Irrigating fresh produce with contaminated water contributes to the burden of foodborne illness. Identifying fecal contamination of irrigation waters and characterizing fecal sources and associated environmental factors can help inform fresh produce safety and health hazard management. Using two previously collected data sets, we developed and evaluated the performance of logistic regression and conditional random forest models for predicting general and human-specific fecal contamination of ponds in southwest Georgia used for fresh produce irrigation. Generic Escherichia coli served as a general fecal indicator, and human-associated Bacteroides (HF183), crAssphage, and F+ coliphage genogroup II were used as indicators of human fecal contamination. Increased rainfall in the previous 7 days and the presence of a building within 152 m (a proxy for proximity to septic systems) were associated with increased odds of human fecal contamination in the training data set. However, the models did not accurately predict the presence of human-associated fecal indicators in a second data set collected from nearby irrigation ponds in different years. Predictive statistical models should be used with caution to assess produce irrigation water quality as models may not reliably predict fecal contamination at other locations and times, even within the same growing region. © 2024 American Chemical Society.

We performed chlorine inactivation experiments for Elizabethkingia anophelis and E. meningoseptica bacterial strains from clinical and environmental sources. Free chlorine concentration × contact time values <0.04 mg·min/L achieved 99.9% inactivation of Elizabethkingia species, indicating chlorine susceptibility. Measures to control biofilm producing pathogens in plumbing are needed to prevent Elizabethkingia bacterial infections.

Sanitary surveys are used in low- and middle-income countries to assess water, sanitation, and hygiene conditions, but have rarely been compared with direct measures of environmental fecal contamination. We conducted a cross-sectional assessment of sanitary conditions and E. coli counts in soils and on surfaces of compounds (household clusters) in low-income neighborhoods of Maputo, Mozambique. We adapted the World Bank's Urban Sanitation Status Index to implement a sanitary survey tool specifically for compounds: a Localized Sanitation Status Index (LSSI) ranging from zero (poor sanitary conditions) to one (better sanitary conditions) calculated from 20 variables that characterized local sanitary conditions. We measured the variation in the LSSI with E. coli counts in soil (nine locations/compound) and surface swabs (seven locations/compound) in 80 compounds to assess reliability. Multivariable regression indicated that a ten-percentage point increase in LSSI was associated with 0.05 (95% CI: 0.00, 0.11) log10 fewer E. coli/dry gram in courtyard soil. Overall, the LSSI may be associated with fecal contamination in compound soil; however, the differences detected may not be meaningful in terms of public health hazards.