Cargando…

Development of Multimorbidity Over Time: An Analysis of Belgium Primary Care Data Using Markov Chains and Weighted Association Rule Mining

BACKGROUND: The prevalence of multimorbidity is increasing in recent years, and patients with multimorbidity often have a decrease in quality of life and require more health care. The aim of this study was to explore the evolution of multimorbidity taking the sequence of diseases into consideration....

Descripción completa

Detalles Bibliográficos
Autores principales: Shi, Xi, Nikolic, Gorana, Van Pottelbergh, Gijs, van den Akker, Marjan, Vos, Rein, De Moor, Bart
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8202155/
https://www.ncbi.nlm.nih.gov/pubmed/33159204
http://dx.doi.org/10.1093/gerona/glaa278
_version_ 1783707924017709056
author Shi, Xi
Nikolic, Gorana
Van Pottelbergh, Gijs
van den Akker, Marjan
Vos, Rein
De Moor, Bart
author_facet Shi, Xi
Nikolic, Gorana
Van Pottelbergh, Gijs
van den Akker, Marjan
Vos, Rein
De Moor, Bart
author_sort Shi, Xi
collection PubMed
description BACKGROUND: The prevalence of multimorbidity is increasing in recent years, and patients with multimorbidity often have a decrease in quality of life and require more health care. The aim of this study was to explore the evolution of multimorbidity taking the sequence of diseases into consideration. METHODS: We used a Belgian database collected by extracting coded parameters and more than 100 chronic conditions from the Electronic Health Records of general practitioners to study patients older than 40 years with multiple diagnoses between 1991 and 2015 (N = 65 939). We applied Markov chains to estimate the probability of developing another condition in the next state after a diagnosis. The results of Weighted Association Rule Mining (WARM) allow us to show strong associations among multiple conditions. RESULTS: About 66.9% of the selected patients had multimorbidity. Conditions with high prevalence, such as hypertension and depressive disorder, were likely to occur after the diagnosis of most conditions. Patterns in several disease groups were apparent based on the results of both Markov chain and WARM, such as musculoskeletal diseases and psychological diseases. Psychological diseases were frequently followed by irritable bowel syndrome. CONCLUSIONS: Our study used Markov chains and WARM for the first time to provide a comprehensive view of the relations among 103 chronic conditions, taking sequential chronology into consideration. Some strong associations among specific conditions were detected and the results were consistent with current knowledge in literature, meaning the approaches were valid to be used on larger data sets, such as National Health care Systems or private insurers.
format Online
Article
Text
id pubmed-8202155
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Oxford University Press
record_format MEDLINE/PubMed
spelling pubmed-82021552021-06-15 Development of Multimorbidity Over Time: An Analysis of Belgium Primary Care Data Using Markov Chains and Weighted Association Rule Mining Shi, Xi Nikolic, Gorana Van Pottelbergh, Gijs van den Akker, Marjan Vos, Rein De Moor, Bart J Gerontol A Biol Sci Med Sci THE JOURNAL OF GERONTOLOGY: Medical Sciences BACKGROUND: The prevalence of multimorbidity is increasing in recent years, and patients with multimorbidity often have a decrease in quality of life and require more health care. The aim of this study was to explore the evolution of multimorbidity taking the sequence of diseases into consideration. METHODS: We used a Belgian database collected by extracting coded parameters and more than 100 chronic conditions from the Electronic Health Records of general practitioners to study patients older than 40 years with multiple diagnoses between 1991 and 2015 (N = 65 939). We applied Markov chains to estimate the probability of developing another condition in the next state after a diagnosis. The results of Weighted Association Rule Mining (WARM) allow us to show strong associations among multiple conditions. RESULTS: About 66.9% of the selected patients had multimorbidity. Conditions with high prevalence, such as hypertension and depressive disorder, were likely to occur after the diagnosis of most conditions. Patterns in several disease groups were apparent based on the results of both Markov chain and WARM, such as musculoskeletal diseases and psychological diseases. Psychological diseases were frequently followed by irritable bowel syndrome. CONCLUSIONS: Our study used Markov chains and WARM for the first time to provide a comprehensive view of the relations among 103 chronic conditions, taking sequential chronology into consideration. Some strong associations among specific conditions were detected and the results were consistent with current knowledge in literature, meaning the approaches were valid to be used on larger data sets, such as National Health care Systems or private insurers. Oxford University Press 2020-11-07 /pmc/articles/PMC8202155/ /pubmed/33159204 http://dx.doi.org/10.1093/gerona/glaa278 Text en © The Author(s) 2020. Published by Oxford University Press on behalf of The Gerontological Society of America. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle THE JOURNAL OF GERONTOLOGY: Medical Sciences
Shi, Xi
Nikolic, Gorana
Van Pottelbergh, Gijs
van den Akker, Marjan
Vos, Rein
De Moor, Bart
Development of Multimorbidity Over Time: An Analysis of Belgium Primary Care Data Using Markov Chains and Weighted Association Rule Mining
title Development of Multimorbidity Over Time: An Analysis of Belgium Primary Care Data Using Markov Chains and Weighted Association Rule Mining
title_full Development of Multimorbidity Over Time: An Analysis of Belgium Primary Care Data Using Markov Chains and Weighted Association Rule Mining
title_fullStr Development of Multimorbidity Over Time: An Analysis of Belgium Primary Care Data Using Markov Chains and Weighted Association Rule Mining
title_full_unstemmed Development of Multimorbidity Over Time: An Analysis of Belgium Primary Care Data Using Markov Chains and Weighted Association Rule Mining
title_short Development of Multimorbidity Over Time: An Analysis of Belgium Primary Care Data Using Markov Chains and Weighted Association Rule Mining
title_sort development of multimorbidity over time: an analysis of belgium primary care data using markov chains and weighted association rule mining
topic THE JOURNAL OF GERONTOLOGY: Medical Sciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8202155/
https://www.ncbi.nlm.nih.gov/pubmed/33159204
http://dx.doi.org/10.1093/gerona/glaa278
work_keys_str_mv AT shixi developmentofmultimorbidityovertimeananalysisofbelgiumprimarycaredatausingmarkovchainsandweightedassociationrulemining
AT nikolicgorana developmentofmultimorbidityovertimeananalysisofbelgiumprimarycaredatausingmarkovchainsandweightedassociationrulemining
AT vanpottelberghgijs developmentofmultimorbidityovertimeananalysisofbelgiumprimarycaredatausingmarkovchainsandweightedassociationrulemining
AT vandenakkermarjan developmentofmultimorbidityovertimeananalysisofbelgiumprimarycaredatausingmarkovchainsandweightedassociationrulemining
AT vosrein developmentofmultimorbidityovertimeananalysisofbelgiumprimarycaredatausingmarkovchainsandweightedassociationrulemining
AT demoorbart developmentofmultimorbidityovertimeananalysisofbelgiumprimarycaredatausingmarkovchainsandweightedassociationrulemining