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A Model for Risk Prediction of Cerebrovascular Disease Prevalence—Based on Community Residents Aged 40 and above in a City in China
Cerebrovascular disease (CVD) is the leading cause of death in many countries including China. Early diagnosis and risk assessment represent one of effective approaches to reduce the CVD-related mortality. The purpose of this study was to understand the prevalence and influencing factors of cerebrov...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8296485/ https://www.ncbi.nlm.nih.gov/pubmed/34207332 http://dx.doi.org/10.3390/ijerph18126584 |
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author | Zhu, Qin Luo, Die Zhou, Xiaojun Cai, Xianxu Li, Qi Lu, Yuanan Chen, Jiayan |
author_facet | Zhu, Qin Luo, Die Zhou, Xiaojun Cai, Xianxu Li, Qi Lu, Yuanan Chen, Jiayan |
author_sort | Zhu, Qin |
collection | PubMed |
description | Cerebrovascular disease (CVD) is the leading cause of death in many countries including China. Early diagnosis and risk assessment represent one of effective approaches to reduce the CVD-related mortality. The purpose of this study was to understand the prevalence and influencing factors of cerebrovascular disease among community residents in Qingyunpu District, Nanchang City, Jiangxi Province, and to construct a model of cerebrovascular disease risk index suitable for local community residents. A stratified cluster sampling method was used to sample 2147 community residents aged 40 and above, and the prevalence of cerebrovascular diseases and possible risk factors were investigated. It was found that the prevalence of cerebrovascular disease among local residents was 4.5%. Poisson regression analysis found that old age, lack of exercise, hypertension, diabetes, smoking, and family history of cerebrovascular disease are the main risk factors for local cerebrovascular disease. The relative risk ORs were 3.284, 2.306, 2.510, 3.194, 1.949, 2.315, respectively. For these six selected risk factors, a cerebrovascular disease risk prediction model was established using the Harvard Cancer Index method. The R value of the risk prediction model was 1.80 (sensitivity 81.8%, specificity 47.0%), which was able to well predict the risk of cerebrovascular disease among local residents. This provides a scientific basis for the further development of local cerebrovascular disease prevention and control work. |
format | Online Article Text |
id | pubmed-8296485 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-82964852021-07-23 A Model for Risk Prediction of Cerebrovascular Disease Prevalence—Based on Community Residents Aged 40 and above in a City in China Zhu, Qin Luo, Die Zhou, Xiaojun Cai, Xianxu Li, Qi Lu, Yuanan Chen, Jiayan Int J Environ Res Public Health Article Cerebrovascular disease (CVD) is the leading cause of death in many countries including China. Early diagnosis and risk assessment represent one of effective approaches to reduce the CVD-related mortality. The purpose of this study was to understand the prevalence and influencing factors of cerebrovascular disease among community residents in Qingyunpu District, Nanchang City, Jiangxi Province, and to construct a model of cerebrovascular disease risk index suitable for local community residents. A stratified cluster sampling method was used to sample 2147 community residents aged 40 and above, and the prevalence of cerebrovascular diseases and possible risk factors were investigated. It was found that the prevalence of cerebrovascular disease among local residents was 4.5%. Poisson regression analysis found that old age, lack of exercise, hypertension, diabetes, smoking, and family history of cerebrovascular disease are the main risk factors for local cerebrovascular disease. The relative risk ORs were 3.284, 2.306, 2.510, 3.194, 1.949, 2.315, respectively. For these six selected risk factors, a cerebrovascular disease risk prediction model was established using the Harvard Cancer Index method. The R value of the risk prediction model was 1.80 (sensitivity 81.8%, specificity 47.0%), which was able to well predict the risk of cerebrovascular disease among local residents. This provides a scientific basis for the further development of local cerebrovascular disease prevention and control work. MDPI 2021-06-18 /pmc/articles/PMC8296485/ /pubmed/34207332 http://dx.doi.org/10.3390/ijerph18126584 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zhu, Qin Luo, Die Zhou, Xiaojun Cai, Xianxu Li, Qi Lu, Yuanan Chen, Jiayan A Model for Risk Prediction of Cerebrovascular Disease Prevalence—Based on Community Residents Aged 40 and above in a City in China |
title | A Model for Risk Prediction of Cerebrovascular Disease Prevalence—Based on Community Residents Aged 40 and above in a City in China |
title_full | A Model for Risk Prediction of Cerebrovascular Disease Prevalence—Based on Community Residents Aged 40 and above in a City in China |
title_fullStr | A Model for Risk Prediction of Cerebrovascular Disease Prevalence—Based on Community Residents Aged 40 and above in a City in China |
title_full_unstemmed | A Model for Risk Prediction of Cerebrovascular Disease Prevalence—Based on Community Residents Aged 40 and above in a City in China |
title_short | A Model for Risk Prediction of Cerebrovascular Disease Prevalence—Based on Community Residents Aged 40 and above in a City in China |
title_sort | model for risk prediction of cerebrovascular disease prevalence—based on community residents aged 40 and above in a city in china |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8296485/ https://www.ncbi.nlm.nih.gov/pubmed/34207332 http://dx.doi.org/10.3390/ijerph18126584 |
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