Analysis of potential categories and influencing factors of chronic disease comorbidity patterns among residents of Yantai City based on the health ecology model.

Objective: To explore the latent category characteristics of chronic disease comorbidity among residents in Yantai City, Shandong Province, based on the theoretical framework of the health ecology model, and to identify key factors influencing different comorbidity patterns, thereby providing scientific basis for formulating targeted chronic disease prevention and control strategies. Methods: A cross-sectional survey was conducted in Yantai City in 2024 using convenience sampling to collect questionnaire data from 10,681 permanent residents aged 18 years and older. Latent class analysis identified typical comorbidity patterns among residents, followed by multinomial logistic regression to analyze factors influencing different comorbidity patterns. Results: Four chronic disease comorbidity patterns were identified among Yantai residents: (1) Low Comorbidity Group (C1, 82.39%), characterized by 1-2 chronic diseases; (2) Musculoskeletal-Chronic Disease Mixed Group (C2, 9.48%), characterized by coexisting musculoskeletal diseases and other chronic conditions; (3) Metabolic Syndrome-Dominant Group (C3, 7.01%), exhibiting clustering of metabolic disorders such as hypertension, diabetes, and dyslipidemia; (4) High Comorbidity-Complex Group (C4, 1.12%), involving complex combinations of three or more systemic chronic diseases. Multivariate logistic regression analysis revealed that increasing age was a common risk factor across all comorbidity groups (OR = 10.841, 95% CI: 8.853, 13.276). Gender effects exhibited pattern specificity: male gender was a protective factor for C2 (OR = 0.664, 95% CI: 0.552, 0.799) but a risk factor for C3 (OR = 1.745, 95% CI: 1.440, 2.116). At the behavioral level, regular physical exercise (OR = 0.755, 95% CI: 0.659, 0.865) and adequate sleep (OR = 0.437, 95% CI: 0.327, 0.583) were protective factors, while high-frequency consumption of pickled foods was a common risk factor (OR = 1.630, 95% CI: 1.387, 1.914), and alcohol consumption was a specific risk factor for Group C3 (OR = 1.425, 95% CI: 1.137, 1.785). Among socioeconomic factors, higher income levels (OR = 1.394, 95% CI: 1.096, 1.772) constituted a risk factor for Groups C2 and C3. Conclusion: Chronic disease comorbidity among Yantai residents exhibits significant population heterogeneity, categorizable into four distinct patterns. Influencing factors span multiple dimensions of the health ecology model. Public health practice should implement precision interventions, targeting high-risk populations with specific comorbidity patterns while integrating local dietary cultural characteristics. Tailored health promotion and disease management strategies are essential to effectively alleviate the burden of chronic disease comorbidity.