title: A national prediction model for PM2.5 component exposures and measurement error-corrected health effect inference.
abstract: BACKGROUND
  Studies estimating health effects of long-term air pollution exposure often use a two-stage approach: building exposure models to assign individual-level exposures, which are then used in regression analyses. This requires accurate exposure modeling and careful treatment of exposure measurement error.
  
  
  OBJECTIVE
  To illustrate the importance of accounting for exposure model characteristics in two-stage air pollution studies, we considered a case study based on data from the Multi-Ethnic Study of Atherosclerosis (MESA).
  
  
  METHODS
  We built national spatial exposure models that used partial least squares and universal kriging to estimate annual average concentrations of four PM2.5 components: elemental carbon (EC), organic carbon (OC), silicon (Si), and sulfur (S). We predicted PM2.5 component exposures for the MESA cohort and estimated cross-sectional associations with carotid intima-media thickness (CIMT), adjusting for subject-specific covariates. We corrected for measurement error using recently developed methods that account for the spatial structure of predicted exposures.
  
  
  RESULTS
  Our models performed well, with cross-validated R2 values ranging from 0.62 to 0.95. Naïve analyses that did not account for measurement error indicated statistically significant associations between CIMT and exposure to OC, Si, and S. EC and OC exhibited little spatial correlation, and the corrected inference was unchanged from the naïve analysis. The Si and S exposure surfaces displayed notable spatial correlation, resulting in corrected confidence intervals (CIs) that were 50% wider than the naïve CIs, but that were still statistically significant.
  
  
  CONCLUSIONS
  The impact of correcting for measurement error on health effect inference is concordant with the degree of spatial correlation in the exposure surfaces. Exposure model characteristics must be considered when performing two-stage air pollution epidemiologic analyses because naïve health effect inference may be inappropriate.
authors.affiliation: Department of Biostatistics, University of Washington, Seattle, Washington 98195, USA.
authors.forename: Silas
authors.initials: S
authors.lastname: Bergen
authors.forename: Lianne
authors.initials: L
authors.lastname: Sheppard
authors.forename: Paul D
authors.initials: PD
authors.lastname: Sampson
authors.forename: Sun-Young
authors.initials: SY
authors.lastname: Kim
authors.forename: Mark
authors.initials: M
authors.lastname: Richards
authors.forename: Sverre
authors.initials: S
authors.lastname: Vedal
authors.forename: Joel D
authors.initials: JD
authors.lastname: Kaufman
authors.forename: Adam A
authors.initials: AA
authors.lastname: Szpiro
mesh_terms: D002244:Carbon / Q000032:analysis; D015331:Cohort Studies; D004781:Environmental Exposure / Q000009:adverse effects*; D004784:Environmental Monitoring / Q000379:methods / Q000706:statistics & numerical data*; D005544:Forecasting / Q000379:methods*; D005843:Geography; D006801:Humans; D016014:Linear Models; D008962:Models, Theoretical; D052638:Particulate Matter / Q000032:analysis / Q000633:toxicity*; D012107:Research Design; D012825:Silicon / Q000032:analysis; D013455:Sulfur / Q000032:analysis; D014481:United States / Q000453:epidemiology
chemical_list: D052638:Particulate Matter; D013455:Sulfur; D002244:Carbon; D012825:Silicon