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Source attribution of campylobacteriosis in Australia, 2017-2019

dc.contributor.authorMcLure, Angus
dc.contributor.authorSmith, James J.
dc.contributor.authorFirestone, Simon
dc.contributor.authorKirk, Martyn
dc.contributor.authorFrench, Nigel
dc.contributor.authorFearnley, Emily
dc.contributor.authorWallace, Rhiannon
dc.contributor.authorValcanis, Mary
dc.contributor.authorBulach, Dieter
dc.contributor.authorMoffatt, Cameron
dc.contributor.authorSelvey, Linda
dc.contributor.authorJennison, Amy V.
dc.contributor.authorCribb, Dani
dc.contributor.authorGlass, Katie
dc.date.accessioned2024-07-14T23:45:01Z
dc.date.available2024-07-14T23:45:01Z
dc.date.issued2023
dc.date.updated2024-05-19T08:17:04Z
dc.description.abstractCampylobacter jejuni and Campylobacter coli infections are the leading cause of foodborne gastroenteritis in high-income countries. Campylobacter colonizes a variety of warm-blooded hosts that are reservoirs for human campylobacteriosis. The proportions of Australian cases attributable to different animal reservoirs are unknown but can be estimated by comparing the frequency of different sequence types in cases and reservoirs. Campylobacter isolates were obtained from notified human cases and raw meat and offal from the major livestock in Australia between 2017 and 2019. Isolates were typed using multi-locus sequence genotyping. We used Bayesian source attribution models including the asymmetric island model, the modified Hald model, and their generalizations. Some models included an “unsampled” source to estimate the proportion of cases attributable to wild, feral, or domestic animal reservoirs not sampled in our study. Model fits were compared using the Watanabe–Akaike information criterion. We included 612 food and 710 human case isolates. The best fitting models attributed >80% of Campylobacter cases to chickens, with a greater proportion of C. coli (>84%) than C. jejuni (>77%). The best fitting model that included an unsampled source attributed 14% (95% credible interval [CrI]: 0.3%–32%) to the unsampled source and only 2% to ruminants (95% CrI: 0.3%–12%) and 2% to pigs (95% CrI: 0.2%–11%) The best fitting model that did not include an unsampled source attributed 12% to ruminants (95% CrI: 1.3%–33%) and 6% to pigs (95% CrI: 1.1%–19%). Chickens were the leading source of human Campylobacter infections in Australia in 2017–2019 and should remain the focus of interventions to reduce burden.
dc.description.sponsorshipAgrifutures Australia; ACT Health; Department of Health, Australian Government; Food Standards Australia New Zealand; Queensland Health; New South Wales Food Authority
dc.format.mimetypeapplication/pdfen_AU
dc.identifier.issn0272-4332
dc.identifier.urihttps://hdl.handle.net/1885/733713873
dc.language.isoen_AUen_AU
dc.provenanceThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium,provided the original work is properly cited and is not used for commercial purposes
dc.publisherBlackwell Publishing Ltd
dc.relationhttp://purl.org/au-research/grants/nhmrc/1116294
dc.relationhttp://purl.org/au-research/grants/nhmrc/145997
dc.relationhttp://purl.org/au-research/grants/arc/ DP180100246
dc.rights© 2023 The authors
dc.rights.licenseCreative Commons Attribution licence
dc.rights.urihttp://creativecommons.org/licenses/ by-nc/4.0/
dc.sourceRisk Analysis
dc.subjectBayesian analysis
dc.subjectCampylobacter
dc.subjectsource attribution
dc.titleSource attribution of campylobacteriosis in Australia, 2017-2019
dc.typeJournal article
dcterms.accessRightsOpen Access
local.bibliographicCitation.issue12
local.bibliographicCitation.lastpage2548
local.bibliographicCitation.startpage2527
local.contributor.affiliationMcLure, Angus, College of Health and Medicine, ANU
local.contributor.affiliationSmith, James J., Queensland University of Technology
local.contributor.affiliationFirestone, Simon, University of Melbourne
local.contributor.affiliationKirk, Martyn, College of Health and Medicine, ANU
local.contributor.affiliationFrench, Nigel, Massey University
local.contributor.affiliationFearnley, Emily, South Australia Health
local.contributor.affiliationWallace, Rhiannon, Agassiz Research and Development Centre
local.contributor.affiliationValcanis, Mary, University of Melbourne
local.contributor.affiliationBulach, Dieter, University of Melbourne
local.contributor.affiliationMoffatt, Cameron, College of Health and Medicine, ANU
local.contributor.affiliationSelvey, Linda, The University of Queensland
local.contributor.affiliationJennison, Amy V., Queensland Health
local.contributor.affiliationCribb, Dani, College of Health and Medicine, ANU
local.contributor.affiliationGlass, Katie, College of Health and Medicine, ANU
local.contributor.authoruidMcLure, Angus, u4859599
local.contributor.authoruidKirk, Martyn, u3853379
local.contributor.authoruidMoffatt, Cameron, u4170813
local.contributor.authoruidCribb, Dani, u5348216
local.contributor.authoruidGlass, Katie, u4053649
local.description.notesImported from ARIES
local.identifier.absfor420205 - Epidemiological modelling
local.identifier.ariespublicationa383154xPUB41221
local.identifier.citationvolume43
local.identifier.doi10.1111/risa.14138
local.identifier.scopusID2-s2.0-85152271058
local.publisher.urlhttps://onlinelibrary.wiley.com/
local.type.statusPublished Version

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