Strain-specific Growth Parameters are Important to Accurately Model Bacterial Growth on Baby Spinach in Simulation Models.

Digital tools to predict produce shelf life have the potential to reduce food waste and improve consumer satisfaction. To address this need, we (i) performed an observational study on the microbial quality of baby spinach, (ii) completed growth experiments of bacteria that are representative of the baby spinach microbiota, and (iii) developed an initial simulation model of bacterial growth on baby spinach. Our observational data showed that the predominant genera found on baby spinach were Pseudomonas, Pantoea and Exiguobacterium. Rifampicin-resistant mutants (rifR mutants) of representative bacterial subtypes were subsequently generated to obtain strain-specific growth parameters on baby spinach. These experiments showed that: (i) it is difficult to select rifR mutants that do not have fitness costs affecting growth (9 of 15 rifR mutants showed substantial differences in growth, compared to their corresponding wild-type strain) and (ii) based on estimates from primary growth models, the mean (geometric) maximum population of rifR mutants on baby spinach (7.6 log10 CFU/g, at 6°C) appears lower than that of the spinach microbiota (9.6 log10 CFU/g, at 6°C), even if rifR mutants did not have substantial growth-related fitness costs. Thus, a simulation model, parameterized with the data obtained here as well as literature data on home refrigeration temperatures, underestimated bacterial growth on baby spinach. The root mean square error of the simulation's output, compared against data from the observational study, was 1.11 log10 CFU/g. Sensitivity analysis was used to identify key parameters (e.g., strain maximum population) that impact the simulation model's output, allowing for prioritization of future data collection to improve the simulation model. Overall, this study provides a roadmap for the development of models to predict bacterial growth on leafy vegetables with strain-specific parameters and suggests that additional data are required to improve these models.

Assessment of Oral Albendazole and Fumagillin in the Treatment of Pseudoloma neurophilia in Adult Zebrafish.

Pseudoloma neurophilia ( Pn ), the causative agent of the most commonly reported disease of zebrafish, is a microsporidian parasite that confounds research by inducing behavioral and physiologic changes in zebrafish. Although a treatment for P. neurophilia has not been documented in zebrafish, albendazole (ALB) and fumagillin (FUM) have been used to treat microsporidian infections of other fish species. To investigate the efficacy of oral ALB and FUM in the treatment of Pn, we performed a pilot study that demonstrated the safety and palatability of novel gel-based diets containing FUM or ALB in adult AB zebrafish. In a subsequent study, approximately 250 adult AB zebrafish (previously infected with Pn ) were treated with these medicated diets for 4 wk. At 4 different time points (weeks 0, 5, 10, and 16 of the study), fish were euthanized and whole-body qPCR was performed to assess Pn prevalence across treatment and control groups. There was no statistically significant association between treatment group and Pn prevalence at any time point, although potential biologically relevant reductions in Pn prevalence occurred in the combination therapy group at weeks 5 and 16 and in the ALB group at week 5. Based on high-performance liquid chromatography analyses, the medicated diets contained less ALB and more FUM than expected, highlighting the importance of validating medicated feed concentrations to ensure safety, efficacy, and consistency. While Pn remains challenging to eradicate and control, results of this study warrant further investigation into the utility of ALB and FUM as potential treatments for this pathogen.

Safer not to know? Shaping liability law and policy to incentivize adoption of predictive AI technologies in the food system.

Governments, researchers, and developers emphasize creating "trustworthy AI," defined as AI that prevents bias, ensures data privacy, and generates reliable results that perform as expected. However, in some cases problems arise not when AI is not trustworthy, technologically, but when it is. This article focuses on such problems in the food system. AI technologies facilitate the generation of masses of data that may illuminate existing food-safety and employee-safety risks. These systems may collect incidental data that could be used, or may be designed specifically, to assess and manage risks. The predictions and knowledge generated by these data and technologies may increase company liability and expense, and discourage adoption of these predictive technologies. Such problems may extend beyond the food system to other industries. Based on interviews and literature, this article discusses vulnerabilities to liability and obstacles to technology adoption that arise, arguing that "trustworthy AI" cannot be achieved through technology alone, but requires social, cultural, political, as well as technical cooperation. Implications for law and further research are also discussed.

Population dynamics of Listeria spp., Salmonella spp., and Escherichia coli on fresh produce: A scoping review.

Collation of the current scope of literature related to population dynamics (i.e., growth, die-off, survival) of foodborne pathogens on fresh produce can aid in informing future research directions and help stakeholders identify relevant research literature. A scoping review was conducted to gather and synthesize literature that investigates population dynamics of pathogenic and non-pathogenic Listeria spp., Salmonella spp., and Escherichia coli on whole unprocessed fresh produce (defined as produce not having undergone chopping, cutting, homogenization, irradiation, or pasteurization). Literature sources were identified using an exhaustive search of research and industry reports published prior to September 23, 2021, followed by screening for relevance based on strict, a priori eligibility criteria. A total of 277 studies that met all eligibility criteria were subjected to an in-depth qualitative review of various factors (e.g., produce commodities, study settings, inoculation methodologies) that affect population dynamics. Included studies represent investigations of population dynamics on produce before (i.e., pre-harvest; n = 143) and after (i.e., post-harvest; n = 144) harvest. Several knowledge gaps were identified, including the limited representation of (i) pre-harvest studies that investigated population dynamics of Listeria spp. on produce (n = 13, 9% of pre-harvest studies), (ii) pre-harvest studies that were carried out on non-sprouts produce types grown using hydroponic cultivation practices (n = 7, 5% of pre-harvest studies), and (iii) post-harvest studies that reported the relative humidity conditions under which experiments were carried out (n = 56, 39% of post-harvest studies). These and other knowledge gaps summarized in this scoping review represent areas of research that can be investigated in future studies.

Predictive Population Dynamics of Escherichia coli O157:H7 and Salmonella enterica on Plants: a Mechanistic Mathematical Model Based on Weather Parameters and Bacterial State.

Weather affects key aspects of bacterial behavior on plants but has not been extensively investigated as a tool to assess risk of crop contamination with human foodborne pathogens. A novel mechanistic model informed by weather factors and bacterial state was developed to predict population dynamics on leafy vegetables and tested against published data tracking Escherichia coli O157:H7 (EcO157) and Salmonella enterica populations on lettuce and cilantro plants. The model utilizes temperature, radiation, and dew point depression to characterize pathogen growth and decay rates. Additionally, the model incorporates the population level effect of bacterial physiological state dynamics in the phyllosphere in terms of the duration and frequency of specific weather parameters. The model accurately predicted EcO157 and S. enterica population sizes on lettuce and cilantro leaves in the laboratory under various conditions of temperature, relative humidity, light intensity, and cycles of leaf wetness and dryness. Importantly, the model successfully predicted EcO157 population dynamics on 4-week-old romaine lettuce plants under variable weather conditions in nearly all field trials. Prediction of initial EcO157 population decay rates after inoculation of 6-week-old romaine plants in the same field study was better than that of long-term survival. This suggests that future augmentation of the model should consider plant age and species morphology by including additional physical parameters. Our results highlight the potential of a comprehensive weather-based model in predicting contamination risk in the field. Such a modeling approach would additionally be valuable for timing field sampling in quality control to ensure the microbial safety of produce. IMPORTANCE Fruits and vegetables are important sources of foodborne disease. Novel approaches to improve the microbial safety of produce are greatly lacking. Given that bacterial behavior on plant surfaces is highly dependent on weather factors, risk assessment informed by meteorological data may be an effective tool to integrate into strategies to prevent crop contamination. A mathematical model was developed to predict the population trends of pathogenic E. coli and S. enterica, two major causal agents of foodborne disease associated with produce, on leaves. Our model is based on weather parameters and rates of switching between the active (growing) and inactive (nongrowing) bacterial state resulting from prevailing environmental conditions on leaf surfaces. We demonstrate that the model has the ability to accurately predict dynamics of enteric pathogens on leaves and, notably, sizes of populations of pathogenic E. coli over time after inoculation onto the leaves of young lettuce plants in the field.

Increasing vaccine uptake among employees within the non-health related critical infrastructure sectors: A review.

This review aimed to identify barriers to employee vaccination, motivators for vaccination, and vaccine uptake strategies within the critical infrastructure sectors. We focused on non-healthcare-related sectors, including food and agriculture, manufacturing, and education where employee vaccination is rarely mandated. We conducted a search for literature published from 2012 to 2022 from MEDLINE-PubMed, PsycINFO, and Web of Science Core Collection, which resulted in 22 studies that met the inclusion criteria. We found that 1) barriers to vaccination differ by infectious disease and population; 2) common motivators for vaccination were about protecting self, family, and community; and 3) common uptake strategies for influenza (which accounted for 83% of uptake strategies in reviewed studies) addressed convenience and confidence barriers such as vaccination cost and education. Our review highlights the need for employers, policymakers, and researchers to identify infectious disease and population-specific barriers to vaccination and implement strategies aimed at addressing the identified barriers.

Responsible antibiotic use labeling and consumers' willingness to buy and pay for fluid milk.

Concerns about antibiotic resistant infections in the United States have called for reduction of antibiotic use in livestock, including dairy cattle. Although effective in curbing antibiotic use, universal organic dairy farming would be impractical and unattainable due to its high land and premium demands. The US Department of Agriculture's organic certification, which completely eliminates antibiotic use in milk production, also raises animal welfare concerns, as it could discourage the use of antibiotics even to treat indicated diseases. Therefore, a proposed alternative for US consumers is a label indicating the responsible antibiotic use (RAU) - not complete elimination - that would minimize antibiotics more than conventional (unlabeled) milk and maximize animal welfare more than organic milk. Our goal was to determine consumers' (1) self-reported preference and (2) willingness to pay for this hypothetical RAU label of milk relative to existing substitutes in organic and unlabeled fluid milk. We conducted (1) a nationally representative survey of US adults and (2) a randomized non-hypothetical experimental Becker-Degroot-Marschak auction with real money and real milk. Although almost half of the survey participants (48.5%) responded that they would buy a RAU-labeled milk, consumers in the experimental auction refused to pay a significant premium for the milk compared with unlabeled milk (mean willingness to pay (95% confidence interval) per half-gallon: $1.92 ($1.65-$2.19) for RAU-labeled milk versus $1.86 ($1.58-$2.13) for unlabeled milk). These results suggest that consumers' survey-identified preferences for RAU-labeled milk could reflect either social desirability bias or a genuine preference for which, however, consumers simply will not pay a significant premium. The study provides preliminary data for future exploration of marketability of the proposed RAU label in the United States and demonstrates the benefits of using complementary survey and experimental auction approaches to understand the potential market for a new dairy product.

Examining Patterns of Persistent Listeria Contamination in Packinghouses Using Agent-Based Models.

ABSTRACT: Persistent Listeria monocytogenes contamination may occur in a packinghouse if the pathogen successfully infiltrates the facility and reaches a harborage site, where it may be difficult to remove and may contaminate produce within the facility. There is a need for simulation-based decision support tools that can predict which equipment sites are more likely to undergo persistent contamination and simulate potential corrective actions to prevent this contamination. Thus, we adapted for longer term simulation two existing applications of an agent-based model of Listeria spp. hourly contamination dynamics in produce packinghouses. Next, we developed a novel approach to identify and analyze persistent and transient Listeria contamination patterns on simulated agents representing equipment sites and employees. Testing of corrective actions showed that methods that involved targeted, facility-specific, risk-based sanitation were the most effective in reducing both the likelihood and duration of persistent contamination. Generic approaches to controlling Listeria (e.g., more concentrated sanitizers) are unlikely to be successful and suggest that use of sanitation schedules produced through facility-specific root cause analysis and hygienic design are key in reducing persistence. Hourly Listeria contamination patterns also suggest that transient contamination may be mistaken for persistent contamination, depending on the frequency of environmental sampling. Likewise, as concentrations of Listeria on most contaminated agents were predicted to be very low, there is also a possibility to mistake persistence for transient contamination of sites, or even miss it outright, due to false-negative environmental Listeria monitoring results. These findings support that agent-based models may be valuable decision support tools, aiding in the identification of contamination patterns within packinghouses and assessing the viability of specific corrective actions.

Understanding Antibiotic Resistance as a Perceived Threat towards Dairy Cattle through Beliefs and Practices: A Survey-Based Study of Dairy Farmers.

Antibiotic use is an important component in dairy herd management both to treat bacterial diseases and to maximize animal welfare. However, there is concern among scientists that antibiotic misuse and/or overuse by farmers might promote the emergence of resistant pathogens. We conducted a cross-sectional web-based questionnaire study with dairy farmers/managers in New York, USA to evaluate their (i) level of concern about antibiotic resistance and (ii) interest in adopting new judicious antibiotic use practices regarding mastitis treatment. A total of 118 responses were subjected to statistical analysis. The findings revealed that nearly half (45%) of study participants were undecided or disagreed that antibiotic resistance due to antibiotic use in dairy farming may negatively impact the health of dairy cattle. In contrast, the majority (78%) of participants self-reported that they do not treat with antibiotics at the first sign of mastitis, and the majority (66%) have either fully or partially implemented culture-based mastitis treatment on their farm. The self-reported adoption of culture-based mastitis treatment practices was statistically significantly associated with higher numbers of injectable and intramammary doses of antibiotics used on the participants' farms. These findings will aid future research investigations on how to promote sustainable antibiotic use practices in dairy cattle.

A perspective on data sharing in digital food safety systems.

In this age of data, digital tools are widely promoted as having tremendous potential for enhancing food safety. However, the potential of these digital tools depends on the availability and quality of data, and a number of obstacles need to be overcome to achieve the goal of digitally enabled "smarter food safety" approaches. One key obstacle is that participants in the food system and in food safety often lack the willingness to share data, due to fears of data abuse, bad publicity, liability, and the need to keep certain data (e.g., human illness data) confidential. As these multifaceted concerns lead to tension between data utility and privacy, the solutions to these challenges need to be multifaceted. This review outlines the data needs in digital food safety systems, exemplified in different data categories and model types, and key concerns associated with sharing of food safety data, including confidentiality and privacy of shared data. To address the data privacy issue a combination of innovative strategies to protect privacy as well as legal protection against data abuse need to be pursued. Existing solutions for maximizing data utility, while not compromising data privacy, are discussed, most notably differential privacy and federated learning.

Correction: Consumer perceptions of antimicrobial use in animal husbandry: A scoping review.

[This corrects the article DOI: 10.1371/journal.pone.0261010.].

Growth and survival of aerobic and Gram-negative bacteria on fresh spinach in a Chinese supply chain from harvest through distribution and refrigerated storage.

Spinach is a highly perishable product that degrades over time, including due to bacteria contaminating the product prior to packaging, yet the dynamics of bacterial spoilage and factors that affect it are not well understood. Notably, while China is the top producer of spinach globally, there is limited available microbiological data in the literature for spinach supply chains in China. The overall goal of this foundational study was to establish a baseline understanding of bacterial population dynamics on spinach from harvest to 10 days postprocessing for a Chinese supply chain that includes distribution via traditional grocery (a local physical store) and eCommerce (an online store). To this end, organic spinach samples were collected at different stages in a Chinese supply chain by following the same 3 lots, starting at point-of-harvest through processing and distribution via a local grocery store and eCommerce. After distribution, the same 3 lots were stored at 4 °C with microbiological testing performed on multiple days up to day 10 postprocessing, simulating storage at the point-of-consumer. Results showed aerobic plate counts and total Gram-negative counts did not significantly differ across stages in the supply chain from harvest through processing. However, packaged spinach from the same processing facility and lots, exhibited different patterns in bacterial levels across 0 to 10 days postprocessing, depending on whether it was distributed via the local grocery store or eCommerce. Evaluation of bacterial populations performed on a subset of the packaged spinach samples indicated Gram-negative bacteria, in particular Pseudomonas, were predominant across all days of testing (days 0, 3, and 10 postprocessing), with populations differing at the genus level by day. Overall, this study improves our understanding of the dynamics of bacterial populations on spinach and provides baseline data needed for future studies.

Using agent-based modeling to compare corrective actions for Listeria contamination in produce packinghouses.

The complex environment of a produce packinghouse can facilitate the spread of pathogens such as Listeria monocytogenes in unexpected ways. This can lead to finished product contamination and potential foodborne disease cases. There is a need for simulation-based decision support tools that can test different corrective actions and are able to account for a facility's interior cross-contamination dynamics. Thus, we developed agent-based models of Listeria contamination dynamics for two produce packinghouse facilities; agents in the models represented equipment surfaces and employees, and models were parameterized using observations, values from published literature and expert opinion. Once validated with historical data from Listeria environmental sampling, each model's baseline conditions were investigated and used to determine the effectiveness of corrective actions in reducing prevalence of agents contaminated with Listeria and concentration of Listeria on contaminated agents. Evaluated corrective actions included reducing incoming Listeria, modifying cleaning and sanitation strategies, and reducing transmission pathways, and combinations thereof. Analysis of Listeria contamination predictions revealed differences between the facilities despite their functional similarities, highlighting that one-size-fits-all approaches may not always be the most effective means for selection of corrective actions in fresh produce packinghouses. Corrective actions targeting Listeria introduced in the facility on raw materials, implementing risk-based cleaning and sanitation, and modifying equipment connectivity were shown to be most effective in reducing Listeria contamination prevalence. Overall, our results suggest that a well-designed cleaning and sanitation schedule, coupled with good manufacturing practices can be effective in controlling contamination, even if incoming Listeria spp. on raw materials cannot be reduced. The presence of water within specific areas was also shown to influence corrective action performance. Our findings support that agent-based models can serve as effective decision support tools in identifying Listeria-specific vulnerabilities within individual packinghouses and hence may help reduce risks of food contamination and potential human exposure.

Weather stressors correlate with Escherichia coli and Salmonella enterica persister formation rates in the phyllosphere: a mathematical modeling study.

Enteric pathogens can enter a persister state in which they survive exposure to antibiotics and physicochemical stresses. Subpopulations of such phenotypic dormant variants have been detected in vivo and in planta in the laboratory, but their formation in the natural environment remains largely unexplored. We applied a mathematical model predicting the switch rate to persister cell in the phyllosphere to identify weather-related stressors associated with E. coli and S. enterica persister formation on plants based on their population dynamics in published field studies from the USA and Spain. Model outputs accurately depicted the bi-phasic decay of bacterial population sizes measured in the lettuce and spinach phyllosphere in these studies. Predicted E. coli persister switch rate on leaves was positively and negatively correlated with solar radiation intensity and wind velocity, respectively. Likewise, predicted S. enterica persister switch rate correlated positively with solar radiation intensity; however, a negative correlation was observed with air temperature, relative humidity, and dew point, factors involved in water deposition onto the phylloplane. These findings suggest that specific environmental factors may enrich for dormant bacterial cells on plants. Our model quantifiably links persister cell subpopulations in the plant habitat with broader physical conditions, spanning processes at different granular scales.

Comparison of different biomass methodologies to adjust sales data on veterinary antimicrobials in the USA.

OBJECTIVES: The United States (US) FDA, European Surveillance of Veterinary Antimicrobial Consumption (ESVAC), Public Health Agency of Canada (PHAC) and World Organisation for Animal Health (OIE) established methodologies that characterize antimicrobial sales for use in food animals by adjusting the sales by animal biomass. Our aim was to review and compare these methodologies on US-specific data. METHODS: Annual antimicrobial sales for cattle, swine, chickens and turkeys in the USA between 2016 and 2018 were adjusted by the FDA, ESVAC, PHAC and OIE methodologies. To better understand the advantages and disadvantages of the four methodologies, their biomass denominators were compared regarding the level of detail accounted for in the estimated US livestock biomass, their ability to observe temporal trends in animal biomass within a country and practicality in biomass estimation for comparing antimicrobial sales across countries. RESULTS: The four methodologies resulted in substantially different estimates of biomass-adjusted antimicrobial sales for use in US food animals. The 2018 estimates were the highest with the ESVAC methodology (314.7 mg of active antimicrobial ingredient/kg of animal biomass), followed by PHAC (191.5 mg/kg), FDA (127.6 mg/kg) and OIE (111.5 mg/kg). The animal weight parameters used in each methodology had the most impact on the biomass-adjusted sales estimates. CONCLUSIONS: In regard to the estimation of the animal biomass, no methodology was found to be perfect; however, the FDA methodology had the best resolution in characterizing the US livestock biomass while the OIE methodology was best for biomass estimation for global monitoring of antimicrobial sales for use in food animals.

Consumer perceptions of antimicrobial use in animal husbandry: A scoping review.

Antimicrobial use in animal agriculture is often perceived to play a role in the emerging threat of antimicrobial resistance. Increased consumer awareness of this issue places pressure on animal husbandry to adopt policies to reduce or eliminate antimicrobial use. We use a scoping review methodology to assess research on consumer perceptions of antimicrobial drugs in meat products in the United States, Canada, or the European Union. Evaluating peer-reviewed and grey literature, we included studies for assessment if they met these topical and geographic requirements, involved primary data collection, and were originally published in English. Our screening process identified 124 relevant studies. Three reviewers jointly developed a data charting form and independently charted the contents of the studies. Of the 105 studies that measured consumer concern, 77.1% found that consumers were concerned about antimicrobial use in meat production. A minority of studies (29.8% of all studies) queried why consumers hold these views. These studies found human health and animal welfare were the main reasons for concern. Antimicrobial resistance rarely registered as an explicit reason for concern. A smaller group of studies (23.3%) measured the personal characteristics of consumers that expressed concern about antimicrobials. Among these studies, the most common and consistent features of these consumers were gender, age, income, and education. Regarding the methodology used, studies tended to be dominated by either willingness-to-pay studies or Likert scale questionnaires (73.64% of all studies). We recommend consideration of qualitative research into consumer views on this topic, which may provide new perspectives that explain consumer decision-making and mentality that are lacking in the literature. In addition, more research into the difference between what consumers claim is of concern and their ultimate purchasing decisions would be especially valuable.

In Silico Models for Design and Optimization of Science-Based Listeria Environmental Monitoring Programs in Fresh-Cut Produce Facilities.

Food facilities need time- and cost-saving methods during the development and optimization of environmental monitoring for pathogens and their surrogates. Rapid virtual experimentation through in silico modeling can alleviate the need for extensive real-world, trial-and-error style program design. Two agent-based models of fresh-cut produce facilities were developed as a way to simulate the dynamics of Listeria in the built environment by modeling the different surfaces of equipment and employees in a facility as agents. Five sampling schemes at three time points were evaluated in silico on their ability to locate the presence of Listeria contamination in a facility with sample sites for each scheme (i.e., scenario, as modeled using scenario analysis) based on the following: the facilities' current environmental monitoring program (scenario 1), Food and Drug Administration recommendations (scenario 2), random selection (scenario 3), sites exclusively from zone 3 (i.e., sites in the production room but not directly adjacent to food contact surfaces) (scenario 4), or model prediction of elevated risk of contamination (scenario 5). Variation was observed between the scenarios on how well the Listeria prevalence of the virtually collected samples reflected the true prevalence of contaminated agents in the modeled operation. The zone 3 only (scenario 4) and model-based (scenario 5) sampling scenarios consistently overestimated true prevalence across time, suggesting that those scenarios could provide a more sensitive approach for determining if Listeria is present in the operation. The random sampling scenario (scenario 3) may be more useful for operations looking for a scheme that is most likely to reflect the true prevalence. Overall, the developed models allow for rapid virtual experimentation and evaluation of sampling schemes specific to unique fresh-cut produce facilities. IMPORTANCE Programs such as environmental monitoring are used to determine the state of a given food facility with regard to the presence of environmental pathogens, such as Listeria monocytogenes, that could potentially cross-contaminate food product. However, the design of environmental monitoring programs is complex, and there are infinite ways to conduct the sampling that is required for these programs. Experimentally evaluating sampling schemes in a food facility is time-consuming, costly, and nearly impossible. Therefore, the food industry needs science-based tools to aid in developing and refining sampling plans that reduce the risk of harboring contamination. Two agent-based models of two fresh-cut produce facilities reported here demonstrate a novel way to evaluate how different sampling schemes can be rapidly evaluated across multiple time points as a way to understand how sampling can be optimized in an effort to locate the presence of Listeria in a food facility.

New York State dairy veterinarians' perceptions of antibiotic use and resistance: A qualitative interview study.

Antimicrobial resistance (AMR) limits the ability to prevent and treat infection, making AMR one of the foremost threats to human and animal health. Animal agriculture's large use of antibiotics in food animals is an important factor in AMR. As such, policies to reduce antibiotic use and combat AMR in animal agriculture in the United States (US) have been in place or are developed. One key to the success of these policies in the US is understanding how a major stakeholder - veterinarians treating dairy cattle - perceive the scale of antibiotic use, the threat of AMR and the utility of antibiotic use policies. We interviewed 9 dairy veterinarians in New York State and conducted an iterative thematic analysis of their responses, through which five themes were identified: 1. veterinarians' views of the frequency and reasons for antibiotic misuse, 2. their ideas on reducing antibiotic use, 3. perceptions of AMR within the dairy industry, 4. view of organic farming and how it relates to animal welfare, and 5. the impact of consumers' beliefs on the dairy industry. Participants viewed antibiotic overuse as largely due to farmers' concern for the welfare of their cattle and desire to treat ailments swiftly. Interviewees believed that it was possible to reduce antibiotic use through regulation, such as the Veterinary Feed Directive and improved herd management activities, such as better colostrum management, culture-based mastitis treatment, and improved housing conditions. They did not view the dairy industry as a significant contributor to AMR, particularly when compared to the human medical industry. Interviewees also offered their (unsolicited) opinion on organic dairy farming in the US and expressed frustration with the limited treatment options available in organic dairy farming and how this dynamic may potentially compromise animal welfare. Finally, they commented on the impact of consumers' beliefs on the dairy industry, expressing frustration with how misinformation about the dairy industry has led to consumer driven changes. As consumer beliefs have an impact on the dairy industry, this influence could be leveraged to further decrease antibiotic use. These findings can help guide future efforts in veterinarian-client communication and the development and implementation of effective policies in New York State. These results also highlight the need for more quantitative research on antibiotic use in the dairy industry, as without this data it will be difficult to ascertain the true impact of policy interventions.

How does public perception of antibiotic use on dairy farms contribute to self-reported purchasing of organic?

The United States regulates the use of antibiotics in agricultural settings to address the global antibiotic resistance problem. Conventional dairy cows treated with antibiotics are kept in the herd and after the withholding period milk is harvested. On organic farms, the US organic standard on antibiotic use requires sick dairy cows to be treated, but treated cows must be removed from the herd and their milk can never again be sold as certified organic. This study investigated the US public's perceptions of the organic dairy farming, antibiotic use on dairy farms, and whether these perceptions affect consumer's self-reported purchasing behavior for organic. We used a nationally representative phone-based survey of 1000 US adults and characterized participants' self-reported (i) knowledge of the legality of antibiotic use on dairy farms (conventional and organic) and (ii) frequency of purchasing organic instead of conventional dairy products, as well as several demographic and other variables. The results indicated that participants' knowledge about antibiotic use practices in dairy farming have no effect on their self-reported purchasing behavior for organic or conventional dairy products. However, respondents who were familiar with the regulations of antibiotic use on dairy farms were more likely to oppose the US organic standard on antibiotic use in dairy farming and thought that past antibiotic use should not permanently remove a cow's organic status. These findings contribute to understanding of public perceptions that shape the US dairy organic market. PRACTICAL APPLICATION: Income, employment, health and political values, but not consumers' knowledge about antibiotic use in dairy farming, affect self-reported purchasing behavior for organic dairy products. However, consumers who are familiar with the regulations of antibiotic use on US dairy farms disagree with the US organic standard on antibiotic use mandating loss of organic status for any cattle treated with antibiotics. These findings may be useful to organic markets.

Survey of perceptions and attitudes of an international group of veterinarians regarding antibiotic use and resistance on dairy cattle farms.

Veterinarians are the main source of information for farmers regarding the responsible use of antibiotics in farm animals and how to reduce the risk of antibiotic resistance. Consequently, understanding how veterinarians perceive their clients' and colleagues' antibiotic use and their own beliefs about the development of antibiotic resistance is essential to determining areas in which antibiotic use practices can be improved to minimize the emergence of antibiotic resistance. An international cross-sectional study was carried out using a questionnaire designed to elucidate perceptions, attitudes, and concerns of dairy veterinarians regarding antibiotic use and the emergence of antibiotic resistance in dairy farming. The questionnaire was initially administered to veterinarians attending the International Bovine Mastitis Conference in Milano, Italy, 2018, followed by veterinarian members of the National Mastitis Council, and all conference registrants. A total of 71 participants from 21 countries participated in the survey, the majority were from the United States and member countries of the European Union. Logistic regression analysis was conducted to identify predictors of veterinarians' level of concern about the development of antibiotic resistance on their clients' farms. Associations were described with odds ratios (ORs) and the associated 95 % confidence intervals (95 % CIs). Free text responses where participants shared their views on the reason for overprescribing antibiotics by veterinarians were analyzed using thematic analysis. Participants perceived that nearly half of their clients overuse or inappropriately use antibiotics, and nearly half of their colleagues overprescribe or inappropriately prescribe antibiotics. After controlling for other factors, the odds of veterinarians being concerned about antibiotic resistance on dairy farms they served decreased by a factor of 0.91 for each additional year of participants' experience working with dairy cattle (OR = 0.91, 95 % CI = 0.84-0.99). Participants concerned about antibiotic resistance on clients' dairy farms were also more likely to consider better adherence to drug labelling as important for reducing farmers' antibiotic use (OR = 6.86, 95 % CI = 1.21-38.93). Thematic analysis revealed four themes surrounding the perceived reasons for veterinarians' overprescribing of antibiotics: (i) knowledge, (ii) attitudes, (iii) barriers, and (iv) rules and regulations. The study findings will aid in the development of strategies to improve antibiotic use in dairy farming and educational initiatives looking to enhance the communication between veterinarians and farmers about judicious use of antibiotics.