No abstract

Although small, rural water supplies may present elevated microbial risks to consumers in some settings, characterizing exposures through representative point-of-consumption sampling is logistically challenging. In order to evaluate the usefulness of consumer self-reported data in predicting measured water quality and risk factors for contamination, we compared matched consumer interview data with point-of-survey, household water quality and pressure data for 910 households served by 14 small water systems in rural Alabama. Participating households completed one survey that included detailed feedback on two key areas of water service conditions: delivery conditions (intermittent service and low water pressure) and general aesthetic characteristics (taste, odor and color), providing five condition values. Microbial water samples were taken at the point-of-use (from kitchen faucets) and as-delivered from the distribution network (from outside flame-sterilized taps, if available), where pressure was also measured. Water samples were analyzed for free and total chlorine, pH, turbidity, and presence of total coliforms and Escherichia coli. Of the 910 households surveyed, 35% of participants reported experiencing low water pressure, 15% reported intermittent service, and almost 20% reported aesthetic problems (taste, odor or color). Consumer-reported low pressure was associated with lower gauge-measured pressure at taps. While total coliforms (TC) were detected in 17% of outside tap samples and 12% of samples from kitchen faucets, no reported water service conditions or aesthetic characteristics were associated with presence of TC. We conclude that consumer-reported data were of limited utility in predicting potential microbial risks associated with small water supplies in this setting, although consumer feedback on low pressure—a risk factor for contamination—may be relatively reliable and therefore useful in future monitoring efforts.

BackgroundCommunity water supplies in underserved areas of the United States may be associated with increased microbiological contamination and risk of gastrointestinal disease. Microbial and health risks affecting such systems have not been systematically characterized outside outbreak investigations. The objective of the study was to evaluate associations between self-reported gastrointestinal illnesses (GII) and household-level water supply characteristics.MethodsWe conducted a cross-sectional study of water quality, water supply characteristics, and GII in 906 households served by 14 small and medium-sized community water supplies in Alabama’s underserved Black Belt region.ResultsWe identified associations between respondent-reported water supply interruption and any symptoms of GII (adjusted odds ratio (aOR): 3.01, 95% confidence interval (CI) = 1.65–5.49), as well as low water pressure and any symptoms of GII (aOR: 4.51, 95% CI = 2.55–7.97). We also identified associations between measured water quality such as lack of total chlorine and any symptoms of GII (aOR: 5.73, 95% CI = 1.09–30.1), and detection of E. coli in water samples and increased reports of vomiting (aOR: 5.01, 95% CI = 1.62–15.52) or diarrhea (aOR: 7.75, 95% CI = 2.06–29.15).ConclusionsIncreased self-reported GII was associated with key water system characteristics as measured at the point of sampling in a cross-sectional study of small and medium water systems in rural Alabama in 2012 suggesting that these water supplies can contribute to endemic gastro-intestinal disease risks. Future studies should focus on further characterizing and managing microbial risks in systems facing similar challenges.

The purpose of this research was to examine how learning space design and implementation of an active learning pedagogy based on the 5E Instructional Model influence university faculty’s teaching practices and students’ engagement. Faculty Fellows were recruited from a public, medium-sized university in the United States to teach courses, typically taught in a traditional classroom setting, in a new Active Learning Center (ALC) classroom.  The classroom was funded by a Steelcase® Education Active Learning Center Grant that provided innovative and dynamic classroom furnishings and technology that allowed mobility and flexibility for both instructors and students.  Quantitative and qualitative data were collected concurrently in this study.  The quantitative analysis results indicated that the ALC learning experience significantly improved students’ class participation and cognitive attentiveness, but had no effect on improving their meaningful processing of new information.  The qualitative analysis results, while providing new insights into the quantitative findings, revealed the faculty fellows’ changes and weaknesses in teaching practices and the mechanism of the ALC in supporting active learning. Implications of these findings and directions for future research are discussed.

Temporal and spatial trends for key water quality measures were evaluated in 12 rural drinking water systems within a three‐county study area in Alabama. The water systems varied in size from very small (25–500 people served) to large (10,001–100,000 people served). Large‐volume water samples were collected from 10 diverse locations within each system on three sampling dates. Sampling locations were assigned to one of five location categories: well, post‐treatment, post‐storage, in‐line, and end‐line. Water quality parameters (i.e., free and total chlorine, pH, turbidity, pressure, heterotrophic plate count) and microbial indicators (i.e., total coliforms, Escherichia coli, Enterococci, male‐specific coliphages) were analyzed for spatial and temporal trends. Analysis of the samples from these rural water systems over nine months did not show a statistically significant association between distribution system sampling locations and water quality measures or microbial indicators. Temporal trends were consistent across sampling locations and were stronger than trends in spatial variability. However, substantial temporal heterogeneity of water quality measures was noted, potentially the result of seasonality, temperature fluctuations, and distribution system operation and maintenance practices. The study results indicate that system‐level sampling efforts intended to inform microbial risk assessments must account for variability in indicators of risk over time.