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The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Mortality Partitions and their Relevance to Research on Senescence

Carnes, Bruce A., Holden, Larry R., Olshansky, S. Jay, Witten, M. Tarynn, Siegel, Jacob S.·2006·Biogerontology
DOI 10.1007/s10522-006-9020-3
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Abstract

The reasons for classifying causes of death into aggregate groups are discussed and the impact of mortality partitions on analyses of mortality is described. Special emphasis is given to a mortality partition that distinguishes between intrinsic causes of death that arise primarily from the failure of biological processes that originate within an organism, and extrinsic causes of death that are primarily imposed on the organism by outside forces. Examples involving mortality data for mice, dogs, and humans are used to illustrate how this mortality partition infuses biological reasoning into mathematical models used to analyze and predict senescent-determined mortality, enhances the information content of the mortality schedules generated from these models, improves mortality comparisons between populations within species separated by time or geographic location, and provides a logical pathology endpoint for making interspecies comparisons of mortality. By bridging biology and the statistics of mortality, a mortality partition based on intrinsic and extrinsic causes of death provides both structure and direction for research on senescent-determined mortality.

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Crossref · 10.1007/s10522-006-9020-3

Cites

1 of 1 references verified0 citation errors · 0 not checkable

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  • The resulting maximum median age at death for humans fell within the mid to upper 80s.

    “The resulting maximum median age at death for humans (an approximation of life expectancy at birth) fell within the mid to upper 80s.”

    From Inconvenient Truths About Human Longevity (2019) · cites this paper as [33]

  • In the comparison of mortality of humans with mice and dogs, causes of death for all three species were verified to be aging related.

    “In a second line of inquiry we compared the mortality of humans with two other species, mice and dogs, where causes of death for all three species were verified to be aging related (33).”

    From Inconvenient Truths About Human Longevity (2019) · cites this paper as [33]

  • Once intrinsic mortality schedules for humans, mice, and dogs were compared and scaled for time, the life expectancy limit for humans was approximately 85 years.

    “Once the intrinsic mortality schedules for all three species were compared and scaled for time (for more on interspecies time scaling see Ref. (8)), it was concluded that the life expectancy limit for humans was approximately 85 years.”

    From Inconvenient Truths About Human Longevity (2019) · cites this paper as [33]

Citations

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Research works printed but not linked: 41

  1. Benjamin B (1959) Actuarial aspects of human lifespans. In: Wolstenholme GEW, Oȁ9Connor M (eds) CIBA foundation colloquia on ageing, vol 5. Little Brown, Boston
  2. Bodenheimer FS (1938) Problem of ANIMAL ECOLOGY. Oxford University Press, Oxford
  3. Bourgeois-Pichat J (1978) Future outlook for mortality decline in the world. Popul Bull United Nations 11:12–41
  4. Brown JH, West GB (eds) (2000) Scaling in biology. Oxford University Press, Oxford · DOI
  5. Carey JR (2003) Longevity: the biology and demography of life span. Princeton University Press, Princeton · DOI
  6. Carey JR, Liedo P, Orozco D, Vaupel JW (1992) Slowing of mortality rates at older ages in large medfly cohorts. Science 258:457–463 · DOI
  7. Carnes BA, Olshansky SJ (1993) Evolutionary perspectives on human senescence. Popul Dev Rev 19:793–806 · DOI
  8. Carnes BA (2004) Darwinian bodies in a Lamarkian world. The Gerontologist 44:274–279 · DOI
  9. Carnes BA, Olshansky SJ (1997) A biologically motivated partitioning of mortality. Exp Gerontol 32(6):615–631 · DOI
  10. Carnes BA, Olshansky SJ (2001) Heterogeneity and its biodemographic implications for longevity and mortality. Exp Gerontol 36:419–430 · DOI
  11. Carnes BA, Olshansky SJ, Grahn D (1996) Continuing the search for a law of mortality. Popul Dev Rev 22:231–264 · DOI · Paper page
  12. Carnes BA, Olshansky SJ, Grahn D (1998) An interspecies prediction of the risk of radiation-induced mortality. Radiat Res 149:487–492 · DOI
  13. Carnes BA, Olshansky SJ, Grahn D (2003a) Biological evidence for limits to the duration of life. Biogerontology 4(1):31–45 · DOI · Paper page
  14. Carnes BA, Grahn D, Hoel D (2003b) Mortality of atomic-bomb survivors predicted from laboratory animals. Radiat Res 160:159–167 · DOI
  15. Carnes BA, Nakasato YR, Olshansky SJ (2005) Medawar revisited: unresolved issues in research on ageing. Ageing Horizons 3:22–27
  16. Clarke RD (1950) A bio-actuarial approach to forecasting rates of mortality. Proceed Cent Assem Inst Act 2:12–27
  17. de Finetti B, Rossi C (1982) Bio-mathematical models of mortality. In: Samuel H Preston (ed) Biological and social aspects of mortality and length of life. Ordina Editions, Liege (Belgium)
  18. Deevey ES Jr (1947) Life tables for natural populations of animals. Q Rev Biol 22:283–314 · DOI
  19. Eakin T, Witten M (1995a) How square is the survival curve of a given species? Exp Gerontol 30(1):33–64 · DOI
  20. Eakin T,Witten M (1995b) A gerontological distance metric for analysis of survival dynamics. Mech Aging Dev 78:85–101 · DOI
  21. Efron B, Tibshirani RJ (1993) An introduction to the bootstrap. Chapman & Hall, London, 436 pp · DOI · Paper page
  22. Elandt-Johnson RC, Johnson NL (1980) Survival models and data analysis. John Wiley and Sons, NY
  23. Fukui HH, Xiu L, Curtsinger JW (1993) Slowing of age-specific mortality rates in Drosophila melanogaster. Exp Gerontol 28:585–599 · DOI
  24. Grahn D (1994) Studies of acute and chronic radiation injury at the Biological and Medical Research Division, Argonne National Laboratory, 1953–1970: Description of individual studies, data files, codes, and summaries of significant findings. Chicago: ANL-94/26 · DOI
  25. Grahn D, Wright BJ, Carnes BA, Williamson FS, Fox C (1995) Studies of acute and chronic radiation injury at the Biological and Medical Research Division, Argonne National Laboratory, 1970–1992: The Janus Program: Survival and Pathology Data. Chicago: ANL-95/3 · DOI
  26. Hayflick L (2000) The future of ageing. Nature 408:267–269 · DOI
  27. Hirsch HR, Liu X, Witten TM (2000). Mortality-rate cross-overs and maximum lifespan in advantaged and dis advantaged populations: accelerated-mortality and sudden-death models. J Theor Biol 205:171–180 · DOI
  28. Kier LB, Witten TM (2005) Cellular automata models of complex biochemical systems. In: Bonchev D, Rouvray DH (eds) Complexity in chemistry, biology and ecology. Springer Science+Business Media, NY
  29. Kirkwood TBL, Holliday FRS (1979) The evolution of ageing and longevity. Proc R Soc Lond B 205:97–112
  30. Kleinbaum DG, Kupper LL, Morgenstern H (1982) Epidemiologic research: principles and quantitative methods. Van Nostrand Reinhold Company, NY
  31. Mackenbach JP, Bouvier-Colle MH, Jougla E (1990) “Avoidable” mortality and health-services: a review of aggregate data studies. J Epidemiol Commun Health 44:106–111 · DOI
  32. Makeham WM (1867) On the law of mortality. J Inst Actuaries 13:325–358 · DOI
  33. McGinnis JM, Foege WH (1993) Actual causes of death in the United States. JAMA 270:2207–2212 · DOI · Paper page
  34. Medawar PB (1952) An unsolved problem of biology. Lewis, London
  35. NCHS [National Center for Health Statistics] (1998) Technical appendix. Vital statistics of the United States, vol II, mortality, part A. Public Health Service, Washington
  36. Olshansky SJ, Carnes BA (1997) Ever since Gompertz. Demography 34: 1–15 · DOI
  37. Olshansky SJ, Carnes BA, Grahn D (1998) Confronting the boundaries of human longevity. Am Sci 86:52–61 · DOI
  38. Olshansky SJ, Hayflick L, Carnes BA et al (2002) Position statement on human aging. Journal of Gerontology: Biological Sciences 57A(8):B1–B6
  39. Pearl R (1921) A biological classification of the causes of death. Metron 1:92–99
  40. Stearns SC, Ackermann M, Doebeli M (1998) The experimental evolution of aging in fruitflies. Exp Gerontol 33:785–792 · DOI
  41. Thomson RC (1989) Life-span effects of ionizing radiation in the Beagle Dog. PNL-6822 UC-408. Pacific Northwest Laboratory, Richland, WA

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Metadata

Authors
Carnes, B. A., Holden, L. R., Olshansky, S. J., Witten, M. T., & Siegel, J. S.
Venue
Biogerontology
Publisher
Springer Science and Business Media LLC
Published
2006-05-27
Volume
7
Issue
4
Pages
183-198
ISSN
  • 1389-5729
  • 1573-6768
DOI
10.1007/s10522-006-9020-3
Cited by
86
References
1
Language
en
Licence
www.springer.com/tdm (tdm)

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