评价空间分辨的森林火灾烟雾模型用于基于人群的流行病暴露的评估
2016/07/28
摘要
置身于森林火灾烟雾（FFS）与多种不良反应有关，主要是呼吸。对心血管的影响的研究结果不一致，可能与评估FFS暴露系统的传统方法的局限性有关。在以往的工作中，我们为加拿大的不列颠哥伦比亚（BC）的人口密集的地区开发了一个实证模型来评估烟雾相关的PM2.5。本文，我们通过模拟和测量的PM2.5之间的流行病关系来评估我们模型的应用情况。每个地方卫生区（LHA），我们使用泊松回归来评估PM2.5 对药物应用和门诊就医次数的评估和测量的影响。 然后，我们使用元回归估计整体效果。模型PM2.5 一个10 μg/m(3)的增加与沙丁胺醇应用（RR = 1.04，95% CI 1.03-1.06）的增加、哮喘（1.06，1.04-1.08）、慢性阻塞性肺疾病（1.02，1.00-1.03）、下呼吸道感染（1.03，1.00-1.05）和中耳炎（1.05，1.03-1.07）就诊的增加等有关。所有的增加都可与测试的PM2.5相当。对心血管结局的影响只在极端火天在所有的地方卫生区使用模型评估时是显著的。这表明，曝露模型通过改进的空间覆盖范围和分辨率，对提高流行病学研究能力来检测森林火灾烟雾（FFS）对健康的影响是一种潜在工具。
（杨冬 审校）
J Expo Sci Environ Epidemiol. 2016 May;26(3):233-40. doi: 10.1038/jes.2014.67. Epub 2014 Oct 8.
Evaluation of a spatially resolved forest fire smoke model for population-based epidemiologic exposure assessment.
Yao J1, Eyamie J2, Henderson SB1,3.
Author information
Abstract
Exposure to forest fire smoke (FFS) is associated with multiple adverse health effects, mostly respiratory. Findings for cardiovascular effects have been inconsistent, possibly related to the limitations of conventional methods to assess FFS exposure. In previous work, we developed an empirical model to estimate smoke-related fine particulate matter (PM2.5) for all populated areas in British Columbia (BC), Canada. Here, we evaluate the utility of our model by comparing epidemiologic associations between modeled and measured PM2.5. For each local health area (LHA), we used Poisson regression to estimate the effects of PM2.5 estimates and measurements on counts of medication dispensations and outpatient physician visits. We then used meta-regression to estimate the overall effects. A 10 μg/m(3) increase in modeled PM2.5 was associated with increased sabutamol dispensations (RR=1.04, 95% CI 1.03-1.06), and physician visits for asthma (1.06, 1.04-1.08), COPD (1.02, 1.00-1.03), lower respiratory infections (1.03, 1.00-1.05), and otitis media (1.05, 1.03-1.07), all comparable to measured PM2.5. Effects on cardiovascular outcomes were only significant using model estimates in all LHAs during extreme fire days. This suggests that the exposure model is a promising tool for increasing the power of epidemiologic studies to detect the health effects of FFS via improved spatial coverage and resolution.
J Expo Sci Environ Epidemiol. 2016 May;26(3):233-40. doi: 10.1038/jes.2014.67. Epub 2014 Oct 8.
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