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Waterborne diseases affect over 7 million people in the U.S. every year, according to the Centers for Disease Control and Prevention, and cost our health care system over $3 billion. But they don’t impact all people equally.
A campuswide collaboration is using sewage surveillance as a vital strategy in the fight against diseases that spread through the water such as legionella and shigella. The ones that are most difficult to combat are diseases with antimicrobial resistance, which means they are able to survive against antibiotics that are intended to kill them.
A recent paper in Nature Water offers an encouraging insight: Monitoring sewage for antimicrobial resistance indicators is proving to be more efficient and more comprehensive than testing individuals. This approach not only detects antimicrobial resistance more effectively but also reveals its connection to socioeconomic factors, which are often key drivers of the spread of resistance.
The team is collaborating across Virginia Tech with experts such as Leigh-Anne Krometis in biological systems engineering and Alasdair Cohen and Julia Gohlke in population health sciences to focus on serving rural communities where the issues are most acute.
Globally, low-to middle-income communities bear the brunt of infectious diseases and the challenges of antimicrobial resistance. Sewage surveillance could be a game changer in addressing these disparities. This method not only captures a snapshot of antimicrobial resistance at the community level, but also reveals how socioeconomic factors drive the issue.
The National Science Foundation Research Traineeship focuses on advancing sewage surveillance to combat antimicrobial resistance. The work is integral to broader efforts led by Vikesland and the Fralin Life Sciences Institute program for technology enabled environmental surveillance and control to sense and monitor waterborne health threats.
The study analyzed data from 275 human fecal samples across 23 countries and 234 urban sewage samples from 62 countries to investigate antibiotic resistance gene levels. Socio-economic data, including health and governance indicators from World Bank databases, were incorporated to explore links between antibiotic resistance genes and socio-economic factors. The group utilized machine learning to assess antibiotic resistance gene abundance in relation to socio-economic factors, revealing significant correlations. Statistical methods supported the finding that within country antibiotic resistance gene variation was lower than between countries.
Big picture, the team’s findings show sewage surveillance is emerging as a powerful tool in the fight against antimicrobial resistance. It even has the potential to protect vulnerable communities more effectively.