Urban Form, Heart Disease, and Geography: A Case Study in Composite Index Formation and Bayesian Spatial Modeling

Urban Form, Heart Disease, and Geography: A Case Study in Composite Index Formation and Bayesian... Recent studies indicate a relationship between measures of urban form as applied to urban and suburban areas, and obesity, a risk factor for heart disease. Measures of urban form for exurban and rural areas are considerably scarce; such measures could prove useful in measuring relationships between urban form and both mortality and morbidity in such areas. In modeling area-level mortality, geographic relationships between counties warrant consideration because geographically adjacent areas tend to have more in common than areas farther from each other. We modify county-level indices of urban form found in the literature so that they can be applied to exurban and rural counties. We then use these indices in a Bayesian spatial model that accounts for spatial autocorrelation to determine if there is a relationship between such measures and cardiovascular disease mortality for white males age 35 and older for the time period 1999–2001. Issues related to the formation and usefulness of the indices, and issues related to the spatial model, are discussed. Maps of observed and expected relative risk of mortality are presented. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Population Research and Policy Review Springer Journals

Urban Form, Heart Disease, and Geography: A Case Study in Composite Index Formation and Bayesian Spatial Modeling

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Publisher
Springer Journals
Copyright
Copyright © 2007 by Springer Science+Business Media B.V.
Subject
Social Sciences; Demography; Sociology, general; Population Economics
ISSN
0167-5923
eISSN
1573-7829
D.O.I.
10.1007/s11113-007-9049-2
Publisher site
See Article on Publisher Site

Abstract

Recent studies indicate a relationship between measures of urban form as applied to urban and suburban areas, and obesity, a risk factor for heart disease. Measures of urban form for exurban and rural areas are considerably scarce; such measures could prove useful in measuring relationships between urban form and both mortality and morbidity in such areas. In modeling area-level mortality, geographic relationships between counties warrant consideration because geographically adjacent areas tend to have more in common than areas farther from each other. We modify county-level indices of urban form found in the literature so that they can be applied to exurban and rural counties. We then use these indices in a Bayesian spatial model that accounts for spatial autocorrelation to determine if there is a relationship between such measures and cardiovascular disease mortality for white males age 35 and older for the time period 1999–2001. Issues related to the formation and usefulness of the indices, and issues related to the spatial model, are discussed. Maps of observed and expected relative risk of mortality are presented.

Journal

Population Research and Policy ReviewSpringer Journals

Published: Sep 27, 2007

References

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