Time series analyses of ambient air pollution often involve comparing a pollutant measured at a point in space with an aggregate measure of health. Spatial misalignment of the exposure and response variables can induce error, the magnitude of which depends on the spatial variation of the exposure of interest. In air pollution epidemiology, there is an increasing focus on estimating the health effects of the chemical components of particulate matter. One issue that is raised by this new focus is the spatial misalignment error introduced by the lack of spatial homogeneity in many of the particulate matter components. Current approaches to time series modeling do not take into account the spatial properties of components and therefore could result in biased estimation of health risks. We present a spatial-temporal statistical model for quantifying spatial misalignment error and show how adjusted heath risk estimates can be obtained using a plug-in approach and a two-stage Bayesian model. We apply our methods to a database containing information on hospital admissions, air pollution, and weather for 20 large urban counties in the United States.
Dr. Roger Peng
Department of BioStatistics
Johns Hopkins Bloomberg School of Public Health