Clinical and Economic impact of updated Fall 2023 COVID-19 vaccines in the Immunocompromised Population in Canada
Bibliographic record
Abstract
ABSTRACT Background Immunocompromised (IC) individuals are at increased risk of COVID-19 infection-related severe outcomes. Moderna and Pfizer-BioNTech COVID-19 mRNA vaccines are available in Canada, and differences in vaccine effectiveness (VE) have been found between the two in IC individuals. The objective of this analysis was to compare the clinical and economic impact of a Moderna XBB.1.5 updated COVID-19 mRNA Fall 2023 vaccine to a Pfizer-BioNTech XBB.1.5 updated COVID-19 mRNA Fall 2023 vaccine in Canadian IC individuals aged ≥18 years. Methods A static decision-analytic model estimated the number of COVID-19 infections, hospitalizations, deaths, and resulting quality-adjusted life years (QALYs) over a one-year time horizon (September 2023-August 2024) in the Canadian IC adult population (n=894,580). Costs associated with COVID-19 infection were estimated from health care and societal perspectives. The predicted VE of the updated Moderna vaccine was based on prior variant versions, which were well-matched to the circulating variant. Pfizer-BioNTech VE was calculated based on a meta-analysis of comparative effectiveness between both vaccines (relative risk for Moderna vaccine: infection=0.85 [95%CI 0.75-0.97], hospitalization=0.88 [95%CI 0.79-0.97]). The model combined VE estimates with COVID-19 incidence and probability of COVID-19 related severe outcomes. Sensitivity analyses tested the impact of uncertainty surrounding incidence, hospitalization and mortality rates, costs, and QALYs. Results Given the expected higher VE against infection and hospitalizations with the Moderna Fall 2023 vaccine, its use is predicted to prevent an additional 2,411 infections (3.6%), 275 hospitalizations (3.7%), and 47 deaths (4.0%) compared to the Pfizer-BioNTech Fall 2023 vaccine, resulting in 330 QALYs gained, and savings of $7.4M in infection treatment costs, and $0.9M in productivity loss costs. Results were most sensitive to variations in VE parameters, specifically the relative risk of infection and hospitalizations between the vaccines, and waning rates. Conclusions If the Moderna and Pfizer-BioNTech Fall 2023 vaccines protect against infection and hospitalizations similar to previous vaccines, using the Moderna Fall 2023 vaccine would result in substantial public health benefits in IC individuals, as well as provide health care and societal cost savings.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".