C index and scaled IBS.

Additionally, although area-level SDI was considered as an optional variable in the recently developed PREVENT equations, the role of individual-level SDOH was not assessed in PREVENT.¹⁹ Therefore, as a secondary analysis, we assessed if adding individual-level SDOH to the PREVENT plus SDI equations may further improve estimate accuracy. In the original PREVENT plus SDI risk model, SDI was calculated at the zip code level and was not available in the current study population.¹⁹ Therefore, we replaced SDI with a similar area-level deprivation index developed by the Agency for Healthcare Research and Quality, which was based on 7 indicators of poverty, education, employment, and physical environment at the census tract level when calculating 10-year ASCVD risk estimated by the PREVENT plus SDI equations.^37,38

Lastly, because approximately 20% of participants were missing information on SDOH, possibly due to unwillingness to report this information, we performed sensitivity analyses by creating missing indicator variables for these participants and including them in all the analyses (34 023 individuals). The threshold for statistical significance was a 2-sided P < .05. All analyses were performed with R version 4.0.2 (R Project for Statistical Computing).

Results

A total of 26 316 participants were included in this study (mean [SD] age at baseline, 61.0 [9.1] years); 15 494 were women (58.9%) and 703 self-identified as Chinese American (2.7%), 1278 as Hispanic (4.9%), 11 365 as non-Hispanic Black (43.2%), and 12 970 as non-Hispanic White (49.3%) (Table). For individual-level SDOH, 2882 participants (11.0%) had less than high school education, 11 107 (42.2%) had low household income, and 568 (2.2%) were unemployed. For area-level SDOH, the median (IQR) for percentage of neighborhood with less than high school education was 21.8% (10.9%-32.5%), median percentage of neighborhood living below federal poverty line was 14.2% (7.0%-25.5%), and median neighborhood unemployment was 7.1% (4.3%-11.3%). Individuals with at least 1 adverse individual- or area-level SDOH were more likely to be non-Hispanic Black or to have a worse cardiovascular risk factor profile (eTables 1 and 2 in Supplement 1). The Cohen κ statistics between individual-level and area-level SDOH were 0.18 (95% CI, 0.17-0.20) for education, 0.21 (95% CI, 0.20-0.22) for income, and 0.01 (95% CI, 0.00-0.02) for unemployment (eTable 3 in Supplement 1).

Table. Baseline Characteristics of Study Participants.

Participant characteristics	Participants, No. (%)
Participant characteristics	Overall (N = 26 316)	FHS Offspring (n = 1181)	JHS (n = 3443)	MESA (n = 5906)	REGARDS (n = 15 786)
Age, mean (SD), y	61.0 (9.1)	58.3 (9.0)	56.0 (10.2)	61.1 (9.5)	62.3 (8.2)
Follow-up time, median (IQR), y	13.0 (9.3-15.0)	21.9 (15.8-23.3)	13.7 (10.4-14.6)	16.8 (13.3-17.5)	12.0 (7.5-13.8)
Gender
Women	15 494 (58.9)	620 (52.5)	2229 (64.7)	3090 (52.3)	9555 (60.5)
Men	10 822 (41.1)	561 (47.5)	1214 (35.3)	2816 (47.7)	6231 (39.5)
Race and ethnicity
Chinese American	703 (2.7)	0	0	703 (11.9)	0
Hispanic	1278 (4.9)	0	0	1278 (21.6)	0
Non-Hispanic Black	11 365 (43.2)	0	3443 (100)	1611 (27.3)	6311 (40.0)
Non-Hispanic White	12 970 (49.3)	1181 (100)	0	2314 (39.2)	9475 (60.0)
Smoking status
Never	13 507 (51.3)	406 (34.4)	2396 (69.6)	2953 (50.0)	7752 (49.1)
Former	9305 (35.4)	574 (48.6)	648 (18.8)	2183 (37.0)	5900 (37.4)
Current	3504 (13.3)	201 (17.0)	399 (11.6)	770 (13.0)	2134 (13.5)
BMI, mean (SD)	29.5 (6.2)	27.9 (5.0)	31.7 (7.0)	28.4 (5.4)	29.5 (6.3)
Lipids, mean (SD), mg/dL
Total cholesterol	196.0 (38.1)	205.2 (37.6)	200.8 (38.9)	194.2 (35.3)	195.0 (38.7)
HDL cholesterol	52.7 (15.9)	51.0 (15.7)	52.4 (14.6)	51.0 (14.7)	53.6 (16.6)
LDL cholesterol	118.4 (33.9)	127.3 (33.6)	127.4 (35.8)	117.4 (31.2)	116.1 (34.0)
Triglycerides	126.2 (83.1)	134.6 (77.1)	106.9 (80.5)	131.2 (87.4)	128.0 (81.9)
Blood pressure, mean (SD), mm Hg
Systolic	125.9 (17.2)	128.5 (18.8)	127.6 (16.3)	125.4 (20.8)	125.5 (15.8)
Diastolic	75.4 (9.7)	75.8 (9.6)	76.2 (8.7)	71.9 (10.2)	76.5 (9.3)
Diabetes	4579 (17.4)	99 (8.4)	547 (15.9)	713 (12.1)	3220 (20.4)
Use of antihypertensive medication	11 772 (44.7)	298 (25.2)	1799 (52.3)	2115 (35.8)	7560 (47.9)
Use of lipid-lowering medication	6058 (23.0)	128 (10.8)	449 (13.0)	952 (16.1)	4529 (28.7)
SDOH characteristics
Individual-level SDOH
Less than high school education	2882 (11.0)	55 (4.7)	539 (15.7)	969 (16.4)	1319 (8.4)
Annual household income <$35 000	11 107 (42.2)	480 (40.6)	1636 (47.5)	2511 (42.5)	6480 (41.0)
Unemployed	568 (2.2)	16 (1.4)	7 (0.2)	131 (2.2)	414 (2.6)
No. of adverse individual-level SDOH
0	14 552 (55.3)	684 (57.9)	1729 (50.2)	3199 (54.2)	8940 (56.6)
1	9030 (34.3)	444 (37.6)	1251 (36.3)	1828 (31.0)	5507 (34.9)
2	2675 (10.2)	52(4.4)	458 (13.3)	854 (14.5)	1311 (8.3)
3	59 (0.2)	1 (0.1)	5 (0.1)	25 (0.4)	28 (0.2)
Area-level SDOH, median (IQR), %
Neighborhood with less than high school education	21.8 (10.9-32.5)	5.3 (3.7-9.5)	25.5 (12.9-36.9)	20.5 (10.3-33.1)	22.3 (12.6-32.0)
Neighborhood living below federal poverty line	14.2 (7.0-25.5)	3.9 (2.4-8.4)	22.2 (11.5-34.7)	13.0 (6.4-22.3)	14.3 (7.2-24.8)
≥25% living below federal poverty line, No. (%)	6810 (25.9)	2 (0.2)	1692 (49.1)	1233 (20.9)	3883 (24.6)
Neighborhood unemployment rate	7.1 (4.3-11.3)	15.1 (13.3-19.4)	9.0 (5.5-12.1)	6.1 (3.8-10.2)	6.6 (3.9-10.3)
No. of adverse area-level SDOH^a
0	15 408 (58.6)	155 (13.1)	1450 (42.1)	3839 (65.0)	9964 (63.1)
1	4937 (18.8)	998 (84.5)	747 (21.7)	875 (14.8)	2317 (14.7)
2	2658 (10.1)	27 (2.3)	407 (11.8)	485 (8.2)	1739 (11.0)
3	3313 (12.6)	1 (0.1)	839 (24.4)	707 (12.0)	1766 (11.2)

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Abbreviations: BMI, body mass index (calculated as weight in kilograms divided by height in meters squared); FHS Offspring, Framingham Heart Study Offspring Cohort; HDL, high-density lipoprotein; JHS, Jackson Heart Study; LDL, low-density lipoprotein; MESA, Multi-Ethnic Study of Atherosclerosis; REGARDS, Reasons for Geographic and Racial Differences in Stroke Study; SDOH, social determinants of health.

SI conversion factor: To convert total, HDL, and LDL cholesterol to millimoles per liter, multiply by 0.0259; triglycerides to millimoles per liter, 0.0113.

^{^a}

Adverse area-level SDOH include (1) neighborhood with less than high school education in the upper quartile (33% or higher), (2) neighborhood living below federal poverty line 25% or higher, and (3) neighborhood with unemployment rate in the upper quartile (11% or higher).

During a median (IQR) follow-up of 13.0 years (9.3-15.0 years), a total of 2673 incident ASCVD events occurred. When adjusted for age and sex, the hazard ratios (HRs) for ASCVD associated with individual-level SDOH were 1.39 (95% CI, 1.25-1.55) for less than high school education, 1.35 (95% CI, 1.25-1.47) for annual household income below $35 000, and 1.61 (95% CI, 1.24-2.10) for unemployment (Figure 1). The corresponding HRs associated with area-level SDOH were 1.31 (95% CI, 1.20-1.42) for neighborhoods with 33% or more residents with less than a high school education, 1.28 (95% CI, 1.17-1.40) for neighborhoods with 25% or more of residents living below the federal poverty line, and 1.25 (95% CI, 1.14-1.37) for neighborhoods with an unemployment rate of 11% or higher. When further adjusted for race and ethnicity, the association between SDOH and ASCVD remained the same, but results were attenuated when further controlling for traditional ASCVD risk factors in PCEs. When the numbers of adverse SDOH were examined, the magnitude of the effect size increased with the number of adverse individual- and area-level SDOH. For example, compared with participants with no adverse individual-level SDOH, the HRs associated with having 1 and 2 adverse individual-level SDOH were 1.32 (95% CI, 1.21-1.44) and 1.63 (95% CI, 1.45-1.84), respectively. Results for individuals with 3 adverse SDOH were not significant (HR, 1.65; 95% CI, 0.74-3.68). When including individual- and area-level SDOH simultaneously in the same model, all 3 individual-level SDOH and area-level education were associated with ASCVD.

Figure 1. — The associations between SDOH and ASCVD were assessed using Cox proportional hazards models. All models were stratified by study cohort, allowing the baseline hazard function to vary across different cohorts.

^aModel 1 was adjusted for sex and age at the baseline visit.

^bModel 2 included adjustments in model 1 and was further adjusted for race and ethnicity.

^cModel 3 included adjustments of the other models and was further adjusted for traditional ASCVD risk factors included in the pooled cohort equations (including smoking status, total cholesterol, high-density lipoprotein cholesterol, systolic blood pressure, use of antihypertension medication, and diabetes status) and use of lipid-lowering medication.

In the analyses stratified by sex, we found similar associations between SDOH and ASCVD in men and women, except that effect sizes for the association between annual household income and ASCVD were higher in women (HR, 1.48; 95% CI, 1.31-1.66) than in men (HR, 1.23; 95% CI, 1.10-1.37; P for interaction = .006) (eTable 4 in Supplement 1). When stratified by race and ethnicity, we found similar effect sizes for the association between individual- and area-level SDOH with ASCVD across all races, except for individual- and area-level education, which had larger effect sizes for ASCVD in non-Hispanic White individuals than Hispanic individuals (eTable 5 in Supplement 1).

Adding area-level SDOH alone to the PCEs did not improve model discrimination but modestly improved calibration (change in scaled IBS in the overall population, 0.114%; 95% CI, 0.031% to 0.257%) (Figure 2). Further adding both individual- and area-level SDOH to the PCEs led to a modest improvement in both model discrimination (change in C index, 0.0051; 95% CI, 0.0011 to 0.0126) and calibration (change in scaled IBS, 0.396%; 95% CI, 0.221% to 0.802%) in non-Hispanic Black individuals, as well as improved calibration in non-Hispanic White individuals (change in scaled IBS, 0.274%; 95% CI, 0.095% to 0.665%), women (0.430%; 95% CI, 0.210% to 0.802%), and men (0.113%; 95% CI, 0.012% to 0.378%). The calibration plot also suggested that calibration was improved in both men and women, as well as in non-Hispanic White, non-Hispanic Black, and Hispanic individuals after adding both individual- and area-level SDOH to the PCEs (Figure 3). Additionally, adding individual-level SDOH to the PREVENT plus SDI risk model did not improve model discrimination, but modestly improved calibration in non-Hispanic White (change in scaled IBS, 0.182%; 95% CI, 0.040% to 0.496%), non-Hispanic Black (0.187%; 95% CI, 0.039% to 0.501%), and in women (0.289%; 95% CI, 0.115% to 0.574%) (eFigure 2 in Supplement 1).

Figure 2. — The 95% CI of change in C index and change in scaled IBS were calculated by nonparametric bootstrapping.

Figure 3. — Calibration plots compared estimated with observed 10-year atherosclerotic cardiovascular disease (ASCVD) risk by decile of the estimated risk. Orange lines represent calibration curves for models with only the PCEs; blue lines, calibration curves for models with PCEs plus individual- and area-level SDOH.

In sensitivity analyses not excluding participants with missing individual- or area-level SDOH, those with missing SDOH were more likely to be older, men, and non-Hispanic White and to have a worse cardiovascular risk factor profile (eTable 6 in Supplement 1). The associations between adverse individual- and area-level SDOH with incident ASCVD events were similar to those in the main analysis (eFigure 3 in Supplement 1). Additionally, missing individual-level income, individual-level employment status, or area-level SDOH in participants were associated with an increased risk of ASCVD. Adding area-level SDOH alone to the PCEs did not improve model discrimination but improved model calibration in all sex and race groups except for Chinese American (eFigure 4 in Supplement 1). Further adding both individual- and area-level SDOH to the PCEs led to a greater improvement in model discrimination in men, non-Hispanic White, and non-Hispanic Black individuals, as well as improved calibration in all sex and race groups.

Discussion

In this analysis of over 26 000 adults from 4 large US prospective cohort studies, we found that low education, low income, and unemployment at both individual and area levels were associated with an increased risk of incident ASCVD events. Area-level education remained associated with ASCVD even after adjusting for individual-level measures. Adding area-level SDOH alone to the PCEs did not improve model discrimination but modestly improved calibration. Further adding both individual- and area-level SDOH to the PCEs led to a modest improvement in both discrimination and calibration in non-Hispanic Black individuals. Additionally, adding individual-level SDOH to PREVENT plus SDI modestly improved calibration in non-Hispanic Black and non-Hispanic White individuals. These findings suggest that both individual- and area-level SDOH may be considered in the development of future ASCVD risk assessment tools, particularly among non-Hispanic Black individuals.

Adverse SDOH are important cardiovascular risk factors and may confer ASCVD risk that is equivalent to or greater than traditional risk factors.^13,16 Education, income, and employment status are commonly collected individual-level SDOH and have been consistently shown to be associated with ASCVD risks.^{12,16,39,40,41} Similarly, neighborhood-level SDOH were also associated with ASCVD risk.^{4,12,15,42,43} In a study of 77 101 individuals with a history of ASCVD from Kaiser Permanente Southern California, individuals living in neighborhoods with lower education and lower household income were associated with a higher risk of recurrent ASCVD events.¹⁵ A Swedish study of 336 295 men and 334 057 women showed that individuals from neighborhoods with higher unemployment rates were associated with a greater CHD risk.⁴²

In past studies, area-level SDOH were sometimes used to approximate individual-level SDOH when individual-level data were not available. However, individual- and area-level SDOH may capture ASCVD risk differently.^{4,6,16,44,45,46,47,48} In a study of over 3 470 000 participants in the Mortality Disparities in American Communities study, there was moderate agreement among binary indicators of education, income, and employment status across individual, census tract, and county levels, with increased precision for census tract compared with county-level measures when approximating individual-level values.⁴⁷ The SDOH-mortality associations were also found to be systematically underestimated when area-level SDOH were used as proxies for individual-level measures.⁴⁷ Similarly, an analysis of over 97 025 individuals from the Canadian Community Health Survey found both individual- and area-level income measures were associated with premature mortality, with low agreement between individual- and area-level income measures and effect sizes that were larger for the mortality associations with individual-level measures.⁴⁹ Consistent with previous studies, the current analysis also found low agreement between individual- and area-level SDOH of education, income, and employment status. Our study also extended those previous reports by examining the association between SDOH and ASCVD outcomes, and found that both individual- and area-level education, income, and unemployment were associated with ASCVD risk. Furthermore, area-level education remained associated with ASCVD even after adjusting for individual-level measures.

The current study found overall similar associations between individual- and area-level SDOH with ASCVD in men and women, except that individual-level household income appeared to be a stronger variable for estimating risk of ASCVD in women than in men. A 2017 meta-analysis³² of 44 studies with over 22 million individuals found that while SDOH were associated with CHD risk in both sexes, adverse SDOH including lower educational attainment, lower income, and higher area deprivation were associated with a significantly greater excess CHD risk in women compared with men. Additionally, our study found similar associations between individual-and area-level SDOH with ASCVD risk in non-Black and Black individuals. However, a previous study³³ of over 25 000 individuals from a nationally representative survey of US adults found a more pronounced effect in results for the association between individual-level education and CVD risk in non-Hispanic White individuals than in non-Hispanic Black individuals. Some previous reports have proposed the hypothesis of marginalization-related diminished returns, which refers to the weaker health effects of SDOH, particularly education and income, for members of socially marginalized groups (eg, Black and Hispanic) compared with socially privileged groups (eg, non-Hispanic White).^33,50,51,52 Findings from the current analysis need to be validated in future studies of diverse racial groups.

Previous studies have shown that the PCEs may overestimate or underestimate 10-year ASCVD risks to various degrees depending on an individual’s socioeconomic status, and adding SDOH measures to risk assessment may improve prediction accuracy.^18,53,54 A study of more than 11 000 participants from the Atherosclerosis Risk in Communities Study found that socioeconomic status modified the association between the PCEs-estimated risk and absolute risk of ASCVD, and adding individual-level education and measures of neighborhood deprivation to the PCEs improved overall model fit.¹⁴ The current analysis found that adding area-level SDOH alone to the PCEs did not improve model discrimination, while further adding both individual- and area-level SDOH to the PCEs led to a modest improvement in both discrimination and calibration in non-Hispanic Black individuals. These findings suggest that both individual- and area-level SDOH may be considered in future ASCVD risk assessment tools, particularly among black individuals.

Additionally, while the recently developed PREVENT equations considered area-level SDOH as an optional predictor, the current study suggests that adding individual-level SDOH may further improve model calibration.¹⁹ Future studies should evaluate the benefits of considering both individual- and area-level SDOH in ASCVD risk prediction across large diverse sociodemographic groups.

Strengths and Limitations

This study has several strengths. The primary strength lies in the unique study design, which pooled data from 4 large prospective cohort studies with high-quality exposure and outcome assessments as well as a large sample size and long follow-up duration. This allowed us to estimate the associations more reliably between SDOH with ASCVD, and more accurately evaluate the impact of SDOH on ASCVD risk assessment.

This study also has several limitations. First, our study only examined selected individual- and area-level SDOH of education, income, and unemployment, because these are the ones most consistently associated with ASCVD outcomes and most collected in research. Future studies are needed to explore the role of other SDOH such as social support, perceived discrimination, and racism in ASCVD risk prediction. Second, in the current analysis, the majority of the participants who were missing SDOH were missing information on individual-level SDOH, likely due to the personal and sensitive nature of such information and participants’ unwillingness to report.^6,55,56 Indeed, in sensitivity analyses not excluding those participants with missing SDOH, we found that participants with missing individual-level income or employment status were significantly associated with an increased risk of ASCVD, suggesting the mechanism of missing SDOH was likely missing not at random. Future studies should explore strategies to improve the collection of relevant SDOH history from participants and reduce the occurrence of missing information. Third, our study primarily included non-Hispanic White and non-Hispanic Black individuals, with a limited number of other racial groups. Further studies are needed to assess the association between SDOH and ASCVD in diverse populations of Hispanics, Asians, and other racial minority groups with larger sample sizes. Fourth, we did not adjust individual-level income for inflation because income was self-reported using fixed categories. This may result in some participants being misclassified into low- or high-income categories. Lastly, we reported nominal statistical associations and P values for all analyses as correction for multiple testing may increase the risk of type II errors.⁵⁷ We recognize that although this approach minimizes loss of true positive findings, it may also risk identification of false associations and results from the current analysis require confirmation in other studies.

Conclusions

Both individual- and area-level SDOH of low education, low income, and unemployment were associated with an increased risk of incident ASCVD events. Adding area-level SDOH alone to the PCEs did not improve model discrimination and modestly improved calibration, while adding both individual- and area-level SDOH to the PCEs modestly improved discrimination and calibration in non-Hispanic Black individuals. Addition of individual-level SDOH to the PREVENT plus SDI also modestly improved calibration in non-Hispanic Black and White individuals. These findings suggest that both individual- and area-level SDOH may be considered in future development of ASCVD risk assessment tools, particularly among non-Hispanic Black individuals.

Supplement 1.

eMethods.

eTable 1. Baseline Characteristics Stratified by Number of Individual-Level SDOH

eTable 2. Baseline Characteristics Stratified by Number of Area-Level SDOH

eTable 3. Cohen Kappa (95% CI) Between Individual-Level and Area-Level SDOH

eTable 4. Associations of Individual-Level and Area-Level Social Determinants of Health (SDOH) With Atherosclerotic Cardiovascular Disease (ASCVD), Stratified by Sex

eTable 5. Associations of Social Determinants of Health (SDOH) With Atherosclerotic Cardiovascular Disease (ASCVD), Stratified by Race

eTable 6. Baseline Characteristics Comparing Participants Included in the Main Analysis vs Those Who Were Excluded Due to Missing SDOH Variables

eFigure 1. Flowchart of Study Design

eFigure 2. Changes in Harrell C-Index and Scaled Integrated Brier Score (Scaled IBS) When Adding Individual-Level SDOH to PREVENT + SDI

eFigure 3. Associations of Individual-Level and Area-Level Social Determinants of Health (SDOH) With Atherosclerotic Cardiovascular Disease (ASCVD), Not Excluding Participants With Missing Individual- or Area-Level SDOH

eFigure 4. Changes in Harrell C-Index and Scaled Integrated Brier Score (Scaled IBS) When Adding Individual-Level and Area-Level SDOH to the Pooled Cohort Equations (PCEs), Not Excluding Participants With Missing Individual- or Area-Level SDOH

eReferences

jamanetwopen-e248584-s001.pdf^{(1.8MB, pdf)}

Supplement 2.

Data Sharing Statement

jamanetwopen-e248584-s002.pdf^{(14.5KB, pdf)}

References

1.US Department of Health and Human Services, Office of Disease Prevention and Health Promotion . Healthy People 2030. 2021. Accessed November 20, 2023. https://health.gov/healthypeople/priority-areas/social-determinants-health
2.Gouri Suresh SS, Schauder SA. Income segregation and access to healthy food. Am J Prev Med. 2020;59(2):e31-e38. doi: 10.1016/j.amepre.2020.02.009 [DOI] [PubMed] [Google Scholar]
3.Krieger N, Chen JT, Waterman PD, Soobader MJ, Subramanian SV, Carson R. Geocoding and monitoring of US socioeconomic inequalities in mortality and cancer incidence: does the choice of area-based measure and geographic level matter?: the Public Health Disparities Geocoding Project. Am J Epidemiol. 2002;156(5):471-482. doi: 10.1093/aje/kwf068 [DOI] [PubMed] [Google Scholar]
4.Powell-Wiley TM, Baumer Y, Baah FO, et al. Social determinants of cardiovascular disease. Circ Res. 2022;130(5):782-799. doi: 10.1161/CIRCRESAHA.121.319811 [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Althubaiti A. Information bias in health research: definition, pitfalls, and adjustment methods. J Multidiscip Healthc. 2016;9:211-217. doi: 10.2147/JMDH.S104807 [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Fiscella K, Tancredi D. Socioeconomic status and coronary heart disease risk prediction. JAMA. 2008;300(22):2666-2668. doi: 10.1001/jama.2008.792 [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Bazemore AW, Cottrell EK, Gold R, et al. “Community vital signs”: incorporating geocoded social determinants into electronic records to promote patient and population health. J Am Med Inform Assoc. 2016;23(2):407-412. doi: 10.1093/jamia/ocv088 [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Frieden TR. A framework for public health action: the health impact pyramid. Am J Public Health. 2010;100(4):590-595. doi: 10.2105/AJPH.2009.185652 [DOI] [PMC free article] [PubMed] [Google Scholar]
9.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(Suppl):S8-S17. doi: 10.1097/01.PHH.0000338382.36695.42 [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Comer KF, Grannis S, Dixon BE, Bodenhamer DJ, Wiehe SE. Incorporating geospatial capacity within clinical data systems to address social determinants of health. Public Health Rep. 2011;126 Suppl 3(Suppl 3):54-61. doi: 10.1177/00333549111260S310 [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Mannoh I, Hussien M, Commodore-Mensah Y, Michos ED. Impact of social determinants of health on cardiovascular disease prevention. Curr Opin Cardiol. 2021;36(5):572-579. doi: 10.1097/HCO.0000000000000893 [DOI] [PubMed] [Google Scholar]
12.Brandt EJ, Tobb K, Cambron JC, et al. Assessing and addressing social determinants of cardiovascular health: JACC state-of-the-art review. J Am Coll Cardiol. 2023;81(14):1368-1385. doi: 10.1016/j.jacc.2023.01.042 [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Havranek EP, Mujahid MS, Barr DA, 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(9):873-898. doi: 10.1161/CIR.0000000000000228 [DOI] [PubMed] [Google Scholar]
14.Henderson K, Kaufman B, Rotter JS, et al. Socioeconomic status and modification of atherosclerotic cardiovascular disease risk prediction: epidemiological analysis using data from the atherosclerosis risk in communities study. BMJ Open. 2022;12(11):e058777. doi: 10.1136/bmjopen-2021-058777 [DOI] [PMC free article] [PubMed] [Google Scholar]
15.An J, Zhang Y, Muntner P, Moran AE, Hsu JW, Reynolds K. Recurrent atherosclerotic cardiovascular event rates differ among patients meeting the very high risk definition according to age, sex, race/ethnicity, and socioeconomic status. J Am Heart Assoc. 2020;9(23):e017310. doi: 10.1161/JAHA.120.017310 [DOI] [PMC free article] [PubMed] [Google Scholar]
16.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]
17.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]
18.Colantonio LD, Richman JS, Carson AP, et al. Performance of the atherosclerotic cardiovascular disease pooled cohort risk equations by social deprivation status. J Am Heart Assoc. 2017;6(3):e005676. doi: 10.1161/JAHA.117.005676 [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Khan SS, Matsushita K, Sang Y, et al. ; Chronic Kidney Disease Prognosis Consortium and the American Heart Association Cardiovascular-Kidney-Metabolic Science Advisory Group . Development and validation of the American Heart Association’s PREVENT equations. Circulation. 2024;149(6):430-449. doi: 10.1161/CIRCULATIONAHA.123.067626 [DOI] [PMC free article] [PubMed] [Google Scholar]
20.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]
21.Sempos CT, Bild DE, Manolio TA. Overview of the Jackson Heart Study: a study of cardiovascular diseases in African American men and women. Am J Med Sci. 1999;317(3):142-146. doi: 10.1016/S0002-9629(15)40495-1 [DOI] [PubMed] [Google Scholar]
22.Bild DE, Bluemke DA, Burke GL, et al. Multi-Ethnic Study of Atherosclerosis: objectives and design. Am J Epidemiol. 2002;156(9):871-881. doi: 10.1093/aje/kwf113 [DOI] [PubMed] [Google Scholar]
23.Howard VJ, Cushman M, Pulley L, et al. The reasons for geographic and racial differences in stroke study: objectives and design. Neuroepidemiology. 2005;25(3):135-143. doi: 10.1159/000086678 [DOI] [PubMed] [Google Scholar]
24.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]
25.Barber S, Hickson DA, Wang X, Sims M, Nelson C, Diez-Roux AV. Neighborhood disadvantage, poor social conditions, and cardiovascular disease incidence among African American adults in the Jackson Heart Study. Am J Public Health. 2016;106(12):2219-2226. doi: 10.2105/AJPH.2016.303471 [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Hajat A, Diez-Roux AV, Adar SD, et al. Air pollution and individual and neighborhood socioeconomic status: evidence from the Multi-Ethnic Study of Atherosclerosis (MESA). Environ Health Perspect. 2013;121(11-12):1325-1333. doi: 10.1289/ehp.1206337 [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Uddin J, Malla G, Long DL, et al. The association between neighborhood social and economic environment and prevalent diabetes in urban and rural communities: the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study. SSM Popul Health. 2022;17:101050. doi: 10.1016/j.ssmph.2022.101050 [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Safford MM, Reshetnyak E, Sterling MR, et al. Number of social determinants of health and fatal and nonfatal incident coronary heart disease in the REGARDS study. Circulation. 2021;143(3):244-253. doi: 10.1161/CIRCULATIONAHA.120.048026 [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Taylor HA, Jr., Wilson JG, Jones DW, et al. Toward resolution of cardiovascular health disparities in African Americans: design and methods of the Jackson Heart Study. Ethn Dis. 2005;15(4 Suppl 6):S6-4-17. [PubMed] [Google Scholar]
30.Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977;33(1):159-174. doi: 10.2307/2529310 [DOI] [PubMed] [Google Scholar]
31.McHugh ML. Interrater reliability: the kappa statistic. Biochem Med (Zagreb). 2012;22(3):276-282. doi: 10.11613/BM.2012.031 [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Backholer K, Peters SAE, Bots SH, Peeters A, Huxley RR, Woodward M. Sex differences in the relationship between socioeconomic status and cardiovascular disease: a systematic review and meta-analysis. J Epidemiol Community Health. 2017;71(6):550-557. doi: 10.1136/jech-2016-207890 [DOI] [PubMed] [Google Scholar]
33.Assari S, Cobb S, Saqib M, Bazargan M. Diminished returns of educational attainment on heart disease among Black Americans. Open Cardiovasc Med J. 2020;14:5-12. doi: 10.2174/1874192402014010005 [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Graf E, Schmoor C, Sauerbrei W, Schumacher M. Assessment and comparison of prognostic classification schemes for survival data. Stat Med. 1999;18(17-18):2529-2545. doi: [DOI] [PubMed] [Google Scholar]
35.Steyerberg EW, Vickers AJ, Cook NR, et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology. 2010;21(1):128-138. doi: 10.1097/EDE.0b013e3181c30fb2 [DOI] [PMC free article] [PubMed] [Google Scholar]
36.McLernon DJ, Giardiello D, Van Calster B, et al. ; topic groups 6 and 8 of the STRATOS Initiative . Assessing performance and clinical usefulness in prediction models with survival outcomes: practical guidance for Cox proportional hazards models. Ann Intern Med. 2023;176(1):105-114. doi: 10.7326/M22-0844 [DOI] [PubMed] [Google Scholar]
37.Agency for Healthcare Research and Quality, Centers for Medicare & Medicaid Services . Creation of New Race-Ethnicity Codes and Socioeconomic Status (SES) Indicators for Medicare Beneficiaries: Final Report. CreateSpace Independent Publishing Platform; 2017. [Google Scholar]
38.Agency for Healthcare Research and Quality . Creation of New Race-Ethnicity Codes and SES Indicators for Medicare Beneficiaries—Chapter 3. AHRQ publication No. 08-0029-EF. Last reviewed January 2008. Accessed November 23, 2023. https://archive.ahrq.gov/research/findings/final-reports/medicareindicators/medicareindicators3.html
39.Kubota Y, Heiss G, MacLehose RF, Roetker NS, Folsom AR. Association of educational attainment with lifetime risk of cardiovascular disease: the Atherosclerosis Risk in Communities Study. JAMA Intern Med. 2017;177(8):1165-1172. doi: 10.1001/jamainternmed.2017.1877 [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Schröder SL, Richter M, Schröder J, Frantz S, Fink A. Socioeconomic inequalities in access to treatment for coronary heart disease: a systematic review. Int J Cardiol. 2016;219:70-78. doi: 10.1016/j.ijcard.2016.05.066 [DOI] [PubMed] [Google Scholar]
41.Khaing W, Vallibhakara SA, Attia J, McEvoy M, Thakkinstian A. Effects of education and income on cardiovascular outcomes: a systematic review and meta-analysis. Eur J Prev Cardiol. 2017;24(10):1032-1042. doi: 10.1177/2047487317705916 [DOI] [PubMed] [Google Scholar]
42.Sundquist K, Theobald H, Yang M, Li X, Johansson SE, Sundquist J. Neighborhood violent crime and unemployment increase the risk of coronary heart disease: a multilevel study in an urban setting. Soc Sci Med. 2006;62(8):2061-2071. doi: 10.1016/j.socscimed.2005.08.051 [DOI] [PubMed] [Google Scholar]
43.Topel ML, Kim JH, Mujahid MS, et al. Neighborhood socioeconomic status and adverse outcomes in patients with cardiovascular disease. Am J Cardiol. 2019;123(2):284-290. doi: 10.1016/j.amjcard.2018.10.011 [DOI] [PMC free article] [PubMed] [Google Scholar]
44.Pollack CE, Slaughter ME, Griffin BA, Dubowitz T, Bird CE. Neighborhood socioeconomic status and coronary heart disease risk prediction in a nationally representative sample. Public Health. 2012;126(10):827-835. doi: 10.1016/j.puhe.2012.05.028 [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Gottlieb LM, Francis DE, Beck AF. Uses and misuses of patient- and neighborhood-level social determinants of health data. Perm J. 2018;22:18-078. doi: 10.7812/TPP/18-078 [DOI] [PMC free article] [PubMed] [Google Scholar]
46.Sundquist J, Malmström M, Johansson SE. Cardiovascular risk factors and the neighbourhood environment: a multilevel analysis. Int J Epidemiol. 1999;28(5):841-845. doi: 10.1093/ije/28.5.841 [DOI] [PubMed] [Google Scholar]
47.Moss JL, Johnson NJ, Yu M, Altekruse SF, Cronin KA. Comparisons of individual- and area-level socioeconomic status as proxies for individual-level measures: evidence from the Mortality Disparities in American Communities study. Popul Health Metr. 2021;19(1):1. doi: 10.1186/s12963-020-00244-x [DOI] [PMC free article] [PubMed] [Google Scholar]
48.Diez-Roux AV, Kiefe CI, Jacobs DR Jr, et al. Area characteristics and individual-level socioeconomic position indicators in three population-based epidemiologic studies. Ann Epidemiol. 2001;11(6):395-405. doi: 10.1016/S1047-2797(01)00221-6 [DOI] [PubMed] [Google Scholar]
49.Buajitti E, Chiodo S, Rosella LC. Agreement between area- and individual-level income measures in a population-based cohort: implications for population health research. SSM Popul Health. 2020;10:100553. doi: 10.1016/j.ssmph.2020.100553 [DOI] [PMC free article] [PubMed] [Google Scholar]
50.Johnson AE, Herbert BM, Stokes N, Brooks MM, Needham BL, Magnani JW. Educational attainment, race, and ethnicity as predictors for ideal cardiovascular health: from the National Health and Nutrition Examination Survey. J Am Heart Assoc. 2022;11(2):e023438. doi: 10.1161/JAHA.121.023438 [DOI] [PMC free article] [PubMed] [Google Scholar]
51.Kumar A, Ogunnowo GO, Khot UN, et al. Interaction between race and income on cardiac outcomes after percutaneous coronary intervention. J Am Heart Assoc. 2022;11(22):e026676. doi: 10.1161/JAHA.122.026676 [DOI] [PMC free article] [PubMed] [Google Scholar]
52.Javed Z, Haisum Maqsood M, Yahya T, et al. Race, racism, and cardiovascular health: applying a social determinants of health framework to racial/ethnic disparities in cardiovascular disease. Circ Cardiovasc Qual Outcomes. 2022;15(1):e007917. doi: 10.1161/CIRCOUTCOMES.121.007917 [DOI] [PubMed] [Google Scholar]
53.Rodriguez F, Chung S, Blum MR, Coulet A, Basu S, Palaniappan LP. Atherosclerotic cardiovascular disease risk prediction in disaggregated Asian and Hispanic subgroups using electronic health records. J Am Heart Assoc. 2019;8(14):e011874. doi: 10.1161/JAHA.118.011874 [DOI] [PMC free article] [PubMed] [Google Scholar]
54.Cook NR, Ridker PM. Calibration of the pooled cohort equations for atherosclerotic cardiovascular disease: an update. Ann Intern Med. 2016;165(11):786-794. doi: 10.7326/M16-1739 [DOI] [PubMed] [Google Scholar]
55.Kim S, Egerter S, Cubbin C, Takahashi ER, Braveman P. Potential implications of missing income data in population-based surveys: an example from a postpartum survey in California. Public Health Rep. 2007;122(6):753-763. doi: 10.1177/003335490712200607 [DOI] [PMC free article] [PubMed] [Google Scholar]
56.Davern M, Rodin H, Beebe TJ, Call KT. The effect of income question design in health surveys on family income, poverty and eligibility estimates. Health Serv Res. 2005;40(5 Pt 1):1534-1552. doi: 10.1111/j.1475-6773.2005.00416.x [DOI] [PMC free article] [PubMed] [Google Scholar]
57.Perneger TV. What’s wrong with Bonferroni adjustments. BMJ. 1998;316(7139):1236-1238. doi: 10.1136/bmj.316.7139.1236 [DOI] [PMC free article] [PubMed] [Google Scholar]
58.MESA Coordinating Center . Multi-Ethnic Study of Atherosclerosis website. Accessed November 20, 2023. https://www.mesa-nhlbi.org/
59.REGARDS Operations Center; University of Alabama at Birmingham . REGARDS Study. Accessed November 20, 2023. https://www.uab.edu/soph/regardsstudy

Associated Data

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

Supplementary Materials

Supplement 1.

eMethods.

eTable 1. Baseline Characteristics Stratified by Number of Individual-Level SDOH

eTable 2. Baseline Characteristics Stratified by Number of Area-Level SDOH

eTable 3. Cohen Kappa (95% CI) Between Individual-Level and Area-Level SDOH

eTable 4. Associations of Individual-Level and Area-Level Social Determinants of Health (SDOH) With Atherosclerotic Cardiovascular Disease (ASCVD), Stratified by Sex

eTable 5. Associations of Social Determinants of Health (SDOH) With Atherosclerotic Cardiovascular Disease (ASCVD), Stratified by Race

eTable 6. Baseline Characteristics Comparing Participants Included in the Main Analysis vs Those Who Were Excluded Due to Missing SDOH Variables

eFigure 1. Flowchart of Study Design

eFigure 2. Changes in Harrell C-Index and Scaled Integrated Brier Score (Scaled IBS) When Adding Individual-Level SDOH to PREVENT + SDI

eReferences

jamanetwopen-e248584-s001.pdf^{(1.8MB, pdf)}

Supplement 2.

Data Sharing Statement

jamanetwopen-e248584-s002.pdf^{(14.5KB, pdf)}

[zoi240317r1] 1.US Department of Health and Human Services, Office of Disease Prevention and Health Promotion . Healthy People 2030. 2021. Accessed November 20, 2023. https://health.gov/healthypeople/priority-areas/social-determinants-health

[zoi240317r2] 2.Gouri Suresh SS, Schauder SA. Income segregation and access to healthy food. Am J Prev Med. 2020;59(2):e31-e38. doi: 10.1016/j.amepre.2020.02.009 [DOI] [PubMed] [Google Scholar]

[zoi240317r3] 3.Krieger N, Chen JT, Waterman PD, Soobader MJ, Subramanian SV, Carson R. Geocoding and monitoring of US socioeconomic inequalities in mortality and cancer incidence: does the choice of area-based measure and geographic level matter?: the Public Health Disparities Geocoding Project. Am J Epidemiol. 2002;156(5):471-482. doi: 10.1093/aje/kwf068 [DOI] [PubMed] [Google Scholar]

[zoi240317r4] 4.Powell-Wiley TM, Baumer Y, Baah FO, et al. Social determinants of cardiovascular disease. Circ Res. 2022;130(5):782-799. doi: 10.1161/CIRCRESAHA.121.319811 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi240317r5] 5.Althubaiti A. Information bias in health research: definition, pitfalls, and adjustment methods. J Multidiscip Healthc. 2016;9:211-217. doi: 10.2147/JMDH.S104807 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi240317r6] 6.Fiscella K, Tancredi D. Socioeconomic status and coronary heart disease risk prediction. JAMA. 2008;300(22):2666-2668. doi: 10.1001/jama.2008.792 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi240317r7] 7.Bazemore AW, Cottrell EK, Gold R, et al. “Community vital signs”: incorporating geocoded social determinants into electronic records to promote patient and population health. J Am Med Inform Assoc. 2016;23(2):407-412. doi: 10.1093/jamia/ocv088 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi240317r8] 8.Frieden TR. A framework for public health action: the health impact pyramid. Am J Public Health. 2010;100(4):590-595. doi: 10.2105/AJPH.2009.185652 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi240317r9] 9.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(Suppl):S8-S17. doi: 10.1097/01.PHH.0000338382.36695.42 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi240317r10] 10.Comer KF, Grannis S, Dixon BE, Bodenhamer DJ, Wiehe SE. Incorporating geospatial capacity within clinical data systems to address social determinants of health. Public Health Rep. 2011;126 Suppl 3(Suppl 3):54-61. doi: 10.1177/00333549111260S310 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi240317r11] 11.Mannoh I, Hussien M, Commodore-Mensah Y, Michos ED. Impact of social determinants of health on cardiovascular disease prevention. Curr Opin Cardiol. 2021;36(5):572-579. doi: 10.1097/HCO.0000000000000893 [DOI] [PubMed] [Google Scholar]

[zoi240317r12] 12.Brandt EJ, Tobb K, Cambron JC, et al. Assessing and addressing social determinants of cardiovascular health: JACC state-of-the-art review. J Am Coll Cardiol. 2023;81(14):1368-1385. doi: 10.1016/j.jacc.2023.01.042 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi240317r13] 13.Havranek EP, Mujahid MS, Barr DA, 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(9):873-898. doi: 10.1161/CIR.0000000000000228 [DOI] [PubMed] [Google Scholar]

[zoi240317r14] 14.Henderson K, Kaufman B, Rotter JS, et al. Socioeconomic status and modification of atherosclerotic cardiovascular disease risk prediction: epidemiological analysis using data from the atherosclerosis risk in communities study. BMJ Open. 2022;12(11):e058777. doi: 10.1136/bmjopen-2021-058777 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi240317r15] 15.An J, Zhang Y, Muntner P, Moran AE, Hsu JW, Reynolds K. Recurrent atherosclerotic cardiovascular event rates differ among patients meeting the very high risk definition according to age, sex, race/ethnicity, and socioeconomic status. J Am Heart Assoc. 2020;9(23):e017310. doi: 10.1161/JAHA.120.017310 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi240317r16] 16.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]

[zoi240317r17] 17.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]

[zoi240317r18] 18.Colantonio LD, Richman JS, Carson AP, et al. Performance of the atherosclerotic cardiovascular disease pooled cohort risk equations by social deprivation status. J Am Heart Assoc. 2017;6(3):e005676. doi: 10.1161/JAHA.117.005676 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi240317r19] 19.Khan SS, Matsushita K, Sang Y, et al. ; Chronic Kidney Disease Prognosis Consortium and the American Heart Association Cardiovascular-Kidney-Metabolic Science Advisory Group . Development and validation of the American Heart Association’s PREVENT equations. Circulation. 2024;149(6):430-449. doi: 10.1161/CIRCULATIONAHA.123.067626 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi240317r20] 20.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]

[zoi240317r21] 21.Sempos CT, Bild DE, Manolio TA. Overview of the Jackson Heart Study: a study of cardiovascular diseases in African American men and women. Am J Med Sci. 1999;317(3):142-146. doi: 10.1016/S0002-9629(15)40495-1 [DOI] [PubMed] [Google Scholar]

[zoi240317r22] 22.Bild DE, Bluemke DA, Burke GL, et al. Multi-Ethnic Study of Atherosclerosis: objectives and design. Am J Epidemiol. 2002;156(9):871-881. doi: 10.1093/aje/kwf113 [DOI] [PubMed] [Google Scholar]

[zoi240317r23] 23.Howard VJ, Cushman M, Pulley L, et al. The reasons for geographic and racial differences in stroke study: objectives and design. Neuroepidemiology. 2005;25(3):135-143. doi: 10.1159/000086678 [DOI] [PubMed] [Google Scholar]

[zoi240317r24] 24.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]

[zoi240317r25] 25.Barber S, Hickson DA, Wang X, Sims M, Nelson C, Diez-Roux AV. Neighborhood disadvantage, poor social conditions, and cardiovascular disease incidence among African American adults in the Jackson Heart Study. Am J Public Health. 2016;106(12):2219-2226. doi: 10.2105/AJPH.2016.303471 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi240317r26] 26.Hajat A, Diez-Roux AV, Adar SD, et al. Air pollution and individual and neighborhood socioeconomic status: evidence from the Multi-Ethnic Study of Atherosclerosis (MESA). Environ Health Perspect. 2013;121(11-12):1325-1333. doi: 10.1289/ehp.1206337 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi240317r27] 27.Uddin J, Malla G, Long DL, et al. The association between neighborhood social and economic environment and prevalent diabetes in urban and rural communities: the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study. SSM Popul Health. 2022;17:101050. doi: 10.1016/j.ssmph.2022.101050 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi240317r28] 28.Safford MM, Reshetnyak E, Sterling MR, et al. Number of social determinants of health and fatal and nonfatal incident coronary heart disease in the REGARDS study. Circulation. 2021;143(3):244-253. doi: 10.1161/CIRCULATIONAHA.120.048026 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi240317r29] 29.Taylor HA, Jr., Wilson JG, Jones DW, et al. Toward resolution of cardiovascular health disparities in African Americans: design and methods of the Jackson Heart Study. Ethn Dis. 2005;15(4 Suppl 6):S6-4-17. [PubMed] [Google Scholar]

[zoi240317r30] 30.Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977;33(1):159-174. doi: 10.2307/2529310 [DOI] [PubMed] [Google Scholar]

[zoi240317r31] 31.McHugh ML. Interrater reliability: the kappa statistic. Biochem Med (Zagreb). 2012;22(3):276-282. doi: 10.11613/BM.2012.031 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi240317r32] 32.Backholer K, Peters SAE, Bots SH, Peeters A, Huxley RR, Woodward M. Sex differences in the relationship between socioeconomic status and cardiovascular disease: a systematic review and meta-analysis. J Epidemiol Community Health. 2017;71(6):550-557. doi: 10.1136/jech-2016-207890 [DOI] [PubMed] [Google Scholar]

[zoi240317r33] 33.Assari S, Cobb S, Saqib M, Bazargan M. Diminished returns of educational attainment on heart disease among Black Americans. Open Cardiovasc Med J. 2020;14:5-12. doi: 10.2174/1874192402014010005 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi240317r34] 34.Graf E, Schmoor C, Sauerbrei W, Schumacher M. Assessment and comparison of prognostic classification schemes for survival data. Stat Med. 1999;18(17-18):2529-2545. doi: [DOI] [PubMed] [Google Scholar]

[zoi240317r35] 35.Steyerberg EW, Vickers AJ, Cook NR, et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology. 2010;21(1):128-138. doi: 10.1097/EDE.0b013e3181c30fb2 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi240317r36] 36.McLernon DJ, Giardiello D, Van Calster B, et al. ; topic groups 6 and 8 of the STRATOS Initiative . Assessing performance and clinical usefulness in prediction models with survival outcomes: practical guidance for Cox proportional hazards models. Ann Intern Med. 2023;176(1):105-114. doi: 10.7326/M22-0844 [DOI] [PubMed] [Google Scholar]

[zoi240317r37] 37.Agency for Healthcare Research and Quality, Centers for Medicare & Medicaid Services . Creation of New Race-Ethnicity Codes and Socioeconomic Status (SES) Indicators for Medicare Beneficiaries: Final Report. CreateSpace Independent Publishing Platform; 2017. [Google Scholar]

[zoi240317r38] 38.Agency for Healthcare Research and Quality . Creation of New Race-Ethnicity Codes and SES Indicators for Medicare Beneficiaries—Chapter 3. AHRQ publication No. 08-0029-EF. Last reviewed January 2008. Accessed November 23, 2023. https://archive.ahrq.gov/research/findings/final-reports/medicareindicators/medicareindicators3.html

[zoi240317r39] 39.Kubota Y, Heiss G, MacLehose RF, Roetker NS, Folsom AR. Association of educational attainment with lifetime risk of cardiovascular disease: the Atherosclerosis Risk in Communities Study. JAMA Intern Med. 2017;177(8):1165-1172. doi: 10.1001/jamainternmed.2017.1877 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi240317r40] 40.Schröder SL, Richter M, Schröder J, Frantz S, Fink A. Socioeconomic inequalities in access to treatment for coronary heart disease: a systematic review. Int J Cardiol. 2016;219:70-78. doi: 10.1016/j.ijcard.2016.05.066 [DOI] [PubMed] [Google Scholar]

[zoi240317r41] 41.Khaing W, Vallibhakara SA, Attia J, McEvoy M, Thakkinstian A. Effects of education and income on cardiovascular outcomes: a systematic review and meta-analysis. Eur J Prev Cardiol. 2017;24(10):1032-1042. doi: 10.1177/2047487317705916 [DOI] [PubMed] [Google Scholar]

[zoi240317r42] 42.Sundquist K, Theobald H, Yang M, Li X, Johansson SE, Sundquist J. Neighborhood violent crime and unemployment increase the risk of coronary heart disease: a multilevel study in an urban setting. Soc Sci Med. 2006;62(8):2061-2071. doi: 10.1016/j.socscimed.2005.08.051 [DOI] [PubMed] [Google Scholar]

[zoi240317r43] 43.Topel ML, Kim JH, Mujahid MS, et al. Neighborhood socioeconomic status and adverse outcomes in patients with cardiovascular disease. Am J Cardiol. 2019;123(2):284-290. doi: 10.1016/j.amjcard.2018.10.011 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi240317r44] 44.Pollack CE, Slaughter ME, Griffin BA, Dubowitz T, Bird CE. Neighborhood socioeconomic status and coronary heart disease risk prediction in a nationally representative sample. Public Health. 2012;126(10):827-835. doi: 10.1016/j.puhe.2012.05.028 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi240317r45] 45.Gottlieb LM, Francis DE, Beck AF. Uses and misuses of patient- and neighborhood-level social determinants of health data. Perm J. 2018;22:18-078. doi: 10.7812/TPP/18-078 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi240317r46] 46.Sundquist J, Malmström M, Johansson SE. Cardiovascular risk factors and the neighbourhood environment: a multilevel analysis. Int J Epidemiol. 1999;28(5):841-845. doi: 10.1093/ije/28.5.841 [DOI] [PubMed] [Google Scholar]

[zoi240317r47] 47.Moss JL, Johnson NJ, Yu M, Altekruse SF, Cronin KA. Comparisons of individual- and area-level socioeconomic status as proxies for individual-level measures: evidence from the Mortality Disparities in American Communities study. Popul Health Metr. 2021;19(1):1. doi: 10.1186/s12963-020-00244-x [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi240317r48] 48.Diez-Roux AV, Kiefe CI, Jacobs DR Jr, et al. Area characteristics and individual-level socioeconomic position indicators in three population-based epidemiologic studies. Ann Epidemiol. 2001;11(6):395-405. doi: 10.1016/S1047-2797(01)00221-6 [DOI] [PubMed] [Google Scholar]

[zoi240317r49] 49.Buajitti E, Chiodo S, Rosella LC. Agreement between area- and individual-level income measures in a population-based cohort: implications for population health research. SSM Popul Health. 2020;10:100553. doi: 10.1016/j.ssmph.2020.100553 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi240317r50] 50.Johnson AE, Herbert BM, Stokes N, Brooks MM, Needham BL, Magnani JW. Educational attainment, race, and ethnicity as predictors for ideal cardiovascular health: from the National Health and Nutrition Examination Survey. J Am Heart Assoc. 2022;11(2):e023438. doi: 10.1161/JAHA.121.023438 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi240317r51] 51.Kumar A, Ogunnowo GO, Khot UN, et al. Interaction between race and income on cardiac outcomes after percutaneous coronary intervention. J Am Heart Assoc. 2022;11(22):e026676. doi: 10.1161/JAHA.122.026676 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi240317r52] 52.Javed Z, Haisum Maqsood M, Yahya T, et al. Race, racism, and cardiovascular health: applying a social determinants of health framework to racial/ethnic disparities in cardiovascular disease. Circ Cardiovasc Qual Outcomes. 2022;15(1):e007917. doi: 10.1161/CIRCOUTCOMES.121.007917 [DOI] [PubMed] [Google Scholar]

[zoi240317r53] 53.Rodriguez F, Chung S, Blum MR, Coulet A, Basu S, Palaniappan LP. Atherosclerotic cardiovascular disease risk prediction in disaggregated Asian and Hispanic subgroups using electronic health records. J Am Heart Assoc. 2019;8(14):e011874. doi: 10.1161/JAHA.118.011874 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi240317r54] 54.Cook NR, Ridker PM. Calibration of the pooled cohort equations for atherosclerotic cardiovascular disease: an update. Ann Intern Med. 2016;165(11):786-794. doi: 10.7326/M16-1739 [DOI] [PubMed] [Google Scholar]

[zoi240317r55] 55.Kim S, Egerter S, Cubbin C, Takahashi ER, Braveman P. Potential implications of missing income data in population-based surveys: an example from a postpartum survey in California. Public Health Rep. 2007;122(6):753-763. doi: 10.1177/003335490712200607 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi240317r56] 56.Davern M, Rodin H, Beebe TJ, Call KT. The effect of income question design in health surveys on family income, poverty and eligibility estimates. Health Serv Res. 2005;40(5 Pt 1):1534-1552. doi: 10.1111/j.1475-6773.2005.00416.x [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi240317r57] 57.Perneger TV. What’s wrong with Bonferroni adjustments. BMJ. 1998;316(7139):1236-1238. doi: 10.1136/bmj.316.7139.1236 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi240317r58] 58.MESA Coordinating Center . Multi-Ethnic Study of Atherosclerosis website. Accessed November 20, 2023. https://www.mesa-nhlbi.org/

[zoi240317r59] 59.REGARDS Operations Center; University of Alabama at Birmingham . REGARDS Study. Accessed November 20, 2023. https://www.uab.edu/soph/regardsstudy

PERMALINK

Cardiovascular Risk Associated With Social Determinants of Health at Individual and Area Levels

Mengying Xia, MPH

Jaejin An, PhD

Monika M Safford, MD

Lisandro D Colantonio, MD, PhD

Mario Sims, PhD, MS

Kristi Reynolds, PhD

Andrew E Moran, MD, MPH

Yiyi Zhang, PhD

Key Points

Question

Findings

Meaning

Abstract

Importance

Objective

Design, Setting, and Participants

Exposures

Main outcomes and measures

Results

Conclusions and Relevance

Introduction

Methods

Study Design and Cohorts

Data Collection

Follow-Up and ASCVD Events

Statistical Analysis

Results

Table. Baseline Characteristics of Study Participants.

Figure 1. Associations of Individual-Level and Area-Level Social Determinants of Health (SDOH) With Atherosclerotic Cardiovascular Disease (ASCVD).

Figure 2. Changes in Harrell C Index and Scaled Integrated Brier Score (IBS) When Adding Individual-Level and Area-Level Social Determinants of Health (SDOH) to the Pooled Cohort Equations.

Figure 3. Calibration Plots of Pooled Cohort Equations (PCEs) and PCEs Plus Social Determinants of Health (SDOH) at Both Individual- and Area-Level by Race and Ethnicity and by Sex.

Discussion

Strengths and Limitations

Conclusions

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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