Cardiovascular Health and Air Pollution: A National Study

 描述（由申请人提供）：在大城市地区，短期和长期暴露于细颗粒物（PM2.5）与心血管疾病风险增加有关。虽然以前的研究包括越来越多的人口和复杂的分析方法，但仍然存在许多知识空白。一个限制是，依靠环境保护局的空气污染监测网络，只有研究将：1）排除较少的城市地区，其中PM2.5没有监测; 2）无法区分与区域/长距离运输相关的健康影响与当地污染源; 3）无法估计低浓度空气污染对健康的影响，这需要大量人口处于该暴露范围内。此外，随着气候变化的威胁越来越大，完全缺乏关于空气污染和热量潜在协同效应的证据。在目标1中，我们将把基于卫星的气溶胶光学厚度（AOD）数据与土地利用回归变量相结合，以1x 1 km网格单元分辨率预测和验证整个美国的每日PM2.5水平。这些数据将用于计算因地点和日期而异的邮政编码暴露，这些暴露将与2000年至2013年期间的整个Medicare队列（90%的美国老年人口）相关联。在目标2中，我们将开发计算效率高的算法来分析这些关联的数据集，并通过在非常局部的范围内使用改善的PM2.5暴露来提供前所未有的心血管疾病急性和慢性风险的证据，包括来自污染监测器不可用的农村人口。我们将估计心血管入院和PM2.5之间的确定性-响应关系的形状，以解决低于空气质量标准的影响问题;并通过农村/城市地区，通过多种社会经济措施，通过个人和土地利用特征评估影响修改。在目标2.1中，我们将把估计的每日污染面分解为不同的空间尺度，代表区域/远距离传输和本地产生的污染。在目标A.2.2中，我们将评估新英格兰城市和农村地区PM2.5成分和气体的影响。在目标A.2.2中，我们将开发使用辅助信息调整混杂偏倚的方法。在目标3中，我们将评估与同时暴露于高温和PM2.5相关的急性和慢性健康风险。在目标4中，我们将向研究界提供在以前目标中获得的成果的地图。我们还将分发软件，以便有效执行分析这些大数据集所需的新开发的统计方法。总之，如果暴露数据的准确性、样本量、方法、暴露响应关系的表征以及热和空气污染的联合效应没有实质性的改进，为未来的空气质量法规提供必要的证据将是具有挑战性的。研究结果将为制定最具成本效益和最有益的空气质量干预措施提供急需的证据基础。

 DESCRIPTION (provided by applicant): Short and long-term exposures to fine particulate matter (PM2.5) have been associated with increased risks of cardiovascular disease in large urban areas. Although previous studies have included increasingly larger populations and sophisticated analytical methods, many gaps of knowledge remain. One limitation is that by relying on the Environmental Protection Agency air pollution monitoring network only studies will: 1) exclude less urban areas where PM2.5 is not monitored; 2) not be able to differentiate health effects associated with regional/ long-range transport versus local pollution sources; and 3) not be able to estimate health effects of air pollution at low concentrations, which requires large populations in that exposure range. In addition, with the mounting threat of climate change, evidence regarding a potential synergistic effect of air pollution and heat is totally lacking. In Aim 1 we will integrate satellite-based aerosol optical depth (AOD) data with land use regression variables to predict and validate daily PM2.5 levels at 1x1 km grid cell resolution for the entire US. This data will be used to calculate zip-code exposures that vary by location and date, which will be linked to the entire Medicare cohort (90% of the US elderly population) over the period 2000 to 2013. In Aim 2 we will develop computationally efficient algorithms to analyze these linked data sets and provide evidence of acute and chronic risks of cardiovascular disease of unprecedented accuracy by using improved exposure to PM2.5 at a very local scale, and including rural populations from locations in which pollution monitors are not available. We will estimate the shape of the exposure-response relationship between cardiovascular admissions and PM2.5 to address questions of effects below the air quality standards; and evaluate effect modification by rural/ urban area, by multiple socioeconomic measures, by individual and by land use characteristics. In Aim 2.1 we will decompose the estimated daily pollution surfaces into different spatial scales, representing regional/long- range transport and locally-generated pollution. In Aim A.2.2 we will estimate the effects of PM2.5 components and gases in urban and rural areas in New England. In Aim A.2.2 we will develop approaches to adjust for confounding bias using auxiliary information. In Aim 3 we will estimate the acute and chronic health risks associated with simultaneous exposure to heat and PM2.5. In Aim 4, we will make available to the research community maps of the output obtained in the previous aims. We will also disseminate software for the efficient implementation of the newly developed statistical methods needed for the analysis of these big data sets. In summary, without a substantial improvement in the accuracy of the exposure data, sample size, methodology, and characterization of the exposure response relationship and the joint effects of heat and air pollution, it will be challenging to provide the necessary evidence for future air quality regulations. The results will provide a much needed evidence base for developing the most cost effective and beneficial air quality interventions.