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Reporting incidence from a surveillance system with an operational case definition of unknown predictive value positive

机译:报告来自监视系统的事件其操作案例定义的预测价值未知

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摘要

When reporting incidence rate estimates for relatively rare health conditions, associated case counts are often assumed to follow a Poisson distribution. Case counts obtained from large-scale electronic surveillance systems are often inflated by the presence of false positives, however, and adjusted case counts based on the results of a validation sample will have variances which are hyper-Poisson. This paper presents a simple method for constructing interval estimates for incidence rates based on case counts that are adjusted downward using an estimate of the predictive value positive of the surveillance case definition.
机译:当报告相对罕见的健康状况的发生率估算值时,通常假定相关病例数遵循泊松分布。从大型电子监视系统获得的病例数通常会因存在误报而被夸大,但是,基于验证样本的结果调整后的病例数将具有极高的Poisson差异。本文提出了一种简单的方法,该方法可基于病例数构建发病率的区间估计值,并使用监视病例定义的阳性预测值的估计值向下调整。

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