Associations of sociodemographic and behavioral factors with frailty transition patterns: a multi-state Markov analysis of the China Health and Retirement Longitudinal Study (CHARLS)

Background Frailty state is dynamic and reversible, but there is a lack of clarity about the patterns of frailty transition and its influencing factors. This study aimed to investigate frailty transitions and the impact of sociodemographic and behavioral factors on these transitions in Chinese middle-aged and older adults. Methods We used five-wave data from the China Health and Retirement Longitudinal Study (CHARLS), and 20140 Chinese adults aged ≥ 45 years were included. Frailty was assessed using a 35-item frailty index. A multi-state Markov model was used to systematically analyze the transition patterns of frailty states (robust, prefrail, frail) and death, and to explore the associations of sociodemographic and behavioral factors with frailty transitions. Results Among the 20140 participants at baseline, the proportions of robust, prefrailty, and frailty were 30.4%, 51.8%, and 17.8%, respectively. In the short term, participants tended to remain in their original state, while over time, the probability of deterioration and recovery increased significantly. Increasing age [HR (hazard ratio): 1.020; 95% CI (confidence interval): 1.016–1.023], being female (1.394, 1.268–1.533), living in a rural area (1.336, 1.255–1.423), being illiterate (1.207, 1.124–1.296), current smoking (1.108, 1.013–1.212), and sleep < 6 or > 8 hours (1.228, 1.157–1.304) increased the risk of frailty deterioration. While current drinking (0.924, 0.859–0.994) reduced the risk of frailty deterioration. Stratified analyses by age and gender showed consistent results with the main analysis. Conclusions Targeted interventions should be developed for at-risk populations and intervenable behavioral factors should be taken to slow or reverse the progression of frailty.