首都医科大学学报 ›› 2025, Vol. 46 ›› Issue (3): 520-526.doi: 10.3969/j.issn.1006-7795.2025.03.017

• 基础研究 • 上一篇    下一篇

城市社区居民卒中相关影响因素分析:基于倾向评分匹配的病例对照研究

任倩薇1,周思怡2,金鑫悦1,郭馥祯1,管仲军3*   

  1. 1.首都医科大学公共卫生学院卫生事业管理系, 北京 100069;2.首都医科大学附属北京天坛医院纪检办公室, 北京 100070;3.首都医科大学宣武医院党委办公室, 北京 100053
  • 收稿日期:2025-01-17 出版日期:2025-06-21 发布日期:2025-06-25
  • 通讯作者: 管仲军 E-mail:guanzhj@ccmu.edu.cn
  • 基金资助:
    首都卫生发展科研专项基金(2024-1G-2013),《智慧社区建设-以AD为例》项目(IPO-20201222-0004-A-R)。

Analysis of stroke-related risk factors among urban community residents: A case-control study based on propensity score matching

Ren Qianwei1, Zhou Siyi2, Jin Xinyue1, Guo Fuzhen1, Guan Zhongjun3*   

  1. 1.Department of Health Administration,School of Public Health, Capital Medical University,Beijing 100069,China;2.Discipline Inspection Office, Beijing Tiantan Hospital, Capital Medical University,Beijing 100070,China; 3.Office of the CPC Committee,Xuanwu Hospital, Capital Medical University,Beijing 100053,China
  • Received:2025-01-17 Online:2025-06-21 Published:2025-06-25
  • Supported by:
    This study was supported by Capital Health Development Research Special Program(2024-1G-2013),Smart Community Development: A Case Study of AD(IPO-20201222-0004-A-R).

摘要: 目的  本研究旨在分析北京市城市社区居民中卒中患者的生活现状及相关危险因素分布,评估高血压、糖尿病等慢性病及心理健康、生活质量对卒中风险的影响,为社区制定有依据的科学筛查流程及干预策略提供参考。方法  选取北京市19个社区50岁及以上常住居民,利用问卷调查收集数据。通过倾向匹配得分法平衡基线特征,结合SPSS和R软件进行统计分析,进行Logistic单因素和多因素逐步回归以及中介效应分析。结果  根据社区筛查结果,卒中组(n=87)与非卒中组(348人)在匹配后基线特征平衡。卒中组慢性病患病率高于非卒中组,心理健康和生活质量评分显著低于非卒中组。高血压显著增加卒中风险(P=0.002)。认知障碍简明评价表 (Cognitive-12 Scale, Cog-12)评分与卒中发病率正相关,欧洲五维健康量表(European Quality of Life-5 Dimensions,EQ-5D)评分与卒中发病率显著负相关(P<0.001)。高血压通过Cog-12和EQ-5D对卒中风险产生中介效应,EQ-5D贡献更显著(26.72%)。结论  在本次调查的城市社区中,卒中防治需关注慢性病管理、认知健康、生活质量提升及心理健康干预,并借助电子化流程进行随访和评估,优化对社区患者的卒中疾病管理方案。

关键词: 社区筛查, 卒中影响因素, 社区卫生服务, 倾向评分匹配, 慢病管理

Abstract: Objective  To analyze stroke-related risk factors and their impact on urban community residents in Beijing, focusing on chronic diseases, psychological health, and quality of life, to provide evidence for community screening and intervention strategies. Methods  Data were collected from residents aged 50+ in 19 communities by using questionnaires and a WeChat App. Propensity score matching (PSM) balanced baseline characteristics. Logistic regression (univariate and stepwise) and mediation effect analysis were conducted by using SPSS and R. Results  After PSM, 87 stroke cases and 348 controls showed balanced baseline characteristics. The stroke group had higher chronic disease prevalence and lower psychological and quality of life scores. Hypertension increased stroke risk (P=0.002). Cognitive-12 Scale (Cog-12) was positively, and European Quality of Life-5 Dimensions (EQ-5D) negatively, associated with stroke (P<0.001). Hypertension mediated stroke risk through Cog-12 and EQ-5D, with EQ-5D contributing 26.72%. Conclusion  In this urban community study, stroke prevention should focus on managing chronic diseases, improving cognitive health and quality of life, and addressing psychological health. Digital tools can enhance follow-up and assessment, optimizing community-based stroke management strategies.

Key words: community screening, stroke risk factors, community health services, propensity score matching, chronic disease control

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