Risk factors associated with transfusion transmissible infections among blood donors at Karongi Regional Centre for Blood Transfusion-Western Province of Rwanda
Bibliographic record
Abstract
Introduction: Blood transfusion saves human lives, but also it can be a route for Transfusion-transmissible Infections (TTIs) such as Human Immuno-Deficiency Virus (HIV), Hepatitis B virus (HBV), Hepatitis C virus (HCV), and Syphilis. TTIs are still a threat to the safety of blood given also the growing magnitude of viral infections (HIV, HBV, and HCV) in the population especially in the Western province of Rwanda. This study aimed to explore the risk factors associated with TTIs among blood donors in the Regional Centre for Blood Transfusion (RCBT) of Karongi, Rwanda. Methods: A secondary data analysis was conducted on cross-sectionally collected data from blood donors collected from 2015 to 2019 in the Regional Centre for Blood Transfusion of Karongi. The key variables for the current analysis were age, gender, residence, blood group, occupation, marital status, and blood donor regularity status and TTIs results reported. We analysed data using Stata version 15 and proportions of TTIs by various characteristics were calculated. Logistic regression was used to identify the factors independently associated with each of the TTIs among blood donors. The significant risk factors were assessed using a P-value of 0.05 and 95% confidence intervals. Results: Among 36,708 donations, the proportion of HBV, HCV, HIV and Syphilis was 1.3%, 0.44%, 0.065% and 0.34% respectively and the overall prevalence of all TTIs was 2.1%. HBV was associated with male gender (AOR: 1.7, 95% CI: 1.4-2.1), age group of 26-35 years (AOR: 1.5, 95% CI: 1.2-1.9), being a new blood donors (AOR:13.2, 95% CI: 8.5-20.6), living in Rusizi District (AOR: 2.5, 95% CI: 1.9-3.3) and being married (AOR: 1.7, 95% CI: 1.2-2.4). HCV was associated with male gender (AOR:1.5, 95% CI: 1.1-2.1), age groups of 26- 35 years (AOR:1.6, 95% CI: 1.1-2.5), 36-45 years (AOR:2.0, 95% CI: 1.2-3.5), 46-55 years (AOR:2.4, 95% CI: 1.2-4.8), 56-65 years (AOR:5.8, 95% CI: 2.4-14.0), being a new blood donors (AOR:4.7, 95% CI: 2.8-7.8), Rusizi District (AOR:2.9, 95% CI: 1.6-5.3), Nyamasheke District (AOR:3.9, 95% CI: 2.1-7.0) and Karongi (AOR:2.6, 95% CI; 1.4-4.8). HIV was related to being new blood donors (AOR:12.1, 95% CI: 1.6-91.0) and living in urban areas (AOR:4.1, 95% CI: 1.2-13.5). Male gender (AOR:1.9, 95% CI: 1.2-2.9) and new blood donors (AOR: 2.3, 95% CI: 1.4-3.6) determined the risk of Syphilis. Conclusion: This study illuminates key risk factors for TTIs in blood donors, emphasizing the importance of improving donor screening and selection processes. Its findings necessitate enhanced health education and continuous monitoring with sensitive testing methods. The results also underscore the need for such measures in similar settings globally for safer blood supply.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".