Spatial Epidemiological Approaches In Disease Mapping And Analysis

Containing method descriptions and step-by-step procedures, the Spatial Epidemiological Approaches in Disease Mapping and Analysis equips readers with skills to prepare health-related data in the proper format, process these data using relevant functions and software, and display the results as mapped or statistical summaries. Describing the wide range of available methods and key GIS concepts for spatial epidemiology, this book illustrates the utilities of the software using real-world data. Additional topics include geographic data models, address matching, geostatistical analysis, universal kriging, point pattern analysis, kernel density, spatio-temporal display, and disease surveillance.

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 imp

Spatial epidemiology is concerned with describing, quantifying and explaining geographical variations in disease, especially with respect to variations in environmental exposures at the small-area scale. The recent and rapid expansion of the field looks set to continue in line with growing public, government and media concern about environment and health issues, and a scientific need to understand and explain the effects of environmental pollutants on health. This book brings together contributions from an international group of practitioners from a wide spectrum of disciplines including epidemiologists, statisticians, geographers, demographers and pollution modellers, providing a comprehensive reference on state-of-the-art methods and applications in the emerging field of spatial epidemiology. The book is divided into four sections. Section one gives an introduction to spatial epidemiological studies and summarises data requirements and problems with respect to modelling health events, including bias and confounding. Section two gives an overview of the state-of-the-art in statistical methodology, including Bayesian approaches to disease mapping, cluster detection, analysis of point exposures, geostatistical methods and methods for ecological correlation studies. Section three gives examples of disease mapping and cluster studies, involving mortality data, communicable disease data, Hodgkins disease, diabetes and childhood leukemias. Section four reviews methods ofexposure assessment for use in spatial epidemiological studies, and discusses possible links between exposure and health data in risk asessment, and in the effects on human health of traffic related pollution, water quality and climate change. This book aims to give an authoritative account of current practice and developments in the field. As such it should be of interest to epidemiologists, public health practitioners, statisticians, geographers, environmental scientists and others concerned with understanding the geographical distribution of disease and the effects of environmental exposures on human health. It will be a a valuable source for undergraduate and postgraduate coursees in epidemiology, medical geography, biostatistics, environmental health and environmental science as well as a useful source of reference for health policy makers, health economists, regulators and others in the field of environmental health.

Providing a practical, comprehensive and up-to-date overview of the use of spatial statistics in epidemiology, this book examines spatial analytical methods in conjunction with GIS and remotely sensed data to provide insights into the patterns and processes that underlie disease transmission.

Since the publication of the first edition, many new Bayesian tools and methods have been developed for space-time data analysis, the predictive modeling of health outcomes, and other spatial biostatistical areas. Exploring these new developments, Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Second Edition provides an up-to-date, cohesive account of the full range of Bayesian disease mapping methods and applications. A biostatistics professor and WHO advisor, the author illustrates the use of Bayesian hierarchical modeling in the geographical analysis of disease through a range of real-world datasets. New to the Second Edition Three new chapters on regression and ecological analysis, putative hazard modeling, and disease map surveillance Expanded material on case event modeling and spatiotemporal analysis New and updated examples Two new appendices featuring examples of integrated nested Laplace approximation (INLA) and conditional autoregressive (CAR) models In addition to these new topics, the book covers more conventional areas such as relative risk estimation, clustering, spatial survival analysis, and longitudinal analysis. After an introduction to Bayesian inference, computation, and model assessment, the text focuses on important themes, including disease map reconstruction, cluster detection, regression and ecological analysis, putative hazard modeling, analysis of multiple scales and multiple diseases, spatial survival and longitudinal studies, spatiotemporal methods, and map surveillance. It shows how Bayesian disease mapping can yield significant insights into georeferenced health data. WinBUGS and R are used throughout for data manipulation and simulation.

Spatial epidemiology is the description and analysis of the geographical distribution of disease. It is more important now than ever, with modern threats such as bio-terrorism making such analysis even more complex. This second edition of Statistical Methods in Spatial Epidemiology is updated and expanded to offer a complete coverage of the analysis and application of spatial statistical methods. The book is divided into two main sections: Part 1 introduces basic definitions and terminology, along with map construction and some basic models. This is expanded upon in Part II by applying this knowledge to the fundamental problems within spatial epidemiology, such as disease mapping, ecological analysis, disease clustering, bio-terrorism, space-time analysis, surveillance and infectious disease modelling. Provides a comprehensive overview of the main statistical methods used in spatial epidemiology. Updated to include a new emphasis on bio-terrorism and disease surveillance. Emphasizes the importance of space-time modelling and outlines the practical application of the method. Discusses the wide range of software available for analyzing spatial data, including WinBUGS, SaTScan and R, and features an accompanying website hosting related software. Contains numerous data sets, each representing a different approach to the analysis, and provides an insight into various modelling techniques. This text is primarily aimed at medical statisticians, researchers and practitioners from public health and epidemiology. It is also suitable for postgraduate students of statistics and epidemiology, as well professionals working in government agencies.

Since the publication of the second edition, many new Bayesian tools and methods have been developed for space-time data analysis, the predictive modeling of health outcomes, and other spatial biostatistical areas. Exploring these new developments, Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Third Edition provides an up-to-date, cohesive account of the full range of Bayesian disease mapping methods and applications. In addition to the new material, the book also covers more conventional areas such as relative risk estimation, clustering, spatial survival analysis, and longitudinal analysis. After an introduction to Bayesian inference, computation, and model assessment, the text focuses on important themes, including disease map reconstruction, cluster detection, regression and ecological analysis, putative hazard modeling, analysis of multiple scales and multiple diseases, spatial survival and longitudinal studies, spatiotemporal methods, and map surveillance. It shows how Bayesian disease mapping can yield significant insights into georeferenced health data. The target audience for this text is public health specialists, epidemiologists, and biostatisticians who need to work with geo-referenced health data.

Focusing on data commonly found in public health databases and clinical settings, Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology provides an overview of the main areas of Bayesian hierarchical modeling and its application to the geographical analysis of disease. The book explores a range of topics in Bayesian inference and