The potential public health and economic value of a hypothetical COVID-19 vaccine in the United States: Use of cost-effectiveness modeling to inform vaccination prioritization

Abstract

Background: Researchers are working at unprecedented speed to develop a SARS-CoV-2 vaccine. We aimed to assess the value of a hypothetical vaccine and its potential public health impact when prioritization is required due to supply constraints.

Methods: A Markov cohort model was used to estimate COVID-19 related direct medical costs and deaths in the United States (US), with and without implementation of a 60% efficacious vaccine. To prioritize the vaccine under constrained supply, the population was divided into tiers based on age; risk and age; and occupation and age; and outcomes were compared across one year under various supply assumptions. The incremental cost per quality-adjusted life-year (QALY) gained versus no vaccine was calculated for the entire adult population and for each tier in the three prioritization schemes.

Results: The incremental cost per QALY gained for the US adult population was $8,200 versus no vaccination. For the tiers at highest risk of complications from COVID-19, such as those ages 65 years and older, vaccination was cost-saving compared to no vaccination. The cost per QALY gained increased to over $94,000 for those with a low risk of hospitalization and death following infection. Results were most sensitive to infection incidence, vaccine price, the cost of treating COVID-19, and vaccine efficacy. Under the most optimistic supply scenario, the hypothetical vaccine may prevent 31% of expected deaths. As supply becomes more constrained, only 23% of deaths may be prevented. In lower supply scenarios, prioritization becomes more important to maximize the number of deaths prevented.

Conclusions: A COVID-19 vaccine is predicted to be good value for money (cost per QALY gained <$50,000). The speed at which an effective vaccine can be made available will determine how much morbidity and mortality may be prevented in the US.

Keywords: COVID-19; Coronavirus; Cost-effectiveness analysis; Economic analysis; SARS-CoV-2; Vaccine.

Fig. 1

Structure of the model of SARS-CoV-2 infection and COVID-19 progression. (A) Markov health states showing allowed transitions. (B) Probability tree linking transitions from the “Detected Infection” state in the Markov model. Arrows represent the movements between the health states. Death from “Detected infection” is due to COVID-19 while death from all other health states is due to other causes. ICU, intensive care unit.

Fig. 2

Tornado diagram showing the impact of the sensitivity analyses on the incremental cost per quality-adjusted life-year gained of vaccination compared to no vaccination (target population: all adults). BC, base case. *Vaccination dominates no vaccination (it is less costly and more effective) when the base case incidence of infection is doubled or the estimates of commercial costs are used as inputs. ^†Alternative values were used for the calibrated probabilities of hospitalization and death following detected infection as described in the Appendix. ^‡For the base case, single dose efficacy was assumed to be 40% and 25% of full efficacy for those under 55 years and those 55+ years, respectively. This was increased to 40% of full efficacy for everyone in the sensitivity analysis. ^§Undetected infection incidence was assumed to be 1.05 times the incidence of detected infection in the base case. This was increased to 1.5 for the sensitivity analysis.