Time Sequence Rules of Chronic Disease Multimorbidity: An Analysis of China Health and Retirement Longitudinal Study Using Sequential Pattern Mining

Chronic disease multimorbidity has become a significant global public health issue, imposing a heavy burden on patients' quality of life and the healthcare system. In China, a country with a large population, it is of vital importance to investigate the sequence relationship between chronic diseases in the middle-aged and elderly population. This study was based on the analysis of data from 10,827 participants in the China Health and Retirement Longitudinal Study(CHARLS 2011–2020). Hierarchical clustering analysis and sequence pattern mining were adopted to identify the temporal correlations in the development of 14 chronic diseases and the differences among different populations, and a chronic disease multimorbidity network was constructed to visualize these relationships. The research results showed that arthritis was at the core of the association network; chronic lung disease, digestive diseases, dyslipidemia, heart attack and hypertension were in the middle layer of the network; while asthma, cancer, diabetes, kidney disease, liver disease, memory related problems and stroke were at the periphery of the network. Meanwhile, significant differences in the multimorbidity pattern of chronic diseases were observed by gender, age, residential area and educational level. Arthritis was identified as a key disease that was prone to occur and could easily trigger other diseases. Patients with chronic lung diseases, digestive diseases, dyslipidemia, heart attack and hypertension had a higher risk of developing other diseases. By exploring the multimorbidity patterns of chronic diseases, this study provides empirical evidence for early prevention and multimorbidity management of diseases, which may help reduce health and social burdens.