The geography of overdose in British Columbia.
Bibliographic record
Abstract
Illicit drug toxicity poisoning (overdose) continues to be a public health emergency in British Columbia (BC) with record high rates of illicit drug toxicity during the COVID-19 pandemic. In 2021, 2224 people died of overdose in BC with some of the highest rates recorded in rural and remote regions. Understanding the geographic variations in overdose mortality risk is necessary to avoid disproportionate risk resulting from service access inequity. Using novel linked administrative health data from the BC Provincial Overdose Cohort we estimated the odds of fatal overdose per event (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. We found that the likelihood of fatal overdose was about 20% higher in rural areas than in large urban centers, with some regions reporting odds 50% higher than others. Temporal variations in fatal risk exhibit an increasing trend over the entire province. However, risks in the Interior and Northern BC increased earlier and faster. The results of this study demonstrate the importance of geography to health outcomes and suggest that rural and remote regions may lack harm reduction services to counteract the province-wide increase in illicit drug toxicity death.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".