Ito Kazuhiko, Thurston George D, Silverman Robert A
NYU School of Medicine, Nelson Institute of Environmental Medicine, Tuxedo, New York 10987, USA.
J Expo Sci Environ Epidemiol. 2007 Dec;17 Suppl 2:S45-60. doi: 10.1038/sj.jes.7500627.
Associations of particulate matter (PM) and ozone with morbidity and mortality have been reported in many recent observational epidemiology studies. These studies often considered other gaseous co-pollutants also as potential confounders, including nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO). However, because each of these air pollutants can have different seasonal patterns and chemical interactions, the estimation and interpretation of each pollutant's individual risk estimates may not be straightforward. Multi-collinearity among the air pollution and weather variables also leaves the possibility of confounding and over- or under-fitting of meteorological variables, thereby potentially influencing the health effect estimates for the various pollutants in differing ways. To investigate these issues, we examined the temporal relationships among air pollution and weather variables in the context of air pollution health effects models. We compiled daily data for PM less than 2.5 mum (PM2.5), ozone, NO2, SO2, CO, temperature, dew point, relative humidity, wind speed, and barometric pressure for New York City for the years 1999-2002. We conducted several sets of analyses to characterize air pollution and weather data interactions, to assess different aspects of these data issues: (1) spatial/temporal variation of PM2.5 and gaseous pollutants measured at multiple monitors; (2) temporal relationships among air pollution and weather variables; and (3) extent and nature of multi-collinearity of air pollution and weather variables in the context of health effects models. The air pollution variables showed a varying extent of intercorrelations with each other and with weather variables, and these correlations also varied across seasons. For example, NO2 exhibited the strongest negative correlation with wind speed among the pollutants considered, while ozone's correlation with PM2.5 changed signs across the seasons (positive in summer and negative in winter). The extent of multi-collinearity problems also varied across pollutants and choice of health effects models commonly used in the literature. These results indicate that the health effects regression need to be run by season for some pollutants to provide the most meaningful results. We also find that model choice and interpretation needs to take into consideration the varying pollutant concurvities with the model co-variables in each pollutant's health effects model specification. Finally, we provide an example for analysis of associations between these air pollutants and asthma emergency department visits in New York City, which evaluate the relationship between the various pollutants' risk estimates and their respective concurvities, and discuss the limitations that these results imply about the interpretability of multi-pollutant health effects models.
近期许多观察性流行病学研究报告了颗粒物（PM）和臭氧与发病率及死亡率之间的关联。这些研究通常也将其他气态共污染物视为潜在混杂因素，包括二氧化氮（NO₂）、二氧化硫（SO₂）和一氧化碳（CO）。然而，由于这些空气污染物中的每一种都可能具有不同的季节模式和化学相互作用，因此对每种污染物个体风险估计值的估算和解读可能并非易事。空气污染和气象变量之间的多重共线性也使得存在气象变量被混淆以及过度拟合或拟合不足的可能性，从而可能以不同方式影响对各种污染物的健康效应估计。为了研究这些问题，我们在空气污染健康效应模型的背景下考察了空气污染和气象变量之间的时间关系。我们汇总了1999 - 2002年纽约市细颗粒物（PM2.5）、臭氧、NO₂、SO₂、CO、温度、露点、相对湿度、风速和气压的每日数据。我们进行了几组分析来描述空气污染和气象数据的相互作用，以评估这些数据问题的不同方面：（1）多个监测点测量的PM2.5和气态污染物的空间/时间变化；（2）空气污染和气象变量之间的时间关系；（3）在健康效应模型背景下空气污染和气象变量多重共线性的程度和性质。空气污染变量之间以及与气象变量之间呈现出不同程度的相互关联，并且这些关联在不同季节也有所变化。例如，在所考虑的污染物中，NO₂与风速呈现出最强的负相关，而臭氧与PM2.5的相关性在不同季节会改变符号（夏季为正，冬季为负）。多重共线性问题的程度在不同污染物以及文献中常用的健康效应模型选择之间也有所不同。这些结果表明，对于某些污染物，健康效应回归需要按季节进行，以提供最有意义的结果。我们还发现，在每种污染物的健康效应模型设定中，模型选择和解读需要考虑污染物与模型协变量之间不同的共曲线性。最后，我们提供了一个分析纽约市这些空气污染物与哮喘急诊就诊之间关联的示例，该示例评估了各种污染物风险估计值与其各自共曲线性之间的关系，并讨论了这些结果对多污染物健康效应模型可解释性所暗示的局限性。
