Spatial misalignment in time series studies of air pollution and health data
Spatial misalignment in time series studies of air pollution and health data
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DOI：
10.1093/biostatistics/kxq017
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发表时间：
2010-10-01
期刊：
影响因子：
2.1
通讯作者：
Bell, Michelle L.
中科院分区：
文献类型：
--
作者：
Peng, Roger D.;Bell, Michelle L.
环境暴露的时间序列研究通常涉及将在空间某一点测量的有毒物质的每日变化与健康综合指标的每日变化进行比较。暴露变量和反应变量的空间错位会使健康风险的估计产生偏差，这种偏差的大小取决于相关暴露的空间变化。在空气污染流行病学中，人们越来越关注估算颗粒物（PM）化学成分对健康的影响。这一新关注点引发的一个问题是，许多PM成分缺乏空间同质性所导致的空间错位误差。目前通过时间序列建模估算短期健康风险的方法没有考虑化学成分的空间特性，因此可能导致对这些风险的估计有偏差。我们提出了一个时空统计模型，用于量化空间错位误差，并展示如何使用回归校准方法和两阶段贝叶斯模型获得经调整的健康风险估计值。我们将我们的方法应用于一个包含美国20个大城市县的住院信息、空气污染和天气信息的数据库。
Time series studies of environmental exposures often involve comparing daily changes in a toxicant measured at a point in space with daily changes in an aggregate measure of health. Spatial misalignment of the exposure and response variables can bias the estimation of health risk, and the magnitude of this bias depends on the spatial variation of the exposure of interest. In air pollution epidemiology, there is an increasing focus on estimating the health effects of the chemical components of particulate matter (PM). One issue that is raised by this new focus is the spatial misalignment error introduced by the lack of spatial homogeneity in many of the PM components. Current approaches to estimating short-term health risks via time series modeling do not take into account the spatial properties of the chemical components and therefore could result in biased estimation of those risks. We present a spatial-temporal statistical model for quantifying spatial misalignment error and show how adjusted health risk estimates can be obtained using a regression calibration approach and a 2-stage Bayesian model. We apply our methods to a database containing information on hospital admissions, air pollution, and weather for 20 large urban counties in the United States.