Using laboratory-based surveillance data for prevention: an algorithm for detecting Salmonella outbreaks.
Using laboratory-based surveillance data for prevention: an algorithm for detecting Salmonella outbreaks.
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DOI：
10.3201/eid0303.970322
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发表时间：
1997-07
影响因子：
11.8
通讯作者：
Martin SM
中科院分区：
文献类型：
--
作者：
Hutwagner LC;Maloney EK;Bean NH;Slutsker L;Martin SM
通过应用累积总和（Cumulative Sums），一种生产中常用的质量控制方法，我们构建了一个检测致病微生物实验室分离株中异常簇的过程。我们开发了一种计算机算法的基础上最小的调整，以累积的总和分离株的频率和预期的手段之间的差异，我们用该算法来确定暴发的沙门氏菌在1993年报告的肠道分离株。通过比较这些检测到的爆发与已知的报告的爆发，我们估计的灵敏度，特异性和假阳性率的方法。报告疫情的州的敏感性为0%（0/1）至100%。特异性为64%~ 100%，假阳性率为0 ~ 1。
By applying cumulative sums (CUSUM), a quality control method commonly used in manufacturing, we constructed a process for detecting unusual clusters among reported laboratory isolates of disease-causing organisms. We developed a computer algorithm based on minimal adjustments to the CUSUM method, which cumulates sums of the differences between frequencies of isolates and their expected means; we used the algorithm to identify outbreaks of Salmonella Enteritidis isolates reported in 1993. By comparing these detected outbreaks with known reported outbreaks, we estimated the sensitivity, specificity, and false-positive rate of the method. Sensitivity by state in which the outbreak was reported was 0%(0/1) to 100%. Specificity was 64% to 100%, and the false-positive rate was 0 to 1.