Spatial analysis in epidemiology / Dirk U. Pfeiffer ... [and others].

Format:

Book

Contributor:

Pfeiffer, Dirk, 1958-

Olive R. Morrison Collection (University of Pennsylvania)

Series:

Oxford biology

Language:

English

Subjects (All):

Epidemiology--Statistical methods.

Epidemiology.

Medical geography--Statistical methods.

Medical geography.

Epidemiologic Methods.

Geography--statistics & numerical data.

Statistics.

Medical Subjects:

Epidemiologic Methods.

Geography--statistics & numerical data.

Penn Provenance:

Morrison, Olive R. (honoree)

Physical Description:

xii, 142 pages, 8 unnumbered pages of plates : illustrations, maps (partly color) ; 26 cm.

Place of Publication:

Oxford ; New York : Oxford University Press, 2008.

Summary:

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.

Contents:

1.1 Framework for spatial analysis 2

1.2 Scientific literature and conferences 3

1.3 Software 4

1.4 Spatial data 5

1.5 Book content and structure 6

1.5.1 Datasets used 6

1.5.1.1 Bovine tuberculosis data 6

1.5.1.2 Environmental data 6

2 Spatial data 9

2.2 Spatial data and GIS 9

2.2.1 Data types 9

2.2.2 Data storage and interchange 11

2.2.3 Data collection and management 12

2.2.4 Data quality 13

2.3 Spatial effects 14

2.3.1 Spatial heterogeneity and dependence 14

2.3.2 Edge effects 14

2.3.3 Representing neighbourhood relationships 15

2.3.4 Statistical significance testing with spatial data 15

3 Spatial visualization 17

3.2 Point data 17

3.3 Aggregated data 17

3.4 Continuous data 23

3.5 Effective data display 23

3.5.1 Media, scale, and area 23

3.5.2 Dynamic display 24

3.5.3 Cartography 26

3.5.3.1 Distance or scale 26

3.5.3.2 Projection 26

3.5.3.3 Direction 27

3.5.3.4 Legends 27

3.5.3.5 Neatlines, and locator and inset maps 27

3.5.3.6 Symbology 27

3.5.3.7 Dealing with statistical generalization 28

4 Spatial clustering of disease and global estimates of spatial clustering 32

4.2 Disease cluster alarms and cluster investigation 32

4.3 Statistical concepts relevant to cluster analysis 33

4.3.1 Stationarity, isotropy, and first- and second-order effects 33

4.3.2 Monte Carlo simulation 33

4.3.3 Statistical power of clustering methods 34

4.4 Methods for aggregated data 34

4.4.1 Moran's I 35

4.4.2 Geary's c 37

4.4.3 Tango's excess events test (EET) and maximized excess events test (MEET) 37

4.5 Methods for point data 37

4.5.1 Cuzick and Edwards' k-nearest neighbour test 37

4.5.2 Ripley's K-function 39

4.5.3 Rogerson's cumulative sum (CUSUM) method 41

4.6 Investigating space-time clustering 41

4.6.1 The Knox test 42

4.6.2 The space-time k-function 42

4.6.3 The Ederer-Myers-Mantel (EMM) test 43

4.6.4 Mantel's test 43

4.6.5 Barton's test 43

4.6.6 Jacquez's k nearest neighbours test 44

5 Local estimates of spatial clustering 45

5.2 Methods for aggregated data 46

5.2.1 Getis and Ord's local Gi(d) statistic 46

5.2.2 Local Moran test 47

5.3 Methods for point data 49

5.3.1 Openshaw's Geographical Analysis Machine (GAM) 49

5.3.2 Turnbull's Cluster Evaluation Permutation Procedure (CEPP) 49

5.3.3 Besag and Newell's method 50

5.3.4 Kulldorff's spatial scan statistic 51

5.3.5 Non-parametric spatial scan statistics 52

5.3.6 Example of local cluster detection 53

5.4 Detecting clusters around a source (focused tests) 56

5.4.1 Stone's test 60

5.4.2 The Lawson-Waller score test 61

5.4.3 Bithell's linear risk score tests 62

5.4.4 Diggle's test 62

5.4.5 Kulldorff's focused spatial scan statistic 62

5.5 Space-time cluster detection 63

5.5.1 Kulldorff's space-time scan statistic 63

5.5.2 Example of space-time cluster detection 64

6 Spatial variation in risk 67

6.2 Smoothing based on kernel functions 67

6.3 Smoothing based on Bayesian models 70

6.4 Spatial interpolation 73

7 Identifying factors associated with the spatial distribution of disease 81

7.2 Principles of regression modelling 81

7.2.1 Linear regression 81

7.2.2 Poisson regression 83

7.2.3 Logistic regression 86

7.2.4 Multilevel models 87

7.3 Accounting for spatial effects 90

7.4 Area data 92

7.4.1 Frequentist approaches 93

7.4.2 Bayesian approaches 94

7.5 Point data 97

7.5.1 Frequentist approaches 97

7.5.2 Bayesian approaches 99

7.6 Continuous data 100

7.6.1 Trend surface analysis 100

7.6.2 Generalized least squares models 102

7.7 Discriminant analysis 103

7.7.1 Variable selection within discriminant analysis 106

8 Spatial risk assessment and management of disease 110

8.2 Spatial data in disease risk assessment 110

8.3 Spatial analysis in disease risk assessment 111

8.4 Data-driven models of disease risk 112

8.5 Knowledge-driven models of disease risk 113

8.5.1 Static knowledge-driven models 113

8.5.2 Dynamic knowledge-driven models 117.

Notes:

Includes bibliographical references and index.

Local Notes:

Presented to the Penn Libraries by Dr. Adrian R. Morrison in honor of his wife Olive R. Morrison.

ISBN:

9780198509882

019850988X

9780198509899

0198509898

OCLC:

183146692