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Covariate adjusted weighted normal spatial scan statistics with applications to study geographic clustering of obesity and lung cancer mortality in the United States
Author(s) -
Huang Lan,
Tiwari Ram C.,
Pickle Linda W.,
Zou Zhaohui
Publication year - 2010
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.3990
Subject(s) - categorical variable , covariate , statistics , statistic , body mass index , cluster analysis , scan statistic , medicine , epidemiology , obesity , behavioral risk factor surveillance system , mortality rate , lung cancer , econometrics , demography , mathematics , oncology , environmental health , population , sociology
In the field of cluster detection, a weighted normal model‐based scan statistic was recently developed to analyze regional continuous data and to evaluate the clustering pattern of pre‐defined cells (such as state, county, tract, school, hospital) that include many individuals. The continuous measures of interest are, for example, the survival rate, mortality rate, length of physical activity, or the obesity measure, namely, body mass index, at the cell level with an uncertainty measure for each cell. In this paper, we extend the method to search for clusters of the cells after adjusting for single/multiple categorical/continuous covariates. We apply the proposed method to 1999–2003 obesity data in the United States (US) collected by CDC's Behavioral Risk Factor Surveillance System with adjustment for age and race, and to 1999–2003 lung cancer age‐adjusted mortality data by gender in the United States from the Surveillance Epidemiology and End Results (SEER Program) with adjustment for smoking and income. Copyright © 2010 John Wiley & Sons, Ltd.