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Health Forecasting and Models of Aging

  • Chapter
Forecasting the Health of Elderly Populations

Part of the book series: Springer Series in Statistics ((SBH))

Abstract

In two prior chapters, legal, financial, and regulatory constraints on actuarial forecasts were discussed (Chapter 2), and the implications of such constraints for projections by the Social Security Administration (SSA) and the U.S. Census Bureau were reviewed (Chapter 3). This chapter discusses statistical and mathematical forecasting techniques that may improve health forecasts by (1) using a broad range of data types, (2) appropriately combining data from multiple sources, and (3) improving the biological realism of forecasts. Identification of methodological issues in health forecasting that need further research is also covered. This need is, in part, evident from past problems with federal projections (e.g., U.S. Senate, 1983) and, in part, is due to the rapid growth of scientific insights on the physiological mechanisms underlying aging which has changed our understanding of the substance of forecasts (e.g., changing concepts of senescence, chronic disease, disability) and their relation to mortality (e.g., Kristal and Yu, 1992; Mooradian and Wong, 1991a,b). Forecasting models, whatever mathematical techniques are used, must take advantage of this new knowledge to be credible. This requires advances in several areas.

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  1. Kenneth G. Manton
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Editors and Affiliations

  1. Center for Demographic Studies, Duke University, 2117 Campus Drive, 27708, Durham, NC, USA

    Kenneth G. Manton (Research Professor) (Research Professor)

  2. Department of Epidemiology and Public Health, School of Medicine, Yale University, 60 College Street, Box 3333, 06510, New Haven, CT, USA

    Burton H. Singer

  3. NIH/NIA, Gateway Building, Room 2C234, 7201 Wisconsin Avenue, 20892, Bethesda, MD, USA

    Richard M. Suzman

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Manton, K.G. (1993). Health Forecasting and Models of Aging. In: Manton, K.G., Singer, B.H., Suzman, R.M. (eds) Forecasting the Health of Elderly Populations. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4613-9332-0_4

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