Annual Variant-Targeted Vaccination to Prevent Severe COVID-19 in Cohorts With Vaccine-Derived and Hybrid Immunity
Bibliographic record
Abstract
BACKGROUND: Current coronavirus disease 2019 (COVID-19) vaccine recommendations in the United States (US) provide guidance for adults to receive at least annual variant-targeted vaccination. We sought to estimate the strength and durability of protection from annual variant-targeted vaccination against severe COVID-19 illness in individuals with vaccine-derived and hybrid immunity. METHODS: We emulated a target trial using an electronic health record-based, propensity score-matched (1:1) cohort of US Veterans. Booster-vaccinated adults were eligible for a variant-targeted messenger RNA (mRNA) booster starting 1 September 2022. Matched sets of those who did and did not receive the variant-targeted booster dose were identified on a weekly basis, and the cohort was followed until 31 August 2023. Outcomes were hospitalization due to COVID-19 pneumonia and in-hospital severe illness. We fit Cox models, overall and stratified by last documented severe acute respiratory syndrome coronavirus 2 infection (pre-Omicron, Omicron), to estimate relative vaccine effectiveness (rVE). RESULTS: The propensity score-matched cohort consisted of 1 576 626 COVID-19 booster-vaccinated adults. Estimates of rVE from variant-targeted mRNA booster against hospitalization due to COVID-19 pneumonia were significant and similar in the cohort with vaccine-derived immunity (rVE, 29% [95% confidence interval {CI}, 25%-34%]) and cohort with hybrid immunity (rVE, 38% [95% CI, 27%-47%]). These protective gains were significant from 0 to 6 months but not 6 to 12 months after vaccination and during pre-XBB and XBB variant eras. Findings were similar for in-hospital severe illness. CONCLUSIONS: In cohorts with vaccine-derived and hybrid immunity, modest but significant gains in protection against hospitalization and severe COVID-19 illness were conferred by the annual variant-targeted booster dose but not sustained beyond 6 months.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".