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预防医学  2026, Vol. 38 Issue (10): 1036-1041,1046    DOI: 10.19485/j.cnki.issn2096-5087.2026.10.013
  疾病控制 本期目录 | 过刊浏览 | 高级检索 |
社区高血压患者收缩压变化轨迹的影响因素分析
陈绍云, 曹思静, 严新凤, 陈健东, 孙明霞, 霍泳琦, 谷超男, 陈瑜, 于传宁, 邹健锋
深圳市龙华区慢性病防治中心,广东 深圳 518110
Influencing factors for systolic blood pressure trajectories among community patients with hypertension
CHEN Shaoyun, CAO Sijing, YAN Xinfeng, CHEN Jiandong, SUN Mingxia, HUO Yongqi, GU Chaonan, CHEN Yu, YU Chuanning, ZOU Jianfeng
Longhua District Chronic Disease Prevention and Control Center, Shenzhen, Guangdong 518110, China
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摘要 目的 分析社区高血压患者收缩压(SBP)变化轨迹及其影响因素,为加强社区高血压患者SBP管理提供依据。方法 选择2020年广东省深圳市龙华区各社区卫生服务中心确诊高血压、建立健康档案且规范随访管理的患者为研究对象。于2026年5月,通过个人健康码匹配国家基本公共卫生服务项目中的健康体检数据库与高血压患者健康管理数据库收集性别、年龄、吸烟、饮酒、体质指数(BMI)、腰围、SBP、空腹血糖(FPG)和低密度脂蛋白胆固醇(LDL-C)等资料。采用2020—2025年SBP随访数据建立潜类别轨迹模型(LCTM)识别SBP变化轨迹,采用无序多分类logistic回归模型分析社区高血压患者SBP变化轨迹的影响因素。结果 纳入社区高血压患者17 281例,其中男性9 951例,占57.58%;女性7 330例,占42.42%。年龄为(55.91±10.95)岁。LCTM识别出3组SBP变化轨迹,根据SBP变化特征定义为低水平稳定组、中水平稳定组和高水平稳定组,分别为5 170、10 005和2 106例,占29.92%、57.90%和12.19%。无序多分类logistic回归分析结果显示,以低水平稳定组为参照,≥65岁、非深圳市户籍、无工作、无医保、高血压病程≥10年、超重肥胖、中心性肥胖、FPG较高和LDL-C较高的社区高血压患者归属于中水平稳定组的可能性较高,初中及以上文化程度的社区高血压患者归属于中水平稳定组的可能性较低;≥65岁、无工作、未婚/离异/丧偶、无医保、高血压病程≥5年、超重肥胖、中心性肥胖、FPG较高和LDL-C较高的社区高血压患者归属于高水平稳定组的可能性较高,初中及以上文化程度、吸烟的社区高血压患者归属于高水平稳定组的可能性较低。结论 社区高血压患者SBP呈低水平稳定、中水平稳定和高水平稳定3种变化轨迹,与人口学特征、社会因素和代谢指标有关。
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陈绍云
曹思静
严新凤
陈健东
孙明霞
霍泳琦
谷超男
陈瑜
于传宁
邹健锋
关键词 : 高血压,  收缩压,  潜类别轨迹模型,  影响因素,  社区    
Abstract:Objective To analyze the trajectories of systolic blood pressure (SBP) and their influencing factors among community patients with hypertension, so as to provide evidence for improving SBP management. Methods Patients diagnosed with hypertension in 2020 at community health service centers in Longhua District, Shenzhen City, Guangdong Province, with established health records and standardized follow-up management, were selected as participants. In May 2026, data on gender, age, smoking, alcohol consumption, body mass index (BMI), waist circumference, SBP, fasting plasma glucose (FPG) and low-density lipoprotein cholesterol (LDL-C) were collected by matching the health examination database and the hypertension patient health management database of the National Basic Public Health Service Program through personal health codes. A latent class trajectory model (LCTM) was established using SBP follow-up data from 2020 to 2025 to identify SBP trajectories. A multinomial logistic regression model was used to analyse factors affecting SBP trajectories among community patients with hypertension. Results A total of 17 281 community patients with hypertension were included, comprising 9 951 (57.58%) males and 7 330 (42.42%) females. The mean age of the participants was (55.91±10.95) years. The LCTM identified three distinct SBP trajectories, defined according to SBP patterns as the low-stable, moderate-stable and high-stable groups, comprising 5 170 (29.92%), 10 005 (57.90%) and 2 106 (12.19%) patients, respectively. Multinomial logistic regression analysis showed that, compared with the low-stable group, patients aged ≥65 years, without Shenzhen household registration, unemployed, without medical insurance, hypertension duration of ≥10 years, overweight or obese, central obesity, higher FPG and higher LDL-C had higher odds of being classified into the moderate-stable group. Patients with a junior high school education or above had lower odds of being classified into the moderate-stable group. Patients aged ≥65 years, unemployed, unmarried, divorced or widowed, without medical insurance, hypertension duration of ≥5 years, overweight or obese, central obesity, higher FPG and higher LDL-C had higher odds of being classified into the high-stable group. Patients with a junior high school education or above and smokers had lower odds of being classified into the high-stable group. Conclusions SBP trajectories among community patients with hypertension were classified into three patterns: low-stable, moderate-stable and high-stable groups. Demographic characteristics, social factors and metabolic indicators were associated with SBP trajectories.
Key words: hypertension    systolic blood pressure    latent class trajectory model    influencing factor    community
收稿日期: 2026-06-18      修回日期: 2026-08-28      出版日期: 2026-10-10
中图分类号:  R743.3  
基金资助:广东省医学科学技术研究基金项目(B2026631)
作者简介: 陈绍云,硕士,主管医师,主要从事慢性非传染性疾病防控工作
通信作者: 邹健锋,E-mail:zjf3103@163.com   
引用本文:   
陈绍云, 曹思静, 严新凤, 陈健东, 孙明霞, 霍泳琦, 谷超男, 陈瑜, 于传宁, 邹健锋. 社区高血压患者收缩压变化轨迹的影响因素分析[J]. 预防医学, 2026, 38(10): 1036-1041,1046.
CHEN Shaoyun, CAO Sijing, YAN Xinfeng, CHEN Jiandong, SUN Mingxia, HUO Yongqi, GU Chaonan, CHEN Yu, YU Chuanning, ZOU Jianfeng. Influencing factors for systolic blood pressure trajectories among community patients with hypertension. Preventive Medicine, 2026, 38(10): 1036-1041,1046.
链接本文:  
https://www.zjyfyxzz.com/CN/10.19485/j.cnki.issn2096-5087.2026.10.013      或      https://www.zjyfyxzz.com/CN/Y2026/V38/I10/1036
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