Seasonal mortality in Denmark

Our paper addresses two questions on seasonal mortality: How do women and men differ with respect to seasonal fluctuations in mortality? How does seasonality in death change with age? The analysis is based on a sample of all Danes aged 50 and older on 1 April 1968 being followed for 30 years. In contrast to previous studies we found remarkable differences between women and men in their seasonal mortality patterns. Men showed larger seasonal fluctuations than women indicating a higher susceptibility to environmental stressful periods. We found that seasonality increases with age. However, we discovered again a sex difference: women's seasonality starts increasing at later ages than men's.age effect, mortality, seasonal fluctuations, sex differentials

10 years after Kannisto: further evidence for mortality decline at advanced ages in developed countries

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Beyond the Kannisto-Thatcher Database on Old Age Mortality: an assessment of data quality at advanced ages

The old age population in developed countries has been increasing remarkably, yet internationally comparable high quality data on oldest-old mortality remain relatively scarce. The Kannisto-Thatcher Old Age Mortality Database (KTD) is a unique source providing uniformly recalculated old-age mortality data for 35 countries. Our study addresses a number of data quality issues relevant to population and death statistics at the most advanced ages. Following previous studies by Väinö Kannisto, we apply the same set of measures. This allows us to identify dubious or irregular mortality patterns. Deviations such as this often suggest that the data quality has serious problems. We update previously published findings by extending the analyses made so far to thirty five countries and by adding data on longer historical periods. In addition, we propose a systematic classification of country- and period-specific data, thus simultaneously accounting for each indicator of data quality. We apply conventional procedures of hierarchical cluster analysis to distinguish four data quality clusters (best data quality, acceptable data quality, conditionally acceptable quality, and weak quality). We show that the reliability of old-age mortality estimates has been improving in time. However, the mortality indicators for the most advanced ages of a number of countries, such as Chile, Canada, and the USA should be treated with caution even for the most recent decade. Canada, Ireland, Finland, Lithuania, New Zealand (Non-Maori), Norway, Portugal, Spain, and the USA have particular problems in their historical data series. After having compared the KTD with official data, we conclude that the methods used for extinct and almost extinct generations produce more accurate population estimates than those published by national statistical offices. The most reliable official data come from the countries with fully functioning population registers.World, data evaluation, mortality, old age

Visualizing Mortality Dynamics in the Lexis Diagram

The goal of this book is simple: We would like to show how mortality dynamics can be visualized in the so-called Lexis diagram. To appeal to as many potential readers as possible, we do not require any specialist knowledge. This approach may be disappointing: Demographers may have liked more information about the mathematical underpinnings of population dynamics on the Lexis surface as demonstrated, for instance, by Arthur and Vaupel in 1984. Statisticians would have probably preferred more information about the underlying smoothing methods that were used. Epidemiologists likewise might miss discussions about the etiology of diseases. Sociologists would have probably expected that our results were more embedded into theoretical frameworks...

Preparing for the future : the changing demographic composition of hospital patients in Denmark between 2013 and 2050

Funding: Both grants – 2P01AG031719, US National Institute of Health and ”On the edge of societies: Vulnerable populations, emerging challenges for social policies and future demands for social innovation. The experience of the Baltic Sea States (2016-2021)” – were received by Prof. James W. Vaupel, Interdisciplinary Centre on Population Dynamics, University of Southern Denmark (https://portal.findresearcher.sdu.dk/en/persons/jvaupel), who is not a co-author on this paper. The experience of the Baltic Sea States (2016-2021) provided by the Max Planck Society has no number.Background Population aging will pose huge challenges for healthcare systems and will require a promotion of positive attitudes towards older people and the encouragement of careers in geriatrics to attract young professionals into the field and to meet the needs of a rapidly growing number of old-aged patients. We describe the current demographic profile of hospital care use in Denmark and make projections for changes in the patient profile up to 2050. Methods The Danish population in 2013 (N = 5.63 million) was followed up for inpatient and emergency admissions recorded in Danish hospitals in 2013 using population-based registers. We combined age- and sex-specific hospital care use in 2013 with official population estimates to forecast the profile of hospital days up to 2050 with respect to age and sex. Results The total number of hospital days per year is projected to increase by 42% between 2013 and 2050, from 4.66 to 6.72 million days. While small changes are projected for the population aged 0–69, the largest change is projected to occur for the population aged 70+. The 2013 levels were 0.82 and 0.93 million days for men and women aged 70+, respectively. By 2050, these levels are projected to have reached 1.94 and 1.84 million days. While the population aged 70+ accounted for 37.5% of all days in 2013, its contribution is projected to increase to 56.2% by 2050. Conclusion Our study shows one possible scenario for changes in the hospital days due to population aging by 2050: Assuming no changes in hospital care use over the forecast period, the absolute contribution of individuals aged 70+ to the total hospital days will more than double, and the relative contribution of persons aged 70+ will account for nearly 60% of all hospital days by 2050, being largest among men.Publisher PDFPeer reviewe

Sex differences in the 1-year risk of dying following all-cause and cause-specific hospital admission after age 50 in comparison with a general and non-hospitalised population : a register-based cohort study of the Danish population

Funding: The work was supported by the US National Institute of Health (P01AG031719, R01AG026786, and 2P01AG031719), the VELUX Foundation and the Max Planck Society within the framework of the project “On the edge of societies: New vulnerable populations, emerging challenges for social policies and future demands for social innovation. The experience of the Baltic Sea States (2016-2021)”.Objectives  We examine the mortality of men and women within the first year after all-cause and cause-specific hospital admission to investigate whether the sex differences in mortality after hospitalisation are higher than in the corresponding general and non-hospitalised population. Design  This is a population-based, longitudinal study with nationwide coverage. The study population was identified by linking the National Patient Register with the Central Population Register using a 5% random sample of the Danish population. Setting  The population born between 1898 and 1961, who was alive and residing in Denmark after 1977, was followed up between 1977 and 2011 with respect to hospital admissions and mortality while aged 50–79. Primary outcome measures  The absolute sex differences in the 1-year risk of dying after all-cause and cause-specific hospital admission. The hospitalised population sex differentials were then compared with the sex differences in a general and a non-hospitalised population, randomly matched by age, sex and hospitalisation status. Results  The risk of dying was consistently higher for hospitalised men and women. At all ages, the absolute sex differences in mortality were largest in the hospitalised population, were smaller in the general population and were smallest in the non-hospitalised population. This pattern was consistent across all-cause admissions, and with respect to admissions for neoplasms, circulatory diseases and respiratory diseases. For all-cause hospital admissions, absolute sex differences in the 1-year risk of dying resulted in 43.8 excess male deaths per 1,000 individuals within the age range 50–79, while the levels were lower in the general and the non-hospitalised population, at levels of 13.5 and 6.6, respectively. Conclusions  This study indicates a larger male disadvantage in mortality following hospitalisation, pointing towards an association between the health status of a population and the magnitude of the female advantage in mortality.Publisher PDFPeer reviewe