Historic Traffic Exposure Maps for Cancer Studies

描述(由申请人提供)： 接触交通废气在城市地区很常见，其组成部分 在流行病学中被发现与肺癌和白血病有关 学习。确定这些关联在本质上是否具有因果关系 尤其受到历史个人层面的缺乏的限制 曝光数据。最近，有人呼吁发展曝光率 使用地理信息显示环境空气污染模拟浓度的地图 信息系统(地理信息系统)，以调查长期健康影响。一项研究 如果建模得当，受试者的住所可以很好地预测接触情况 空气污染水平是在适当的空间尺度上实现的。通常， 对空气污染的模拟已经在区域(空气流域)或 本地化级别。区域方法导致粗略的曝光图，这 缺乏流行病学研究所需的空间分辨率，并可能导致 在曝光错误分类中。局部化模型实现了一个合适的空间 可扩展，但需要复杂的数据输入，并且数据密集度太高，无法绘制地图 在大范围内暴露。我们之前已经开发了地理信息系统流量暴露 适当比例的地图，用于流行病学研究，说明 风的特性和特定污染物的扩散行为。在这 研究中，我们建议通过考虑表面来进一步发展这一模型 利用地理信息系统进行土地利用层次的景观纹理分析。我们还建议 评估和验证此模型以及其他两个内部开发的模型和四个 加州一个有NO2的县的外部交通尾气暴露模型 使用被动扩散管进行现场测量。我们将比较 从每个模型到每个模型的实际NO2浓度的预测暴露水平 监控地点。我们将评估由于暴露而产生的偏倚的量 通过对肺癌地址进行地理编码来对每个模型进行错误分类 来自加州癌症登记处的病例和随机对照系列和 比较NO2的预测值和观测值。最后，我们会 开发一系列历史交通尾气暴露地图，利用最佳 使用交通计数、土地使用、 气象学、点源和环境空气监测数据。由此产生的这些 暴露图可以应用于现有的研究对象队列来分配 以前的暴露将极大地减少时间和成本 关于交通尾气暴露和癌症风险的昂贵的前瞻性研究。

DESCRIPTION (provided by applicant): Exposure to traffic exhaust is common in urban areas and its components have been found to be associated with lung cancer and leukemia in epidemiological studies. Determining whether or not these associations are causal in nature has been especially limited by the lack of historical individual-level exposure data. Recently, there have been calls for the development of exposure maps showing modeled concentrations of ambient air pollution using geographic information systems (GIS) to investigate long-term health effects. A study subject's residence can be a good predictor of exposure if suitably modeled levels of air pollution are achieved at a proper spatial scale. Typically, modeling of air pollution has been done either at a regional (air basin) or localized level. The regional approach results in coarse exposure maps, which lack the spatial resolution needed for epidemiological studies and can result in exposure misclassification. The localized models achieve a proper spatial scale but require complex data inputs and are too data intensive for mapping exposure over wide areas. We have previously developed GIS traffic exposure maps at an appropriate scale for epidemiological studies that account for properties of wind and dispersal behavior of specific pollutants. In this study, we propose to further develop this model by accounting for the surface texture of the landscape using GIS land use layers. We also propose to evaluate and validate this and two other in-house developed models and four external traffic exhaust exposure models in one California county with NO2 field measurements using passive diffusion tubes. We will compare the predicted exposure level from each model to actual NO2 concentrations at each monitored location. We will assess the amount of bias due to exposure misclassification for each model by geocoding the addresses of lung cancer cases from the California Cancer Registry and a random control series and comparing the predicted and observed NO2 measurements. Finally, we will develop a series of historic traffic exhaust exposure maps using the best evaluated traffic model using retrospective data on traffic counts, land use, meteorology, point sources, and ambient air monitoring data. These resulting exposure maps could be applied to existing cohorts of study subjects to assign previous exposures which would dramatically reduce the time and cost of expensive prospective studies of traffic exhaust exposure and cancer risk.