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Why do lifespan variability trends for the young and old diverge? A perturbation analysis
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 u...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4326020/ https://www.ncbi.nlm.nih.gov/pubmed/25685053 http://dx.doi.org/10.4054/DemRes.2014.30.48 |
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author | Engelman, Michal Caswell, Hal Agree, Emily M. |
author_facet | Engelman, Michal Caswell, Hal Agree, Emily M. |
author_sort | Engelman, Michal |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-4326020 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
record_format | MEDLINE/PubMed |
spelling | pubmed-43260202015-02-12 Why do lifespan variability trends for the young and old diverge? A perturbation analysis Engelman, Michal Caswell, Hal Agree, Emily M. Demogr Res Article 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. 2014-05-01 /pmc/articles/PMC4326020/ /pubmed/25685053 http://dx.doi.org/10.4054/DemRes.2014.30.48 Text en © 2014 Michal Engelman, Hal Caswell & Emily M. Agree. http://creativecommons.org/licenses/by-nc/2.0/de/ This open-access work is published under the terms of the Creative Commons Attribution NonCommercial License 2.0 Germany, which permits use, reproduction & distribution in any medium for non-commercial purposes, provided the original author(s) and source are given credit. See http://creativecommons.org/licenses/by-nc/2.0/de/ |
spellingShingle | Article Engelman, Michal Caswell, Hal Agree, Emily M. Why do lifespan variability trends for the young and old diverge? A perturbation analysis |
title | Why do lifespan variability trends for the young and old diverge? A perturbation analysis |
title_full | Why do lifespan variability trends for the young and old diverge? A perturbation analysis |
title_fullStr | Why do lifespan variability trends for the young and old diverge? A perturbation analysis |
title_full_unstemmed | Why do lifespan variability trends for the young and old diverge? A perturbation analysis |
title_short | Why do lifespan variability trends for the young and old diverge? A perturbation analysis |
title_sort | why do lifespan variability trends for the young and old diverge? a perturbation analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4326020/ https://www.ncbi.nlm.nih.gov/pubmed/25685053 http://dx.doi.org/10.4054/DemRes.2014.30.48 |
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