Human adenovirus-associated health risk in the recreational waters of the Yal-ku lagoon in the Mexican Caribbean.

The study objective was to evaluate human faecal contamination impacts in the Yal-ku lagoon in the Mexican Caribbean and to estimate adenovirus infection and illness risks associated with recreational exposure during water activities. A total of 20 water samples (10 from each site × two sites) (50 L) were collected monthly over a period of 12 months from two selected sampling sites in the swimming area of the Yal-ku lagoon. The occurrence of faecal-associated viruses was explored, and human adenovirus (HAdV) and pepper mild mottle virus (PMMoV) concentrations were quantified. A quantitative microbial risk assessment (QMRA) model was used to estimate exposure and subsequent adenovirus infection and illness risk for 1 h of swimming or snorkelling. Somatic and F + -specific coliphages occurred in 100% of the samples. Both HAdV and PMMoV were detected at a 60% frequency thereby indicating persistent faecal inputs. PMMoV concentrations (44-370 GC/L) were relatively lower than the concentrations of HAdV (64-1,000 GC/L). Estimated mean adenovirus risks were greater for snorkelling than for swimming by roughly one to two orders of magnitude and estimated mean illness risks for snorkelling were >32/1,000. Human faecal contamination is frequent in the Yal-ku lagoon, which is associated with human gastrointestinal illness.

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.

School health systems under strain: an example of COVID-19 experiences & burnout among school health staff in Pima County, Arizona.

BACKGROUND: School health staff lead and provide a variety of care for children in schools. As school districts have navigated the COVID-19 pandemic, school health staff have faced unprecedented challenges in protecting the health of students and school staff. Our objective was to qualitatively characterize these pandemic challenges and experiences of school health staff in Pima County, Arizona to identify gaps in school health staff support for improving future emergency preparedness. METHODS: We conducted two focus group discussions (FGDs) with 48 school health staff in Pima County, Arizona in two school districts using a discussion guide including ten open-ended questions. The FGDs were audio recorded and transcribed verbatim. We used the socioecological model (SEM) to organize the thematic analysis and generate codes and themes; data were analyzed using Atlas.ti software. FINDINGS: The pandemic has significantly challenged school health staff with new pandemic-related job tasks: managing isolation, vaccination, and developing/implementing new and evolving COVID-19 guidelines. School health staff also reported increased stress related to interactions with parents and school administration as well as frustrations with rapid changes to guidance from the health department and policy makers. A common issue was not having enough staff or resources to complete regular job responsibilities, such as providing care for students with non-COVID-19 related health issues. CONCLUSIONS: Increased workload for school health staff resulted in physical burnout, mental distress, and disruption of core functions with long term implications for children's health. These focus groups highlight the need for improved emergency preparedness in schools during pandemics or infectious disease outbreaks. These include basic infrastructure changes (e.g., personnel support from health departments for tasks such as contact tracing to enable school nurses to continue core functions), and increased funding to allow for hazard pay and more school health personnel during emergency situations. In addition, basic school health infrastructure is lacking, and we should include a licensed school health nurse in every school.

Exposure frequency, intensity, and duration: What we know about work-related asthma risks for healthcare workers from cleaning and disinfection.

The objective of this review was to scope the current evidence base related to three exposure assessment concepts: frequency, intensity, and duration (latency) for cleaning and disinfection exposures in healthcare and subsequent work-related asthma risks. A search strategy was developed addressing intersections of four main concepts: (1) work-related asthma; (2) occupation (healthcare workers/nurses); (3) cleaning and disinfection; and (4) exposure. Three databases were searched: Embase, PubMed, and the Cumulative Index to Nursing and Allied Health Literature (CINAHL) database. Data were extracted related to three main components of risk assessment: (1) exposure frequency, (2) exposure intensity, and (3) exposure duration. Latency data were analyzed using an exponential distribution fit, and extracted concentration data were compared to occupational exposure limits. The final number of included sources from which data were extracted was 133. Latency periods for occupational asthma were exponentially distributed, with a mean waiting time (1/λ) of 4.55 years. No extracted concentration data were above OELs except for some formaldehyde and glutaraldehyde concentrations. Data from included sources also indicated some evidence for a dose-response relationship regarding increased frequency yielding increased risk, but this relationship is unclear due to potential confounders (differences in role/task and associated exposure) and the healthy worker effect. Data priority needs to include linking concentration data to health outcomes, as most current literature does not include both types of measurements in a single study, leading to uncertainty in dose-response relationships.

Transmission of Respiratory Viral Diseases to Health Care Workers: COVID-19 as an Example.

Health care workers (HCWs) can acquire infectious diseases, including coronavirus disease 2019 (COVID-19), from patients. Herein, COVID-19 is used with the source-pathway-receptor framework as an example to assess evidence for the roles of aerosol transmission and indirect contact transmission in viral respiratory infectious diseases. Evidence for both routes is strong for COVID-19 and other respiratory viruses, but aerosol transmission is likely dominant for COVID-19. Key knowledge gaps about transmission processes and control strategies include the distribution of viable virus among respiratory aerosols of different sizes, the mechanisms and efficiency by which virus deposited on the facial mucous membrane moves to infection sites inside the body, and the performance of source controls such as face coverings and aerosol containment devices. To ensure that HCWs are adequately protected from infection, guidelines and regulations must be updated to reflect the evidence that respiratory viruses are transmitted via aerosols.

Minding the matrix: The importance of inoculum suspensions on finger transfer efficiency of virus.

AIMS: The aim of this study was to determine how the transfer efficiency of MS-2 coliphage from the toilet seat to hands and fingertip to lip differs according to the suspension of the inoculum. METHODS AND RESULTS: Hands were sampled after lifting a toilet seat which was inoculated with MS-2 on the underneath side. MS-2 was suspended in a spectrum of proteinaceous and non-proteinaceous solutions. Transfer efficiencies were greatest with the ASTM tripartite soil load (3.02% ± 4.03) and lowest with phosphate-buffered saline (PBS) (1.10% ± 0.81) for hand-to-toilet seat contacts. Finger-to-lip transfer rates were significantly different (p < 0.05) depending on suspension matrix, with PBS yielding the highest transfer (52.53% ± 4.48%) and tryptose soy broth (TSB) the lowest (23.15% ± 24.27%). Quantitative microbial risk assessment was used to estimate the probability of infection from adenovirus and norovirus from finger contact with a toilet seat. CONCLUSIONS: The greatest transfer as well as the largest variation of transfer were measured for finger-to-lip contacts as opposed to toilet seat-to-finger contacts. These factors influence the estimation of the probability of infection from micro-activity, that is, toilet seat adjustment. SIGNIFICANCE AND IMPACT: Viruses may be transferred from various human excreta with differing transfer efficiencies, depending on the protein content.

Modeling fomite-mediated SARS-CoV-2 exposure through personal protective equipment doffing in a hospital environment.

Self-contamination during doffing of personal protective equipment (PPE) is a concern for healthcare workers (HCW) following SARS-CoV-2-positive patient care. Staff may subconsciously become contaminated through improper glove removal; so, quantifying this exposure is critical for safe working procedures. HCW surface contact sequences on a respiratory ward were modeled using a discrete-time Markov chain for: IV-drip care, blood pressure monitoring, and doctors' rounds. Accretion of viral RNA on gloves during care was modeled using a stochastic recurrence relation. In the simulation, the HCW then doffed PPE and contaminated themselves in a fraction of cases based on increasing caseload. A parametric study was conducted to analyze the effect of: (1a) increasing patient numbers on the ward, (1b) the proportion of COVID-19 cases, (2) the length of a shift, and (3) the probability of touching contaminated PPE. The driving factors for the exposure were surface contamination and the number of surface contacts. The results simulate generally low viral exposures in most of the scenarios considered including on 100% COVID-19 positive wards, although this is where the highest self-inoculated dose is likely to occur with median 0.0305 viruses (95% CI =0-0.6 viruses). Dose correlates highly with surface contamination showing that this can be a determining factor for the exposure. The infection risk resulting from the exposure is challenging to estimate, as it will be influenced by the factors such as virus variant and vaccination rates.

Risk perceptions of drinking bottled vs. tap water in a low-income community on the US-Mexico Border.

BACKGROUND: Previous studies have shown that low-income Latinos generally drink bottled water over tap water and might be at increased risks for cavities from unfluoridated bottled water. In order to better design interventions, it is important to understand the risk perceptions of this unique high-risk yet historically marginalized group. METHODS: We interviewed low-income Latino households (n = 90) from Nogales, Arizona who primarily drink bottled water and asked them to evaluate potential health risks of drinking tap water compared to 16 other voluntary activities. Unpaired t-tests were used to determine if statistically significant (α = 0.05) differences occurred in perceived risk by drinking-water source and differences among demographic groups in their level of (dis)agreement with statements regarding tap or bottled water safety. To assess significant differences (α = 0.05) in perceived risks and voluntariness to engage in a number of activities, including drinking local tap water and drinking water in different geographic regions, a one-way analysis of variance (ANOVA) followed by Scheffe's post-hoc test (a conservative post-hoc test) with adjustment for the number of pairwise comparisons was used. RESULTS: Participants viewed bottled water to be significantly safer to consume than tap water (p < 0.001). On a Likert scale from 1 (low risk) to 5 (high risk), "drinking tap water in Nogales, Arizona" received an average score of 4.7, which was significantly higher than the average perceived risk of drinking San Francisco, California tap water (µ = 3.4, p < 0.001), and as risky as drinking and driving (µ = 4.8, p = 1.00) and drinking Nogales, Sonora, Mexico tap water (µ = 4.8, p = 1.00). Ninety-eight percent of participants feared that drinking local tap water could result in illness, 79% did not drink their water because of fear of microbial and chemical contamination and 73% would drink their water if they knew it was safe regardless of taste. CONCLUSIONS: These results suggest that fear of illness from tap-water consumption is an important contributing factor to increased bottled water use. Future efforts should focus on the development of educational and outreach efforts to assess the safety and risks associated with tap-water consumption.

Quantifying SARS-CoV-2 Infection Risk Within the Google/Apple Exposure Notification Framework to Inform Quarantine Recommendations.

Most early Bluetooth-based exposure notification apps use three binary classifications to recommend quarantine following SARS-CoV-2 exposure: a window of infectiousness in the transmitter, ≥15 minutes duration, and Bluetooth attenuation below a threshold. However, Bluetooth attenuation is not a reliable measure of distance, and infection risk is not a binary function of distance, nor duration, nor timing. We model uncertainty in the shape and orientation of an exhaled virus-containing plume and in inhalation parameters, and measure uncertainty in distance as a function of Bluetooth attenuation. We calculate expected dose by combining this with estimated infectiousness based on timing relative to symptom onset. We calibrate an exponential dose-response curve based on infection probabilities of household contacts. The probability of current or future infectiousness, conditioned on how long postexposure an exposed individual has been symptom-free, decreases during quarantine, with shape determined by incubation periods, proportion of asymptomatic cases, and asymptomatic shedding durations. It can be adjusted for negative test results using Bayes' theorem. We capture a 10-fold range of risk using six infectiousness values, 11-fold range using three Bluetooth attenuation bins, ∼sixfold range from exposure duration given the 30 minute duration cap imposed by the Google/Apple v1.1, and ∼11-fold between the beginning and end of 14 day quarantine. Public health authorities can either set a threshold on initial infection risk to determine 14-day quarantine onset, or on the conditional probability of current and future infectiousness conditions to determine both quarantine and duration.

Effects of patient room layout on viral accruement on healthcare professionals' hands.

Healthcare professionals (HCPs) are exposed to highly infectious viruses, such as norovirus, through multiple exposure routes. Understanding exposure mechanisms will inform exposure mitigation interventions. The study objective was to evaluate the influences of hospital patient room layout on differences in HCPs' predicted hand contamination from deposited norovirus particles. Computational fluid dynamic (CFD) simulations of a hospital patient room were investigated to find differences in spatial deposition patterns of bioaerosols for right-facing and left-facing bed layouts under different ventilation conditions. A microbial transfer model underpinned by observed mock care for three care types (intravenous therapy (IV) care, observational care, and doctors' rounds) was applied to estimate HCP hand contamination. Viral accruement was contrasted between room orientation, care type, and by assumptions about whether bioaerosol deposition was the same or variable by room orientation. Differences in sequences of surface contacts were observed for care type and room orientation. Simulated viral accruement differences between room types were influenced by mostly by differences in bioaerosol deposition and by behavior sequences when deposition patterns for the room orientations were similar. Differences between care types were likely driven by differences in hand-to-patient contact frequency, with doctors' rounds resulting in the greatest predicted viral accruement on hands.

Respirators, face masks, and their risk reductions via multiple transmission routes for first responders within an ambulance.

First responders may have high SARS-CoV-2 infection risks due to working with potentially infected patients in enclosed spaces. The study objective was to estimate infection risks per transport for first responders and quantify how first responder use of N95 respirators and patient use of cloth masks can reduce these risks. A model was developed for two Scenarios: an ambulance transport with a patient actively emitting a virus in small aerosols that could lead to airborne transmission (Scenario 1) and a subsequent transport with the same respirator or mask use conditions, an uninfected patient; and remaining airborne SARS-CoV-2 and contaminated surfaces due to aerosol deposition from the previous transport (Scenario 2). A compartmental Monte Carlo simulation model was used to estimate the dispersion and deposition of SARS-CoV-2 and subsequent infection risks for first responders, accounting for variability and uncertainty in input parameters (i.e., transport duration, transfer efficiencies, SARS-CoV-2 emission rates from infected patients, etc.). Infection risk distributions and changes in concentration on hands and surfaces over time were estimated across sub-Scenarios of first responder respirator use and patient cloth mask use. For Scenario 1, predicted mean infection risks were reduced by 69%, 48%, and 85% from a baseline risk (no respirators or face masks used) of 2.9 × 10-2 ± 3.4 × 10-2 when simulated first responders wore respirators, the patient wore a cloth mask, and when first responders and the patient wore respirators or a cloth mask, respectively. For Scenario 2, infection risk reductions for these same Scenarios were 69%, 50%, and 85%, respectively (baseline risk of 7.2 × 10-3 ± 1.0 × 10-2). While aerosol transmission routes contributed more to viral dose in Scenario 1, our simulations demonstrate the ability of face masks worn by patients to additionally reduce surface transmission by reducing viral deposition on surfaces. Based on these simulations, we recommend the patient wear a face mask and first responders wear respirators, when possible, and disinfection should prioritize high use equipment.

Bacterial transfer to fingertips during sequential surface contacts with and without gloves.

Bacterial transmission from contaminated surfaces via hand contact plays a critical role in disease spread. However, the fomite-to-finger transfer efficiency of microorganisms during multiple sequential surface contacts with and without gloves has not been formerly investigated. We measured the quantity of Escherichia coli on fingertips of participants after 1-8 sequential contacts with inoculated plastic coupons with and without nitrile gloves. A Bayesian approach was used to develop a mechanistic model of pathogen accretion to examine finger loading as a function of the difference between E coli on surfaces and fingers. We used the model to determine the coefficient of transfer efficiency (λ), and influence of swabbing efficiency and finger area. Results showed that λ for bare skin was higher (49%, 95% CI = 32%-72%) than for gloved hands (30%, CI = 17%-49%). Microbial load tended toward a dynamic equilibrium after four and six contacts for gloved hands and bare skin, respectively. Individual differences between volunteers' hands had a negligible effect compared with use of gloves (P < .01). Gloves reduced loading by 4.7% (CI = -12%-21%) over bare skin contacts, while 20% of participants accrued more microorganisms on gloved hands. This was due to poor fitting, which created a larger finger surface area than bare hands.

Assessing virus infection probability in an office setting using stochastic simulation.

Viral infections are an occupational health concern for office workers and employers. The objectives of this study were to estimate rotavirus, rhinovirus, and influenza A virus infection risks in an office setting and quantify infection risk reductions for two hygiene interventions. In the first intervention, research staff used an ethanol-based spray disinfectant to clean high-touch non-porous surfaces in a shared office space. The second intervention included surface disinfection and also provided workers with alcohol-based hand sanitizer gel and hand sanitizing wipes to promote hand hygiene. Expected changes in surface concentrations due to these interventions were calculated. Human exposure and dose were simulated using a validated, steady-state model incorporated into a Monte Carlo framework. Stochastic inputs representing human behavior, pathogen transfer efficiency, and pathogen fate were utilized, in addition to a mixed distribution that accounted for surface concentrations above and below a limit of detection. Dose-response curves were then used to estimate infection risk. Estimates of percent risk reduction using mean values from baseline and surface disinfection simulations for rotavirus, rhinovirus, and influenza A infection risk were 14.5%, 16.1%, and 32.9%, respectively. For interventions with both surface disinfection and the promotion of personal hand hygiene, reductions based on mean values of infection risk were 58.9%, 60.8%, and 87.8%, respectively. This study demonstrated that surface disinfection and the use of personal hand hygiene products can help decrease virus infection risk in communal offices. Additionally, a variance-based sensitivity analysis revealed a greater relative importance of surface concentrations, assumptions of relevant exposure routes, and inputs representing human behavior in estimating risk reductions.

Microbial study of household hygiene conditions and associated Listeria monocytogenes infection risks for Peruvian women.

OBJECTIVES: To develop an exposure and risk assessment model to estimate listeriosis infection risks for Peruvian women. METHODS: A simulation model was developed utilising Listeria monocytogenes concentrations on kitchen and latrine surfaces in Peruvian homes, hand trace data from Peruvian women and behavioural data from literature. Scenarios involving varying proportions of uncontaminated, or 'clean', surfaces and non-porous surfaces were simulated. Infection risks were estimated for 4, 6 and 8 h of behaviours and interactions with surfaces. RESULTS: Although infection risks were estimated across scenarios for various time points (e.g. 4, 6, 8 h), overall mean estimated infection risks for all scenarios were ≥ 0.31. Infection risks increased as the proportions of clean surfaces decreased. Hand-to-general surface contacts accounted for the most cumulative change in L. monocytogenes concentration on hands. CONCLUSIONS: In addition to gaining insights on how human behaviours affect exposure and infection risk, this model addressed uncertainties regarding the influence of household surface contamination levels. Understanding the influence of surface contamination in preventing pathogen transmission in households could help to develop intervention strategies to reduce L. monocytogenes infection and associated health risks.

OBJECTIFS: Développer un modèle d'exposition et d'évaluation des risques pour estimer les risques d'infection par la listériose chez les femmes péruviennes. MÉTHODES: Un modèle de simulation a été développé en utilisant des concentrations de Listeria monocytogenes sur la surface des cuisines et des latrines dans des foyers péruviens, des données de traces de mains de femmes péruviennes et des données comportementales de la littérature. Des scénarios impliquant différentes proportions de surfaces non contaminées ou «propres¼ et de surfaces non poreuses ont été simulés. Les risques d'infection ont été estimés pour 4, 6 et 8 heures de comportements et d'interactions avec les surfaces. RÉSULTATS: Bien que les risques d'infection aient été estimés pour tous les scénarios à différents moments (par ex. à 4, 6 ou 8 heures), les risques d'infection globaux moyens estimés pour tous les scénarios étaient ≥ 0,31. Les risques d'infection augmentaient à mesure que les proportions de surfaces propres diminuaient. Les contacts entre la main et les surfaces générales contribuent pour le plus de changement cumulatif de la concentration de L. monocytogenes sur les mains. CONCLUSIONS: En plus de comprendre comment les comportements humains affectent l'exposition et le risque d'infection, ce modèle a traité des incertitudes quant à l'influence des niveaux de contamination des surfaces ménagers. Comprendre l'influence de la contamination de surface dans la prévention de la transmission d'agents pathogènes dans les ménages pourrait aider à développer des stratégies d'intervention pour réduire l'infection à L. monocytogenes et les risques associés pour la santé.

Validation of a Stochastic Discrete Event Model Predicting Virus Concentration on Nurse Hands.

Understanding healthcare viral disease transmission and the effect of infection control interventions will inform current and future infection control protocols. In this study, a model was developed to predict virus concentration on nurses' hands using data from a bacteriophage tracer study conducted in Tucson, Arizona, in an urgent care facility. Surfaces were swabbed 2 hours, 3.5 hours, and 6 hours postseeding to measure virus spread over time. To estimate the full viral load that would have been present on hands without sampling, virus concentrations were summed across time points for 3.5- and 6-hour measurements. A stochastic discrete event model was developed to predict virus concentrations on nurses' hands, given a distribution of virus concentrations on surfaces and expected frequencies of hand-to-surface and orifice contacts and handwashing. Box plots and statistical hypothesis testing were used to compare the model-predicted and experimentally measured virus concentrations on nurses' hands. The model was validated with the experimental bacteriophage tracer data because the distribution for model-predicted virus concentrations on hands captured all observed value ranges, and interquartile ranges for model and experimental values overlapped for all comparison time points. Wilcoxon rank sum tests showed no significant differences in distributions of model-predicted and experimentally measured virus concentrations on hands. However, limitations in the tracer study indicate that more data are needed to instill more confidence in this validation. Next model development steps include addressing viral concentrations that would be found naturally in healthcare environments and measuring the risk reductions predicted for various infection control interventions.

Modeling the role of fomites in a norovirus outbreak.

Norovirus accounts for a large portion of the gastroenteritis disease burden, and outbreaks have occurred in a wide variety of environments. Understanding the role of fomites in norovirus transmission will inform behavioral interventions, such as hand washing and surface disinfection. The purpose of this study was to estimate the contribution of fomite-mediated exposures to infection and illness risks in outbreaks. A simulation model in discrete time that accounted for hand-to-porous surfaces, hand-to-nonporous surfaces, hand-to-mouth, -eyes, -nose, and hand washing events was used to predict 17 hr of simulated human behavior. Norovirus concentrations originated from monitoring contamination levels on surfaces during an outbreak on houseboats. To predict infection risk, two dose-response models (fractional Poisson and 2F1 hypergeometric) were used to capture a range of infection risks. A triangular distribution describing the conditional probability of illness given an infection was multiplied by modeled infection risks to estimate illness risks. Infection risks ranged from 70.22% to 72.20% and illness risks ranged from 21.29% to 70.36%. A sensitivity analysis revealed that the number of hand-to-mouth contacts and the number of hand washing events had strong relationships with model-predicted doses. Predicted illness risks overlapped with leisure setting and environmental attack rates reported in the literature. In the outbreak associated with the viral concentrations used in this study, attack rates ranged from 50% to 86%. This model suggests that fomites may have accounted for 25% to 82% of illnesses in this outbreak. Fomite-mediated exposures may contribute to a large portion of total attack rates in outbreaks involving multiple transmission modes. The findings of this study reinforce the importance of frequent fomite cleaning and hand washing, especially when ill persons are present.

Modeling Surface Disinfection Needs To Meet Microbial Risk Reduction Targets.

Nosocomial viral infections are an important cause of health care-acquired infections where fomites have a role in transmission. Using stochastic modeling to quantify the effects of surface disinfection practices on nosocomial pathogen exposures and infection risk can inform cleaning practices. The purpose of this study was to predict the effect of surface disinfection on viral infection risks and to determine needed viral reductions to achieve risk targets. Rotavirus, rhinovirus, and influenza A virus infection risks for two cases were modeled. Case 1 utilized a single fomite contact approach, while case 2 assumed 6 h of contact activities. A 94.1% viral reduction on surfaces and hands was measured following a single cleaning round using an Environmental Protection Agency (EPA)-registered disinfectant in an urgent care facility. This value was used to model the effect of a surface disinfection intervention on infection risk. Risk reductions for other surface-cleaning efficacies were also simulated. Surface reductions required to achieve risk probability targets were estimated. Under case 1 conditions, a 94.1% reduction in virus surface concentration reduced infection risks by 94.1%. Under case 2 conditions, a 94.1% reduction on surfaces resulted in median viral infection risks being reduced by 92.96 to 94.1% and an influenza A virus infection risk below one in a million. Surface concentration in the equations was highly correlated with dose and infection risk outputs. For rotavirus and rhinovirus, a >99.99% viral surface reduction would be needed to achieve a one-in-a-million risk target. This study quantifies reductions of infection risk relative to surface disinfectant use and demonstrates that risk targets for low-infectious-dose organisms may be more challenging to achieve.IMPORTANCE It is known that the use of EPA-registered surface disinfectant sprays can reduce infection risk if used according to the manufacturer's instructions. However, there are currently no standards for health care environments related to contamination levels on surfaces. The significance of this research is in quantifying needed reductions to meet various risk targets using realistic viral concentrations on surfaces for health care environments. This research informs the design of cleaning protocols by demonstrating that multiple applications may be needed to reduce risk and by highlighting a need for more models exploring the relationship among microbial contamination of surfaces, patient and health care worker behaviors, and infection risks.

Methods for Handling Left-Censored Data in Quantitative Microbial Risk Assessment.

Data below detection limits, left-censored data, are common in environmental microbiology, and decisions in handling censored data may have implications for quantitative microbial risk assessment (QMRA). In this paper, we utilize simulated data sets informed by real-world enterovirus water data to evaluate methods for handling left-censored data. Data sets were simulated with four censoring degrees (low [10%], medium [35%], high [65%], and severe [90%]) and one real-life censoring example (97%) and were informed by enterovirus data assuming a lognormal distribution with a limit of detection (LOD) of 2.3 genome copies/liter. For each data set, five methods for handling left-censored data were applied: (i) substitution with LOD/[Formula: see text], (ii) lognormal maximum likelihood estimation (MLE) to estimate mean and standard deviation, (iii) Kaplan-Meier estimation (KM), (iv) imputation method using MLE to estimate distribution parameters (MI method 1), and (v) imputation from a uniform distribution (MI method 2). Each data set mean was used to estimate enterovirus dose and infection risk. Root mean square error (RMSE) and bias were used to compare estimated and known doses and infection risks. MI method 1 resulted in the lowest dose and infection risk RMSE and bias ranges for most censoring degrees, predicting infection risks at most 1.17 × 10-2 from known values under 97% censoring. MI method 2 was the next overall best method. For medium to severe censoring, MI method 1 may result in the least error. If unsure of the distribution, MI method 2 may be a preferred method to avoid distribution misspecification.IMPORTANCE This study evaluates methods for handling data with low (10%) to severe (90%) left-censoring within an environmental microbiology context and demonstrates that some of these methods may be appropriate when using data containing concentrations below a limit of detection to estimate infection risks. Additionally, this study uses a skewed data set, which is an issue typically faced by environmental microbiologists.

Recruitment strategies in marginalized industries for occupational health research: an example in a pilot study of cleaning staff during COVID-19.

This commentary describes challenges in recruiting workers from marginalized industries with examples from a pilot study of janitors, custodians, and maids and their experiences of cleaning and disinfection protocols during COVID-19 and potentially associated respiratory symptoms. Recruitment strategies included contacting a local hospital, national workers' unions, and a large Arizona employer; using online Facebook groups; and contacting Arizona maid service companies and a school district. English and Spanish online and hard copy surveys about cleaning protocols and respiratory symptoms were used. Participants could also participate in online/phone interviews. Worker's compensation, liability, and confidentiality were concerns across organizations. Online surveys yielded unreliable data. Hard copy surveys were used due to workers' limited technology access. We reflect upon these challenges and discuss other strategies for recruiting from marginalized populations for occupational health research. Building trust with organizations and workers and considering technology access may ultimately increase recruitment feasibility.