Evaluation of a Statewide Foodborne Illness Complaint Surveillance System in Minnesota, 2000 through 2006
Source: Journal of Food Protection®, Number 11, November 2010, pp. 1962-2140 , pp. 2059-2064(6)
Abstract:Foodborne outbreaks are detected by recognition of similar illnesses among persons with a common exposure or by identification of case clusters through pathogen-specific surveillance. PulseNet USA has created a national framework for pathogen-specific surveillance, but no comparable effort has been made to improve surveillance of consumer complaints of suspected foodborne illness. The purpose of this study was to characterize the complaint surveillance system in Minnesota and to evaluate its use for detecting outbreaks. Minnesota Department of Health foodborne illness surveillance data from 2000 through 2006 were analyzed for this study. During this period, consumer complaint surveillance led to detection of 79% of confirmed foodborne outbreaks. Most norovirus infection outbreaks were detected through complaints. Complaint surveillance also directly led or contributed to detection of 25% of salmonellosis outbreaks. Eighty-one percent of complainants did not seek medical attention. The number of ill persons in a complainant's party was significantly associated with a complaint ultimately resulting in identification of a foodborne outbreak. Outbreak confirmation was related to a complainant's ability to identify a common exposure and was likely related to the process by which the Minnesota Department of Health chooses complaints to investigate. A significant difference (P < 0.001) was found in incubation periods between complaints that were outbreak associated (median, 27 h) and those that were not outbreak associated (median, 6 h). Complaint systems can be used to detect outbreaks caused by a variety of pathogens. Case detection for foodborne disease surveillance in Minnesota happens through a multitude of mechanisms. The ability to integrate these mechanisms and carry out rapid investigations leads to improved outbreak detection.
Document Type: Research Article
Publication date: November 2010
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