Search

Purpose: One manifestation of systemic inequities in communication sciences and disorders (CSD) is the chronic underreporting and underrepresentation of sex, gender, race, and ethnicity in research. The present study characterized recent demographic reporting practices and representation of participants across CSD research. Method: We systematically reviewed and extracted key reporting and participant data from empirical studies conducted in the United States with human participants published in the year 2020 in journals by the American Speech- Language-Hearing Association (ASHA; 407 articles comprising a total of 80,058 research participants, search completed in November 2021). Sex, gender, race, and ethnicity were operationalized per National Institutes of Health guidelines. Results: Sex or gender was reported in 85.5% of included studies; race in 33.7%; and ethnicity in 13.8%. Sex and gender were clearly differentiated in 3.4% of relevant studies. Where reported, median proportions for race and ethnicity were significantly different from the U.S. population, with underrepresentation noted for all non-White racial groups and Hispanic participants. Moreover, 64.7% of studies that reported sex or gender and 67.2% of studies that reported race or ethnicity did not consider these respective variables in analyses or discussions. Conclusions: At present, research published in ASHA journals frequently fails to report key demographic data summarizing the characteristics of participants. Moreover, apparent gaps in the representation of minoritized racial and ethnic groups threaten the external validity of CSD research and broader health care equity endeavors in the United States. Although our study is limited to a single year and publisher, our results point to several steps for readers that may bring greater accountability, consistency, and diversity to the discipline. [ABSTRACT FROM AUTHOR]

Purpose: Memory impairments are among the most commonly reported deficits and among the most frequent rehabilitation targets for individuals with traumatic brain injury (TBI). Memory and learning are also critical for rehabilitation success and broader long-term outcomes. Speech-language pathologists (SLPs) play a central role in memory management for individuals with TBI across the continuum of care. Yet, little is known about the current practice patterns of SLPs for post-TBI memory disorders. This study aims to examine the clinical management of memory disorders in adults with TBI by SLPs and identify opportunities to improve post-TBI memory outcomes. Method: SLPs from across the continuum of care were recruited to complete an online survey. The survey assessed key practice areas specific to memory and memory disorders post-TBI, including education and training, knowledge and confidence, and assessment and treatment patterns. Results: Surveys from 155 SLPs were analyzed. Results revealed that TBIspecific training remains low in the field. Respondents varied in their practice patterns in assessing and treating memory disorders. Most SLPs do not appear to have access to appropriate standardized assessments to measure unique forms of memory. Respondents also reported a range of barriers and opportunities to advance memory outcomes following TBI and provided suggestions of areas in which they would like to see more basic and clinical research. Conclusions: These findings establish a baseline of the current practices for clinical management of memory impairment in adults with TBI by SLPs. Improved opportunities for clinician training, the development of a single tool to assess multiple forms of memory, better access to existing memory assessments, and implementation of evidence-based interventions promise to lead to improved memory outcomes for individuals with TBI. [ABSTRACT FROM AUTHOR]

Problem-solving (PS) has been identified as a therapeutic technique found in multiple evidence-based treatments for depression. To further understand for whom and how this intervention works, we undertook a systematic review of the evidence for PS's effectiveness in preventing and treating depression among adolescents and young adults. We searched electronic databases (PsycINFO, Medline, and Cochrane Library) for studies published between 2000 and 2022. Studies meeting the following criteria were included: (a) the intervention was described by authors as a PS intervention or including PS; (b) the intervention was used to treat or prevent depression; (c) mean or median age between 13–25 years; (d) at least one depression outcome was reported. Risk of bias of included studies was assessed using the Cochrane Risk of Bias 2.0 tool. A narrative synthesis was undertaken given the high level of heterogeneity in study variables. Twenty-five out of 874 studies met inclusion criteria. The interventions studied were heterogeneous in population, intervention, modality, comparison condition, study design, and outcome. Twelve studies focused purely on PS; 13 used PS as part of a more comprehensive intervention. Eleven studies found positive effects in reducing depressive symptoms and two in reducing suicidality. There was little evidence that the intervention impacted PS skills or that PS skills acted as a mediator or moderator of effects on depression. There is mixed evidence about the effectiveness of PS as a prevention and treatment of depression among AYA. Our findings indicate that pure PS interventions to treat clinical depression have the strongest evidence, while pure PS interventions used to prevent or treat sub-clinical depression and PS as part of a more comprehensive intervention show mixed results. Possible explanations for limited effectiveness are discussed, including missing outcome bias, variability in quality, dosage, and fidelity monitoring; small sample sizes and short follow-up periods. [ABSTRACT FROM AUTHOR]

Guanabara Bay has been known to be polluted with trash and sewage from the surrounding areas, but health risks from recreational contact with water in the basin have not been well characterized. In this paper, fecal indicator bacteria (FIB) monitoring data are used to predict risks in three different exposure scenarios: (1) bathing in freshwater rivers that discharge into Guanabara Bay, (2) wading in these freshwater rivers, and (3) sailing in Guanabara Bay. Concentrations of indicator bacteria in river samples were measured directly, and concentrations of indicator bacteria in bay samples were sourced from publicly available government data sets. Ratios between pathogens and fecal indicator concentrations were used to estimate risks for five selected pathogens based on the indicator concentrations. The median risk of disease estimated from E. coli indicator concentrations was 1.0, 9.9 × 10−1 and 8.2 × 10−4 for the swimming, wading, and sailing exposure pathways, respectively. Risks estimates based on concentrations of the enterococci indicator bacteria in the sailing exposure scenario were comparable, at 3.4 × 10−4. The sum of total risk estimated from the five selected pathogens was 5.9 × 10−1, 3.6 × 10−1, and 1.0 × 10−3 for the swimming, wading, and sailing exposure pathways, respectively. Estimated risks of swimming and wading in the rivers far exceeded risks associated with U.S. recreational contact standards, while estimated risks for sailing in the bay were well below these risk guidelines. The 95th percentile of the sailing risk was estimated to exceed the U.S. recreational contact risk level. This paper exemplifies an approach to conducting quantitative microbial risk assessments when only fecal indicator bacteria data are available. Context-specific data on the relevant exposure routes, exposure frequency, and site-specific indicator: pathogen ratios were lacking, which ultimately led to uncertainty in the model. This study is intended to provide a framework for estimating GI risk based on fecal indicator concentrations while acknowledging that the substantial variation in indicator:pathogen ratios make the results of such efforts uncertain. [ABSTRACT FROM AUTHOR]

Elizabethkingia spp. are common environmental pathogens responsible for infections in more vulnerable populations. Although the exposure routes of concern are not well understood, some hospital‐associated outbreaks have indicated possible waterborne transmission. In order to facilitate quantitative microbial risk assessment (QMRA) for Elizabethkingia spp., this study fit dose–response models to frog and mice datasets that evaluated intramuscular and intraperitoneal exposure to Elizabethkingia spp. The frog datasets could be pooled, and the exact beta‐Poisson model was the best fitting model with optimized parameters α = 0.52 and β = 86,351. Using the exact beta‐Poisson model, the dose of Elizabethkingia miricola resulting in a 50% morbidity response (LD50) was estimated to be approximately 237,000 CFU. The model developed herein was used to estimate the probability of infection for a hospital patient under a modeled exposure scenario involving a contaminated medical device and reported Elizabethkingia spp. concentrations isolated from hospital sinks after an outbreak. The median exposure dose was approximately 3 CFU/insertion event, and the corresponding median risk of infection was 3.4E‐05. The median risk estimated in this case study was lower than the 3% attack rate observed in a previous outbreak, however, there are noted gaps pertaining to the possible concentrations of Elizabethkingia spp. in tap water and the most likely exposure routes. This is the first dose–response model developed for Elizabethkingia spp. thus enabling future risk assessments to help determine levels of risk and potential effective risk management strategies. [ABSTRACT FROM AUTHOR]

The residence time of water in residential building water systems is a critical factor regarding water quality at end use. Published literature has highlighted the importance of water age in these systems and its relationship with pathogenic bacteria such as Legionella pneumophila. However, tools to measure water age in such plumbing systems are typically repurposed from other applications and include limitations that make them inappropriate for some plumbing systems. This work presents a novel means of estimating water age by assuming these systems operate without mixing. Data for this study was collected from a full-scale home equipped with an extensive array of flow meters to monitor water use. Further, 408 individual water quality samples were collected to ascertain water quality changes that take place in the plumbing. Model results show weak correlation with EPANET 2.2 (ρ = 0.666), a commonly used hydraulic modeling software. The results of the water age model were also evaluated with several variable selection tools. These analyses indicate that this method's water age results are a statistically significant (p < .05) predictor of Legionella concentrations. Model results from this approach could be used in plumbing design and/or operation to assist in managing Legionella risks. [ABSTRACT FROM AUTHOR]

The results show that human risk and organizational risk of public health administration, CDC, and medical institutions are the key sources of risk in the complex interconnected EPC system, exacerbating the risk and producing a significant domino effect. The COVID-19 pandemic has had global effects on the intersections of medical, political, economic, industrial, infrastructure, work force, and other domains. The approach includes track features extraction, infection risk indicator system construction, and infection risk categories classification. [Extracted from the article]

Significant seasonal changes in chemical and microbiological water quality can occur in buildings at different fixture locations due to temperature and time dependent reaction rates. Here, a series of calibrated plumbing hydraulic‐water quality models were developed for the extensively monitored Retrofitted Net‐zero Energy, Water & Waste (ReNEWW) house in West Lafayette, Indiana, USA. The eight new models predict the absolute level of free chlorine, total trihalomethanes, copper, iron, lead, NO3− (nitrate‐nitrogen), heterotrophic plate count (HPC), and Legionella spp. concentration at each fixture for plumbing use, operational characteristics, and design layouts of the plumbing system. Model development revealed that the carrying capacity to describe Legionella spp. growth (and other organisms) under water usage and plumbing design conditions is lacking in the literature. Reducing simulated building water use by 25% prompted increased absolute concentrations of HPC and Legionella spp., each increasing by a factor of about 105. When the simulated service line length was increased, Legionella spp. concentrations increased by up to 106 gene copies /L in the Summer season. The proposed modeling framework can be used to support better planning, design, analysis, and operational decision‐making. [ABSTRACT FROM AUTHOR]

Eradication of poliovirus (PV) is a global public health priority, and as clinical cases decrease, the role of environmental surveillance becomes more important. Persistence of PV and the environmental factors that influence it (such as temperature and sample type) are an important part of understanding and interpreting positive environmental surveillance samples. The objective of this study was to evaluate the persistence of poliovirus type 2 (PV2) and type 3 (PV3) in wastewater and sediment. Microcosms containing either 1) influent wastewater or 2) influent wastewater with a sediment matrix were seeded with either PV2 or PV3, and stored for up to 126 days at three temperatures (4°C, room temperature [RT], and 30°C). Active PV in the liquid of (1), and the sediment and liquid portions of (2) were sampled and quantified at up to 10 time points via plaque assay and RT-qPCR. A suite of 17 models were tested for best fit to characterize decay of PV2 and PV3 over time and determine the time points at which >90% (T90) and >99% (T99) reduction was reached. Linear models assessed the influence of experimental factors (matrix, temperature, virus type and method of detection) on the predicted T90 and T99 values. Results showed that when T90 was the dependent variable, virus type, matrix, and temperature significantly affected decay, and there was a clear interaction between the sediment matrix and temperature. When T99 was the dependent variable, only temperature and matrix type significantly influenced the decay metric. This study characterizes the persistence of both active and molecular PV2 and PV3 in relevant environmental conditions, and demonstrates that temperature and sediment both play important roles in PV viability. As eradication nears and clinical cases decrease, environmental surveillance and knowledge of PV persistence will play a key role in understanding the silent circulation in endemic countries. [ABSTRACT FROM AUTHOR]

The ongoing pandemic has evolved and is posing diverse challenges for the world. Countermeasures for risks are needed to address both direct and indirect effects of disease on the healthcare system, economic and industrial sectors, governance, environment, transportation, energy, and communication systems. There are indicators of a forthcoming postpandemic era. The rethinking and reevaluation of policies adopted throughout the pandemic are ongoing to address cascading threats of emerging and reemerging infectious diseases. The first Special Issue introduced the topic. This second Special Issue describes international collaboration and innovation for pandemic risk and resilience, with a focus on future policy and operations of global systems toward a postandemic era. [ABSTRACT FROM AUTHOR]

This Special Issue is dedicated to issues and challenges related to pandemic risk and resilience, with a focus on policy and operations of global systems in the COVID‐19 pandemic. The cascading effects of emerging and reemerging infectious diseases to the global economy are a critical interest. Measures to confront the ongoing pandemic are an urgent need. Data analysis at regional and global scales is helping to prioritize response and resilience across locations of high risks. The risk sciences are available for addressing human health and infection risks; the evaluation of risk management strategies and tradeoffs; risk perception as it relates to information processing and receiving risk communication; and tracking system resilience as it relates to various imposed measures. [ABSTRACT FROM AUTHOR]

This study developed dose response models for determining the probability of eye or central nervous system infections from previously conducted studies using different strains of Acanthamoeba spp. The data were a result of animal experiments using mice and rats exposed corneally and intranasally to the pathogens. The corneal inoculations of Acanthamoeba isolate Ac 118 included varied amounts of Corynebacterium xerosis and were best fit by the exponential model. Virulence increased with higher levels of C. xerosis. The Acanthamoeba culbertsoni intranasal study with death as an endpoint of response was best fit by the beta‐Poisson model. The HN‐3 strain of A. castellanii was studied with an intranasal exposure and three different endpoints of response. For all three studies, the exponential model was the best fit. A model based on pooling data sets of the intranasal exposure and death endpoint resulted in an LD50 of 19,357 amebae. The dose response models developed in this study are an important step towards characterizing the risk associated with free‐living amoeba like Acanthamoeba in drinking water distribution systems. Understanding the human health risk posed by free‐living amoeba will allow for quantitative microbial risk assessments that support building design decisions to minimize opportunities for pathogen growth and survival. [ABSTRACT FROM AUTHOR]

This study develops dose–response models for Ebolavirus using previously published data sets from the open literature. Two such articles were identified in which three different species of nonhuman primates were challenged by aerosolized Ebolavirus in order to study pathology and clinical disease progression. Dose groups were combined and pooled across each study in order to facilitate modeling. The endpoint of each experiment was death. The exponential and exact beta‐Poisson models were fit to the data using maximum likelihood estimation. The exact beta‐Poisson was deemed the recommended model because it more closely approximated the probability of response at low doses though both models provided a good fit. Although transmission is generally considered to be dominated by person‐to‐person contact, aerosolization is a possible route of exposure. If possible, this route of exposure could be particularly concerning for persons in occupational roles managing contaminated liquid wastes from patients being treated for Ebola infection and the wastewater community responsible for disinfection. Therefore, this study produces a necessary mathematical relationship between exposure dose and risk of death for the inhalation route of exposure that can support quantitative microbial risk assessment aimed at informing risk mitigation strategies including personal protection policies against occupational exposures. [ABSTRACT FROM AUTHOR]

Middle Eastern respiratory syndrome, an emerging viral infection with a global case fatality rate of 35.5%, caused major outbreaks first in 2012 and 2015, though new cases are continuously reported around the world. Transmission is believed to mainly occur in healthcare settings through aerosolized particles. This study uses Quantitative Microbial Risk Assessment to develop a generalizable model that can assist with interpreting reported outbreak data or predict risk of infection with or without the recommended strategies. The exposure scenario includes a single index patient emitting virus‐containing aerosols into the air by coughing, leading to short‐ and long‐range airborne exposures for other patients in the same room, nurses, healthcare workers, and family visitors. Aerosol transport modeling was coupled with Monte Carlo simulation to evaluate the risk of MERS illness for the exposed population. Results from a typical scenario show the daily mean risk of infection to be the highest for the nurses and healthcare workers (8.49 × 10−4 and 7.91 × 10−4, respectively), and the lowest for family visitors and patients staying in the same room (3.12 × 10−4 and 1.29 × 10−4, respectively). Sensitivity analysis indicates that more than 90% of the uncertainty in the risk characterization is due to the viral concentration in saliva. Assessment of risk interventions showed that respiratory masks were found to have a greater effect in reducing the risks for all the groups evaluated (>90% risk reduction), while increasing the air exchange was effective for the other patients in the same room only (up to 58% risk reduction). [ABSTRACT FROM AUTHOR]

This study develops novel dose-response models for Naegleria fowleri from selected peer-reviewed experiments on the virulence based on the intranasal exposure pathway. One data set measured the response of mice intranasally inoculated with the amebae and the other study addressed the response of mice swimming in N. fowleri infected water. The measured response for both studies was death. All experimental data were best fit by the beta-Poisson dose-response model. The three swimming experiments could be pooled, and this is the final recommended model with an LD50 of 13,257 amebae. The results of this study provide a better estimate of the probability of the risk to N. fowleri exposure than the previous models developed based on an intravenous exposure. An accurate dose-response model is the first step in quantifying the risk of free-living amebae like N. fowleri, which pose risks in recreational environments and have been detected in drinking water and premise plumbing systems. A better understanding of this risk will allow for risk management that limits the ability for pathogen growth, proliferation, and exposure. [ABSTRACT FROM AUTHOR]

Summary: Bacillus spores resist inactivation, but the extent of their persistence on common surfaces is unclear. This work addresses knowledge gaps regarding biothreat agents in the environment to reduce uncertainty in risk assessment models. Studies were conducted to investigate the long‐term inactivation of Bacillus anthracis and three commonly used surrogate organisms – B. cereus, B. atrophaeus and B. thuringiensis on three materials: laminate countertop, stainless steel and polystyrene Petri dishes. Viable spores were measured at 1, 30, 90, 196, 304 and 1038 days. Twelve different persistence models were fit to the data using maximum likelihood estimation and compared. The study found that (1) spore inactivation was not log‐linear, as commonly modelled; (2) B. thuringiensis counts increased at 24 h on all materials, followed by a subsequent decline; (3) several experiments showed evidence of a 'U' shape, with spore counts apparently decreasing and then increasing between 1 and 304 days; (4) spores on polystyrene showed little inactivation; and (5) the maximum inactivation of 56% was observed for B. atrophaeus spores on steel at 196 days. Over the range of surfaces, time durations and conditions (humidity controlled vs. uncontrolled) examined, B. thuringiensis most closely matched the behaviour of B. anthracis. This work is the first of its kind to jointly present an experimental and modelling study that characterizes the long‐term persistence of Bacillus spores on common surfaces by natural decay over a 3 year period. The implications of model selection and extrapolation beyond the final time point are elucidated for microorganisms that persist for long periods of time on environmental media. The study also informs the selection of suitable surrogates based on their behaviour under similar conditions over time. [ABSTRACT FROM AUTHOR]

Antimicrobial spray products are used by millions of people around the world for cleaning and disinfection of commonly touched surfaces. Influenza A is a pathogen of major concern, leading to up to 49,000 deaths and 114,000 hospitalizations per year in the United States alone. One of the recognized routes of transmission for influenza A is by transfer of viruses from surfaces to hands and subsequently to mucous membranes. Therefore, routine cleaning and disinfection of surfaces is an important part of the environmental management of influenza A. While the emphasis is generally on spraying hard surfaces and laundering cloth and linens with high temperature machine drying, not all surfaces can be treated in this manner. The quantitative microbial risk assessment (QMRA) approach was used to develop a stochastic risk model for estimating the risk of infection from indirect contact with porous fomite with and without surface treatment with an antimicrobial spray. The data collected from laboratory analysis combined with the risk model show that influenza A infection risk can be lowered by four logs after using an antimicrobial spray on a porous surface. Median risk associated with a single touch to a contaminated fabric was estimated to be 1.25 × 10−4 for the untreated surface, and 3.6 × 10−8 for the treated surface as a base case assumption. This single touch scenario was used to develop a generalizable model for estimating risks and comparing scenarios with and without treatment to more realistic multiple touch scenarios over time periods and with contact rates previously reported in the literature. The results of this study and understanding of product efficacy on risk reduction inform and broaden the range of risk management strategies for influenzaAby demonstrating effective risk reduction associated with treating nonporous fomites that cannot be laundered at high temperatures. [ABSTRACT FROM AUTHOR]

Microbial dose response modelling is vital to a well-characterized microbial risk estimate. Dose response modelling is an inherently multidisciplinary field, which collates knowledge and data from disparate scientific fields. This multidisciplinary nature presents a key challenge to the expansion of microbial dose response modelling into new groups of researchers and modelers. This research employs a dose response optimization R code used in 18 peer-reviewed research studies to develop a multi-functional dose response software. The underlying R code performs an optimization of the two primary dose response models using the MLE method and outputs statistical analyses of the fits and bootstrapped uncertainty information for the models. VizDR (Visual Dose Response) was developed to provide microbial dose response modelling capabilities to a larger audience. VizDR is programmed in JavaScript with underlying Python scripts for intercommunication with Rserve. VizDR allows for dose response model visualization and optimization of a user's own experimental data. [ABSTRACT FROM AUTHOR]

Hyperaminoacidemia following ingestion of cows-milk may stimulate muscle anabolism and attenuate exercise-induced muscle damage (EIMD). However, as dairy-intolerant athletes do not obtain the reported benefits from milk-based products, A2 milk may offer a suitable alternative as it lacks the A1-protein. This study aimed to determine the effect of A2 milk on recovery from a sports-specific muscle damage model. Twenty-one male team sport players were allocated to three independent groups: A2 milk (n = 7), regular milk (n = 7), and placebo (PLA) (n = 7). Immediately following muscle-damaging exercise, participants consumed either A2 milk, regular milk or PLA (500 mL each). Visual analogue scale (muscle soreness), maximal voluntary isometric contraction (MVIC), countermovement jump (CMJ) and 20-m sprint were measured prior to and 24, 48, and 72 h post EIMD. At 48 h post-EIMD, CMJ and 20-m sprint recovered quicker in A2 (33.4 ± 6.6 and 3.3 ± 0.1, respectively) and regular milk (33.1 ± 7.1 and 3.3 ± 0.3, respectively) vs. PLA (29.2 ± 3.6 and 3.6 ± 0.3, respectively) (p < 0.05). Relative to baseline, decrements in 48 h CMJ and 20-m sprint were minimised in A2 (by 7.2 and 5.1%, respectively) and regular milk (by 6.3 and 5.2%, respectively) vs. PLA. There was a trend for milk treatments to attenuate decrements in MVIC, however statistical significance was not reached (p = 0.069). Milk treatments had no apparent effect on muscle soreness (p = 0.152). Following muscle-damaging exercise, ingestion of 500 mL of A2 or regular milk can limit decrements in dynamic muscle function in male athletes, thus hastening recovery and improving subsequent performance. The findings propose A2 milk as an ergogenic aid following EIMD, and may offer an alternative to athletes intolerant to the A1 protein. [ABSTRACT FROM AUTHOR]

Abstract: While only limited data are available to characterize the potential toxicity of over 8million 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 &y& Elsevier]

The manufacture of novel synthetic chemicals has increased in volume and variety, but often the environmental and health risks are not fully understood in terms of toxicity and, in particular, exposure. While efforts to assess risks have generally been effective when sufficient data are available, the hazard and exposure data necessary to assess risks adequately are unavailable for the vast majority of chemicals in commerce. The US Environmental Protection Agency has initiated the ExpoCast Program to develop tools for rapid chemical evaluation based on potential for exposure. In this context, a model is presented in which chemicals are evaluated based on inherent chemical properties and behaviorally-based usage characteristics over the chemical’s life cycle. These criteria are assessed and integrated within a decision analytic framework, facilitating rapid assessment and prioritization for future targeted testing and systems modeling. A case study outlines the prioritization process using 51 chemicals. The results show a preliminary relative ranking of chemicals based on exposure potential. The strength of this approach is the ability to integrate relevant statistical and mechanistic data with expert judgment, allowing for an initial tier assessment that can further inform targeted testing and risk management strategies. [ABSTRACT FROM AUTHOR]

Handwashing with soap is an effective and economical means to reduce the likelihood of Escherichia coli infection from indirect contact with contaminated surfaces during food preparation. The purpose of this study was to conduct a quantitative microbial risk assessment (QMRA) to evaluate the risk of infection from indirect contact with fomites contaminated with E. coli after hand washing with antimicrobial hand soaps. A Monte Carlo simulation was done with a total of 10,000 simulations to compare the effectiveness of two antimicrobial and one control (non-antimicrobial) bar soaps in reducing the exposure and infection risk compared to no hand washing. The numbers of E. coli on several fomites commonly found in household kitchens, as well as the transfer rates between fomites and onto fingertips, were collected from the literature and experimental data. The sponsor company provided the E. coli survival on hands after washing with antimicrobial and control soaps. A number of scenarios were evaluated at two different exposure doses (high and low). Exposure scenarios included transfer of E. coli between meat-to-cutting board surface-to-hands, meat-to-knife surface-to-hands, and from a countertop surface-to-hands, kitchen sponge-to-hands, hand towel-to-hands, and dishcloth-to-hands. Results showed that the risks of illness after washing with the control soap was reduced approximately 5-fold compared to no handwashing. Washing with antimicrobial soap reduced the risk of E. coli infection by an average of about 40-fold compared with no handwashing. The antimicrobial soaps ranged from 3 to 32 times more effective than the non-antimicrobial soap, depending on the specific exposure scenario. Importance: The Centers for Disease Control and Prevention indicate the yearly incidence rate of Shiga Toxin producing E. coli infections is about 1.7/100,000, with about 10% of cases leading to life-threatening hemolytic uremic syndrome and 3–5% leading to death. Our findings confirm handwashing with soap reduces the risks associated with indirect transmission of E. coli infection from contact with fomites during food preparation. Further, in these exposure scenarios, antimicrobial soaps were more effective overall than the non-antimicrobial soap in reducing exposure to E. coli and risk of infection. [ABSTRACT FROM AUTHOR]

Quantitative microbial risk assessment (QMRA) is a computational science leveraged to optimize infectious disease controls at both population and individual levels. Often, diverse populations will have different health risks based on a population's susceptibility or outcome severity due to heterogeneity within the host. Unfortunately, due to a host homogeneity assumption in the microbial dose-response models' derivation, the current QMRA method of modeling exposure volume heterogeneity is not an accurate method for pathogens such as Legionella pneumophila. Therefore, a new method to model within-group heterogeneity is needed. The method developed in this research uses USA national incidence rates from the Centers for Disease Control and Prevention (CDC) to calculate proxies for the morbidity ratio that are descriptive of the within-group variability. From these proxies, an example QMRA model is developed to demonstrate their use. This method makes the QMRA results more representative of clinical outcomes and increases population-specific precision. Further, the risks estimated demonstrate a significant difference between demographic groups known to have heterogeneous health outcomes after infection. The method both improves fidelity to the real health impacts resulting from L. pneumophila infection and allows for the estimation of severe disability-adjusted life years (DALYs) for Legionnaires' disease, moderate DALYs for Pontiac fever, and post-acute DALYs for sequela after recovering from Legionnaires' disease. [ABSTRACT FROM AUTHOR]

In order to determine the relationship between an exposure dose of Staphylococcus aureus (S. aureus) on the skin and the risk of infection, an understanding of the bacterial growth and decay kinetics is very important. Models are essential tools for understanding and predicting bacterial kinetics and are necessary to predict the dose of organisms post-exposure that results in a skin infection. One of the challenges in modeling bacterial kinetics is the estimation of model parameters, which can be addressed using an inverse problem approach. The objective of this study is to construct a microbial kinetic model of S. aureus on human skin and use the model to predict concentrations of S. aureus that result in human infection. In order to model the growth and decay of S. aureus on skin, a Gompertz inactivation model was coupled with a Gompertz growth model. A series of analyses, including ordinary least squares regression, scaled sensitivity coefficient analysis, residual analysis, and parameter correlation analysis were conducted to estimate the parameters and to describe the model uncertainty. Based on these analyses, the proposed model parameters were estimated with high accuracy. The model was then used to develop a new dose-response model for S. aureus using the exponential dose–response model. The new S. aureus model has an optimized k parameter equivalent to 8.05 × 10−8 with 95th percentile confidence intervals between 6.46 × 10−8 and 1.00 × 10−7. [ABSTRACT FROM AUTHOR]

To combat the spread of antimicrobial resistance (AMR), it is vital to link the behavior of donor and recipient bacteria in dynamic environments to horizontal gene transfer (HGT) potentialspecifically, conjugation the primary means of spread of AMR genes. However, HGT is poorly understood under dynamic conditions, such as those in the gut of humans and animals. Most experiments are done under static conditions at viscosities similar to water, but these methods do not accurately represent the higher gut viscosities or movement. Hence, a next step to increase understanding of conjugation is with experiments using generic donor and recipient pairs at different viscosities. Accordingly, it is necessary to establish the relationship between viscosity and bacterial growth in these experiments, for which our hypothesis is that the rate of bacterial growth in fluids with higher viscosities will be lower due to water displacement. To test this hypothesis, experiments were designed to measure the number of donors, recipients and transconjugant bacteria using optical density. Varying concentrations of the thickeners agar and xanthan gum will be used to achieve different viscosity levels in the media. Media of thicknesses closer to that of bodily fluids, which are more alike to pancake syrup or batter, will be evaluated. Concentrations will be tracked at half hour intervals as a means to obtain data and to formulate a growth curve model. Some preliminary results indicate that our hypothesis has a good probability of being correct. Linear growth curve models were applied to the data for comparison purposes. [ABSTRACT FROM AUTHOR]

Residential plumbing is critical for the health and safety of populations worldwide. A case study was conducted to understand fixture water use, drinking water quality and their possible link, in a newly plumbed residential green building. Water use and water quality were monitored at four in-building locations from September 2015 through December 2015. Once the home was fully inhabited average water stagnation periods were shortest at the 2nd floor hot fixture (90 percentile of 0.6–1.2 h). The maximum water stagnation time was 72.0 h. Bacteria and organic carbon levels increased inside the plumbing system compared to the municipal tap water entering the building. A greater amount of bacteria was detected in hot water samples (6–74,002 gene copy number/mL) compared to cold water (2–597 gene copy number/mL). This suggested that hot water plumbing promoted greater microbial growth. The basement fixture brass needle valve may have caused maximum Zn (5.9 mg/L), Fe (4.1 mg/L), and Pb (23 μg/L) levels compared to other fixture water samples (Zn ≤ 2.1 mg/L, Fe ≤ 0.5 mg/L and Pb ≤ 8 μg/L). At the basement fixture, where the least amount of water use events occurred (cold: 60–105, hot: 21–69 event/month) compared to the other fixtures in the building (cold: 145–856, hot: 326–2230 event/month), greater organic carbon, bacteria, and heavy metal levels were detected. Different fixture use patterns resulted in disparate water quality within a single-family home. The greatest drinking water quality changes were detected at the least frequently used fixture. [ABSTRACT FROM AUTHOR]

• Virus persistence in water has inactivation kinetics not fully captured by linear models. • Three nonlinear models best fit viral persistence data most frequently. • Classification trees provide a novel framework for persistence model selection. 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. [ABSTRACT FROM AUTHOR]

The accuracy of predictive microbial models used in quantitative microbial risk assessment (QMRA) relies on the relevancy of conditions influencing growth or inactivation. The continued use of log-linear models in studies remains widespread, despite evidence that they fail to accurately account for biphasic kinetics or include parameters to account for the effect of environmental conditions within the model equation. Although many experimental studies detail conditions of interest, studies that do not do so lead to uncertainty in QMRA modeling because the applicability of the predictive microbial models to the conditions in the risk scenarios is questionable or must be extrapolated. The current study systematically reviewed 65 articles that provided quantitative data and documented the conditions influencing the inactivation or growth of Shiga toxin-producing Escherichia coli (STEC) O157:H7 in leafy greens. The conditions were identified and categorized as environmental, biological, chemical, and/or processing. Our study found that temperature (n = 37 studies) and sanitizing and washing procedures (n = 12 studies) were the most studied conditions in the farm-to-table continuum of leafy greens. In addition, relative humidity was also established to affect growth and inactivation in more than one stage in the continuum. This study proposes the evaluation of the interactive effects of multiple conditions in processing and storage stages from controlled experiments as they relate to the fate of STEC O157:H7 in leafy greens for future quantitative analysis., (© 2024 The Author(s). Comprehensive Reviews in Food Science and Food Safety published by Wiley Periodicals LLC on behalf of Institute of Food Technologists.)

Recent advancements in modeling suggest that microbial inactivation in leafy greens follows a nonlinear pattern, rather than the simple first-order kinetics. In this study, we evaluated 17 inactivation models commonly used to describe microbial decline and established the conditions that govern microbial survival on leafy greens. Through a systematic review of 65 articles, we extracted 530 datasets to model the fate of Shiga toxin-producing Escherichia coli O157:H7 on leafy greens. Various factor analysis methods were employed to evaluate the impact of identified conditions on survival metrics. A two-parameter model (jm2) provided the best fit to most of both natural and antimicrobial-induced persistence datasets, whereas the one-parameter exponential model provided the best fit to less than 20% of the datasets. The jm2 model (adjusted R 2  = .89) also outperformed the exponential model (adjusted R 2  = .58) in fitting the pooled microbial survival data. In the context of survival metrics, the model averaging approach generated higher values than the exponential model for >4 log reduction times (LRTs), suggesting that the exponential model may be overpredicting inactivation at later time points. The random forest technique revealed that temperature and inoculum size were common factors determining inactivation in both natural and antimicrobial-induced die-offs.. The findings show the limitations of relying on the first-order survival metric of 1 LRT and considering nonlinear inactivation in produce safety decision-making., (© 2024 The Author(s). Comprehensive Reviews in Food Science and Food Safety published by Wiley Periodicals LLC on behalf of Institute of Food Technologists.)

Quantitative microbial risk assessment (QMRA) is a growing interdisciplinary field addressing exposures to microbial pathogens and infectious disease processes. Risk science is inherently interdisciplinary, but few of the contributing disciplinary programs offer courses and training specifically in QMRA. To develop multidisciplinary training in QMRA, an annual 10-day long intensive workshop was conducted from 2015 to 2019-the Quantitative Microbial Risk Assessment Interdisciplinary Instructional Institute (QMRA III). National leaders in the fields of public health, engineering, microbiology, epidemiology, communications, public policy, and QMRA served as instructors and mentors over the course of the program. To provide cross-training, multidisciplinary teams of 5-6 trainees were created from the approximately 30 trainees each year. A formal assessment of the program was performed based on observations and surveys containing Likert-type scales and open-ended prompts. In addition, a longitudinal alumni survey was also disseminated to facilitate the future redevelopment of QMRA institutes and determine the impact of the program. Across all years, trainees experienced statistically significant increases ( P < 0.05) in their perceptions of their QMRA abilities (e.g., use of specific computer programs) and knowledge of QMRA constructs (e.g., risk management). In addition, 12 publications, three conference presentations, and two research grants were derived from the QMRA III institute projects or tangential research. The success of QMRA III indicates that a short course format can effectively address many multidisciplinary training needs. Key features of QMRA III, including the inter-disciplinary training approach, hands-on exercises, real-world institute projects, and interaction through a mentoring process, were vital for training multidisciplinary teams housing multiple forms of expertise. Future QMRA institutes are being redeveloped to leverage hybrid learning formats that can further the multidisciplinary training and mentoring objectives., Competing Interests: The authors declare no conflict of interest.

The coronavirus disease 2019 pandemic highlighted the need for more rapid and routine application of modeling approaches such as quantitative microbial risk assessment (QMRA) for protecting public health. QMRA is a transdisciplinary science dedicated to understanding, predicting, and mitigating infectious disease risks. To better equip QMRA researchers to inform policy and public health management, an Advances in Research for QMRA workshop was held to synthesize a path forward for QMRA research. We summarize insights from 41 QMRA researchers and experts to clarify the role of QMRA in risk analysis by (1) identifying key research needs, (2) highlighting emerging applications of QMRA; and (3) describing data needs and key scientific efforts to improve the science of QMRA. Key identified research priorities included using molecular tools in QMRA, advancing dose-response methodology, addressing needed exposure assessments, harmonizing environmental monitoring for QMRA, unifying a divide between disease transmission and QMRA models, calibrating and/or validating QMRA models, modeling co-exposures and mixtures, and standardizing practices for incorporating variability and uncertainty throughout the source-to-outcome continuum. Cross-cutting needs identified were to: develop a community of research and practice, integrate QMRA with other scientific approaches, increase QMRA translation and impacts, build communication strategies, and encourage sustainable funding mechanisms. Ultimately, a vision for advancing the science of QMRA is outlined for informing national to global health assessments, controls, and policies., (© 2024 The Author(s). Risk Analysis published by Wiley Periodicals LLC on behalf of Society for Risk Analysis.)