Risk of transfusion‐associated malaria in sub‐Saharan Africa: The case of Mali
Bibliographic record
Abstract
Abstract Introduction Malaria is a health threat in sub‐Saharan Africa, where Plasmodium is not tested in blood bags. Our objective was to determine the prevalence of plasmodial carriage in blood bags and the associated factors, and the involvement of these bags in the occurrence of malaria in recipients. Methods From 1st April to 30th November, 2020, we conducted a prospective cross‐sectional study of 348 blood bags stored at 4°C in Bamako. Using SPSS 21.0 software, statistical analyses were performed using a binary logistic regression model with a significance threshold of p < 0.05 and the odds ratio (OR) framed by its 95% confidence interval (CI). Results During this period, 348 blood bags were transfused into 108/152 hospitalised patients, generating a transfusion frequency of 71.1%, with a prevalence of plasmodial carriage of 22%. Among the 54 initially malaria‐negative recipients, all 20 (37%) who received malaria‐positive blood bags and slept under long‐acting insecticide‐treated nets (LLINs) developed malaria. We recorded 33.3% deaths. Donor age ≤ 34 years ( p = 0.011; OR = 2.55[CI.95% = 1.25–5.23]), replacement donation ( p = 0.000; OR = 0.04[CI.95% = 0.0–0.19]) and not regular use of LLINs by donors ( p = 0.048; OR = 0.53[CI.95% = 0.29–1]) were factors associated with plasmodial carriage of blood bags. CD4 count<200 cells/mm 3 ( p = 0.002; OR = 0.2[CI.95% = 0.10–0.52]), severe anaemia ( p = 0.034; OR = 0.26[CI.95% = 0.10–0.90]) and decompensated anaemia ( p = 0.034; OR = 3.88[CI.95% = 1.11–13.56]) were factors independently associated with recipient death. Conclusion The prevalence of plasmodial carriage among blood donors is increasing in Mali. Transfusion malaria is a reality to be feared, with the risk increasing with the level of malaria endemicity of the blood donor.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".