pro vaccination thesis statement

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• Volume 47, Issue 2
• Good reasons to vaccinate: mandatory or payment for risk?
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• http://orcid.org/0000-0003-1691-6403 Julian Savulescu 1 , 2 , 3
• 1 Faculty of Philosophy , University of Oxford , Oxford , UK
• 2 Murdoch Childrens Research Institute , Parkville , Victoria , Australia
• 3 Melbourne Law School , University of Melbourne , Melbourne , Victoria , Australia
• Correspondence to Professor Julian Savulescu, Faculty of Philosophy, University of Oxford, Oxford, UK; julian.savulescu{at}philosophy.ox.ac.uk

Mandatory vaccination, including for COVID-19, can be ethically justified if the threat to public health is grave, the confidence in safety and effectiveness is high, the expected utility of mandatory vaccination is greater than the alternatives, and the penalties or costs for non-compliance are proportionate. I describe an algorithm for justified mandatory vaccination. Penalties or costs could include withholding of benefits, imposition of fines, provision of community service or loss of freedoms. I argue that under conditions of risk or perceived risk of a novel vaccination, a system of payment for risk in vaccination may be superior. I defend a payment model against various objections, including that it constitutes coercion and undermines solidarity. I argue that payment can be in cash or in kind, and opportunity for altruistic vaccinations can be preserved by offering people who have been vaccinated the opportunity to donate any cash payment back to the health service.

• behaviour modification
• technology/risk assessment
• philosophical ethics
• public health ethics

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/ .

https://doi.org/10.1136/medethics-2020-106821

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Introduction

We are in the midst of a global pandemic with COVID-19 and there is a race to develop a vaccine. At the time of writing, there are 53 vaccines in clinical trials on humans (plus five that have bypassed the full trial process) and at least 92 preclinical vaccines under active investigation in animals. There are a number of different approaches: (1) genetic—using mRNA to cause the body to produce viral proteins; (2) viral vector—using genetically modified viruses such as adenovirus to carry sections of coronavirus genetic material; (3) protein—delivering viral proteins (but not genetic material) to provoke an immune response; (4) inactivated or attenuated coronavirus; (5) repurposing existing vaccines, eg, BCG (bacillus Calmette–Guérin). 1

Given the mounting number of deaths globally, and the apparent failure of many countries to contain the pandemic without severely damaging or problematic lockdowns and other measures, there have been calls to make a vaccine, if it were approved, mandatory. 2 Mandatory vaccination has not been ruled out within the UK. 3

The first part of this article asks when, if ever, a vaccine should be mandatory. I will create a set of criteria and a decision algorithm for mandatory vaccination. I will argue that in the case of COVID-19, some of these criteria may not be satisfied. The second part of the article argues that in the case of COVID-19, it may be ethically preferable to incentivise vaccine uptake. I will justify incentivisation and discuss different kinds of incentives.

Ethics of mandatory COVID-19 vaccination

There is a large body of literature on the justification for the use of coercion in public health and infectious disease in particular. Mandatory vaccination is typically justified on Millian grounds: harm to others. According to John Stuart Mill, the sole ground for the use of state coercion (and restriction of liberty) is when one individual risks harming others. 4 The most prominent arguments from bioethicists appeal to preventing harm to others. 5–7 In the case of children, significant risk of harm to the child is also a ground for state protection. Bambery et al 8 give the example of a child taking a box of toxic bleach to school, potentially harming himself and other children. Teachers are entitled to restrain the child and remove the poison both because of risk to the child and to other children. 8 Flanigan uses a similar example of a person shooting a gun into a crowd. 5

The Nuffield Council of Bioethics produced an influential report on public health which considers when coercion and mandatory vaccination might be justified:

When assessing whether more directive policies are acceptable, the following factors should be taken into account: the risks associated with the vaccination and with the disease itself, and the seriousness of the threat of the disease to the population. In the case of incentivised policies, the size of the incentive involved should be appropriate so that it would not unduly compromise the voluntariness of consent. We identified two circumstances in which quasi-mandatory vaccination measures are more likely to be justified. First, for highly contagious and serious diseases, for example with characteristics similar to smallpox. Second, for disease eradication if the disease is serious and if eradication is within reach. 9

I will elaborate on these brief suggestions and provide a novel structured algorithm for when vaccination should be mandatory.

COVID-19 is almost unique because of the gravity of the problem at the global level: not only is there cost in terms of lives from COVID-19, there is also the extraordinary economic, health and well-being consequences of various virus-control measures, including lockdown, which will extend into the future. Probably never before has a vaccine been developed so rapidly and the pressure to use it so great, at least at the global level.

There is a strong case for making any vaccination mandatory (or compulsory) if four conditions are met:

There is a grave threat to public health

The vaccine is safe and effective

Mandatory vaccination has a superior cost/benefit profile compared with other alternatives

The level of coercion is proportionate.

Each of these conditions involves value judgements.

Grave threat to public health

So far, there have been over 1 million deaths attributed to COVID-19 globally (as of 30 September 2020). 10 In the UK alone, it was predicted in influential early modelling that 500 000 would have died if nothing was done to prevent its spread. Even with the subsequent introduction of a range of highly restrictive lockdown measures (measures which could themselves come at a cost of 200 000 non-COVID-19 lives according to a recent UK government report), 11 more than 42 000 (as of 30 September 2020) 12 have died in the UK within 28 days of a positive test.

The case fatality rate was originally estimated to be as high as 11%, but (as is typical with new diseases) this was quickly scaled down to 1.5% or even lower. 13 The infection fatality rate (IFR, which accounts for asymptomatic and undiagnosed cases) is lower still as it has become clear that there are a large number of asymptomatic and mild cases. For example, the Centre for Evidence Based Medicine reports that “In Iceland, where the most testing per capita has occurred, the IFR lies somewhere between 0.03% and 0.28%”. 14

Of course, how you define “grave” is a value judgement. One of the worst-affected countries in the world in terms of COVID-19-attributed deaths per million is Belgium. The mortality is (at the time of writing) around 877 per million population, which is still under 0.1%, and the average age of death is 80. Of course, Belgium and most other countries have taken strict measures to control the virus and so we are not seeing the greatest possible impact the virus could have. Yet others such as Brazil and Sweden have intervened to a much lesser degree, yet (currently) have rates of 687 and 578 deaths per million respectively. Sweden’s April all-cause deaths and death rate at the peak of its pandemic so far, while extremely high, were surpassed by months in 1993 and 2000. 15

The data are complex and difficult to compare with different testing rates, and ways of assigning deaths and collecting data differing from country to country. For example, Belgium counts deaths in care homes where there is a suspicion that COVID-19 was the cause (without the need for a positive test) and, until recently, the UK counted a death which followed any time from a COVID-19 positive test as a COVID-19 death. Moreover, there have been huge behavioural changes even in countries without legally enforced lockdowns. Furthermore, the IFR varies wildly by age-group and other factors. Even among survivors, there is emerging evidence that there may be long-term consequences for those who have been infected but survived. Long COVID-19 health implications may present a grave future public health problem. Nevertheless, some might still argue that this disease has not entered the “grave” range that would warrant mandatory vaccination. The Spanish influenza killed many more (50–100 million), 16 and it afflicted younger rather than older people, meaning even more “life years” were lost. It is not difficult to imagine a Superflu, or bioengineered bug, which killed 10% across all ages. This would certainly be a grave public health emergency where it is likely mandatory vaccination would be employed.

Deciding whether COVID-19 is sufficiently grave requires both more data than we have and also a value judgement over the gravity that would warrant this kind of intervention. But let us grant for the sake of argument that COVID-19 is a grave public health emergency.

Vaccine is safe and effective

There are concerns that testing has been rushed and the vaccine may not be safe or effective. 17

First, although the technology being used in many of these vaccine candidates has been successfully used in other vaccines, no country has ever produced a safe and effective vaccine against a coronavirus. So in one way, we are all in uncharted waters.

Second, any vaccine development will be accelerated in the context of a grave public health emergency.The inherent probabilistic nature of the development of any biologic means that no vaccine could be said to be 100% safe. There will be risks and those risks are likely to be greater than with well-established vaccines.

Thirdly, some side effects may take time to manifest.

This is not to support the anti-vaccination movement. Vaccines are one of the greatest medical accomplishments and a cornerstone of public health. There are robust testing procedures in place in most jurisdictions to ensure that licensed COVID-19 vaccines are both effective and safe. It is only to acknowledge that everything, including vaccination, has risks. Perhaps the biggest challenge in the development of a vaccine for COVID-19 will be to be honest about the extent of those risks and convey the limitations of confidence in safety and efficacy relative to the evidence accrued.

There is an ethical balance to be struck: introducing a vaccine early and saving more lives from COVID-19, but risking side effects or ineffectiveness versus engaging in longer and more rigorous testing, and having more confidence in safety and efficacy, but more people dying of COVID-19 while such testing occurs. There is no magic answer and, given the economic, social and health catastrophe of various anti-COVID-19 measures such as lockdown, there will be considerable pressure to introduce a vaccine earlier.

To be maximally effective, particularly in protecting the most vulnerable in the population, vaccination would need to achieve herd immunity (the exact percentage of the population that would need to be immune for herd immunity to be reached depends on various factors, but current estimates range up to 82% of the population). 18

There are huge logistical issues around finding a vaccine, proving it to be safe, and then producing and administering it to the world’s population. Even if those issues are resolved, the pandemic has come at a time where there is another growing problem in public health: vaccine hesitancy.

US polls “suggest only 3 in 4 people would get vaccinated if a COVID-19 vaccine were available, and only 30% would want to receive the vaccine soon after it becomes available.” 18

Indeed, vaccine refusal appears to be going up. A recent Pew survey suggested 49% of adults in the USA would refuse a COVID-19 vaccine in September 2020. 19

If these results prove accurate then even if a safe and effective vaccine is produced, at best, herd immunity will be significantly delayed by vaccine hesitancy at a cost both to lives and to the resumption of normal life, and at worst, it may never be achieved.

There remain some community concerns about the safety of all pre-existing vaccines, including many that have been rigorously tested and employed for years.

In the case of COVID-19, the hesitancy may be exacerbated by the accelerated testing and approval process which applies not only to Sputnik V (the controversial “Russian vaccine”). Speaking about America’s vaccine programme, Warp Speed, Donald Trump applauded its unprecedented pace:

…my administration cut through every piece of red tape to achieve the fastest-ever, by far, launch of a vaccine trial for this new virus, this very vicious virus. And I want to thank all of the doctors and scientists and researchers involved because they’ve never moved like this, or never even close. 20

The large impact on society means the vaccine will be put to market much more quickly than usual, perhaps employing challenge studies or other innovative designs, or by condensing or running certain non-safety critical parts of the process in parallel (for example, creating candidate vaccines before its approval).

While the speed is welcomed by politicians and some members of the public, the pressure to produce a candidate vaccine, and the speed at which it has been done, may be also perceived (perhaps unfairly) to increase the likelihood of the kind of concerns that lead to vaccine hesitancy: concerns over side-effects that are unexpected or rare, or that take longer to appear than the testing process allows for, or that for another reason may be missed in the testing process.

The job to be done will not only be to prove scientifically that the vaccine is safe and effective, but also to inform and reassure the public, especially the group who are willing to take the vaccine in theory—but only after others have tried it out first.

The question remains of how safe is safe enough to warrant mandatory vaccination. It is vanishingly unlikely that there will be absolutely no risk of harm from any biomedical intervention, and the disease itself has dramatically different risk profiles in different groups of the population. In an ideal world, the vaccine would be proven to be 100% safe. But there will likely be some risk remaining. Any mandatory vaccination programme would therefore need to make a value judgement about what level of safety and what level of certainty are safe and certain enough. Of course, it would need to be very high, but a 0% risk option is very unlikely.

A COVID-19 vaccine may be effective in reducing community spread and/or preventing disease in individuals. Mandatory vaccination is most justifiable when there are benefits to both the individual and in terms of preventing transmission. If the benefits are only to individual adults, it is more difficult to support mandatory vaccination. One justification would be to prevent exhaustion of healthcare services in an emergency (eg, running out of ventilators), which has been used a basis of restriction of liberty (it was the main justification for lockdown). It could also be justified in the case of protection of children and others who cannot decide for themselves, and of other adults who either cannot be vaccinated for medical reasons.

Better than the alternatives

It is a standard principle of decision theory that the expected utility of a proposed option must be compared with the expected utility of relevant alternatives. There are many alternatives to mandatory vaccination. See figure 1 for a summary of the range of strategies for preventing infectious disease.

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Strategies for prevention of infectious disease.

A popular position, especially among medical professionals, 7 is that we don’t need mandatory vaccination because people are self-interested or altruistic enough to come forward for vaccination. We can reach herd immunity without mandatory vaccination.

If this were true, all well and good, but the surveys mentioned above cast doubt on this claim with regard to the future COVID-19 vaccine. Moreover, reaching herd immunity is not good enough.

First, how fast we reach herd immunity is also important. In a pandemic, time is lives. If it takes a year to reach herd immunity, that could be thousands or tens of thousands of lives in one country.

Second, herd immunity is necessary because some people cannot be vaccinated for medical reasons: they have allergies, immune problems, or other illnesses. The elderly often don’t mount a strong immune response (that is why it is better to vaccinate children for influenza because they are the biggest spreaders of that disease 7 —although COVID-19 appears to be different on the current evidence). And immunity wanes over time—so even people previously vaccinated may become vulnerable.

Even when national herd immunity is achieved, local areas can fall below that level over time, causing outbreaks, as happened with measles recently. This is especially likely to happen where people opposed to vaccines tend to cluster toghether—for example, in the case of certain religious communities. So ideally we need better than herd immunity to ensure that people are protected both over time and in every place.

These are thus reasons to doubt whether a policy of voluntary vaccination will be good enough, though it remains to be seen.

There are other policies that might obviate the need for mandatory vaccination. South Korea has kept deaths down to about 300 (at the time of writing) with a population of 60 000 000 with a vigorous track and trace programme (although it was criticised for exposing extra-marital affairs and other stigmatised behaviours). 21 Other countries have enforced quarantine with tracking devices. There could be selective lockdown of certain groups, 22 or for intermittent periods of time.

The long-term costs and benefits of such policies would have to be evaluated. That is, we should calculate the expected utility of mandatory vaccination (in combination with other policies) and compare it to alternative strategies (or some other combination of these). How utility should be evaluated is an ethical question. Should we count deaths averted (no matter how old), life years lost or lost well-being (perhaps measured by quality adjusted life years)? 23 Should we count loss of liberty or privacy into the other side the equation?

It may be that a one-off mandatory vaccination is a significantly smaller loss of well-being or liberty than these other complex resource intensive strategies.

So we cannot say whether a mandatory policy of COVID-19 vaccination is ethically justified until we can assess the nature of the vaccine, the gravity of the problem and the likely costs/benefit of alternatives. But it is certainly feasible that it could be justified.

It is important to recognise that coercive vaccination can be justified. This is easy to see by comparing it to other coercive interventions in the public interest.

Conscription in war

In the gravest emergencies, where the existence and freedom of the whole population is at stake, people are conscripted to serve their country, often with high risk of death or permanent injury. We often analogise the pandemic to a war: we are fighting the virus. If people can be sent to war against their will, in certain circumstances some levels of coercion are justified in the war on the virus. Notably, in conditions of extreme emergency in past wars (graver than currently exist for COVID-19), imprisonment or compulsion have even been employed. 24

A more mundane example is the payment of taxes. Taxes benefit individuals because tax revenue allows the preservation of public goods. But if sufficient numbers of others are paying their taxes, it is in a person’s self-interest to free ride and avoid taxes. Indeed, paying taxes may result in harm in some circumstances. 24 In the USA, where there is a large private healthcare sector, paying your taxes may mean you cannot pay for lifesaving medical care that you would otherwise have been able to afford. Still, taxes are mandatory based on considerations of fairness and utility.

Seat belts are mandatory in the UK and many other countries, whereas they were previously voluntary. Interestingly, 50% or so of Americans initially opposed making seat belts mandatory, but now 70% believe mandatory laws are justified. 25

Seat belts reduce the chance of death if you are involved in a car accident by 50%. They are very safe and effective. Notably, they do cause injuries (seat belt syndrome) and even, very occasionally, death. But the chances of being benefitted by wearing them vastly outweigh these risks, so they are mandatory, with enforcement through fines . I have previously likened vaccination to wearing a seat belt. 25

Pre-existing mandatory vaccination

Mandatory vaccination policies are already in place in different parts of the world. Mandatory vaccination policies are those that include a non-voluntary element to vaccine consent and impose a penalty or cost for unjustified refusal (justified refusal includes those who have a contraindicating medical condition, or those who already have natural immunity). There are a range of possible penalties or costs which can coerce people. Australia has the “No Jab, No Pay” scheme which withholds child benefits if the child is not vaccinated, and a “No Jab, No Play” scheme which withholds kindergarten childcare benefits. Italy introduced fines for unvaccinated children who attend school. In the USA, state regulations mandate that children cannot attend school if they are not vaccinated, and healthcare workers are required to vaccinate. Some US states (eg, Michigan) make exemptions difficult to obtain by requiring parents to attend immunisation education courses 26 (see also 27 28 ).

Figure 2 summarises the range of coercive policies that can constitute mandatory vaccination.

Cost of mandatory/coercive vaccination.

Coercion is proportionate

In public health ethics, there is a familiar concept of the “least restrictive alternative”. 28 The least restrictive alternative is the option which achieves a given outcome with the least coercion (and least restriction of liberty).

This is a very weak principle: it uses liberty as tie breaker between options with the same expected utility. More commonly, however, we need to weigh utility against liberty. That is, a more restrictive policy will achieve more expected utility—but is it justified?

According to a principle of proportionality, the additional coercion or infringement in liberty is justified if it is proportionate to the gain in expected utility of the more coercive intervention compared with next best option. That is, additional coercion is justified when the restriction of liberty is both minimised and proportionate to the expected advantages offered by the more coercive policy.

As we can see from the previous section and figure 2, there are a variety of coercive measures. (The Nuffield Council has created a related “Intervention Ladder”, 29 though this includes education and incentives, as well as coercive measures.) Penalties can be high. In war, those who conscientiously objected to fighting went to jail or were forced to perform community service (or participate in medical research). In France, parents were given a suspended prison sentence for refusing to vaccinate their child. 30

While there are legitimate concerns that the effectiveness of these policies in different contexts has been inadequately investigated, a number of these policies have been shown to increase vaccination rates. 31

Notably, the fine or punishment for avoiding taxes varies according to the gravity of the offence. The fine for not wearing a seat belt is typically small. A modest penalty for not being vaccinated in a grave public health emergency could be justifiable. For example, a fine or restriction of movement might be justified.

Figure 3 combines these four factors into an algorithm for justified mandatory vaccination.

Algorithm for mandatory vaccination.

These four factors can be justified in several ways. They represent a distillation and development of existing principles in public health ethics, for example, the least restrictive alternative. They can also be justified by the four principles of biomedical ethics.

For example, justice is about the distribution of benefits and burdens across a population in a fair manner. Justice and beneficence, in the context of vaccination policies, both require that the problem addressed is significant and vaccination is an effective means of addressing it. Non-maleficence requires that the risk imposed on individuals be small. Respect for autonomy and justice both require that coercion be applied only if necessary and that it be proportionate to additional utility of mandatory vaccination (and that such coercion be minimised, which is a feature of proportionality).

It is important to recognise that vaccines may have benefits both to the individual and to others (the community). If the vaccine has an overall net expected utility for the individual, beneficence supports its administration.

How great a sacrifice (loss of liberty or risk) can be justified? The most plausible account is provided by a duty of easy rescue: when the cost to an individual is small of some act, but the benefit or harm to another is large, then there is a moral obligation to perform that act. I have elsewhere argued for a collective duty of easy rescue: where the cost of some act to an individual is small, and the benefit of everyone doing that act to the collective is large, there is a collective duty of easy rescue. 32 Such a principle appropriately balances respect for autonomy with justice.

Whether mandatory vaccination for any disease can be justified will depend on precise facts around the magnitude of the problem, the nature of the disease and vaccination, the availability and effectiveness of alternative strategies and the level of coercion. Elsewhere I compare mandatory vaccination for influenza and COVID-19 in more detail. 27

Better than coercion? Payment for risk

Given the risks, or perceived risks, of a novel COVID-19 vaccine, it would be practically and perhaps ethically problematic to introduce a mandatory policy, at least initially (when uncertainty around safety will be greater). Is there a more attractive alternative?

The arguments in favour of vaccination, particularly for those at lower risk (children, young people and those previously infected) can be based on a principle of solidarity. After all, “We are in this together” has been a recurrent slogan supporting pandemic measures in different countries. Those at low risk are asked to do their duty to their fellow citizens, which is a kind of community service. Yet they have little to personally gain from vaccination. The risk/benefit profile looms large for those at lowest risk.

However, another way of looking at this is that those at low risk are being asked to do a job which entails some risk., so they should be paid for the risk they are taking for the sake of providing a public good. And although it may be unlikely to influence so-called 'anti-vaxxers', it may influence a good portion of the 60% of American adults who responded in a March 2020 poll that they would either delay vaccination or didn’t know about vaccination. 33

I have previously argued that we should reconceive live organ donation and participation in risky research, including challenge studies, 34 as jobs where risk should be remunerated, much like we pay construction workers and other dangerous professions both for the job and for the risk involved. 35 36 While the risk profile for approved vaccinations means that it differs from these examples, it could be compared to a job such as social work as a further argument in favour of payment. People may legitimately be incentivised to take on risks, as the Nuffield Council recognises in its Intervention Ladder. 29

The advantage of payment for risk is that people are choosing voluntarily to take it on. As long as we are accurate in conveying the limitations in our confidence about the risks and benefits of a vaccine, then it is up to individuals to judge whether they are worth payment.

Of course, that is a big ask. It would require government to be transparent, explicit and comprehensive in disclosure of data, what should be inferred and the limitations on the data and confidence. This has often not been the case—one only has to remember the denial of the risks of bovine spongiform encephalopathy (BSE) at the height of the crisis by the British government, when in 1990 the Minister for Agriculture, Fisheries and Food, John Gummer proudly fed his 4-year-old daughter, Cordelia, a hamburger in front of the world’s media, declaring British beef safe. (Gummer was awarded a peerage in 2010 and is now Lord Deben.) 37

There is also a danger that payment might signal lack of confidence in safety. That is a real risk and one that I will address in the “payment in kind” section below.

But the basic ethical point (public acceptability aside) is that, if a vaccine is judged to be safe enough to be used without payment, then it is safe enough to be used with payment. 36 Payment itself does not make a vaccine riskier. If a vaccine is considered too risky to be administered to the population, then it should not be administered, no matter whether coercively, through incentives, or through some other policy.

A standard objection to payment for risk (whether it is risky research or live organ donation) is that it is coercive: it forces people to take risks against their better judgement. In Macklin’s words:

The reason for holding that it is ethically inappropriate to pay patients to be research subjects is that it is likely to be coercive, violating the ethical requirement that participation in research should be fully voluntary. 38

As I have previously argued, 39 this demonstrates deep conceptual confusion. Coercion exists when an option which is either desired or good is removed or made very unappealing. The standard example is a robber who demands “Your money or your life”. This removes the most desired and best option: your money and your life. The Australian “No Jab, No Pay”scheme arguably does constitute coercion as it removes an option that one is entitled to, that is, non-vaccination with the “Pay”. So too is the Italian scheme of fines coercive.

However, paying people is not coercive. Adding an option, like payment, without affecting the status quo is not coercive. If a person chooses that option, it is because they believe that overall their life will go better with it, in this case, with the vaccination and the payment. The gamble may not pay off: some risk might eventuate and then it wasn’t worth it. But that is life—and probability.

It is true that the value of the option might exercise force over our rational capacities, but that is no different from offering a lot of money to attract a favoured job applicant.

What can be problematic about offers is exploitation. Exploitation exists where one offers less than a fair deal and a person only accepts it because of vulnerability from background injustice.

There are two ways to prevent exploitation. First, we can correct any background injustice that might cause it. In this case, the person would have little reason to accept the offer. Second, we can pay a fair minimum price for risk, as when we pay construction workers danger money. Paradoxically, this requires paying more, rather than less. 40

But there is an important additional feature of vaccination. If a vaccine were deemed to be safe enough to offer on a voluntary basis without payment, it must be safe enough to incentivise with payment because the risks are reasonable. It may be that those who are poorer may be more inclined to take the money and the risk, but this applies to all risky or unpleasant jobs in a market economy. It is not necessarily exploitation if there are protections in place such as a minimum wage or a fair price is paid to take on risk.

So payment for vaccination which passes independent safety standards (and could reasonably be offered without payment) is not exploitation, if the payment is adequate.

Undue influence?

A related concern is undue influence. Undue influence means that because of the attractiveness of the offer, I can’t autonomously and rationally weigh up the risks and benefits. It is sometimes understood as “were it not for the money, he would not do it”.

But that formulation is too broad—were it not for the money, many people would not go to work. Rather what the concept of ‘undue influence’ intends to capture is that the offer, usually money, bedazzles a person so that he or she makes a mistake in weighing up the risks and benefits. Someone offers Jones a million dollars to take on a risk of 99.99% of dying in a dangerous experiment. He just focuses on the money and takes a deal which is unfair and unreasonable. However, taking such an offer might be rational. If Jones’ daughter is about to die without a million dollars and he values her life more than his own, it might be both autonomous and rational to take the deal.

Because we cannot get into people’s minds, it is difficult in practice to unravel whether undue influence is occurring—how can you differentiate it from a rational decision? In practice, if it would be acceptable to be vaccinated for nothing, it is acceptable to do it for money. Concerns about undue influence are best met by implementing procedures to minimise bias and irrational decision making, such as cooling off periods, information reframing, and so on.

There remains a lurking concern that a decision where payment is involved may not be fully autonomous or authentic. For example, racial and ethnic minorities are among the groups most gravely affected by COVID-19, but given concerns about systemic racism in research and medicine, these communities may have good reason to distrust the medical machine. Is it acceptable to use payment to get over those concerns?

All we can do practically to address concerns about autonomy and authenticity is to redouble efforts: to ensure we do know the risks and they are reasonable (and that the underpinning research is not itself subject to concerns about systemic racism), and try to foster trust with such public education campaigns. This can work alongside a payment scheme. People still need to understand what the facts are. They still need to make as autonomous and authentic a decision as possible.

Practical advantages

A payment model could also be superior to a mandatory model from a practical point of view. There may be considerable resistance to a mandatory model which may make it difficult, expensive and time-consuming to implement, with considerable invasion of liberty. In a payment model, people are doing what they want to do.

A payment model could also be very cheap, compared with the alternatives. The cost of the UK’s furlough scheme is estimated to reach £60 billion by its planned end in October, 41 and the economic shut down is likely to cost many billions more, as well as the estimated 200 000 lives expected to be lost as a result. 11 It would make economic sense to pay people quite a lot to incentivise them to vaccinate sooner rather than later—which, for example, would speed up their full return to work.

It may be that payment is only required to incentivise certain groups. For example, it may be that young people require incentivising because they are at lower risk from the disease itself. On the other hand, justice might require payment for all taking the risk. Although the elderly and those at higher risk have more to gain personally, they are also providing a service by being vaccinated and not using limited health resources. (There is an enormous backlog of patients in the NHS—another grave threat to public health.)

One particularly difficult case is paying parents to vaccinate their children. It is one thing to pay people to take on risk for themselves; it is quite another to pay them to enable their children to take on risks, particularly when the children have little to gain as they are at lowest risk. In part, the answer to this issue is determined by how safe the vaccine is and how confident we can be in that assessment. If it were safe, to a level that even a mandatory programme would be justified, it may be appropriate to instead incentivise parents to volunteer their children for vaccination. If safety is less certain, payment for risk in this group is the most problematic.

It is true that some mandatory vaccination programmes already fine parents for failure to vaccinate their children. However, in those cases vaccination is clearly in the child’s best interest, as the child receives the benefit of immunity to diseases such as measles, that pose a greater risk to that child than we currently believe COVID-19 does. Moreover, they are for vaccines that have been in place for many years and have a well-established safety profile.

A standard objection to paying people to do their duty, particularly civic duty, is that it undermines solidarity, trust, reciprocity and other community values. This is the argument given by Richard Titmuss for a voluntary blood donation scheme. 42

The UK does not pay donors for blood or blood products, but does purchase blood products from other countries, including Austria where donors are paid a “travel allowance” for plasma donation. In Australia, which runs a donor system, more than 50% of the plasma comes from paid donors in the USA. 43 Altruism is insufficient. Germany recently moved to paying for plasma donors. It does not appear to have undermined German society.

In the end, the policy we should adopt towards COVID-19 vaccination will depend on the precise risks and benefits of the vaccine (and our confidence in them), the state of the pandemic, the nature of the alternatives, and particularly the public appetite for a vaccine.

In the right circumstances, mandatory vaccination could be ethically justified, if the penalty is suitably proportionate.

Payment for vaccination, perhaps, has even more to be said for it.

For those attached to the gift of altruism, the vaccinated could be offered the opportunity to donate their fee back to the NHS (or similar health service provider). This combined “payment-donation” model would be a happy marriage of ethics and economics. It would give altruists a double chance to be altruistic: first by vaccinating and second by donating the fee. It would also couple self-interest with morality for free-riders (as they would have greater self-interest to do what is moral), and it would give those who face obstacles to vaccination an additional reason to overcome these.

Payment in kind

Of course, benefits can come in cash or kind. An alternative “payment” model is to pay those who vaccinate in kind. This could take the form of greater freedom to travel, opportunity to work or socialise. With some colleagues, I have given similar arguments in favour of immunity passports. 44

One attractive benefit would be the freedom to not wear a mask in public places if you carried a vaccination certificate, and not to socially distance. Currently, everyone has to wear a mask and practise social distancing. Relaxing this requirement for those who have been vaccinated (or otherwise have immunity) would be an attractive benefit. Moreover, it would help ameliorate the risks the unvaccinated would pose to others.

Payment in kind has one advantage over cash in that it might not send the signal that vaccination is perceived to be unsafe. A cash payment may paradoxically undermine vaccination uptake by introducing unwarranted suspicion (though this is an intuition that may need to be tested). Benefits in kind are less susceptible to this concern because they are directly linked to the benefit provided by the vaccine itself: the vaccinated person is no longer a threat to others.

Some might object that this represents a form of shaming the non-vaccinators (some of whom might be excluded from vaccination for health reasons), just as presenting those who evaded conscription with a white feather was a method of shaming perceived free-riders. But this could be managed through an education campaign about the justification for face covering requirements. There is a good reason to require the non-vaccinated to continue to wear masks and practice social distancing, regardless of whether their refusal is justified—they do represent a greater direct threat to others.

It is quite possible that some mixture of altruism, financial and non-financial benefits will obviate the need to introduce mandatory vaccination. It is better that people voluntarily choose on the basis of reasons to act well, rather than being forced to do so. Structuring the rewards and punishments in a just and fair way is one way of giving people reasons for action.

Mandatory vaccination can be ethically justified (see figure 3), but when risks are more uncertain, payment for vaccination (whether in cash or kind) may be an ethically superior option.

Acknowledgments

This piece builds on a previous piece I published on the JME blog, Good Reasons to Vaccinate: COVID19 Vaccine, Mandatory or Payment Model? [ https://blogs.bmj.com/medical-ethics/2020/07/29/good-reasons-to-vaccinate-covid19-vaccine-mandatory-or-payment-model/ ]. I would like to thank an anonymous reviewer for very many helpful and constructive comments. I would also like to thank Alberto Giubilini for his help.

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Supplementary materials

• Press release 

Contributors Sole authorship.

Funding JS is supported by the Uehiro Foundation on Ethics and Education. He received funding from the Wellcome Trust WT104848 and WT203132. Through his involvement with the Murdoch Children’s Research Institute, he has received funding through from the Victorian State Government through the Operational Infrastructure Support (OIS) Program.

Competing interests None declared.

Patient consent for publication Not required.

Provenance and peer review Not commissioned; externally peer reviewed.

Data availability statement No data are available.

Linked Articles

• Response Persuasion, not coercion or incentivisation, is the best means of promoting COVID-19 vaccination Susan Pennings Xavier Symons Journal of Medical Ethics 2021; 47 709-711 Published Online First: 27 Jan 2021. doi: 10.1136/medethics-2020-107076

Read the full text or download the PDF:

Other content recommended for you.

• Spoonful of honey or a gallon of vinegar? A conditional COVID-19 vaccination policy for front-line healthcare workers Owen M Bradfield et al., Journal of Medical Ethics, 2021
• The unintended consequences of COVID-19 vaccine policy: why mandates, passports and restrictions may cause more harm than good Kevin Bardosh et al., BMJ Global Health, 2022
• Exploring vaccine hesitancy in care home employees in North West England: a qualitative study Amelia Dennis et al., BMJ Open, 2022
• Persuasion, not coercion or incentivisation, is the best means of promoting COVID-19 vaccination Susan Pennings et al., Journal of Medical Ethics, 2021
• COVID-19 vaccine boosters for young adults: a risk benefit assessment and ethical analysis of mandate policies at universities Kevin Bardosh et al., Journal of Medical Ethics, 2022
• Vaccine mandates for healthcare workers beyond COVID-19 Alberto Giubilini et al., Journal of Medical Ethics, 2022
• No Jab, No Job? Ethical Issues in Mandatory COVID-19 Vaccination of Healthcare Personnel Rachel Gur-Arie et al., BMJ Global Health, 2021
• Evaluating potential unintended consequences of COVID-19 vaccine mandates and passports Maxwell J Smith, BMJ Global Health, 2022
• Healthcare workers’ (HCWs) attitudes and related factors towards COVID-19 vaccination: a rapid systematic review Mei Li et al., Postgraduate Medical Journal, 2021
• Covid-19: Is the UK heading towards mandatory vaccination of healthcare workers? Jacqui Wise, BMJ, 2021

• Open access
• Published: 09 November 2023

To vaccinate or not to vaccinate? The interplay between pro- and against- vaccination reasons

• Marta Caserotti 1 ,
• Paolo Girardi 2 ,
• Roberta Sellaro 1 ,
• Enrico Rubaltelli 1 ,
• Alessandra Tasso 3 ,
• Lorella Lotto 1 &
• Teresa Gavaruzzi 4  

BMC Public Health volume  23 , Article number:  2207 ( 2023 ) Cite this article

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By mid 2023, European countries reached 75% of vaccine coverage for COVID-19 and although vaccination rates are quite high, many people are still hesitant. A plethora of studies have investigated factors associated with COVID-19 vaccine hesitancy, however, insufficient attention has been paid to the reasons why people get vaccinated against COVID-19. Our work aims to investigate the role of reasons in the decision to get vaccinated against COVID-19 in a representative sample of 1,689 adult Italians (March–April 2021) balanced in terms of age, gender, educational level and area of residence.

Through an online questionnaire, we asked participants to freely report up to three reasons for and against COVID-19 vaccination, and the weight each had in the decision to get vaccinated. We first investigated the role of emotional competence and COVID-19 risk perception in the generation of both reasons using regression models. Next, we studied the role that the different reasons had in the vaccination decision, considering both the intention to vaccinate (using a beta regression model) and the decision made by the participants who already had the opportunity to get vaccinated (using a logistic regression model). Finally, two different classification tree analyses were carried out to characterize profiles with a low or high willingness to get vaccinated or with a low or high probability to accept/book the vaccine.

High emotional competence positively influences the generation of both reasons (ORs > 1.5), whereas high risk perception increases the generation of positive reasons (ORs > 1.4) while decreasing reasons against vaccination (OR = 0.64). As pro-reasons increase, vaccination acceptance increases, while the opposite happens as against-reasons increase (all p  < 0.001). One strong reason in favor of vaccines is enough to unbalance the decision toward acceptance of vaccination, even when reasons against it are also present ( p  < 0.001). Protection and absence of distrust are the reasons that mostly drive willingness to be vaccinated and acceptance of an offered vaccine.

Conclusions

Knowing the reasons that drive people’s decision about such an important choice can suggest new communication insights to reduce possible negative reactions toward vaccination and people's hesitancy. Results are discussed considering results of other national and international studies.

Peer Review reports

Introduction

By mid 2023 the European Union reached nearly 75% vaccine coverage for the primary vaccine cycle against COVID-19, with countries such as Croatia, Slovakia, and Poland falling short of 60% and others such as France, Portugal, and Italy close to 90% [ 1 ]. Although vaccination rates are, on average, quite high, many people are still hesitant. Vaccine hesitancy indicates the delay or refusal of a vaccine despite availability in vaccine services [ 2 , 3 ] and is a multidimensional construct, resulting from the interaction between individual, social, and community aspects [ 4 ]. In the last two years, a plethora of studies have investigated factors associated with COVID-19 vaccine hesitancy showing, for example, that vaccine hesitancy is higher in women [ 5 , 6 ], in young people [ 5 , 7 , 8 ], in people with low education [ 8 , 9 ], low trust in authorities [ 10 , 11 ], and strong conspiracy beliefs [ 5 , 12 , 13 ]. However, to the best of our knowledge no one has investigated the interplay that pro- and against- vaccination reasons may play in the choice to get vaccinated, namely what happens when a person has both pro- and against-vaccine considerations. Trying to fill this gap in the literature, our work aims to investigate how different reasons and the importance people place on them are likely to influence the decision to get vaccinated against COVID-19.

In line with the vaccine hesitancy continuum defined by SAGE [ 2 ], while extremely pro-vax people are likely to express only reasons pro-vaccination and extremely no-vax people are likely to express only reasons against vaccination, individuals who fall between the two extreme end-points are likely to feel some doubts. This large number of people offer us the unique opportunity to assess which category of reasons (pro- vs. against- vaccination) is more impactful in driving people's vaccination decisions. As it is reasonable to imagine, among the reasons for choosing to get (or not) vaccinated some reasons are more rational, while others are more related to affect. For example, there are people who rationally recognize the importance of vaccines but at the same time are frightened by the side effects. Thus, the decision to get (or not) vaccinated is the result of a complex process, in which costs and benefits are weighed more or less rationally. Indeed, while several studies have pointed out that the decision to vaccinate is due to cognitive rather than emotional processes [ 14 , 15 , 16 , 17 ], others have highlighted the role of affect and risk perception in the vaccination decision [ 18 , 19 , 20 ]. Thus, the intention to accept the vaccine is driven by emotional and affective feelings as much as by cognitive and rational judgments. Particular attention to what people feel and think about vaccine-preventable diseases and vaccination in general is paid in the model developed by the “Measuring Behavioral and Social Drivers of Vaccination” (BeSD), a global group of experts established by the World Health Organization [ 21 ]. This model encompasses two groups of proximal antecedents of vaccination, namely, what people think and feel (e.g., perceived risk, worry, confidence, trust and safety concerns) and social processes (e.g., provider recommendation, social norms and rumors). Antecedents affect vaccination motivation (i.e., vaccination readiness, willingness, intention, hesitancy), which can then be strengthened or weakened by practical issues (such as vaccine availability, convenience and cost but also requirements and incentives), resulting in acceptance, delay or refusal of vaccination (vaccination behavior).

Although some studies have considered whether the cognitive or affective component has greater weight in determining the intention to vaccinate, no one, to the best of our knowledge, has studied the interplay between pro- and against- vaccination reasons, nor the weight these have in the choice to vaccinate. In addition to the drivers already studied in the literature [ 5 , 6 , 7 , 8 , 11 , 12 ], we believe that the focus on this interaction may be relevant to better understand the complex phenomena related to vaccine hesitancy. Few recent studies have attempted to investigate the complexity of vaccination choice by studying the reasons why people choose to get (or not) vaccinated against COVID-19. Fieselmann and colleagues [ 22 ] highlighted that among the reasons that reduce adherence to vaccination are a low perception of its benefits, a low perception of the risk of contracting COVID-19, health concerns, lack of information, distrust of the system, and spiritual or religious reasons. Another study, instead, shed light on the reasons that encourage hesitant people to consider vaccination, such as protecting themselves, their family, friends and community from COVID-19, and being able to return to normal life [ 23 ].

In the present study we asked the participants to spontaneously come up with their own reasons to get (or not) vaccinated, without limiting or influencing them with a set of predefined options to choose from, thus aiming to obtain more genuine answers that may better capture the intuitive aspect of people’s opinions (for a similar reasoning see [ 24 ]). The procedure we used has been implemented by Moore et al. [ 23 ], the only study, as far as we know, that asked for reasons with an open-ended question. Critically, in their study, participants were asked to report only reasons in favor of vaccination (e.g., "What are your reasons for getting the COVID-19 vaccine?"), excluding reasons against. By contrast, we asked participants to freely report up to three reasons in favor and up to three reasons against COVID-19 vaccination and to rate on a 5-point Likert scale their weight in the decision about getting (or not) vaccinated.

From a theoretical point of view, the reasons pro- and against vaccination may be seen within the framework of prospect theory [ 25 , 26 ] which suggests that people evaluate the outcome of a choice based on a reference point, against which losses and gains are determined: the former below this point, the latter above this point. Importantly, especially in this specific context, losses and negative consequences are weighted more than gains and benefits, making us hypothesize that if a person has one reason for and one reason against the vaccine, which are of equal importance, they will more likely lean toward choosing not to vaccinate. Consistently, it is known that negative experiences have a greater impact than neutral or positive ones (i.e., the negativity bias [ 27 ]).

Besides delving into the reasons that may influence the choice to get (or not) vaccinated, it would be interesting to also look at the individual differences that may determine the reporting of pro- and against- vaccination reasons and their valence. In this regard, the literature suggests that risk perception and emotion regulation can both have a great impact in the decision to get vaccinated. For instance, studies conducted during H1N1 influenza have shown that perception of disease-related risk is one of the strongest predictors of vaccine adherence [ 28 , 29 ]. Additional insights have been provided by more recent studies investigating the role of COVID-19 risk perception in the decision to get vaccinated against COVID-19. Viswanath and colleagues [ 30 ] showed that people are more willing to vaccinate themselves and those under their care to the extent to which they feel more vulnerable to COVID-19 and rate the consequences of a possible infection as severe. Such a relationship between COVID-19 risk perception and intention to vaccinate was confirmed by another study using a cross-sectional design, which focused on the early months of the pandemic [ 31 ]. This study also examined how risk perception changed during the pandemic phases and showed that during the lockdown, compared to the pre-lockdown phase, also those who reported some hesitancy were more likely to get vaccinated when they perceived a strong COVID-19 risk.

With regard to emotion regulation, the literature suggests that people react differently to affective stimuli [ 32 ] and that their decisions are influenced by their abilities to regulate emotions [ 33 , 34 ]. Recent works investigating the relationship between hesitancy in pediatric vaccinations and the emotional load associated with vaccinations, have shown that a negative affective reaction is one of the factors leading to lower vaccine uptake [ 35 , 36 ]. Specifically, Gavaruzzi and colleagues [ 36 ] showed that concerns about vaccine safety and extreme views against vaccines are associated with vaccine refusal. Interestingly, they also showed that parents' intrapersonal emotional competences, i.e., their ability to manage, identify, and recognize their own emotions, is critical to vaccine acceptance for their children. Therefore, in our study we measured people's risk perception and emotional competencies to assess their possible role in the production of reasons in favor and against vaccination.

As described in Fig.  1 , the relationship between different domains of interest can be hierarchically structured, using a directed acyclic graph, starting from the risk perception and emotion regulation, to the generation of pro- and against- vaccination reasons and their valence, and finally to the vaccination willingness/adherence. With respect to the mentioned structure, we are interested to investigate the following research hypotheses:

The number and weight associated with reasons pro- and against-vaccination should be influenced by individual differences in the ability to regulate emotions;

The number and weight associated with pro-vaccination reasons should be influenced by individual differences in COVID-19 risk perception;

A higher number of strong (i.e., with high weight) reasons pro- (vs. against-) vaccination should correspond to a more (vs. less) likelihood to accept the vaccination.

Generating an equal number of reasons in favor and against vaccination should lead to a weaker likelihood to accept the vaccination.

Directed Acyclic Graph (DAG) between variables considered in the study (PEC: Short Profile of Emotional Competence scale)

As we conducted the study between March and April 2021, a time when vaccinations were being progressively rolled out, we decided to consider the role of personal reasons on both the intention to get vaccinated (for those who had not yet had the opportunity to get vaccinated) and the choice already made (e.g., vaccine received or booked vs. refused).

Finally, through a non-parametric classification analysis, we will explore how specific pro- and against-vaccination reasons impact the decision to get (or not) vaccinated. Specifically, we will investigate the role that different categories of reasons play in the choice to vaccinate.

Participants

Data collection was commissioned to a survey and market research agency (Demetra Opinions.net), with the aim of securing a representative sample of the adult (+ 18) Italian population, estimated at 49.8 million [ 37 ]. The sample was balanced in terms of age, gender, educational level (middle school or lower, high school, degree or higher), and area of residence (North, Center, South, and Islands). The agency distributed via email the survey link to its panelists, who freely decided whether to participate in the study in exchange for financial compensation. Out of 1,833 participants who started the questionnaire, 77 (4%) were excluded because they did not complete the survey and 16 (0.9%) were excluded since they reported offensive content in open-ended questions. Finally, 124 (6.8%) participants were excluded because of missing information. Thus, the final sample consisted of 1,689 participants. The project was approved by the ethical committee for Psychology Research of the University of Padova (Italy), with protocol number 3911/2020 and informed consent was obtained for all participants.

We developed an ad-hoc questionnaire including a series of open-ended and closed questions (see Additional file 1 : Appendix 2 for the full material). We first investigated the vaccination status of the participants, asking whether they already had received at least the first dose, whether they had booked it or were still ineligible, and finally whether they had refused the vaccination. Those not yet eligible were asked to rate how likely they would be to get vaccinated at the time they responded (0 =  Not at all likely , 100 =  Extremely likely ). Then, we asked participants to report a maximum of three reasons both in favor of the COVID-19 vaccine and against it (in counterbalanced order) and to rate how much each of the reported reasons weighed in their choice to vaccinate or not, on a 5-point likert scale (1 =  Not at all , 5 =  Extremely ). Due to the sparsity on the rate and the number of provided reasons we re-coded the provided information into two semi-quantitative variables, one for pro- and one for against- vaccination reasons, as following: missing/invalid reasons, low average rating (answers 1–3 on the Likert scale) and 1–3 reasons, high rating (answers 4–5 points on the Likert scale) and 1 reason, and high average rating (answer 4–5 points on the Likert scale) and 2–3 reasons.

The questionnaire also included the 20-item Short Profile of Emotional Competence scale (S-PEC; [ 38 ]) to measure intra- and inter-personal emotional competences separately. The intra-personal scale (10 items) refers to emotional competences related to oneself and it includes items such as "In my life I never make decisions based on my emotions'' or "I don't always understand why I react in a certain way". The inter-personal scale (10 items) refers to emotional competences related to other people and it includes items such as “If I wanted, I could easily make someone feel uneasy” or “Most of the time, I understand why the people feel the way they do”. All items are answered on a 7-point likert scale (1 =  Not at all agree , 7 =  Completely agree ). The internal consistency of the S-PEC scale, measured by means of Cronbach’s α, was adequate (α = 0.81). Further, we measured participants' risk perception of COVID-19 by asking them to indicate how scared they felt of the virus, how serious they think the disease is, how likely they think they are to get sick, and how worried they feel about the various mutations [ 10 , 31 ]. We then asked participants to report their age, gender, educational level, their occupation (health workers, white-collar workers, entrepreneurs, other non-health-related contract forms, and the unemployed), whether they already had COVID-19 (No or don't know, Yes asymptomatic, Yes with few symptoms, and Yes with severe symptoms). The questionnaire was pilot tested by 30 participants who filled the questionnaire first then were asked to discuss and comment on the comprehension of the wording of questions and answer options. Two questions were slightly reworded to improve clarity.

Scoring of reasons

In the first instance, a bottom-up process from reasons to categories was followed by reading a sample of both types of reasons, with the aim of constructing initial categorizing patterns. Examples of pro-vaccination reasons include protection of personal and public health, return to normality, and civic duty; while reasons against vaccination include fears for one's health, sociopolitical perplexity, and distrust of science and institutions (see Additional file 1 : Appendix 1). At this stage, response information was added to the categorizations indicating whether the responses were valid or missing/invalid. Specifically, valid responses had both a reason and the respective weight; missing/invalid responses were those where reason, weight or both were missing or with utterly unrelated concepts or meaningless strings or letters. Finally, by applying a top-down process, we constructed macro categories by merging specific conceptually assimilated categories, so as to avoid the dispersion of data into too many ramifications (see Table S 5 ).

Statistical analysis

Descriptive analysis.

All the analyses were performed only on respondents with no missing observations on the variables of interest (1,681, 92%) excluding also a limited number of those with a non-valid set of pro- or against-vaccination reasons (Table S 1 ; 0.9%). The study variables were summarized in frequency tables and figures (frequency for categorical variables, median and Interquartile Range (IQR) for continuous variables). Kruskal–Wallis tests were computed to compare the distribution of continuous variables across the categories of vaccine status. Categorical variables were compared using chi-squared or Fisher's exact test where expected frequencies in any combination were less than 10. Statistical significance was assumed at the 5% level.

COVID-19 Perceived risk—exploratory factor analysis

An Exploratory Factorial Analysis (EFA) was performed on groups of variables related to COVID-19 perceived risk: scare, severity, contagiousness, and the likelihood of mutation. Since the presence of limited support (0–100 scale) and non-normal marginal distribution, the EFA was performed using a weighted least square mean and variance adjusted (WLSMV) estimator. We extracted from the EFA only the first factor, which explained the highest percentage of variance (Table S 2 ; 61%). The estimated loadings were then used to calculate the regression factor scores. The number and the name of items included, their internal consistency (Cronbach’s α), the estimated loadings, and the proportion of deviance explained are reported in Table S 2 .

Propensity score weighting

At the time of data collection (March–April 2021), the vaccine offer was not opened to the entire population. To adjust the estimates of the following regression models for the propensity to receive the vaccine, we estimated a logistic regression model in which the dependent variable was the response to the question about a previous vaccination offer (Yes/No), while all the factors that can influence the vaccine proposal served as independent variables: age-class (young ≤ 25, young adult 26–45, adult 46–65, elderly 66–84), gender (male, female), occupational status (health worker, not at work, not health worker-employer, not health worker-entrepreneur, not health worker-other), educational level (low = middle school or lower, medium = high school, high = degree or higher), key worker status (yes, no, I don’t know), past COVID-19 contagion (no, yes asymptomatic, yes low symptoms, yes severe symptoms), and familiar status (single/in a relation, married/cohabitant, divorced/separated/other). The predicted probability was used to estimate the weights for the following regression models using a framework based on an inverse probability of treatment weighting (IPTW; for further details, see [ 39 ]).

Regression models

Our research questions can be summarized by trying to describe the relationship exploited by the directed acyclic graph in Fig.  1 . The first step regression model aims to assess how S-PEC scores (inter- and intra-personal) and COVID-19 risk perception influenced the reasons pro- and against-vaccination produced by participants while considering the presence of a set of confounders (age-class, gender, occupational status, educational level, key worker status, and familial status).

Since both the pro- and against-vaccination reasons are formed by a categorical variable with 4 levels (missing/invalid, low 1/2/3 reasons, high 1 reason, high 2/3 reasons), we evaluated whether S-PEC and COVID-19 risk perception scores influenced the distribution of pro- and against-vaccination reasons employing two different multinomial regression models including all the previously mentioned variables (S-PEC, COVID-19 risk perception, and confounders). The overall significance of a variable in the model was tested using an analysis of the variance (ANOVA).

The second step in the analyses was taken to investigate whether the generation of pro- and/or against-vaccination reasons affected the willingness to be vaccinated or the vaccine acceptance. Each participant reported their willingness to get vaccinated on a 0–100 scale or, in case a COVID-19 vaccine had been already offered, their vaccination status (done, booked, or refused). For respondents who had not yet been contacted for booking/getting the vaccination, we evaluated whether pro- and/or against vaccination reasons influenced the willingness to be vaccinated by employing a beta regression model in which the respondent variable scale (0–100) was rescaled to be a relative frequency [ 40 ]. The full models included the semi-quantitative pro- and against-vaccination reasons variables and, even if non-statistically significant, all the confounders in order to adjust for age class, gender, educational level, occupational status, familial status, and key worker status. Beta regression coefficients were estimated using a maximum likelihood estimator (MLE). Results were presented in terms of Odds Ratios (ORs) by exponentiating the estimated coefficients and producing a relative 95% Confidence Interval (95% CI).

A further regression analysis was conducted through a logistic regression model to explain which factors influenced vaccine acceptance (done/booked vs. refused) among those who already received the vaccine offers. The full model included the same variables considered in the previous beta regression model, after recoding the variables related to pro- and against-vaccination reasons into a binary form (missing/invalid vs. presence of at least one valid reason) due to low sample size and the sparsity of the response variable. As a consequence, we tested a simplified version of Hypothesis 3, considering the presence (vs. missing/invalid) of pro- or against-vaccination reasons in order to test their influence on the probability of having accepted/booked the vaccination.

Results were reported employing ORs and relative 95% Confidence Interval (95% CI).

Both the beta regression and logistic regression were weighed using an IPTW scheme to take into account the presence of a different probability of a vaccine offer among respondents.

The presence of an interaction between pro- and against-vaccination reasons was tested by means of a likelihood ratio test. The regression models were estimated through the R 4.0 program (R Core Team, 2021), and for the beta regression we employed the betareg package [ 41 ].

Classification tree analysis

Two different classification tree analyses were carried out to characterize profiles with a low or high willingness to get vaccinated (respondents who had not yet been offered a vaccine) or with a low or high probability to accept/book the vaccine (respondents who had already received a vaccine offer).

Although the dependent variables were non-normally distributed (scale 0–100 or binary 0/1), we considered them continuously distributed adopting a splitting criterion based on the analysis of the variance (ANOVA). We tested the inclusion in the model considering the type of pro- or against-vaccination reasons. A tree pruning strategy was adopted to reduce classification tree overfitting considering the overall determination coefficient (R 2 ) as an indicator and fixing that at each classification step in the tree if the R 2 did not increase by 0.5% the tree should be stopped. Classification tree analysis was performed using the rpart package [ 42 ] on R environment [ 43 ].

The main characteristics of the respondents by vaccination status (received, booked, not yet, and refused) were reported in Table 1 . Among respondents, 23.3% were offered the vaccination and, among them, 13.8% refused it (Fig. S 1 ). Among those not yet eligible, willingness to be vaccinated showed a median value of 80 points (average: 68.7). The distribution of gender was almost equal (51% females, 49% male), and the median age was 47 years old (IQR: 34–57 years). Educational level was low in 41% of the sample, while the most represented employment status was not at work (39%) followed by employed (37%), and entrepreneur (9.8%). A quarter (26%) of respondents classified themselves as key workers during the COVID-19 pandemic. The predominance of respondents (63%) were married or living with a partner, while only 9% had had a COVID-19 infection.

COVID-19 risk perception and the S-PEC score (intra- and inter-personal) were categorized into three categories according to empirical tertiles (low:1 st tertile, medium: 2 nd tertile, high: 3 rd tertile). The level of COVID-19 risk perception differed across vaccination status ( p  < 0.001). The reasons pro- and against-vaccination have a different distribution according to COVID-19 vaccination status (Table 2 ). The highest frequency of pro-vaccination reasons was reported by those who received the COVID-19 vaccination; conversely the lowest frequency of pro-vaccination reasons was generated by those who refused the vaccine, whereas, intermediate frequencies were shown by people who were not yet offered the vaccination and those who had booked the vaccine, who reported a comparable distribution of the number of pro-vaccination reasons. A reverse pattern was exhibited for against-vaccination reasons, which were generated with the highest percentage by respondents who refused the vaccine (in particular high and multiple reasons). Conversely those who have booked/done the COVID-19 vaccine showed the lowest frequency of reasons against vaccination, while respondents without a vaccine offer reported an intermediate frequency of reasons against vaccination.

The estimated results of the propensity score model for the vaccine offer are shown in Table S 3 . Respondents older than 65 years exhibited a nearly four-fold increase in the probability to be contacted for the vaccination with respect to the reference age-class (≤ 25 years). All non-health employees showed a high drop in the probability of having received the vaccination offer, while the probability increased as the educational level increased. Being a key worker during pandemic resulted in an increased probability of having received the vaccination proposal while no statistical significant influence was observed for the past COVID-19 contagion or for familial status. The distribution of the propensity score by vaccine status obtained by the model is reported in Fig. S 1 , in which it is shown that the distribution is different by vaccine offer, but the two density functions partially overlap. The discriminant power of the propensity score estimated was only discrete (ROC analysis, AUC: 71.8%).

The results of the multinomial regression models which investigated the effect of emotional competences and risk perception on the generation and the predictors of pro- and against-vaccination reasons with respect to missing/invalid level and the reference categories are presented in Table 3 (see also Fig.  1 ). Compared to the reference category (missing/invalid), high values of S-PEC-self were associated with a higher probability to report pro- and against-vaccination reasons (all ORs > 1.5), while high values of S-PEC-others were associated with a mild probability to report multiple pro-vaccination reasons (all ORs > 1.42). A high (vs. low) COVID-19 risk perception increased the frequency of one strong pro-vaccination reason while it had a null or low decremental effect on the frequency of against weak vaccination reasons. Further, medium (vs. low) COVID-19 risk perception only increased the strong pro-vaccination. Compared to the reference age-class (young), adults and elderly showed a higher probability to generate a strong unique pro-vaccination reason (adults vs. young OR: 1.72, 95%CI: 1.07–2.77); elderly vs. young OR: 2.24, 95%CI: 1.26–4.00), while lower probability to generate against vaccination reasons was observed for elderly compared to young respondents (OR: 0.48, 95%CI: 0.26–0.90). Female participants generated fewer strong pro-vaccination reasons (ORs < 0.73), and also fewer multiple weak against-vaccination reasons (OR: 0.68, 95%CI: 0.51–0.91) compared to male participants. Overall, the occupational status did not affect the generation of pro- and against-vaccination reasons (ANOVA test p  > 0.05); however an increased frequency of low 1/2/3 against-vaccination reasons emerged among the category “Other—not health workers” compared to the reference group represented by health workers (OR: 2.52, 95%CI:1.09–5.86). Pro-vaccination reasons are more frequent as the educational level becomes higher, while the relation of the educational level with against- vaccination reasons appears weaker and significantly increased only for the presence of multiple weak reasons against vaccination (High vs. Low educational level, OR: 2.10, 95%CI: 1.45–3.03). Not being a key worker is related to a higher frequency of multiple strong both pro- and against vaccination reasons. The familiar status did not seem to be related to the frequency or the strength of the reasons, except for the status of divorced/separate/other that, with respect to the reference category single/in a relation, showed a twofold increase in the frequency of a strong unique against vaccination reason.

Through a beta regression model we investigated the predictors of willingness to be vaccinated for the participants who had not yet received the vaccination offer. As shown in Table 4 , the generation of pro- and against-vaccination reasons strongly influences the willingness to be vaccinated. The predicted probability from the combination of pro- and against-vaccination reasons is shown in Fig.  2 (and Table S 4 ): respondents who did not report any reasons had an average predicted probability above 60%, while the presence of at least one reason against vaccination decreased the willingness to be vaccinated, in particular in the case of strong multiple against vaccination reasons. On the other hand, the presence of at least one pro-vaccination reason strongly increased the probability. In the end, the presence of both strong multiple pro and against vaccination reasons resulted in a high probability of getting the vaccine. Regression models adjusted by propensity score weighting allowed us to comment the influence of potential confounders: males reported an increased willingness to be vaccinated (vs. females; OR: 1.26, 95%CI: 1.11–1.42), and so did those with a high educational level (vs. low; OR: 1.22, 95%CI: 1.04–1.44) while the opposite was true among no key workers (vs. key workers; OR: 0.85, 95%CI: 0.72–0.99).

Predicted willingness to get vaccinated by interaction between pro- and against-vaccination reasons

Finally, with a logistic model we investigated the predictors of vaccine acceptance\booking. As shown in Table 5 , people who accepted or booked the COVID-19 vaccine were more likely to show pro-vaccination reasons and less likely to show against-vaccination reasons. Interestingly, when both kinds of reasons were provided, the probability of getting/booking the vaccine remained nevertheless very high (Fig.  3 ). Compared to the age class [46-65], younger age classes reported a strong reduction in the probability to have accepted/booked the vaccine. Male participants (OR: 1.53, 95%CI: 1.10–2.12) and those with a high educational level (OR: 2.65, 95%CI: 1.60–4.54) showed an increased probability of vaccine acceptance/booking when compared to females and participants with medium educational level, respectively. Being a health worker had a strong and positive influence on the probability of getting/booking the vaccine with respect to those employed as no health workers (OR: 6.61, 95%CI: 2.10–30.9).

Predicted COVID-19 vaccine acceptance/booking probability by interaction between pro- and against-vaccination reasons

Two regression tree models were estimated separately on the willingness to be vaccinated for those who had not yet received the vaccine offer and on the booking/acceptance of the vaccination in case of vaccine offer. Results are shown in Fig.  4 . Considering the willingness to be vaccinated, the presence of distrust in the vaccination was the most discriminant variable; this latter in conjunction with reasons related to protection, herd immunity, and the absence of no clinical trials guided the willingness to be vaccinated. In particular, the combination of the absence of reasons related to distrust and the presence of protection reasons showed the highest values on the intention to get vaccinated (average value = 83 points, 22% of the sample). On the other side, the presence of at least one reason related to distrust without any positive reasons concerning protection, herd immunity, and trust predicted the lowest willingness to be vaccinated (average value = 29 points, 6% of the sample).

Regression tree for the willingness to be vaccinated (left) and for COVID-19 vaccine acceptance/booking (right) by selected type of pro- and against-vaccination reasons

The sense of protection given by the vaccine or the trust in the vaccination was the main reason for vaccination acceptance/booking (average probability = 0.96 and 1.00, 33% and 5% of the sample, respectively). The combination of the absence of protective reasons and the presence of doubts about the lack of clinical studies results in the lowest likelihood of accepting/booking the vaccination (average probability = 0.40, 3% of the sample). The presence of distrust and the belief in herd immunity were the other discriminant reasons with intermediate results in terms of the probability to accept/book the vaccination.

The frequency of each category of pro- and against-vaccination reasons by COVID-19 vaccine status is shown in Table S 5 .

In the present study we aimed to investigate the reasons behind the decision to get (or not) vaccinated against COVID-19 by asking participants to report up to three reasons in favor and three reasons against the COVID-19 vaccination and to indicate the weight these reasons had in their decision. Although some researchers discourage categorization, the sparsity of the responses related to the number of reasons and their weight implies a semi-quantitative solution since a simple variable multiplication between rating and frequency (recoding to zero in case of zero reasons) is not feasible. In this case, this approach was not satisfactory as such coding would not allow differences underlying identical scores to emerge. For example, only 1 strong motivation (rating 5) would be coded in the same way as three motivations with weights 1, 2, and 2. Instead, we decided to categorize the combination of frequency-weight reasons as categorical variables (missing/invalid, low 1/2/3 reasons, high 1 reason, high 2/3 reasons) in which rating and number of reasons are combined into a single variable. This categorization allows us not only to study the weight that different categories have on the decision to get vaccinated but also to overcome the risk of imputing a specific value for missing responses.

As shown in Fig.  1 , analyses were run in two steps. The first step aimed to assess how emotional competences and risk perception impacted the generation of reasons pro- and against-vaccination (Hypotheses 1A and 1B), whereas the second step investigated how different reasons affected the intention to get vaccinated (Hypotheses 2 and 3). The results support the hypotheses that emotional competences and risk perception play a significant role. Regarding emotional competence as measured by the S-PEC, the results show that high intra-personal emotional competence positively influences the production of stronger and more numerous pro-vaccination and against-vaccination reasons (confirming Hypothesis 1A). This result suggests that greater awareness of one's emotions and of what one is feeling promotes higher fluency in the production of reasons about the vaccination. Research has shown that people can be ambivalent about vaccines and hold both positive and negative reasons [ 2 , 44 ]. It is reasonable to assume that, compared to people with low intra-personal emotional competences, those with high intra-personal emotional competences are more likely to have higher awareness of these contrasting attitudes and to embrace them without suppressing one of the two stances. Furthermore, the results showed that only high inter-personal emotional competences influence the generation of multiple strong reasons in favor of vaccination, and this appears to be related to the perception of vaccines as a public good and a tool to protect others. As for risk perception, a moderate to high perception of risk associated with COVID-19 influences the generation of strong pro-vaccination reasons (confirming Hypothesis 1B). These results are in line with the literature showing that a high perception of risk associated with COVID-19 positively influences the decision to get vaccinated [ 30 , 31 , 45 , 46 , 47 ]. In particular, perceiving a medium/high risk leads to generating a high number of reasons strongly in favor of vaccination, while reducing the number and weight of the reasons against the vaccine. The main premise of the psychological literature examining the relationship between risk perception and affect is that one’s behaviors are affected by rapid and intuitive evaluations, either positive or negative, people make while assessing things happening around them [ 48 , 49 ]. Thus, an event is evaluated not only on the basis of objective information, but also on the basis of the experienced feelings. Emotional competence, which is clearly related to affect, also modulates how we perceive and process the emotional component underlying our judgments [ 36 ].

The results also show that, compared with younger people, those over 45 more frequently produce reasons in favor of vaccines while those over 65 produce fewer reasons against vaccination. These results are in line with the fact that younger people are at lower risk of severe consequences than older people [ 50 ], but can also be explained by considering that age was particularly salient during the period of the data collection, as the vaccination campaign was phased out by age groups, starting from the elderly. As for gender, women produced less strong pro-vaccine and weak-against vaccine reasons than men. These results are congruent with the general findings in the literature on vaccine hesitancy showing that females are more hesitant than males [ 5 , 51 , 52 ]. Furthermore, medium and high educational levels favored the production of both pro- and against-vaccination reasons, whereas not being in a relationship or being divorced/separated increased the generation of a strong reason against vaccination. Consistent with previous work [ 53 ], we confirmed that non-health professionals participants or non-key workers categories showed a lower intention to get vaccinated and a higher likelihood of having refused the vaccine compared to health care and key workers.

Once the role of demographics aspects and individual differences on the generation of reasons pro and/or against vaccination had been established, we ran two additional models to assess the role that those reasons have on the decision to accept the vaccination (see Fig.  1 ). More specifically, we tested the hypothesis that a higher number of pro- (vs. against-) vaccination reasons, connoted by a higher weight, corresponded to a stronger (vs. weaker) acceptance of vaccination (Hypothesis 2). Since data collection took place between March and April 2021, when the vaccination campaign had already started in Italy, we developed two different regression models, with the first investigating the willingness to be vaccinated in participants who were not yet offered the vaccine and the second investigating the likelihood of accepting/booking or refusing the vaccine in those who already received the offer. In particular, thanks to the propensity score weighting technique, we managed to reduce the estimates bias, especially for those factors (age, occupational status, and educational level) that influenced the vaccine offer the most [ 54 ]. The results of the two models are very similar, as the intention to get vaccinated and the likelihood of having accepted/booked the vaccine are predicted by the same factors. Specifically, the production of strong positive reasons increases either the intention to get vaccinated or having accepted/booked the vaccination. In contrast, generating strong negative reasons reduces vaccination intention and predicts the refusal of the vaccination. Hypothesis 2 is thus confirmed.

Results on the interactions between reasons, pro- and against-vaccination, and vaccination intention or vaccination choice are particularly worthy of attention. The third hypothesis was derived from the literature on prospect theory [ 25 , 26 ], suggesting that at equal intensity subjective losses are more important in determining a decision than subjective gains. We therefore expected that negative reasons would count more than positive reasons in deciding whether to get vaccinated or to accept the vaccine. However, in contrast to our hypothesis, the results showed that just the generation of a single positive reason with a strong weight was enough to shift behavior and attitude in favor of the vaccination, regardless of the number and weight of negative reasons. In other words, vaccine refusal is predicted by the absence of any positive strong reasons, while when people generate both positive and negative reasons, the positive ones seem to yield a particularly important role when having a strong weight. According to prospect theory, people evaluate their goals depending on the reference point they focus on. During the pandemic, the vaccination offered an opportunity to be safer, reduced the risk of infection, and more generally appeared as the best way to re-open and get back to life as it was before COVID-19. After a year of pandemic characterized by periods of lockdown and some re-opening attempts, people were likely feeling in a state of loss (e.g., the lost freedom to go out and meet with friends and family, the lost freedom of traveling) and were looking forward to whatever chance available to recover and return to their previous lifestyle and habits. Just as those who gamble are willing to do anything to make up for a loss, so probably those who were not entirely certain about the vaccine were more willing to take risks to recover the loss in quality of life. It follows that the pandemic emergency made people forgo some of their doubts about the vaccine when, at the same time, they had reasons to get their shot. In addition, several studies [ 19 , 55 , 56 ] have highlighted the relationship between anticipated regret and vaccination, showing that anticipated regret is associated with an increased likelihood of adhering, or having one's children adhere, to vaccine offerings. Trusting that the vaccine would work, focusing less on its potential side effects, made sense for people who were looking forward to recovering what was perceived (and was indeed) a loss of quality of life and freedom, because they desired to be back doing the things had ever enjoyed doing (e.g., going to restaurants, movies, etc.). This finding is also interesting from a communicative perspective: providing positive reasons that resonate well with people and have therefore a strong weight for them could offset their doubts, yielding to a greater acceptance of COVID-19 vaccination.

Therefore, it is crucial to consider what kind of reasons drive the decision toward or against vaccination. Allowing participants to openly report their reasons pro- or against- vaccination can facilitate a freer exploration of the concerns and reservations of the most hesitant individuals [ 24 ], thus providing valuable insights for shaping future vaccine-related communications. In fact, thanks to the regression tree on vaccination intention, it emerges that positive attitudes toward vaccines are strongly determined by "Protection" and "Community Protection" reasons. The fact that the sense of individual and collective protection is among the principal determinants of the decision with respect to COVID-19 vaccines suggests that in general vaccination is seen as a means of avoiding nefarious clinical consequences. The effect of the sense of communal protection as the reason favoring vaccination and of other-oriented S-PEC in determining the generation of multiple pro-vaccine motivations confirms previous results suggesting that people often are more willing to get vaccinated primarily to protect their loved ones [ 57 , 58 , 59 ], especially when they have a good understanding of how community immunity works [ 60 , 61 ]. However, it is worth mentioning that, at the time the study was conducted (March–April 2021), there was still uncertainty about whether COVID-19 vaccines could provide sterilizing immunity (i.e., could prevent the transmission of the infection) in addition to protecting the individual. To foster people's willingness to get vaccinated, it is crucial from a public health perspective that people understand that even when vaccines do not yield sterilizing immunity, vaccination can still increase protection of others by reducing the circulation of the virus.

The reasons that influenced the willingness to be vaccinated or the vaccination acceptance/booking were generally in line with the existing literature, although they differed depending on whether respondents had already been offered a vaccine or not: among those who did not received a vaccination offer, the main reasons promoting vaccination acceptance were protection against COVID-19 for oneself, one's family, friends, and community [ 23 ], while among the main reasons that reduced vaccination adherence for those who got the vaccine offer we found the lack of clinical trials [ 62 , 63 ], as well as the distrust of institutions and science [ 22 ]. This latter emerged as the most reported negative reason by those who have refused the vaccine and those who have not yet received the vaccine offer. Thus, effective communication aimed at defusing the perception of risk regarding vaccines themselves should focus on enhancing trust in the scientific process and experimental rigor. Indeed, these reasons were deemed as very important not only by those who refused the vaccination, but also by those who had not yet been offered the vaccine, and even by those who held mixed feelings but eventually chose to get vaccinated. While it is unlikely that individuals firmly against vaccination will be persuaded by simple interventions [ 64 ], we should keep in mind that vaccine hesitancy is a dynamic process. As such, reducing hesitancy or enhancing ambivalence, for example through motivational interviewing (e.g., [ 65 , 66 ]), could potentially lead to small shifts towards greater vaccine acceptance.

Our findings are also in line with the results of other international studies that have used a qualitative approach to examine reasons for and against vaccinations. For example, Hamilton and colleagues [ 67 ] employed a qualitative content analysis to extract the main motivations for and concerns about COVID-19 vaccination from medical records obtained by 102 consults in Australia. The study was conducted in June 2021, and revealed that most consults were driven by doubts about the vaccine available and recommended at that time (i.e., ChAdOx1-S, also known as Vaxzevria), followed by need for further information regarding vaccines and vaccination, also considering specific comorbidities. Notwithstanding the peculiarity of the Australian context in which a very low number of COVID-19 infections was observed, the analysis performed by Hamilton et al. [ 67 ] revealed a set of themes that largely overlaps with the reasons identified in our study. Indeed, among the reason to get vaccinated, 5 themes emerged: a) Protection, b) Occupational or facility responsibility or requirement, c) Trust in primary healthcare physician, d) Autonomy, and e) Civic duty, likewise, concerns about vaccination were mainly in terms of: a) Perceived vaccine risks, b) Perceived vaccine performance, c) Uncertainty, d) Autonomy, and e) Fairness in access. An aspect worth noting is that after the consultation, 81% of participants received the vaccination, 19% did not. Consistent results were observed in another study by Purvis and colleagues [ 68 ] conducted in the USA, which focused specifically on “hesitant adopters”, i.e. those who accepted vaccination but showed some level of hesitancy. To note that in this study the focus was on factors influencing the decision to get the COVID-19 vaccine, not on reasons against it. The authors interviewed 49 participants as a follow up of a larger study ( N  = 2022) conducted from mid-September 2021 through mid-October 2021, to explore factors that influenced their decision-making process about COVID-19 vaccination [ 68 ]. Two main themes emerged, each with four subthemes: 1) sociocultural context (political, cultural, health professionals, employment, and media environment) and 2) individual and group influences (attitudes and beliefs related to vaccines, family and social networks, free to return to normal, and COVID-19 outcomes).

As for the Italian context, to the best of our knowledge, only one study (i.e., [ 69 ]) attempted to provide a qualitative examination of the concept associated with vaccination in general, through open-ended and closed questions. Notably, this study was conducted a year later than our own study (April–May 2022) and was administered to a non-representative sample of Italians. The authors used a combination of closed and open-ended questions to assess concepts associated with vaccination in general. Consistent with our findings, Boragno et al. reported that participants who had been vaccinated against COVID-19 (92% of the sample) frequently mentioned concepts related to protection and salvation, whereas those who were not vaccinated frequently mentioned mistrust and ambivalence as concepts associated with vaccination [ 69 ].

This study has some limitations. First, COVID-19 perceived risk score was obtained only with respect to the disease and a similar score should be of interest for the COVID-19 vaccine. Second, data were collected during a vaccine offer limited to a well-defined slice of the population and the investigation on the vaccine acceptance/booking has, as a consequence, a limited sample size. Finally, the lack of a longitudinal perspective does not allow us to evaluate how strong the association is between the willingness to get vaccinated, vaccine acceptance and potential changes in risk perception. Thus, we cannot generalize our results beyond the period of data collection and to other countries or health systems. Since the dynamics have now changed, results may not apply to the decision to get a booster shot or not or an annual shot, however it might be interesting to study what motivations are most relevant now. Likewise, it remains to be established whether our results are generalisable to other populations.

Future studies could consider how the interaction between perceived risk associated with the disease and perceived risk associated with the vaccine influences the choice to get the shot. Furthermore, it would be important to explore how we can harness the reasons that most hold back vaccination in a specific communication strategy for the most hesitant people. Moreover, at the time of data collection, the vaccination campaign was still at an early stage, and only a small portion of the population had already received their shot. Therefore, we believe that it might be of particular interest to know more in detail, with a larger sample, what are the reasons that to date, almost 2 years after the release of the vaccine, still make some people reject the vaccine. Only by knowing these reasons will it be possible to develop appropriate vaccination campaigns.

In conclusion, our work examined pro- and against-vaccination reasons and how these, and their interaction, influence the decision to get vaccinated or not. Specifically, high emotional competence and risk perception influence the generation of pro- and against-vaccination reasons and that the presence of a strong pro-vaccination reason shifts intention toward vaccination. We also highlighted the category of reasons that influence intention to vaccinate. That said, given that the discussion about the next doses is still open and that in any case the next pandemic is a matter of when and not if [ 70 ], it is of paramount importance to know the best way to counteract vaccine hesitancy, fostering more effective communication strategies.

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Open access funding provided by Università degli Studi di Padova. The project was developed thanks to institutional research funding of TG and AT.

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Department of Developmental Psychology and Socialization, University of Padova, Padua, Italy

Marta Caserotti, Roberta Sellaro, Enrico Rubaltelli & Lorella Lotto

Department of Environmental Sciences, Informatics and Statistics, Ca’ Foscari University of Venezia, Venice, Italy

Paolo Girardi

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MC: Conceptualization, Formal analysis, Visualization, Writing—original draft and Writing—review & editing. PG: Conceptualization, Formal analysis, Visualization, Writing—original draft. RS: Conceptualization, Writing—review & editing. ER: Conceptualization, Writing—review & editing. AT: Conceptualization, Writing—review & editing. LL: Conceptualization, Writing—review & editing. TG: Conceptualization, Visualization, Writing—review & editing.

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Supplementary Information

Additional file 1: appendix 1..

Scoring for pro- and against-vaccination reasons.  Appendix 2. Structure of the questionnaire. Table S1. Selection criteria. Table S2. Number of items, internal consistency (Cronbach’s α), name of the items and their estimated loadings, total deviance explained by the loadings and proportion of variance explained by EFA for COVID-19 perceived risk. Table S3. Odds ratios (ORs) estimated by the logistic model for the propensity score weighting for the COVID-19 vaccine offer. Table S4 . Predicted willingness to get vaccinated by combination of pro- and against-vaccination reasons by category of reference.  Table S5. Frequency of reported categories of pro- and against-vaccination reasons overall, and by COVID-19 vaccine status. Figure S1. Distribution of the propensity scores by vaccine offer.

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Caserotti, M., Girardi, P., Sellaro, R. et al. To vaccinate or not to vaccinate? The interplay between pro- and against- vaccination reasons. BMC Public Health 23 , 2207 (2023). https://doi.org/10.1186/s12889-023-17112-6

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Analysis of the Anti-Vaccine Movement in Social Networks: A Systematic Review

Elvira ortiz-sánchez.

1 Faculty of Health Sciences, University of Granada, 18016 Granada, Spain; se.rgu.oerroc@zehcnasoe (E.O.-S.); se.rgu@aziuqruglj (J.L.G.-U.); se.rgu@fcag (G.A.C.-D.l.F.)

Almudena Velando-Soriano

2 Andalusian Health Service, Virgen de las Nieves University Hospital, 18014 Granada, Spain

Laura Pradas-Hernández

3 Andalusian Health Service, San Cecilio Clinical University Hospital, 18016 Granada, Spain; moc.liamg@9lhparual

Keyla Vargas-Román

4 Faculty of Psychology, University of Granada, 18071 Granada, Spain; se.rgu@moravyek

Jose L. Gómez-Urquiza

Guillermo a. cañadas-de la fuente, luis albendín-garcía.

5 La Chana Health Center, Granada Metropolitan District, Andalusian Health Service, 18015 Granada, Spain; se.oohay@nidneblal

The aim of this study was to analyze social networks’ information about the anti-vaccine movement. A systematic review was performed in PubMed, Scopus, CINAHL and CUIDEN databases. The search equations were: “vaccine AND social network” and “vaccine AND (Facebook[title] OR Twitter[title] OR Instagram[title] OR YouTube[title])”. The final sample was n = 12, including only articles published in the last 10 years, in English or Spanish. Social networks are used by the anti-vaccine groups to disseminate their information. To do this, these groups use different methods, including bots and trolls that generate anti-vaccination messages and spread quickly. In addition, the arguments that they use focus on possible harmful effects and the distrust of pharmaceuticals, promoting the use of social networks as a resource for finding health-related information. The anti-vaccine groups are able to use social networks and their resources to increase their number and do so through controversial arguments, such as the economic benefit of pharmaceuticals or personal stories of children to move the population without using reliable or evidence-based content.

1. Introduction

The rejection of immunization by the population was born with the creation of the first vaccine in 1796 when Jenner presented protection against smallpox to the Royal Society of London. From this point, a rise in England’s first compulsory vaccination through a strong campaign began to appear unhappy [ 1 ], which led to the constitution of the League against Mandatory Vaccination of London in 1867, and it began to spread this movement to the rest of Europe [ 2 ].

This movement won strength in 1998 when Wakefield published an article in “The Lancet”, in which he related the possibility of suffering autism with the administration of the vaccine against rubella, mumps and measles. This sonorous article caused a 9% drop in the vaccination rate in the United Kingdom in just one year. In the end, it was proved that Wakefield and the co-authors of the article had conflicts of interest and the journal was forced to publish a retraction but, despite that, this belief is still maintained today [ 3 ].

Currently, a study by the American Academy of Pediatrics reveals that 74% of pediatricians find parents who oppose or have delayed the administration of vaccines to their children. Another survey of parents with children aged 6 months to 6 years shows that 13% opted for an alternative immunization program, 53% rejected one vaccine and 17% rejected all [ 4 ].

The anti-vaccination groups base their arguments on their lack of confidence in the information provided by health professionals and official sources about vaccines. Generally, they have doubts about the administration of multiple vaccines at such early ages and the lack of individualization of these drugs. Their fear lies in the possible adverse effects and the constant change in the vaccination schedule, as well as in the differences between autonomous communities. This is linked to the belief that because the disease has very low incidence it is not necessary to vaccinate their children (which is, in fact, due to the vaccine) or because they believe in natural remedies or alternative medicine, so people in the anti-vaccine group end up looking for information that confirms their beliefs [ 5 , 6 ]. In addition, it shows that people who refuse vaccines are more likely to obtain information from social networks, not from health professionals or verified healthcare websites [ 6 ]. Another study informs that 52% of people who use the Internet consider this medium reliable in terms of health issues [ 7 ].

Despite the fact that healthcare professionals remain the main source of health information, including vaccines, the Internet has grown as a resource for finding information due to its high accessibility [ 8 ]. In fact, vaccine-related searches on the Internet have increased, most of them coinciding with the start of influenza vaccination campaigns [ 9 ]. The increase of web information search causes an opportunity for the appearance of websites with unreliable content generating false beliefs [ 10 ].

Considering the importance that the anti-vaccine movement has gradually acquired, the aim of this study was to analyze social networks’ information about the anti-vaccine movement. Thus, the PICO question that guided the review was: Which information (O) does the anti-vaccine movement (P) use in social networks (I) to influence the global population (C)?

2. Materials and Methods

A systematic literature review was conducted following the PRISMA guideline. No protocol was registered.

2.1. Eligibility Criteria

We included primary studies related to the use of social networks and the anti-vaccine movement that collect samples on the main social networks (Twitter, Facebook, Instagram and YouTube), published in English and Spanish and conducted over the last 10 years.

All articles were exclusively related to the measles outbreak and the HPV vaccine; articles using samples not obtained on Twitter, Facebook, Instagram or YouTube and articles without statistical information and duplicate articles were excluded.

2.2. Information Sources and Search

The databases used were CINHAL, CUIDEN, PubMed (Medline) and Scopus. The first search equation based on MeSH terms was “Vaccine and social networking”. The second search equation was “vaccine AND (Facebook[title] OR Twitter[title] OR Instagram[title] OR Youtube[title])”. The search equations were adapted to each database. The search was conducted in December 2019. In addition, a reverse search was performed in the selected studies.

2.3. Study Selection and Risk of Bias

The selection of the studies was done independently by two researchers and had 4 phases. Firstly, title and abstract were read. Secondly, the full text was read. Then, a reverse search was done with the included studies to locate as many documents as possible; and finally, a critical reading of the studies was carried out to evaluate possible biases in the methodology. The SIGN classification (2011) was used to provide the level of evidence.

2.4. Data Collection, Variables and Data Analysis

A data collection notebook was used to extract the data from each study. The following data were collected from each study:

• Variables about the characteristics of the sample: year of publication, country of study, study design, number of tweets, Facebook or Instagram comments or YouTube videos.
• Variables about the study: aim and main results of each article.

The analysis of the data was descriptive.

3.1. Study Selection

A total of 503 articles were obtained from the database search as of December 2019. After reading the title and abstract, 486 studies were excluded for not meeting the inclusion criteria or being duplicates. After reading the full text of the remaining articles, eight were excluded for not meeting the inclusion criteria. So, eight articles from the search were included in the review. Finally, four studies were included after the reverse search, leaving a sample of n = 12 articles. The flow chart with the study selection process is shown in Figure 1 .

Flow diagram of the study selection.

3.2. Study Characteristics

All selected articles were descriptive cross-sectional studies. The evidence level according to SIGN [ 11 ] evidence scale was 3D. The 41.66% of the articles were published in 2019, 50% show data obtained on Twitter and 50% have been made in the United States. The 12 articles included showed data on three social networks: YouTube, Twitter and Facebook. Half of the studies (50%) were based on Twitter. After the analysis, two categories of results were established.

3.3. Twitter and Vaccine Information

The study by Gunaratne et al [ 12 ] shows an increase in Twitter content in favor of vaccines; despite this, it highlights an increase in users defending the anti-vaccine movement. This information is reinforced in the study led by Blankenship et al [ 13 ], which highlights that everything related to the anti-vaccine group has a greater number of interactions, retweets or likes.

Another study indicates that approximately 12% of websites with vaccine content shared on Twitter have low credibility [ 14 ] and use a different language between those webs that are in favor and those that are against vaccines. For example, those that favor vaccines refer to all stages of life, while the anti-vaccine refer only to childhood. In addition, the type of words most often used by websites against vaccine are names of diseases that may have been related to the administration of vaccines, such as autism, relating it to vaccine components [ 15 ]. This is supported by the study of Love et al. [ 16 ], which determines that tweets against vaccines focus on the supposed harm that they cause. Twitter also has users called “trolls” or “bots” that generate more content about vaccines than a normal user, being mostly against them [ 17 ]. All studies agree that the mechanisms to spread the anti-vaccine message in Twitter are the use of personal stories, talking about the risks of vaccines and their components, the business of the pharmaceutical industry and conspiracy theories, sometimes supported with links to websites based on no evidence [ 12 , 13 , 14 , 15 , 16 , 17 ]. Nevertheless, pro-vaccine tweets and users have more presence on Twitter than anti-vaccine tweets and users [ 12 , 13 , 14 , 15 , 16 , 17 ].

3.4. Facebook and YouTube and Vaccine Infringement

Facebook users who showed negative feelings about vaccines are introduced as a “pro-science” group that tries to give information about vaccines that is supposedly being hidden [ 18 ]. Tustin et al. [ 19 ] revealed that the comments of this social network mostly speak about distrust towards pharmacists or healthcare providers and include negative experiences with vaccines. These studies are based on the new advertising tool from Facebook, in which anti-vaccination ads have been included talking about alleged institutional fraud and promoting vaccination [ 20 ]. In terms of interaction, comments in favor of immunization receive more “likes” than those against [ 21 ], although the latter group consumes more content [ 22 ]. As on Twitter, antivaccine users based their posts and comments on personal stories, vaccines risks, vaccine components, distrust in pharmaceutical industry and conspiratory theories [ 18 , 19 , 20 , 21 , 22 ]. Even though pro-vaccine users and posts have more presence, anti-vaccine users seems to grow more cohesively on Facebook than pro-vaccine groups [ 22 ].

On YouTube the most watched videos about vaccines are about personal stories that had more views than videos by health agencies. In addition, the search terms are similar for videos presented for and against vaccines [ 23 ].

Table 1 summarizes the information from the included studies.

Summary of included studies ( N = 12).

4. Discussion

After the literature review, it was observed that Twitter seemed to be the most used social network by the anti-vaccine movement. The anti-vaccine users are fewer than pro-vaccine, but they are more active. The anti-vaccine groups usually use the same reasons in their tweets or posts (vaccine risks, autism, vaccine component and conspiracy theories) and base their speech on personal stories. This is linked to the distrust of pharmaceuticals by the anti-vaccine group [ 17 ], which is based on the belief that they have great economic gains from vaccines, having no evidence for the information that they disseminate [ 5 , 24 ]. They also argue that because there is no incidence of some diseases, there is no need for vaccination; but the low incidence of some diseases is due to vaccines [ 6 , 8 ].

It is also interesting to see how people who are against immunization use words in their language related to vaccine components such as "mercury", inciting users’ distrust [ 22 ]. This is due to the rejection of chemical products for fear of suffering health problems [ 25 ] and the preference for alternatives with natural products by the anti-vaccine group [ 5 ]. This movement is mainly growing in western countries partly due to unlimited access to health-related information on the Internet [ 26 ].

Another example of the spread of false information on social networks can be seen with the recent SARS-Covid-19 pandemic that we are suffering. The novelty of the disease causes false news of both its origin and its treatment to spread rapidly. One of the most popular formulas has been to mix sodium chlorite with citric acid as a treatment against the virus, a remedy that has no evidence [ 27 , 28 ]. This type of news can confuse the population, as well as be dangerous to their health, as is the case with vaccines. Moreover, as it happens with other vaccines, some false information is growing on the Internet and social networks with the COVID-19 pandemic, with some groups saying that the virus does not exist or that future vaccine will have a microchip to control us [ 29 , 30 ]. Thus, even with the danger of the pandemic, the anti-vaccine movement is still there. The trend and future of this movement depend on the efforts of healthcare professionals, health organizations and social networks to prevent fake information dissemination which is the main technique that they use to hook people [ 31 , 32 ] . Without any intervention, surely this movement will grow.

The influence of the anti-vaccine movement on social networks can be prevented with strategies that are already working, like creating social networks accounts for official health organizations or modifying the search logarithm of social networks, showing the official information from verified sources first when a user looks for information about vaccines or the COVID-19 pandemic (as it is being done on the main social networks) [ 33 , 34 ]. Furthermore, a strong emphasis on parents’ education about how to find and trust checked information in health institutions should be taken into account [ 26 ].

This study shows the need for greater training for the population to learn how to detect fake information and, on the part of health agencies, to be attentive to possible misinformation that may arise and refute it with true and accessible data in a simple language accessible to the population. They should also promote strategies to try to reach more people on the net to combat misinformation and fake news.

Limitations

This study has some limitations. First, the number of articles is low, mainly because the use of social networks to find health-related information is a recent phenomenon. Differences between countries should also be taken into account in the interpretation of the results, as some have less access to the Internet or its influence on the population is lower. Also, some terms related to the topic were not included in the search equation. Future research could analyze this movement against concrete vaccines, like the HPV, or to analyze how training courses on fake information detection can influence the beliefs of the population.

5. Conclusions

Anti-vaccine groups are using social networks to spread health information, creating their own content without any evidence to confuse users who access their pages. To do this, most of the time they use alleged stories about children who have suffered side effects that end up moving the readers—a fact that impacts more than the scientific data provided by health agencies.

Another method used by the anti-vaccine groups to attract different users is the debate generated by bots and trolls about vaccination. They create contentious debates arguing that pharmaceutical companies make a profit. The methods used by groups against vaccination on social networks are diverse and in many cases are useful for their task.

Author Contributions

Conceptualization, E.O.-S. and J.L.G.-U.; methodology, L.P.-H.; validation, L.P.-H., K.V.-R. and L.A.-G.; formal analysis, E.O.-S.; investigation, E.O.-S.; resources, J.L.G.-U.; data curation, L.A.-G.; writing—original draft preparation, E.S.-O.; writing—review and editing, G.A.C.-D.l.F. and A.V.-S.; visualization, A.V.-S.; supervision, J.L.G.-U. and G.A.C.-D.l.F.; project administration, K.V.-R. All authors have read and agreed to the published version of the manuscript.

This research received no external funding.

Conflicts of Interest

The authors declare no conflicts of interest.