What Is the Economic Benefit of Annual COVID-19 Vaccination From the Adult Individual Perspective?

Abstract

Background

With coronavirus disease 2019 (COVID-19) vaccination no longer mandated by many businesses/organizations, it is now up to individuals to decide whether to get any new boosters/updated vaccines going forward.

Methods

We developed a Markov model representing the potential clinical/economic outcomes from an individual perspective in the United States of getting versus not getting an annual COVID-19 vaccine.

Results

For an 18–49 year old, getting vaccinated at its current price ($60) can save the individual on average $30–$603 if the individual is uninsured and $4–$437 if the individual has private insurance, as long as the starting vaccine efficacy against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is ≥50% and the weekly risk of getting infected is ≥0.2%, corresponding to an individual interacting with 9 other people in a day under Winter 2023–2024 Omicron SARS-CoV-2 variant conditions with an average infection prevalence of 10%. For a 50–64 year old, these cost-savings increase to $111–$1278 and $119–$1706 for someone without and with insurance, respectively. The risk threshold increases to ≥0.4% (interacting with 19 people/day), when the individual has 13.4% preexisting protection against infection (eg, vaccinated 9 months earlier).

Conclusions

There is both clinical and economic incentive for the individual to continue to get vaccinated against COVID-19 each year.

COVID-19, vaccination, booster, individual, economic, model

With boosters or updated coronavirus disease 2019 (COVID-19) vaccination no longer required/mandated by many businesses and organizations [1–3], individuals now must decide whether to get the updated annual COVID-19 vaccine. Moreover, with the cost of vaccines no longer covered by the government [4, 5], individuals will either have to use their insurance to cover the cost or pay out-of-pocket (OOP). Additionally, COVID-19 hospitalization and death rates dropped from 115.6 to 61.3 per 100 000 people between 2021 and 2022 [6], perhaps leading to the perception that COVID-19 is no longer the risk it once was. All of these factors could be contributing to the decrease in vaccination coverages in the United States from the primary series (69.5%) to the boosters made available in September 2021 (17%) and the updated vaccine in September 2023 (35.3%) [7, 8]. Such decreasing vaccination coverages could significantly affect the control of COVID-19 as the protection from previous severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and COVID-19 vaccination wane over time, which could leave the individual unprotected [9–11].

Therefore, it would be useful to better understand the value of annual COVID-19 immunization from the perspective of the individual, as we have done previously for influenza vaccines [12]. Studies have already demonstrated the clinical (eg, preventing hospitalizations and deaths [13, 14]) and economic value of the COVID-19 vaccine from the societal and third-party payer perspective [15–19]. While such studies can guide recommendations from governments and coverage decisions by insurance companies, they may not speak to the individual, who may wonder whether the cost of vaccination, which can include the price of the vaccine and the cost of getting to the vaccination location (eg, transportation, time away from work and other activities), is worth it. To help an individual better understand the trade-offs and value of getting vaccinated, we developed a computational simulation model to determine the economic value of annual COVID-19 vaccination to an adult from the individual's perspective and how this may vary with their age, insurance status, and preexisting protection level.

METHODS

Markov Model Structure and States

We developed a Markov model (in TreeAge Pro 2022) representing the decision of whether an adult individual will get the annual COVID-19 vaccine and the resulting cost-benefit. The model consists of 8 mutually exclusive SARS-CoV-2 infection states (Figure 1), representing the worst state a person could experience in a cycle (1 week). Table 1 describes these states and their accompanying clinical and economic outcomes. Supplementary Table 1 provides the model input parameters, values, and sources, with key parameters shown in Table 2. Individuals start in the “no infection” state and could have preexisting protection from either infection in the past year or previous vaccination. An individual remains in a state for 1 cycle and then has probabilities of remaining in the same state or moving to another. An individual's probability of moving to a COVID-19 state depends on the risk of infection, age-specific probabilities of developing various clinical outcomes, and his/her level of preexisting protection (which reduces the infection risk and severe disease by 1 minus preexisting protection).

Figure 1.

Markov model structure with 8 mutually exclusive SARS-CoV-2 disease and infection states.

Table 1.

Markov States, Descriptions, Accompanying Clinical Outcomes, Health Effects, and Costs

Markov State	Description of State	Clinical Outcomes	Health Care Utilization	Costsa
Not infected	Individual is not infected with SARS-CoV-2	…	…	…
Asymptomatic COVID-19	Individual has COVID-19 and is infectious but not displaying any symptoms	…	…	…
Symptomatic COVID-19: mild (not hospitalized)	Individual has COVID-19 with mild symptoms and is not hospitalized	General symptoms (eg, headache, runny nose, fever)	OTCs, Ambulatory care, Paxlovid/other COVID-specific medications	OTCs, Medications, Copays/coinsurance/deductible for ambulatory care based on insurance type
Symptomatic COVID-19: moderate (not hospitalized)	Individual has COVID-19 with moderate symptoms and is not hospitalized	Moderate symptoms (eg, shortness of breath, loss of taste/smell, coughing)	OTCs, Ambulatory care, Paxlovid/other COVID-specific medications	OTCs, Medications, Copays/coinsurance/deductible for ambulatory care based on insurance type
Symptomatic COVID-19: severe, hospitalized, non-ICU	Individual has severe symptoms of COVID-19 and is hospitalized in a general ward	Hospitalization, pneumonia, acute respiratory failure, sepsis, renal failure, ARDS	Hospitalization, Medications, Diagnostic tests	Copays/coinsurance/deductible for hospitalization
Symptomatic COVID-19: severe, ICU admission, nonventilated	Individual has severe COVID-19 symptoms and is admitted to the ICU, but does not require the use of a ventilator	Pneumonia, acute respiratory failure, sepsis, renal failure, ARDS	Hospitalization, Medications, Diagnostic tests	Copays/coinsurance/deductible for hospitalization
Symptomatic COVID-19: severe, ICU admission, ventilated	Individual has severe COVID-19 symptoms, is admitted to the ICU, and requires the use of a ventilator	Acute respiratory failure ARDS (with or without sepsis), ventilator use	Hospitalization, Medications, Diagnostic tests, Ventilator use	Copays/coinsurance/deductible for hospitalization
Dead	Individual dies of COVID-19	Death	…	Lifetime productivity losses

Abbreviations: ARDS, acute respiratory distress syndrome; COVID-19, coronavirus disease 2019; OTC, over the counter; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2.

^aCopays, coinsurance, and deductible depended on individual's insurance status, type of insurance, and associated cost-sharing.

Table 2.

Key Model Input Parameters, Values, and Sources

Parameter	Distribution Type	Value	Source
Costs, 2024 US$
COVID-19 vaccinea	Point estimate	60	[4, 20]
Annual wages, all occupations, median (10th–90th percentile)	Triangular	50 003 (32 727–74 950)	[21]
Hospitalization, for COVID-19, mean (SD)
18–44 y old	Gamma	18 504.49 (384.92)	[22]
45–64 y old	Gamma	22 819.69 (305.01)	[22]
Probabilities
Asymptomatic infection, median (range)b	Beta pert	0.324 (0.253–0.3951)	[23]
Mild illness, given nonsevere infection	Point estimate	0.55	[24]
Ambulatory care, mild/moderate infection, median (range)	Beta pert	0.247 (0.2223–0.2717)	[25]
Hospitalization, given infection, median (range)	Triangular	0.0416 (0.0375–0.0458)	[26]
ICU admission, given hospitalization, median (range)
18–49 y old	Triangular	0.095 (0.0855–0.1045)	[27]
≥50 y old	Triangular	0.147 (0.1323–0.1617)	[27]
Mortality, given hospitalization, median (range)
18–50 y oldb	Triangular	0.023 (0.0207–0.0253)	[27]
≥50 y oldb	Triangular	0.10 (0.09–0.11)	[27]
Side effects from vaccines
Mild/moderate side effects, range	Uniform	0.33–0.42	[28–30]
Myocarditis/pericarditis, mean (95% confidence interval)	Triangular	0.000023 (0.0000156–0.000027)	[31]
Allergic reaction/anaphylaxis, range	Uniform	0.000003–0.000011	[32, 33]
Vaccine efficacy, rangea	…	0.5–0.8
Miss work if mild illness, range	Uniform	0.525–0.838	[34]
Duration (days)
Minor vaccine side effects, (range)	Uniform	1–2	[28–30]
Ambulatory care	Point estimate	0.5	Assumption
Hospitalization, ICU and general ward, median (range)	Triangular	3.9 (1.9–8.7)	[35]

Costs reported in past years (eg, prior to 2024) are converted to 2024 values using a 3% annual rate.

^aVaried in sensitivity analysis.

^bValues for range are ± 10% of median/mean value.

If an individual became exposed and infected during a cycle, he/she had probabilities of developing symptoms and could develop mild, moderate, or severe symptoms (requiring hospitalization) during the first several days of his/her infection. Each symptomatic individual draws a severity-specific illness duration from a distribution. If asymptomatic, he/she has a probability of developing long COVID [36–38] and then recovers (returns to the not infected state). If he/she has a mild/moderate infection that does not progress to severe disease, he/she has a probability of developing long COVID and returning to the not infected state if his/her illness has resolved. However, if he/she does not recover, he/she has probabilities of progressing to severe disease or remaining in the same state. If he/she has severe disease (in the first week of infection) or progresses to severe disease requiring hospitalization, he/she has probabilities of intensive care unit admission and ventilator use and moving to the corresponding state (Table 1). If hospitalized, he/she draws a hospitalization duration and has probabilities of COVID-19–associated mortality and developing long COVID. An individual could no longer move once in the “dead” state. If an individual survives his/her infection and returns to the not infected state, he/she has natural protection to reinfection. Because we evaluated an annual vaccine, an individual continued to cycle in the model until he/she ended up in the “dead” state or 52 cycles (ie, 1 year) elapsed, whichever came first.

Annual COVID-19 Vaccination

Each individual journeys through the model twice, once with and once without COVID-19 vaccination. Vaccination has probabilities of causing minor side effects (eg, soreness), requiring over-the-counter medications, and severe adverse effects (eg, myocarditis/pericarditis), requiring hospitalization. Vaccination attenuates an individual's risk of SARS-CoV-2 infection (1 − vaccine efficacy against infection) [39–41]. Vaccination similarly reduces a person's probability of severe disease if infected. Vaccine efficacy against infection remains at its starting efficacy for 4 months, and to represent waning, decreases linearly over 2 months to half the starting efficacy at 6 months [9, 10], and then decreases linearly to 0% by 1 year postvaccination [11].

Economic Measures

All costs and health effects accrue at the time they occur in the model, regardless of when the cost is actually incurred (eg, billed later). The individual perspective includes the productivity losses due to absenteeism (eg, COVID-19 illness, side effects, time to get vaccinated), presenteeism, and mortality and OOP costs for vaccination (vaccine itself, vaccine administration), medications, and health care visits. Hourly wages serve as a proxy for productivity losses. Absenteeism results in productivity losses for the duration of symptoms or hospitalization. We calculate presenteeism productivity losses by attenuating an individual's wage by the utility weight for long COVID symptoms. Death results in the loss of the net present value of productivity losses for the remainder of an individual's lifetime [42]. Based on the US health insurance system, an individual's OOP costs depends on their insurance status, insurance type, and associated cost-sharing (described in the Supplementary Material).

For each scenario, we calculated the cost-benefit of vaccination as:

$$\begin{array}{rl}\mathrm{Cost-Benefit}& =\mathrm{Benefit}-\mathrm{Cost}\\ & =\mathrm{Direct}\mathrm{Medical}\mathrm{Costs}\mathrm{and}\mathrm{Productivity}\\ & \mathrm{Losses}\mathrm{of}\mathrm{Averted}\mathrm{Infections}\\ & -\mathrm{Cost}\mathrm{of}\mathrm{Vaccination}\mathrm{and}\mathrm{Vaccine}\mathrm{Side}\mathrm{Effects}\end{array}$$

All costs are reported in 2024 values, converting all costs using a 3% annual rate.

Experiments and Sensitivity Analyses

Each simulation run sent 1000 individuals through the model 1000 times and compared the use of annual COVID-19 vaccination to no vaccination for each individual. Different sets of experiments simulated individuals 18–49 years old and 50–64 years old, given differences in COVID-19–related outcomes. Different scenarios explored the value for an insured individual and uninsured individual. Sensitivity analysis varied the weekly infection risk (0.2%–2.2%; corresponding to 10%–70% over a year), preexisting protection level [43, 44] against infection (0%–26.8%) and severe disease (0%–50%), starting vaccine efficacy (50%–80%), long COVID cost ($138–$5762 [45]), and vaccination OOP cost [4, 20] ($0–$130). As Table 1 shows, the probability of more severe outcomes drew from a distribution constructed from numbers reported in the literature. Because an individual's probability of different outcomes may not mirror that of the population and these probabilities may vary depending on the particular SARS-CoV-2 variant, we conducted sensitivity analyses varying the probability of severe disease from 0.5 to 3 times the reported value. Additional scenarios represented a vaccine that only protects against severe disease. Probabilistic sensitivity analyses (ie, Monte Carlo simulation) varied each parameter through its range/distribution (Supplementary Table 1).

RESULTS

An 18–49 Year Old With No Insurance

Figure 2 shows how COVID-19 vaccination for an uninsured 18–49 year old becomes net cost-saving when the weekly risk of infection exceeds 0.2%, which corresponds to an individual interacting with 9 other people in a day under winter 2023–2024 Omicron conditions with an average infection prevalence of 10%. With a 0.2% weekly infection risk, vaccination saves $30, all resulting from saved productivity losses, because the average medical OOP costs saved due to illness do not offset the vaccination cost (an OOP medical cost). However, when excluding the vaccination cost, 31% of cost-savings come from OOP medical costs due to illness and 69% come from productivity losses. The weekly risk threshold increases to ≥0.3% (corresponding to interacting with 14 other people/day with a 10% infection prevalence; Figure 2A) when the individual has 13.4%/36% preexisting protection against infection/hospitalization due to prior vaccination/infection. When an individual has 26.8%/50% preexisting protection against infection/hospitalization, vaccination becomes net cost-saving when the weekly infection risk is ≥1.27% (eg, interacting with 59 other people/day). Even if the vaccine only protects against severe disease, it becomes cost-saving when the weekly infection risk exceeds 0.3% (50% starting efficacy, no preexisting protection).

Figure 2.

Cost-benefit of annual COVID-19 vaccination to an adult 18–49 years old and how it varies with (A) the individual's level of preexisting protection when they have no insurance; (B) starting vaccine efficacy when they have no insurance; (C) the individual's level of preexisting protection when they have private insurance; and (D) starting vaccine efficacy when they have private insurance.

As Figure 2B shows, increases in the starting vaccine efficacy against SARS-CoV-2 infection decrease the weekly risk at which vaccination becomes cost-saving (eg, from 1.4% with a 50% efficacy to 0.02% with an 80% efficacy, assuming 26.8% preexisting protection). Additionally, net cost-savings increase relatively linearly with increases in starting vaccine efficacy (eg, for every percentage point increase in starting efficacy between 50% and 80%, vaccination saves $7; 1.32% infection risk, no preexisting protection). Cost-savings also increase with higher long COVID costs (eg, saving $50 when long COVID cost $5762 OOP, 50% efficacy, 1.32% risk infection, 26.8%/50% preexisting protection against infection/hospitalization). Cost-savings also increase with disease severity, saving $19 when the hospitalization probability is 3 times higher (weekly infection risk 1.32%). At this infection risk, vaccination saves $2 even when the severe disease probability is reduced by half.

Figure 3A shows threshold maps illustrating the combinations of vaccination cost and starting vaccine efficacy for which annual vaccination is net cost-saving for an uninsured adult 18–49 years old. For example, with a 50% efficacy and 0.68% weekly infection risk, vaccination could cost up to $20 and remain cost-saving. These cost thresholds increase by 1.4–3.7 times with increases in vaccine efficacy (eg, up to $154 at 80% efficacy) and increase 1.5–4 times with increases in infection risk (eg, up to $56 at 1.32% weekly infection risk).

Figure 3.

COVID-19 vaccination cost in $US and starting vaccine efficacy thresholds at which the vaccine is net cost-saving from the individual perspective for (A) an uninsured adult 18–49  years old; (B) an insured adult 18–49 years old; (C) an uninsured adult 50–64 years old; and (D) an insured adult 50–64 years old.

An 18–49 Year Old With Private Insurance

As Figure 2C shows, the net cost-savings to an insured 18–49 year old is approximately half as much as that of an uninsured individual but remains saving as long as the weekly infection risk is ≥0.2% (≥50% vaccine efficacy against SARS-CoV-2 infection). Again, this corresponds to interacting with 9 other people/day. OOP medical costs due to illness represent 4% of the savings, while the remaining 96% are saved productivity losses, when excluding the cost of vaccination (otherwise, all cost-savings come from productivity losses). For an individual who has been previously vaccinated/infected, getting the annual vaccine is net cost-saving when the weekly infection risk exceeds 0.45% (13.6%/36% preexisting protection), corresponding to interacting with 19 other people/day (10% infection prevalence). This further increases to a 1.3% weekly risk with 26.8%/50% preexisting protection (eg, interacting with 59 people/day). If the vaccine only protects against severe disease, vaccination becomes net cost-saving when the weekly infection risk exceeds 0.3% (≥50% starting vaccine efficacy, no preexisting protection).

As Figure 2D shows, net cost-savings increase linearly with increases in starting vaccine efficacy (eg, saving an additional $4 for every 1% increase in efficacy between 50% and 80%, 0.68% infection risk, 26.8%/50% preexisting protection against infection/hospitalization). Moreover, as Figure 2D shows, annual vaccination with a higher starting efficacy becomes net cost-saving with lower weekly infection risk (eg, becoming net cost-saving with a ≥0.73% weekly risk, corresponding to interacting with 34 other people/day, with a 60% vaccine efficacy). Further, cost-savings increase slightly when long COVID costs $5762 OOP (eg, saving $69 with a 50% efficacy, 2.29% weekly infection risk, and 26.8%/50% preexisting protection). Similar to uninsured adults, cost-savings increase as COVID severity increases, increasing to $16 when the probability of hospitalization is 3-fold higher (1.32% weekly infection risk). Furthermore, when the hospitalization probability is reduced by half, cost-savings decrease to $2 (1.32% weekly infection risk).

The cost at which vaccination is net cost-saving (Figure 3B) ranges from $4 (50% efficacy, 0.2% weekly infection risk) to $313 (80% efficacy, 2.29% weekly infection risk). These cost thresholds are 1.1–3.4 times higher with increases in vaccine efficacy (eg, ≤$263 with 70% efficacy and 2.29% weekly infection risk) and 1.5–4 times higher with increases in infection risk (eg, ≤$100 with a 60% efficacy and 1.32% weekly infection risk).

A 50–64 Year Old With No Insurance

As Figure 4A shows, the net cost-savings of vaccination to an uninsured 50–64 year old is 1–3 times greater than that of an 18–49 year old. Figure 4 shows how COVID-19 vaccination becomes net cost-saving when the weekly infection risk exceeds 0.2% (eg, interacting with 9 other people/day). Again, all the cost-savings result from saved productivity losses as saved OOP medical costs due to illness are not enough to offset the vaccination cost (when excluding the vaccination cost, 17% of cost-savings are saved OOP medical illness costs and 83% are saved productivity losses). A weekly risk exceeding 0.2% is also needed for vaccination to become net cost-saving with a 13.4% preexisting protection level against infection (Figure 4A). However, this increases to 1.2%, corresponding to interacting with 54 other people/day (10% infection prevalence), with 26.8% preexisting protection against infection. If the vaccine only protects against severe disease, vaccination remains net cost-saving when the weekly infection risk is ≥0.2% (saving ≥$100 with a 50% efficacy, no preexisting protection).

Figure 4.

Cost-benefit of annual COVID-19 vaccination to an adult 50–64 years old and how it varies with (A) the individual's level of preexisting protection when they have no insurance; (B) starting vaccine efficacy when they have no insurance; (C) the individual's level of preexisting protection when they have private insurance; and (D) starting vaccine efficacy when they have private insurance.

Again, net cost-savings increase fairly linearly with increases in starting vaccine efficacy (Figure 4B). For example, every 1% increase in vaccine efficacy between 50% and 80% saves $8 (0.68% infection risk). Additionally, it decreases the weekly risk at which vaccination becomes cost-saving to 0.29% (interacting with 14 people/day) with a 60% starting efficacy and 0.2% (interacting with 9 people/day) with a ≥70% starting efficacy. If long COVID costs $5762 OOP, vaccination becomes cost-saving with an infection risk ≥0.68% (≤26.8%/50% preexisting protection levels; ≥50% efficacy). Again, net cost-savings increase and decrease with increases and decreases in disease severity (eg, at a 1.32% weekly infection risk, cost-savings increase to $64 when the hospitalization risk is 3 times higher and decrease to $4 with the hospitalization risk is half as much).

As Figure 3C shows, vaccination can cost up to $5 (50% efficacy, 0.2% weekly infection risk) to $731 (80% efficacy, 2.29% weekly infection risk) and remain net cost-saving. These cost thresholds increase 1.3–3.4 times with increases in starting vaccine efficacy (eg, up to $191 with a 70% efficacy and 0.68% weekly infection risk) and 1.4–8.3 times with increases in infection risk (eg, up to $372 with a 70% efficacy and 1.32% weekly infection risk).

A 50–64 Year Old With Private Insurance

The net cost-savings of vaccination to an insured 50–64 year old is 2–3 times higher than the cost-savings to an insured 18–49 year old, and less than the cost-savings (0.7–0.9 times lower) to an uninsured 50–64 year old (Figure 4C). As Figure 4 shows, vaccination becomes net cost-saving when the weekly infection risk exceeds 0.2% (interacting with 9 other people/day). This increases to 0.25% (interacting with 14 other people/day) and 1.32% (interacting with 59 people/day) when the individual has 13.4% and 26.8% preexisting protection against infection, respectively. At a 0.2% weekly infection risk, vaccination saves $119, of which OOP medical costs of illness represent 2% and productivity losses represent 98% when excluding vaccination cost (when including vaccination, all savings come from productivity losses). If only protecting against severe disease, vaccination is net cost-saving when the weekly infection risk is ≥0.2% (saving ≥$54 with a 50% efficacy, no preexisting immunity).

Again, net cost-savings increase approximately linearly as starting vaccine efficacy increases (Figure 4D) and decreases the weekly risk threshold at which vaccination becomes cost-saving. For example, with a 1.32% weekly infection risk, every 1% increase in starting efficacy between 50% and 80% saves an additional $11. As another example, COVID-19 vaccination (60% efficacy) becomes cost-saving when the weekly risk exceeds 0.41% (interacting with 19 other people/day). Similar to uninsured adults, cost-savings increase with increased long COVID costs (eg, saving $35 when long COVID costs $5762, 1.32% weekly infection risk) and severity (eg, saving $21 when the hospitalization risk is 3 times higher), and decrease with lower risk of severe outcomes (eg, saving $9 when hospitalization risk is reduced by half).

The vaccination can cost up to $5 (50% efficacy against SARS-CoV-2 infection, 0.2% weekly infection risk) to $637 (80% efficacy, 2.29% weekly infection risk) and remain net cost-saving (Figure 3D). These cost thresholds increase 1.2–6 times with increases in starting vaccine efficacy (eg, ≤$93 with a 60% efficacy and 0.68% weekly infection risk) and increase 1.5–4.8 times with increases in infection risk (eg, ≤$187 with a 70% efficacy and 0.68% weekly infection risk).

DISCUSSION

Our results show that the COVID-19 vaccine at its currently measured efficacy levels (54%) [46] and cost ($48.88–$97.75) [20] would provide individuals cost-savings as long as the risk of infection is ≥1.32% per week. Such a risk threshold would correspond to interacting with 59 other people in a day (10% infection prevalence) assuming winter 2023–2024 Omicron variant conditions. However, this risk threshold decreases to 0.2% (interacting with 9 other people/day) when an individual has no preexisting protection. Cost-savings means that getting vaccinated would not only pay for itself, but also on average would provide a net monetary return to the person getting vaccinated via saved productivity losses. While vaccination reduces OOP medical costs due to illness (2%–31% of total cost-saving excluding vaccination costs), these savings do not quite make up the cost of a $60 vaccination. Moreover, the cost-savings increase by 1.7–1.9 for those who are uninsured compared to insured, because their OOP costs of illness are not shared by an insurance plan.

Compared to the cost-benefit of other currently recommended vaccines, our results show even greater cost-savings. To our knowledge, there is only one other study that evaluated the cost-benefit of vaccination from the individual perspective in the United States. This study found that the influenza vaccine had a net cost to the individual per prevented illness ranging from $16 to $4000, depending on the strain (seasonal vs pandemic), attack rate, and amount of time to get vaccinated [12]. Most cost-benefit analyses focus on the value from the third-party payer and societal perspectives to inform insurance coverage and vaccination policies/campaigns. Thus, there is a need for more studies from the individual perspective to quantify the vaccination's value to one person, as individuals are the final decision-makers about vaccination and may need to pay OOP.

Helping people understand the net financial benefits of getting vaccinated has become increasingly important as most businesses and other organizations have dropped COVID-19 vaccination requirements, the government is no longer bearing the full cost of the vaccines, and vaccination rates have dropped [47]. While economic considerations may not be at the forefront when people decide whether they want to get vaccinated, cost can be a barrier in some situations. For example, people may not be able to afford taking time off work, hiring childcare, or getting transportation to get vaccinated. Elucidating such trade-offs can help design programs to offset some of these costs such as providing vaccination in more convenient locations. Moreover, during the COVID-19 pandemic, there was some belief that economic incentives could help tip some towards getting vaccinated as some state governments offered financial incentives (eg, lotteries for cash prizes). Furthermore, knowing the cost trade-offs for the individual can help with determining vaccination pricing and insurance coverage. Since September 2023, manufacturers have raised the vaccine price from $26.36–$30.48 [48] to $120–$130 per dose [49], which may act as a further deterrent to getting vaccinated. Our results show that in some situations, even if the vaccination cost were as high as $731, getting vaccinated could still be net cost-saving to the individual. Of course, this does not necessarily mean that implementing such a price would be justified, as a higher price could further deter people from getting vaccinated.

Our study does have some limitations. All models, by definition, are simplifications of real life and cannot account for every possibility. We sought to remain as conservative as possible about the OOP costs associated with COVID-19 and thus vaccination's potential cost-savings. For example, we assumed an insured individual would seek care within the insurance provider's covered network. If the individual were to seek care outside the network, that would only raise costs and increase vaccination's net cost-savings. We also assumed only COVID-19–related costs counted toward the individual's OOP maximum and deductible, in reality, other health care costs (eg, care for other illnesses) would also count. The infection risk thresholds reported above were annual average weekly infection risk; in actuality, COVID-19 transmission can vary with the weather/season, SARS-CoV-2 variant, and vaccination coverage. Of course, an individual's decision to get vaccinated can contribute to decreasing the infection risk due to potential herd immunity effects, thus possibly adding more cost-savings to vaccination. However, because this study focused on the individual, independent of the vaccination status of those around the individual, and the goal was to be conservative about the vaccine's potential impact to the individual, we chose not to account for such potential herd effects.

CONCLUSIONS

Our study shows that annual COVID-19 vaccination is net cost-saving from the adult individual perspective over the course of a year, and thus provides support for individuals to get the COVID-19 vaccine, even if they must pay OOP. While vaccination is net cost-saving up to $731, our results should not imply that manufacturers should charge up to this amount for an annual COVID-19 vaccine as cost can be a barrier to vaccination.

Supplementary Data

Supplementary materials are available at The Journal of Infectious Diseases online (http://jid.oxfordjournals.org/). Supplementary materials consist of data provided by the author that are published to benefit the reader. The posted materials are not copyedited. The contents of all supplementary data are the sole responsibility of the authors. Questions or messages regarding errors should be addressed to the author.

Notes

Disclaimer. Statements in the manuscript do not necessarily represent the official views of, or imply endorsement by, the National Institute of Health, Agency for Healthcare Research and Quality, the US Department of Health and Human Services, City University of New York, or the Pandemic Response Institute.

Financial support. This work was supported by the National Science Foundation (grant number 2054858); the Agency for Healthcare Research and Quality (grant number 1R01HS028165–01); the National Institute of General Medical Sciences as part of the Models of Infectious Disease Agent Study Network (grant numbers R01GM127512 and 3R01GM127512–01A1S1); the National Center for Advancing Translational Sciences of the National Institutes of Health (grant number U54TR004279); the National Institute of Allergy and Infectious Diseases of the National Institutes of Health (grant number P01AI172725); and by the City University of New York in support of the Pandemic Response Institute.

Author notes

Potential conflicts of interest. P. J. H., M. E. B., and U. S. are coinventors of a COVID-19 recombinant protein vaccine technology owned by Baylor College of Medicine (BCM) that was recently licensed by BCM nonexclusively and with no patent restrictions to several companies committed to advance vaccines for low- and middle-income countries. The coinventors have no involvement in license negotiations conducted by BCM. Similar to other research universities, a long-standing BCM policy provides its faculty and staff, who make discoveries that result in a commercial license, a share of any royalty income, according to BCM policy. All other authors report no potential conflicts.

All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.