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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
[Transition rules of cognitive frailty and influencing factors in the elderly in China]
1Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen 361000, China.
Abstract:
Objective: To understand the transition rules of cognitive frailty and its influencing factors in the elderly in China and provide evidence for the early intervention of cognitive frailty. Methods: Data were retrospectively collected from China Health and Retirement Longitudinal Study with 3 round consecutive survey (2011, 2013, 2015) and the state of the subjects were classified into four categories: robust-normal cognitive, cognitive impairment, physical frailty, and cognitive frailty. A multi-state Markov model was established to explore the transition rules of cognitive frailty and its influencing factors. Results: A total of 3 470 older adults were included, and 350 (10.09%) had cognitive frailty at baseline. After two years, the probability of cognitive frailty in the cognitive impairment population was higher than that in people with physical frailty (31.6% vs. 7.6%). Persons with cognitive frailty were more likely to become physical frailty (29.7% vs. 15.6%). Being women (HR=1.599, 95%CI: 1.058-2.417), comorbidity (HR=3.035, 95%CI: 1.090-8.450), and depression (HR=1.678, 95%CI: 1.153-2.441) were the risk factors associated with cognitive frailty in the elderly, while being educated (HR=2.367, 95%CI: 1.567-3.575) was a protective factor for the transition of cognitive frailty to physical frailty. Conclusions: The prevalence of cognitive frailty is relatively high in the elderly in China. Those with cognitive impairment have a higher probability of cognitive frailty. Gender, education level, comorbidity, and depression are the main influencing factors for the occurrence and transition of cognitive frailty.
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