Tolbert Paige E, Klein Mitchel, Peel Jennifer L, Sarnat Stefanie E, Sarnat Jeremy A
Rollins School of Public Health, Department of Environmental and Occupational Health, Emory University, Atlanta, Georgia 30322, USA.
J Expo Sci Environ Epidemiol. 2007 Dec;17 Suppl 2:S29-35. doi: 10.1038/sj.jes.7500625.
Multipollutant models are frequently used to differentiate roles of multiple pollutants in epidemiologic studies of ambient air pollution. In the presence of differing levels of measurement error across pollutants under consideration, however, they can be biased and as misleading as single-pollutant models. Their appropriate interpretation depends on the relationships among the pollutant measurements and the outcomes in question. In situations where two or more pollutant variables may be acting as surrogates for the etiologic agent(s), multipollutant models can help identify the best surrogate, but the risk estimates may be influenced by inclusion of a second variable that is not itself an independent risk factor for the outcome in question. In this paper, these issues will be illustrated in the context of an ongoing study of emergency visits in Atlanta. Emergency department visits from 41 of 42 hospitals serving the 20-county Atlanta metropolitan area for the period 1993-2004 (n=10,206,389 visits) were studied in relation to ambient pollutant levels, including speciated particle measurements from an intensive monitoring campaign at a downtown station starting in 1998. Relative to our earlier publications, reporting results through 2000, the period for which the speciated data are available is now tripled (6 years in length). Poisson generalized linear models were used to examine outcome counts in relation to 3-day moving average concentrations of pollutants of a priori interest (ozone, nitrogen dioxide, carbon monoxide, sulfur dioxide, oxygenated hydrocarbons, PM10, coarse PM, PM2.5, and the following components of PM2.5: elemental carbon, organic carbon, sulfate, and water-soluble transition metals). In the present analysis, we report results for two outcome groups: a respiratory outcomes group and a cardiovascular outcomes group. For cardiovascular visits, associations were observed with CO, NO2, and PM2.5 elemental carbon and organic carbon. In multipollutant models, CO was the strongest predictor. For respiratory visits, associations were observed with ozone, PM10, CO, and NO2 in single-pollutant models. In multipollutant models, PM10 and ozone persisted as predictors, with ozone the stronger predictor. Caveats and considerations in interpreting the multipollutant model results are discussed.
多污染物模型常用于在环境空气污染的流行病学研究中区分多种污染物的作用。然而，在所考虑的污染物存在不同程度测量误差的情况下，它们可能会产生偏差，并且与单污染物模型一样具有误导性。其恰当的解释取决于污染物测量值与所讨论结果之间的关系。在两种或更多污染物变量可能作为病因替代物的情况下，多污染物模型有助于识别最佳替代物，但风险估计可能会受到纳入第二个并非所讨论结果独立风险因素的变量的影响。在本文中，这些问题将在亚特兰大正在进行的急诊就诊研究背景下进行阐述。研究了1993 - 2004年期间为亚特兰大大都市20个县服务的42家医院中的41家医院的急诊就诊情况（n = 10,206,389次就诊）与环境污染物水平的关系，包括自1998年起在市中心站点进行的强化监测活动中特定颗粒物的测量。与我们之前截至2000年发表的报告结果相比，现有特定数据的时间段现在延长了两倍（长达6年）。使用泊松广义线性模型来检查结果计数与先验关注的污染物3天移动平均浓度（臭氧、二氧化氮、一氧化碳、二氧化硫、含氧烃、PM10、粗颗粒物、PM2.5以及PM2.5的以下成分：元素碳、有机碳、硫酸盐和水溶性过渡金属）之间的关系。在当前分析中，我们报告了两个结果组的结果：一个呼吸结果组和一个心血管结果组。对于心血管就诊，观察到与一氧化碳、二氧化氮以及PM2.5的元素碳和有机碳存在关联。在多污染物模型中，一氧化碳是最强的预测因子。对于呼吸就诊，在单污染物模型中观察到与臭氧、PM10、一氧化碳和二氧化氮存在关联。在多污染物模型中，PM10和臭氧仍然是预测因子，其中臭氧是更强的预测因子。文中讨论了解释多污染物模型结果时的注意事项和考虑因素。
