ABO and Rh blood groups and risk of infection: systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Persons with non-O and Rh-positive blood types are purported to be more susceptible to infection, including SARS-CoV-2, but there remains uncertainty about the degree to which this is so for both non-viral and viral infections. Methods We systematically reviewed Embase and PubMed from January 1st 1960 to May 31st 2022. English-language publications were selected that separately investigated the relation between ABO and/or Rh blood group and risk of SARS-CoV-2 and non-SARS-CoV-2 infection. Pooled odds ratios (ORp) and 95% confidence intervals (CI) were then generated for each. Results Non-O blood groups had a higher ORp for SARS-CoV-2 than O blood groups, both within 22 case–control studies (2.13, 95% CI 1.49- 3.04) and 15 cohort studies (1.89, 95% CI 1.56- 2.29). For non-SARS-CoV-2 viral infections, the respective ORp were 1.98 (95% CI 1.49–2.65; 4 case–control studies) and 1.87 (95% CI 1.53–2.29; 12 cohort studies). For non-viral infections, the ORp were 1.56 (95% CI 0.98–2.46; 13 case–control studies) and 2.11 (95% CI 1.67–6.67; 4 cohort studies). Rh-positive status had a higher ORp for SARS-CoV-2 infection within 6 case–control studies (13.83, 95% CI 6.18–30.96) and 6 cohort studies (19.04, 95% CI 11.63–31.17), compared to Rh-negative persons. For Rh status, non-SARS-CoV-2 infections, the ORp were 23.45 (95% CI 16.28–33.76) among 7 case–control studies, and 9.25 (95% CI 2.72–31.48) within 4 cohort studies. High measures of heterogeneity were notably observed for all analyses. Conclusions Non-O and Rh-positive blood status are each associated with a higher risk of SARS-CoV-2 infection, in addition to other viral and non-viral infections.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.027 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".