Analysis of Wastewater Samples to Explore Community Substance Use in the United States: Pilot Correlative and Machine Learning Study

Marie A Severson; Sathaporn Onanong; Alexandra Dolezal; Shannon L Bartelt-Hunt; Daniel D Snow; Lisa M McFadden

doi:10.2196/45353

Analysis of Wastewater Samples to Explore Community Substance Use in the United States: Pilot Correlative and Machine Learning Study

JMIR Form Res. 2023 Oct 26:7:e45353. doi: 10.2196/45353.

Authors

Marie A Severson¹, Sathaporn Onanong², Alexandra Dolezal¹, Shannon L Bartelt-Hunt³, Daniel D Snow², Lisa M McFadden¹

Affiliations

¹ Division of Basic Biomedical Sciences, University of South Dakota, Vermillion, SD, United States.
² Water Sciences Laboratory & Nebraska Water Center, part of the Daugherty Water for Food Global Institute, University of Nebraska-Lincoln, Lincoln, NE, United States.
³ Department of Civil and Environmental Engineering, University of Nebraska-Lincoln, Lincoln, NE, United States.

PMID: 37883150
PMCID: PMC10636622
DOI: 10.2196/45353

Abstract

Background: Substance use disorder and associated deaths have increased in the United States, but methods for detecting and monitoring substance use using rapid and unbiased techniques are lacking. Wastewater-based surveillance is a cost-effective method for monitoring community drug use. However, the examination of the results often focuses on descriptive analysis.

Objective: The objective of this study was to explore community substance use in the United States by analyzing wastewater samples. Geographic differences and commonalities of substance use were explored.

Methods: Wastewater was sampled across the United States (n=12). Selected drugs with misuse potential, prescriptions, and over-the-counter drugs and their metabolites were tested across geographic locations for 7 days. Methods used included wastewater assessment of substances and metabolites paired with machine learning, specifically discriminant analysis and cluster analysis, to explore similarities and differences in wastewater measures.

Results: Geographic variations in the wastewater drug or metabolite levels were found. Results revealed a higher use of methamphetamine (z=-2.27, P=.02) and opioids-to-methadone ratios (oxycodone-to-methadone: z=-1.95, P=.05; hydrocodone-to-methadone: z=-1.95, P=.05) in states west of the Mississippi River compared to the east. Discriminant analysis suggested temazepam and methadone were significant predictors of geographical locations. Precision, sensitivity, specificity, and F₁-scores were 0.88, 1, 0.80, and 0.93, respectively. Finally, cluster analysis revealed similarities in substance use among communities.

Conclusions: These findings suggest that wastewater-based surveillance has the potential to become an effective form of surveillance for substance use. Further, advanced analytical techniques may help uncover geographical patterns and detect communities with similar needs for resources to address substance use disorders. Using automated analytics, these advanced surveillance techniques may help communities develop timely, tailored treatment and prevention efforts.

Keywords: community; detecting; drug detection; drugs; methamphetamine; monitoring; opioids; pilot study; substance use; substance use disorder; surveillance; wastewater-based surveillance.

©Marie A Severson, Sathaporn Onanong, Alexandra Dolezal, Shannon L Bartelt-Hunt, Daniel D Snow, Lisa M McFadden. Originally published in JMIR Formative Research (https://formative.jmir.org), 26.10.2023.

Abstract

Grants and funding