Using synthetic controls to estimate the population-level effects of Ontario’s recently implemented overdose prevention sites and consumption and treatment services
Bibliographic record
Abstract
ABSTRACT Background Between 2017 and 2020, Ontario implemented overdose prevention sites (OPS) and consumption and treatment services (CTS) in nine of its 34 public health units (PHU). We tested for the effect of booth-hours (spaces within OPS/CTSs for supervised consumption) on opioid-related health service use and mortality rates at the provincial-(aggregate) and PHU-level. Methods We used monthly rates of all opioid-related emergency department (ED) visits, hospitalizations, and deaths between January 2015 and March 2021 as our three outcomes. For each PHU that implemented OPS/CTSs, we created a synthetic control as a weighted combination of unexposed PHUs. Our exposure was the time-varying rate of booth-hours provided. We estimated the population-level effects of the intervention on each outcome per treated/synthetic-control pair using controlled interrupted time series with segmented regression; and tested for the aggregate effect using a multiple baseline approach. We adjusted for time-varying provision of prescription opioids for pain management, opioid agonist treatment (OAT), and naloxone kits; and corrected for seasonality and autocorrelation. All rates were per 100,000 population. For sensitivity analysis, we restricted the post-implementation period to before COVID-19 public health measures were implemented (March 2020). Results Our aggregate analyses found no effect per booth-hour on ED visit (0.00, 95% CI: -0.01, 0.01; p-value=0.6684), hospitalization (0.00, 95% CI: 0.00, 0.00; p-value=0.9710) or deaths (0.00, 95% CI: 0.00, 0.00; p-value=0.2466). However, OAT reduced ED visits (−0.20, 95% CI: -0.35, -0.05; p-value=0.0103) and deaths (−0.04, 95% CI: -0.05, -0.03; p-value=<0.0001). Conversely, prescription opioids for pain management modestly increased deaths (0.0008, 95% CI: 0.0002, 0.0015; p-value=0.0157) per 100,000 population, respectively. Except for a few treated PHU/synthetic control pairs, disaggregate results were congruent with overall findings. Conclusion Booth-hours had no population-level effect on opioid-related overdose ED visit, hospitalization, or death 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 | 0.021 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".