Seroprevalence of SARS-CoV-2 antibodies among healthy blood donors: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Introduction: The development of a potent immune response and antibodies against SARS-CoV-2 is important for attaining herd immunity. This serological response could be due to past infection or vaccines. Healthy blood donors could represent and provide information on the immune status of the general population. Hence, we estimated the global and regional prevalence of SARS-CoV-2 antibodies among healthy asymptomatic blood donors. Methods: We systematically searched PubMed, Scopus, and ProQuest for eligible articles published between December 1, 2019, and January 12, 2023, without language restrictions. After critical appraisal and quality assessment, a qualitative synthesis of the identified pertinent articles was performed. The random-effect 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 I2 statistics. Results: A total of 70 peer-reviewed articles were selected and included 2,453,937 blood donors. The global estimated pooled prevalence of SARS-CoV-2 antibodies among healthy blood donors was 10.9% (95% CI: 5.0 – 18.8%, n=68). A high seroprevalence of SARS-CoV-2 was observed in Asia (20.4%, 95% CI: 10.1 – 33.1%, n=24), followed by Africa (16.1%, 95% CI: 6.8 – 28.3%, n=7). The seroprevalence of SARS-CoV-2 in studies conducted before the introduction of the vaccine was 6.5% (95% CI: 4.9 – 8.3%, n=50), while that of studies conducted after the vaccine was 27.6% (95% CI: 12.4 – 46.2% n=18). High seroprevalence was observed in studies that measured antibodies against the S protein of the virus (16.2%, 95% CI: 11.4 – 21.8%, n=27), while it was 12.5% (95% CI: 5.3 – 22.1%, n=16) in those that measured antibodies against the N protein. A high seroprevalence of SARS-CoV-2 was observed in studies that only measured IgG antibodies (17.2%, 95% CI: 10.5 – 25.1%, n=33) and in studies that measured total antibodies to SARS-CoV-2 (6.2%, 95% CI: 0.7 – 16.5%, n=33). Conclusion: In view of all evidence, there is variation in the prevalence of SARS-CoV-2 antibodies among healthy blood donors globally. Noticeably, there is a regional difference that could depict differences in transmission and vaccination rates. Based on the results of our analysis, we recommend evidence-based booster vaccination strategies informed by seroprevalence trends and waning immunity and reinforcing seroprevalence surveillance for outbreak management. It is advisable to mitigate socioeconomic disparities through inclusive health policies coupled with adaptable public health measures given local seroprevalence rates. These will contribute to informed policy decisions to build community resilience during the post-PHEIC phase of the COVID-19 pandemic.
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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 | 0.013 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.026 |
| 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".