Examining the Role of Structural Factors in Racial and Ethnic Disparities in Cardiovascular Disease

检查结构性因素在心血管疾病种族和民族差异中的作用

• 批准号: 10723870
• 负责人: Shawna Follis
• 金额: $ 15.64万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-18 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10723870
• 关键词: Acceleration; Accounting; Adult; Alaska Native; American; American Indians; Award; Behavioral; Black race; Cardiovascular Diseases; Censuses; Centers for Disease Control and Prevention (U.S.); Childhood; Clinical; Cohort Studies; Community Surveys; Data; Data Science; Data Set; Development Plans; Disease Outcome; Disease Pathway; Disparity; Economics; Epidemiology; Ethnic Origin; Etiology; Faculty; Foundations; Future; Gender; Geography; Health Policy; Health Promotion; High Risk Woman; Incidence; Income; Indigenous; Individual; Inequality; Intervention; Knowledge; Latina; Level of Evidence; Life; Life Cycle Stages; Link; Maps; Measures; Mediator; Medical; Mentors; Methodology; Methods; National Heart, Lung, and Blood Institute; Neighborhoods; Paper; Participant; Pathway interactions; Phase; Policies; Population; Positioning Attribute; Postmenopause; Prospective cohort; Psychosocial Stress; Public Health; Publishing; Quechua; Race; Research; Research Personnel; Resources; Risk; Risk Factors; Role; Scientist; Site; Source; Strategic vision; Structural Models; Structural Racism; Techniques; Time; Training; Woman; Women' s Health; access restrictions; cardiovascular disorder risk; career development; clinical center; cohort; data ecosystem; data fusion; design; disease disparity; disparity reduction; effective intervention; ethnic disparity; experience; follow-up; health difference; improved; indexing; innovation; intersectionality; longitudinal dataset; marginalization; novel; programs; prospective; racial disparity; residence; residential segregation; segregation; skills; social culture; social health determinants; social organization; social structure; social vulnerability; statistics; structural determinants; tenure track; theories

Project Summary Accumulating research suggests that barriers to eliminating the persistent racial disparities in cardiovascular disease (CVD) are related to structural-level social determinants of health (SDOH). The majority of this evidence is cross-sectional, from studies using administrative datasets (i.e., US Census) to quantify structural SDOH and structural racism associations with ecological-level measures of CVD. Prospective and clinical CVD outcome data are needed to advance from descriptive-level evidence; however, well-established cohort studies typically lack access to novel structural determinants. The scientific objective of the research plan is an innovative solution to generate the needed high-quality dataset, by employing data fusion techniques to link structural determinants from administrative datasets with prospective cohort data. I will generate four structural-level determinants at the neighborhood-level using geographic linkages between the Women’s Health Initiative (WHI) cohort with 1) US Census 2) American Community Survey (ACS) 3) Center for Disease Control and 4) Neighborhood Redlining Maps. Each structural determinant includes a measure of racialization and adheres to recent conceptual frameworks for advancing the quantification of structural racism in CVD research. I uniquely measure determinants longitudinally to account for changes in residence and the duration of exposure. In Aim 1 (K99 phase), I will quantify structural racism at the intersection of race and income using the index of concentration at the extremes (ICE). The causal effects of ICE on CVD incidence over 30 years of follow-up will be estimated. This mentored research and training prepare me for the R00 phase research. In Aim 2, I propose to link the Social Vulnerability Index to evaluate a hypothesized structural intervention on CVD. In Aim 3, I propose to estimate CVD risk associated with racial residential segregation and residence in a historically redlined neighborhood. Evaluating causal mechanisms, temporality, life-course exposure, and accounting for race and gender intersectionality would markedly advance the current level of evidence. The public health implications of which may help design future interventions to target modifiable structural policies and practices. The career development plan will advance my scientific training in data fusion techniques, the modeling of structural racism, and pathways to CVD. Through mentored training combined with this research plan, the MOSAIC K99/R00 will prepare me to transition to an independent investigator in a tenure-track faculty position. This award would advance three Objectives of the NHLBI Strategic Vision through the use of (3) an emerging opportunity in data science to accelerate understanding of (7) factors that account for differences in health among populations, led by (8) a scientist who would diversify the scientific workforce.

项目摘要 累积研究表明，消除心血管中持续的种族分布的障碍 疾病（CVD）与卫生的结构水平社会决定者（SDOH）有关。大多数 证据是横断面的，从使用管理数据集（即美国人口普查）进行量化结构性的研究。 SDOH和结构性种族主义与CVD的生态水平衡量标准的关联。前瞻性和临床CVD 结果数据需要从描述性的证据中进步；但是，成熟的队列研究 通常缺乏获得新型结构决定者的访问。研究计划的科学目标是 通过使用数据融合技术链接的创新解决方案来生成所需的高质量数据集 带有前瞻性队列数据的管理数据集中的结构性决定剂。我会生成四个 使用妇女之间的地理联系，在社区层面的结构级别的决定师 健康倡议（WHI）队列与1）美国人口普查2）美国社区调查（ACS）3）疾病中心 控制和4）邻里红线图。每个结构确定器都包括种族化的测量 并遵守最新的概念框架，以推进CVD中结构性种族主义的数量 研究。我独特地测量了确定词，以说明居住的变化和持续时间的变化 暴露。在AIM 1（K99阶段）中，我将在种族与收入的交汇处量化结构性种族主义 极端（冰）的浓度指数。冰对CVD事件的因果影响30年 随访将被估算。这种修改的研究和培训为我为R00阶段研究做好了准备。在 AIM 2，我建议将社会脆弱性指数联系起来，以评估假设的结构干预 CVD。在AIM 3中，我建议估计与种族居住隔离和住所相关的CVD风险 历史上红线的社区。评估因果机制，临时性，生命课程的暴露和 对种族和性别交叉的考虑将显着提高当前的证据水平。这 公共卫生的影响可能有助于设计未来的干预措施，以针对可修改的结构性政策 和实践。职业发展计划将推进我在数据融合技术方面的科学培训， 结构性种族主义的建模和通往CVD的途径。通过指导的培训与这项研究相结合 计划，马赛克K99/R00将使我准备过渡到终身教师的独立调查员 位置。该奖项将通过使用（3）一个 数据科学中新兴的机会，以加速对（7）因素的理解 人口中的健康，由（8）领导的科学家将使科学劳动力多样化。