Explanations for diverging mortality trends in high-income countries

Rates of suicide mortality have declined substantially over the past 25 years in most OECD countries. Unfortunately, since 1985 suicide rates have increased by 20% in Japan and by 250% in South Korea. Suicide mortality has increased at an especially rapid pace in South Korea since 2000. To help disentangle the effects of age-related factors, secular change, and birth cohort membership, we estimated a series of intrinsic estimator age-period-cohort models of suicide mortality rates in Japan and South Korea between 1985 and 2010. Results indicate that age-related factors explain much of the increase in Japan, where a large segment of the population has moved into a high-risk age range of 40-65. In South Korea, the increase is driven by multiple factors – including rising period effects, a growing elderly population, and strong cohort effects for those born between the Great Depression and the aftermath of World War II.

For a long time the basic assumption has linked the change in life expectancy between the Flemish and French talking regions in Belgium to the uneven economic development between the regions and to the evolution in relative wealth and socio-economic composition of the population. There is no doubt of the importance of socio-economic factors on population health and mortality, but the pure association over time does not explain the particular mechanisms at work. Moreover, when analysing the shift in age specific mortality between the regions, the age pattern tells another story, not exactly in sync with the shift in socio-economic wealth between north and south. Using historical mortality data and recent individual cause specific mortality data, we try to explore more in depth the factors that caused this inversion in life expectancy and that are still contributing to the enduring lower life expectancy in the Walloon region compared to the Flemish region. The insights based on the detailed analysis of this process illustrates the Belgian case and add insight in the complexity of the general process of the health transition.

In spite of significant gains in life expectancy at birth (e0) that both Arabs and Jews in Israel have experienced throughout the last 40 years, a gap ranging between 3 to 4 years has persisted between the two groups. Using decomposition methods, this paper estimates the contribution of specific age groups and causes of death to the total Arab-Jewish gap during 1970-08. Results show that trends in the total gap reflect two opposing processes. During 1970-80 modest declines in the total gap were driven by converging infant and child mortality rates. Thereafter, growing inequalities in older-age mortality resulted in widening the gap. Cancer mortality which was a major suppressor of the total Arab-Jewish gap for both sexes has recently turned to a small positive contributor. Faster increase in diabetes mortality rates among the Arab minority also positively contributed to enlarging the gap.

People often consider the question of where the limits of the lengths of human life lie. Length of life is usually expressed as the life expectancy at births (or more generally the life expectancy at exact age x years). The values of the life expectancy at births and of the modal age at deaths differ from one another and their development trends in time also differ. This is due first and foremost to the fact that the life expectancy at births is the average age of deaths in the stationary population, which is strongly influenced by the extreme values, especially the level of infant mortality. In contrast to this, the modal age at deaths is given by the age at which people most frequently die (with the exception of childhood). The term modal age at deaths was already introduced by Ch. Bernoulli and defined precisely by Lexis (1877) as the age when the number of deaths in adulthood (in the life tables) attains its maximum. The modal age at deaths is often used as a characteristic of longevity. This paper will analyse and compare trends in the development of the life expectancy and the modal age at deaths in European countries using various methods for the smoothing and modelling the mortality at higher ages. An estimate will also be made of the possible future development of mortality with the help of the analysis of time series.

Life expectancy is increasing in most high-income countries. The development is not uniform, though. Whereas some countries experienced steady progress during recent decades, others have periods of stagnation and, eventually, phases of catching up.

The aim of our paper is to investigate whether comparable developments
in life expectancy are based on the same underlying mortality dynamics. We use surfaces of rates of mortality improvement as our tool of analysis. Based on two-dimensional plots of smoothed death rates, our ``maps'' depict the rate of mortality change over time. We argue that this approach provides an excellent exploratory tool to visually analyze mortality dynamics, in particular to detect age-, period-, and cohort-effects. Preliminary results demonstrate for international comparisons that periods of stagnation followed by rapid increases can be caused by cohort factors (e.g. Denmark) as well as by period factors (e.g. East Germany).

An analysis by major causes of death for the United States shows that
antagonistic cohort effects were instrumental for the slow life
expectancy increase during the 1980s and the 1990s: If negative cohort
effects of respiratory diseases and cancer had been absent, life
expectancy would have increased much faster due to improvements in
survival for heart diseases.

Life Expectancy at birth for women in the United States rose to nearly 78 years in 2007. While this is a new milestone for low mortality among Americans, the United States lags significantly behind its counterparts in Europe, partially reflecting a history of heavy smoking among Americans. However, health and mortality experience in the United States is far from homogeneous. The same factors that are responsible for the shortfall of American life expectancy may also be manifested in geographic inequalities in mortality within the United States. The current geographic pattern in adult mortality in the US is a relatively recent phenomenon, the result of increasing divergence in mortality experience between the southern states and the rest of the United States over past several decades. The goal of this paper is to examine the contribution of cigarette smoking to the increasing mortality gap between the southern states and the rest of the US since 1965. Data come from US vital statistics and the impact of smoking is estimated using indirect methods. Preliminary results indicate that the divergence in all-cause mortality occurs concomitantly with divergence in mortality attributable to cigarette smoking.

Introduction: Aim of this study is to identify how socio-demographic factors contribute to the mortality differences among men and women in Sweden.
Methods: We used data from the Linnaeus Database in 2005. We analyzed using multivariate logistic regression to estimate the adjusted effects of each socio-demographic factor on mortality among men and women. Later we used a variant of the Blinder-Oaxaca decomposition technique partition to measure mortality differences among men and women.
Results:Mortality rates ratio among women and men was 1.03. The difference mean of mortality rate among men and women was 0.004156. Approximately 23% of the inequality was attributed to the difference in “explained” component and 77% due to “unexplained” component. Of the “explained” components, 86% of the contribution came from education, whilst number of sibling and country of birth contributed less. The remaining 19% of the “explained” inequality was attributed to differences in the distribution of age among men and women. Of the “unexplained” component, education and number of siblings made negative contributions. Country of birth made small but statistically significant contributions.
Conclusion: This study confirmed how differences in socio-demographic and economic characteristics explained the mortality differences by gender in Sweden.