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Patterns of multimorbidity in the middle-aged and elderly population in China: a cluster analysis based on self-organizing map
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Fu-Lin Wang1, 2, Chao Yang3, 4, 5, *, Jian Du2, Gui-Lan Kong2, Lu-Xia Zhang2, 3, 4, 5
Medical Journal of Chinese People’s Liberation Army | 2022, 47(12) : 1217 - 1225
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Medical Journal of Chinese People’s Liberation Army | 2022, 47(12): 1217-1225
Clinical Research
Patterns of multimorbidity in the middle-aged and elderly population in China: a cluster analysis based on self-organizing map
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Fu-Lin Wang1, 2, Chao Yang3, 4, 5, *, Jian Du2, Gui-Lan Kong2, Lu-Xia Zhang2, 3, 4, 5
Affiliations
  • 1Institute of Medical Technology, Peking University Health Science Center, Beijing 100191, China
  • 2National Institute of Health Data Science at Peking University, Beijing 100191, China
  • 3Renal Division, Department of Medicine, Peking University First Hospital/Peking University Institute of Nephrology, Beijing 100034, China
  • 4Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing 100034, China
  • 5Advanced Institute of Information Technology, Peking University, Hangzhou, Zhejiang 311215, China
Published: 2022-12-28 doi: 10.11855/j.issn.0577-7402.2022.12.1217
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Objective To reveal the pattern of multimorbidity in the middle-aged and elderly population in China by visual cluster analysis. Methods Using the 2015 data of China Health and Retirement Longitudinal Study, the age, gender, place of residence, and 14 kinds of non-communicable chronic diseases of the study population were extracted. A total of 18 542 subjects with complete information were included. A two-step clustering algorithm combining self-organizing map and K-Means was used to visually cluster the existence of chronic diseases among the middle-aged and elderly population. Results There were 8044 patients with 2 or more chronic diseases. The prevalence of multimorbidity was 43.38% in those patients, and 52.28% in the elderly population aged 60 years or above. Among the 14 chronic diseases, arthritis or rheumatism had the highest prevalence (33.02%),followed by hypertension (31.07%), stomach or other digestive diseases (23.60%). The patterns of multimorbidity included the following four categories: (1) 97.72% of patients in group A had chronic lung diseases, with more than half (55.05%) suffering from arthritis or rheumatism; (2) the prevalence of hypertension in group B was 98.21%; (3) the prevalence of dyslipidemia in group C was as high as 99.49%, and 91.72% of patients had hypertension; (4) 73.39% of patients in group D suffered from arthritis or rheumatism, and 68.11% had stomach or other digestive diseases. The patterns of multimorbidity were slightly different in women and urban populations. Conclusions The situation of chronic diseases in the middle-aged and elderly population in China is not optimistic, and the patterns of multimorbidity among different genders, urban and rural populations are different. Those results based on visual cluster analysis are of great significance for co-prevention of multiple conditions and reducing the burden of chronic diseases.

non-communicable chronic disease  /  pattern of multimorbidity  /  cluster analysis  /  self-organizing map  /  visualization
Fu-Lin Wang, Chao Yang, Jian Du, Gui-Lan Kong, Lu-Xia Zhang. Patterns of multimorbidity in the middle-aged and elderly population in China: a cluster analysis based on self-organizing map[J]. Medical Journal of Chinese People’s Liberation Army, 2022 , 47 (12) : 1217 -1225 . DOI: 10.11855/j.issn.0577-7402.2022.12.1217
  • National Natural Science Foundation of China(82003529)
  • National Natural Science Foundation of China(72125009)
  • CAMS Innovation Fund for Medical Sciences(2019-I2M-5-046)
Year 2022 volume 47 Issue 12
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Article Info
doi: 10.11855/j.issn.0577-7402.2022.12.1217
  • Receive Date:2022-03-01
  • Online Date:2025-12-14
  • Published:2022-12-28
Article Data
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History
  • Received:2022-03-01
  • Accepted:2022-06-16
Funding
National Natural Science Foundation of China(82003529)
National Natural Science Foundation of China(72125009)
CAMS Innovation Fund for Medical Sciences(2019-I2M-5-046)
Affiliations
    1Institute of Medical Technology, Peking University Health Science Center, Beijing 100191, China
    2National Institute of Health Data Science at Peking University, Beijing 100191, China
    3Renal Division, Department of Medicine, Peking University First Hospital/Peking University Institute of Nephrology, Beijing 100034, China
    4Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing 100034, China
    5Advanced Institute of Information Technology, Peking University, Hangzhou, Zhejiang 311215, China

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表12种不同金属材料的力学参数

Family
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Number of
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Number of
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Percentage of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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