Meta-Analysis of Risk of Vaccine-Induced Immune Thrombotic Thrombocytopenia Following ChAdOx1-S Recombinant Vaccine
Bibliographic record
Abstract
Abstract Context Vaccine-induced immune thrombotic thrombocytopenia (VITT) has been reported after administering ChAdOx1-S recombinant COVID-19 vaccine (marketed as Vaxzevira™ by Astra-Zeneca, Covishield™). Estimates of incidence vary between countries, due to different age distributions chosen, case definitions and choice of denominator (persons vaccinated vs immunizations given). This study clarifies these estimates by pooling data from ten countries and examining differences by age group. Methods We examined case reports, press releases and immunization data and calculated pooled estimates of VITT incidence using random effects models. Sensitivity analyses considered different combinations of countries and varying assumptions on time between vaccination and reporting of cases. Results Pooling all countries, VITT incidence was 0.73 per 100,000 persons receiving first dose of Covishield/Vaxzevira [95% CI .43,1.23]. Incidence for age 65 and over was 0.11 per 100,000 persons [95% CI .05-.26], and significantly higher among those under age 55: 1.67 per 100,000 persons [95% CI 1.30-2.14] in the UK, 5.06 per 100,000 persons in Norway [95% CI 2.16, 11.86]. The latter had the best data on counts of persons vaccinated. Incidence for age 55 to 64 years was 0.34 [95% CI 0.13, 0.85] in the UK, lower than for under age 55. Conclusion VITT is a rare vaccine-associated adverse event. Incidence estimates vary between jurisdictions. However, even the highest reported incidence from Norway is low – and in settings with high community transmission, lower than risk of serious outcomes associated with Covid-19. Policymakers and individuals can use these data to calculate risk-benefit ratios and better target vaccine distribution. Essentials This paper measures risk of vaccine-induced immune thrombotic thrombocytopenia (VITT) after ChAdOx1-S recombinant COVID-19 vaccine Pooled estimates of incidence were calculated with a random effects model based on data from 10 countries Overall risk is 1 in 139,000; for age 65 and over, about 1 in 1,000,000; for age under 55, between 1 in 20,000 to 60,000 VITT risk is low and varies by age. These data can inform policies around vaccination distribution.
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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 | 0.021 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.055 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".