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 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.
Hutwagner
L C
LC
Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
Maloney
E K
EK
Bean
N H
NH
Slutsker
L
L
Martin
S M
SM
D000465:Algorithms; D016000:Cluster Analysis; D004196:Disease Outbreaks; D006801:Humans; D007753:Laboratories; D011159:Population Surveillance / Q000379:methods; D011634:Public Health; D012480:Salmonella Infections / Q000453:epidemiology* / Q000517:prevention & control*; D012477:Salmonella enteritidis / Q000302:isolation & purification; D012680:Sensitivity and Specificity; D014481:United States / Q000453:epidemiology