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Tables of Contents for Disease Mapping and Risk Assessment for Public Health
Chapter/Section Title
Page #
Page Count
Editors' Preface
xiii
List of Contributors
xv
PART I: DISEASE MAPPING
Disease Mapping and Its Uses
3
12
Introduction
3
1
Simple statistical representation
4
4
Model-based approaches
8
3
Spatio-temporal modelling
11
2
Conclusions
13
2
Bayesian and Empirical Bayes Approaches to Disease Mapping
15
16
Introduction
15
1
Maximum likelihood estimation of relative risks of mortality
16
3
Hierarchical Bayesian model of relative risks
19
5
Empirical Bayes estimation of relative risks
24
1
Fully Bayesian estimation of the relative risks
25
4
Conclusion
29
2
Addressing Multiple Goals Evaluating Region-Specific Risk Using Bayesian Methods
31
18
Introduction
31
1
Models
32
3
Goals and inferences
35
3
Using Monte-Carlo output
38
1
Scottish lip cancer data analysis
38
7
Conclusion
45
4
Disease Mapping with Hidden Structures Using Mixture Models
49
14
Introduction
49
2
The empirical Bayes approach
51
4
The validity of the mixture model approach for map construction
55
1
Extensions of the mixture model approach
56
2
Discussion and conclusions
58
1
Appendix: Details about the program DismapWin
59
4
PART II: CLUSTERING OF DISEASE
Inference for Extremes in Disease Mapping
63
22
Introduction
63
1
Spatial models for disease incidence or mortality
64
3
Bayesian inference via simulation
67
8
Bayes and constrained Bayes estimates
75
3
Loss functions for extreme values
78
2
Results for the Scotland lip-cancer data
80
5
Edge Effects in Disease Mapping
85
14
Introduction
85
1
Edge effect problems
86
1
Edge effect compensation methods
87
2
A hierarchical Bayesian model for disease mapping of tract count data
89
3
The Tuscany example
92
5
Conclusions
97
2
A Review of Cluster Detection Methods
99
12
Introduction
99
3
Reasons for studying disease clustering
102
2
Definition of clusters and clustering
104
3
Modelling issues
107
1
Hypothesis tests for clustering
108
3
Comparison of General Tests for Spatial Clustering
111
8
Introduction
111
1
Inappropriate tests
111
2
Available tests
113
3
Discussion
116
3
Markov Chain Monte Carlo Methods for Putative Sources of Hazard and General Clustering
119
24
Introduction
119
1
Definitions
120
1
The analysis of health risk related to pollution sources
121
1
The analysis of non-focused disease clustering
122
1
A general model formulation for specific clustering
123
1
Markov chain Monte Carlo methods
124
1
Putative hazard example
125
7
Non-focused clustering example
132
9
Conclusions
141
2
Statistical Evaluation of Disease Cluster Alarms
143
8
Introduction
143
1
Focused cluster tests applied at other similar locations
144
1
Post-alarm monitoring
145
1
The spatial scan statistic
146
1
A proactive approach
147
1
Discussion
148
3
Disease Clustering for Uncertain Locations
151
18
Introduction
151
1
Classical statistics and randomisation tests
152
1
Cluster statistics as randomisation tests
153
1
Sample-based randomisation tests are epidemiologically unreasonable
154
1
Statistical inference
155
2
Location models
157
4
Location model applications
161
1
Spatial randomisation
162
2
Statistical inference for uncertain locations
164
1
An application
164
1
Discussion
165
4
Empirical Studies of Cluster Detection---Different Cluster Tests in Application to German Cancer Maps
169
12
Introduction
169
1
Methods
170
2
Results of application to German cancer mortality data
172
4
Discussion
176
5
PART III: ECOLOGICAL ANALYSIS
Introduction to Spatial Models in Ecological Analysis
181
12
Introduction
181
1
Ecological fallacy in spatial data
182
3
Statistical models
185
6
Example
191
1
Conclusions
191
2
Bayesian Ecological Modelling
193
10
Introduction
193
1
Statistical issues
194
2
Data issues
196
3
Problems with the interpretation of ecological regression studies
199
1
Technical implementation
200
1
Conclusions
200
3
Spatial Regression Models in Epidemiological Studies
203
14
Truncated auto-Poisson vs. random effects Poisson regression
205
3
Model fitting using Monte Carlo Newton--Raphson
208
2
Prostate cancer in Valencia, 1975--1980
210
4
Discussion
214
3
Multilevel Modelling of Area-Based Health Data
217
14
Introduction
217
2
Developing a Poisson spatial multilevel model
219
4
Incidence of prostate cancer in Scottish local authority districts
223
4
Discussion
227
4
PART IV: RISK ASSESSMENT FOR PUTATIVE SOURCES OF HAZARD
A Review of Modelling Approaches in Health Risk Assessment around Putative Sources
231
16
Introduction
231
2
Problems of inference
233
1
Exploratory techniques
234
1
Models for point data
235
7
Models for count data
242
2
Modelling vs. hypothesis testing
244
1
Conclusions
245
2
Disease Mapping Using the Relative Risk Function Estimated from Areal Data
247
10
Introduction
247
1
Definition of the Relative Risk Function
248
1
Estimation of the relative risk function
249
1
Application to childhood cancer data
249
3
General heterogeneity of risk
252
2
Selecting the degree of smoothing
254
1
Discussion
254
3
The Power of Focused Score Tests Under Misspecified Cluster Models
257
14
Introduction
257
1
Models of clustering and score tests
258
3
Homogeneous population results
261
5
Post hoc power analysis: New York leukaemia data
266
2
Discussion
268
3
Case--Control Analysis of Risk around Putative Source
271
16
Introduction
271
1
General definitions
272
2
Estimation
274
4
Crude trend tests on distance from source
278
4
Stratified analysis
282
1
Logistic regression analysis
282
3
Conclusions
285
2
Lung Cancer Near Point Emission Sources
287
8
Introduction
287
1
Intustrial areas
288
1
Urban areas
289
1
Methodological implications
290
2
Conclusions
292
3
PART V: PUBLIC HEALTH APPLICATIONS AND CASE STUDIES
Environmental Epidemiology, Public Health Advocacy and Policy
295
6
Introduction
295
1
`Washing Whiter': A pitfall to avoid
296
2
A societal context to take into account
298
1
Conclusion
299
2
The Character and the Public Health Implications of Ecological Analyses
301
10
Introduction
301
1
Some remarks on the assessment of ecological exposures
302
1
Comment on study designs
303
1
Credibility of ecological analyses
304
1
Scientific aspects
305
2
Societal attitudes
307
1
Conclusions
308
3
Computer Geographic Analysis: A Commentary on Its Use and Misuse in Public Health
311
10
Introduction
311
1
Hypothesis generation
312
4
Ecological studies
316
1
Descriptive/administrative uses of GIS and geographic analysis
317
1
Hypothesis testing
318
1
Conclusion
319
2
Estimating the Presence and the Degree of Heterogeneity of Disease Rates
321
8
Introduction
321
1
The Poisson case
322
2
Binomial case
324
2
Discussion
326
3
Ecological Regression with Errors in Covariates: An Application
329
20
Introduction
329
2
Background and data
331
2
The statistical model
333
7
The data
340
1
Estimation
340
1
Results
340
6
Discussion
346
3
Case Studies in Bayesian Disease Mapping for Health and Health Service Research in Ireland
349
16
Introduction
349
1
Background
350
2
Low birth weight and area deprivation
352
6
Avoidable mortality for asthma
358
5
Conclusion
363
2
An Analysis of Determinants of Regional Variation in Cancer Incidence: Ontario, Canada
365
18
Introduction
365
1
Background and objectives
366
1
Methods
367
2
Results
369
11
Conclusions
380
3
Congenital Anomalies Near Hazardous Waste Landfill Sites in Europe
383
12
Introduction
383
1
Methods
384
4
Results
388
3
Discussion
391
4
An Analysis of the Geographical Distribution of Leukaemia Incidence in the Vicinity of a Suspected Point Source: A Case Study
395
16
Introduction
395
1
Materials and methods
396
5
Results
401
5
Discussion
406
2
Conclusion
408
3
Lung Cancer Mortality in Women in Germany 1995: A Case Study in Disease Mapping
411
42
Introduction
411
1
The data
412
1
The methods
413
40
Appendix: Disease Mapping and Risk Assessment for Public Health Decision Making: Report on a WHO/Biomed2 International Workshop
453
16
Index
469