Fecal Indicator Bacteria Data to Characterize Drinking Water Quality in Low-Resource Settings: Summary of Current Practices and Recommendations for Improving Validity

Abstract

Fecal indicator bacteria (FIB) values are widely used to assess microbial contamination in drinking water and to advance the modeling of infectious disease risks. The membrane filtration (MF) testing technique for FIB is widely adapted for use in low- and middle-income countries (LMICs). We conducted a systematic literature review on the use of MF-based FIB data in LMICs and summarized statistical methods from 172 articles. We then applied the commonly used statistical methods from the review on publicly available datasets to illustrate how data analysis methods affect FIB results and interpretation. Our findings indicate that standard methods for processing samples are not widely reported, the selection of statistical tests is rarely justified, and, depending on the application, statistical methods can change risk perception and present misleading results. These results raise concerns about the validity of FIB data collection, analysis, and presentation in LMICs. To improve evidence quality, we propose a FIB data reporting checklist to use as a reminder for researchers and practitioners.

Water, Sanitation and Cholera in sub-Saharan Africa

“…Additionally, these estimates represented an average over time, which may have attenuated the apparent associations between measures. Previous work has suggested that incomplete and erroneous reporting in water quality can produce misleading results 20 , which would then propagate to modeled estimates. Finally, our analysis measures cholera risk only through mean annual incidence, but metrics like outbreak size and frequency 21 may better capture the relationship between water and sanitation access and cholera transmission efficiency.…”

Water, Sanitation, and Cholera in Sub-Saharan Africa

“…Additionally, these estimates represented an average over time, which may have attenuated the apparent associations between measures. Previous work has suggested that incomplete and erroneous reporting in water quality can produce misleading results, 26 which would then propagate to modeled estimates. Our results are based on suspected cholera incidence estimates from 2010 to 2016; as newer and perhaps better data become available, updates to these analyses should be performed.…”

“…Additionally, these estimates represented an average over time, which may have attenuated the apparent associations between measures. Previous work has suggested that incomplete and erroneous reporting in water quality can produce misleading results, 26 which would then propagate to modeled estimates.…”