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.

Conclusion: 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.

Figure 1

Locations of IMPROVE and CSN monitors and predicted national average PM_2.5 component concentrations from final predictions models. (A) EC, (B) OC, (C) Si, and (D) S. Insets show predictions for St. Paul, MN.

Figure 2

Coefficients of the PLS fit, where the coefficients describe the associations of each geographic covariate with exposure for (A) EC, (B) OC, (C) Si, and (D) S. The size of each circle represents covariate buffer size, with larger circles indicating larger buffers. Each closed circle for “distance to feature” represents a different feature (listed in Table 2): A1 road, nearest road, airport, large airport, port, coastline, commercial or service center, railroad, and rail yard. Variable abbreviations and buffer sizes are indicated in Table 2. Most of the variables shown here were used for modeling all four pollutants, but not all. Variables used for modeling Si and S but not EC and OC were PM2.5 and PM10 emissions, streams and canals within a 3-km buffer, other urban or built-up land use within a 400-m buffer, lakes within a 10-km buffer, industrial and commercial complexes within a 15-km buffer, and herbaceous rangeland within a 3-km buffer. The variables used for modeling EC and OC but not Si and S were industrial land use within 1- and 1.5-km buffers.