Invited commentary: motivating better methods—and better data collection—for measuring the prevalence of drug misuse
Bibliographic record
Abstract
The United States continues to suffer a drug overdose crisis that has resulted in over 100 000 deaths annually since 2021. Despite decades of attention, estimates of the prevalence of drug use at the spatiotemporal resolutions necessary for resource allocation and intervention evaluation are lacking. Current approaches for measuring the prevalence of drug use, such as population surveys, capture-recapture, and multiplier methods, have significant limitations. In a recent article, Santaella-Tenorio et al (Am J Epidemiol. 2024;193(7):959-967) used a novel joint bayesian spatiotemporal modeling approach to estimate the county-level prevalence of opioid misuse in New York State from 2007 to 2018 and identify significant intrastate variation. By leveraging 5 data sources and simultaneously modeling different opioid-related outcomes-such as numbers of deaths, emergency department visits, and treatment visits-they obtained policy-relevant insights into the prevalence of opioid misuse and opioid-related outcomes at high spatiotemporal resolutions. The study provides future researchers with a sophisticated modeling approach that will allow them to incorporate multiple data sources in a rigorous statistical framework. The limitations of the study reflect the constraints of the broader field and underscore the importance of enhancing current surveillance with better, newer, and more timely data that are both standardized and easily accessible to inform public health policies and interventions. This article is part of a Special Collection on Mental Health.
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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 | 0.038 | 0.229 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.054 | 0.063 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".