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The Use of a Network Analysis to Identify Associations and Temporal Patterns Among Non-communicable Diseases in Japan Based on a Large Medical Claims Database

BACKGROUND: Reducing the considerable non-communicable disease (NCD) burden in the aging Japanese population depends on better understanding of the comorbid and temporal relationships between different NCDs. OBJECTIVE: We aimed to identify associations between NCDs and temporal patterns of NCDs in J...

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Autores principales: Higa, Shingo, Nozawa, Kazutaka, Karasawa, Yusuke, Shirai, Chikako, Matsuyama, Satoshi, Yamamoto, Yuji, Laurent, Thomas, Asami, Yuko
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9392665/
https://www.ncbi.nlm.nih.gov/pubmed/35780274
http://dx.doi.org/10.1007/s40801-022-00310-w
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author Higa, Shingo
Nozawa, Kazutaka
Karasawa, Yusuke
Shirai, Chikako
Matsuyama, Satoshi
Yamamoto, Yuji
Laurent, Thomas
Asami, Yuko
author_facet Higa, Shingo
Nozawa, Kazutaka
Karasawa, Yusuke
Shirai, Chikako
Matsuyama, Satoshi
Yamamoto, Yuji
Laurent, Thomas
Asami, Yuko
author_sort Higa, Shingo
collection PubMed
description BACKGROUND: Reducing the considerable non-communicable disease (NCD) burden in the aging Japanese population depends on better understanding of the comorbid and temporal relationships between different NCDs. OBJECTIVE: We aimed to identify associations between NCDs and temporal patterns of NCDs in Japan using data from a large medical claims database. METHODS: The study used three-digit International Classification of Diseases, Tenth Revision codes for NCDs for employees and their dependents included in the MinaCare database, which covers the period since 2010. Associations between pairs of NCDs were assessed by calculating risk ratios. The calculated risk ratios were used to create a network of closely associated NCDs (risk ratio > 15, statistically significant) and to assess temporal patterns of NCD diagnoses (risk ratio ≥ 5). The Infomap algorithm was used to identify clusters of diseases for different sex and age strata. RESULTS: The analysis included 4,200,254 individuals (age < 65 years: 98%). Many of the temporal associations and patterns of the diseases of interest identified in this study were previously known. Regarding the diseases of interest, these associations can be classified as comorbidities, early manifestations initially diagnosed as something else, diseases attributable to or that cause the disease of interest, or caused by pharmacological treatment. International Classification of Diseases, Tenth Revision chapters that were most associated with other chapters included L Diseases of the skin and subcutaneous tissue. In the age-stratified and gender-stratified networks, clusters with the highest numbers of International Classification of Diseases, Tenth Revision codes included I Diseases of the circulatory system and F Mental and behavioral disorders. CONCLUSIONS: Our findings reinforce established associations between NCDs and underline the importance of comprehensive NCD care. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40801-022-00310-w.
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spelling pubmed-93926652022-08-22 The Use of a Network Analysis to Identify Associations and Temporal Patterns Among Non-communicable Diseases in Japan Based on a Large Medical Claims Database Higa, Shingo Nozawa, Kazutaka Karasawa, Yusuke Shirai, Chikako Matsuyama, Satoshi Yamamoto, Yuji Laurent, Thomas Asami, Yuko Drugs Real World Outcomes Original Research Article BACKGROUND: Reducing the considerable non-communicable disease (NCD) burden in the aging Japanese population depends on better understanding of the comorbid and temporal relationships between different NCDs. OBJECTIVE: We aimed to identify associations between NCDs and temporal patterns of NCDs in Japan using data from a large medical claims database. METHODS: The study used three-digit International Classification of Diseases, Tenth Revision codes for NCDs for employees and their dependents included in the MinaCare database, which covers the period since 2010. Associations between pairs of NCDs were assessed by calculating risk ratios. The calculated risk ratios were used to create a network of closely associated NCDs (risk ratio > 15, statistically significant) and to assess temporal patterns of NCD diagnoses (risk ratio ≥ 5). The Infomap algorithm was used to identify clusters of diseases for different sex and age strata. RESULTS: The analysis included 4,200,254 individuals (age < 65 years: 98%). Many of the temporal associations and patterns of the diseases of interest identified in this study were previously known. Regarding the diseases of interest, these associations can be classified as comorbidities, early manifestations initially diagnosed as something else, diseases attributable to or that cause the disease of interest, or caused by pharmacological treatment. International Classification of Diseases, Tenth Revision chapters that were most associated with other chapters included L Diseases of the skin and subcutaneous tissue. In the age-stratified and gender-stratified networks, clusters with the highest numbers of International Classification of Diseases, Tenth Revision codes included I Diseases of the circulatory system and F Mental and behavioral disorders. CONCLUSIONS: Our findings reinforce established associations between NCDs and underline the importance of comprehensive NCD care. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40801-022-00310-w. Springer International Publishing 2022-07-02 /pmc/articles/PMC9392665/ /pubmed/35780274 http://dx.doi.org/10.1007/s40801-022-00310-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by-nc/4.0/Open AccessThis article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial 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-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Original Research Article
Higa, Shingo
Nozawa, Kazutaka
Karasawa, Yusuke
Shirai, Chikako
Matsuyama, Satoshi
Yamamoto, Yuji
Laurent, Thomas
Asami, Yuko
The Use of a Network Analysis to Identify Associations and Temporal Patterns Among Non-communicable Diseases in Japan Based on a Large Medical Claims Database
title The Use of a Network Analysis to Identify Associations and Temporal Patterns Among Non-communicable Diseases in Japan Based on a Large Medical Claims Database
title_full The Use of a Network Analysis to Identify Associations and Temporal Patterns Among Non-communicable Diseases in Japan Based on a Large Medical Claims Database
title_fullStr The Use of a Network Analysis to Identify Associations and Temporal Patterns Among Non-communicable Diseases in Japan Based on a Large Medical Claims Database
title_full_unstemmed The Use of a Network Analysis to Identify Associations and Temporal Patterns Among Non-communicable Diseases in Japan Based on a Large Medical Claims Database
title_short The Use of a Network Analysis to Identify Associations and Temporal Patterns Among Non-communicable Diseases in Japan Based on a Large Medical Claims Database
title_sort use of a network analysis to identify associations and temporal patterns among non-communicable diseases in japan based on a large medical claims database
topic Original Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9392665/
https://www.ncbi.nlm.nih.gov/pubmed/35780274
http://dx.doi.org/10.1007/s40801-022-00310-w
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