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Exp Gerontol. 2013 Aug;48(8):801-11. doi: 10.1016/j.exger.2013.05.054. Epub 2013 May 23.

A mathematical model of mortality dynamics across the lifespan combining heterogeneity and stochastic effects.

Author information

  • 1Department of Mathematical Sciences, University of Liverpool, Liverpool, UK.

Abstract

The mortality patterns in human populations reflect biological, social and medical factors affecting our lives, and mathematical modelling is an important tool for the analysis of these patterns. It is known that the mortality rate in all human populations increases with age after sexual maturity. This increase is predominantly exponential and satisfies the Gompertz equation. Although the exponential growth of mortality rates is observed over a wide range of ages, it excludes early- and late-life intervals. In this work we accept the fact that the mortality rate is an exponential function of age and analyse possible mechanisms underlying the deviations from the exponential law across the human lifespan. We consider the effect of heterogeneity as well as stochastic factors in altering the exponential law and compare our results to publicly available age-dependent mortality data for Swedish and US populations. In a model of heterogeneous populations we study how differences in parameters of the Gompertz equation describing different subpopulations account for mortality dynamics at different ages. Particularly, we show that the mortality data on Swedish populations can be reproduced fairly well by a model comprising four subpopulations. We then analyse the influence of stochastic effects on the mortality dynamics to show that they play a role only at early and late ages, when only a few individuals contribute to mortality. We conclude that the deviations from exponential law at young ages can be explained by heterogeneity, namely by the presence of a subpopulation with high initial mortality rate presumably due to congenital defects, while those for old ages can be viewed as fluctuations and explained by stochastic effects.

KEYWORDS:

Ageing; Demography; Mathematical modelling; Model fitting; Stochastic processes

PMID:
23707231
DOI:
10.1016/j.exger.2013.05.054
[PubMed - indexed for MEDLINE]
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