Social Determinants, Cardiovascular Disease, and Health Care Cost: A Nationwide Study in the United States Using Machine Learning

Aug 5, 2026

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This nationwide study evaluates how social determinants shape cardiovascular disease (CVD) prevalence and health care costs across U.S. counties. Using extreme gradient boosting on data from the Interactive Atlas of Heart Disease and Stroke and multiple national datasets, it analyzes 3,137 counties. Demographic makeup and individual risk factors relate to inpatient costs and CVD prevalence, while social vulnerability and racial/ethnic segregation are especially important for total and outpatient cost outcomes. Poverty and income inequality drive total costs in nonmetro or highly segregated or vulnerable counties. Results indicate targeted interventions in economically and socially marginalized areas may reduce CVD burden.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages19

Reading Time48 min

AccessFree to download

Background

Methods and Results

Predictors of inpatient and total cost outcomes

Role of social vulnerability, poverty, and segregation

Consistent predictors across scenarios

Conclusions

Clinical Perspective

What Is New

Clinical Implications

Washington University School of Medicine Digital Commons@Becker | 2020-Current year OA Pubs | Open Access Publications | | --- | --- | 3-7-2023 Social determinants, cardiovascular disease, and health care cost: A nationwide study in the United States using machine learning Feinuo Sun Jie Yao Shichao Du Feng Qian Allison A Appleton See next page for additional authors Follow this and additional works at: [https://digitalcommons.wustl.edu/oa_4](https://digitalcommons.wustl.edu/oa_4) Part of the Medicine and Health Sciences Commons Please let us know how this document benefits you. Authors Feinuo Sun, Jie Yao, Shichao Du, Feng Qian, Allison A Appleton, Cui Tao, Hua Xu, Lei Liu, Qi Dai, Brian T Joyce, Drew R Nannini, Lifang Hou, and Kai Zhang Downloaded from [http://ahajournals.org by on November 30](http://ahajournals.org by on November 30), 2023 Journal of the American Heart Association ORIGINAL RESEARCH | Social Determinants, Cardiovascular Disease, and Health Care Cost: A Nationwide Study in the United States Using Machine Learning
Feinuo Sun , PhD; Jie Yao, MS; Shichao Du , BA; Feng Qian, PhD; Allison A. Appleton, ScD ;
Cui Tao , PhD; Hua Xu , PhD; Lei Liu, PhD; Qi Dai , PhD, MD; Brian T. Joyce , PhD; Drew R. Nannini , PhD; Lifang Hou , PhD, MD; Kai Zhang , PhD
BACKGROUND: Existing studies on cardiovascular diseases (CVDs) often focus on individual-level behavioral risk factors, but research examining social determinants is limited. This study applies a novel machine learning approach to identify the key predictors of county-level care costs and prevalence of CVDs (including atrial fibrillation, acute myocardial infarction, congestive heart failure, and ischemic heart disease) .
METHODS AND RESULTS: We applied the extreme gradient boosting machine learning approach to a total of 3137 counties. Data are from the Interactive Atlas of Heart Disease and Stroke and a variety of national data sets. We found that although demographic composition (eg, percentages of Black people and older adults) and risk factors (eg, smoking and physical inactivity) are among the most important predictors for inpatient care costs and CVD prevalence, contextual factors such as social vulnerability and racial and ethnic segregation are particularly important for the total and outpatient care costs. Poverty and income inequality are the major contributors to the total care costs for counties that are in nonmetro areas or have high segregation or social vulnerability levels. Racial and ethnic segregation is particularly important in shaping the total care costs for counties with low poverty rates or social vulnerability level. Demographic composition, education, and social vulnerability are consistently important across different scenarios.
CONCLUSIONS: The findings highlight the differences in predictors for different types of CVD cost outcomes and the importance of social determinants. Interventions directed toward areas that have been economically and socially marginalized may aid in reducing the impact of CVDs.
Key Words: cardiovascular disease ■ health care costs ■ machine learning ■ racial and ethnic segregation ■ social determinants of
health | | | --- | --- | | Cardiovascular disease (CVD) is the leading cause
of death in the United States. It accounted for
≈875 000 deaths in 2019, and the average annual estimated direct and indirect economic cost was $378.0 billion in 2017 to 2018.1 Cardiovascular outcomes vary by geographical location across the United | States. For example, counties with high CVD mortality rates cluster in southeastern Oklahoma along the Mississippi River Valley to eastern Kentucky, whereas counties with low CVD mortality rates are found in the Southwest, Northeast, and southern Florida.2 Understanding the determinants of population-based | Correspondence to: Kai Zhang, PhD, Department of Environmental Health Sciences, School of Public Health, Univers

FAQs

What was the goal of this nationwide machine-learning study?

To identify key predictors of county-level health care costs and CVD prevalence in the United States using a machine learning approach.

Which factors were found to be most important for different cost outcomes?

Demographics and individual risk factors contributed to inpatient costs and CVD prevalence, while contextual social factors—especially social vulnerability and racial/ethnic segregation—were particularly important for total and outpatient care costs.

How do poverty and income inequality affect county health care costs?

Poverty and income inequality were major contributors to total care costs in counties that are nonmetro and/or have high segregation or social vulnerability levels.

Aug 5, 2026

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