Exceptional longevity in Okinawa: Demographic trends since 1975

Demographers have studied the Japanese mortality pattern since Japan became the most longevous population worldwide, half a century ago. Nutrition and lifestyle were considered by epidemiologists, gerontologists and other scientists as the most important reasons explaining the Japanese superiority. In Okinawa, the mortality pattern is even more exceptional, but few demographers have pointed out this exception. Other scientists proposed different explanations - for example some genetic characteristics, less salt and more animal protein in the food, a mild climate, a higher level of activity, a better consideration of the oldest in the population and, globally speaking, a more traditional lifestyle. At the end of the 1980s, lower improvements of mortality among young adults were identified in Okinawa. In 2002, Okinawa fell from the 4th to the 26th place in the ranking of the 47 Japanese prefectures by male life expectancy. This has been considered by the population of Okinawa as a 'shock'. Our in-depth analysis of available life tables and associated mortality rates proves that the population of Okinawa is divided into two groups of generations: those born before World War II and those born after. The older generations clearly experience a highly favourable mortality pattern, whereas the younger generations show mortality levels that are definitively higher compared to mainland Japan. This contribution considers which factors may explain such a situation, including the plausible invalidation of the age of some oldest in the population. We plea for in-depth demographic age validation that will enhance all scientific findings so far and boost the exceptional longevity in Okinawa.

Exploring Patterns of Human Mortality and Aging: A Reliability Theory Viewpoint

The most important manifestation of aging is an increased risk of death with advancing age, a mortality pattern characterized by empirical regularities known as mortality laws. We highlight three significant ones: the Gompertz law, compensation effect of mortality (CEM), and late-life mortality deceleration and describe new developments in this area. It is predicted that CEM should result in declining relative variability of mortality at older ages. The quiescent phase hypothesis of negligible actuarial aging at younger adult ages is tested and refuted by analyzing mortality of the most recent birth cohorts. To comprehend the aging mechanisms, it is crucial to explain the observed empirical mortality patterns. As an illustrative example of data-directed modeling and the insights it provides, we briefly describe two different reliability models applied to human mortality patterns. The explanation of aging using a reliability theory approach aligns with evolutionary theories of aging, including idea of chronic phenoptosis. This alignment stems from their focus on elucidating the process of organismal deterioration itself, rather than addressing the reasons why organisms are not designed for perpetual existence. This article is a part of a special issue of the journal that commemorates the legacy of the eminent Russian scientist Vladimir Petrovich Skulachev (1935-2023) and his bold ideas about evolution of biological aging and phenoptosis.

COVID-19 and All-Cause Mortality by Race, Ethnicity, and Age Across Five Periods of the Pandemic in the United States

Racial/ethnic and age disparities in COVID-19 and all-cause mortality during 2020 are well documented, but less is known about their evolution over time. We examine changes in age-specific mortality across five pandemic periods in the United States from March 2020 to December 2022 among four racial/ethnic groups (non-Hispanic White, non-Hispanic Black, Hispanic, and non-Hispanic Asian) for ages 35+. We fit Gompertz models to all-cause and COVID-19 death rates by 5-year age groups and construct age-specific racial/ethnic mortality ratios across an Initial peak (Mar-Aug 2020), Winter peak (Nov 2020-Feb 2021), Delta peak (Aug-Oct 2021), Omicron peak (Nov 2021-Feb 2022), and Endemic period (Mar-Dec 2022). We then compare to all-cause patterns observed in 2019. The steep age gradients in COVID-19 mortality in the Initial and Winter peak shifted during the Delta peak, with substantial increases in mortality at working ages, before gradually returning to an older age pattern in the subsequent periods. We find a disproportionate COVID-19 mortality burden on racial and ethnic minority populations early in the pandemic, which led to an increase in all-cause mortality disparities and a temporary elimination of the Hispanic mortality advantage at certain age groups. Mortality disparities narrowed over time, with racial/ethnic all-cause inequalities during the Endemic period generally returning to pre-pandemic levels. Black and Hispanic populations, however, faced a younger age gradient in all-cause mortality in the Endemic period relative to 2019, with younger Hispanic and Black adults in a slightly disadvantageous position and older Black adults in a slightly advantageous position, relative to before the pandemic.

Physiological aging in India: The role of the epidemiological transition

We construct a cohort-based frailty index from age-related health deficits to investigate physiological aging in India over the period 1990-2019. During this period, the Indian states underwent at different speeds the epidemiological transition and experienced unprecedented economic growth. We show that the rate of physiological aging remained remarkably stable to the changing environment. Age-related health deficits increased by about 3 percent per year of age with little variation across states, ages, cohorts, and over time. We find that, with advancing epidemiological transition, health deficits for given age declined at the individual level (within states and within cohorts). Across cohorts born between 1900 and 1995, we show that, for given age, health deficits are higher for later-born cohorts until birth years around 1940 and remained trendless afterwards. We propose a selection-based theory of aging during the epidemiological transition that explains these facts.

The role of age inequalities in cause of death in the slow pace of epidemiological transition in India

In developed countries, low disparity in lifespan contributed by the reduction in the burden of noncommunicable diseases (NCDs) is the key to advances in epidemiological transition. Contrarily, India passing through a phase of the dual burden of CDs and NCDs shows a heavy burden of NCDs responsible for the high disparity in lifespan. The Gini coefficient was decomposed for examining the contribution of 22 causes of death and their repercussions for inequality in age at death for 30 years between 1990-1994 and 2015-2019, using Global Burden of Disease data. The outcomes of the study reveal that India's epidemiological transition has been just modest on account of high inequality in mortality by NCDs emplaced in the middle through old age despite a consistent mortality decline at infant through old age for communicable diseases (CDs). The structural changes in causes of death structure is shaped by CDs rather than NCDs, but overall bolstered by the adult mortality decline, especially in women. However, the process is restrained by the small contribution of the middle age group and a benign contribution of old mortality decline owing to the low threshold age. India needs to target health interventions in seeking significant mortality decline in the middle age group of 50-69 years that is warranted for epidemiological transition apace as evident in the developed nations.

Association between ambient temperature and age-specific mortality from the elderly: Epidemiological evidence from the Chinese prefecture with most serious aging

Older people are main susceptible group affected by non-optimal temperature. The aim of the study was to determine how mortality of older people with different ages are affected by temperatures. For this study, we collected data of all-cause death of 256,037 people aged between 65 and 104 years of age from a prefecture located in the north subtropical area with most serious aging rate in 2000, 2010 and 2020 in China. A distributed lag nonlinear model under different age groups was used to estimate non-optimal temperature associations to mortality. The results revealed: (1) With increasing age, older people were more likely to die during moderate low temperature, the proportion of attributable fraction of moderate low temperature in all temperature gradually increased with age. (2) Moderate low temperature could be divided into two parts, the lower part caused most death at age 65-79 and the higher part was not so dangerous, while for age 80+, preventive actions should be taken for both parts. (3) A leveling-off and deceleration phenomenon was observed at age 95-99 for low temperature, but not 100-104, it may be virtually a consequence of "harvesting effect" in that susceptible and common people have died before age 95, it was coincidence with mortality deceleration at extreme old ages found by demographic scholars over the past 200 years. (4) Heat wave had much higher relative risk than cold spell compared with moderate high and low temperature because of steeper slope of relative risk at the period of moderate-extreme conversion of high temperature, the older people should pay more attention to weather with moderate-extreme conversion of high temperature. Furthermore, our findings could help improve the understanding of non-optimal temperature on health of older people and support the development of response strategies for different seasons at different ages.

A Mechanistic Theory of Development-Aging Continuity in Humans and Other Mammals

There is consensus among biogerontologists that aging occurs either as the result of a purposeful genome-based, evolved program or due to spontaneous, randomly occurring, maladaptive events. Neither concept has yet identified a specific mechanism to explain aging’s emergence and acceleration during mid-life and beyond. Presented herein is a novel, unifying mechanism with empirical evidence that describes how aging becomes continuous with development. It assumes that aging emerges from deterioration of a regulatory process that directs morphogenesis and morphostasis. The regulatory system consists of a genome-wide “backbone” within which its specific genes are differentially expressed by the local epigenetic landscapes of cells and tissues within which they reside, thereby explaining its holistic nature. Morphostasis evolved in humans to ensure the nurturing of dependent offspring during the first decade of young adulthood when peak parental vitality prevails in the absence of aging. The strict redundancy of each morphostasis regulatory cycle requires sensitive dependence upon initial conditions to avoid initiating deterministic chaos behavior. However, when natural selection declines as midlife approaches, persistent, progressive, and specific DNA damage and misrepair changes the initial conditions of the regulatory process, thereby compromising morphostasis regulatory redundancy, instigating chaos, initiating senescence, and accelerating aging thereafter.

Projecting Mortality Rates to Extreme Old Age with the CBDX Model

We introduce a simple extension to the CBDX model to project cohort mortality rates to extreme old age. The proposed approach fits a polynomial to a sample of age effects, uses the fitted polynomial to project the age effects to ages beyond the sample age range, then splices the sample and projected age effects, and uses the spliced age effects to obtain mortality rates for the higher ages. The proposed approach can be used to value financial instruments such as life annuities that depend on projections of extreme old age mortality rates.

Evaluation of Epigenetic Age Based on DNA Methylation Analysis of Several CpG Sites in Ukrainian Population

Epigenetic clocks are the models, which use CpG methylation levels for the age prediction of an organism. Although there were several epigenetic clocks developed there is a demand for development and evaluation of the relatively accurate and sensitive epigenetic clocks that can be used for routine research purposes. In this study, we evaluated two epigenetic clock models based on the 4 CpG sites and 2 CpG sites in the human genome using the pyrosequencing method for their methylation level estimation. The study sample included 153 people from the Ukrainian population with the age from 0 to 101. Both models showed a high correlation with the chronological age in our study sample (R² = 0.85 for the 2 CpG model and R² = 0.92 for the 4 CpG model). We also estimated the accuracy metrics of the age prediction in our study sample. For the age group from 18 to 80 MAD was 5.1 years for the 2 CpG model and 4.1 years for the 4 CpG model. In this regard, we can conclude, that the models evaluated in the study have good age predictive accuracy, and can be used for the epigenetic age evaluation due to the relative simplicity and time-effectiveness.

What have we learned about mortality patterns over the past 25 years?

In this paper, I examine progress in the field of mortality over the past 25 years. I argue that we have been most successful in taking advantage of an increasingly data-rich environment to improve aggregate mortality models and test pre-existing theories. Less progress has been made in relating our estimates of mortality risk at the individual level to broader mortality patterns at the population level while appropriately accounting for contextual differences and compositional change. Overall, I find that the field of mortality continues to be highly visible in demographic journals, including Population Studies. However much of what is published today in field journals could just as easily appear in neighbouring disciplinary journals, as disciplinary boundaries are shrinking.

Demographic perspectives on the rise of longevity

This article reviews some key strands of demographic research on past trends in human longevity and explores possible future trends in life expectancy at birth. 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.

A Mixture-Function Mortality Model: Illustration of the Evolution of Premature Mortality

Premature mortality is often a neglected component of overall deaths, and the most difficult to identify. However, it is important to estimate its prevalence. Following Pearson's theory about mortality components, a definition of premature deaths and a parametric model to study its transformations are introduced. The model is a mixture of three distributions: a Half Normal for the first part of the death curve and two Skew Normals to fit the remaining pieces. One advantage of the model is the possibility of obtaining an explicit equation to compute life expectancy at birth and to break it down into mortality components. We estimated the mixture model for Sweden, France, East Germany and Czech Republic. In addition, to the well-known reduction in infant deaths, and compression and shifting trend of adult mortality, we were able to study the trend of the central part of the distribution of deaths in detail. In general, a right shift of the modal age at death for young adults is observed; in some cases, it is also accompanied by an increase in the number of deaths at these ages: in particular for France, in the last twenty years, premature mortality increases.

Senescent cells and the incidence of age-related diseases

Age-related diseases such as cancer, cardiovascular disease, kidney failure, and osteoarthritis have universal features: Their incidence rises exponentially with age with a slope of 6-8% per year and decreases at very old ages. There is no conceptual model which explains these features in so many diverse diseases in terms of a single shared biological factor. Here, we develop such a model, and test it using a nationwide medical record dataset on the incidence of nearly 1000 diseases over 50 million life-years, which we provide as a resource. The model explains incidence using the accumulation of senescent cells, damaged cells that cause inflammation and reduce regeneration, whose level rise stochastically with age. The exponential rise and late drop in incidence are captured by two parameters for each disease: the susceptible fraction of the population and the threshold concentration of senescent cells that causes disease onset. We propose a physiological mechanism for the threshold concentration for several disease classes, including an etiology for diseases of unknown origin such as idiopathic pulmonary fibrosis and osteoarthritis. The model can be used to design optimal treatments that remove senescent cells, suggeting that treatment starting at old age can sharply reduce the incidence of all age-related diseases, and thus increase the healthspan.

Are We Approaching a Biological Limit to Human Longevity?

Until recently human longevity records continued to grow in history, with no indication of approaching a hypothetical longevity limit. Also, earlier studies found that age-specific death rates cease to increase at advanced ages (mortality plateau) suggesting the absence of fixed limit to longevity too. In this study, we reexamine both claims with more recent and reliable data on supercentenarians (persons aged 110 years and older). We found that despite a dramatic historical increase in the number of supercentenarians, further growth of human longevity records in subsequent birth cohorts slowed down significantly and almost stopped for those born after 1879. We also found an exponential acceleration of age-specific death rates for persons older than 113 years in more recent data. Slowing down the historical progress in maximum reported age at death and accelerated growth of age-specific death rates after age 113 years in recent birth cohorts may indicate the need for more conservative estimates for future longevity records unless a scientific breakthrough in delaying aging would happen. The hypothesis of approaching a biological limit to human longevity has received some empirical support and it deserves further study and testing.

Multidimensional Mortality Selection: Why Individual Dimensions of Frailty Don't Act Like Frailty

Theoretical models of mortality selection have great utility in explaining otherwise puzzling phenomena. The most famous example may be the Black-White mortality crossover: at old ages, Blacks outlive Whites, presumably because few frail Blacks survive to old ages while some frail Whites do. Yet theoretical models of unidimensional heterogeneity, or frailty, do not speak to the most common empirical situation for mortality researchers: the case in which some important population heterogeneity is observed and some is not. I show that, when one dimension of heterogeneity is observed and another is unobserved, neither the observed nor the unobserved dimension need behave as classic frailty models predict. For example, in a multidimensional model, mortality selection can increase the proportion of survivors who are disadvantaged, or "frail," and can lead Black survivors to be more frail than Whites, along some dimensions of disadvantage. Transferring theoretical results about unidimensional heterogeneity to settings with both observed and unobserved heterogeneity produces misleading inferences about mortality disparities. The unusually flexible behavior of individual dimensions of multidimensional heterogeneity creates previously unrecognized challenges for empirically testing selection models of disparities, such as models of mortality crossovers.

Beyond One Hundred: A Cohort Analysis of Italian Centenarians and Semisupercentenarians

Although the increase in the number of centenarians is well documented today in countries with advanced demographic data, the same is not true for those aged 105 years and over. The first aim of this paper was to analyze the demographic characteristics of the 4,626 validated semisuper and 102 supercentenarians for the cohorts born between 1896 and 1910, referring to Italian Semi-Supercentenarians Survey. Then, starting from this data and from the survival histories in old ages-reconstructed by Vincent's Extinct - Cohort Method-for the cohorts born between 1870 and 1904, the most important aim was to analyze longevity history and the trend of gender gap of the Italian oldest cohorts beyond 100 years old. The Italian centenarians and semisupercentenarians increase from the first to the last cohort is due to the survival rise in old ages and the increase in the gender gap at extreme ages depends on the higher survival of women than men after 60 years old. Around 110-112 for both genders (for women in particular) a kind of resistance to further progress seems to appear in our analysis as in more recent studies on supercentenarians.

Spatial Differences in China’s Population Aging and Influencing Factors: The Perspectives of Spatial Dependence and Spatial Heterogeneity

Since China became an aging society in 2000, the regional inequality of population aging has been highlighted, and the phenomenon of “aging before getting rich” has gradually become a core issue in China’s coordinated socioeconomic development. This paper aims to comprehensively assess the spatial differences and driving forces of China’s population aging through two-stage nested Theil decomposition, ESDA, and spatial econometric models. Empirical results show that spatial differences in population aging were evident at different spatial scales, and the distribution gradually decreased from east to west, showing a positive spatial correlation of similar value aggregation. Moreover, China’s population aging was determined by the demographic, socioeconomic, and natural environment, and there are different leading factors in different regions. The demographic aspects played a decisive role and had a direct influence, while the socioeconomic and natural environment indirectly affected population aging through demographic factors and became the root cause of regional differences in population aging. These findings provide an empirical basis for establishing a cooperative mechanism and formulating a targeted response to the problem of population aging in various regions in China.

Cell division rates decrease with age, providing a potential explanation for the age-dependent deceleration in cancer incidence

A new evaluation of previously published data suggested to us that the accumulation of mutations might slow, rather than increase, as individuals age. To explain this unexpected finding, we hypothesized that normal stem cell division rates might decrease as we age. To test this hypothesis, we evaluated cell division rates in the epithelium of human colonic, duodenal, esophageal, and posterior ethmoid sinonasal tissues. In all 4 tissues, there was a significant decrease in cell division rates with age. In contrast, cell division rates did not decrease in the colon of aged mice, and only small decreases were observed in their small intestine or esophagus. These results have important implications for understanding the relationship between normal stem cells, aging, and cancer. Moreover, they provide a plausible explanation for the enigmatic age-dependent deceleration in cancer incidence in very old humans but not in mice.

New Trend in Old-Age Mortality: Gompertzialization of Mortality Trajectory

There is great interest among gerontologists, demographers, and actuaries in the question concerning the limits to human longevity. Attempts at getting answers to this important question have stimulated many studies on late-life mortality trajectories, often with opposing conclusions. One group of researchers believes that mortality stops growing with age at extreme old ages, and that hence there is no fixed limit to the human life span. Other studies found that mortality continues to grow with age up to extreme old ages. Our study suggests a possible solution to this controversy. We found that mortality deceleration is best observed when older, less accurate life span data are analyzed, while in the case of more recent and reliable data there is a persistent mortality growth with age. We compared the performance (goodness of fit) of two competing mortality models - the Gompertz model and the Kannisto ("mortality deceleration") model - at ages of 80-105 years using data for 1880-1899 single-year birth cohorts of US men and women. The mortality modeling approach suggests a transition from mortality deceleration to the Gompertzian mortality pattern over time for both men and women. These results are consistent with the hypothesis about disappearing mortality deceleration over time due to improvement in the accuracy of age reporting. In the case of more recent data, mortality continues to grow with age even at very old ages. This observation may lead to more conservative estimates of future human longevity records.

The Effect of Gaussian Noise on Maximum Likelihood Fitting of Gompertz and Weibull Mortality Models with Yeast Lifespan Data

Background/study context: Empirical lifespan data sets are often studied with the best-fitted mathematical model for aging. Here, we studied how experimental noises can influence the determination of the best-fitted aging model. We investigated the influence of Gaussian white noise in lifespan data sets on the fitting outcomes of two-parameter Gompertz and Weibull mortality models, commonly adopted in aging research.

METHODS

To un-equivocally demonstrate the effect of Gaussian white noises, we simulated lifespans based on Gompertz and Weibull models with added white noises. To gauge the influence of white noise on model fitting, we defined a single index, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi>δ</mml:mi><mml:mrow><mml:mi>L</mml:mi><mml:mi>L</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math> , for the difference between the maximal log-likelihoods of the Weibull and Gompertz model fittings. We then applied the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi>δ</mml:mi><mml:mrow><mml:mi>L</mml:mi><mml:mi>L</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math> approach using experimental replicative lifespan data sets for the laboratory BY4741 and BY4742 wildtype reference strains.

RESULTS

We systematically evaluated how Gaussian white noise can influence the maximal likelihood-based comparison of the Gompertz and Weibull models. Our comparative study showed that the Weibull model is generally more tolerant to Gaussian white noise than the Gompertz model. The effect of noise on model fitting is also sensitive to model parameters.

CONCLUSION

Our study shows that Gaussian white noise can influence the fitting of an aging model for yeast replicative lifespans. Given that yeast replicative lifespans are hard to measure and are often pooled from different experiments, our study highlights that interpreting model fitting results should take experimental procedure variation into account, and the best fitting model may not necessarily offer more biological insights.

Late-life mortality is underestimated because of data errors

Knowledge of true mortality trajectory at extreme old ages is important for biologists who test their theories of aging with demographic data. Studies using both simulation and direct age validation found that longevity records for ages 105 years and older are often incorrect and may lead to spurious mortality deceleration and mortality plateau. After age 105 years, longevity claims should be considered as extraordinary claims that require extraordinary evidence. Traditional methods of data cleaning and data quality control are just not sufficient. New, more strict methodologies of data quality control need to be developed and tested. Before this happens, all mortality estimates for ages above 105 years should be treated with caution.

This Primer explores recent evidence that demographic data errors can explain away apparent late-life mortality plateaus, proposing that traditional methods of data quality control are insufficient, and that claims of human longevity greater than 105 years should be considered extraordinary and require extraordinary evidence.

Errors as a primary cause of late-life mortality deceleration and plateaus

Several organisms, including humans, display a deceleration in mortality rates at advanced ages. This mortality deceleration is sufficiently rapid to allow late-life mortality to plateau in old age in several species, causing the apparent cessation of biological ageing. Here, it is shown that late-life mortality deceleration (LLMD) and late-life plateaus are caused by common demographic errors. Age estimation and cohort blending errors introduced at rates below 1 in 10,000 are sufficient to cause LLMD and plateaus. In humans, observed error rates of birth and death registration predict the magnitude of LLMD. Correction for these sources of demographic error using a mixed linear model eliminates LLMD and late-life mortality plateaus (LLMPs) without recourse to biological or evolutionary models. These results suggest models developed to explain LLMD have been fitted to an error distribution, that ageing does not slow or stop during old age in humans, and that there is a finite limit to human longevity.

In diverse species, mortality rates increase with age at a relatively fixed rate within populations. However, recent discoveries have suggested this relationship breaks down in advanced old age, with mortality rate increases slowing and even reaching a plateau. This late-life mortality deceleration has initiated sustained debate on the cause of late-life deceleration and plateaus. Proposed explanations include evolutionary patterns, the exhaustion of selective pressure, population heterogeneity, and even the cessation of the ageing process. Here, I demonstrate that apparent late-life mortality decelerations and plateaus can be generated by low-frequency errors. I then reveal how indicators of demographic data quality predict the magnitude of late-life mortality deceleration and the existence of late-life plateaus in human populations. These findings suggest that human late-life mortality plateaus are largely, if not entirely, artefacts of error processes. As a result, late-life mortality plateaus and decelerations may be explained by error patterns in humans and many other species without invoking complex biological, heterogeneity, or evolutionary models. This finding has immediate consequences for demographic modelling, evolutionary biology, and the projected upper limits of human and nonhuman life.

Separating the Signal From the Noise: Evidence for Deceleration in Old-Age Death Rates

Widespread population aging has made it critical to understand death rates at old ages. However, studying mortality at old ages is challenging because the data are sparse: numbers of survivors and deaths get smaller and smaller with age. I show how to address this challenge by using principled model selection techniques to empirically evaluate theoretical mortality models. I test nine models of old-age death rates by fitting them to 360 high-quality data sets on cohort mortality after age 80. Models that allow for the possibility of decelerating death rates tend to fit better than models that assume exponentially increasing death rates. No single model is capable of universally explaining observed old-age mortality patterns, but the log-quadratic model most consistently predicts well. Patterns of model fit differ by country and sex. I discuss possible mechanisms, including sample size, period effects, and regional or cultural factors that may be important keys to understanding patterns of old-age mortality. I introduce mortfit, a freely available R package that enables researchers to extend the analysis to other models, age ranges, and data sources.

Cause-specific mortality patterns among hospital deaths in Tanzania, 2006-2015

Background

Understanding the causes of inpatient mortality in hospitals is important for monitoring the population health and evidence-based planning for curative and public health care. Dearth of information on causes and trends of hospital mortality in most countries of Sub-Saharan Africa has resulted to wide use of model-based estimation methods which are characterized by estimation errors. This retrospective analysis used primary data to determine the cause-specific mortality patterns among inpatient hospital deaths in Tanzania from 2006–2015.

Materials and methods

The analysis was carried out from July to December 2016 and involved 39 hospitals in Tanzania. A review of hospital in-patient death registers and report forms was done to cover a period of 10 years. Information collected included demographic characteristics of the deceased and immediate underlying cause of death. Causes of death were coded using international classification of diseases (ICD)-10. Data were analysed to provide information on cause-specific, trends and distribution of death by demographic and geographical characteristics.

Principal findings

A total of 247,976 deaths were captured over a 10-year period. The median age at death was 30 years, interquartile range (IQR) 1, 50. The five leading causes of death were malaria (12.75%), respiratory diseases (10.08%), HIV/AIDS (8.04%), anaemia (7.78%) and cardio-circulatory diseases (6.31%). From 2006 to 2015, there was a noted decline in the number of deaths due to malaria (by 47%), HIV/AIDS (28%) and tuberculosis (26%). However, there was an increase in number of deaths due to neonatal disorders by 128%. Malaria and anaemia killed more infants and children under 5 years while HIV/AIDS and Tuberculosis accounted for most of the deaths among adults.

Conclusion

The leading causes of inpatient hospital death were malaria, respiratory diseases, HIV/AIDS, anaemia and cardio-circulatory diseases. Death among children under 5 years has shown an increasing trend. The observed trends in mortality indicates that the country is lagging behind towards attaining the global and national goals for sustainable development. The increasing pattern of respiratory diseases, cancers and septicaemia requires immediate attention of the health system.

[Gender Differences in Projected Life Expectancy in Japan (2023-2047) Determined by Bayesian Age-Period-Cohort Analysis]

OBJECTIVES

In this study, we aimed to (1) determine the effects of age, period, and cohort on mortality rate trends between 1958 and 2012 in Japan and (2) assess gender differences in projected life expectancy (LE) for the 2023-2047 period.

METHODS

A time trend study was conducted using age-period-cohort (APC) analysis. A Bayesian APC model was fitted to describe mortality rate trends for the 1958-2012 period and to project mortality rates for 2023-2047. LE was predicted by Chiang's method using projected mortality rates.

RESULTS

Age, period, and cohort effects showed similar patterns between males and females. As time passes, gender differences in projected LE were larger among individuals over 65 years than among those under 65 years. Time series change rates of the extension of projected LE after excluding specific causes of death showed the following: smaller extension of projected LE in males in terms of mortality risk from malignant neoplasms, heart diseases, pneumonia, and accidents (under 65 years) and in females in terms of mortality risk from heart diseases, cerebrovascular diseases, and suicide (over 65 years).

CONCLUSIONS

Gender differences in projected LE are expected to be smaller before middle age and to be larger among seniors. These projected gender differences stem in part from the lower mortality risk among men than among women from malignant neoplasms, heart diseases, pneumonia, and accidents (under 65 years), and among women compared to men from heart disease, cerebrovascular disease, and suicide (over 65 years).

The plateau of human mortality: Demography of longevity pioneers

Theories about biological limits to life span and evolutionary shaping of human longevity depend on facts about mortality at extreme ages, but these facts have remained a matter of debate. Do hazard curves typically level out into high plateaus eventually, as seen in other species, or do exponential increases persist? In this study, we estimated hazard rates from data on all inhabitants of Italy aged 105 and older between 2009 and 2015 (born 1896-1910), a total of 3836 documented cases. We observed level hazard curves, which were essentially constant beyond age 105. Our estimates are free from artifacts of aggregation that limited earlier studies and provide the best evidence to date for the existence of extreme-age mortality plateaus in humans.

A Cause-of-Death Decomposition of Young Adult Excess Mortality

We propose a method to decompose the young adult mortality hump by cause of death. This method is based on a flexible shape decomposition of mortality rates that separates cause-of-death contributions to the hump from senescent mortality. We apply the method to U.S. males and females from 1959 to 2015. Results show divergence between time trends of hump and observed deaths, both for all-cause and cause-specific mortality. The study of the hump shape reveals age, period, and cohort effects, suggesting that it is formed by a complex combination of different forces of biological and socioeconomic nature. Male and female humps share some traits in all-cause shape and trend, but they also differ by their overall magnitude and cause-specific contributions. Notably, among males, the contributions of traffic and other accidents were progressively replaced by those of suicides, homicides, and poisonings; among females, traffic accidents remained the major contributor to the hump.

Rethinking mortality deceleration

The evolution of mortality shows a marked deceleration at older ages. This phenomenon is generally thought to be an effect of selection: mortality decelerates because it progressively selects the most robust individuals in the cohort. Other processes, however, may contribute to mortality deceleration as well. People may not be passive in the face of ageing and may try to counter it by modifying their behaviours and lifestyles. In this paper, I propose a method to test whether selection is to be considered the unique mechanism responsible for mortality deceleration. I applied this method to the life tables of selected female cohorts drawn from the Human Mortality Database. The results indicate mortality decelerates more rapidly than predicted by the selection theory.

Expectation of life at old age: revisiting Horiuchi-Coale and reconciling with Mitra

Data quality issues at advanced old age, such as incompleteness of registration of vital events and age misreporting, compromise estimates of the death rates and remaining life expectancy at those ages. Following up on Horiuchi and Coale (Population Studies 36: 317-326, 1982), Mitra (Population Studies 38: 313-319, 1984, Population Studies 39: 511-512, 1985), and Coale (Population Studies 39: 507-509, 1985), we examine the conventional approaches to constructing life tables from data deficient at advanced ages and the two adjustment methods by the mentioned authors. Contrary to earlier reports by Horiuchi, Coale, and Mitra, we show that the two methods are consistent and useful in drastically reducing the estimation errors in life expectancy as compared to the conventional approaches, i.e., the classical open age interval model and extrapolation of the death rates. Our results suggest complementing the classical estimates of life expectancy by adjustments using Horiuchi-Coale, Mitra, or other appropriate methods and avoiding the extrapolation method as a tool for estimating the life expectancy.

Stochasticity, heterogeneity, and variance in longevity in human populations

Inter-individual variance in longevity (or any other demographic outcome) may arise from heterogeneity or from individual stochasticity. Heterogeneity refers to differences among individuals in the demographic rates experienced at a given age or stage. Stochasticity refers to variation due to the random outcome of demographic rates applied to individuals with the same properties. The variance due to individual stochasticity can be calculated from a Markov chain description of the life cycle. The variance due to heterogeneity can be calculated from a multistate model that incorporates the heterogeneity. We show how to use this approach to decompose the variance in longevity into contributions from stochasticity and heterogeneous frailty for male and female cohorts from Sweden (1751–1899), France (1816–1903), and Italy (1872–1899), and also for a selection of period data for the same countries.

Heterogeneity in mortality is described by the gamma-Gompertz–Makeham model, in which a gamma distributed “frailty” modifies a baseline Gompertz–Makeham mortality schedule. Model parameters were estimated by maximum likelihood for a range of starting ages. The estimates were used to construct an age×frailty-classified matrix model, from which we compute the variance of longevity and its components due to heterogeneous frailty and to individual stochasticity. The estimated fraction of the variance in longevity due to heterogeneous frailty (averaged over time) is less than 10% for all countries and for both sexes. These results suggest that most of the variance in human longevity arises from stochasticity, rather than from heterogeneous frailty.

A new parametric model to assess delay and compression of mortality

BACKGROUND

A decrease in mortality across all ages causes a shift of the age pattern of mortality, or mortality delay, while differences in the rate of decrease across ages cause a change in the shape of the age-at-death distribution, mortality compression or expansion. Evidence exists for both compression and delay of mortality. Existing parametric models to describe the full age pattern of mortality are not able to capture mortality delay versus mortality compression. More recent models that assess delay versus compression mostly focused on the adult or old ages alone and did not distinguish mortality compression below and above the modal age at death, although they represent different mechanisms.

METHODS

This paper presents a new parametric model that describes the full age pattern of mortality and assesses compression - at different stages of life - and delay of mortality: the CoDe model. The model includes 10 parameters, of which five are constant over time. The five time-varying parameters reflect delay of mortality and compression of mortality in infancy, adolescence, young adulthood, late adulthood, and old age. The model describes infant and background mortality by two simple functions, uses a mixed logistic model with different slopes in adult, middle, and old age, and includes the modal age at death as a parameter to account for the delay in mortality.

RESULTS

Applying the CoDe model to age-specific probabilities of death for Japanese, French, American, and Danish men and women between 1950 and 2010 showed a very good fit of the full age pattern of mortality. Delay of mortality explained about two-thirds of the increase in life expectancy at birth, whereas compression of mortality due to mortality declines in young age explained about one-third. No strong compression of mortality in late adulthood age was observed. Mortality compression in old age has had a small negative impact on life expectancy.

CONCLUSIONS

The CoDe model proved a valid instrument for describing the full age pattern of mortality and for disentangling the effects of mortality delay and compression - at different stages of life - on the increase in life expectancy.

On the heterogeneity of human populations as reflected by mortality dynamics

The heterogeneity of populations is used to explain the variability of mortality rates across the lifespan and their deviations from an exponential growth at young and very old ages. A mathematical model that combines the heterogeneity with the assumption that the mortality of each constituent subpopulation increases exponentially with age, has been shown to successfully reproduce the entire mortality pattern across the lifespan and its evolution over time. In this work we aim to show that the heterogeneity is not only a convenient consideration for fitting mortality data but is indeed the actual structure of the population as reflected by the mortality dynamics over age and time. In particular, we show that the model of heterogeneous population fits mortality data better than other commonly used mortality models. This was demonstrated using cohort data taken for the entire lifespan as well as for only old ages. Also, we show that the model can reproduce seemingly contradicting observations in late-life mortality dynamics. Finally, we show that the homogenisation of a population, observed by fitting the model to actual data of consecutive periods, can be associated with the evolution of allele frequencies if the heterogeneity is assumed to reflect the genetic variations within the population.

Measuring senescence rates of patients with end-stage renal disease while accounting for population heterogeneity: an analysis of data from the ERA-EDTA Registry

PURPOSE

Although a population's senescence rate is classically measured as the increase in mortality rate with age on a logarithmic scale, it may be more accurately measured as the increase on a linear scale. Patients on dialysis, who suffer from accelerated senescence, exhibit a smaller increase in their mortality rate on a logarithmic scale, but a larger increase on a linear scale than patients with a functioning kidney transplant. However, this comparison may be biased by population heterogeneity.

METHODS

Follow-up data on 323,308 patients on dialysis and 91,679 patients with a functioning kidney transplant were derived from the ERA-EDTA Registry. We measured the increases in their mortality rates using Gompertz frailty models that allow individual variation in this increase.

RESULTS

According to these models, the senescence rate measured as the increase in mortality rate on a logarithmic scale was smaller in patients on dialysis, while the senescence rate measured as the increase on a linear scale was larger in patients on dialysis than patients with a functioning kidney transplant.

CONCLUSIONS

Also when accounting for population heterogeneity, a population's senescence rate is more accurately measured as the increase in mortality rate on a linear scale than a logarithmic scale.

Black-White Differences in Mortality Compression and Deceleration and the Mortality Crossover Reconsidered

Most studies investigating Black-White differences in mortality patterns have focused exclusively on the well-known crossover but have ignored other aspects of the mortality curves, such as deceleration and compression. Yet compression and deceleration are also important features of mortality curves that may vary by race. In this research, the authors developed models for data from 1972 to 1990 and estimated them using naive and more stringent assumptions about Black data quality. They found that mortality deceleration begins at older ages for Blacks than for Whites but that the ages of deceleration onset are converging. The authors also found that mortality compression is occurring for Blacks but not for Whites and that compression is more apparent for Blacks when data quality is considered. Finally, the authors found that a crossover exists, that the age at crossover is increasing across time, and that the age at crossover is later in adjusted data than in unadjusted data.

Long-term Cognitive Trajectories and Mortality in Older Women

BACKGROUND

Few studies have examined whether change in cognition is linked to mortality. This study examined the relationship between cognitive trajectories in older age and risk of death.

METHODS

We studied community-dwelling, nondemented women aged 65+ (mean age = 71) enrolled in a prospective study of aging and followed up to 25 years. A modified Mini-Mental State Examination (mMMSE) and Trail Making Task Part B (TMTB) were administered at multiple visits during follow-up. We examined the association between cognitive trajectories (analyzed by quintiles) from baseline to age 80 (n = 7,477 for mMMSE and n = 6,503 for TMTB) and all-cause mortality after age 80 using Cox regression models, both unadjusted and adjusted for education, physical activity, alcohol, depression score, current smoking and history of hypertension and diabetes. Cause of death was determined from death certificates, classified as cardiovascular, cancer and other.

RESULTS

Women with greater rate of decline were older, less educated, less physically active, had higher depression score and were more likely to have a history of hypertension and diabetes (all p < .01). Participants with the greatest decline (quintile 1) had an increased risk of death (mMMSE hazard ratio [HR] = 1.28; TMTB HR = 1.43] and those with the least decline (quintile 5) had a decreased risk of death (mMMSE HR = 0.73; TMTB HR = 0.61) compared with intermediate decliners (quintiles 2-4). Cognitive trajectories were associated with cardiovascular mortality and other causes of death, but not cancer deaths.

CONCLUSIONS

Our study suggests that greater decline in general cognition or executive function is associated with higher rates of mortality in oldest-old women.

Sums of smooth exponentials to decompose complex series of counts

Representing the conditional mean in Poisson regression directly as a sum of smooth components can provide a realistic model of the data generating process. Here, we present an approach that allows such an additive decomposition of the expected values of counts. The model can be formulated as a penalized composite link model and can, therefore, be estimated by a modified iteratively weighted least-squares algorithm. Further shape constraints on the smooth additive components can be enforced by additional penalties, and the model is extended to two dimensions. We present two applications that motivate the model and demonstrate the versatility of the approach.

Distinct age and self-rated health crossover mortality effects for African Americans: Evidence from a national cohort study

The predictive effects of age and self-rated health (SRH) on all-cause mortality are known to differ across race and ethnic groups. African American adults have higher mortality rates than Whites at younger ages, but this mortality disparity diminishes with advancing age and may "crossover" at about 75-80 years of age, when African Americans may show lower mortality rates. This pattern of findings reflects a lower overall association between age and mortality for African Americans than for Whites, and health-related mechanisms are typically cited as the reason for this age-based crossover mortality effect. However, a lower association between poor SRH and mortality has also been found for African Americans than for Whites, and it is not known if the reduced age and SRH associations with mortality for African Americans reflect independent or overlapping mechanisms. This study examined these two mortality predictors simultaneously in a large epidemiological study of 12,181 African Americans and 17,436 Whites. Participants were 45 or more years of age when they enrolled in the national REasons for Geographic and Racial Differences in Stroke (REGARDS) study between 2003 and 2007. Consistent with previous studies, African Americans had poorer SRH than Whites even after adjusting for demographic and health history covariates. Survival analysis models indicated statistically significant and independent race*age, race*SRH, and age*SRH interaction effects on all-cause mortality over an average 9-year follow-up period. Advanced age and poorer SRH were both weaker mortality risk factors for African Americans than for Whites. These two effects were distinct and presumably tapped different causal mechanisms. This calls into question the health-related explanation for the age-based mortality crossover effect and suggests that other mechanisms, including behavioral, social, and cultural factors, should be considered in efforts to better understand the age-based mortality crossover effect and other longevity disparities.

Frailty still matters to health and survival in centenarians: the case of China

Background

Frailty indicates accumulated vulnerability of adverse health outcomes in later life. Its robustness in predicting dependent living, falls, comorbidity, disability, health change, mortality, and health care utilization at older ages is well-documented. However, almost no studies have ever attempted to examine its robustness in centenarians, mainly due to data unavailability. This study examines prevalence of frailty in centenarians and its predictive powers on subsequent mortality and health conditions.

Methods

We use a sample of 4434 centenarians from the 2002, 2005, 2008, and 2011 waves of the Chinese Longitudinal Healthy Longevity Survey (CLHLS), with elders in three younger age groups 65–79, 80–89, and 90–99 as comparisons. Frailty is measured by a cumulative deficit index (DI) that is constructed from 39 variables covering physical and cognitive function, disease conditions, psychological well-being, and other health dimensions. Survival analysis is conducted to examine how frailty is associated with subsequent mortality at an average follow-up length of 3.7 years (2.6 years for deceased persons died in 2002–2011 and 7.6 years for survived persons at the 2011 wave). Logistic regressions are applied to examine how frailty is associated with subsequent physical and cognitive functions, disease conditions, and self-rated health with an average follow-up length of 3.0 years.

Results

The study reveals that centenarians are frailer than younger elders. The DI scores increase from less than 0.1 at ages 65–79 to over 0.30 in centenarians. Women are frailer than men at all ages. However, there is a great variation in frailty among all age groups. We also find that each additional increase of 0.01 score of the DI is associated with 1.6 % higher mortality risk (95 % CI: 1.014–1.018) in female centenarians and 1.4 % higher mortality risk (95 % CI: 1.010–1.018) in male centenarians, although these associations are weaker than those in other three younger age groups.

Conclusions

Frailty still plays an important role in determining subsequent health outcomes and mortality in centenarians.

On the beginning of mortality acceleration

Physiological senescence is characterized by the increasing limitation of capabilities of an organism resulting from the progressive accumulation of molecular damage, which at group (cohort) level translates into, among other things, an increase in mortality risks with age. Physiological senescence is generally thought to begin at birth, if not earlier, but models of demographic aging (i.e., an increase in mortality risks) normally start at considerably later ages. This apparent inconsistency can be solved by assuming the existence of two mortality regimes: "latent" and "manifest" aging. Up to a certain age, there is only latent aging: physiological senescence occurs, but its low level does not trigger any measurable increase in mortality. Past a certain level (and age), molecular damage is such that mortality risks start to increase. We first discuss why this transition from latent to manifest aging should exist at all, and then we turn to the empirical estimation of the corresponding threshold age by applying Bai's approach to the estimation of breakpoints in time series. Our analysis, which covers several cohorts born between 1850 and 1938 in 14 of the countries included in the Human Mortality Database, indicates that an age at the onset of manifest aging can be identified. However, it has not remained constant: it has declined from about 43 and 47 years, respectively, for males and females at the beginning of the period (cohorts born in 1850-1869) to about 31 for both males and females toward its end (cohorts born in 1920-1938). A discussion of why this may have happened ensues.

A solution to debates over the behavior of mortality at old ages

As humans live longer, the precise modeling of mortality curves in very old age is becoming more important in aging research and public health. Here, we address a methodology that utilizes a modified stretched exponential survival function where a stretched exponent is relevant to heterogeneity in human populations. This function allows better estimation of the maximum human lifespan by providing a good description of the mortality curves in very old age. Demographic analysis of Swedish females over three recent decades revealed an important trend: the maximum human lifespan (existing around 125 years) gradually decreased at a constant rate of ~1.6 years per decade, while the characteristic life gradually increased at a constant rate of ~1.2 years per decade. This trend indicates that the number of aging people is increasingly concentrated at very old age, which is consistent with the definition of population aging. Importantly analyzing the stretched exponents would help in evaluating the heterogeneity trends in human populations.

The Strehler-Mildvan correlation from the perspective of a two-process vitality model

The Strehler and Mildvan (SM) general theory of ageing and mortality provides a mechanism-based explanation of Gompertz's law and predicts a log-linear relationship between the two Gompertz coefficients, known as the SM correlation. While the SM correlation is supported by data from developed countries before the second half of the twentieth century, the recent breakdown of the correlation pattern in these countries has prompted demographers to conclude that SM theory needs to be reassessed. In this paper we use a newly developed two-process vitality model to explain the SM correlation and its breakdown in terms of asynchronous trends in acute (extrinsic) and chronic (intrinsic) mortality factors. We propose that the mortality change in the first half of the twentieth century is largely determined by the elimination of immediate hazards to death, whereas the mortality change in the second half is primarily driven by the slowdown of the deterioration rate of intrinsic survival capacity.

Biodemography of old-age mortality in humans and rodents

The growing number of persons living beyond age 80 underscores the need for accurate measurement of mortality at advanced ages and understanding the old-age mortality trajectories. It is believed that exponential growth of mortality with age (Gompertz law) is followed by a period of deceleration, with slower rates of mortality increase at older ages. This pattern of mortality deceleration is traditionally described by the logistic (Kannisto) model, which is considered as an alternative to the Gompertz model. Mortality deceleration was observed for many invertebrate species, but the evidence for mammals is controversial. We compared the performance (goodness-of-fit) of two competing models-the Gompertz model and the logistic (Kannisto) model using data for three mammalian species: 22 birth cohorts of U.S. men and women, eight cohorts of laboratory mice, and 10 cohorts of laboratory rats. For all three mammalian species, the Gompertz model fits mortality data significantly better than the "mortality deceleration" Kannisto model (according to the Akaike's information criterion as the goodness-of-fit measure). These results suggest that mortality deceleration at advanced ages is not a universal phenomenon, and survival of mammalian species follows the Gompertz law up to very old ages.

New Developments in the Biodemography of Aging and Longevity

Biodemography is a promising scientific approach based on using demographic data and methods for getting insights into biological mechanisms of observed processes. Recently, new important developments have happened in biodemographic studies of aging and longevity that call into question conventional aging theories and open up novel research directions. Recent studies found that the exponential increase of the mortality risk with age (the famous Gompertz law) continues even at extreme old ages in humans, rats, and mice, thus challenging traditional views about old-age mortality deceleration, mortality leveling-off, and late-life mortality plateaus. This new finding represents a challenge to many aging theories, including the evolutionary theory that explains senescence by a declining force of natural selection with age. Innovative ideas are needed to explain why exactly the same exponential pattern of mortality growth is observed not only at reproductive ages, but also at very-old postreproductive ages (up to 106 years), long after the force of natural selection becomes negligible (when there is no room for its further decline). Another important recent development is the discovery of long-term 'memory' for early-life experiences in longevity determination. Siblings born to young mothers have significantly higher chances to live up to 100 years, and this new finding, confirmed by two independent research groups, calls for its explanation. As recent studies found, even the place and season of birth matter for human longevity. Beneficial longevity effects of young maternal age are observed only when children of the same parents are compared, while the maternal age effect often could not be detected in across-families studies, presumably being masked by between-family variation. It was also found that male gender of centenarian has a significant positive effect on the survival of adult male biological relatives (brothers and fathers) but not of female relatives. Finally, large gender differences are found in longevity determinants for males and females, suggesting a higher importance of occupation history for male centenarians as well as a higher importance of home environment history for female centenarians.