The impact of an integrated safer use space and safer supply program on non-fatal overdose among emergency shelter residents during a COVID-19 outbreak: a case study
Bibliographic record
Abstract
BACKGROUND: Opioid-related harms, including fatal and non-fatal overdoses, rose dramatically during the COVID-19 pandemic and presented unique challenges during outbreaks in congregate settings such as shelters. People who are deprived of permanent housing have a high prevalence of substance use and substance use disorders, and need nimble, rapid, and portable harm reduction interventions to address the harms of criminalized substance use in an evidence-based manner. CASE STUDY: In February 2021, a COVID-19 outbreak was declared at an emergency men's shelter in Hamilton, Ontario, Canada. Building on pre-existing relationships, community and hospital-based addictions medicine providers and a local harm reduction group collaborated to establish a shelter-based opioid agonist treatment and safer supply program, and a volunteer run safer drug use space that also distributed harm reduction supplies. In the 4 weeks preceding the program, the rate of non-fatal overdoses was 0.93 per 100 nights of shelter bed occupancy. During the 26 days of program operation, there were no overdoses in the safer use space and the rate of non-fatal overdoses in the shelter was 0.17 per 100 nights of shelter bed occupancy. The odds ratio of non-fatal overdose pre-intervention to during intervention was 5.5 (95% CI 1.63-18.55, p = 0.0059). We were not able to evaluate the impact of providing harm reduction supplies and did not evaluate the impact of the program on facilitating adherence to public health isolation and quarantine orders. The program ended as the outbreak waned, as per the direction from the shelter operator. CONCLUSIONS: There was a significant reduction in the non-fatal overdose rate after the safer drug use and safer supply harm reduction program was introduced. Pre-existing relationships between shelter providers, harm reduction groups, and healthcare providers were critical to implementing the program. This is a promising approach to reducing harms from the criminalization of substance use in congregate settings, particularly in populations with a higher prevalence of substance use and substance use disorders.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".