The impact of wildfire smoke exposure on cardiovascular health in the western US

项目摘要 心血管疾病（CVD）是美国的主要疾病负担。广泛的研究表明， 暴露于PM2.5和臭氧等空气污染物是CVD死亡率增加的原因或可能原因， 发病率。虽然美国在减少工业和汽车空气污染方面取得了稳步进展， 最近野火的增加减缓甚至逆转了这一进展，特别是在西部地区。 我们火灾烟雾中大量的空气污染物可以传播数百公里， 远离原始火灾的州的人口稠密地区。由于气候变化预计将大大增加 该地区已经存在很高的野火风险，因此有重要的公共卫生需求来确定 烟雾暴露具有与环境空气污染明显不同的化学成分， 风险了解烟雾成分的心脏毒性作用之间的关键区别， 设计有效的火灾烟雾应急响应措施，并评估长期医疗需求 在美国西部。我们将研究心血管疾病风险与急性或慢性暴露于 烟雾中的主要空气污染物，包括PM2.5的主要成分（即，硫酸盐，硝酸盐，有机碳， 元素碳）和气体污染物如臭氧和甲醛（HCHO）。具体来说，我们将首先 估计2001年至2012年期间每日烟雾和非烟雾PM2.5成分、臭氧和甲醛的1公里分辨率 2020年在美国西部使用EPA的社区多尺度空气质量模型（CMAQ）， 在机器学习模型中使用EPA级测量和卫星数据。第二，我们要把这个富 来自美国西部六个州的艾德访视和住院数据的暴露数据集，以评估不良反应 急性暴露于火灾烟雾对各种心血管疾病结局的健康影响，包括急性心肌梗死， 中风、心力衰竭、房颤和所有CVD事件。第三，我们将把这个暴露数据集与 美国西部的医疗保险数据，以表征慢性暴露的影响（即，前几周、前几个月或 年），以消防烟对这些心血管疾病的结果。作为子分析，我们将开发一个增强的机器学习 模型，包括2016 - 2020年可用的低成本传感器测量值，以估计每日PM2.5，并使用 该数据集进行与主要分析相似的流行病学分析。最后，我们将评估未来 2050 - 2100年气候变化导致的慢性烟雾暴露的心血管疾病负担 本研究中开发的浓度-反应函数。有大量的研究人群和高质量的 暴露估计，我们预计将产生强有力的估计，急性和慢性暴露的关联 野火烟雾和心血管疾病的风险。通过高分辨率的区域气候模型模拟，我们还将能够 预测气候变化造成的与吸烟相关的长期心血管疾病负担。

PROJECT SUMMARY Cardiovascular diseases (CVD) are the primary disease burden in the US. Extensive research has established exposure to air pollutants such as PM2.5 and ozone as cause or likely cause of increased CVD mortality and morbidity. While the US has made steady progress in reducing air pollution from industrial and vehicle emissions, recent increase in wildfires has slowed or even reversed this progress particularly in the Western US. Massive quantities of air pollutants in fire smoke can travel hundreds of kilometers affecting highly populated areas in states far from the original fire. As climate change is projected to substantially increase the already high wildfire risk in this region, there is an important public health need to establish the effects of smoke exposure, which has a distinctly different chemical composition from ambient air pollution, on CVD risks. Understanding the key distinctions between the cardiotoxic effects of smoke constituents is important to design effective emergency response measures to fire smoke, and to assess the long-term health care needs in the Western US. We will investigate the relationship between CVD risks and acute or chronic exposure to major air pollutants in smoke, including major PM2.5 constituents (i.e., sulfate, nitrate, organic carbon, elemental carbon), and gas pollutants such as ozone and formaldehyde (HCHO). Specifically, we will first estimate daily smoke and non-smoke PM2.5 constituents, ozone and HCHO at 1 km resolution from 2001 to 2020 in the Western US using EPA’s Community Multi-scale Air Quality Model (CMAQ) calibrated by ground- level EPA measurements and satellite data in a machine learning model. Second, we will link this rich exposure dataset with ED visits and hospitalization data from six Western US states to assess the adverse health effects of acute exposure to fire smoke on various CVD outcomes including acute myocardial infarction, stroke, heart failure, atrial fibrillation, and all CVD events. Third, we will link this exposure dataset with Medicare data in Western US to characterize the effect of chronic exposure (i.e., previous weeks, months or year) to fire smoke on these CVD outcomes. As a sub-analysis, we will develop an enhanced machine learning model by including available low-cost sensor measurements for 2016 – 2020 to estimate daily PM2.5, and use this dataset to conduct similar epidemiological analysis to the main analysis. Finally, we will assess the future CVD disease burden of chronic smoke exposure due to climate change from 2050 to 2100 using the concentration-response functions developed in this study. With large study populations and high-quality exposure estimates, we expect to generate robust estimates of the associations of acute and chronic exposure to wildfire smoke and CVD risks. With high-resolution regional climate model simulations, we will also be able to project smoke-related long-term CVD burden imposed by climate change.