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The foodborne pathogen Listeria monocytogenes generally infects immunocompromised individuals, such as cancer patients, more frequently and with higher morbidity and mortality than the general population. Because of the anticipated risk associated with L. monocytogenes and other pathogens in produce, immunocompromised individuals are often placed on neutropenic diets that exclude fresh produce, though these risks have not been quantified. Therefore, this study developed a data-driven risk model for listeriosis in cancer patients who consume ready-to-eat (RTE) salads, consisting of leafy greens, cucumbers, and tomatoes, as influenced by kitchen-scale treatments and storage practices. Monte Carlo simulations were used to model the risk of invasive listeriosis during one chemotherapy cycle. Refrigerating all salad components decreased the median risk by approximately one-half log. For refrigerated salads with no treatment, the predicted median risk was ≤ 4.3 × 10 -08 . When salad ingredients were surface blanched with greens rinsed, the predicted risk decreased to 5.4 × 10 -10 . Predicted risk was lowest (1.4 × 10 -13 ) for a blanched "salad" consisting of solely cucumbers and tomatoes. Interestingly, rinsing, as recommended by FDA, only decreased the median risk by 1 log. A sensitivity analysis revealed that the highly variable dose-response parameter k strongly influenced risk, indicating that reducing uncertainty in this variable may improve model accuracy. Overall, this study demonstrates that kitchen-scale pathogen reduction approaches have high risk reduction efficacy and could be considered as an alternative to diets that exclude produce when making risk management decisions., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

The environmental implications of antimicrobial resistance arising from food animal farm practice are still a knowledge gap. This study investigates the fate and transport of antimicrobial resistance genes related to the use of antibiotics on a dairy farm in Michigan. Manure, soil, animal feed, animal drinking water, surface and groundwater samples were taken and the abundance of antimicrobial resistance genes (ARGs) and mobile genetic elements (MGEs) were subsequently measured using high parallel quantitative PCR targeting 136 genes. The total abundance and detected numbers of ARGs were found to be highest in the stagnant lagoon. Up to 44 ARG subtypes with high abundance were found in drinking water in pen which was very close to those in manure compost. The ARGs pattern clustered by soil depth although they were treated by different manure. ARGs and MGEs were detected in surface and groundwater surrounded by dairy farmlands, with the occurrence of carbapenemase-encoding KPC gene in two waters, which may be due to transport of ARGs through runoff or other sources. Overall, the results of the study suggest high prevalence of ARGs both inside and outside the animal raising area and their potential contribution to environmental ARGs., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021. Published by Elsevier B.V.)

Objectives: To evaluate the effect of Hospital Admission Risk Program (HARP) on unplanned hospitalization, bed days, and mortality of enrolled individuals and to evaluate the cost-effectiveness of HARP., Design: A retrospective longitudinal analysis of hospital administrative data., Intervention: Individuals at risk of hospitalization were provided with multidisciplinary, community-based care support managed by care coordinators including integrated care planning, education, monitoring, service linkages, and general practitioner liaison over 6-9 months., Setting and Participants: Individuals who were enrolled into 1 of 8 HARP chronic disease management programs between July 1, 2017, and June 30, 2018, at the Royal Melbourne Hospital, Australia., Methods: Hospital admissions between 18 months before and 18 months after HARP enrollment were analyzed. Total hospital costs were compared between 18 months before and 12 months after HARP enrollment., Results: A total of 1553 individuals with a median age of 71 years (interquartile range 60-81), 63.4% males, were admitted to HARP. Both unplanned hospitalizations and bed days were reduced during the HARP intervention compared to within 3 months before enrollment in each of the HARP management programs. After the HARP intervention, cardiac coach, cardiac heart failure, chronic respiratory, diabetes comanagement, and medication management programs had higher hospitalizations and bed days than individuals' baseline of at least 3 months before HARP enrollment. Individuals in cardiac heart failure and chronic respiratory management programs had a higher mortality rate than other HARP chronic disease management programs. Individuals in cardiac coach, diabetes comanagement, and medication management programs had lower hospital costs during the HARP intervention compared to within 3 months before HARP enrollment., Conclusions and Implications: HARP reduced unplanned hospitalization and bed days but did not return individuals' hospital use to baseline before the intervention. The variations in mortality between HARP chronic disease management programs implies that condition-specific goals between programs is preferable., (Copyright © 2020 AMDA – The Society for Post-Acute and Long-Term Care Medicine. Published by Elsevier Inc. All rights reserved.)

Several factors can affect virus behavior and persistence in water sources. Historically linear models have been used to describe persistence over time; however, these models do not consider all of the factors that can affect inactivation kinetics or the observed patterns of decay. Meanwhile, applying the appropriate persistence model is critical for ensuring that decision makers are minimizing human health risk in the event of contamination and exposure to contaminated groundwater. Therefore, to address uncertainty in predictions of decay or virus concentrations over time, this study fit seventeen different linear and nonlinear mathematical models to persistence data from a previously conducted sampling study on drinking water wells throughout the United States. The models were fit using Maximum Likelihood Estimation and the best fitting models were determined by the Bayesian Information Criterion. The purpose of the study was to identify the best model for estimating decay of viruses in groundwater and to determine if model uncertainty contributes to erroneous predictions of viral contamination when only conventional models are considered. For the datasets analyzed in this study, the Juneja and Marks models and the exponential damped model were more representative of the persistence of viruses in groundwater than the traditionally used linear models. The results from this study were then evaluated with classification trees in order to identify more relevant modeling methodology for future research. The classification trees aid in narrowing the scope of appropriate persistence models based on characteristics of the experimental conditions and water sampled., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2019 Elsevier B.V. All rights reserved.)

While only limited data are available to characterize the potential toxicity of over 8 million commercially available chemical substances, there is even less information available on the exposure and use-scenarios that are required to link potential toxicity to human and ecological health outcomes. Recent improvements and advances such as high throughput data gathering, high performance computational capabilities, and predictive chemical inherency methodology make this an opportune time to develop an exposure-based prioritization approach that can systematically utilize and link the asymmetrical bodies of knowledge for hazard and exposure. In response to the US EPA's need to develop novel approaches and tools for rapidly prioritizing chemicals, a "Challenge" was issued to several exposure model developers to aid the understanding of current systems in a broader sense and to assist the US EPA's effort to develop an approach comparable to other international efforts. A common set of chemicals were prioritized under each current approach. The results are presented herein along with a comparative analysis of the rankings of the chemicals based on metrics of exposure potential or actual exposure estimates. The analysis illustrates the similarities and differences across the domains of information incorporated in each modeling approach. The overall findings indicate a need to reconcile exposures from diffuse, indirect sources (far-field) with exposures from directly, applied chemicals in consumer products or resulting from the presence of a chemical in a microenvironment like a home or vehicle. Additionally, the exposure scenario, including the mode of entry into the environment (i.e. through air, water or sediment) appears to be an important determinant of the level of agreement between modeling approaches., (Copyright © 2013 Elsevier B.V. All rights reserved.)