Air Pollutants and Cardiovascular Risk: Investigating Thresholds with Pooled Cohorts and Electronic Health Records

空气污染物和心血管风险：通过汇总队列和电子健康记录调查阈值

• 批准号: 9392461
• 负责人: Joel Daniel Kaufman
• 金额: $ 68.79万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-15 至 2021-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9392461
• 关键词: Address; Air Pollutants; Air Pollution; Area; Beds; Big Data; Biological; California; Cardiovascular Diseases; Cardiovascular system; Cessation of life; Chemicals; Classification; Cohort Studies; Complex; Congestive Heart Failure; Data; Data Set; Data Sources; Detection; Developed Countries; Development; Disease; Electronic Health Record; Environmental Health; Epidemiologic Methods; Epidemiology; Event; Exposure to; Future; Geography; Gold; Health; Health system; Heart Diseases; Home environment; Hybrids; Individual; Knowledge; Least-Squares Analysis; Long-Term Effects; Methods; Modeling; Morbidity - disease rate; Myocardial Infarction; Nature; Nitrogen Dioxide; Nitrogen Oxides; Non-accidental; Outcome; Ozone; Participant; Particulate; Particulate Matter; Persons; Policies; Policy Making; Population; Public Health; Records; Research; Risk Factors; Sample Size; Science; Shapes; Source; Statistical Methods; Stroke; System; Testing; Translating; United States; base; cardiogenesis; cardiovascular risk factor; cohort; cost; cost effective; epidemiology study; experience; fine art; fine particles; follow-up; health inequalities; insight; member; mortality; novel strategies; pollutant; premature; programs; remote sensing; response; spatiotemporal; trend

Project Summary Long-term exposure to air pollutants is recognized as a risk factor for cardiovascular disease (CVD); exposure to ambient fine particulates (PM2.5) is considered among the top ten global risk factors for premature death and morbidity primarily due to CVD effects. Public health action on a national and global level still requires better information on the nature of the association between pollutants and CVD: Is there a threshold for exposure, below which effects do not occur? Is the effect linear or does it follow a more biologically likely function? This proposal addresses these critical questions with two approaches: 1) pooling high-quality cohort studies; and 2) analyzing health system records based on electronic health records (EHR). The former approach is the epidemiological standard while the latter represents the “big data” future. The combination and comparison of these two approaches in the same research program affords a unique opportunity to address another key public health question: Can “big data” provide the same answer as traditional cohort approaches? This proposal addresses low-level air pollution health effects using state-of-the-art exposure assessment, fine- scale hybrid modeling of concentrations (PM2.5, oxides of nitrogen, and ozone) and advanced statistical methods. The cohorts permit optimal minimization of bias due to confounding and misclassification by pooling information from a set of well-established cohorts in the US totaling nearly one million participants, each with appropriate outcome, home address, and individual level covariate detail. The consortium cohorts feature geographic and exposure diversity. This project will also study one unusually well-characterized large-scale integrated health delivery system, Kaiser Permanente Northern California, with detailed EHR on more than 4.7 million members. Results of this study will provide critically important knowledge to guide policy in the United States and globally. Further, this proposal will directly compare traditional cohort and new “big data” approaches for answering complex epidemiological research questions, allowing for better understanding of the ability of “big data” to replace and/or supplement traditional approaches.

项目概要 长期接触空气污染物被认为是心血管疾病（CVD）的危险因素； 环境细颗粒物 (PM2.5) 被认为是导致过早死亡的全球十大风险因素之一 主要由于心血管疾病影响而导致的发病率仍然需要在国家和全球层面采取更好的公共卫生行动。 有关污染物与 CVD 之间关联性质的信息：是否存在暴露阈值， 以下哪些效应不会发生？该效应是线性的还是遵循生物学上更可能的函数？ 该提案通过两种方法解决这些关键问题：1）汇集高质量的队列研究； 2）基于电子健康记录（EHR）分析健康系统记录。 流行病学标准，而后者则代表“大数据”的未来。 同一研究计划中的这两种方法提供了解决另一个关键问题的独特机会 公共卫生问题：“大数据”能否提供与传统队列方法相同的答案？ 该提案利用最先进的暴露评估、精细- 浓度（PM2.5、氮氧化物和臭氧）的比例混合建模和高级统计 这些方法可以最大程度地减少由于合并而导致的混杂和错误分类造成的偏差。 来自美国一组完善的队列的信息，总计近百万参与者，每个队列都有 适当的结果、家庭住址和个人层面的协变量详细信息。 该项目还将研究一个异常明确的大规模地区。 综合医疗服务系统，Kaiser Permanente 北加州，详细 EHR 超过 4.7 这项研究的结果将为指导美国的政策提供至关重要的知识。 此外，该提案将直接比较传统队列和新的“大数据”。 回答复杂的流行病学研究问题的方法，以便更好地理解 “大数据”取代和/或补充传统方法的能力。