ArticleEpidemiology and infection2015
Using surveillance and monitoring data of different origins in a Salmonella source attribution model: a European Union example with challenges and proposed solutions.
Article in Epidemiology and infection, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Surveillance of antimicrobial resistance, phenotypic, and genotypic patterns ofVeterinary world · 2023Article
- Source Attribution and Risk Assessment of Antimicrobial Resistance.Microbiology spectrum · 2018Review
- Low Prevalence ofItalian journal of food safety · 2017Article
- Attributing foodborne salmonellosis in humans to animal reservoirs in the European Union using a multi-country stochastic model.Epidemiology and infection · 2015Article
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Authors and funding
3 authors.
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Abstract
Microbial subtyping approaches are commonly used for source attribution of human salmonellosis. Such methods require data on Salmonella in animals and humans, outbreaks, infection abroad and amounts of food available for consumption. A source attribution model was applied to 24 European countries, requiring special data management to produce a standardized dataset. Salmonellosis data on animals and humans were obtained from datasets provided by the European Food Safety Authority. The amount of food available for consumption was calculated based on production and trade data. Limitations included different types of underreporting, non-participation in prevalence studies, and non-availability of trade data. Cases without travel information were assumed to be domestic; non-subtyped human or animal records were re-identified according to proportions observed in reference datasets; missing trade information was estimated based on previous years. The resulting dataset included data on 24 serovars in humans, broilers, laying hens, pigs and turkeys in 24 countries.
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