Clinical and Economic Impact of Differential COVID-19 Vaccine Effectiveness in the United States
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».