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

Abstract

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.

Attributable Costs of Stroke in Ontario, Canada and Their Variation by Stroke Type and Social Determinants of Health

“…In a recent study in the United States, social determinants measured at a county level such as racial and ethnic segregation and poverty and income inequality were important contributing factors for higher costs associated with cardiovascular disease. 22 In 1 prior study on sex differences in direct health care costs of stroke, higher unadjusted costs of stroke were found in female compared with male patients; however, these were lower after adjusting for comorbidities. 6 A systematic review on poststroke care costs across different health segments found inpatient rehabilitation to be the main driver of costs, 21 and sex differences in the receipt of inpatient rehabilitation are not well-known.…”

Exposomics and Cardiovascular Diseases: A Scoping Review of Machine Learning Approaches

“…Proceeding with the remaining 30% of pertinent literature that does not conduct performance comparison whatsoever, a part of research opted for a non-linear algorithm, 42,43,56,57 while another part has focused on an ensemble algorithm of their selection. 50,51,58,59 As it becomes obvious from Figure 4 ensemble algorithms are the most popular choice encountered in pertinent literature, 35,[39][40][41][44][45][46][47][48][49][50][51][52][53][54][55]58,[60][61][62][63][66][67][68] closely followed by non-linear algorithms. 35,[41][42][43][44][45][46][47][48][49]54,59,60,62,[64]…”

“…39,41,47,48,51,55,67,71 Less often, extra variables such as lack of private health insurance, home ownership, housing type or severe housing cost burden are also considered. 42,44,58,66,70 In terms of lifestyle factors the focus is predominantly on health-related habits and adopted dietary, physical activity and sleep patterns/quality. 11,39,41,42,44,48,51,52,55,58,67,70,71 The most commonly considered health habit-related parameters is smoking status and/or alcohol consumption.…”

“…42,44,58,66,70 In terms of lifestyle factors the focus is predominantly on health-related habits and adopted dietary, physical activity and sleep patterns/quality. 11,39,41,42,44,48,51,52,55,58,67,70,71 The most commonly considered health habit-related parameters is smoking status and/or alcohol consumption. 11,41,44,52,55,58,67,70,71 A considerable part of literature explores combinations of all three categories i.e.…”

Epigenetics of Early Cardiometabolic Disease: Mechanisms and Precision Medicine

“…Social determinants of health, including socioeconomic status (SES), social support, and psychological stress, have been identified as risk factors for CVD. [14][15][16][17][18] In recent years, epigenetic modifications, such as DNA methylation (DNAm), have emerged as potential mechanisms linking social components to cardiovascular health. 19,20 Some investigators have focused on one of those Social determinants of health; however, it is essential to recognize that these SDH are interconnected and can have a cascading effect on health outcomes.…”