Bateson Thomas F, Coull Brent A, Hubbell Bryan, Ito Kazuhiko, Jerrett Michael, Lumley Thomas, Thomas Duncan, Vedal Sverre, Ross Mary
National Center for Environmental Assessment, US Environmental Protection Agency, Washington, District of Columbia 20460, USA.
J Expo Sci Environ Epidemiol. 2007 Dec;17 Suppl 2:S90-6. doi: 10.1038/sj.jes.7500631.
The Clean Air Act mandates that the US Environmental Protection Agency (EPA) develop National Ambient Air Quality Standards for criteria air pollutants and conduct periodic reviews of the standards based on new scientific evidence. In recent reviews, evidence from epidemiologic studies has played a key role. Epidemiologic studies often provide evidence for effects of several air pollutants. Determining whether there are independent effects of the separate pollutants is a challenge. Among the many issues confronting the interpretation of epidemiologic studies of multi-pollutant exposures and health effects are those specifically related to statistical modeling. The EPA convened a workshop on 13 and 14 December 2006 in Chapel Hill, North Carolina, USA, to discuss these and other issues; Session Three of the workshop was devoted specifically to statistical modeling. Prominent statistical modeling issues in epidemiologic studies of air pollution include (1) measurement error across the co-pollutants; (2) correlation and multi-collinearity among the co-pollutants; (3) the timing of the concentration-response function; (4) confounding; and (5) spatial analyses.
《清洁空气法》规定，美国环境保护局（EPA）要制定针对空气污染物的国家环境空气质量标准，并根据新的科学证据定期对这些标准进行审查。在最近的审查中，流行病学研究的证据发挥了关键作用。流行病学研究常常能提供多种空气污染物影响的证据。确定单一污染物是否具有独立影响是一项挑战。在解释多污染物暴露与健康影响的流行病学研究时面临的诸多问题中，有一些是与统计建模特别相关的。美国环境保护局于2006年12月13日至14日在美国北卡罗来纳州教堂山召开了一次研讨会，讨论这些及其他问题；研讨会的第三场会议专门讨论了统计建模。空气污染流行病学研究中突出的统计建模问题包括：（1）共同污染物的测量误差；（2）共同污染物之间的相关性和多重共线性；（3）浓度-反应函数的时间；（4）混杂因素；以及（5）空间分析。
