Cost-effectiveness of drug consumption rooms in France: a modelling study
Bibliographic record
Abstract
BACKGROUND: People who inject drugs (PWID) experience many health problems which result in a heavy economic and public health burden. To tackle this issue, France opened two drug consumption rooms (DCRs) in Paris and Strasbourg in 2016. This study assessed their long-term health benefits, costs and cost-effectiveness. METHODS: We developed a model to simulate two fictive cohorts for each city (n=2,997 in Paris and n=2,971 in Strasbourg) i) PWID attending a DCR over the period 2016-2026, ii) PWID attending no DCR. The model accounted for HIV and HCV infections, skin abscesses and related infective endocarditis, drug overdoses and emergency department visits. We estimated the number of health events and associated costs over 2016-2026, the lifetime number of quality-adjusted life-years (QALYs) and costs, and the incremental cost-effectiveness ratio (ICER). RESULTS: The numbers of abscesses and associated infective endocarditis, drug overdoses, and emergency department visits decreased significantly in PWID attending DCRs (-77%, -69%, and -65%, respectively) but the impact on HIV and HCV infections was modest (-11% and -6%, respectively). This resulted in savings of €6.6 (Paris) and €5.8 (Strasbourg) millions of medical costs. The ICER of DRCs was €30,600/QALY (Paris) and €9,200/QALY (Strasbourg). In scenario analysis where drug consumption spaces are implemented inside existing harm reduction structures, these ICERs decreased to €21,400/QALY and €2,500/QALY, respectively. CONCLUSIONS: Our findings show that DCRs are highly effective and efficient to prevent harms in PWID in France, and advocate extending this intervention to other cities by adding drug consumption spaces inside existing harm reduction centers.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".