The healthy workplace project: Reduced viral exposure in an office setting

Reynolds, Kelly A.; Beamer, Paloma I.; Plotkin, Kevin R.; Sifuentes, Laura Y.; Koenig, David W.; Gerba, Charles P.

doi:10.1080/19338244.2015.1058234

Cited by 31 publications

(28 citation statements)

References 29 publications

(22 reference statements)

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“…To determine the best collection, processing, and results representation approach, three sampling approaches Additionally, coliphage MS2 (ATCC 15597-B1; American Type Culture Collection, Manassas, VA) was used to seed the same three canals (one experiment per canal) to serve as a microbial tracer organism. This non-pathogenic virus has been used as a surrogate for many pathogens in various environments (Reynolds et al, 2015;Sinclair et al, 2009;Valdez et al, 2015). Coliphage samples were serially diluted with sterile phosphate-buffered saline (PBS; pH 7.4; Sigma-Aldrich, St. Louis, MO) to create 10, 1, -1 , 10 -2 , 10 -3 , and -4 ml duplicate subsamples.…”

Section: Field Analysis and Sample Collectionmentioning

confidence: 99%

Optimal strategies for monitoring irrigation water quality

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Sexton

et al. 2018

Agricultural Water Management

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The quality of irrigation water drawn from surface water sources varies greatly. This is particularly true for waters that are subject to intermittent contamination events such as runoff from rainfall or direct entry of livestock upstream of use. Such pollution in irrigation systems increases the risk of food crop contamination and require adoption of best monitoring practices. Therefore, this study aimed to define optimal strategies for monitoring irrigation water quality. Following the analysis of 1,357 irrigation water samples for Escherichia coli, total coliforms, and physical and chemical parameters, the following key irrigation water collection approaches are suggested: 1) explore up to 950 m upstream to ensure no major contamination or outfalls exists; 2) collect samples before 12:00 PM local time; 3) collect samples at the surface of the water at any point across the canal where safe access is available; and 4) composite five samples and perform a single E. coli assay. These recommendations comprehensively consider the results as well as sampling costs, personnel effort, and current scientific knowledge of water quality characterization. These strategies will help to better characterize risks from microbial pathogen contamination in irrigation waters in the Southwest United States and aid in risk reduction practices for agricultural water use in regions with similar water quality, climate, and canal construction. HIGHLIGHTS • Microbial testing practices must be based on irrigation water-specific research. • Assaying sample composites is the most cost-effective best representation of microbial content. • Contamination events have 2-log10 reductions 950 m downstream. • Microbial concentrations are highest before noon. • Microbial concentrations are homogenous throughout the canal water column.

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Section: Field Analysis and Sample Collectionmentioning

confidence: 99%

Optimal strategies for monitoring irrigation water quality

Lothrop

Bright

Sexton

et al. 2018

Agricultural Water Management

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“…Transfer efficiency distributions used in other viral exposure models were used in this study (Julian et al., ; Lopez et al., ; Reynolds et al., ; Rusin, Maxwell, & Gerba, ). Fractions of hand in contact with orifices during orifice contacts were constructed using distributions of surface area of hand in contact with orifices divided by a value sampled from a distribution of total hand surface area.…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2019

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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.

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“…12 As more care is provided in outpatient facilities, it is increasingly important to understand the potential for disease transmission and study the practices that ensure infection prevention in this setting. Although disinfection interventions have been quantitatively evaluated in hospitals 13,14 and workplaces, 15,16 studies have not been published measuring their effect in health care facilities beyond hospitals, despite evidence of environmental contamination in outpatient care sites. 17,18 Understanding the dynamics of transmission and reservoirs of contamination in an outpatient setting can help inform effective infection control guidelines and practices.…”

Section: Introductionmentioning

confidence: 99%