ABSTRACT
Antimicrobial resistance (AMR) is a growing public health threat. Improved surveillance of AMR's genetic indicators in environmental reservoirs should lead to a more comprehensive understanding of the problem at a global scale, as with SARS-CoV-2 monitoring in sewage. However, the "best" monitoring approach is unclear. Some scientific works have emphasized monitoring for the abundance of already-known antimicrobial resistance genes (ARGs);others have emphasized monitoring for the potential of new ARGs to arise. The goal of this study was to examine which methods were employed by highly-cited papers studying AMR in environmental engineering and agricultural systems, thus providing insight into current and future methodological trends for monitoring ARGs. We searched recent (2018-2020) literature documenting AMR in five environmental matrices: wastewater, surface water, drinking water, stormwater, and livestock manure. We selected the most highly-cited papers across these matrices (89 papers from 17 809 initial results) and categorized them as using targeted methods (e.g., qPCR), non-targeted methods (e.g., shotgun metagenomics), or both. More than 80% of papers employed targeted methods. Only 33% employed non-targeted methods, and the use of targeted versus non-targeted methods varied by environmental matrix. We posit that improving AMR surveillance in environmental reservoirs requires assessing risk, and that different monitoring approaches imply different objectives for risk assessment. Targeted methods are appropriate for quantifying known threats, particularly in environmental matrices where direct human exposure is likely (e.g., drinking water). However, long-term studies employing non-targeted methods are needed to provide an understanding of how frequently new threats (i.e., novel ARGs) arise.
ABSTRACT
Wastewater surveillance for SARS-CoV-2 provides an approach for assessing the infection burden across a sewer service area. For these data to be useful for public health, measurement variability and the relationship to case data need to be established. We determined SARS-CoV-2 RNA concentrations in the influent of 12 wastewater treatment plants from August 2020 to January 2021. Technical replicates for N1 gene concentrations showed a relative standard deviation of 24%, suggesting it is possible to track relatively small (similar to 30%) changes in SARS-CoV-2 concentrations over time. COVID-19 cases were correlated significantly (rho >= 0.70) to wastewater SARS-CoV-2 RNA concentrations across large and small service areas, with weaker relationships (rho >= 0.59) in two communities. SARS-CoV-2 concentrations normalized to per capita slightly improved correlations to COVID-19 incidence, but normalizing to a spiked recovery control (BCoV) or a fecal marker (PMMoV or HF183) reduced correlations for a number of plants. Daily sampling demonstrated that a minimum of two samples collected per week were needed to maintain accuracy in trend analysis. The differences in the strength of SARS-CoV-2 relationships to COVID-19 incidence and the effect of normalization on these data among communities demonstrate that rigorous validation should be performed at individual sites where wastewater surveillance programs are implemented.