{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T16:45:41Z","timestamp":1790441141228,"version":"4.1.0"},"reference-count":51,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2022,3,10]],"date-time":"2022-03-10T00:00:00Z","timestamp":1646870400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100013890","name":"Ministerie van Landbouw, Natuur en Voedselkwaliteit","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100013890","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Zoonoses and Public Health"],"published-print":{"date-parts":[[2022,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Numerous source attribution studies for foodborne pathogens based on epidemiological and microbiological methods are available. These studies provide empirical data for modelling frameworks that synthetize the quantitative evidence at our disposal and reduce reliance on expert elicitations. Here, we develop a statistical model within a Bayesian estimation framework to integrate attribution estimates from expert elicitations with estimates from microbial subtyping and case\u2010control studies for sporadic infections with four major bacterial zoonotic pathogens in the Netherlands (<jats:italic>Campylobacter<\/jats:italic>, <jats:italic>Salmonella<\/jats:italic>, Shiga toxin\u2010producing <jats:italic>E. coli<\/jats:italic> [STEC] O157 and <jats:italic>Listeria<\/jats:italic>). For each pathogen, we pooled the published fractions of human cases attributable to each animal reservoir from the microbial subtyping studies, accounting for the uncertainty arising from the different typing methods, attribution models, and year(s) of data collection. We then combined the population attributable fractions (PAFs) from the case\u2010control studies according to five transmission pathways (domestic food, environment, direct animal contact, human\u2013human transmission and travel) and 11 groups within the foodborne pathway (beef\/lamb, pork, poultry meat, eggs, dairy, fish\/shellfish, fruit\/vegetables, beverages, grains, composite foods and food handlers\/vermin). The attribution estimates were biologically plausible, allowing the human cases to be attributed in several ways according to reservoirs, transmission pathways and food groups. All pathogens were predominantly foodborne, with <jats:italic>Campylobacter<\/jats:italic> being mostly attributable to the chicken reservoir, <jats:italic>Salmonella<\/jats:italic> to pigs (albeit closely followed by layers), and <jats:italic>Listeria<\/jats:italic> and STEC O157 to cattle. Food\u2010wise, the attributions reflected those at the reservoir level in terms of ranking. We provided a modelling solution to reach consensus attribution estimates reflecting the empirical evidence in the literature that is particularly useful for policy\u2010making and is extensible to other pathogens and domains.<\/jats:p>","DOI":"10.1111\/zph.12937","type":"journal-article","created":{"date-parts":[[2022,3,10]],"date-time":"2022-03-10T16:46:38Z","timestamp":1646930798000},"page":"475-486","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["A statistical modelling approach for source attribution meta\u2010analysis of sporadic infection with foodborne pathogens"],"prefix":"10.1111","volume":"69","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5420-949X","authenticated-orcid":false,"given":"Lapo","family":"Mughini\u2010Gras","sequence":"first","affiliation":[{"name":"Centre for Infectious Disease Control (CIb) National Institute for Public Health and the Environment (RIVM)  Bilthoven The Netherlands"},{"name":"Institute for Risk Assessment Sciences (IRAS), Faculty of Veterinary Medicine Utrecht University  Utrecht The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elisa","family":"Beninc\u00e0","sequence":"additional","affiliation":[{"name":"Centre for Infectious Disease Control (CIb) National Institute for Public Health and the Environment (RIVM)  Bilthoven The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Scott A.","family":"McDonald","sequence":"additional","affiliation":[{"name":"Centre for Infectious Disease Control (CIb) National Institute for Public Health and the Environment (RIVM)  Bilthoven The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aarieke","family":"de Jong","sequence":"additional","affiliation":[{"name":"Office for Risk Assessment &amp; Research (BuRO) Netherlands Food and Consumer Product Safety Authority  Utrecht The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jurgen","family":"Chardon","sequence":"additional","affiliation":[{"name":"Centre for Infectious Disease Control (CIb) National Institute for Public Health and the Environment (RIVM)  Bilthoven The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric","family":"Evers","sequence":"additional","affiliation":[{"name":"Centre for Infectious Disease Control (CIb) National Institute for Public Health and the Environment (RIVM)  Bilthoven The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Axel A.","family":"Bona\u010di\u0107 Marinovi\u0107","sequence":"additional","affiliation":[{"name":"Centre for Infectious Disease Control (CIb) National Institute for Public Health and the Environment (RIVM)  Bilthoven The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2022,3,10]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijfoodmicro.2013.03.005"},{"key":"e_1_2_8_3_1","unstructured":"Beninc\u00e0 E. Lagerweij G. R. Pijnacker R. Friesema I. Kretzschmar M\/ Franz E. &Mughini Gras L.(2021).Disease burden of food\u2010related pathogens in the Netherlands 2020. Bilthoven: Rijksinstituut voor Volksgezondheid en Milieu (RIVM). 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