HIV Prevention Among People Who Inject Drugs (PWID): A Narrative Review
Bibliographic record
Abstract
People who inject drugs (PWID) face a significantly higher risk of HIV infection than the general population. Effective prevention for this group depends on combining several strategies: needle and syringe programs (NSPs), opioid agonist therapy (OAT), antiretroviral therapy (ART), pre-exposure prophylaxis (PrEP), overdose prevention, and changes to structural and policy environments. This narrative review draws on evidence published between 2000 and 2024, including systematic reviews, clinical trials, surveillance reports, and global health guidelines. Programs that offer high coverage of NSPs and OAT have consistently been shown to reduce HIV incidence. ART helps lower viral load and prevents transmission, while both daily and long-acting PrEP offer additional protection for those at risk. However, access remains limited, particularly in low- and middle-income countries, due to criminalization, stigma, and a lack of investment in harm reduction services. Outcomes vary widely by region. In places like Portugal and Canada, where policies support decriminalization and integrate harm reduction into routine care, HIV incidence among PWID has dropped significantly. In contrast, countries that maintain punitive drug laws, such as Russia, continue to struggle with sustained epidemics. Reducing HIV transmission in this population will require expanding access to proven interventions, integrating services more effectively, and reforming policies that act as barriers to care. Priorities moving forward include scaling up NSP and OAT, addressing gender-based inequities, reducing stigma, and aligning national policies with international harm reduction standards.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| 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".