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Analysis of multimorbidity networks associated with different factors in Northeast China: a cross-sectional analysis

OBJECTIVES: This study aimed to identify and study the associations and co-occurrence of multimorbidity, and assessed the associations of diseases with sex, age and hospitalisation duration. DESIGN: Cross-sectional. SETTING: 15 general hospitals in Jilin Province, China. PARTICIPANTS: A total of 431...

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Autores principales: Yu, Jianxing, Li, Yingying, Zheng, Zhou, Jia, Huanhuan, Cao, Peng, Qiangba, Yuzhen, Yu, Xihe
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8572406/
https://www.ncbi.nlm.nih.gov/pubmed/34732482
http://dx.doi.org/10.1136/bmjopen-2021-051050
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author Yu, Jianxing
Li, Yingying
Zheng, Zhou
Jia, Huanhuan
Cao, Peng
Qiangba, Yuzhen
Yu, Xihe
author_facet Yu, Jianxing
Li, Yingying
Zheng, Zhou
Jia, Huanhuan
Cao, Peng
Qiangba, Yuzhen
Yu, Xihe
author_sort Yu, Jianxing
collection PubMed
description OBJECTIVES: This study aimed to identify and study the associations and co-occurrence of multimorbidity, and assessed the associations of diseases with sex, age and hospitalisation duration. DESIGN: Cross-sectional. SETTING: 15 general hospitals in Jilin Province, China. PARTICIPANTS: A total of 431 295 inpatients were enrolled through a cross-sectional study in Jilin Province, China. PRIMARY OUTCOME MEASURES: The complex relationships of multimorbidity were presented as weighted networks. RESULTS: The distributions of the numbers of diseases differed significantly by sex, age and hospitalisation duration (p<0.001). Cerebrovascular diseases (CD), hypertensive diseases (HyD), ischaemic heart diseases (IHD) and other forms of heart disease (OFHD) showed the highest weights in the multimorbidity networks. The connections between different sexes or hospitalisation duration and diseases were similar, while those between different age groups and diseases were different. CONCLUSIONS: CD, HyD, IHD and OFHD were the central points of disease clusters and directly or indirectly related to other diseases or factors. Thus, effective interventions for these diseases should be adopted. Furthermore, different intervention strategies should be developed according to multimorbidity patterns in different age groups.
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spelling pubmed-85724062021-11-17 Analysis of multimorbidity networks associated with different factors in Northeast China: a cross-sectional analysis Yu, Jianxing Li, Yingying Zheng, Zhou Jia, Huanhuan Cao, Peng Qiangba, Yuzhen Yu, Xihe BMJ Open Public Health OBJECTIVES: This study aimed to identify and study the associations and co-occurrence of multimorbidity, and assessed the associations of diseases with sex, age and hospitalisation duration. DESIGN: Cross-sectional. SETTING: 15 general hospitals in Jilin Province, China. PARTICIPANTS: A total of 431 295 inpatients were enrolled through a cross-sectional study in Jilin Province, China. PRIMARY OUTCOME MEASURES: The complex relationships of multimorbidity were presented as weighted networks. RESULTS: The distributions of the numbers of diseases differed significantly by sex, age and hospitalisation duration (p<0.001). Cerebrovascular diseases (CD), hypertensive diseases (HyD), ischaemic heart diseases (IHD) and other forms of heart disease (OFHD) showed the highest weights in the multimorbidity networks. The connections between different sexes or hospitalisation duration and diseases were similar, while those between different age groups and diseases were different. CONCLUSIONS: CD, HyD, IHD and OFHD were the central points of disease clusters and directly or indirectly related to other diseases or factors. Thus, effective interventions for these diseases should be adopted. Furthermore, different intervention strategies should be developed according to multimorbidity patterns in different age groups. BMJ Publishing Group 2021-11-03 /pmc/articles/PMC8572406/ /pubmed/34732482 http://dx.doi.org/10.1136/bmjopen-2021-051050 Text en © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Public Health
Yu, Jianxing
Li, Yingying
Zheng, Zhou
Jia, Huanhuan
Cao, Peng
Qiangba, Yuzhen
Yu, Xihe
Analysis of multimorbidity networks associated with different factors in Northeast China: a cross-sectional analysis
title Analysis of multimorbidity networks associated with different factors in Northeast China: a cross-sectional analysis
title_full Analysis of multimorbidity networks associated with different factors in Northeast China: a cross-sectional analysis
title_fullStr Analysis of multimorbidity networks associated with different factors in Northeast China: a cross-sectional analysis
title_full_unstemmed Analysis of multimorbidity networks associated with different factors in Northeast China: a cross-sectional analysis
title_short Analysis of multimorbidity networks associated with different factors in Northeast China: a cross-sectional analysis
title_sort analysis of multimorbidity networks associated with different factors in northeast china: a cross-sectional analysis
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8572406/
https://www.ncbi.nlm.nih.gov/pubmed/34732482
http://dx.doi.org/10.1136/bmjopen-2021-051050
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