Identifying Shared Opioid Supplies from Public Data on Opioid Deaths (Preprint)
Bibliographic record
Abstract
UNSTRUCTURED Opioid-related, accidental death counts have increased in North America over the past decade, leading to the notion of an ‘opioid crisis’. In recent years, the majority of deaths involving opioids were due to illicit fentanyl, connecting deaths more strongly with illicit activities. This study presents a straightforward approach to determine if two locations shared the same opioid supply during a specific period of time. It is based on the assumption that synchronized death counts in two locations over a specific period of time highlight locations that shared opioid supplies. Data on accidental opioid deaths is publicly available in many health jurisdictions. The proposed analysis operates on data from pairs of locations of interest and involves calculating the Pearson correlation coefficients and the related p-values to check for statistical significance. Statistically significant coefficients higher than 0.5 identify locations as sharing access to the same opioid supply. As an example, data from the “Alberta substance use surveillance system” for the time from January 2019 until June 2022 is analyzed to identify connected opioid supplies in seven cities in Alberta, Canada. The limitations of this approach are discussed. This study describes how to provide information about shared supplies of illicit opioids to support health and social service providers, municipal administrators, or law enforcement agencies to respond to the public health concerns related to the opioid crisis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".