Leveraging a clinical emergency department dataset to estimate two-dose COVID-19 vaccine effectiveness and duration of protection in Canada
Bibliographic record
Abstract
BACKGROUND: During the COVID-19 pandemic, clinical care shifted toward virtual and Emergency Department care. We explored the feasibility of mRNA vaccine effectiveness (VE) estimation against SARS-CoV-2-related Emergency Department visits and hospitalizations using prospectively collected Emergency Department data. METHODS: We estimated two-dose VE using a test-negative design and data from 10 participating sites of the Canadian COVID-19 Emergency Department Rapid Response Network (CCEDRRN). We included Emergency Department patients presenting with COVID-19 symptoms and nucleic acid amplification testing for SARS-CoV-2 between July 19 and December 31, 2021. We excluded patients with unclear vaccination and one or more than 2 vaccine doses by their Emergency Department visit. RESULTS: Among 3,405 eligible patients, adjusted two-dose mRNA VE against SARS-CoV-2-related Emergency Department visits was 93.3 % (95 % CI 87.9-96.3 %) between 7-55 days, sustained over 80 % through 139 days post-vaccination. In stratified analyses, VE was similar among patients with select immune-compromising conditions, chronic kidney disease, lung disease, unstable housing, and reported illicit substance use. CONCLUSIONS: Two-dose mRNA VE against SARS-CoV-2-related Emergency Department visit was high and sustained, including among vulnerable subgroups. Compared to administrative datasets, active Emergency Department enrolment enables standardization for testing access and indication and supports separate VE assessment among special population subgroups. Compared to other active enrolment settings, Emergency Departments more consistently function during crises when alternate healthcare sectors become variably closed. TRIAL REGISTRATION: Clinicaltrials.gov, NCT0470294.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".