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2 Age-specific contributions to the increase in life expectancy among women during the past 10 years in Denmark, 1960-2014 (Data source: Human Mortality Database)
Source publication
Rates of mortality improvement, as presented in the previous two chapters, are an excellent tool to illustrate mortality dynamics. They can not be directly translated to contributions to changes in life expectancy. Using Arriaga’s decomposition approach, we plot maps for selected countries from the Human Mortality Database, showing which ages contr...
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Risk assessment models are conceptual constructs (translated into mathematical forms), built on a set of assumptions (hypotheses) made on the available knowledge. In this sense, the risk assessment outcomes are conditional on the available knowledge. Risk assessment provides informative support to decision making (DM), and assurance must be provide...
Citations
... Thus, living a long life in retirement is a desirable goal of many people. As mortality reductions at ages 65 and over have been the main driver of life expectancy gains since the mid-20th century (Rau et al., 2018), people are expected to live longer in retirement now than in the past. ...
Objectives
The length of retirement life may be highly unequal due to persistent and significant discrepancies in old-age mortality. This study assesses gender and educational differences in the average retirement lifespan and the variation in retirement lifespan, taking into account individual labor force exit and re-entry dynamics.
Methods
We used longitudinal data from the Health and Retirement Study from 1996–2016, focusing on respondents aged 50 and above (N = 32,228). Multistate life tables were estimated using discrete-time event history models. The average retirement lifespan, as well as absolute and relative variation in retirement lifespan, were calculated analytically.
Results
Among women, we found a persistent educational gradient in average retirement lifespan over the whole period studied; among men, the relationship between education and retirement expectancy differed across periods. Women and the lower-educated had higher absolute variation in retirement lifespan than men and the higher-educated—yet these relationships were reversed when examined by relative variation.
Discussion
Our multistate approach provides an accurate and comprehensive picture of the retirement lifespan of older Americans over the past two decades. Such findings should be considered in high-level discussions on Social Security. Potential reforms such as raising the eligibility age or cutting benefits may have unexpected implications for different social groups due to their differential impacts on retirement initiation and re-entry dynamics.
... Age-specific contributions to e 0 and G 0 , India, 2010-2020 Table 3 shows the application of decomposition methods for analysing the age-specific contributions to Δe 0 by discrete [77] and the replacement [75] methods [82,83]. The results from both methods are similar. ...
Background
Quantifying excess deaths and their impact on life expectancy at birth (e0) provide a more comprehensive understanding of the burden of coronavirus disease of 2019 (COVID-19) on mortality. The study aims to comprehend the repercussions of the burden of COVID-19 disease on the life expectancy at birth and inequality in age at death in India.
Methods
The mortality schedule of COVID-19 disease in the pandemic year 2020 was considered one of the causes of death in the category of other infectious diseases in addition to other 21 causes of death in the non-pandemic year 2019 in the Global Burden of Disease (GBD) data. The measures e0 and Gini coefficient at age zero (G0) and then sex differences in e0 and G0 over time were analysed by assessing the age-specific contributions based on the application of decomposition analyses in the entire period of 2010–2020.
Results
The e0 for men and women decline from 69.5 and 72.0 years in 2019 to 67.5 and 69.8 years, respectively, in 2020. The e0 shows a drop of approximately 2.0 years in 2020 when compared to 2019. The sex differences in e0 and G0 are negatively skewed towards men. The trends in e0 and G0 value reveal that its value in 2020 is comparable to that in the early 2010s. The age group of 35–79 years showed a remarkable negative contribution to Δe0 and ΔG0. By causes of death, the COVID-19 disease has contributed − 1.5 and − 9.5%, respectively, whereas cardiovascular diseases contributed the largest value of was 44.6 and 45.9%, respectively, to sex differences in e0 and G0 in 2020. The outcomes reveal a significant impact of excess deaths caused by the COVID-19 disease on mortality patterns.
Conclusions
The COVID-19 pandemic has negative repercussions on e0 and G0 in the pandemic year 2020. It has severely affected the distribution of age at death in India, resulting in widening the sex differences in e0 and G0. The COVID-19 disease demonstrates its potential to cancel the gains of six to eight years in e0 and five years in G0 and has slowed the mortality transition in India.
... The Arriaga's method of decomposition is widely applied for analysing age-specific contributions to G 0 and e 0 (Shkolnikov et al. 2003, pp. 311-312;Rau et al. 2018). The principle of standardisation or replacement (Kitagawa 1964) of ASDR is the basis for applying the technique of decomposition over time or regions or subgroups of the population. ...
India has seen a reduction in infant and child mortality rates for both the sexes since the early 1980s. However, a decline in mortality at adult ages is marked by significant differences in the subgroups of sex and regions. This study assesses the progress of inequality in age at death with the advances in mortality transition during 36 years period between 1981–1985 and 2012–2016 in India, using the Gini coefficients at the age of zero (G0). The Gini coefficients show that in the mid-2000s, women outpaced men in G0. The reduction in inequality in age at death is a manifestation of the process of homogeneity in mortality. The low G0 is concomitant of high life expectancy at birth (e0) in India. The results show the dominance of adult mortality over child mortality in the medium-mortality and low-mortality regimes. Varying adult mortality in the subgroups of sex and variance in the mortality levels of regions are the predominant factors for the variation in inequality in age at death. By lowering of the mortality rates in the age group of 15–29 years, India can achieve a high e0 that appears at high demographic development and the narrow sex differentials in e0 and G0 in a short time. Men in the age group of 15–29 years are the most vulnerable subgroup with respect to mortality. There is an immediate need for health policies in India to prioritise the aversion of premature deaths in men aged 15–29 years.
The study of the mortality differences between groups has traditionally focused on metrics that describe average levels of mortality, for example life expectancy and standardized mortality rates. Additional insights can be gained by using statistical distance metrics to examine differences in lifespan distributions between groups. Here, we use a distance metric, the non-overlap index, to capture the sociological concept of stratification, which emphasizes the emergence of unique, hierarchically layered social strata. We show an application using Finnish registration data that cover the entire population over the period from 1996 to 2017. The results indicate that lifespan stratification and life-expectancy differences between income groups both increased substantially from 1996 to 2008; subsequently, life-expectancy differences declined, whereas stratification stagnated for men and increased for women. We conclude that the non-overlap index uncovers a unique domain of inequalities in mortality and helps to capture important between-group differences that conventional approaches miss.
