Opioid-related emergency department visits and deaths after a harm-reduction intervention: a retrospective observational cohort time series analysis
Bibliographic record
Abstract
BACKGROUND: To date, there has been little research on the effect of safe consumption site and community-based naloxone programs on regional opioid-related emergency department visits and deaths. We sought to determine the impact of these interventions on regional opioid-related emergency department visit and death rates in the province of Alberta. METHODS: We used a retrospective observational design, via interrupted time series analysis, to assess municipal opioid-related emergency department visit volume and opioid-related deaths (defined by poisoning and opioid use disorder). We compared rates before and after program implementation in individual Alberta municipalities and province-wide after safe consumption site (March 2018 to October 2018) and community-based naloxone (January 2016) program implementation. RESULTS: A total of 24 107 emergency department visits and 2413 deaths were included in the study. After safe consumption site opening, we saw decreased opioid-related emergency department visits in Calgary (level change -22.7 [-20%] visits per month, 95% confidence interval [CI] -29.7 to -15.8) and Lethbridge (level change -8.8 [-50%] visits per month, 95% CI -11.7 to -5.9), and decreased deaths in Edmonton (level change -5.9 [-55%] deaths per month, 95% CI -8.9 to -2.9). We observed increased emergency department visits after community-based naloxone program implementation in urban Alberta (level change 38.9 [46%] visits, 95% CI 33.3 to 44.4). We also observed an increase in urban opioid-related deaths (level change 9.1 [40%] deaths, 95% CI 6.7 to 11.5). INTERPRETATION: The results of this study suggest differences exist between municipalities employing similar interventions. Our results also suggest contextual variation; for example, illicit drug supply toxicity may modify the ability of a community-based naloxone program to prevent opioid overdose without a thorough public health response.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.004 | 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 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".