Quantifying Intrinsic and Extrinsic Contributions to Human Longevity: Application of a Two-Process Vitality Model to the Human Mortality Database

The rise in human life expectancy has involved declines in intrinsic and extrinsic mortality processes associated, respectively, with senescence and environmental challenges. To better understand the factors driving this rise, we apply a two-process vitality model to data from the Human Mortality Database. Model parameters yield intrinsic and extrinsic cumulative survival curves from which we derive intrinsic and extrinsic expected life spans (ELS). Intrinsic ELS, a measure of longevity acted on by intrinsic, physiological factors, changed slowly over two centuries and then entered a second phase of increasing longevity ostensibly brought on by improvements in old-age death reduction technologies and cumulative health behaviors throughout life. The model partitions the majority of the increase in life expectancy before 1950 to increasing extrinsic ELS driven by reductions in environmental, event-based health challenges in both childhood and adulthood. In the post-1950 era, the extrinsic ELS of females appears to be converging to the intrinsic ELS, whereas the extrinsic ELS of males is approximately 20 years lower than the intrinsic ELS.     

The implications of increased survivorship for mortality variation in aging populations

The remarkable growth in life expectancy during the twentieth century inspired predictions of a future in which all people, not just a fortunate few, will live long lives ending at or near the maximum human life span. We show that increased longevity has been accompanied by less variation in ages at death, but survivors to the oldest ages have grown increasingly heterogeneous in their mortality risks. These trends are consistent across countries, and apply even to populations with record-low variability in the length of life. We argue that as a result of continuing improvements in survival, delayed mortality selection has shifted health disparities from early to later life, where they manifest in the growing inequalities in late-life mortality.     

Can higher life expectancy induce more schooling and earlier retirement?

In this paper, we show that it may be optimal for individuals to educate more and retire earlier when life expectancy increases. This result reconciles the findings of Hazan (Econometrica 77:1829–1863, 2009) with theory. Further, the paper contributes to a better understanding of the conflicting empirical findings on the causal effect on income per capita from increased life expectancy.     

Lifespan Dispersion in Times of Life Expectancy Fluctuation: The Case of Central and Eastern Europe

Central and Eastern Europe (CEE) have experienced considerable instability in mortality since the 1960s. Long periods of stagnating life expectancy were followed by rapid increases in life expectancy and, in some cases, even more rapid declines, before more recent periods of improvement. These trends have been well documented, but to date, no study has comprehensively explored trends in lifespan variation. We improved such analyses by incorporating life disparity as a health indicator alongside life expectancy, examining trends since the 1960s for 12 countries from the region. Generally, life disparity was high and fluctuated strongly over the period. For nearly 30 of these years, life expectancy and life disparity varied independently of each other, largely because mortality trends ran in opposite directions over different ages. Furthermore, we quantified the impact of large classes of diseases on life disparity trends since 1994 using a newly harmonized cause-of-death time series for eight countries in the region. Mortality patterns in CEE countries were heterogeneous and ran counter to the common patterns observed in most developed countries. They contribute to the discussion about life expectancy disparity by showing that expansion/compression levels do not necessarily mean lower/higher life expectancy or mortality deterioration/improvements.     

On the decomposition of life expectancy and limits to life

Life expectancy is a measure of how long people are expected to live and is widely used as a measure of human development. Variations in the measure reflect not only the process of ageing but also the impacts of such events as epidemics, wars, and economic recessions. Since 1950, the influence of these events in the most developed countries has waned and life expectancy continues to lengthen unabated. As a result, it has become more difficult to forecast long-run trends accurately, or identify possible upper limits. We present new methods for comparing past improvements in life expectancy and also future prospects, using data from five developed, low-mortality countries. We consider life expectancy in 10-year age intervals rather than over the remaining lifetime, and show how natural limits to life expectancy can be used to extrapolate trends. We discuss the implications and compare our approach with other commonly used methods.

Supplementary material for this article is available at: http://dx.doi.org/10.1080/00324728.2014.972433     

Indonesia’s social capacity for population health: the educational gap in active life expectancy

In this paper, we lay the initial groundwork for anticipating Indonesia’s future burden of disease by developing a demographic model of population health. We develop this model within the analytic framework of a Markov-based multistate life table model to calculate an important indicator of the burden of disease, the expected years of active life of elderly Indonesians. The magnitude of the gap points to the potential consequences of improvements in the nation’s educational level for the future burden of disease. The results show that having some education increases life expectancy but it also expands the expected years with a major functional problem. Overall educational attainment levels, however, are very low, indicating that Indonesia’s elderly are at the leading edge of improvements in the nation’s social capacity for health. The life tables suggest that at the early stages of development, longer life is accompanied by an expansion of morbidity.     

Potential support ratios: Cohort versus period perspectives

The ‘prospective potential support ratio’ has been proposed by researchers as a measure that accurately quantifies the burden of ageing, by identifying the fraction of a population that has passed a certain measure of longevity, for example, 17?years of life expectancy. Nevertheless, the prospective potential support ratio usually focuses on the current mortality schedule, or period life expectancy. Instead, in this paper we look at the actual mortality experienced by cohorts in a population, using cohort life tables. We analyse differences between the two perspectives using mortality models, historical data, and forecasted data. Cohort life expectancy takes future mortality improvements into account, unlike period life expectancy, leading to a higher prospective potential support ratio. Our results indicate that using cohort instead of period life expectancy returns around 0.5 extra younger people per older person among the analysed countries. We discuss the policy implications implied by our cohort measures.     

Life Expectancy as an Objective Factor of a Subjective Well-Being

The paper has two parts. In the first part we offer a definition of well-being which makes life expectancy an explicit variable. We recognize the importance of happiness as a significant aspect of any definition of well-being, but we side-step the issue of what determines its level or how to measure it, and concentrate instead on the consequences of our new variable, life expectancy. We argue that life is valued for its quality, and, if positive, its extension is an improvement of well-being. From this we show how, given certain assumptions, disparate problems that have moral and/or social significance can be approached from the perspective of improving well-being. We close the first part by showing that our definition has enough flexibility to be used for that class of decisions which require tradeoffs between quality of life (happiness) and life expectancy. As a corollary we show that attitudes toward risk depend on expectations, and on some occasions, age itself. In the second part we argue, first, that real economic factors, not reducible to mere psychological ones, may still offer an adequate explanation for the fact that absolute income and happiness do not always correlate well. However, we take no position on the many controversies, such as whether it is relative or absolute increases in wealth that bears most directly on changes in happiness. We confirm through statistical analysis (simple regressions) the well established influence that absolute income has on life expectancy, and, hence, by inference and definition, we argue that this must also be the case with well-being. Secondly, we find through statistical analysis that healthcare has as much impact on life expectancy as does absolute income, leading us to theoretically examine the appropriate income cost for access to healthcare if life expectancy is to improve. And thirdly, by assuming a homogeneous function of life expectancy, we theoretically show how a market oriented healthcare system can exacerbate inequities in life expectancy, and so on well-being. Lastly, we consider some policy implications of those inequities.     

Long‐Term Trends in Life Expectancy and Active Life Expectancy in the United States

Changes in life expectancy and in active life expectancy may have effects on the fiscal integrity of both the Social Security and Medicare programs. Analysis of the fiscal stability of these programs shows that the most serious problem may be the growth of Medicare expenditures projected to surpass, in about 2024, Social Security costs. This is aggravated by the associated rapid growth of the Medicaid program. To understand how the growth of Medicare, Medicaid, and Social Security might be correlated we present estimates of changes in life expectancy and active life expectancy from 1935 to 1999 and then project those values to 2080. How the correlation of life expectancy and active life expectancy changes over time, and by age, may provide insights into how increased health care expenditures, if effective in changing health in the elderly, could modify the age structure of the labor force and the availability of human capital.     

The Segmented Trend Line of Highest Life Expectancies

The well‐known Oeppen–Vaupel straight line of maximum female life expectancies showed that the highest life expectancy observed in a given year increased linearly from 1840 to 2000. Their analysis fueled major controversy, especially when used to extrapolate future improvements in life expectancy at the same pace. We improve on the empirical analysis by enriching the dataset, expanding the period to 1750–2005, and considering both maximum life expectancy at birth and lowest age‐specific survival rates. It clearly appears that the original Oeppen–Vaupel straight line must be divided into several segments characterized by different slopes and that each segment corresponds to a major advance in the health transition. There is room to push life expectancy higher, but unless some new breakthrough increases the human life span, progress will very likely decelerate as mortality reduction affects individuals at older and older ages. The main key to the future lies not in knowing whether the observed straight line can be extrapolated but in anticipating the next major health improvement that will lead to an additional increase in life expectancy.     

The contribution of a history of heavy smoking to Scotland's mortality disadvantage

Scotland has a lower life expectancy than any country in Western Europe or North America, and this disadvantage is concentrated above age 50. According to the Human Mortality Database, life expectancy at age 50 has been lower in Scotland than in any other developed country since 1980. Relative to 15 developed countries that we have chosen for comparison, Scotland's life expectancy in 2009 at age 50 was lower by an average of 2.5?years for women and 1.6?years for men. We estimate that Scottish women lost 3.6?years of life expectancy at age 50 as a result of smoking, compared to 1.4?years for the comparison countries. The equivalent figures among men are 3.1 and 2.1?years. These differences are large enough for the history of heavy smoking in Scotland to account both for most of the shortfall in life expectancy for both sexes and for the country's unusually narrow sex differences in life expectancy.     

Human Capabilities Versus Human Capital: Guaging the Value of Education in Developing Countries

The purpose of this study is to defend the view that education should be evaluated in terms of the capability to achieve valued functionings, rather than mental satisfaction or resources. In keeping with Amartya Sen’s capabilities approach we argue that mental satisfaction provides an inaccurate metric of well-being because of the phenomenon of adaptive preferences. Equally, resources cannot be used as a metric of well-being because of inequalities in the ability to convert income and commodities into valued functionings. Hence, interpreting education as a means to create human capital is also impoverished because it evaluates education solely in terms of the accumulation of resources. In order to provide evidence in support of the human capabilities approach we statistically examine the channels through which educational attainment affects the health functionings implied by life expectancy. Using panel data analysis for 35 developing countries for the years 1990, 1995 and 2000 we compare the health functionings (as indicated by life expectancy) that are achieved by the income growth generated by educational attainment, with the total health functionings that are achieved by educational attainment. We find that educational attainment (as indicated by average years of schooling) has a significant effect on life expectancy independently of its effect by way of income growth. A 1% increase in per capita income increases life expectancy by 0.073954% while a 1% increase in average years of schooling directly increases life expectancy by 0.055324%. Because it shows that income underestimates the health functionings achieved by educational attainment, our empirical findings lend support to the claim that the value of education should be measured in terms of the capability for functioning, rather than resources.     

Modeling and Forecasting Health Expectancy: Theoretical Framework and Application

Life expectancy continues to grow in most Western countries; however, a major remaining question is whether longer life expectancy will be associated with more or fewer life years spent with poor health. Therefore, complementing forecasts of life expectancy with forecasts of health expectancies is useful. To forecast health expectancy, an extension of the stochastic extrapolative models developed for forecasting total life expectancy could be applied, but instead of projecting total mortality and using regular life tables, one could project transition probabilities between health states simultaneously and use multistate life table methods. In this article, we present a theoretical framework for a multistate life table model in which the transition probabilities depend on age and calendar time. The goal of our study is to describe a model that projects transition probabilities by the Lee-Carter method, and to illustrate how it can be used to forecast future health expectancy with prediction intervals around the estimates. We applied the method to data on the Dutch population aged 55 and older, and projected transition probabilities until 2030 to obtain forecasts of life expectancy, disability-free life expectancy, and probability of compression of disability.     

Changing Life Expectancy and Health Expectancy Among Russian Adults: Results from the Past 20 Years

The decade following the collapse of the Soviet Union was characterized by wide fluctuations in Russian mortality rates, but since the early 2000s, life expectancy has improved progressively. Recent upturns in longevity have promoted policy debates over extending the retirement age in the country. However, whether observed gains in life expectancy are accompanied by improving health remains to be addressed. Using data from the 1994–2014 Russian Longitudinal Monitoring Survey of the Higher School of Economics, this study investigates trends over 20 years in healthy life expectancy (HLE) and illness-free life expectancy (IFLE) for men and women at adult ages. Analyses using the Sullivan method show that men and women at adult ages have experienced large increases in health expectancies during the post-Soviet period. Increases in HLE exceeded increases in total life expectancy for both genders. Further, health expectancies have evolved over time through cycles of increases and decreases, just like life expectancy. These results suggest increases in good-quality years among men and women at working ages, offering support for changing the official retirement age. The extent of the change in the retirement age, however, needs to be carefully considered, given that, despite recent improvements, the health expectancy of the Russian population still remains low.     

A statistical analysis of the life expectancy of the residents of our country, 1973-75]

Statistical analysis of life expectancy is important in assessing population health and its characteristics and in studying human diseases and natural population changes. Life tables are constructed and statistical analysis is performed retrospectively on data accumulated over a 3-year (1973-1975) period. The data were supplied by the Office of Cancer Prevention and Treatment of the Ministry of Health, which originated from 24 provinces, representing an accumulated population of 2.04 billion, with a total mortality of 15.29 million. Results show that life expectancy in China has greatly improved since Liberation. Thus, in 1935, the average life expectancy for Nanjing residents was under 35 years. In 1951, the average life expectancy for male and female residents of Shanghai were 42.74 and 46.76 years respectively. But for the 1973-1975 period, the average Chinese life expectancy was 63.62 years for males and 66.31 years for females, with higher life expectancy for coastal provinces than for inland provinces. Cardiovascular diseases (excluding arteriosclerotic heart diseases), malignant tumors, and cerebrovascular diseases were the major causes of death in regions with higher life expectancy, while respiratory diseases, infectious diseases, and diseases of the newborn were the major causes of death in regions with lower life expectancy.     

A Biodemographic Interpretation of Life Span

The life span of individuals and the life expectancy of the populations they comprise have always been topics of interest to scientists and the lay population. In modern times, forecasts of life span and life expectancy have become particularly important public policy issues because of their influence on the future solvency of age‐entitlement programs. The authors present a brief discussion of the origin of the notion of life span, discuss its relevance and importance in light of recent developments in the emerging field of the biodemography of aging, and explore the theoretical and biological forces that influence the duration of life of sexually reproducing species.     

The length of working life

The rapid growth of the aged population in recent years has underscored the economic problems of dependency in old age and has focused attention on the length of working life as compared to the total life span. In addition to interest in the average work-life span, students of labour-force dynamics have found a need for related measures of withdrawals from the labour force at different ages. In this paper, the author adopts the technique of the life table to a measurement of the work-life span by the construction of a table of working life.

Significant contrast is shown between total life expectancy and working life expectancy. In the United States, the average male worker at 20, for example, could expect to live for an additional 46.8 years and to remain in the labour force for an additional 41.1 years. He would therefore have to provide for almost 6 years of retirement. Significant differences are also indicated in comparisons by colour and residence. Further areas of study in applying the concept of working life are suggested.     

中国各地区人口预期寿命及地理分布分析

平均预期寿命是世界公认的衡量人类进步的标准。本文以生命表为基础,主要依据我国第六次人口普查资料,计算各省市区的预期寿命并进行相关分析,计算结果表明,2010年中国人口平均预期寿命为78.17岁,男75.86岁,女80.68岁,已进入发达国家行列。预期寿命与婴儿死亡率呈负相关r＝-0.789,与人均国民总收入呈正相关r＝0.604。预期寿命女性比男性长4～7岁,符合一般规律。影响男性寿命低于女性的原因很多,本文重点讨论了遗传因素和生活方式的影响,并对预期寿命的地理分布进行分析。     

Testing the relationship between income inequality and life expectancy: a simple correction for the aggregation effect when using aggregated data

In this paper, we show a simple correction for the aggregation effect when testing the relationship between income inequality and life expectancy using aggregated data. While there is evidence for a negative correlation between income inequality and a population’s average life expectancy, it is not clear whether this is due to an aggregation effect based on a non-linear relationship between income and life expectancy or to income inequality being a health hazard in itself. The proposed correction method is general and independent of measures of income inequality, functional form assumptions of the health production function, and assumptions on the income distribution. We apply it to data from the Human Development Report and find that the relationship between income inequality and life expectancy can be explained entirely by the aggregation effect. Hence, there is no evidence that income inequality itself is a health hazard.     

A Realist View of Aging,Mortality, and Future Longevity

Differences in methodology and philosophy have led scientists analyzing the same mortality data to arrive at very different conclusions about the behavior of mortality trajectories, the nature of aging, and the future of human longevity. This note describes the authors’views on these issues, which taken together can be termed a “realist” position. In this view, life expectancy is unlikely to exceed an average of 85 years absent significant advances in the control of aging. We identify a number of myths that have been attached to our work: 1) Reaching an average life expectancy of 85 years is a pessimistic outlook for human longevity, 2) Species possess an intrinsic mortality schedule that cannot be modified by human intervention, 3) Realist scenarios of the future course of human longevity are based on notions of biological determinism, 4) Realists assert that there is an age beyond which there can be no survivors, 5) Hypothesized biological barriers to longer life spans have been scientifically studied and refuted, and 6) Realists claim that life expectancy at birth cannot exceed 85 years. In dispelling these myths, we hope to provide a more accurate representation of our school of biodemographic thought.