ArticlemedRxiv : the preprint server for health sciences2025
Effectiveness of 2024-2025 COVID-19 Vaccination Against COVID-19 Hospitalization and Severe In-Hospital Outcomes - IVY Network, 26 Hospitals, September 1, 2024-April 30, 2025.
Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
50 authors.
Funding
Abstract
Importance: As SARS-CoV-2 JN.1 lineage descendants continue to evolve, evaluating COVID-19 vaccine effectiveness (VE) against severe COVID-19 is necessary to inform vaccine composition updates. Objective: To estimate effectiveness of 2024-2025 COVID-19 vaccines against COVID-19-associated hospitalizations and severe in-hospital outcomes overall and by time since dose (7-89, 90-179, and ≥180 days), JN.1 descendant lineage (KP.3.1.1, XEC, LP.8.1), and spike mutations potentially associated with immune evasion. Design setting and participants: This test-negative, case-control analysis included adult patients hospitalized during September 1, 2024-April 30, 2025 at 26 hospitals in 20 U.S. states. Cases presented with COVID-19-like illness and a positive SARS-CoV-2 nucleic acid or antigen test; controls had COVID-19-like illness but tested negative. Exposure: Receipt of 2024-2025 COVID-19 vaccine ≥7 days before illness onset. Main Outcomes and Measures: Main outcomes were COVID-19-associated hospitalization and severe in-hospital outcomes (supplemental oxygen therapy, acute respiratory failure, intensive care unit admission, invasive mechanical ventilation [IMV] or death). Logistic regression was used to estimate the odds of vaccination in cases and controls adjusting for demographics, clinical characteristics, and enrollment region. VE was estimated as (1 - adjusted odds ratio) x 100%. Results: 1,888 COVID-19 cases (including 348 with KP.3.1.1, 218 with XEC, and 134 with LP.8.1 infections) and 6,605 controls were enrolled (median [IQR] age, 66 [54-76] years; 4,338 [51%] female). VE against COVID-19-associated hospitalization was 40% (95% CI, 27%-51%) and protection was sustained through 90-179 days after vaccination. VE was higher against the most severe outcome of IMV or death at 79% (95% CI, 55%-92%). VE was 49% (95% CI, 25%-67%) against hospitalization with KP.3.1.1, 34% (95% CI, 4%-56%) against XEC, and 24% (95% CI, -19% to 53%) against LP.8.1, with increasing median time since dose receipt due to sequential circulation patterns (60, 89, and 141 days, respectively). VE was similar against lineages with spike protein S31 deletion (41% [95% CI, 22%-56%]) and T22N and F59S substitutions (37% [95% CI, 9%-57%]). Conclusion and Relevance: 2024-2025 COVID-19 vaccines provided additional protection against severe disease as multiple JN.1 descendant lineages circulated.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.