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.

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.

This book provides not only the theory of biostatistics, but also the opportunity of applying it in practice. In fact, each chapter presents one or more specific examples on how to perform an epidemiological or statistical data analysis and includes download access to the software and databases, giving the reader the possibility of replicating the analyses described.

A practical guide to the most important techniques available for longitudinal data analysis, essential for non-statisticians and researchers.

This graduate-level community nutrition textbook presents a conceptual framework for understanding the course of health and disease and matching community nutrition or applied nutrition epidemiology to the model.

The book is a comprehensive, self-contained introduction to the mathematical modeling and analysis of infectious diseases. It includes model building, fitting to data, local and global analysis techniques. Various types of deterministic dynamical models are considered: ordinary differential equation models, delay-differential equation models, difference equation models, age-structured PDE models and diffusion models. It includes various techniques for the computation of the basic reproduction number as well as approaches to the epidemiological interpretation of the reproduction number. MATLAB code is included to facilitate the data fitting and the simulation with age-structured models.

A NEW AND ESSENTIAL RESOURCE FOR THE PRACTICE OF EPIDEMIOLOGY AND PUBLIC HEALTH The CDC Field Epidemiology Manual is a definitive guide to investigating acute public health events on the ground and in real time. Assembled and written by experts from the Centers for Disease Control and Prevention as well as other leading public health agencies, it offers current and field-tested guidance for every stage of an outbreak investigation -- from identification to intervention and other core considerations along the way. Modeled after Michael Gregg's seminal book Field Epidemiology, this CDC manual ushers investigators through the core elements of field work, including many of the challenges inherent to outbreaks: working with multiple state and federal agencies or multinational organizations; legal considerations; and effective utilization of an incident-management approach. Additional coverage includes: � Updated guidance for new tools in field investigations, including the latest technologies for data collection and incorporating data from geographic information systems (GIS) � Tips for investigations in unique settings, including healthcare and community-congregate sites � Advice for responding to different types of outbreaks, including acute enteric disease; suspected biologic or toxic agents; and outbreaks of violence, suicide, and other forms of injury For the ever-changing public health landscape, The CDC Field Epidemiology Manual offers a new, authoritative resource for effective outbreak response to acute and emerging threats. *** Oxford University Press will donate a portion of the proceeds from this book to the CDC Foundation, an independent nonprofit and the sole entity created by Congress to mobilize philanthropic and private-sector resources to support the Centers for Disease Control and Prevention's critical health protection work. To learn more about the CDC Foundation, visit www.cdcfoundation.org.

The goal of this book is to search for a balance between simple and analyzable models and unsolvable models which are capable of addressing important questions on population biology. Part I focusses on single species simple models including those which have been used to predict the growth of human and animal population in the past. Single population models are, in some sense, the building blocks of more realistic models -- the subject of Part II. Their role is fundamental to the study of ecological and demographic processes including the role of population structure and spatial heterogeneity -- the subject of Part III. This book, which will include both examples and exercises, is of use to practitioners, graduate students, and scientists working in the field.

Now in its Fifth Edition, Clinical Epidemiology: The Essentials is a comprehensive, concise, and clinically oriented introduction to the subject of epidemiology. Written by expert educators, this text introduces students to the principles of evidence-based medicine that will help them develop and apply methods of clinical observation in order to form accurate conclusions. The Fifth Edition includes more complete coverage of systematic reviews and knowledge management, as well as other key topics such as abnormality, diagnosis, frequency and risk, prognosis, treatment, prevention, chance, studying cases and cause.

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