Association of sedentary and physical activity behaviours with body composition: a genome-wide association and Mendelian randomisation study.

Objectives: Studies suggest that body composition can be independently improved through physical activity (PA). We performed a Mendelian randomisation (MR) study to test the incremental benefits of sedentary behaviour and various PA exposures on body composition outcomes as assessed by anthropometric indices, lean body mass (kg), body fat (%) and visceral adipose tissue (VAT) (kg). Methods: Genetic instruments were identified for both self-reported and accelerometer-measured sedentary behaviour and PA. Outcomes included anthropometric and dual-energy X-ray absorptiometry measures of adiposity, extracted from the UK Biobank and the largest available consortia. Multivariable MR (MVMR) included educational attainment as a covariate to address potential confounding. Sensitivity analyses were evaluated for weak instrument bias and pleiotropic effects. Results: We did not identify consistent associations between genetically predicted self-reported and accelerometer-measured sedentary behaviour and body composition outcomes. All analyses for self-reported moderate PA were null for body composition outcomes. Genetically predicted PA at higher intensities was protective against VAT in MR and MVMR analyses of both accelerometer-measured vigorous PA (MVMR ß=-0.15, 95% CI: -0.24 to -0.07, p<0.001) and self-reported participation in strenuous sports or other exercises (MVMR ß=-0.27, 95% CI: -0.52 to -0.01, p=0.034) was robust across several sensitivity analyses. Conclusions: We did not identify evidence of a causal relationship between genetically predicted PA and body composition, with the exception of a putatively protective effect of higher-intensity PA on VAT. Protective effects of PA against VAT may support prior evidence of biological pathways through which PA decreases risk of downstream cardiometabolic diseases.

Cross-Sectional Study of Clinical Predictors of Coccidioidomycosis, Arizona, USA.

Demographic and clinical indicators have been described to support identification of coccidioidomycosis; however, the interplay of these conditions has not been explored in a clinical setting. In 2019, we enrolled 392 participants in a cross-sectional study for suspected coccidioidomycosis in emergency departments and inpatient units in Coccidioides-endemic regions. We aimed to develop a predictive model among participants with suspected coccidioidomycosis. We applied a least absolute shrinkage and selection operator to specific coccidioidomycosis predictors and developed univariable and multivariable logistic regression models. Univariable models identified elevated eosinophil count as a statistically significant predictive feature of coccidioidomycosis in both inpatient and outpatient settings. Our multivariable outpatient model also identified rash (adjusted odds ratio 9.74 [95% CI 1.03-92.24]; p = 0.047) as a predictor. Our results suggest preliminary support for developing a coccidioidomycosis prediction model for use in clinical settings.

Sampling Low Air Pollution Concentrations at a Neighborhood Scale in a Desert U.S. Metropolis with Volatile Weather Patterns.

BACKGROUND: Neighborhood-scale air pollution sampling methods have been used in a range of settings but not in low air pollution airsheds with extreme weather events such as volatile precipitation patterns and extreme summer heat and aridity-all of which will become increasingly common with climate change. The desert U.S. metropolis of Tucson, AZ, has historically low air pollution and a climate marked by volatile weather, presenting a unique opportunity. METHODS: We adapted neighborhood-scale air pollution sampling methods to measure ambient NO2, NOx, and PM2.5 and PM10 in Tucson, AZ. RESULTS: The air pollution concentrations in this location were well below regulatory guidelines and those of other locations using the same methods. While NO2 and NOx were reliably measured, PM2.5 measurements were moderately correlated with those from a collocated reference monitor (r = 0.41, p = 0.13), potentially because of a combination of differences in inlet heights, oversampling of acutely high PM2.5 events, and/or pump operation beyond temperature specifications. CONCLUSION: As the climate changes, sampling methods should be reevaluated for accuracy and precision, especially those that do not operate continuously. This is even more critical for low-pollution airsheds, as studies on low air pollution concentrations will help determine how such ambient exposures relate to health outcomes.

Quantification and Analysis of Micro-Level Activities Data from Children Aged 1-12 Years Old for Use in the Assessments of Exposure to Recycled Tire on Turf and Playgrounds.

BACKGROUND: There are growing health concerns about exposure to toxicants released from recycled tire rubber, which is commonly used in synthetic turf and playground mats. To better estimate children's exposure and risk from recycled tire rubber used in synthetic turf and playground mats, there is a need to collect detailed accurate information on mouthing activity and dermal contact behaviors. The objective of this study was to quantify and analyze micro-level activity time series (MLATS) data from children aged 1-12 years old while playing (non-sport-related games) at turf-like locations and playgrounds. Another objective was to estimate the incidental ingestion rate of rubber crumb among children. METHODS: Hand and mouth contact frequency, hourly duration, and median contact duration with different objects were calculated for children playing on turf (i.e., parks, lawns, and gardens) (n = 56) and for children playing on playground structures (n = 24). Statistically significant differences between males and females as well as children's age groups were evaluated. The daily incidental ingestion rate of rubber crumb was calculated. RESULTS: For children playing on turf, there were significant differences between younger (1-6 y) and older (7-12 y) children for the mouthing median duration with non-dietary objects and all objects. For children playing on playground structures, we found significant mouthing frequency differences between younger (1-6 y) and older children (7-12 y) with all objects, and for mouthing median duration with non-dietary objects. There were no significant differences between males and females playing on artificial turf-like surfaces or playground mats. Our estimated mean incidental ingestion rate was 0.08, 0.07, and 0.08 g rubber crumb/day for children <2, 2-6, and 6-11 years old, respectively. DISCUSSION: our results suggest that age and contact duration should be considered in risk assessment models to evaluate mouthing activities when children are playing on artificial turf surfaces or playground mats.

Household and behavioral determinants of indoor PM2.5 in a rural solid fuel burning Native American community.

Indoor and outdoor concentrations of PM2.5 were measured for 24 h during heating and non-heating seasons in a rural solid fuel burning Native American community. Household building characteristics were collected during the initial home sampling visit using technician walkthrough questionnaires, and behavioral factors were collected through questionnaires by interviewers. To identify seasonal behavioral factors and household characteristics associated with indoor PM2.5 , data were analyzed separately by heating and non-heating seasons using multivariable regression. Concentrations of PM2.5 were significantly higher during the heating season (indoor: 36.2 µg/m3 ; outdoor: 22.1 µg/m3 ) compared with the non-heating season (indoor: 14.6 µg/m3 ; outdoor: 9.3 µg/m3 ). Heating season indoor PM2.5 was strongly associated with heating fuel type, housing type, indoor pests, use of a climate control unit, number of interior doors, and indoor relative humidity. During the non-heating season, different behavioral and household characteristics were associated with indoor PM2.5 concentrations (indoor smoking and/or burning incense, opening doors and windows, area of surrounding environment, building size and height, and outdoor PM2.5 ). Homes heated with coal and/or wood, or a combination of coal and/or wood with electricity and/or natural gas had elevated indoor PM2.5 concentrations that exceeded both the EPA ambient standard (35 µg/m3 ) and the WHO guideline (25 µg/m3 ).

Longitudinal assessment of kidney function in migrant farm workers.

Chronic kidney disease of unknown etiology (CKDu) is an epidemic that affects young agricultural workers in several warm regions of the world. However, there is a lack of monitoring of kidney issues in regions with extremely warm environments such as the Northwest of Mexico, a semi-arid region with a growing agricultural industry, where migrant and seasonal farm workers (MSFWs) travel to work in the fields. The objective of this study was to longitudinally assess kidney functioning of MSFWs in relation to pesticide exposure, heat stress and dehydration in a large-scale farm in Mexico. We enrolled 101 MSFWs, of whom 50 were randomly selected to work in an organic certified area and 51 were randomly selected to work in a conventional area. We also enrolled 50 office workers within the same region as a reference group. We collected urine and blood samples from all workers in addition to demographic, behavioral, and occupational characteristics. The physiological strain index (PSI) was used to estimate workers' heat strain. Sampling was conducted at pre-harvest (March) and late in the harvest (July). Linear mixed models were built with the estimated glomerular filtration rate (eGFR) as the dependent variable. We found a significant decrease in kidney function in MSFWs compared to office workers. By the late harvest, one MSFW developed kidney disease, two MSFWs suffered a kidney injury, and 14 MSFWs were at risk of a kidney injury. We found that the eGFR in MSFWs decreased significantly from pre-harvest (125 ± 13.0 mL/min/1.73 m2) to late harvest (109 ± 13.6 mL/min/1.73 m2) (p < 0.001), while no significant change was observed in office workers. The eGFR was significantly lower in MSFWs who worked in the conventional field (101.2 ± 19.4 mL/min/1.73 m2) vs the organic field (110.9 ± 13.6 mL/min/1.73 m2) (p = 0.002). In our final model, we found that dehydration was associated with the decrease of eGFR. We also found an interaction between heat strain and job category, as a significant decline in eGFR by job category (conventional/organic MSFWs and office workers) was related to an increase in heat strain. This suggests that pesticide exposure needs to be considered in combination with heat stress and dehydration. This study provides valuable information on kidney function in MSFWs, and it shows the importance of early long-term monitoring in farm workers in other regions where CKDu has not been evaluated yet.

Impacts to Diné activities with the San Juan River after the Gold King Mine Spill.

BACKGROUND: On August 5th, 2015, 3 million gallons of acid mine drainage was accidentally discharged from the Gold King Mine near Silverton, Colorado into Cement Creek which is a tributary to the Animas and San Juan Rivers. The government-initiated risk assessment only assessed a recreational scenario (i.e. hiker drinking from the river), failing to recognize the deep connection of the Diné (Navajo) with the San Juan River. METHODS: Utilizing a mixed-methods approach we determined the impacts of the 2015 Gold King Mine Spill (GKMS or Spill) on Diné activities. We developed a questionnaire to collect pre- and post-GKMS Diné activity frequency and duration. Navajo Nation Community Health Representatives administered the questionnaire to 63 Diné adults and 27 children living in three Navajo communities along the River. RESULTS: Through analysis of the focus group transcripts we identified 43 unique activities between the Diné and San Juan River. There were significant reductions in the total number, frequency, and duration of livelihood, dietary, recreational, cultural/spiritual and arts and craft activities. On average, Diné activities with the San Juan River following the GKMS decreased by 56.2%. SIGNIFICANCE: The significant reduction in activities following the GKMS may lead to long-term trauma, impacting the ability of the Diné to pass down teachings to their children affecting future generations to come. The 43 distinct activities between the Diné and the San Juan River highlight the importance for scientists and disaster responders to consider cultural and spiritual impacts when responding to environmental disasters and conducting risk assessments among Indigenous communities.

Evaluating imidacloprid exposure among grape field male workers using biological and environmental assessment tools: An exploratory study.

Imidacloprid is a neonicotinoid insecticide commonly injected through agricultural drip irrigation systems to reduce the population of vine mealybugs (P. ficus) in grape farms. There is a growing concern of potential human health effects of imidacloprid, however, there is limited information on the exposure to imidacloprid in farm workers. Imidacloprid exposure was evaluated in this exploratory study of 20 male migrant grape workers sampled five days after imidacloprid was injected in the irrigation system during winter and summer seasons. We administered a questionnaire on work activities, exposure characteristics, and socio-demographics and collected personal air, hand wipe, and spot urine samples. Heat exposure was also assessed. Spearman's correlation coefficients and Wilcoxon rank-sum tests were utilized to evaluate associations and differences in imidacloprid exposures with socio-demographic, occupational, and environmental characteristics. All participants had less than a high school education and about half identified an Indigenous language as their primary language. Although not detected in air samples, imidacloprid was detected in 85% of the hand wipes (median: 0.26: 0.41 µg/wipe, range: 0.05-7.10 µg/wipe). The majority of participants (75%) had detectable urinary concentrations of imidacloprid (median: 0.11 µg/g creatinine, range: 0.05-3.90 µg/g of creatinine), and nearly all (95%) had detectable urinary concentrations of 5-hydroxy-Imidacloprid (5-OH-IMI), a metabolite of imidacloprid (median: 1.28 µg/g creatinine, range: 0.20-27.89 µg/g creatinine). There was a significant correlation (p < 0.001) between imidacloprid in hand wipes and urinary imidacloprid and 5-OH-IMI (rs: 0.67 for imidacloprid and 0.80 for 5-OH-IMI). Hand temperature was significantly and positively correlated (p < 0.05) with imidacloprid concentration on hand wipes (rs: 0.70), and urinary biomarkers (rs: 0.68 for imidacloprid, and 0.60 for 5-OH-IMI) suggesting that working in high temperatures may influence the exposure and absorption of imidacloprid. Thus, research on farm workers would benefit in the future by evaluating imidacloprid exposure in relation to heat stress and other occupational factors.

Evaluating a transfer gradient assumption in a fomite-mediated microbial transmission model using an experimental and Bayesian approach.

Current microbial exposure models assume that microbial exchange follows a concentration gradient during hand-to-surface contacts. Our objectives were to evaluate this assumption using transfer efficiency experiments and to evaluate a model's ability to explain concentration changes using approximate Bayesian computation (ABC) on these experimental data. Experiments were conducted with two phages (MS2, ΦX174) simultaneously to study bidirectional transfer. Concentrations on the fingertip and surface were quantified before and after fingertip-to-surface contacts. Prior distributions for surface and fingertip swabbing efficiencies and transfer efficiency were used to estimate concentrations on the fingertip and surface post contact. To inform posterior distributions, Euclidean distances were calculated for predicted detectable concentrations (log10 PFU cm-2) on the fingertip and surface post contact in comparison with experimental values. To demonstrate the usefulness of posterior distributions in calibrated model applications, posterior transfer efficiencies were used to estimate rotavirus infection risks for a fingertip-to-surface and subsequent fingertip-to-mouth contact. Experimental findings supported the transfer gradient assumption. Through ABC, the model explained concentration changes more consistently when concentrations on the fingertip and surface were similar. Future studies evaluating microbial transfer should consider accounting for differing fingertip-to-surface and surface-to-fingertip transfer efficiencies and extend this work for other microbial types.

Imputation methods for addressing missing data in short-term monitoring of air pollutants.

Monitoring of environmental contaminants is a critical part of exposure sciences research and public health practice. Missing data are often encountered when performing short-term monitoring (<24 h) of air pollutants with real-time monitors, especially in resource-limited areas. Approaches for handling consecutive periods of missing and incomplete data in this context remain unclear. Our aim is to evaluate existing imputation methods for handling missing data for real-time monitors operating for short durations. In a current field-study, realtime PM2.5 monitors were placed outside of 20 households and ran for 24-hours. Missing data was simulated in these households at four consecutive periods of missingness (20%, 40%, 60%, 80%). Univariate (Mean, Median, Last Observation Carried Forward, Kalman Filter, Random, Markov) and multivariate time-series (Predictive Mean Matching, Row Mean Method) methods were used to impute missing concentrations, and performance was evaluated using five error metrics (Absolute Bias, Percent Absolute Error in Means, R2 Coefficient of Determination, Root Mean Square Error, Mean Absolute Error). Univariate methods of Markov, random, and mean imputations were the best performing methods that yielded 24-hour mean concentrations with the lowest error and highest R2 values across all levels of missingness. When evaluating error metrics minute-by-minute, Kalman filters, median, and Markov methods performed well at low levels of missingness (20-40%). However, at higher levels of missingness (60-80%), Markov, random, median, and mean imputation performed best on average. Multivariate methods were the worst performing imputation methods across all levels of missingness. Imputation using univariate methods may provide a reasonable solution to addressing missing data for short-term monitoring of air pollutants, especially in resource-limited areas. Further efforts are needed to evaluate imputation methods that are generalizable across a diverse range of study environments.

An Occupational Heat Stress and Hydration Assessment of Agricultural Workers in North Mexico.

Expanding agribusiness in Northern Mexico has increased demand for workers from Southern Mexico, with hundreds of thousands migrating for work annually. Extreme temperatures, physical labor, and low fluid consumption place workers at risk for heat strain and dehydration, commonly underreported hazards in the agricultural industry. The objectives of this pilot study were to assess heat exposure and hydration status of a population of migratory agricultural workers in Northern Mexico throughout the grape harvest season. In addition to demographic information, environmental conditions, hydration status, and core body temperatures were collected. The majority listed Chiapas as their home state, nearly half spoke an Indigenous language, and none had completed high school. The wet-bulb globe temperature was significantly higher during the harvest and post-harvest seasons compared to the pre-harvest season. Across the different seasons, the majority were dehydrated post-shift, and mean core body temperature of workers was not significantly different. This project highlights the need for targeted interventions to improve hydration and prevent heat stress in this region. As the number of warm days is expected to rise each year worldwide, it will be increasingly important to engage in practices to protect vulnerable populations, such as migratory agriculture workers.

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.

Estimating the effect of hand hygiene compliance and surface cleaning timing on infection risk reductions with a mathematical modeling approach.

BACKGROUND: Quantitative tools are needed to relate infection control interventions to infection risk reductions. METHODS: A model for predicting virus concentrations on hands was used to predict rotavirus, rhinovirus, and influenza A virus doses. Variability in behaviors, transfer efficiencies for various contact types, and surface areas of contact were included. Dose-response curves were used to relate estimated doses to infection risks. Percent reductions from baseline in average rotavirus, rhinovirus, and influenza A virus dose and infection risk were calculated for interventions. RESULTS: Baseline average infection risks for rotavirus, rhinovirus, and influenza A virus were 0.43, 0.20, and 5.51 × 10-6, respectively. One and 2 cleaning events decreased average viral infection risks by 6.98%-17.06% and 13.95%-34.66%, respectively. A 15% increase in hand compliance decreased average infection risks by 6.98%-20.51%. A 15% increase in hand hygiene compliance paired with 2 cleaning events decreased average infection risks by 20.93%-47.55%. DISCUSSION: This study demonstrates the infection risk benefits of combined interventions. CONCLUSIONS: Models such as the one in this study could be used to optimize timing and frequency of cleaning events and to create hand hygiene compliance goals to achieve infection risk targets.

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.

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.

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.

Quantifying human-environment interactions using videography in the context of infectious disease transmission.

Quantitative data on human-environment interactions are needed to fully understand infectious disease transmission processes and conduct accurate risk assessments. Interaction events occur during an individual's movement through, and contact with, the environment, and can be quantified using diverse methodologies. Methods that utilize videography, coupled with specialized software, can provide a permanent record of events, collect detailed interactions in high resolution, be reviewed for accuracy, capture events difficult to observe in real-time, and gather multiple concurrent phenomena. In the accompanying video, the use of specialized software to capture humanenvironment interactions for human exposure and disease transmission is highlighted. Use of videography, combined with specialized software, allows for the collection of accurate quantitative representations of human-environment interactions in high resolution. Two specialized programs include the Virtual Timing Device for the Personal Computer, which collects sequential microlevel activity time series of contact events and interactions, and LiveTrak, which is optimized to facilitate annotation of events in real-time. Opportunities to annotate behaviors at high resolution using these tools are promising, permitting detailed records that can be summarized to gain information on infectious disease transmission and incorporated into more complex models of human exposure and risk.

High time-resolution simulation of E. coli on hands reveals large variation in microbial exposures amongst Vietnamese farmers using human excreta for agriculture.

Infectious disease transmission is frequently mediated by the environment, where people's movements through and interactions with the environment dictate risks of infection and/or illness. Capturing these interactions, and quantifying their importance, offers important insights into effective interventions. In this study, we capture high time-resolution activity data for twenty-five Vietnamese farmers during collection and land application of human excreta for agriculture. Although human excreta use improves productivity, the use increases risks of enteric infections for both farmers and end users. In our study, the activity data are integrated with environmental microbial sampling data into a stochastic-mechanistic simulation of E. coli contamination on hands and E. coli ingested. Results from the study include frequent and variable contact rates for farmers' hands (from 34 to 1344 objects contacted per hour per hand), including highly variable hand-to-mouth contact rates (from 0 to 9 contacts per hour per hand). The frequency of hand-to-mouth contacts was substantially lower than the widely-used frequency previously reported for U.S. Office Workers. Environmental microbial contamination data highlighted ubiquitous E. coli contamination in the environment, including excreta, hands, toilet pit, handheld tools, soils, surfaces, and water. Results from the simulation suggest dynamic changes in E. coli contamination on hands, and wide variation in hand contamination and E. coli ingested amongst the farmers studied. Sensitivity analysis suggests that E. coli contamination on hands and ingested doses are most influenced by contamination of handheld tools, excreta, and the toilet pit as well as by frequency of hand-to-mouth contacts. The study findings are especially relevant given the context: no farmers reported adequate storage time of human excreta, and personal protective mask availability did not prevent hand-to-mouth contacts. Integrating high time-resolution activity data into exposure assessments highlights variation in exposures amongst farmers, and offers greater insight into effective interventions and their potential impacts.