Evaluating Climate Change Impacts discusses assessing and quantifying climate change and its impacts from a multi-faceted perspective of ecosystem, social, and infrastructure resilience, given through a lens of statistics and data science. It provides a multi-disciplinary view on the implications of climate variability and shows how the new data science paradigm can help us to mitigate climate-induced risk and to enhance climate adaptation strategies. This book consists of chapters solicited from leading topical experts and presents their perspectives on climate change effects in two general areas: natural ecosystems and socio-economic impacts. The chapters unveil topics of atmospheric circulation, climate modeling, and long-term prediction; approach the problems of increasing frequency of extreme events, sea level rise, and forest fires, as well as economic losses, analysis of climate impacts for insurance, agriculture, fisheries, and electric and transport infrastructures. The reader will be exposed to the current research using a variety of methods from physical modeling, statistics, and machine learning, including the global circulation models (GCM) and ocean models, statistical generalized additive models (GAM) and generalized linear models (GLM), state space and graphical models, causality networks, Bayesian ensembles, a variety of index methods and statistical tests, and machine learning methods. The reader will learn about data from various sources, including GCM and ocean model outputs, satellite observations, and data collected by different agencies and research units. Many of the chapters provide references to open source software R and Python code that are available for implementing the methods.

The Millennium Development Goals, adopted at the UN Millennium Summit in 2000, are the world's targets for dramatically reducing extreme poverty in its many dimensions by 2015?income poverty, hunger, disease, exclusion, lack of infrastructure and shelter while promoting gender equality, education, health and environmental sustainability. These bold goals can be met in all parts of the world if nations follow through on their commitments to work together to meet them. Achieving the Millennium Development Goals offers the prospect of a more secure, just, and prosperous world for all. The UN Millennium Project was commissioned by United Nations Secretary-General Kofi Annan to develop a practical plan of action to meet the Millennium Development Goals. As an independent advisory body directed by Professor Jeffrey D. Sachs, the UN Millennium Project submitted its recommendations to the UN Secretary General in January 2005. The core of the UN Millennium Project's work has been carried out by 10 thematic Task Forces comprising more than 250 experts from around the world, including scientists, development practitioners, parliamentarians, policymakers, and representatives from civil society, UN agencies, the World Bank, the IMF, and the private sector. Coming to Grips with Malaria in the New Millennium presents an innovative strategic framework for relieving the burden that malaria imposes on society through the implementation of tried and tested anti-malarial interventions designed to improve health nationally and to promote economic development locally. Recommendations include early diagnosis, treatment with effective anti-malarial medicines, the use of insecticide treated nets, indoor residual spraying, managing the environment, improving housing, extending health education and improving monitoring and evaluation systems.

This book is suitable to be used as a textbook for all levels of students in medical school. It is also useful as a reference book for students interested in the application of biostatistics in medicine. Materials from the Introduction to Chapter 6 are similar to those of an elementary statistical textbook.This book is more modern than the current textbook in medical statistics. In this book, biostatistics and epidemiologic concepts are nicely blended. In contrast to the fallacy of the p-value, it introduces the Bayes factor as a measure of the evidence hidden in the sample data. It illustrates the application of the regression to the mean in medicine. Many epidemiologic concepts such as sensitivity and specificity of the diagnostic test, classification and discrimination, types of bias, etc. are discussed in the book.Chapter 7 on 'Correlation and Regression' includes the concept of regression to the mean, generalized linear (Poisson and Logistic) regression models, and discrimination of new data to belong to which sample data sets. Chapter 8 covers the nonparametric inference, including Kolmogorov and Smirnov test. Via the estimation and hypothesis testing, sample sizes are determined in Chapter 9. Chapter 10 discusses the study of design for collecting sample data, including cohort, cross-sectional, case-control, and clinical trial. In addition, types of bias are expounded as a last section in Chapter 10.Chapter 11 covers in detail the inference on contingency tables, including 2 x 2, two-way, and three-way. Five tests (Pearson, log-odds-ratio, Fisher-Irwin, McNemar, and Ejigou-McHugh) are listed in Section 11.1. Six tests (Pearson, First-order interaction, Yate's linear trend, Stuart's marginal homogeneity, Kendall, and Wilcoxon-Mann-Whitney) are described in Section 11.2. Three tests (Pearson, log-odds-ratio on first-order interaction, Barlett's on second-order interaction) and Simpson's paradox are covered in Section 11.3.Chapter 12 covers analysis of survival data. Two methods (life-table and Kaplan-Meier) are introduced for estimating the survivor function in Section 12.2. Four methods (maximum likelihood, Armitage's preference, Wald's sequential sign, and Armitage's restricted sequential) for comparing two survival curves are covered in Section 12.3. Proportional hazard model and the log-rank test are discussed, respectively, in Section 12.4 and 12.5.In addition, advanced techniques in comparing two survival curves are included in the book such as Armitage's preference method, Armitage's restricted sequential test and Wald's sequential sign test. Also, inference on contingency tables are treated in more detail than other books.

Interval-Censored Time-to-Event Data: Methods and Applications collects the most recent techniques, models, and computational tools for interval-censored time-to-event data. Top biostatisticians from academia, biopharmaceutical industries, and government agencies discuss how these advances are impacting clinical trials and biomedical research. Divided into three parts, the book begins with an overview of interval-censored data modeling, including nonparametric estimation, survival functions, regression analysis, multivariate data analysis, competing risks analysis, and other models for interval-censored data. The next part presents interval-censored methods for current status data, Bayesian semiparametric regression analysis of interval-censored data with monotone splines, Bayesian inferential models for interval-censored data, an estimator for identifying causal effect of treatment, and consistent variance estimation for interval-censored data. In the final part, the contributors use Monte Carlo simulation to assess biases in progression-free survival analysis as well as correct bias in interval-censored time-to-event applications. They also present adaptive decision making methods to optimize the rapid treatment of stroke, explore practical issues in using weighted logrank tests, and describe how to use two R packages. A practical guide for biomedical researchers, clinicians, biostatisticians, and graduate students in biostatistics, this volume covers the latest developments in the analysis and modeling of interval-censored time-to-event data. It shows how up-to-date statistical methods are used in biopharmaceutical and public health applications.

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.

Epidemic trend analysis, timeline progression, prediction, and recommendation are critical for initiating effective public health control strategies, and AI and data analytics play an important role in epidemiology, diagnostic, and clinical fronts. The focus of this book is data analytics for COVID-19, which includes an overview of COVID-19 in terms of epidemic/pandemic, data processing and knowledge extraction. Data sources, storage and platforms are discussed along with discussions on data models, their performance, different big data techniques, tools and technologies. This book also addresses the challenges in applying analytics to pandemic scenarios, case studies and control strategies. Aimed at Data Analysts, Epidemiologists and associated researchers, this book: discusses challenges of AI model for big data analytics in pandemic scenarios; explains how different big data analytics techniques can be implemented; provides a set of recommendations to minimize infection rate of COVID-19; summarizes various techniques of data processing and knowledge extraction; enables users to understand big data analytics techniques required for prediction purposes.

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.

Geographies of Plague Pandemics synthesizes our current understanding of the spatial and temporal dynamics of plague, Yersinia pestis. The environmental, political, economic, and social impacts of the plague from Ancient Greece to the modern day are examined. Chapters explore the identity of plague DNA, its human mortality, and the source of ancient and modern plagues. This book also discusses the role plague has played in shifting power from Mediterranean Europe to north-western Europe during the 500 years that plague has raged across the continent. The book demonstrates how recent colonial structures influenced the spread and mortality of plague while changing colonial histories. In addition, this book provides critical insight into how plague has shaped modern medicine, public health, and disease monitoring, and what role, if any, it might play as a terror weapon. The scope and breadth of Geographies of Plague Pandemics offers geographers, historians, biologists, and public health educators the opportunity to explore the deep connections among disease and human existence.

During the mid to late 19th century, Detroit and the American Midwest were the sites of five major cholera epidemics. The first of these, the 1832 outbreak, was of particular significance - an unexpected consequence of the Black Hawk War. In order to suppress the American Indian uprising then taking place in regions around present-day Illinois, General Winfield Scott had been ordered by President Andrew Jackson to transport his troops from Virginia to the Midwest. While passing through New York State the men were exposed to cholera, and then transmitted the disease to the population of Detroit once they reached that city. As a result, cholera was established as an endemic disease in the upper Midwest. Further outbreaks took place in 1834, 1849, 1854 and 1866, ultimately resulting in the deaths of hundreds of individuals. Only the efforts of local health authorities prevented mortality from reaching the level of that in other cities along the Mississippi. This book is the story of those outbreaks and the efforts to control them.

Recognised as the most influential publication in the field, ARM facilitates deep understanding of the Rasch model and its practical applications. The authors review the crucial properties of the model and demonstrate its use with examples across the human sciences. Readers will be able to understand and critically evaluate Rasch measurement research, perform their own Rasch analyses and interpret their results. The glossary and illustrations support that understanding, and the accessible approach means that it is ideal for readers without a mathematical background. Highlights of the new edition include: More learning tools to strengthen readers' understanding including chapter introductions, boldfaced key terms, chapter summaries, activities and suggested readings. Greater emphasis on the use of R packages; readers can download the R code from the Routledge website. Explores the distinction between numerical values, quantity and units, to understand the measurement and the role of the Rasch logit scale (Chapter 4). A new four-option data set from the IASQ (Instrumental Attitude towards Self-assessment Questionnaire) for the Rating Scale Model (RSM) analysis exemplar (Chapter 6). Clarifies the relationship between Rasch measurement, path analysis and SEM, with a host of new examples of Rasch measurement applied across health sciences, education and psychology (Chapter 10). Intended as a text for graduate courses in measurement, item response theory, (advanced) research methods or quantitative analysis taught in psychology, education, human development, business, and other social and health sciences. Professionals in these areas will also appreciate the book's accessible introduction.

Recognised as the most influential publication in the field, ARM facilitates deep understanding of the Rasch model and its practical applications. The authors review the crucial properties of the model and demonstrate its use with examples across the human sciences.

Readers will be able to understand and critically evaluate Rasch measurement research, perform their own Rasch analyses and interpret their results. The glossary and illustrations support that understanding, and the accessible approach means that it is ideal for readers without a mathematical background.

Highlights of the new edition include:

• More learning tools to strengthen readers’ understanding including chapter introductions, boldfaced key terms, chapter summaries, activities and suggested readings.
• Greater emphasis on the use of R packages; readers can download the R code from the Routledge website.
• Explores the distinction between numerical values, quantity and units, to understand the measurement and the role of the Rasch logit scale (Chapter 4).
• A new four-option data set from the IASQ (Instrumental Attitude towards Self-assessment Questionnaire) for the Rating Scale Model (RSM) analysis exemplar (Chapter 6).
• Clarifies the relationship between Rasch measurement, path analysis and SEM, with a host of new examples of Rasch measurement applied across health sciences, education and psychology (Chapter 10).

Intended as a text for graduate courses in measurement, item response theory, (advanced) research methods or quantitative analysis taught in psychology, education, human development, business, and other social and health sciences. Professionals in these areas will also appreciate the book’s accessible introduction.

Epidemiology is often referred to as the science of public health. However, unlike other major sciences, its theoretical foundations are rarely articulated. While the idea of epidemiologic theory may seem dry and arcane, it is at its core about explaining the people's health. It is about life and death. It is about biology and society. It is about ecology and the economy. It is about how myriad aspects of people's lives - involving work, dignity, desire, love, play, conflict, discrimination, and injustice - become literally incorporated into our bodies and manifest in our health status, individually and collectively. And it is about essential knowledge critical for improving the people's health and minimizing inequitable burdens of disease, disability, and death. Woven from a vast array of schools of thought, including those in the natural, social, and biomedical sciences, epidemiologic theory is a rich tapestry whose time for analysis is long overdue. By tracing its history and contours from ancient societies on through the development of - and debates within - contemporary epidemiology worldwide, Dr. Krieger shows how epidemiologic theory has long shaped epidemiologic practice, knowledge, and the politics of public health. Outlining an ecosocial theory of disease distribution that situates both population health and epidemiologic theory in societal and ecologic context, she offers a more holistic picture of how we embody the human experience. This concise, conceptually rich, and accessible book is a rallying cry for a return to the study and discussion of epidemiologic theory: what it is, why it matters, how it has changed over time, and its implications for improving population health and promoting health equity. It should be required reading for all epidemiologists, or anyone involved in the study of human health and well-being.

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

This book covers applications of fractional calculus used for medical and health science. It offers a collection of research articles built into chapters on classical and modern dynamical systems formulated by fractional differential equations describing human diseases and how to control them. The mathematical results included in the book will be helpful to mathematicians and doctors by enabling them to explain real-life problems accurately. The book will also offer case studies of real-life situations with an emphasis on describing the mathematical results and showing how to apply the results to medical and health science, and at the same time highlighting modeling strategies. The book will be useful to graduate level students, educators and researchers interested in mathematics and medical science.

A fully updated edition of this key text on mixed models, focusing on applications in medical research The application of mixed models is an increasingly popular way of analysing medical data, particularly in the pharmaceutical industry. A mixed model allows the incorporation of both fixed and random variables within a statistical analysis, enabling efficient inferences and more information to be gained from the data. There have been many recent advances in mixed modelling, particularly regarding the software and applications. This third edition of Brown and Prescott's groundbreaking text provides an update on the latest developments, and includes guidance on the use of current SAS techniques across a wide range of applications. Presents an overview of the theory and applications of mixed models in medical research, including the latest developments and new sections on incomplete block designs and the analysis of bilateral data. Easily accessible to practitioners in any area where mixed models are used, including medical statisticians and economists. Includes numerous examples using real data from medical and health research, and epidemiology, illustrated with SAS code and output. Features the new version of SAS, including new graphics for model diagnostics and the procedure PROC MCMC. Supported by a website featuring computer code, data sets, and further material. This third edition will appeal to applied statisticians working in medical research and the pharmaceutical industry, as well as teachers and students of statistics courses in mixed models. The book will also be of great value to a broad range of scientists, particularly those working in the medical and pharmaceutical areas.

Building on its best-selling predecessors, Basic Statistics and Pharmaceutical Statistical Applications, Third Edition covers statistical topics most relevant to those in the pharmaceutical industry and pharmacy practice. It focuses on the fundamentals required to understand descriptive and inferential statistics for problem solving. Incorporating new material in virtually every chapter, this third edition now provides information on software applications to assist with evaluating data. New to the Third Edition Use of Excel (R) and Minitab (R) for performing statistical analysis Discussions of nonprobability sampling procedures, determining if data is normally distributed, evaluation of covariances, and testing for precision equivalence Expanded sections on regression analysis, chi square tests, tests for trends with ordinal data, and tests related to survival statistics Additional nonparametric procedures, including the one-sided sign test, Wilcoxon signed-ranks test, and Mood's median test With the help of flow charts and tables, the author dispels some of the anxiety associated with using basic statistical tests in the pharmacy profession and helps readers correctly interpret their results using statistical software. Through the text's worked-out examples, readers better understand how the mathematics works, the logic behind many of the equations, and the tests' outcomes.

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.

Bayesian Demographic Estimation and Forecasting presents three statistical frameworks for modern demographic estimation and forecasting. The frameworks draw on recent advances in statistical methodology to provide new tools for tackling challenges such as disaggregation, measurement error, missing data, and combining multiple data sources. The methods apply to single demographic series, or to entire demographic systems. The methods unify estimation and forecasting, and yield detailed measures of uncertainty. The book assumes minimal knowledge of statistics, and no previous knowledge of demography. The authors have developed a set of R packages implementing the methods. Data and code for all applications in the book are available on www.bdef-book.com. "This book will be welcome for the scientific community of forecasters...as it presents a new approach which has already given important results and which, in my opinion, will increase its importance in the future." ~Daniel Courgeau, Institut national d'etudes demographiques

The aim of this book is to equip biostatisticians and other quantitative scientists with the necessary skills, knowledge, and habits to collaborate effectively with clinicians in the healthcare field. The book provides valuable insight on where to look for information and material on sample size and statistical techniques commonly used in clinical research, and on how best to communicate with clinicians. It also covers the best practices to adopt in terms of project, time, and data management; relationship with collaborators; etc.

Disease Surveillance: Technological Contributions to Global Health Security reminds us of the continued vulnerability of the world to contagious infections. The book presents examples of disease surveillance systems and evaluates promising advances as well as opportunities for new systems. It also explains how newer technologies can allow countries to comply with the International Health Regulations established by the World Health Organization. The book covers various topics including international health regulations policy, challenges surrounding system deployment and implementation, data visualization techniques, the strengths and weaknesses of open source software, and legal considerations surrounding such software. This book will show you how new reporting requirements, combined with new technologies, big data sources, and sophisticated analytic approaches now enable the public health community to identify potential outbreaks and initiate a response earlier than at any other time in history.

Accurate sample size calculation ensures that clinical studies have adequate power to detect clinically meaningful effects. This results in the efficient use of resources and avoids exposing a disproportionate number of patients to experimental treatments caused by an overpowered study. Sample Size Calculations for Clustered and Longitudinal Outcomes in Clinical Research explains how to determine sample size for studies with correlated outcomes, which are widely implemented in medical, epidemiological, and behavioral studies. The book focuses on issues specific to the two types of correlated outcomes: longitudinal and clustered. For clustered studies, the authors provide sample size formulas that accommodate variable cluster sizes and within-cluster correlation. For longitudinal studies, they present sample size formulas to account for within-subject correlation among repeated measurements and various missing data patterns. For multiple levels of clustering, the level at which to perform randomization actually becomes a design parameter. The authors show how this can greatly impact trial administration, analysis, and sample size requirement. Addressing the overarching theme of sample size determination for correlated outcomes, this book provides a useful resource for biostatisticians, clinical investigators, epidemiologists, and social scientists whose research involves trials with correlated outcomes. Each chapter is self-contained so readers can explore topics relevant to their research projects without having to refer to other chapters.

Designed for students training to become biostatisticians as well as practicing biostatisticians, Inference Principles for Biostatisticians presents the theoretical and conceptual foundations of biostatistics. It covers the theoretical underpinnings essential to understanding subsequent core methodologies in the field. Drawing on his extensive experience teaching graduate-level biostatistics courses and working in the pharmaceutical industry, the author explains the main principles of statistical inference with many examples and exercises. Extended examples illustrate key concepts in depth using a specific biostatistical context. In addition, the author uses simulation to reinforce the repeated sampling interpretation of numerous statistical concepts. Reducing the computational complexities, he provides simple R functions for conducting simulation studies. This text gives graduate students with diverse backgrounds across the health, medical, social, and mathematical sciences a solid, unified foundation in the principles of statistical inference. This groundwork will lead students to develop a thorough understanding of biostatistical methodology.

In clinical trial practice, controversial statistical issues inevitably occur regardless of the compliance with good statistical practice and good clinical practice. But by identifying the causes of the issues and correcting them, the study objectives of clinical trials can be better achieved. Controversial Statistical Issues in Clinical Trials covers commonly encountered controversial statistical issues in clinical trials and, whenever possible, makes recommendations to resolve these problems. The book focuses on issues occurring at various stages of clinical research and development, including early-phase clinical development (such as bioavailability/bioequivalence), bench-to-bedside translational research, and late-phase clinical development. Numerous examples illustrate the impact of these issues on the evaluation of the safety and efficacy of the test treatment under investigation. The author also offers recommendations regarding possible resolutions of the problems. Written by one of the preeminent experts in the field, this book provides a useful desk reference and state-of-the art examination of problematic issues in clinical trials for scientists in the pharmaceutical industry, medical/statistical reviewers in government regulatory agencies, and researchers and students in academia.

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.