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Adolescent experience predicts longevity: evidence from historical epidemiology

Published online by Cambridge University Press:  27 February 2014

A. Falconi ,
A. Gemmill ,
R. E. Dahl  and
R. Catalano
A. Falconi*
Affiliation:
School of Public Health, University of California, Berkeley, CA, USA
A. Gemmill
Affiliation:
Department of Demography, University of California, Berkeley, CA, USA
R. E. Dahl
Affiliation:
School of Public Health, University of California, Berkeley, CA, USA
R. Catalano
Affiliation:
School of Public Health, University of California, Berkeley, CA, USA
*
*Address for correspondence: A. Falconi, School of Public Health, University of California, 50 University Hall, Berkeley, CA 94720, USA. (Email april.falconi@berkeley.edu)
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Abstract

Human development reportedly includes critical and sensitive periods during which environmental stressors can affect traits that persist throughout life. Controversy remains over which of these periods provides an opportunity for such stressors to affect health and longevity. The elaboration of reproductive biology and its behavioral sequelae during adolescence suggests such a sensitive period, particularly among males. We test the hypothesis that life expectancy at age 20 among males exposed to life-threatening stressors during early adolescence will fall below that among other males. We apply time-series methods to cohort mortality data in France between 1816 and 1919, England and Wales between 1841 and 1919, and Sweden between 1861 and 1919. Our results indicate an inverse association between cohort death rates at ages 10–14 and cohort life expectancy at age 20. Our findings imply that better-informed and more strategic management of the stressors encountered by early adolescents may improve population health.

Keywords

adolescencecritical periodslongevitymortality
Type
Brief Report
Information
Journal of Developmental Origins of Health and Disease , Volume 5 , Issue 3 , June 2014 , pp. 171 - 177
Copyright
© Cambridge University Press and the International Society for Developmental Origins of Health and Disease 2014 

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References

1. Ben-Shlomo, Y, Kuh, D. A life course approach to chronic disease epidemiology: conceptual models, empirical challenges, and interdisciplinary perspectives. Int J Epidemiol. 2002; 31, 285–293.Google Scholar
2. Barker, DJ. The origins of the developmental origins theory. J Intern Med. 2007; 261, 412–417.CrossRefGoogle ScholarPubMed
3. Gluckman, PD, Hanson, MA, Beedle, AS, Raubenheimer, D. Fetal and neonatal pathways to obesity. Front Horm Res. 2008; 36, 61–72.Google Scholar
4. Catalano, R. Selection in utero contributes to the male longevity deficit. Soc Sci Med. 2011; 72, 999–1003.Google Scholar
5. Störmer, C. Sex differences in the consequences of early-life exposure to epidemiological stress – A life history approach. Am J Hum Biol. 2011; 23, 201–208.Google Scholar
6. Kuh, D, Ben-Shlomo, Y. A Life Course Approach to Chronic Disease Epidemiology, 2004. Oxford University Press: Oxford.Google Scholar
7. Crone, EA, Dahl, RE. Understanding adolescence as a period of social-affective engagement and goal flexibility. Nat Rev Neurosci. 2012; 13, 636–650.CrossRefGoogle ScholarPubMed
8. Ellis, BJ, Boyce, WT, Belsky, J, Bakermans-Kranenburg, MJ, van Ijzendoorn, MH. Differential susceptibility to the environment: an evolutionary-neurodevelopmental theory. Dev Psychopathol. 2011; 23, 7–12.Google Scholar
9. Selevan, SG, Kimmel, CA, Mendola, P. Identifying critical windows of exposure for children’s health. Environ Health Perspect. 2000; 108, 451–455.Google Scholar
10. Ellis, BJ, Del Giudice, M, Dishion, TJ, et al. The evolutionary basis of risky adolescent behavior: implications for science, policy, and practice. Dev Psychol. 2012; 48, 598–623.CrossRefGoogle ScholarPubMed
11. Dodge, KA, Albert, D. Evolving science in adolescence: comment on Ellis et al . Dev Psychol. 2012; 48, 624–627.Google Scholar
12. Horiuchi, S. The long-term impact of war on mortality: old age mortality of the First World War survivors in the Federal Republic of Germany. Popul Bull UN. 1983; 15, 80–92.Google Scholar
13. Falkstedt, D, Lundberg, I, Hemmingsson, T. Childhood socio-economic position and risk of coronary heart disease in middle age: a study of 49,231 male conscripts. Eur J Public Health. 2011; 21, 713–718.Google Scholar
14. Belsky, J, Steinberg, L, Houts, RM, Halpern-Felsher, BL, NICHD Early Child Care Research Network. The development of reproductive strategy in females: early maternal harshness → earlier menarche → increased sexual risk taking. Dev Psychol. 2010; 46, 120–128.Google Scholar
15. Gluckman, PD, Hanson, MA. Changing times: the evolution of puberty. Mol Cell Endocrinol. 2006; 254–255, 26–31.CrossRefGoogle ScholarPubMed
16. Placek, C, Quinlan, R. Adolescent fertility and risky environments: a population-level perspective across the lifespan. Proc Biol Sci. 2012; 279, 4003–4008.Google Scholar
17. Cameron, N, Demerath, E. Critical periods in human growth and their relationship to diseases of aging. Am J Phys Anthropol Suppl. 2002; 23, 159–184.Google Scholar
18. Spear, LP. The adolescent brain and age-related behavioral manifestations. Neurosci Biobehav Rev. 2000; 24, 417–463.Google Scholar
19 Human Mortality Database. University of California, Berkeley, USA and Max Planck Institute for Demographic Research, Germany). Retrieved from 4 April 2012 from www.mortality.org or www.humanmortality.de Google Scholar
20. Fisher, RA. Studies in crop variation: an examination of the yield of dressed grain from Broadbalk. J Agri Sci. 1921; 11, 107–135.CrossRefGoogle Scholar
21. Box, G, Jenkins, G, Reinsel, G. Time Series Analysis: Forecasting and Control, 3rd ed. 1994. Prentice Hall: London.Google Scholar
22. Ljung, G, Box, G. On a measure of lack of fit in time series models. Biometrika. 1978; 65, 297–303.CrossRefGoogle Scholar
23. Chang, I, Tiao, G, Chen, C. Estimation of time series parameters in the presence of outliers. Technometrics. 1988; 30, 193–204.Google Scholar
24. Goldstein, JR. A secular trend toward earlier male sexual maturity: evidence from shifting ages of male young adult mortality. PLoS One. 2011; 6. doi: 10.1371/0014826.Google Scholar
25. Lifshitz, F. Normal male development. In Pediatric Endocrinology (ed. Lifshitz F), 2006; pp. 275–277. Informa Healthcare: New York.Google Scholar
26. Daw, S. Age of boys’ puberty in Leipzig, 1727–49, as indicated by voice breaking in J.S. Bach’s choir members. Hum Biol. 1970; 42, 87–89.Google Scholar
27. Mashall, WA, Turner, JM. Variations in the pattern of pubertal changes in boys. Arch Dis Child. 1970; 45, 13–23.CrossRefGoogle Scholar
28. Dufty, AM Jr, Clobert, J, Møller, AP. Hormones, developmental plasticity, and adaptation. Trends Ecol Evol. 2002; 24, 439–446.Google Scholar
29. Hochberg, Z, Feil, R, Constancia, M, et al. Child health, developmental plasticity, and epigenetic programming. Endoc Rev. 2011; 32, 59–124.Google Scholar
30. Catalano, R, Bruckner, T. Secondary sex ratios and male lifespan: damaged or culled cohorts. Proc Natl Acad Sci. 2006; 103, 1639–1643.Google Scholar
31. Razzell, P, Spence, C. The hazards of wealth: adult mortality in pre-twentieth century England. Soc Hist Med. 2006; 19, 381–405.CrossRefGoogle Scholar
32. Preston, SH, Haines, MR. Fatal Years – Child Mortality in Late Nineteenth-Century America, 1991. Princeton University Press: Princeton.CrossRefGoogle Scholar
33. Hatton, TJ, Williamson, JG. What drove the mass migrations from Europe in the late 19th century? Pop and Dev Rev. 1994; 20, 533–559.Google Scholar
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