Wastewater-Based Surveillance of Substances of Potential Abuse
Bibliographic record
Abstract
ObjectiveAmid a drug poisoning epidemic, UCalgary adapted its comprehensive wastewater monitoring program to track illicit drug use. The joint initiative between the academic community and Alberta municipalities aims to provide early warning information on changes in the drug supply to healthcare workers, policymakers, and people with substance use disorders (pwSUD). ApproachThe logistic network, first used for SARS-CoV-2 monitoring, has evolved to track 48 substances linked to substance use. These include the parent drugs (e.g., cocaine, methamphetamines, various opiates) and their metabolites. The comprehensive nature of this tracking provides a nuanced understanding of trends. Collected up to 3X-weekly across various locations in Alberta including municipal wastewater treatment plants, entire neighbourhoods, and shelters, the data reveals geographical and temporal patterns in substance use. Findings were cross validated with Alberta’s substance use surveillance system to predict trends in wastewater, correlating with emergency and inpatient admissions and overdose deaths. ResultsThe research uncovered noteworthy trends. Intermittent, alarming, spikes in carfentanyl, benzodiazepines, and xylazine in wastewater were observed during periods of elevated Emergency Medical Services (EMS) responses to opioid-related incidents in the year 2023. This correlation underscores the real-world impact of the identified substance trends, emphasizing the urgency for targeted interventions to address the escalating opioid crisis. Further work to create a dashboard with additional data sources has begun. ConclusionsTimely data on the dynamic drug supply is crucial in addressing the ongoing drug poisoning crisis. The team is exploring ways to share information with emergency responders, healthcare providers, government officials, and pwSUD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".