Statistical Methods in Spatial Epidemiology / Edition 2

Statistical Methods in Spatial Epidemiology / Edition 2

by Andrew B. Lawson
     
 

ISBN-10: 0470014849

ISBN-13: 9780470014844

Pub. Date: 07/11/2006

Publisher: Wiley

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

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Overview

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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Product Details

ISBN-13:
9780470014844
Publisher:
Wiley
Publication date:
07/11/2006
Series:
Wiley Series in Probability and Statistics Series, #657
Edition description:
Revised Edition
Pages:
424
Product dimensions:
6.24(w) x 9.02(h) x 1.11(d)

Related Subjects

Table of Contents


Preface and Acknowledgements to Second Edition     xv
Preface and Acknowledgements     xvii
The Nature of Spatial Epidemiology     1
Definitions, Terminology and Data Sets     3
Map Hypotheses and Modelling Approaches     5
Definitions and Data Examples     7
Case event data     7
Count data     8
Further Definitions     10
Control events and processes     10
Census tract information     10
Clustering definitions     10
Some Data Examples     11
Case event examples     11
Count data examples     19
Scales of Measurement and Data Availability     25
Small Scale     26
Large Scale     26
Rate Dependence     27
Data Quality and the Ecological Fallacy     27
Edge Effects     28
Geographical Representation and Mapping     31
Introduction and Definitions     31
Maps and Mapping     32
Statistical maps and mapping     34
Object process mapping     34
Geostatistical mapping     36
Statistical Accuracy     37
Aggregation     37
Mapping Issues Related to Aggregated Data     37
Conclusions     39
Basic Models     41
Sampling Considerations     41
Likelihood-Based and Bayesian Approaches     42
Point Event Models     42
Point process models and applications     43
The basic Poisson process model     44
Hybrid models and regionalisation     49
Bayesian models and random effects     50
MAP estimation, empirical Bayes and full Bayesian analysis     52
Bivariate/multivariate models     53
Hidden structure and mixture models     56
Space-time extensions     56
Count Models     58
Standard models     60
Approximations     63
Random-effect extensions     63
Hidden structure and mixture models     64
Space-time extensions     65
Exploratory Approaches, Parametric Estimation and Inference     67
Exploratory Methods     68
Cartographic issues     69
Case event mapping     71
Count mapping     75
Parameter Estimation     80
Case event likelihood models     80
Count event likelihood models     85
Approximations     87
Bayesian models     88
Residual Diagnostics     96
Hypothesis Testing     98
Edge Effects     99
Edge effects in case events     101
Edge effects in counts     101
Edge weighting schemes and MCMC methods     102
Discussion     104
The Tuscany example     105
Important Problems in Spatial Epidemiology     109
Small Scale: Disease Clustering     111
Definition of Clusters and Clustering     112
Modelling Issues     115
Hypothesis Tests for Clustering     118
General non-specific clustering     118
Specific clustering     121
Space-Time Clustering     123
Modelling issues     123
Hypothesis testing     126
Clustering Examples     127
Humberside example     127
Larynx cancer example     131
Count data clustering example     133
Space-time clustering examples     136
Other Methods Related to Clustering     138
Wombling     140
Small Scale: Putative Sources of Hazard      143
Introduction     143
Study Design     144
Retrospective and prospective studies     144
Study region design     145
Replication and controls     146
Problems of Inference     147
Exploratory techniques     148
Modelling the Hazard Exposure Risk     153
Models for Case Event Data     162
Estimation     164
Hypothesis tests     164
Diagnostic techniques     166
A Case Event Example     167
Models for Count Data     169
Estimation     171
Hypothesis tests     171
A Count Data Example     172
Other Directions     174
Multiple disease analysis     174
Space-time modelling     184
Space-time exploratory analysis     184
Space-time Bayesian analysis     185
Large Scale: Disease Mapping     189
Introduction     189
Simple Statistical Representation     189
Crude rates     190
Standardised mortality/morbidity ratios, standardisation and relative risk surfaces     191
Interpolation     193
Exploratory mapping methods     193
Basic Models     194
Likelihood models     194
Random effects and Bayesian models     197
Advanced Methods     201
Non-parametric methods     202
Incorporating spatially correlated heterogeneity     203
Case event modelling     206
Model Variants and Extensions     209
Semiparametric modelling     209
Geographically weighted regression     210
Mixture models     211
Approximate Methods     212
Multivariate Methods     213
Evaluation of Model Performance     216
Hypothesis Testing in Disease Mapping     219
First-order effects     219
Second-order and variance effects     221
Space-Time Disease Mapping     222
Spatial Survival and Longitudinal Data     229
Spatial survival analysis     229
Spatial longitudinal analysis     231
Spatial multiple event modelling     232
Disease Mapping: Case Studies     232
Eastern Germany     232
Ohio respiratory cancer     239
Ecological Analysis and Scale Change     247
Ecological Analysis: Introduction      247
Small-Scale Modelling Issues     252
Hypothesis tests     253
Ecological aggregation effects     253
Changes of Scale and MAUP     255
MAUP: the modifiable areal unit problem     255
Large-scale issues     260
A Simple Example: Sudden Infant Death in North Carolina     261
A Case Study: Malaria and IDDM     263
Infectious Disease Modelling     269
Introduction     269
General Model Development     270
Spatial Model Development     273
Count data     273
Individual-level data     278
Modelling Special Cases for Individual-Level Data     280
Proportional hazards interpretation     280
Subgroup modifications     281
Cluster function specification     282
Survival Analysis with Spatial Dependence     283
Individual-Level Data Example     284
Distribution of susceptibles S(x, t)     285
The spatial distance function h     285
The function g     285
Fitting the model     286
Revised model     287
Underascertainment and Censoring     288
Conclusions      289
Large Scale: Surveillance     293
Process Control Methodology     294
Spatio-Temporal Modelling     295
S-T Monitoring     297
Fixed spatial and temporal frame     297
Fixed spatial frame and dynamic temporal frame     301
Syndromic Surveillance     304
Multivariate-Multifocus Surveillance     305
Bayesian Approaches     308
Bayesian alarm functions, Bayes factors and syndromic analyses     308
Computational Considerations     310
Infectious Diseases     311
Conclusions     312
Monte Carlo Testing, Parametric Bootstrap and Simulation Envelopes     313
Nuisance Parameters and Test Statistics     313
Monte Carlo Tests     314
Null Hypothesis Simulation     315
Spatial case     316
Spatio-temporal case     318
Parametric Bootstrap     319
Bayesian spatial models     322
Spatio-temporal case     323
Simulation Envelopes     324
Markov Chain Monte Carlo Methods     325
Definitions     325
Metropolis and Metropolis-Hastings Algorithms     326
Metropolis algorithm     326
Metropolis-Hastings extension     327
The Gibbs sampler     327
M-H versus Gibbs algorithms     328
Examples     328
Algorithms and Code     331
Data Exploration     331
Likelihood and Bayesian Models     335
Likelihood Models     336
Case event data     336
Count data     340
Bayesian Hierarchical Models     341
Case event data     341
Count data     344
Space-Time Analysis     346
Data exploration     346
Likelihood models     349
Bayesian models     351
Infectious disease models     357
Glossary of Estimators     359
Case Event Estimators     359
Tract Count Estimators     361
Software     363
Software     363
Spatial statistical tools     363
Geographical information systems     365
Bibliography     367
Index     389

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