The transitions and predictors of cognitive frailty with multi-state Markov model: a cohort study

Manqiong Yuan; Chuanhai Xu; Ya Fang

doi:10.1186/s12877-022-03220-2

The transitions and predictors of cognitive frailty with multi-state Markov model: a cohort study

BMC Geriatr. 2022 Jul 1;22(1):550. doi: 10.1186/s12877-022-03220-2.

Authors

Manqiong Yuan^#^{1

2}, Chuanhai Xu^#², Ya Fang^{3

4}

Affiliations

¹ State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102, Fujian, China.
² Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, China.
³ State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102, Fujian, China. fangya@xmu.edu.cn.
⁴ Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, China. fangya@xmu.edu.cn.

^# Contributed equally.

Abstract

Background: Cognitive frailty (CF) is characterized by the simultaneous presence of physical frailty and cognitive impairment. Previous studies have investigated its prevalence and impact on different adverse health-related outcomes. Few studies have focused on the progression and reversibility of CF and their potential predictors.

Methods: Data were derived from the China Health and Retirement Longitudinal Study (CHARLS). A total of 4051 older adults with complete data on three waves of the survey (2011, 2013, and 2015) were included and categorized into four groups: normal state (NS), cognitive impairment (CI) only, physical frailty (PF) only and CF (with both PF and CI). A multi-state Markov model was constructed to explore the transitions and predicting factors of CF.

Results: The incidence and improvement rates of CF were 1.70 and 11.90 per 100 person-years, respectively. The 1-year transition probability of progression to CF in those with CI was higher than that in the PF population (0.340 vs. 0.054), and those with CF were more likely to move to PF (0.208). Being female [hazard ratio (HR) = 1.46, 95%CI = 1.06, 2.02)], dissatisfied with life (HR = 4.94, 95%CI = 1.04, 23.61), had a history of falls (HR = 2.36, 95%CI = 1.02, 5.51), rural household registration (HR = 2.98, 95%CI = 1.61, 5.48), multimorbidity (HR = 2.17, 95%CI = 1.03, 4.59), and depression (HR = 1.75, 95%CI = 1.26, 2.45) increased the risk of progression to CF, whereas literacy (HR = 0.46, 95%CI = 0.33, 0.64) decreased such risk. Depression (HR = 0.43, 95%CI = 0.22, 0.84) reduced the likelihood of CF improvement, whereas literacy (HR = 2.23, 95%CI = 1.63, 3.07) increased such likelihood.

Conclusions: Cognitive frailty is a dynamically changing condition in older adults. Possible interventions aimed at preventing the onset and facilitating the recovery of cognitive frailty should focus on improving cognitive function in older adults.

Keywords: Cognitive frailty; Cognitive impairment; Multi-state Markov model; Physical frailty.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Aged
Cognition
Cohort Studies
Female
Frail Elderly / psychology
Frailty* / diagnosis
Frailty* / epidemiology
Frailty* / psychology
Humans
Longitudinal Studies
Male