Abstract
The rise in human life expectancy has involved declines in intrinsic and extrinsic mortality processes associated, respectively, with senescence and environmental challenges. To better understand the factors driving this rise, we apply a two-process vitality model to data from the Human Mortality Database. Model parameters yield intrinsic and extrinsic cumulative survival curves from which we derive intrinsic and extrinsic expected life spans (ELS). Intrinsic ELS, a measure of longevity acted on by intrinsic, physiological factors, changed slowly over two centuries and then entered a second phase of increasing longevity ostensibly brought on by improvements in old-age death reduction technologies and cumulative health behaviors throughout life. The model partitions the majority of the increase in life expectancy before 1950 to increasing extrinsic ELS driven by reductions in environmental, event-based health challenges in both childhood and adulthood. In the post-1950 era, the extrinsic ELS of females appears to be converging to the intrinsic ELS, whereas the extrinsic ELS of males is approximately 20 years lower than the intrinsic ELS.
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Notes
Defining independent intrinsic and extrinsic processes achieves a closed-form solution of total mortality that can be fit to data to yield parameter estimates (Li and Anderson 2013). We are currently exploring a version of the model with greater interaction between these processes.
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Li and Anderson (2013) showed that extrinsic challenges to stochastic vitality trajectories preferentially eliminate lower vitality paths, reshaping the distribution of vitality at each age. Because the effect of this reshaping cannot be captured in a closed-form solution that could be fit to data and yield parameter estimates, Li and Anderson expressed the extrinsic mortality process as challenges to the mean rate of loss of vitality—a deterministic function. This modified version of the two-process model with independent intrinsic and extrinsic parts is the model form used in this article.
Fitting the closed-form solution of the model to data can result in slightly biased parameter estimates, with r and β slightly low, and s and λ slightly high. Li and Anderson (2013:350) provided bias correction formulas for the four adult parameters based on simulation with a numerical model with greater parameter interaction. However, we avoid direct parameter interpretation here in favor of the summary metric ELS, which is unaffected by the parameter bias.
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This work was supported by Grant No. 1R21AG046760-01 from the National Institute on Aging.
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An erratum to this article is available at http://dx.doi.org/10.1007/s13524-016-0547-x.
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Sharrow, D.J., Anderson, J.J. Quantifying Intrinsic and Extrinsic Contributions to Human Longevity: Application of a Two-Process Vitality Model to the Human Mortality Database. Demography 53, 2105–2119 (2016). https://doi.org/10.1007/s13524-016-0524-4
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DOI: https://doi.org/10.1007/s13524-016-0524-4