Roberts Steven, Martin Michael
School of Finance and Applied Statistics, College of Business and Economics, Australian National University, Canberra, Australian Capital Territory, Australia.
J Expo Sci Environ Epidemiol. 2008 Jan;18(1):88-94. doi: 10.1038/sj.jes.7500597. Epub 2007 Aug 8.
In time-series studies on the effect of particulate matter (PM) air pollution on an adverse health outcome, PM time-series data are often available from multiple monitoring stations. Published studies have combined the data from the multiple monitors using a simple or trimmed average. We investigate an alternative method of combining the data available from multiple PM-monitoring sites. This method uses time-series data to assign each PM monitor a weight. The weights are then used to combine the data from the multiple PM monitors into a single air pollution time series. The resulting model will identify important monitors for describing the relationship between PM and the adverse health outcome of interest. Subsequent investigations of why certain monitors are more informative than others may provide valuable information concerning the location of vulnerable subpopulations or locations where the meteorological and/or land-use conditions are better for assessing population exposure to PM. The new model is illustrated by applying it to actual data from Cook County, IL, USA and through a simulation study. Using the new model, for the Cook County data, it was found that two of the six monitors provided essentially as much information about the effect of PM on mortality as all six monitors combined. The simulation study suggests that the weights assigned to each monitor by the new model are appropriate, that is, that the model assigns the largest weight to the monitor most highly correlated with the underlying PM time series used to generate mortality.
在关于颗粒物（PM）空气污染对不良健康结局影响的时间序列研究中，PM时间序列数据通常可从多个监测站获取。已发表的研究使用简单平均或截尾平均来合并多个监测器的数据。我们研究了一种合并多个PM监测站点可用数据的替代方法。该方法使用时间序列数据为每个PM监测器分配一个权重。然后使用这些权重将来自多个PM监测器的数据合并为一个单一的空气污染时间序列。所得模型将识别出对于描述PM与感兴趣的不良健康结局之间关系至关重要的监测器。随后对某些监测器为何比其他监测器更具信息价值的调查，可能会提供有关脆弱亚人群位置或气象和/或土地利用条件更有利于评估人群PM暴露的位置的宝贵信息。通过将新模型应用于美国伊利诺伊州库克县的实际数据以及通过模拟研究对新模型进行了说明。对于库克县的数据，使用新模型发现，六个监测器中的两个提供的关于PM对死亡率影响的信息基本上与所有六个监测器合并提供的信息一样多。模拟研究表明，新模型为每个监测器分配的权重是合适的，也就是说，该模型将最大权重分配给与用于生成死亡率的基础PM时间序列相关性最高的监测器。
