Over the past fifty years, more than three hundred infectious diseases have either newly emerged or reemerged, appearing in territories where they’ve never been seen before. Ninety percent of epidemiologists expect that one of them will cause a deadly pandemic sometime in the next two generations. It could be Ebola, avian flu, a drug-resistant superbug, or something completely new. While we can’t know which pathogen will cause the next pandemic, by unraveling the story of how pathogens have caused pandemics in the past, we can make predictions about the future. In Pandemic: Tracking Contagions, from Cholera to Ebola and Beyond, the prizewinning journalist Sonia Shah—whose book on malaria, The Fever, was called a “tour-de-force history” (The New York Times) and “revelatory” (The New Republic)—interweaves history, original reportage, and personal narrative to explore the origins of contagions, drawing parallels between cholera, one of history’s most deadly and disruptive pandemic-causing pathogens, and the new diseases that stalk humankind today.
To reveal how a new pandemic might develop, Sonia Shah tracks each stage of cholera’s dramatic journey, from its emergence in the South Asian hinterlands as a harmless microbe to its rapid dispersal across the nineteenth-century world, all the way to its latest beachhead in Haiti. Along the way she reports on the pathogens now following in cholera’s footsteps, from the MRSA bacterium that besieges her own family to the never-before-seen killers coming out of China’s wet markets, the surgical wards of New Delhi, and the suburban backyards of the East Coast.
By delving into the convoluted science, strange politics, and checkered history of one of the world’s deadliest diseases, Pandemic reveals what the next global contagion might look like— and what we can do to prevent it.
· Downloadable data sets
· Library of computer programs in SAS, SPSS, Stata, HLM, MLwiN, and more
· Additional material for data analysis
Treating these topics together takes advantage of all they have in common. The authors point out the many-shared elements in the methods they present for selecting, estimating, checking, and interpreting each of these models. They also show that these regression methods deal with confounding, mediation, and interaction of causal effects in essentially the same way.
The examples, analyzed using Stata, are drawn from the biomedical context but generalize to other areas of application. While a first course in statistics is assumed, a chapter reviewing basic statistical methods is included. Some advanced topics are covered but the presentation remains intuitive. A brief introduction to regression analysis of complex surveys and notes for further reading are provided. For many students and researchers learning to use these methods, this one book may be all they need to conduct and interpret multipredictor regression analyses.
The authors are on the faculty in the Division of Biostatistics, Department of Epidemiology and Biostatistics, University of California, San Francisco, and are authors or co-authors of more than 200 methodological as well as applied papers in the biological and biomedical sciences. The senior author, Charles E. McCulloch, is head of the Division and author of Generalized Linear Mixed Models (2003), Generalized, Linear, and Mixed Models (2000), and Variance Components (1992).
From the reviews:
"This book provides a unified introduction to the regression methods listed in the title...The methods are well illustrated by data drawn from medical studies...A real strength of this book is the careful discussion of issues common to all of the multipredictor methods covered." Journal of Biopharmaceutical Statistics, 2005
"This book is not just for biostatisticians. It is, in fact, a very good, and relatively nonmathematical, overview of multipredictor regression models. Although the examples are biologically oriented, they are generally easy to understand and follow...I heartily recommend the book" Technometrics, February 2006
"Overall, the text provides an overview of regression methods that is particularly strong in its breadth of coverage and emphasis on insight in place of mathematical detail. As intended, this well-unified approach should appeal to students who learn conceptually and verbally." Journal of the American Statistical Association, March 2006
The real story of AIDS—how it originated with a virus in a chimpanzee, jumped to one human, and then infected more than 60 million people—is very different from what most of us think we know. Recent research has revealed dark surprises and yielded a radically new scenario of how AIDS began and spread. Excerpted and adapted from the book Spillover, with a new introduction by the author, Quammen's hair-raising investigation tracks the virus from chimp populations in the jungles of southeastern Cameroon to laboratories across the globe, as he unravels the mysteries of when, where, and under what circumstances such a consequential "spillover" can happen. An audacious search for answers amid more than a century of data, The Chimp and the River tells the haunting tale of one of the most devastating pandemics of our time.
Christine Murphy has compiled a book that presents the vaccination dilemma from multiple perspectives. It clearly describes the immune system and its workings--and what science does and does not know about them. It offers suggestions and resources for parents whose children are sick, whether from a common childhood illness or from a vaccination reaction. And it makes a case for an alternate view of disease--as a teacher that allows us to develop physically and spiritually, and as a necessary test of strength that we have chosen out of our destiny.
This book will help educate parents about the vaccination dilemma and prepare them to make, in consultation with one or more health professionals, educated vaccination decisions for their children.
“This book will serve to greatly complement the growing number of texts dealing with mixed models, and I highly recommend including it in one’s personal library.”
—Journal of the American Statistical Association
Mixed modeling is a crucial area of statistics, enabling the analysis of clustered and longitudinal data. Mixed Models: Theory and Applications with R, Second Edition fills a gap in existing literature between mathematical and applied statistical books by presenting a powerful examination of mixed model theory and application with special attention given to the implementation in R.
The new edition provides in-depth mathematical coverage of mixed models’ statistical properties and numerical algorithms, as well as nontraditional applications, such as regrowth curves, shapes, and images. The book features the latest topics in statistics including modeling of complex clustered or longitudinal data, modeling data with multiple sources of variation, modeling biological variety and heterogeneity, Healthy Akaike Information Criterion (HAIC), parameter multidimensionality, and statistics of image processing.
Mixed Models: Theory and Applications with R, Second Edition features unique applications of mixed model methodology, as well as:Comprehensive theoretical discussions illustrated by examples and figures Over 300 exercises, end-of-section problems, updated data sets, and R subroutines Problems and extended projects requiring simulations in R intended to reinforce material Summaries of major results and general points of discussion at the end of each chapter Open problems in mixed modeling methodology, which can be used as the basis for research or PhD dissertations
Ideal for graduate-level courses in mixed statistical modeling, the book is also an excellent reference for professionals in a range of fields, including cancer research, computer science, and engineering.
Visualize the most recent topics in cutaneous pathology such as sporothrix and cutaneous t-cell lymphoma as well as classic problems like alopecia and neurofibromatosis, informed by the latest developments in molecular biology and histologic imaging.
See current dermatologic concepts captured in the visually rich Netter artistic tradition via major new contributions from Netter disciple Carlos Machado, MD - making complex concepts easy to understand and remember through the precision, clarity, detail, and realism for which Netter’s work has always been known.
Get complete, integrated visual guidance on the skin, hair, and nails in a single source, from basic sciences and normal anatomy and function through pathologic conditions.
Adeptly navigate current controversies and timely topics in clinical medicine with guidance from the Editor and informed by an experienced international advisory board.
This volume provides formulas and procedures for determination of sample size required not only for testing equality, but also for testing non-inferiority/superiority, and equivalence (similarity) based on both untransformed (raw) data and log-transformed data under a parallel-group design or a crossover design with equal or unequal ratio of treatment allocations. It contains a comprehensive and unified presentation of statistical procedures for sample size calculation that are commonly employed at various phases of clinical development. Each chapter includes, whenever possible, real examples of clinical studies from therapeutic areas such as cardiovascular, central nervous system, anti-infective, oncology, and women's health to demonstrate the clinical and statistical concepts, interpretations, and their relationships and interactions.
The book highlights statistical procedures for sample size calculation and justification that are commonly employed in clinical research and development. It provides clear, illustrated explanations of how the derived formulas and/or statistical procedures can be used.
Since the third edition, there have been many developments in statistical techniques. The fourth edition provides the medical statistician with an accessible guide to these techniques and to reflect the extent of their usage in medical research.
The new edition takes a much more comprehensive approach to its subject. There has been a radical reorganization of the text to improve the continuity and cohesion of the presentation and to extend the scope by covering many new ideas now being introduced into the analysis of medical research data. The authors have tried to maintain the modest level of mathematical exposition that characterized the earlier editions, essentially confining the mathematics to the statement of algebraic formulae rather than pursuing mathematical proofs.
Received the Highly Commended Certificate in the Public Health Category of the 2002 BMA Books Competition.
Includes practical examples from recent trials
Bringing together leading statisticians, scientists, and clinicians from the pharmaceutical industry, academia, and regulatory agencies, Multiple Testing Problems in Pharmaceutical Statistics explores the rapidly growing area of multiple comparison research with an emphasis on pharmaceutical applications. In each chapter, the expert contributors describe important multiplicity problems encountered in pre-clinical and clinical trial settings.
The book begins with a broad introduction from a regulatory perspective to different types of multiplicity problems that commonly arise in confirmatory controlled clinical trials, before giving an overview of the concepts, principles, and procedures of multiple testing. It then presents statistical methods for analyzing clinical dose response studies that compare several dose levels with a control as well as statistical methods for analyzing multiple endpoints in clinical trials. After covering gatekeeping procedures for testing hierarchically ordered hypotheses, the book discusses statistical approaches for the design and analysis of adaptive designs and related confirmatory hypothesis testing problems. The final chapter focuses on the design of pharmacogenomic studies based on established statistical principles. It also describes the analysis of data collected in these studies, taking into account the numerous multiplicity issues that occur.
This volume explains how to solve critical issues in multiple testing encountered in pre-clinical and clinical trial applications. It presents the necessary statistical methodology, along with examples and software code to show how to use the methods in practice.
"A major work of interpretation of medical and social thought . . . this volume is also to be commended for its skillful, absorbing presentation of the background and the effects of this dread disease."—I.B. Cohen, New York Times
"The Cholera Years is a masterful analysis of the moral and social interest attached to epidemic disease, providing generally applicable insights into how the connections between social change, changes in knowledge and changes in technical practice may be conceived."—Steven Shapin, Times Literary Supplement
"In a way that is all too rarely done, Rosenberg has skillfully interwoven medical, social, and intellectual history to show how medicine and society interacted and changed during the 19th century. The history of medicine here takes its rightful place in the tapestry of human history."—John B. Blake, Science
This comprehensive, up-to-date volume aims to define issues and potential solutions to the challenges of antimicrobial resistance. The chapter authors are leading international experts on antimicrobial resistance among a variety of bacteria (Streptococcus pneumoniae, enteroccoci, staphylococci, gram-negative bacilli, mycobacteria species) viruses (HIV, herpesviruses), and fungi (Candida species, fusarium etc.). The chapters will explore the molecular mechanisms of drug resistance, the immunology and epidemiology of resistance strains, clinical implications and implications on research and lack thereof, and prevention and future directions. This volume will also describe the steps that researchers are taking to develop molecular methods for detecting resistance; develop drugs and other means to deal with newly-resistant organisms. A special chapter to address the issues on strategies to limit antimicrobial resistance propagation will be included in this volume.
The New Public Health will help students and practitioners understand factors affecting the reform process of health care organization and delivery. It links the classic public health issues such as environmental sanitation, health education, and epidemiology with the new issues of universal health care, economics, and management of health systems for the new century.Provides a comprehensive overview of public health from a global perspectiveAssesses health systems models of the United States, Russia, the United Kingdom, Germany, Canada, Scandinavian countries, and developing countries including China, Nigeria, and ColombiaAnalyzes critical issues of health economics, including forces associated with escalating costs and the strategies to control those costsDiscusses strategies for dealing with the many ramifications of managed careLinks medicine with the social sciences, technology, and health management issues as they evolve
In Deadly River, Ralph R. Frerichs tells the story of the epidemic—of a French disease detective determined to trace its origins so that he could help contain the spread and possibly eliminate the disease—and the political intrigue that has made that effort so difficult. The story involves political maneuvering by powerful organizations such as the United Nations and its peacekeeping troops in Haiti, as well as by the World Health Organization and the U.S. Centers for Disease Control. Frerichs explores a quest for scientific truth and dissects a scientific disagreement involving world-renowned cholera experts who find themselves embroiled in intellectual and political turmoil in a poverty-stricken country.
Frerichs’s narrative highlights how the world’s wealthy nations, nongovernmental agencies, and international institutions respond when their interests clash with the needs of the world’s most vulnerable people. The story poses big social questions and offers insights not only on how to eliminate cholera in Haiti but also how nations, NGOs, and international organizations such as the UN and CDC deal with catastrophic infectious disease epidemics.
· Downloadable data sets
· Library of computer programs in SAS, SPSS, Stata, HLM, MLwiN, and more
· Additional material for data analysis
Practical examples demonstrate the most important points
The author first discusses how to write computer code using HTML as a concrete example. He then covers a variety of data storage topics, including different file formats, XML, and the structure and design issues of relational databases. After illustrating how to extract data from a relational database using SQL, the book presents tools and techniques for searching, sorting, tabulating, and manipulating data. It also introduces some very basic programming concepts as well as the R language for statistical computing. Each of these topics has supporting chapters that offer reference material on HTML, CSS, XML, DTD, SQL, R, and regular expressions.
One-stop shop of introductory computing information
Written by a member of the R Development Core Team, this resource shows readers how to apply data technologies to tasks within a research setting. Collecting material otherwise scattered across many books and the web, it explores how to publish information via the web, how to access information stored in different formats, and how to write small programs to automate simple, repetitive tasks.
* Easy-to-follow format incorporates medical examples, step-by-step methods, and check yourself exercises
* Two-part design features course material and a professional reference section
* Chapter summaries provide a review of formulas, method algorithms, and check lists
* Companion site links to statistical databases that can be downloaded and used to perform the exercises from the book and practice statistical methods
New in this Edition:
* New chapters on: multifactor tests on means of continuous data, equivalence testing, and advanced methods
* New topics include: trial randomization, treatment ethics in medical research, imputation of missing data, and making evidence-based medical decisions
* Updated database coverage and additional exercises
* Expanded coverage of numbers needed to treat and to benefit, and regression analysis including stepwise regression and Cox regression
Thorough discussion on required sample size
This 3e provides a unified approach to public health appropriate for all masters' level students and practitioners—specifically for courses in MPH programs, community health and preventive medicine programs, community health education programs, and community health nursing programs, as well as programs for other medical professionals such as pharmacy, physiotherapy, and other public health courses.Changes in infectious and chronic disease epidemiology including vaccines, health promotion, human resources for health and health technology Lessons from H1N1, pandemic threats, disease eradication, nutritional health Trends of health systems and reforms and consequences of current economic crisis for health Public health law, ethics, scientific d health technology advances and assessment Global Health environment, Millennium Development Goals and international NGOs
By quantifying various ideas underlying drug development, the book shows how to systematically address problems, such as:
Sizing a phase 2 trial and choosing the range of p-values that will trigger a follow-up phase 3 trial Deciding whether a drug should receive marketing approval based on its phase 2/3 development program and recent experience with other drugs in the same clinical area Determining the impact of adaptive designs on the quality of drugs that receive marketing approval Designing a phase 3 pivotal study that permits the data-driven adjustment of the treatment effect estimate Knowing when enough information has been gathered to show that a drug improves the survival time for the whole patient population
Drawing on his extensive work as a statistician in the pharmaceutical industry, the author focuses on the efficient development of drugs and the quantification of evidence in drug development. He provides a rationale for underpowered phase 2 trials based on the notion of efficiency, which leads to the identification of an admissible family of phase 2 designs. He also develops a framework for evaluating the strength of evidence generated by clinical trials. This approach is based on the ratio of power to type 1 error and transcends typical Bayesian and frequentist statistical analyses.
This book provides much-needed guidance on data analysis using R for the growing number of scientists in hospital departments who are responsible for producing reports, and who may have limited statistical expertise.
This book explores data analysis using R and is aimed at scientists in hospital departments who are responsible for producing reports, and who are involved in improving safety. Professionals working in the healthcare quality and safety community will also find this book of interest
Statistical Methods for Hospital Monitoring with R:Provides functions to perform quality improvement and infection management data analysis. Explores the characteristics of complex systems, such as self-organisation and emergent behaviour, along with their implications for such activities as root-cause analysis and the Pareto principle that seek few key causes of adverse events. Provides a summary of key non-statistical aspects of hospital safety and easy to use functions. Provides R scripts in an accompanying web site enabling analyses to be performed by the reader http://www.wiley.com/go/hospital_monitoring Covers issues that will be of increasing importance in the future, such as, generalised additive models, and complex systems, networks and power laws.
The authors begin with an overview of signal processing and machine learning approaches and continue on to introduce specific applications, which illustrate CI’s importance in medical diagnosis and healthcare. They provide an extensive review of signal processing techniques commonly employed in the analysis of biomedical signals and in the improvement of signal to noise ratio. The text covers recent CI techniques for post processing ECG signals in the diagnosis of cardiovascular disease and as well as various studies with a particular focus on CI’s potential as a tool for gait diagnostics.
In addition to its detailed accounts of the most recent research, Computational Intelligence in Biomedical Engineering provides useful applications and information on the benefits of applying computation intelligence techniques to improve medical diagnostics.
With contributions from: Peter Jones, Jean-Pierre Issa, Gavin Kelsey, Robert Waterland, and many other experts in epigenetics!
Some of the topics covered in the book are the sick role in Western Societies; sickness behavior in a traditional society; statistics vital to social medicine; geographical pathology of cancer; scope and methods of epidemiology; possibilities and limitations of health education; and health in industry and external disability. The definition and description of social provisions for health and welfare are fully covered. An in-depth account of the common features and development of social medicine are provided. The epidemiology of the cancer of the esophagus is completely presented. A chapter is devoted to description and diagnosis of ischaemic heart disease. Another section focuses on the practical applications of social medicine.
The book can provide useful information to doctors, students, and researchers.
Urinary tract infections are among the most frequent diseases caused by microbial pathogens. In this volume, researchers, clinical microbiologists and clinicians exchange the latest ideas covering four major aspects of this important topic: Genetic information, synthesis and assembly of virulence factors in urinary pathogens; Regulation of genes involved in the phenotypic appearance of virulence; Host-parasite interactions determining the process and outcome of the infection; Possible applications of the above aspects in diagnosis, therapy and prevention.