title: Development of a Salmonella screening tool for consumer complaint-based foodborne illness surveillance systems.
abstract: Foodborne illness surveillance based on consumer complaints detects outbreaks by finding common exposures among callers, but this process is often difficult. Laboratory testing of ill callers could also help identify potential outbreaks. However, collection of stool samples from all callers is not feasible. Methods to help screen calls for etiology are needed to increase the efficiency of complaint surveillance systems and increase the likelihood of detecting foodborne outbreaks caused by Salmonella. Data from the Minnesota Department of Health foodborne illness surveillance database (2000 to 2008) were analyzed. Complaints with identified etiologies were examined to create a predictive model for Salmonella. Bootstrap methods were used to internally validate the model. Seventy-one percent of complaints in the foodborne illness database with known etiologies were due to norovirus. The predictive model had a good discriminatory ability to identify Salmonella calls. Three cutoffs for the predictive model were tested: one that maximized sensitivity, one that maximized specificity, and one that maximized predictive ability, providing sensitivities and specificities of 32 and 96%, 100 and 54%, and 89 and 72%, respectively. Development of a predictive model for Salmonella could help screen calls for etiology. The cutoff that provided the best predictive ability for Salmonella corresponded to a caller reporting diarrhea and fever with no vomiting, and five or fewer people ill. Screening calls for etiology would help identify complaints for further follow-up and result in identifying Salmonella cases that would otherwise go unconfirmed; in turn, this could lead to the identification of more outbreaks.
authors.affiliation: University of Minnesota, 1158 Mayo MMC 807, 1158 Delaware Street S.E., Minneapolis, Minnesota 55455, USA. lixxx607@umn.edu
authors.forename: John
authors.initials: J
authors.lastname: Li
authors.forename: Rich
authors.initials: R
authors.lastname: Maclehose
authors.forename: Kirk
authors.initials: K
authors.lastname: Smith
authors.forename: Dawn
authors.initials: D
authors.lastname: Kaehler
authors.forename: Craig
authors.initials: C
authors.lastname: Hedberg
mesh_terms: D003937:Diagnosis, Differential; D004196:Disease Outbreaks / Q000706:statistics & numerical data; D005506:Food Contamination / Q000706:statistics & numerical data; D005516:Food Microbiology; D006801:Humans; D008403:Mass Screening / Q000295:instrumentation* / Q000379:methods*; D008910:Minnesota / Q000453:epidemiology; D011237:Predictive Value of Tests; D012475:Salmonella / Q000302:isolation & purification*; D012478:Salmonella Food Poisoning / Q000175:diagnosis / Q000453:epidemiology* / Q000209:etiology / Q000517:prevention & control; D012680:Sensitivity and Specificity; D018571:Sentinel Surveillance