上海交通大学学报（医学版）

上海交通大学学报（医学版）

• 论著（公共卫生） • 上一篇    下一篇

心血管疾病患病率的系统动力学初步研究

成杰1,2,施莉莉1,3,江骏杰1,蔡雨阳1   

  1. 1.上海交通大学 公共卫生学院， 上海 200025； 2.上海交通大学 医学院附属瑞金医院院办, 上海 200025; 3.挪威卑尔根大学系统动力学中心， 卑尔根 N5020, 挪威
  • 出版日期:2016-03-28 发布日期:2017-06-02
  • 通讯作者: 蔡雨阳, 电子信箱: caiyuyang@sjtu.edu.cn
  • 作者简介:成杰（1983—）， 女， 硕士生; 电子信箱: chengxu1125@hotmail.com。
  • 基金资助:

    国家高技术研究发展计划（“863”计划）(2013AA020418);第四轮公共卫生三年行动计划重点学科(15GWZK0901)

Preliminary system dynamics study on incidence of cardiovascular disease

CHENG Jie1,2, SHI Li-li1,3, JIANG Jun-jie1, CAI Yu-yang1   

  1. 1.School of Public Health, Shanghai Jiao Tong University, Shanghai 200025, China; 2.Deans office Ruijin Hospital affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025; 3.System Dynamics Group, University of Bergen, Bergen N5020, Norway

  • Online:2016-03-28 Published:2017-06-02
  • Supported by:

    Hi-Tech Research and Development Program of China，“863”Program， 2013AA020418; the Fourth Round of Three-year Action Plan on Public Health Discipline and Talent Program: Evidence-based Public Health and Health Economics， 15GWZK0901

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摘要：

目的 构建针对上海市户籍人口心血管疾病患病率的系统动力学仿真模型，模拟心血管疾病流行发展过程并预测相关发展趋势。方法 根据心血管疾病危险因素研究现状，搭建针对心血管疾病患病率的系统动力学模型结构，并采用文献研究法获得相关危险因素的研究数据，完成对应模型的模拟、修正与检验。结果 模型模拟了2002—2010年上海市户籍人口心血管疾病的流行数据，模拟结果与历史数据较为吻合。在数据测试通过的基础上，对2020年上海市户籍人口的心血管疾病流行状况进行了预测。结论 模型能够模拟并预测在心血管疾病危险因素没有受到干预的情况下上海市户籍人口每年新增心血管疾病患病人群数量，进而为有效干预政策的提出提供相应理论支持。

关键词: 心血管疾病, 系统动力学, 疾病预防干预

Abstract:

Objective To build a system dynamics simulation model for incidence of cardiovascular disease (CVD) in Shanghai registered population, simulate the development process of CVD and predict the development trend. Methods A system dynamics model structure for incidence of CVD was constructed according to current studies on risk factors in CVD. Data of related risk factors were obtained via literature research. The corresponding model was simulated, corrected, and verified. Results The resultant model simulated the data of CVD during 2002-2010 in Shanghai registered population and simulation results matched the historical data very well. The incidence of CVD in Shanghai registered population in 2020 was predicted based on successful data test. Conclusion This model can simulate and predict the annual increase in CVD population in Shanghai registered population if risk factors in CVD are not affected and provide theoretic support for developing efficient intervention policies.

Key words: cardiovascular disease;system dynamics, intervention on disease prevention