ArticleBMC infectious diseases2018
Diversity of Salmonella serotypes from humans, food, domestic animals and wildlife in New South Wales, Australia.
Article in BMC infectious diseases, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it, 71 citations in OpenAlex.
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- Aptamer-Based Electrochemical Biosensors for the Detection ofDiagnostics (Basel, Switzerland) · 2022Article
- Genotypic virulence profiles and associations in Salmonella isolated from meat samples in wet markets and abattoirs of Metro Manila, Philippines.BMC microbiology · 2022Article
- Is Salmonella enterica shared between wildlife and cattle in cattle farming areas? An 11-year retrospective study in Tokachi district, Hokkaido, Japan.Veterinary medicine and science · 2022Article
- Investigating the dynamics of Salmonella contamination in integrated poultry companies using a whole genome sequencing approach.Poultry science · 2022Article
- Source attribution of salmonellosis by time and geography in New South Wales, Australia.BMC infectious diseases · 2022Article
- Salmonella enterica serotypes from human and nonhuman sources in Sao Paulo State, Brazil, 2004-2020.Revista do Instituto de Medicina Tropical de Sao Paulo · 2022Article
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Authors and funding
4 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSalmonella is an important human pathogen in Australia and annual case rates continue to increase. In addition to foodborne exposures, cases have been associated with animal and contaminated environment contact. However, routine surveillance in Australia has tended to focus on humans and food, with no reported attempts to collate and compare Salmonella data from a wider range of potential sources of exposure.
methodsSalmonella data from humans, food, animals and environments were collated from a range of surveillance and diagnostic sources in New South Wales (NSW). Data were categorised to reflect one of 29 sample origins. Serotype diversity was described for each category, and the distribution of serotypes commonly isolated from humans was examined for each sample origin. The distribution of serotypes along the livestock-food-human continuum and at the companion animal-wildlife interface was also examined.
resultsIn total, 49,872 Salmonella isolates were included in this analysis, comprising 325 serotypes. The vast majority of these isolates were from humans (n = 38,106). Overall S. Typhimurium was the most frequently isolated serotype and was isolated from all sample categories except natural environment and game meat. S. Enteriditis was not isolated from any livestock animal, however sporadic cases were documented in food, companion animals and a reptile. Many serotypes that were frequently isolated from livestock animals and associated food products were only rarely isolated from humans. In addition, a number of key human serotypes were only sporadically isolated from livestock and food products, suggesting alternative sources of infection. In particular, S. Paratyphi B Java and S. Wangata were more often isolated from wild animals. Finally, there was some overlap between serotypes in companion animals and wildlife, with cats in particular having a large number of serotypes in common with wild birds.
conclusionsThis is the most comprehensive description of Salmonella data from humans, food, livestock, wildlife, companion animals and various environments in Australia reported to date. Results confirm that livestock and food are important sources of salmonellosis in humans but that alternative sources - such as contact with wildlife and environments - warrant further investigation. Surveillance in NSW is largely human-focussed: major knowledge gaps exist regarding the diversity and frequency of serotypes in animals. More systematic surveillance of domestic animals and wildlife is needed to inform targeted control strategies and quantitative source attribution modelling in this state.
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