Seroprevalence of SARS-CoV-2 antibodies among healthy blood donors: a systematic review and meta-analysis
Bibliographic record
Abstract
The development of a potent immune response and antibodies against SARS-CoV-2 is important for herd immunity. The serological response may be due to a previous infection or vaccination. Healthy blood donors could represent and provide information on the immune status of the general population. Therefore, we estimated the global and regional prevalence of SARS-CoV-2 antibodies among healthy blood donors. We conducted a systematic search of PubMed, Scopus, and ProQuest from December 2019 to January 2023. After critical appraisal and quality assessment, a qualitative synthesis of the identified relevant articles was performed. The random-effects model was used to estimate the pooled prevalence of SARS-CoV-2 antibodies. Funnel plots and Egger ’s test were used to assess publication bias. Sensitivity analysis was performed, and heterogeneity was quantified using I 2 statistics . A total of 70 peer-reviewed articles were selected that together included 2,454,192 blood donors. The global estimated pooled prevalence of SARS-CoV-2 antibodies among healthy blood donors was 10.3% (95% CI: 4.6 – 18.0%, n = 70). The highest seroprevalence was observed in Asia (17.7%), followed by Africa (16.1%). The seroprevalence in studies conducted before the introduction of the vaccine was 6.1%, whereas those of studies conducted after vaccines were available was 27.6%. High seroprevalence was observed in studies that measured antibodies against the S protein of the virus (15.2%), while lower (12.5%) in those that measured antibodies against the N protein . A high seroprevalence was observed in studies that only measured IgG antibodies (16.3%), and it was 5.9% in studies that measured total antibodies. The prevalence of SARS-CoV-2 antibodies among healthy blood donors varies, potentially indicating geographical disparities in transmission and vaccination rates. To enhance community resilience, addressing these differences through inclusive health policies and adaptable public health measures is crucial.
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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 | 0.014 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.029 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".