Urban–Rural Differences in Patterns and Associated Factors of Multimorbidity Among Older Adults in China: A Cross-Sectional Study Based on Apriori Algorithm and Multinomial Logistic Regression - Zhang, Chichen; Xiao, Shujuan; Shi, Lei; Xue, Yaqing; Zheng, Xiao; Dong, Fang; Zhang, Jiachi; Xue, Benli; Lin, Huang; Ouyang, Ping (Frontiers in Public Health, 2021)
Introduction: Multimorbidity has become one of the key issues in the public health sector. This study aimed to explore the urban–rural differences in patterns and associated factors of multimorbidity in China and to provide scientific reference for the development of health management strategies to reduce health inequality between urban and rural areas. Methods: A cross-sectional study, which used a multi-stage random sampling method, was conducted effectively among 3250 participants in the Shanxi province of China. The chi-square test was used to compare the prevalence of chronic diseases among older adults with different demographic characteristics. The Apriori Algorithm and Multinomial logistic regression were used to explore the patterns and associated factors of multimorbidity among older adults, respectively. Results: The findings showed that 30.3% of older adults reported multimorbidity, with significantly higher proportions in rural areas. Ten binary chronic disease combinations with strong association strength were obtained among urban older adults. Eleven binary chronic disease combinations and three ternary chronic disease combinations with strong association strength were obtained in rural older adults. In rural and urban areas, there is a large gap in patterns and factors associated with multimorbidity. Conclusions: Multimorbidity was prevalent among older adults, which patterns mainly consisted of two or three chronic diseases. The patterns and associated factors of multimorbidity varied from urban to rural regions. Expanding the study of urban-rural differences in multimorbidity will help the country formulate more reasonable public health policies to maximize the benefits of medical services for all.