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[病例交叉设计对空气污染短期健康影响分析的贡献:空气污染与健康数据的重新分析]

[Contribution of case-crossover design to the analysis of short-term health effects of air pollution: reanalysis of air pollution and health data].

作者信息

Chardon B, Host S, Pedrono G, Gremy I

机构信息

ORS Ile-de-France, 21-23 rue Miollis, Paris cedex 15, France.

出版信息

Rev Epidemiol Sante Publique. 2008 Feb;56(1):31-40. doi: 10.1016/j.respe.2007.11.002. Epub 2008 Feb 8.

Abstract

BACKGROUND

During the last decades, numerous studies have shown significant links between short-term exposure to air pollution and health. Time series design have been widely used in order to study these associations. In recent years, the case-crossover design has been applied to the analysis of acute effects of environmental exposures, especially air pollution. The aims of this paper are to describe the case-crossover design and to compare this approach with time series design to assess the association between air pollution and health.

METHODS

In the case-crossover approach, a case-control study is conducted whereby each person who had a health event (case period) is matched with himself/herself on a nearby time period where he/she did not have the event (control period). Review of the literature shows that the referent selection strategies can be associated to a bias in the estimation of the health effect of air pollution. In comparison with time series design, the case-crossover design is easier to conduct, and individual factors can be taken into account. Nevertheless, it is not possible to take into account the overdispersion in the health indicator with this approach.

RESULTS AND CONCLUSION

In conclusion, we suggest to use time series analysis with population data and case-crossover design with individual data.

摘要

背景

在过去几十年中,大量研究表明短期接触空气污染与健康之间存在显著关联。时间序列设计已被广泛用于研究这些关联。近年来,病例交叉设计已应用于环境暴露尤其是空气污染的急性效应分析。本文的目的是描述病例交叉设计,并将该方法与时间序列设计进行比较,以评估空气污染与健康之间的关联。

方法

在病例交叉方法中,进行一项病例对照研究,即每个发生健康事件的人(病例期)在附近其未发生该事件的时间段(对照期)与自身进行匹配。文献综述表明,参照选择策略可能与空气污染对健康影响估计中的偏差相关。与时间序列设计相比,病例交叉设计更易于实施,并且可以考虑个体因素。然而,使用这种方法无法考虑健康指标中的过度离散情况。

结果与结论

总之,我们建议使用基于人群数据的时间序列分析和基于个体数据的病例交叉设计。

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