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Updated: May 27, 2025
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Multimorbidity patterns and influencing factors in older Chinese adults: a national population-based cross-sectional
Xinyu Xue1,2, Ziyi Wang3, Yana Qi1
1Chinese Evidence-based Medicine Center, West China Hospital, Sichuan University, Chengdu, China.
Background:
This study aims to develop specific multimorbidity relationships among the elderly and to explore the association of multidimensional factors with these relationships, thereby facilitating the formulation of personalised strategies for multimorbidity management.
Methods:
Cluster analysis identified chronic conditions that tend to cluster together, and then association rule mining was used to investigate relationships within these identified clusters more closely. Stepwise logistic regression analysis was conducted to explore the relationship between influencing factors and different health statuses in older adults. The results of this study were presented by network graph visualisation.
Results:
A total of 15 045 individuals were included in this study. The average age was 73.0 ± 6.8 years. The number of patients with multimorbidity was 7426 (49.4%). The most common binary disease combination was hypertension and depression. The four major multimorbidity clusters identified were the tumour-digestive disease cluster, the metabolic-circulatory disease cluster, the metal-psychological disease cluster, and the age-related degenerative disease cluster. Cluster analysis by sex and region revealed similar numbers and types of conditions in each cluster, with some variations. Gender and number of medications had a consistent effect across all disease clusters, while aging, body mass index (BMI), waist-to-hip ratio (WHR), cognitive impairment, plant-based foods, animal-based foods, highly processed foods and marital status had varying effects across different disease clusters.
Conclusions:
Multimorbidity is highly prevalent in the older population. The impact of lifestyle varies between different clusters of multimorbidity, and there is a need to implement different strategies according to different clusters of multimorbidity rather than an integrated approach to multimorbidity management.
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