Antimicrobial resistance (AMR) poses a global health threat, causing millions of deaths annually, with expectations of increased impact in the future. Wastewater surveillance offers a cost-effective, non-invasive tool to understand AMR carriage trends within a population. We monitored extended-spectrum β-lactamase producing Escherichia coli (ESBL-E. coli) weekly in influent wastewater from six wastewater treatment plants (WWTPs) in Switzerland (November 2021 to November 2022) to investigate spatio-temporal variations, explore correlations with environmental variables, develop a predictive model for ESBL-E. coli carriage in the community, and detect the most prevalent ESBL-genes. We cultured total and ESBL-E. coli in 300 wastewater samples to quantify daily loads and percentage of ESBL-E. coli. Additionally, we screened 234 ESBL-E. coli isolates using molecular methods for the presence of 18 ESBL-gene families. We found a population-weighted mean percentage of ESBL-E. coli of 1.9% (95% confidence interval: 1.8–2%) across all sites and weeks, which can inform ESBL-E. coli carriage. Concentrations of ESBL-E. coli varied across WWTPs and time, with higher values observed in WWTPs serving larger populations. Recent precipitations (previous 24/96 h) showed no significant association with ESBL-E. coli, while temperature occasionally had a moderate impact (P < 0.05, correlation coefficients approximately 0.40) in some locations. We identified blaCTX-M-1, blaCTX-M-9, and blaTEM as the predominant ESBL-gene families. Our study demonstrates that wastewater-based surveillance of culturable ESBL-E. coli provides insights into AMR trends in Switzerland and may also inform resistance. These findings establish a foundation for long term, nationally established monitoring protocols and provide information that may help inform targeted public health interventions.@en

Greywater reuse is a strategy to address water scarcity, necessitating the selection of treatment processes that balance cost-efficiency and human health risks. A key aspect in evaluating these risks is understanding pathogen contamination levels in greywater, a complex task due to intermittent pathogen occurrences. To address this, faecal indicator organisms like E. coli are often monitored as proxies to evaluate faecal contamination levels and infer pathogen concentrations. However, the wide variability in faecal indicator concentrations poses challenges in their modelling for quantitative microbial risk assessment (QMRA). Our study critically assesses the adequacy of parametric models in predicting the variability in E. coli concentrations in greywater. We found that models that build on summary statistics, like medians and standard deviations, can substantially underestimate the variability in E. coli concentrations. More appropriate models may provide more accurate estimations of, and uncertainty around, peak E. coli concentrations. To demonstrate this, a Poisson lognormal distribution model is fit to a data set of E. coli concentrations measured in shower and laundry greywater sources. This model estimated arithmetic mean E. coli concentrations in laundry waters at approximately 1.0E + 06 MPN 100 mL−1. These results are around 2.0 log10 units higher than estimations from a previously used hierarchical lognormal model based on aggregated summary data from multiple studies. Such differences are considerable when assessing human health risks and setting pathogen reduction targets for greywater reuse. This research highlights the importance of making raw monitoring data available for more accurate statistical evaluations than those based on summary statistics. It also emphasizes the crucial role of model comparison, selection, and validation to inform policy-relevant outcomes.@en