people aged 45 to 64—is assumed to face the same risks as everyone else; thus, such studies cannot be used to analyze such things as the change in the health of given individuals, nor are they useful for assessing the efficacy and cost of interventions.

Medical demography, the study of chronic disease, disability, and mortality in mature and aging populations, has roots in actuarial and biometric models of mortality and insurance of health-contingent events (Spiegelman, 1968). The tradition includes Graunt's ''bills of mortality" (1662), Bernoulli's life-table models of smallpox vaccination (1760), and the models of adult mortality devised by Gompertz (1825), Makeham (1867), Perks (1932), and Beard (1963a,b). Other well known examples include Clarke's use of "bioactuarial" models to isolate mortality associated with old age from mortality from exogenous causes (1950), and Bourgeois-Pichat's (1952, 1978) and others attempts to estimate the biological "limits" to life expectancy.

But medical demography lost impetus as research into the epidemiology of chronic disease specialized. That work, however, had its limitations in its reliance on case-control studies or studies of occupational cohorts and on longitudinal studies of selected populations from which it was difficult to estimate national rates of health events.

Recently, medical demography regained impetus for several reasons. First, concern has arisen because federal forecasts of mortality and population growth show biases with important implications for Social Security, Medicare, and other federal programs. The Social Security "crisis" of 1982-1983 showed that the population aged 65 and older was consistently underestimated, a bias that may still exist (Myers et al., 1987; Preston, 1993). Second, forecasts of the effects of disease on populations and of the health- and cost-effectiveness of interventions were often inaccurate (Walker, 1982; Frank et al., 1992). Third, the need to assess health trends and to characterize the natural history of chronic disease in the very old—those 85 or older— has intensified as life expectancy has lengthened and as that group has grown. Finally, longitudinal surveys of changes in the health of elderly Americans became available that were linked to Medicare data whose age reporting was better than that in decennial censuses (for example, the 1982, 1984, and 1989 National Long-Term Care Surveys, NLTCS).

Medical demography requires biomedically detailed models of the relation of age to health, to change in the ability to function, and to mortality in individuals. Biologically naive models do not accurately anticipate change in health or in the population health burden or the effects of intervention (Evens et al., 1992; Frank et al., 1992; Selikoff 1981; Tsevat et al., 1991). This is especially the case for the very old because of the special nature of the health processes of this group: they experience comorbidity—combinations of problems—impairment of function, a decrease in the ability to maintain biological stability with emergent nonlinearities in the change of



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