Evaluating the population-level effects of overdose prevention sites and supervised consumption sites in British Columbia, Canada: Controlled interrupted time series
Bibliographic record
Abstract
BACKGROUND: On 14 April 2016, British Columbia's Provincial Medical Health Officer declared the overdose crisis a public health emergency, sanctioning the implementation of new overdose prevention sites (OPS) and supervised consumption sites (SCS) across the province. METHODS: We used the BC Centre for Disease Control's Provincial Overdose Cohort of all overdose events between 1 January 2015 and 31 December 2017 to evaluate the population-level effects of OPSs and SCSs on acute health service use and mortality. We matched local health areas (LHA) that implemented any site with propensity score matched controls and conducted controlled interrupted time series analysis. RESULTS: During the study period, twenty-five OPSs and SCSs opened across fourteen of British Columbia's 89 LHAs. Results from analysis of LHAs with matched controls (i.e. excluding Vancouver DTES) were mixed. Significant declines in reported overdose events, paramedic attendance, and emergency department visits were observed. However, there were no changes to trends in monthly hospitalization or mortality rates. Extensive sensitivity analyses found these results persisted. CONCLUSIONS: We found OPSs and SCSs reduce opioid-related paramedic attendance and emergency department visit rates but no evidence that they reduce local hospitalization or mortality rates.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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 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".