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

项目摘要 在美国，每年约有840,000名美国人死于心血管疾病。流行率 心血管疾病的发病率正在上升，在经济、种族和民族之间存在着广泛的差距。在……里面 为了应对日益增长的心血管疾病患病率和持续存在的差距，已将重点转向公众 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 无心血管疾病。尽管最近将重点放在改善CVH上，但仍然存在广泛的差距。社交 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“人出生的结构性决定因素和条件， 成长、生活、工作和变老。在研究一组不同的SDOH如何变化方面，工作一直有限 预测CVH的时间和效果。为了满足这一需求，我们将确定SDOH暴露的模式 并确定SDOH变量的添加是否允许更好地预测个体的CVH 状态。主要的假设是，一组不同的SDOH将与预测相关联并改善预测 CVH，与基线CVH和其他协变量无关。为了检验这一假设，我们将继续 以下具体目标：1)确定50岁以下SDOH暴露的模式，并确定暴露亚组和 2)确定从青壮年到中年，SDOH暴露的总体和特定领域的模式 目标1中确定的年龄与CVH及其组成部分指标的预测相关，并改善了预测。我们 将利用青年冠状动脉风险发展(CARDIA)研究，这是一项前瞻性队列研究 提供关于不同地理和种族的心血管危险因素和疾病的详细信息 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之间的联系，并确定从基线到50岁的SDOH暴露模式。目标1 SDOH暴露定义将被纳入为50岁及以上的CVH的预测因子 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 临床医生可能会对低CVH的高危患者有更好的了解，并可能更好地定制社会 并针对每个患者的需要进行临床干预。如果获奖，这笔奖学金将使我能够为小说作贡献 对SDOH和心血管健康领域的研究，获得新研究技能的能力，并提高 我的写作和演讲技巧。这个项目的完成和培训将为我的长期职业生涯做好准备 在学术背景下成为一名独立研究科学家的职业目标，研究SDOH并使用 数据科学和信息学工具，以改善公众健康。

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.