Literature DB >> 21219718

Evaluation of a statewide foodborne illness complaint surveillance system in Minnesota, 2000 through 2006.

John Li1, Kirk Smith, Dawn Kaehler, Karen Everstine, Josh Rounds, Craig Hedberg.   

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.

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Mesh:

Year:  2010        PMID: 21219718     DOI: 10.4315/0362-028x-73.11.2059

Source DB:  PubMed          Journal:  J Food Prot        ISSN: 0362-028X            Impact factor:   2.077


  5 in total

1.  Using Twitter to Identify and Respond to Food Poisoning: The Food Safety STL Project.

Authors:  Jenine K Harris; Jared B Hawkins; Leila Nguyen; Elaine O Nsoesie; Gaurav Tuli; Raed Mansour; John S Brownstein
Journal:  J Public Health Manag Pract       Date:  2017 Nov/Dec

2.  Prevention and Control of Youth Camp-Associated Acute Gastroenteritis Outbreaks.

Authors:  Anita K Kambhampati; Zachary A Marsh; Michele C Hlavsa; Virginia A Roberts; Antonio R Vieira; Jonathan S Yoder; Aron J Hall
Journal:  J Pediatric Infect Dis Soc       Date:  2019-11-06       Impact factor: 3.164

3.  Norovirus surveillance among callers to foodborne illness complaint hotline, Minnesota, USA, 2011-2013.

Authors:  Amy A Saupe; Dawn Kaehler; Elizabeth A Cebelinski; Brian Nefzger; Aron J Hall; Kirk E Smith
Journal:  Emerg Infect Dis       Date:  2013-08       Impact factor: 6.883

4.  Reporting of foodborne illness by U.S. consumers and healthcare professionals.

Authors:  Susan Arendt; Lakshman Rajagopal; Catherine Strohbehn; Nathan Stokes; Janell Meyer; Steven Mandernach
Journal:  Int J Environ Res Public Health       Date:  2013-08-19       Impact factor: 3.390

5.  Evaluating the Implementation of a Twitter-Based Foodborne Illness Reporting Tool in the City of St. Louis Department of Health.

Authors:  Jenine K Harris; Leslie Hinyard; Kate Beatty; Jared B Hawkins; Elaine O Nsoesie; Raed Mansour; John S Brownstein
Journal:  Int J Environ Res Public Health       Date:  2018-04-24       Impact factor: 3.390

  5 in total

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