Life Course, Environmental Change, and Life Span JEAN-MARIE ROBINE
From: Carey, James R. and Shripad Tuljapurkar (eds.). Life Span: Evolutionary, Ecological, and Demographic Perspectives, Supplement to Population and Development Review, vol. 29, 2003. New York: Population Council. ©2003 by The Population Council, Inc. All rights reserved.
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Life Course, Environmental Change, and Life Span JEAN-MARIE ROBINE
Biodemographic models, incorporating biological, social, and environmental heterogeneity, are essential to understanding the determinants of human longevity (Carnes and Olshansky 2001). Yet these conventional factors are not sufficient, even in an interactive way, to explain the life history and the longevity of species, including the human species. Finch and Kirkwood (2000) have proposed the addition to the conventional models of intrinsic chance, known to physicists as chaos. I suggest the addition of the life course. In recent publications (Robine and Vaupel 2001; Robine 2001a), I have proposed a new biodemographic model to explain the trajectory of mortality currently observed for humans. It is generally believed that life span is genetically determined and that the environment is responsible for individual differences. I propose to consider the opposite hypothesis: that the environment plays an essential part in defining the limits of the life span while genetic heterogeneity explains a large part of the individual differences in the duration of life. The aim of this chapter is to elaborate this hypothesis in which human longevity is seen as a “plastic” outcome, especially in its ability to explain the evolution of the mortality trajectory observed through the twentieth century. In this approach, environment is understood to include the built environment, living and working conditions, and changes in medical knowledge such as the discoveries of vaccines and antibiotics. For human populations the notion of transition is important (Meslé and Vallin 2000). The demographic transition allowed the human species to move from high mortality and high fertility to the current demographic situation characterized by low mortality and low fertility. The epidemiological transition, paralleling the demographic changes (Omran 1971; McKeown 1976), allowed the human species to move from a situation dominated by mortality resulting from infectious diseases to the current epidemiological situation where most deaths are associated with the aging process. Myers 229
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and Lamb (1993) extended the notion of transition to changes in functional health status that accompanied the demographic and the epidemiological changes. In the same way, the notion of transition must be extended to gerontological changes. Before entering the epidemiological transition, humans age though rarely become old; apart from a few exceptional cases, they die of accidents and infectious diseases before reaching old age. During the transition, mortality attributable to infectious disease recedes, and humans have increasing access to old age—becoming sick and then old or, alternatively, old and then sick before dying. After the transition, if the pathological causes of death are controlled, virtually all humans will reach old age and will eventually die of frailty. The mortality trajectory observed through the twentieth century corresponds to the transitional phase. Before the transition, that is, before the eighteenth century in Europe, the environment was relatively homogeneous and changed very little for an individual through his or her life course. During the transition the environment became more complex, with an increase in the number of specific environments corresponding to the main life periods. The environmental change can be seen, at an individual level, as a series of breaks in the life course (school conditions, higher education conditions, working conditions, retirement conditions, nursing home conditions). Further into the transition, the number of environments could be much higher (village or residence for seniors; residence associated with a nursing home; or specialized nursing home), and the changes between environments could be seen as a more nearly continuous process. During the life course individuals enter different environments (i.e., life conditions) with associated theoretical life spans and with a final change (institutionalization) before dying. I will discuss these changes in relation to the mortality trajectory and provide some specifications to develop the mathematics of such a model. The “ages” of life are not a new notion. Already in the sixteenth century Shakespeare distinguished the seven ages of man: infancy, school days, the young man, the soldier, the grave citizen, the retired old man, and finally second childhood. But in practice, before the demographic transition, this complete life course was applicable to only a small portion of the population. Moreover, the associated environments were more constant across the life span in the past than today.
The biodemographic model of mortality trajectory For biologists, mortality primarily serves to measure the aging process. For demographers, mortality measures the quality of the current ecological and social environment, the current conditions. In view of the fact that human beings spend the greater part of their time improving the quality of their physical and social environment, making it more and more favorable for the
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realization of their potential longevity, I proposed a biodemographic model to explain the trajectory of mortality for the human species that takes into account the combined effects of the quality of the environment and of aging (Robine 2001a). Starting with youth, the age when individuals are the most robust and the most resistant to environmental hazards, the increase in mortality measures the aging process translating young, vigorous individuals into frail, senile elders. When individuals become frail and are no longer able to resist environmental hazards or resist them extremely weakly, the mortality rate becomes constant. The lowest mortality rate recorded at the starting point and the level of the final plateau of mortality measure the quality of the ecological and social environment. Between these two measures, the mortality trajectory measures the aging process. Excluding infant and child mortality, we can summarize the mortality trajectory corresponding to this model by (1) a lowest point, similar to the initial mortality rate proposed by Finch and his collaborators (Finch et al. 1990), followed by (2) an increase in the mortality rate and (3) a plateau of mortality, corresponding to the three stages of the developmental transition: young and robust/aging/old and frail. Nothing is fixed in this model: neither the age with the lowest mortality rate, the level of the lowest mortality rate, the rate of mortality increase during the aging phase, the age at which the mortality increase reaches a plateau, nor the level of the final plateau. The model does not imply a constant rate of increase for the mortality rate during the aging phase, but that phase may be a “Gompertzian segment” (Finch and Pike 1996). One can also imagine a slower rate of increase at both ends of the aging phase: at the beginning because initially robust young people can resist the transition and at the end because aged people can fight to retain resources to continue resisting, even very weakly, the environmental hazards. This model is complementary to the frailty model (Vaupel et al. 1979) since the aging trajectory, the central part of the mortality trajectory, may incorporate differential aging trajectories characterizing different homogeneous groups within the population.
Life course and environmental changes The specific contribution of the new model is to incorporate the life course and the environmental changes as part of the biological aging process to explain the mortality trajectory and its transition over the twentieth century. One explanation could be the substantial improvement, since 1945, in the general environment in countries with low mortality, leading to a fall of mortality at the oldest ages. At each year of age, individuals who should be higher on their theoretical trajectory of mortality are living in an environment that is increasingly favorable to the realization of their poten-
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tial for longevity. Another explanation could be that when individuals become too fragile to face the difficulties of the common environment, they are placed in a protected environment, such as that provided by a nursing home, where their aging is slowed considerably. Such a model with two contrasted environments supports representations of a mortality trajectory with two successive segments (whether Gompertzian or not). Variations between individuals in the ages at which the environment changes could support the existence of a progressive transition between the two segments.
The gerontological transition The next question is how the three elements summarizing the mortality trajectory (lowest point, increase, plateau) could have changed during the demographic and epidemiological transition. First, the mortality level at the lowest mortality point, correctly observed since the eighteenth century, has diminished considerably since the beginning of the demographic transition— unambiguous testimony to a substantial improvement in the physical and social environment, specifically in conditions during childhood. With the transition, children have benefited from more-protected environments—being excluded from the labor force, for example. It is noteworthy that no change is detectable in the age with the lowest mortality rate—suggesting no environmental impact on the time needed to reach the maximum survival from birth.1 Second, we have no evidence that the rate of mortality increase with age during the aging phase has slowed during the demographic transition. Whatever the model used to summarize the mortality trajectory during the aging phase, the rate of mortality increase with age appears to be constant, or even to increase, through the demographic transition (Thatcher 1999). This suggests that the demographic and epidemiological transitions have had little if any impact on the aging process itself, although they may have contributed toward its acceleration. The latter hypothesis could be explained by a decrease in selection in infancy before the starting point of the aging process and also by the fact that the climb to the plateau now starts at a lower level of minimum mortality. However, during the aging phase, mortality rates double approximately every eight years for the human species. Third, the existence of a plateau of mortality when individuals become frail and cease resisting environmental hazards is still a hypothesis, although it is substantiated by the models that best fit current mortality data (Vaupel et al. 1998; Thatcher et al. 1999; Lynch and Brown 2001) and by empirical observations among centenarians and supercentenarians (Robine and Vaupel 2001, 2002). In the future the mortality plateau, if it exists, will be better observed when the size of the population reaching the age where it appears increases. Alternative models have been proposed, however, such as quadratic trajectories that involve a decrease in the mortality rate after a maximum is reached (Vaupel et al. 1998). Only time will tell.
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The evolution of the mortality trajectory through the twentieth century Figures 1 and 2 summarize the mortality trajectories of low-mortality countries (i.e., the countries most advanced in the transition) through the twentieth century. The first trajectory was extrapolated by Vincent (1951) using data from France, the Netherlands, Sweden, and Switzerland circa 1900– 45. He observed regularly increasing death rates (i.e., the quotient of mortality) to ages beyond 100 years that exceeded the value 0.6. By simple linear extrapolation of the death rates plotted on a semi-logarithmic graph, he found that the mortality trajectory reached a death rate of 1.0 at age 110 for both sexes. He therefore concluded that human life is limited to 110 years (Vincent 1951). The second trajectory was extrapolated by Dépoid (1973), using the same method as Vincent and with data from the same countries but for a later period, circa 1945–70. Dépoid found limit values of 117 years for men and 119 years for women. Surprisingly, there was no further discussion about the eight years separating the limits found using the same method but with a 50-year difference in measurement period. The third trajectory was extrapolated by Thatcher et al. (1998) with a logistic model fitted to the mortality data of the 13 countries with the best human mortality data for the period 1980–90 (Austria, Denmark, England and Wales, Finland, France, West Germany, Iceland, Italy, Japan, the Nether-
FIGURE 1 Mortality trajectories beyond the age of 95 years, arithmetic scale: France, Netherlands, Sweden, and Switzerland (circa 1900–45 and 1945–70); 13 countries (1980–90); French centenarians (1980–90); supercentenarians (1960–2000) 1.0 Adjusted Vincent
Probability of death
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Observed Vincent 1951
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0.2 Quadratic model: Vaupel et al. 1998 Logistic model: Thatcher et al. 1998
0.0 92
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Age SOURCE: Robine and Vaupel (2001).
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FIGURE 2 Mortality trajectories beyond the age of 95 years, logarithmic scale: France, Netherlands, Sweden, and Switzerland (circa 1900–45 and 1945–70); 13 countries (1980–90); French centenarians (1980–90); supercentenarians (1960–2000) 1 Dépoid 1973 Vincent 1951
Probability of death
Logistic
French centenarians Quadratic
Quadratic model: Vaupel et al. 1998 Logistic model : Thatcher et al. 1998
0.1 92
96
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Age
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120
SOURCE: Robine and Vaupel (2001).
lands, Norway, Sweden, and Switzerland). Thatcher et al. found that a logistic model is the best to fit the observations since 1960 and that this model suggests that the mortality quotient will never increase above about 0.6 (Thatcher et al. 1998; Kannisto 1999). This does not mean that man will become immortal but that, even at the highest ages, the few survivors have a small chance to survive to the next year. Similarly, if man does not age, he would not become immortal. The fourth and last trajectory was extrapolated by Vaupel et al. (1998), using the same data base for the period 1980– 90. Vaupel et al. found that a quadratic model fits the most recent data very well. After having reached a maximum at age 109 years with a mortality level slightly above 0.5, the mortality trajectory goes down (Vaupel et al. 1998). In addition to these four trajectories, Figures 1 and 2 display the observed rates used by Vincent in 1951 and by Thatcher et al. in 1998. The figures also display the mortality rates of French centenarians for the period 1980–90 (Kannisto 1996) and the mortality rates of French supercentenarians (Robine and Vaupel 2001). A more recent study confirms the mortality rates of supercentenarians but with greater precision (Robine and Vaupel 2002).
The plasticity of longevity Altogether the extrapolated trajectories and the observed data support the hypothesis of substantial changes in the mortality trajectory of the human
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species during the twentieth century. These observations provide strong support for the new concept of plasticity of aging (Finch 1998; Finch and Tanzi 1997). But what has changed, the shape of the trajectory or its level? My first hypothesis is that only the level of the trajectory has changed with the demographic and epidemiological transition because of the substantial improvement in the physical and social environment. My second hypothesis is that the shape of the mortality trajectory has always appeared constant since it is for the most part determined by the biological life history of humans. A classic assumption in demography is that the age pattern of mortality is independent of the level of mortality, this being the rationale for model life tables (Coale and Demeny 1983). Before the transition the mortality plateau (i.e., the level of mortality reached by frail persons who can no longer resist environmental hazards or have only a weak resistance) was much higher than today, although the level was below unity and was reached much earlier in the life course. So few people reached this stage that its observation was impossible. With the demographic transition and the fall of mortality among the aging population, more people are getting old and frail, thus increasing the chance of observing a mortality plateau, even if it occurs at an older age. At the same time, the level of mortality at the plateau has decreased because of improvements in the physical and social environment during the transition. Good historical data, such as the French-Canadian data for the seventeenth century, show an exponential increase in mortality with age without any evidence of a leveling off at extreme ages (Bourbeau and Desjardins 2002). However, the extreme ages involved, 85 to 95 years, as well as the level of mortality, make the results of these studies compatible with our model. Before the demographic transition, the level of mortality was so high that only the aging segment could be observed. At an individual level, aging today is often a long retirement process. When individuals reach age 60 or 65 years, the majority retire from work. With time they reduce or stop various elements of their activity and begin to reduce mobility to the confines of their community and then their neighborhood. These successive environments could be associated with specific conditions more favorable to their survival, but the changes are generally gradual. When a person becomes too frail to survive in the common environment, he/she moves to a radically different environment—a nursing home or home confinement—that provides more secure conditions in relation to the individual’s health status. This series of changes may not only change the rate of increase in mortality rates but may also eventually lead to a plateau of mortality for those living in a nursing home or confined to their own home. Although some time may be required to acknowledge the necessity of a move to a new environment to improve the chance of survival, the move can immediately produce lower mortality conditions. In
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addition if the price to pay for the benefit of better mortality conditions is high in terms of quality of life (confinement, social isolation), the move can be delayed as long as possible, increasing the gap between the mortality conditions before and after the move and leading eventually to a decrease in the mortality level. Three concomitant factors can contribute to lowering the level of the plateau of mortality reached by the frail oldest old in the future. First, the recognition of the syndrome of frailty itself and its association with a higher risk of mortality (Fried et al. 2001) will help in the development of specific interventions. Second, general improvement and the development of specialized nursing homes, better adapted to the specific health status of the residents, will increase the level of safety of such homes. Third, the growing recognition that the preservation of the quality of life of the oldest old is an important social goal will help to increase the quality and variety of the services and possibilities offered by the nursing homes, making them more attractive. Acting in synergy these three factors—specific interventions targeted to frail persons and general improvement of the safety and quality of the environment provided by nursing homes—have an enormous potential to lower the final plateau of mortality.
A note on mathematical specification According to the proposed biodemographic model, the mortality trajectory can be summarized by three elements, (1) a lowest mortality point, (2) an aging segment, and (3) a mortality plateau, corresponding to the three stages of the developmental transition: young and robust/aging/old and frail. We have seen that nothing is fixed in this model. The associated mathematical model must allow these elements to occur at different ages and thereby to shape the aging segment. Recent work on mortality deceleration suggests several solutions for such an approach, including an arctangent model (Lynch and Brown 2001). However, the logistic model suggested by Thatcher (1999) seems to be the most promising mathematical approach as it includes three parameters: one governing the lowest mortality rate, one governing the rate of mortality increase during the aging phase, and the third governing the age at which mortality starts to decelerate. The logistic approach does not strictly fit a mortality plateau but determines through its parameters a ceiling, which gives the highest possible value for the probability of death (i.e., the quotient of mortality) as well as ceilings for the force of mortality and central death rate. Changes over time in the basic parameters of such a model would allow us both to reconstruct past trajectories when the plateau was invisible and to forecast future trajectories.
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Notes I thank Carol Jagger and Roger Thatcher for comments on an earlier version of this chapter. 1 This point deserves to be carefully reassessed, as at first glance a retrospective French yearbook suggests that the age with the lowest mortality rate moved from ages 12 years and 11 years for males and females in 1898–
1903 to age 10 years for both sexes in 1952– 56 (INSEE 1961). However, a special tabulation made for us by INSEE from 1890–94 to 1990–94 (Robine 2001b) shows no trend in France in the lowest mortality rate through the twentieth century. It is exceptionally stable for females between ages 10 and 11 years.
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