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Mortality Modeling Perspectives

In: Recent Advances in Reliability and Quality in Design

Author

Listed:
  • Hoang Pham

    (Rutgers University)

Abstract

As the human lifespan increases, more and more people are becoming interested in mortality rates at higher ages. Since 1909, the birth rate in the United States has been decreasing except for a major significant increase after World War II, between the years 1946 and 1964, also known as the baby boom period. People born during the baby boom are now between the ages of 44 and 62. According to the National Center for Health Statistics, US Department Health and Human Services, in 1900–1902, one could expect to live for 49 years on average. Today, an infant can expect to live about 77 years. As of recent years and in prediction, the life expectancy for an infant born may be even higher. With the human lifespan increasing and a large part of the United States population aging, many researchers in various fields have recently become interested in studying quantitative models of mortality rates. Scientists in biological fields are not only interested in organisms and how they are made, they are also interested in what happens to organisms over time. A study of yeast, which would interest biologists, showed the effects of senescence as well as a model that accurately represents the experimental data. It has been shown that the addition of a Sir2 gene can prolong life in yeast. Once we can model human aging, we can look for ways to extend our lifespan and counteract the negative aspects of aging.

Suggested Citation

  • Hoang Pham, 2008. "Mortality Modeling Perspectives," Springer Series in Reliability Engineering, in: Hoang Pham (ed.), Recent Advances in Reliability and Quality in Design, chapter 25, pages 509-516, Springer.
  • Handle: RePEc:spr:ssrchp:978-1-84800-113-8_25
    DOI: 10.1007/978-1-84800-113-8_25
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    Cited by:

    1. I. A. Lakman & R. A. Askarov & V. B. Prudnikov & Z. F. Askarova & V. M. Timiryanova, 2021. "Predicting Mortality by Causes in the Republic of Bashkortostan Using the Lee–Carter Model," Studies on Russian Economic Development, Springer, vol. 32(5), pages 536-548, September.
    2. Ying Jiao & Yahia Salhi & Shihua Wang, 2021. "Dynamic Bivariate Mortality Modelling," Working Papers hal-03244324, HAL.
    3. Ying Jiao & Yahia Salhi & Shihua Wang, 2022. "Dynamic Bivariate Mortality Modelling," Methodology and Computing in Applied Probability, Springer, vol. 24(2), pages 917-938, June.

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