Sewage monitoring is becoming a crucial method in combating antimicrobial resistance, which could significantly safeguard at-risk communities.
According to the Centers for Disease Control and Prevention, over 7 million individuals in the U.S. are impacted by waterborne illnesses every year, resulting in costs exceeding $3 billion for our healthcare system. However, these diseases do not affect everyone equally.
A collaborative effort on campus is leveraging sewage surveillance as an essential tactic against waterborne illnesses like legionella and shigella. The most challenging diseases to address are those exhibiting antimicrobial resistance; these germs can withstand antibiotics meant to eliminate them.
According to a recent study published in Nature Water, monitoring sewage for signs of antimicrobial resistance is proving to be a more effective and thorough method than conducting individual tests. This strategy not only identifies antimicrobial resistance more accurately but also highlights connections to socioeconomic factors that often contribute to its spread.
The Virginia Tech team includes experts like Leigh-Anne Krometis from biological systems engineering, and Alasdair Cohen and Julia Gohlke from population health sciences, focusing on assisting rural communities that face the most severe issues.
Worldwide, low- to middle-income communities are most affected by infectious diseases and the struggle against antimicrobial resistance. Sewage monitoring could be revolutionary in bridging these gaps. This approach captures a detailed view of antimicrobial resistance in communities and illustrates how socioeconomic factors influence the problem.
The National Science Foundation Research Traineeship is dedicated to enhancing sewage monitoring to tackle antimicrobial resistance. This work plays a vital role in larger initiatives led by Vikesland and the Fralin Life Sciences Institute aimed at using technology for environmental monitoring and management of waterborne health threats.
The research analyzed data from 275 human fecal samples across 23 countries and 234 urban sewage samples from 62 countries to measure antibiotic resistance gene levels. It incorporated socioeconomic data, including health and governance indicators from World Bank sources, to investigate connections between antibiotic resistance genes and socioeconomic status. The team applied machine learning techniques to evaluate the relationship between antibiotic resistance gene prevalence and socioeconomic factors, uncovering notable correlations. Their statistical analysis indicated that variations in antibiotic resistance genes within a country were smaller compared to those observed between different countries.
Overall, the insights from this team indicate that sewage surveillance is becoming a formidable asset in the combat against antimicrobial resistance, with the potential to offer better protection for vulnerable communities.
