Wastewater surveillance has proven effective in combating antimicrobial resistance

According to the Centers for Disease Control and Prevention, waterborne illnesses affect more than 7 million people in the United States each year and cost our healthcare system more than $3 billion. But they do not affect all people equally.

The campus-wide collaboration uses wastewater surveillance as a vital strategy in the fight against waterborne diseases such as Legionella and Shigella. The most difficult diseases to fight are those that are antimicrobial resistant, meaning they can survive the antibiotics designed to kill them.

Recent article in Nature Water offers encouraging information: monitoring wastewater for indicators of antimicrobial resistance appears to be more effective and comprehensive than testing individuals. This approach not only more effectively identifies antimicrobial resistance, but also reveals its relationship with socioeconomic factors, which are often key factors in the spread of resistance.

The team is collaborating at Virginia Tech with experts such as Leigh-Ann Crometis in biological systems engineering and Alasdair Cohen and Julia Gohlke in population health sciences to focus on serving rural communities where the problems are most acute.

Around the world, low- and middle-income communities bear the brunt of infectious diseases and challenges associated with antimicrobial resistance. Wastewater surveillance could be a game-changer in eliminating these disparities. This method not only provides insight into antimicrobial resistance at the community level, but also shows how socioeconomic factors shape the problem.

A National Science Foundation research fellowship aims to improve wastewater surveillance to combat antimicrobial resistance. This work is an integral part of a broader effort led by Vikesland and the Fralin Institute for Biological Sciences program in environmental surveillance and control technologies to detect and monitor waterborne health threats.

The study analyzed data from 275 human fecal samples from 23 countries and 234 municipal wastewater samples from 62 countries to examine levels of antibiotic resistance genes. Socioeconomic data, including health and governance indicators from World Bank databases, were included to examine associations between antibiotic resistance genes and socioeconomic factors. The team used machine learning to estimate the prevalence of antibiotic resistance genes by socioeconomic factors, finding significant correlations. Statistical methods supported the finding that variation in antibiotic resistance genes within countries was lower than between countries.

Overall, the team’s findings show that wastewater surveillance is becoming a powerful tool in the fight against antimicrobial resistance. It even has the potential to better protect vulnerable communities.