UCSF

Wastewater-based surveillance is increasingly recognized as an important approach to monitoring population-level antimicrobial resistance (AMR). In this exploratory study, we examined the use of metagenomics to evaluate AMR using untreated wastewater samples routinely collected by the Niger national polio surveillance program. Forty-eight stored samples from two seasons each year over 4 years (2016-2019) in three regions were selected for inclusion in this study and processed using unbiased DNA deep sequencing. Normalized number of reads of genetic determinants for different antibiotic classes were compared over time, by season, and by location. Correlations in resistance were examined among classes. Changes in reads per million per year were demonstrated for several classes, including decreases over time in resistance determinants for phenicols (-3.3, 95% CI: -8.7 to -0.1, P = 0.029) and increases over time for aminocoumarins (3.8, 95% CI: 0.0 to 11.4, P = 0.043), fluoroquinolones (6.8, 95% CI: 0.0 to 20.5, P = 0.048), and beta-lactams (0.85, 95% CI: 0.1 to 1.7, P = 0.006). Sulfonamide resistance was higher in the post-rainy season compared with the dry season (5.2-fold change, 95% CI: 3.4 to 7.9, P < 0.001). No differences were detected when comparing other classes by season or by site for any antibiotic class. Positive correlations were identified in genetic determinants of resistance among several antibiotic classes. These results demonstrate the potential utility of leveraging existing wastewater sample collection in this setting for AMR surveillance.