An investigation of distributed lag models in the context of air pollution and mortality tune series analysis
An investigation of distributed lag models in the context of air pollution and mortality tune series analysis
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DOI：
10.1080/10473289.2005.10464620
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发表时间：
2005-03-01
影响因子：
2.7
通讯作者：
Roberts, S
中科院分区：
文献类型：
--
作者：
Roberts, S
在颗粒物空气污染死亡率时间序列研究中，使用的颗粒物空气污染暴露测量通常是当天或前一天的空气污染浓度或多日移动平均空气污染浓度。分布式滞后模型 (DLM) 允许分布在多天内的不同空气污染影响，被视为比使用单日或多日移动平均空气污染暴露测量方法的改进。然而，目前，DLM 作为空气污染暴露衡量指标的统计特性尚未得到研究。本文通过模拟研究研究了 DLM 作为空气污染暴露衡量指标的性能，并与各种形式下的单日和多日移动平均空气污染暴露衡量指标进行比较，以了解空气污染对死亡率的真实影响。模拟研究表明，与单日或多日移动平均空气污染暴露指标相比，DLM 可以更可靠地衡量空气污染对死亡率的影响，并避免出现较大负偏差的可能性。这是重要信息。在美国许多城市，颗粒物空气污染浓度每六天才观测一次，这意味着通常只能使用单日颗粒物空气污染暴露措施。本文的结果将有助于量化使用单日暴露测量可能导致的负面偏差的程度。讨论了这项工作对未来空气污染死亡率时间序列研究的影响。本文使用的数据是 1987 年至 1994 年期间伊利诺伊州库克县死亡率、天气和颗粒物空气污染的每日同步时间序列。
In particulate air pollution mortality time series studies, the particulate air pollution exposure measure used is typically the current day's or the previous day's air pollution concentration or a multi-day moving average air pollution concentration. Distributed lag models (DLMs) that allow for differential air pollution effects that are spread over multiple days are seen as an improvement over using a single- or multi-day moving average air pollution exposure measure. However, at the current time, the statistical properties of DLMs as a measure of air pollution exposure have not been investigated. In this paper, a simulation study is used to investigate the performance of DLMs as a measure of air pollution exposure in comparison with single- and multi-day moving average air pollution exposure measures under various forms for the true effect of air pollution on mortality. The simulation study shows that DLMs offer a more robust measure of the effect of air pollution on mortality and avoid the potential for a large negative bias compared with singleor multi-day moving average air pollution exposure measures. This is important information. In many U.S. cities, particulate air pollution concentrations are observed only once every six days, meaning it is often only possible to use single-day particulate air pollution exposure measures. The results from this paper will help quantify the magnitude of the negative bias that can result from using single-day exposure measures. The implications of this work for future air pollution mortality time series studiesare discussed. The data used in this paper are concurrent daily time series of mortality, weather, and particulate air pollution from Cook County, IL, for the period 1987-1994.