کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
1151198 1489830 2012 12 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Estimating spatial variation in disease risk from locations coarsened by incomplete geocoding
موضوعات مرتبط
مهندسی و علوم پایه ریاضیات آمار و احتمال
پیش نمایش صفحه اول مقاله
Estimating spatial variation in disease risk from locations coarsened by incomplete geocoding
چکیده انگلیسی

Inference for spatial variation in relative risk of disease is an important problem in spatial epidemiologic studies. A standard component of data assimilation in these studies is the assignment of a geocode, i.e. point-level spatial coordinates, 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. Typically, 10-30% of the addresses in the study population fail to geocode, which can adversely affect relative risk estimation, especially if one of the disease groups (e.g. cases) has a different geocoding success rate than another (e.g. controls). The possibility exists, however, for ameliorating this effect by incorporating geographic information coarser than a point (e.g. a Zip code) that is measured for the observations that fail to geocode. This article develops 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.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Statistical Methodology - Volume 9, Issues 1–2, January–March 2012, Pages 239–250
نویسندگان
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