Estimating Spatial Variation in Disease Risk from Locations Coarsened by Incomplete Geocoding

• Activity Number: 674
• Type: Contributed
• Date/Time: Thursday, August 4, 2011 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Dale Zimmerman and Xiangming Fang*+
• Companies: University of Iowa and East Carolina University
• Keywords: Coarsened data ; Geocoding ; Relative risk ; Spatial epidemiology

Abstract

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.