Trends of blood-borne infectious diseases in a rural blood donation center of southeast Gabon (Koula-Moutou)
Bibliographic record
Abstract
INTRODUCTION: Blood-borne pathogens such as human immunodeficiency virus (HIV), hepatitis B and C (HBV and HCV) viruses and Treponema pallidum remain a major public health problem in sub-Saharan Africa. The purpose of this study was to assess the frequency and clinical implications of HIV, HBV, HCV and Treponema pallidum markers in blood donors in a rural area of Southeast Gabon (Koula-Moutou) from 2012 to 2017. METHODS: hepatitis B surface antigen (HBsAg), anti-HIV, anti-HCV and anti-Treponema pallidum antibodies were screened using (RDTs). RESULTS: of a total of 5,706 blood donors, 1,054 (18.5%) were seropositive for at least one infectious marker and 59 (5.6%) had serologic evidence of multiple infections. The overall seroprevalence of HIV, HBsAg, HCV, and syphilis was 3.1%; 5.9%; 6.2% and 3.3%, respectively. HIV, syphilis and HCV distributions were associated with neither the sex nor the age of the donors. Only HBsAg seroprevalence was significantly higher in donors of the age group 26-35 years old compared to donors of the age group 36-45 years (OR = 1.43 (95% CI: 1.01-2.04), P = 0.045). There was a significant increase in the frequencies of HIV and syphilis and a regression of HBsAg and HCV among blood donors. CONCLUSION: this study presents the epidemiology of the main pathogens detected in blood donors in a rural area in Gabon. We found that the overall distribution of transfusion transmitted infectious diseases were lower than those observed in the general population but could be underestimated due to the use of RDTs in the screening process of the blood donations.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".