Abstract
BACKGROUND: The rising prevalence of multimorbidity strains hospital-centric healthcare. Urgent attention is needed to understand potential out-of-hospital health service needs and inform policy for effective public health practices. METHODS: Utilizing data from the China Health and Retirement Longitudinal Study (CHARLS), we first employed latent class analysis (LCA) to identify distinct patterns of multimorbidity. Subsequently, basic characteristics associated with each identified multimorbidity pattern were investigated using logistic regression models. Third, employing logistic mixed-effects models, we examined the associations between multimorbidity status, specific multimorbidity patterns, and Potential out-of-hospital clinical services need (POHCN). Fourth, network analysis was performed to explore the complex comorbidity network and identify central nodes within the patterns of multimorbidity. Finally, a stratified analysis by sex and age groups was conducted to examine the patterns and relationships between multimorbidity and POHCN across different sex and age categories. RESULTS: Incorporating 11,215 participants aged 45 and above, with 51.4% being women, our study employed latent class analysis to delineate four latent patterns for 13 chronic diseases: "Kidney arthritic" (20%), "Lung-stomach disorder" (58%), "Asthma pattern" (5%), and "Multisystem pattern" (17%). Participants with multimorbidity exhibited a heightened potential demand for out-of-hospital care (OR = 2.53, 95% CI: 2.17-2.96). Notably, the "Multisystem pattern" displayed the highest demand (OR = 3.93, 95% CI: 3.23-4.79), followed by "Kidney arthritic" (OR = 3.50, 95% CI: 2.56-4.78), "Lung-stomach disorder" (OR = 3.09, 95% CI: 2.48-3.86), and "Asthma pattern" (OR = 2.07, 95% CI: 1.77-2.43). These associations persisted across diverse age groups (45-59, 60 + years). The results of the sex measurement uncertainty analysis indicated that the sex index adheres to the principles of measurement uncertainty. Network analysis identified heart disease, memory-related disease, and heart as pivotal nodes in the comorbid network. Furthermore, stratified analysis revealed statistically significant heterogeneity in the association between multimorbidity and POHCN across different sex and age groups. CONCLUSION: This study links multimorbidity to potential out-of-hospital medical service needs, identifying crucial diseases in the network. Crafting effective medical policies necessitates aligning clinical and public health practices with the characteristics of multimorbidity and its pivotal diseases.