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Activity Number: 326 - Exploring the Impact of Air Pollution on Alzheimer's Disease and Other Indicators of Dementia
Type: Topic Contributed
Date/Time: Tuesday, August 9, 2022 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #322954
Title: Accounting for Exposure Measurement Error in Air Pollution and Neuroimaging Analysis
Author(s): Eun Sug Park* and Richard Smith and Xiaohui Xu and Eric Whitsel and James Stewart and Qi Ying and Katie Lynch and Erin Bennett and Melinda Power
Companies: TTI and University of North Carolina Chapel Hill and Texas A&M University and University of North Carolina at Chapel Hill and University of North Carolina at Chapel Hill and Texas A&M University and George Washington University and George Washington University and George Washington University
Keywords: Air pollution; Exposure measurement error; Cognitive health; Neuroimaging outcomes; Atherosclerosis Risk in Communities study cohort
Abstract:

While there has been growing research suggesting adverse health effects of PM2.5 on cognitive health and MRI biomarkers from neuroimaging analysis, most previous studies examined the association by using the direct plug-in of the exposure estimates as an independent variable in a health effects analysis. However, such methods are subject to non-ignorable exposure measurement error resulting from uncertainty in exposure prediction models as well as spatial misalignment between estimated exposures and health outcomes. It is well known that ignoring exposure measurement error can lead to biased health effect estimates and incorrect uncertainty estimates. We explicitly account for measurement error in the exposure estimates obtained from a combination of air quality models and data-fusing approach in the health effects evaluation of PM2.5 in the Atherosclerosis Risk in Communities (ARIC) study by using a Bayesian hierarchical analysis linking the unobserved true ambient exposure at the geocoded residential address for each ARIC participant to neuroimaging outcomes. This allows us to estimate the health effects of PM2.5 on neuroimaging outcomes along with uncertainty more accurately.


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