Injection Drug use Practices and HIV infection among People Who Inject Drugs in Kigali, Rwanda
Bibliographic record
Abstract
Abstract Background In Rwanda, epidemiological data characterizing people who inject drugs (PWID) and their burden of HIV are limited. We examined injecting drug use (IDU) history, practices, and HIV infection in a sample of PWID in Kigali. Methods From October 2019–February 2020, 322 PWID aged ≥18 were enrolled in a cross-sectional study using convenience sampling in Kigali. Participants underwent a structured interview and HIV testing. We used Poisson regression with robust variance estimation to assess IDU practices associated with HIV infection. Results The median age was 28 years(IQR:24-31) and 81%(248) were male. The median age at first injection was 23 years (IQR:20-27). HIV prevalence was 9.5%(95%CI:8.7-9.3). In the six months preceding the study, heroin was the primary drug of choice for 99%(303); but cocaine and methamphetamine were also reported by 10%(31) and 4%(12) respectively. Furthermore, 31%(94) and 33%(103) of participants, shared or reused needles in the previous six months, respectively. Up to 43%(133) knew someone who died from a drug-related overdose. PWID reporting sharing needles at least half the time in the previous six months had increased likelihood of HIV-infection, compared to those who did not (aPR: 2.67; 95%CI:1.23–5.78). Conclusion HIV infection was common in this sample of PWID in Kigali. The high prevalence of needle reuse and sharing practices highlight significant risk for onward transmission and acquisition of HIV and hepatitis B and C. PWID-focused harm reduction services, including needle and syringe programs, safer injection education, naloxone distribution, and substance use disorder treatment programs, are needed in Rwanda.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".