Prenatal exposure to traffic emissions and incident asthma in a birth cohort

 描述(由申请人提供)：越来越多的证据表明，产前环境在儿童哮喘的病因中发挥了作用。流行病学证据表明，学龄前患哮喘的儿童在出生时已有40%的相关肺缺陷，动物实验研究表明，产前暴露于烟草烟雾、柴油废气和其他与燃烧有关的颗粒物可导致后代哮喘。这项拟议的研究旨在调查怀孕期间暴露于交通排放的环境空气污染对儿童哮喘发病率的影响。我们将研究乔治亚州Kaiser Permanente的19,169对母婴出生队列，这是一个哮喘负担高的人群，目前正在作为美国环保局资助的清洁空气研究中心东南空气污染和流行病学中心(SCAPE)的一部分进行研究。使用SCAPE研究人员最近开发的一种新的贝叶斯分层方法，每天空间分辨的氮氧化物(NOx)、一氧化碳(CO)、直径2.5微米的颗粒物(PM2.5)和PM2.5元素碳的浓度将分配给每个母亲地址。校准的社区多尺度空气质量模式(CMAQ)在4公里网格分辨率下使用贝叶斯时空降尺度模型将网格分辨率缩小到250米，该模型结合了额外的细尺度交通排放数据、土地利用信息和气象学。统一的方法能够通过流行病学模型传播来自所有来源的暴露估计不确定性。使用队列中儿童的综合纵向病史，我们将评估出生前交通污染物浓度与哮喘事件的关系，分别为2、4、6和 8岁，包括仅限于哮喘病例的子分析，有证据表明每个随访年龄继续发病。我们还将在考虑到暴露窗口之间可能的协同效应的情况下，估计从产前到出生第一年的累积暴露的影响；估计与交通有关的多种污染物的联合影响；并深入评估个人水平和背景社会经济因素造成的混杂。我们在产前获得完整的母亲住所信息，将使我们能够表征怀孕期间住所流动的模式 一大批当代美国人，并估计这种流动性对暴露估计的影响。这些结果将与广泛的流行病学研究的设计和解释有关，这些流行病学研究依赖于出生时的居住地来分配怀孕期间空间变化的暴露。通过利用以前收集的健康数据和集成了多种空气质量信息来源的新型空气质量模型，我们将能够有效地调查研究问题，并提高我们对哮喘的可改变风险因素的理解。哮喘是儿童最常见的慢性疾病。

 DESCRIPTION (provided by applicant): There is growing evidence that the prenatal environment plays a role in the etiology of childhood asthma. Epidemiologic evidence suggests that children who develop asthma by school age already have 40% of their associated lung deficit at birth, and experimental studies in animals demonstrate that prenatal exposures to tobacco smoke, diesel exhaust, and other combustion-related particles can induce asthma in the offspring. The proposed research seeks to investigate the effect of exposure to ambient air pollution from traffic emissions during pregnancy on asthma incidence in childhood. We will study a well-characterized historical birth cohort of 19,169 mother-child pairs from Kaiser Permanente Georgia, a population with a high burden of asthma currently being studied as part of the Southeastern Center for Air Pollution and Epidemiology (SCAPE), an EPA-funded Clean Air Research Center. Daily spatially-resolved concentrations of nitrogen oxides (NOx), carbon monoxide (CO), particulate matter <2.5 micrometers in diameter (PM2.5), and PM2.5 elemental carbon will be assigned to each maternal address using a novel Bayesian hierarchical approach recently developed by SCAPE researchers. Calibrated Community Multi-scale Air Quality Model (CMAQ) simulations at 4 kilometer grid resolution are downscaled to 250 meter grid resolution using a Bayesian space- time downscaler model that incorporates additional fine-scale traffic emissions data, land-use information and meteorology. The unified approach enables the propagation of exposure estimation uncertainty from all sources through the epidemiologic models. Using comprehensive longitudinal medical histories on the children in the cohort we will assess prenatal concentrations of traffic pollutants in relation to incident asthma by 2, 4, 6, and 8 years of age, including subanalyses restricted to asthma cases with evidence of continued morbidity at each follow-up age. We will also estimate the effect of cumulative exposures for the prenatal period through the first year of life taking into account possible synergistic effects between exposure windows; estimate the joint effects of multiple traffic-related pollutants; and conduct an in-depth assessment of confounding by individual-level and contextual socioeconomic factors. Our access to complete maternal residence information during the prenatal period will allow us to characterize patterns of residential mobility during pregnancy for a large contemporary U.S. cohort and estimate impacts of this mobility on exposure estimation. These results will be relevant to the design and interpretation of a broad range of epidemiologic studies relying on residence location at the time of birth to assign spatially-varying exposures during pregnancy. By leveraging previously collected health data and novel air quality models that integrate multiple sources of air quality information we will be able to efficiently investigae the study questions and advance our understanding of modifiable risk factors for asthma, the most common chronic disease of childhood.