×

Uh-oh, it looks like your Internet Explorer is out of date.

For a better shopping experience, please upgrade now.

Spatial Analysis in Epidemiology
     

Spatial Analysis in Epidemiology

by Dirk U. Pfeiffer, Timothy P. Robinson, Mark Stevenson, Kim B. Stevens, David J. Rogers
 

See All Formats & Editions

ISBN-10: 019850988X

ISBN-13: 9780198509882

Pub. Date: 07/25/2008

Publisher: Oxford University Press


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

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 is 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.

Product Details

ISBN-13:
9780198509882
Publisher:
Oxford University Press
Publication date:
07/25/2008
Pages:
208
Product dimensions:
7.40(w) x 9.80(h) x 0.60(d)

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

Customer Reviews

Average Review:

Post to your social network

     

Most Helpful Customer Reviews

See all customer reviews