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The Strathprints institutional repository is a digital archive of University of Strathclyde research outputs. Strathprints provides access to thousands of Open Access research papers by University of Strathclyde researchers, including those from the School of Psychological Sciences & Health - but also papers by researchers based within the Faculties of Science, Engineering, Humanities & Social Sciences, and from the Strathclyde Business School.

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Assessing the probability of detecting antimicrobial resistant Salmonella in livestock using mathematical models

Kavanagh, Kimberley and Kelly, Louise Anne and Snary, Emma and Gettinby, George (2008) Assessing the probability of detecting antimicrobial resistant Salmonella in livestock using mathematical models. In: Young Statisticians’ Meeting. Office for National Statistics, Newport. (Unpublished)

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Abstract

The extent to which farmed animals are infected with antimicrobial resistant zoonotic bacteria such as Salmonella is of concern due to the potential exposure of humans to an additional pool of resistance genes via the food chain, which may present a risk to public health. In Great Britain, monitoring the levels of antimicrobial resistant Salmonella in livestock occurs both as part of a passive surveillance system and as structured surveys. To provide insight into such surveillance activities, a probabilistic model, adapted for non perfect test sensitivity and specificity, has been developed to assess the probability of detecting resistance at the faecal, pen and farm level. Using this model, it is concluded that the probability of detecting resistant Salmonella is dependent upon the level of resistance within sample/pen/farm and the diagnostic power of the test used. The likelihood of detecting low level (e.g. emerging) resistance on individual farms was low and therefore the use of selective plating (antimicrobial present in the plate at the specified breakpoint concentration so growth confirms the presence of resistant Salmonella) is recommended. Importantly, the models provide an insight into the sampling and testing methods and could therefore be used to inform any future on-farm surveillance programmes or research projects.