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In 1949 the U.S. National Cancer Institute (NCI) and the Canadian Department of National Health and Welfare (DNHW) commissioned a film, eventually called Challenge. Science Against Cancer, as part of a major effort to recruit young scientists into cancer research. Both organizations feared that poor recruitment would stifle the development of the field at a time when funding for research was growing dramatically. The fear was that there would not be enough new young scientists to meet the demand, and that the shortfall would undermine cancer research and the hopes invested in it. Challenge aimed to persuade young scientists to think of cancer research as a career. This book is the story of that forgotten film and what it tells us about mid-twentieth century American and Canadian cancer research, educational filmmaking, and health education campaigns. It explores why Canadian and American health agencies turned to film to address the problem of scientist recruitment; how filmmakers turned such recruitment concerns into something they thought would work as a film; and how information officers at the NCI and DNHW sought to shape the impact of Challenge by embedding it in a broader educational and propaganda program. It is, in short, an account of the important, but hitherto undocumented, roles of filmmakers and information officers in the promotion of post-Second World War cancer research.
In the late 1980s, the National Cancer Institute initiated an investigation of cancer risks in populations near 52 commercial nuclear power plants and 10 Department of Energy nuclear facilities (including research and nuclear weapons production facilities and one reprocessing plant) in the United States. The results of the NCI investigation were used a primary resource for communicating with the public about the cancer risks near the nuclear facilities. However, this study is now over 20 years old. The U.S. Nuclear Regulatory Commission requested that the National Academy of Sciences provide an updated assessment of cancer risks in populations near USNRC-licensed nuclear facilities that utilize or process uranium for the production of electricity. Analysis of Cancer Risks in Populations near Nuclear Facilities: Phase 1 focuses on identifying scientifically sound approaches for carrying out an assessment of cancer risks associated with living near a nuclear facility, judgments about the strengths and weaknesses of various statistical power, ability to assess potential confounding factors, possible biases, and required effort. The results from this Phase 1 study will be used to inform the design of cancer risk assessment, which will be carried out in Phase 2. This report is beneficial for the general public, communities near nuclear facilities, stakeholders, healthcare providers, policy makers, state and local officials, community leaders, and the media.
A physician-anthropologist explores how public health practices--from epidemiological modeling to outbreak containment--help perpetuate global inequities. In Epidemic Illusions, Eugene Richardson, a physician and an anthropologist, contends that public health practices--from epidemiological modeling and outbreak containment to Big Data and causal inference--play an essential role in perpetuating a range of global inequities. Drawing on postcolonial theory, medical anthropology, and critical science studies, Richardson demonstrates the ways in which the flagship discipline of epidemiology has been shaped by the colonial, racist, and patriarchal system that had its inception in 1492. Deploying a range of rhetorical tools and drawing on his clinical work in a variety of epidemics, including Ebola in West Africa and the Democratic Republic of Congo, leishmania in the Sudan, HIV/TB in southern Africa, diphtheria in Bangladesh, and SARS-CoV-2 in the United States, Richardson concludes that the biggest epidemic we currently face is an epidemic of illusions—one that is propagated by the coloniality of knowledge production.
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
This comprehensive account of the human herpesviruses provides an encyclopedic overview of their basic virology and clinical manifestations. This group of viruses includes human simplex type 1 and 2, Epstein–Barr virus, Kaposi's Sarcoma-associated herpesvirus, cytomegalovirus, HHV6A, 6B and 7, and varicella-zoster virus. The viral diseases and cancers they cause are significant and often recurrent. Their prevalence in the developed world accounts for a major burden of disease, and as a result there is a great deal of research into the pathophysiology of infection and immunobiology. Another important area covered within this volume concerns antiviral therapy and the development of vaccines. All these aspects are covered in depth, both scientifically and in terms of clinical guidelines for patient care. The text is illustrated generously throughout and is fully referenced to the latest research and developments.
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.
Epidemiology faces its limits: the search for subtle links between diet, lifestyle, or environmental factors and disease is an unending source of fear - but often yields little certainty. Studies on weak associations - or small effects - often produce contradictory results which confuse the public. In this book, numerous contributions and illustrated examples show the effects of this problem, and sets out how future research should be approached in order to minimize the problems, thus producing clear results of significance.
This practical guide to survival data and its analysis for readers with a minimal background in statistics shows why the analytic methods work and how to effectively analyze and interpret epidemiologic and medical survival data with the help of modern computer systems. The introduction presents a review of a variety of statistical methods that are not only key elements of survival analysis but are also central to statistical analysis in general. Techniques such as statistical tests, transformations, confidence intervals, and analytic modeling are presented in the context of survival data but are, in fact, statistical tools that apply to understanding the analysis of many kinds of data. Similarly, discussions of such statistical concepts as bias, confounding, independence, and interaction are presented in the context of survival analysis and also are basic components of a broad range of applications. These topics make up essentially a 'second-year', one-semester biostatistics course in survival analysis concepts and techniques for non-statisticians.
Strategic health planning, the cornerstone of initiatives designed to achieve health improvement goals around the world, requires an understanding of the comparative burden of diseases and injuries, their corresponding risk factors and the likely effects of invervention options. The Global Burden of Disease framework, originally published in 1990, has been widely adopted as the preferred method for health accounting and has become the standard to guide the setting of health research priorities. This publication sets out an updated assessment of the situation, with an analysis of trends observed since 1990 and a chapter on the sensitivity of GBD estimates to various sources of uncertainty in methods and data.