Economic evaluation for mass vaccination against COVID-19

Abstract

Background Vaccine is supposed to be the most effective means to prevent COVID-19 as it may not only save lives but also reduce productivity loss due to resuming pre-pandemic activities. Providing the results of economic evaluation for mass vaccination is paramount important for all stakeholders worldwide. Methods We developed a Markov decision tree for the economic evaluation of mass vaccination against COVID-19. The effectiveness of reducing outcomes after the administration of three COVID-19 vaccines (BNT162b2 (Pfizer-BioNTech), mRNA-1273 (Moderna), and AZD1222 (Oxford-AstraZeneca)) were modelled with empirical parameters obtained from literatures. The direct cost of vaccine and COVID-19 related medical cost, the indirect cost of productivity loss due to vaccine jabs and hospitalization, and the productivity loss were accumulated given different vaccination scenarios. We reported the incremental cost-utility ratio and benefit/cost (B/C) ratio of three vaccines compared to no vaccination with a probabilistic approach. Results Moderna and Pfizer vaccines won the greatest effectiveness among the three vaccines under consideration. After taking both direct and indirect costs into account, all of the three vaccines dominated no vaccination strategy. The results of B/C ratio show that one dollar invested in vaccine would have USD $13, USD $23, and USD $28 in return for Moderna, Pfizer, and AstraZeneca, respectively when health and education loss are considered. The corresponding figures taking value of the statistical life into account were USD $176, USD $300, and USD $443. Conclusion Mass vaccination against COVID-19 with three current available vaccines is cost-saving for gaining more lives and less cost incurred.

Prioritization of the Target Population for Coronavirus Disease 2019 (COVID-19) Vaccination Program in Thailand

“…In addition, our sensitivity analysis found that even with a very humble assumption of VE (VE = 50% for base analysis and VE = 20% and 80% for sensitivity analysis), the benefit of mass vaccination, especially on outbreak-prone communities such as migrant populations, was still evident. Our findings pointed in the same direction as the study by Wang et al [39], which applied the Markov decision tree for cost-benefit analysis on COVID-19 vaccines. However, the vaccines of interest in Wang et al's study were mRNA or viral-vector vaccines, which demonstrated a higher degree of VE (70-75%) relative to live-attenuated vaccines such as those in our study.…”

Análisis de impacto presupuestal de la vacunación contra COVID-19 en América Latina

“…En EEUU por ejemplo, Kholi, Maschio, Becker y Weinsten ( 8 ) desarrollaron un Modelo de Markov, para estimar los costos médicos directos y las muertes relacionadas con COVID-19 en una población de pacientes mayores de 65 años, costo por AVAC ganado < USD 50 000. Estos resultados son similares a los obtenidos por Wang et al ( 9 ) en Taiwán, para tres vacunas contra COVID-19: BNT162b2 (Pfizer-BioNTech), mRNA-1273 (Moderna), y AZD1222 (Oxford-AstraZeneca), donde su principal resultado es que la vacunación masiva contra COVID-19 disminuye la cantidad de vidas pérdidas por la pandemia, y reduce los costos directos e indirectos en comparación al escenario sin vacunación. En Reino Unido también se evaluó la costó efectividad de los programas de vacunación contra COVID-19 y las medidas de cuarentena y distanciamiento físico, poniendo a la vacunación como el mejor escenario ( 10 ).…”

COVID-19 Induced Economic Slowdown and Mental Health Issues

“…A study in the United States suggested that social distancing would not be a cost-effective strategy unless an effective therapy or vaccine could be introduced within 11.1 months of late May 2020 (Thunstrom et al, 2020). Increasing studies have been established and informed the vaccination prioritization (Kohli et al, 2021;Padula et al, 2021;Wang W. C. et al, 2021). Third, stigmatization resulted from misinformation and invalid communication can hinder the application of cost-effective strategies in flattening the COVID-19 curve.…”