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The Gompertz force of mortality in terms of the modal age at death

Author

Listed:
  • Trifon Missov

    (Syddansk Universitet)

  • Adam Lenart

    (Syddansk Universitet)

  • Laszlo Nemeth

    (Max-Planck-Institut für Demografische Forschung)

  • Vladimir Canudas-Romo

    (Australian National University)

  • James W. Vaupel

    (Syddansk Universitet)

Abstract

Background: The Gompertz force of mortality (hazard function) is usually expressed in terms of a, the initial level of mortality, and b, the rate at which mortality increases with age. Objective: We express the Gompertz force of mortality in terms of b and the old-age modal age at death M, and present similar relationships for other widely-used mortality models. Our objective is to explain the advantages of using the parameterization in terms of M. Methods: Using relationships among life table functions at the modal age at death, we express the Gompertz force of mortality as a function of the old-age mode. We estimate the correlation between the estimators of old (a and b) and new (M and b) parameters from simulated data. Results: When the Gompertz parameters are statistically estimated from simulated data, the correlation between estimated values of b and M is much less than the correlation between estimated values of a and b. For the populations in the Human Mortality Database, there is a negative association between a and b and a positive association between M and b. Conclusions: Using M, the old-age mode, instead of a, the level of mortality at the starting age, has two major advantages. First, statistical estimation is facilitated by the lower correlation between the estimators of model parameters. Second, estimated values of M are more easily comprehended and interpreted than estimated values of a.

Suggested Citation

  • 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.
  • Handle: RePEc:dem:demres:v:32:y:2015:i:36
    DOI: 10.4054/DemRes.2015.32.36
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    References listed on IDEAS

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    Cited by:

    1. Lucia Zanotto & Vladimir Canudas-Romo & Stefano Mazzuco, 2021. "A Mixture-Function Mortality Model: Illustration of the Evolution of Premature Mortality," European Journal of Population, Springer;European Association for Population Studies, vol. 37(1), pages 1-27, March.
    2. Hartemink, Nienke & Missov, Trifon I. & Caswell, Hal, 2017. "Stochasticity, heterogeneity, and variance in longevity in human populations," Theoretical Population Biology, Elsevier, vol. 114(C), pages 107-116.
    3. Alois Pichler & Dana Uhlig, 2021. "Mortality in Germany during the Covid-19 pandemic," Papers 2107.12899, arXiv.org, revised Apr 2022.
    4. 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.
    5. Jonas Šiaulys & Rokas Puišys, 2022. "Survival with Random Effect," Mathematics, MDPI, vol. 10(7), pages 1-17, March.
    6. Casey Breen & Joshua R. Goldstein, 2022. "Berkeley Unified Numident Mortality Database: Public administrative records for individual-level mortality research," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 47(5), pages 111-142.
    7. Alois Pichler & Dana Uhlig, 2023. "Mortality in Germany during the COVID-19 Pandemic," IJERPH, MDPI, vol. 20(20), pages 1-11, October.
    8. Ugofilippo Basellini & Vladimir Canudas-Romo & Adam Lenart, 2019. "Location–Scale Models in Demography: A Useful Re-parameterization of Mortality Models," European Journal of Population, Springer;European Association for Population Studies, vol. 35(4), pages 645-673, October.
    9. Joel E. Cohen & Christina Bohk-Ewald & Roland Rau, 2018. "Gompertz, Makeham, and Siler models explain Taylor's law in human mortality data," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 38(29), pages 773-842.
    10. 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.
    11. Alexander, Monica, 2018. "Deaths without denominators: using a matched dataset to study mortality patterns in the United States," SocArXiv q79ye, Center for Open Science.
    12. María-Dolores Huete-Morales & Esteban Navarrete-Álvarez & María-Jesús Rosales-Moreno & María-José Del-Moral-Ávila & José-Manuel Quesada-Rubio, 2020. "Modelling the survival function of the Spanish population by the Wong–Tsui model with the incorporation of frailty and covariates," Letters in Spatial and Resource Sciences, Springer, vol. 13(2), pages 151-163, August.
    13. Søren Kjærgaard & Vladimir Canudas-Romo, 2017. "Potential support ratios: Cohort versus period perspectives," Population Studies, Taylor & Francis Journals, vol. 71(2), pages 171-186, May.
    14. Lindholm, Mathias, 2017. "A note on the connection between some classical mortality laws and proportional frailty," Statistics & Probability Letters, Elsevier, vol. 126(C), pages 76-82.
    15. Marie-Pier Bergeron-Boucher & Marcus Ebeling & Vladimir Canudas-Romo, 2015. "Decomposing changes in life expectancy: Compression versus shifting mortality," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 33(14), pages 391-424.
    16. Breen, Casey & Goldstein, Joshua R., 2022. "Berkeley Unified Numident Mortality Database: Public Administrative Records for Individual-Level Mortality Research," SocArXiv pc294, Center for Open Science.
    17. Nico Keilman & Dinh Q. Pham & Astri Syse, 2018. "Mortality shifts and mortality compression. The case of Norway, 1900-2060," Discussion Papers 884, Statistics Norway, Research Department.
    18. Alfredo Omar Palafox-Roca, 2023. "Consumo óptimo en el retiro con diferentes leyes de mortalidad," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 18(3), pages 1-30, Julio - S.

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    More about this item

    Keywords

    modal age at death; Gompertz force of mortality; Gumbel distribution; parameter correlation; maximum likelihood;
    All these keywords.

    JEL classification:

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

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