Introduction: Foodborne illnesses are common, with an estimated 76 million cases in the U.S. annually. They are also becoming harder to prevent with the increasing complexity of food distribution networks and product types. There has been an increased call to improve the food safety in the United States through improved foodborne disease surveillance. The aim of this dissertation was to improve foodborne illness surveillance, by examining a statewide complaint surveillance system and development of methods to more effectively use incoming data.
Methods: In manuscript one, the complaint surveillance system in Minnesota from 2000-2006 was evaluated and characteristics of outbreak related complaints were analyzed. In manuscript two, predictors for Salmonella complaint calls were examined to develop a screening tool to be used on incoming complaint data. A predictive model for Salmonella-like calls was developed and validated using bootstrap methods. The third manuscript used cusum methods to detect temporal correlations in complaint calls and flag weeks of unusually high calls. The fourth manuscript, described the current use of complaint based surveillance systems by local health departments in the U.S.
Results: Complaint based surveillance was responsible for detection of 72% of outbreaks in Minnesota. The predictive model for Salmonella was able to discriminate between Salmonella-like and non-Salmonella-like calls with an adjusted AUC of 0.88. An algorithm to flag suspected outbreak weeks had a sensitivity and specificity of 63% and 84% in detection of norovirus outbreaks. An estimated 81% of health departments in the U.S. use a complaint based surveillance system; however, ability of the system to detect outbreaks varies between jurisdictions.]
Conclusions: This dissertation provides a framework to improve food safety in the U.S. through the development of complaint based surveillance systems and application of methods to better use incoming data. Complaint systems are a powerful tool to complement pathogen specific surveillance.
University of Minnesota Ph.D. dissertation. September 2010. Major: Epidemiology. Advisors: Craig Hedberg, PhD and Alan Lifson, MD, MPH. 1 computer file (PDF); x, 136 pages, appendices A-C.
Li, John Jiuhan.
Foodborne disease surveillance: evaluation of a consumer driven complaint system and development of methods for screening of pathogens and cluster detection..
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