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.

项目概要 越来越多的研究表明，消除心血管疾病中持续存在的种族差异的障碍 疾病（CVD）与结构层面的健康社会决定因素（SDOH）有关。 证据是横截面的，来自使用行政数据集（即美国人口普查）来量化结构性的研究 SDOH 和结构性种族主义与 CVD 的生态水平测量相关。 需要从描述性证据中获得结果数据；然而，完善的队列研究 通常缺乏新的结构决定因素 研究计划的科学目标是 通过采用数据融合技术链接来生成所需的高质量数据集的创新解决方案 我将根据行政数据集和前瞻性队列数据生成四个结构决定因素。 利用妇女社区之间的地理联系，确定邻里层面的结构层面决定因素 健康倡议 (WHI) 队列包括 1) 美国人口普查 2) 美国社区调查 (ACS) 3) 疾病中心 控制和 4) 社区红线地图。每个结构性决定因素都包括种族化的衡量标准。 并遵守最新的概念框架，以推进 CVD 中结构性种族主义的量化 我独特地纵向测量决定因素，以解释居住地和持续时间的变化。 在目标 1（K99 阶段）中，我将使用种族和收入的交叉点来量化结构性种族主义。 极端浓度指数 (ICE) 30 年来 ICE 对 CVD 发病率的因果影响。 这项指导性研究和培训将为我的 R00 阶段研究做好准备。 目标 2，我建议将社会脆弱性指数联系起来，以评估已开发的结构性干预措施 在目标 3 中，我建议估计与种族居住隔离和居住在某个地区相关的 CVD 风险。 评估历史红线社区。 考虑到种族和性别交叉性将显着提高目前的证据水平。 其对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 职业发展计划将促进我在数据融合技术方面的科学培训。 通过指导培训与这项研究相结合，对结构性种族主义和心血管疾病的途径进行建模。 计划，MOSAIC K99/R00 将使我做好过渡到终身教授的独立研究者的准备 该奖项将通过使用 (3) 推进 NHLBI 战略愿景的三个目标。 数据科学中出现的新机遇可加速对 (7) 造成差异的因素的理解 人口健康，由 (8) 一位科学家领导，他将使科学队伍多样化。