The global health and economic value of COVID-19 vaccination

Abstract

Introduction

The COVID-19 pandemic triggered one of the largest global health and economic crises in recent history. COVID-19 vaccination (CV) has been the central tool for global health and macroeconomic recovery, yet estimates of CV’s global health and macroeconomic value remain scarce.

Methods

We used regression analyses to measure the impact of CV on gross domestic product (GDP), infections and deaths. We combined regression estimates of vaccine-averted infections and deaths with estimates of quality-adjusted life years (QALY) losses, and direct and indirect costs, to estimate three broad value components: (i) QALY gains, (ii) direct and indirect costs averted and (iii) GDP impacts. The global value is the sum of components over 148 countries between January 2020 and December 2021 for CV generally and for Pfizer-BioNTech specifically.

Results

CV’s global value was US$5.2 (95% CI US$4.1 to US$6.2) trillion, with Pfizer-BioNTech’s vaccines contributing over US$1.9 (95% CI US$1.5 to US$2.3) trillion. Varying key parameters results in values 10%–20% higher or lower than the base-case value. The largest value component was GDP impacts, followed by QALY gains, then direct and indirect costs averted. CV provided US$740 of value per dose, while Pfizer-BioNTech specifically provided >US$1600 per dose. We estimated conservative benefit-cost ratios of 13.9 and 30.8 for CV and Pfizer-BioNTech, respectively.

Conclusions

We provide the first estimates of the broad value of CV incorporating GDP, QALY and direct and indirect cost impacts. Through December 2021, CV produced significant health and economic value, represented strong value for money and produced significant macroeconomic benefits that should be considered in vaccine evaluation.

WHAT IS ALREADY KNOWN ON THIS TOPIC

• Only three studies have evaluated the global health and/or macroeconomic impact of COVID-19 vaccination (CV), but (i) none translated global mortality or morbidity impacts into summary health measures such as quality-adjusted life years (QALYs), (ii) studies that estimated the global economic impacts of CV did not assess its impacts on the key macroeconomic indicator, gross domestic product (GDP) and (iii) none of the global studies provided a single comprehensive value measure integrating both health and macroeconomic values, or estimated the value-for-money of CV relating its benefits to its costs.

WHAT THIS STUDY ADDS

• Our study addresses all three of the above deficits in the literature and in our understanding of the value of CV, finding that CV provided >US$5 trillion in value globally through December 2021, that GDP gains constituted the largest value component (US$2.6 trillion), QALY gains constituted the second largest component (US$2.1 trillion) and direct and indirect costs averted constituted the smallest component (US$0.4 trillion).

• CV provided a value of US$741 per dose, which corresponds to a very strong benefit-cost ratio of almost 14.

• The GDP benefits alone yielded a benefit-cost ratio of 6.5, indicating that CV’s costs are justified many times over by GDP benefits alone.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

• This study demonstrates the significant value and value-for-money of CV, and the substantial economic and health benefits it provides.

• It supports investments in, and prioritisation of, global vaccination to end the pandemics; clarifies that addressing pandemics is as much an economic imperative as a health imperative; and reinforces the need to value pandemic vaccines’ economic benefits alongside their health benefits to achieve optimal levels of global investment.

Introduction

The COVID-19 pandemic has been one of the largest pandemics and global economic crises of the past century. COVID-19 vaccination (CV) has been claimed the single most important policy tool allowing global health and macroeconomic recovery.^{1 2} Yet there is limited research quantifying the broad impact of CV on the global economy or global health.

We found only three studies that quantified the global health and/or economic impact of CV. Watson et al³ estimated the impact of CV solely in terms of averted global COVID-19-related deaths. Bell et al⁴ and Yang et al⁵ measured both the health (ie, averted global infections and/or deaths) and global economic impacts of CV. Yang et al⁵ estimated CV economic impacts solely in terms of averted direct and indirect costs; however, the study did not incorporate the value of unpaid work into its indirect cost estimates. And Bell et al⁴ estimated CV’s economic impacts on direct costs and global trade, the latter of which was based on hypothetical CV scenarios as opposed to actual CV data. None of the global studies that estimated the health impacts of CV translated these effects into quality-adjusted life years (QALYs) or disability-adjusted life years (DALYs) gains. And those studies that estimated the global economic impacts of CV did not evaluate such impacts on gross domestic product (GDP). The valuation methods used in the existing literature do not allow direct comparison of the relative magnitudes of global economic and health values. Additionally, none of the global studies derived a single comprehensive value measure spanning its health and macroeconomic values, nor an estimate of the value-for-money (VfM) of CV relating its benefits to its costs. We also found no published estimates of the global health or macroeconomic impact of Pfizer-BioNTech vaccines specifically.

Yet, value assessments are essential for decisions regarding coverage of CV within population vaccination programmes by non-commercial vaccine payers such as national governments, multilaterals like the World Bank and non-profits like Gavi who are motivated by public goals such as population health and well-being. And appropriate value assessments should adopt a societal perspective, reflecting both health sector impacts of CV and broader societal impacts, including on the macroeconomy and productivity.⁶ We make progress in this direction and add to the existing literature by quantifying CV’s broad VfM, reflecting its contribution to global GDP recovery, QALY gains and averted direct and indirect costs.

Methods

Overview

We retrospectively assess the VfM of non-commercial payers’ (NCP) spending on population CV programmes, adopting a societal perspective, and estimating three components of the broad health and economic value (‘broad value’) of CV: (i) health impacts in terms of QALY gains, (ii) averted direct and indirect costs of COVID-19 infections and deaths and (iii) GDP impacts. We estimate these values for each country or territory in our sample (hereafter, ‘country’) and for the globe, and for all vaccine brands combined and for the Pfizer-BioNTech vaccine specifically.

Our focus on the VfM of NCP spending on population CV programmes allows us to ignore CV-related R&D costs, manufacturing costs and vaccine manufacturer profits. In a market economy, it is not the NCP’s responsibility to account for these. Instead, it is the vaccine manufacturer’s burden as a commercial entity to develop a vaccine that is sufficiently valuable to NCPs that negotiated prices cover these costs while allowing for profits. R&D costs, whether private or public, are also irrelevant to NCP coverage decisions because such costs will have already been incurred regardless of whether or not NCPs decide to cover CV. They are therefore not incremental to the coverage decision and drop out of a comparison of vaccination and no vaccination scenarios.

To estimate these broad values, we perform a regression analysis to measure the impact of CV doses on GDP, COVID-19 infections (‘infections’) and COVID-19 deaths (‘deaths’). We combine estimates of vaccine-averted infections and deaths from these regressions with estimates of QALY losses, direct costs per non-fatal infection and death and indirect costs per non-fatal infection to estimate QALY gains and costs averted by vaccination. We value QALY gains at per capita full income. The broad value of CV (VoCV) is the sum of monetised QALY gains, GDP gains and averted direct and indirect costs. The global VoCV sums up country-level broad values, which are themselves the sum of a country’s period-specific broad values.

Box 1 describes the steps of the analysis. The online supplemental appendix provides further details on data sources, variable construction and methods.

Box 1. Analysis steps for the valuation.

1. Estimate infection-, death- and PCGDP regressions.

2. Compute averted infections, deaths and PCGDP losses from vaccination, relative to a no-vaccination counterfactual, using estimates from (1).

3. Compute averted non-fatal cases from vaccination by taking the difference between averted infections and deaths from (2).

4. Estimate QALYs per fatal case and per non-fatal case.

5. Estimate direct costs per case.

6. Estimate indirect costs (unpaid work) per non-fatal case.

7. Estimate population-level QALY gains by combining averted deaths from (2), averted non-fatal cases from (3) and QALY losses per fatal and non-fatal cases from (4).

8. Estimate population-level averted direct costs by combining averted deaths from (2), averted non-fatal cases from (3) and direct costs per case from (5).

9. Estimate population-level averted indirect costs by combining averted non-fatal cases from (3) and indirect costs per non-fatal case from (6).

10. Compute full income.

11. Compute the monetary value of population-level QALY gains by multiplying population-level QALY gains from (7) by full income from (10).

12. Compute population-level vaccination benefits by summing the population-level monetary value of QALY gains from (11), averted direct costs from (8) and averted indirect costs from (9).

13. Compute benefit per dose by dividing population-level vaccination benefits from (12) by vaccine doses.

14. Assume each vaccine dose costs US$53.19.

15. Compute BCR as ratio of (13) to (14).

BCR, benefit-cost ratio; PCGDP, per capita gross domestic product; QALY, quality-adjusted life year.

Data

Data sources and summary statistics are provided in table 1. We rely on licensed Institute for Health Metrics and Evaluation (IHME) COVID-19 Projections data⁷ for vaccine doses, infections, deaths, hospitalisations and intensive care unit (ICU) beds. All other data sources are in the public domain.

Table 1. Data sources and summary statistics.

	Minimum	Maximum	Mean	Median	Source(s)
Health variables
Per capita COVID-19 vaccine doses administered per quarter*
Pfizer-BioNTech new doses, 2020Q4–2021Q3	0	0.608	0.060	0.00057	Constructed by authors from IHME COVID-19 vaccine administration data
Non-Pfizer-BioNTech new doses, 2020Q4 –2021Q3	0	0.971	0.105	0.027	Constructed by authors from IHME COVID-19 vaccine administration data
Cumulative Pfizer-BioNTech doses through 2021Q4	0	1.600	0.142	0.015	Constructed by authors from IHME COVID-19 vaccine administration data
Cumulative non-Pfizer-BioNTech doses through 2021Q4	0.044	1.984	0.642	0.551	Constructed by authors from IHME COVID-19 vaccine administration data
Per capita COVID-19 deaths per quarter†
Scaled for excess mortality (base-case)	0	0.0040	0.00033	0.00014	IHME official deaths adjusted by IHME for under-reporting
Unscaled (sensitivity analysis)	0	0.0015	0.00013	0.000025	IHME unadjusted official reported deaths
Per capita COVID-19 infections per quarter‡
Mean estimate (base-case)	0	1.197	0.103	0.064	IHME data on daily COVID-19 infections
Lower bound estimate (sensitivity analysis)	0	0.465	0.060	0.035	IHME data on daily COVID-19 infections
Upper bound estimate (sensitivity analysis)	0	2.355	0.165	0.088	IHME data on daily COVID-19 infections
COVID-19 hospitalisations per quarter
COVID-19 hospital admissions	0	5 949 463	38 961	5700	IHME
COVID-19 ICU beds	0	14 705 973	99 444	10 423	IHME
Infection severity levels§
Proportion asymptomatic	0.192	0.192	0.192	0.192	Derived by authors using IHME data. See S4.1.2.1 for details
Proportion mild	0.656	0.808	0.789	0.795	Derived by authors using IHME data. See S4.1.2.1 for details
Proportion severe	0.0000717	0.076	0.012	0.008	Derived by authors using IHME data. See S4.1.2.1 for details
Proportion critical	0.0000047	0.039	0.002	0.001	Derived by authors using IHME data. See S4.1.2.1 for details
Proportion fatal	0	0.0764	0.0043	0.0023	Derived by authors using IHME data. See S4.1.2.1 for details
QALY loss per fatal infection	2.74	14.76	7.63	7.56	Constructed by authors. See S4.1.1 for details
QALY loss per non-fatal infection	0.0074	0.0166	0.0080	0.0078	Constructed by authors. See S4.1.2 for details
Healthcare utilisation variables
Healthcare utilisation, length of stay (days)
Asymptomatic infection				0	Assumption
Mild infection				1	Constructed from Di Fusco et al.20 See S4.1.2.1 for details
Severe infection	6.21	6.36	6.27	6.26	Constructed from Di Fusco et al.20 See S4.1.2.1 for details
Critical infection	12.85	14.08	13.46	13.46	Constructed from Di Fusco et al.20 See S4.1.2.1 for details
Healthcare unit cost (2019 US$)
Asymptomatic infection, cost per infection				0	Assumption
Mild infection, cost per outpatient visit	0.01	82.04	13.93	5.61	Constructed by authors. See S4.2.1 for details
Severe infection, cost per bed day	0.03	1208.57	170.01	46.79	Constructed by authors. See S4.2.1 for details
Critical infection, cost per bed day	0.04	1629.34	229.20	63.08	Constructed by authors. See S4.2.1 for details
Direct medical cost per infection (2019 US$)	0.01	637.38	46.13	8.71	Constructed by authors. See S4.2 for details
Economic variables
Per capita quarterly projected GDP (2019 US$)	553.92	21 207.93	7264.94	5285.60	Constructed by authors. See S2.4.2 for details
Per capita quarterly actual GDP (2019 US$)	386.18	24 762.16	7075.66	5249.14	Constructed by authors. See S2.4.2 for details
Quarterly GDP shortfall	0.69	1.20	0.96	0.97	Constructed by authors. See S2.4.2 for details
Per capita annual projected GDP (2019 US$)	392.44	75 547.90	9372.72	4434.08	Constructed by authors. See S2.4.2 for details
Per capita annual actual GDP (2019 US$	485.07	66 369.04	8896.87	4116.10	Constructed by authors. See S2.4.2 for details
Annual GDP shortfall	0.49	2.07	0.98	0.96	Constructed by authors. See S2.4.2 for details
Unpaid work (hours per day)	2.20	4.12	3.13	3.01	Constructed by authors. See S4.3.2 for details
Non-market time (hours per day)	6.40	9.14	7.89	8.11	Constructed by authors. See S4.5 for details
Daily value of unpaid work, ≥15 years old (2019 US$)	0.47	124.11	21.33	7.67	Constructed by authors. See S4.5 for details
COVID-19-related worktime lost (days)
Mild infection				10.00	Constructed by authors. See S4.3.3 for details
Severe infection without long COVID	24.18	28.04	25.89	25.68	Constructed by authors. See S4.3.3 for details
Severe infection with long COVID	69.18	73.04	70.89	70.68	Constructed by authors. See S4.3.3 for details
Critical infection without long COVID	93.10	107.75	100.87	100.63	Constructed by authors. See S4.3.3 for details
Critical infection with long COVID	641.10	655.75	648.87	648.63	Constructed by authors. See S4.3.3 for details
Indirect cost per non-fatal infection (2019 US$)	3.84	1432.51	215.38	67.85	Constructed by authors. See S4.3 for details
Full income (2019 US$)	770.89	191 931.31	34 505.67	12 329.47	Constructed by authors. See S4.5 for details
Discount rate				3%
Other variables and data sources
Countries (deaths and infections)				145
Countries (quarterly GDP)				52
Countries (annual GDP)				66
Government Response Index	0	85.8	44.4	52.4	Oxford COVID-19 Government Response Tracker
Lifetables					United Nations, World Population Prospects

^*. A value of 0.01 would correspond to 1one vaccine dose received within the country-quarter per 100 in the general population.

^†. A value of 0.01 would correspond to 1one death within the country-quarter per 100 in the general population.

^‡. A value of 0.1 would correspond to 1one infection occurring within the country-quarter per 10 in the general population.

^§. Asymptomatic, mild, severe, critical, and fatal proportions within a country-quarter sum to 1. Thus, the means of such proportions across country-quarters sum to 1one but the minimums, maximums, and medians do not.

GDP, gross domestic product; ICUintensive care unitIHME, Institute for Health Metrics and Evaluation; QALY, quality-adjusted life year

We conduct our analysis in Excel, Stata V.18 and Python V.3.11.

Study countries, horizon and currency

Our study population consists of 148 countries with available data and >1 mllion population, covering nearly 98% of the global population (online supplemental table S1).

The highest frequency at which some data are available is quarterly. Thus, the time-period in most of our analysis is the calendar-quarter (‘quarter’). However, in countries where only annual GDP data are available, the time-period in the GDP regression is annual.

Given data limitations, we limit the study horizon to the pre-Omicron period, spanning 2020Q1–2021Q4. Our base currency, to avoid postpandemic volatility, is 2019 US$. See online supplemental appendix for inflation and exchange rate adjustments.

Regression specification

Infections and deaths

To estimate the impact of vaccinations on infections and deaths, we estimate the following panel regression equations:

$${\displaystyle \begin{array}{c}{n}_{i,t}={\delta }_1{v}_{i,t-1}^p+{\delta }_2{v}_{i,t-1}^{np}+{\delta }_{3}c{v}_{i,t-2}^p+{\delta }_{4}c{v}_{i,t-2}^{np}+{\delta }_5{n}_{i,t-1}\\ +{\delta }_{6}c{n}_{i,t-2}+{\delta }_7{s}_{i,t-1}+{\omega }_i+{\lambda }_t+{\kappa }_{i,t}\end{array}}$$ (1)

$${\displaystyle \begin{array}{c}{d}_{i,t}={\gamma }_1{v}_{i,t-1}^p+{\gamma }_2{v}_{i,t-1}^{np}+{\gamma }_{3}c{v}_{i,t-2}^p+{\gamma }_{4}c{v}_{i,t-2}^{np}+{\gamma }_5{n}_{i,t-1}\\ +{\gamma }_{6}c{n}_{i,t-2}+{\gamma }_7{s}_{i,t-1}+{\pi }_i+{\rho }_t+{\theta }_{i,t}\end{array}}$$ (2)

The dependent variables are per capita infections ${\displaystyle n_{i,t}}$ and per capita deaths ${\displaystyle d_{i,t}}$ in country ${\displaystyle i}$ and quarter ${\displaystyle t}$.The equations have identical independent variables. The variables $v_{i,t}^p$ and $v_{i,t}^{np}$ denote per capita vaccine doses administered of Pfizer-BioNTech vaccines (superscripted by $p$) and all non-Pfizer-BioNTech brands combined (superscripted by $np$), respectively. The variables ${cv}_{i,t}^p$ and $c{v}_{i,t}^{np}$ denote per capita cumulative vaccine doses administered since the start of the pandemic up to and including the last day of quarter $t$. We account for the impact of natural immunity with one lag of new infections, $n_{i,t}$, and two lags of cumulative infections, ${cn}_{i,t}$. We include two lags of per capita vaccine doses and infections (denoted $t-1$ and $t-2$, respectively) because the efficacy of vaccine-induced and infection-induced immunity can last longer than one-quarter, and waning suggests more recent vaccinations and infections have stronger impacts than less recent ones. The second lags of cumulative doses and infections allow the full histories of these variables to have causal impact.

The variable $s_{i,t}$ is the value of the Government Response Index, a composite index of lockdown stringency, non-pharmaceutical and economic policies on a scale of 0 (no response) to 100 (maximal response).⁸ We include country fixed effects (${\omega }_i,{\pi }_i)$ and quarter fixed effects (${\lambda }_t,{\rho }_t$) to control for country-specific time-invariant factors and global time-varying factors. The terms ${\kappa }_{i,t},{\theta }_{i,t}$ are error terms. To allow for correlated error terms across equations (1) and (2), and other violations of classical error variance assumptions, we estimate these with seemingly unrelated regression and robust standard errors.

We lag all independent variables to avoid reverse causality from dependent variables.

Gross domestic product

For countries with quarterly GDP data, we estimate a panel regression with an identical specification as (1) and (2) except for the dependent variable:

$${\displaystyle \begin{array}{c}{g}_{i,t}={\beta }_1{v}_{i,t-1}^p+{\beta }_2{v}_{i,t-1}^{np}+{\beta }_{3}c{v}_{i,t-2}^p+{\beta }_{4}c{v}_{i,t-2}^{np}+{\beta }_5{n}_{i,t-1}\\ +{\beta }_{6}c{n}_{i,t-2}+{\beta }_7{s}_{i,t-1}+{\mu }_i+{\tau }_t+{\epsilon }_{i,t}\end{array}}$$ (3)

The dependent variable $g_{i,t}$ is the GDP shortfall in country-quarter $i,t$, where the shortfall is the ratio of actual GDP to its prepandemic projected value. The variables ${\mu }_i$ and ${\tau }_t$ are the country and quarter fixed effects, respectively, and ${\epsilon }_{i,t}$ is the error term.

For the 66 countries without quarterly GDP data but with annual GDP data and vaccination starting in either 2020Q4 or 2021Q1, we estimate a cross-sectional regression:

$${\displaystyle \begin{array}{c}{g}_{i,2021}-g_{i,2020}={\alpha }_0+{\alpha }_1{g}_{i,2020}+{\alpha }_{2}c{v}_{i,2021Q4}^p+{\alpha }_{3}c{v}_{i,2021Q4}^{np}\\ +{\alpha }_{4}c{n}_{i,2020Q4}+{\sigma }_i\end{array}}$$ (4)

$g_{i,2021}$ and $g_{i,2020}$ are the GDP shortfall in 2021 and 2020, respectively; ${cv}_{i,2021Q4}^p$ and ${cv}_{i,2021Q4}^{np}$ are cumulative Pfizer-BioNTech and non-Pfizer-BioNTech doses per capita at the end of 2021Q4, ${cn}_{i,2020Q4}$ is cumulative infections per capita at the end of 2020Q4 and ${\sigma }_i$ is the error term. We omit variables reflecting infections in 2021 from the right-hand side of $\left(4\right)$ because of potential reverse causality from economic activity during 2021 to infections during 2021 (eg, resulting from workplace exposure).

We allow for a lagged GDP shortfall on the right hand side to allow for dynamic convergence, whereby larger initial shortfalls may imply larger ‘rebounds’. We include lagged cumulative per capita infections to represent natural immunity and pandemic trajectory.

Comments on regression specifications

Regression equations (1)–(3) are parsimonious in that they have relatively few explanatory variables. We chose parsimonious specifications to reduce the risk of overfitting chance correlations, preserve the precision of estimates, preserve sample size (ie, avoid dropping countries without the requisite data) and avoid introducing endogenous and collinear variables. Some endogenous variables such as household and firm behaviours, for example, are likely driven by vaccination (eg, households and firms will likely re-engage in workplace-centred or public-facing economic activity or reduce social distancing when vaccination rates are high), so behavioural changes are part of the mechanism for vaccine impact and should not be controlled for when estimating that impact. While we include lagged infections as explanatory variables, we do not also include lagged deaths: deaths are relatively stable proportions of infections, so lagged infections can proxy for, and will be highly collinear with, lagged deaths. Parsimony also reflects scarcity of relevant variables measured on a quarterly basis and available for many countries. For example, measures of health system capacity (eg, hospital beds per capita) are not available on a quarterly basis during the study time horizon. We note that while our regression specifications are linear in variables, they are non-linear in infections and vaccinations, since we allow two lagged terms for each, thus allowing their timing to matter (we tested more complex lag structures, but these did not improve fit).

A technical issue: in contrast to equations (2) and (3) which are static panel equations, equation (1) is a dynamic panel equation since it has a lagged dependent variable $n_{i,t-1}$ as an explanatory variable. Dynamic panels with only a small number of time periods face the well-known problem of Nickell bias: data transformations (either demeaning or first differencing) required to control for the fixed effects induce a correlation between the transformed lagged dependent variable and the transformed error term.⁹ The standard solution is to use instrumental variables for the transformed lagged dependent variable, where the instruments are even earlier lags of levels and differences of the dependent variable.^{10 11} However, under certain circumstances, such instrumentation can yield estimates that are inconsistent, inefficient or have poor finite sample properties.¹² Having to use earlier lags as instruments requires dropping earlier observations from the regression since these would not have the necessary lagged instruments, impairing precision. Importantly, this standard solution is not available to us because these earlier values already play an explanatory role in (1) through the ${cn}_{i,t-2}$ term, which represents natural immunity from earlier infections, rendering them ineligible as instruments. We are aware of no widely accepted solutions to Nickell bias that use instruments distinct from these earlier values. Seeking and justifying such distinct instruments would bring us to the frontier of econometrics research, which is far outside our study scope. For simplicity, we therefore do not attempt to correct for Nickell bias.

Vaccination’s impacts

We separately measure the impact of all CVs relative to a ‘no vaccination’ counterfactual and the impact of ‘Pfizer-BioNTech’ vaccines relative to ‘no Pfizer-BioNTech’ counterfactual. These counterfactuals are simulated worlds where no CV is one scenario and no Pfizer-BioNTech is another scenario. We simulate these counterfactuals using the results of our regression analyses as we describe in online supplemental appendix S3.

Quality-adjusted life years

We compute country-specific QALY losses per fatal and non-fatal infection. QALY losses per fatal infection equal the weighted average of age-specific QALY losses from death,^{13 14} with weights reflecting the age distribution of COVID-19 deaths.¹⁵ QALY losses from death are discounted at 3% following WHO recommendations.¹⁶ QALY losses per non-fatal infection are age-invariant and equal the weighted average of QALY losses from infections of different severity levels,¹⁷ with weights reflecting the relative probabilities of those levels (‘severity-weighted average’). The severity levels are asymptomatic, mild (not requiring hospitalisation), severe (requiring hospitalisation without ICU admission) and critical (requiring ICU admission). We allow long COVID to affect QALY losses following severe and critical infections.¹⁸ We compute QALY gains from vaccination from the product of averted fatal infections from vaccination and the QALY loss per fatal infection, as well as the product of averted non-fatal infections from vaccination and the QALY loss per non-fatal infection.

Direct and indirect costs

We estimate country-specific age-invariant direct costs per infection (which we apply equally to fatal and non-fatal infections) as a severity-weighted average of the product of inpatient or outpatient unit costs¹⁹ and severity-specific durations of utilisation (eg, hospital or ICU length of stay, or one outpatient visit per mild infection).²⁰

Country-specific age-invariant indirect costs per non-fatal infection consist of lost unpaid work related to infection, which is a severity-weighted average of the product of severity-specific workdays lost (our proxy for the days of unpaid work lost), daily hours spent on unpaid work and the hourly wage. We exclude paid work from indirect costs to eliminate double counting given our consideration of GDP impacts. We exclude all indirect costs of fatal cases to eliminate double counting given our monetisation of QALYs.

Full income

We monetise QALYs at country-specific age-invariant full income, equal to the sum of per capita annual income²¹ and the per capita value of annual non-market time. Annual non-market time consists of unpaid work and leisure time,^{22 23} where each hour is valued at the hourly wage.²⁴ As discussed below and shown in the online supplemental appendix, full income is a conservative approximation to individuals’ willingness-to-pay (WTP) per QALY.

The broad value formula

We take the broad VoCV to be the sum of monetised QALY gains, GDP gains and averted direct and indirect costs. We theoretically derive and justify this broad value measure more fully in online supplemental appendix S1. Put simply, when utility is a multiplicative function of health and full income, then the impact of a shock on utility can be approximated by the sum of two terms. The first term is the impact on utility of the shock-induced impact on health holding full income fixed, and the second is the impact on utility of the shock-induced impact on full income holding health fixed. We convert these utility impacts into WTP to avoid such impacts by dividing them by the expected marginal utility of full income. Under certain functional form and simplifying assumptions, we find that the first term can be conservatively approximated by the product of full income (serving as a conservative estimate of WTP per QALY) and the QALY impacts of the policy, while the second term equals the policy-induced impact on full income. (See Murphy and Topel²⁵ (their equation 13) for an example of full income as we define it being a component of, and a conservative approximation to, WTP for health.) Since the COVID-19 pandemic is a shock and CV is a reduction in the magnitude of the shock, the broad VoCV can be approximated by the resulting form.

Since our broad value measure reflects WTP for the health and economic impacts of policy, our analysis is a form of cost-benefit analysis (CBA). Since we derive our broad value measure from a utility framework in which QALYs represent health-related utilities, our analysis is also a form of cost-utility analysis (CUA). However, given that we value QALYs at individuals’ WTP per QALY, ours is a societal perspective CUA, as opposed to a health payer perspective CUA where QALYs are valued using the shadow price of some exogenously given health payer budget. Our broad value measure can also be understood within a societal-perspective cost of illness (CoI) framework in which costs of illness are defined broadly to encompass health and productivity losses, health losses are valued in terms of individuals’ WTP for health, health is measured by QALYs and the broad value of vaccination equals its averted CoI.

Marginal versus non-marginal risks

The approximations we use to derive the broad value formula are valid for marginal risks, but the risks imposed by COVID-19 are potentially non-marginal. Standard models of WTP for mortality risk reductions suggest that the rate of substitution of wealth for risk reduction declines when the magnitude of the risk reduction grows,²⁶ raising the prospect that formulas like ours derived for marginal risks will overestimate WTP for non-marginal risks. Despite this concern, we persist with our approximations for three reasons. First, a published calibration exercise suggests such overestimation is modest in the range of risk reductions we consider. Table 1 shows median per capita deaths per calendar quarter of 14/100 000, implying an annualised mortality risk shock of 56/100 000=0.56/1000, which is within the 1/1000 to 1/10 000 range in which overestimation appears modest.²⁶ Second, the same standard models ignore several salient features of COVID-19—dread, uncertainty, ambiguity and catastrophe—that may double the WTP.²⁶ This would considerably offset the modest overestimation, suggesting that our approach is likely on balance conservative. Third, WTP for non-marginal risks is significantly understudied, and there are no broadly accepted or well-established empirical estimates of such WTP.²⁶

Scenario and sensitivity analyses

We perform one-way scenario analyses, using lower and upper bound estimates of infections in place of mean estimates, using confirmed deaths in place of under-reporting adjusted deaths and using 0% and 6% discount rates.

We perform a probabilistic sensitivity analysis (PSA), taking 1000 draws from a multivariate normal distribution constructed using the model coefficients and variance-covariance matrix. For each draw, we calculate the VoCV. We estimate a 95% CI from these 1000 VoCVs.

Results

As shown in table 1, on average, fatal infections incur vastly larger QALY losses (2.74) than non-fatal infections (0.0074), and indirect costs per non-fatal infection (US$489.33) are an order of magnitude larger than the direct cost per fatal or non-fatal infection (US$46.13).

We present the results of the base-case regression models in table 2.

Table 2. Base-case regression results.

Dependent variable	Coefficients (robust SEs)
	Quarterly infections per capita	Quarterly deaths per capita	Quarterly GDP shortfall	2021 annual GDP shortfall minus 2020 annual GDP shortfall
First lag doses per capita
Pfizer-BioNTech	−0.1866*** (0.038)	−0.0015*** (0.0002)	0.0377** (0.0155)
Non-Pfizer-BioNTech brands	−0.1609*** (0.0563)	−0.0003*** (0.0001)	0.0288* (0.0164)
Second lag cumulative doses per capita
Pfizer-BioNTech	−0.0735 (0.0489)	0.0008*** (0.0002)	0.0377** (0.0150)
Non-Pfizer-BioNTech brands	−0.1318*** (0.0429)	−0.0003** (0.0001)	0.07589*** (0.0145)
First lag infections per capita	−0.0109 (0.0554)	0.0007*** (0.0002)	−0.0001233 (0.0244)
Second lag cumulative infections per capita	−0.0792** (0.0343)	−0.0004*** (0.0001)	0.0314** (0.0127)
First lag stringency index	−0.0009** (0.0004)	−0.0000011 (0.0000015)	0.00034 (0.00021)
GDP shortfall in 2020				0.0333 (0.0351)
Cumulative Pfizer doses per capita at the end of 2021Q4				0.0374** (0.0160)
Cumulative non-Pfizer doses per capita at the end of 2021Q4				0.0228* (0.0134)
Cumulative infections per capita at the end of 2020Q4				0.0958** (0.0392)
Observations	1160	1160	407	66
R2	0.5573	0.4476	0.8872	0.187
Tests of joint significance
Calendar quarter fixed effects	251.81***	251.81***	54.78***
Country fixed effects	988.71***	988.71***	27.03***

Regression samples: Tthere are 145 countries in the infections and deaths regressions, with eight8 quarterly observations each. There are 51 countries in the quarterly GDP regression, some of which have fewer than eight8 quarterly GDP observations. The are 66 countries in the annual GDP regression. There are 31 study countries excluded from the GDP analysis either because they do not have GDP data or they have annual GDP data, but vaccinations started after 2021Q1.

*P<0.10.

**P<0.05.

***P<0.01.

GDP, gross domestic product

Vaccination coefficients generally have the correct sign (negative in the infections and deaths regressions, and positive in the GDP regressions) and are statistically significant. We provide our interpretation of the coefficients as infection and mortality risk reductions and percentage-point GDP gains in online supplemental appendix S3.

We summarise our central findings in tables3 4. Table 3 presents pandemic outcomes (columns A, B), outcomes in the no vaccination counterfactuals (columns C, D) and attributes the differences to CV (columns E, F). Table 4 converts the aggregated values in table 3 (colums E, F) to per capita and per dose values.

Table 3. Global VoCV by value component and focal brand (2019 US$).

	Pandemic	Predicted pandemic	Predicted zero vaccination	Predicted zero Pfizer-BioNTech vaccination	VoCV all vaccine brands	VoCV Pfizer-BioNTech
	(A)	(B)	(C)	(D)	(E)=(C)−(B)	(F)=(D)−(B)
Global GDP shortfall	US$5.5T	US$5.6T	US$8.2T	US$6.5T	US$2.6T	US$877.8B
GDP share of value	54.6%	54.2%	53.2%	52.9%	51.2%	46.0%
Global infections	5.2B	5.2B	6.9B	5.5B	1.7B	270.7M
Global deaths	15.3M	15.4M	19.4M	16.5M	4.1M	1.1M
Global QALY losses	156.6M	159.3M	214.1M	171.2M	54.7M	11.8M
Global monetary value of QALY losses	US$4.0T	US$4.1T	US$6.2T	US$4.9T	US$2.1T	US$873.3B
Monetised QALY share of value	39.1%	39.4%	39.9%	40.4%	40.9%	45.8%
Global direct costs	US$101.2B	US$110.6B	US$173.2B	US$138.5B	US$62.6B	US$27.8B
Direct costs share of value	1.0%	1.1%	1.1%	1.1%	1.2%	1.5%
Global indirect costs	US$533.9B	US$540.4B	US$881.2B	US$669.3B	US$340.9B	US$128.9B
Indirect costs share of value	5.3%	5.3%	5.7%	5.5%	6.6%	6.8%
Global total losses or value (GDP+monetary QALY+direct costs+indirect costs)	US$10.1T	US$10.3T	US$15.4T	US$12.2T	US$5.2T	US$1.9T

Notes: (i) The results cover a sample of 148 countries over the years 2020–2021; (ii) Eeach row in table 3 represents a value element. (A) and (B) provide estimates of pandemic impact on those value elements, where (A) relies on actual data and (B) relies on our regression predicted values;. (C) and (D) provide estimates of these value elements in our no-vaccination counterfactuals;. (E) and (F) provide estimates of the impact of vaccination on these value elements, where (E) reflects the impact of all vaccines taken together, and where (F) reflects the impact of Pfizer-BioNTech vaccination alone. All rounded results in (A) to (F) are computed from unrounded underlying inputs, so that the rounded differences in (E) and (F) will not equal differences of the rounded numbers in (B), (C), and (D).

B, billion; GDP, gross domestic product; M, million; QALY, quality-adjusted life year; T, trillion; VoCV, value of COVID-19 vaccination

Table 4. Global value of COVID-19 vaccination (2019 US$) per capita and per dose by value component.

	All vaccine brands*	Pfizer-BioNTech†
(1) GDP gain, quarterly regression	US$1.5T	US$639.0B
(2) Population, quarterly GDP regression	3.6B	3.5B
(3) Doses, quarterly GDP regression‡	3.1B	848.7M
(4) GDP gain per capita, quarterly regression (1)/(2)	US$423.24	US$183.64
(5) GDP gain per dose, quarterly (1)/(3)	US$497.70	US$752.91
(6) GDP gain, annual regression	US$1.1T	US$238.7B
(7) Population, annual regression	3.3B	3.3B
(8) Doses, annual regression§	4.5B	585.7M
(9) GDP gain per capita, annual regression (6)/(7)	US$331.87	US$72.39
(10) GDP gain per dose, annual regression (6)/(8)	US$244.95	US$407.65
(11) GDP gain per capita, pooled (4)×((2)/((2)+(7)))+(9)×((7)/((2)+(7)))	US$379.45	US$129.50
(12) GDP gain per dose, pooled (5)×((3)/((3)+(8)))+(10)×((8)/((3)+(8)))	US$347.45	US$611.94
(13) Monetary value of averted QALYs	US$2.1T	US$873.3B
(14) Averted direct and indirect costs	US$403.4B	US$156.7B
(15) Population, SUR regression	7.6B	7.4B
(16) Doses, SUR regression¶	6.4B	1.0B
(17) Monetary value of averted QALYs per capita (13)/(15)	US$277.65	US$118.50
(18) Monetary value of averted QALYs per dose (13)/(16)	US$330.49	US$871.73
(19) Averted direct and indirect costs per capita (14)/(15)	US$53.07	US$21.27
(20) Averted direct and indirect costs per dose (14)/(16)	US$63.17	US$156.45
(21) Total value per capita (11)+(17)+(19)	US$710.18	US$269.27
(22) Total value per dose (12)+(18)+(20)	US$741.10	US$1640.12

^*All rounded results in the rows below are computed from unrounded underlying inputs.

^†All rounded results in the rows below are computed from unrounded underlying inputs.

^‡Doses in the quarterly GDP regression are counted through 2021 Q3.

^§Doses in the annual GDP regression are counted through 2021 Q4.

^¶Doses in the quarterly deaths and infections (SUR) are counted through 2021 Q3.

B, billion; GDPgross domestic productM, millionQALYquality-adjusted life yearSURseemingly unrelated regressionT, trillion

Base-case population-level results

The similarity across (A) and (B) in table 3 indicates that our regressions provide reasonable predictions of infections, deaths and GDP. Table 3 also shows that the pandemic caused US$5.6 trillion in GDP losses, that the absence of all CV and Pfizer-BioNTech vaccines would have raised this loss to US$8.2 and US$6.5 trillion, respectively. Therefore, we estimate that CV as a whole and Pfizer-BioNTech vaccines specifically produced GDP benefits of US$2.6 trillion and US$877.8 billion, respectively.

Our results suggest that the pandemic caused 5.2 billion infections (symptomatic and asymptomatic), that the absence of all CV and Pfizer-BioNTech vaccines would have raised this to 6.9 and 5.5 billion infections, respectively and that therefore CV as a whole and Pfizer-BioNTech vaccines in particular, averted 1.7 billion and 270.7 million infections, respectively. The pandemic caused 15.4 million deaths, the absence of all CV and Pfizer-BioNTech vaccines would have raised this to 19.4 and 16.5 million deaths, respectively, and therefore CV as a whole and Pfizer-BioNTech vaccines in particular averted 4.1 and 1.1 million deaths, respectively.

Attributing differences in predicted and counterfactual outcomes to vaccines in this manner, we find that CV (Pfizer-BioNTech alone) averted 54.7 (11.8) million QALYs whose monetary value is US$2.1 trillion (US$873.3 billion), US$62.6 (US$27.8) billion in direct costs and US$340.9 (US$128.9) billion in indirect costs.

Combining GDP losses, monetised QALY losses and direct and indirect costs, the pandemic caused a total loss of US$10.2 trillion, the absence of all CV and Pfizer-BioNTech vaccines would have raised this to US$15.4 and US$12.2 trillion, respectively, and therefore CV as a whole and Pfizer-BioNTech vaccine specifically produced US$5.2 and US$1.9 trillion in value, respectively.

Considering the postvaccination period (2021Q1–2021Q4), we estimate the pandemic burden to be US$4.98 trillion, which would have been US$10.13 trillion without all CV or US$6.89 trillion without Pfizer-BioNTech specifically. Thus, once the vaccine rollout began, all CV reduced the burden of the pandemic by 50.9% ((US$10.13 trillion−US$4.98 trillion)/US$10.13 trillion) and Pfizer-BioNTech specifically reduced it by 27.7% ((US$6.89 trillion–US$4.98 trillion)/US$6.89 trillion).

Recall that we value QALYs conservatively using full income, implying that our estimated share of health in total burdens and benefits is likewise conservative. Subject to that caveat, we find that GDP losses constitute at least half (54%) the total pandemic burden (table 3), suggesting pandemics are as much a threat to the global economy as they are to global health, and that addressing pandemics is as much an economic imperative as a health imperative. Furthermore, the GDP-related value of vaccination is at least as large as the value of its impact on global population health as measured by monetised QALY gains (51% vs 41% for all CV and 46% vs 46% for Pfizer-BioNTech vaccination). We find that the broader economic value of vaccination (reflecting its impact on global GDP and indirect costs) exceeds its health system-related values (reflecting its impact on global population health and direct costs): economic values comprise 51.2%+6.6%=57.8% of the VoCV while health-related values comprise 40.9%+1.2%=42.1% of its value; economic values comprise 46.0%+6.8%=52.8% of the value of Pfizer-BioNTech vaccination while health-relative values comprise 45.8%+1.5%=47.3%.

Per capita and per dose results

On average, each person in our sample receives US$710 in value from CV and US$269 in value from Pfizer-BioNTech. On average, each dose of CV and Pfizer-BioNTech produces US$741 and US$1640 in value, respectively. However, these values can vary widely by country. For example, the 25th percentile VoCV per dose for all vaccines (Pfizer-BioNTech) is US$93.00 (US$252.16) and the 75th percentile is US$965.92 (US$2482.13) with a few major outliers (figure 1).

Figure 1. Country-specific broad value per dose for all COVID-19 vaccination (top) and Pfizer-BioNTech specifically.

Ratios of our per capita and per dose values yield a pooled estimate of doses per capita: US$710.06/US$741.16=0.96 and US$269.23/US$1640.28=0.16 doses per capita for CV and Pfizer-BioNTech, respectively. Fewer doses per capita explains why Pfizer-BioNTech has greater value per dose but lesser value per capita.

Benefit-cost ratio

In the USA, the average cost per original monovalent booster dose was US$20.69, and administration costs were between US$25 and US$40 per dose,²⁷ yielding a per dose cost estimate inclusive of administration costs of US$20.69+(US$25+US$40)/2=US$53.19. We treat this as a simple upper-bound estimate of the cost per dose. Given the per dose benefits, the benefit-cost ratio for CV in general is US$741.16/US$53.19=13.93 and for Pfizer-BioNTech vaccination is US$1640.28/US$53.19=30.84. Thus, vaccination benefits are an order of magnitude larger than costs. Our calculations suggest that macroeconomic benefits alone rationalise CV’s costs multiple times over⁴: macroeconomic benefits per dose are US$347.45/US$53.19=6.5 and US$611.94/US$53.19=11.5 times per dose costs for CV and Pfizer-BioNTech, respectively.

Sensitivity analyses

Our PSA suggests that 95% confidence intervals are given by VoCVs 21% above and below our main estimates (table 5). Using IHME’s lower bound of infections reduces the VoCV by 20.6% for all vaccines and 20.1% for Pfizer-BioNTech. Using the upper bound increases the VoCV by 21.8% and 22.3%, respectively. Using IHME’s unscaled deaths variable reduces the VoCV by 18.2% and 20.7%, respectively. Reducing the annual discount rate to 0% increases the VoCV by 13.3% for all vaccines and 14.4% for Pfizer-BioNTech. Increasing the annual discount rate to 6% decreases it 7.2% and 7.9%, respectively. In summary, varying values for key parameters result in estimates that are about 10%–20% above or below the base-case estimates.

Table 5. Total Value of vaccination sensitivity analysis results (2019 US$).

	Total VoCV	2.5th Percentile	97.5th Percentile
Probabilistic sensitivity analysis
All vaccines	US$5.2T	US$4.1T	US$6.2T
% different from total VoCV		−21.3%	21.2%
Pfizer-BioNTech	US$1.9T	US$1.5T	US$2.3T
% different from total VoCV		−21.2%	20.5%
One-way sensitivity analyses
IHME lower bound infections
All vaccines	US$4.1T
% different from total VoCV	−20.6%
Pfizer-BioNTech	US$1.5T
% different from total VoCV	−20.1%
IHME upper bound infections
All vaccines	US$6.3T
% different from total VoCV	21.8%
Pfizer-BioNTech	US$2.3T
% different from total VoCV	22.3%
IHME unscaled deaths
All vaccines	US$4.2T
% different from total VoCV	−18.2%
Pfizer-BioNTech	US$1.5T
% different from total VoCV	−20.7%
0% discount rate
All vaccines	US$5.8T
% different from total VoCV	13.3%
Pfizer-BioNTech	US$2.2T
% different from total VoCV	14.3%
6% discount rate
All vaccines	US$4.8T
% different from total VoCV	−7.2%
Pfizer-BioNTech	US$1.8T
% different from total VoCV	−7.9%

IHMEInstitute for Health Metrics and EvaluationT, trillionVoCVvalue of COVID-19 vaccination

Vaccine effectiveness

We estimate vaccine effectiveness against infections and deaths to be 36.5% and 30.6%, respectively, for CV, and 49.3% and 63.2%, respectively, for Pfizer-BioNTech. Thus, Pfizer-BioNTech effectiveness against infections and deaths is at least 50% and 100% higher, respectively, than that of CVs on average (see online supplemental appendix S5.1 for details).

Discussion

This study uses a regression-based approach to estimate the global health and macroeconomic impact of CV over the first 2 years of the pandemic. Combining GDP losses, monetised QALY losses and direct and indirect costs, we find that the pandemic caused an economic loss of US$10.2 trillion. The absence of all CV and Pfizer-BioNTech vaccines would have raised this loss to US$15.4 and US$12.2 trillion, respectively. Therefore, CV as a whole, and Pfizer-BioNTech vaccines in particular, generated values of US$5.2 and US$1.9 trillion, respectively.

Table 6 compares our results with those of other studies.

Table 6. Comparisons of our results with those from other selected studies.

Estimates from the literature	Our estimates
Bell et al.4 Globally through 2021, 1.4 million deaths averted by CV.	Globally through 2021, 4.1 million averted deaths by CV.
Watson et al.3 Globally through 2021, 14.4–19.8 million deaths averted by CV.	Globally through 2021, 4.1 million averted deaths by CV.
Yang et al.5 Globally through September 2021, 152 million symptomatic infections, 0.6–2.1 million deaths averted and US$135.1 billion (2021 US$) in averted indirect costs (paid work only) from non-fatal infections.	Globally through September 2021, 588 million symptomatic infections averted, 1.77 million deaths averted and US$184 billion averted indirect costs (unpaid work) from non-fatal infections.
Di Fusco et al.20 In the USA through 2021, 9 million symptomatic infections, 110 000 deaths, US$30 billion in direct costs and US$43.7 billion in indirect costs averted by Pfizer-BioNTech vaccination.	In the USA through 2021, 38 million averted non-fatal symptomatic infections, 109 000 deaths, US$10.7 billion direct costs and US$37.9 billion in indirect costs averted by Pfizer-BioNTech vaccination.
Kirson et al.33 In the USA through June 2024, US$5 trillion in averted health and economic costs (depression, GDP, health) from CV. If we spread out the US$5 trillion over 3.5 years, this is equivalent to US$5/3.5=US$1.43 trillion per year.	In the USA through 2021, the total value of CV is US$1.2 trillion.
Steele et al.28 In the USA through September 2021, 27 million infections and 235 000 deaths averted by CV.	In the USA through September 2021, 70 million infections and 280 000 deaths averted.
Sandmann et al.34 In the UK over 10 years, CV could avert £35 billion in health and GDP losses.	In the UK through 2021, CV produces a total value of US$194 billion, or £152 billion in 2019 terms.
Cutler and Summers.30 In the USA, US$16 trillion on health and economic costs from 2020 to 2030. Components include US$7.6 trillion in lost GDP, 450 000 COVID-19 deaths through the end of 2021 (valued at US$3.15 trillion using a value of a statistical life of US$7 million), costs of long COVID of US$2.6 trillion through the end of 2021.	Through 2021, the USA incurred US$4.3 trillion in health and economic costs in the absence of vaccination. Components include US$2.0 trillion in monetised QALY losses and US$343 billion in direct and indirect costs (resulting from 329 million infections and 1.3 million deaths), and US$2.0 trillion in GDP losses.
Fan et al.32 Globally through April 2021, 32 million DALYs lost, with a value of US$5.1 trillion using contingent valuation methods or US$0.6 trillion using hedonic wage methods.	Globally through 2021Q1, 72.1 million QALYs lost with a monetary value of US$2.2 trillion.
The Economist.29 Globally, US$10 trillion in foregone GDP by the end of 2021.	Globally in the absence of CV, US$8.2 trillion in foregone GDP by the end of 2021.
Walmsley et al.31 In the USA through 2023, GDP loss is US$14 trillion.	In the USA through 2021, GDP loss is US$1.3 trillion.

CVCOVID-19 vaccinationDALYdisability-adjusted life yearGDPgross domestic productQALYquality-adjusted life year

Our estimates of averted infections are higher than those found in the literature (Di Fusco et al²⁰; Yang et al⁵); our averted deaths from vaccination are similar to those of other studies (Di Fusco et al²⁰; Steele et al²⁸; Yang et al⁵) and fall considerably below the least conservative estimates (Watson et al³); our estimates of the GDP loss from the pandemic are similar to those of The Economist²⁹ and Cutler and Summers,³⁰ but smaller than less conservative estimates of Walmsley et al³¹; our monetised QALY losses during the pandemic is smaller than the most comparable estimates we found for the globe³² and for the USA³⁰; our VoCV is similar to that of Kirson et al³³ for the USA, but much higher than that of Sandmann et al³⁴ for the UK.

Substantial differences between our study and other studies are likely due to methods (regression vs models, see section S2.1) and data input differences. For example, IHME data on infections (symptomatic and asymptomatic)³⁵ used in this study far exceed Our World in Data estimates of confirmed cases.³⁶ While estimates in table 6 vary widely across studies, the literature reveals that the COVID-19 pandemic has imposed profound health and economic burdens. Our study contributes to this literature by estimating the broad loss of the pandemic and the broad benefit of CV.

The following are limitations of our study. Like all regression analyses, our predicted values may underestimate or overestimate observed values for individual country-quarter observations even if such deviations wash out in aggregate, thereby making country-specific projections less reliable than global projections. We also have data limitations: some countries only have annual as opposed to quarterly GDP data; infections, deaths and other important quantities are not age-disaggregated; COVID-19 health utility impacts are inferred from proxy conditions; some variables are missing for some countries, which require extrapolation from other countries. We do not measure potentially important value elements, such as fiscal effects, mental health, education, community transmission reduction and caregiver burdens, which makes our VoCV estimates conservative. We do not address the relationship between COVID-19 and comorbidities. We assume long COVID only affects QALY losses in severe and critical infections, while there is evidence that it could also affect patients with mild infections.¹⁸ We ignore the equity issues raised by cross-country differences in WTP resulting from global inequality in per capita gross domestic product. We are unable to address concerns regarding Nickell bias. And we use formulas derived for marginal risks and do not more fully address the impact on WTP of non-marginal risks, dread, uncertainty, anxiety and catastrophe.

There are many possible extensions of our study. First, a more rigorous treatment of VfM calculations could include country-specific and manufacturer-specific prices. Second, we adopt a CBA approach where every dollar’s worth of benefit has equal value. This implies that when we value QALYs using full income, a rich country’s QALY counts more than a poor country’s QALY (because of the former’s higher full income), and that US$1 of GDP gain counts equally in rich and poor countries, even if such GDP gain may produce larger well-being gains in poor countries given diminishing marginal utility of income. Therefore, future extensions could use more equity-sensitive value frameworks like social welfare functions. Other extensions include the VoCV in controlling variant emergence, the global health and economic costs of CV-related vaccine hesitancy and optimal spending on pandemic preparedness.

We find CV impacts to be empirically large, and though we do not fully investigate CV costs, simple approximations suggest significant VfM. Importantly, we find that the broader economic costs of the pandemic exceed its narrower health-related costs, that the broader economic benefits of CV exceed its narrower health-related benefits and that the macroeconomic benefits alone justify the costs of CV many times over.

These results have implications for perhaps the most important policy debate in vaccine evaluation: whether to value vaccines narrowly (ie, focusing only on health-centric outcomes) or broadly (ie, incorporating broader socio-economic and other outcomes). Our results suggest narrow approaches ignore perhaps half the harm of pandemics, and perhaps half the benefits of pandemic vaccines. This suggests that achieving optimal societal and global investment in CV requires valuing it broadly.

Conclusions

We find that the COVID-19 pandemic had a profound impact on global health as measured by QALY losses, and on the global economy as measured by GDP losses. Our study finds that GDP losses constitute at least half the total health and economic burden of the COVID-19 pandemic, suggesting that addressing pandemics is as much an economic imperative as a health imperative. In per capita terms, the broad VoCV confers considerable value to each person in the world on average. Our benefit-cost ratios suggest that vaccination benefits are an order of magnitude larger than vaccination costs, indicating that CV offers significant value for money. The GDP-related VoCV is at least as large as its QALY-related value, suggesting that the broader economic VoCV exceeds its narrow health-related value, and macroeconomic benefits alone rationalise CV’s costs multiple times over. Perhaps the most important policy debate in vaccine evaluation is whether to value vaccines narrowly (focusing only on health impacts) or broadly (including broader socio-economic impacts). Our findings suggest that narrow approaches neglect at least half the harm of pandemics and at least half the benefits of pandemic vaccines, and that broad valuation is required to achieve optimal societal investment in CV. Our results highlight the profound health and economic impacts of the COVID-19 pandemic and of CV, and the importance of valuing CV from a societal perspective that takes into account economic impacts.

Data availability statement

Data not subject to license restrictions are available in a public open access repository.