Modeling the impact of a supervised consumption site on HIV and HCV transmission among people who inject drugs in three counties in California, USA
Bibliographic record
Abstract
BACKGROUND: Supervised consumption sites (SCS) have been shown to reduce receptive syringe sharing among people who inject drugs (PWID) in the United States and elsewhere, which can prevent HIV and hepatitis C virus (HCV) transmission. PWID are at risk of disease transmission and may benefit from SCS, however legislation has yet to support their implementation. This study aims to determine the potential impact of SCS implementation on HIV and HCV incidence among PWID in three California counties. METHODS: A dynamic HIV and HCV joint transmission model among PWID (sexual and injecting transmission of HIV, injecting transmission of HCV) was calibrated to epidemiological data for three counties: San Francisco, Los Angeles, and San Diego. The model incorporated HIV and HCV disease stages and HIV and HCV treatment. Based on United States data, we assumed access to SCS reduced receptive syringe sharing by a relative risk of 0.17 (95 % CI: 0.04-1.03). This model examined scaling-up SCS coverage from 0 % to 20 % of the PWID population within the respective counties and assessed its impact on HIV and HCV incidence rates after 10 years. RESULTS: By increasing SCS from 0 % to 20 % coverage among PWID, 21.8 % (95 % CI: -1.2-32.9 %) of new HIV infections and 28.3 % (95 % CI: -2.0-34.5 %) of new HCV infections among PWID in San Francisco County, 17.7 % (95 % CI: -1.0-30.8 %) of new HIV infections and 29.8 % (95 % CI: -2.1-36.1 %) of new HCV infections in Los Angeles County, and 32.1 % (95 % CI: -2.8-41.5 %) of new HIV infections and 24.3 % (95 % CI: -1.6-29.0 %) of new HCV infections in San Diego County could be prevented over ten years. CONCLUSION: Our models suggest that SCS is an important intervention to enable HCV elimination and could help end the HIV epidemic among PWID in California. It could also have additional benefits such facilitating pathways into drug treatment programs and preventing fatal overdose.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".