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Activity Number: 461
Type: Invited
Date/Time: Wednesday, August 7, 2013 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics and the Environment
Abstract - #307156
Title: A Localized Conditional Autoregressive Model for Residual Spatial Confounding in Air Pollution and Health Studies
Author(s): Duncan Paul Lee*+
Companies: University of Glasgow
Keywords: Air pollution and health ; Conditional autoregressive models ; Localised spatial smoothing
Abstract:

The health impact of long-term exposure to air pollution can be estimated by regressing disease counts in non-overlapping areal units against air pollution concentrations and other covariates. Poisson log-linear models are used for the analysis, and a set of spatially correlated random effects are typically added to the model to account for any residual spatial correlation. Conditional autoregressive (CAR) models are commonly specified for the random effects, which make the restrictive assumption that there is a single level of spatial smoothness across the study region. However this is unrealistic, because the inclusion of spatially smooth covariates in the model, such as air pollution concentrations, means that the residual correlation is likely to be exhibit localised rather than global spatial smoothness. Therefore we develop a new CAR model for localised spatial smoothing, which can model both sub-regions of spatial smoothness and step-changes in the random effects surface. We apply our methodology to a study of air pollution and respiratory ill health in Greater Glasgow, Scotland in 2010, and its efficacy is compared to existing modelling approaches by simulation.


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