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The population attributable risk and clustering of stroke risk factors in different economical regions of China

The greatest regional variation in stroke prevalence exists in China. However, whether there are differences in population attributable risk (PAR) and clustering of stroke risk factors among regions resulting in stroke geographic variation is unclear. We conducted face-to-face surveys of residents o...

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Autores principales: Dong, Shuju, Fang, Jinghuan, Li, Yanbo, Ma, Mengmeng, Hong, Ye, He, Li
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Wolters Kluwer Health 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7220510/
https://www.ncbi.nlm.nih.gov/pubmed/32311944
http://dx.doi.org/10.1097/MD.0000000000019689
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author Dong, Shuju
Fang, Jinghuan
Li, Yanbo
Ma, Mengmeng
Hong, Ye
He, Li
author_facet Dong, Shuju
Fang, Jinghuan
Li, Yanbo
Ma, Mengmeng
Hong, Ye
He, Li
author_sort Dong, Shuju
collection PubMed
description The greatest regional variation in stroke prevalence exists in China. However, whether there are differences in population attributable risk (PAR) and clustering of stroke risk factors among regions resulting in stroke geographic variation is unclear. We conducted face-to-face surveys of residents of 14 provinces from September 2016 to May 2017 who participated in the Chinese Stroke Screening and Prevention Project. We compared the specific PAR values of eight risk factors and the different cluster rates and patterns in China. A total of 84,751partipants were included. Eight factors accounted for 70% to 80% of the PAR of overall stroke in China. Not only did the PAR of the total risk factors differ among the 3 regions, but the PAR of the same risk factor also varied among different regions. The top 3 factors with the greatest PAR variations among the 3 regions were dyslipidemia, physical inactivity and family history of stroke. The clustering rates and patterns varied by regions. The overall proportion of participants with 0, 1, 2, 3, and ≥4 risk factors were 34.4%, 28.0%, 17.4%, 9.2%, and 10.3% in eastern China; 31.0%, 27.9%, 19.8%, 10.8%, and 9.9% in Central China and 28.2%, 29.5%, 19.9%, 10.8%, and 11.0% in western China, respectively. On basis of hypertension, the most common risk cluster patterns were overweight or smoking, dyslipidemia and physical inactivity, with other risk factors in the eastern, central and western regions, respectively. The rates and patterns of clustering and the potential importance of stroke risk factors in different regions may together contribute to the geographical variation in stroke prevalence in China.
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spelling pubmed-72205102020-06-15 The population attributable risk and clustering of stroke risk factors in different economical regions of China Dong, Shuju Fang, Jinghuan Li, Yanbo Ma, Mengmeng Hong, Ye He, Li Medicine (Baltimore) 3400 The greatest regional variation in stroke prevalence exists in China. However, whether there are differences in population attributable risk (PAR) and clustering of stroke risk factors among regions resulting in stroke geographic variation is unclear. We conducted face-to-face surveys of residents of 14 provinces from September 2016 to May 2017 who participated in the Chinese Stroke Screening and Prevention Project. We compared the specific PAR values of eight risk factors and the different cluster rates and patterns in China. A total of 84,751partipants were included. Eight factors accounted for 70% to 80% of the PAR of overall stroke in China. Not only did the PAR of the total risk factors differ among the 3 regions, but the PAR of the same risk factor also varied among different regions. The top 3 factors with the greatest PAR variations among the 3 regions were dyslipidemia, physical inactivity and family history of stroke. The clustering rates and patterns varied by regions. The overall proportion of participants with 0, 1, 2, 3, and ≥4 risk factors were 34.4%, 28.0%, 17.4%, 9.2%, and 10.3% in eastern China; 31.0%, 27.9%, 19.8%, 10.8%, and 9.9% in Central China and 28.2%, 29.5%, 19.9%, 10.8%, and 11.0% in western China, respectively. On basis of hypertension, the most common risk cluster patterns were overweight or smoking, dyslipidemia and physical inactivity, with other risk factors in the eastern, central and western regions, respectively. The rates and patterns of clustering and the potential importance of stroke risk factors in different regions may together contribute to the geographical variation in stroke prevalence in China. Wolters Kluwer Health 2020-04-17 /pmc/articles/PMC7220510/ /pubmed/32311944 http://dx.doi.org/10.1097/MD.0000000000019689 Text en Copyright © 2020 the Author(s). Published by Wolters Kluwer Health, Inc. http://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 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc/4.0
spellingShingle 3400
Dong, Shuju
Fang, Jinghuan
Li, Yanbo
Ma, Mengmeng
Hong, Ye
He, Li
The population attributable risk and clustering of stroke risk factors in different economical regions of China
title The population attributable risk and clustering of stroke risk factors in different economical regions of China
title_full The population attributable risk and clustering of stroke risk factors in different economical regions of China
title_fullStr The population attributable risk and clustering of stroke risk factors in different economical regions of China
title_full_unstemmed The population attributable risk and clustering of stroke risk factors in different economical regions of China
title_short The population attributable risk and clustering of stroke risk factors in different economical regions of China
title_sort population attributable risk and clustering of stroke risk factors in different economical regions of china
topic 3400
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7220510/
https://www.ncbi.nlm.nih.gov/pubmed/32311944
http://dx.doi.org/10.1097/MD.0000000000019689
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