Understanding illicit drug use patterns through wastewater analysis
Bibliographic record
Abstract
Illicit drug use is a perennial societal problem that has serious ramifications on the health and socioeconomic well-being of a society. Its identification and estimation in communities is difficult because of the privacy laws and doctor-patient confidentiality agreements, which may limit the availability of reports and data. Various analytical strategies and research methodologies rely on surveys and reports from health and other government agencies. In this study, we employ a relatively new approach called "wastewater epidemiology" to study the patterns of cocaine use in Belgium. We compiled and analyzed census data and previously published water quality data from 37 wastewater treatment plants across Belgium. Using Pearson correlation and stepwise multiple regression analyses, we examined the relationship of cocaine loads at the treatment plants with service coverage, and the population age group, education, and employment. Our results show strong correlations (p2= 0.89), younger population (2= 0.89), manual labor (blue collar jobs, RR2= 0.88), and tertiary level education (R2= 0.88). Interestingly, cocaine loads were positively correlated with higher education level (Coeff. = 2.0, p < 0.0001); and negatively correlated with lower education level (Coeff. = -47.6, p = 0.02). Our findings suggest that wastewater epidemiology can potentially be used as a complementary tool to existing survey-based studies of illicit drug consumption in society. Further, this method provides real-time data and a non-invasive alternative to understanding the patterns of drug use in communities.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".