Spatial-temporal trends in the risk of illicit drug toxicity death in British Columbia
Bibliographic record
Abstract
Abstract Background Illicit drug poisoning (overdose) continues to be an important public health problem with overdose-related deaths currently recorded at an unprecedented level. Understanding the geographic variations in fatal overdose mortality is necessary to avoid disproportionate risk resulting from service access inequity. Methods We estimated the odds of fatal overdose per event from all cases captured by the overdose surveillance system in British Columbia (2015 - 2018), using both conventional logistic regression and Generalized Additive Models (GAM). The results of GAM were mapped to identify spatial-temporal trends in the risk of fatal overdose. Results We found that the odds of fatal overdose were about 30% higher in rural areas than in large urban centers, with some regions reporting odds 50% higher than others. Temporal variations in fatal overdose revealed an increasing trend over the entire province. However, the increase occurred earlier and faster in the Interior and Northern regions. Conclusion Rural areas were disproportionately affected by fatal overdose; lack of access to harm reduction services may partly explain the elevated risk in these areas.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".