Analysis of health outcome time series data in epidemiological studies
Analysis of health outcome time series data in epidemiological studies
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DOI：
10.1002/env.623
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发表时间：
2004-03-01
期刊：
影响因子：
1.7
通讯作者：
Katsouyanni, K
中科院分区：
文献类型：
--
作者：
Touloumi, G;Atkinson, R;Katsouyanni, K
最近的几项研究报告了空气污染对健康的重大影响，即使是在空气污染物水平较低的情况下。这些研究因统计方法和城市之间结果的不一致而受到批评。空气污染流行病学的一个重要进展来自多中心研究。在APHEA-2项目中，我们开发了一种统计方法，使用来自欧洲30个城市的数据评估空气污染对健康的短期影响。分析采用分层建模方法，分两个阶段实施：（a）使用广义加性泊松回归模型分别分析每个城市的数据，以考虑局部差异;（B）根据城市特定协变量对城市特定效应估计值进行回归，以获得总体估计值并探索城市间异质性。为了说明我们的方法，我们目前的结果为PM10的影响。结果发现，PM10或NO2浓度每增加10 mug/m3，总死亡率分别增加0.67%（95%CI：0.50 ~ 0.90）和0.33%（0.20 ~ 0.40）。经过相互调整后，PM10的影响减少了40%，NO2的影响减少了20%，但两个合并估计值仍然显着。长期平均NO2浓度作为PM10效应的效应修正剂，即使在NO2混杂效应调整后。在第二阶段，我们探索了两种不同的模型，用于结合调整后的NO2，PM10对城市的影响：双变量，它解释了PM10和NO2的城市内相关性;单变量，它忽略了这种相关性。两种模型给出了大致相同的结果。版权所有（C）2004约翰威利父子有限公司。
Several recent studies have reported significant health effects of air pollution even at low levels of air pollutants. These studies have been criticized for the statistical methods and for inconsistency in results between cities. An important development in air pollution epidemiology has come from multicenter studies. Within the APHEA-2 project we have developed a statistical methodology to evaluate short-term health effects of air pollution using data from 30 cities across Europe. For the analysis, a hierarchical modelling approach was adopted and implemented in two stages: (a) data from each city were analyzed separately to allow for local differences, using generalized additive Poisson regression models; (b) city-specific effects estimates were regressed on city-specific covariates to obtain an overall estimate and to explore heterogeneity across cities. In order to illustrate our methodology we present results for PM10 effects. It was found that a 10 mug/m(3) increase in PM10 or NO2 concentrations is associated with a 0.67% (95% Cl: 0.50 to 0.90) and 0.33% (0.20 to 0.40) increase in total mortality, respectively. After mutual adjustment, the PM10 effect was reduced by 40% and that of NO2 by 20%, but both pooled estimates remained significant. Long-term mean NO2 concentrations act as an effect modifier for PM10 effects, even after adjustment for NO2 confounding effects. In the second stage we explored two different models for combining the adjusted for NO2, PM10 effects across cities: bivariate, which accounts for within-city correlation of PM10 and NO2; and univariate, which ignores this correlation. Both models gave broadly the same results. Copyright (C) 2004 John Wiley Sons, Ltd.