Spatial Epidemiological Approaches In Disease Mapping And Analysis
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Spatial Epidemiological Approaches in Disease Mapping and Analysis by Poh-Chin Lai,Fun-Mun So,Ka-Wing Chan Pdf
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 r
Handbook of Spatial Epidemiology by Andrew B. Lawson,Sudipto Banerjee,Robert P. Haining,Maria Dolores Ugarte Pdf
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.
Spatial Analysis in Epidemiology by Dirk U. Pfeiffer,Timothy P. Robinson,Mark Stevenson,Kim B. Stevens,David J. Rogers,Archie C.A. Clements Pdf
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.
Author : Andrew B. Lawson Publisher : John Wiley & Sons Page : 302 pages File Size : 53,7 Mb Release : 2013-07-08 Category : Medical ISBN : 9781118723173
Statistical Methods in Spatial Epidemiology by Andrew B. Lawson Pdf
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
Dirk U. Pfeiffer,Timothy P. Robinson,Mark Stevenson,Kim B. Stevens,David J. Rogers,Archie C. A. Clements
Author : Dirk U. Pfeiffer,Timothy P. Robinson,Mark Stevenson,Kim B. Stevens,David J. Rogers,Archie C. A. Clements Publisher : OUP Oxford Page : 154 pages File Size : 40,7 Mb Release : 2008-05-29 Category : Medical ISBN : 9780191523274
Spatial Analysis in Epidemiology by Dirk U. Pfeiffer,Timothy P. Robinson,Mark Stevenson,Kim B. Stevens,David J. Rogers,Archie C. A. Clements Pdf
This book provides a practical, comprehensive and up-to-date overview of the use of spatial statistics in epidemiology - the study of the incidence and distribution of diseases. Used appropriately, spatial analytical methods in conjunction with GIS and remotely sensed data can provide significant insights into the biological patterns and processes that underlie disease transmission. In turn, these can be used to understand and predict disease prevalence. This user-friendly text brings together the specialised and widely-dispersed literature on spatial analysis to make these methodological tools accessible to epidemiologists for the first time. With its focus is on application rather than theory, Spatial Analysis in Epidemiology includes a wide range of examples taken from both medical (human) and veterinary (animal) disciplines, and describes both infectious diseases and non-infectious conditions. Furthermore, it provides worked examples of methodologies using a single data set from the same disease example throughout, and is structured to follow the logical sequence of description of spatial data, visualisation, exploration, modelling and decision support. This accessible text is aimed at graduate students and researchers dealing with spatial data in the fields of epidemiology (both medical and veterinary), ecology, zoology and parasitology, environmental science, geography and statistics.
Using R for Bayesian Spatial and Spatio-Temporal Health Modeling by Andrew B. Lawson Pdf
Progressively more and more attention has been paid to how location affects health outcomes. The area of disease mapping focusses on these problems, and the Bayesian paradigm has a major role to play in the understanding of the complex interplay of context and individual predisposition in such studies of disease. Using R for Bayesian Spatial and Spatio-Temporal Health Modeling provides a major resource for those interested in applying Bayesian methodology in small area health data studies. Features: Review of R graphics relevant to spatial health data Overview of Bayesian methods and Bayesian hierarchical modeling as applied to spatial data Bayesian Computation and goodness-of-fit Review of basic Bayesian disease mapping models Spatio-temporal modeling with MCMC and INLA Special topics include multivariate models, survival analysis, missing data, measurement error, variable selection, individual event modeling, and infectious disease modeling Software for fitting models based on BRugs, Nimble, CARBayes and INLA Provides code relevant to fitting all examples throughout the book at a supplementary website The book fills a void in the literature and available software, providing a crucial link for students and professionals alike to engage in the analysis of spatial and spatio-temporal health data from a Bayesian perspective using R. The book emphasizes the use of MCMC via Nimble, BRugs, and CARBAyes, but also includes INLA for comparative purposes. In addition, a wide range of packages useful in the analysis of geo-referenced spatial data are employed and code is provided. It will likely become a key reference for researchers and students from biostatistics, epidemiology, public health, and environmental science.
Disease Mapping by Miguel A. Martinez-Beneito,Paloma Botella-Rocamora Pdf
Disease Mapping: From Foundations to Multidimensional Modeling guides the reader from the basics of disease mapping to the most advanced topics in this field. A multidimensional framework is offered that makes possible the joint modeling of several risks patterns corresponding to combinations of several factors, including age group, time period, disease, etc. Although theory will be covered, the applied component will be equally as important with lots of practical examples offered. Features: Discusses the very latest developments on multivariate and multidimensional mapping. Gives a single state-of-the-art framework that unifies most of the previously proposed disease mapping approaches. Balances epidemiological and statistical points-of-view. Requires no previous knowledge of disease mapping. Includes practical sessions at the end of each chapter with WinBUGs/INLA and real world datasets. Supplies R code for the examples in the book so that they can be reproduced by the reader. About the Authors: Miguel A. Martinez Beneito has spent his whole career working as a statistician for public health services, first at the epidemiology unit of the Valencia (Spain) regional health administration and later as a researcher at the public health division of FISABIO, a regional bio-sanitary research center. He has been also the Bayesian Hierarchical Models professor for several seasons at the University of Valencia Biostatics Master. Paloma Botella Rocamora has spent most of her professional career in academia although she now works as a statistician for the epidemiology unit of the Valencia regional health administration. Most of her research has been devoted to developing and applying disease mapping models to real data, although her work as a statistician in an epidemiology unit makes her develop and apply statistical methods to health data, in general.
Localization is involved everywhere in epidemiology: health phenomena often involve spatial relationships among individuals and risk factors related to geography and environment. Therefore, the use of localization in the analysis and comprehension of health phenomena is essential. This book describes the objectives, principles, methods and tools of spatial analysis and geographic information systems applied to the field of health, and more specifically to the study of the spatial distribution of disease and health–environment relationships. It is a practical introduction to spatial and spatio-temporal analysis for epidemiology and health geography, and takes an educational approach illustrated with real-world examples. Epidemiology and Geography presents a complete and straightforward overview of the use of spatial analysis in epidemiology for students, public health professionals, epidemiologists, health geographers and specialists in health–environment studies.
GIS and Health by Anthony Gattrell,Markku Löytönen Pdf
Methodological Issues. GIS and Health Research: An Introduction. GIS as an Enabling Technology. Spatial Statistics and the Analysis of Health Data. Statistical Methods for Spatial Epidemiology: Tests for Randomness. Improving the Geographic Basis of Health Surveillance Using GIS. Modelling Spatial Variations in Air Quality Using GIS. GIS, Time Geography and Health. Applications. GIS Applications for Environment and Health in Italy. A Multipurpose Interactive Mortality Atlas of Italy. Bayesian Analysis of Emerging Neoplasms in Spain. The Development of an Epidemiological Spatial Information Sys ...
Andrew B. Lawson,William J. Browne,Carmen L. Vidal Rodeiro
Author : Andrew B. Lawson,William J. Browne,Carmen L. Vidal Rodeiro Publisher : John Wiley & Sons Page : 292 pages File Size : 54,7 Mb Release : 2003-10-31 Category : Mathematics ISBN : 9780470856055
Disease Mapping with WinBUGS and MLwiN by Andrew B. Lawson,William J. Browne,Carmen L. Vidal Rodeiro Pdf
Disease mapping involves the analysis of geo-referenced disease incidence data and has many applications, for example within resource allocation, cluster alarm analysis, and ecological studies. There is a real need amongst public health workers for simpler and more efficient tools for the analysis of geo-referenced disease incidence data. Bayesian and multilevel methods provide the required efficiency, and with the emergence of software packages – such as WinBUGS and MLwiN – are now easy to implement in practice. Provides an introduction to Bayesian and multilevel modelling in disease mapping. Adopts a practical approach, with many detailed worked examples. Includes introductory material on WinBUGS and MLwiN. Discusses three applications in detail – relative risk estimation, focused clustering, and ecological analysis. Suitable for public health workers and epidemiologists with a sound statistical knowledge. Supported by a Website featuring data sets and WinBUGS and MLwiN programs. Disease Mapping with WinBUGS and MLwiN provides a practical introduction to the use of software for disease mapping for researchers, practitioners and graduate students from statistics, public health and epidemiology who analyse disease incidence data.
Spatial Analysis in Health Geography by Pavlos Kanaroglou,Eric Delmelle Pdf
Presenting current research on spatial epidemiology, this book covers topics such as exposure, chronic disease, infectious disease, accessibility to health care settings and new methods in Geographical Information Science and Systems. For epidemiologists, and for the management and administration of health care settings, it is critical to understand the spatial dynamics of disease. For instance, it is crucial that hospital administrators develop an understanding of the flow of patients over time, especially during an outbreak of a particular disease, so they can plan for appropriate levels of staffing and to carry out adaptive prevention measures. Furthermore, understanding where and why a disease occurs at a certain geographic location is vital for decision makers to formulate policy to increase the accessibility to health services (either by prevention, or adding new facilities). Spatial epidemiology relies increasingly on new methodologies, such as clustering algorithms, visualization and space-time modelling, the domain of Geographic Information Science. Implementation of those techniques appears at an increasing pace in commercial Geographic Information Systems, alongside more traditional techniques that are already part of such systems. This book provides the latest methods in GI Science and their use in health related problems.