Theory of illness causation is an important issue in all biomedical sciences, and solid etiological explanations are needed in order to develop therapeutic approaches in medicine and preventive interventions in public health. Until now, the literature about the theoretical underpinnings of illness causation research has been scarce and fragmented, and lacking a convenient summary. This interdisciplinary book provides a convenient and accessible distillation of the current status of research into this developing field, and adds a personal flavor to the discussion by proposing the etiological stance as a comprehensive approach to identify modifiable causes of illness. Key Features * Provides a synthesis of the epidemiological and philosophical concepts in this growing research area * Gives an accessible overview of current methods in biomedical causal metaphysics what is a cause of illness? and epistemology how do we identify it? * Proposes a novel approach that integrates modern epidemiological methodology and recent theories from philosophy of science Written for postgraduate students and researchers in the health and biomedical sciences, including those undertaking courses in the philosophy of medicine/science, public and global health, introduction to epidemiology, research methods, and advanced reasoning, the content will also be of interest to practicing public health workers, biomedical scientists, and physicians. ABOUT THE AUTHOR Olaf Dammann is Professor and Vice Chair of Public Health and Community Medicine at Tufts University School of Medicine, Boston, Massachusetts, USA; as well as a Professor in the Department of Gynecology and Obstetrics at Hannover Medical School, Hannover, Germany. Cover image: Mask used by "Eskimo" shaman in causation of illness. Credit: Wellcome Collection. CC BY https://creativecommons.org/licenses/by/4.0

Medical Data Management is a systematic introduction to the basic methodology of professional clinical data management. It emphasizes generic methods of medical documentation applicable to such diverse tasks as the electronic patient record, maintaining a clinical trials database, and building a tumor registry. This book is for all students in medical informatics and health information management, and it is ideal for both the undergraduate and the graduate levels. The book also guides professionals in the design and use of clinical information systems in various health care settings. It is an invaluable resource for all health care professionals involved in designing, assessing, adapting, or using clinical data management systems in hospitals, outpatient clinics, study centers, health plans, etc. The book combines a consistent theoretical foundation of medical documentation methods outlining their practical applicability in real clinical data management systems. Two new chapters detail hospital information systems and clinical trials. There is a focus on the international classification of diseases (ICD-9 and -10) systems, as well as a discussion on the difference between the two codes. All chapters feature exercises, bullet points, and a summary to provide the reader with essential points to remember. New to the Third Edition is a comprehensive section comprised of a combined Thesaurus and Glossary which aims to clarify the unclear and sometimes inconsistent terminology surrounding the topic.

This monograph on measurement error and misclassification covers a broad range of problems and emphasizes unique features in modeling and analyzing problems arising from medical research and epidemiological studies. Many measurement error and misclassification problems have been addressed in various fields over the years as well as with a wide spectrum of data, including event history data (such as survival data and recurrent event data), correlated data (such as longitudinal data and clustered data), multi-state event data, and data arising from case-control studies. Statistical Analysis with Measurement Error or Misclassification: Strategy, Method and Application brings together assorted methods in a single text and provides an update of recent developments for a variety of settings. Measurement error effects and strategies of handling mismeasurement for different models are closely examined in combination with applications to specific problems. Readers with diverse backgrounds and objectives can utilize this text. Familiarity with inference methods-such as likelihood and estimating function theory-or modeling schemes in varying settings-such as survival analysis and longitudinal data analysis-can result in a full appreciation of the material, but it is not essential since each chapter provides basic inference frameworks and background information on an individual topic to ease the access of the material. The text is presented in a coherent and self-contained manner and highlights the essence of commonly used modeling and inference methods. This text can serve as a reference book for researchers interested in statistical methodology for handling data with measurement error or misclassification; as a textbook for graduate students, especially for those majoring in statistics and biostatistics; or as a book for applied statisticians whose interest focuses on analysis of error-contaminated data. Grace Y. Yi is Professor of Statistics and University Research Chair at the University of Waterloo. She is the 2010 winner of the CRM-SSC Prize, an honor awarded in recognition of a statistical scientist's professional accomplishments in research during the first 15 years after having received a doctorate. She is a Fellow of the American Statistical Association and an Elected Member of the International Statistical Institute.

Authoritative, clear and concise, this highly acclaimed book continues to be an essential text for all medical, surgical and allied health professionals who are seeking a quick and respected guide to systematically reviewing the literature. The third edition features a new chapter on 'Publishing systematic reviews' and numerous new case studies covering the latest developments in the field, including umbrella reviews, reviews of test accuracy, qualitative evidence and prognostic studies, and practice guidelines. The highly experienced authors, who have each contributed to numerous systematic reviews that have helped form policy and practice, ensure that this practical text, which avoids technical jargon, continues to be the reference of choice for all health professionals undertaking literature reviews, including those specializing in epidemiology and public health.

Statistical Analysis of Contingency Tables is an invaluable tool for statistical inference in contingency tables. It covers effect size estimation, confidence intervals, and hypothesis tests for the binomial and the multinomial distributions, unpaired and paired 2x2 tables, rxc tables, ordered rx2 and 2xc tables, paired cxc tables, and stratified tables. For each type of table, key concepts are introduced, and a wide range of intervals and tests, including recent and unpublished methods and developments, are presented and evaluated. Topics such as diagnostic accuracy, inter-rater reliability, and missing data are also covered. The presentation is concise and easily accessible for readers with diverse professional backgrounds, with the mathematical details kept to a minimum. For more information, including a sample chapter and software, please visit the authors' website.

This updated second edition of Molecular Typing in Bacterial Infections, presented in two volumes, covers both common and neglected bacterial pathogenic agents, highlighting the most effective methods for their identification and classification in the light of their specific epidemiology. New chapters have been included to add new species, as well as another view of how bacterial typing can be used. These books are valuable resources for the molecular typing of infectious disease agents encountered in both research and hospital clinical laboratory settings, as well as in culture collections and in the industry. Each of the 21 chapters provides an overview of specific molecular approaches to efficiently detect and type different bacterial pathogens. The chapters are grouped in five parts, covering respiratory and urogenital pathogens (Volume I), and gastrointestinal and healthcare-associated pathogens, as well as a new group of vector-borne and Biosafety level 3 pathogens including a description of typing methods used in the traditional microbiology laboratory in comparison to molecular methods of epidemiology (Volume II). Comprehensive and updated, Molecular Typing in Bacterial Infections provides state-of-the-art methods for accurate diagnosis and for the correct classification of different types which will prove to be critical in unravelling the transmission routes of human pathogens.

The concepts of estimands, analyses (estimators), and sensitivity are interrelated. Therefore, great need exists for an integrated approach to these topics. This book acts as a practical guide to developing and implementing statistical analysis plans by explaining fundamental concepts using accessible language, providing technical details, real-world examples, and SAS and R code to implement analyses. The updated ICH guideline raises new analytic and cross-functional challenges for statisticians. Gaps between different communities have come to surface, such as between causal inference and clinical trialists, as well as among clinicians, statisticians, and regulators when it comes to communicating decision-making objectives, assumptions, and interpretations of evidence. This book lays out a path toward bridging some of these gaps. It offers A common language and unifying framework along with the technical details and practical guidance to help statisticians meet the challenges A thorough treatment of intercurrent events (ICEs), i.e., postrandomization events that confound interpretation of outcomes and five strategies for ICEs in ICH E9 (R1) Details on how estimands, integrated into a principled study development process, lay a foundation for coherent specification of trial design, conduct, and analysis needed to overcome the issues caused by ICEs: A perspective on the role of the intention-to-treat principle Examples and case studies from various areas Example code in SAS and R A connection with causal inference Implications and methods for analysis of longitudinal trials with missing data Together, the authors have offered the readers their ample expertise in clinical trial design and analysis, from an industrial and academic perspective.

With COVID-19 sweeping across the globe with near impunity, it is thwarting governments and health organizations efforts to contain it. Not since the 1918 Spanish Flu have citizens of developed countries experienced such a large-scale disease outbreak that is having devastating health and economic impacts. One reason such outbreaks are not more common has been the success of the public health community, including epidemiologists and biostatisticians, in identifying and then mitigating or eliminating the outbreaks. Monitoring the Health of Populations by Tracking Disease Outbreaks: Saving Humanity from the Next Plague is the story of the application of statistics for disease detection and tracking. The work of public health officials often crucially depends on statistical methods to help discern whether an outbreak may be occurring and, if there is sufficient evidence of an outbreak, then to locate and track it. Statisticians also help collect critical information, and they analyze the resulting data to help investigators zero in on a cause for a disease. With the recent outbreaks of diseases such as swine and bird flu, Ebola, and now COVID-19, the role that epidemiologists and biostatisticians play is more important than ever. Features: * Discusses the crucial roles of statistics in early disease detection. * Outlines the concepts and methods of disease surveillance. * Covers surveillance techniques for communicable diseases like Zika and chronic diseases such as cancer. * Gives real world examples of disease investigations including smallpox, syphilis, anthrax, yellow fever, and microcephaly (and its relationship to the Zika virus). Via the process of identifying an outbreak, finding its cause, and developing a plan to prevent its reoccurrence, this book tells the story of how medical and public health professionals use statistics to help mitigate the effects of disease. This book will help readers understand how statisticians and epidemiologists help combat the spread of such diseases in order to improve public health across the world.

Environmental epidemiology is the study of the environmental causes of disease in populations and how these risks vary in relation to intensity and duration of exposure and other factors like genetic susceptibility. As such, it is the basic science upon which governmental safety standards and compensation policies for environmental and occupational exposure are based. Profusely illustrated with examples from the epidemiologic literature on ionizing radiation and air ollution, this text provides a systematic treatment of the statistical challenges that arise in environmental health studies and the use of epidemiologic data in formulating public policy, at a level suitable for graduate students and epidemiologic researchers.
After a general overview of study design and statistical methods for epidemiology generally, the book goes on to address the problems that are unique to environmental health studies, special-purpose designs like two-phase case-control studies and countermatching, statistical methods for modeling exposure-time-response relationships, longitudinal and time-series studies, spatial and ecologic methods, exposure measurement error, interactions, and mechanistic models. It also discusses studies aimed at evaluating the public health benefits of interventions to improve the environment, the use of epidemiologic data to establish environmental safety standards and compensation policy, and concludes with emerging problems in reproductive epidemiology, natural and man-made disasters like global warming, and the global burden of environmentally caused disease. No other book provides such a broad perspective on the methodological challenges in this field at a level accessible to both epidemiologists and statisticians.

Although the spatial dimension of ecosystem dynamics is now widely recognized, the specific mechanisms behind species patterning in space are still poorly understood and the corresponding theoretical framework is underdeveloped. Going beyond the classical Turing scenario of pattern formation, Spatiotemporal Patterns in Ecology and Epidemiology: Theory, Models, and Simulation illustrates how mathematical modeling and numerical simulations can lead to greater understanding of these issues. It takes a unified approach to population dynamics and epidemiology by presenting several ecoepidemiological models where both the basic interspecies interactions of population dynamics and the impact of an infectious disease are explicitly considered. The book first describes relevant phenomena in ecology and epidemiology, provides examples of pattern formation in natural systems, and summarizes existing modeling approaches. The authors then explore nonspatial models of population dynamics and epidemiology. They present the main scenarios of spatial and spatiotemporal pattern formation in deterministic models of population dynamics. The book also addresses the interaction between deterministic and stochastic processes in ecosystem and epidemic dynamics, discusses the corresponding modeling approaches, and examines how noise and stochasticity affect pattern formation. Reviewing the significant progress made in understanding spatiotemporal patterning in ecological and epidemiological systems, this resource shows that mathematical modeling and numerical simulations are effective tools in the study of population ecology and epidemiology.

The consequences of childhood obesity are serious and far reaching, with both physical and psychological components that add to its complexity. Childhood Obesity: Contemporary Issues provides an up-to-date account of the increase of obesity in children, its causes, and its prevention. The expert editorial panel has chosen contributors with considerable practical and research experience. They explore why childhood obesity is so difficult to prevent and treat. Focusing less on clinical issues and more on environmental factors, the book brings together social, psychological, biological, and socio-biological approaches to the experience and problem of obesity. Delineating the scope and impact of childhood obesity, the book provides a unique view of the obese child. It examines the link between food intake and physical activity, which are the immediate determinants of energy balance, and discusses how to measure and assess them. The World Health Organization describes obesity as one of today's most blatantly visible - yet most neglected - public health problems. This book highlights obesity in children and discusses the need to develop multifactorial and multi-agency strategic plans to contain this epidemic.

This volume is comprised of papers presented at the Third International Conference on Pharmacoepidemiology, held September 9-11, 1987, in Minneapolis, Minnesota. The book is divided into four sections, which reflect the four themes of the conference: Social Impact of Pharmacoepidemiology; Drug Epidemiology and the Law; Drug Surveillance; and Drugs, Populations, and Outcomes: Specific Studies. The collection of papers discusses the social and legal impact of epidemiology, the system of checks and balances that is necessary for the field, the importance of core support for researchers, and the goal of an enlightened and informed public, including the media, consumer advocates, and the courts. Contributing authors offer perspectives from academia, practice, government, industry and the law. Numerous tables and figures are included to illustrate many of the papers within the text. This book offers substantial reading for epidemiologists and individuals interested in the field of pharmacoepidemiology.

Introductory Medical Statistics, now in its third edition, is an introductory textbook on basic statistical techniques. It is written for physicians, surgeons, radiation oncologists, medical physicists, radiographers, hospital administrators, medical statisticians in training, biochemists, and other professionals allied to medicine. It is suitable as a teaching text for clinicians working towards their professional examinations. It is also suitable for Maters degree courses in medical physics. The third edition has been extensively revised and expanded to include: Clinical trial design and analysis] Multivariate analysis Cox proportional hazards model McNemar, Wicoxon, Mann-Whitney, Kruskal-Wallis, Mantel-Haenszel, and Kappa tests Kaplan-Meier survival rates Sensitivity and Specificity Specification of treatment success, cure, and quality of life Risk specification Case-control and cohort epidemiological studies Glossary of terms The major change has been the advent of personal computing, so people rely on the power of their machine, and its software to number crunch. What is missing is that the software may not use the appropriate statistical error standard - Dick Mould

This completely revised and updated edition of an outstanding text addresses the fundamental knowledge of epidemiological methods and statistics that can be applied to evolving systems, programs, technologies, and policies. This edition presents new chapters on causal thinking, ethics, and web resources, analyzes data on multinational increases in poverty and longevity, details the control of transmissible diseases, and explains quality management, and the evaluation of healthcare system performance.

Viruses do not behave as other microbes; their life cycles require infecting healthy cells, commandeering their cellular apparatus, replicating and then killing the host cell. Methods for virus detection and identification have been developed only in the past few decades. These recently developed methods include molecular, physical, and proteomic techniques. All these approaches (Electron Microscopy, Molecular, Direct Counting, and Mass Spectrometry Proteomics) to detection and identification are reviewed in this succinct volume. It is written in approachable language with enough detail for trained professionals to follow and want to recommend to others. Key Features Covers common detection methods Reviews the history of detection from antiquity to the present Documents the strengths and weaknesses of various detection methods Describes how to detect newly discovered viruses Recommends specific applications for clinical, hospital, environmental, and public health uses

TEXTBOOK OF EPIDEMIOLOGY The gold standard in epidemiological texts In the second edition of Textbook of Epidemiology, a distinguished team of researchers deliver an extensively updated and comprehensive exploration of epidemiological methods, illuminating the tools for studying the distribution and risk factors of health states and events in populations. An introduction to epidemiological methods with recent and broadly applicable examples End-of-chapter self-assessment questions for readers to check their understanding of key concepts, with answer keys and further enrichment materials available on a companion website A brand-new chapter covering methods for systematic reviews and meta-analysis Accessible material appropriate for clinical practitioners and researchers from around the world Perfect for professionals working in clinical medicine and public health, Textbook of Epidemiology will also earn a place in the libraries of allied health professionals seeking a one-stop resource or to re-immerse themselves in specific methodological topics and practices.

"Blackwell Publishing is delighted to announce that this book has been Highly Commended in the 2004 BMA Medical Book Competition. Here is the judges' summary of this book: "

""This is a technical book on a technical subject but presented in a delightful way. There are many books on statistics for doctors but there are few that are excellent and this is certainly one of them. Statistics is not an easy subject to teach or write about. The authors have succeeded in producing a book that is as good as it can get. For the keen student who does not want a book for mathematicians, this is an excellent first book on medical statistics.""

"Essential Medical Statistics" is a classic amongst medical statisticians. An introductory textbook, it presents statistics with a clarity and logic that demystifies the subject, while providing a comprehensive coverage of advanced as well as basic methods.

The second edition of "Essential Medical Statistics" has been comprehensively revised and updated to include modern statistical methods and modern approaches to statistical analysis, while retaining the approachable and non-mathematical style of the first edition. The book now includes full coverage of the most commonly used regression models, multiple linear regression, logistic regression, Poisson regression and Cox regression, as well as a chapter on general issues in regression modelling. In addition, new chapters introduce more advanced topics such as meta-analysis, likelihood, bootstrapping and robust standard errors, and analysis of clustered data.

Aimed at students of medical statistics, medical researchers, public health practitioners andpractising clinicians using statistics in their daily work, the book is designed as both a teaching and a reference text. The format of the book is clear with highlighted formulae and worked examples, so that all concepts are presented in a simple, practical and easy-to-understand way. The second edition enhances the emphasis on choice of appropriate methods with new chapters on strategies for analysis and measures of association and impact.

"Essential Medical Statistics" is supported by a web site at www.blackwellpublishing.com/essentialmedstats. This useful online resource provides statistical datasets to download, as well as sample chapters and future updates.

While biomedical researchers may be able to follow instructions in the manuals accompanying the statistical software packages, they do not always have sufficient knowledge to choose the appropriate statistical methods and correctly interpret their results. Statistical Thinking in Epidemiology examines common methodological and statistical problems in the use of correlation and regression in medical and epidemiological research: mathematical coupling, regression to the mean, collinearity, the reversal paradox, and statistical interaction. Statistical Thinking in Epidemiology is about thinking statistically when looking at problems in epidemiology. The authors focus on several methods and look at them in detail: specific examples in epidemiology illustrate how different model specifications can imply different causal relationships amongst variables, and model interpretation is undertaken with appropriate consideration of the context of implicit or explicit causal relationships. This book is intended for applied statisticians and epidemiologists, but can also be very useful for clinical and applied health researchers who want to have a better understanding of statistical thinking. Throughout the book, statistical software packages R and Stata are used for general statistical modeling, and Amos and Mplus are used for structural equation modeling.

This unique volume provides self-contained accounts of some recent trends in Biostatistics methodology and their applications. It includes state-of-the-art reviews and original contributions.

The articles included in this volume are based on a careful selection of peer-reviewed papers, authored by eminent experts in the field, representing a well balanced mix of researchers from the academia, R&D sectors of government and the pharmaceutical industry.

The book is also intended to give advanced graduate students and new researchers a scholarly overview of several research frontiers in biostatistics, which they can use to further advance the field through development of new techniques and results.

In the past, disease pattern mapping depended on census tracts based on political units, such as states and counties. However, with the advent of geographic information systems (GIS), researchers can now achieve a new level of precision and flexibility in geographic locating. This emerging technology allows the mapping of many different kinds of geographies, including disease rates in relation to pollution sources. Geocoding Health Data presents a state-of-the-art discussion on the current technical and administrative developments in geographic information science. In particular, it discusses how geocoded residential addresses can be used to examine the spatial patterns of cancer incidence, staging, survival, and mortality. The book begins with an introduction of various codes and their uses, including census geographic, health area, and street level codes. It goes on to describe the specific application of geocodes to cancer, detailing methods, materials, and technical issues. The text illustrates how to compile data maps for analysis and addresses issues, such as mismatch correction and data quality. It describes the current state of geocoding practices and discusses the use of individually geocoded cancer incidences in spatial epidemiology, distance estimation and spatial accessibilities, and tips for handling non-geocoded cases. Special consideration is given to privacy and confidentiality issues by focusing on disclosure limitation methods. With recent disease outbreaks and escalating concerns about bioterrorism, interest in the application of GIS to individual data is growing. The fundamental concepts presented by this book are of great value to anyone trying to understand the causes, prevention, and control of cancer as well as a variety of other diseases.

"Provides current models, tools, and examples for the formulation and evaluation of scientific hypotheses in causal terms. Introduces a new method of model parametritization. Illustrates structural equations and graphical elements for complex causal systems."

Orthopoxviruses Pathogenic for Humans covers those viruses capable of causing disease in man, including monkeypox, smallpox, cowpox, and vaccinia. The coverage of each virus is comprehensive, covering the biology, molecular biology, and ecology of the virus as well as the clinical and epidemiological aspects of these viruses in humans and animals. In addition, this volume highlights developments in genetic engineering that are paving the way for potential therapeutic treatments of these viruses.

Influenza is a continuing news story with three clearly defined pandemics. Spanning a history of over fifty years, Mass Mediated Disease focuses on the Spanish influenza pandemic of 1918, the Asian influenza pandemic of 1957, and the Hong Kong influenza pandemic of 1968, by discussing news stories reported by the New York Times, London Times, and other popular magazines. This groundbreaking work demonstrates that the social construction of influenza changed over time, which was reflected by the mass media, and ultimately resulted in public policies that clearly illustrate the link between media and policy formation in the United States. This comprehensive history serves as an example for future incidents involving mass casualties or mass contamination from infectious agents_especially on what to expect, what to do, and what not to do regarding the crisis response, reportage, and resulting policies.

Genetic epidemiology is a very rapidly developing field that has acquired a central role in modern biomedical science. Driven by fast-paced technological advances arising from the sequencing of the human genome, and the rolling-out of these technologies into population-based studies, remarkable advances have occurred recently in our ability to detect genetic polymorphisms which contribute to susceptibility to complex human disease. This new science has begun to transform epidemiology, clinical medicine, and public health. Epidemiologists and public health students who in the past only had to learn a minimum of genetics are now required to acquire more detailed knowledge of the field, yet until now there has been no text set at the appropriate introductory level. This book brings together leading international experts to provide an introduction to genetic epidemiology that begins with a primer in human molecular genetics (requiring no prior specialist knowledge) and then examines all the standard methods in population genetics and genetic epidemiology required for an adequate grounding in the field. Among much else, the book contains discussion of the public health aspects of the new genetics, and of epidemiological methods for studying genes and environmental factors in complex diseases. Supplemented by a glossary and in-chapter guides to further reading, this introduction to genetic epidemiology will be widely welcomed by teachers and students on a burgeoning number of courses across the world.

A practical introduction to epidemiology, biostatistics, and research methodology for the whole health care community This comprehensive text, which has been extensively revised with new material and additional topics, utilizes a practical slant to introduce health professionals and students to epidemiology, biostatistics, and research methodology. It draws examples from a wide range of topics, covering all of the main contemporary health research methods, including survival analysis, Cox regression, and systematic reviews and meta-analysis the explanation of which go beyond introductory concepts. This second edition of Quantitative Methods for Health Research: A Practical Interactive Guide to Epidemiology and Statistics also helps develop critical skills that will prepare students to move on to more advanced and specialized methods. A clear distinction is made between knowledge and concepts that all students should ensure they understand, and those that can be pursued further by those who wish to do so. Self-assessment exercises throughout the text help students explore and reflect on their understanding. A program of practical exercises in SPSS (using a prepared data set) helps to consolidate the theory and develop skills and confidence in data handling, analysis, and interpretation. Highlights of the book include: * Combining epidemiology and bio-statistics to demonstrate the relevance and strength of statistical methods * Emphasis on the interpretation of statistics using examples from a variety of public health and health care situations to stress relevance and application * Use of concepts related to examples of published research to show the application of methods and balance between ideals and the realities of research in practice * Integration of practical data analysis exercises to develop skills and confidence * Supplementation by a student companion website which provides guidance on data handling in SPSS and study data sets as referred to in the text Quantitative Methods for Health Research, Second Edition is a practical learning resource for students, practitioners and researchers in public health, health care and related disciplines, providing both a course book and a useful introductory reference.