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

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  • Saul Justin Newman

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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  • Saul Justin Newman, 2018. "Errors as a primary cause of late-life mortality deceleration and plateaus," PLOS Biology, Public Library of Science, vol. 16(12), pages 1-12, December.
    • Handle: RePEc:plo:pbio00:2006776
    DOI: 10.1371/journal.pbio.2006776
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    References listed on IDEAS

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    1. Dan A. Black & Yu-Chieh Hsu & Seth G. Sanders & Lynne Steuerle Schofield & Lowell J. Taylor, 2017. "The Methuselah Effect: The Pernicious Impact of Unreported Deaths on Old-Age Mortality Estimates," Demography, Springer;Population Association of America (PAA), vol. 54(6), pages 2001-2024, December.
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    2. Seaman, Rosie & Riffe, Tim & Leyland, Alastair H. & Popham, Frank & van Raalte, Alyson, 2019. "The increasing lifespan variation gradient by area-level deprivation: A decomposition analysis of Scotland 1981–2011," Social Science & Medicine, Elsevier, vol. 230(C), pages 147-157.
    3. Li, Hong & Tan, Ken Seng & Tuljapurkar, Shripad & Zhu, Wenjun, 2021. "Gompertz law revisited: Forecasting mortality with a multi-factor exponential model," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 268-281.
    4. Linh Hoang Khanh Dang & Carlo Giovanni Camarda & France Meslé & Nadine Ouellette & Jean-Marie Robine & Jacques Vallin, 2023. "The question of the human mortality plateau: Contrasting insights by longevity pioneers," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 48(11), pages 321-338.
    5. Cássio M. Turra & Fernando Fernandes & Júlia Almeida Calazans & Marília R. Nepomuceno, 2023. "Age reporting for the oldest old in the Brazilian COVID-19 vaccination database: What can we learn from it?," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 48(28), pages 829-848.
    6. Camarda, Carlo Giovanni, 2022. "The curse of the plateau. Measuring confidence in human mortality estimates at extreme ages," Theoretical Population Biology, Elsevier, vol. 144(C), pages 24-36.
    7. Saul Justin Newman, 2018. "Plane inclinations: A critique of hypothesis and model choice in Barbi et al," PLOS Biology, Public Library of Science, vol. 16(12), pages 1-4, December.

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