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This book examines the history of the Victorian Cancer Registry (VCR) in Australia from its establishment in the late 1930s through to the present day. It sheds new light on the history of medicine and the broader social and cultural histories affected by advances in cancer control science, providing a historical account of cancer registration that is empirically grounded in new archival and oral sources. It addresses the obstacles that proponents of cancer registration faced, how governments came to support permanent registries, and the subsequent contributions of the VCR and other registries to cancer research. In charting this history, the book discusses some of the political, social, and cultural implications of registry-driven science, and the links between developments in scientific knowledge and campaigning for policy changes around cancer.
This atlas illustrates the latest available data on the cancer epidemic, showing causes, stages of development, and prevalence rates of different types of cancers by gender, income group, and region. It also examines the cost of the disease, both in terms of health care and commercial interests, and the steps being taken to curb the epidemic, from research and screening to cancer management programs and health education.
The demand for health information continues to increase, but the ability of health professionals to provide it clearly remains variable. The aim of this book is (1) to summarize and synthesize research on the selection and presentation of data pertinent to public health, and (2) to provide practical suggestions, based on this research summary and synthesis, on how scientists and other public health practitioners can better communicate data to the public, policy makers, and the press in typical real-world situations. Because communication is complex and no one approach works for all audiences, the authors emphasize how to communicate data "better" (and in some instances, contrast this with how to communicate data "worse"), rather than attempting a cookbook approach. The book contains a wealth of case studies and other examples to illustrate major points, and actual situations whenever possible. Key principles and recommendations are summarized at the end of each chapter. This book will stimulate interest among public health practitioners, scholars, and students to more seriously consider ways they can understand and improve communication about data and other types of scientific information with the public, policy makers, and the press. Improved data communication will increase the chances that evidence-based scientific findings can play a greater role in improving the public's health.
This is the first book to compare eight LDFs by different types of datasets, such as Fisher’s iris data, medical data with collinearities, Swiss banknote data that is a linearly separable data (LSD), student pass/fail determination using student attributes, 18 pass/fail determinations using exam scores, Japanese automobile data, and six microarray datasets (the datasets) that are LSD. We developed the 100-fold cross-validation for the small sample method (Method 1) instead of the LOO method. We proposed a simple model selection procedure to choose the best model having minimum M2 and Revised IP-OLDF based on MNM criterion was found to be better than other M2s in the above datasets. We compared two statistical LDFs and six MP-based LDFs. Those were Fisher’s LDF, logistic regression, three SVMs, Revised IP-OLDF, and another two OLDFs. Only a hard-margin SVM (H-SVM) and Revised IP-OLDF could discriminate LSD theoretically (Problem 2). We solved the defect of the generalized inverse matrices (Problem 3). For more than 10 years, many researchers have struggled to analyze the microarray dataset that is LSD (Problem 5). If we call the linearly separable model "Matroska," the dataset consists of numerous smaller Matroskas in it. We develop the Matroska feature selection method (Method 2). It finds the surprising structure of the dataset that is the disjoint union of several small Matroskas. Our theory and methods reveal new facts of gene analysis.
When the woman he loved was diagnosed with a metastatic cancer, science writer George Johnson embarked on a journey to learn everything he could about the disease and the people who dedicate their lives to understanding and combating it. What he discovered is a revolution under way—an explosion of new ideas about what cancer really is and where it comes from. In a provocative and intellectually vibrant exploration, he takes us on an adventure through the history and recent advances of cancer research that will challenge everything you thought you knew about the disease. Deftly excavating and illuminating decades of investigation and analysis, he reveals what we know and don’t know about cancer, showing why a cure remains such a slippery concept. We follow him as he combs through the realms of epidemiology, clinical trials, laboratory experiments, and scientific hypotheses—rooted in every discipline from evolutionary biology to game theory and physics. Cogently extracting fact from a towering canon of myth and hype, he describes tumors that evolve like alien creatures inside the body, paleo-oncologists who uncover petrified tumors clinging to the skeletons of dinosaurs and ancient human ancestors, and the surprising reversals in science’s comprehension of the causes of cancer, with the foods we eat and environmental toxins playing a lesser role. Perhaps most fascinating of all is how cancer borrows natural processes involved in the healing of a wound or the unfolding of a human embryo and turns them, jujitsu-like, against the body. Throughout his pursuit, Johnson clarifies the human experience of cancer with elegiac grace, bearing witness to the punishing gauntlet of consultations, surgeries, targeted therapies, and other treatments. He finds compassion, solace, and community among a vast network of patients and professionals committed to the fight and wrestles to comprehend the cruel randomness cancer metes out in his own family. For anyone whose life has been affected by cancer and has found themselves asking why?, this book provides a new understanding. In good company with the works of Atul Gawande, Siddhartha Mukherjee, and Abraham Verghese, The Cancer Chronicles is endlessly surprising and as radiant in its prose as it is authoritative in its eye-opening science.
In the past, disease pattern mapping depended on census tracts based on political units, such as states and counties. However, with the advent of geographic information systems (GIS), researchers can now achieve a new level of precision and flexibility in geographic locating. This emerging technology allows the mapping of many different kinds of ge
Cancer is low or absent on the health agendas of low- and middle-income countries (LMCs) despite the fact that more people die from cancer in these countries than from AIDS and malaria combined. International health organizations, bilateral aid agencies, and major foundations—which are instrumental in setting health priorities—also have largely ignored cancer in these countries. This book identifies feasible, affordable steps for LMCs and their international partners to begin to reduce the cancer burden for current and future generations. Stemming the growth of cigarette smoking tops the list to prevent cancer and all the other major chronic diseases. Other priorities include infant vaccination against the hepatitis B virus to prevent liver cancers and vaccination to prevent cervical cancer. Developing and increasing capacity for cancer screening and treatment of highly curable cancers (including most childhood malignancies) can be accomplished using "resource-level appropriateness" as a guide. And there are ways to make inexpensive oral morphine available to ease the pain of the many who will still die from cancer.
Cancer is a dreaded disease. One in two people will be diagnosed with cancer within their lifetime. Medical Statistics for Cancer Studies shows how cancer data can be analysed in a variety of ways, covering cancer clinical trial data, epidemiological data, biological data, and genetic data. It gives some background in cancer biology and genetics, followed by detailed overviews of survival analysis, clinical trials, regression analysis, epidemiology, meta-analysis, biomarkers, and cancer informatics. It includes lots of examples using real data from the author’s many years of experience working in a cancer clinical trials unit. Features: A broad and accessible overview of statistical methods in cancer research Necessary background in cancer biology and genetics Details of statistical methodology with minimal algebra Many examples using real data from cancer clinical trials Appendix giving statistics revision.
The purpose of this book is to examine the etiology of cancer in large human populations using mathematical models developed from an inter-disciplinary perspective of the population epidemiological, biodemographic, genetic and physiological basis of the mechanisms of cancer initiation and progression. In addition an investigation of how the basic mechanism of tumor initiation relates to general processes of senescence and to other major chronic diseases (e.g., heart disease and stroke) will be conducted.