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 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".