USING LABORATORY-BASED SURVEILLANCE DATA FOR PREVENTION - AN ALGORITHM FOR DETECTING SALMONELLA OUTBREAKS

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 organi sms. We developed a computer algorithm based on minimal adjustments to the CUSUM method, which cumulates sums of the differences between fre quencies of isolates and their expected means; we used the algorithm t o identify outbreaks of Salmonella Enteritidis isolates reported in 19 93. By comparing these detected outbreaks with known reported outbreak s, 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-posit ive rate was 0 to 1.