Spatial Analysis in Epidemiology

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Overview

This book provides a practical, comprehensive, and up-to-date overview of the use of spatial statistics in epidemiology - the study of the incidence and distribution of diseases. Used appropriately, spatial analytical methods in conjunction with GIS and remotely sensed data can provide significant insights into the biological patterns and processes that underlie disease transmission. In turn, these can be used to understand and predict disease prevalence. This user-friendly text brings together the specialised and widely-dispersed literature on spatial analysis to make these methodological tools accessible to epidemiologists for the first time.

With its focus on application rather than theory, Spatial Analysis in Epidemiology includes a wide range of examples taken from both medical (human) and veterinary (animal) disciplines, and describes both infectious diseases and non-infectious conditions. Furthermore, it provides worked examples of methodologies using a single data set from the same disease example throughout, and is structured to follow the logical sequence of description of spatial data, visualisation, exploration, modelling, and decision support. This accessible text is aimed at graduate students and researchers dealing with spatial data in the fields of epidemiology (both medical and veterinary), ecology, zoology and parasitology, environmental science, geography, and statistics.

About the Author:
Dirk U. Pfeiffer, Epidemiology Division, Royal Veterinary College, University of London, United Kingdom

About the Author:
Timothy P. Robinson, Food and Agricultural Organization of the United Nations, Italy

About the Author:
MarkStevenson, Epicentre, Institute of Veterinary, Animal and Biomedical Sciences, Massey University, New Zealand

About the Author:
Kim B. Stevens, Epidemiology Division, Royal Veterinary College, University of London, United Kingdom

About the Author:
David J. Rogers, Department of Zoology, Oxford University, United Kingdom

About the Author:
Archie C. A. Clements, Division of Epidemiology and Social Medicine, School of Population Health, University of Queensland, Australia

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Editorial Reviews

From the Publisher

"I found that the book provides a very useful introduction to spatial analysis in epidemiology and, I am sure, a reference that I will often revisit."--Maria-Gloria Basanez, Parasites & Vectors

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Product Details

  • ISBN-13: 9780198509882
  • Publisher: Oxford University Press
  • Publication date: 7/25/2008
  • Pages: 208
  • Product dimensions: 7.40 (w) x 9.80 (h) x 0.60 (d)

Meet the Author

Dirk Pfeiffer graduated in Veterinary Medicine in Germany in 1984. He obtained his PhD in Veterinary Epidemiology from Massey University, Palmerston North, New Zealand in 1994. He has worked as an academic in New Zealand until accepting a professorship in veterinary epidemiology at the Royal Veterinary College in 1999. His particular interest is the epidemiology and control of infectious diseases, and his technical expertise includes field epidemiological and ecological research methods, advanced epidemiological analysis, spatial and temporal analysis of epidemiological data, risk analysis, computer modelling of animal disease, animal health economics and development of animal health information systems. Dirk provides scientific expertise to various organizations including the European Food Safety Authority, Defra, the Food and Agriculture Organization, as well as various international governments.

Timothy Robinson graduated from the University of Oxford with a degree in pure and applied biology in 1988. His PhD, at the University of Reading, was on the ecology of the African armyworm, and involved extensive fieldwork in Kenya. After his doctorate he went on to work in Zambia (1992-1996) as a field ecologist, providing technical support to the Regional Tsetse and Trypanosomiasis Control Programme. This was followed by a stint of research at the University of Oxford (1996-1999), as a zoology research fellow and a fellow of Linacre College. From 1999-2002 he was employed as a scientist at the International Livestock Research Institute (ILRI) in Nairobi, again working on diseases of livestock. From ILRI, Timothy moved to the United Nations Food and Agriculture Organisation, where he currently works in the Livestock Information, Sector Analysis and Policy Branch.

Mark Stevenson is senior lecturer in veterinary epidemiology at Massey University, Palmerston North New Zealand. He received his PhD in veterinary epidemiology in 2003 from Massey University. Dr. Stevenson was awarded the Chris Baldock Prize for Early Career Researcher from the Australian Biosecurity Cooperative Research Centre in 2006 and is a member of the Australian College of Veterinary Scientists.

After completing an MSc in Agriculture in 1995 Kim Stevens worked for the Veterinary Faculty of the University of Pretoria (South Africa), first as a Technical Assistant in the Department of Veterinary Physiology and then as a Senior Technical Assistant for the Equine Research Centre. She moved to England in 2000, and joined the Royal Veterinary College in 2002 as a Clinical Research Assistant.

David Rogers is Professor of Ecology in Oxford University. His interests include population ecology of pests and vectors of disease, mathematical modelling, epidemiology and the application of remotely sensed environmental data to conservation and epidemiology/epizootiology.

Archie Clements graduated with a Bachelor of Veterinary Science degree from the University of Sydney in 1996. He then spent two years working in veterinary practice before undertaking an internship and concurrent Masters degree in Veterinary Medicine at the University of Glasgow; going on to study a PhD in veterinary epidemiology at the Royal Veterinary College, University of London, starting in October 2000. His thesis focussed on the application of new spatial analytical methods to decision-making and resource-allocation in veterinary diseases. He spent two years working as an epidemiologist at Imperial College London before moving to the School of Population Health, University of Queensland, where he is currently employed as a Senior Lecturer in epidemiology.

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Table of Contents


Contents     v
Abbreviations     ix
Preface     xi
Introduction     1
Framework for spatial analysis     2
Scientific literature and conferences     3
Software     4
Spatial data     5
Book content and structure     6
Datasets used     6
Bovine tuberculosis data     6
Environmental data     6
Spatial data     9
Introduction     9
Spatial data and GIS     9
Data types     9
Data storage and interchange     11
Data collection and management     12
Data quality     13
Spatial effects     14
Spatial heterogeneity and dependence     14
Edge effects     14
Representing neighbourhood relationships     15
Statistical significance testing with spatial data     15
Conclusion     16
Spatial visualization     17
Introduction     17
Point data     17
Aggregated data     17
Continuous data     23
Effective data display     23
Media, scale, andarea     23
Dynamic display     24
Cartography     26
Distance or scale     26
Projection     26
Direction     27
Legends     27
Neatlines, and locator and inset maps     27
Symbology     27
Dealing with statistical generalization     28
Conclusion     31
Spatial clustering of disease and global estimates of spatial clustering     32
Introduction     32
Disease cluster alarms and cluster investigation     32
Statistical concepts relevant to cluster analysis     33
Stationarity, isotropy, and first- and second-order effects     33
Monte Carlo simulation     33
Statistical power of clustering methods     34
Methods for aggregated data     34
Moran's I     35
Geary's c     37
Tango's excess events test (EET) and maximized excess events test (MEET)     37
Methods for point data     37
Cuzick and Edwards' k-nearest neighbour test     37
Ripley's K-function     39
Rogerson's cumulative sum (CUSUM) method     41
Investigating space-time clustering     41
The Knox test     42
The space-time k-function     42
The Ederer-Myers-Mantel (EMM) test     43
Mantel's test     43
Barton's test     43
Jacquez's k nearest neighbours test     44
Conclusion     44
Local estimates of spatial clustering     45
Introduction     45
Methods for aggregated data     46
Getis and Ord's local Gi(d) statistic     46
Local Moran test     47
Methods for point data     49
Openshaw's Geographical Analysis Machine (GAM)     49
Turnbull's Cluster Evaluation Permutation Procedure (CEPP)     49
Besag and Newell's method     50
Kulldorff's spatial scan statistic     51
Non-parametric spatial scan statistics     52
Example of local cluster detection     53
Detecting clusters around a source (focused tests)     56
Stone's test     60
The Lawson-Waller score test     61
Bithell's linear risk score tests     62
Diggle's test     62
Kulldorff's focused spatial scan statistic     62
Space-time cluster detection     63
Kulldorff's space-time scan statistic      63
Example of space-time cluster detection     64
Conclusion     64
Spatial variation in risk     67
Introduction     67
Smoothing based on kernel functions     67
Smoothing based on Bayesian models     70
Spatial interpolation     73
Conclusion     80
Identifying factors associated with the spatial distribution of disease     81
Introduction     81
Principles of regression modelling     81
Linear regression     81
Poisson regression     83
Logistic regression     86
Multilevel models     87
Accounting for spatial effects     90
Area data     92
Frequentist approaches     93
Bayesian approaches     94
Point data     97
Frequentist approaches     97
Bayesian approaches     99
Continuous data     100
Trend surface analysis     100
Generalized least squares models     102
Discriminant analysis     103
Variable selection within discriminant analysis     106
Conclusions     107
Spatial risk assessment and management of disease     110
Introduction     110
Spatial data in disease risk assessment     110
Spatial analysis in disease risk assessment     111
Data-driven models of disease risk     112
Knowledge-driven models of disease risk     113
Static knowledge-driven models     113
Dynamic knowledge-driven models     117
Conclusion     118
References     120
Index     137
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