Protection of Messenger RNA Vaccines Against Hospitalized Coronavirus Disease 2019 in Adults Over the First Year Following Authorization in the United States
Bibliographic record
Abstract
BACKGROUND: Coronavirus disease 2019 (COVID-19) messenger RNA (mRNA) vaccines were authorized in the United States in December 2020. Although vaccine effectiveness (VE) against mild infection declines markedly after several months, limited understanding exists on the long-term durability of protection against COVID-19-associated hospitalization. METHODS: Case-control analysis of adults (≥18 years) hospitalized at 21 hospitals in 18 states 11 March-15 December 2021, including COVID-19 case patients and reverse transcriptase-polymerase chain reaction-negative controls. We included adults who were unvaccinated or vaccinated with 2 doses of a mRNA vaccine before the date of illness onset. VE over time was assessed using logistic regression comparing odds of vaccination in cases versus controls, adjusting for confounders. Models included dichotomous time (<180 vs ≥180 days since dose 2) and continuous time modeled using restricted cubic splines. RESULTS: A total of 10 078 patients were included, 4906 cases (23% vaccinated) and 5172 controls (62% vaccinated). Median age was 60 years (interquartile range, 46-70), 56% were non-Hispanic White, and 81% had ≥1 medical condition. Among immunocompetent adults, VE <180 days was 90% (95% confidence interval [CI], 88-91) versus 82% (95% CI, 79-85) at ≥180 days (P < .001). VE declined for Pfizer-BioNTech (88% to 79%, P < .001) and Moderna (93% to 87%, P < .001) products, for younger adults (18-64 years) (91% to 87%, P = .005), and for adults ≥65 years of age (87% to 78%, P < .001). In models using restricted cubic splines, similar changes were observed. CONCLUSIONS: In a period largely predating Omicron variant circulation, effectiveness of 2 mRNA doses against COVID-19-associated hospitalization was largely sustained through 9 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".