The Global Burden of Disease Study (GBD) is one of the largest-scale research collaborations in global health, distilling a wide range of health information to provide estimates and projections for more than 350 diseases, injuries, and risk factors in 195 countries. Its results are a critical tool informing researchers, policy-makers, and others working to promote health around the globe. A study like the GBD is, of course, extremely complex from an empirical perspective. But it also raises a large number of complex ethical and philosophical questions that have been explored in a series of collaborations over the past twenty years among epidemiologists, philosophers, economists, and policy scholars. The essays in this volume address issues of current and urgent concern to the GBD and other epidemiological studies, including rival understandings of causation, the aggregation of complex health data, temporal discounting, age-weighting, and the valuation of health states. The volume concludes with a set of chapters discussing how epidemiological data should and should not be used. Better appreciating the philosophical dimensions of a study like the GBD can make possible a more sophisticated interpretation of its results, and it can improve epidemiological studies in the future, so that they are better suited to produce results that can help us to improve global health.

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.