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Spatiotemporal Analysis of the Prevalence and Pattern of Multimorbidity in Older Chinese Adults
BACKGROUND: Multimorbidity presents an enormous problem to societal and healthcare utilization under the context of aging population in low- and middle-income countries (LMICs). Currently, systematic studies on the profile of multimorbidity and its characteristics among Chinese elderly are lacking....
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8811186/ https://www.ncbi.nlm.nih.gov/pubmed/35127761 http://dx.doi.org/10.3389/fmed.2021.806616 |
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author | Chen, Shimin Wang, Shengshu Jia, Wangping Han, Ke Song, Yang Liu, Shaohua Li, Xuehang Liu, Miao He, Yao |
author_facet | Chen, Shimin Wang, Shengshu Jia, Wangping Han, Ke Song, Yang Liu, Shaohua Li, Xuehang Liu, Miao He, Yao |
author_sort | Chen, Shimin |
collection | PubMed |
description | BACKGROUND: Multimorbidity presents an enormous problem to societal and healthcare utilization under the context of aging population in low- and middle-income countries (LMICs). Currently, systematic studies on the profile of multimorbidity and its characteristics among Chinese elderly are lacking. We described the temporal and spatial trends in the prevalence of multimorbidity and explored chronological changes of comorbidity patterns in a large elderly population survey. METHODS: Data were extracted from the Chinese Longitudinal Healthy Longevity Study (CLHLS) conducted between 1998 and 2018 in a random selection of half of the counties and city districts. All the elderly aged 65 and older were included in the survey of eight waves. We used 13 investigated chronic diseases to measure the prevalence of multimorbidity by means of geography, subpopulation, and chronological changes. The patterns of multimorbidity were assessed by computing the value of relative risk (RR indicates the likelihood of certain diseases to be associated with multimorbidity) and the observed-to-expected ratio (O/E indicates the likelihood of the coexistence of a multimorbidity combination). RESULTS: From 1998 to 2018, the prevalence of multimorbidity went from 15.60 to 30.76%, increasing in the fluctuation across the survey of eight waves (p (for trend) = 0.020). Increasing trends were observed similarly in a different gender group (p (male) = 0.009; p (female) = 0.004) and age groups among female participants (p(~80) = 0.009; p(81−90) = 0.004; p(91−100) = 0.035; p(101~) = 0.018). The gap in the prevalence of multimorbidity between the north and the south was getting narrow across the survey of eight waves. Hypertension was the highest prevalent chronic condition while diabetes was most likely to coexist with other chronic conditions in the CLHLS survey. The most frequently occurring clusters were hypertension and heart disease, hypertension and cataract, and hypertension and chronic lung disease. And, the cancer, TB, and Parkinson's disease cluster took the domination of O/E rankings over time, which had a higher probability of coexistence in all the multimorbidity combinations. CONCLUSIONS: The prevalence of multimorbidity has been increasing nationwide, and more attention should be paid to a rapid growth in the southern part of China. It demands the effective diagnosis and treatment adopted to the highly prevalent comorbidities, and strategies and measures were adjusted to strongly relevant clusters. |
format | Online Article Text |
id | pubmed-8811186 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-88111862022-02-04 Spatiotemporal Analysis of the Prevalence and Pattern of Multimorbidity in Older Chinese Adults Chen, Shimin Wang, Shengshu Jia, Wangping Han, Ke Song, Yang Liu, Shaohua Li, Xuehang Liu, Miao He, Yao Front Med (Lausanne) Medicine BACKGROUND: Multimorbidity presents an enormous problem to societal and healthcare utilization under the context of aging population in low- and middle-income countries (LMICs). Currently, systematic studies on the profile of multimorbidity and its characteristics among Chinese elderly are lacking. We described the temporal and spatial trends in the prevalence of multimorbidity and explored chronological changes of comorbidity patterns in a large elderly population survey. METHODS: Data were extracted from the Chinese Longitudinal Healthy Longevity Study (CLHLS) conducted between 1998 and 2018 in a random selection of half of the counties and city districts. All the elderly aged 65 and older were included in the survey of eight waves. We used 13 investigated chronic diseases to measure the prevalence of multimorbidity by means of geography, subpopulation, and chronological changes. The patterns of multimorbidity were assessed by computing the value of relative risk (RR indicates the likelihood of certain diseases to be associated with multimorbidity) and the observed-to-expected ratio (O/E indicates the likelihood of the coexistence of a multimorbidity combination). RESULTS: From 1998 to 2018, the prevalence of multimorbidity went from 15.60 to 30.76%, increasing in the fluctuation across the survey of eight waves (p (for trend) = 0.020). Increasing trends were observed similarly in a different gender group (p (male) = 0.009; p (female) = 0.004) and age groups among female participants (p(~80) = 0.009; p(81−90) = 0.004; p(91−100) = 0.035; p(101~) = 0.018). The gap in the prevalence of multimorbidity between the north and the south was getting narrow across the survey of eight waves. Hypertension was the highest prevalent chronic condition while diabetes was most likely to coexist with other chronic conditions in the CLHLS survey. The most frequently occurring clusters were hypertension and heart disease, hypertension and cataract, and hypertension and chronic lung disease. And, the cancer, TB, and Parkinson's disease cluster took the domination of O/E rankings over time, which had a higher probability of coexistence in all the multimorbidity combinations. CONCLUSIONS: The prevalence of multimorbidity has been increasing nationwide, and more attention should be paid to a rapid growth in the southern part of China. It demands the effective diagnosis and treatment adopted to the highly prevalent comorbidities, and strategies and measures were adjusted to strongly relevant clusters. Frontiers Media S.A. 2022-01-20 /pmc/articles/PMC8811186/ /pubmed/35127761 http://dx.doi.org/10.3389/fmed.2021.806616 Text en Copyright © 2022 Chen, Wang, Jia, Han, Song, Liu, Li, Liu and He. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Medicine Chen, Shimin Wang, Shengshu Jia, Wangping Han, Ke Song, Yang Liu, Shaohua Li, Xuehang Liu, Miao He, Yao Spatiotemporal Analysis of the Prevalence and Pattern of Multimorbidity in Older Chinese Adults |
title | Spatiotemporal Analysis of the Prevalence and Pattern of Multimorbidity in Older Chinese Adults |
title_full | Spatiotemporal Analysis of the Prevalence and Pattern of Multimorbidity in Older Chinese Adults |
title_fullStr | Spatiotemporal Analysis of the Prevalence and Pattern of Multimorbidity in Older Chinese Adults |
title_full_unstemmed | Spatiotemporal Analysis of the Prevalence and Pattern of Multimorbidity in Older Chinese Adults |
title_short | Spatiotemporal Analysis of the Prevalence and Pattern of Multimorbidity in Older Chinese Adults |
title_sort | spatiotemporal analysis of the prevalence and pattern of multimorbidity in older chinese adults |
topic | Medicine |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8811186/ https://www.ncbi.nlm.nih.gov/pubmed/35127761 http://dx.doi.org/10.3389/fmed.2021.806616 |
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