To provide the tools and knowledge needed in efforts to improve the health of the world's populations, researchers collaborated on the Global Burden of Diseases, Injuries, and Risk Factors Study 2010. The study produced comprehensive estimates of over 200 diseases and health risk factors in 187 countries over two decades, results that will be used by governments and non-governmental agencies to inform priorities for global health research, policies, and funding.
Integrated Meta-Regression Framework for Descriptive Epidemiology is the first book-length treatment of model-based meta-analytic methods for descriptive epidemiology used in the Global Burden of Disease Study 2010. In addition to collecting the prior work on compartmental modeling of disease, this book significantly extends the model, by formally connecting the system dynamics model of disease progression to a statistical model of epidemiological rates and demonstrates how the two models were combined to allow researchers to integrate relevant data. Practical applications of the model to meta-analysis of more than a dozen different diseases complement the theoretical foundations of the integrative systems modeling of disease in populations. The book concludes with a detailed description of the future directions for research in model-based meta-analysis of descriptive epidemiological data.
Abraham Flaxman is assistant professor of global health in the Institute for Health Metrics and Evaluation at the University of Washington.
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Abraham D. Flaxman is assistant professor of global health at the Institute for Health Metrics and Evaluation at the University of Washington.Theo Vos is professor of global health at the Institute for Health Metrics and Evaluation at the University of Washington. Christopher J. L. Murray is professor of global health and director of the Institute for Health Metrics and Evaluation at the University of Washington.
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Anbieter: PBShop.store UK, Fairford, GLOS, Vereinigtes Königreich
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Zustand: New. First book-length treatment of model-based meta-analytic methods for descriptive epidemiology Editor(s): Flaxman, Abraham. Series: Publications on Global Health, Institute for Health Metrics and Evaluation. Num Pages: 600 pages, black & white illustrations. BIC Classification: MBNS. Category: (P) Professional & Vocational. Dimension: 263 x 186 x 22. Weight in Grams: 646. . 2015. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland. Artikel-Nr. V9780295991849
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Anbieter: Optimon Books, Gravesend, KENT, Vereinigtes Königreich
Hardcover. Zustand: Good. THERE ARE NO TARIFFS OR CUSTOMS DUTIES ON BOOKS. Book in good condition. Pages clean, tight and unmarked. Cover also in good condition, with only very minor spine creasing and scuffing. To provide the tools and knowledge needed in efforts to improve the health of the world's populations, researchers collaborated on the Global Burden of Diseases, Injuries, and Risk Factors Study 2010. The study produced comprehensive estimates of more than 200 diseases and health risk factors in 187 countries over two decades, results that will be used by governments and non-governmental agencies to inform priorities for global health research, policies, and funding. An Integrative Metaregression Framework for Descriptive Epidemiology is the first book-length treatment of model-based meta-analytic methods for descriptive epidemiology used in the Global Burden of Disease Study 2010. In addition to collecting the prior work on compartmental modeling of disease, this book significantly extends the model by formally connecting the system dynamics model of disease progression to a statistical model of epidemiological rates and demonstrates how the two models were combined to allow researchers to integrate all available relevant data. Practical applications of the model to meta-analysis of several different diseases complement the theoretical foundations of what the editors call the integrative systems modeling of disease in populations. The book concludes with a detailed description of the future directions for research in model-based meta-analysis of descriptive epidemiological data. Artikel-Nr. 444785
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