Short term effects of air pollution on health: A European approach using epidemiologic time series data: The APHEA protocol
Short term effects of air pollution on health: A European approach using epidemiologic time series data: The APHEA protocol
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DOI：
10.1136/jech.50.suppl_1.s12
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发表时间：
1996-04-01
影响因子：
6.3
通讯作者：
Anderson, HR
中科院分区：
文献类型：
--
作者：
Katsouyanni, K;Schwartz, J;Anderson, HR
背景和目的-过去五年的几项研究结果表明，欧洲和北美目前的污染物水平对健康有短期的不利影响。APHEA项目旨在使用标准化方法量化欧洲的这些。设计-收集了几种空气污染物（二氧化硫;颗粒物，测量为总颗粒或空气动力学直径小于某一临界值的颗粒部分，或黑烟;二氧化氮;臭氧）和健康结果（死亡和急诊入院的总数和具体原因）的每日时间序列数据。数据包括满足质量标准所设定的APHEA protocol.Setting -15个欧洲城市从10个不同的国家，总人口超过25 million.Methodology -APHEA协作组决定了一个具体的方法程序，以控制混杂效应和评估的假设。与此同时，也有充分的灵活性，可以考虑到当地的特点。该程序包括模拟所有潜在的混淆因素（即季节和长期模式，气象因素，星期几，假期和其他不寻常的事件），选择“最佳”的空气污染模型，并应用诊断工具来检查模型的适当性。最后的分析使用自回归泊松模型，允许过度分散。影响报告为相对风险，与相应污染物水平的确定增加相对照。每个参与组适用于自己的data.Conclusions分析-这种方法使许多不同的欧洲设置的结果被集体考虑。在使用最初并非为流行病学研究目的收集的汇总时间序列数据时，这是可行性、可比性和地方适应性之间的现有最佳折衷办法。
Background and objectives - Results from several studies over the past five years have shown that the current levels of pollutants in Europe and North America have adverse shortterm effects on health. The APHEA project aims to quantifying these in Europe, using standardised methodology. The project protocol and analytical methodology are presented here.Design - Daily time series data were gathered for several air pollutants (sulphur dioxide; particulate matter, measured as total particles or as the particle fraction with an aerodynamic diameter smaller than a certain cut off, or as black smoke; nitrogen dioxide; and ozone) and health outcomes (the total and cause specific number of deaths and emergency hospital admissions). The data included fulfilled the quality criteria set by the APHEA protocol.Setting - Fifteen European cities from 10 different countries with a total population over 25 million.Methodology - The APHEA collaborative group decided on a specific methodological procedure to control for confounding effects and evaluate the hypothesis. At the same time there was sufficient flexibility to allow local characteristics to be taken into account. The procedure included modelling of all potential confounding factors (that is, seasonal and long term patterns, meteorological factors, day of the week, holidays, and other unusual events), choosing the ''best'' air pollution models, and applying diagnostic tools to check the adequacy of the models. The final analysis used autoregressive Poisson models allowing for overdispersion. Effects were reported as relative risks contrasting defined increases in the corresponding pollutant levels. Each participating group applied the analyses to their own data.Conclusions - This methodology enabled results from many different European settings to be considered collectively. It represented the best available compromise between feasibility, comparability, and local adaptibility when using aggregated time series data not originally collected for the purpose of epidemiological studies.