Spatial Cluster Detection for Weighted Outcomes Using Cumulative Geographic Residuals |
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Authors: | Andrea J. Cook Yi Li David Arterburn Ram C. Tiwari |
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Affiliation: | 1. Biostatistics Unit, Group Health Research Institute, Seattle, Washington 98101, U.S.A.;2. Department of Biostatistics, University of Washington, Seattle, Washington 98105, U.S.A.;3. Department of Biostatistics, Harvard School of Public Health and the Dana Farber Cancer Institute, Boston, Massachusetts 02115, U.S.A.;4. Office of Biostatistics, CDR, FDA, Silver Spring, Maryland 20993, U.S.A. |
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Abstract: | Summary Spatial cluster detection is an important methodology for identifying regions with excessive numbers of adverse health events without making strong model assumptions on the underlying spatial dependence structure. Previous work has focused on point or individual‐level outcome data and few advances have been made when the outcome data are reported at an aggregated level, for example, at the county‐ or census‐tract level. This article proposes a new class of spatial cluster detection methods for point or aggregate data, comprising of continuous, binary, and count data. Compared with the existing spatial cluster detection methods it has the following advantages. First, it readily incorporates region‐specific weights, for example, based on a region's population or a region's outcome variance, which is the key for aggregate data. Second, the established general framework allows for area‐level and individual‐level covariate adjustment. A simulation study is conducted to evaluate the performance of the method. The proposed method is then applied to assess spatial clustering of high Body Mass Index in a health maintenance organization population in the Seattle, Washington, USA area. |
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Keywords: | Body mass index Cumulative residuals Generalized estimating equations Socioeconomic status Spatial cluster detection Weighted linear regression |
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