Attributing the human disease burden of foodborne infections to specific sources

Foodborne Illnesses from Leafy Greens in the United States: Attribution, Burden, and Cost

Leafy green vegetables are a major source of foodborne illnesses. Nevertheless, few studies have attempted to estimate attribution and burden of illness estimates for leafy greens. This study combines results from three outbreak-based attribution models with illness incidence and economic cost models to develop comprehensive pathogen-specific burden estimates for leafy greens and their subcategories in the United States. We find that up to 9.18% (90% CI: 5.81%-15.18%) of foodborne illnesses linked to identified pathogens are attributed to leafy greens. Including 'Unknown' illnesses not linked to specific pathogens, leafy greens account for as many as 2,307,558 (90% CI: 1,077,815-4,075,642) illnesses annually in the United States. The economic cost of these illnesses is estimated to be up to $5.278 billion (90% CI: $3.230-$8.221 billion) annually. Excluding the pathogens with small outbreak sizes, Norovirus, Shiga toxin-producingEscherichia coli (both non-O157 and O157:H7), Campylobacter spp., and nontyphoidal Salmonella, are associated with the highest number of illnesses and greatest costs from leafy greens. While lettuce (romaine, iceberg, "other lettuce") takes 60.8% of leafy green outbreaks, it accounts for up to 75.7% of leafy green foodborne illnesses and 70% of costs. Finally, we highlighted that 19.8% of Shiga toxin-producingEscherichia coli O157:H7 illnesses are associated with romaine among all food commodities, resulting in 12,496 estimated illnesses and $324.64 million annually in the United States.

Estimating the health burden of foodborne gastroenteritis caused by non-typhoidal Salmonella enterica and Vibrio parahaemolyticus in Zhejiang province, China

As acute gastrointestinal (AGI) disease patients only sometimes seek medical care or submit stool samples for testing, and laboratories cannot detect or identify the pathogen, most cases of foodborne gastroenteritis still need to be identified through public health monitoring. We conducted a population survey and sentinel hospital surveillance to determine the burden of foodborne gastroenteritis caused by non-typhoidal Salmonella enterica (NTS) and Vibrio parahaemolyticus infection, from July 2018 to June 2019 in Zhejiang province, China, and a model for calculating disease burden established. Using the burden of illness pyramid model, we estimated that there were 140.3 cases of NTS infection and 136.2 cases of V. parahaemolyticus infection. We estimated annual incidence per 100,000 population in Zhejiang province as 236 (95% confidence interval [CI] 208-267) and 206 (95% CI 155-232) cases for foodborne NTS and V. parahaemolyticus gastroenteritis, respectively. The results show that AGI caused by these two pathogens constitutes a substantial burden in the Zhejiang population. The health burden of AGI estimations caused by NTS and V. parahaemolyticus in this study can serve as a strategic framework to direct policy and intervention.

Frequency of isolation and phenotypic antimicrobial resistance of fecal Salmonella enterica recovered from dairy cattle in Canada

Salmonellosis is one of the leading causes of gastrointestinal infections in humans. In Canada, it is estimated that approximately 87,500 cases of salmonellosis occur every year in humans, resulting in 17 deaths. In the United States, it is estimated that 26,500 hospitalizations and 420 deaths occur every year. In dairy cattle, infections caused by nontyphoidal Salmonella enterica can cause mild to severe disease, including enteritis, pneumonia, and septicemia. Our study objectives were to determine the proportion of fecal samples positive for Salmonella in dairy cattle in Canada and determine the resistance pattern of these isolates. We used data collected through the Canadian Dairy Network for Antimicrobial Stewardship and Resistance (CaDNetASR). Pooled fecal samples from preweaning calves, postweaning heifers, lactating cows, and manure storage were cultured for Salmonella, and the isolates were identified using matrix-assisted laser desorption/ionization-time of flight mass spectrometry. Antimicrobial susceptibilities were determined using the minimum inhibitory concentration test, and resistance interpretation was made according to the Clinical and Laboratory Standards Institute. A 2-level, multivariable logistic regression model was built to determine the probability of recovering Salmonella from a sample, accounting for province, year, and sample source. The proportion of farms with at least one positive sample were 12% (17/140), 19% (28/144), and 17% (24/144) for the sampling years 2019, 2020, and 2021, respectively. Out of the 113 Salmonella isolates, 23 different serovars were identified. The occurrence of Salmonella appeared to be clustered by farms and provinces. The most common serovars identified were Infantis (14%) and Typhimurium (14%). Overall, 21% (24/113) of the Salmonella isolates were resistant to at least one antimicrobial. Resistance to tetracycline was commonly observed (17%); however, very limited resistance to category I antimicrobials (categorization according to Health Canada that includes third-generation cephalosporins, fluoroquinolones, polymyxins, and carbapenems) was observed, with one isolate resistant to amoxicillin and clavulanic acid. The proportion of Salmonella isolates resistant to 2 and 3 antimicrobial classes was 3.5% and 8.8%, respectively. Our study provided valuable information on the proportion of fecal samples positive for Salmonella, the serovars identified, and the associated resistance patterns across CaDNetASR herds, at regional and national levels.

Development and validation of a random forest algorithm for source attribution of animal and human Salmonella Typhimurium and monophasic variants of S. Typhimurium isolates in England and Wales utilising whole genome sequencing data

Source attribution has traditionally involved combining epidemiological data with different pathogen characterisation methods, including 7-gene multi locus sequence typing (MLST) or serotyping, however, these approaches have limited resolution. In contrast, whole genome sequencing data provide an overview of the whole genome that can be used by attribution algorithms. Here, we applied a random forest (RF) algorithm to predict the primary sources of human clinical Salmonella Typhimurium (S. Typhimurium) and monophasic variants (monophasic S. Typhimurium) isolates. To this end, we utilised single nucleotide polymorphism diversity in the core genome MLST alleles obtained from 1,061 laboratory-confirmed human and animal S. Typhimurium and monophasic S. Typhimurium isolates as inputs into a RF model. The algorithm was used for supervised learning to classify 399 animal S. Typhimurium and monophasic S. Typhimurium isolates into one of eight distinct primary source classes comprising common livestock and pet animal species: cattle, pigs, sheep, other mammals (pets: mostly dogs and horses), broilers, layers, turkeys, and game birds (pheasants, quail, and pigeons). When applied to the training set animal isolates, model accuracy was 0.929 and kappa 0.905, whereas for the test set animal isolates, for which the primary source class information was withheld from the model, the accuracy was 0.779 and kappa 0.700. Subsequently, the model was applied to assign 662 human clinical cases to the eight primary source classes. In the dataset, 60/399 (15.0%) of the animal and 141/662 (21.3%) of the human isolates were associated with a known outbreak of S. Typhimurium definitive type (DT) 104. All but two of the 141 DT104 outbreak linked human isolates were correctly attributed by the model to the primary source classes identified as the origin of the DT104 outbreak. A model that was run without the clonal DT104 animal isolates produced largely congruent outputs (training set accuracy 0.989 and kappa 0.985; test set accuracy 0.781 and kappa 0.663). Overall, our results show that RF offers considerable promise as a suitable methodology for epidemiological tracking and source attribution for foodborne pathogens.

A Review on Microbiological Source Attribution Methods of Human Salmonellosis: From Subtyping to Whole-Genome Sequencing

Salmonella is one of the main causes of human foodborne illness. It is endemic worldwide, with different animals and animal-based food products as reservoirs and vehicles of infection. Identifying animal reservoirs and potential transmission pathways of Salmonella is essential for prevention and control. There are many approaches for source attribution, each using different statistical models and data streams. Some aim to identify the animal reservoir, while others aim to determine the point at which exposure occurred. With the advance of whole-genome sequencing (WGS) technologies, new source attribution models will greatly benefit from the discriminating power gained with WGS. This review discusses some key source attribution methods and their mathematical and statistical tools. We also highlight recent studies utilizing WGS for source attribution and discuss open questions and challenges in developing new WGS methods. We aim to provide a better understanding of the current state of these methodologies with application to Salmonella and other foodborne pathogens that are common sources of illness in the poultry and human sectors.

Foodborne disease outbreaks in flour and flour-based food products from microbial pathogens in the United States, and their health economic burden

The most comprehensive and inclusive estimates for the economic burden of foodborne illness yield values as high as $97.4 billion USD annually. However, broad incidence and cost estimates have limited use if they cannot be attributed to specific foods, for the purposes of food safety control. In this study, we estimated the economic burden of foodborne illnesses resulting from flour and flour-based food products in the United States from the years 2001 to 2021. The outbreak, illness burden, and health economic data are combined to generate these estimates. Our model combined outbreak data with published Centers for Disease Control and Prevention multipliers to estimate the annual number of illnesses associated with flour-borne pathogens. We then integrated illness severity data with an updated economic model that accounts for costs related to medical care, productivity loss, loss of life, along with the quality of life loss that entails pain and suffering. In total, 752 cases and 223 hospitalizations from flour-related illnesses were reported from 2001 to 2021, with an average of 37.6 cases of reported cases annually. However, the actual number of cases, accounting for underreporting and underdiagnosis, can be as high as 19,440 annually. Pathogens involved in these outbreaks are Salmonella, E. coli O157:H7, and E. coli O121. Our estimates suggest average annual economic losses, including healthy years of life lost, of $108 and $258 million using two alternative models.

Transforming Shiga toxin-producing Escherichia coli surveillance through whole genome sequencing in food safety practices

Introduction

Shiga toxin-producing Escherichia coli (STEC) is a gastrointestinal pathogen causing foodborne outbreaks. Whole Genome Sequencing (WGS) in STEC surveillance holds promise in outbreak prevention and confinement, in broadening STEC epidemiology and in contributing to risk assessment and source attribution. However, despite international recommendations, WGS is often restricted to assist outbreak investigation and is not yet fully implemented in food safety surveillance across all European countries, in contrast to for example in the United States.

Methods

In this study, WGS was retrospectively applied to isolates collected within the context of Belgian food safety surveillance and combined with data from clinical isolates to evaluate its benefits. A cross-sector WGS-based collection of 754 strains from 1998 to 2020 was analyzed.

Results

We confirmed that WGS in food safety surveillance allows accurate detection of genomic relationships between human cases and strains isolated from food samples, including those dispersed over time and geographical locations. Identifying these links can reveal new insights into outbreaks and direct epidemiological investigations to facilitate outbreak management. Complete WGS-based isolate characterization enabled expanding epidemiological insights related to circulating serotypes, virulence genes and antimicrobial resistance across different reservoirs. Moreover, associations between virulence genes and severe disease were determined by incorporating human metadata into the data analysis. Gaps in the surveillance system were identified and suggestions for optimization related to sample centralization, harmonizing isolation methods, and expanding sampling strategies were formulated.

Discussion

This study contributes to developing a representative WGS-based collection of circulating STEC strains and by illustrating its benefits, it aims to incite policymakers to support WGS uptake in food safety surveillance.

Comparison of Source Attribution Methodologies for Human Campylobacteriosis

Campylobacter spp. are the most common cause of bacterial gastrointestinal infection in humans both in Denmark and worldwide. Studies have found microbial subtyping to be a powerful tool for source attribution, but comparisons of different methodologies are limited. In this study, we compare three source attribution approaches (Machine Learning, Network Analysis, and Bayesian modeling) using three types of whole genome sequences (WGS) data inputs (cgMLST, 5-Mers and 7-Mers). We predicted and compared the sources of human campylobacteriosis cases in Denmark. Using 7mer as an input feature provided the best model performance. The network analysis algorithm had a CSC value of 78.99% and an F1-score value of 67%, while the machine-learning algorithm showed the highest accuracy (98%). The models attributed between 965 and all of the 1224 human cases to a source (network applying 5mer and machine learning applying 7mer, respectively). Chicken from Denmark was the primary source of human campylobacteriosis with an average percentage probability of attribution of 45.8% to 65.4%, representing Bayesian with 7mer and machine learning with cgMLST, respectively. Our results indicate that the different source attribution methodologies based on WGS have great potential for the surveillance and source tracking of Campylobacter. The results of such models may support decision makers to prioritize and target interventions.

Building an International One Health Strain Level Database to Characterise the Epidemiology of AMR Threats: ESBL—AmpC Producing E. coli as An Example—Challenges and Perspectives

Antimicrobial resistance (AMR) is one of the top public health threats nowadays. Among the most important AMR pathogens, Escherichia coli resistant to extended spectrum cephalosporins (ESC-EC) is a perfect example of the One Health problem due to its global distribution in animal, human, and environmental sources and its resistant phenotype, derived from the carriage of plasmid-borne extended-spectrum and AmpC β-lactamases, which limits the choice of effective antimicrobial therapies. The epidemiology of ESC-EC infection is complex as a result of the multiple possible sources involved in its transmission, and its study would require databases ideally comprising information from animal (livestock, companion, wildlife), human, and environmental sources. Here, we present the steps taken to assemble a database with phenotypic and genetic information on 10,763 ESC-EC isolates retrieved from multiple sources provided by 13 partners located in eight European countries, in the frame of the DiSCoVeR Joint Research project funded by the One Health European Joint Programme (OH-EJP), along with its strengths and limitations. This database represents a first step to help in the assessment of different geographical and temporal trends and transmission dynamics in animals and humans. The work performed highlights aspects that should be considered in future international efforts, such as the one presented here.

Molecular Epidemiological Evidence Implicates Cattle as a Primary Reservoir of Campylobacter jejuni Infecting People via Contaminated Chickens

The study aimed to determine the relative contribution of cattle to the burden of illness in a model agroecosystem with high rates of human campylobacteriosis (≥ 115 cases/100 K), and high densities of cattle, including large numbers of cattle housed in confined feeding operations (i.e., in southwestern Alberta, Canada). To accomplish this, a large-scale molecular epidemiological analysis of Campylobacter jejuni circulating within the study location was completed. In excess of 8000 isolates of C. jejuni from people (n = 2548 isolates), chickens (n = 1849 isolates), cattle (n = 2921 isolates), and water (n = 771 isolates) were subtyped. In contrast to previous studies, the source attribution estimates of clinical cases attributable to cattle vastly exceeded those attributed to chicken (i.e., three- to six-fold). Moreover, cattle were often colonized by C. jejuni (51%) and shed the bacterium in their feces. A large proportion of study isolates were found in subtypes primarily associated with cattle (46%), including subtypes infecting people and those associated with chickens (19%). The implication of cattle as a primary amplifying reservoir of C. jejuni subtypes in circulation in the study location is supported by the strong cattle association with subtypes that were found in chickens and in people, a lack of evidence indicating the foodborne transmission of C. jejuni from beef and dairy, and the large number of cattle and the substantial quantities of untreated manure containing C. jejuni cells. Importantly, the evidence implicated cattle as a source of C. jejuni which was infecting people through a transmission pathway from cattle to people via the consumption of chicken. This has implications for reducing the burden of campylobacteriosis in the study location and elsewhere.

Health and Economic Burden of Seven Foodborne Diseases in Denmark, 2019

We ranked seven foodborne pathogens in Denmark on the basis of their health and economic impact on society in 2019. We estimated burden of disease of infections with Campylobacter spp., Salmonella spp., Shiga toxin-producing Escherichia coli (STEC), Yersinia enterocolitica, Listeria monocytogenes, norovirus, and hepatitis A virus in terms of incidence, mortality, disability-adjusted life years (DALY), and economic burden in terms of direct and indirect health costs. These seven pathogens accounted for 268,372 cases, 98 deaths, and 3121 DALYs, and led to a total expenditure of 434 million Euro in 1 year in a country with 5.8 million citizens. Foodborne infections by Campylobacter, Salmonella, and norovirus caused the most DALYs, whereas Campylobacter, and norovirus and STEC had the higher costs. A combination of disease burden and cost of illness estimates is useful to inform policymaking and establish food safety priorities at the national level.

Comparison of approaches for source attribution of ESBL-producing Escherichia coli in Germany

Extended-spectrum beta-lactamase (ESBL)-producing Escherichia (E.) coli have been widely described as the cause of treatment failures in humans around the world. The origin of human infections with these microorganisms is discussed controversially and in most cases hard to identify. Since they pose a relevant risk to human health, it becomes crucial to understand their sources and the transmission pathways. In this study, we analyzed data from different studies in Germany and grouped ESBL-producing E. coli from different sources and human cases into subtypes based on their phenotypic and genotypic characteristics (ESBL-genotype, E. coli phylogenetic group and phenotypic antimicrobial resistance pattern). Then, a source attribution model was developed in order to attribute the human cases to the considered sources. The sources were from different animal species (cattle, pig, chicken, dog and horse) and also from patients with nosocomial infections. The human isolates were gathered from community cases which showed to be colonized with ESBL-producing E. coli. We used the attribution model first with only the animal sources (Approach A) and then additionally with the nosocomial infections (Approach B). We observed that all sources contributed to the human cases, nevertheless, isolates from nosocomial infections were more related to those from human cases than any of the other sources. We identified subtypes that were only detected in the considered animal species and others that were observed only in the human population. Some subtypes from the human cases could not be allocated to any of the sources from this study and were attributed to an unknown source. Our study emphasizes the importance of human-to-human transmission of ESBL-producing E. coli and the different role that pets, livestock and healthcare facilities may play in the transmission of these resistant bacteria. The developed source attribution model can be further used to monitor future trends. A One Health approach is necessary to develop source attribution models further to integrate also wildlife, environmental as well as food sources in addition to human and animal data.

A Machine Learning Model for Food Source Attribution of Listeria monocytogenes

Despite its low morbidity, listeriosis has a high mortality rate due to the severity of its clinical manifestations. The source of human listeriosis is often unclear. In this study, we investigate the ability of machine learning to predict the food source from which clinical Listeria monocytogenes isolates originated. Four machine learning classification algorithms were trained on core genome multilocus sequence typing data of 1212 L. monocytogenes isolates from various food sources. The average accuracies of random forest, support vector machine radial kernel, stochastic gradient boosting, and logit boost were found to be 0.72, 0.61, 0.7, and 0.73, respectively. Logit boost showed the best performance and was used in model testing on 154 L. monocytogenes clinical isolates. The model attributed 17.5 % of human clinical cases to dairy, 32.5% to fruits, 14.3% to leafy greens, 9.7% to meat, 4.6% to poultry, and 18.8% to vegetables. The final model also provided us with genetic features that were predictive of specific sources. Thus, this combination of genomic data and machine learning-based models can greatly enhance our ability to track L. monocytogenes from different food sources.

Source Attribution of Human Campylobacteriosis Using Whole-Genome Sequencing Data and Network Analysis

Campylobacter spp. are a leading and increasing cause of gastrointestinal infections worldwide. Source attribution, which apportions human infection cases to different animal species and food reservoirs, has been instrumental in control- and evidence-based intervention efforts. The rapid increase in whole-genome sequencing data provides an opportunity for higher-resolution source attribution models. Important challenges, including the high dimension and complex structure of WGS data, have inspired concerted research efforts to develop new models. We propose network analysis models as an accurate, high-resolution source attribution approach for the sources of human campylobacteriosis. A weighted network analysis approach was used in this study for source attribution comparing different WGS data inputs. The compared model inputs consisted of cgMLST and wgMLST distance matrices from 717 human and 717 animal isolates from cattle, chickens, dogs, ducks, pigs and turkeys. SNP distance matrices from 720 human and 720 animal isolates were also used. The data were collected from 2015 to 2017 in Denmark, with the animal sources consisting of domestic and imports from 7 European countries. Clusters consisted of network nodes representing respective genomes and links representing distances between genomes. Based on the results, animal sources were the main driving factor for cluster formation, followed by type of species and sampling year. The coherence source clustering (CSC) values based on animal sources were 78%, 81% and 78% for cgMLST, wgMLST and SNP, respectively. The CSC values based on Campylobacter species were 78%, 79% and 69% for cgMLST, wgMLST and SNP, respectively. Including human isolates in the network resulted in 88%, 77% and 88% of the total human isolates being clustered with the different animal sources for cgMLST, wgMLST and SNP, respectively. Between 12% and 23% of human isolates were not attributed to any animal source. Most of the human genomes were attributed to chickens from Denmark, with an average attribution percentage of 52.8%, 52.2% and 51.2% for cgMLST, wgMLST and SNP distance matrices respectively, while ducks from Denmark showed the least attribution of 0% for all three distance matrices. The best-performing model was the one using wgMLST distance matrix as input data, which had a CSC value of 81%. Results from our study show that the weighted network-based approach for source attribution is reliable and can be used as an alternative method for source attribution considering the high performance of the model. The model is also robust across the different Campylobacter species, animal sources and WGS data types used as input.

A statistical modelling approach for source attribution meta-analysis of sporadic infection with foodborne pathogens

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-control studies for sporadic infections with four major bacterial zoonotic pathogens in the Netherlands (Campylobacter, Salmonella, Shiga toxin-producing E. coli [STEC] O157 and Listeria). 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-control studies according to five transmission pathways (domestic food, environment, direct animal contact, human-human 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 Campylobacter being mostly attributable to the chicken reservoir, Salmonella to pigs (albeit closely followed by layers), and Listeria and STEC O157 to cattle. Food-wise, 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-making and is extensible to other pathogens and domains.

Cyst detection and viability assessment of Balantioides coli in environmental samples: Current status and future needs

The ciliate Balantioides coli is a human enteric parasite that can cause life-threatening infections. It is a food- and waterborne parasite, with cysts being the infective stage. Despite its importance as a potential pathogen, few reports have investigated its presence in environmental samples, and some issues need attention including i) The accuracy of B. coli identification. In most cases, the protozoa is identified only by its morphological traits, which can be identical to those from other parasitic ciliates of animals. Genetic analysis of cysts recovered from environmental samples is necessary for species confirmation. In addition, genetic methods used with faecal samples need to be adequately validated with environmental matrices. ii) The methodology for searching this parasite in environmental samples. The protocols include an initial phase to isolate the cysts from the matrix followed by a second phase in which concentration procedures are usually applied. The methods may be valid but are not standardised and differences between studies could affect the results obtained. iii) The areas that needs further research. The development of genetic identification methods and standardised analytical protocols in environmental samples are required, as well as the assessment of viability and infectivity of B. coli cysts. The development of axenic culture systems will boost research on this parasite.

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Balantioides coli is mainly considered a foodborne parasite for humans.

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Detection methods in environmental samples are not standardised.

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Correct identification should be made by genetic analysis.

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Methods for B. coli cyst viability and infectivity assessment are to be developed.

Benefits and Risks of Smallholder Livestock Production on Child Nutrition in Low- and Middle-Income Countries

Livestock production may improve nutritional outcomes of pregnant women and children by increasing household income, availability of nutrient-dense foods, and women's empowerment. Nevertheless, the relationship is complex, and the nutritional status of children may be impaired by presence of or proximity to livestock and their pathogens. In this paper, we review the benefits and risks of livestock production on child nutrition. Evidence supports the nutritional benefits of livestock farming through income, production, and women's empowerment. Increasing animal source food consumption requires a combination of efforts, including improved animal management so that herd size is adequate to meet household income needs and consumption and addressing sociocultural and gendered norms. Evidence supports the inclusion of behavior change communication strategies into livestock production interventions to facilitate the sustainability of nutritional benefits over time, particularly interventions that engage women and foster dimensions of women's empowerment. In evaluating the risks of livestock production, evidence indicates that a broad range of enteric pathogens may chronically infect the intestines of children and, in combination with dietary deficits, may cause environmental enteric dysfunction (EED), a chronic inflammation of the gut. Some of the most important pathogens associated with EED are zoonotic in nature with livestock as their main reservoir. Very few studies have aimed to understand which livestock species contribute most to colonization with these pathogens, or how to reduce transmission. Control at the point of exposure has been investigated in a few studies, but much less effort has been spent on improving animal husbandry practices, which may have additional benefits. There is an urgent need for dedicated and long-term research to understand which livestock species contribute most to exposure of young children to zoonotic enteric pathogens, to test the potential of a wide range of intervention methods, to assess their effectiveness in randomized trials, and to assure their broad adaptation and sustainability. This review highlights the benefits and risks of livestock production on child nutrition. In addition to identifying research gaps, findings support inclusion of poor gut health as an immediate determinant of child undernutrition, expanding the established UNICEF framework which includes only inadequate diet and disease.

3D Hierarchical Nanotopography for On-Site Rapid Capture and Sensitive Detection of Infectious Microbial Pathogens

Effective capture and rapid detection of pathogenic bacteria causing pandemic/epidemic diseases is an important task for global surveillance and prevention of human health threats. Here, we present an advanced approach for the on-site capture and detection of pathogenic bacteria through the combination of hierarchical nanostructures and a nuclease-responsive DNA probe. The specially designed hierarchical nanocilia and network structures on the pillar arrays, termed 3D bacterial capturing nanotopographical trap, exhibit excellent mechanical reliability and rapid (<30 s) and irreversible bacterial capturability. Moreover, the nuclease-responsive DNA probe enables the highly sensitive and extremely fast (<1 min) detection of bacteria. The bacterial capturing nanotopographical trap (b-CNT) facilitates the on-site capture and detection of notorious infectious pathogens (Escherichia coli O157:H7, Salmonella enteritidis, Staphylococcus aureus, and Bacillus cereus) from kitchen tools and food samples. Accordingly, the usefulness of the b-CNT is confirmed as a simple, fast, sensitive, portable, and robust on-site capture and detection tool for point-of-care testing.

Comparative history of Campylobacter contamination on chicken meat and campylobacteriosis cases in the United States: 1994-2018

In many countries campylobacteriosis ranks as one of the most frequently reported foodborne illnesses and poultry is the commodity that is most often associated with these illnesses. Nevertheless, efforts to reduce the occurrence of pathogen contamination on poultry are often more focused on Salmonella. While some control measures are pathogen specific, such as pre-harvest vaccination for Salmonella, improvements in sanitary dressing and interventions applied during the slaughter process can be effective against all forms of microbial contamination. To investigate the potential effectiveness of these non-specific pathogen reduction strategies in the United States, it is helpful to assess if, and by how much, Campylobacter contamination of chicken meat has changed across time. This study assesses change considering data collected in both slaughter and retail establishments and comparing observed trends in contamination with trends in human surveillance data. The results support the assertion that substantial reductions in Campylobacter contamination of chicken meat in the late 1990s and early 2000s contributed to a reduction in the human case rate of campylobacteriosis. Further reductions in chicken meat contamination between 2013 and 2018 are more difficult to associate with trends in human illnesses, with one contributing factor being the inclusion of culture independent diagnostic test results in the official case counts during that time. Other contributing factors are discussed.

The Data Behind Risk Analysis of Campylobacter Jejuni and Campylobacter Coli Infections

Campylobacter jejuni and Campylobacter coli are major causes of food-borne enteritis in humans. Poultry meat is known to be responsible for a large proportion of cases of human campylobacteriosis. However, other food-borne, environmental and animal sources are frequently associated with the disease in humans as well. Human campylobacteriosis causes gastroenteritis that in most cases is self-limiting. Nevertheless, the burden of the disease is relatively large compared with other food-borne diseases, which is mostly due to rare but long-lasting symptoms related to immunological sequelae. In order to pave the way to improved surveillance and control of human campylobacteriosis, we review here the data that is typically used for risk analysis to quantify the risk and disease burden, identify specific surveillance strategies and assist in choosing the most effective control strategies. Such data are mostly collected from the literature, and their nature is discussed here, for each of the three processes that are essential for a complete risk analysis procedure: risk assessment, risk management and risk communication. Of these, the first, risk assessment, is most dependent on data, and this process is subdivided into the steps of hazard identification, hazard characterization, exposure assessment and risk characterization. For each of these steps of risk assessment, information from published material that is typically collected will be summarized here. In addition, surveillance data are highly valuable for risk assessments. Different surveillance systems are employed in different countries, which can make international comparison of data challenging. Risk analysis typically results in targeted control strategies, and these again differ between countries. The applied control strategies are as yet not sufficient to eradicate human campylobacteriosis. The surveillance tools of Campylobacter in humans and exposure sources in place in different countries are briefly reviewed to better understand the Campylobacter dynamics and guide control strategies. Finally, the available control measures on different risk factors and exposure sources are presented.

Food Safety Implications of Transitions Toward Sustainable Healthy Diets

Despite increased political attention, foodborne diseases still cause a substantial public health, economic, and social burden worldwide. Children younger than 5 years, people living in developing regions, and in the poorest areas of the world are disproportionally affected, bearing a large proportion of the global burden of foodborne disease. Yet, food safety is a prerequisite to ensuring food security globally: Foods that are responsible for important food safety problems are also crucial to ensure food security in some regions and are essential sources of nutrition. Moreover, together with calls for action to meeting international sustainable development goals, global efforts to promote food security and healthy diets have now highlighted the need to modify food systems globally. This article therefore explores the food safety dimensions of transitions toward food systems that promote sustainable healthy diets. The current body of evidence points to the combined health and environmental benefits of shifting toward a more plant-based diet, including vegetables and fruits, nuts, pulses, and whole grains. As a shift toward more plant-based diets may also lead to higher exposures to chemicals or pathogens present in these foods, an evaluation of food safety implications of such transitions is now imperative. We conclude that several synergies between public health, environmental, and food safety strategies can be identified to support dietary transitions.

Methicillin-Resistant Staphylococcus aureus (MRSA) in Slaughter Houses and Meat Shops in Capital Territory of Pakistan During 2018–2019

Livestock-associated methicillin-resistant Staphylococcus aureus (LA-MRSA) is a major concern in many parts of the world, including Pakistan. The aim of this study was to investigate the prevalence of MRSA in slaughterhouses and meat shops in Rawalpindi-Islamabad, Pakistan, 2018–2019. A total of 300 samples were collected: 40 from each of working area, tools (knives, hooks), butcher hands and beef, 30 from each of chicken and mutton, 20 from each of nasal and rectal swabs. S. aureus was phenotypically identified by performing gram staining and biochemical tests. 150 of the 300 samples were confirmed to be S. aureus by phenotypic identification. MRSA was identified among S. aureus positive isolates by performing disk diffusion test and by detecting S. aureus-specific genes such as 16s rRNA, nuc, mecA, spa, and coa. Out of 150 isolates 96 (63%) showed resistance to antibiotic cefoxitin, known as a potential marker for detecting MRSA. While all 150 isolates have shown complete resistance to the four antibiotics neomycin, methicillin, ciprofloxacin and tetracycline. The nuc and 16s rRNA genes were detected in all 150 S. aureus-positive isolates and 118 (79%) were confirmed to be MRSA through the detection of the mecA gene. MRSA prevalence was highest in chicken (23/30, 77%) followed by beef (25/40, 63%), mutton (15/30, 50%), knives (18/40, 45%), nasal swabs (7/20, 35%), working area (11/40, 28%), rectal swabs (5/20, 25%), hooks (7/40, 18%), and butcher hands (7/40, 18%). 50 MRSA-positive isolates were chosen to identify two virulence factors (spa and coa gene). Of the 50 MRSA isolates subject to coa and spa gene typing, 27 (54%) were positive for the coa gene and 18 (36%) were positive for the spa gene, respectively. To the best of our knowledge, this was the first study on the molecular identification of MRSA in meat samples from Pakistan. High prevalence of MRSA in meat samples demand for implementation of proper hygienic practices and procedures during the slaughtering, transport and marketing of meat and meat products in order to prevent the spread of these bacteria to the human population.

Lactobacillus delbrueckii subsp. bulgaricus KLDS 1.0207 Exerts Antimicrobial and Cytotoxic Effects in vitro and Improves Blood Biochemical Parameters in vivo Against Notable Foodborne Pathogens

Globally, foodborne diseases (FBDs) result in millions of sicknesses and deaths annually. Cumulative evidence suggests that the use of probiotic lactic acid bacteria (LAB) strains could be a viable alternative in inhibiting the activities of foodborne pathogens. This study aims to evaluate the in vitro antimicrobial, cytotoxic, and tolerance levels of Lactobacillus bulgaricus KLDS 1.0207 against two notable foodborne pathogens – Escherichia coli ATCC25922 and Staphylococcus aureus ATCC25923. Afterward, a 48 BALB/c mice-trial was used to assess its ameliorative effects on weight and serum biochemical parameters. Results showed that the cell-free supernatant (CFS) of this strain significantly inhibited both pathogens, but these effects were abolished at pH 6.5 and 7.0 (P < 0.05). Also, 6.96 ± 0.02 log CFU mL^–1 of L. bulgaricus KLDS 1.0207 was still viable after three hours in simulated gastric juice and at pH 3.0, indicating that this strain was a potential probiotic candidate. Also, inflammatory activities in RAW264.7 cells were significantly inhibited using 10⁹ CFU mL^–1 of L. bulgaricus KLDS 1.0207 cells (P < 0.05). Significant weight losses were also prevented in the T_LBSA (from 19.42 ± 1.04 to 19.55 ± 0.55 g) and T_LBEC (from 22.86 ± 0.90 to 14.77 ± 9.86 g) groups compared to their respective model groups (T_SA – from 21.65 ± 1.80 to 20.14 ± 1.84, and T_EC – from 21.45 ± 0.82 to 14.45 ± 9.70 g). Besides, there was a slight weight gain in the S. aureus prevention group (T_LBSA) compared to the model group (T_SA). Serum biochemical analyses revealed that the total cholesterol (TC), triglycerides (TG), low-density lipoprotein (LDL), and some mineral levels were markedly increased by S. aureus and E. coli administrations but were reversed to normalcy in both prevention groups (T_LBSA and T_LBEC). Interestingly, high-density lipoprotein (HDL) levels, which were initially disrupted in the model groups, were restored in the prevention groups (T_LBSA and T_LBEC). This study presents L. bulgaricus KLDS 1.0207 as a promising probiotic candidate with antimicrobial, anti-inflammatory, acid, and bile tolerant and lipid-regulating applications. It also gives valuable insights for targeted future in vivo treatment and prevention studies involving other probiotic LAB candidates. Future in vivo studies elucidating specific mechanisms behind the in vitro antimicrobial, cytotoxic, and in vivo ameliorative effects are warranted.

Application of Whole-Genome Sequences and Machine Learning in Source Attribution of Salmonella Typhimurium

Prevention of the emergence and spread of foodborne diseases is an important prerequisite for the improvement of public health. Source attribution models link sporadic human cases of a specific illness to food sources and animal reservoirs. With the next generation sequencing technology, it is possible to develop novel source attribution models. We investigated the potential of machine learning to predict the animal reservoir from which a bacterial strain isolated from a human salmonellosis case originated based on whole-genome sequencing. Machine learning methods recognize patterns in large and complex data sets and use this knowledge to build models. The model learns patterns associated with genetic variations in bacteria isolated from the different animal reservoirs. We selected different machine learning algorithms to predict sources of human salmonellosis cases and trained the model with Danish Salmonella Typhimurium isolates sampled from broilers (n = 34), cattle (n = 2), ducks (n = 11), layers (n = 4), and pigs (n = 159). Using cgMLST as input features, the model yielded an average accuracy of 0.783 (95% CI: 0.77-0.80) in the source prediction for the random forest and 0.933 (95% CI: 0.92-0.94) for the logit boost algorithm. Logit boost algorithm was most accurate (valid accuracy: 92%, CI: 0.8706-0.9579) and predicted the origin of 81% of the domestic sporadic human salmonellosis cases. The most important source was Danish produced pigs (53%) followed by imported pigs (16%), imported broilers (6%), imported ducks (2%), Danish produced layers (2%), Danish produced cattle and imported cattle (<1%) while 18% was not predicted. Machine learning has potential for improving source attribution modeling based on sequence data. Results of such models can inform risk managers to identify and prioritize food safety interventions.

Mining whole genome sequence data to efficiently attribute individuals to source populations

Whole genome sequence (WGS) data could transform our ability to attribute individuals to source populations. However, methods that efficiently mine these data are yet to be developed. We present a minimal multilocus distance (MMD) method which rapidly deals with these large data sets as well as methods for optimally selecting loci. This was applied on WGS data to determine the source of human campylobacteriosis, the geographical origin of diverse biological species including humans and proteomic data to classify breast cancer tumours. The MMD method provides a highly accurate attribution which is computationally efficient for extended genotypes. These methods are generic, easy to implement for WGS and proteomic data and have wide application.

Network Approach to Source Attribution of Salmonella enterica Serovar Typhimurium and Its Monophasic Variant

Salmonella enterica subspecies enterica serovar Typhimurium and its monophasic variant are among the most common Salmonella serovars associated with human salmonellosis each year. Related infections are often due to consumption of contaminated meat of pig, cattle, and poultry origin. In order to evaluate novel microbial subtyping methods for source attribution, an approach based on weighted networks was applied on 141 human and 210 food and animal isolates of pigs, broilers, layers, ducks, and cattle collected in Denmark from 2013 to 2014. A whole-genome SNP calling was performed along with cgMLST and wgMLST. Based on these genomic input data, pairwise distance matrices were built and used as input for construction of a weighted network where nodes represent genomes and links to distances. Analyzing food and animal Typhimurium genomes, the coherence of source clustering ranged from 89 to 90% for animal source, from 84 to 85% for country, and from 63 to 65% for year of isolation and was equal to 82% for serotype, suggesting animal source as the first driver of clustering formation. Adding human isolate genomes to the network, a percentage between 93.6 and 97.2% clustered with the existing component and only a percentage between 2.8 and 6.4% appeared as not attributed to any animal sources. The majority of human genomes were attributed to pigs with probabilities ranging from 83.9 to 84.5%, followed by broilers, ducks, cattle, and layers in descending order. In conclusion, a weighted network approach based on pairwise SNPs, cgMLST, and wgMLST matrices showed promising results for source attribution studies.

Global and regional source attribution of Shiga toxin-producing Escherichia coli infections using analysis of outbreak surveillance data

Shiga toxin-producing Escherichia coli (STEC) infections pose a substantial health and economic burden worldwide. To target interventions to prevent foodborne infections, it is important to determine the types of foods leading to illness. Our objective was to determine the food sources of STEC globally and for the six World Health Organization regions. We used data from STEC outbreaks that have occurred globally to estimate source attribution fractions. We categorised foods according to their ingredients and applied a probabilistic model that used information on implicated foods for source attribution. Data were received from 27 countries covering the period between 1998 and 2017 and three regions: the Americas (AMR), Europe (EUR) and Western-Pacific (WPR). Results showed that the top foods varied across regions. The most important sources in AMR were beef (40%; 95% Uncertainty Interval 39-41%) and produce (35%; 95% UI 34-36%). In EUR, the ranking was similar though with less marked differences between sources (beef 31%; 95% UI 28-34% and produce 30%; 95% UI 27-33%). In contrast, the most common source of STEC in WPR was produce (43%; 95% UI 36-46%), followed by dairy (27%; 95% UI 27-27%). Possible explanations for regional variability include differences in food consumption and preparation, frequency of STEC contamination, the potential of regionally predominant STEC strains to cause severe illness and differences in outbreak investigation and reporting. Despite data gaps, these results provide important information to inform the development of strategies for lowering the global burden of STEC infections.

Associating sporadic, foodborne illness caused by Shiga toxin-producing Escherichia coli with specific foods: a systematic review and meta-analysis of case-control studies

Shiga toxin-producing Escherichia coli (STEC) infections are a significant public health issue, with foodborne transmission causing >1 million illnesses worldwide each year. We conducted a systematic review and meta-analysis (PROSPERO registry # CRD42017074239), to determine the relative association of different food types with sporadic illnesses caused by STEC. Searches were conducted from 01 August to 30 September 2017, using bibliographic and grey literature databases, websites and expert consultation. We identified 22 case-control studies of sporadic STEC infection in humans, from 10 countries within four World Health Organization subregions, from 1985 to 2012. We extracted data from 21 studies, for 237 individual measures in 11 food categories and across three status types (raw or undercooked, not raw and unknown). Beef was the most significant food item associated with STEC illness in the Americas and Europe, but in the Western Pacific region, chicken was most significant. These findings were not significantly moderated by the raw or cooked status of the food item, nor the publication year of the study. Data from the African, South-East Asian and Eastern Mediterranean subregions were lacking and it is unclear whether our results are relevant to these regions.

Four European Salmonella Typhimurium datasets collected to develop WGS-based source attribution methods

Zoonotic Salmonella causes millions of human salmonellosis infections worldwide each year. Information about the source of the bacteria guides risk managers on control and preventive strategies. Source attribution is the effort to quantify the number of sporadic human cases of a specific illness to specific sources and animal reservoirs. Source attribution methods for Salmonella have so far been based on traditional wet-lab typing methods. With the change to whole genome sequencing there is a need to develop new methods for source attribution based on sequencing data. Four European datasets collected in Denmark (DK), Germany (DE), the United Kingdom (UK) and France (FR) are presented in this descriptor. The datasets contain sequenced samples of Salmonella Typhimurium and its monophasic variants isolated from human, food, animal and the environment. The objective of the datasets was either to attribute the human salmonellosis cases to animal reservoirs or to investigate contamination of the environment by attributing the environmental isolates to different animal reservoirs.

Attribution of Listeria monocytogenes human infections to food and animal sources in Northern Italy

Listeriosis is a foodborne illness characterized by a relatively low morbidity, but a large disease burden due to the severity of clinical manifestations and the high case fatality rate. Increased listeriosis notifications have been observed in Europe since the 2000s. However, the reasons for this increase are largely unknown, with the sources of sporadic human listerioris often remaining elusive. Here we inferred the relative contributions of several putative sources of Listeria monocytogenes strains from listerioris patients in Northern Italy (Piedmont and Lombardy regions), using two established source attribution models (i.e. 'Dutch' and 'STRUCTURE') in comparative fashion. We compared the Multi-Locus Sequence Typing and Multi-Virulence-Locus Sequence Typing profiles of strains collected from beef, dairy, fish, game, mixed foods, mixed meat, pork, and poultry. Overall, 634 L. monocytogenes isolates were collected from 2005 to 2016. In total, 40 clonal complexes and 51 virulence types were identified, with 36% of the isolates belonging to possible epidemic clones (i.e. genetically related strains from unrelated outbreaks). Source attribution analysis showed that 50% of human listerioris cases (95% Confidence Interval 44-55%) could be attributed to dairy products, followed by poultry and pork (15% each), and mixed foods (15%). Since the contamination of dairy, poultry and pork products are closely linked to primary production, expanding actions currently limited to ready-to-eat products to the reservoir level may help reducing the risk of cross-contamination at the consumer level.

Bayesian estimation of diagnostic accuracy of fecal culture and PCR-based tests for the detection of Salmonella enterica in California cull dairy cattle

Epidemiological studies of low prevalence disease problems are often hindered by the high cost of diagnostic testing. The objective of this study was to evaluate PCR screening of both individual and pooled fecal samples from culled dairy cows for the invA gene of Salmonella followed by culture to determine if the sensitivity and specificity were comparable to the results from traditional culture methods applied to individual samples. Cows from six different dairies were sampled in all four seasons. A total of 240 individual cow fecal samples, 24 fecal pools and 24 pools of 24-hour tetrathionate enrichment broth were tested. Diagnostic sensitivity of PCR screening followed by culture of PCR positive or indeterminate samples (i.e PCR-CUL method) was lower than that of culture (CUL) when applied to individual fecal samples (94.8%, 99.5%), however the specificity was comparable (99.6% and 97.7% respectively). For pools of five fecal samples and pools of five, 24 h tetrathionate broth samples, the specificity of both tests were comparable (∼98%); however, their sensitivity was only comparable in pooled fecal samples (∼93%) but greater for culture compared to PCR-CUL in pooled broth samples (∼99% versus ∼93%). Compared to culture results from testing of individual fecal samples, testing pooled fecal samples by culture had a relative sensitivity of 74% and relative specificity of 96%, testing pooled fecal samples by PCR-CUL resulted in relative sensitivity of 90% and relative specificity of 96%. Testing of pooled 24-hour enrichment broth by PCR-CUL increased the relative sensitivity and specificity to 100%. PCR testing followed by culture of positive or indeterminate samples is a time saving alternative to traditional methods. In addition, pooling of samples may be a useful method for decreasing cost if study aims can accommodate a moderate loss of relative sensitivity.

Characterization of Salmonella Isolates from Wastewater Treatment Plant Influents to Estimate Unreported Cases and Infection Sources of Salmonellosis

Salmonella enterica is a major cause of gastroenteritis usually caused by animal-based contaminated foods. Since the current passive surveillance is not sufficient to detect all infections and infection sources, we determined the prevalence of Salmonella isolated from sewage influent of wastewater treatment plants (WWTPs) and compared the characteristics of human and food isolates to identify the infection sources. Sewage influent samples were collected monthly from two WWTPs located in the Yamanashi Prefecture, Japan, for three years. Serotypes, antimicrobial resistances, isolation periods, isolated areas, and pulsed-field gel electrophoresis patterns of six isolates belonging to five serotypes were consistent with those of the isolates from patients. Real-time PCR for Salmonella indicated that sewage influents reflect cases of patients infected with Salmonella, including unreported cases. Serovars Schwarzengrund and Anatum were predominant in sewage, but not in humans, and their characteristics were closely related or identical to those isolated from poultry heart and liver, respectively. These results suggest that sewage influent contains Salmonella isolates from humans and that some originated from unreported human cases infected by poultry-associated products. Therefore, it is necessary to take countermeasures against Salmonella infection based on the unreported cases, which would be disclosed by analysis of sewage influent.

Whole genome sequencing and metagenomics for outbreak investigation, source attribution and risk assessment of food-borne microorganisms

This Opinion considers the application of whole genome sequencing (WGS) and metagenomics for outbreak investigation, source attribution and risk assessment of food‐borne pathogens. WGS offers the highest level of bacterial strain discrimination for food‐borne outbreak investigation and source‐attribution as well as potential for more precise hazard identification, thereby facilitating more targeted risk assessment and risk management. WGS improves linking of sporadic cases associated with different food products and geographical regions to a point source outbreak and can facilitate epidemiological investigations, allowing also the use of previously sequenced genomes. Source attribution may be favoured by improved identification of transmission pathways, through the integration of spatial‐temporal factors and the detection of multidirectional transmission and pathogen–host interactions. Metagenomics has potential, especially in relation to the detection and characterisation of non‐culturable, difficult‐to‐culture or slow‐growing microorganisms, for tracking of hazard‐related genetic determinants and the dynamic evaluation of the composition and functionality of complex microbial communities. A SWOT analysis is provided on the use of WGS and metagenomics for Salmonella and Shigatoxin‐producing Escherichia coli (STEC) serotyping and the identification of antimicrobial resistance determinants in bacteria. Close agreement between phenotypic and WGS‐based genotyping data has been observed. WGS provides additional information on the nature and localisation of antimicrobial resistance determinants and on their dissemination potential by horizontal gene transfer, as well as on genes relating to virulence and biological fitness. Interoperable data will play a major role in the future use of WGS and metagenomic data. Capacity building based on harmonised, quality controlled operational systems within European laboratories and worldwide is essential for the investigation of cross‐border outbreaks and for the development of international standardised risk assessments of food‐borne microorganisms.

Critical Orientation in the Jungle of Currently Available Methods and Types of Data for Source Attribution of Foodborne Diseases

With increased interest in source attribution of foodborne pathogens, there is a need to sort and assess the applicability of currently available methods. Herewith we reviewed the most frequently applied methods for source attribution of foodborne diseases, discussing their main strengths and weaknesses to be considered when choosing the most appropriate methods based on the type, quality, and quantity of data available, the research questions to be addressed, and the (epidemiological and microbiological) characteristics of the pathogens in question. A variety of source attribution approaches have been applied in recent years. These methods can be defined as top–down, bottom–up, or combined. Top–down approaches assign the human cases back to their sources of infection based on epidemiological (e.g., outbreak data analysis, case-control/cohort studies, etc.), microbiological (i.e., microbial subtyping), or combined (e.g., the so-called ‘source-assigned case-control study’ design) methods. Methods based on microbial subtyping are further differentiable according to the modeling framework adopted as frequency-matching (e.g., the Dutch and Danish models) or population genetics (e.g., Asymmetric Island Models and STRUCTURE) models, relying on the modeling of either phenotyping or genotyping data of pathogen strains from human cases and putative sources. Conversely, bottom–up approaches like comparative exposure assessment start from the level of contamination (prevalence and concentration) of a given pathogen in each source, and then go upwards in the transmission chain incorporating factors related to human exposure to these sources and dose-response relationships. Other approaches are intervention studies, including ‘natural experiments,’ and expert elicitations. A number of methodological challenges concerning all these approaches are discussed. In absence of an universally agreed upon ‘gold’ standard, i.e., a single method that satisfies all situations and needs for all pathogens, combining different approaches or applying them in a comparative fashion seems to be a promising way forward.

The role of parameterization in comparing source attribution models based on microbial subtyping for salmonellosis

Source attribution methods attribute human cases of a zoonotic disease to a certain source putatively responsible for this disease. Identifying and quantifying the contribution of different sources to human infections is important for taking appropriate actions for reducing the exposure of the consumer to zoonotic pathogens. One widely used method is the microbial subtyping approach, whose principle is to compare the frequency of pathogen subtypes from different sources (e.g. animals or food) with the frequency of these subtypes in human cases. This paper studies the relationship between a Bayesian microbial subtyping approach described by Hald and coworkers subsequently modified by David and coworkers, here called the Hald model, and a frequentist approach known as the "Dutch model." The comparison between the Bayesian and frequentist model is done for two data sets on salmonellosis in Germany from different time periods (year 2004-2007 and 2010-2011). The results of both approaches are in good agreement with each other for the used data. It is shown here mathematically that a certain parameterization can be used to transform the probabilistic Hald model into a deterministic form, which is equivalent to the Dutch model. That certain parameterization secures independence of the model outcomes from the choice of so-called unique subtypes (which are unique in the sense that they are found exclusively in one of the sources). It is shown that deviating from that certain parameterization leads variations in the model outcome dependent on which unique subtypes are chosen in the process of modelling.

A Modular Bayesian Salmonella Source Attribution Model for Sparse Data

Several statistical models for salmonella source attribution have been presented in the literature. However, these models have often been found to be sensitive to the model parameterization, as well as the specifics of the data set used. The Bayesian salmonella source attribution model presented here was developed to be generally applicable with small and sparse annual data sets obtained over several years. The full Bayesian model was modularized into three parts (an exposure model, a subtype distribution model, and an epidemiological model) in order to separately estimate unknown parameters in each module. The proposed model takes advantage of the consumption and overall salmonella prevalence of the studied sources, as well as bacteria typing results from adjacent years. The latter were used for a smoothed estimation of the annual relative proportions of different salmonella subtypes in each of the sources. The source-specific effects and the salmonella subtype-specific effects were included in the epidemiological model to describe the differences between sources and between subtypes in their ability to infect humans. The estimation of these parameters was based on data from multiple years. Finally, the model combines the total evidence from different modules to proportion human salmonellosis cases according to their sources. The model was applied to allocate reported human salmonellosis cases from the years 2008 to 2015 to eight food sources.

Zoonotic Source Attribution of Salmonella enterica Serotype Typhimurium Using Genomic Surveillance Data, United States

Increasingly, routine surveillance and monitoring of foodborne pathogens using whole-genome sequencing is creating opportunities to study foodborne illness epidemiology beyond routine outbreak investigations and case–control studies. Using a global phylogeny of Salmonella enterica serotype Typhimurium, we found that major livestock sources of the pathogen in the United States can be predicted through whole-genome sequencing data. Relatively steady rates of sequence divergence in livestock lineages enabled the inference of their recent origins. Elevated accumulation of lineage-specific pseudogenes after divergence from generalist populations and possible metabolic acclimation in a representative swine isolate indicates possible emergence of host adaptation. We developed and retrospectively applied a machine learning Random Forest classifier for genomic source prediction of Salmonella Typhimurium that correctly attributed 7 of 8 major zoonotic outbreaks in the United States during 1998–2013. We further identified 50 key genetic features that were sufficient for robust livestock source prediction.

Global disease burden of pathogens in animal source foods, 2010

Animal source foods (ASF) such as dairy, eggs, fish and meat are an important source of high-quality nutrients. Lack of ASF in diets can result in developmental disorders including stunting, anemia, poor cognitive and motor development. ASF are more effective in preventing stunting than other foods and promoting ASF consumption in low- and middle-income countries could help improve health, particularly among pregnant women and young children. Production and consumption of ASF are, however, also associated with potential food safety risks. Strengthening of food control systems, informed by quantitative assessments of the disease burden associated with ASF is necessary to meet global nutrition goals. We present the human disease burden associated with 13 pathogens (bacteria and parasites) in ASF, based on an analysis of global burden of foodborne disease (FBD) estimates of the WHO Foodborne Disease Burden Epidemiology Reference Group (FERG). The FBD burden of these pathogens was combined with estimates of the proportion of disease transmitted by eight main groups of ASF. Uncertainty in all estimates was accounted for by Monte Carlo simulation. In 2010, the global burden of ASF was 168 (95% uncertainty interval (UI 137-219) Disability Adjusted Life Years (DALYs) per 100,000 population, which is approximately 35% of the estimated total burden of FBD. Main pathogens contributing to this burden included non-typhoidal Salmonella enterica, Taenia solium, and Campylobacter spp. The proportion of FBD burden associated with ASF varied considerably between subregions and between countries within subregions. Likewise, the contribution of different pathogens and ASF groups varied strongly between subregions. Pathogens with a localized distribution included T. solium and fishborne trematodes. Pathogens with a global distribution included non-typhoidal S. enterica, Campylobacter spp., Toxoplasma gondii, and Mycobacterium bovis. Control methods exist for many hazards associated with ASF, and their implementation is linked to economic development and effective food safety systems.

The use of next generation sequencing for improving food safety: Translation into practice

Next Generation Sequencing (NGS) combined with powerful bioinformatic approaches are revolutionising food microbiology. Whole genome sequencing (WGS) of single isolates allows the most detailed comparison possible hitherto of individual strains. The two principle approaches for strain discrimination, single nucleotide polymorphism (SNP) analysis and genomic multi-locus sequence typing (MLST) are showing concordant results for phylogenetic clustering and are complementary to each other. Metabarcoding and metagenomics, applied to total DNA isolated from either food materials or the production environment, allows the identification of complete microbial populations. Metagenomics identifies the entire gene content and when coupled to transcriptomics or proteomics, allows the identification of functional capacity and biochemical activity of microbial populations. The focus of this review is on the recent use and future potential of NGS in food microbiology and on current challenges. Guidance is provided for new users, such as public health departments and the food industry, on the implementation of NGS and how to critically interpret results and place them in a broader context. The review aims to promote the broader application of NGS technologies within the food industry as well as highlight knowledge gaps and novel applications of NGS with the aim of driving future research and increasing food safety outputs from its wider use.

One Health - Cycling of diverse microbial communities as a connecting force for soil, plant, animal, human and ecosystem health

The One Health concept proposes that there is a connection between human, animal and environmental health. Plants and their health are not explicitly included. In this review, we broaden the One Health concept to include soil, plant, animal and ecosystem health. We argue that the health conditions of all organisms in an ecosystem are interconnected through the cycling of subsets of microbial communities from the environment (in particular the soil) to plants, animals and humans, and back into the environment. After an introduction on health concepts, we present examples of community stability and resilience, diversity and interconnectedness as affected by pollutants, and integrity of nutrient cycles and energy flows. Next, we explain our concept of microbial cycling in relation to ecosystem health, and end with examples of plant and animal disease outbreaks in relation to microbial community composition and diversity. We conclude that we need a better understanding of the role of interconnected microbiomes in promoting plant and animal health and possible ways to stimulate a healthy, diverse microbiome throughout human-dominated ecosystems. We suggest that it is essential to maintain ecosystem and soil health through diversification of plant communities and oligotrophication of managed ecosystems.

Prevalence and Serotype Diversity of Salmonella in Apparently Healthy Cattle: Systematic Review and Meta-Analysis of Published Studies, 2000-2017

Salmonellosis is a leading cause of foodborne illnesses in humans with cattle being one of the reservoirs for Salmonella. We estimated a pooled prevalence of Salmonella in apparently healthy cattle and examined serotype diversity through systematic review and meta-analysis of studies published between 2000 and 2017. Peer reviewed publications reporting the prevalence of Salmonella in cattle were searched through five electronic databases (PubMed, Google scholar, Agricola, Scopus, CAB direct) and through manual search. We obtained 71 publications with 75 datasets consisting a total of 52,766 animals examined and 5,010 Salmonella positive cattle from 29 countries in six continents (except from Antarctica). Pooled prevalence of Salmonella in cattle was 9% (95% confidence interval: 7-11%). Significantly high heterogeneity (I 2 = 98.7%, P < 0.01) was observed among all studies as well as within continents. Prevalence varied from 2% (Europe) to 16% (North America). Overall, 143 different serotypes were reported with the most diverse serotypes being reported from Africa (76 different serotypes) followed by North America (49 serotypes). The 10 most frequently reported serotypes (Montevideo, Typhimurium, Kentucky, Meleagridis, Anatum, Cerro, Mbandaka, Muenster, Newport, and Senftenberg) accounted for 65% of the isolates for which specific serotype information was reported. Salmonella Montevideo and S. Dublin are the most frequently reported serotypes in North America and Europe, respectively, while S. Typhimurium was the most frequent in Africa, Asia and Australasia. Our results indicated variability both in the prevalence and serotype diversity of Salmonella in cattle across continents. Although all Salmonella serotypes are potentially pathogenic to humans, five (Montevideo, Typhimurium, Anatum, Mbandaka, and Newport) of the top 10 serotypes identified in this study are among the serotypes most commonly associated with clinical illnesses in humans.

Source Attribution and Risk Assessment of Antimicrobial Resistance

Source attribution and microbial risk assessment methods have been widely applied for the control of several foodborne pathogens worldwide by identifying (i) the most important pathogen sources and (ii) the risk represented by specific foods and the critical points in these foods' production chains for microbial control. Such evidence has proved crucial for risk managers to identify and prioritize effective food safety and public health strategies. In the context of antimicrobial resistance (AMR) from livestock and pets, the utility of these methods is recognized, but a number of challenges have largely prevented their application and routine use. One key challenge has been to define the hazard in question: Is it the antimicrobial drug use in animals, the antimicrobial-resistant bacteria in animals and foods, or the antimicrobial resistance genes that can be transferred between commensal and pathogenic bacteria in the animal or human gut or in the environment? Other important limitations include the lack of occurrence and transmission data and the lack of evidence to inform dose-response relationships. We present the main principles, available methods, strengths, and weaknesses of source attribution and risk assessment methods, discuss their utility to identify sources and estimate risks of AMR from livestock and pets, and provide an overview of conducted studies. In addition, we discuss remaining challenges and current and future opportunities to improve methods and knowledge of the sources and transmission routes of AMR from animals through food, direct contact, or the environment, including improvements in surveillance and developments in genotypic typing methods.