Burton H. Singer – författare

This volume brings together, for the first time, inquiries into the size and proximity of social networks and emotion in social relationships to advance understanding of how emotion in significant social relationships influences health. The collection integrates knowledge from those with expertise in mapping the nature of emotional experience in human relations with those who are linking social ties to health outcomes, and those who explicate underlying neurobiological mechanisms. The book puts forth the idea that full explication of how emotion, social relationships, and health are woven together demands multidisciplinary inquiry and brings together leading experts from fields of affective science, clinical and social psychology, epidemiology, psychiatry, psychoneuroimmunology, psychoneuroendocrinology, and health to promote the above synthesis.

This volume brings together, for the first time, inquiries into the size and proximity of social networks and emotion in social relationships to advance understanding of how emotion in significant social relationships influences health. The collection integrates knowledge from those with expertise in mapping the nature of emotional experience in human relations with those who are linking social ties to health outcomes, and those who explicate underlying neurobiological mechanisms. The book puts forth the idea that full explication of how emotion, social relationships, and health are woven together demands multidisciplinary inquiry and brings together leading experts from fields of affective science, clinical and social psychology, epidemiology, psychiatry, psychoneuroimmunology, psychoneuroendocrinology, and health to promote the above synthesis.

New Horizons in Health discusses how the National Institutes of Health (NIH) can integrate research in the social, behavioral, and biomedical sciences to better understand the causes of disease as well as interventions that promote health. It outlines a set of research priorities for consideration by the Office of Behavioral and Social Sciences Research (OBSSR), with particular attention to research that can support and complement the work of the National Institutes of Health. By addressing the range of interactions among social settings, behavioral patterns, and important health concerns, it highlights areas of scientific opportunity where significant investment is most likely to improve national—and global—health outcomes. These opportunities will apply the knowledge and methods of the behavioral and social sciences to contemporary health needs, and give attention to the chief health concerns of the general public.

New Horizons in Health discusses how the National Institutes of Health (NIH) can integrate research in the social, behavioral, and biomedical sciences to better understand the causes of disease as well as interventions that promote health. It outlines a set of research priorities for consideration by the Office of Behavioral and Social Sciences Research (OBSSR), with particular attention to research that can support and complement the work of the National Institutes of Health. By addressing the range of interactions among social settings, behavioral patterns, and important health concerns, it highlights areas of scientific opportunity where significant investment is most likely to improve national—and global—health outcomes. These opportunities will apply the knowledge and methods of the behavioral and social sciences to contemporary health needs, and give attention to the chief health concerns of the general public.

Multiple complex pathways, characterized by interrelated events and c- ditions, represent routes to many illnesses, diseases, and ultimately death. Although there are substantial data and plausibility arguments suppo- ing many conditions as contributory components of pathways to illness and disease end points, we have, historically, lacked an e?ective method- ogy for identifying the structure of the full pathways. Regression methods, with strong linearity assumptions and data-basedconstraints onthe extent and order of interaction terms, have traditionally been the strategies of choice for relating outcomes to potentially complex explanatory pathways. However, nonlinear relationships among candidate explanatory variables are a generic feature that must be dealt with in any characterization of how health outcomes come about. It is noteworthy that similar challenges arise from data analyses in Economics, Finance, Engineering, etc. Thus, the purpose of this book is to demonstrate the e?ectiveness of a relatively recently developed methodology—recursive partitioning—as a response to this challenge. We also compare and contrast what is learned via rec- sive partitioning with results obtained on the same data sets using more traditional methods. This serves to highlight exactly where—and for what kinds of questions—recursive partitioning–based strategies have a decisive advantage over classical regression techniques.

Multiple complex pathways, characterized by interrelated events and c- ditions, represent routes to many illnesses, diseases, and ultimately death. Although there are substantial data and plausibility arguments suppo- ing many conditions as contributory components of pathways to illness and disease end points, we have, historically, lacked an e?ective method- ogy for identifying the structure of the full pathways. Regression methods, with strong linearity assumptions and data-basedconstraints onthe extent and order of interaction terms, have traditionally been the strategies of choice for relating outcomes to potentially complex explanatory pathways. However, nonlinear relationships among candidate explanatory variables are a generic feature that must be dealt with in any characterization of how health outcomes come about. It is noteworthy that similar challenges arise from data analyses in Economics, Finance, Engineering, etc. Thus, the purpose of this book is to demonstrate the e?ectiveness of a relatively recently developed methodology—recursive partitioning—as a response to this challenge. We also compare and contrast what is learned via rec- sive partitioning with results obtained on the same data sets using more traditional methods. This serves to highlight exactly where—and for what kinds of questions—recursive partitioning–based strategies have a decisive advantage over classical regression techniques.

Models to forecast changes in mortality, morbidity, and disability in elderly populations are essential to national and state policies for health and welfare programs. This volume presents a wide-ranging survey of the forecasting of health of elderly populations, including the modelling of the incidence of chronic diseases in the elderly, the differing perspectives of actuarial and health care statistics, and an assessment of the impact of new technologies on the elderly population. Amongst the topics covered are - uncertainties in projections from census and social security data and actuarial approaches to forecasting - plausible ranges for population growth using biol ogical models and epidemiological time series data - the financing of long term care programs - the effects of major disabling diseases on health expenditures - forecasting cancer risks and risk factors As a result, this wide-ranging volume will become an indispensable reference for all those whose research touches on these topics.

Models to forecast changes in mortality, morbidity, and disability in elderly populations are essential to national and state policies for health and welfare programs. This volume presents a wide-ranging survey of the forecasting of health of elderly populations, including the modelling of the incidence of chronic diseases in the elderly, the differing perspectives of actuarial and health care statistics, and an assessment of the impact of new technologies on the elderly population. Amongst the topics covered are - uncertainties in projections from census and social security data and actuarial approaches to forecasting - plausible ranges for population growth using biol ogical models and epidemiological time series data - the financing of long term care programs - the effects of major disabling diseases on health expenditures - forecasting cancer risks and risk factors As a result, this wide-ranging volume will become an indispensable reference for all those whose research touches on these topics.

Multiple complex pathways, characterized by interrelated events and c- ditions, represent routes to many illnesses, diseases, and ultimately death. Although there are substantial data and plausibility arguments suppo- ing many conditions as contributory components of pathways to illness and disease end points, we have, historically, lacked an e?ective method- ogy for identifying the structure of the full pathways. Regression methods, with strong linearity assumptions and data-basedconstraints onthe extent and order of interaction terms, have traditionally been the strategies of choice for relating outcomes to potentially complex explanatory pathways. However, nonlinear relationships among candidate explanatory variables are a generic feature that must be dealt with in any characterization of how health outcomes come about. It is noteworthy that similar challenges arise from data analyses in Economics, Finance, Engineering, etc. Thus, the purpose of this book is to demonstrate the e?ectiveness of a relatively recently developed methodology—recursive partitioning—as a response to this challenge. We also compare and contrast what is learned via rec- sive partitioning with results obtained on the same data sets using more traditional methods. This serves to highlight exactly where—and for what kinds of questions—recursive partitioning–based strategies have a decisive advantage over classical regression techniques.

Multiple complex pathways, characterized by interrelated events and con­ ditions, represent routes to many illnesses, diseases, and ultimately death. Although there are substantial data and plausibility arguments supporting many conditions as contributory components of pathways to illness and disease end points, we have, historically, lacked an effective methodology for identifying the structure of the full pathways. Regression methods, with strong linearity assumptions and data-based constraints on the extent and order of interaction terms, have traditionally been the strategies of choice for relating outcomes to potentially complex explanatory pathways. How­ ever, nonlinear relationships among candidate explanatory variables are a generic feature that must be dealt with in any characterization of how health outcomes come about. Thus, the purpose of this book is to demon­ strate the effectiveness of a relatively recently developed methodology­ recursive partitioning-as a response to this challenge. We also compare and contrast what is learned via recursive partitioning with results ob­ tained on the same data sets using more traditional methods. This serves to highlight exactly where--and for what kinds of questions-recursive partitioning-based strategies have a decisive advantage over classical re­ gression techniques. This book is suitable for three broad groups of readers: (1) biomedical re­ searchers, clinicians, public health practitioners including epidemiologists, health service researchers, environmental policy advisers; (2) consulting statisticians who can use the recursive partitioning technique as a guide in providing effective and insightful solutions to clients'' problems; and (3) statisticians interested in methodological and theoretical issues.