Biomedical Research and Changing Concepts of Disease and Aging: Implications for Long-Term Health Forecasts for Elderly Populations

Abstract

In prior chapters, we reviewed models for forecasting the health of the U.S. elderly and oldest-old population. In Chapter 4 we suggest that a major stimulus to the development of forecasting models is increased scientific knowledge about human senescence and chronic disease. As knowledge increases, the boundaries of senescence and disease become less distinct (Mooradian and Wong, 1991a,b). Models of senescence have evolved from a unidimensional, genetically fixed failure process, to multidimensional stochastic processes where polygenic determination of multiple pathologies and losses of function interact with environment (Finch, 1991; Manton, Stallard, et al., 1992). Forecasting models must keep pace with, and anticipate the future direction of, this evolution—with the complication that, even using multiple data sets, processes will be only partially observed. This chapter reviews and illustrates how biomedical research can be used to construct models, and substantive scenarios, to forecast the effects of future health interventions.