Journal article
Spatial-temporal association between fine particulate matter and daily mortality
Computational Statistics and Data Analysis, Vol.53(8), pp.2989-3000
06/15/2009
DOI: 10.1016/j.csda.2008.05.018
PMCID: PMC2685284
PMID: 19652691
Abstract
Fine particulate matter (PM.sub.2.5) is a mixture of pollutants that has been linked to serious health problems, including premature mortality. Since the chemical composition of PM.sub.2.5 varies across space and time, the association between PM.sub.2.5 and mortality could also change with space and season. A statistical multi-stage Bayesian framework is developed and implemented, which provides a very broad and flexible approach to studying the spatiotemporal associations between mortality and population exposure to daily PM.sub.2.5 mass, while accounting for different sources of uncertainty. The first stage of the framework maps ambient PM.sub.2.5 air concentrations using all available monitoring data (IMPROVE and FRM) and an air quality model (CMAQ) at different spatial and temporal scales. The second stage of the framework examines the spatial temporal relationships between the health end-points and the exposures to PM.sub.2.5 by introducing a spatial-temporal generalized Poisson regression model. A method to adjust for time-varying confounders such as seasonal trends is proposed. A common seasonal trends model uses a fixed number of basis functions to account for these confounders, but the results can be sensitive to the number of basis functions. Thus, instead the number of the basis functions is treated as an unknown parameter in the Bayesian model, and a space-time stochastic search variable selection approach is used. The framework is illustrated using a data set in North Carolina for the year 2001.
Details
- Title: Subtitle
- Spatial-temporal association between fine particulate matter and daily mortality
- Creators
- Jungsoon Choi - North Carolina State UniversityMontserrat Fuentes - North Carolina State UniversityBrian J Reich - North Carolina State University
- Resource Type
- Journal article
- Publication Details
- Computational Statistics and Data Analysis, Vol.53(8), pp.2989-3000
- Publisher
- Elsevier B.V
- DOI
- 10.1016/j.csda.2008.05.018
- PMID
- 19652691
- PMCID
- PMC2685284
- ISSN
- 0167-9473
- eISSN
- 1872-7352
- Language
- English
- Date published
- 06/15/2009
- Description audience
- Academic
- Academic Unit
- Statistics and Actuarial Science; Biostatistics; Provost Office Administration
- Record Identifier
- 9983763498102771
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