Multimorbidity Analysis of 13 Systemic Diseases in Northeast China

Jianxing Yu, Fangying Song, Yingying Li, Zhou Zheng, Huanhuan Jia, Yuzhe Sun, Lina Jin and Xihe Yu
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Jianxing Yu: Social Medicine and Health Service Management, School of Public Health, No. 1163 Xinmin Street, Jilin University, Changchun 130021, Jilin, China
Fangying Song: Social Medicine and Health Service Management, School of Public Health, No. 1163 Xinmin Street, Jilin University, Changchun 130021, Jilin, China
Yingying Li: Social Medicine and Health Service Management, School of Public Health, No. 1163 Xinmin Street, Jilin University, Changchun 130021, Jilin, China
Zhou Zheng: Social Medicine and Health Service Management, School of Public Health, No. 1163 Xinmin Street, Jilin University, Changchun 130021, Jilin, China
Huanhuan Jia: Social Medicine and Health Service Management, School of Public Health, No. 1163 Xinmin Street, Jilin University, Changchun 130021, Jilin, China
Yuzhe Sun: Social Medicine and Health Service Management, School of Public Health, No. 1163 Xinmin Street, Jilin University, Changchun 130021, Jilin, China
Lina Jin: Epidemiology and Biostatistics, School of Public Health, No. 1163 Xinmin Street, Jilin University, Changchun 130021, Jilin, China
Xihe Yu: Social Medicine and Health Service Management, School of Public Health, No. 1163 Xinmin Street, Jilin University, Changchun 130021, Jilin, China

IJERPH, 2020, vol. 17, issue 6, 1-12

Abstract: Background: Multimorbidity not only affects the quality of patients’ lives, but can also bring a heavy economic burden to individuals, families and society. The purpose of this study was to reveal the connections between diseases, especially the important role each disease played in the entire multimorbidity network. Methods: A total of 1,155,734 inpatients were enrolled through multistage stratified random sampling in Jilin Province in 2017. Categorical variables were compared using the Rao–Scott-χ2 test. Weighted networks were adopted to present the complex relationships of multimorbidity. Results: The distributions of the number of diseases differed significantly by gender, age and health insurance scheme ( P < 0.001). Diseases of the respiratory system had the highest weight in multimorbidity in young people. Endocrine, nutritional and metabolic diseases and circulatory system diseases were often associated with other systemic diseases in middle aged and old people. Conclusions: Multimorbidity with respiratory system diseases in young people should not be overlooked. Additionally, effective prevention efforts that target endocrine, nutritional and metabolic diseases and circulatory system diseases are needed in middle aged and old people.

Keywords: multimorbidity; systemic diseases; weighted networks (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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