title: Bayesian Source Attribution of Salmonellosis in South Australia.
abstract: Salmonellosis is a significant cause of foodborne gastroenteritis in Australia, and rates of illness have increased over recent years. We adopt a Bayesian source attribution model to estimate the contribution of different animal reservoirs to illness due to Salmonella spp. in South Australia between 2000 and 2010, together with 95% credible intervals (CrI). We excluded known travel associated cases and those of rare subtypes (fewer than 20 human cases or fewer than 10 isolates from included sources over the 11-year period), and the remaining 76% of cases were classified as sporadic or outbreak associated. Source-related parameters were included to allow for different handling and consumption practices. We attributed 35% (95% CrI: 20-49) of sporadic cases to chicken meat and 37% (95% CrI: 23-53) of sporadic cases to eggs. Of outbreak-related cases, 33% (95% CrI: 20-62) were attributed to chicken meat and 59% (95% CrI: 29-75) to eggs. A comparison of alternative model assumptions indicated that biases due to possible clustering of samples from sources had relatively minor effects on these estimates. Analysis of source-related parameters showed higher risk of illness from contaminated eggs than from contaminated chicken meat, suggesting that consumption and handling practices potentially play a bigger role in illness due to eggs, considering low Salmonella prevalence on eggs. Our results strengthen the evidence that eggs and chicken meat are important vehicles for salmonellosis in South Australia.
authors.affiliation: National Centre for Epidemiology and Population Health, Australian National University, Canberra, ACT, 0200, Australia.
authors.forename: K
authors.initials: K
authors.lastname: Glass
authors.affiliation: National Centre for Epidemiology and Population Health, Australian National University, Canberra, ACT, 0200, Australia.|Communicable Disease Control Branch, South Australian Department for Health, Adelaide, SA, 5000, Australia.
authors.forename: E
authors.initials: E
authors.lastname: Fearnley
authors.affiliation: Australian Salmonella Reference Centre, S.A. Pathology, Adelaide, Australia.
authors.forename: H
authors.initials: H
authors.lastname: Hocking
authors.affiliation: Communicable Disease Control Branch, South Australian Department for Health, Adelaide, SA, 5000, Australia.
authors.forename: J
authors.initials: J
authors.lastname: Raupach
authors.affiliation: Tasmanian Department of Health and Human Services, Hobart, Tasmania.
authors.forename: M
authors.initials: M
authors.lastname: Veitch
authors.affiliation: National Centre for Epidemiology and Population Health, Australian National University, Canberra, ACT, 0200, Australia.
authors.forename: L
authors.initials: L
authors.lastname: Ford
authors.affiliation: National Centre for Epidemiology and Population Health, Australian National University, Canberra, ACT, 0200, Australia.
authors.forename: M D
authors.initials: MD
authors.lastname: Kirk
mesh_terms: D000818:Animals; D015373:Bacterial Typing Techniques; D001499:Bayes Theorem; D002645:Chickens; D004196:Disease Outbreaks; D004531:Eggs; D005516:Food Microbiology; D059022:Food Safety; D006291:Health Policy; D006801:Humans; D008460:Meat; D012478:Salmonella Food Poisoning / Q000453:epidemiology* / Q000209:etiology / Q000517:prevention & control*; D012480:Salmonella Infections / Q000453:epidemiology; D013021:South Australia; D014195:Travel
keywords: Attribution model; Australia; Salmonella; chicken; eggs