Analysis of Comorbidity Patterns of Chronic Diseases in Elderly Population in China Based on CHARLES Data

This study aims to reveal the network pattern of comorbidity of chronic diseases and its main influencing factors, and provide scientific basis for comprehensive management of chronic diseases. The study used network analysis, hierarchical clustering and negative binomial regression model to analyze CHARLES large-scale questionnaire data, and systematically discussed the characteristics, patterns and mechanisms of health behaviors of chronic diseases. The results show that high-frequency co-occurring diseases (such as zdisease_1_and zdisease_14_) are concentrated in the center of the comorbid network, forming a strongly correlated core network; while diseases such as zdisease_12_appear as isolated nodes, indicating their unique etiology or low co-occurrence frequency. Hierarchical clustering further verified the close connection and local branch structure between diseases, and identified several small subgroups with high co-occurrence and bridging diseases (such as zdisease_10_). Negative binomial regression analysis revealed that health behaviors such as smoking, alcohol consumption, and physical activity significantly affected the number of comorbidities. For every unit increase in physical activity, the number of comorbidities decreases by approximately 2.5%; moderate drinkers have a significantly lower risk of comorbidities than non-drinkers, while the risk of comorbidities among current smokers is temporarily reduced due to compensation mechanisms. This study provides important reference for the prevention and control of chronic diseases.