The Effect of Pandemic Prevalence on the Reported Efficacy of SARS-CoV-2 Vaccine Candidates: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract Importance The efficacy of SARS-CoV-2 vaccine candidates reported in Phase 3 trials varies from ∼45% to ∼95%. It is important to explain the reasons for this heterogeneity. Objective To test the hypothesis that the efficacy of SARS-CoV-2 vaccine candidates falls with increasing prevalence of the COVID-19 pandemic. Data Sources ClinicalTrials.gov , WHO, McGill and LSHTM trackers of COVID-19 candidate vaccines, peer reviewed publications, and press releases were searched until March 31 st , 2021. Study Selection All RCTs reporting efficacy outcomes from Phase 3 trials till March 31 st , 2021 were included. Of the 11 vaccine candidates that had started their Phase 3 trials by November 1, 2020. Phase 3 efficacy outcomes were available for 8 vaccine candidates. (PROSPERO CRD42021243121). Data Extraction and Synthesis Both authors independently extracted the data required from identified sources, using PRISMA guidelines. The analysis included all RCTs reported in peer reviewed publications and publicly available sources. A random effects model with restricted maximum likelihood estimator was used to summarize the treatment effects. Cochrane Risk of Bias Assessment Tool was used to assess risk of bias. Certainty of evidence was assessed using the GRADE tool. Main Outcomes and Measures SARS-CoV-2 infections per protocol in vaccine and placebo groups, risk ratio, prevalence of the COVID-19 infection rate in the populations where the Phase 3 trials were conducted. Results 8 vaccine candidates had reported efficacy data from a total of 20 independent Phase 3 trials, representing a total of 221,968 subjects, 453 infections across the vaccinated groups and 1,554 infections across the placebo groups. The overall estimate of the risk-ratio is 0.24 (95% CI, 0.17-0.34, p < 0.01), with an I 2 statistic of 88.73%. The meta-regression analysis with pandemic prevalence as the moderator explains almost half the variance in risk ratios across trials (R 2 =49.06%, p<0.01). Conclusion and Relevance Pandemic prevalence explains almost half of the between-trial variance in reported efficacies. Efficacy of SARS-CoV-2 vaccine candidates declines as the pandemic prevalence increases. Key Points Question Does the prevalence of the COVID-19 pandemic explain the heterogeneity in efficacies reported across Phase 3 trials of SARS-CoV-2 vaccine candidates? Findings Almost 50% of the variance in efficacies reported across Phase 3 trials can be explained by differences in COVID-19 infection rate prevailing across trials. Efficacy of evaluated SARS-CoV-2 vaccine candidates falls significantly with increasing prevalence of the COVID-19 pandemic across trial sites. Meaning Efficacy of SARS-CoV-2 vaccine candidates needs to be interpreted in conjunction with the prevalence of the COVID-19 pandemic. Adjustment for location-level prevalence analysis would provide better insights into the efficacy results of Phase 3 trials.
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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 | 0.036 | 0.093 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.027 | 0.043 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".