With the deepening of population aging, the rising prevalence of multimorbidity and disability poses a significant threat to the quality of life among middle-aged and older adults. Existing research, both domestic and international, rarely explores influencing factors of disability specifically in older adults with multimorbidity and seldom analyzes the impact of variable interactions on disability.

This study aimed to investigate factors influencing disability status in older adults with multimorbidity, reveal the contribution of various variables and their intrinsic relationships to disability, and provide empirical support for developing targeted prevention and control strategies.

From November 2023 to December 2024, based on the health ecological model theory and data from the China Health and Retirement Longitudinal Study (CHARLS 2020) , an indicator system of influencing factors was constructed. Kruskal-Wallis tests, multinomial Logistic regression, and Decision tree models were employed to analyze the impact of variables on disability status.

The study included 9 124 middle-aged and elderly individuals with chronic disease comorbidity. Among them, 5 355 (58.7%) had no impairment in physical functioning, 3 442 (37.7%) had mild impairment, and 327 (3.6%) had moderate or greater impairment. Multinomial Logistic regression analysis showed age <80 years, male, primary education, mild/moderate/severe depression, self-rated health as very poor/poor/fair, visual impairment, no history of falls, no history of hip fractures, fewer sites of bodily pain, current or former smoker, alcohol consumption, no exercise or low exercise intensity, minimal night-time sleep, and low/moderate personal annual income are factors associated with preserved physical functioning in older adults with chronic disease comorbidity (P<0.05) ; Self-rated health as very poor, abnormal vision, no history of falls, no history of hip fractures, lack of exercise, and non-participation in social activities were factors associated with moderate or greater impairment of physical functioning in older adults with chronic disease comorbidity (P<0.05) . The decision tree model identified self-rated health as the primary influencing factor for disability status, followed by depression, number of pain sites, physical activity level, age, and history of falls (P<0.05) . Model comparison showed that the area under the curve (AUC) for multinomial Logistic regression was higher than that for the Decision tree model across all disability levels: no impairment [0.808 (95%CI=0.800-0.817) vs. 0.768 (95%CI=0.759-0.778) ], mild impairment [0.773 (95%CI=0.764-0.783) vs. 0.734 (95%CI=0.724-0.745) ], and moderate-to-severe impairment [0.891 (95%CI=0.875-0.907) vs. 0.833 (95%CI=0.812-0.854) ].

Age, gender, education level, depression, self-rated health, vision, fall history, hip fracture history, number of bodily pain sites, smoking status, alcohol consumption, physical activity level, nighttime sleep duration, social participation, and personal annual income significantly influence disability status in older adults with multimorbidity. Relevant authorities should implement effective preventive measures across these dimensions to reduce the disability rate. While the logistic regression model demonstrates slightly better predictive performance, both models offer distinct advantages: logistic regression clearly demonstrates the dependence between independent and dependent variables, while the decision tree elucidates the impact of variable interactions. Both models can be utilized for supplementary analysis in future research.