Development of a Method for Assessing Illicit Drug Consumption during Brazilian Carnival through Wastewater-Based Epidemiology Using Gas Chromatography–Mass Spectrometry
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Wastewater-based epidemiology (WBE) is a noninvasive and real-time method for assessing illicit drug consumption and the impact of events on community drug use. In this study, a WBE method combining solid-phase extraction (SPE) and gas chromatography–mass spectrometry (GC-MS) was applied to monitor assessing illicit drugs in raw wastewater samples, which were collected during the 2023 Carnival, and in a reference week in Recife (community A) and Olinda (community B) cities. The method was then validated for linearity, limit of quantification, precision, and accuracy. Population-normalized daily drug loads followed the consumption trend of cannabis > cocaine > MDMA > methamphetamine. Cannabis was the most consumed drug, with weekly consumption rates of 8575 mg –1 day –1 1000 inhabitants in community A and 16,978 mg –1 day –1 1000 inhabitants in community B, especially during the Carnival period. Methamphetamine was the least consumed, detected only during Carnival days, underscoring its recreational use. Additionally, stimulant drug use more than doubled during Carnival compared to the reference week, highlighting the significant impact of the festivities. The statistical analysis enabled distinguishing collection periods and highlighting key consumption trends. These findings provide valuable insights into drug use patterns and demonstrate the effectiveness of WBE for monitoring illicit drug consumption.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".