Time-Series Analysis of Air Pollution and Health Accounting for Spatial Exposure Uncertainty

• Activity Number: 290
• Type: Topic Contributed
• Date/Time: Tuesday, August 6, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Howard Chang*+ and Yang Liu and Stefanie Sarnat
• Companies: Emory University and Emory University and Emory University
• Keywords: Air pollution ; Spatial statistics ; Measurement error ; Time series ; Health effects

Abstract

Population studies of air pollution and health routinely assign exposures using measurements from outdoor monitoring networks that have limited spatial coverage. Moreover, ambient concentrations may not reflect human exposure to pollution from outdoor sources. As such, exposure uncertainty can arise in these studies due to unobserved spatial variation in ambient air pollution concentrations, as well as spatial variations in population or environmental characteristics that contribute to differential exposure. We will describe a daily time-series study of fine particulate matter and emergency department visits in Atlanta. To account for spatial exposure uncertainties, additional data sources are being incorporated to supplement ambient monitor measurements in a unified statistical modeling framework. Specifically, we first utilize remotely sensed data to obtain spatially-resolved concentration predictions. These predictions are then combined with data from stochastic exposure simulators to obtain estimated personal exposures.