Impact of drug consumption rooms on non-fatal overdoses, abscesses and emergency department visits in people who inject drugs in France: results from the COSINUS cohort
Bibliographic record
Abstract
BACKGROUND: The effectiveness of drug consumption rooms (DCRs) for people who inject drugs (PWID) has been demonstrated for HIV and hepatitis C virus risk practices, and access to care for substance use disorders. However, data on other health-related complications are scarce. Using data from the French COSINUS cohort, we investigated the impact of DCR exposure on non-fatal overdoses, abscesses and emergency department (ED) visits, all in the previous 6 months. METHODS: COSINUS is a 12-month prospective cohort study of 665 PWID in France studying DCR effectiveness on health. We collected data from face-to-face interviews at enrolment, and at 6 and 12 months of follow-up. After adjusting for other correlates (P-value < 0.05), the impact of DCR exposure on each outcome was assessed using a two-step Heckman mixed-effects probit model, allowing us to adjust for potential non-randomization bias due to differences between DCR-exposed and DCR-unexposed participants, while taking into account the correlation between repeated measures. RESULTS: At enrolment, 21%, 6% and 38% of the 665 participants reported overdoses, abscesses and ED visits, respectively. Multivariable models found that DCR-exposed participants were less likely to report overdoses [adjusted coefficient (95% CI): -0.47 (-0.88; -0.07), P = 0.023], abscesses [-0.74 (-1.11; -0.37), P < 0.001] and ED visits [-0.74 (-1.27; -0.20), P = 0.007]. CONCLUSION: This is the first study to show the positive impact of DCR exposure on abscesses and ED visits, and confirms DCR effectiveness in reducing overdoses, when adjusting for potential non-randomization bias. Our findings strengthen the argument to expand DCR implementation to improve PWID injection environment and health.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".