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2020年广州市越秀区社区老年居民慢性病共病模式:基于网络分析

Multimorbidity patterns of chronic diseases among community-dwelling older adults in Yuexiu District, Guangzhou, 2020: a network analysis

  • 摘要:
    目的 分析广东省广州市越秀区社区老年居民慢性病共病模式,为社区老年居民慢性病管理工作提供参考依据。
    方法 提取广州市越秀区社区卫生服务中心信息系统中2020年1—12月参加国家免费体检的54 829名≥65岁老年居民的体检数据。采用Python 3.14.0软件进行系统聚类,分析慢性病共病模式。并应用Gephi 0.9.2软件将慢性病共病模式进行可视化展示。随后基于性别和聚类结果进行亚组分析。
    结果 广州市越秀区社区老年居民慢性病共病患病率为45.5%(24 922/54 829);老年男性居民慢性病共病患病率44.2%(10 594/23 957)低于老年女性居民慢性病共病患病率46.4%(14 328/30 872),差异有统计学意义(χ2=28.00,P<0.001)。系统聚类分析结果显示,越秀区社区老年居民中共发现105种二元共病组合和455 种三元共病组合,患病率居于前3位的二元共病组合依次为冠心病+高血压(36.9%)、高脂血症+高血压(18.6%)和痛风+骨关节炎(5.7%);患病率居于前3位的三元共病组合依次为冠心病+高血压+心力衰竭(8.8%)、支气管疾病+慢性阻塞性肺疾病+哮喘(2.3%)和心律失常+高脂血症+骨质疏松(1.5%)。15种慢性病的网络分析结果显示,该网络包含15个节点和87条边,网络密度为0.83。核心慢性病为骨关节炎、痛风和冠心病,加权度依次为2.9、2.6和2.1。基于性别的亚组分析结果显示,男性共病模式密度为0.78,冠心病、心力衰竭和脑卒中为关键枢纽,介数中心性系数分别为3.7、3.7和3.1;女性共病模式密度为0.83,痛风、高脂血症和骨关节炎为关键枢纽,介数中心性系数分别为2.3、2.1和1.8。基于聚类结果的亚组分析结果显示,冠心病+高血压+心力衰竭共病组合中,高血压与冠心病关系密切(权重为0.39),高血压为核心慢性病(加权度为0.73);支气管疾病+慢性阻塞性肺疾病+哮喘共病组合中,慢性阻塞性肺疾病与支气管疾病关系密切(权重为0.50),慢性阻塞性肺疾病为核心慢性病(加权度为0.77);心律失常+高脂血症+骨质疏松共病组合中,高脂血症与骨质疏松关系密切(权重为0.07),骨质疏松为核心慢性病(加权度为0.11)。
    结论 广州市越秀区社区老年居民慢性病共病患病率较高且共病模式关系复杂,其中女性共病模式较男性更为复杂。基层社区需依性别差异采取有针对性的干预措施。

     

    Abstract:
    Objective To analyze multimorbidity networks in community-dwelling elderly adults in Yuexiu district, Guangzhou and provide evidence to inform chronic disease management in community settings.
    Methods A total of 54 829 individuals aged ≥ 65 years who participated in the National Free Health Examination Program in Yuexiu district, Guangzhou from January to December 2020 were included. Python 3.14.0 was used for hierarchical cluster analysis of the multimorbidity patterns, and Gephi 0.9.2 was employed to visually analyze the multimorbidity networks. Subsequently, subgroup analyses were conducted based on gender and clustering results.
    Results The overall prevalence of multimorbidity in community-dwelling elderly adults in Yuexiu district, Guangzhou was 45.5% (24 922/54 829). The prevalence among males 44.2% (10 594/23 957) was lower than that among females 46.4% (14 328/30 872) (χ2 = 28.00, P < 0.001). Hierarchical cluster analysis identified 105 binary and 455 ternary disease combinations. The top three binary combinations were coronary heart disease (CHD) + hypertension (36.9%), hyperlipidemia + hypertension (18.6%), and gout + osteoarthritis (5.7%), while the top three ternary combinations were CHD + hypertension + heart failure (8.8%), bronchial diseases + chronic obstructive pulmonary disease (COPD) + asthma (2.3%), and arrhythmia + hyperlipidemia + osteoporosis (1.5%). Network analysis of 15 chronic diseases revealed a network consisting of 15 nodes and 87 edges, with a network density of 0.83. The core diseases were osteoarthritis, gout, and CHD, with weighted degrees of 2.9, 2.6, and 2.1, respectively. Gender-specific subgroup analyses showed that the network density for males was 0.78, with CHD, heart failure, and stroke as key hubs (betweenness centrality: 3.7, 3.7, and 3.1, respectively). For females, the network density was 0.83, with gout, hyperlipidemia, and osteoarthritis as key hubs (betweenness centrality: 2.3, 2.1, and 1.8, respectively). Clustering-based subgroup analysis showed that in the CHD + hypertension + heart failure group, hypertension was closely associated with CHD (weight: 0.39) and acted as the core disease (weighted degree: 0.73). For bronchial diseases + COPD + asthma, COPD was strongly connected to bronchial diseases (weight: 0.50) and served as the core disease (weighted degree: 0.77). In the arrhythmia + hyperlipidemia + osteoporosis combination, hyperlipidemia was associated with osteoporosis (weight: 0.07), with osteoporosis identified as the core disease (weighted degree: 0.11).
    Conclusions Multimorbidity is highly prevalent among community-dwelling elderly adults in Yuexiu district, Guangzhou, with complex interrelationships among chronic diseases. The multimorbidity network in females is more intricate than that in males. These findings highlight the need for community health institutions to implement targeted interventions tailored to gender differences.

     

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