Estimating the total utilization of take home naloxone during an unregulated drug toxicity crisis: A Bayesian modeling approach
Bibliographic record
Abstract
BACKGROUND: British Columbia (BC) Canada has a large take-home naloxone (THN) program, implemented as part of the provincial response to the ongoing toxic unregulated drug supply emergency. Ascertaining the rate of use of THN kits is vital to understanding the full impact of the program. However, this is a challenging problem due to under-reporting of kit distribution. This study aims to estimate the total number of THN kits used based on the number of THN kits shipped, the number of THN kits reported as distributed, and the number of THN kits reported as used. METHODS: We used BC THN shipment and distribution records (February 2015 to August 2023) to inform a simple Bayesian model of naloxone kit distribution and use. A logistic regression term by health region and distribution site type was incorporated to account for variable under-reporting, and a convolution term was incorporated to account for kit distribution. RESULTS: We find the number of THN kits reported as used, and the number of total THN kits distributed, are largely under-reported. An estimated 1,500 (95 % CrI: 1,430 - 1,590) THN kits per 10,000 BC population were used, of which 288 per 10,000 had been reported as used. Of all the THN kits shipped, the model estimated that 43 % (95 % CrI: 41-45 %) of kits were used. We also found variation in both distribution and use by distribution site type, with kits distributed from overdose prevention sites having the highest rate of use (56 %; 95 % CrI: 53-59 %). CONCLUSION: Across all sites, kit use is approximately five times higher than has been reported. Our framework can also be applied to other localities where THN programs operate, in order to better estimate the true reach and impact of take home naloxone distribution.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".