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

社会环境在空气污染与心血管疾病关系中的作用

• 批准号: 10284896
• 负责人: Anjum Hajat
• 金额: $ 25.18万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-03 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10284896
• 关键词: Address; Air Pollution; Animals; Cardiovascular Diseases; Cardiovascular system; Characteristics; Chronic Disease; Cohort Studies; Collection; Color; Communities; Data; Data Sources; Disease Outcome; Environment; Epidemiology; Event; Exposure to; Future; Goals; Health; Human; Individual; Intervention; Joints; Light; Literature; Long-Term Effects; Measurement; Measures; National Institute of Environmental Health Sciences; Nature; Neighborhoods; Nitrogen Dioxide; Observational Study; Outcome; Participant; Pathway interactions; Policies; Policy Making; Population; Principal Component Analysis; Public Health; Research; Residual state; Resources; Rest; Risk Factors; Role; Social Environment; Socioeconomic Status; Standardization; Survival Analysis; Systematic Bias; Time; Toxicology; Ursidae Family; Work; ambient air pollution; cardiovascular health; cohort; contextual factors; data harmonization; design; environmental health disparity; environmental justice; epidemiology study; fine particles; follow-up; health disparity; high school; indexing; mortality; residential segregation; study population

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.

抽象的 邻里社会经济地位（NSE）和种族住宅隔离等上下文级别的因素 （RR）是可能偏向空气污染流行病学研究的潜在混杂因素。很少有研究 系统地检查了这些重要的上下文因素的混淆大小。向我们 知识没有研究包括RRS作为空气污染 - 心脏疾病（CVD）的混杂因素 研究。我们建议对SES的混杂和协同作用进行系统评估（均 在社区和个人级别）和RRS中的独特数据源，由八个很好的数据源组成 慢性病队列研究的特征。广泛的协变量数据，一致且冗长的随访 参与者，高质量的空气污染暴露和标准化的CVD结果收集使这成为 进行这项重要工作的理想数据源。现有的项目正在进行 这些队列的数据。我们的目的是：1）评估SES不同指标与 RR和长期环境空气污染在横截面和随着时间的流逝，通过发展最新的 SES和RRS的艺术度量2）进行定量偏差分析以评估混杂的幅度 长期空气污染与CVD死亡率和事件之间的关联以及3） 检查长期空气污染以及SES和RR对心血管事件和死亡率的联合影响。 我们将从1990年至2015年的主要组件分析中创建一个随时间变化的NSE索引 以及均衡度（差异指数）和隔离（隔离指数）以评估RR。我们将使用 AIM 1的空间回归方法，AIM 2和生存分析的概率定量偏差分析 对于AIM 3。该提案的公共卫生影响是三倍。首先，评估混杂的大小 SES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的探索可以建立一个新的空气污染上下文混杂因素 流行病学。第三，开发一个严格且理论上的扎根框架来评估 多个空间变化的因素将有助于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。