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Background: Mortality rates have been reduced by half over the last 60 years for nonagenarians, and the progress is continuing. The greater survival might be due to overtreatment of severely physically and cognitively disabled individuals, which is a big concern for societies and individuals., Methods: The study population comprised two Danish birth cohorts: the 1905 Cohort and the 1915 Cohort. At age 95, all from the two cohorts who were still alive and living in Denmark were invited to participate in a health survey that used the same assessment instrument. A total of 2,670 (56.8%) persons participated in the two surveys and survival was assessed through a 7.3-year follow-up period during which 2,497 (93.5%) had died, and with virtually no loss to follow-up., Results: Despite the increasing chance of surviving to age 95, the 1915 Cohort had significantly better health and functioning than the 1905 Cohort. The survival advantage in the 1915 Cohort continued in the follow-up period after age 95: Median survival length was 2.4 months longer, p = .011. This advantage was not statistically associated with different levels of activities of daily living, physical performance, cognitive functioning, self-rated health and life satisfaction. However, the advantage tended to be more pronounced among people with better health., Conclusions: Life span and health increases among the oldest old. The improvement in survival for 95-year olds born in 1915 compared with 1905 was seen across the whole spectrum of health and functioning, with a tendency towards bigger improvement among those in good health., (© The Author(s) 2020. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Although socioeconomic statuses affect cancer mortality rates, the specific difference between metropolitan and non-metropolitan areas in Japan has not been evaluated. This study analyzed differences in cancer mortality between metropolitan and non-metropolitan areas in Japan, using an age-period-cohort (APC) analysis., Methods: Data on cancer mortality from 1999 to 2018 for metropolitan and non-metropolitan areas in Japan were used. Here metropolitan areas were defined as government ordinance-designated municipalities in 1999 and special wards of Tokyo. In addition to general mortality data for all cancer sites, data on mortality for stomach, colorectal, liver, gallbladder, pancreatic, lung, prostate, and breast cancers were used for analysis. A Bayesian APC analysis was administered to the data for each type of cancer for area and for sex-distinguished data. Additionally, the ratios for estimated mortality rate by periods and cohorts between the two areas were calculated., Results: The age-standardized mortality rate for cancer in all sites in non-metropolitan areas was lower than that in metropolitan areas throughout the analyzed years for both men and women, but the mortality difference decreased during the periods for men. The rates of decrease in mortality rate in cohorts differed for some cancers between the two area types, and the mortality rate ratios of metropolitan compared with non-metropolitan areas decreased for cancer in all sites over the analyzed cohorts for men. Also, the rate of decrease in mortality rate over the cohorts was completely different between the areas for stomach cancer in men and for liver cancer for women., Conclusion: Mortality rates for cancer in all sites tended to diverge between the two area types in younger cohorts for men, and people in younger cohorts in non-metropolitan areas should take more extensive preventive measures against cancer than their counterparts in metropolitan areas.

Background: The current study aimed to analyze the sex differences in cancer mortality rates in Japan via an age-period-cohort (APC) analysis., Methods: We used data about cancer mortality rates from 1995 to 2015 in Japan based on the Vital Statistics survey. In addition to the data about mortality from all sites of cancer, we specifically used data about stomach, lung, colorectal, and liver cancers. A Bayesian APC analysis was performed to identify changes in mortality rates based on three effects, which were as follows: age, period, and cohort. Then, we finally calculated the mortality rate ratios for each effect between men and women., Results: The sex differences in age-adjusted mortality rates for all-sites cancer, lung cancer, and liver cancer were decreasing from 1995 to 2015, and the mortality rate ratios in terms of sex decreased from 2.17 in 1995 to 1.93 in 2015. Based on the results of the APC analyses, only minimal changes were observed in the mortality rate ratios for all types of cancer between men and women during the analyzed periods. The cohort effects began to decrease from the early 20th century in all types of cancer in both men and women, and the mortality rate ratios for all types of cancer between men and women began to increase in the cohorts born from 1926 to 1935. For all-sites cancer, the ratio increased from 0.49 (0.44, 0.57) in the cohort born from 1926 to 1930 to 0.81 (0.60, 1.03) in the cohort born from 1971 to 1975., Conclusion: The sex differences in cancer mortality rates were decreasing in the more recent born generations in Japan. If this trend continues, there will be a minimal difference in the morality rates in the following generations., .

Period life expectancy is one of the most used summary indicators for the overall health of a population. Its levels and trends direct health policies, and researchers try to identify the determining risk factors to assess and forecast future developments. The use of period life expectancy is often based on the assumption that it directly reflects the mortality conditions of a certain year. Accordingly, the explanation for changes in life expectancy are typically sought in factors that have an immediate impact on current mortality conditions. It is frequently overlooked, however, that this indicator can also be affected by at least three kinds of effects, in particular in the situation of short-term fluctuations: cohort effects, heterogeneity effects, and tempo effects. We demonstrate their possible impact with the example of the almost Europe-wide decrease in life expectancy in 2015, which caused a series of reports about an upsurge of a health crisis, and we show that the consideration of these effects can lead to different conclusions. Therefore, we want to raise an awareness concerning the sensitivity of life expectancy to sudden changes and the menaces a misled interpretation of this indicator can cause., (© 2019 The Author(s)Published by S. Karger AG, Basel.)

Background: With aging, health deficits accumulate: people with few deficits for their age are fit, and those with more are frail. Despite recent reports of improved health in old age, how deficit accumulation is changing is not clear. Our objectives were to evaluate changes over 30 years in the degree of deficit accumulation and in the relationship between frailty and mortality in older adults., Methods: We analyzed data from two population based, prospective longitudinal cohorts, assembled in 1971-1972 and 2000-2001, respectively. Residents of Gothenburg Sweden, systematically drawn from the Swedish population registry. The 1901-1902 cohort (N = 973) had a response rate of 84.8%; the 1930 cohort (N = 500) had a response rate of 65.1%. A frailty index using 36 deficits was calculated using data from physical examinations, assessments of physical activity, daily, sensory and social function, and laboratory tests. We evaluated mortality over 12.5 years in relation to the frailty index., Results: Mean frailty levels were the same (x¯ = 0.20, p = .37) in the 1901-1902 cohort as in the 1930 cohort. Although the frailty index was linked to the risk of death in both cohorts, the hazards ratio decreased from 1.67 per 0.1 increment in the frailty index for the first cohort to 1.32 for the second cohort (interaction term p = .005)., Discussion: Although frailty was as common at age 70 as before, its lethality appears to be less. Just why this is so should be explored further., (© The Author 2016. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: To examine temporal variations of age, period, and cohort on suicide mortality rate in Hong Kong (HK) from 1976 to 2010, and speculate the macroenvironmental mechanisms of the observed trends., Methods: Poisson age-period-cohort modeling was used to delineate the effects of age, period, and cohort on suicide mortality. Analysis by sex was also conducted to examine if gender difference exists for suicidal behaviours., Results: Age-cohort model provides the best fit to the mortality data, implying that the cohort effect is likely to explain more of the contributions to HK's suicide mortality pattern than the period effect. Risk of suicide mortality increases nonlinearly with age and accelerates after age 65-69 for both sexes. Moreover, the cohort effects differ between the sexes-risk of mortality increases continually for men born after 1961, but no change is observed for women since the 1941 cohort., Conclusions: With increased risk of suicide mortality in younger cohorts and the age effect of suicide mortality, we may see future increase in suicide mortality as these younger cohorts age. Further studies are needed to clarify plausible associations between broader sociohistorical changes in the population impacting psychological risk factors and suicidal behaviour to better inform suicide prevention strategies., (© 2015 Wiley Periodicals, Inc.)

Purpose: Identification is a central problem with age-period-cohort analysis. Because age + cohort = period, there is no unique solution to the linear effect using generalized linear modeling, but cohort effects have caused greater controversy than age and period effects. To illustrate the magnitude of cohort effects given the presence of collinearity, we reanalyze data from the seminal study by Kermack et al, with an update., Methods: Relative mortality data in England and Wales between year 1845 and 1995 were analyzed using partial least squares regression. There were seven age groups ranging from 5 to 74 years old and 16 periods with 22 cohorts., Results: Our reanalysis seemed to support the existence of cohort effects in the mortality trends. Period and cohort effects were generally consistent with changes in the social, economic, and environmental factors taking place in the last two centuries. Our analysis also showed a declining trend in period effects up to 1950s., Conclusions: Partial least squares and related methods provide intuitive pointers toward the separation of linear age, period, and cohort effects. Because statistical algorithms cannot distinguish between relative and actual mortality rates, cohort effects may be underestimated because of contamination by negative age effects., (Copyright © 2014 Elsevier Inc. All rights reserved.)

The increasing amount of attention paid to longevity risk and funding for old age has created the need for precise mortality models and accurate future mortality forecasts. Orthogonal polynomials have been widely used in technical fields and there have also been applications in mortality modeling. In this paper we adopt a flexible functional form approach using two-dimensional Legendre orthogonal polynomials to fit and forecast mortality rates. Unlike some of the existing mortality models in the literature, the model we propose does not impose any restrictions on the age, time or cohort structure of the data and thus allows for different model designs for different countries' mortality experience. We conduct an empirical study using male mortality data from a range of developed countries and explore the possibility of using age-time effects to capture cohort effects in the underlying mortality data. It is found that, for some countries, cohort dummies still need to be incorporated into the model. Moreover, when comparing the proposed model with well-known mortality models in the literature, we find that our model provides comparable fitting but with a much smaller number of parameters. Based on 5-year-ahead mortality forecasts, it can be concluded that the proposed model improves the overall accuracy of the future mortality projection. [ABSTRACT FROM AUTHOR]

The general health status of a population changes over time, generally in a positive direction. Some generations experience more unfavourable conditions than others. The health of Danish women in the interwar generations is an example of such a phenomenon. The stagnation in their life expectancy between 1977 and 1995 is thought to be related to their smoking behaviour. So far, no study has measured the absolute effect of smoking on the mortality of the interwar generations of Danish women and thus the stagnation in Danish women's life expectancy. We applied a method to estimate age-specific smoking-attributable number of deaths to examine the effect of smoking on the trends in partial life expectancy of Danish women between age 50 and 85 from 1950 to 2012. We compared these trends to those for women in Sweden, where there was no similar stagnation in life expectancy. When smoking-attributable mortality was excluded, the gap in partial life expectancy at age 50 between Swedish and Danish women diminished substantially. The effect was most pronounced in the interwar generations. The major reason for the stagnation in Danish women's partial life expectancy at age 50 was found to be smoking-related mortality in the interwar generations. [ABSTRACT FROM AUTHOR]

Purpose Identification is a central problem with age-period-cohort analysis. Because age + cohort = period, there is no unique solution to the linear effect using generalized linear modeling, but cohort effects have caused greater controversy than age and period effects. To illustrate the magnitude of cohort effects given the presence of collinearity, we reanalyze data from the seminal study by Kermack et al, with an update. Methods Relative mortality data in England and Wales between year 1845 and 1995 were analyzed using partial least squares regression. There were seven age groups ranging from 5 to 74 years old and 16 periods with 22 cohorts. Results Our reanalysis seemed to support the existence of cohort effects in the mortality trends. Period and cohort effects were generally consistent with changes in the social, economic, and environmental factors taking place in the last two centuries. Our analysis also showed a declining trend in period effects up to 1950s. Conclusions Partial least squares and related methods provide intuitive pointers toward the separation of linear age, period, and cohort effects. Because statistical algorithms cannot distinguish between relative and actual mortality rates, cohort effects may be underestimated because of contamination by negative age effects. [ABSTRACT FROM AUTHOR]

Individual-level research reports that adverse environmental conditions during infancy increase the risk of mortality later in life. Extending this model to populations implies what we call the “diminished entelechy” hypothesis in which birth cohorts subjected to virulent environmental insults early in life experience increased mortality at older ages and do not realize their otherwise expected lifespan. Controversy remains as to whether the individual-level findings generalize to populations. We test the “diminished entelechy” hypothesis by measuring the association between infant mortality and life expectancy at age one for males and females born in Sweden (1751–1912), Denmark (1835–1913), and England and Wales (1841–1912). Time-series methods control for trends and other forms of autocorrelation that could confound the associations. Results support diminished entelechy in Sweden and England and Wales, but not in Denmark, in that environmental insults during infancy appear associated with reduced cohort lifespan. We then explored when in the life course the sequelae of infancy appear most salient. We examined cohort associations between infant mortality and mortality during childhood (1–4 years), youth (5–19 years), adulthood (20–54 years), and old-age (55–79 years). We generally find that infant cohort effects appear to “program” the mortality experience in youth, but not in adulthood or old-age. These findings conflict with the notion that improved conditions during infancy shaped the historical decline of old-age mortality. [Copyright &y& Elsevier]

Many studies have shown that health conditions experienced in childhood play an important role on an individual's adult mortality. Recent research suggests that past reductions in early life exposure to infectious diseases have been a major contributor to the historical decline in old-age mortality. Drawing on French-Canadian data from cohorts born in the 17th and 18th centuries, we test whether a progressive deterioration in early life conditions (as revealed by an increasing infant mortality rate) translates into a decrease in survival prospects in late life. We use traditional demographic measures such as the age-specific probability of death, and a series of proportional hazard models to control for familial and environmental conditions. Results point toward little evidence of any early life effects. The trend of increasing infant mortality does not correlate with a general increase of mortality in older ages within the same cohorts. Period changes affecting survival at older ages (war, epidemics) as well as demographic and biological characteristics shared within families have a much larger role in old-age mortality than early life characteristics shared within the same cohorts. [Copyright &y& Elsevier]

Aims: The authors examined causes of death contributing to the relatively high mortality of Danish women born 1915–45, and evaluated the impact of smoking related causes of death. Methods: Age–period–cohort analysis of mortality of Danish women aged 40–89 in 1960–98. Estimate of the negative curvature in parabola patterns for 50 causes of death. Results: A total of 34 causes of death contributed to the relatively high mortality for women born 1915–45. The main contribution came from smoking‐related causes of death. Conclusion: The results indicate a high smoking prevalence to be the main explanation behind the relatively low life expectancy of Danish women born 1915–45. [ABSTRACT FROM AUTHOR]

In this paper we propose a hierarchical Bayesian model to study the variation in space and time of disease risk. We represent spatial effects following the usual Bayesian specification of a Gaussian convolution of unstructured and structured components, while we adopt the birth cohort (instead of the commonly used period of death) as the main time scale. The model also includes space–time interaction terms to take into account structured inseparable space–time variability. The model is applied to lung cancer death certificate data in Tuscany, for males during the period 1971–94. While a calendar period analysis points out a general increase of mortality levelling off in the last period (1990–94), the cohort model shows a general and substantial decrease of the relative risk for the youngest cohorts born after 1930. Moreover, the pattern of the epidemic by birth cohort presents a maximum which varies by municipalities, with a strong north-west/south-east gradient. [ABSTRACT FROM AUTHOR]

I study how shocks in cohort-level early life conditions, as represented by deviations from trend in mortality before age 5, affect later mortality. I use data for six European countries and find that shocks that increase infant mortality decrease later life expectancy between ages 5-30. The effect is strong for England and Wales but small or insignificant for other countries. Shocks that increase mortality at ages 1-5 increase life expectancy between ages 5-30 and compress the mortality distribution. For both shocks the effects are weak at older ages. These results suggest that early life conditions have a transitory effect and potentially only little influence on old-age mortality.

Mortality rates typically vary smoothly over age and time. Exceptions occur owing to events such as wars and epidemics, which create ridges in the age‐period surface of mortality rates in a particular year or for cohorts born in a particular year. We propose a new practical method for modelling the age‐period surface of mortality rates. Our approach uses L1 regularization with bivariate smoothing and allows for age‐varying period and cohort ridges in the otherwise smooth surface. Cross‐validation on data from many countries and from simulations demonstrates that our approach is superior to existing approaches in estimating the "true" age‐period mortality surface. It also provides greater insight into the underlying mortality dynamics, informing mortality modelling, analysis and forecasting. Although designed for the modelling of mortality rates, our method can also be applied to any bivariate data with occasional ridges and extends the statistical literature on quantile smoothing. © 2018 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]