不同诊断情景下结核病负担预测的动力学模型研究

王岳; 王文灿; 李涛; 陈仕敏; 汪业胜; 陈伟; 王伟炳

文章摘要

王岳,王文灿,李涛,陈仕敏,汪业胜,陈伟,王伟炳.不同诊断情景下结核病负担预测的动力学模型研究[J].中华流行病学杂志,2020,41(4):580-584

不同诊断情景下结核病负担预测的动力学模型研究

Disease burden of tuberculosis under different diagnostic scenarios in China: a dynamic modeling study

收稿日期:2019-07-06 出版日期:2020-04-24

DOI：10.3760/cma.j.cn112338-20190706-00497

中文关键词: 结核病 SEIR模型基本再生数延迟诊断时间及时就诊率

英文关键词: Tuberculosis SEIR model Basic reproductive number Delayed diagnosis time Timely hospital visit rate

基金项目:国家重大传染病防治科技重大专项（2017ZX10202302-005，2017ZX10201302-007-003）；国家自然科学基金（81673233）

作者	单位	E-mail
王岳	复旦大学公共卫生学院公共卫生安全教育部重点实验室, 上海 200032
王文灿	复旦大学公共卫生学院公共卫生安全教育部重点实验室, 上海 200032
李涛	中国疾病预防控制中心结核病控制中心, 北京 102206
陈仕敏	复旦大学公共卫生学院公共卫生安全教育部重点实验室, 上海 200032
汪业胜	复旦大学公共卫生学院公共卫生安全教育部重点实验室, 上海 200032
陈伟	中国疾病预防控制中心结核病控制中心, 北京 102206	chenwei@chinacdc.cn
王伟炳	复旦大学公共卫生学院公共卫生安全教育部重点实验室, 上海 200032	wwb@fudan.edu.cn

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中文摘要:

目的通过建立考虑不同诊断情景的结核病动力学模型,预测不同延迟诊断时间和及时就诊率下发病负担,为控制结核病的传播提供建议。方法建立系统动力学模型拟合中国CDC 2005-2018年结核病年发病例数,计算结核病基本再生数(R0),通过诊断情景相关参数的数值变化探究其对结核病发病负担影响。结果模型符合程度的χ²检验结果:χ²=1.102,P=1.000。通过本模型计算得到的结核病R₀=0.063<1,说明在中国结核病会逐渐走向消亡。减少延迟诊断时间和提高及时就诊率短期内引起感染后到医院诊断治疗者人数波动,长期可使感染后未到医院就诊人数持续减少。结论本研究模型是对2005-2018年结核病发病趋势的良好拟合。在减少延迟诊断时间和提高及时就诊率的诊断情景下,对于结核病长期负担的减少有重要意义,并进一步探讨模型有待改进的地方。

英文摘要:

Objective Under different diagnostic scenarios, we tried to establish a tuberculosis dynamic model, to predict the incidence burden and to provide evidence for developing the prevention and control programs of tuberculosis. Methods A systematic dynamic model was established to fit the annual incidence rates of tuberculosis data from the China CDC, between 2005 and 2018. Basic reproductive number (R0) was calculated. Impact of different diagnostic scenarios on tuberculosis burden was explored by numerical changes in diagnosis-related parameters. Results Results from the Chi-square test indicated that the model accuracy appeared as:χ²=1.102 (P=1.000). Also, the computed result showed that R₀=0.063<1, indicating that tuberculosis would gradually be disappearing in China. Approaches that including ‘reducing the delayed diagnosis time’ or ‘improving the timely medical treatment’ would end the fluctuations of the number of infectious and hospitalized patients and thus leading to continuous reduction in the number of these patients, in a long run. Conclusions This model fitted well for the trend of tuberculosis incidence rates between 2005 and 2018. Reducing the delay time in diagnosis and improving the rate of timely medical treatment could effectively reduce the long-term burden of tuberculosis. Improvement of this model would be further explored.

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