Research gaps and priorities for quantitative microbial risk assessment (QMRA).

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.

Memory and Traumatic Brain Injury: Assessment and Management Practices of Speech-Language Pathologists.

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 TBI-specific 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.

Problem-solving interventions and depression among adolescents and young adults: A systematic review of the effectiveness of problem-solving interventions in preventing or treating depression.

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.

Listeriosis Risk Model for Cancer Patients Who Consume Ready-to-Eat Salad.

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.

Development and application of a dose-response model for Elizabethkingia spp.

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 ß = 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.

Meta-Analysis Addressing the Implications of Model Uncertainty in Understanding the Persistence of Indicators and Pathogens in Natural Surface Waters.

This study evaluates the impact persistence model selection has on the prediction of persistence values of interest and the identification of influential water quality and environmental factors for microorganisms in natural surface waters. Five persistence models representing first-order decay and nonlinear decay profiles were fit to a comprehensive database of 629 data sets for fecal indicator bacteria (FIB), bacteriophages, bacteria, viruses, and protozoa mined from the literature. Initial periods of minimal decay and decay rates tapering off over time were often observed, and a two-parameter model, based on the logistic probability distribution, provided the best fit to the data most frequently. First-order decay kinetics provided the best fit to less than 20% of the analyzed data. Using the best fitting models in this analysis, T90 and T99 metrics were calculated for each data set and used as the dependent variable in a variety of exploratory factor analyses. Random forest methods identified temperature and predation as some of the most important water quality factors influencing persistence, and the protozoa target type differed the most from FIB. This analysis further confirmed the interactions between temperature and predation and suggests that pH and turbidity be more frequently documented in persistence studies to further elucidate their impact on target persistence. The findings from this analysis and the calculated persistence metrics can be used to better inform quantitative microbial risk assessments and may lead to improved predictions of human health risks and water management decisions.

Large-Scale SARS-CoV-2 Testing Utilizing Saliva and Transposition Sample Pooling.

Identification and isolation of contagious individuals along with quarantine of close contacts, is critical for slowing the spread of COVID-19. Large-scale testing in a surveillance or screening capacity for asymptomatic carriers of COVID-19 provides both data on viral spread and the follow-up ability to rapidly test individuals during suspected outbreaks. The COVID-19 early detection program at Michigan State University has been utilizing large-scale testing in a surveillance or screening capacity since fall of 2020. The methods adapted here take advantage of the reliability, large sample volume, and self-collection benefits of saliva, paired with a cost-effective, reagent conserving two-dimensional pooling scheme. The process was designed to be adaptable to supply shortages, with many components of the kits and the assay easily substituted. The processes outlined for collecting and processing SARS-CoV-2 samples can be adapted to test for future viral pathogens reliably expressed in saliva. By providing this blueprint for universities or other organizations, preparedness plans for future viral outbreaks can be developed.

Global systemic risk and resilience for novel coronavirus in postpandemic era.

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.

Identifying water quality and environmental factors that influence indicator and pathogen decay in natural surface waters.

Biphasic decay has been observed for indicators and pathogens in bench-scale and in-situ water experiments for decades, however, first-order decay kinetics continue to be applied to persistence data because of their simplicity and ease of application. Model uncertainty introduced by broadly applying first-order decay kinetics to persistence data may lead to erroneous decision making in the fields of water management and protection. As surface waters are exposed to highly variable environmental and water quality factors that influence microbial and viral persistence, it is expected that first-order decay kinetics are not representative of most of the persistence literature for indicators and pathogens in surface water matrices. This review compiled the methods and results of 61 studies that conducted experiments evaluating the persistence of fecal indicator bacteria (FIB), bacteriophages, pathogenic bacteria, viruses, and protozoa in natural surface water matrices. The goals of this review were trifold: (1) collate studies in the literature with data available for future persistence modeling, (2) present the current state of knowledge with regards to the environmental and water quality factors affecting persistence in natural surface waters, and (3) identify recurrent evidence for interactions between the frequently studied factors to inform future factor analyses. Comparing the methods and results across the 61 studies suggest potential interactions between sunlight and water type; sunlight and method of detection; predation and water type; predation and temperature; and water type and method of detection. The majority of the identified literature evaluated FIB or bacteria persistence; future experiments are needed that focus on protozoa, brackish or marine water types, and molecular-based methods of detection.

Persistence of poliovirus types 2 and 3 in waste-impacted water and sediment.

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.

Critical Capability Needs for Reduction of Transmission of SARS-CoV-2 Indoors.

Coordination of efforts to assess the challenges and pain points felt by industries from around the globe working to reduce COVID-19 transmission in the indoor environment as well as innovative solutions applied to meet these challenges is mandatory. Indoor infectious viral disease transmission (such as coronavirus, norovirus, influenza) is a complex problem that needs better integration of our current knowledge and intervention strategies. Critical to providing a reduction in transmission is to map the four core technical areas of environmental microbiology, transmission science, building science, and social science. To that end a three-stage science and innovation Summit was held to gather information on current standards, policies and procedures applied to reduce transmission in built spaces, as well as the technical challenges, science needs, and research priorities. The Summit elucidated steps than can be taken to reduce transmission of SARS-CoV-2 indoors and calls for significant investments in research to enhance our knowledge of viral pathogen persistence and transport in the built environment, risk assessment and mitigation strategy such as processes and procedures to reduce the risk of exposure and infection through building systems operations, biosurveillance capacity, communication form leadership, and stakeholder engagement for optimal response. These findings reflect the effective application of existing knowledge and standards, emerging science, and lessons-learned from current efforts to confront SARS-CoV-2.

Distribution of antimicrobial resistance across the overall environment of dairy farms - A case study.

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.

Global Systemic Risk and Resilience for Novel Coronavirus and COVID-19.

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.

A Multidisciplinary, Community-Based Program to Reduce Unplanned Hospital Admissions.

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.

Modeling the Dose Response Relationship of Waterborne Acanthamoeba.

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.

Quantitation of Risk Reduction of E. coli Transmission After Using Antimicrobial Hand Soap.

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.

Impacts of Municipal Water-Rainwater Source Transitions on Microbial and Chemical Water Quality Dynamics at the Tap.

When rainwater harvesting is utilized as an alternative water resource in buildings, a combination of municipal water and rainwater is typically required to meet water demands. Altering source water chemistry can disrupt pipe scale and biofilm and negatively impact water quality at the distribution level. Still, it is unknown if similar reactions occur within building plumbing following a transition in source water quality. The goal of this study was to investigate changes in water chemistry and microbiology at a green building following a transition between municipal water and rainwater. We monitored water chemistry (metals, alkalinity, and disinfectant byproducts) and microbiology (total cell counts, plate counts, and opportunistic pathogen gene markers) throughout two source water transitions. Several constituents including alkalinity and disinfectant byproducts served as indicators of municipal water remaining in the system since the rainwater source does not contain these constituents. In the treated rainwater, microbial proliferation and Legionella spp. gene copy numbers were often three logs higher than those in municipal water. Because of differences in source water chemistry, rainwater and municipal water uniquely interacted with building plumbing and generated distinctively different drinking water chemical and microbial quality profiles.

Ebola Virus Dose Response Model for Aerosolized Exposures: Insights from Primate Data.

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.

Modeling the persistence of viruses in untreated groundwater.

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.