COVID-19 vaccine effectiveness against hospitalisation in the Netherlands, 2021: Improved stratified estimates
Bibliographic record
Abstract
In the Netherlands, medical risk data and SARS-CoV-2 test results were not available for stratifying vaccine effectiveness (VE) against COVID-19 hospitalisation during the COVID-19 pandemic. Such data became available afterwards, allowing for re-estimating VE and the number needed to vaccinate (NNV) by medical risk group for severe COVID-19. We conducted a nationwide register-based cohort study, estimating VE against first-time COVID-19 hospitalisation from 06 to 01-2021 to 31-12-2021 in persons aged ≥12 years without registered prior SARS-CoV-2 infection. VE of one and two vaccinations, versus unvaccinated, were estimated by age, medical risk, vaccine type and time since vaccination using Cox models with vaccination status as time-varying exposure. Additionally, we computed the NNV to prevent one hospitalisation. Among 14.3 million individuals in this study, 43,405 COVID-19 first-time hospitalisations were recorded. VE of two doses was >80 % in the first quarter post-vaccination across all strata. VE decreased over time, but vaccination remained protective three quarters post-vaccination. Among persons aged ≥60 years without medical risk conditions, VE was 96.1 %, 91.0 % and 84.9 % in the first, second and third quarter since vaccination. VE was lower and waned faster among persons with medical risk conditions: 91.1 %, 80.0 %, 70.8 % and 85.4 %, 73.4 %, 60.6 %, in the first three quarters post-vaccination in the moderate and high medical risk groups, respectively. Nonetheless, the NNV was lower among medical risk groups. These findings suggest that prioritising medical risk populations for booster vaccination was warranted. To adequately respond to future epidemics and optimise vaccination programmes, the data infrastructure should allow near-real-time stratified VE analyses. • COVID-19 vaccination was highly effective against COVID-19 hospitalisation. • Effectiveness decreased over time, but was significant for at least 9 months. • Effectiveness was lower and declined faster in persons with high risk of severe COVID. • Medical risk populations were rightly targeted for additional vaccination rounds. • Timely stratified vaccine effectiveness analyses can inform targeted vaccine policy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".