The role of the social environment in the association between air pollution and cardiovascular disease

摘要 环境层面的因素，如邻里社会经济地位(NSE)和种族居住隔离 (RR)是可能影响空气污染流行病学研究的潜在混杂因素。很少有研究发现 系统地考察了这些重要背景因素造成的混乱程度。致我们的 知识没有研究将RRS作为空气污染-心血管疾病(CVD)的混杂因素 学习。我们建议对经济局局长(两者)的混淆和协同作用进行有系统的评估 在社区和个人级别)和RRS在由八个井组成的唯一且强大的数据源中 以慢性病队列研究为特征。广泛的协变量数据，一致和长期的随访 参与者、高质量的空气污染暴露和CVD结果的标准化收集使这成为 用于执行这项重要工作的理想数据源。一个现有的项目正在进行中，以协调 这些队列中的数据。我们的目标是：1)评估不同的SES指标和 RRS和长期环境空气污染暴露的横截面和随时间的变化，通过制定最新状况- SES和RRS的ART测量2)进行定量偏差分析，以评估混杂程度 SES和RRS在长期空气污染与心血管疾病死亡率和事件之间的关联和3) 检查长期空气污染以及SES和RRS对心血管事件和死亡率的联合影响。 我们将根据主成分分析为1990-2015年的时间段创建一个随时间变化的NSE指数 用均匀度(相异度指数)和隔离度(隔离指数)来评价RRS。我们将使用 目标1的空间回归方法、目标2的概率定量偏差分析和生存分析 目标3.这项建议对公众健康有三方面的影响。第一，评估混乱的程度 环境保护局局长在空气污染流行病学研究中将加强用于国家空气污染的证据 标准。其次，对RRS的探索可能会为空气污染建立一个新的背景混杂因素 流行病学。第三，制定一个严谨和具有强大理论基础的评估框架 多个空间变化因素将帮助我们更好地理解长期存在的环境健康差距 并可能有助于设计未来的干预措施和政策。

Abstract Contextual level factors like neighborhood socioeconomic status (NSES) and racial residential segregation (RRS) are potential confounders that may bias air pollution epidemiology studies. Few studies have systematically examined the magnitude of confounding by these important contextual factors. To our knowledge no studies have included RRS as a confounder in air pollution-cardiovascular disease (CVD) studies. We propose to conduct a systematic assessment of the confounding and synergist roles of SES (both at the neighborhood and individual levels) and RRS in a unique and robust data source made-up of eight well- characterized chronic disease cohort studies. Extensive covariate data, consistent and lengthy follow-up of participants, high quality air pollution exposures and standardized collection of CVD outcomes makes this an ideal data source within which to conduct this important work. An existing project is underway to harmonize data across these cohorts. Our aims are to: 1) assess the association between different indicators of SES and RRS and long-term ambient air pollution exposures cross-sectionally and over time, by developing state-of-the- art measures of SES and RRS 2) conduct quantitative bias analysis to evaluate the magnitude of confounding by SES and RRS in the association between long-term air pollution and CVD mortality and events and 3) examine the joint effects of long-term air pollution and SES and RRS on cardiovascular events and mortality. We will create a time-varying NSES index from principal components analysis for the time period 1990 - 2015 and measures of evenness (dissimilarity index) and isolation (isolation index) to evaluate RRS. We will use spatial regression approaches for aim 1, probabilistic quantitative bias analysis for aim 2 and survival analysis for aim 3. The public health impact of this proposal is three-fold. First, evaluating the magnitude of confounding by SES in air pollution epidemiology studies will strengthen the evidence used for national air pollution standards. Second, exploration of RRS could establish a new contextual confounder for air pollution epidemiology. And third, developing a rigorous and strongly theoretically grounded framework for assessing multiple spatially varying factors will help us better understand longstanding environmental health disparities and may help design future interventions and policy.