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Shaping human mortality patterns through intrinsic and extrinsic vitality processes

Author

Listed:
  • Ting Li

    (Renmin University of China)

  • James Anderson

    (University of Washington)

Abstract

Background: While historical declines in human mortality are clearly shaped by lifestyle and environmental improvements, modeling patterns is difficult because intrinsic and extrinsic processes shape mortality through complex stochastic interactions. Objective: To develop a stochastic model describing intrinsic and extrinsic mortality rates and quantify historical mortality trends in terms of parameters describing the rates. Methods: Based on vitality, a stochastic age-declining measure of survival capacity, extrinsic mortality occurs when an extrinsic challenge exceeds the remaining vitality and intrinsic mortality occurs with the complete loss of vitality by aging. Total mortality depends on the stochastic loss rate of vitality and the magnitude and frequency of extrinsic challenges. Parameters are estimated using maximum likelihood. Results: Fitting the model to two centuries of adult Swedish period data, intrinsic mortality dominated in old age and gradually declined over years. Extrinsic mortality increased with age and exhibited step-like decline over years driven by high-magnitude, low-frequency challenges in the 19th century and low-magnitude high-frequency challenges in the 20th century. Conclusions: The Swedish mortality was driven by asynchronous intrinsic and extrinsic processes, coinciding with well-known epidemiological patterns involving lifestyle and health care. Because the processes are largely independent, predicting future mortality requires projecting trends of both processes. Comments: The model merges point-of-view and classical hazard rate mortality models and yields insights not available from either model individually. To obtain a closed form the intrinsic-extrinsic interactions were simplified, resulting in biased, but correctable, parameters estimates.

Suggested Citation

  • Ting Li & James Anderson, 2013. "Shaping human mortality patterns through intrinsic and extrinsic vitality processes," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 28(12), pages 341-372.
  • Handle: RePEc:dem:demres:v:28:y:2013:i:12
    DOI: 10.4054/DemRes.2013.28.12
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    References listed on IDEAS

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    Cited by:

    1. Cha, Ji Hwan & Finkelstein, Maxim, 2016. "Justifying the Gompertz curve of mortality via the generalized Polya process of shocks," Theoretical Population Biology, Elsevier, vol. 109(C), pages 54-62.
    2. David J. Sharrow & James J. Anderson, 2016. "Quantifying Intrinsic and Extrinsic Contributions to Human Longevity: Application of a Two-Process Vitality Model to the Human Mortality Database," Demography, Springer;Population Association of America (PAA), vol. 53(6), pages 2105-2119, December.
    3. Xiaobai Zhu & Kenneth Q. Zhou & Zijia Wang, 2024. "A new paradigm of mortality modeling via individual vitality dynamics," Papers 2407.15388, arXiv.org, revised Oct 2024.
    4. Ting Li & Yang Yang & James Anderson, 2013. "Mortality Increase in Late-Middle and Early-Old Age: Heterogeneity in Death Processes as a New Explanation," Demography, Springer;Population Association of America (PAA), vol. 50(5), pages 1563-1591, October.
    5. Carlo Maccheroni & Samuel Nocito, 2017. "Backtesting the Lee–Carter and the Cairns–Blake–Dowd Stochastic Mortality Models on Italian Death Rates," Risks, MDPI, vol. 5(3), pages 1-23, July.
    6. Ji Cha & Maxim Finkelstein, 2015. "A dynamic stress–strength model with stochastically decreasing strength," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 78(7), pages 807-827, October.

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    More about this item

    Keywords

    stochastic processes; intrinsic mortality; vitality; extrinsic mortality; adult mortality; mortality;
    All these keywords.

    JEL classification:

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

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