Social Determinants, Cardiovascular Disease, and Health Care Cost: A Nationwide Study in the United States Using Machine Learning.
Social Determinants, Cardiovascular Disease, and Health Care Cost: A Nationwide Study in the United States Using Machine Learning.
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DOI：
10.1161/jaha.122.027919
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发表时间：
2023-03-07
影响因子：
5.4
通讯作者：
Zhang, Kai
中科院分区：
文献类型：
--
作者：
Sun, Feinuo;Yao, Jie;Du, Shichao;Qian, Feng;Appleton, Allison A.;Tao, Cui;Xu, Hua;Liu, Lei;Dai, Qi;Joyce, Brian T.;Nannini, Drew R.;Hou, Lifang;Zhang, Kai
关键词：
现有的心血管疾病（CVD）研究通常集中在个人水平的行为风险因素，但研究社会决定因素是有限的。本研究采用了一种新的机器学习方法来确定县级医疗成本和心血管疾病（包括房颤、急性心肌梗死、充血性心力衰竭和缺血性心脏病）患病率的关键预测因素。我们将极端梯度提升机器学习方法应用于总共3137个县。数据来自心脏病和中风的交互式地图集和各种国家数据集。我们发现，虽然人口构成（如黑人和老年人的百分比）和风险因素（如吸烟和缺乏身体活动）是住院治疗费用和心血管疾病患病率的最重要预测因素，但社会脆弱性和种族隔离等背景因素对总费用和门诊费用尤为重要。贫困和收入不平等是非都市地区或隔离程度高或社会脆弱程度高的县的总护理费用的主要贡献者。种族和民族隔离对于贫困率低或社会脆弱程度低的县的总护理费用的形成尤为重要。人口构成、教育和社会脆弱性在不同的情景中始终很重要。研究结果强调了不同类型CVD成本结果的预测因素的差异以及社会决定因素的重要性。针对经济和社会边缘化地区的干预措施可能有助于减少心血管疾病的影响。
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). 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. 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.