Demographic Perspectives On The Rise Of Longevity

Abstract:

Demographic data on age-specific mortality are used to estimate life expectancy, and validated data on exceptional life spans are used to study the maximum length of life. In the countries doing best each year, life expectancy started to increase around 1840 at a pace of almost 2.5 y per decade. This trend has continued until the present. Contrary to classical evolutionary theories of senescence and contrary to the predictions of many experts, the frontier of survival is advancing to higher ages. Furthermore, individual life spans are becoming more equal, reducing inequalities, with octogenarians and nonagenarians accounting for most deaths in countries with the highest life expectancy.

If the current pace of progress in life expectancy continues, most children born this millennium will celebrate their 100th birthday. Considerable uncertainty, however, clouds forecasts: Life expectancy and maximum life span might increase very little if at all, or longevity might rise much faster than in the past. Substantial progress has been made over the past three decades in deepening understanding of how long humans have lived and how long they might live. The social, economic, health, cultural, and political consequences of further increases in longevity are so significant that the development of more powerful methods of forecasting is a priority.

The relation between high life expectancy and life span equality is attributable to reductions in premature mortality. “The countries that have the highest life expectancy today are those which have been most successful at postponing the premature deaths that contribute to early-life disparity.” The increase in life expectancy in the countries doing best has also been accompanied by an increase in maximum life span—the oldest age attained as verified by reliable data. 

By projecting the historical pace of progress into the future, it is possible to estimate the age that at least 50% of babies born in some country in some year will attain. Such forecasts show that most children born in the last two decades in countries with high life expectancy will, if past progress continues, celebrate their 100th birthday. Very long lives are the likely destiny of children alive today, provided life expectancy continues to increase at the historical pace of more than 2 y per decade. These forecasts depend, however, on substantial improvements being made in reducing death rates at high ages. An important question is whether such improvements will happen.

Among researchers who are willing to speculate about the future of life expectancy, there are, broadly speaking, three views: 1) Some argue that life expectancy will rise more slowly than in the past, perhaps approaching a limit that is not much greater than the current best-practice level, with some chance that life expectancy will fall; 2) others think that life expectancy will continue to rise and mortality to decline at the historical pace for the next several decades, and perhaps longer; 3) finally, some futurologists predict that life expectancy will rise substantially faster than this because of major biomedical breakthroughs. Most demographers, actuaries, and gerontologists appear to think that the future will be somewhere between the first and second scenarios. Although some think that the second view is more plausible, many support the first and a few are open to the third.

Since 1840, the country with the highest life expectancy has shifted from Sweden to Japan, and a different country—perhaps Singapore or Spain—might become the leader in the future. The causes of death against which progress has been made have shifted from infectious to chronic diseases. Before 1950, the rise in life expectancy was largely fueled by reductions in infant, child, and young adult mortality. Today, the rise is largely attributable to declines in death rates after age 65, and especially after age 80 when the majority of deaths now occur in the most developed countries.

The kinds of mortality improvements that might occur in the future include:

• More effective public health strategies might be devised (perhaps as a consequence of the COVID-19 pandemic) that could improve health, e.g., by reducing the spread of infectious disease, controlling obesity and drug abuse, and slowing smoking initiation.
• In the next decade or two, substantial progress might be made in reducing the incidence of cancer and in treating it. Various diseases, including cancer, multiple sclerosis, and HIV, might be treated by enhanced immune therapies.
• There is evidence that over recent decades dementia has been postponed by roughly 2 to 4 y per decade, and this trend might continue.
• The new initiative of “precision medicine” aims to develop alternative treatments that are optimal for people with various genetic makeups. Such therapies might substantially reduce mortality. Furthermore, recent breakthroughs in CRISPR technology might lead to strategies for replacing deleterious genes a person might have with variants that decrease disease risks.
• Extensive research on reconstructing or regenerating tissues and organs, such as reconstructing skin or regenerating heart tissue damaged by a heart attack, might lead to better treatment and perhaps, in several decades, even to strategies for rejuvenating tissues and organs.
• Research on nanotechnology might eventually lead to the development of new tools for the manipulation of submicroscopic particles to repair damage or to destroy pathogens or cancerous cells.
• Most significantly, but perhaps less likely, research on the basic biology of aging might lead to interventions that slow down the rate of aging. For example, breakthroughs might be achieved such that it would take 2 y for a person to suffer the deterioration that older people currently experience in 1 y: that is, roughly speaking, it would take 2 y to grow 1 y older.

On the other side of the coin, there are things that could slow or even reverse the increase of life expectancy such as, economic growth could slow down, there could be less money available for the prevention and treatment of disease, resources available for biomedical research could decline, new diseases and more deadly disease could emerge, wars may break out, the increasing obesity epidemic or other behavioral risk factors could severely damage health, along with other possibilities, and perhaps it may not be possible to reduce mortality after age 100. Influences on mortality include economic, social, and political conditions, genetics, events in utero and early childhood, educational levels, diet, smoking and other aspects of personal behavior, epidemics, public health interventions, the quality of health care, the development of more effective pharmaceutical products, improvements in medical treatments and surgical procedures, and revolutionary biomedical breakthroughs, among others.  

Extrapolative methods are often being used to forecast life expectancy based on historical data on age-specific death rates. These methods generally assume that the age-specific pace of decline in death rates will persist into the future, sometimes with some modest acceleration. Because death rates at advanced ages have declined at a slower pace than death rates at younger ages, the methods generally yield what most experts believe, namely that life expectancy will rise more slowly in the future.

Alternative models have been suggested to forecast mortality, using the age distributions of deaths rather than death rates, reduce forecast bias by allowing the pace of mortality decline to accelerate over time. A direct approach is to forecast life expectancy by extrapolating historical data on life expectancy. Some pioneering research has been done on this approach that takes advantage of the remarkable regularity of time trends in best-practice life expectancy. 

If best-practice life expectancy is forecast linearly, then the gap between it and life expectancy for a given population can be forecast using data on gaps in the past. Age-specific death rates can be forecast by exploiting the strong relationship between life expectancy and the pattern of age-specific mortality. This use of the best-practice life expectancy in forecasting is part of a broader approach that recognizes that mortality trajectories are not independent between populations. Methods have been developed to integrate this coherence between populations in the forecasts, generally assuming that population-specific life expectancies are converging toward an average or toward best practice.

It is important to note that extrapolative approaches are not assumption-free. Each model is based on specific assumptions about future mortality, e.g., constant rate of improvement, convergence toward a benchmark, etc. These models are also often sensitive to different factors or choices made by the forecasters, such as the length of the fitting period, the indicator used, or if a coherent model is used, to the choice of the reference populations.

The best-performing model varies across populations and time periods, making model selection problematic. Assessing whether progress in mortality at older ages, when most deaths occur, will stay constant or will accelerate is of crucial importance in selecting the appropriate forecast model. The models, including the national forecasts, produce very different forecasts at high ages. Other more powerful strategies than those mentioned exist and/or could be developed, directions for research could include options for better exploiting empirical data about past rends on health and mortality.

The ongoing and unprecedented rise of longevity over the past two centuries is so remarkable that the future of longevity may be similarly rich in unexpected developments. The future will almost certainly be surprising, but it might be possible to anticipate some general trends. The social, economic, health, cultural, and political consequences of further increases in longevity are of such significance that the development of more powerful methods of forecasting is a priority.

The report was authored by James W. Vaupel of the Danish Centre for Demographic Research at the University of Southern Denmark Of the Departement of International Health at the Bloomberg School of Public Health Johns Hopkins University, Francisco Villavicencio, and Marie-Pier Bergeron-Boucher from the Danish Centre for Demographic Research and Interdisciplinary Center on Population Dynamics at the University of Southern Denmark.