Spatial Analysis in Epidemiology

Name: Spatial Analysis in Epidemiology
Brand: Oxford University Press
Price: 66.09 EUR
Availability: OnlineOnly

CLEMENTS DIRK U. PF(Autor*in)

Oxford University Press

1. Auflage

Erschienen am 29. Mai 2008

171 Seiten

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978-0-19-152327-4 (ISBN)

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Inhalt

Intro
Contents
Abbreviations
Preface
1 Introduction
1.1 Framework for spatial analysis
1.2 Scientific literature and conferences
1.3 Software
1.4 Spatial data
1.5 Book content and structure
1.5.1 Datasets used
2 Spatial data
2.1 Introduction
2.2 Spatial data and GIS
2.2.1 Data types
2.2.2 Data storage and interchange
2.2.3 Data collection and management
2.2.4 Data quality
2.3 Spatial effects
2.3.1 Spatial heterogeneity and dependence
2.3.2 Edge effects
2.3.3 Representing neighbourhood relationships
2.3.4 Statistical significance testing with spatial data
2.4 Conclusion
3 Spatial visualization
3.1 Introduction
3.2 Point data
3.3 Aggregated data
3.4 Continuous data
3.5 Effective data display
3.5.1 Media, scale, and area
3.5.2 Dynamic display
3.5.3 Cartography
3.6 Conclusion
4 Spatial clustering of disease and global estimates of spatial clustering
4.1 Introduction
4.2 Disease cluster alarms and cluster investigation
4.3 Statistical concepts relevant to cluster analysis
4.3.1 Stationarity, isotropy, and first- and second-order effects
4.3.2 Monte Carlo simulation
4.3.3 Statistical power of clustering methods
4.4 Methods for aggregated data
4.4.1 Moran's I
4.4.2 Geary's c
4.4.3 Tango's excess events test (EET) and maximized excess events test (MEET)
4.5 Methods for point data
4.5.1 Cuzick and Edwards' k-nearest neighbour test
4.5.2 Ripley's K-function
4.5.3 Rogerson's cumulative sum (CUSUM) method
4.6 Investigating space-time clustering
4.6.1 The Knox test
4.6.2 The space-time k-function
4.6.3 The Ederer-Myers-Mantel (EMM) test
4.6.4 Mantel's test
4.6.5 Barton's test
4.6.6 Jacquez's k nearest neighbours test
4.7 Conclusion
5 Local estimates of spatial clustering
5.1 Introduction
5.2 Methods for aggregated data
5.2.1 Getis and Ord's local Gi(d) statistic
5.2.2 Local Moran test
5.3 Methods for point data
5.3.1 Openshaw's Geographical Analysis Machine (GAM)
5.3.2 Turnbull's Cluster Evaluation Permutation Procedure (CEPP)
5.3.3 Besag and Newell's method
5.3.4 Kulldorff's spatial scan statistic
5.3.5 Non-parametric spatial scan statistics
5.3.6 Example of local cluster detection
5.4 Detecting clusters around a source (focused tests)
5.4.1 Stone's test
5.4.2 The Lawson-Waller score test
5.4.3 Bithell's linear risk score tests
5.4.4 Diggle's test
5.4.5 Kulldorff's focused spatial scan statistic
5.5 Space-time cluster detection
5.5.1 Kulldorff's space-time scan statistic
5.5.2 Example of space-time cluster detection
5.6 Conclusion
6 Spatial variation in risk
6.1 Introduction
6.2 Smoothing based on kernel functions
6.3 Smoothing based on Bayesian models
6.4 Spatial interpolation
6.5 Conclusion
7 Identifying factors associated with the spatial distribution of disease
7.1 Introduction
7.2 Principles of regression modelling
7.2.1 Linear regression
7.2.2 Poisson regression
7.2.3 Logistic regression
7.2.4 Multilevel models
7.3 Accounting for spatial effects
7.4 Area data
7.4.1 Frequentist approaches
7.4.2 Bayesian approaches
7.5 Point data
7.5.1 Frequentist approaches
7.5.2 Bayesian approaches
7.6 Continuous data
7.6.1 Trend surface analysis
7.6.2 Generalized least squares models
7.7 Discriminant analysis
7.7.1 Variable selection within discriminant analysis
7.8 Conclusions
8 Spatial risk assessment and management of disease
8.1 Introduction
8.2 Spatial data in disease risk assessment
8.3 Spatial analysis in disease risk assessment
8.4 Data-driven models of disease risk
8.5 Knowledge-driven models of disease risk
8.5.1 Static knowledge-driven models
8.5.2 Dynamic knowledge-driven models
8.6 Conclusion
References
Index
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
U
V
W

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