PM Exposure and Covariate Adjustment in Spatio-Temporal Latent Small-Area Health Modeling

• Activity Number: 7
• Type: Invited
• Date/Time: Sunday, July 31, 2011 : 2:00 PM to 3:50 PM
• Sponsor: Environmental and Ecological Statistics
• Author(s): Andrew B. Lawson*+
• Companies: Medical University of South Carolina
• Keywords: latent ; mixture ; asthma ; space-time ; Bayesian ; PM2.5

Abstract

Latent structure models can be developed for the mean level of a space-time count data observation process. The focus is on small area health outcomes observed in fixed spatial units and fixed time periods. We assume a Poisson data level model with mean parameterized as a weighted mixture of temporal components. Each area has a distribution of weights assigning the area to a component. Posterior sampling can be used to estimate both weights and components. Identification and label switching are considered in relation to single/double chain dynamics. Allocation paradigms are considered as we focus on spatial 'clustering' of temporal profiles. Goodness of fit measures suggest that from a relative risk viewpoint these mixtures can outperform conventional ST random effect models (such as proposed by Knorr-Held, 2000), while also providing latent component information. Time-varying environmental covariates, such as PM2.5 concentration, could be important in the prediction of latent health effects and we consider an example of county-level asthma admissions data with temporally-varying interpolated PM 2.5 at county level for the state of Georgia