Linking fecal indicator bacteria concentrations in large mixed-use watersheds back to diffuse human sources, such as septic systems, has met limited success. In this study, 64 rivers that drain 84% of Michigan's Lower Peninsula were sampled under baseflow conditions for Escherichia coli, Bacteroides thetaiotaomicron (a human source-tracking marker), landscape characteristics, and geochemical and hydrologic variables. E. coli and B. thetaiotaomicron were routinely detected in sampled rivers and an E. coli reference level was defined (1.4 log 10 most probable number·100 mL −1). Using classification and regression tree analysis and demographic estimates of wastewater treatments per watershed, septic systems seem to be the primary driver of fecal bacteria levels. In particular, watersheds with more than 1,621 septic systems exhibited significantly higher concentrations of B. thetaiotaomicron. This information is vital for evaluating water quality and health implications, determining the impacts of septic systems on watersheds, and improving management decisions for locating, constructing, and maintaining on-site wastewater treatment systems.Escherichia coli | Bacteroides thetaiotaomicron | baseflow | reference conditions | septic system W ater quality degradation influenced by diffuse sources at large watershed scales has been difficult to describe. Human modifications of natural landscapes can permanently alter hydrologic cycles and affect water quality (1, 2). Deforestation (3) and increased impervious surface area (4) have been linked with decreased infiltration and thus increased surface runoff. Overland flows concentrate pollutants and rapidly transport them down gradient where they eventually enter surface water systems and affect water quality (5, 6). A number of models have been developed to calculate overland and surface water flows (7,8) and nutrient/chemical transport (9), but few studies have focused on microbial movement from land to water, particularly nontraditional fecal indicator bacteria that can be used to track human sources of pollution.Microbial contamination poses one of the greatest health risks to swimming areas, drinking water intakes, and fishing/shellfish harvesting zones where human exposures are highest (10-12). These highly visible areas often receive more attention than sources of contamination because identifying the origin of pollution in complex watersheds requires costly comprehensive investigation of environmental and hydrologic conditions across temporal and spatial scales (13). Grayson et al. (14) suggest using a "snapshot" approach that captures water quality characteristics at a single point in time across broad areas to provide information frequently missed during routine monitoring. Compared with long-term comprehensive investigations, the snapshot approach reduces the number of samples, cost, and personnel required to examine pollution sources.Escherichia coli concentrations are commonly used to describe the relative human health risk during water quality monitoring in li...

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 2.3 genome copies/L limit of detection (LOD). For each data set, 5 methods for handling left-censored data were applied: 1) Substitution with LOD/√2, 2) Lognormal maximum likelihood estimation (MLE) to estimate mean and standard deviation, 3) Kaplan-Meier estimates (KM), 4) Imputation method using MLE to estimate distribution parameters (MI Method 1), 5) Imputing 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 lowest dose and infection risk RMSE and bias ranges for most censoring degrees, predicting infection risks at most 1.17 x 10 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.This study evaluates methods for handling low (10%) to severe (90%) left-censored data 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.

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.

Drinking water quality in the United States (US) is among the safest in the world. However, many residents, often in rural areas, rely on unregulated private wells or small municipal utilities for water needs. These utilities may violate the Safe Drinking Water Act contaminant guidelines, often because they lack the required financial resources. Residents may use alternative water sources or install a home water treatment system. Despite increased home water treatment adoption, few studies have examined their use and effectiveness in the US. Our study addresses this knowledge gap by examining home water treatment in a rural Arizona community. Water samples were analyzed for metal(loid)s, and home treatment and demographic data were recorded in 31 homes. Approximately 42% of homes treated their water. Independent of source water quality, residents with higher income (OR = 1.25; 95%CI (1.00 – 1.64)) and education levels (OR = 1.49; 95%CI (1.12 – 2.12)) were more likely to treat their water. Some contaminant concentrations were effectively reduced with treatment, while some were not. We conclude that increased educational outreach on contaminant testing and treatment, especially to rural areas with endemic water contamination, would result in a greater public health impact while reducing rural health disparities.

This study investigated healthcare workers’ perceptions of hand hygiene practices by comparing personal reports, as assessed by questionnaires, to direct observations of the workers’ hand hygiene practices. The study employed a cross-sectional research design. Observations were made using a 16-item checklist, based on three sources: Centers for Disease Control and Prevention (CDC), World Health Organization (WHO), and Boyce and Pittet’s guidelines of hand hygiene. The checklist was used for both direct-observation and self-reported data collection purposes. Pearson correlation and Multivariate Analysis of Covariance (MANCOVA) were utilized to statistically determine the relationship between healthcare workers’ reports of hand hygiene practices and observed hand hygiene behaviors. The study was conducted in the outpatient examination rooms and emergency departments of three types of hospitals in the Eastern region of Saudi Arabia where Middle East respiratory syndrome coronavirus (MERS-CoV) is endemic and is observed in routine cases and outbreaks. The total sample size included 87 physicians and nurses recruited while on duty during the scheduled observation periods, with each healthcare worker being observed during individual medical examinations with at least three patients. No statistically significant correlations between the healthcare workers’ perceptions of hand hygiene practices and healthcare workers’ actual behaviors were evident. Based on the self-report questionnaires, significant differences were found between physicians’ and nurses’ hand hygiene practices reports. Healthcare workers clearly understand the importance of careful hand hygiene practices, but based on researchers’ observations, the medical personnel failed to properly implement protocol-driven hand hygiene applications. However, the significant differences between physicians’ and nurses’ self-reports suggest further inquiry is needed to fully explore these discrepancies.