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;
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