Handbook of Spatial Epidemiology: 1st Edition (Hardback) book cover

Handbook of Spatial Epidemiology

1st Edition

Edited by Andrew B. Lawson, Sudipto Banerjee, Robert P. Haining, Maria Dolores Ugarte

Chapman and Hall/CRC

686 pages | 129 B/W Illus.

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pub: 2016-04-04
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Handbook of Spatial Epidemiology explains how to model epidemiological problems and improve inference about disease etiology from a geographical perspective. Top epidemiologists, geographers, and statisticians share interdisciplinary viewpoints on analyzing spatial data and space–time variations in disease incidences. These analyses can provide important information that leads to better decision making in public health.

The first part of the book addresses general issues related to epidemiology, GIS, environmental studies, clustering, and ecological analysis. The second part presents basic statistical methods used in spatial epidemiology, including fundamental likelihood principles, Bayesian methods, and testing and nonparametric approaches. With a focus on special methods, the third part describes geostatistical models, splines, quantile regression, focused clustering, mixtures, multivariate methods, and much more. The final part examines special problems and application areas, such as residential history analysis, segregation, health services research, health surveys, infectious disease, veterinary topics, and health surveillance and clustering.

Spatial epidemiology, also known as disease mapping, studies the geographical or spatial distribution of health outcomes. This handbook offers a wide-ranging overview of state-of-the-art approaches to determine the relationships between health and various risk factors, empowering researchers and policy makers to tackle public health problems.


"In 2008, CRC Press started publishing the Handbooks of Modern Statistical Methods. Apparently the series ispopular as it is growing rapidly, with 13 volumesprinted now and 7 announced. It is easy to understandwhy: the books are attractive in content, presentationand price. The present volume is no exception.The book has been edited by first-class experts, whoalso contributed a number of chapters. The book’swebsite gives a table of the contents of the 35chapters. It documents the rich variety of subjects. … I can only recommend this book."

—Paul Eilers, ISCB News, May 2017

Table of Contents


Integration of Different Epidemiologic Perspectives and Applications to Spatial Epidemiology

Sara Wagner Robb, Sarah E. Bauer, and John E. Vena

Environmental Studies

Mark J. Nieuwenhuijsen

Interpreting Clusters of Health Events

Geoffrey Jacquez and Jared Aldstadt

Geographic Information Systems in Spatial Epidemiology and Public Health

Robert Haining and Ravi Maheswaran

Ecological Modeling: General Issues

Jon Wakefield and Theresa R. Smith

Basic Methods

Case Event and Count Data Modeling

Andrew B. Lawson

Bayesian Modeling and Inference

Georgiana Onicescu and Andrew B. Lawson

Statistical Tests for Clustering and Surveillance

Peter A. Rogerson and Geoffrey Jacquez

Scan Tests

Inkyung Jung

Kernel Smoothing Methods

Martin L. Hazelton

Special Methods

Geostatistics in Small-Area Health Applications

Patrick E. Brown

Splines in Disease Mapping

Tomás Goicoa, Jaione Etxeberria, and María Dolores Ugarte

Quantile Regression for Epidemiological Applications

Brian J. Reich

Focused Clustering: Statistical Analysis of Spatial Patterns of Disease around Putative Sources of Increased Risk

Lance A. Waller, David C. Wheeler, and Jeffrey M. Switchenko

Estimating the Health Impact of Air Pollution Fields

Duncan Lee and Sujit K. Sahu

Data Assimilation for Environmental Pollution Fields

Howard H. Chang

Spatial Survival Models

Sudipto Banerjee

Spatial Longitudinal Analysis

Andrew B. Lawson

Spatiotemporal Disease Mapping

Andrew B. Lawson and Jungsoon Choi

Mixtures and Latent Structure in Spatial Epidemiology

Md. Monir Hossain and Andrew B. Lawson

Bayesian Nonparametric Modeling for Disease Incidence Data

Athanasios Kottas

Multivariate Spatial Models

Sudipto Banerjee

Special Problems and Applications

Bayesian Variable Selection in Semiparametric and Nonstationary Geostatistical Models: An Application to Mapping Malaria Risk in Mali

Federica Giardina, Nafomon Sogoba, and Penelope Vounatsou

Computational Issues and R Packages for Spatial Data Analysis

Marta Blangiardo and Michela Cameletti

The Role of Spatial Analysis in Risk-Based Animal Disease Management

Kim B. Stevens and Dirk U. Pfeiffer

Infectious Disease Modelling

Michael Höhle

Spatial Health Surveillance

Ana Corberán-Vallet and Andrew B. Lawson

Cluster Modeling and Detection

Andrew B. Lawson

Spatial Data Analysis for Health Services Research

Brian Neelon

Spatial Health Survey Data

Christel Faes, Yannick Vandendijck, and Andrew B. Lawson

Graphical Modeling of Spatial Health Data

Adrian Dobra

Smoothed ANOVA Modeling

Miguel A. Martinez-Beneito, James S. Hodges, and Marc Marí-Dell’Olmo

Sociospatial Epidemiology: Segregation

Sue C. Grady

Sociospatial Epidemiology: Residential History Analysis

David C. Wheeler and Catherine A. Calder

Spatiotemporal Modeling of Preterm Birth

Joshua L. Warren, Montserrat Fuentes, Amy H. Herring, and Peter H. Langlois


About the Editors

Andrew B. Lawson is a professor of biostatistics in the Division of Biostatistics, Department of Public Health Sciences, College of Medicine at the Medical University of South Carolina (MUSC). He is an MUSC eminent scholar and American Statistical Association (ASA) fellow. He is also an advisor in disease mapping and risk assessment for the World Health Organization, the founding editor of the journal Spatial and Spatio-Temporal Epidemiology, and the author of eight books, including the highly regarded Chapman & Hall/CRC book Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Second Edition. He has published more than 150 journal articles on spatial epidemiology, spatial statistics, and related areas. He earned a PhD in spatial statistics from the University of St. Andrews.

Sudipto Banerjee is a professor and chair in the Department of Biostatistics at the University of California, Los Angeles. He is an elected fellow of the ASA, the Institute of Mathematical Statistics, and the International Statistical Institute. He is also a recipient of the Mortimer Spiegelman Award from the American Public Health Association. He is the author/coauthor of more than 100 peer-reviewed publications and two highly regarded Chapman & Hall/CRC books: Hierarchical Modeling and Analysis for Spatial Data, Second Edition and Linear Algebra and Matrix Analysis for Statistics. His research interests include hierarchical modeling and Bayesian inference for spatially referenced data.

Robert Haining retired as a professor of human geography from the University of Cambridge in September 2015. He is the author/coauthor of more than 150 articles and two books. His research focuses on the quantitative analysis of geographical data, including the geography of health, spatial representation, spatial sampling, exploratory data analysis, small-area estimation and hypothesis testing, spatial data analysis, and spatial econometrics. His past work has involved the evaluation of the impact of air pollution on health status using small-area statistics as well as the development of new methods for evaluating the effectiveness of small-area targeted police interventions.

María Dolores Ugarte is a professor of statistics at the Public University of Navarre. She is the author/coauthor of many papers on statistics and epidemiology and several books, including the recent Chapman & Hall/CRC book Probability and Statistics with R, Second Edition. She is also an associate editor for Statistical Modelling, TEST, and Computational Statistics and Data Analysis as well as an editorial panel member of Spatial and Spatio-Temporal Epidemiology. Her research focuses on spatiotemporal disease mapping and small-area estimation with applications in several fields. She earned a PhD in statistics from the Public University of Navarre.

About the Series

Chapman & Hall/CRC Handbooks of Modern Statistical Methods

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Subject Categories

BISAC Subject Codes/Headings:
MATHEMATICS / Probability & Statistics / General
MEDICAL / Epidemiology
SCIENCE / Environmental Science