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Life expectancy improvement for multiple cure distributions

In many circumstances, the increase in life expectancy when certain causes of death are eliminated is sought. These calculations are typically based on the assumption that the causes in question are simply omitted, which is equivalent to the causes being taken out of consideration, from the outset,...

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Autores principales: Naik, Shanoja, Adamic, Peter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7319305/
https://www.ncbi.nlm.nih.gov/pubmed/32626640
http://dx.doi.org/10.1007/s13385-020-00229-y
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author Naik, Shanoja
Adamic, Peter
author_facet Naik, Shanoja
Adamic, Peter
author_sort Naik, Shanoja
collection PubMed
description In many circumstances, the increase in life expectancy when certain causes of death are eliminated is sought. These calculations are typically based on the assumption that the causes in question are simply omitted, which is equivalent to the causes being taken out of consideration, from the outset, with certainty. In this paper, we propose models whereby probability distributions for the cures of specific causes of death over time can be incorporated so as to more accurately predict the increase in life expectancy that would ensue. The theoretical results are applied to a real data set involving Diabetes and HIV-related deaths from Denver, Colorado, United States of America, between the years 1990 and 2015 inclusive.
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spelling pubmed-73193052020-07-01 Life expectancy improvement for multiple cure distributions Naik, Shanoja Adamic, Peter Eur Actuar J Original Research Paper In many circumstances, the increase in life expectancy when certain causes of death are eliminated is sought. These calculations are typically based on the assumption that the causes in question are simply omitted, which is equivalent to the causes being taken out of consideration, from the outset, with certainty. In this paper, we propose models whereby probability distributions for the cures of specific causes of death over time can be incorporated so as to more accurately predict the increase in life expectancy that would ensue. The theoretical results are applied to a real data set involving Diabetes and HIV-related deaths from Denver, Colorado, United States of America, between the years 1990 and 2015 inclusive. Springer Berlin Heidelberg 2020-04-27 2020 /pmc/articles/PMC7319305/ /pubmed/32626640 http://dx.doi.org/10.1007/s13385-020-00229-y Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Original Research Paper
Naik, Shanoja
Adamic, Peter
Life expectancy improvement for multiple cure distributions
title Life expectancy improvement for multiple cure distributions
title_full Life expectancy improvement for multiple cure distributions
title_fullStr Life expectancy improvement for multiple cure distributions
title_full_unstemmed Life expectancy improvement for multiple cure distributions
title_short Life expectancy improvement for multiple cure distributions
title_sort life expectancy improvement for multiple cure distributions
topic Original Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7319305/
https://www.ncbi.nlm.nih.gov/pubmed/32626640
http://dx.doi.org/10.1007/s13385-020-00229-y
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