Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA.
Am J Epidemiol. 2012 Aug 15;176(4):317-26. doi: 10.1093/aje/kws018. Epub 2012 Jul 31.
There is increasing interest in evaluating the association between specific fine-particle (particles with aerodynamic diameters less than 2.5 µm; PM2.5) constituents and adverse health outcomes rather than focusing solely on the impact of total PM2.5. Because PM2.5 may be related to both constituent concentration and health outcomes, constituents that are more strongly correlated with PM2.5 may appear more closely related to adverse health outcomes than other constituents even if they are not inherently more toxic. Therefore, it is important to properly account for potential confounding by PM2.5 in these analyses. Usually, confounding is due to a factor that is distinct from the exposure and outcome. However, because constituents are a component of PM2.5, standard covariate adjustment is not appropriate. Similar considerations apply to source-apportioned concentrations and studies assessing either short-term or long-term impacts of constituents. Using data on 18 constituents and data from 1,060 patients admitted to a Boston medical center with ischemic stroke in 2003-2008, the authors illustrate several options for modeling the association between constituents and health outcomes that account for the impact of PM2.5. Although the different methods yield results with different interpretations, the relative rankings of the association between constituents and ischemic stroke were fairly consistent across models.
人们越来越关注评估特定细颗粒物(空气动力学直径小于 2.5μm 的颗粒;PM2.5)成分与不良健康结果之间的关联,而不仅仅关注 PM2.5 的总体影响。因为 PM2.5 可能与成分浓度和健康结果都有关,所以与 PM2.5 相关性更强的成分,即使它们本身并不具有更高的毒性,也可能与不良健康结果更为密切相关。因此,在这些分析中,正确考虑 PM2.5 可能产生的混杂因素非常重要。通常,混杂因素是指与暴露和结果不同的因素。然而,由于成分是 PM2.5 的一个组成部分,因此标准的协变量调整并不适用。类似的考虑也适用于源分配浓度和评估成分短期或长期影响的研究。本文作者利用 18 种成分的数据和 2003-2008 年期间在波士顿医疗中心因缺血性中风入院的 1060 名患者的数据,说明了几种可用于建模成分与健康结果之间关联的方法,这些方法考虑了 PM2.5 的影响。尽管不同的方法得出的结果具有不同的解释,但成分与缺血性中风之间关联的相对排名在不同模型中相当一致。