Spatial-temporal association between fine particulate matter and daily mortality
AbstractFine particulate matter (PM2.5) is a mixture of pollutants that has been linked to serious health problems, including premature mortality. Since the chemical composition of PM2.5 varies across space and time, the association between PM2.5 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 PM2.5 mass, while accounting for different sources of uncertainty. The first stage of the framework maps ambient PM2.5 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 PM2.5 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Bibliographic InfoArticle provided by Elsevier in its journal Computational Statistics & Data Analysis.
Volume (Year): 53 (2009)
Issue (Month): 8 (June)
Contact details of provider:
Web page: http://www.elsevier.com/locate/csda
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Smith, M. & Kohn, R., .
"Nonparametric Regression using Bayesian Variable Selection,"
Statistics Working Paper
_009, Australian Graduate School of Management.
- Smith, Michael & Kohn, Robert, 1996. "Nonparametric regression using Bayesian variable selection," Journal of Econometrics, Elsevier, vol. 75(2), pages 317-343, December.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei).
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.