Injection drug use practices and HIV infection among people who inject drugs in Kigali, Rwanda: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: In Rwanda, epidemiological data characterizing people who inject drugs (PWID) and their burden of HIV are limited. We examined injection drug use (IDU) history and practices, and HIV infection in a sample of PWID in Kigali. METHODS: From October 2019 to February 2020, 307 PWID aged ≥ 18 were enrolled in a cross-sectional study using convenience sampling in Kigali. Participants completed interviewer-administered questionnaires on IDU history and practices and HIV testing. We used Poisson regression with robust variance estimation to assess IDU practices associated with HIV infection and assessed factors associated with needle sharing in the six months preceding the study. RESULTS: The median age was 28 years (IQR 24-31); 81% (251) were males. Female PWID were more likely to report recent IDU initiation, selling sex for drugs, and to have been injected by a sex partner (p < 0.05). In the prior six months, heroin was the primary drug of choice for 99% (303) of participants, with cocaine and methamphetamine also reported by 10% (31/307) and 4% (12/307), respectively. In total, 91% (280/307) of participants reported ever sharing needles in their lifetime and 43% (133) knew someone who died from a drug-related overdose. HIV prevalence was 9.5% (95% CI 8.7-9.3). Sharing needles at least half of the time in the previous six months was positively associated with HIV infection (adjusted prevalence ratio (aPR) 2.67; 95% CI 1.23-5.78). Overall, 31% (94/307) shared needles and 33% (103/307) reused needles in the prior six months. Female PWID were more likely to share needles compared to males (aPR 1.68; 95% CI 1.09-2.59). Additionally, bisexual PWID (aPR 1.68; 95% CI 1.09-2.59), those who shared needles at the first injection (aPR 2.18; 95% CI 1.59-2.99), reused needles recently (aPR 2.27; 95% CI 1.51-3.43) and shared other drug paraphernalia (aPR 3.56; 95% CI 2.19-5.81) were more likely to report recent needle sharing. CONCLUSION: HIV infection was common in this study. The high prevalence of needle reuse and sharing practices highlights significant risks for onward transmission and acquisition of HIV and viral hepatitis. These data highlight the urgent need for PWID-focused harm reduction services in Rwanda, including syringe services programs, safe injection education, naloxone distribution, and substance use disorder treatment programs and optimizing these services to the varied needs of people who use drugs in Rwanda.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| 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 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".