Explore the vast range of titles available.

A practical introduction to epidemiology, biostatistics, and research methodology for the whole health care community This comprehensive text, which has been extensively revised with new material and additional topics, utilizes a practical slant to introduce health professionals and students to epidemiology, biostatistics, and research methodology. It draws examples from a wide range of topics, covering all of the main contemporary health research methods, including survival analysis, Cox regression, and systematic reviews and meta-analysis—the explanation of which go beyond introductory concepts. This second edition of Quantitative Methods for Health Research: A Practical Interactive Guide to Epidemiology and Statistics also helps develop critical skills that will prepare students to move on to more advanced and specialized methods. A clear distinction is made between knowledge and concepts that all students should ensure they understand, and those that can be pursued further by those who wish to do so. Self-assessment exercises throughout the text help students explore and reflect on their understanding. A program of practical exercises in SPSS (using a prepared data set) helps to consolidate the theory and develop skills and confidence in data handling, analysis, and interpretation. Highlights of the book include: * Combining epidemiology and bio-statistics to demonstrate the relevance and strength of statistical methods * Emphasis on the interpretation of statistics using examples from a variety of public health and health care situations to stress relevance and application * Use of concepts related to examples of published research to show the application of methods and balance between ideals and the realities of research in practice * Integration of practical data analysis exercises to develop skills and confidence * Supplementation by a student companion website which provides guidance on data handling in SPSS and study data sets as referred to in the text Quantitative Methods for Health Research, Second Edition is a practical learning resource for students, practitioners and researchers in public health, health care and related disciplines, providing both a course book and a useful introductory reference. 

This comprehensive text focuses on reasoning, critical thinking and pragmatic decision making in medicine. Based on the author's extensive experience and filled with definitions, formulae, flowcharts and checklists, this fully revised second edition continues to provide invaluable guidance to the crucial role that clinical epidemiology plays in the expanding field of evidence-based medicine. Key Features: * Considers evidence-based medicine as a universal initiative common to all health sciences and professions, and all specialties within those disciplines * Demonstrates how effective practice is reliant on proper foundations, such as clinical and fundamental epidemiology, and biostatistics * Introduces the reader to basic epidemiological methods, meta-analysis and decision analysis * Shows that structured, modern, argumentative reasoning is required to build the best possible evidence and use it in practice and research * Outlines how to make the most appropriate decisions in clinical care, disease prevention and health promotion Presenting a range of topics seldom seen in a single resource, the innovative blend of informal logic and structured evidence-based reasoning makes this book invaluable for anyone seeking broad, in-depth and readable coverage of this complex and sometimes controversial field.

A one-stop guide for public health students and practitioners learning the applications of classical regression models in epidemiology This book is written for public health professionals and students interested in applying regression models in the field of epidemiology.

The 1918-19 influenza epidemic killed more than fifty million people worldwide. The SARS epidemic of 2002-3, by comparison, killed fewer than a thousand. The success in containing the spread of SARS was due largely to the rapid global response of public health authorities, which was aided by insights resulting from mathematical models. Models enabled authorities to better understand how the disease spread and to assess the relative effectiveness of different control strategies. In this book, Lisa Sattenspiel and Alun Lloyd provide a comprehensive introduction to mathematical models in epidemiology and show how they can be used to predict and control the geographic spread of major infectious diseases. Key concepts in infectious disease modeling are explained, readers are guided from simple mathematical models to more complex ones, and the strengths and weaknesses of these models are explored. The book highlights the breadth of techniques available to modelers today, such as population-based and individual-based models, and covers specific applications as well. Sattenspiel and Lloyd examine the powerful mathematical models that health authorities have developed to understand the spatial distribution and geographic spread of influenza, measles, foot-and-mouth disease, and SARS. Analytic methods geographers use to study human infectious diseases and the dynamics of epidemics are also discussed. A must-read for students, researchers, and practitioners, no other book provides such an accessible introduction to this exciting and fast-evolving field.

The success of the Apgar score demonstrates the astounding power of an appropriate clinical instrument. This down-to-earth book provides practical advice, underpinned by theoretical principles, on developing and evaluating measurement instruments in all fields of medicine. It equips you to choose the most appropriate instrument for specific purposes. The book covers measurement theories, methods and criteria for evaluating and selecting instruments. It provides methods to assess measurement properties, such as reliability, validity and responsiveness, and interpret the results. Worked examples and end-of-chapter assignments use real data and well-known instruments to build your skills at implementation and interpretation through hands-on analysis of real-life cases. All data and solutions are available online. This is a perfect course book for students and a perfect companion for professionals/researchers in the medical and health sciences who care about the quality and meaning of the measurements they perform.

In the analysis of data from longitudinal cohort studies, there is a growing interest in the analysis of developmental trajectories in subpopulations of the cohort under study. There are different advanced statistical methods available to analyze these trajectories, but in the epidemiologic literature, most of those are never used. The purpose of the present study is to compare five statistical methods to detect developmental trajectories in a longitudinal epidemiological data set. All five statistical methods (K-means clustering, a “two-step” approach with mixed modeling and K-means clustering, latent class analysis [LCA], latent class growth analysis [LCGA], and latent class growth mixture modeling [LCGMM]) were performed on a real-life data set and two manipulated data sets. The first manipulated data set contained four different linear developments over time, whereas the second contained two linear and two quadratic developments. For the real-life data set, all five classification methods revealed comparable trajectories. Regarding the manipulated data sets, LCGA performed best in detecting linear trajectories, whereas none of the methods performed well in detecting a combination of linear and quadratic trajectories. Furthermore, the optimal solution for LCA and LCGA contained more classes compared with LCGMM. Although LCGA and LCGMM seem to be preferable above the more simple methods, all classification methods should be applied with great caution.

The increasing diversity of population of the United States presents many challenges to conducting health research that is representative and informative. Dispersion and accessibility issues can increase logistical costs; populations for which it is difficult to obtain adequate sample size are also likely to be expensive to study. Hence, even if it is technically feasible to study a small population, it may not be easy to obtain the funding to do so. In order to address the issues associated with improving health research of small populations, the National Academies of Sciences, Engineering, and Medicine convened a workshop in January 2018. Participants considered ways of addressing the challenges of conducting epidemiological studies or intervention research with small population groups, including alternative study designs, innovative methodologies for data collection, and innovative statistical techniques for analysis.

New forms of evidence are needed to complement evidence generated from randomised controlled trials (RCTs). Real-World Evidence (RWE) is a potential new form of evidence, but remains undefined.This paper sets to fill that gap by defining RWE as the output from a rigorous research process which: (1) includes a clear a priori statement of a hypothesis to be tested or research question to be answered; (2) defines the data sources that will be used and critically appraises their strengths and weaknesses; and (3) applies appropriate methods, including advanced analytics. These elements should be set out in advance of the study commencing, ideally in a published protocol.The strengths of RWE studies are that they are more inclusive than RCTs and can enable an evidence base to be developed around real-world effectiveness and to start to address the complications of managing other real-world problems such as multimorbidity. Computerised medical record systems and big data provide a rich source of data for RWE studies.However, guidance is needed to help assess the rigour of RWE studies so that the strength of recommendations based on their output can be determined. Additionally, RWE advanced analytics methods need better categorisation and validation.We predict that the core role of RCTs will shift towards assessing safety and achieving regulatory compliance. RWE studies, notwithstanding their limitations, may become established as the best vehicle to assess efficacy.

Background Long-term multicentre studies are subject to numerous factors that may affect the integrity of their conclusions. Quality control and standardization of data collection are crucial to minimise the biases induced by these factors. Nevertheless, tools implemented to manage biases are rarely described in publications about population-based cohorts. This report aims to describe the processes implemented to control biases in the Constances cohort taking lung function results as an example. Methods Constances is a general-purpose population-based cohort of 200,000 participants. Volunteers attend physical examinations at baseline and then every 5 years at selected study sites. Medical device specifications and measurement methods have to comply with Standard Operating Procedures developed by experts. Protocol deviations are assessed by on-site inspections and database controls. In February 2016, more than 94,000 participants yielding around 30 million readings from physical exams, had been covered by our quality program. Results Participating centres accepted to revise their practices in accordance with the study research specifications. Distributors of medical devices were asked to comply with international guidelines and Constances requirements. Close monitoring enhanced the quality of measurements and recordings of the physical exams. Regarding lung function testing, spirometry acceptability rates per operator doubled in some sites within a few months and global repeatability reached 96.7 % for 29,772 acceptable maneuvers. Conclusions Despite Constances volunteers being followed in multiple sites with heterogeneous materials, the investment of significant resources to set up and maintain a continuous quality management process has proved effective in preventing drifts and improving accuracy of collected data.

Background The previously published “Dose Response Multicentre International Collaborative Initiative (DoReMi)” study concluded that the high mortality of critically ill patients with acute kidney injury (AKI) was unlikely to be related to an inadequate dose of renal replacement therapy (RRT) and other factors were contributing. This follow-up study aimed to investigate the impact of daily fluid balance and fluid accumulation on mortality of critically ill patients without AKI (N-AKI), with AKI (AKI) and with AKI on RRT (AKI-RRT) receiving an adequate dose of RRT. Methods We prospectively enrolled all consecutive patients admitted to 21 intensive care units (ICUs) from nine countries and collected baseline characteristics, comorbidities, severity of illness, presence of sepsis, daily physiologic parameters and fluid intake-output, AKI stage, need for RRT and survival status. Daily fluid balance was computed and fluid overload (FO) was defined as percentage of admission body weight (BW). Maximum fluid overload (MFO) was the peak value of FO. Results We analysed 1734 patients. A total of 991 (57 %) had N-AKI, 560 (32 %) had AKI but did not have RRT and 183 (11 %) had AKI-RRT. ICU mortality was 22.3 % in AKI patients and 5.6 % in those without AKI ( p  < 0.0001). Progressive fluid accumulation was seen in all three groups. Maximum fluid accumulation occurred on day 2 in N-AKI patients (2.8 % of BW), on day 3 in AKI patients not receiving RRT (4.3 % of BW) and on day 5 in AKI-RRT patients (7.9 % of BW). The main findings were: (1) the odds ratio (OR) for hospital mortality increased by 1.075 (95 % confidence interval 1.055–1.095) with every 1 % increase of MFO. When adjusting for severity of illness and AKI status, the OR changed to 1.044. This phenomenon was a continuum and independent of thresholds as previously reported. (2) Multivariate analysis confirmed that the speed of fluid accumulation was independently associated with ICU mortality. (3) Fluid accumulation increased significantly in the 3-day period prior to the diagnosis of AKI and peaked 3 days later. Conclusions In critically ill patients, the severity and speed of fluid accumulation are independent risk factors for ICU mortality. Fluid balance abnormality precedes and follows the diagnosis of AKI.