| Popis: |
BACKGROUND Substance use disorder and associated deaths have increased in the United States, but methods for detecting and monitoring substance use utilizing 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 current study aimed to utilize machine learning to better understand geographic differences and explore commonalities of substance use in the United States. Further, it looked to validate trends in wastewater levels of drugs and metabolites with other forms of substance use surveillance. 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 sampled across geographic locations for 7 days. Machine learning was utilized to assess geographical patterns of drug use. RESULTS Geographic variations in the wastewater drug or metabolite levels were found. Specifically, results revealed a higher use of methamphetamine (z=-2.27, p=0.02) and opioids-to-methadone ratios (oxycodone-to-methadone: z=-1.95, p=0.05; hydrocodone-to-methadone: z=-1.95, p=0.05) in states west of the Mississippi River compared to the east. Machine learning 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 is an effective form of surveillance for substance use. Further, machine learning may help uncover geographical patterns and detect communities with similar needs for resources to address substance use disorders. Utilizing automated machine learning, these advanced surveillance techniques may help communities develop tailored treatment and prevention efforts. |
