Schaeffer Laura, de Crouy-Chanel Perrine, Wagner Vérène, Desplat Julien, Pascal Mathilde
Environmental Health Department, Institut de Veille Sanitaire (French Institute for Public Health Surveillance), Saint-Maurice, France.
Ile-de-France Interregional Centre, Météo-France, Paris, France.
Int J Biometeorol. 2016 Jan;60(1):73-83. doi: 10.1007/s00484-015-1006-x. Epub 2015 May 15.
Time series studies assessing the effect of temperature on mortality generally use temperatures measured by a single weather station. In the Paris region, there is a substantial measurement network, and a variety of exposure indicators created from multiple stations can be tested. The aim of this study is to test the influence of exposure indicators on the temperature-mortality relationship in the Paris region. The relationship between temperature and non-accidental mortality was assessed based on a time series analysis using Poisson regression and a generalised additive model. Twenty-five stations in Paris and its three neighbouring departments were used to create four exposure indicators. These indicators were (1) the temperature recorded by one reference station, (2) a simple average of the temperatures of all stations, (3) an average weighted on the departmental population and (4) a classification of the stations based on land use and an average weighted on the population in each class. The relative risks and the Akaike criteria were similar for all the exposure indicators. The estimated temperature-mortality relationship therefore did not appear to be significantly affected by the indicator used, regardless of study zone (departments or region) or age group. The increase in temperatures from the 90(th) to the 99(th) percentile of the temperature distribution led to a significant increase in mortality over 75 years (RR = 1.10 [95% CI, 1.07; 1.14]). Conversely, the decrease in temperature between the 10(th) and 1(st) percentile had a significant effect on the mortality under 75 years (RR = 1.04 [95% CI, 1.01; 1.06]). In the Paris area, there is no added value in taking multiple climatic stations into account when estimating exposure in time series studies. Methods to better represent the subtle temperature variations in densely populated areas in epidemiological studies are needed.
评估温度对死亡率影响的时间序列研究通常使用单个气象站测量的温度。在巴黎地区，有一个庞大的测量网络，可以测试从多个站点创建的各种暴露指标。本研究的目的是测试暴露指标对巴黎地区温度 - 死亡率关系的影响。基于使用泊松回归和广义相加模型的时间序列分析评估了温度与非意外死亡率之间的关系。使用巴黎及其三个相邻部门的25个站点创建了四个暴露指标。这些指标分别是：（1）一个参考站记录的温度；（2）所有站点温度的简单平均值；（3）按部门人口加权的平均值；（4）根据土地利用对站点进行分类并对每个类别中的人口进行加权平均。所有暴露指标的相对风险和赤池准则相似。因此，无论研究区域（部门或地区）或年龄组如何，估计的温度 - 死亡率关系似乎并未受到所使用指标的显著影响。温度分布从第90百分位数增加到第99百分位数导致75岁以上人群的死亡率显著增加（RR = 1.10 [95% CI，1.07；1.14]）。相反，温度从第10百分位数下降到第1百分位数对75岁以下人群的死亡率有显著影响（RR = 1.04 [95% CI，1.01；1.06]）。在巴黎地区，在时间序列研究中估计暴露时考虑多个气象站并没有额外价值。需要在流行病学研究中更好地表示人口密集地区细微温度变化的方法。
