空气污染
环境卫生
概化理论
暴露评估
环境流行病学
圣杯
人口
相关性(法律)
公共卫生
环境规划
计算机科学
环境科学
医学
病理
化学
统计
数学
有机化学
万维网
政治学
法学
作者
Cole Brokamp,Eric B. Brandt,Patrick Ryan
标识
DOI:10.1016/j.jaci.2019.04.019
摘要
Epidemiologic studies have found air pollution to be causally linked to respiratory health including the exacerbation and development of childhood asthma. Accurately characterizing exposure is paramount in these studies to ensure valid estimates of health effects. Here, we provide a brief overview of the evolution of air pollution exposure assessment ranging from the use of ground-based, single-site air monitoring stations for population-level estimates to recent advances in spatiotemporal models, which use advanced machine learning algorithms and satellite-based data to accurately estimate individual-level daily exposures at high spatial resolutions. In addition, we review recent advances in sensor technology that enable the use of personal monitoring in epidemiologic studies, long-considered the "holy grail" of air pollution exposure assessment. Finally, we highlight key advantages and uses of each approach including the generalizability and public health relevance of air pollution models and the accuracy of personal monitors that are useful to guide personalized prevention strategies. Investigators and clinicians interested in the effects of air pollution on allergic disease and asthma should carefully consider the pros and cons of each approach to guide their application in research and practice.
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