Role of unsafe medical practices and sexual behaviours in the hepatitis B and C syndemic and HIV co-infection in Rwanda: a cross-sectional study
Bibliographic record
Abstract
OBJECTIVES: This study describes the burden of the hepatitis B, C and HIV co-infections and assesses associated risk factors. SETTING: This analysis used data from a viral hepatitis screening campaign conducted in six districts in Rwanda from April to May 2019. Ten health centres per district were selected according to population size and distance. PARTICIPANTS: The campaign collected information from 156 499 participants (51 496 males and 104 953 females) on sociodemographic, clinical and behavioural characteristics. People who were not Rwandan by nationality or under 15 years old were excluded. PRIMARY AND SECONDARY OUTCOMES: The outcomes of interest included chronic hepatitis C virus (HCV) infection, chronic hepatitis B virus (HBV) infection, HIV infection, co-infection HIV/HBV, co-infection HIV/HCV, co-infection HBV/HCV and co-infection HCV/HBV/HIV. Multivariable logistic regressions were used to assess factors associated with HBV, HCV and HIV, mono and co-infections. RESULTS: Of 156 499 individuals screened, 3465 (2.2%) were hepatitis B surface antigen positive and 83% (2872/3465) of them had detectable HBV desoxy-nucleic acid (HBV DNA). A total of 4382 (2.8%) individuals were positive for antibody-HCV (anti-HCV) and 3163 (72.2%) had detectable HCV ribo-nucleic acid (RNA). Overall, 36 (0.02%) had HBV/HCV co-infection, 153 (0.1%) HBV/HIV co-infection, 238 (0.15%) HCV/HIV co-infection and 3 (0.002%) had triple infection. Scarification or receiving an operation from traditional healer was associated with all infections. Healthcare risk factors-history of surgery or transfusion-were associated with higher likelihood of HIV infection with OR 1.42 (95% CI 1.21 to 1.66) and OR 1.48 (1.29 to 1.70), respectively, while history of physical traumatic assault was associated with a higher likelihood of HIV and HBV/HIV co-infections with OR 1.69 (95% CI 1.51 to 1.88) and OR 1.82 (1.08 to 3.05), respectively. CONCLUSIONS: Overall, mono-infections were common and there were differences in significant risk factors associated with various infections. These findings highlight the magnitude of co-infections and differences in underlying risk factors that are important for designing prevention and care programmes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".