Document Detail


Spatial-temporal association between fine particulate matter and daily mortality.
MedLine Citation:
PMID:  19652691     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
Fine particulate matter (PM(2.5)) is a mixture of pollutants that has been linked to serious health problems, including premature mortality. Since the chemical composition of PM(2.5) varies across space and time, the association between PM(2.5) and mortality could also change with space and season. In this work we develop and implement a statistical multi-stage Bayesian framework that provides a very broad, flexible approach to studying the spatiotemporal associations between mortality and population exposure to daily PM(2.5) mass, while accounting for different sources of uncertainty. In stage 1, we map ambient PM(2.5) air concentrations using all available monitoring data (IMPROVE and FRM) and an air quality model (CMAQ) at different spatial and temporal scales. In stage 2, we examine the spatial temporal relationships between the health end-points and the exposures to PM(2.5) by introducing a spatial-temporal generalized Poisson regression model. We adjust for time-varying confounders, such as seasonal trends. A common seasonal trends model is to use a fixed number of basis functions to account for these confounders, but the results can be sensitive to the number of basis functions. In this study, the number of the basis functions is treated as an unknown parameter in our Bayesian model and we use a space-time stochastic search variable selection approach. We apply our methods to a data set in North Carolina for the year 2001.
Authors:
Jungsoon Choi; Montserrat Fuentes; Brian J Reich
Publication Detail:
Type:  JOURNAL ARTICLE    
Journal Detail:
Title:  Computational statistics & data analysis     Volume:  53     ISSN:  -     ISO Abbreviation:  Comput Stat Data Anal     Publication Date:  2009 Jun 
Date Detail:
Created Date:  2009-8-6     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  100960938     Medline TA:  Comput Stat Data Anal     Country:  -    
Other Details:
Languages:  ENG     Pagination:  2989-3000     Citation Subset:  -    
Affiliation:
Department of Statistics, North Carolina State University, Raleigh, NC, 27695-8203, USA.
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Descriptor/Qualifier:
Grant Support
ID/Acronym/Agency:
R01 ES014843-01A2//NIEHS NIH HHS; R01 ES014843-02//NIEHS NIH HHS

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