Disease mapping using Bayesian hierarchical models
Alston, Clair L., Cramb, Susanna M., & White, Nicole M. (2013) Disease mapping using Bayesian hierarchical models. In Alston, Clair, Mengersen, Kerrie L., & Pettitt, Anthony N. (Eds.) Case Studies in Bayesian Statistical Modelling and Analysis. John Wiley & Sons, Ltd, West Sussex, pp. 221-239.
description and analysis of geographically indexed health data with respect to demographic, environmental, behavioural, socioeconomic, genetic, and infectious risk factors (Elliott andWartenberg 2004). Disease maps can be useful for estimating relative risk; ecological analyses, incorporating area and/or individual-level covariates; or cluster analyses (Lawson 2009). As aggregated data are often more readily available, one common method of mapping disease is to aggregate the counts of disease at some geographical areal level, and present them as choropleth maps (Devesa et al. 1999; Population Health Division 2006). Therefore, this chapter will focus exclusively on methods appropriate for areal data...
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|Item Type:||Book Chapter|
|Keywords:||Spatial modelling, Bayesian statistics, Hierarchical models, Disease mapping, Spatial Epidemiology|
|Subjects:||Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400) > Applied Statistics (010401)|
|Divisions:||Current > QUT Faculties and Divisions > Science & Engineering Faculty
Past > Schools > Mathematical Sciences
|Deposited On:||25 Nov 2015 23:42|
|Last Modified:||08 Dec 2015 05:45|
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