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This article examines the evidence for two propositions: that health and ability to function can often be sustained into advanced old age through interventions that (1) control risk factors among people already old and (2) improve lifelong health behaviors and life-styles starting with people currently still young. Beginning with a general model of age-related changes in health, function, and survival, the article shows how the interrelationship between the three varies between two extreme types of diseases: the fast, lethal type, where death occurs early and rapidly, with few years spent in unhealthy or disabled conditions; and the slow, degenerative type, where prolonged survival allows for added years in unhealthy or disabled conditions. Since this latter type is predominant among older people in the United States today, interventions to prevent morbidity and functional loss are of critical importance. The need for an improved research base to guide such interventions is strongly urged.

Based on a review of the literature, evidence for two propositions is examined: that health & ability to function can often be sustained into advanced old age through interventions that: (1) control risk factors among people already old; & (2) improve lifelong health behaviors & lifestyles among people currently still young. Beginning with a general model of age-related changes in health, function, & survival, it is shown how the interrelationship between the three varies between two extreme types of diseases: the fast, lethal type, where death occurs early & rapidly, with few years spent in unhealthy or disabled conditions; & the slow, degenerative type, where prolonged survival allows for added years in unhealthy or disabled conditions. Since this latter type is predominant among older people in the US today, interventions to prevent morbidity & functional loss are of critical importance & require an improved research base to guide them. 2 Tables, 3 Figures. HA

Information on 7,184 respondents of the Duke University and Johns Hopkins University Epidemiologic Catchment Areas (ECA) studies were analyzed using a fuzzy set classification procedure called Grade of Membership (GoM) analysis. A formal description of GoM analysis and its relationship to other multivariate procedures, some of which are described in other articles in this issue, is provided. Symptoms were selected for analysis that represented seven clinically defined psychiatric disorders: depression, generalized anxiety, panic disorder, simple phobia, social phobia, agoraphobia, and somatization. In the sample there were persons who expressed symptoms for these diseases as well as a large group of persons free of any symptoms. In the GoM analyses, we used both the 41 symptom variables initially provided to define disorder groups and a derivative set of 33 symptom variables with certain of the original symptoms grouped to increase the stability of symptom sets. In the analysis it was found that six pure types best explained the variation of both the sets of 41 and 33 symptoms. The structure of the 33 variable set provided a better identification of the underlying disease groups. The six pure types that emerged from the 33 variable analysis were identified as depression, generalized anxiety, phobias, somatization, and two groups that were initially classified as asymptomatic persons. One of these groups was clearly asymptomatic and the other possessed only mild symptoms not strongly indicative of any of the disorders. This group may represent a neurotic subpopulation. Perhaps a surprising result was the failure for panic to emerge as a separate disorder but instead to be associated with generalized anxiety. The failure to identify the phobias as separate disorders may be attributable to the small number of variables used to define them. In a previous analysis using a larger set of variables, simple and social phobias clearly separated.

The analysis of many social and health policy issues requires the use of multiple data sources from a diverse body of scientific and technical studies. Although individual data sets are rigorously analyzed, integration of the results of these analyses to resolve policy questions is often accomplished by informal or subjective strategies based on procedures designed to generate consensus among scientific experts. In this article we discuss a model for conducting a more formal integration of multiple data sources (including subjective or theoretical judgments). The advantages of such models over consensus generation by informal means are that (1) they can produce very specific quantitative measures of the implications of alternative policies; (2) their assumptions are more readily reviewable; (3) they can be validated against data; and (4) they formally link experimental and survey data, organizing our knowledge base so that priorities for improving the knowledge base can be determined systematically. These advantages suggest that the use of formal models can be a valuable adjunct to informal consensus-generating procedures. An example of how such a modeling strategy is applied to the monitoring of population health is presented and discussed.

Nonlinear hazard models are used to examine temporal trends in the age‐specific mortality risks of chronic obstructive lung diseases for the U.S. population. These hazard functions are fit to age‐specific mortality rates for 1968 and 1977 for four race/sex groups. Changes in the parameters of these models are used to assess two types of differences in the age pattern of the rates between 1968 and 1977. The first measure of trend in the age‐specific mortality rates is the temporal change in the proportionality constant in the function used to model their age variation. By allowing only this proportionality parameter to vary between 1968 and 1977, it is possible to determine an age‐constant percentage increase or decrease. The second measure reflects the absolute displacement in terms of years of life of the fitted mortality curves for the two time points. This second index can be interpreted as the acceleration or deceleration of mortality risks over the life span, i.e., the number of years that is needed for mortality rates to achieve the same level as in the comparison group. The analysis showed that the age changes in chronic obstructive lung disease mortality rates differed by race/sex group and for both measures of change over the period. Adjustment of the fitted curves for the effects of individual variability in risk was significant for three of four groups.

A microsimulation model, allowing one to forecast short‐ and long‐term population changes conditional on the prevalence of a risk factor in a population, is presented. In this model, population changes result from the aggregation of changes in individual event histories, which, in turn, result from mortality and infertility rates recalculated in accordance with their known relative risks in population groups exposed to a risk factor. Smoking, being the most widespread and influential preventable public health risk factor, is chosen to demonstrate the abilities of the model to forecast the population effects of different hypothetical smoking prevalences. The demographic and population health effects on 20‐, 50‐, and 100‐year projections with the current, hypothetically doubled, and hypothetically halved the current smoking prevalence are analyzed in detail. The model predicts an increase in life expectancy (0.99 year for males and 0.64 years for females), and an increase in population size (2.2–7.5% dependent on the age group) if smoking prevalence is reduced by half. Sensitivity analyses of all findings are performed. The generalization of the model to account for multiple risk factors (e.g., the simultaneous effects of alcohol consumption, obesity, and smoking) and effects on medical expenditures are discussed.