Detection of pathogens in food processing facilities by routine environmental monitoring (EM) is essential to reduce the risk of foodborne illness but is complicated by the complexity of equipment ...and environment surfaces. To optimize design of EM programs, we developed EnABLe ("Environmental monitoring with an Agent-Based Model of Listeria"), a detailed and customizable agent-based simulation of a built environment. EnABLe is presented here in a model system, tracing Listeria spp. (LS) (an indicator for conditions that allow the presence of the foodborne pathogen Listeria monocytogenes) on equipment and environment surfaces in a cold-smoked salmon facility. EnABLe was parameterized by existing literature and expert elicitation and validated with historical data. Simulations revealed different contamination dynamics and risks among equipment surfaces in terms of the presence, level and persistence of LS. Grouping of surfaces by their LS contamination dynamics identified connectivity and sanitary design as predictors of contamination, indicating that these features should be considered in the design of EM programs to detect LS. The EnABLe modeling approach is particularly timely for the frozen food industry, seeking science-based recommendations for EM, and may also be relevant to other complex environments where pathogen contamination presents risks for direct or indirect human exposure.
The objective of this study was to perform a systematic review of risk factors for contamination of fruits and vegetables with Listeria monocytogenes, Salmonella, and Escherichia coli O157:H7 at the ...preharvest level. Relevant studies were identified by searching six electronic databases: MEDLINE, EMBASE, CAB Abstracts, AGRIS, AGRICOLA, and FSTA, using the following thesaurus terms: L. monocytogenes, Salmonella, E. coli O157 AND fruit, vegetable. All search terms were exploded to find all related subheadings. To be eligible, studies had to be prospective controlled trials or observational studies at the preharvest level and had to show clear and sufficient information on the process in which the produce was contaminated. Of the 3,463 citations identified, 68 studies fulfilled the eligibility criteria. Most of these studies were on leafy greens and tomatoes. Six studies assessed produce contamination with respect to animal host-related risk factors, and 20 studies assessed contamination with respect to pathogen characteristics. Sixty-two studies assessed the association between produce contamination and factors related to produce, water, and soil, as well as local ecological conditions of the production location. While evaluations of many risk factors for preharvest-level produce contamination have been reported, the quality assessment of the reviewed studies confirmed the existence of solid evidence for only some of them, including growing produce on clay-type soil, the application of contaminated or non-pH-stabilized manure, and the use of spray irrigation with contaminated water, with a particular risk of contamination on the lower leaf surface. In conclusion, synthesis of the reviewed studies suggests that reducing microbial contamination of irrigation water and soil are the most effective targets for the prevention and control of produce contamination. Furthermore, this review provides an inventory of the evaluated risk factors, including those requiring more research.
Antibiotic resistance is a global problem affecting both human and animal health. Ensuring the strategic and effective use of antibiotics is paramount to combatting the emergence and spread of ...resistance. This study explored New York State (NYS) dairy farmers' perceptions regarding antibiotic use in dairy farming and antibiotic resistance. Dairy farmers' perceptions were assessed through semi-structured, in-person interviews. Twenty interviews with farm owners and/or managers of 15 conventional and five USDA certified organic dairy farms with 40 to 2,300 lactating cows were conducted. Thematic analysis was used to assess, compare and contrast transcripts for farmers' characterization of their beliefs, values, and concerns. Conventional dairy farmers had a low level of concern about the possible impacts of on-farm antibiotic resistance on human health and believed their antibiotic use was already judicious. Generally, they believed their cattle's health would suffer if antibiotic use were further curtailed. Conventional farmers expressed frustration over the possibility of more stringent governmental, milk cooperative, buyer, or marketer requirements for antibiotic use and associated animal welfare in the future. They attributed expanding regulations in part to misinformed consumer preferences, that farmers felt were influenced by the marketing of organic dairy products. Organic dairy farmers were generally more concerned about issues related to antibiotic resistance than conventional farmers. Both conventional and organic farmers placed emphasis on disease prevention through herd health management rather than treatment. In conclusion, the conventional NYS dairy farmers in this study were skeptical of the need for and benefits of reduced antibiotic use on their dairy farms. Interventions for farmers, delivered by a trusted source such as a veterinarian, that provide training about proper antibiotic use practices and information of possible financial benefits of refining antibiotic use may hold promise.
•Agent-based model can aid decision-making for retailers.•Better data on Listeria transfer coefficients can improve model prediction.•Improving supplier qualification may effectively reduce Listeria ...at retail.
Contamination of fresh produce with Listeria monocytogenes can occur throughout the supply chain, including at retail, where Listeria spp., including L. monocytogenes, may be introduced and spread via various routes. However, limited tools are available for retailers to assess practices that can enhance control of Listeria transmission to fresh produce. Therefore, we developed an agent-based model that can simulate Listeria transmission in retail produce sections to optimize environmental sampling programs and evaluate control strategies. A single retail store was used as a model environment, in which various routes of Listeria introduction into and transmission between environmental surfaces were modeled. Model prediction (i.e., Listeria prevalence) was validated using a published longitudinal study for all surfaces that were included in both the model and the validation data. Sensitivity analysis using the Partial Rank Correlation Coefficient showed that (i) initial Listeria concentration from incoming produce, (ii) transfer coefficient from produce to employee’s hands, and (iii) transfer coefficient from consumer to produce were the top three parameters that were significantly (p < 0.0018) associated with the mean Listeria prevalence across all agents, suggesting that the accuracy of these parameters are important for prediction of overall Listeria prevalence at retail. Cluster analysis grouped agents with similar contamination patterns into six unique clusters; this information can be used to optimize the sampling plans for retail environments. Scenario analysis suggested that (i) more stringent supplier control as well as (ii) practices reducing Listeria transmission via consumer’s hands may have the largest impact on reducing finished product contamination. Overall, we show that an agent-based model can serve as a foundational tool to help with decision-making on Listeria control strategies at retail.
•Listeriosis can cause devastating outcomes in pregnant individuals.•Frozen vegetables are consumed without prior heating which is risky for listeriosis.•Lack of listeriosis knowledge was associated ...with uncooked vegetable consumption.•Consumer behavior supports the need for updated food safety guidance.
In recent years, there have been numerous recalls of frozen vegetable products due to Listeria monocytogenes contamination, which causes listeriosis. In pregnant women, listeriosis can cause miscarriage, stillbirth, and other serious complications. Manufacturing guidelines are created with the intention that frozen vegetables will be cooked prior to consumption. However, consumers may prepare and eat frozen vegetables without prior cooking. Therefore, it is necessary to assess behaviors that could be risky for L. monocytogenes exposure. A 10-question online survey was distributed to women between the ages of 18–54 to investigate frozen vegetable consumption behaviors. The prevalence of uncooked frozen vegetable consumption, reading preparation instructions, and listeriosis knowledge was assessed. Data were analyzed using logistic and ordered logit regression. Of 1,001 complete responses, 531 (53%) indicated that they consumed frozen vegetables in the past week, and of those 35.6% (n = 189) indicated that they consumed frozen vegetables without prior heating. Women who had not heard of listeriosis and had not read preparation instructions had significantly higher odds of uncooked frozen vegetable consumption (Odds Ratio (OR): 2.30, 95% Confidence Interval (CI): 1.48, 3.55; OR: 1.85, 95% CI: 1.13, 3.01, respectively). These results will guide future research on safe food handling practices for frozen vegetable products. The findings support the need for updating public health guidelines to include frozen vegetables as foods that are risky for listeriosis in pregnancy. Additionally, these findings have implications for future research to inform food policy governing labeling regulation on frozen vegetable products to reflect current consumer behavior.
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.
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.
Agricultural water is an important source of foodborne pathogens on produce farms. Managing water-associated risks does not lend itself to one-size-fits-all approaches due to the heterogeneous nature ...of freshwater environments. To improve our ability to develop location-specific risk management practices, a study was conducted in two produce-growing regions to (i) characterize the relationship between
levels and pathogen presence in agricultural water, and (ii) identify environmental factors associated with pathogen detection. Three AZ and six NY waterways were sampled longitudinally using 10-L grab samples (GS) and 24-h Moore swabs (MS). Regression showed that the likelihood of
detection (Odds Ratio OR = 2.18), and
codetection (OR = 6.49) was significantly greater for MS compared to GS, while the likelihood of detecting
was not. Regression also showed that
codetection in AZ (OR = 50.2) and NY (OR = 18.4), and
detection in AZ (OR = 4.4) were significantly associated with
levels, while
detection in NY was not. Random forest analysis indicated that interactions between environmental factors (e.g., rainfall, temperature, turbidity) (i) were associated with likelihood of pathogen detection and (ii) mediated the relationship between
levels and likelihood of pathogen detection. Our findings suggest that (i) environmental heterogeneity, including interactions between factors, affects microbial water quality, and (ii)
levels alone may not be a suitable indicator of food safety risks. Instead, targeted methods that utilize environmental and microbial data (e.g., models that use turbidity and
levels to predict when there is a high or low risk of surface water being contaminated by pathogens) are needed to assess and mitigate the food safety risks associated with preharvest water use. By identifying environmental factors associated with an increased likelihood of detecting pathogens in agricultural water, this study provides information that (i) can be used to assess when pathogen contamination of agricultural water is likely to occur, and (ii) facilitate development of targeted interventions for individual water sources, providing an alternative to existing one-size-fits-all approaches.
•Fitness costs limit the use of rifampicin resistance as a selective marker.•On spinach, rifR mutants have a lower maximum population than the native microbiota.•Further data collection on strain ...maximum population will improve model performance.
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.
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.
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