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

摘要 背景水平因素，如社区社会经济地位（NSES）和种族居住隔离 (RRS)是可能使空气污染流行病学研究产生偏差的潜在混杂因素。很少有研究 系统地研究了这些重要的背景因素造成的混淆程度。对我们 没有研究将RRS作为空气污染-心血管疾病（CVD）的混杂因素 问题研究我们建议对SES的混杂和干扰作用进行系统评估（两者都是 在社区和个人水平）和RRS在一个独特的和强大的数据源，由八个良好的， 特征性慢性病队列研究。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标准化的心血管疾病结果的收集，使这成为一个 理想的数据源，在其中进行这项重要的工作。一个现有的项目正在进行中， 这些群体的数据。我们的目标是：1）评估SES的不同指标之间的关联， RRS和长期环境空气污染暴露的横截面和随着时间的推移，通过制定国家的- SES和RRS的艺术测量2）进行定量偏倚分析以评估混杂的程度 SES和RRS在长期空气污染与CVD死亡率和事件之间的关联中的作用，以及3） 研究长期空气污染、SES和RRS对心血管事件和死亡率的联合影响。 我们将从1990 - 2015年的时间段的主成分分析中创建一个随时间变化的NSES指数 用均匀度（相异性指数）和隔离度（隔离指数）评价RRS。我们将使用 aim 1的空间回归方法，aim 2的概率定量偏倚分析和生存分析 目标3。这项建议对公众健康的影响有三方面。首先，评估混杂的程度 由SES在空气污染流行病学研究将加强用于国家空气污染的证据 标准其次，RRS的探索可以为空气污染建立一个新的背景混杂因子 流行病学第三，建立一个严格的、有很强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了解长期存在的环境健康差异 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.