Effectiveness of Pfizer-BioNTech COVID-19 vaccine as evidence for policy action: A rapid systematic review and meta-analysis of non-randomized studies
Bibliographic record
Abstract
In December 2020, an interim recommendation for the use of Pfizer-BioNTech COVID-19 vaccine in persons aged ≥16 years was made under Food and Drug Administration's Emergency Use Authorization. In preparation for Biologics License Application approval, we conducted a systematic review and meta-analysis to inform the U.S. Centers for Disease Control and Prevention's Advisory Committee for Immunization Practice's (ACIP) decision-making for a standard recommendation. We conducted a rapid systematic review and meta-analysis of Pfizer-BioNTech vaccine effectiveness (VE) against symptomatic COVID-19, hospitalization due to COVID-19, death due to COVID-19, and asymptomatic SARS-CoV-2 infection. We identified studies through August 20, 2021 from an ongoing systematic review conducted by the International Vaccine Access Center and the World Health Organization. We evaluated each study for risk of bias using the Newcastle-Ottawa Scale. Pooled estimates were calculated using meta-analysis. The body of evidence for each outcome was assessed using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach. We identified 80 articles, selected 35 for full-text review, and included 26. The pooled VE of Pfizer-BioNTech COVID-19 vaccine was 92.4% (95% CI: 87.5%-95.3%) against symptomatic COVID-19 with moderate evidence certainty (eight studies), 94.3% (95% CI: 87.9%-97.3%) against hospitalization due to COVID-19 with moderate certainty (eight studies), 96.1% (95% CI: 91.5%-98.2%) against death due to COVID-19 with moderate certainty (four studies), and 89.3% (88.4%-90.1%) against asymptomatic SARS-CoV-2 infection with very low certainty (two studies). The Pfizer-BioNTech COVID-19 vaccine demonstrated high effectiveness in all pre-specified outcomes and extended knowledge of the vaccine's benefits to outcomes and populations not informed by the RCTs. Use of an existing systematic review facilitated a rapid meta-analysis to inform an ACIP policy decision. This approach can be utilized as additional COVID-19 vaccines are considered for standard recommendations by ACIP.
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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 | 0.070 | 0.131 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.024 | 0.050 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".