Impact of Differential Vaccine Effectiveness on COVID-19 Hospitalization Cases: Projections for 10 Developed Countries where Booster Vaccines were Recommended
Bibliographic record
Abstract
ABSTRACT Background & Objectives In a previous analysis, a decision-analytic model was used to analyze the clinical and economic impact of the differences in effectiveness between the two licensed mRNA COVID-19 booster vaccines, mRNA-1273 and BNT162b2, in 2022 for adults aged 18 years and older in the United States (US). In this analysis, the same model was used to estimate the impact that administering first booster doses with mRNA-1273 could have had on COVID-related hospitalizations and costs over a 6-month period in 10 developed countries (Australia, Canada, France, Germany, Italy, Japan, South Korea, Spain, United Kingdom [UK], and US), considering updated effectiveness data. Methods The model was used to estimate number of hospitalizations and related costs using the actual vaccine distribution for the first COVID-19 booster from each country. These estimates were compared to a scenario where 100% of doses for that 6-month period was assumed to be mRNA-1273. The effectiveness of mRNA-1273 compared to BNT162b2 was estimated from real world data from the UK. Results The total number of doses switched to the mRNA-1273 booster would range from 4.3 million in Spain to 39.4 million in Japan. The number of hospitalizations and associated hospitalization costs would be expected to fall in all countries, with the proportional decrease ranging from 1.1% (16,800 fewer) in Germany to 8.8% (25,100 fewer) in Australia. Conclusions Real-world effectiveness data suggest that a booster dose of the mRNA-1273 vaccine may be more effective compared to other vaccines used for booster doses. Given this difference in effectiveness, results of this analysis demonstrate that switching to 100% mRNA-1273 boosters would have reduced the number of hospitalizations and associated costs in each country during the first 6 months of the omicron period.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".