SARS-CoV-2 vaccination, ABO blood group and risk of COVID-19: population-based cohort study
Bibliographic record
Abstract
OBJECTIVE: To compare outcomes between O and non-O blood groups, and by modified RNA (mRNA) and adenovirus-vectored (Ad-V) vaccines. DESIGN: Population-based cohort study. SETTING: All of Ontario, Canada. Linked data sets captured clinical encounters, vaccinations and laboratory testing for SARS-CoV-2. PARTICIPANTS: Individuals aged 12+ years with known ABO blood group and free of SARS-CoV-2 before 15 January 2021. MAIN OUTCOMES MEASURES: The main exposure, first SARS-CoV-2 vaccination, was modelled in a time-varying manner. O and non-O blood group was known prior to vaccination. SARS-CoV-2 infection, and severe COVID-19 (hospitalisation or death), were assessed starting 14 days after vaccination, up to 27 June 2021. RESULTS: 2 472 261 individuals were included. 1 743 916 (70.5%) had at least one vaccination, of which 24.6% were fully vaccinated. Those vaccinated were more likely to be women, older in age, residing in a higher-income area and have higher rates of certain comorbid conditions, like cancer, diabetes and hypertension. Relative to unvaccinated, after receiving their first mRNA (adjusted HR (aHR) 0.46, 95% CI 0.44 to 0.47) or Ad-V (aHR 0.49, 95% CI 0.44 to 0.54) vaccine, the risk of SARS-CoV-2 infection was lower, as was severe COVID-19 (aHR 0.29, 95% CI 0.20 to 0.43 (mRNA); aHR 0.29, 95% CI 0.26 to 0.33 (Ad-V)). Stratifying by blood group produced similar results. For example, after first mRNA vaccination, the aHR of severe COVID-19 was 0.31 (95% CI 0.27 to 0.36) among non-O blood groups, and 0.27 (95% CI 0.22 to 0.32) among O blood groups, relative to unvaccinated. Fully vaccinated individuals had the lowest risk of SARS-CoV-2 and severe COVID-19. CONCLUSIONS: SARS-CoV-2 infection and severe COVID-19 are reduced by vaccination. This effect does not vary by vaccine type or blood group, but is more pronounced among fully, than partially, vaccinated individuals.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".