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Monitoring Drinking Water Quality in Nationally Representative Household Surveys in Low- and Middle-Income Countries:
Robert Bain1, Richard Johnston2, Shane Khan1
1Division of Data, Analytics, Planning and Monitoring, United Nations Children's Fund, New York, New York, USA.
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. https://doi.org/10.1289/EHP8459.
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