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

项目摘要 越来越多的研究表明，消除心血管疾病长期存在的种族差异的障碍 疾病(CVD)与结构性的健康社会决定因素(SDOH)有关。这其中的大部分 证据是横截面的，来自使用行政数据集(即美国人口普查)来量化结构的研究 SDOH和结构性种族主义与心血管疾病的生态水平措施相关联。前瞻性和临床性心血管疾病 结果数据需要从描述性水平的证据中发展出来；然而，建立良好的队列研究 通常缺乏获得新的结构决定因素的机会。该研究计划的科学目标是 创新的解决方案，通过使用数据融合技术链接来生成所需的高质量数据集 来自行政数据集的结构性决定因素与预期的队列数据。我将生成四个 利用妇女之间的地理联系在邻里一级的结构层面的决定因素 健康倡议(WHI)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)邻里红线地图。每个结构性决定因素都包括一种种族主义的衡量标准 并坚持最近的概念框架，以推动对心血管疾病中结构性种族主义的量化 研究。我独一无二地纵向测量决定因素，以说明居住地和持续时间的变化 曝光率。在目标1(K99阶段)中，我将量化种族和收入交汇处的结构性种族主义 极值集中度指数(ICE)。30年来ICE对心脑血管疾病发病率的影响 将对后续行动进行评估。这种有指导的研究和培训让我为R00阶段的研究做好了准备。在……里面 目标2，我建议将社会脆弱性指数联系起来，以评估假设的结构性干预 心血管疾病。在目标3中，我建议估计与种族居住隔离和居住在一个 历史上的红线社区。评估因果机制、暂时性、生命过程暴露，以及 考虑到种族和性别的交叉，将显著提高目前的证据水平。这个 其对公共健康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 和练习。职业发展计划将推进我在数据融合技术方面的科学培训， 结构性种族主义的模型，以及通向心血管疾病的途径。通过导师培训与本研究相结合 计划中，MASIC K99/R00将为我过渡到终身教职员工的独立调查员做好准备 位置。该奖项将通过使用(3)和 数据科学中正在出现的机会，以加速理解(7)导致 人口中的健康，由一位科学家领导，他将使科学劳动力多样化。

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.