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. To illustrate the importance of carefully accounting for exposure model characteristics in two-stage air pollution studies, we consider a case study based on data from the Multi-Ethnic Study of Atherosclerosis (MESA). We present national spatial exposure models that use partial least squares and universal kriging to estimate annual average concentrations of four PM2.5 components: elemental carbon (EC), organic carbon (OC), sulfur (S), and silicon (Si). Our models perform well, with cross-validated R2s ranging from 0.62 to 0.95. We predict PM2.5 component exposures for the MESA cohort and estimate cross-sectional associations with carotid intima-media thickness (CIMT), adjusting for subject-specific covariates. In naïve analyses that do not account for measurement error, we find statistically significant associations between CIMT and increased exposure to OC, S, and Si. We correct for measurement error using recently developed methods that account for the spatial structure of predicted exposures. OC exhibits little spatial correlation, and the corrected inference is unchanged from the naïve analysis. The S and Si exposure surfaces display notable spatial correlation, resulting in corrected confidence intervals (CIs) that are 50% wider than the naïve CIs, but that are still statistically significant. The impact on health effect inference is concordant with the degree of spatial correlation in the exposure surfaces.



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