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Effect of housing factors and surficial uranium on the spatial prediction of residential radon in Iowa.

Authors
Smith-BJ; Field-RW
Source
Environmetrics 2007 Aug; 18(5):481-497
NIOSHTIC No.
20040682
Abstract
Growing epidemiologic evidence suggests that residential radon is an important risk factor for lung cancer. Consequently, public health professionals have expressed interest in characterizing the spatial distribution of radon concentrations in order to identify geographic regions of high exposure. Ambient radon concentrations are a function of geologic features including soil radium content. Indoor radon concentrations can vary based on building characteristics that affect the entry of radon into the building and movement between rooms therein. We present a geostatistical hierarchical Bayesian model for radon that allows for spatial prediction based on geologic data and housing characteristics. Our model is applied to radon data from an epidemiologic study in Iowa that consist of 136 outdoor measurements and 2590 indoor measurements from 614 residential homes. Housing characteristics collected in the Iowa Study are included as predictors in the model. Geologic data in the form of county-average surficial uranium concentrations from the USGS National Uranium Resource Evaluation project are also considered. A 'change of support' approach is implemented to combine the radon measurements, collected at points in space, and the uranium concentrations, averaged over counties, so that point-source concentrations for the latter are available for the analysis. Estimates of the effect of select housing factors on radon are provided along with spatial maps of predicted radon concentrations in Iowa.
Keywords
Environmental-exposure; Exposure-levels; Exposure-limits; Risk-factors; Lung-disease; Lung-cancer; Lung; Cancer; Pulmonary-cancer; Pulmonary-disorders; Pulmonary-function; Pulmonary-system; Pulmonary-system-disorders; Spatial-perception; Models; Author Keywords: environmental exposure; hierarchical Bayesian model; Markov chain Monte Carlo simulation; radon gas; spatial statistics; uranium
Contact
Brian J. Smith, Department of Biostatistics, 200 Hawkins Drive, C22 GH, The University of Iowa, IowaCity, Iowa 52242-1009
CODEN
ENVCEE
CAS No.
10043-92-2; 7440-14-4; 7440-61-1
Publication Date
20070801
Document Type
Journal Article
Email Address
brian-j-smith@uiowa.edu
Funding Type
Grant
Fiscal Year
2007
NTIS Accession No.
NTIS Price
Identifying No.
Grant-Number-T42-OH-008491
Issue of Publication
5
ISSN
1180-4009
Source Name
Environmetrics
State
IA
Performing Organization
University of Iowa
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