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Frailty Risk Prediction Model among Older Adults: A Chinese Nation-Wide Cross-Sectional Study

Objectives: Numerous studies have been performed on frailty, but rarely do studies explore the integrated impact of socio-demographic, behavioural and social support factors on frailty. This study aims to establish a comprehensive frailty risk prediction model including multiple risk factors. Method...

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Autores principales: Li, Siying, Fan, Wenye, Zhu, Boya, Ma, Chao, Tan, Xiaodong, Gu, Yaohua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9322778/
https://www.ncbi.nlm.nih.gov/pubmed/35886260
http://dx.doi.org/10.3390/ijerph19148410
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author Li, Siying
Fan, Wenye
Zhu, Boya
Ma, Chao
Tan, Xiaodong
Gu, Yaohua
author_facet Li, Siying
Fan, Wenye
Zhu, Boya
Ma, Chao
Tan, Xiaodong
Gu, Yaohua
author_sort Li, Siying
collection PubMed
description Objectives: Numerous studies have been performed on frailty, but rarely do studies explore the integrated impact of socio-demographic, behavioural and social support factors on frailty. This study aims to establish a comprehensive frailty risk prediction model including multiple risk factors. Methods: The 2018 wave of the Chinese Longevity and Health Longitudinal Survey was used. Univariate and multivariate logistic regressions were performed to identify the relationship between frailty and multiple risk factors and establish the frailty risk prediction model. A nomogram was utilized to illustrate the prediction model. The area under the receiver operating characteristic curve (AUC), Hosmer–Lemeshow test and calibration curve were used to appraise the prediction model. Results: Variables from socio-demographic, social support and behavioural dimensions were included in the final frailty risk prediction model. Risk factors include older age, working as professionals and technicians before 60 years old, poor economic condition and poor oral hygiene. Protective factors include eating rice as a staple food, regular exercise, having a spouse as the first person to share thoughts with, doing physical examination once a year and not needing a caregiver when ill. The AUC (0.881), Hosmer–Lemeshow test (p = 0.618), and calibration curve showed that the risk prediction model was valid. Conclusion: Risk factors from socio-demographic, behavioural and social support dimensions had a comprehensive effect on frailty, further supporting that a comprehensive and individualized intervention is necessary to prevent frailty.
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spelling pubmed-93227782022-07-27 Frailty Risk Prediction Model among Older Adults: A Chinese Nation-Wide Cross-Sectional Study Li, Siying Fan, Wenye Zhu, Boya Ma, Chao Tan, Xiaodong Gu, Yaohua Int J Environ Res Public Health Article Objectives: Numerous studies have been performed on frailty, but rarely do studies explore the integrated impact of socio-demographic, behavioural and social support factors on frailty. This study aims to establish a comprehensive frailty risk prediction model including multiple risk factors. Methods: The 2018 wave of the Chinese Longevity and Health Longitudinal Survey was used. Univariate and multivariate logistic regressions were performed to identify the relationship between frailty and multiple risk factors and establish the frailty risk prediction model. A nomogram was utilized to illustrate the prediction model. The area under the receiver operating characteristic curve (AUC), Hosmer–Lemeshow test and calibration curve were used to appraise the prediction model. Results: Variables from socio-demographic, social support and behavioural dimensions were included in the final frailty risk prediction model. Risk factors include older age, working as professionals and technicians before 60 years old, poor economic condition and poor oral hygiene. Protective factors include eating rice as a staple food, regular exercise, having a spouse as the first person to share thoughts with, doing physical examination once a year and not needing a caregiver when ill. The AUC (0.881), Hosmer–Lemeshow test (p = 0.618), and calibration curve showed that the risk prediction model was valid. Conclusion: Risk factors from socio-demographic, behavioural and social support dimensions had a comprehensive effect on frailty, further supporting that a comprehensive and individualized intervention is necessary to prevent frailty. MDPI 2022-07-09 /pmc/articles/PMC9322778/ /pubmed/35886260 http://dx.doi.org/10.3390/ijerph19148410 Text en © 2022 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
Li, Siying
Fan, Wenye
Zhu, Boya
Ma, Chao
Tan, Xiaodong
Gu, Yaohua
Frailty Risk Prediction Model among Older Adults: A Chinese Nation-Wide Cross-Sectional Study
title Frailty Risk Prediction Model among Older Adults: A Chinese Nation-Wide Cross-Sectional Study
title_full Frailty Risk Prediction Model among Older Adults: A Chinese Nation-Wide Cross-Sectional Study
title_fullStr Frailty Risk Prediction Model among Older Adults: A Chinese Nation-Wide Cross-Sectional Study
title_full_unstemmed Frailty Risk Prediction Model among Older Adults: A Chinese Nation-Wide Cross-Sectional Study
title_short Frailty Risk Prediction Model among Older Adults: A Chinese Nation-Wide Cross-Sectional Study
title_sort frailty risk prediction model among older adults: a chinese nation-wide cross-sectional study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9322778/
https://www.ncbi.nlm.nih.gov/pubmed/35886260
http://dx.doi.org/10.3390/ijerph19148410
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