Spatio-Temporal Methods in Environmental Epidemiology

Spatio-Temporal Methods in Environmental Epidemiology

by Gavin Shaddick, James V. Zidek


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Product Details

ISBN-13: 9781482237030
Publisher: Taylor & Francis
Publication date: 07/07/2015
Series: Chapman & Hall/CRC Texts in Statistical Science Series
Pages: 395
Product dimensions: 6.20(w) x 9.40(h) x 1.00(d)

About the Author

Gavin Shaddick is a reader in statistics in the Department of Mathematical Sciences at the University of Bath. He received his master’s in applied stochastic systems from University College London and his PhD in statistics and epidemiology from Imperial College London.

His research interests include the theory and application of Bayesian statistics to the areas of spatial epidemiology, environmental health risk, and the modeling of spatio-temporal fields of environmental hazards. Of particular interest are computational techniques that allow the implementation of complex statistical models to real-life applications where the scope over both space and time may be very large.

Dr. Shaddick is actively involved in a number of substantive epidemiological projects related to the effects of air pollution to health. He has worked on many large-scale funded projects, including the high-resolution mapping of environmental pollutants, the utilization of information from multiple sources in estimating exposures to environmental hazards, and the characterization of uncertainty in scenario assessment and policy support.

He is a co-author of the Oxford Handbook of Epidemiology for Clinicians, which was Highly Commended in the Basis of Medicine Category, BMA Book Awards 2013.

James V. Zidek is a professor emeritus in the Department of Statistics at the University of British Columbia. Professor Zidek received his MSc and PhD in statistics from the University of Alberta and Stanford University, respectively.

He began his research career working on Wald’s statistical decision theory. That interest shifted into Bayesian decision analysis. His interest in applications also emerged early in his career and as a consultant, published with engineering collaborators, the first design code for long-span bridges, such as the famous Golden Gate Bridge in San Francisco. The combination of theory and practice led him to an EPA project on acid rain where he, with a few of his collaborators, started to lay the foundations of environmetrics as it is now called, notably on the design of environmental monitoring networks and spatio-temporal modeling of environmental processes. That work led naturally into spatio-temporal epidemiology, which remains an area of interest. He has published about 100 refereed articles and a book on modeling environmental processes.

His contributions to statistics have been recognized by a number of honors. He is a fellow of the ASA, IMS, and Royal Society of Canada; member of the ISI; and a recipient of the Gold Medal of the Statistical Society of Canada (its highest honor).

Table of Contents

Why spatio-temporal epidemiology?
Health-exposure models
Dependencies over space and time
Examples of spatio-temporal epidemiological analyses
Bayesian hierarchical models
Spatial data
Good spatio-temporal modelling approaches

Modelling health risks
Types of epidemiological study
Measures of risk
Standardised mortality ratios (SMRs)
Generalised linear models
Generalised additive models
Generalised estimating equations
Poisson models for count data
Estimating relative risks in relation to exposures
Modelling the cumulative effects of exposure
Logistic models for case-controls studies

The importance of uncertainty
The wider world of uncertainty
Quantitative uncertainty
Methods for assessing uncertainty
Quantifying uncertainty

Embracing uncertainty: the Bayesian approach
Introduction to Bayesian inference
Using the posterior for inference
Transformations of parameters
Prior formulation

The Bayesian approach in practice
Analytical approximations
Markov chain Monte Carlo (MCMC)
Using samples for inference

Strategies for modelling
Hierarchical models
Generalised linear mixed models
Linking exposure and health models
Model selection and comparison
What about the p-value?
Comparison of models—Bayes factors
Bayesian model averaging

Is ‘real’ data always quite so real?
Missing Values
Measurement error
Preferential sampling

Spatial patterns in disease
The Markov random field (MRF)
The conditional autoregressive (CAR) model
Spatial models for disease mapping

From points to fields: modelling environmental hazards over space
A brief history of spatial modelling
Exploring spatial data
Modelling spatial data
Spatial trend
Spatial prediction
Stationary and isotropic spatial processes
Fitting variogram models
Extensions of simple kriging
A hierarchical model for spatially varying exposures
INLA and spatial modelling in a continuous domain
Non-stationary random fields

Why time also matters
Time series epidemiology
Time series modelling
Modelling the irregular components
The spectral representation theorem and Bochner’s lemma
State space models
A hierarchical model for temporally varying exposures

The interplay between space and time in exposure assessment
Spatio-temporal models
Dynamic linear models for space and time
An empirical Bayes approach
A hierarchical model for spatio-temporal exposure data
Approaches to modelling non-separable processes

Roadblocks on the way to causality: exposure pathways, aggregation and other sources of bias
Ecological bias
Acknowledging ecological bias
Exposure pathways
Personal exposure models

Better exposure measurements through better design
Design objectives?
Design paradigms
Geometry-based designs
Probability-based designs
An entropy-based approach
Implementation challenges

New frontiers
Non-stationary fields
Physical–statistical modelling
The problem of extreme values

Appendix 1: Distribution theory
Appendix 2: Entropy decomposition



Author index

A Summary and Exercises appear at the end of each chapter.

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