Costs and consequences of the Portuguese needle-exchange program in community pharmacies
Bibliographic record
Abstract
BACKGROUND: Needle-exchange programs (NEPs) reduce infections in people who inject drugs. This study assesses the impact community pharmacies have had in the Needle-Exchange Program in Portugal since 2015. METHODS: Health gains were measured by the number of human immunodeficiency virus (HIV) and hepatitis C virus (HCV) infections averted, which were estimated, in each scenario, based on a standard model in the literature, calibrated to national data. The costs per infection were taken from national literature; costs of manufacturing, logistics and incineration of injection materials were also considered. The results were presented as net costs (i.e., incremental costs of the program with community pharmacies less the costs of additional infections avoided). RESULTS: = 22) of HCV and HIV infections, respectively. The present value of net savings generated by the participation of community pharmacies in the program was estimated at €2,073,347. The average discounted net benefit per syringe exchanged is €3.01, already taking into account a payment to community pharmacies per needle exchanged. INTERPRETATION: We estimate that the participation of community pharmacies in the Needle Exchange Program will lead to a reduction of HIV and HCV infections and will generate over €2 million in savings for the health system. CONCLUSIONS: The intervention is estimated to generate better health outcomes at lower costs, contributing to improving the efficiency of the public health system in Portugal.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".