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Abstract Details
Activity Number:
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7
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Type:
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Invited
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Date/Time:
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Sunday, July 31, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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Environmental and Ecological Statistics
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Abstract - #300082 |
Title:
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PM Exposure and Covariate Adjustment in Spatio-Temporal Latent Small-Area Health Modeling
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Author(s):
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Andrew B. Lawson*+
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Companies:
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Medical University of South Carolina
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Address:
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135 Cannon Street, Charleston, SC, 29466, USA
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Keywords:
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latent ;
mixture ;
asthma ;
space-time ;
Bayesian ;
PM2.5
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Abstract:
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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
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