HIV treatment as prevention among people who inject drugs – a re-evaluation of the evidence
Bibliographic record
Abstract
Background: Population-level associations between community measures of HIV viral load and HIV incidence have been interpreted as evidence for HIV anti-retroviral treatment (ART) as prevention among people who inject drugs (PWID). However, investigation of concurrent HCV and HIV incidence trends allows examination of alternative explanations for the fall in HIV incidence. We estimate the contribution of ART and reductions in injecting risk for reducing HIV incidence in Vancouver between 1996 and 2007. Methods: A deterministic model of HIV and HCV transmission among PWID was calibrated to the baseline (1996) HIV and HCV epidemic among PWID in Vancouver. While incorporating parameter uncertainty, the model projected what levels of ART protection and decreases in injecting risk could reproduce the observed reduction in HIV and HCV incidence for 1996-2007, and so what impact would have been achieved with just ART or just reductions in injecting risk. Results: Model predictions suggest the estimated reduction (84%) in HCV incidence for 1996-2007 required a 59% (2.5-97.5 percentile range 49-76%) reduction in injecting risk, which accounted for nine-tenths of the observed decrease in HIV incidence; the remainder was achieved with a moderate ART efficacy for reducing sexual HIV infectivity (70%, 51-89%) and an uncertain ART efficacy for reducing injection-related HIV infectivity (44%, 0-96%). Despite this uncertainty, projections suggest that the decrease in injecting risk reduced HIV incidence by 76% (63-85%) and ART further reduced HIV incidence by 8% (2-19%), or on its own by 3% (-34-37%). Conclusions: Observed declines in HIV incidence in Vancouver between 1996 and 2007 should be seen as a success for intensive harm reduction, whereas ART probably played a small role.
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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 | 0.078 | 0.178 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".