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Abstract Details
Activity Number:
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674
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Type:
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Contributed
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Date/Time:
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Thursday, August 4, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistics in Epidemiology
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Abstract - #303189 |
Title:
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Estimating Spatial Variation in Disease Risk from Locations Coarsened by Incomplete Geocoding
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Author(s):
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Dale Zimmerman and Xiangming Fang*+
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Companies:
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University of Iowa and East Carolina University
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Address:
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, , ,
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Keywords:
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Coarsened data ;
Geocoding ;
Relative risk ;
Spatial epidemiology
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Abstract:
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A standard component of data assimilation in spatial epidemiologic studies is the assignment of a geocode to the address of each subject in the study population. Unfortunately, when geocoding is performed by the standard procedure of street-segment matching to a georeferenced road file and subsequent interpolation, it is rarely completely successful, which can adversely affect relative risk estimation, especially if one of the disease groups has a different geocoding success rate than another. The possibility exists for ameliorating this effect by incorporating geographic information coarser than a point that is measured for the observations that fail to geocode. We propose coarsened-data methods for relative risk estimation from incompletely geocoded data. Nonparametric (kernel smoothing) estimation procedures are featured; parametric (likelihood-based) procedures are described as well, but their applicability is much more limited. We demonstrate, via simulation and a real example of childhood asthma cases in an Iowa county, that substantial improvements in the quality of relative risk estimates are possible using the proposed nonparametric coarsened-data methods.
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Authors who are presenting talks have a * after their name.
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