Factors associated with take-home naloxone kit usage in British Columbia: an analysis of administrative data
Bibliographic record
Abstract
BACKGROUND: The British Columbia (BC) Take-Home Naloxone (THN) program provides naloxone to people at risk of experiencing or witnessing an opioid overdose for use in reversing suspected overdose events. This study seeks to examine trends and correlates of individuals obtaining a THN kit in BC between 2017 and 2020. METHODS: Records of THN kits distributed between 2017 and 2020 were the primary source of data for this analysis. Frequency tables were used to describe characteristics of people obtaining kits from THN sites. Correlates of individuals obtaining a THN kit to replace a previous kit reported as used to reverse an overdose were assessed with multivariate logistic regression. RESULTS: Between January 1, 2017, and December 31, 2020, 240,606 THN kits were reported distributed by registered sites to members of the public, with 90,011 records indicating that a kit was obtained to replace a previous kit that had been used to reverse an overdose. There was a significant trend in increasing kits reported used by year (p < 0.01). The kit recipient's risk of overdose was a significant predictor of having reported using a THN kit, and the strength of the association was dependent on gender (Male: Adjusted odds ratio (AOR) 5.37 [95% confidence interval (CI) 5.08 - 5.67]; Female: AOR 8.35 [95% CI 7.90 - 8.82]; Trans and gender expansive: AOR 3.68 [95% CI 2.82 - 4.79]). CONCLUSIONS: Between 2017 and 2020, THN kits were used to reverse tens of thousands of overdose events in BC, with people at risk of overdose (i.e. people who use drugs [PWUD]) having greater odds of using a kit to reverse an overdose than those not at risk. Thus, PWUD are responsible for reversing the vast majority of overdoses. THN kits are being distributed to the people who use them most. However, additional strategies in conjunction with community-based naloxone distribution programs are needed to address the rising number of illicit drug toxicity deaths.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".