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中国防痨杂志 ›› 2004, Vol. 26 ›› Issue (1): 10-15.

• 论著 • 上一篇    下一篇

结核病疫情发展预测模型的建立及评价

贺晓新,金水高,张立兴,赵丰曾,屠德华,安燕生   

  1. 北京结核病控制研究所 北京 100035
  • 出版日期:2004-01-10 发布日期:2004-11-03

Establishment and evaluation of the prediction model for tuberculosis epidemic trend

He Xiaoxin,Jin Shuigao,Zhang Lixing,et al.   

  1. Beijing Institute for Tuberculosis Control,Beijing 100035
  • Online:2004-01-10 Published:2004-11-03

摘要: 目的 建立中国结核病疫情发展预测模型。方法 根据中国结核病流行病学规律 ,针对Aruma模型的局限性 ,建立联立递推方程模型。利用所建模型对北京市 1979-2000年的疫情发展过程进行模拟 ,并与实际进行比较以评价模型的拟合优度。结果 从活动性肺结核、涂阳肺结核新登记人数、人群结核感染率以及全国结核病流行病学抽样调查结果4个方面 ,模型的模拟结果与实际拟合良好。模型中可控制因素的灵敏度分析结果提示,只有提高了结防机构对肺结核病人特别是涂阳肺结核病人的管理比例后 ,DOTS策略才能发挥显著降低结核病疫情的作用。结论 结核病疫情发展预测模型能良好反映结核病流行实际过程,适合对结核病干预措施、控制策略的评价以及疫情发展预测。

关键词: 结核,肺/流行病学, 理论模型, 预测

Abstract: Objective To establish the tuberculosis epidemic situation prediction model.Methods Aiming at the limitations of Aruma tuberculosis model,the tuberculosis epidemiology in china had been translated into a model including series of equations. The evolution of tuberculosis epidemic situation for 1979-2000 in Beijing had been simulated with the new established model.And the simulated data was compared with the actual data.Goodness-of-model fitting has been evaluated.Results The simulated data numbers of registered active tuberculosis patients number, registered sputum positive tuberculosis patients, tuberculous infection rate, and the tuberculosis prevalence from national tuberculosis sampling epidemiological survey were fitted with the acual datas. The Results of sensitivity analysis of the key factors in model indicated that only the proportion of tuberculosis patients managed by the Tuberculosis Control Stations elevated, then the DOTS strategy could affect the epidemic situation significantly.Conclusion The tuberculosis epidemic situation prediction model could reflect the actual tuberculosis evolution exactly, and was fit for tuberculosis control measures evaluation and epidemic situation prediction.

Key words: Pulmonary tuberculosis/epidemiology, Mathematical model, Prediction