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Background In the last decade, veterinary antimicrobial usage (AMU) and antimicrobial resistance (AMR) among indicator bacteria in livestock have decreased substantially in the Netherlands. The extent to which this decrease has affected AMR levels among human infections remains unclear. Objectives To assess the association between AMU in livestock and AMR in Escherichia coli isolates from human urinary tract infections (UTIs). Methods Data on AMR and AMU between 2009 and 2020 from Dutch national surveillance programmes for humans and livestock were used. Associations between AMU in four major livestock sectors and AMR in humans were assessed for 10 antimicrobial classes and the ESBL resistance profile, using logistic regression analysis. Associations between AMU and AMR in livestock, between AMR in livestock and in humans, and between AMU and AMR in humans were also assessed. Results Statistical significance was reached for 16/31 of the tested associations between AMU in livestock and AMR in human E. coli UTIs. Of the significant associations, 11 were positive (OR 1.01–1.24), whereas 5 were negative (OR 0.96–0.99). All associations between human AMU and AMR in E. coli isolates from UTIs were positive and statistically significant. Weak but significant positive correlations were also observed between livestock AMR and human AMR. Conclusions Although several significant associations between AMU in livestock and AMR in human UTIs caused by E. coli were observed, the associations between AMU and AMR were generally stronger within the human and animal populations. This indicates that potential zoonotic spread of AMR in E. coli causing human UTIs from livestock sources is limited. [ABSTRACT FROM AUTHOR]

Improvements in sequencing quality, availability, speed and costs results in an increased presence of genomics in infectious disease applications. Nevertheless, there are still hurdles in regard to the optimal use of WGS for public health purposes. Here, we discuss the current state ("status quo") and future directions ("quo vadis") based on literature regarding the use of genomics in surveillance, hazard characterization and source attribution of foodborne pathogens. The future directions include the application of new techniques, such as machine learning and network approaches that may overcome the current shortcomings. These include the use of fixed genomic distances in cluster delineation, disentangling similarity or lack thereof in source attribution, and difficulties ascertaining function in hazard characterization. Although, the aforementioned methods can relatively easily be applied technically, an overarching challenge is the inference and biological/epidemiological interpretation of these large amounts of high-resolution data. Understanding the context in terms of bacterial isolate and host diversity allows to assess the level of representativeness in regard to sources and isolates in the dataset, which in turn defines the level of certainty associated with defining clusters, sources and risks. This also marks the importance of metadata (clinical, epidemiological, and biological) when using genomics for public health purposes. [ABSTRACT FROM AUTHOR]

Drinking water distribution systems (DWDSs) represent an ideal environment for biofilm formation, which can harbor pathogenic and antimicrobial-resistant bacteria. This study aimed to assess longitudinally the microbial community composition and antimicrobial resistance (AMR), as determined by 16S rRNA NGS and qPCR, respectively, in drinking water (DW) and biofilm from DWDSs, as well as faeces, of free-range organic broiler farms. The role of DWDSs in AMR gene (ARG) dissemination within the farm environment and transmission to animals, was also assessed. DW and biofilm microbial communities differed from those of faecal samples. Moreover, potentially pathogenic and opportunistic bacteria (e.g., Staphylococcaceae) were identified in water and biofilms. High prevalence and abundance of ARGs conferring resistance to carbapenems (i.e., blaNDM), 3rd and 4th generation cephalosporins (i.e., blaCMY-2), (fluoro)quinolones (i.e., qnrS), and polymyxins (i.e., mcr-3 and mcr-5) were detected in DW, biofilm, and faecal samples, which is of concern for both animal and human health. Although other factors (e.g., feed, pests, and wildlife) may contribute to the dissemination of AMR in free-range organic poultry farms, this study indicates that DWDSs can also play a role. [ABSTRACT FROM AUTHOR]

Aims Methods and Results Conclusions Most human infections with non‐typhoid Salmonella (NTS) or Campylobacter are zoonotic in nature and acquired though consumption of contaminated food of mainly animal origin. However, individuals may also acquire salmonellosis or campylobacteriosis through non‐foodborne transmission pathways, such as those mediated by the environment. This emphasizes the need to consider both direct and indirect exposure to livestock sources as a possible transmission route for NTS and Campylobacter. Therefore, this study aimed at assessing whether salmonellosis and campylobacteriosis incidence is spatially associated with exposure to livestock (i.e. small ruminants, dairy cows, veal calves, laying hens, broiler chickens and pigs) in the Netherlands for the years 2007–2019 and 2014–2019 respectively.Risk factors (population‐weighted number of animals) and their population attributable fractions were determined using a Poisson regression model with a log‐link function fitted using integrated nested Laplace approximation. The analyses were performed for different hexagonal sizes (90, 50, 25 and 10 km2) and accounted for geographical coverage of the diagnostic laboratory catchment areas. Moreover, serological data were used to look into the possible effects of acquired immunity due to repeated exposure to the pathogen through the environment that would potentially hinder the analyses based on the incidence of reported cases. A linear mixed‐effects model was then fitted where the postal code areas were included as a random effect. Livestock was not consistently significantly associated with acquiring salmonellosis or campylobacteriosis in the Netherlands.Results showed that living in livestock‐rich areas in the Netherlands is not a consistently significant, spatially restricted risk factor for acquiring salmonellosis or campylobacteriosis, thereby supporting current knowledge that human infections with Salmonella and Campylobacter are mainly foodborne. [ABSTRACT FROM AUTHOR]

In total, 4069 SE and 5336 ST/STM isolates were included, 18 and 6% of which were travel-related, respectively (SE: 5% within and 13% outside EU/EEA; ST/STM: 2% within and 4% outside EU/EEA). Keywords: Enteric fever; AMR; fluoroquinolones; water sanitation; food hygiene; traveller's diarrhoea EN Enteric fever AMR fluoroquinolones water sanitation food hygiene traveller's diarrhoea 1 4 4 11/08/23 20230801 NES 230801 Non-typhoidal salmonellosis is the second most reported zoonosis in the European Union (EU) and European Economic Area (EEA). [Extracted from the article]

Introduction: Whole genome sequencing (WGS) is increasingly used for characterizing foodborne pathogens and it has become a standard typing technique for surveillance and research purposes. WGS data can help assessing microbial risks and defining risk mitigating strategies for foodborne pathogens, including Salmonella enterica. Methods: To test the hypothesis that (combinations of) different genes can predict the probability of infection [P(inf)] given exposure to a certain pathogen strain, we determined P(inf) based on invasion potential of 87 S. enterica strains belonging to 15 serovars isolated from animals, foodstuffs and human patients, in an in vitro gastrointestinal tract (GIT) model system. These genomes were sequenced with WGS and screened for genes potentially involved in virulence. A random forest (RF) model was applied to assess whether P(inf) of a strain could be predicted based on the presence/absence of those genes. Moreover, the association between P(inf) and biofilm formation in different experimental conditions was assessed. Results and Discussion: P(inf) values ranged from 6.7E-05 to 5.2E-01, showing variability both among and within serovars. P(inf) values also varied between isolation sources, but no unambiguous pattern was observed in the tested serovars. Interestingly, serovars causing the highest number of human infections did not show better ability to invade cells in the GIT model system, with strains belonging to other serovars displaying even higher infectivity. The RF model did not identify any virulence factor as significant P(inf) predictors. Significant associations of P(inf) with biofilm formation were found in all the different conditions for a limited number of serovars, indicating that the two phenotypes are governed by different mechanisms and that the ability to form biofilm does not correlate with the ability to invade epithelial cells. Other omics techniques therefore seem more promising as alternatives to identify genes associated with P(inf), and different hypotheses, such as gene expression rather than presence/absence, could be tested to explain phenotypic virulence [P(inf)]. [ABSTRACT FROM AUTHOR]

Hantaviruses (family Bunuyaviriade, genus Hantavirus) are primarily rodent-borne pathogens that are a suspected cause of hemorrhagic fever with renal syndrome (HFRS) in Eurasia (1). They are transmitted to humans mainly [...]

Broiler chickens are among the main livestock sectors worldwide. With individual treatments being inapplicable, contrary to many other animal species, the need for antimicrobial use (AMU) is relatively high. AMU in animals is known to drive the emergence and spread of antimicrobial resistance (AMR). High farm biosecurity is a cornerstone for animal health and welfare, as well as food safety, as it protects animals from the introduction and spread of pathogens and therefore the need for AMU. The goal of this study was to identify the main biosecurity practices associated with AMU in broiler farms and to develop a statistical model that produces customised recommendations as to which biosecurity measures could be implemented on a farm to reduce its AMU, including a cost-effectiveness analysis of the recommended measures. AMU and biosecurity data were obtained cross-sectionally in 2014 from 181 broiler farms across nine European countries (Belgium, Bulgaria, Denmark, France, Germany, Italy, the Netherlands, Poland and Spain). Using mixed-effects random forest analysis (Mix-RF), recursive feature elimination was implemented to determine the biosecurity measures that best predicted AMU at the farm level. Subsequently, an algorithm was developed to generate AMU reduction scenarios based on the implementation of these measures. In the final Mix-RF model, 21 factors were present: 10 about internal biosecurity, 8 about external biosecurity and 3 about farm size and productivity, with the latter showing the largest (Gini) importance. Other AMU predictors, in order of importance, were the number of depopulation steps, compliance with a vaccination protocol for non-officially controlled diseases, and requiring visitors to check in before entering the farm. K -means clustering on the proximity matrix of the final Mix-RF model revealed that several measures interacted with each other, indicating that high AMU levels can arise for various reasons depending on the situation. The algorithm utilised the AMU predictive power of biosecurity measures while accounting also for their interactions, representing a first step toward aiding the decision-making process of veterinarians and farmers who are in need of implementing on-farm biosecurity measures to reduce their AMU. [ABSTRACT FROM AUTHOR]

Bacterial antimicrobial resistance (AMR) is among the leading global health challenges of the century. Animals and their products are known contributors to the human AMR burden, but the extent of this contribution is not clear. This systematic literature review aimed to identify studies investigating the direct impact of animal sources, defined as livestock, aquaculture, pets, and animal-based food, on human AMR. We searched four scientific databases and identified 31 relevant publications, including 12 risk assessments, 16 source attribution studies, and three other studies. Most studies were published between 2012 and 2022, and most came from Europe and North America, but we also identified five articles from South and South-East Asia. The studies differed in their methodologies, conceptual approaches (bottom-up, top-down, and complex), definitions of the AMR hazard and outcome, the number and type of sources they addressed, and the outcome measures they reported. The most frequently addressed animal source was chicken, followed by cattle and pigs. Most studies investigated bacteria–resistance combinations. Overall, studies on the direct contribution of animal sources of AMR are rare but increasing. More recent publications tailor their methodologies increasingly towards the AMR hazard as a whole, providing grounds for future research to build on. [ABSTRACT FROM AUTHOR]

Broiler chickens are among the main livestock sectors worldwide. With individual treatments being inapplicable, contrary to many other animal species, the need for antimicrobial use (AMU) is relatively high. AMU in animals is known to drive the emergence and spread of antimicrobial resistance (AMR). High farm biosecurity is a cornerstone for animal health and welfare, as well as food safety, as it protects animals from the introduction and spread of pathogens and therefore the need for AMU. The goal of this study was to identify the main biosecurity practices associated with AMU in broiler farms and to develop a statistical model that produces customised recommendations as to which biosecurity measures could be implemented on a farm to reduce its AMU, including a cost-effectiveness analysis of the recommended measures. AMU and biosecurity data were obtained cross-sectionally in 2014 from 181 broiler farms across nine European countries (Belgium, Bulgaria, Denmark, France, Germany, Italy, the Netherlands, Poland and Spain). Using mixed-effects random forest analysis (Mix-RF), recursive feature elimination was implemented to determine the biosecurity measures that best predicted AMU at the farm level. Subsequently, an algorithm was developed to generate AMU reduction scenarios based on the implementation of these measures. In the final Mix-RF model, 21 factors were present: 10 about internal biosecurity, 8 about external biosecurity and 3 about farm size and productivity, with the latter showing the largest (Gini) importance. Other AMU predictors, in order of importance, were the number of depopulation steps, compliance with a vaccination protocol for non-officially controlled diseases, and requiring visitors to check in before entering the farm. K -means clustering on the proximity matrix of the final Mix-RF model revealed that several measures interacted with each other, indicating that high AMU levels can arise for various reasons depending on the situation. The algorithm utilised the AMU predictive power of biosecurity measures while accounting also for their interactions, representing a first step toward aiding the decision-making process of veterinarians and farmers who are in need of implementing on-farm biosecurity measures to reduce their AMU. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Simple Summary: In intensive animal farming, biosecurity represents a crucial point to guarantee animal welfare and health. Therefore, it is necessary that proper biosecurity measures are complied with and implemented. To date, several tools to assess biosecurity compliance in poultry farms are available with the general purpose of providing an evaluation of the implemented biosecurity measures. With this study, we aimed to assess the level of compliance with biosecurity measures in poultry farms located in the most densely populated Italian poultry area. Standardized biosecurity checklists according to national legislation were used to collect information. The main findings of our study show an overall good level of biosecurity implementation with some measures still requiring improvement. Our study may help to highlight the importance of biosecurity compliance in poultry farms by identifying the main achievements/challenges in a context in which measures are to be implemented by law and poultry production is vertically integrated. Biosecurity in poultry farms represents the first line of defense against the entry and spread of pathogens that may have animal health, food safety, and economic consequences. The aim of this study was to assess biosecurity compliance in poultry farms located in a densely populated poultry area in North East Italy. A total of 259 poultry farms (i.e., broilers, turkeys, and layers) were surveyed between 2018 and 2019 using standardized checklists, and differences in biosecurity compliance between the poultry sectors and years (only for turkey farms) were tested for significance. Among the three sectors, turkey farms showed the highest compliance. Farm hygiene, infrastructure condition, cleaning and disinfection tools, and procedures were the biosecurity measures most complied with. Some deficiencies were observed in the cleanliness of the farm hygiene lock in broiler farms, as well as the presence of the house hygiene lock in broiler and layer farms and an adequate coverage of built-up litter in turkey and broiler farms. In conclusion, this study highlighted a generally high level of biosecurity in the visited poultry farms (probably due to the stringent national regulation and the integration of the poultry industry) and identified some measures that still need to be improved. [ABSTRACT FROM AUTHOR]

Antimicrobials have been widely used in poultry, promoting antimicrobial resistance (AMR) emergence and spread. Resistant bacteria selected by antimicrobial use (AMU) can contaminate the farm environment and transfer resistance genes to other bacteria, providing opportunities for persistence and (re-)colonization of subsequent flocks and potentially jeopardizing antimicrobial treatments. We investigated the effects of AMU on AMR in poultry in the long-term (due to historical AMU in the farm) and in the short-term (due to current AMU in a flock). Litter samples from 35 broiler and 35 turkey farms in North-East Italy were sampled longitudinally for AMR testing of E. coli indicator bacteria in 2019/2020. Differences in AMR as a function of historical AMU (Defined Daily Doses in 2016–2018), current AMU in the sampled flock, farm size and season were tested using Generalized Estimating Equation regression analysis. In both broilers and turkeys, the highest resistance levels were observed for sulfamethoxazole (>70%), followed by ampicillin (54–60%). Only a few positive associations between historical levels of penicillin use and the specific resistance levels to penicillin in broiler farms, and the overall historical AMU and resistance to trimethoprim in turkey flocks, were significant. Current AMU showed significant effects on resistance to sulfamethoxazole, trimethoprim, ciprofloxacin and tetracycline in turkey flocks. Significant effects of farm size on some AMR levels were also identified. We found a stronger association between current AMU and AMR compared to historical AMU and AMR. AMR persistence in the farm environment in the absence of direct AMU pressure needs to be further investigated. Sulfamethoxazole and ampicillin resistance are the highest in E. coli isolates of both broilers and turkeys. Short-term effects of antibiotic use on resistance are seen more often than long-term effects. Historical penicillin use is associated with penicillin resistance in broilers. There is an association between historical use and resistance to trimethoprim in turkeys. [ABSTRACT FROM AUTHOR]