Statistical Methods in Spatial Epidemiology / Edition 2

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Spatial epidemiology is the description and analysis of the geographical distribution of disease. It is more important now than ever, with modern threats such as bio-terrorism making such analysis even more complex. This second edition of Statistical Methods in Spatial Epidemiology is updated and expanded to offer a complete coverage of the analysis and application of spatial statistical methods. The book is divided into two main sections: Part 1 introduces basic definitions and terminology, along with map construction and some basic models. This is expanded upon in Part II by applying this knowledge to the fundamental problems within spatial epidemiology, such as disease mapping, ecological analysis, disease clustering, bio-terrorism, space-time analysis, surveillance and infectious disease modelling. Provides a comprehensive overview of the main statistical methods used in spatial epidemiology. Updated to include a new emphasis on bio-terrorism and disease surveillance. Emphasizes the importance of space-time modelling and outlines the practical application of the method. Discusses the wide range of software available for analyzing spatial data, including WinBUGS, SaTScan and R, and features an accompanying website hosting related software. Contains numerous data sets, each representing a different approach to the analysis, and provides an insight into various modelling techniques. This text is primarily aimed at medical statisticians, researchers and practitioners from public health and epidemiology. It is also suitable for postgraduate students of statistics and epidemiology, as well professionals working in government agencies.

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Editorial Reviews

Doody's Review Service
Reviewer: Eric Westhus, BS (Saint Louis University)
Description: This is a comprehensive review of current methods used in the analysis of spatial epidemiology data.
Purpose: The purpose, as stated by the author, is "to provide an overview of the main statistical methods currently available in the field of spatial epidemiology." Spatial statistics have become increasingly important with the growth of GIS technology. A comprehensive review like this was much needed and this book meets the author's objective.
Audience: According to the author, the book was written for PhDs with some statistical background or postgraduate students in statistics or epidemiology. Researchers with a strong background in statistics and GIS will get the most out of this book, while others could skim it for a general familiarity with modern techniques. The author delivers the material with credible authority.
Features: The book provides an overview of the types of spatial data available to epidemiology researchers, the types of statistical models that can be used to analyze spatial data, and common problems encountered during a spatial epidemiological study. The author's extensive use of examples and figures do well at illustrating the concepts put forth by the author. This book is not, however, a how-to manual. It will serve nicely as a theory reference for someone conducting a spatial study, but it does not tell the reader how to apply these techniques using modern computer software.
Assessment: This is a very useful reference for the theoretical foundations of spatial epidemiological statistics. Alone, it will help researchers to understand spatial epidemiological literature. In conjunction with various software packages and manuals, researchers should be able to use this book to apply spatial statistical methods in their own research.
From the Publisher
"…the second edition is a substantial improvement on what was already a valuable, well structured and comprehensive reference…" (Biometrics, September 2007)
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Product Details

  • ISBN-13: 9780470014844
  • Publisher: Wiley
  • Publication date: 7/11/2006
  • Series: Wiley Series in Probability and Statistics Series, #657
  • Edition description: Revised Edition
  • Edition number: 2
  • Pages: 424
  • Product dimensions: 6.24 (w) x 9.02 (h) x 1.11 (d)

Meet the Author

Professor Andrew B. Lawson is a respected and well-known academic. He has published many papers in leading journals, and a number of books on spatial statistics, including five for Wiley.

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Table of Contents

Preface and Acknowledgements to Second Edition.

Preface and Acknowledgements.

I: The Nature of Spatial Epidemiology.

1. Definitions, Terminolgy and Data Sets.

1.1 Map Hypotheses and Modelling Approaches.

1.2 Definitions and Data Examples.

1.3 Further definitions.

1.4 Some Data Examples.

2.Scales of Measurement and Data Availability.

2.1 Small Scale.

2.2 Large Scale.

2.3 Rate Dependence.

2.4 DataQuality and the Ecological Fallacy.

2.5 Edge E.ects.

3.Geographical Representation and Mapping.

3.1 Introduction and Definitions.

3.2 Maps and Mapping.

3.3 Statistical Accuracy.

3.4 Aggregation.

3.5 Mapping Issues related toAggregated Data.

3.6 Conclusions.

4.Basic Models.

4.1 Sampling Considerations.

4.2 Likelihood-based and Bayesian Approaches.

4.3 Point EventModels.

4.4 CountModels.

5.Exploratory Approaches, Parametric Estimation and Inference.

5.1 ExploratoryMethods.

5.2 Parameter Estimation.

5.3 Residual Diagnostics.

5.4 Hypothesis Testing.

5.5 Edge E.ects.

II:Important Problems in Spatial Epidemiology.

6.Small Scale: Disease Clustering.

6.1 Definition of Clusters and Clustering.

6.2 Modelling Issues.

6.3 Hypothesis Tests for Clustering.

6.4 Space-Time Clustering.

6.5 Clustering Examples.

6.6 OtherMethods related to clustering.

7.Small Scale: Putative Sources of Hazard.

7.1 Introduction.

7.2 StudyDesign.

7.3 Problems of Inference.

7.4 Modelling the Hazard Exposure Risk.

7.5 Models for Case Event Data.

7.6 ACase Event Example.

7.7 Models for CountData.

7.8 ACountData Example.

7.9 OtherDirections.

8. Large Scale: Disease Mapping.

8.1 Introduction.

8.2 Simple Statistical Representation.

8.3 BasicModels.

8.4 AdvancedMethods.

8.5 Model Variants and Extensions.

8.6 ApproximateMethods.

8.7 MultivariateMethods.

8.8 Evaluation ofModel Performance.

8.9 Hypothesis Testing in DiseaseMapping.

8.10 Space-Time DiseaseMapping.

8.11 Spatial Survival and longitudinal data.

8.12 DiseaseMapping: Case Studies.

9.Ecological Analysis and Scale Change.

9.1 Ecological Analysis: Introduction.

9.2 Small-ScaleModelling Issues.

9.3 Changes of Scale andMAUP.

9.4 A Simple Example: Sudden Infant Death in North Carolina.

9.5 ACase Study: Malaria and IDDM.

10.Infectious Disease Modelling.

10.1 Introduction.

10.2 GeneralModelDevelopment.

10.3 SpatialModelDevelopment.

10.4 Modelling Special Cases for Individual Level Data.

10.5 Survival Analysis with spatial dependence.

10.6 Individual level data example.

10.7 Underascertainment and Censoring.

10.8 Conclusions.

11.Large Scale: Surveillance.

11.1 Process ControlMethodology.

11.2 Spatio-Temporal Modelling.

11.3 Spatio-TemporalMonitoring.

11.4 Syndromic Surveillance.

11.5 Multivariate-Mulitfocus Surveillance.

11.6 Bayesian Approaches.

11.7 Computational Considerations.

11.8 Infectious Diseases.

11.9 Conclusions.

Appendix A:Monte Carlo Testing, Parametric Bootstrap and Simulation Envelopes.

Appendix B:Markov ChainMonte Carlo Methods.

Appendix C:Algorithms and Software.

Appendix D: Glossary of Estimators.

Appendix E:Software.



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