Social Determinants of Health and Novel Prediction of Cardiovascular Health from Young Adulthood to Middle Age in the CARDIA Study

CARDIA 研究中健康的社会决定因素和从青年期到中年心血管健康的新预测

• 批准号: 9911103
• 负责人: Lindsay Paige Zimmerman
• 金额: $ 4.07万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-12-01 至 2021-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9911103
• 关键词: Accounting; Address; Age; American; American Heart Association; Award; Behavior; Behavioral; Blood Pressure; Body mass index; Cardiovascular Diseases; Cause of Death; Cessation of life; Cholesterol; Clinical; Communities; Competence; Coronary Artery Risk Development in Young Adults Study; Data; Data Science; Disease; Economics; Education; Environment; Epidemiology; Ethnic group; Event; Fellowship; Geography; Goals; Health; Health Professional; Health Services; Health Status; Health behavior; Healthcare; Individual; Intervention; Life; Life Cycle Stages; Link; Machine Learning; Maintenance; Measures; Mediating; Methods; Mining; Modeling; Morbidity - disease rate; Neighborhoods; Patients; Pattern; Performance; Phenotype; Physical activity; Policies; Population; Predictive Analytics; Prevalence; Prospective cohort study; Public Health; Race; Research; Research Training; Risk Behaviors; Risk Factors; Sampling; Scientist; Site; Smoking Status; Social Identification; Socioeconomic Factors; Stroke; Structure; Subgroup; Techniques; Time; Training; Work; World Health Organization; Writing; age group; base; biracial; built environment; burden of illness; cardiovascular health; cardiovascular risk factor; career; cohort; fasting plasma glucose; good diet; health disparity; health economics; high risk; improved; informatics tool; machine learning algorithm; machine learning method; middle age; mortality; novel; patient population; predictive modeling; programs; racial difference; racial diversity; secondary analysis; sex; skills; social; social factors; social health determinants; supervised learning; theories; training opportunity; young adult

Project Summary In the U.S., approximately 840,000 Americans die from cardiovascular disease (CVD) each year. The prevalence of CVD is on the rise and widespread disparities in CVD exist across economic, racial, and ethnic groups. In order to address the rising prevalence of CVD and persistent disparities, there has been a shift in focus to public health strategies addressing cardiovascular health (CVH). CVH is a broader and more positive construct beyond the absence of CVD. Despite this recent focus on improving CVH, widespread disparities still exist. Social determinants of health (SDOH) may be important contributors to these continued disparities. The World Health Organization (WHO) defines SDOH as the “structural determinants and conditions in which people are born, grow, live, work, and age.” There has been limited work in studying how a diverse set of SDOH change over time and perform in the prediction of CVH. To address this need, we will identify patterns of SDOH exposure over time and determine if the addition of SDOH variables allows for better prediction of an individual's CVH status. The primary hypothesis is that a diverse set of SDOH will be associated with and improve the prediction of CVH, independent of baseline CVH and other covariates. To examine this hypothesis, we will pursue the following Specific Aims: 1) identify patterns of SDOH exposure up to age 50 and define exposure subgroups and 2) determine whether overall and domain-specific patterns of SDOH exposures from young adulthood to middle age, identified in Aim 1, are associated with and improve the prediction of CVH and its component metrics. We will utilize the Coronary Artery Risk Development in Young Adults (CARDIA) study, a prospective cohort study with detailed information on cardiovascular risk factors and disease in a geographically and racially diverse sample of young adults. In Aim 1, we will use a novel sequential pattern mining method to identify the associations among SDOH and determine the SDOH exposure patterns from baseline to age 50. The Aim 1 SDOH exposure definitions will be included as predictors of CVH at age 50 and beyond using supervised machine learning techniques. By including SDOH in predictive models, health services professionals and clinicians may have an improved understanding of patients at high-risk for low CVH and may better tailor social and clinical interventions to each patient's needs. If awarded, this fellowship will allow me to contribute novel research to the SDOH and cardiovascular health fields, gain competency in new research skills, and improve my writing and presentation skills. The completion of this project and training will prepare me for my long-term career goal of becoming an independent research scientist in an academic setting, studying SDOH and using data science and informatics tools to improve public health.

项目摘要 在美国，每年大约有840，000美国人死于心血管疾病（CVD）。之时尚 心血管疾病的发病率呈上升趋势，不同经济、种族和民族群体之间心血管疾病的发病率存在广泛差异。在 为了解决心血管疾病发病率上升和持续的差距，重点已经转移到公众 针对心血管健康（CVH）的健康战略。CVH是一个更广泛、更积极的概念， 没有CVD。尽管最近的重点是改善CVH，但仍然存在广泛的差异。社会 健康决定因素（SDOH）可能是造成这些持续差异的重要因素。世界卫生 世界卫生组织（WHO）将SDOH定义为“人们出生的结构性决定因素和条件， 成长，生活，工作，和年龄。”在研究一组不同的SDOH如何改变方面， 在CVH预测中的应用。为了满足这一需求，我们将确定SDOH暴露的模式 并确定是否添加SDOH变量可以更好地预测个体的CVH status.主要的假设是，不同的SDOH集合将与预测相关联并改善预测 CVH，独立于基线CVH和其他协变量。为了检验这一假设，我们将继续研究 以下具体目标：1）确定50岁以下的SDOH暴露模式，并定义暴露亚组， 2)确定从青年到中年的SDOH暴露的总体和特定领域模式是否 目标1中确定的年龄与CVH及其分量度量的预测相关并改善其预测。我们 将利用年轻人冠状动脉风险发展（CARDIA）研究，一项前瞻性队列研究 在地理和种族多样的地区， 年轻人的样本。在目标1中，我们将使用一种新的序列模式挖掘方法来识别 SDOH之间的关联，并确定从基线到50岁的SDOH暴露模式。目标1 将纳入SDOH暴露定义作为50岁及以上CVH的预测因子，使用监督 机器学习技术通过将SDOH纳入预测模型，卫生服务专业人员和 临床医生可能对低CVH高风险患者有更好的了解， 和临床干预来满足每个病人的需求。如果被授予，这个奖学金将允许我贡献小说 研究SDOH和心血管健康领域，获得新的研究技能的能力，并提高 我的写作和表达能力。这个项目的完成和培训将为我的长期工作做好准备。 成为一个独立的研究科学家在学术环境中的职业目标，研究SDOH和使用 数据科学和信息学工具，以改善公共卫生。