Clinical impact and cost-effectiveness of updated 2023/24 COVID-19 mRNA vaccination in high-risk populations in the United States
Bibliographic record
Abstract
Abstract Introduction In the post-pandemic era, people with underlying medical conditions continue to be at increased risk for severe COVID-19 disease, yet COVID-19 vaccination uptake remains low. This study estimated the clinical and economic impact of updated 2023/24 Moderna COVID-19 vaccination among high-risk adults versus no updated vaccination and versus updated Pfizer/BioNTech vaccination. Methods A static Markov model was adapted for high-risk adults, including immunocompromised (IC), chronic lung disease (CLD), chronic kidney disease (CKD), cardiovascular disease (CVD), and diabetes mellitus (DM) populations in the United States. Results Vaccination with the updated Moderna vaccine at current coverage rates was estimated to prevent considerable COVID-19 hospitalizations in CLD (101,309), DM (97,358), CVD (47,830), IC (14,834) and CKD (13,558) populations versus no updated vaccination. Vaccination also provided net medical cost savings of $399M–2,129M (healthcare payer) and $457M–2,531M (societal perspective), depending on population. The return-on-investment was positive across all conditions ($1.10–$2.60 gain for every $1 invested). Healthcare savings increased with a relative 10% increase in current vaccination coverage ($439M–$2,342M), and from meeting US 2030 targets of 70% coverage ($1,096M–$5,707M). Based on higher vaccine effectiveness observed in real-world evidence studies, updated Moderna vaccination was estimated to prevent additional COVID-19 hospitalizations in DM (13,105), CLD (10,359), CVD (6,241), IC (1,979), and CKD (942) versus Pfizer/BioNTech’s updated vaccine, with healthcare payer and societal cost savings, making it the dominant strategy. Healthcare savings per patient vaccinated with Moderna versus Pfizer/BioNTech’s updated vaccine were $31-59, depending on population. Results were robust across sensitivity/scenario analyses. Conclusions Updated 2023/24 Moderna COVID-19 vaccination was estimated to provide significant health benefits through prevention of COVID-19 in high-risk populations, and cost-savings to healthcare payers and society, versus no vaccination and updated Pfizer/BioNTech vaccination. Increasing current low COVID-19 vaccination coverage rates was estimated to be cost-saving while preventing many more severe infections and hospitalizations in these high-risk populations. Key Summary Points Why carry out this study? In the US, people with underlying medical conditions continue to be at high risk of severe COVID-19, yet vaccination rates are low. The CDC recommends an updated 2024/25 COVID-19 vaccination for everyone aged >6 months. The objective of this study was to estimate the clinical benefits and cost-effectiveness of updated 2023/24 Moderna COVID-19 vaccination in people with high-risk conditions, versus no updated vaccination, and versus updated Pfizer/BioNTech COVID-19 vaccination. What was learned from the study? COVID-19 vaccination with the updated Moderna mRNA vaccine of people with underlying medical conditions at high-risk of severe COVID-19 was cost-saving versus no updated vaccination. It also provided more health gains with cost savings versus Pfizer/BioNTech, making it the dominant strategy. For every $1 spent on vaccination, the updated Moderna vaccination provided a return-on-investment of $1.10–$2.60 versus no updated vaccination, depending on the high-risk population. Healthcare cost savings were $31-59 per patient vaccinated with Moderna’s versus Pfizer/BioNTech’s updated vaccination, depending on the high-risk population. A relative 10% increase in vaccination coverage rates prevented 10% more hospitalizations and deaths, and increased healthcare and societal cost savings in all high-risk populations, with the potential for significant health and financial benefits with greater vaccine coverage rates.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".