协助结核病控制实践:预测传播的决策支持模型

H. Mamiya, K. Schwartzman, Aman Verma, Christian Jauvin, M. Behr, D. Buckeridge
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引用次数: 0

摘要

新发结核病例可分为:(1)导致其他继发性活动性结核病例的传播源病例;(2)最近传播引起的继发性病例;或(3)孤立病例,与最近的传播无关(即既不是源也不是接受者)。源病例和继发病例由于参与传播链,需要更有力的干预;因此,准确和快速的新患者分类应有助于公共卫生人员有效地优先考虑控制活动。然而,目前公认的分类方法,DNA指纹分析,需要数周才能产生结果(1);因此,公共卫生人员往往完全依靠他们的直觉来确定最有可能参与传播的病例。已知各种临床和社会人口学特征与结核病传播有关(2)。通过在诊断时使用这些现成的数据,可以快速估计病例为源性、继发性和孤立性的概率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Aiding the practice of tuberculosis control: a decision support model to predict transmission
Introduction A new TB case can be classified as: (1) a source case for transmission leading to other, secondary active TB cases; (2) a secondary case, resulting from recent transmission; or (3) an isolated case, uninvolved in recent transmission (i.e., neither source nor recipient). Source and secondary cases require more intense intervention due to their involvement in a chain of transmission; thus, accurate and rapid classification of new patients should help public health personnel to effectively prioritize control activities. However, the currently accepted method for classification, DNA fingerprint analysis, takes many weeks to produce the results (1); therefore, public health personnel often solely rely on their intuition to identify the case who is most likely to be involved in transmission. Various clinical and sociodemographic features are known to be associated with TB transmission (2). By using these readily available data at the time of diagnosis, it is possible to rapidly estimate the probabilities of the case being source, secondary and isolated.
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