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

Siying Li, Wenye Fan, Boya Zhu, Chao Ma, Xiaodong Tan and Yaohua Gu
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Siying Li: School of Public Health, Wuhan University, Wuhan 430071, China
Wenye Fan: School of Public Health, Wuhan University, Wuhan 430071, China
Boya Zhu: School of Public Health, Wuhan University, Wuhan 430071, China
Chao Ma: School of Public Health, Wuhan University, Wuhan 430071, China
Xiaodong Tan: School of Public Health, Wuhan University, Wuhan 430071, China
Yaohua Gu: School of Nursing, Wuhan University, Wuhan 430071, China

IJERPH, 2022, vol. 19, issue 14, 1-13

Abstract: 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.

Keywords: frailty; risk factor; prediction model; social support; lifestyle (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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