Clinical and Economic Impact of Differential COVID-19 Vaccine Effectiveness in the United States
Bibliographic record
Abstract
Abstract Background In the United States (US), three vaccines are currently available for primary vaccination and booster doses to prevent coronavirus disease 2019 (COVID-19), including the 2-dose messenger ribonucleic acid (mRNA) BNT162b2 (COMIRNATY®, Pfizer Inc) and mRNA-1273 (SPIKEVAX®, Moderna Inc) vaccines, which are preferred by the Centers for Disease Control and Prevention’s (CDC) Advisory Committee on Immunization Practice (ACIP), and the adenovirus vector Ad26.COV2.S (Johnson & Johnson) vaccine. A substantial body of evidence has now been published on the real-world effectiveness and waning of the primary series and booster doses against specific SARS-CoV2-variants. The study objective was to determine the clinical and economic impact of differences in effectiveness between mRNA-1273 and BNT162b2 booster vaccinations over one year (2022) in US adults ≥18 years. Methods A decision analytic model was used to compare three mRNA booster market share scenarios: (1) Current Scenario, where the booster mix observed in December 2021 continues throughout 2022; (2) mRNA-1273 Scenario, where the only booster administered in 2022 is mRNA-1273, and (3) BNT162b2 Scenario, where the only booster administered in 2022 is BNT162b2. Analyses were performed from the US healthcare system perspective. Sensitivity analyses were performed to explore the impact of COVID-19 incidence in the unvaccinated population and vaccine effectiveness (VE) on model results. Results In the Current Scenario, the model predicts 65.2 million outpatient visits, 3.4 million hospitalizations, and 636,100 deaths from COVID-19 in 2022. The mRNA-1273 Scenario reduced each of these outcomes compared to the Current Scenario. Specifically, 684,400 fewer outpatient visits, 48,700 fewer hospitalizations and 9,500 fewer deaths would be expected. Exclusive of vaccine costs, the mRNA-1273 Scenario is expected to decrease direct medical costs by $1.3 billion. Conversely, the BNT162b2 Scenario increased outcomes compared to the Current Scenario: specifically, 391,500 more outpatient visits, 34,500 more hospitalizations and 7,200 more deaths would be expected in 2022, costing an additional $946 million in direct medical costs. For both the mRNA-1273 and BNT162b2 booster scenarios, the percent change in direct treatment costs for COVID-19 is similar to the percent change in hospitalizations as the rate of hospitalizations is the driver of the overall costs. Changing the number of projected COVID-19 cases in 2022 by varying the incidence rate has a direct effect on model outcomes. Higher incidence rates leads to higher outpatient visits, hospitalizations and deaths for all scenarios. Varying VE has an inverse effect on model outcomes. All outcomes increase when VE is lower for all vaccines and decrease when VE is higher. In all cases, additional use of mRNA-1273 leads to fewer infection outcomes while additional use of BNT126b2 results to higher infection outcomes. Conclusion As the real-world effectiveness evidence to date indicates that mRNA-1273 may be more effective at preventing COVID-19 infection and hospitalization over time than BNT-162b2, increasing the proportion of people receiving this as a booster are expected to reduce COVID-19-related outcomes and costs in 2022, regardless of COVID-19 incidence or variant.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".