Rating
Title	Bayesian 2-stage space-time mixture modeling with spatial misalignment of the exposure in small area health data
Author	Lawson, Andrew B.; Choi, Jungsoon; Cai, Bo; Hossain, Md. Monir; Kirby, Russell S.; Liu, Jihong
Date	2011
Description	Abstract: We develop a new Bayesian two-stage space-time mixture model to investigate the effects of air pollution on asthma. The two-stage mixture model proposed allows for the identification of temporal latent structure as well as the estimation of the effects of covariates on health outcomes. In the paper, we also consider spatial misalignment of exposure and health data. A simulation study is conducted to assess the performance of the 2-stage mixture model. We apply our statistical framework to a county-level ambulatory care asthma data set in the US state of Georgia for the years 1999-2008. Key words: Space-time mixture model; air pollution; covariate adjustment; asthma; Bayesian modeling
Collection	MUSC Department of Public Health Sciences Working Papers Working Papers
Contributing Institution	Waring Historical Library (MUSC)
Date Digital	2011
Rights	Copyright is held by the author.
Language	English
Type	Text
Format	application/pdf
Media Type	Manuscripts
Resource Identifier	11-007
Bibliographic Citation	Lawson, Andrew B.; Choi, Jungsoon; Cai, Bo; Hossain, Md. Monir; Kirby, Russell S.; Liu, Jihong. Bayesian 2-stage space-time mixture modeling with spatial misalignment of the exposure in small area health data, MUSC Department of Public Health Sciences Working Papers, 2011. http://medica.library.musc.edu/p/bew/108

Bayesian 2-stage space-time mixture modeling with spatial misalignment of the exposure in small area health data

Description

Tags

Comments