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Area variations in multiple morbidity using a life table methodology

Analysis of healthy life expectancy is typically based on a binary distinction between health and ill-health. By contrast, this paper considers spatial modelling of disease free life expectancy taking account of the number of chronic conditions. Thus the analysis is based on population sub-groups wi...

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Detalles Bibliográficos
Autor principal: Congdon, Peter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4867778/
https://www.ncbi.nlm.nih.gov/pubmed/27257403
http://dx.doi.org/10.1007/s10742-015-0142-4
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author Congdon, Peter
author_facet Congdon, Peter
author_sort Congdon, Peter
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description Analysis of healthy life expectancy is typically based on a binary distinction between health and ill-health. By contrast, this paper considers spatial modelling of disease free life expectancy taking account of the number of chronic conditions. Thus the analysis is based on population sub-groups with no disease, those with one disease only, and those with two or more diseases (multiple morbidity). Data on health status is accordingly modelled using a multinomial likelihood. The analysis uses data for 258 small areas in north London, and shows wide differences in the disease burden related to multiple morbidity. Strong associations between area socioeconomic deprivation and multiple morbidity are demonstrated, as well as strong spatial clustering.
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spelling pubmed-48677782016-05-31 Area variations in multiple morbidity using a life table methodology Congdon, Peter Health Serv Outcomes Res Methodol Article Analysis of healthy life expectancy is typically based on a binary distinction between health and ill-health. By contrast, this paper considers spatial modelling of disease free life expectancy taking account of the number of chronic conditions. Thus the analysis is based on population sub-groups with no disease, those with one disease only, and those with two or more diseases (multiple morbidity). Data on health status is accordingly modelled using a multinomial likelihood. The analysis uses data for 258 small areas in north London, and shows wide differences in the disease burden related to multiple morbidity. Strong associations between area socioeconomic deprivation and multiple morbidity are demonstrated, as well as strong spatial clustering. Springer US 2016-01-08 2016 /pmc/articles/PMC4867778/ /pubmed/27257403 http://dx.doi.org/10.1007/s10742-015-0142-4 Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Article
Congdon, Peter
Area variations in multiple morbidity using a life table methodology
title Area variations in multiple morbidity using a life table methodology
title_full Area variations in multiple morbidity using a life table methodology
title_fullStr Area variations in multiple morbidity using a life table methodology
title_full_unstemmed Area variations in multiple morbidity using a life table methodology
title_short Area variations in multiple morbidity using a life table methodology
title_sort area variations in multiple morbidity using a life table methodology
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4867778/
https://www.ncbi.nlm.nih.gov/pubmed/27257403
http://dx.doi.org/10.1007/s10742-015-0142-4
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