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BACKGROUND: To make the pension system robust to population ageing, Denmark will increase the statutory retirement age in tandem with national life expectancy. By universally increasing this age, this pension indexation policy might amplify known inequalities in mortality, such as those between people in different living arrangements. OBJECTIVE: We aim to quantify inequalities in mortality before retirement age by living arrangement over time and to estimate whether an increase in the retirement age could disproportionately affect disadvantaged groups. METHODS: We estimate the probability of dying between ages 50 and 65/67/70 for several cohorts of Danes living in different household types, stratifying by socioeconomic status. To give a more complete picture, we also calculate equivalent age for each sub-group. RESULTS: In Denmark considerable mortality inequalities exist depending on living arrangements, and they are becoming larger. Across all the dimensions of mortality we examine, single individuals cumulate a clear disadvantage. An increased retirement age would increase absolute and (for single individuals) relative differences in pre-retirement survival, adding to the widening trends across cohorts. CONCLUSIONS: Relying on the national average to set the statutory retirement age risks maintaining these subpopulations' disadvantage. While early retirement schemes exist in Denmark, they mostly impact people based on their occupation. We highlight that other characteristics are strongly tied to mortality and should also be considered. CONTRIBUTION: This paper falls within a project developed and directed by Jim Vaupel from 2019 until his death. He believed that the Danish pension indexation policy risked unfairly damaging specific groups. The results of this paper show that by neglecting existing and widening inequalities, this might indeed be the case. [ABSTRACT FROM AUTHOR]

The population-level case-fatality rate (CFR) associated with COVID-19 varies substantially, both across countries at any given time and within countries over time. We analyze the contribution of two key determinants of the variation in the observed CFR: the age-structure of diagnosed infection cases and age-specific case-fatality rates. We use data on diagnosed COVID-19 cases and death counts attributable to COVID-19 by age for China, Germany, Italy, South Korea, Spain, the United States, and New York City. We calculate the CFR for each population at the latest data point and also for Italy, Germany, Spain, and New York City over time. We use demographic decomposition to break the difference between CFRs into unique contributions arising from the age-structure of confirmed cases and the age-specific case-fatality. In late June 2020, CFRs varied from 2.2% in South Korea to 14.0% in Italy. The age-structure of detected cases often explains more than two-thirds of cross-country variation in the CFR. In Italy, the CFR increased from 4.2% to 14.0% between March 9 and June 30, 2020, and more than 90% of the change was due to increasing age-specific case-fatality rates. The importance of the age-structure of confirmed cases likely reflects several factors, including different testing regimes and differences in transmission trajectories; while increasing age-specific case-fatality rates in Italy could indicate other factors, such as the worsening health outcomes of those infected with COVID-19. Our findings lend support to recommendations for data to be disaggregated by age, and potentially other variables, to facilitate a better understanding of population-level differences in CFRs. They also show the need for well-designed seroprevalence studies to ascertain the extent to which differences in testing regimes drive differences in the age-structure of detected cases. [ABSTRACT FROM AUTHOR]

Background: Health, as defined by the WHO, is a multidimensional concept that includes different aspects. Interest in the health conditions of the oldest-old has increased as a consequence of the phenomenon of population aging. This study investigates whether (1) it is possible to identify health profiles among the oldest-old, taking into account physical, emotional and psychological information about health, and (2) there are demographic and socioeconomic differences among the health profiles.Methods: Latent Class Analysis with covariates was applied to the Mugello Study data to identify health profiles among the 504 nonagenarians residing in the Mugello district (Tuscany, Italy) and to evaluate the association between socioeconomic characteristics and the health profiles resulting from the analysis.Results: This study highlights four groups labeled according to the posterior probability of determining a certain health characteristic: "healthy", "physically healthy with cognitive impairment", "unhealthy", and "severely unhealthy". Some demographic and socioeconomic characteristics were found to be associated with the final groups: older nonagenarians are more likely to be in worse health conditions; men are in general healthier than women; more educated individuals are less likely to be in extremely poor health conditions, while the lowest-educated are more likely to be cognitively impaired; and office or intellectual workers are less likely to be in poor health conditions than are farmers.Conclusions: Considering multiple dimensions of health to determine health profiles among the oldest-old could help to better evaluate their care needs according to their health status. [ABSTRACT FROM AUTHOR]

Objective: To quantify inequalities in lifespan across multiple social determinants of health, how they act in tandem with one another, and to create a scoring system that can accurately identify subgroups of the population at high risk of mortality., Design: Comparison of life tables across 54 subpopulations defined by combinations of four social determinants of health: sex, marital status, education and race, using data from the Multiple Cause of Death dataset and the American Community Survey., Setting: United States, 2015-2019., Main Outcome Measures: We compared the partial life expectancies (PLEs) between age 30 and 90 years of all subpopulations. We also developed a scoring system to identify subgroups at high risk of mortality., Results: There is an 18.0-year difference between the subpopulations with the lowest and highest PLE. Differences in PLE between subpopulations are not significant in most pairwise comparisons. We visually illustrate how the PLE changes across social determinants of health. There is a complex interaction among social determinants of health, with no single determinant fully explaining the observed variation in lifespan. The proposed scoring system adds clarification to this interaction by yielding a single score that can be used to identify subgroups that might be at high risk of mortality. A similar scoring system by cause of death was also created to identify which subgroups could be considered at high risk of mortality from specific causes. Even if subgroups have similar mortality levels, they are often subject to different cause-specific mortality risks., Conclusions: Having one characteristic associated with higher mortality is often not sufficient to be considered at high risk of mortality, but the risk increases with the number of such characteristics. Reducing inequalities is vital for societies, and better identifying individuals and subgroups at high risk of mortality is necessary for public health policy., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)