An essential collection that advances our understanding of how cities influence our health More than half the world's population lives in cities - a figure that will grow to two-thirds by 2030. As global populations rapidly consolidate around urban centers, the scientific understanding of what this means for human health faces a new and greater urgency. Urban Health connects urban exposures - the experiences, choices, and behaviors shaped by living in a city - to their impact on population health. By using the ubiquitous aspects of the urban experience as a lens to study these exposures across borders and demographics, it offers a new, scalable framework for understanding health and disease. Its applications to public health, epidemiology, and social science are virtually unlimited. Enriched with case studies that consider the state of health in cities all over the world, this book does more than capture the state of a nascent field; it holds a critical mirror to itself, considering the next decade and arming a new generation with the tools for research and practice.

While regression models have become standard tools in medical research, understanding how to properly apply the models and interpret the results is often challenging for beginners. Regression Models as a Tool in Medical Research presents the fundamental concepts and important aspects of regression models most commonly used in medical research, including the classical regression model for continuous outcomes, the logistic regression model for binary outcomes, and the Cox proportional hazards model for survival data. The text emphasizes adequate use, correct interpretation of results, appropriate presentation of results, and avoidance of potential pitfalls. After reviewing popular models and basic methods, the book focuses on advanced topics and techniques. It considers the comparison of regression coefficients, the selection of covariates, the modeling of nonlinear and nonadditive effects, and the analysis of clustered and longitudinal data, highlighting the impact of selection mechanisms, measurement error, and incomplete covariate data. The text then covers the use of regression models to construct risk scores and predictors. It also gives an overview of more specific regression models and their applications as well as alternatives to regression modeling. The mathematical details underlying the estimation and inference techniques are provided in the appendices.

The get-it-over-with-quickly approach to statistics has been encouraged - and often necessitated - by the short time allotted to it in most curriculums. If included at all, statistics is presented briefly, as a task to be endured mainly because pertinent questions may appear in subsequent examinations for licensure or other certifications. However, in later professional activities, clinicians and biomedical researchers will constantly be confronted with reports containing statistical expressions and analyses. Not just a set of cookbook recipes, Principles of Medical Statistics is designed to get you thinking about data and statistical procedures. It covers many new statistical methods and approaches like box plots, stem and leaf plots, concepts of stability, the bootstrap, and the jackknife methods of resampling. The book is arranged in a logical sequence that advances from simple to more elaborate results. The text describes all the conventional statistical procedures, and offers reasonably rigorous accounts of many of their mathematical justifications. Although the conventional mathematical principles are given a respectful account, the book provides a distinctly clinical orientation with examples and teaching exercises drawn from real world medical phenomena. Statistical procedures are an integral part of the basic background needed by biomedical researchers, students, and clinicians. Containing much more than most elementary texts, Principles of Medical Statistics fills the gap often found in the current curriculum. It repairs the imbalance that gives so little attention to the role of statistics as a prime component of basic biomedical education.

Wide-Ranging Coverage of Parametric Modeling in Linear and Nonlinear Mixed Effects ModelsMixed Effects Models for the Population Approach: Models, Tasks, Methods and Tools presents a rigorous framework for describing, implementing, and using mixed effects models. With these models, readers can perform parameter estimation and modeling across a whole population of individuals at the same time. Easy-to-Use Techniques and Tools for Real-World Data ModelingThe book first shows how the framework allows model representation for different data types, including continuous, categorical, count, and time-to-event data. This leads to the use of generic methods, such as the stochastic approximation of the EM algorithm (SAEM), for modeling these diverse data types. The book also covers other essential methods, including Markov chain Monte Carlo (MCMC) and importance sampling techniques. The author uses publicly available software tools to illustrate modeling tasks. Methods are implemented in Monolix, and models are visually explored using Mlxplore and simulated using Simulx. Careful Balance of Mathematical Representation and Practical ImplementationThis book takes readers through the whole modeling process, from defining/creating a parametric model to performing tasks on the model using various mathematical methods. Statisticians and mathematicians will appreciate the rigorous representation of the models and theoretical properties of the methods while modelers will welcome the practical capabilities of the tools. The book is also useful for training and teaching in any field where population modeling occurs.

Epidemiology: A Research Manual for South Africa offers an applied introduction to epidemiological research methods, for students of various health science disciplines who are required to conduct or reference epidemiological research. Directed at readers who are new to epidemiological theory and practice, the text explains fundamental concepts and methods in a manner that is clear and accessible. The text offers a step-by-step guide to the development of a research protocol and the application of epidemiological methods, and addresses specific content areas and methodologies that are important in contemporary epidemiological research. Examples focus on the South African setting, highlighting regional conditions, diseases and health services.

COVID-19: Surviving a Pandemic provides critical insights into survival strategies employed by communities and individuals around the world during the pandemic. A central question since this pandemic began has been how to survive it. That question has applied not just to staying alive, but also to staying healthy, both physically and mentally. Survival is certainly key, but surviving, and what that means, is also critical. The scholarship included in this volume will take a closer look at what it means to survive by addressing such issues as the importance of ethnicity in vaccine uptake, the gendered and racialized impacts of the pandemic, the impact on those with disabilities, questions of food security, and what it means to grieve. Drawing on the expertise of scholars from around the world, the work presented here represents a remarkable diversity and quality of impassioned scholarship on the impact of COVID-19 and is a timely and critical advance in knowledge related to the pandemic.

Explore Important Tools for High-Quality Work in Pharmaceutical Safety Statistical Methods for Drug Safety presents a wide variety of statistical approaches for analyzing pharmacoepidemiologic data. It covers both commonly used techniques, such as proportional reporting ratios for the analysis of spontaneous adverse event reports, and newer approaches, such as the use of marginal structural models for controlling dynamic selection bias in the analysis of large-scale longitudinal observational data. Choose the Right Statistical Approach for Analyzing Your Drug Safety Data The book describes linear and non-linear mixed-effects models, discrete-time survival models, and new approaches to the meta-analysis of rare binary adverse events. It explores research involving the re-analysis of complete longitudinal patient records from randomized clinical trials. The book discusses causal inference models, including propensity score matching, marginal structural models, and differential effects, as well as mixed-effects Poisson regression models for analyzing ecological data, such as county-level adverse event rates. The authors also cover numerous other methods useful for the analysis of within-subject and between-subject variation in adverse events abstracted from large-scale medical claims databases, electronic health records, and additional observational data streams. Advance Statistical Practice in Pharmacoepidemiology Authored by two professors at the forefront of developing new statistical methodologies to address pharmacoepidemiologic problems, this book provides a cohesive compendium of statistical methods that pharmacoepidemiologists can readily use in their work. It also encourages statistical scientists to develop new methods that go beyond the foundation covered in the text.

This book provides practical knowledge to clinicians and biomedical researchers using biological and biochemical specimen/samples in order to understand health and disease processes at cellular, clinical, and population levels. Concepts and techniques provided will help researchers design and conduct studies, then translate data from bench to clinics in attempt to improve the health of patients and populations. This book presents the extreme complexity of epidemiologic research in a concise manner that will address the issue of confounders, thus allowing for more valid inferences and yielding results that are more reliable and accurate.

This volume elaborately studies the challenges posed and impact made by the Covid 19 pandemic. Through detailed case studies, it presents ethical, political, economic, medical, logistical and social impediments faced by contemporary states in the EU. The book focuses on the short and long-term consequences of the economic shock caused by the COVID-19 pandemic and covers issues concerning the world economy, the EU economy, as well as the Visegrad economies. The essays in this volume: Probes into the response of states to the economic phenomena resulting from the pandemic and analyses the institutional framework of the resulting crisis, lapses in social communication, social protests and the decline in democratic standards in countries such as the Czech Republic, Poland, Slovakia, and Hungary; Discusses issues related to state security under conditions of the pandemic, the effectiveness of state and self-government administration, the transition of states from an external controllability to an internal controllability model of power, as well as challenges related to security in the digital space; Presents policy actions at three basic levels, i.e. at the global, regional and sub-regional, and investigate strategies of the UN, WHO, the EU and the Visegrad Group as they play the most important role in the fight against COVID-19; This insightful and timely volume will be of great interest to scholars, researchers and anyone inquisitive about political theory, public policy, public health and social care, international relations, governance, security studies, and public administration.

- The only textbook that combines an introduction to both the technical operations and conceptual premises of biostatistics - Details the conceptual consequences of the mathematical premises of biostatistics that distort our understanding of holistic and contextual nature of health and medicine - Emphasizes the qualitative and subjective decisions that precede any quantitative analysis - The conceptual implications of biostatistical tools and techniques-such as contriving to fit objects to tools rather than fitting tools to objects-apply equally to statistics in general - Presents univariate, bivariate, and regression analysis through a single common scenario to facilitate student comprehension - Organizes biostatistical tools and techniques into nine configurations that help frame use and rationale of statistical tests and measures - Walks the reader through the technical steps for the examples associated with the nine configurations

Encyclopedic in breadth, yet practical and concise, Medical Biostatistics, Fourth Edition focuses on the statistical aspects ofmedicine with a medical perspective, showing the utility of biostatistics as a tool to manage many medical uncertainties. This edition includes more topics in order to fill gaps in the previous edition. Various topics have been enlarged and modified as per the new understanding of the subject.

With ever-rising healthcare costs, evidence generation through Health Economics and Outcomes Research (HEOR) plays an increasingly important role in decision-making about the allocation of resources. Accordingly, it is now customary for health technology assessment and reimbursement agencies to request for HEOR evidence, in addition to data from clinical trials, to inform decisions about patient access to new treatment options. While there is a great deal of literature on HEOR, there is a need for a volume that presents a coherent and unified review of the major issues that arise in application, especially from a statistical perspective. Statistical Topics in Health Economics and Outcomes Research fulfils that need by presenting an overview of the key analytical issues and best practice. Special attention is paid to key assumptions and other salient features of statistical methods customarily used in the area, and appropriate and relatively comprehensive references are made to emerging trends. The content of the book is purposefully designed to be accessible to readers with basic quantitative backgrounds, while providing an in-depth coverage of relatively complex statistical issues. The book will make a very useful reference for researchers in the pharmaceutical industry, academia, and research institutions involved with HEOR studies. The targeted readers may include statisticians, data scientists, epidemiologists, outcomes researchers, health economists, and healthcare policy and decision-makers.

Using real data sets throughout, Survival Analysis in Medicine and Genetics introduces the latest methods for analyzing high-dimensional survival data. It provides thorough coverage of recent statistical developments in the medical and genetics fields. The text mainly addresses special concerns of the survival model. After covering the fundamentals, it discusses interval censoring, nonparametric and semiparametric hazard regression, multivariate survival data analysis, the sub-distribution method for competing risks data, the cure rate model, and Bayesian inference methods. The authors then focus on time-dependent diagnostic medicine and high-dimensional genetic data analysis. Many of the methods are illustrated with clinical examples. Emphasizing the applications of survival analysis techniques in genetics, this book presents a statistical framework for burgeoning research in this area and offers a set of established approaches for statistical analysis. It reveals a new way of looking at how predictors are associated with censored survival time and extracts novel statistical genetic methods for censored survival time outcome from the vast amount of research results in genomics.

The case-control approach is a powerful method for investigating factors that may explain a particular event. It is extensively used in epidemiology to study disease incidence, one of the best-known examples being Bradford Hill and Doll's investigation of the possible connection between cigarette smoking and lung cancer. More recently, case-control studies have been increasingly used in other fields, including sociology and econometrics. With a particular focus on statistical analysis, this book is ideal for applied and theoretical statisticians wanting an up-to-date introduction to the field. It covers the fundamentals of case-control study design and analysis as well as more recent developments, including two-stage studies, case-only studies and methods for case-control sampling in time. The latter have important applications in large prospective cohorts which require case-control sampling designs to make efficient use of resources. More theoretical background is provided in an appendix for those new to the field.

Many new challenges have arisen in the area of oncology clinical trials. New cancer therapies are often based on cytostatic or targeted agents, which pose new challenges in the design and analysis of all phases of trials. The literature on adaptive trial designs and early stopping has been exploding. Inclusion of high-dimensional data and imaging techniques have become common practice, and statistical methods on how to analyse such data have been refined in this area. A compilation of statistical topics relevant to these new advances in cancer research, this third edition of Handbook of Statistics in Clinical Oncology focuses on the design and analysis of oncology clinical trials and translational research. Addressing the many challenges that have arisen since the publication of its predecessor, this third edition covers the newest developments involved in the design and analysis of cancer clinical trials, incorporating updates to all four parts: Phase I trials: Updated recommendations regarding the standard 3 + 3 and continual reassessment approaches, along with new chapters on phase 0 trials and phase I trial design for targeted agents. Phase II trials: Updates to current experience in single-arm and randomized phase II trial designs. New chapters include phase II designs with multiple strata and phase II/III designs. Phase III trials: Many new chapters include interim analyses and early stopping considerations, phase III trial designs for targeted agents and for testing the ability of markers, adaptive trial designs, cure rate survival models, statistical methods of imaging, as well as a thorough review of software for the design and analysis of clinical trials. Exploratory and high-dimensional data analyses: All chapters in this part have been thoroughly updated since the last edition. New chapters address methods for analyzing SNP data and for developing a score based on gene expression data. In addition, chapters on risk calculators and forensic bioinformatics have been added. Accessible to statisticians and oncologists interested in clinical trial methodology, the book is a single-source collection of up-to-date statistical approaches to research in clinical oncology.

In Cancer Screening: A Practical Guide for Physicians, a panel of highly experienced clinicians and researchers from around the world present their up-to-date screening techniques for a wide variety of cancers. The techniques range from screening for breast, gynecological, and gastrointestinal cancers, to testing for urogenital, dermatological, and respiratory cancers. In addition to providing the busy practitioner with quick access to guidelines for particular cancers, the epidemiology and biology of the various cancers, as well as the sensitivity and specificity of the methods, are discussed in detail. Authoritative and physician-friendly, Cancer Screening: A Practical Guide for Physicians offers to all internists, oncologists, various subspecialists, and primary care physicians a concise practical review of cancer screening designed specifically for daily use in the consulting room.

Features Represents the first book to provide comprehensive coverage of model-assisted designs for various types of dose-finding and optimization clinical trials Describes the up-to-date theory and practice for model-assisted designs Presents many practical challenges and issues arising from early-phase clinical trials Illustrates with many real trial applications Offers numerous tips and guidance on designing dose finding and optimization trials Provides step-by-step illustration of using software to design trials Develops a companion website (www.trialdesign.org) to provide easy-to-use software to assist learning and implementing model-assisted designs.

This book offers an accessible and up-to-date reference on primate zoonoses. Recent years have witnessed a rise in human diseases zoonotically transferred from animals, with wild primates implicated in the spread of numerous newly emerging infections. The authors go beyond simply providing an inventory of diseases, helping readers to understand how and why they are transmitted. Important consideration is given to the contemporary cultural and ecological factors involved.

Somewhere in the world, in the next forty seconds, a person is going to commit suicide. Globally, suicides account for 50 percent of all violent deaths among men and 71 percent for women. Despite suicide prevention programs, therapy, and pharmacological treatments, the suicide rate is either increasing or remaining high around the world. Media and Suicide holds traditional and emergent media accountable for influencing an individual's decision to commit suicide. Global experts present research, historical analysis, theoretical disputes (including discussion on the Werther and Papageno effects), and policy regarding the media's impact on suicide. They answer questions about the effects of different types of media and storytelling, show how the impact of social media can be diminished, discuss internet bullying, mass-shootings and mass-suicides, show the effects of recovery stories, and much more. The editors also present examples of suicide policy in the United States, Switzerland, the United Kingdom, Ireland, and Hong Kong on how to best communicate reporting guidelines to decrease the copycat effect, especially in less developed nations where most of the world's nearly one million suicides occur each year. Although there is much work to be done to prevent media-influenced suicide, this innovative volume will contribute a large piece to this complex puzzle.

"All disasters are in some sense man-made." Setting the annus horribilis of 2020 in historical perspective, Niall Ferguson explains why we are getting worse, not better, at handling disasters. Disasters are inherently hard to predict. Pandemics, like earthquakes, wildfires, financial crises. and wars, are not normally distributed; there is no cycle of history to help us anticipate the next catastrophe. But when disaster strikes, we ought to be better prepared than the Romans were when Vesuvius erupted, or medieval Italians when the Black Death struck. We have science on our side, after all. Yet in 2020 the responses of many developed countries, including the United States, to a new virus from China were badly bungled. Why? Why did only a few Asian countries learn the right lessons from SARS and MERS? While populist leaders certainly performed poorly in the face of the COVID-19 pandemic, Niall Ferguson argues that more profound pathologies were at work--pathologies already visible in our responses to earlier disasters. In books going back nearly twenty years, including Colossus, The Great Degeneration, and The Square and the Tower, Ferguson has studied the foibles of modern America, from imperial hubris to bureaucratic sclerosis and online fragmentation. Drawing from multiple disciplines, including economics, cliodynamics, and network science, Doom offers not just a history but a general theory of disasters, showing why our ever more bureaucratic and complex systems are getting worse at handling them. Doom is the lesson of history that this country--indeed the West as a whole--urgently needs to learn, if we want to handle the next crisis better, and to avoid the ultimate doom of irreversible decline.

Capture-recapture methods have been used in biology and ecology for more than 100 years. However, it is only recently that these methods have become popular in the social and medical sciences to estimate the size of elusive populations such as illegal immigrants, illicit drug users, or people with a drinking problem. Capture-Recapture Methods for the Social and Medical Sciences brings together important developments which allow the application of these methods. It has contributions from more than 40 researchers, and is divided into eight parts, including topics such as ratio regression models, capture-recapture meta-analysis, extensions of single and multiple source models, latent variable models and Bayesian approaches. The book is suitable for everyone who is interested in applying capture-recapture methods in the social and medical sciences. Furthermore, it is also of interest to those working with capture-recapture methods in biology and ecology, as there are some important developments covered in the book that also apply to these classical application areas.

Clinical Trial Optimization Using R explores a unified and broadly applicable framework for optimizing decision making and strategy selection in clinical development, through a series of examples and case studies. It provides the clinical researcher with a powerful evaluation paradigm, as well as supportive R tools, to evaluate and select among simultaneous competing designs or analysis options. It is applicable broadly to statisticians and other quantitative clinical trialists, who have an interest in optimizing clinical trials, clinical trial programs, or associated analytics and decision making. This book presents in depth the Clinical Scenario Evaluation (CSE) framework, and discusses optimization strategies, including the quantitative assessment of tradeoffs. A variety of common development challenges are evaluated as case studies, and used to show how this framework both simplifies and optimizes strategy selection. Specific settings include optimizing adaptive designs, multiplicity and subgroup analysis strategies, and overall development decision-making criteria around Go/No-Go. After this book, the reader will be equipped to extend the CSE framework to their particular development challenges as well.

Since early-on in the epidemic, there has been much interest in the role that bisexual behaviour among men may play in HIV transmission. This text reviews from an international perspective what has been learned about male bisexuality in countries as diverse as Peru and Britain. Its authors examine the forms that bisexuality takes in different cultures, what it means to the men concerned, and whether or not such behaviour poses special risks. The implications of such enquiry for HIV prevention efforts are also examined.

Provides an excellent grounding in the basics of both statistics and epidemiology. Full step-by-step guidance on performing statistical calculations. Numerous examples and exercises with detailed answers to help readers navigate these complex subjects with ease and confidence. Enables students and practitioners to make sense of the many research studies that underpin evidence-based practice. Fully revised and updated for this fifth edition, now with additional exercises and question and answers online for self-testing.

How do we identify and measure human disease in the past? In the absence of soft tissue, paleoepidemiologists have developed ingenious ways of assessing illness and mortality in archaeological populations. In this volume, the key methods of epidemiology are outlined for non-specialists, showing the importance of studying prevalence over incidence, adjustments needed in studying past groups, how to compare studies, and the dangers of assessing occupation based upon bone evidence. A model for planning a proper paleoepidemiological study concludes the volume. Both as an introduction to epidemiology for archaeologists, and as a primer on archaeological analysis for epidemiologists, this book should serve the needs of both populations.