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Why do lifespan variability trends for the young and old diverge? A perturbation analysis.

Engelman M, Caswell H, Agree EM - Demogr Res (2014)

Bottom Line: Variation in lifespan has followed strikingly different trends for the young and old: while total lifespan variability has decreased as life expectancy at birth has risen, the variability conditional on survival to older ages has increased.Our analysis quantifies the influence of changing demographic parameters on lifespan variability at all ages, highlighting the influence of declining childhood mortality on the reduction of lifespan variability, and the influence of subsequent improvements in adult survival on the rising variability of lifespans at older ages.These findings provide insight into the dynamic relationship between the age pattern of survival improvements and time trends in lifespan variability.

View Article: PubMed Central - PubMed

Affiliation: Department of Sociology and Center for Demography and Ecology, University of Wisconsin-Madison, USA.

ABSTRACT

Background: Variation in lifespan has followed strikingly different trends for the young and old: while total lifespan variability has decreased as life expectancy at birth has risen, the variability conditional on survival to older ages has increased. These diverging trends reflect changes in the underlying demographic parameters determining age-specific mortality.

Objective: We ask why the variation in the ages at death after survival to adult ages has followed a different trend than the variation at younger ages, and aim to explain the divergence in terms of the age pattern of historical mortality changes.

Methods: Using simulations, we show that the empirical trends in lifespan variation are well characterized using the Siler model, which describes the mortality trajectory using functions representing early-life, later-life, and background mortality. We then obtain maximum likelihood estimates of the Siler parameters for Swedish females from 1900 to 2010. We express mortality in terms of a Markov chain model, and apply matrix calculus to compute the sensitivity of age-specific variance trends to the changes in Siler model parameters.

Results: Our analysis quantifies the influence of changing demographic parameters on lifespan variability at all ages, highlighting the influence of declining childhood mortality on the reduction of lifespan variability, and the influence of subsequent improvements in adult survival on the rising variability of lifespans at older ages.

Conclusions: These findings provide insight into the dynamic relationship between the age pattern of survival improvements and time trends in lifespan variability.

No MeSH data available.


Simulated lifespan variability trends for 111 years based on a Siler modelNotes: Starting parameters are estimated from a life table for Swedish females, 1900. The scenarios depicted are those where the rate of childhood mortality change (ρc) is faster than the rate of change in adult mortality (ρa) and the rate of change in background mortality (ρb) is slowest (1), intermediate (2), or fastest (3).
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Figure 7: Simulated lifespan variability trends for 111 years based on a Siler modelNotes: Starting parameters are estimated from a life table for Swedish females, 1900. The scenarios depicted are those where the rate of childhood mortality change (ρc) is faster than the rate of change in adult mortality (ρa) and the rate of change in background mortality (ρb) is slowest (1), intermediate (2), or fastest (3).


Why do lifespan variability trends for the young and old diverge? A perturbation analysis.

Engelman M, Caswell H, Agree EM - Demogr Res (2014)

Simulated lifespan variability trends for 111 years based on a Siler modelNotes: Starting parameters are estimated from a life table for Swedish females, 1900. The scenarios depicted are those where the rate of childhood mortality change (ρc) is faster than the rate of change in adult mortality (ρa) and the rate of change in background mortality (ρb) is slowest (1), intermediate (2), or fastest (3).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4326020&req=5

Figure 7: Simulated lifespan variability trends for 111 years based on a Siler modelNotes: Starting parameters are estimated from a life table for Swedish females, 1900. The scenarios depicted are those where the rate of childhood mortality change (ρc) is faster than the rate of change in adult mortality (ρa) and the rate of change in background mortality (ρb) is slowest (1), intermediate (2), or fastest (3).
Bottom Line: Variation in lifespan has followed strikingly different trends for the young and old: while total lifespan variability has decreased as life expectancy at birth has risen, the variability conditional on survival to older ages has increased.Our analysis quantifies the influence of changing demographic parameters on lifespan variability at all ages, highlighting the influence of declining childhood mortality on the reduction of lifespan variability, and the influence of subsequent improvements in adult survival on the rising variability of lifespans at older ages.These findings provide insight into the dynamic relationship between the age pattern of survival improvements and time trends in lifespan variability.

View Article: PubMed Central - PubMed

Affiliation: Department of Sociology and Center for Demography and Ecology, University of Wisconsin-Madison, USA.

ABSTRACT

Background: Variation in lifespan has followed strikingly different trends for the young and old: while total lifespan variability has decreased as life expectancy at birth has risen, the variability conditional on survival to older ages has increased. These diverging trends reflect changes in the underlying demographic parameters determining age-specific mortality.

Objective: We ask why the variation in the ages at death after survival to adult ages has followed a different trend than the variation at younger ages, and aim to explain the divergence in terms of the age pattern of historical mortality changes.

Methods: Using simulations, we show that the empirical trends in lifespan variation are well characterized using the Siler model, which describes the mortality trajectory using functions representing early-life, later-life, and background mortality. We then obtain maximum likelihood estimates of the Siler parameters for Swedish females from 1900 to 2010. We express mortality in terms of a Markov chain model, and apply matrix calculus to compute the sensitivity of age-specific variance trends to the changes in Siler model parameters.

Results: Our analysis quantifies the influence of changing demographic parameters on lifespan variability at all ages, highlighting the influence of declining childhood mortality on the reduction of lifespan variability, and the influence of subsequent improvements in adult survival on the rising variability of lifespans at older ages.

Conclusions: These findings provide insight into the dynamic relationship between the age pattern of survival improvements and time trends in lifespan variability.

No MeSH data available.