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      Errors as a primary cause of late-life mortality deceleration and plateaus

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      PLoS Biology
      Public Library of Science

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          Abstract

          Several organisms, including humans, display a deceleration in mortality rates at advanced ages. This mortality deceleration is sufficiently rapid to allow late-life mortality to plateau in old age in several species, causing the apparent cessation of biological ageing. Here, it is shown that late-life mortality deceleration (LLMD) and late-life plateaus are caused by common demographic errors. Age estimation and cohort blending errors introduced at rates below 1 in 10,000 are sufficient to cause LLMD and plateaus. In humans, observed error rates of birth and death registration predict the magnitude of LLMD. Correction for these sources of demographic error using a mixed linear model eliminates LLMD and late-life mortality plateaus (LLMPs) without recourse to biological or evolutionary models. These results suggest models developed to explain LLMD have been fitted to an error distribution, that ageing does not slow or stop during old age in humans, and that there is a finite limit to human longevity.

          Author summary

          In diverse species, mortality rates increase with age at a relatively fixed rate within populations. However, recent discoveries have suggested this relationship breaks down in advanced old age, with mortality rate increases slowing and even reaching a plateau. This late-life mortality deceleration has initiated sustained debate on the cause of late-life deceleration and plateaus. Proposed explanations include evolutionary patterns, the exhaustion of selective pressure, population heterogeneity, and even the cessation of the ageing process. Here, I demonstrate that apparent late-life mortality decelerations and plateaus can be generated by low-frequency errors. I then reveal how indicators of demographic data quality predict the magnitude of late-life mortality deceleration and the existence of late-life plateaus in human populations. These findings suggest that human late-life mortality plateaus are largely, if not entirely, artefacts of error processes. As a result, late-life mortality plateaus and decelerations may be explained by error patterns in humans and many other species without invoking complex biological, heterogeneity, or evolutionary models. This finding has immediate consequences for demographic modelling, evolutionary biology, and the projected upper limits of human and nonhuman life.

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          Most cited references25

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          Diversity of ageing across the tree of life.

          Evolution drives, and is driven by, demography. A genotype moulds its phenotype's age patterns of mortality and fertility in an environment; these two patterns in turn determine the genotype's fitness in that environment. Hence, to understand the evolution of ageing, age patterns of mortality and reproduction need to be compared for species across the tree of life. However, few studies have done so and only for a limited range of taxa. Here we contrast standardized patterns over age for 11 mammals, 12 other vertebrates, 10 invertebrates, 12 vascular plants and a green alga. Although it has been predicted that evolution should inevitably lead to increasing mortality and declining fertility with age after maturity, there is great variation among these species, including increasing, constant, decreasing, humped and bowed trajectories for both long- and short-lived species. This diversity challenges theoreticians to develop broader perspectives on the evolution of ageing and empiricists to study the demography of more species.
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            Biodemographic trajectories of longevity.

            Old-age survival has increased substantially since 1950. Death rates decelerate with age for insects, worms, and yeast, as well as humans. This evidence of extended postreproductive survival is puzzling. Three biodemographic insights--concerning the correlation of death rates across age, individual differences in survival chances, and induced alterations in age patterns of fertility and mortality--offer clues and suggest research on the failure of complicated systems, on new demographic equations for evolutionary theory, and on fertility-longevity interactions. Nongenetic changes account for increases in human life-spans to date. Explication of these causes and the genetic license for extended survival, as well as discovery of genes and other survival attributes affecting longevity, will lead to even longer lives.
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              • Article: not found

              The plateau of human mortality: Demography of longevity pioneers

              Theories about biological limits to lifespan and evolutionary shaping of human longevity depend on facts about mortality at extreme ages. The facts have remained in dispute. Do hazard curves typically ultimately level out into high plateaus, as seen in other species, or do exponential increases go on and on? Here we estimate hazard rates from data on all Italian inhabitants aged over 105 between 2009 and 2015 (born 1896–1910), 3836 carefully documented cases. We find level hazard curves, essentially constant beyond age 105. The estimates are free from artifacts of aggregation limiting earlier studies and provide the best evidence so far for the existence of extreme-age mortality plateaus in humans. Above age 105 human mortality appears constant over age at levels that are slowly declining across cohorts.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Academic Editor
                Journal
                PLoS Biol
                PLoS Biol
                plos
                plosbiol
                PLoS Biology
                Public Library of Science (San Francisco, CA USA )
                1544-9173
                1545-7885
                20 December 2018
                December 2018
                20 December 2018
                : 16
                : 12
                : e2006776
                Affiliations
                [001]Research School of Biology, The Australian National University, Acton, ACT, Australia
                Charité—Universitätsmedizin Berlin, Germany
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0001-9841-1518
                Article
                pbio.2006776
                10.1371/journal.pbio.2006776
                6301557
                30571676
                8bdf04eb-48e6-4c88-98f9-dd65b38d4d96
                © 2018 Saul Justin Newman

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 1 June 2018
                : 19 November 2018
                Page count
                Figures: 4, Tables: 0, Pages: 12
                Funding
                The author(s) received no specific funding for this work.
                Categories
                Short Reports
                Biology and Life Sciences
                Population Biology
                Population Metrics
                Death Rates
                Biology and Life Sciences
                Developmental Biology
                Organism Development
                Aging
                Biology and Life Sciences
                Physiology
                Physiological Processes
                Aging
                Medicine and Health Sciences
                Physiology
                Physiological Processes
                Aging
                Biology and Life Sciences
                Evolutionary Biology
                Biology and Life Sciences
                Population Biology
                Population Metrics
                Life Expectancy
                Medicine and Health Sciences
                Public and Occupational Health
                Life Expectancy
                Biology and Life Sciences
                Ecology
                Ecological Metrics
                Species Diversity
                Ecology and Environmental Sciences
                Ecology
                Ecological Metrics
                Species Diversity
                Research and Analysis Methods
                Research Design
                Cohort Studies
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Statistical Methods
                Forecasting
                Physical Sciences
                Mathematics
                Statistics
                Statistical Methods
                Forecasting
                Biology and Life Sciences
                Population Biology
                Population Metrics
                Birth Rates
                Custom metadata
                All data are available publicly from the World Population Prospects report and in the WPP2017 R package ( https://cran.r-project.org/web/packages/wpp2017/index.html), from the Human Mortality Database ( http://www.mortality.org), and via code embedded in the supplementary materials.

                Life sciences
                Life sciences

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