title: Modeling the association between particle constituents of air pollution and health outcomes.
abstract: 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.
authors: Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA.
authors: Elizabeth
authors: E
authors: Mostofsky
authors: Joel
authors: J
authors: Schwartz
authors: Brent A
authors: BA
authors: Coull
authors: Petros
authors: P
authors: Koutrakis
authors: Gregory A
authors: GA
authors: Wellenius
authors: Helen H
authors: HH
authors: Suh
authors: Diane R
authors: DR
authors: Gold
authors: Murray A
authors: MA
authors: Mittleman
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000393:Air Pollutants / Q000009:adverse effects* / Q000032:analysis / Q000737:chemistry; D000397:Air Pollution / Q000009:adverse effects* / Q000032:analysis / Q000706:statistics & numerical data; D001900:Boston; D015986:Confounding Factors, Epidemiologic; D018592:Cross-Over Studies; D003627:Data Interpretation, Statistical; D004781:Environmental Exposure / Q000009:adverse effects* / Q000706:statistics & numerical data; D015340:Epidemiologic Research Design; D005260:Female; D006801:Humans; D016014:Linear Models; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D052638:Particulate Matter / Q000009:adverse effects* / Q000032:analysis / Q000737:chemistry; D020521:Stroke / Q000209:etiology*
chemical_list: D000393:Air Pollutants; D052638:Particulate Matter