Statistics for the Environment, Statistical Aspects of Health and the Environment

, by Barnett, Vic; Stein, Alfred; Turkman, K. Feridun

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ISBN: 9780471976455 | 0471976458
Cover: Hardcover
Copyright: 3/12/1999

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In line with the SPRUCE initiative, concerned as it is with Statistics in Public Resources, Utilities and Care of the Environment, the recent conference brought together leading workers from a widely multidisciplinary background. The SPRUCE IV programme was divided into five thematic sections: * Small Area Studies and Disease Mapping * Atmospheric Pollution Studies * Disease Risks and Social Effects * Effects of Radiation * Agriculture and the Food Chain Providing a stimulating overview of current methods and developing research trends across the field of Statistical Aspects of Health and the Environment, this volume presents a co-ordinated and structured review of the recent SPRUCE conference. This volume is the fourth in the Wiley Series on Statistics and the Environment. Environmental issues continue to be of major concern to scientists and those involved with policy formulation. The concise presentation of arguments, statistical models and their application will make this volume essential reading.

Preface

xiii

(2)

Introduction

PART I SMALL AREA STUDIES AND DISEASE MAPPING

(70)

1 Small-Area Studies of Environment and Health

(26)

P. Elliott

J. C. Wakefield

1.1 Introduction

(2)

1.2 An idealized model of environment-disease relationships

(1)

1.3 Data

(7)

1.3.1 Health data

(1)

1.3.2 Population data

(2)

1.3.3 Environmental exposure data

(2)

1.3.4 Confounder data

(2)

1.4 Questions of scale

(1)

1.5 Statistical framework for small-area studies

(2)

1.6 Conduct and analysis of small-area studies

(10)

1.6.1 Point and line source exposures

(5)

1.6.2 Disease mapping

(2)

1.6.3 Ecological correlation studies of environment and health

(2)

1.6.4 Cluster investigation

(1)

1.7 Conclusion

(1)

Acknowledgements

(2)

2 Bayesian Analysis of Ecological Data for Studying the Association Between Insulin-Dependent Diabetes Mellitus and Malaria

(20)

L. Bernardinelli

C. Pascutto

C. Montomoli

J. Komakec

W. Gilks

M. Songini

O. Fiorani

A. Lisa

G. Zei

G. Solinas

G. F. Bottazzo

2.1 Introduction

(2)

2.2 Data

(1)

2.2.1 IDDM

(1)

2.2.2 Malaria

(1)

2.2.3 Altitude

(1)

2.3 Analysis of spatial variation of IDDM risk

(3)

2.3.1 The Bayesian model for IDDM risk

(1)

2.3.2 A Markov random field prior

(1)

2.3.3 Measurement error in the prevalence of malaria

(1)

2.4 Analysis of temporal variation of IDDM risk

(2)

2.5 Estimation

(1)

2.6 Results

(6)

2.6.1 Spatial analysis

(4)

2.6.2 Temporal analysis

(2)

2.7 Conclusions

(3)

2.7.1 Sustantive conclusions

(1)

2.7.2 Methodological issues

(2)

Acknowledgements

(2)

3 On the Analysis of Spatial Point Process Data with Inaccurately Observed Covariate Information

(12)

J. Currie

3.1 Introduction

(1)

3.2 Monte Carlo simulation study

(7)

3.2.1 Simulation of an inhomogeneous planar Poisson process

(3)

3.2.2 Asymptotic approximations

(2)

3.2.3 Comparing the empirical and estimated variance of coefficient estimates

(1)

3.2.4 Estimating the signal

(1)

3.3 Results

(3)

3.3.1 Prediction error only

(1)

3.3.2 Prediction and measurement error

(2)

3.4 Discussion and further work

(1)

4 Geographical and Statistical Patterns of Cholera

(10)

R. J. Borroto

E. Duarte

R. Martinez-Piedra

4.1 Introduction

(1)

4.2 Methods

(2)

4.3 Results

(2)

4.4 Four hypotheses

(3)

4.5 Research limitations

(1)

4.6 Conclusions

(1)

Appendix

(1)

PART II ATMOSPHERIC POLLUTION STUDIES

(80)

5. Quantifying the Risks from Residential Radon

(18)

E. Whitley

S. C. Darby

5.1 Introduction

(2)

5.2 Radon as a carcinogen

(3)

5.3 Residential radon exposure

(2)

5.4 Challenges in quantifying residential radon risk

(11)

5.4.1 Study design

(1)

5.4.2 Study population

(1)

5.4.3 Smokers vs. non-smokers

(1)

5.4.4 Coverage of radon measurements

(1)

5.4.5 Estimation of missing values

(2)

5.4.6 Uncertainties of radon measurements

(1)

5.4.7 Methods of analysis that take uncertainties into account

(1)

5.4.8 Pooling of individual studies

(3)

6 Human Health Effects of Environmental Pollution in the Atmosphere

(26)

R. L. Smith

J. M. Davis

P. Speckman

6.1 Introduction

(1)

6.2 Methodology

(3)

6.3 Linear modelling results

(2)

6.4 Non-linear relationships between PM(10) and mortality

(3)

6.5 Alternative approach via generalized additive models

100

(2)

6.6 Mortality displacement

102

(11)

6.6.1 A model for mortality displacement

104

(2)

6.6.2 Bayesian inference

106

(2)

6.6.3 Results

108

(2)

6.6.4 A simulation study

110

(2)

6.6.5 Conclusions

112

(1)

6.7 Other issues

113

(1)

6.8 Conclusions

114

(1)

Acknowledgements

115

(2)

7 Using Spatial Data in Assessing the Association Between Air Pollution Episodes and Respiratory Morbidity

117

(20)

J.V. Zidek

R. White

N. D. Le

7.1 Introduction

117

(2)

7.2 Methods that use all relevant information

119

(7)

7.2.1 Relevance weighted likelihood methods

120

(3)

7.2.2 Hierarchical Bayes methods

123

(2)

7.2.3 Discussion

125

(1)

7.3 Cluster-specific methods for health impact assessment

126

(5)

7.4 Cluster-specific health impact analysis and conclusions

131

(4)

Acknowledgements

135

(2)

8 Spatial Models of Pollution Exposure from a Road Network: The Effect on Asthma

137

(14)

K. Rennolls

Y. Zhang

N. I. Ramesh

A. Chen

S. Patel

8.1 Introduction

137

(1)

8.2 Data

138

(2)

8.2.1 The post-code file

139

(1)

8.2.2 Roads

139

(1)

8.3 Models

140

(4)

8.3.1 Spread of pollution exposure

141

(1)

8.3.2 Combining the exposures from separate point sources

141

(1)

8.3.3 Exposure from a road line-segment

142

(1)

8.3.4 Total pollution exposure

142

(1)

8.3.5 Models of asthma incidence

143

(1)

8.3.6 Inference

144

(1)

8.4 Results

144

(5)

8.4.1 Numerical optimization difficulties

144

(1)

8.4.2 Asthma prevalence

144

(1)

8.4.3 Parameter constraints

145

(1)

8.4.4 Estimated models

145

(4)

8.5 Discussion

149

(2)

PART III DISEASE RISKS AND SOCIAL EFFECTS

151

(84)

9 Deriving Safe Exposure Levels for Chemicals from Animal Studies Using Statistical Methods: Recent Developments

153

(22)

W. Slob

9.1 Introduction

153

(1)

9.2 Current procedure

154

(2)

9.3 The benchmark approach as an alternative to the NOAEL

156

(10)

9.3.1 Critical effect size (CES)

157

(4)

9.3.2 Choice of model

161

(3)

9.3.3 Choice of study design

164

(2)

9.4 Distributions for extrapolation factors

166

(4)

9.4.1 Quantification of EF distributions

169

(1)

9.5 Illustrative examples

170

(2)

9.5.1 Deriving an RfD (TDI, ADI)

170

(2)

9.5.2 Estimating actual risk

172

(1)

9.6 Discussion

172

(3)

10 What's the Point? Exploring the Spatial Epidemiology of Motor Neurone Disease in Part of North-West England

175

(18)

A. C. Gatrell

C. E. Sabel

J. D. Mitchell

10.1 Introduction

175

(1)

10.2 On spatial epidemiology

176

(4)

10.3 On time in spatial epidemiology

180

(2)

10.4 Motor neurone disease

182

(3)

10.4.1 Incidence

183

(1)

10.4.2 Aetiology

183

(2)

10.5 Analysis of motor neurone disease in Lancashire and South Cumbria

185

(6)

10.5.1 Does MND show evidence of clustering?

186

(1)

10.5.2 Associations with water quality

186

(2)

10.5.3 Epidemiological findings

188

(1)

10.5.4 Spatial analysis of residential locations

188

(3)

10.6 Conclusions

191

(1)

Acknowledgements

192

(1)

11 Decline of Semen Quality During the Last 50 Years and Possible Links With Environmental Chemicals

193

(16)

N. Keiding

11.1 Introduction

193

(1)

11.2 Ecological analysis

194

(5)

11.2.1 Meta-analysis issues

195

(1)

11.2.2 Ecological analysis issues

196

(1)

11.2.3 Debate following Carlsen et al.

197

(2)

11.3 Individual case series

199

(2)

11.4 The oestrogen hypothesis and various reactions

201

(4)

11.4.1 Reactions in the media

202

(1)

11.4.2 Reactions from the chemical industry

203

(1)

11.4.3 Reaction of governmental and inter-governmental agencies

204

(1)

11.5 Epidemiological research strategy issues

205

(1)

11.5.1 Trends in sex ratios among newborns

205

(1)

11.5.2 Future studies on semen quality

205

(1)

Appendix: Assessing effects of age, period and cohort on human sperm concentration

206

(2)

Acknowledgement

208

(1)

12 Quantitative Risk Assessment for Developmental Toxicants in Non-Homogeneous Populations

209

(12)

M. Razzaghi

12.1 Introduction

209

(2)

12.2 The quasi-likelihood method

211

(2)

12.3 Dose-response modelling

213

(1)

12.4 Quantitative risk assessment

214

(2)

12.5 Application

216

(3)

12.6 Discussion

219

(1)

Acknowledgements

220

(1)

13 An Approach to Intervention Analysis in Spatio-Temporal Modelling

221

(14)

O. Thas

J. P. Ottoy

13.1 Introduction

221

(1)

13.2 Statistical spatio-temporal models

222

(1)

13.3 Kalman filter as a statistical tool

222

(1)

13.4 Spatio-temporal model for rivers

223

(5)

13.4.1 Spatial structure

223

(1)

13.4.2 Temporal structure

224

(1)

13.4.3 The complete spatio-temporal model

224

(1)

13.4.4 Structural equation model

225

(1)

13.4.5 State-space model: reformulation

226

(1)

13.4.6 Non-zero mean model

227

(1)

13.5 Parameter estimation and statistical inference

228

(2)

13.5.1 Kalman filter

228

(2)

13.5.2 Statistical inference

230

(1)

13.6 Case study

230

(3)

13.6.1 Data description

230

(2)

13.6.2 Mean model

232

(1)

13.6.3 Statistical analysis

232

(1)

13.7 Discussion and conclusion

233

(1)

Acknowledgement

234

(1)

PART IV EFFECTS OF RADIATION

235

(56)

14 Stochastic Models for Estimation and Prediction of Cancer Risk

237

(22)

S. H. Moolgavkar

14.1 Introduction

237

(2)

14.2 Multistage models

239

(1)

14.3 The Armitage-Doll multistage model

239

(2)

14.4 The two-mutation clonal expansion model

241

(6)

14.5 Some properties of the hazard functions of multistage models

247

(2)

14.6 Incidence of solid cancers among A-bomb survivors

249

(4)

14.7 Implications for radiation risk assessment

253

(4)

Acknowledgements

257

(2)

15 Bayesian Modelling of Measurement Error Problems with Reference to the Analysis of Atomic Bomb Survivor Data

259

(22)

S. Richardson

I. Deltour

15.1 Introduction

259

(2)

15.2 Error model structures and graphical representation

261

(3)

15.2.1 Surrogate information recorded for each individual

261

(1)

15.2.2 Group-level risk factor information

262

(2)

15.3 Bayesian analyses of measurement error problems

264

(6)

15.3.1 Principles

264

(1)

15.3.2 Conditional independence structure

265

(1)

15.3.3 Designs

266

(4)

15.4 Measurement error modelling in the analysis of the atomic bomb survivors data

270

(8)

15.4.1 Context

270

(1)

15.4.2 Individual level model

271

(2)

15.4.3 Data structure released from RERF

273

(1)

15.4.4 Group level model

274

(2)

15.4.5 Implementation and summary of findings

276

(2)

15.5 Discussion

278

(1)

Acknowledgement

279

(2)

16 Piecewise Linear Cox Model for Estimating Relative Risks Adjusting for the Heterogeneity of the Sample

281

(10)

T. Nakamura

K. Akazawa

N. Kinukawa

Y. Nose

16.1 Introduction

281

(1)

16.2 Atomic bomb survivors data

282

(3)

16.3 Cancer clinical trial 1

285

(2)

16.4 Cancer clinical trial 2

287

(1)

16.5 Discussion

288

(1)

Acknowledgements

289

(2)

PART V AGRICULTURE AND THE FOOD CHAIN

291

(76)

17 Environmental Threshold Values for Agricultural Production Systems Varying in Space and Time

293

(10)

I. Bouma

J. Verhagen

17.1 Introduction

293

(1)

17.2 Materials and methods

294

(2)

17.2.1 A farmer's field

294

(1)

17.2.2 Using simulation modelling to obtain data for production and leaching

295

(1)

17.2.3 Geostatistical techniques

295

(1)

17.3 Results and discussion

296

(4)

17.3.1 Potato production in 1994

296

(1)

17.3.2 Backward-looking precision fertilization in 1994

297

(1)

17.3.3 Weather variation among the years

298

(1)

17.3.4 Leaching in the wet season

299

(1)

17.3.5 Exceedance of thresholds in space and time: making choices

299

(1)

17.4 Conclusions

300

(3)

18 Linear and Non-Linear Kriging Methods: Tools for Monitoring Soil Pollution

303

(34)

A. Papritz

R. A. Moyeed

18.1 Introduction

303

(4)

18.2 Review of spatial mean square prediction

307

(10)

18.2.1 Linear kriging (SK, UK, OK)

308

(2)

18.2.2 Lognormal kriging (LK)

310

(1)

18.2.3 Disjunctive kriging (DK)

311

(3)

18.2.4 Indicator kriging (IK)

314

(2)

18.2.5 Model-based kriging (MK)

316

(1)

18.3 Data sets

317

(4)

18.3.1 Copper and cobalt in the Border region (SE Scotland)

318

(1)

18.3.2 Heavy metals around La Chaux-de-Fonds (Swiss Jura)

319

(2)

18.3.3 Simulated data

321

(1)

18.4 Implementation

321

(3)

18.4.1 Estimating and modelling the variograms

322

(1)

18.4.2 Kriging

322

(2)

18.5 Validation criteria

324

(1)

18.5.1 Precision of prediction

324

(1)

18.5.2 Modelling prediction uncertainty

324

(1)

18.6 Results

325

(8)

18.6.1 Co data Jura

325

(1)

18.6.2 Skewed heavy metal data

326

(5)

18.6.3 Simulated data

331

(2)

18.7 Discussion and conclusions

333

(2)

Acknowledgements

335

(2)

19 Bayesian Discrimination with Uncertain Covariates for Pesticide Contamination

337

(18)

D. A. Wooff

A. H. Seheult

F. P. A. Coolen

F. Worrall

19.1 Introduction

337

(1)

19.2 Data description

338

(4)

19.2.1 Groundwater database

338

(2)

19.2.2 Chemical properties database

340

(1)

19.2.3 Data quality and plausibility

341

(1)

19.3 Discriminating leachers from non-leachers

342

(3)

19.4 Are the EA data consistent with the Gustafson model?

345

(6)

19.4.1 Prior information for x(1) and x(2)

345

(1)

19.4.2 Prior information for Beta and Epsilon

346

(1)

19.4.3 Bayes linear estimation of the discrimination model

346

(2)

19.4.4 Choosing K(oc) and t(soil)1/2 values for the EA data

348

(3)

19.5 Discussion

351

(4)

20 Assessing Monitoring Strategies by Statistical Methods-Application to Soil Contamination Studies

355

(12)

J. Gampe

20.1 Introduction

355

(1)

20.2 Data description and exploratory analysis

356

(2)

20.3 Searching for models-transformation of variables

358

(4)

20.3.1 Choosing transformations

359

(1)

20.3.2 Parametric model estimation

360

(2)

20.4 Supporting the model by non-parametric regression

362

(3)

20.4.1 Smoothing splines with correlated error terms

362

(3)

20.5 Concluding remarks

365

(1)

Acknowledgments

366

(1)

References

367

(28)

Index

395

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Statistics for the Environment, Statistical Aspects of Health and the Environment

Summary

Author Biography

Table of Contents

Supplemental Materials