文章摘要
刘秋萍,王佳敏,巩超,高培,唐迅,胡永华.微观模拟模型在流行病学筛查成本效果分析中的应用[J].中华流行病学杂志,2022,43(6):931-937
微观模拟模型在流行病学筛查成本效果分析中的应用
Applications of microsimulation model for cost-effectiveness analysis on screening in epidemiology
收稿日期:2021-08-02  出版日期:2022-06-16
DOI:10.3760/cma.j.cn112338-20210802-00601
中文关键词: 微观模拟模型;筛查;成本效果分析
英文关键词: Microsimulation model;Screening;Cost-effectiveness analysis
基金项目:国家自然科学基金(81973132,81961128006,81872695)
作者单位E-mail
刘秋萍 北京大学公共卫生学院流行病与卫生统计学系, 北京 100191  
王佳敏 北京大学公共卫生学院流行病与卫生统计学系, 北京 100191  
巩超 北京大学公共卫生学院流行病与卫生统计学系, 北京 100191  
高培 北京大学公共卫生学院流行病与卫生统计学系, 北京 100191  
唐迅 北京大学公共卫生学院流行病与卫生统计学系, 北京 100191 tangxun@bjmu.edu.cn 
胡永华 北京大学医学信息学中心, 北京 100191 yhhu@bjmu.edu.cn 
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中文摘要:
      微观模拟模型通过使用个体水平的数据估计状态转换概率,模拟人群中个体的疾病发展过程。这种方法可以处理人群异质性或疾病史等个体特征的影响,从而解决流行病学中复杂疾病筛查的成本效果问题。本文介绍了微观模拟模型的基本原理、构建步骤、分析方法和相关注意事项。结合在美国人群中开展的一项慢性肾脏病微量蛋白尿筛查的成本效果分析的研究实例,从模型构建、模型分析和结果解读等方面,详细讨论了微观模拟模型在筛查成本效果分析中应用的要点。微观模拟模型通过估计广泛的个体特征,并考虑复杂疾病的动态发展过程,越来越多地用于解决马尔科夫模型假设受限的复杂问题。为了更好地支持公共卫生领域的循证决策,后续研究应注意决策模型参数的准确性和研究结果的透明度,并且需要按照相应的报告规范开展流行病学筛查的成本效果分析。
英文摘要:
      Microsimulation model simulates individuals and estimates transition probabilities within the population using individual participant data. This approach could deal with the heterogeneous characteristics among the people or personal history of diseases and may be relevant in addressing cost-effectiveness problems of screening for complex conditions in epidemiology. This paper introduces the general principles, basic steps involved in implementation, analytic methods, and other related issues of the microsimulation model. Based on a practical research case of estimating the cost-effectiveness of microalbuminuria screening for chronic kidney disease in the United States, critical points in applications of the microsimulation model for cost-effectiveness analysis of screening were discussed in detail, including model development, model analysis, and the interpretation of the results. The microsimulation model considers the dynamic nature of complex diseases by estimating a broad range of individual characteristics and increasingly used to provide insights into complex problems that the Markov model does not efficiently address. For better supporting evidence-informed decision-making in public health, future studies should be aware of the accuracy of parameters in the decision-analytic model and the transparency of the models and results, as well as complying with the relevant reporting standards.
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