Pérez-Hoyos S, Sáez Zafra M, Barceló M A, Cambra C, Figueiras Guzmán A, Ordóñez J M, Guillén Grima F, Ocaña R, Bellido J, Cirera Suárez L, López A A, Rodríguez V, Alcalá Nalvaiz T, Ballester Díez F
Institut Valencià d'Estudis en Salut Pública (IVESP).
Rev Esp Salud Publica. 1999 Mar-Apr;73(2):177-85.
The aim of this study is to Mortality show the protocol of analysis which was set out as part of the EMECAM Project, illustrating the application thereof to the effect of pollution has on the mortality in the city of Valencia. The response variables considered will be the daily deaths rate resulting from all causes, except external ones. The explicative variables are the daily series of different pollutants (black smoke, SO2, NO2, CO, O3). As possible confusion variables, weather factors, structural factors and weekly cases of flu are taken into account. A Poisson regression model is built up for each one of the four deaths series in two stages. In the first stage, a baseline model is fitted using the possible confusion variables. In the second stage, the pollution variables or the time legs thereof are included, controlling the residual autocorrelation by including mortality time lags. The process of fitting the baseline model is as follows: 1) Include the significant sinusoidal terms up to the sixth order. 2) Include the significant temperature or temperature squared terms with the time lags thereof up to the 7th order. 3) Repeat this process with the relative humidity. 4) Add in the significant terms of calendar years, daily tendency and tendency squared. 5) The days of the week as dummy variables are always included in the model. 6) Include the holidays and the significant time lags of up to two weeks of flu. Following the reassessment of the model, each one of the pollutants and the time lags thereof up to the fifth order are proven out. The impact is analyzed by six-month periods, including interaction terms.
本研究的目的是展示作为EMECAM项目一部分所制定的分析方案,说明其在污染对瓦伦西亚市死亡率影响方面的应用。所考虑的响应变量将是除外部原因外所有原因导致的每日死亡率。解释变量是不同污染物(黑烟、二氧化硫、二氧化氮、一氧化碳、臭氧)的每日序列。作为可能的混淆变量,考虑了天气因素、结构因素和每周流感病例。分两个阶段为四个死亡序列中的每一个建立泊松回归模型。在第一阶段,使用可能的混淆变量拟合一个基线模型。在第二阶段,纳入污染变量或其时间滞后项,通过纳入死亡率时间滞后项来控制残差自相关。拟合基线模型的过程如下:1)纳入直至六阶的显著正弦项。2)纳入显著的温度或温度平方项及其直至七阶的时间滞后项。3)对相对湿度重复此过程。4)添加历年、每日趋势和趋势平方的显著项。5)模型中始终纳入作为虚拟变量的一周中的天数。6)纳入节假日和长达两周的流感的显著时间滞后项。在对模型进行重新评估之后,对每种污染物及其直至五阶的时间滞后项进行检验。通过包括交互项的六个月时间段分析影响。