Demography

, Volume 54, Issue 3, pp 1097–1118 | Cite as

A Quiescent Phase in Human Mortality? Exploring the Ages of Least Vulnerability

  • Michal Engelman
  • Christopher L. Seplaki
  • Ravi Varadhan
Article

Abstract

Demographic studies of mortality often emphasize the two ends of the lifespan, focusing on the declining hazard after birth or the increasing risk of death at older ages. We call attention to the intervening phase, when humans are least vulnerable to the force of mortality, and consider its features in both evolutionary and historical perspectives. We define this quiescent phase (Q-phase) formally, estimate its bounds using life tables for Swedish cohorts born between 1800 and 1920, and describe changes in the morphology of the Q-phase. We show that for cohorts aging during Sweden’s demographic and epidemiological transitions, the Q-phase became longer and more pronounced, reflecting the retreat of infections and maternal mortality as key causes of death. These changes revealed an underlying hazard trajectory that remains relatively low and constant during the prime ages for reproduction and investment in both personal capital and relationships with others. Our characterization of the Q-phase highlights it as a unique, dynamic, and historically contingent cohort feature, whose increased visibility was made possible by the rapid pace of survival improvements in the nineteenth and twentieth centuries. This visibility may be reduced or sustained under subsequent demographic regimes.

Keywords

Mortality Quiescent phase Cohorts Demographic transition 

Notes

Acknowledgments

Michal Engelman began work on this project while supported by a postdoctoral fellowship in the Epidemiology and Biostatistics of Aging at the Johns Hopkins Center on Aging and Health (NIA T32AG000247). She is now supported by the Center for Demography and Ecology (NICHD R24 HD047873) and Center for Demography of Health and Aging (NIA P30 AG17266) at the University of Wisconsin–Madison. Christopher L. Seplaki was supported by Mentored Research Scientist Development Award number K01AG031332 from the National Institute on Aging. Ravi Varadhan was a Brookdale Leadership in Aging Fellow. This work was also funded in part by the Older Americans’ Independence Center (OAIC) at the Johns Hopkins University. Previous versions of this article were presented at meetings of the Population Association of America and at the Berkeley Formal Demography Workshop. We thank John Wilmoth, Ron Lee, Joshua Goldstein, and Joshua Garoon for helpful comments and discussion. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute on Aging or the National Institutes of Health.

References

  1. Austad, S. N., & Partridge, L. (1997). Why we age: What science is discovering about the body’s journey through life. New York, NY: Wiley & Sons.Google Scholar
  2. Barker, D. J. P. (2004). The developmental origins of well-being. Philosophical Transactions of the Royal Society of London: Series B (Biological Sciences), 359, 1359–1366.CrossRefGoogle Scholar
  3. Bebbington, M., Lai, C.-D., & Zitikis, R. (2006). Useful periods for lifetime distributions with bathtub shaped hazard rate functions. IEEE Transactions on Reliability, 55(2), 245–251.CrossRefGoogle Scholar
  4. Beltrán-Sánchez, H., Crimmins, E. M., & Finch, C. E. (2012). Early cohort mortality predicts the rate of aging in the cohort: A historical analysis. Journal of Developmental Origins of Health and Disease, 3, 380–386.CrossRefGoogle Scholar
  5. Bengtsson, T. (2004). Life under pressure: Mortality and living standards in Europe and Asia, 1700–1900. Cambridge, MA: MIT Press.Google Scholar
  6. Bengtsson, T., & Dribe, M. (2011). The late emergence of socioeconomic mortality differentials: A micro-level study of adult mortality in southern Sweden 1815–1968. Explorations in Economic History, 48, 389–400.CrossRefGoogle Scholar
  7. Bengtsson, T., & Lindström, M. (2000). Childhood misery and disease in later life: The effects on mortality in old age of hazards experienced in early life, southern Sweden, 1760–1894. Population Studies, 54, 263–277.CrossRefGoogle Scholar
  8. Black, R. E., Cousens, S., Johnson, H. L., Lawn, J. E., Rudan, I., Bassani, D. G., . . . Mathers, C. (2010). Global, regional, and national causes of child mortality in 2008: A systematic analysis. Lancet, 375, 1969–1987.Google Scholar
  9. Bongaarts, J., & Feeney, G. (2002). How long do we live? Population and Development Review, 28, 13–29.CrossRefGoogle Scholar
  10. Booth, H., & Tickle, L. (2008). Mortality modelling and forecasting: A review of methods. Annals of Actuarial Science, 3, 3–43.CrossRefGoogle Scholar
  11. Bribiescas, R. G. (2001). Reproductive ecology and life history of the human male. American Journal of Physical Anthropology, 116(S33), 148–176.CrossRefGoogle Scholar
  12. Carnes, B. A., & Olshansky, S. J. (1997). A biologically motivated partitioning of mortality. Experimental Gerontology, 32, 615–631.CrossRefGoogle Scholar
  13. Caughley, G. (1966). Mortality patterns in mammals. Ecology, 47, 906–918.CrossRefGoogle Scholar
  14. Charlesworth, B. (1994). Evolution in age-structured populations (2nd ed.). Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
  15. Chu, C. Y. C., Chien, H.-K., & Lee, R. D. (2008). Explaining the optimality of U-shaped age-specific mortality. Theoretical Population Biology, 73, 171–180.CrossRefGoogle Scholar
  16. Crimmins, E. M., & Finch, C. E. (2006). Infection, inflammation, height, and longevity. Proceedings of the National Academy of Sciences, 103, 498–503.CrossRefGoogle Scholar
  17. Engelman, M., Caswell, H., & Agree, E. M. (2014). Why do lifespan variability trends for the young and old diverge? A perturbation analysis. Demographic Research, 30(article 48), 1367–1396. doi: 10.4054/DemRes.2014.30.48 CrossRefGoogle Scholar
  18. Finch, C. E. (2012). Evolution of the human lifespan, past, present, and future: Phases in the evolution of human life expectancy in relation to the inflammatory load. Proceedings of the American Philosophical Society, 156, 9–44.Google Scholar
  19. Finch, C. E., Pike, M. C., & Witten, M. (1990). Slow mortality rate accelerations during aging in some animals approximate that of humans. Science, 249, 902–905.CrossRefGoogle Scholar
  20. Fridlizius, G. (1989). The deformation of cohorts: Nineteenth century mortality decline in a generational perspective. Scandinavian Economic History Review, 37(3), 3–17.CrossRefGoogle Scholar
  21. Gage, T. B. (1998). The comparative demography of primates: With some comments on the evolution of life histories. Annual Review of Anthropology, 27, 197–221.CrossRefGoogle Scholar
  22. Gage, T. B., & Dyke, B. (1986). Parameterizing abridged mortality tables: The Siler three-component hazard model. Human Biology, 58, 275–291.Google Scholar
  23. Gage, T. B., & Mode, C. J. (1993). Some laws of mortality: How well do they fit? Human Biology, 65, 445–461.Google Scholar
  24. Gavrilov, L. A., & Gavrilova, N. S. (2001). The reliability theory of aging and longevity. Journal of Theoretical Biology, 213, 527–545.CrossRefGoogle Scholar
  25. Goldstein, J. R. (2011). A secular trend toward earlier male sexual maturity: Evidence from shifting ages of male young adult mortality. PloS One, 6(8), e14826. doi: 10.1371/journal.pone.0014826 CrossRefGoogle Scholar
  26. Goldstein, J. R., & Wachter, K. W. (2006). Relationships between period and cohort life expectancy: Gaps and lags. Population Studies, 60, 257–269.CrossRefGoogle Scholar
  27. Gompertz, B. (1825). On the nature of the function expressive of the law of human mortality, and on a new mode of determining the value of life contingencies. Philosophical Transactions of the Royal Society of London, 115, 513–583.CrossRefGoogle Scholar
  28. Guillot, M. (2003). The cross-sectional average length of life (CAL): A cross-sectional mortality measure that reflects the experience of cohorts. Population Studies, 57, 41–54.CrossRefGoogle Scholar
  29. Guillot, M. (2011). Period versus cohort life expectancy. In R. G. Rogers & E. M. Crimmins (Eds.), International handbook of adult mortality (pp. 533–549). Dordrecht, The Netherlands: Springer.CrossRefGoogle Scholar
  30. Gurven, M., & Kaplan, H. (2007). Longevity among hunter-gatherers: A cross-cultural examination. Population and Development Review, 33, 321–365.CrossRefGoogle Scholar
  31. Hamilton, W. D. (1966). The moulding of senescence by natural selection. Journal of Theoretical Biology, 12, 12–45.CrossRefGoogle Scholar
  32. Hawkes, K., O’Connell, J. F., Jones, N. G. B., Alvarez, H., & Charnov, E. L. (1998). Grandmothering, menopause, and the evolution of human life histories. Proceedings of the National Academy of Sciences, 95, 1336–1339.CrossRefGoogle Scholar
  33. Heligman, L., & Pollard, J. H. (1980). The age pattern of mortality. Journal of the Institute of Actuaries, 107, 49–80.CrossRefGoogle Scholar
  34. Human Mortality Database. (2015). Berkeley: University of California, Berkeley; and Rostock, Germany: Max Planck Institute for Demographic Research. Available from www.mortality.org
  35. Jones, H. B. (1961). Mechanism of aging suggested from study of altered death risks. In J. Neyman (Ed.), Proceedings of the Berkeley Symposium on Mathematical Statistics and Probability (Vol. 4, pp. 267–292). Berkeley: University of California Press.Google Scholar
  36. Kermack, W. O., McKendrick, A. G., & McKinlay, P. L. (2001). Death-rates in Great Britain and Sweden. Some general regularities and their significance. International Journal of Epidemiology, 30, 678–683.CrossRefGoogle Scholar
  37. Kruger, D. J., & Nesse, R. M. (2006). An evolutionary life-history framework for understanding sex differences in human mortality rates. Human Nature, 17, 74–97.CrossRefGoogle Scholar
  38. Lee, R. D. (2003). Rethinking the evolutionary theory of aging: Transfers, not births, shape senescence in social species. Proceedings of the National Academy of Sciences, 100, 9637–9642.CrossRefGoogle Scholar
  39. Levitis, D. A. (2011). Before senescence: The evolutionary demography of ontogenesis. Proceedings of the Royal Society B: Biological Sciences, 278, 801–809.CrossRefGoogle Scholar
  40. Livi-Bacci, M. (2012). A concise history of world population (5th ed.). West Sussex, UK: John Wiley & Sons.Google Scholar
  41. Loudon, I. (1992). Death in childbirth: An international study of maternal care and maternal mortality 1800–1950. Oxford, UK: Clarendon Press.CrossRefGoogle Scholar
  42. Makeham, W. M. (1889). On the further development of Gompertz’s law. Journal of the Institute of Actuaries, 28, 152–159.Google Scholar
  43. Marmot, M. G., Bosma, H., Hemingway, H., Brunner, E., & Stansfeld, S. (1997). Contribution of job control and other risk factors to social variations in coronary heart disease incidence. Lancet, 350, 235–239.CrossRefGoogle Scholar
  44. Medawar, P. B. (1952). An unsolved problem of biology. London, UK: H.K. Lewis.Google Scholar
  45. Medawar, P. B. (1955). The definition and measurement of senescence. In G. E. W. Wolstenholme & M. P. Cameron (Eds.), Ciba Foundation Symposium: Colloquia on ageing - General aspects (Vol. 1, pp. 4–15). Boston, MA: Little Brown & Company.Google Scholar
  46. Medawar, P. B. (1957). Uniqueness of the individual. London, UK: Methuen.CrossRefGoogle Scholar
  47. Nash, J. C., & Varadhan, R. (2011). Unifying optimization algorithms to aid software system users: Optimx for R. Journal of Statistical Software, 43(9), 1–14.CrossRefGoogle Scholar
  48. Olshansky, S. J., & Carnes, B. A. (1997). Ever since Gompertz. Demography, 34, 1–15.CrossRefGoogle Scholar
  49. Ouellette, N., Barbieri, M., & Wilmoth, J. R. (2014). Period-based mortality change: Turning points in trends since 1950. Population and Development Review, 40, 77–106.CrossRefGoogle Scholar
  50. Partridge, L. (2010). The new biology of ageing. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 365, 147–154.CrossRefGoogle Scholar
  51. Pearl, R., & Miner, J. R. (1935). Experimental studies on the duration of life. XIV. The comparative mortality of certain lower organisms. Quarterly Review of Biology, 10, 60–79.CrossRefGoogle Scholar
  52. Riley, J. C. (2001). Rising life expectancy: A global history. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
  53. Robson, A. J., & Kaplan, H. S. (2003). The evolution of human life expectancy and intelligence in hunter-gatherer economies. American Economic Review, 93, 150–169.CrossRefGoogle Scholar
  54. Ryder, N. B. (1964). The process of demographic translation. Demography, 1, 74–82.CrossRefGoogle Scholar
  55. Schön, L., & Schubert, K. (2010). Sweden’s road to modernity: An economic history. Stockholm, Sweden: SNS Förlag.Google Scholar
  56. Siler, W. (1979). A competing-risk model for animal mortality. Ecology, 60, 750–757.CrossRefGoogle Scholar
  57. Thiele, T. B., & Sprague, T. B. (1871). On a mathematical formula to express the rate of mortality throughout the whole of life, tested by a series of observations made use of by the Danish life insurance company of 1871. Journal of the Institute of Actuaries, 16, 313–329.CrossRefGoogle Scholar
  58. Vaupel, J. W. (2003). Post-Darwinian longevity. Population and Development Review, 29, 258–269.Google Scholar
  59. Vaupel, J. W., Carey, J. R., Christensen, K., Johnson, T. E., Yashin, A. I., Holm, N. V., . . . Curtsinger, J. W. (1998). Biodemographic trajectories of longevity. Science, 280, 855–860.Google Scholar
  60. Vaupel, J. W., & Yashin, A. I. (1985). Heterogeneity’s ruses: Some surprising effects of selection on population dynamics. American Statistician, 39, 176–185.Google Scholar
  61. Wachter, K. (2003). Hazard curves and lifespan prospects. Population and Development Review, 29(Suppl.), 270–291.Google Scholar
  62. Wachter, K., Steinsaltz, D., & Evans, S. N. (2014). Evolutionary shaping of demographic schedules. Proceedings of the National Academy of Sciences, 111, 10846–10853.CrossRefGoogle Scholar
  63. White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48, 817–838.CrossRefGoogle Scholar
  64. Wilmoth, J. R. (1989). Variation in vital rates by age, period, and cohort. Sociological Methodology, 20, 295–335.CrossRefGoogle Scholar
  65. Zeileis, A. (2006). Object-oriented computation of sandwich estimators (Research Report Series, No. 37). Vienna, Austria: WU Vienna University of Economics and Business, Department of Statistics and Mathematics. Retrieved from http://epub.wu.ac.at/1644/

Copyright information

© Population Association of America 2017

Authors and Affiliations

  • Michal Engelman
    • 1
  • Christopher L. Seplaki
    • 2
  • Ravi Varadhan
    • 3
  1. 1.Department of Sociology and Center for Demography and EcologyUniversity of Wisconsin–MadisonMadisonUSA
  2. 2.University of Rochester School of Medicine and DentistryRochesterUSA
  3. 3.Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer CenterJohns Hopkins UniversityBaltimoreUSA

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