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The compression of deaths above the mode

Author

Listed:
  • A. Roger Thatcher

    (Office for National Statistics)

  • Siu Lan Karen Cheung

    (University of Hong Kong)

  • Shiro Horiuchi

    (City University of New York)

  • Jean-Marie Robine

    (Institut National de la Santé et de la Recherche Médicale (INSERM))

Abstract

Kannisto (2001) has shown that as the frequency distribution of ages at death has shifted to the right, the age distribution of deaths above the modal age has become more compressed. In order to further investigate this old-age mortality compression, we adopt the simple logistic model with two parameters, which is known to fit data on old-age mortality well (Thatcher 1999). Based on the model, we show that three key measures of old-age mortality (the modal age of adult deaths, the life expectancy at the modal age, and the standard deviation of ages at death above the mode) can be estimated fairly accurately from death rates at only two suitably chosen high ages (70 and 90 in this study). The distribution of deaths above the modal age becomes compressed when the logits of death rates fall more at the lower age than at the higher age. Our analysis of mortality time series in six countries, using the logistic model, endorsed Kannisto’s conclusion. Some possible reasons for the compression are discussed.

Suggested Citation

  • A. Roger Thatcher & Siu Lan Karen Cheung & Shiro Horiuchi & Jean-Marie Robine, 2010. "The compression of deaths above the mode," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 22(17), pages 505-538.
  • Handle: RePEc:dem:demres:v:22:y:2010:i:17
    DOI: 10.4054/DemRes.2010.22.17
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    References listed on IDEAS

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    1. Siu Lan Karen Cheung & Jean-Marie Robine & Fred Paccaud & Alfio Marazzi, 2009. "Dissecting the compression of mortality in Switzerland, 1876-2005," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 21(19), pages 569-598.
    2. repec:cai:popine:popu_p2001_13n1_0171 is not listed on IDEAS
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    Cited by:

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    2. Viorela Diaconu & Nadine Ouellette & Robert Bourbeau, 2020. "Modal lifespan and disparity at older ages by leading causes of death: a Canada-U.S. comparison," Journal of Population Research, Springer, vol. 37(4), pages 323-344, December.
    3. Jean-Marie Robine & Siu Lan Karen Cheung & Shiro Horiuchi, 2010. "Arthur Roger Thatcher's contributions to longevity research," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 22(18), pages 539-548.
    4. Viorela Diaconu & Nadine Ouellette & Carlo Giovanni Camarda & Robert Bourbeau, 2016. "Insight on 'typical' longevity: An analysis of the modal lifespan by leading causes of death in Canada," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 35(17), pages 471-504.
    5. Matthias Börger & Martin Genz & Jochen Ruß, 2018. "Extension, Compression, and Beyond: A Unique Classification System for Mortality Evolution Patterns," Demography, Springer;Population Association of America (PAA), vol. 55(4), pages 1343-1361, August.
    6. Magdalena Muszyńska, 2012. "Zróżnicowanie długości trwania życia w Polsce," Collegium of Economic Analysis Annals, Warsaw School of Economics, Collegium of Economic Analysis, issue 28, pages 85-96.
    7. Ainhoa-Elena Léger & Stefano Mazzuco, 2021. "What Can We Learn from the Functional Clustering of Mortality Data? An Application to the Human Mortality Database," European Journal of Population, Springer;European Association for Population Studies, vol. 37(4), pages 769-798, November.
    8. Dustin Brown & Mark Hayward & Jennifer Montez & Robert Hummer & Chi-Tsun Chiu & Mira Hidajat, 2012. "The Significance of Education for Mortality Compression in the United States," Demography, Springer;Population Association of America (PAA), vol. 49(3), pages 819-840, August.
    9. Viorela Diaconu & Alyson van Raalte & Pekka Martikainen, 2022. "Why we should monitor disparities in old-age mortality with the modal age at death," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-13, February.
    10. Alyson Raalte & Hal Caswell, 2013. "Perturbation Analysis of Indices of Lifespan Variability," Demography, Springer;Population Association of America (PAA), vol. 50(5), pages 1615-1640, October.
    11. Alyson A. van Raalte & Pekka Martikainen & Mikko Myrskylä, 2012. "Lifespan variation by occupational class: compression or stagnation over time?," MPIDR Working Papers WP-2012-010, Max Planck Institute for Demographic Research, Rostock, Germany.
    12. Trifon Missov & Adam Lenart & Laszlo Nemeth & Vladimir Canudas-Romo & James W. Vaupel, 2015. "The Gompertz force of mortality in terms of the modal age at death," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 32(36), pages 1031-1048.
    13. Alberto Palloni & Laeticia Souza, 2013. "The fragility of the future and the tug of the past," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 29(21), pages 543-578.
    14. Mikhail A. Maksimov, 2019. "Dynamics of modal age at death in 1959−2014 in Russia," Population and Economics, ARPHA Platform, vol. 3(4), pages 86-96, December.
    15. Konstantinos N. Zafeiris, 2023. "Greece since the 1960s: the mortality transition revisited: a joinpoint regression analysis," Journal of Population Research, Springer, vol. 40(1), pages 1-31, March.
    16. Isaac Sasson, 2016. "Trends in Life Expectancy and Lifespan Variation by Educational Attainment: United States, 1990–2010," Demography, Springer;Population Association of America (PAA), vol. 53(2), pages 269-293, April.
    17. Paola Vazquez-Castillo & Marie-Pier Bergeron-Boucher & Trifon Missov, 2024. "Longevity à la mode: A discretized derivative tests method for accurate estimation of the adult modal age at death," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 50(11), pages 325-346.
    18. Basellini, Ugofilippo & Kjærgaard, Søren & Camarda, Carlo Giovanni, 2020. "An age-at-death distribution approach to forecast cohort mortality," Insurance: Mathematics and Economics, Elsevier, vol. 91(C), pages 129-143.

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    More about this item

    Keywords

    compression of mortality; logistic model; modal age of death; standard deviation; oldest old mortality decline; Lexis model;
    All these keywords.

    JEL classification:

    • J1 - Labor and Demographic Economics - - Demographic Economics
    • Z0 - Other Special Topics - - General

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