Table 1.

Characteristics of the included studies

Study	Location	Level	Description of study aims (relevant to this review)	Methods	Relevant outcomes
Microbial subtyping studies
Hald, 2007 [33]	Denmark	National	Attribute resistant salmonellosis cases to different animal reservoirs, using resistance profiles	Typing: serotyping & antibiotyping (phenotypic); phage typing (for susceptible isolates) Analysis: based on Hald model [49]	Attribution of all infections; % of resistant cases due to each source
Vieira, 2016 [32]	United States	National	Evaluate the use of Salmonella Hadar retail contamination data and antibiotyping to attribute human illnesses to food sources, comparing four models	Typing: PFGE (comparison) & antibiotyping (PT) Analysis: used Dutch model [50]	Attribution (%)
Mughini-Gras, 2019 [31]	Netherlands	National	Attribute ESBL- and pAmpC-producing E. coli carriage in the community to different sources based on gene, prevalence, and human exposure data	Typing: ESBL & pAmpC gene occurrence Analysis: based on modified Hald model [51]	Attribution (%)
Parisi, 2020 [47]	Vietnam	Sub-national	Attribute invasive and non-invasive human non-typhoidal salmonellosis in Southern Vietnam to animal sources using serotyping and/or antibiotyping	Typing: molecular serotyping (MLST) & antibiotyping (phenotypic) Analysis: own, Bayesian multinomial mixture model	Attribution (% and total cases)
Duarte, 2021 [19]	Europe	Regional	Demonstrate the use of metagenomics for the attribution of the human resistome to different animal reservoirs with three models differing on whether they include a human/unknown source and on whether they were country dependent or independent	Typing: metagenomics Analysis: own, random forests & dissimilarity analysis (SIMPER)	Different plots illustrating attribution
Mitchell, 2021 [46]	India	Sub-national	Estimate the contribution of animals and water sources to resistant E. coli infections in children in rural India	Typing: antibiotyping (phenotypic) Analysis: SourceR [52]	Attribution (% and total cases)
Perestrelo, 2022 [30]	Germany	National	Attribute human ESBL-producing E. coli colonisation to animal sources and nosocomial infections, using different combinations of three typing methods	Typing: ESBL genotyping, phylogenetic grouping (PCR) & antibiotyping (phenotypic) Analysis: based on Hald model [49]	Attribution (% and total cases)
Comparative exposure assessments
Carmo, 2014 [24]	Denmark	National	Assess the relative contribution of different meat types to the consumer exposure to ESBL-/AmpC-producing E. coli	Quantitative Guideline: not specified	Exposure attribution (%)
Evers, 2017 [23]	Netherlands	National	Quantify ESBL- & pAmpC-producing E. coli exposure in humans via meat consumption from pre-retail to exposure	Quantitative Guideline: based on swift QMRA [53]	Exposure/ portion; exposure attribution (total and %)
Lechner, 202 [22]	Switzerland	National	Identify the most relevant AMR transmission pathways from animals to humans based on Swiss expert opinions	Qualitative Guideline: OIE framework for AMR [9]	Person days at risk; bubble chart relating exposure, release & person days at risk
Investigation of outbreaks
Holmberg, 1984 [29]	United States	National	Describe animal sources of antibiotic-resistant Salmonella outbreaks between 1971 and 1983	Linked outbreak reports with resistance information	Number of resistant outbreaks caused by each source
Sahin, 2012 [28]	United States	National	Report of several outbreaks caused by Campylobacter jejuni clone SA as part of their investigation of its presence in human isolates	Identified relevant outbreaks via the PulseNet database for Campylobacter	Description of all clone SA outbreaks & their source
Brown, 2017 [27]	United States	National	Compare foods associated with antibiotic-resistant Salmonella outbreaks from 2003 to 2012	Linked outbreak info to antibiotic susceptibility data	Numbers of resistant (& multidrug-resistant) outbreaks caused by each source
Folster, 2017 [26]	United States	National	Report the number of outbreaks caused by ceftriaxone-resistant Salmonella by source between 2011 and 2012 as part of a genetic analysis of the outbreak strains	Tested outbreak samples for ceftriaxone-resistance & linked positive samples to source information	Number of resistant outbreaks caused by each source
Waltenburg, 2021 [25]	United States	National	Describe all salmonellosis outbreaks caused by reptiles or amphibians between 2009 and 2018, including a description of the sources by resistance profile	Tested outbreak samples for resistance	Number of resistant outbreaks caused by each pet species
Other source attribution studies
de Freitas Costa, 2022 [34]	Netherlands	National	Develop a dynamic risk model that accounts for the multi-directional spread of ESBL-producing E. coli between populations over time and may be used for exploring the effects of different food chain interventions	Source attribution Analysis: discrete-time model	Attribution at equilibrium (%)
Risk assessments
Vose, 2000 [11]	United States	National	Develop a model to assess the human health impact of fluoroquinolone-resistant Campylobacter attributed to chicken consumption that also allows for modelling future changes in the system	Quantitative Framework: own (FDA-CVM)	% and ‘1 in x’ of being affected for all citizens, cases, cases seeking care & care-seeking cases who are prescribed antibiotics
Alban, 2022 [44]	Denmark	National	Assess whether dry-cured sausages produced with pork with Salmonella Typhimurium DT104 are a risk for consumers	Quantitative Framework: Codex [54]	Maximal observed number of diarrhoea cases per year within 100 years
Presi, 2009 [43]	Switzerland	National	Compare the health risk for consumers arising from their exposure to resistant bacteria from meat of four different types	Semi-Quantitative (Risk scoring) Framework: own model	Ranking of different meat products according to high human health risk
Cox, 2014 [42]	United States	National	Estimate the excess number of human MRSA infections attributable to MRSA ST398 from pigs and pork	Quantitative Framework: own model	Excess cases per year
Otto, 2014 [45]	Canada	Sub-national	Estimate number of ceftiofur-resistant Salmonella enterica Heidelberg cases in humans in Québec and Ontario attributable to chicken consumption	Quantitative Framework: based on FDA-CVM [11]	Annual mean incidence due to chicken consumption
Doménech, 2015 [48]	Spain	Sub-national	Characterise the human health risk due to different resistances in Salmonella from pork, beef, and poultry meat	Qualitative Framework: Codex AMR [55]	Level of risk for humans due to different resistances from different meats
Chereau, 2017 [20]	South East Asia	Regional	Characterise the level of risk of the emergence and spread of AMR in the WHO Southeast Asian region	Qualitative Framework: WHO rapid risk assessment guideline [56]	High, medium, low & negligible risk transmission routes
Collineau, 2018 [41]	Switzerland	National	Develop a framework to rank the human health importance of combinations of pathogens, resistance to antimicrobials, and different meat types	Semi-Quantitative (Risk ranking) Framework: Codex AMR & MCDA, identified via EFSA risk ranking review [55, 57]	List of meat-pathogen-resistance combinations with the highest human health risk
Collineau, 2020 [40]	Canada	National	Define the baseline (2013) risk of human ceftiofur-resistant Salmonella Heidelberg infection due to chicken and compare it to alternative scenarios	Qualitative Framework: Based on Codex AMR & FAO/WHO model [55, 58]	% of illness per serving; number of cases per year
Costard, 2020 [39]	United States	National	Estimate the risk for resistant non-typhoidal salmonellosis per beef meal using the yearly cases of resistant infections and number of meals made with beef and evaluate the change over time	Quantitative Framework: Based on [33] and USDA framework [10]	Annual incidence attributable to beef, cases per 1 million beef meals
Schoen, 2020 [38]	United States	National	Assess the risk for MRSA colonisation from preparing contaminated pork meat	Quantitative Framework: not specified	Risk per preparation event
Opatowski, 2021 [21]	South or South East Asia	National	Develop a model to combine annual ESBL-producing E. coli colonisation incidence due to five One Health transmission routes. Illustrate its application in hypothetical high- and low-income settings	Quantitative Framework: complementary to [20]	Incidence due to animal-based food and animal contact per 100 persons per year
Other studies
Bosch, 2016 [37]	Netherlands	National	Describe changing characteristics of livestock-associated MRSA, including the percentage of cases reporting livestock contact	/	% of cases who were in contact with livestock
Larsen, 2017 [36]	Denmark	National	Describe the emergence of livestock-associated MRSA CC398 in invasive human cases, including a summary of cases not reporting livestock contact	/	% of cases who were in contact with livestock
Booton, 2021 [35]	Thailand	National	Develop a One Health model to predict the maximum impact of reducing different AMR drivers in Thailand on the human AMR burden between 2020 and 2040	Prediction model, One Health Analysis: compartmental model of ordinary differential equations	Maximum human AMR reduction via elimination of animal-to-human transmission (%)

Abbreviations: Codex, Codex Alimentarius; EFSA, The European Food Safety Authority; ESBL, extended-spectrum β-lactamase; FAO, Food and Agriculture Organization; FDA-VCM, Food and Drug Administration Center for Veterinary Medicine; MRSA, methicillin-resistant S. aureus; OIE, World Organisation for Animal Health (now WOAH); (p)AmpC, (plasmid)-mediated AmpC β-lactamase; QMRA, quantitative microbial risk assessment; USDA, United States Department of Agriculture; WHO, World Health Organization.