A framework for monitoring the safety of water services: from measurements to security

Charles, Katrina; Nowicki, Saskia; Bartram, Jamie

doi:10.1038/s41545-020-00083-1

Cited by 38 publications

(31 citation statements)

References 39 publications

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“…coli do not correlate with concentrations of pathogens: the transport and survival patterns of E . coli vary considerably from those of faecal pathogens, particularly viruses and protozoa which tend to be more robust [ 25 ]–so the likelihood that water has been contaminated with faecal matter must be prioritised over E . coli sampling results.…”

Section: Resultsmentioning

confidence: 99%

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The utility of Escherichia coli as a contamination indicator for rural drinking water: Evidence from whole genome sequencing

et al. 2021

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Across the water sector, Escherichia coli is the preferred microbial water quality indicator and current guidance upholds that it indicates recent faecal contamination. This has been challenged, however, by research demonstrating growth of E. coli in the environment. In this study, we used whole genome sequencing to investigate the links between E. coli and recent faecal contamination in drinking water. We sequenced 103 E. coli isolates sampled from 9 water supplies in rural Kitui County, Kenya, including points of collection (n = 14) and use (n = 30). Biomarkers for definitive source tracking remain elusive, so we analysed the phylogenetic grouping, multi-locus sequence types (MLSTs), allelic diversity, and virulence and antimicrobial resistance (AMR) genes of the isolates for insight into their likely source. Phylogroup B1, which is generally better adapted to water environments, is dominant in our samples (n = 69) and allelic diversity differences (z = 2.12, p = 0.03) suggest that naturalised populations may be particularly relevant at collection points with lower E. coli concentrations (<50 / 100mL). The strains that are more likely to have originated from human and/or recent faecal contamination (n = 50), were found at poorly protected collection points (4 sites) or at points of use (12 sites). We discuss the difficulty of interpreting health risk from E. coli grab samples, especially at household level, and our findings support the use of E. coli risk categories and encourage monitoring that accounts for sanitary conditions and temporal variability.

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Section: Resultsmentioning

confidence: 99%

“…It can be difficult to interpret E . coli sampling results, particularly when water system safety controls have not been validated and when sampling is infrequent [ 25 ]. Thus, the severity and immediacy of the potential threat indicated by an E .…”

Section: Introductionmentioning

confidence: 99%

The utility of Escherichia coli as a contamination indicator for rural drinking water: Evidence from whole genome sequencing

et al. 2021

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Section: Introductionmentioning

confidence: 99%

“…Globally, the highest priority parameters for health are fecal contamination, arsenic, and fluoride. The presence of E. coli is considered a reliable indicator of fecal contamination, but brief events may escape detection even with regular testing, so its absence indicates low risk but does not guarantee safety (Charles et al 2020). For the purposes of SDG monitoring, "free from contamination" implies that drinking water does not contain E. coli or thermotolerant coliforms (in a 100-mL sample) or elevated levels of arsenic (>10-lg=L), or fluoride (>1:5-mg=L).…”

Section: Introductionmentioning

confidence: 99%

Monitoring Drinking Water Quality in Nationally Representative Household Surveys in Low- and Middle-Income Countries: Cross-Sectional Analysis of 27 Multiple Indicator Cluster Surveys 2014–2020

Bain

Johnston

Khan

et al. 2021

Environ Health Perspect

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Background: The 2030 Sustainable Development Goals (SDGs) set an ambitious new benchmark for safely managed drinking water services (SMDWs), but many countries lack national data on the availability and quality of drinking water. Objectives: We quantified the availability and microbiological quality of drinking water, monitored SMDWs, and examined risk factors for Escherichia coli ( E. coli ) contamination in 27 low-and middle-income countries (LMICs). Methods: A new water quality module for household surveys was implemented in 27 Multiple Indicator Cluster Surveys. Teams used portable equipment to measure E. coli at the point of collection (PoC, ) and at the point of use (PoU, ) and asked respondents about the availability and accessibility of drinking water. Households were classified as having SMDW services if they used an improved water source that was free of E. coli contamination at PoC, accessible on premises, and available when needed. Compliance with individual SMDW criteria was also assessed. Modified Poisson regression was used to explore household and community risk factors for E. coli contamination. Results: E. coli was commonly detected at the PoC (range 16–90%) and was more likely at the PoU (range 19–99%). On average, 84% of households used an improved drinking water source, and 31% met all of the SMDW criteria. E. coli contamination was the primary reason SMDW criteria were not met (15 of 27 countries). The prevalence of E. coli in PoC samples was lower among households using improved water sources [ ; 95% confidence interval (CI): 0.64, 0.85] but not for households with water accessible on premises ( ; 95% CI: 0.94, 1.05) or available when needed ( ; 95% CI: 0.88, 1.02). E. coli contamination of PoU samples was less common for households in the richest vs. poorest wealth quintile ( ; 95% CI: 0.55, 0.88) and in communities with high ( ) improved sanitation coverage ( ; 95% CI: 0.90, 0.97). Livestock ownership ( ; 95% CI: 1.04, 1.13), rural vs. urban residence ( ; 95% CI: 1.04, 1.16), and wet vs. dry season sampling ( ; 95% CI: 1.01, 1.15) were positively associated with contamination at the PoU. Discussion: Cross-sectional water quality data can be collected in household surveys and can be used to assess inequalities in service levels, to track the SDG indicator of SMDWs, and to examine risk factors for contamination. There is an urgent need for better risk management to reduce widespread exposure to fecal contamination through drinking water services in LMICs. ...

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“…For PoU contamination, risk factors included animal ownership, poor sanitation, and rural locales. Although there are limitations to the use of grab samples for E. coli ( Charles et al. 2020 ), the authors have undertaken detailed sensitivity analyses to provide a high degree of confidence in these associations.…”

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confidence: 99%

Invited Perspective: Beyond National Water Quality Surveys: Improving Water Quality Surveillance to Achieve Safe Drinking Water for All (Sustainable Development Goal 6.1)

Charles

Greggio

2021

Environ Health Perspect

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A framework for monitoring the safety of water services: from measurements to security

Cited by 38 publications

References 39 publications

The utility of Escherichia coli as a contamination indicator for rural drinking water: Evidence from whole genome sequencing

The utility of Escherichia coli as a contamination indicator for rural drinking water: Evidence from whole genome sequencing

Monitoring Drinking Water Quality in Nationally Representative Household Surveys in Low- and Middle-Income Countries: Cross-Sectional Analysis of 27 Multiple Indicator Cluster Surveys 2014–2020

Invited Perspective: Beyond National Water Quality Surveys: Improving Water Quality Surveillance to Achieve Safe Drinking Water for All (Sustainable Development Goal 6.1)

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