Discordant multimorbidity linked to poorer health status and higher healthcare use in Chinese adults

Background: Despite the growing global burden of multimorbidity, the patterns of disease combinations, have not been extensively categorized. We aimed to explore the predictors, health consequences, and patterns of discordant and concordant multimorbidity. Methods: We used the 2018 China Health and Retirement Longitudinal Study (CHARLS), a representative database of adults aged >45 years from China. We conducted logistic regression analyses to assess the likelihood of having discordant (conditions from different disease systems) versus concordant (only cardiometabolic, or only respiratory diseases) multimorbidity, and to compare the health status and healthcare utilization between patients with discordant and concordant multimorbidity. Latent class analysis (LCA) was applied to both the entire sample and to patients with discordant multimorbidity to identify clusters of disease combinations. Results: The sample included 1668 patients with concordant (mainly cardiometabolic), and 7306 patients with discordant, multimorbidity. Female patients, patients living in rural settings, former and current smokers, and patients engaging in high-intensity physical activity, were more likely to have discordant instead of concordant multimorbidity. Depression, limitations in daily activities, poor self-reported health, and frequent healthcare use were more common in patients with discordant than concordant multimorbidity. The LCA identified five clusters when all multimorbid patients were included (cardiometabolic, arthritis-digestive, respiratory, multisystem, and arthritis-hypertension classes), and four clusters when restricted to discordant multimorbidity (digestive, arthritis-cardiometabolic, respiratory, and multisystem classes). Conclusion: Discordant multimorbidity is associated with poorer health and increased use of healthcare. Cardiometabolic diseases, arthritis, and digestive diseases have a central role in defining disease patterns.