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A Canadian Application of One Health: Integration of Salmonella Data from Various Canadian Surveillance Programs (2005-2010)

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FOODBORNE PATHOGENS AND DISEASE
卷 10, 期 9, 页码 747-756

出版社

MARY ANN LIEBERT, INC
DOI: 10.1089/fpd.2012.1438

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  1. PHAC
  2. Health Canada
  3. AAFC
  4. Canadian Food Inspection Agency

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Most bacterial pathogens associated with human enteric illness have zoonotic origins and can be transmitted directly from animals to people or indirectly through food and water. This multitude of potential exposure routes and sources makes the epidemiology of these infectious agents complex. To better understand these illnesses and identify solutions to reduce human disease, an integrative approach like One Health is needed. This article considers the issue of Salmonella in Canada and interprets data collected by several Canadian surveillance and research programs. We describe recovery of Salmonella from various samples collected along the exposure pathway and compare the serovars detected in the different components under surveillance (animal, food, environment, and human). We then present three examples to illustrate how an approach that interprets multiple sources of surveillance data together is able to address issues that transcend multiple departments and jurisdictions. First, differences observed in recovery of Salmonella from different cuts of fresh chicken collected by different programs emphasize the importance of considering the surveillance objectives and how they may influence the information that is generated. Second, the high number of Salmonella Enteritidis cases in Canada is used to illustrate the importance of ongoing, concurrent surveillance of human cases and exposure sources to information domestic control and prevention strategies. Finally, changing patterns in the occurrence of ceftiofur-resistant Salmonella Heidelberg in retail meats and humans demonstrates how integrated surveillance can identify an issue in an exposure source and link it to a trend in human disease. Taken together, surveillance models that encompass different scales can leverage infrastructure, costs, and benefits and generate a multidimensional picture that can better inform disease prevention and control programs.

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