Using Vaccine Safety Data to Demonstrate the Potential of Pooled Data Analysis
Bibliographic record
Abstract
In Canada, vaccine safety studies are often conducted at the provincial/territorial level where the primary data on vaccination reside. Combining health services data from multiple jurisdictions using a pooled data analytic approach would reduce the amount of time needed to detect vaccine safety signals. To determine the difference in the time it would take to identify safety signals using different proportions of the Canadian population, we conducted power and sample size calculations for a hypothetical self-controlled case series-based surveillance analysis. We used scenarios modeled after the real-world examples of myocarditis and vaccine-induced immune thrombotic thrombocytopenia (VITT) following COVID-19 vaccination as our base cases. Our calculations demonstrated that in the case of a myocarditis-type event, a pooled analysis would reduce the time needed to detect a safety signal by over 60% compared to using Ontario data alone. In the case of a VITT-type event, a pooled analysis could detect a safety signal 49 days sooner than using Ontario data alone, potentially averting as many as 30 events. Our analysis demonstrates that there is substantial value in using pan-Canadian health services data to evaluate the safety of vaccines. Efforts should be made to develop a pan-Canadian vaccine data source to allow for an earlier evaluation of suspected adverse events following immunization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".