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      Lifespan Dispersion in Times of Life Expectancy Fluctuation: The Case of Central and Eastern Europe

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          Abstract

          Central and Eastern Europe (CEE) have experienced considerable instability in mortality since the 1960s. Long periods of stagnating life expectancy were followed by rapid increases in life expectancy and, in some cases, even more rapid declines, before more recent periods of improvement. These trends have been well documented, but to date, no study has comprehensively explored trends in lifespan variation. We improved such analyses by incorporating life disparity as a health indicator alongside life expectancy, examining trends since the 1960s for 12 countries from the region. Generally, life disparity was high and fluctuated strongly over the period. For nearly 30 of these years, life expectancy and life disparity varied independently of each other, largely because mortality trends ran in opposite directions over different ages. Furthermore, we quantified the impact of large classes of diseases on life disparity trends since 1994 using a newly harmonized cause-of-death time series for eight countries in the region. Mortality patterns in CEE countries were heterogeneous and ran counter to the common patterns observed in most developed countries. They contribute to the discussion about life expectancy disparity by showing that expansion/compression levels do not necessarily mean lower/higher life expectancy or mortality deterioration/improvements.

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          The online version of this article (10.1007/s13524-018-0729-9) contains supplementary material, which is available to authorized users.

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          Evaluating Random Forests for Survival Analysis using Prediction Error Curves.

          Prediction error curves are increasingly used to assess and compare predictions in survival analysis. This article surveys the R package pec which provides a set of functions for efficient computation of prediction error curves. The software implements inverse probability of censoring weights to deal with right censored data and several variants of cross-validation to deal with the apparent error problem. In principle, all kinds of prediction models can be assessed, and the package readily supports most traditional regression modeling strategies, like Cox regression or additive hazard regression, as well as state of the art machine learning methods such as random forests, a nonparametric method which provides promising alternatives to traditional strategies in low and high-dimensional settings. We show how the functionality of pec can be extended to yet unsupported prediction models. As an example, we implement support for random forest prediction models based on the R-packages randomSurvivalForest and party. Using data of the Copenhagen Stroke Study we use pec to compare random forests to a Cox regression model derived from stepwise variable selection. Reproducible results on the user level are given for publicly available data from the German breast cancer study group.
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            The relationship of average volume of alcohol consumption and patterns of drinking to burden of disease: an overview.

            As part of a larger study to estimate the global burden of disease attributable to alcohol: to quantify the relationships between average volume of alcohol consumption, patterns of drinking and disease and injury outcomes, and to combine exposure and risk estimates to determine regional and global alcohol-attributable fractions (AAFs) for major disease and injury categories. DESIGN, METHODS, SETTING: Systematic literature reviews were used to select diseases related to alcohol consumption. Meta-analyses of the relationship between alcohol consumption and disease and multi-level analyses of aggregate data to fill alcohol-disease relationships not currently covered by individual-level data were used to determine the risk relationships between alcohol and disease. AAFs were estimated as a function of prevalence of exposure and relative risk, or from combining the aggregate multi-level analyses with prevalence data. Average volume of alcohol consumption was found to increase risk for the following major chronic diseases: mouth and oropharyngeal cancer; oesophageal cancer; liver cancer; breast cancer; unipolar major depression; epilepsy; alcohol use disorders; hypertensive disease; hemorrhagic stroke; and cirrhosis of the liver. Coronary heart disease (CHD), unintentional and intentional injuries were found to depend on patterns of drinking in addition to average volume of alcohol consumption. Most effects of alcohol on disease were detrimental, but for certain patterns of drinking, a beneficial influence on CHD, stroke and diabetes mellitus was observed. Alcohol is related to many major disease outcomes, mainly in a detrimental fashion. While average volume of consumption was related to all disease and injury categories under consideration, pattern of drinking was found to be an additional influencing factor for CHD and injury. The influence of patterns of drinking may be underestimated because pattern measures have not been included in many epidemiologic studies. Generalizability of the results is limited by methodological problems of the underlying studies used in the present analyses. Future studies need to address these methodological issues in order to obtain more accurate risk estimates.
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              Rectangularization revisited: variability of age at death within human populations.

              Rectangularization of human survival curves is associated with decreasing variability in the distribution of ages at death. This variability, as measured by the interquartile range of life table ages at death, has decreased from about 65 years to 15 years since 1751 in Sweden. Most of this decline occurred between the 1870s and the 1950s. Since then, variability in age at death has been nearly constant in Sweden, Japan, and the United States, defying predictions of a continuing rectangularization. The United States is characterized by a relatively high degree of variability, compared with both Sweden and Japan. We suggest that the historical compression of mortality may have had significant psychological and behavioral impacts.
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                Author and article information

                Contributors
                jmaburto@sdu.dk
                vanRaalte@demogr.mpg.de
                Journal
                Demography
                Demography
                Demography
                Springer US (New York )
                0070-3370
                1533-7790
                12 November 2018
                12 November 2018
                December 2018
                : 55
                : 6
                : 2071-2096
                Affiliations
                [1 ]ISNI 0000 0001 0728 0170, GRID grid.10825.3e, Interdisciplinary Center on Population Dynamics, , University of Southern Denmark, ; Odense, Denmark
                [2 ]ISNI 0000 0001 2033 8007, GRID grid.419511.9, Max Planck Institute for Demographic Research, ; Rostock, Germany
                Article
                729
                10.1007/s13524-018-0729-9
                6290692
                30519844
                bf8e85e0-210c-44f1-a746-1c5590890059
                © The Author(s) 2018

                Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                Funding
                Funded by: Max Planck Institute for Demographic Research
                Categories
                Article
                Custom metadata
                © Population Association of America 2018

                Sociology
                causes of death,alcohol consumption,mortality,health inequalities,decomposition techniques

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