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Investigation of multimorbidity and prevalent disease combinations in older Irish adults using network analysis and association rules

Multimorbidity (the presence of multiple medical conditions) is well known to increase with age. People with multimorbidities often have higher physical and functional decline as well as increased mortality. Despite growing evidence that integrated and collaborative care improves many undesirable ou...

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Autores principales: Hernández, Belinda, Reilly, Richard B., Kenny, Rose Anne
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6787335/
https://www.ncbi.nlm.nih.gov/pubmed/31601959
http://dx.doi.org/10.1038/s41598-019-51135-7
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author Hernández, Belinda
Reilly, Richard B.
Kenny, Rose Anne
author_facet Hernández, Belinda
Reilly, Richard B.
Kenny, Rose Anne
author_sort Hernández, Belinda
collection PubMed
description Multimorbidity (the presence of multiple medical conditions) is well known to increase with age. People with multimorbidities often have higher physical and functional decline as well as increased mortality. Despite growing evidence that integrated and collaborative care improves many undesirable outcomes of multimorbidity, the majority of health systems are based around treating individual diseases. A pattern analysis of comorbidities using network graphs and a novel use of association rules was conducted to investigate disease associations on 6101 Irish adults aged 50+. The complex network of morbidities and differences in the prevalence and interactions of these morbidities by sex was also assessed. Gender specific differences in disease prevalence was found for 22/31 medical conditions included in this study. Females had a more complex network of disease associations than males with strong associations found between arthritis, osteoporosis and thyroid issues among others. To assess the strength of these associations we provide probabilities of being diagnosed with a comorbid condition given the presence of an index morbidity for 639 pairwise combinations. This information can be used to guide clinicians in deciding which comorbidities should be incorporated into comprehensive assessments in addition to anticipating likely future morbidities and thus developing prevention strategies.
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spelling pubmed-67873352019-10-17 Investigation of multimorbidity and prevalent disease combinations in older Irish adults using network analysis and association rules Hernández, Belinda Reilly, Richard B. Kenny, Rose Anne Sci Rep Article Multimorbidity (the presence of multiple medical conditions) is well known to increase with age. People with multimorbidities often have higher physical and functional decline as well as increased mortality. Despite growing evidence that integrated and collaborative care improves many undesirable outcomes of multimorbidity, the majority of health systems are based around treating individual diseases. A pattern analysis of comorbidities using network graphs and a novel use of association rules was conducted to investigate disease associations on 6101 Irish adults aged 50+. The complex network of morbidities and differences in the prevalence and interactions of these morbidities by sex was also assessed. Gender specific differences in disease prevalence was found for 22/31 medical conditions included in this study. Females had a more complex network of disease associations than males with strong associations found between arthritis, osteoporosis and thyroid issues among others. To assess the strength of these associations we provide probabilities of being diagnosed with a comorbid condition given the presence of an index morbidity for 639 pairwise combinations. This information can be used to guide clinicians in deciding which comorbidities should be incorporated into comprehensive assessments in addition to anticipating likely future morbidities and thus developing prevention strategies. Nature Publishing Group UK 2019-10-10 /pmc/articles/PMC6787335/ /pubmed/31601959 http://dx.doi.org/10.1038/s41598-019-51135-7 Text en © The Author(s) 2019 Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Hernández, Belinda
Reilly, Richard B.
Kenny, Rose Anne
Investigation of multimorbidity and prevalent disease combinations in older Irish adults using network analysis and association rules
title Investigation of multimorbidity and prevalent disease combinations in older Irish adults using network analysis and association rules
title_full Investigation of multimorbidity and prevalent disease combinations in older Irish adults using network analysis and association rules
title_fullStr Investigation of multimorbidity and prevalent disease combinations in older Irish adults using network analysis and association rules
title_full_unstemmed Investigation of multimorbidity and prevalent disease combinations in older Irish adults using network analysis and association rules
title_short Investigation of multimorbidity and prevalent disease combinations in older Irish adults using network analysis and association rules
title_sort investigation of multimorbidity and prevalent disease combinations in older irish adults using network analysis and association rules
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6787335/
https://www.ncbi.nlm.nih.gov/pubmed/31601959
http://dx.doi.org/10.1038/s41598-019-51135-7
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