Disease mapping involves the analysis of geo-referenced disease incidence data and has many applications, for example within resource allocation, cluster alarm analysis, and ecological studies. There is a real need amongst public health workers for simpler and more efficient tools for the analysis of geo-referenced disease incidence data. Bayesian and multilevel methods provide the required efficiency, and with the emergence of software packages - such as WinBUGS and MLwiN - are now easy to implement in practice.

• Provides an introduction to Bayesian and multilevel modelling in disease mapping.
• Adopts a practical approach, with many detailed worked examples.
• Includes introductory material on WinBUGS and MLwiN.
• Discusses three applications in detail - relative risk estimation, focused clustering, and ecological analysis.
• Suitable for public health workers and epidemiologists with a sound statistical knowledge.
• Supported by a Website featuring data sets and WinBUGS and MLwiN programs.

Disease Mapping with WinBUGS and MLwiN provides a practical introduction to the use of software for disease mapping for researchers, practitioners and graduate students from statistics, public health and epidemiology who analyse disease incidence data.

Contents

• Disease mapping basics.
• Bayesian hierarchical modelling.
• Multilevel modelling.
• WinBUGS basics.
• MLwiN basics.
• Relative risk estimation.
• Focused clustering: the analysis of putative health hazards.
• Ecological analysis.
• Spatially-correlated survival analysis.
• Epilogue.
• Appendix 1: WinBUGS code for focused clustering models.
• Appendix 2: S-Plus function for conversion to GeoBUGS format.