{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T03:50:51Z","timestamp":1791258651147,"version":"4.1.0"},"reference-count":33,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T00:00:00Z","timestamp":1790640000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T00:00:00Z","timestamp":1790640000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001665","name":"Agence Nationale de la Recherche","doi-asserted-by":"publisher","award":["ANR\u201021\u2010AAMR\u2010003\u201004"],"award-info":[{"award-number":["ANR\u201021\u2010AAMR\u2010003\u201004"]}],"id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100013281","name":"Joint Programming Initiative on Antimicrobial Resistance","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100013281","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Risk Analysis"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    This article revisits the risk characterization phase in Quantitative Microbial Risk Assessment (QMRA) and proposes a methodological framework to translate probabilistic risk estimates into easily interpretable public health metrics, thereby facilitating risk communication. This framework adopts a top\u2010down perspective, aiming to estimate the public health burden on a particular population associated with the exposure to a specific microbial hazard originating from a predefined production process. Such an approach is useful for risk managers to compare the relative health impacts of a food production system under different scenarios. It also allows a mathematically rigorous use of QMRA model outputs, even when model validation is constrained by unavoidable data and knowledge gaps. The proposed approach is illustrated using a multi\u2010pathway QMRA model from the literature, assessing the transmission of Extended\u2010Spectrum Beta\u2010Lactamase (ESBL)\u2010producing\n                    <jats:italic>Escherichia coli<\/jats:italic>\n                    (\n                    <jats:italic>E.\u00a0coli<\/jats:italic>\n                    ) to humans through broiler production. Under a baseline scenario defined by a parameterized production system and for a given population, the proposed approach classifies different exposure pathways according to their per\u2010individual risk of colonization, while accounting for the proportion of the general population exposed to each pathway.\n                  <\/jats:p>","DOI":"10.1111\/risa.70372","type":"journal-article","created":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T03:03:12Z","timestamp":1790737392000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Risk Characterization for a Multi\u2010Pathway Quantitative Risk Assessment Model: A Case Study on Extended\u2010Spectrum Beta\u2010Lactamas Producing\n                    <i>E. coli<\/i>\n                    Spread From Broiler Production"],"prefix":"10.1111","volume":"46","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3522-1236","authenticated-orcid":false,"given":"Subhasish","family":"Basak","sequence":"first","affiliation":[{"name":"Epidemiology and Surveillance Support Unit University of Lyon \u2010 French Agency for Food, Environmental and Occupational Health and Safety (ANSES) Lyon France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4670-9552","authenticated-orcid":false,"given":"Nunzio","family":"Sarnino","sequence":"additional","affiliation":[{"name":"Freie Universitat Berlin Institute of Veterinary Epidemiology and Biostatistics, Veterinary Center for Resistance Research Berlin Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lucie","family":"Collineau","sequence":"additional","affiliation":[{"name":"Epidemiology and Surveillance Support Unit University of Lyon \u2010 French Agency for Food, Environmental and Occupational Health and Safety (ANSES) Lyon France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,9,29]]},"reference":[{"key":"e_1_2_9_2_1","unstructured":"Agreste.2024.Graph'Agri 2024: Agriculture For\u00eat P\u00eache Alimentation Industries Agroalimentaires Environnement Territoire. 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CAC\/GL 30\u20101999.World Health Organization."},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijfoodmicro.2020.108559"},{"key":"e_1_2_9_17_1","unstructured":"Faverjon C. A.Cameron andM.De Nardi.2022.\"Modelling Framework to Quantify the Risk of AMR Exposure Via Food Products\u2014Example of Chicken and Lettuce.\" UK: Food Standards Agency."},{"key":"e_1_2_9_18_1","doi-asserted-by":"crossref","unstructured":"Furusawa M. S.Widgren E. G.Evers andE. A. 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