| data science and epidemiology: Epidemiology Mark Woodward, 2013-12-19 Highly praised for its broad, practical coverage, the second edition of this popular text incorporated the major statistical models and issues relevant to epidemiological studies. Epidemiology: Study Design and Data Analysis, Third Edition continues to focus on the quantitative aspects of epidemiological research. Updated and expanded, this edition shows students how statistical principles and techniques can help solve epidemiological problems. New to the Third Edition New chapter on risk scores and clinical decision rules New chapter on computer-intensive methods, including the bootstrap, permutation tests, and missing value imputation New sections on binomial regression models, competing risk, information criteria, propensity scoring, and splines Many more exercises and examples using both Stata and SAS More than 60 new figures After introducing study design and reviewing all the standard methods, this self-contained book takes students through analytical methods for both general and specific epidemiological study designs, including cohort, case-control, and intervention studies. In addition to classical methods, it now covers modern methods that exploit the enormous power of contemporary computers. The book also addresses the problem of determining the appropriate size for a study, discusses statistical modeling in epidemiology, covers methods for comparing and summarizing the evidence from several studies, and explains how to use statistical models in risk forecasting and assessing new biomarkers. The author illustrates the techniques with numerous real-world examples and interprets results in a practical way. He also includes an extensive list of references for further reading along with exercises to reinforce understanding. Web Resource A wealth of supporting material can be downloaded from the book’s CRC Press web page, including: Real-life data sets used in the text SAS and Stata programs used for examples in the text SAS and Stata programs for special techniques covered Sample size spreadsheet |
| data science and epidemiology: Eras in Epidemiology Mervyn Susser, Zena Stein, 2009 At its core, epidemiology is concerned with changes in health and disease. The discipline requires counts and measures: of births, health disorders, and deaths, and in order to make sense of these counts it requires a population base defined by place and time. Epidemiology relies on closely defined concepts of cause - experimental or observational - of the physical or social environment, or in the laboratory. Epidemiologists are guided by these concepts, and have often contributed to their development. Because the disciplinary focus is on health and disease in populations, epidemiology has always been an integral driver of public health, the vehicle that societies have evolved to combat and contain the scourges of mass diseases.In this book, the authors trace the evolution of epidemiological ideas from earliest times to the present. Beginning with the early concepts of magic and the humors of Hippocrates, it moves forward through the dawn of observational methods, the systematic counts of deaths initiated in 16th-century London by John Graunt and William Petty, the late 18th-century Enlightenment and the French Revolution, which established the philosophical argument for health as a human right, the national public health system begun in 19th-century Britain, up to the development of eco-epidemiology, which attempts to re-integrate the fragmented fields as they currently exist. By examining the evolution of epidemiology as it follows the evolution of human societies, this book provides insight into our shared intellectual history and shows a way forward for future study. |
| data science and epidemiology: Computational Epidemiology Ellen Kuhl, 2021-09-22 This innovative textbook brings together modern concepts in mathematical epidemiology, computational modeling, physics-based simulation, data science, and machine learning to understand one of the most significant problems of our current time, the outbreak dynamics and outbreak control of COVID-19. It teaches the relevant tools to model and simulate nonlinear dynamic systems in view of a global pandemic that is acutely relevant to human health. If you are a student, educator, basic scientist, or medical researcher in the natural or social sciences, or someone passionate about big data and human health: This book is for you! It serves as a textbook for undergraduates and graduate students, and a monograph for researchers and scientists. It can be used in the mathematical life sciences suitable for courses in applied mathematics, biomedical engineering, biostatistics, computer science, data science, epidemiology, health sciences, machine learning, mathematical biology, numerical methods, and probabilistic programming. This book is a personal reflection on the role of data-driven modeling during the COVID-19 pandemic, motivated by the curiosity to understand it. |
| data science and epidemiology: Applied Longitudinal Data Analysis for Epidemiology Jos W. R. Twisk, 2013-05-09 A practical guide to the most important techniques available for longitudinal data analysis, essential for non-statisticians and researchers. |
| data science and epidemiology: Statistics for Epidemiology Nicholas P. Jewell, 2003-08-26 Statistical ideas have been integral to the development of epidemiology and continue to provide the tools needed to interpret epidemiological studies. Although epidemiologists do not need a highly mathematical background in statistical theory to conduct and interpret such studies, they do need more than an encyclopedia of recipes. Statistics for Epidemiology achieves just the right balance between the two approaches, building an intuitive understanding of the methods most important to practitioners and the skills to use them effectively. It develops the techniques for analyzing simple risk factors and disease data, with step-by-step extensions that include the use of binary regression. It covers the logistic regression model in detail and contrasts it with the Cox model for time-to-incidence data. The author uses a few simple case studies to guide readers from elementary analyses to more complex regression modeling. Following these examples through several chapters makes it easy to compare the interpretations that emerge from varying approaches. Written by one of the top biostatisticians in the field, Statistics for Epidemiology stands apart in its focus on interpretation and in the depth of understanding it provides. It lays the groundwork that all public health professionals, epidemiologists, and biostatisticians need to successfully design, conduct, and analyze epidemiological studies. |
| data science and epidemiology: Data Science for Infectious Disease Data Analytics Lily Wang, 2022-12-05 Data Science for Infectious Disease Data Analytics: An Introduction with R provides an overview of modern data science tools and methods that have been developed specifically to analyze infectious disease data. With a quick start guide to epidemiological data visualization and analysis in R, this book spans the gulf between academia and practices providing many lively, instructive data analysis examples using the most up-to-date data, such as the newly discovered coronavirus disease (COVID-19). The primary emphasis of this book is the data science procedures in epidemiological studies, including data wrangling, visualization, interpretation, predictive modeling, and inference, which is of immense importance due to increasingly diverse and nonexperimental data across a wide range of fields. The knowledge and skills readers gain from this book are also transferable to other areas, such as public health, business analytics, environmental studies, or spatio-temporal data visualization and analysis in general. Aimed at readers with an undergraduate knowledge of mathematics and statistics, this book is an ideal introduction to the development and implementation of data science in epidemiology. Features Describes the entire data science procedure of how the infectious disease data are collected, curated, visualized, and fed to predictive models, which facilitates effective communication between data sources, scientists, and decision-makers. Explains practical concepts of infectious disease data and provides particular data science perspectives. Overview of the unique features and issues of infectious disease data and how they impact epidemic modeling and projection. Introduces various classes of models and state-of-the-art learning methods to analyze infectious diseases data with valuable insights on how different models and methods could be connected. |
| data science and epidemiology: Big Data Viktor Mayer-Schönberger, Kenneth Cukier, 2013 A exploration of the latest trend in technology and the impact it will have on the economy, science, and society at large. |
| data science and epidemiology: Research Anthology on Big Data Analytics, Architectures, and Applications Information Resources Management Association, 2022 Society is now completely driven by data with many industries relying on data to conduct business or basic functions within the organization. With the efficiencies that big data bring to all institutions, data is continuously being collected and analyzed. However, data sets may be too complex for traditional data-processing, and therefore, different strategies must evolve to solve the issue. The field of big data works as a valuable tool for many different industries. The Research Anthology on Big Data Analytics, Architectures, and Applications is a complete reference source on big data analytics that offers the latest, innovative architectures and frameworks and explores a variety of applications within various industries. Offering an international perspective, the applications discussed within this anthology feature global representation. Covering topics such as advertising curricula, driven supply chain, and smart cities, this research anthology is ideal for data scientists, data analysts, computer engineers, software engineers, technologists, government officials, managers, CEOs, professors, graduate students, researchers, and academicians. |
| data science and epidemiology: Epidemiology and Medical Statistics , 2007-11-21 This volume, representing a compilation of authoritative reviews on a multitude of uses of statistics in epidemiology and medical statistics written by internationally renowned experts, is addressed to statisticians working in biomedical and epidemiological fields who use statistical and quantitative methods in their work. While the use of statistics in these fields has a long and rich history, explosive growth of science in general and clinical and epidemiological sciences in particular have gone through a see of change, spawning the development of new methods and innovative adaptations of standard methods. Since the literature is highly scattered, the Editors have undertaken this humble exercise to document a representative collection of topics of broad interest to diverse users. The volume spans a cross section of standard topics oriented toward users in the current evolving field, as well as special topics in much need which have more recent origins. This volume was prepared especially keeping the applied statisticians in mind, emphasizing applications-oriented methods and techniques, including references to appropriate software when relevant.· Contributors are internationally renowned experts in their respective areas· Addresses emerging statistical challenges in epidemiological, biomedical, and pharmaceutical research· Methods for assessing Biomarkers, analysis of competing risks· Clinical trials including sequential and group sequential, crossover designs, cluster randomized, and adaptive designs· Structural equations modelling and longitudinal data analysis |
| data science and epidemiology: Statistics for Health Data Science Ruth Etzioni, Micha Mandel, Roman Gulati, 2021-01-04 Students and researchers in the health sciences are faced with greater opportunity and challenge than ever before. The opportunity stems from the explosion in publicly available data that simultaneously informs and inspires new avenues of investigation. The challenge is that the analytic tools required go far beyond the standard methods and models of basic statistics. This textbook aims to equip health care researchers with the most important elements of a modern health analytics toolkit, drawing from the fields of statistics, health econometrics, and data science. This textbook is designed to overcome students’ anxiety about data and statistics and to help them to become confident users of appropriate analytic methods for health care research studies. Methods are presented organically, with new material building naturally on what has come before. Each technique is motivated by a topical research question, explained in non-technical terms, and accompanied by engaging explanations and examples. In this way, the authors cultivate a deep (“organic”) understanding of a range of analytic techniques, their assumptions and data requirements, and their advantages and limitations. They illustrate all lessons via analyses of real data from a variety of publicly available databases, addressing relevant research questions and comparing findings to those of published studies. Ultimately, this textbook is designed to cultivate health services researchers that are thoughtful and well informed about health data science, rather than data analysts. This textbook differs from the competition in its unique blend of methods and its determination to ensure that readers gain an understanding of how, when, and why to apply them. It provides the public health researcher with a way to think analytically about scientific questions, and it offers well-founded guidance for pairing data with methods for valid analysis. Readers should feel emboldened to tackle analysis of real public datasets using traditional statistical models, health econometrics methods, and even predictive algorithms. Accompanying code and data sets are provided in an author site: https://roman-gulati.github.io/statistics-for-health-data-science/ |
| data science and epidemiology: Eating Disorders in Boys and Men Jason M. Nagata, Tiffany A. Brown, Stuart B. Murray, Jason M. Lavender, 2021-04-12 Boys and men with eating disorders remain a population that is under-recognized and underserved within both research and clinical contexts. It has been well documented that boys and men with eating disorders often exhibit distinct clinical presentations with regard to core cognitive (e.g., body image) and behavioral (e.g., pathological exercise) symptoms. Such differences, along with the greater likelihood of muscularity-oriented disordered eating among boys and men, emphasize the importance of understanding and recognizing unique factors of clinical relevance within this population. This book reviews the most up-to-date research findings on eating disorders among boys and men, with an emphasis on clinically salient information across multiple domains. Five sections are included, with the first focused on a historical overview and the unique nature and prevalence of specific forms of eating disorder symptoms and body image concerns in boys and men. The second section details population-specific considerations for the diagnosis and assessment of eating disorders, body image concerns, and muscle dysmorphia in boys and men. The third section identifies unique concerns regarding medical complications and care in this population, including medical complications of appearance and performance-enhancing substances. The fourth section reviews current findings and considerations for eating disorder prevention and intervention for boys and men. The fifth section of the book focuses on specific populations (e.g., sexual minorities, gender minorities) and addresses sociocultural factors of particular relevance for eating disorders in boys and men (e.g., racial and ethnic considerations, cross-cultural considerations). The book then concludes with a concise overview of key takeaways and a focused summary of current evidence gaps and unanswered questions, as well as directions for future research. Written by experts in the field, Eating Disorders in Boys and Men is a comprehensive guide to an under-reported topic. It is an excellent resource for primary care physicians, adolescent medicine physicians, pediatricians, psychologists, clinical social workers, and any other professional conducting research with or providing clinical care for boys and men with eating disorders. It is also an excellent resource for students, residents, fellows, and trainees across various disciplines. |
| data science and epidemiology: Statistical Epidemiology Graham R. Law, Shane W. Pascoe, 2013 Statistics are a vital skill for epidemiologists and form an essential part of clinical medicine. This textbook introduces students to statistical epidemiology methods in a carefully structured and accessible format with clearly defined learning outcomes and suggested chapter orders that can be tailored to the needs of students at both undergraduate and graduate level from a range of academic backgrounds. The book covers study design, disease measuring, bias, error, analysis and modelling and is illustrated with figures, focus boxes, study questions and examples applicable to everyday clinical problems. Drawing on the authors' extensive teaching experience, the text provides an introduction to core statistical epidemiology that will be a valuable resource for students and lecturers in health and medical sciences and applied statistics, health staff, clinical researchers and data managers. |
| data science and epidemiology: Epidemiology and Biostatistics Bryan Kestenbaum, 2009-08-28 Concise, fast-paced, intensive introduction to clinical research design for students and clinical research professionals Readers will gain sufficient knowledge to pass the United States Medical Licensing Examination part I section in Epidemiology |
| data science and epidemiology: Biostatistics for Epidemiology and Public Health Using R Bertram K.C. Chan, PhD, 2015-11-05 Since it first appeared in 1996, the open-source programming language R has become increasingly popular as an environment for statistical analysis and graphical output. In addition to being freely available, R offers several advantages for biostatistics, including strong graphics capabilities, the ability to write customized functions, and its extensibility. This is the first textbook to present classical biostatistical analysis for epidemiology and related public health sciences to students using the R language. Based on the assumption that readers have minimal familiarity with statistical concepts, the author uses a step-bystep approach to building skills. The text encompasses biostatistics from basic descriptive and quantitative statistics to survival analysis and missing data analysis in epidemiology. Illustrative examples, including real-life research problems and exercises drawn from such areas as nutrition, environmental health, and behavioral health, engage students and reinforce the understanding of R. These examples illustrate the replication of R for biostatistical calculations and graphical display of results. The text covers both essential and advanced techniques and applications in biostatistics that are relevant to epidemiology. This text is supplemented with teaching resources, including an online guide for students in solving exercises and an instructor's manual. KEY FEATURES: First overview biostatistics textbook for epidemiology and public health that uses the open-source R program Covers essential and advanced techniques and applications in biostatistics as relevant to epidemiology Features abundant examples and exercises to illustrate the application of R language for biostatistical calculations and graphical displays of results Includes online student solutions guide and instructor's manual |
| data science and epidemiology: Data-Driven Science and Engineering Steven L. Brunton, J. Nathan Kutz, 2022-05-05 A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®. |
| data science and epidemiology: Spatial Analysis in Epidemiology Dirk U. Pfeiffer, Timothy P. Robinson, Mark Stevenson, Kim B. Stevens, David J. Rogers, Archie C. A. Clements, 2008-05-29 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. |
| data science and epidemiology: Statistical Models in Epidemiology David Clayton, Michael Hills, 2013-01-17 This self-contained account of the statistical basis of epidemiology has been written for those with a basic training in biology. It is specifically intended for students enrolled for a masters degree in epidemiology, clinical epidemiology, or biostatistics. |
| data science and epidemiology: Epidemiology, Biostatistics, and Preventive Medicine James F. Jekel, 2007-01-01 You'll find the latest on healthcare policy and financing, infectious diseases, chronic disease, and disease prevention technology. |
| data science and epidemiology: Applying Quantitative Bias Analysis to Epidemiologic Data Timothy L. Lash, Matthew P. Fox, Aliza K. Fink, 2011-04-14 Bias analysis quantifies the influence of systematic error on an epidemiology study’s estimate of association. The fundamental methods of bias analysis in epi- miology have been well described for decades, yet are seldom applied in published presentations of epidemiologic research. More recent advances in bias analysis, such as probabilistic bias analysis, appear even more rarely. We suspect that there are both supply-side and demand-side explanations for the scarcity of bias analysis. On the demand side, journal reviewers and editors seldom request that authors address systematic error aside from listing them as limitations of their particular study. This listing is often accompanied by explanations for why the limitations should not pose much concern. On the supply side, methods for bias analysis receive little attention in most epidemiology curriculums, are often scattered throughout textbooks or absent from them altogether, and cannot be implemented easily using standard statistical computing software. Our objective in this text is to reduce these supply-side barriers, with the hope that demand for quantitative bias analysis will follow. |
| data science and epidemiology: R for Health Data Science Ewen Harrison, Riinu Pius, 2020-12-31 In this age of information, the manipulation, analysis, and interpretation of data have become a fundamental part of professional life; nowhere more so than in the delivery of healthcare. From the understanding of disease and the development of new treatments, to the diagnosis and management of individual patients, the use of data and technology is now an integral part of the business of healthcare. Those working in healthcare interact daily with data, often without realising it. The conversion of this avalanche of information to useful knowledge is essential for high-quality patient care. R for Health Data Science includes everything a healthcare professional needs to go from R novice to R guru. By the end of this book, you will be taking a sophisticated approach to health data science with beautiful visualisations, elegant tables, and nuanced analyses. Features Provides an introduction to the fundamentals of R for healthcare professionals Highlights the most popular statistical approaches to health data science Written to be as accessible as possible with minimal mathematics Emphasises the importance of truly understanding the underlying data through the use of plots Includes numerous examples that can be adapted for your own data Helps you create publishable documents and collaborate across teams With this book, you are in safe hands – Prof. Harrison is a clinician and Dr. Pius is a data scientist, bringing 25 years’ combined experience of using R at the coal face. This content has been taught to hundreds of individuals from a variety of backgrounds, from rank beginners to experts moving to R from other platforms. |
| data science and epidemiology: Decision Quality Carl Spetzler, Hannah Winter, Jennifer Meyer, 2016-02-24 Add value with every decision using a simple yet powerful framework Few things are as valuable in business, and in life, as the ability to make good decisions. Can you imagine how much more rewarding your life and your business would be if every decision you made were the best it could be? Decision Quality empowers you to make the best possible choice and get more of what you truly want from every decision. Dr. Carl Spetzler is a leader in the field of decision science and has worked with organizations across industries to improve their decision-making capabilities. He and his co-authors, all experienced consultants and educators in this field, show you how to frame a problem or opportunity, create a set of attractive alternatives, identify relevant uncertain information, clarify the values that are important in the decision, apply tools of analysis, and develop buy-in among stakeholders. Their straightforward approach is elegantly simple, yet practical and powerful. It can be applied to all types of decisions. Our business and our personal lives are marked by a stream of decisions. Some are small. Some are large. Some are life-altering or strategic. How well we make those decisions truly matters. This book gives you a framework and thinking tools that will help you to improve the odds of getting more of what you value from every choice. You will learn: The six requirements for decision quality, and how to apply them The difference between a good decision and a good outcome Why a decision can only be as good as the best of the available alternatives Methods for making both significant and strategic decisions The mental traps that undermine decision quality and how to avoid them How to deal with uncertainty—a factor in every important choice How to judge the quality of a decision at the time you're making it How organizations have benefited from building quality into their decisions. Many people are satisfied with 'good enough' when making important decisions. This book provides a method that will take you and your co-workers beyond 'good enough' to true Decision Quality. |
| data science and epidemiology: Statistical Methods in Epidemiology Harold A. Kahn, Christopher T. Sempos, 1989 This book is an expanded version of the Kahn's widely used text, An Introduction to Epidemiologic Methods (Oxford, 1983). It provides clear insight into the basic statistical tools used in epidemiology and is written so that those without advanced statistical training can comprehend the ideas underlying the analytical techniques. The authors emphasize the extent to which similar results are obtained from different methods, both simple and complex. To this edition they have added a new chapter on Comparison of Numerical Results for Various Methods of Adjustment and also one on The Primacy of Data Collection. New topics include the Kaplan-Meier product-limit method and the Cox proportional hazards model for analysis of time-related outcomes. An appendix of data from the Framingham Heart Study is used to illustrate the application of various analytical methods to an identical set of real data and provides source material for student exercises. The text has been updated throughout. |
| data science and epidemiology: Data Analysis for the Life Sciences with R Rafael A. Irizarry, Michael I. Love, 2016-10-04 This book covers several of the statistical concepts and data analytic skills needed to succeed in data-driven life science research. The authors proceed from relatively basic concepts related to computed p-values to advanced topics related to analyzing highthroughput data. They include the R code that performs this analysis and connect the lines of code to the statistical and mathematical concepts explained. |
| data science and epidemiology: Handbook of Parametric and Nonparametric Statistical Procedures David Sheskin, 1997 This book offers unparalleled coverage of parametric and nonparametric statistical procedures: Detailing nearly 75 statistical procedures, the text shows: - How to select and conduct the appropiate statistical analysis for evaluating data from an empirical study - How to discriminate acceptable from unacceptable research when considering experimental control, and statistical analysis - How to interpret and better understand results of published research across a spectrum of disciplines |
| data science and epidemiology: Epidemiology by Design Daniel Westreich, 2020 A (LONG OVERDUE) CAUSAL APPROACH TO INTRODUCTORY EPIDEMIOLOGY Epidemiology is recognized as the science of public health, evidence-based medicine, and comparative effectiveness research. Causal inference is the theoretical foundation underlying all of the above. No introduction to epidemiology is complete without extensive discussion of causal inference; what's missing is a textbook that takes such an approach. Epidemiology by Design takes a causal approach to the foundations of traditional introductory epidemiology. Through an organizing principle of study designs, it teaches epidemiology through modern causal inference approaches, including potential outcomes, counterfactuals, and causal identification conditions. Coverage in this textbook includes: � Introduction to measures of prevalence and incidence (survival curves, risks, rates, odds) and measures of contrast (differences, ratios); the fundamentals of causal inference; and principles of diagnostic testing, screening, and surveillance � Description of three key study designs through the lens of causal inference: randomized trials, prospective observational cohort studies, and case-control studies � Discussion of internal validity (within a sample), external validity, and population impact: the foundations of an epidemiologic approach to implementation science For first-year graduate students and advanced undergraduates in epidemiology and public health fields more broadly, Epidemiology by Design offers a rigorous foundation in epidemiologic methods and an introduction to methods and thinking in causal inference. This new textbook will serve as a foundation not just for further study of the field, but as a head start on where the field is going. |
| data science and epidemiology: Molecular Tools and Infectious Disease Epidemiology Betsy Foxman, 2010-12-28 Molecular Tools and Infectious Disease Epidemiology examines the opportunities and methodologic challenges in the application of modern molecular genetic and biologic techniques to infectious disease epidemiology. The application of these techniques dramatically improves the measurement of disease and putative risk factors, increasing our ability to detect and track outbreaks, identify risk factors and detect new infectious agents. However, integration of these techniques into epidemiologic studies also poses new challenges in the design, conduct, and analysis. This book presents the key points of consideration when integrating molecular biology and epidemiology; discusses how using molecular tools in epidemiologic research affects program design and conduct; considers the ethical concerns that arise in molecular epidemiologic studies; and provides a context for understanding and interpreting scientific literature as a foundation for subsequent practical experience in the laboratory and in the field. The book is recommended for graduate and advanced undergraduate students studying infectious disease epidemiology and molecular epidemiology; and for the epidemiologist wishing to integrate molecular techniques into his or her studies. - Presents the key points of consideration when integrating molecular biology and epidemiology - Discusses how using molecular tools in epidemiologic research affects program design and conduct - Considers the ethical concerns that arise in molecular epidemiologic studies - Provides a context for understanding and interpreting scientific literature as a foundation for subsequent practical experience in the laboratory and in the field |
| data science and epidemiology: Applied Epidemiology Ross C. Brownson, Diana B. Petitti, 2006 Applies traditional epideiologic methods for determining disease etiology to the real-life applications of public health and health services research. This text contains a chapter on the development and use of systematic reviews and one on epidemiology and the law. |
| data science and epidemiology: Quantitative Methods for Health Research Nigel Bruce, Daniel Pope, Debbi Stanistreet, 2013-03-18 Quantitative Research Methods for Health Professionals: A Practical Interactive Course is a superb introduction to epidemiology, biostatistics, and research methodology for the whole health care community. Drawing examples from a wide range of health research, this practical handbook covers important contemporary health research methods such as survival analysis, Cox regression, and meta-analysis, the understanding of which go beyond introductory concepts. The book includes self-assessment exercises throughout to help students explore and reflect on their understanding and a clear distinction is made between a) knowledge and concepts that all students should ensure they understand and b) those that can be pursued by students who wish to do so. The authors incorporate a program of practical exercises in SPSS using a prepared data set that helps to consolidate the theory and develop skills and confidence in data handling, analysis and interpretation. |
| data science and epidemiology: Secondary Data Sources for Public Health Sarah Boslaugh, 2007-04-09 Secondary data play an increasingly important role in epidemiology and public health research and practice; examples of secondary data sources include national surveys such as the BRFSS and NHIS, claims data for the Medicare and Medicaid systems, and public vital statistics records. Although a wealth of secondary data is available, it is not always easy to locate and access appropriate data to address a research or policy question. This practical guide circumvents these difficulties by providing an introduction to secondary data and issues specific to its management and analysis, followed by an enumeration of major sources of secondary data in the United States. Entries for each data source include the principal focus of the data, years for which it is available, history and methodology of the data collection process, and information about how to access the data and supporting materials, including relevant details about file structure and format. |
| data science and epidemiology: Rethinking Social Epidemiology Patricia O’Campo, James R. Dunn, 2011-10-05 To date, much of the empirical work in social epidemiology has demonstrated the existence of health inequalities along a number of axes of social differentiation. However, this research, in isolation, will not inform effective solutions to health inequalities. Rethinking Social Epidemiology provides an expanded vision of social epidemiology as a science of change, one that seeks to better address key questions related to both the causes of social inequalities in health (problem-focused research) as well as the implementation of interventions to alleviate conditions of marginalization and poverty (solution-focused research). This book is ideally suited for emerging and practicing social epidemiologists as well as graduate students and health professionals in related disciplines. |
| data science and epidemiology: Modern Epidemiology Kenneth J. Rothman, Sander Greenland, Timothy L. Lash, 2008 The thoroughly revised and updated Third Edition of the acclaimed Modern Epidemiology reflects both the conceptual development of this evolving science and the increasingly focal role that epidemiology plays in dealing with public health and medical problems. Coauthored by three leading epidemiologists, with sixteen additional contributors, this Third Edition is the most comprehensive and cohesive text on the principles and methods of epidemiologic research. The book covers a broad range of concepts and methods, such as basic measures of disease frequency and associations, study design, field methods, threats to validity, and assessing precision. It also covers advanced topics in data analysis such as Bayesian analysis, bias analysis, and hierarchical regression. Chapters examine specific areas of research such as disease surveillance, ecologic studies, social epidemiology, infectious disease epidemiology, genetic and molecular epidemiology, nutritional epidemiology, environmental epidemiology, reproductive epidemiology, and clinical epidemiology. |
| data science and epidemiology: Epidemiology Leon Gordis, 2008-07-02 This popular book is written by the award-winning teacher, Dr. Leon Gordis of the Bloomberg School of Public Health at Johns Hopkins University. He introduces the basic principles and concepts of epidemiology in clear, concise writing and his inimitable style. This book provides an understanding of the key concepts in the following 3 fully updated sections: Section I: The Epidemiologic Approach to Disease and Intervention; Section II: Using Epidemiology to Identify the Causes of Disease; Section III: Applying Epidemiology to Evaluation and Policy. Clear, practical graphs and charts, cartoons, and review questions with answers reinforce the text and aid in comprehension. Utilizes new full-color format to enhance readability and clarity. Provides new and updated figures, references and concept examples to keep you absolutely current - new information has been added on Registration of Clinical Trials, Case-Cohort Design, Case-Crossover Design, and Sources and Impact of Uncertainty (disease topics include: Obesity, Asthma, Thyroid Cancer, Helicobacter Pylori and gastric/duodenal ulcer and gastric cancer, Mammography for women in their forties) - expanded topics include Person-time. Please note: electronic rights were not granted for several images in this product. Introduces both the underlying concepts as well as the practical uses of epidemiology in public health and in clinical practice. Systemizes learning and review with study questions in each section and an answer key and index. Illustrates textual information with clear and informative full-color illustrations, many created by the author and tested in the classroom. |
| data science and epidemiology: Encyclopedia of Epidemiology Sarah Boslaugh, 2008 Presents information from the field of epidemiology in a less technical, more accessible format. Covers major topics in epidemiology, from risk ratios to case-control studies to mediating and moderating variables, and more. Relevant topics from related fields such as biostatistics and health economics are also included. |
| data science and epidemiology: Gordis Epidemiology David D Celentano, Moyses Szklo, 2018-10-19 From the Department of Epidemiology at Johns Hopkins University and continuing in the tradition of award-winning educator and epidemiologist Dr. Leon Gordis, comes the fully revised 6th Edition of Gordis Epidemiology. This bestselling text provides a solid introduction to basic epidemiologic principles as well as practical applications in public health and clinical practice, highlighted by real-world examples throughout. New coverage includes expanded information on genetic epidemiology, epidemiology and public policy, and ethical and professional issues in epidemiology, providing a strong basis for understanding the role and importance of epidemiology in today's data-driven society. - Covers the basic principles and concepts of epidemiology in a clear, uniquely memorable way, using a wealth of full-color figures, graphs, charts, and cartoons to help you understand and retain key information. - Reflects how epidemiology is practiced today, with a new chapter organization progressing from observation and developing hypotheses to data collection and analyses. - Features new end-of-chapter questions for quick self-assessment, and a glossary of genetic terminology. - Provides more than 200 additional multiple-choice epidemiology self-assessment questions online. - Evolve Instructor Resources, including a downloadable image and test bank, are available to instructors through their Elsevier sales rep or via request at: https://evolve.elsevier.com |
| data science and epidemiology: Causal Inference in Statistics Judea Pearl, Madelyn Glymour, Nicholas P. Jewell, 2016-01-25 CAUSAL INFERENCE IN STATISTICS A Primer Causality is central to the understanding and use of data. Without an understanding of cause–effect relationships, we cannot use data to answer questions as basic as Does this treatment harm or help patients? But though hundreds of introductory texts are available on statistical methods of data analysis, until now, no beginner-level book has been written about the exploding arsenal of methods that can tease causal information from data. Causal Inference in Statistics fills that gap. Using simple examples and plain language, the book lays out how to define causal parameters; the assumptions necessary to estimate causal parameters in a variety of situations; how to express those assumptions mathematically; whether those assumptions have testable implications; how to predict the effects of interventions; and how to reason counterfactually. These are the foundational tools that any student of statistics needs to acquire in order to use statistical methods to answer causal questions of interest. This book is accessible to anyone with an interest in interpreting data, from undergraduates, professors, researchers, or to the interested layperson. Examples are drawn from a wide variety of fields, including medicine, public policy, and law; a brief introduction to probability and statistics is provided for the uninitiated; and each chapter comes with study questions to reinforce the readers understanding. |
| data science and epidemiology: Methods in Observational Epidemiology Jennifer L. Kelsey, 1996 This is the second edition of the first book to provide a complete picture of the design, conduct and analysis of observational studies, the most common type of epidemiologic study. Stressing sample size estimation, sampling, and measurement error, the authors cover the full scope of observational studies, describing cohort studies, case-control studies, cross-sectional studies, and epidemic investigation. The use of statistical procedures is described in easy-to-understand terms. |
| data science and epidemiology: Statistical Methods in Genetic Epidemiology Duncan C. Thomas, 2004-01-29 This well-organized and clearly written text has a unique focus on methods of identifying the joint effects of genes and environment on disease patterns. It follows the natural sequence of research, taking readers through the study designs and statistical analysis techniques for determining whether a trait runs in families, testing hypotheses about whether a familial tendency is due to genetic or environmental factors or both, estimating the parameters of a genetic model, localizing and ultimately isolating the responsible genes, and finally characterizing their effects in the population. Examples from the literature on the genetic epidemiology of breast and colorectal cancer, among other diseases, illustrate this process. Although the book is oriented primarily towards graduate students in epidemiology, biostatistics and human genetics, it will also serve as a comprehensive reference work for researchers. Introductory chapters on molecular biology, Mendelian genetics, epidemiology, statistics, and population genetics will help make the book accessible to those coming from one of these fields without a background in the others. It strikes a good balance between epidemiologic study designs and statistical methods of data analysis. |
| data science and epidemiology: Heart Failure Longjian Liu, 2017-09-14 Get a quick, expert overview of the many key facets of heart failure research with this concise, practical resource by Dr. Longjian Liu. This easy-to-read reference focuses on the incidence, distribution, and possible control of this significant clinical and public health problem which is often associated with higher mortality and morbidity, as well as increased healthcare expenditures. This practical resource brings you up to date with what's new in the field and how it can benefit your patients. - Features a wealth of information on epidemiology and research methods related to heart failure. - Discusses pathophysiology and risk profile of heart failure, research and design, biostatistical basis of inference in heart failure study, advanced biostatistics and epidemiology applied in heart failure study, and precision medicine and areas of future research. - Consolidates today's available information and guidance in this timely area into one convenient resource. |
| data science and epidemiology: Disaster Epidemiology Jennifer Horney, 2017-10-31 Disaster Epidemiology: Methods and Applications applies the core methods of epidemiological research and practice to the assessment of the short- and long-term health effects of disasters. The persistent movement of people and economic development to regions vulnerable to natural disasters, as well as new vulnerabilities related to environmental, technological, and terrorism incidents, means that in spite of large global efforts to reduce the impacts and costs of disasters, average annual expenditures to fund rebuilding from catastrophic losses is rising faster than either population or the gross world product. Improving the resilience of individuals and communities to these natural and technological disasters, climate change, and other natural and manmade stressors is one of the grand challenges of the 21st century. This book provides a guide to disaster epidemiology methods, supported with applications from practice. It helps researchers, public health practitioners, and governmental policy makers to better quantify the impacts of disaster on the health of individuals and communities to enhance resilience to future disasters. Disaster Epidemiology: Methods and Applications explains how public health surveillance, rapid assessments, and other epidemiologic studies can be conducted in the post-disaster setting to prevent injury, illness, or death; provide accurate and timely information for decisions makers; and improve prevention and mitigation strategies for future disasters. These methods can also be applied to the study of other types of public health emergencies, such as infectious outbreaks, emerging and re-emerging diseases, and refugee health. This book gives both the public health practitioner and researcher the tools they need to conduct epidemiological studies in a disaster setting and can be used as a reference or as part of a course. - Provides a holistic perspective to epidemiology with an integration of academic and practical approaches - Showcases the use of hands-on techniques and principles to solve real-world problems - Includes contributions from both established and emerging scholars in the field of disaster epidemiology |
| data science and epidemiology: Computational Epidemiology Jiming Liu, Shang Xia, 2020-09-18 This book provides a comprehensive introduction to computational epidemiology, highlighting its major methodological paradigms throughout the development of the field while emphasizing the needs for a new paradigm shift in order to most effectively address the increasingly complex real-world challenges in disease control and prevention. Specifically, the book presents the basic concepts, related computational models, and tools that are useful for characterizing disease transmission dynamics with respect to a heterogeneous host population. In addition, it shows how to develop and apply computational methods to tackle the challenges involved in population-level intervention, such as prioritized vaccine allocation. A unique feature of this book is that its examination on the issues of vaccination decision-making is not confined only to the question of how to develop strategic policies on prioritized interventions, as it further approaches the issues from the perspective of individuals, offering a well integrated cost-benefit and social-influence account for voluntary vaccination decisions. One of the most important contributions of this book lies in it offers a blueprint on a novel methodological paradigm in epidemiology, namely, systems epidemiology, with detailed systems modeling principles, as well as practical steps and real-world examples, which can readily be applied in addressing future systems epidemiological challenges. The book is intended to serve as a reference book for researchers and practitioners in the fields of computer science and epidemiology. Together with the provided references on the key concepts, methods, and examples being introduced, the book can also readily be adopted as an introductory text for undergraduate and graduate courses in computational epidemiology as well as systems epidemiology, and as training materials for practitioners and field workers. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a Transnationa…
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and …
Belmont Forum Adopts Open Data Principles for Environmental Chan…
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues …
Belmont Forum Adopts Open Data Principles for Environme…
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to …
Belmont Forum Data Accessibility Statement an…
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their …
