Abstract
Current guidelines for testing drinking water quality recommend that the sampling rate, which is the number of samples tested for fecal indicator bacteria (FIB) per year, increases as the population served by the drinking water system increases. However, in low-resource settings, prevalence of contamination tends to be higher, potentially requiring higher sampling rates and different statistical methods not addressed by current sampling recommendations. We analyzed 27,930 tests for FIB collected from 351 piped water systems in eight countries in sub-Saharan Africa to assess current sampling rates, observed contamination prevalences, and the ability of monitoring agencies to complete two common objectives of sampling programs: determine regulatory compliance and detect a change over time. Although FIB were never detected in samples from 75% of piped water systems, only 14% were sampled often enough to conclude with 90% confidence that the true contamination prevalence met an example guideline (≤5% chance of any sample positive for FIB). Similarly, after observing a ten percentage point increase in contaminated samples, 43% of PWS would still require more than a year before their monitoring agency could be confident that contamination had actually increased. We conclude that current sampling practices in these settings may provide insufficient information because they collect too few samples. We also conclude that current guidelines could be improved by specifying how to increase sampling after contamination has been detected. Our results suggest that future recommendations should explicitly consider the regulatory limit and desired confidence in results, and adapt when FIB is detected.
Highlights
While more than 2.6 billion people have gained access to an improved water source over the last 25 years, recent evidence suggests that many of these improved sources do not provide drinking water that is safe (Onda et al, 2012; Bain et al, 2014a; Shaheed et al, 2014; WHO and UNICEF, 2015)
Using the observed contamination prevalence, for each Piped water systems (PWS), we evaluated the range in which the true contamination prevalence was expected to lie below 90% of the time and the range in which the true contamination prevalence was expected lie above 90% of the time, implemented using the Clopper-Pearson algorithm in R (R Core Team 2016, version 3.2.1)
We demonstrated how sampling rates, contamination prevalences, desired confidence, and evaluation criterion can affect interpretation of water quality testing results
Summary
While more than 2.6 billion people have gained access to an improved water source over the last 25 years, recent evidence suggests that many of these improved sources do not provide drinking water that is safe (Onda et al, 2012; Bain et al, 2014a; Shaheed et al, 2014; WHO and UNICEF, 2015). Water management agencies around the world sample microbial drinking water quality to assess whether systems provide water that minimizes risks to health (Rahman et al, 2011; Peletz et al, 2016). D.D.J. Taylor et al / Water Research 134 (2018) 115e125 resource settings where testing activities are constrained and the prevalence of contamination tends to be higher. Previous research on improving the efficacy of sampling has focused on piped supplies in high-income countries that have generous information about their network (e.g. historical data, pipe maps), reliably continuous water supply, and infrequent contamination (Speight et al, 2004; Grayman et al, 2007; van Lieverloo et al, 2007; Horowitz, 2013; Rosen et al, 2009).
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