Accounting for Exposure Measurement Error in Gridded Air Pollution Estimates in Assessing the Association of PM Exposures with Health Outcomes in Cohort Studies

Authors

Eun Sug Park, Texas A&M Transportation Institute, The Texas A&M University System, College Station, Texas 77843, United States.
Richard L. Smith, Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States.
Xiaohui Xu, Department of Epidemiology & Biostatistics, Texas A&M Health Science Center School of Public Health, College Station, Texas 77843, United States.
Qi Ying, Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, Texas 77840, United States.
Eric A. Whitsel, Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27516, United States.
James D. Stewart, Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27516, United States.
Katie M. Lynch, Department of Epidemiology, Milken Institute School of Public Health, George Washington University, Washington, District of Columbia 20052, United States.
Erin E. Bennett, Department of Epidemiology, Milken Institute School of Public Health, George Washington University, Washington, District of Columbia 20052, United States.
Melinda C. Power, Department of Epidemiology, Milken Institute School of Public Health, George Washington University, Washington, District of Columbia 20052, United States.

Document Type

Journal Article

Publication Date

12-18-2025

Journal

Environmental science & technology

DOI

10.1021/acs.est.4c13918

Keywords

Bayesian hierarchical models; air pollution; atherosclerosis risk in communities study cohort; cardiovascular outcomes; community multiscale air quality model (CMAQ); exposure measurement error; neuroimaging outcomes; remote sensing; satellite

Abstract

There is increasing interest in addressing measurement error in gridded exposure estimates. Here, we provide a framework to account for exposure measurement error in gridded air pollution estimates used for health effects estimation, i.e., when numerical air quality models or satellite-derived data are the principal source of exposure data. We employed a two-stage Bayesian hierarchical modeling framework consisting of an exposure measurement error model and a health model, linking the unobserved true ambient exposure at the residential address for each participant in the cohort to health outcomes. In sensitivity analysis, we considered different health models, spatial smoothing parameters, and spatial resolutions (1.33 and 4 km grid cells) for observation-fused CMAQ output. In an example application, comparison of gridded observation-fused CMAQ estimates and spatially smoothed observation-fused CMAQ estimates by leave-one-out cross-validation at monitoring stations indicated that prediction accuracy at those locations is comparable between the 1.33 km resolution gridded estimates and the smoothed estimates, while the spatially smoothed estimates slightly outperform the 4 km resolution gridded estimates. Accounting for exposure measurement error also resulted in somewhat greater PM health effect estimates on continuous neuroimaging outcomes and smaller health effect estimates on binary cardiovascular outcomes, although overall conclusions remained similar.

APA Citation

Park, Eun Sug; Smith, Richard L.; Xu, Xiaohui; Ying, Qi; Whitsel, Eric A.; Stewart, James D.; Lynch, Katie M.; Bennett, Erin E.; and Power, Melinda C., "Accounting for Exposure Measurement Error in Gridded Air Pollution Estimates in Assessing the Association of PM Exposures with Health Outcomes in Cohort Studies" (2025). GW Authored Works. Paper 8387.
https://hsrc.himmelfarb.gwu.edu/gwhpubs/8387

Department

Epidemiology