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Published: 14 May 2021

Public attitudes toward COVID-19 vaccination: The role of vaccine attributes, incentives, and misinformation

Sarah Kreps 1 ,
Nabarun Dasgupta 2 ,
John S. Brownstein 3 , 4 ,
Yulin Hswen 5 &
Douglas L. Kriner ORCID: orcid.org/0000-0002-9353-2334 1

npj Vaccines volume 6 , Article number: 73 ( 2021 ) Cite this article

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While efficacious vaccines have been developed to inoculate against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; also known as COVID-19), public vaccine hesitancy could still undermine efforts to combat the pandemic. Employing a survey of 1096 adult Americans recruited via the Lucid platform, we examined the relationships between vaccine attributes, proposed policy interventions such as financial incentives, and misinformation on public vaccination preferences. Higher degrees of vaccine efficacy significantly increased individuals’ willingness to receive a COVID-19 vaccine, while a high incidence of minor side effects, a co-pay, and Emergency Use Authorization to fast-track the vaccine decreased willingness. The vaccine manufacturer had no influence on public willingness to vaccinate. We also found no evidence that belief in misinformation about COVID-19 treatments was positively associated with vaccine hesitancy. The findings have implications for public health strategies intending to increase levels of community vaccination.

Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA

example of argumentative essay about covid 19 vaccine

Vaccine hesitancy and monetary incentives

Providing normative information increases intentions to accept a COVID-19 vaccine

Introduction.

In less than a year, an array of vaccines was developed to bring an end to the SARS-CoV-2 pandemic. As impressive as the speed of development was the efficacy of vaccines such as Moderna and Pfizer, which are over 90%. Despite the growing availability and efficacy, however, vaccine hesitancy remains a potential impediment to widespread community uptake. While previous surveys indicate that overall levels of vaccine acceptance may be around 70% in the United States 1 , the case of Israel may offer a cautionary tale about self-reported preferences and vaccination in practice. Prospective studies 2 of vaccine acceptance in Israel showed that about 75% of the Israeli population would vaccinate, but Israel’s initial vaccination surge stalled around 42%. The government, which then augmented its vaccination efforts with incentive programs, attributed unexpected resistance to online misinformation 3 .

Research on vaccine hesitancy in the context of viruses such as influenza and measles, mumps, and rubella, suggests that misinformation surrounding vaccines is prevalent 4 , 5 . Emerging research on COVID-19 vaccine preferences, however, points to vaccine attributes as dominant determinants of attitudes toward vaccination. Higher efficacy is associated with greater likelihood of vaccinating 6 , 7 , whereas an FDA Emergency Use Authorization 6 or politicized approval timing 8 is associated with more hesitancy. Whether COVID-19 misinformation contributes to vaccine preferences or whether these attributes or policy interventions such as incentives play a larger role has not been studied. Further, while previous research has focused on a set of attributes that was relevant at one particular point in time, the evidence and context about the available vaccines has continued to shift in ways that could shape public willingness to accept the vaccine. For example, governments, employers, and economists have begun to think about or even devise ways to incentivize monetarily COVID-19 vaccine uptake, but researchers have not yet studied whether paying people to receive the COVID-19 vaccine would actually affect likely behavior. As supply problems wane and hesitancy becomes a limiting factor, understanding whether financial incentives can overcome hesitancy becomes a crucial question for public health. Further, as new vaccines such as Johnson and Johnson are authorized, knowing whether the vaccine manufacturer name elicits or deters interest in individuals is also important, as are the corresponding efficacy rates of different vaccines and the extent to which those affect vaccine preferences. The purpose of this study is to examine how information about vaccine attributes such as efficacy rates, the incidence of side effects, the nature of the governmental approval process, identity of the manufacturers, and policy interventions, including economic incentives, affect intention to vaccinate, and to examine the association between belief in an important category of misinformation—false claims concerning COVID-19 treatments—and willingness to vaccinate.

General characteristics of study population

Table 1 presents sample demographics, which largely reflect those of the US population as a whole. Of the 1335 US adults recruited for the study, a convenience sample of 1100 participants consented to begin the survey, and 1096 completed the full questionnaire. The sample was 51% female; 75% white; and had a median age of 43 with an interquartile range of 31–58. Comparisons of the sample demographics to those of other prominent social science surveys and U.S. Census figures are shown in Supplementary Table 1 .

Vaccination preferences

Each subject was asked to evaluate a series of seven hypothetical vaccines. For each hypothetical vaccine, our conjoint experiment randomly assigned values of five different vaccine attributes—efficacy, the incidence of minor side effects, government approval process, manufacturer, and cost/financial inducement. Descriptions of each attribute and the specific levels used in the experiment are summarized in Table 2 . After seeing the profile of each vaccine, the subject was asked whether she would choose to receive the vaccine described, or whether she would choose not to be vaccinated. Finally, subjects were asked to indicate how likely they would be to take the vaccine on a seven-point likert scale.

Across all choice sets, in 4419 cases (58%) subjects said they would choose the vaccine described in the profile rather than not being vaccinated. As shown in Fig. 1 , several characteristics of the vaccine significantly influenced willingness to vaccinate.

Circles present the estimated effect of each attribute level on the probability of a subject accepting vaccination from the attribute’s baseline level. Horizontal lines through points indicate 95% confidence intervals. Points without error bars denote the baseline value for each attribute. The average marginal component effects (AMCEs) are the regression coefficients reported in model 1 of Table 3 .

Efficacy had the largest effect on individual vaccine preferences. An efficacy rate of 90% increased uptake by about 20% relative to the baseline at 50% efficacy. Even a high incidence of minor side effects (1 in 2) had only a modest negative effect (about 5%) on willingness to vaccinate. Whether the vaccine went through full FDA approval or received an Emergency Use Authorization (EUA), an authority that allows the Food and Drug Administration mechanisms to accelerate the availability and use of treatments or medicines during medical emergencies 9 , significantly influenced willingness to vaccinate. An EUA decreased the likelihood of vaccination by 7% compared to a full FDA authorization; such a decline would translate into about 23 million Americans. While a $20 co-pay reduced the likelihood of vaccination relative to a no-cost baseline, financial incentives did not increase willingness to vaccinate. Lastly, the manufacturer had no effect on vaccination attitudes, despite the public pause of the AstraZeneca trial and prominence of Johnson & Johnson as a household name (our experiment was fielded before the pause in the administration of the Johnson & Johnson shot in the United States).

Model 2 of Table 3 presents an expanded model specification to investigate the association between misinformation and willingness to vaccinate. The primary additional independent variable of interest is a misinformation index that captures the extent to which each subject believes or rejects eight claims (five false; three true) about COVID-19 treatments. Additional analyses using alternate operationalizations of the misinformation index yield substantively similar results (Supplementary Table 4 ). This model also includes a number of demographic control variables, including indicators for political partisanship, gender, educational attainment, age, and race/ethnicity, all of which are also associated with belief in misinformation about the vaccine (Supplementary Table 2 ). Finally, the model also controls for subjects’ health insurance status, past experience vaccinating against seasonal influenza, attitudes toward the pharmaceutical industry, and beliefs about vaccine safety generally.

Greater levels of belief in misinformation about COVID-19 treatments were not associated with greater vaccine hesitancy. Instead, the relevant coefficient is positive and statistically significant, indicating that, all else being equal, individuals who scored higher on our index of misinformation about COVID-19 treatments were more willing to vaccinate than those who were less susceptible to believing false claims.

Strong beliefs that vaccines are safe generally was positively associated with willingness to accept a COVID-19 vaccine, as were past histories of frequent influenza vaccination and favorable attitudes toward the pharmaceutical industry. Women and older subjects were significantly less likely to report willingness to vaccinate than men and younger subjects, all else equal. Education was positively associated with willingness to vaccinate.

This research offers a comprehensive examination of attitudes toward COVID-19 vaccination, particularly the role of vaccine attributes, potential policy interventions, and misinformation. Several previous studies have analyzed the effects of vaccine characteristics on willingness to vaccinate, but the modal approach is to gauge willingness to accept a generic COVID-19 vaccine 10 , 11 . Large volumes of research show, however, that vaccine preferences hinge on specific vaccine attributes. Recent research considering the influence of attributes such as efficacy, side effects, and country of origin take a step toward understanding how properties affect individuals’ intentions to vaccinate 6 , 7 , 8 , 12 , 13 , but evidence about the attributes of actual vaccines, debates about how to promote vaccination within the population, and questions about the influence of misinformation have moved quickly 14 .

Our conjoint experiment therefore examined the influence of five vaccine attributes on vaccination willingness. The first category of attributes involved aspects of the vaccine itself. Since efficacy is one of the most common determinants of vaccine acceptance, we considered different levels of efficacy, 50%, 70%, and 90%, levels that are common in the literature 7 , 15 . Evidence from Phase III trials suggests that even the 90% efficacy level in our design, which is well above the 50% threshold from the FDA Guidance for minimal effectiveness for Emergency Use Authorization 16 , has been exceeded by both Pfizer’s and Moderna’s vaccines 17 , 18 . The 70% efficacy threshold is closer to the initial reports of the efficacy of the Johnson & Johnson vaccine, whose efficacy varied across regions 19 . Our analysis suggests that efficacy levels associated with recent mRNA vaccine trials increases public vaccine uptake by 20% over a baseline of a vaccine with 50% efficacy. A 70% efficacy rate increases public willingness to vaccinate by 13% over a baseline vaccine with 50% efficacy.

An additional set of epidemiological attributes consisted of the frequency of minor side effects. While severe side effects were plausible going into early clinical trials, evidence clearly suggests that minor side effects are more common, ranging from 10% to 100% of people vaccinated depending on the number of doses and the dose group (25–250 mcg) 20 . Since the 100 mcg dose was supported in Phase III trials 21 , we include the highest adverse event probability—approximating 60% as 1 in 2—and 1 in 10 as the lowest likelihood, approximating the number of people who experienced mild arthralgia 20 . Our findings suggest that a the prevalence of minor side effects associated with recent trials (i.e. a 1 in 2 chance), intention to vaccinate decreased by about 5% versus a 1 in 10 chance of minor side effects baseline. However, at a 25% rate of minor side effects, respondents did not indicate any lower likelihood of vaccination compared to the 10% baseline. Public communications about how to reduce well-known side effects, such as pain at the injection site, could contribute to improved acceptance of the vaccine, as it is unlikely that development of vaccine-related minor side effects will change.

We then considered the effect of EUA versus full FDA approval. The influenza H1N1 virus brought the process of EUA into public discourse 22 , and the COVID-19 virus has again raised the debate about whether and how to use EUA. Compared to recent studies also employing conjoint experimental designs that showed just a 3% decline in support conditional on EUA 6 , we found decreases in support of more than twice that with an EUA compared to full FDA approval. Statements made by the Trump administration promising an intensely rapid roll-out or isolated adverse events from vaccination in the UK may have exacerbated concerns about EUA versus full approval 8 , 23 , 24 , 25 . This negative effect is even greater among some subsets of the population. As shown in additional analyses reported in the Supplementary Information (Supplementary Fig. 5 ), the negative effects are greatest among those who believe vaccines are generally safe. Among those who believe vaccines generally are extremely safe, the EUA decreased willingness to vaccinate by 11%, all else equal. This suggests that outreach campaigns seeking to assure those troubled by the authorization process used for currently available vaccines should target their efforts on those who are generally predisposed to believe vaccines are safe.

Next, we compared receptiveness as a function of the manufacturer: Moderna, Pfizer, Johnson and Johnson, and AstraZeneca, all firms at advanced stages of vaccine development. Vaccine manufacturers in the US have not yet attempted to use trade names to differentiate their vaccines, instead relying on the association with manufacturer reputation. In other countries, vaccine brand names have been more intentionally publicized, such as Bharat Biotech’s Covaxin in India and Gamaleya Research Institute of Epidemiology and Microbiology Sputnik V in Russia. We found that manufacturer names had no impact on willingness to vaccinate. As with hepatitis and H. influenzae vaccines 26 , 27 , interchangeability has been an active topic of debate with coronavirus mRNA vaccines which require a second shot for full immunity. Our research suggests that at least as far as public receptiveness goes, interchangeability would not introduce concerns. We found no significant differences in vaccination uptake across any of the manufacturer treatments. Future research should investigate if a manufacturer preference develops as new evidence about efficacy and side effects becomes available, particularly depending on whether future booster shots, if needed, are deemed interchangeable with the initial vaccination.

Taking up the question of how cost and financial incentives shape behavior, we looked at paying and being paid to vaccinate. While existing research suggests that individuals are often willing to pay for vaccines 28 , 29 , some economists have proposed that the government pay individuals up to $1,000 to take the COVID-19 vaccine 30 . However, because a cost of $300 billion to vaccinate the population may be prohibitive, we posed a more modest $100 incentive. We also compared this with a $10 incentive, which previous studies suggest is sufficient for actions that do not require individuals to change behavior on a sustained basis 31 . While having to pay a $20 co-pay for the vaccine did deter individuals, the additional economic incentives had no positive effect although they did not discourage vaccination 32 . Consistent with past research 31 , 33 , further analysis shows that the negative effect of the $20 co-pay was concentrated among low-income earners (Supplementary Fig. 7 ). Financial incentives failed to increase vaccination willingness across income levels.

Our study also yields important insights into the relationship between one prominent category of COVID-19 misinformation and vaccination preferences. We find that susceptibility to misinformation about COVID-19 treatments—based on whether individuals can distinguish between factual and false information about efforts to combat COVID-19—is considerable. A quarter of subjects scored no higher on our misinformation index than random guessing or uniform abstention/unsure responses (for the full distribution, see Supplementary Fig. 2 ). However, subjects who scored higher on our misinformation index did not exhibit greater vaccination hesitancy. These subjects actually were more likely to believe in vaccine safety more generally and to accept a COVID-19 vaccine, all else being equal. These results run counter to recent findings of public opinion in France where greater conspiracy beliefs were negatively correlated with willingness to vaccinate against COVID-19 34 and in Korea where greater misinformation exposure and belief were negatively correlated with taking preventative actions 35 . Nevertheless, the results are robust to alternate operationalizations of belief in misinformation (i.e., constructing the index only using false claims, or measuring misinformation beliefs as the number of false claims believed: see Supplementary Table 4 ).

We recommend further study to understand the observed positive relationship between beliefs in COVID-19 misinformation about fake treatments and willingness to receive the COVID-19 vaccine. To be clear, we do not posit a causal relationship between the two. Rather, we suspect that belief in misinformation may be correlated with an omitted factor related to concerns about contracting COVID-19. For example, those who believe COVID-19 misinformation may have a higher perception of risk of COVID-19, and therefore be more willing to take a vaccine, all else equal 36 . Additional analyses reported in the Supplementary Information (Supplementary Fig. 6 ) show that the negative effect of an EUA on willingness to vaccinate was concentrated among those who scored low on the misinformation index. An EUA had little effect on the vaccination preferences of subjects most susceptible to misinformation. This pattern is consistent with the possibility that these subjects were more concerned with the disease and therefore more likely to vaccinate, regardless of the process through which the vaccine was brought to market.

We also observe that skepticism toward vaccines in general does not correlate perfectly with skepticism toward the COVID-19 vaccine. Therefore, it is important not to conflate people who are wary of the COVID-19 vaccine and those who are anti-vaccination, as even medically informed individuals may be hesitant because of the speed at which the COVID-19 vaccine was developed. For example, older people are more likely to believe vaccines are safe but less willing to receive the COVID-19 vaccine in our survey, perhaps following the high rates of vaccine skepticism among medical staff expressing concerns regarding the safety of a rapidly-developed vaccine 2 . This inverse relationship between age and willingness to vaccinate is also surprising. Most opinion surveys find older adults are more likely to vaccinate than younger adults 37 . However, most of these survey questions ask about willingness to take a generic vaccine. Two prior studies, both recruiting subjects from the Lucid platform and employing conjoint experiments to examine the effects of vaccine attributes on public willingness to vaccinate, also find greater vaccine hesitancy among older Americans 6 , 7 . Future research could explore whether these divergent results are a product of the characteristics of the sample or of the methodological design in which subjects have much more information about the vaccines when indicating their vaccination preferences.

An important limitation of our study is that it necessarily offers a snapshot in time, specifically prior to both the election and vaccine roll-out. We recommend further study to understand more how vaccine perceptions evolve both in terms of the perceived political ownership of the vaccine—now that President Biden is in office—and as evidence has emerged from the millions of people who have been vaccinated. Similarly, researchers should consider analyzing vaccine preferences in the context of online vaccine controversies that have been framed in terms of patient autonomy and right to refuse 38 , 39 . Vaccination mandates may evoke feelings of powerlessness, which may be exacerbated by misinformation about the vaccines themselves. Further, researchers should more fully consider how individual attributes such as political ideology and race intersect with vaccine preferences. Our study registered increased vaccine hesitancy among Blacks, but did not find that skepticism was directly related to misinformation. Perceptions and realities of race-based maltreatment could also be moderating factors worth exploring in future analyses 40 , 41 .

Overall, we found that the most important factor influencing vaccine preferences is vaccine efficacy, consistent with a number of previous studies about attitudes toward a range of vaccines 6 , 42 , 43 . Other attributes offer potential cautionary flags and opportunities for public outreach. The prospect of a 50% likelihood of mild side effects, consistent with the evidence about current COVID-19 vaccines being employed, dampens likelihood of uptake. Public health officials should reinforce the relatively mild nature of the side effects—pain at the injection site and fatigue being the most common 44 —and especially the temporary nature of these effects to assuage public concerns. Additionally, in considering policy interventions, public health authorities should recognize that a $20 co-pay will likely discourage uptake while financial incentives are unlikely to have a significant positive effect. Lastly, belief in misinformation about COVID-19 does not appear to be a strong predictor of vaccine hesitancy; belief in misinformation and willingness to vaccinate were positively correlated in our data. Future research should explore the possibility that exposure to and belief in misinformation is correlated with other factors associated with vaccine preferences.

Survey sample and procedures

This study was approved by the Cornell Institutional Review Board for Human Participant Research (protocol ID 2004009569). We conducted the study on October 29–30, 2020, prior to vaccine approval, which means we captured sentiments prospectively rather than based on information emerging from an ongoing vaccination campaign. We recruited a sample of 1096 adult Americans via the Lucid platform, which uses quota sampling to produce samples matched to the demographics of the U.S. population on age, gender, ethnicity, and geographic region. Research has shown that experimental effects observed in Lucid samples largely mirror those found using probability-based samples 45 . Supplementary Table 1 presents the demographics of our sample and comparisons to both the U.S. Census American Community Survey and the demographics of prominent social science surveys.

After providing informed consent on the first screen of the online survey, participants turned to a choice-based conjoint experiment that varied five attributes of the COVID-19 vaccine. Conjoint analyses are often used in marketing to research how different aspects of a product or service affect consumer choice. We build on public health studies that have analyzed the influence of vaccine characteristics on uptake within the population 42 , 46 .

Conjoint experiment

We first designed a choice-based conjoint experiment that allowed us to evaluate the relative influence of a range of vaccine attributes on respondents’ vaccine preferences. We examined five attributes summarized in Table 2 . Past research has shown that the first two attributes, efficacy and the incidence of side effects, are significant drivers of public preferences on a range of vaccines 47 , 48 , 49 , including COVID-19 6 , 7 , 13 , 50 . In this study, we increased the expected incidence of minor side effects from previous research 6 to reflect emerging evidence from Phase III trials. The third attribute, whether the vaccine received full FDA approval or an EUA, examines whether the speed of the approval process affects public vaccination preferences 6 . The fourth attribute, the manufacturer of the vaccine, allows us to examine whether the highly public pause in the AstraZeneca trial following an adverse event, and the significant differences in brand familiarity between smaller and less broadly known companies like Moderna and household name Johnson & Johnson affects public willingness to vaccinate. The fifth attribute examines the influence of a policy tool—offsetting the costs of vaccination or even incentivizing it financially—on public willingness to vaccinate.

Attribute levels and attribute order were randomly assigned across participants. A sample choice set is presented in Supplementary Fig. 1 . After viewing each profile individually, subjects were asked: “If you had to choose, would you choose to get this vaccine, or would you choose not to be vaccinated?” Subjects then made a binary choice, responding either that they “would choose to get this vaccine” or that they “would choose not to be vaccinated.” This is the dependent variable for the regression analyses in Table 3 . After making a binary choice to take the vaccine or not be vaccinated, we also asked subjects “how likely or unlikely would you be to get the vaccine described above?” Subjects indicated their vaccination preference on a seven-point scale ranging from “extremely likely” to “extremely unlikely.” Additional analyses using this ordinal dependent variable reported in Supplementary Table 3 yield substantively similar results to those presented in Table 3 .

To determine the effect of each attribute-level on willingness to vaccinate, we followed Hainmueller, Hopkins, and Yamamoto and employed an ordinary least squares (OLS) regression with standard errors clustered on respondent to estimate the average marginal component effects (AMCEs) for each attribute 51 . The AMCE represents the average difference in a subject choosing a vaccine when comparing two different attribute values—for example, 50% efficacy vs. 90% efficacy—averaged across all possible combinations of the other vaccine attribute values. The AMCEs are nonparametrically identified under a modest set of assumptions, many of which (such as randomization of attribute levels) are guaranteed by design. Model 1 in Table 3 estimates the AMCEs for each attribute. These AMCEs are illustrated in Fig. 1 .

Analyzing additional correlates of vaccine acceptance

To explore the association between respondents’ embrace of misinformation about COVID-19 treatments and vaccination willingness, the survey included an additional question battery. To measure the extent of belief in COVID-19 misinformation, we constructed a list of both accurate and inaccurate headlines about the coronavirus. We focused on treatments, relying on the World Health Organization’s list of myths, such as “Hand dryers are effective in killing the new coronavirus” and true headlines such as “Avoiding shaking hands can help limit the spread of the new coronavirus 52 .” Complete wording for each claim is provided in Supplementary Appendix 1 . Individuals read three true headlines and five myths, and then responded whether they believed each headline was true or false, or whether they were unsure. We coded responses to each headline so that an incorrect accuracy assessment yielded a 1; a correct accuracy assessment a -1; and a response of unsure was coded as 0. From this, we created an additive index of belief in misinformation that ranged from -8 to 8. The distribution of the misinformation index is presented in Supplementary Fig. 2 . A possible limitation of this measure is that because the survey was conducted online, some individuals could have searched for the answers to the questions before responding. However, the median misinformation index score for subjects in the top quartile in terms of time spent taking the survey was identical to the median for all other respondents. This may suggest that systematic searching for correct answers is unlikely.

To ensure that any association observed between belief in misinformation and willingness to vaccinate is not an artifact of how we operationalized susceptibility to misinformation, we also constructed two alternate measures of belief in misinformation. These measures are described in detail in the Supplementary Information (see Supplementary Figs. 3 and 4 ). Additional regression analyses using these alternate measures of misinformation beliefs yield substantively similar results (see Supplementary Table 4 ). Additional analyses examining whether belief in misinformation moderates the effect of efficacy and an FDA EUA on vaccine acceptance are presented in Supplementary Fig. 6 .

Finally, model 2 of Table 3 includes a range of additional control variables. Following past research, it includes a number of demographic variables, including indicator variables identifying subjects who identify as Democrats or Republicans; an indicator variable identifying females; a continuous variable measuring age (alternate analyses employing a categorical variable yield substantively similar results); an eight-point measure of educational attainment; and indicator variables identifying subjects who self-identify as Black or Latinx. Following previous research 6 , the model also controlled for three additional factors often associated with willingness to vaccinate: an indicator variable identifying whether each subject had health insurance; a variable measuring past frequency of influenza vaccination on a four-point scale ranging from “never” to “every year”; beliefs about the general safety of vaccines measured on a four-point scale ranging from “not at all safe” to “extremely safe”; and a measure of attitudes toward the pharmaceutical industry ranging from “very positive” to “very negative.”

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

All data and statistical code to reproduce the tables and figures in the manuscript and Supplementary Information are published at the Harvard Dataverse via this link: 10.7910/DVN/ZYU6CO.

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S.K. and D.K. would like to thank the Cornell Atkinson Center for Sustainability for financial support.

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Kreps, S., Dasgupta, N., Brownstein, J.S. et al. Public attitudes toward COVID-19 vaccination: The role of vaccine attributes, incentives, and misinformation. npj Vaccines 6 , 73 (2021). https://doi.org/10.1038/s41541-021-00335-2

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A woman administers a COVID-19 vaccination to a man seated with his sleeve rolled up

There are plenty of moral reasons to be vaccinated – but that doesn’t mean it’s your ethical duty

Director of the Master of Bioethics degree program at the Berman Institute of Bioethics, Johns Hopkins University

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Travis N. Rieder does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

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With the news that all U.S. adults are now eligible to receive the COVID-19 vaccine, the holy grail of infectious disease mitigation – herd immunity – feels tantalizingly close. If enough people take the vaccine, likely at least 70% of the population, disease prevalence will slowly decline and most of us will safely get back to normal. But if not enough people get vaccinated, COVID-19 could stick around indefinitely.

The urgency of reaching that milestone has led some to claim that individuals have a civic duty or moral obligation to get vaccinated.

As a moral philosopher who has written on the nature of obligation in other contexts, I want to explore how the seemingly straightforward ethics of vaccine choice is in fact rather complex.

The simple argument

The discussion of whether or not one should take the COVID-19 vaccine is often framed in terms of individual self-interest: The benefits outweigh the risk, so you should do it.

That’s not a moral argument.

Most people likely believe that others have wide latitude in determining how they care for their own health, so it can be permissible to engage in risky activities – such as motorcycling or base jumping – even when it’s not in one’s interest. Whether one should get vaccinated, however, is a moral issue because it affects others, and in a couple of ways.

First, effective vaccines are expected to decrease not only rates of infection but also rates of virus transmission . This means that getting the vaccine can protect others from you and contribute to the population reaching herd immunity.

Second, high disease prevalence allows for more genetic mutation of a virus, which is how new variants arise. If enough people aren’t vaccinated quickly, new variants may develop that are more infectious, are more dangerous or evade current vaccines.

The straightforward ethical argument, then, says: Getting vaccinated isn’t just about you. Yes, you have the right to take risks with your own safety. But as the British philosopher John Stuart Mill argued in 1859, your freedom is limited by the harm it could do to others. In other words, you do not have the right to risk other people’s health, and so you are obligated to do your part to reduce infection and transmission rates.

It’s a plausible argument. But the case is rather more complicated.

Individual action, collective good

The first problem with the argument above is that it moves from the claim that “My freedom is limited by the harm it would cause others” to the much more contentious claim that “My freedom is limited by very small contributions my action might make to large, collective harms.”

Refusing to be vaccinated does not violate Mill’s harm principle , as it does not directly threaten some particular other with significant harm. Rather, it contributes a very small amount to a large, collective harm.

Since no individual vaccination achieves herd immunity or eliminates genetic mutation, it is natural to wonder: Could we really have a duty to make such a very small contribution to the collective good?

A version of this problem has been well explored in the climate ethics literature, since individual actions are also inadequate to address the threat of climate change. In that context, a well-known paper argues that the answer is “no”: There is simply no duty to act if your action won’t make a meaningful difference to the outcome.

Others, however, have explored a variety of ways to rescue the idea that individuals must not contribute to collective harms.

One strategy is to argue that small individual actions may actually make a difference to large collective effects, even if it’s difficult to see.

For instance: Although it appears that an individual getting vaccinated doesn’t make a significant difference to the outcome, perhaps that is just the result of uncareful moral mathematics. One’s chance of saving a life by reducing infection or transmission is very small, but saving a life is very valuable. The expected value of the outcome, then, is still high enough to justify taking it to be a moral requirement.

Another strategy concedes that individual actions don’t make a meaningful difference to large, structural problems, but this doesn’t mean morality must be silent with regard to those actions. Considerations of fairness , virtue and integrity all might recommend taking individual action toward a collective goal – even if that action did not by itself make a difference.

In addition, these and other considerations can provide reasons to act , even if they don’t imply an obligation to act.

New York Gov. Andrew Cuomo walks past students getting vaccinated at Suffolk County Community College

The contours of obligation

There is yet another challenge in justifying an obligation to get vaccinated, which has to do with the very nature of obligations.

Obligations are requirements on actions, and, as such, those actions often seem demandable by members of the moral community. If a person is obligated to donate to charity, then other members of the community have the moral standing to demand a percentage of their income. That money is owed to others.

The relevant question here, then, is: Are there moral grounds to demand another person get vaccinated?

Philosopher Margaret Little has argued that very intimate actions, such as sex and gestation – the continuation of a pregnancy – are not demandable. In my own work, I’ve suggested that this is also true for deciding how to form a family – for example, adopting a child versus procreating. The intimacy of the actions, I argue, make it the case that no one is entitled to them. Someone can ask you for sex, and there are good reasons to adopt rather than procreate; but no one in the community has the moral standing to demand that you do either. These sorts of examples suggest that particularly intimate actions are not the appropriate targets of obligation.

Is getting vaccinated intimate? While it may not appear so at first blush, it involves having a substance injected into your body, which is a form of bodily intimacy. It requires allowing another to puncture the barrier between your body and the world. In fact, most medical procedures are the sort of thing that it seems inappropriate to demand of someone, as individuals have unilateral moral authority over what happens to their bodies.

The argument presented here objects to intimate duties because they seem too invasive. However, even if members of the moral community don’t have the standing to demand that others vaccinate, they are not required to stay silent; they may ask, request or entreat, based on very good reasons. And of course, no one is required to interact with those who decline.

I am certainly not trying to convince anyone that it’s OK not to get vaccinated. Indeed, the arguments throughout indicate, I think, that there is overwhelming reason to get vaccinated. But reasons – even when overwhelming – don’t constitute a duty, and they don’t make an action demandable.

Acting as though the moral case is straightforward can be alienating to those who disagree. And minimizing the moral stakes when we ask others to have a substance injected into their body can be disrespectful. A much better way, I think, is to engage others rather than demand from them, even if the force of reason ends up clearly on one side.

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February 17, 2021

COVID Vaccines Are Safe and Effective—What the Research Says

As more coronavirus vaccines are rolled out, researchers are learning about the extent and nature of side effects

By Ariana Remmel & Nature magazine

A healthcare worker administers a dose of the Pfizer-BioNTech Covid-19 vaccine at the Sun City Anthem Community Center vaccination site in Henderson, Nevada, U.S., on Thursday, Feb. 11, 2021.

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As people around the world receive COVID-19 vaccines, reports of temporary side effects such as headaches and fevers are rolling in. Much of this was expected—clinical-trial data for the vaccines authorized so far suggested as much. But now that millions of people are vaccinated, compared with the thousands enrolled in early studies, reports of some rare, allergic reactions are surfacing, and questions are arising about whether any deaths are linked to the shots.

There is no question that the current vaccines are effective and safe. The risk of severe reaction to a COVID-19 jab, say researchers, is outweighed by the protection it offers against the deadly coronavirus. Nature looks at what scientists are learning about the frequency and nature of side effects as huge numbers of people report their reactions to physicians and through safety-monitoring systems, such as smartphone apps.

How many people experience common side effects from COVID-19 vaccines?

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For the two available messenger RNA (mRNA) vaccines—one made by Moderna at Cambridge, Massachusetts, and the other developed through a collaboration between Pfizer in New York City and BioNTech in Mainz, Germany—a significant portion of people experience non-serious reactions, such as injection-site pain, headache and fatigue. These vaccines deliver bits of RNA that code for coronavirus proteins, which the body mounts a response against.

According to data from the US Vaccine Adverse Event Reporting System (VAERS), about 372 out of every million administered doses of the mRNA vaccines lead to a non-serious reaction report. This number is lower than would be expected from clinical-trial data, which indicated that at least 80% of people would experience injection-site pain. Researchers running trials monitor patients closely and record every reaction. VAERS, meanwhile, relies on health-care workers and vaccinated individuals to self-report side effects.

So far, reactions to the mRNA vaccines are similar. These vaccines are administered in a two-dose regimen: the first shot triggers an immune reaction, and the second is a ‘booster’ that strengthens the body’s ability to fight the coronavirus. For the Pfizer–BioNTech vaccine, which has been in use longer than the Moderna vaccine and therefore has generated more data, side effects increase with the second dose.

In the United Kingdom, three million doses of another vaccine, developed by the University of Oxford and pharmaceutical firm AstraZeneca, have been doled out. This vaccine, which also requires a two-dose regimen, contains a inactivated cold-causing adenovirus with genetic instructions for making coronavirus proteins to trigger immunity. According to UK safety-monitoring system the Yellow Card Scheme, about 4,000 doses out of every million administered lead to adverse reactions. Again, clinical-trial data suggest that a higher frequency is more accurate: around 50% of participants had injection-site pain, headache or fatigue, according to data reported to the European Medicines Agency (EMA).

Few people have received a second dose of the Oxford–AstraZeneca vaccine because the United Kingdom used its supplies to administer a first dose to as many people as possible, but clinical-trial data presented to the EMA suggest that side effects of the second shot are milder than those caused by the first.

Safety data for shots rolling out in other parts of the world, such as the COVID-19 vaccines in China, are harder to come by. Preliminary data from clinical trials of the adenovirus-based Sputnik V vaccine in Russia suggest its most common side effects include flu-like symptoms and injection-site reactions.

How does that compare with side effects from an annual flu shot?

At least for the mRNA vaccines, physicians are seeing more side effects than for flu shots, says Helen Chu, an infectious-disease specialist at the University of Washington School of Medicine in Seattle, who directs the Seattle Flu Study. In clinical trials for the Pfizer–BioNTech vaccine, for instance, 75% of participants reported a ‘systemic reaction’, such as headache, fever or chills. In a clinical trial for the common influenza vaccine Flubok Quadravalent, around 34% of participants aged 18–49 had a systemic reaction. Side effects were even less frequent in study participants who were at least 50 years old.

Chu says the mRNA COVID-19 vaccines generate a particularly strong immune response that increases the risk of side effects, although this also means that the vaccines are working. She notes that her second dose of the Pfizer–BioNTech vaccine made her ill. “I got the vaccine, and 6 hours later, I had chills, a high fever, muscle aches and I went to bed for 24 hours,” she says. “Then by 36 hours later, it was totally over and I was back to normal.” But Chu would rather be temporarily ill from a vaccine than deal with COVID-19, “a potentially mortal disease that could kill me”, she says.

Have investigations linked any deaths to a COVID-19 vaccine?

Although some have questioned whether the vaccines have led to deaths, none have been directly attributed to a COVID-19 jab. After 33 elderly care-home residents in Norway died within 6 days of receiving the Pfizer–BioNTech vaccine, investigations by both the Norwegian Medicines Agency and the World Health Organization concluded that these deaths were in line with normal death rates in this age group and that the vaccine is still safe for older people. India's Ministry of Health and Family Welfare reported 27 deaths in the country, but none of these have been linked directly to a COVID-19 vaccine either.

It is “extremely difficult” to definitively link a death to the vaccine itself, says Hilda Bastian, a writer and scientist who specializes in validating evidence-based health claims. That is partially because the deaths reported so far have occurred days or weeks after an injection, making it hard to rule out other circumstances. Another reason is that, right now, clinicians are prioritizing vaccines largely for a population of older people with underlying health conditions. Most of those who have died after vaccination have been in this group, according to reports from the United Kingdom and the United States .

What do researchers know about the rare, but severe, allergic reactions to the vaccines?

The Moderna vaccine elicits about three anaphylactic reactions per million doses administered, and the Pfizer–BioNTech vaccine triggers five reactions per million doses, according to VAERS data . This is a higher rate than most other vaccines—including annual flu shots, which trigger anaphylaxis for only one out of every million doses administered. For the Oxford–AstraZeneca vaccine, 30 cases of anaphylaxis have been confirmed overall so far, out of a little more than 3 million administered doses. Vaccine specialists expect that these rates might change as more shots are administered.

Although some people have required hospitalization, all have fully recovered. Public-health officials advise people with a history of allergies to any of the vaccines’ ingredients not to get a COVID-19 jab.

Unlike COVID-19, anaphylaxis is treatable with drugs such as epinephrine if caught quickly, says Paul Offit, a vaccine and infectious-disease specialist at the Children’s Hospital of Philadelphia in Pennsylvania, who participated in the US Food and Drug Administration advisory-committee meetings that led the agency to authorize both mRNA vaccines. “I wish that SARS-CoV-2 could be immediately treated with a shot of epinephrine!” he says.

Most of the people who experienced anaphylaxis had reacted to other substances before: about 80% of people who reacted to the Pfizer–BioNTech vaccine, and 86% to the Moderna vaccine, had a history of allergies, according to the US Centers for Disease Control and Prevention.

The specific cause of the anaphylactic reactions remains unknown, but the US National Institute of Allergy and Infectious Diseases told Nature in an e-mail that the agency has designed a clinical trial to determine the underlying mechanism, but did not specify when the trial would begin.

What could be causing the allergic reactions?

Some researchers have had their eye on polyethylene glycol (PEG) as the anaphylaxis-causing agent in the mRNA vaccines. The Moderna and Pfizer–BioNTech vaccines use hollow lipid nanoparticles to store and then deliver their mRNA payload to cells. PEG is linked to the lipids in these particles and, under normal circumstances, helps them to sneak by the immune system. Although PEG-linked molecules are found in a variety of products, such as laxatives and gout medicines, they have been known to cause allergic reactions.

Follow-up studies in people who experienced anaphylaxis could help to determine whether PEG is the culprit, says Samuel Lai, a pharmaco-engineer at the University of North Carolina at Chapel Hill. If blood samples from these people contain anti-PEG antibodies, it could be an indicator, says Lai, but it is as yet unclear how long these proteins remain in the bloodstream after anaphylaxis.

Vaccines that don’t use PEG—such as the not-yet-authorized shot from Johnson & Johnson, which also uses an adenovirus to trigger immunity to the coronavirus—might be a way to vaccinate people with a sensitivity to the polymer, he adds.

Because mRNA vaccines have shown such promise, Ulrich Schubert, a polymer scientist at the University of Jena in Germany, thinks now is the time to invest in developing vaccine-compatible polymers that don’t cause allergic reactions. At the German Research Foundation-funded collaborative research center PolyTarget, where Schubert works, these studies are already in progress. “If we want to be ready for the next pandemic—which will come—we have to start now,” he says.

This article is reproduced with permission and was first published on February 16 2021.

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What are the benefits and risks of vaccines for preventing COVID-19?

Key messages

– Most vaccines reduce, or probably reduce, the number of people who get COVID-19 disease and severe COVID-19 disease.

– Many vaccines likely increase number of people experiencing events such as fever or headache compared to placebo (sham vaccine that contains no medicine but looks identical to the vaccine being tested). This is expected because these events are mainly due to the body's response to the vaccine; they are usually mild and short-term.

– Many vaccines have little or no difference in the incidence of serious adverse events compared to placebo.

– There is insufficient evidence to determine whether there was a difference between the vaccine and placebo in terms of death because the numbers of deaths were low in the trials.

– Most trials assessed vaccine efficacy over a short time, and did not evaluate efficacy to the COVID variants of concern.

What is SARS-CoV-2 and COVID-19?

SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is the virus that causes COVID-19 disease. Not everyone infected with SARS-CoV-2 will develop symptoms of COVID-19. Symptoms can be mild (e.g. fever and headaches) to life-threatening (e.g. difficulty breathing), or death.

How do vaccines prevent COVID-19?

While vaccines work slightly differently, they all prepare the body's immune system to prevent people from getting infected with SARS-CoV-2 or, if they do get infected, to prevent severe disease.

What did we want to find out?

We wanted to find out how well each vaccine works in reducing SARS-CoV-2 infection, COVID-19 disease with symptoms, severe COVID-19 disease, and total number of deaths (including any death, not only those related to COVID-19).

We wanted to find out about serious adverse events that might require hospitalization, be life-threatening, or both; systemic reactogenicity events (immediate short-term reactions to vaccines mainly due to immunological responses; e.g. fever, headache, body aches, fatigue); and any adverse events (which include non-serious adverse events).

What did we do?

We searched for studies that examined any COVID-19 vaccine compared to placebo, no vaccine, or another COVID-19 vaccine.

We selected only randomized trials (a study design that provides the most robust evidence because they evaluate interventions under ideal conditions among participants assigned by chance to one of two or more groups). We compared and summarized the results of the studies, and rated our confidence in the evidence based on factors such as how the study was conducted.

What did we find?

We found 41 worldwide studies involving 433,838 people assessing 12 different vaccines. Thirty-five studies included only healthy people who had never had COVID-19. Thirty-six studies included only adults, two only adolescents, two children and adolescents, and one included adolescents and adults. Three studied people with weakened immune systems, and none studied pregnant women.

Most cases assessed results less than six months after the primary vaccination. Most received co-funding from academic institutions and pharmaceutical companies. Most studies compared a COVID-19 vaccine with placebo. Five evaluated the addition of a 'mix and match' booster dose.

Main results

We report below results for three main outcomes and for 10 World Health Organization (WHO)-approved vaccines (for the remaining outcomes and vaccines, see main text). There is insufficient evidence regarding deaths between vaccines and placebo (mainly because the number of deaths was low), except for the Janssen vaccine, which probably reduces the risk of all-cause deaths.

People with symptoms

The Pfizer, Moderna, AstraZeneca, Sinopharm-Beijing, and Bharat vaccines produce a large reduction in the number of people with symptomatic COVID-19.

The Janssen vaccine reduces the number of people with symptomatic COVID-19.

The Novavax vaccine probably has a large reduction in the number of people with symptomatic COVID-19.

There is insufficient evidence to determine whether CoronaVac vaccine affects the number of people with symptomatic COVID-19 because results differed between the two studies (one involved only healthcare workers with a higher risk of exposure).

Severe disease

The Pfizer, Moderna, Janssen, and Bharat vaccines produce a large reduction in the number of people with severe disease.

There is insufficient evidence about CoronaVac vaccine on severe disease because results differed between the two studies (one involved only healthcare workers with a higher risk of exposure).

Serious adverse events

For the Pfizer, CoronaVac, Sinopharm-Beijing, and Novavax vaccines, there is insufficient evidence to determine whether there was a difference between the vaccine and placebo mainly because the number of serious adverse events was low.

Moderna, AstraZeneca, Janssen, and Bharat vaccines probably result in no or little difference in the number of serious adverse events.

What are the limitations of the evidence?

Most studies assessed the vaccine for a short time after injection, and it is unclear if and how vaccine protection wanes over time. Due to the exclusion criteria of COVID-19 vaccine trials, results cannot be generalized to pregnant women, people with a history of SARS-CoV-2 infection, or people with weakened immune systems. More research is needed comparing vaccines and vaccine schedules, and effectiveness and safety in specific populations and outcomes (e.g. preventing long COVID-19). Further, most studies were conducted before the emergence of variants of concerns.

How up to date is this evidence?

The evidence is up to date to November 2021. This is a living systematic review. Our results are available and updated bi-weekly on the COVID-NMA platform at covid-nma.com.

Compared to placebo, most vaccines reduce, or likely reduce, the proportion of participants with confirmed symptomatic COVID-19, and for some, there is high-certainty evidence that they reduce severe or critical disease. There is probably little or no difference between most vaccines and placebo for serious adverse events. Over 300 registered RCTs are evaluating the efficacy of COVID-19 vaccines, and this review is updated regularly on the COVID-NMA platform ( covid-nma.com ).

Implications for practice

Due to the trial exclusions, these results cannot be generalized to pregnant women, individuals with a history of SARS-CoV-2 infection, or immunocompromized people. Most trials had a short follow-up and were conducted before the emergence of variants of concern.

Implications for research

Future research should evaluate the long-term effect of vaccines, compare different vaccines and vaccine schedules, assess vaccine efficacy and safety in specific populations, and include outcomes such as preventing long COVID-19. Ongoing evaluation of vaccine efficacy and effectiveness against emerging variants of concern is also vital.

Different forms of vaccines have been developed to prevent the SARS-CoV-2 virus and subsequent COVID-19 disease. Several are in widespread use globally.

To assess the efficacy and safety of COVID-19 vaccines (as a full primary vaccination series or a booster dose) against SARS-CoV-2.

We searched the Cochrane COVID-19 Study Register and the COVID-19 L·OVE platform (last search date 5 November 2021). We also searched the WHO International Clinical Trials Registry Platform, regulatory agency websites, and Retraction Watch.

We included randomized controlled trials (RCTs) comparing COVID-19 vaccines to placebo, no vaccine, other active vaccines, or other vaccine schedules.

We used standard Cochrane methods. We used GRADE to assess the certainty of evidence for all except immunogenicity outcomes.

We synthesized data for each vaccine separately and presented summary effect estimates with 95% confidence intervals (CIs).

We included and analyzed 41 RCTs assessing 12 different vaccines, including homologous and heterologous vaccine schedules and the effect of booster doses. Thirty-two RCTs were multicentre and five were multinational. The sample sizes of RCTs were 60 to 44,325 participants. Participants were aged: 18 years or older in 36 RCTs; 12 years or older in one RCT; 12 to 17 years in two RCTs; and three to 17 years in two RCTs. Twenty-nine RCTs provided results for individuals aged over 60 years, and three RCTs included immunocompromized patients. No trials included pregnant women. Sixteen RCTs had two-month follow-up or less, 20 RCTs had two to six months, and five RCTs had greater than six to 12 months or less. Eighteen reports were based on preplanned interim analyses.

Overall risk of bias was low for all outcomes in eight RCTs, while 33 had concerns for at least one outcome.

We identified 343 registered RCTs with results not yet available.

This abstract reports results for the critical outcomes of confirmed symptomatic COVID-19, severe and critical COVID-19, and serious adverse events only for the 10 WHO-approved vaccines. For remaining outcomes and vaccines, see main text. The evidence for mortality was generally sparse and of low or very low certainty for all WHO-approved vaccines, except AD26.COV2.S (Janssen), which probably reduces the risk of all-cause mortality (risk ratio (RR) 0.25, 95% CI 0.09 to 0.67; 1 RCT, 43,783 participants; high-certainty evidence).

Confirmed symptomatic COVID-19

High-certainty evidence found that BNT162b2 (BioNtech/Fosun Pharma/Pfizer), mRNA-1273 (ModernaTx), ChAdOx1 (Oxford/AstraZeneca), Ad26.COV2.S, BBIBP-CorV (Sinopharm-Beijing), and BBV152 (Bharat Biotect) reduce the incidence of symptomatic COVID-19 compared to placebo (vaccine efficacy (VE): BNT162b2: 97.84%, 95% CI 44.25% to 99.92%; 2 RCTs, 44,077 participants; mRNA-1273: 93.20%, 95% CI 91.06% to 94.83%; 2 RCTs, 31,632 participants; ChAdOx1: 70.23%, 95% CI 62.10% to 76.62%; 2 RCTs, 43,390 participants; Ad26.COV2.S: 66.90%, 95% CI 59.10% to 73.40%; 1 RCT, 39,058 participants; BBIBP-CorV: 78.10%, 95% CI 64.80% to 86.30%; 1 RCT, 25,463 participants; BBV152: 77.80%, 95% CI 65.20% to 86.40%; 1 RCT, 16,973 participants).

Moderate-certainty evidence found that NVX-CoV2373 (Novavax) probably reduces the incidence of symptomatic COVID-19 compared to placebo (VE 82.91%, 95% CI 50.49% to 94.10%; 3 RCTs, 42,175 participants).

There is low-certainty evidence for CoronaVac (Sinovac) for this outcome (VE 69.81%, 95% CI 12.27% to 89.61%; 2 RCTs, 19,852 participants).

Severe or critical COVID-19

High-certainty evidence found that BNT162b2, mRNA-1273, Ad26.COV2.S, and BBV152 result in a large reduction in incidence of severe or critical disease due to COVID-19 compared to placebo (VE: BNT162b2: 95.70%, 95% CI 73.90% to 99.90%; 1 RCT, 46,077 participants; mRNA-1273: 98.20%, 95% CI 92.80% to 99.60%; 1 RCT, 28,451 participants; AD26.COV2.S: 76.30%, 95% CI 57.90% to 87.50%; 1 RCT, 39,058 participants; BBV152: 93.40%, 95% CI 57.10% to 99.80%; 1 RCT, 16,976 participants).

Moderate-certainty evidence found that NVX-CoV2373 probably reduces the incidence of severe or critical COVID-19 (VE 100.00%, 95% CI 86.99% to 100.00%; 1 RCT, 25,452 participants).

Two trials reported high efficacy of CoronaVac for severe or critical disease with wide CIs, but these results could not be pooled.

Serious adverse events (SAEs)

mRNA-1273, ChAdOx1 (Oxford-AstraZeneca)/SII-ChAdOx1 (Serum Institute of India), Ad26.COV2.S, and BBV152 probably result in little or no difference in SAEs compared to placebo (RR: mRNA-1273: 0.92, 95% CI 0.78 to 1.08; 2 RCTs, 34,072 participants; ChAdOx1/SII-ChAdOx1: 0.88, 95% CI 0.72 to 1.07; 7 RCTs, 58,182 participants; Ad26.COV2.S: 0.92, 95% CI 0.69 to 1.22; 1 RCT, 43,783 participants); BBV152: 0.65, 95% CI 0.43 to 0.97; 1 RCT, 25,928 participants). In each of these, the likely absolute difference in effects was fewer than 5/1000 participants.

Evidence for SAEs is uncertain for BNT162b2, CoronaVac, BBIBP-CorV, and NVX-CoV2373 compared to placebo (RR: BNT162b2: 1.30, 95% CI 0.55 to 3.07; 2 RCTs, 46,107 participants; CoronaVac: 0.97, 95% CI 0.62 to 1.51; 4 RCTs, 23,139 participants; BBIBP-CorV: 0.76, 95% CI 0.54 to 1.06; 1 RCT, 26,924 participants; NVX-CoV2373: 0.92, 95% CI 0.74 to 1.14; 4 RCTs, 38,802 participants).

For the evaluation of heterologous schedules, booster doses, and efficacy against variants of concern, see main text of review.

Special Issue: COVID-19

This essay was published as part of a Special Issue on Misinformation and COVID-19, guest-edited by Dr. Meghan McGinty (Director of Emergency Management, NYC Health + Hospitals) and Nat Gyenes (Director, Meedan Digital Health Lab).

Peer Reviewed

Not just conspiracy theories: Vaccine opponents and proponents add to the COVID-19 ‘infodemic’ on Twitter

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In February 2020, the World Health Organization announced an ‘infodemic’—a deluge of both accurate and inaccurate health information—that accompanied the global pandemic of COVID-19 as a major challenge to effective health communication. We assessed content from the most active vaccine accounts on Twitter to understand how existing online communities contributed to the ‘infodemic’ during the early stages of the pandemic. While we expected vaccine opponents to share misleading information about COVID-19, we also found vaccine proponents were not immune to spreading less reliable claims. In both groups, the single largest topic of discussion consisted of narratives comparing COVID-19 to other diseases like seasonal influenza, often downplaying the severity of the novel coronavirus. When considering the scope of the ‘infodemic,’ researchers and health communicators must move beyond focusing on known bad actors and the most egregious types of misinformation to scrutinize the full spectrum of information — from both reliable and unreliable sources — that the public is likely to encounter online.

Center for Health Equity, University of Maryland, USA

Institute for Data, Democracy, and Politics, & Department of Engineering Management and Systems Engineering, The George Washington University, USA

Department of Computer Science, Johns Hopkins University, USA

Department of Engineering Management & Systems Engineering, The George Washington University, USA

Department of Family Science, & Center for Health Equity, University of Maryland, USA

Research Questions

How did existing online communities of vaccine opponents and proponents respond to early news of the novel coronavirus?
What were the dominant topics of conversation related to COVID-19? What types of misleading or false information were being shared?
Which types of accounts shared misleading or false content? How do these topics vary between and within vaccine opponent and vaccine proponent Twitter communities?
What do these findings mean for efforts to limit online health misinformation?

Essay Summary

We identified the 2,000 most active Twitter accounts in the vaccine discourse from 2019, identifying both vaccine opponents and proponents. Only 17% of this sample appeared to be bots. In addition to tweeting about vaccines, vaccine opponents also tweeted about conservative politics and conspiracy theories. Vaccine proponents tended to represent doctors, researchers, or health organizations, but also included non-medical accounts.
On February 20, we collected the most recent tweets for each account and automatically extracted 35 distinct topics of conversation related to COVID-19 (roughly 80,000 tweets). Topics were categorized as: more reliable (public health updates & news), less reliable (discussion), and unreliable (misinformation). Misinformation included conspiracy theories, unverifiable rumors, and scams promoting untested prevention/cures.
Vaccine opponents shared the greatest proportion (35.4%) of unreliable information topics including a mix of conspiracy theories, rumors, and scams. Vaccine proponents shared a much lower proportion of unreliable information topics (11.3%).
Across both vaccine proponents and vaccine opponents, the largest single topic of conversation was “Disease & Vaccine Narratives,” a discussion-based topic where users made comparisons between COVID-19 and other diseases—most notably influenza. These messages likely added to public confusion around the seriousness and nature of COVID-19 that endures months later.
In the context of an ‘infodemic,’ efforts to address and correct misinformation are complicated by the high levels of scientific uncertainty. Focusing on only the most conspicuous forms of misinformation—blatant conspiracy theories, bot-driven narratives, and known communities linked by conspiracist ideologies—is one approach to addressing misinformation, but given the complexity of the current moment, this strategy may fail to address the more subtle types of falsehoods that may be shared more broadly.

Implications

On February 2, 2020, the World Health Organization declared that the spread of the novel coronavirus, nCov-2019, was accompanied by an ‘ infodemic ’ described as “an overabundance of information—some accurate and some not” that was inhibiting the spread of trustworthy and reliable information (World Health Organization, 2020). Even under the best circumstances, effective health communication during the early stages of a pandemic is challenging, as it can be difficult to communicate high levels of scientific uncertainty to the public and information is constantly changing (Vaughan & Tinker, 2009). With the ‘infodemic,’ health information is competing against a veritable “tsunami” of competing claims, many of which are amplified across social media with greater speed and reach (Zarocostas, 2020). While much of the ensuing media attention has focused narrowly on misinformation, an ‘infodemic’ is characterized by volume, not by the quality of information. Online health misinformation, defined as “a health-related claim of fact that is currently false due to a lack of scientific evidence” (Chou et al., 2018), was a growing issue prior to the pandemic, but the scope of the problem has increased dramatically with the spread of COVID-19.

Misinformation is commonly attributed to sources with low journalistic integrity and, therefore, low credibility (e.g., Grinberg et al., 2019). These sources typically promote conspiracy theories and other fringe opinions. In this study, we argue that even high-credibility sources emphasizing factual and accurate reporting can be vectors of misinformation when faced with a highly uncertain context (see also, Reyna, 2020). It is important to recognize the full spectrum of misinformation—from false facts, to misleading use of data, unverifiable rumors, and fully developed conspiracy theories—as all types can impede effective communication.

In this analysis, we focused on communication within existing vaccine-focused communities on Twitter. Recent studies have demonstrated that messages pertaining to vaccination are one of the most active vectors for the spread of health misinformation and disinformation (Broniatowski et al., 2018; Larson, 2018; Walter et al., 2020). Although a small fraction of the general public, vaccine opponents have an outsized presence online and especially on Twitter (Pew Research Center, 2017). Anti-vaccine arguments have also been amplified by known malicious actors including bots and state sponsored trolls (Broniatowski et al., 2018). Despite Twitter’s recent efforts to limit the spread of misleading and false health claims, many vaccine opponent accounts remain active on the platform. As news of a novel coronavirus outbreak in Wuhan, China started to spread in the American media, accounts that frequently tweet about vaccines—both those in favor and those opposed—were among the first to start regularly tweeting about the novel virus and the public health community’s reaction.

It is also important to recognize the diversity of viewpoints within the broader Twitter vaccine community. Although some accounts tweet almost exclusively about vaccines, many others also discuss other types of content, making it possible to identify subgroups based on shared interests such as politics, public health, or current events. To better understand the spread of online health misinformation, and potentially mitigate its impact, we believe it is necessary to first understand the communication norms and topics of conversation that shape these varied subgroups. Qualitative research has demonstrated that vaccine-hesitant attitudes likely serve as a signal of social identity and can form a type of social capital in community development (Attwell & Smith, 2017). We believe a similar function may help explain the emergence of online subgroups with shared interests and communication norms, for both vaccine opponents and proponents. While a great deal of research has focused on “anti-vaxxers” online (i.e., vaccine opponents), less has studied their pro-vaccine counterparts, many of whom use Twitter as a platform not to promote vaccines directly, but to debunk and refute claims made by vaccine opponents.

To date, the majority of news coverage on COVID-19 misinformation has tended to focus on unmistakably false claims including conspiracy theories, unchecked rumors, false prevention methods, and dubious “cures” (see also Liu et al., 2020). Many of these claims have been promulgated by vaccine opponents. This should not be a surprise; many of these accounts are adept at quickly adapting new information to fit it into existing narratives that align with worldviews. Most recently this took the form of criticizing US public health measures to limit the disease spread—from launching the #FireFauci hashtag calling for Dr. Anthony Fauci’s dismissal from the President’s COVID-19 Response Team (Nguyen, 2020), to the dissemination of once-fringe conspiracy theories targeting Bill Gates and 5G wireless to more mainstream audiences (Wakabayashi et al., 2020), to appearing as a prominent presence in the multiple “resistance” protests held in state capitals to protest social distancing measures (Wilson, 2020). Most egregious was the Plandemic propaganda undermining and discrediting research on a future COVID-19 vaccine (Zadrozny & Collins, 2020). These instances also suggest an increasing overlap between the alt-right and vaccine opposition in the United States (Frenkel et al., 2020; Zhou et al., 2019). Although this type of highly visible misinformation can be dangerous, it is often easily debunked and may primarily appeal to fringe audiences. We wanted to know what vaccine opponents were sharing, beyond conspiracy theories, and whether topics varied by subgroup. We found that while roughly ⅓ of topics were likely misinformation, another ⅓ were discussion topics, and slightly under ⅓ relied on more reliable information sources, including public health announcements.

We found that vaccine advocates, and especially those who did not have medical, scientific, or public health expertise (i.e., they were not doctors, researchers, or affiliated with health organizations), but were primarily engaging in debates with vaccine opponents, have promoted mixed messages, some of which overlapped with vaccine opponents’ messages downplaying the severity of the pandemic. This is where the nuances of types of misinformation are important—tweeting a fact misrepresenting disease severity is not the same as tweeting a conspiracy theory that suggests vaccines are a government plot to track citizens—they are both part of what makes an ‘infodemic’ so challenging. COVID-19 has proven to be both highly contagious and deadly, a combination that makes the current pandemic particularly challenging to contain (CDC COVID-19 Response Team et al., 2020). By focusing on the greater risks of influenza and urging Americans to get an influenza vaccine, early messaging on COVID-19’s severity was not clearly communicated. In late May, a Gallup Poll found that while roughly two-thirds of Americans believed that coronavirus was deadlier than seasonal flu, there were still a significant number who persisted in believing the virus was less deadly including a majority of Republicans (Ritter, 2020). Singh et al. found “Flu Comparisons” to be among the most prevalent myths on Twitter early in the COVID-19 pandemic (2020). Recent work suggests that exposure to misinformation on social media—including messages downplaying disease severity—was associated with a lower likelihood of individuals engaging in social distancing practices (Bridgman et al., 2020).

What does this mean for efforts to address the COVID-19 ‘infodemic’ on Twitter? First, we need to know which accounts are spreading misinformation. To the best of our knowledge, most these highly active vaccine accounts are genuine users — not bots. While recent reports suggest that up to 50% of accounts tweeting about COVID-19 could be likely bots, our findings highlight the role of real people disseminating information within existing online communities (Roberts, 2020). Limiting fully automated accounts and misuse of amplification tools is still useful to weed out the most egregious violations but is unlikely to be helpful in this specific context. Fact checking content may be more effective, but may still not capture more nuanced forms of misinformation, especially in discussion-based topics. Online communities are not homogenous; our findings suggest online rumors tend to circulate within smaller subgroups. While many topics overlap, specific arguments are likely to vary. This suggests that a one-size-fits-all approach to combating misinformation is unlikely to work on both vaccine-oriented vaccine opponents and vaccine opponents that are motivated by conservative politics. Similarly, while health organizations and health professionals may be sharing reliable information on Twitter, some of their well-meaning allies, especially the self-proclaimed vaccine activists, may inadvertently be sending out conflicting health information. Staying aware of this subgroup of proponents and developing easily shared evidence-based online resources may be one way to improve the quality of information being shared.Second, we need to recognize that while the most obvious sources of misinformation (for instance, known anti-vaccine and conspiracy theory accounts) pre-COVID-19 will continue to spread misinformation during the pandemic, even trusted and reliable sources can still contribute to the ‘infodemic’ by spreading falsehoods and misleading facts. Further, given that these sources may be more trusted by the public, these claims are less easily debunked or dismissed. An ‘infodemic’ consists of information from multiple sources: the scientific community, policy and practice, the media, and social media (Eysenbach, 2020). As information is translated between sources, distortions can occur particularly as complex science is reinterpreted by lay audiences. As the most accessible and least filtered source, social media is likely to be most vulnerable to misinformation. By focusing only on the most blatant forms of misinformation and the actors sharing them, both the media and scholars may inadvertently mislead the public into believing that they haven’t been exposed to misinformation about COVID-19, or that this egregious forms of misinformation are the most common. By drawing attention to fringe theories, well-meaning users may also further amplify misinformation (Ahmed et al., 2020). This is why we urge scholars of misinformation to go beyond the most visible forms of misinformation to highlight the complexities and subtleties that make an ‘infodemic’ so challenging.

Findings

Finding 1: A plurality of top accounts oppose vaccination, and likely represent human users.

Of the 2,000 accounts in our sample, 45% ( n =905) opposed vaccination, 24% ( n= 479) were in favor of vaccination, 15% ( n= 311) were no longer publicly available on Twitter, and 15% ( n =305) did not indicate a clear position on vaccines. Among accounts that were retweeted at least once, vaccine opponents were retweeted significantly more frequently than vaccine proponents, t(1210)=6.86, p<0.001 , after applying a logarithmic transform to correct for skewed data .

Using Botometer (Davis et al., 2016), the majority of accounts had low scores (< 0.2), indicating a low likelihood of automation. After applying a logistic transform to control for floor and ceiling effects, we did not detect any significant difference in bot scores between vaccine opponent and proponent accounts, t(1368) = 0.79, p=0.43 . Pew Research Center uses a threshold of 0.43 to determine likely bots (Wojcik et al., 2018). Applying this criterion, of the 1,754 accounts with complete data, 17% ( n= 298) were likely to be bots. Additionally, 12% of all accounts ( n =246) did not have available bot scores, suggesting the accounts had switched privacy settings (n=198, 10%) or had been closed (n=48, 2%) either voluntarily or because they had been removed and purged from Twitter for violation of terms of service (Twitter Help Center, n.d.).

Finding 2: A significant proportion of both opponents and proponents’ tweet primarily about vaccines. Vaccine opponent subgroups emerged around conservative politics and conspiracy theories. Vaccine proponent subgroups emerged around doctors and researchers and health organizations.

Roughly a quarter of all accounts ( n =498; 25%) posted primarily vaccine content. These dedicated accounts disproportionately opposed vaccination (oppose n= 398, 80%, favor n =100, 20%), ( X 2 = 72.56, p<0.001). For vaccine opponents, vaccine-focused accounts are those that post almost entirely vaccine related content with few posts about other topics. This is one of the more cohesive communities we observed, with accounts retweeting information disseminated from a handful of anti-vaccine activists. For vaccine proponents, vaccine-focused accounts represent a combination of health advocates who tweet almost solely about vaccines but also accounts we dubbed “ anti anti-vax” who use Twitter to engage in debates with anti-vaccine accounts and to debunk or mock anti-vaccine arguments. Another 347 (17%) accounts were sharing “mixed content” not easily classified into a single category. These represented a significantly larger proportion of vaccine proponents (favor n = 116, 33%, oppose n =102, 29%, X 2 = 39.56 , p <0.001).

Thirteen percent shared political content, with 9% ( n =175) of accounts posting conservative content and 4% ( n =78) sharing liberal content. We defined “conservative” as tweeting in support of major Republican political figures (most commonly President Trump) or against major Democratic political figures or identities (e.g. disparaging “libs”). We defined “liberal” as the reverse. Accounts sharing conservative political content were more likely to also oppose vaccines (oppose n= 160, 91%, favor n =0, 0%). Although a similar number of accounts supporting vaccines ( n= 33) and opposing vaccines ( n =26) also shared liberal politics content, this represented a significantly larger proportion of the vaccine proponents than vaccine opponents (7% vs 4%, X 2 = 12.38, p =0.0004).

Nine percent (n=184) shared conspiracist content, split into general conspiracy theories ( n =104, 5%) and conservative conspiracy theories (e.g. QAnon, New World Order, etc.) ( n =82, 4%). By conspiracist, we are describing attempts to “explain events as secret acts of powerful, malevolent forces” (Jolley & Douglas, 2014) . In this dataset this included allusions to shadowy groups like the Illuminati, complex theories that assume technology (e.g. 5G Wireless, “chemtrails”, etc.) is part of a plot to harm citizens, and more extreme theories proposing cover-ups, wrong-doing, and corruption within public health agencies, pharmaceutical companies, and charitable organizations. We made a distinction between conspiracy theories that were largely apolitical and did not explicitly name political figures, and those that were political, and frequently overlapped with conservative politics. Accounts tended to use distinctive hashtags representing both (e.g. using #QAnon or #WWG1WGA alongside #MAGA and #Trump2020). Recognizing that this distinction may be arbitrary, we also ran analysis using the combined conspiracy theory group. Almost all (96%, n =176) conspiracist content was shared by accounts also opposing vaccines.

Doctor/researcher accounts represented 6% ( n = 114) and health organizations represented 5% ( n = 102) of all accounts, with the majority (95%) of accounts in favor of vaccines. Both types of accounts tended to tweet about health issues more generally, which sometimes included vaccines.

Retweet counts for vaccine opponents (X 2 (7) =62.02 p<0.001) and proponents (X 2 (5)=64.12, p<0.001) both differed significantly by category using a non-parametric Kruskal-Wallis rank sum test. Among vaccine opponents, accounts that focused primarily on vaccination, and those focusing on conservative politics, had the most retweets — both with a median of more than 100. In contrast, no accounts promoting vaccination achieved this degree of engagement. The most popular account category—doctors/researchers— achieved a median of 77 retweets and accounts focused primarily on vaccines were retweeted a median of 59.5 times.

After applying a logistic transform to control for floor and ceiling effects, bot scores also varied significantly by category among both vaccine proponents, F(5, 469) = 15.25, p < 0.001 , and vaccine opponents, F(7, 887) = 7.14, p < 0.001 , with news aggregators the most bot-like accounts for both opponents and proponents.

Finding 3: The quality of COVID-19 information varied. The most reliable information included public health announcements and retweets of news, less reliable information came from discussion-oriented topics. Unreliable information included 5 distinct topics on conspiracy theories, 3 on rumors and insinuations, and 1 featuring scam “cures.”

We extracted 35 topics from COVID-19 related tweets generated by these accounts (more details provided in supplemental files). Only 5 of these 35 topics featured vaccines. We grouped these four categories: public health updates, news, discussion, and misinformation. These categories can also be thought of as more reliable information (public health updates and news topics), less reliable information and opinions (discussion topics), and unreliable information (misinformation topics).

Among the most reliable information topics, public health topics tended to feature direct retweets from health organizations (See Table 3, example: “Prevention Techniques”). News topics also tended to feature direct retweets of news headlines, often without additional commentary (example: “Deaths”). Most news topics focused on disease updates and the public health response, including emerging facts about the disease, continually updated case and death statistics, and news of vaccines in development.

Discussion topics featured fewer direct retweets and more personal comments, making these topics less reliable than news and public health topics. Several of these topics are very straightforward, with a clear theme that is consistent across most tweets (example: “Advocating Science”). Other discussion topics featured users agreeing on a general problem but disagreeing on specifics (example: “Infodemic”). The most debated topics were those that combined two or more similar arguments built around shared content (example: “Disease and Vaccine Narratives”). These claims were used both for and against vaccination, but the majority were used to downplay the severity of COVID-19. Many ended with appeals to vaccinate, but others took a different tack and decried “fear campaigns” from the media and “big pharma.”

Among the unreliable topics, we identified 5 topics on conspiracy theories, 3 based on rumors and insinuation, and 1 promoting scam cures. These conspiracy theory topics featured secret plots that connected powerful individuals and institutions to intentional harm. Some of these theories have already been widely covered in the mainstream media (example: “Chinese Bioweapon”) others were less widely circulated (example: “Engineered DNA”). The category of rumors/insinuation included topics that hinted at or suggested misdeeds, but often failed to commit fully to those claims. These topics were less disease focused and seemed to have more political agendas (example: “Chinese Coverup”). The single scam topic featured attempts to market natural cures.

Finding 4: Anti-vaccine accounts were more likely to share unreliable information, including conspiracy theories about disease origins and criticism of China’s disease response. Pro-vaccine accounts shared more public health information, but also more discussion topics. Both types of accounts shared comparisons of disease severity, often downplaying the risks of COVID-19.

Assessing the distribution of topics among vaccine opponents we found approximately ⅓ of topics fell into each category: more reliable (30.1%), less reliable (34.5%), and unreliable (35.4%). In contrast, the distribution for vaccine proponents was roughly evenly split between more reliable (45.3%) and less reliable (43.4%), with only 11.3% unreliable topics. There are two important takeaways here: first, that vaccine opponents use a variety of content including both reliable and unreliable sources to make their arguments, and second, although it is less common, vaccine proponents are not exempt from spreading misinformation.

To focus on specific subgroups, we assessed the top three subgroups for both vaccine opponents and proponents. For vaccine opponents, the top three subgroups — primarily vaccines, combined conspiracy theories, and conservative politics — all presented similar topic breakdowns as the overall anti-vaccine topic model. Slight differences emerged: accounts tweeting primarily about vaccines and conspiracy theories shared a greater proportion of conspiracy topics than those tweeting about conservative politics. Accounts tweeting primarily about vaccines were also more clearly vaccine focused, featuring news of vaccine development and the greatest proportion of natural cures topics (6.4%). The vaccine-focused subgroup shared roughly the same proportion of conspiracy theory topics as the combined conspiracist subgroup (19.7% vs 19.8%). In addition to conspiracy theory topics, the combined conspiracy theories subgroup also incorporated reliable news (in the form of death rates and economic impacts) and discussion topics (both “disease narratives” downplaying severity, but also “coming pandemic” amplifying concerns).

Among vaccine proponents, doctors/researchers and health organizations were more closely aligned, featuring a greater proportion of reliable news and public health information. Health organizations were the only subgroup sharing a greater proportion of reliable news and public health topics than opinion topics and shared the lowest proportion of misinformation topics (7.5%). The accounts tweeting primarily about vaccines (not affiliated with doctors, researchers, or health organizations) shared a higher proportion of less reliable discussion topics (54.1%) including the largest share of the category “Disease & Vaccine Narratives”. We surmise that this subgroup was most active in spreading the “flu is worse so get a flu vaccine” type narratives within this topic.

Methods

We utilized an archive of vaccine-related tweets (Dredze et al. 2014) to compile a list of the 2,000 most active vaccine-related accounts on Twitter. Accounts were included based on the total number of messages containing the keyword “vaccine” from January 1 to December 31, 2019. The most active account in our dataset shared 16,924 tweets meeting our inclusion criterion and the least active accounts shared 120 tweets. The distribution was skewed, with a max of 16,924 mentions, median of 218 mentions (IQR: 152.75-376.25), and minimum of 120 mentions.

We examined the screen name, Twitter handle, and if available, user-provided description for each account. Over a 12-day period in March 2020, two independent annotators (AJ, AS) manually assigned each account into one of 3 categories: pro-vaccine, anti-vaccine, or other. Annotators accessed each users’ Twitter page and public profile to assess recent content. Any accounts that were no longer available on Twitter, or had switched privacy settings to be private, were excluded from annotation.

After assessing vaccine sentiment, annotators noted what other types of content were shared by each account. While many accounts posted almost exclusively about vaccines, we also identified groups of accounts focusing on both liberal and conservative politics (e.g., expressing support for, or opposition to, specific political candidates or political parties), conservative conspiracy theories (e.g. QAnon, “Deep State,” etc.), general conspiracy theories (e.g. eugenics plots, opposition to 5G wireless, theories regarding mind control, chemtrails, etc.), natural/alternative health (e.g. veganism, chiropractors, naturopaths), doctors/researchers (e.g. “tweetatricians,” science journalists, epidemiologists), news accounts (including both original content and news aggregators), and a broad category of other accounts (e.g. joke bots, pet vaccines, music stations, general content) that fell under “mixed content.” If an account posted content from across multiple content areas or no discernable pattern was observed, it was also included as “mixed content.” Intercoder reliability was assessed with a random sample of 45 accounts, with 93% agreement on vaccine sentiment (Fleiss Kappa of 0.91, 0.81-1.00) and 78% agreement on subgroup assignment (Fleiss Kappa 0.75, CI: 0.61-0.89). Discrepancies in subgroup assignments were most common between accounts as doctor/researcher and health organization, conservative politics and conservative conspiracy theories, and in assignment of “mixed.” To address inconsistencies, annotators agreed that if no clear pattern emerged, “mixed” was the best choice.

Simultaneously, using Latent Dirichlet Allocation (LDA) (Blei et al., 2003), as implemented using the MALLET software package, we fit a topic model to 80,153 tweets collected from the 2,000 accounts (McCallum, 2002). These tweets all contained keywords related to COVID-19 including: ‘coronavirus,’ ‘wuhan,’ ‘2019ncov,’ ‘sars,’ ‘mers,’ ‘2019-ncov,’ ‘ncov,’ ‘wuflu,’ ‘covid-19,’ ‘covid,’ ‘#covid19,’ ‘covid19,’ ‘sars2,’ ‘sarscov19,’ ‘covid–19,’ ‘caronavirus,’ ‘#trumpvirus,’ ‘#pencedemic,’ ‘#covid19us,’ ‘#covid19usa,’ ‘#trumpliesaboutcoronavirus,’ ‘#pencepandemic.’ Using Bayesian hyperparameter optimization (Wallach et al., 2009), we de-rived 35 topics and generated 50 representative tweets from each topic. LDA Topic modelling assumes that words that commonly co-occur are likely to belong to a topic and that documents—in this instance tweets—that share common words are likely to share a similar topic. Each tweet receives a score that indicates how likely it is that the given tweet is related to each topic, higher scores reflect a higher degree of relatedness to the topic.

After reading the most representative tweets from each topic, the same annotators (AJ, AS) independently assigned a descriptive label to each topic. Labels were assessed as having high (nearly synonymous), moderate (significant overlap), or low agreement (little overlap). The majority (77%) were found to have high agreement; for instance, for topic 1, both annotators provided labels that were roughly synonymous: “How to protect yourself from coronavirus: washing hands, covering sneezes, etc.” and “How to protect yourself, and others, from the coronavirus.” In instances of moderate or low agreement, a third team member (DAB) was introduced and the team discursively addressed concerns until a unified topic was agreed upon. For instance, topic 6 was ultimately labelled “disease and vaccine”. It included both pro-vaccine sentiment (e.g. “Flu vaccines won’t prevent coronavirus but are still necessary”) and anti-vaccine sentiment (e.g. “a COVID-19 vaccine is unnecessary”) as well as general comparisons of disease severity between COVID-19 and other diseases. Annotators agreed on the broader label to capture the overlap between the multiple perspectives reflected in the topic. The two topics with low agreement were specific responses in non-US locales (Philippines and Hong Kong) and annotators may not have recognized place names.

Topics were also labelled into information categories. These included; “public health” meaning content was largely retweets from official public health sources; “news” meaning content was largely retweets of neutral news headlines (broadly-defined); “discussion” topics included more original content from users as well as retweets of commentary; and “misinformation” which included topics sharing conspiracy theories, unverified prevention and/or treatment options, as well as topics presenting unverifiable rumors or insinuations.

Limitations

Topic models rely on probabilistic clustering algorithms to capture clusters of words that tend to co-occur in a given set of messages. Tweets that are the most representative of the underlying clusters are considered most relevant. By assessing the full-text versions of the most relevant tweets annotators were generally able to determine the prevailing sentiment of a topic (e.g. disseminating misinformation vs. debunking misinformation). However, especially for less relevant tweets, it is possible that tweets sharing similar keywords, but not similar sentiment were included but these tweets were less representative and therefore would have had minimal impact on analysis results. While this is important, given a dataset of this size it becomes less of a consideration.

We limited our analysis to English-language accounts and tweets but did not include any geographic bounds. A recent study over the same period found that while English language tweets accounted for only 34% of all tweets, they accounted for 58.7% of COVID-19 tweets (Singh et al., 2020).

Conspiracy Theories
/ Public Health
/ Twitter/X

Cite this Essay

Jamison, M. A., Broniatowski, D. A., Dredze, M., Sangraula, A., Smith, M. C., & Quinn, S. C. (2020). Not just conspiracy theories: Vaccine opponents and proponents add to the COVID-19 ‘infodemic’ on Twitter. Harvard Kennedy School (HKS) Misinformation Review . https://doi.org/10.37016/mr-2020-38

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This research was supported by the National Institute of General Medical Sciences, National Institutes of Health (NIH; award 5R01GM114771).

Competing Interests

The authors report no conflicting interests.

Our research project and protocols were reviewed and approved by the Institutional Review Board’s at the Johns Hopkins Homewood University and were determined to be exempt from human subjects review (approval no. 2011123).

This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided that the original author and source are properly credited.

Data Availability

All materials needed to replicate this study are available via the Harvard Dataverse: https://doi.org/10.7910/DVN/9ICICY

The Covid vaccine is safe, whatever anti-vaxxers say. Here's why we can trust it.

A pharmacist dilutes the Pfizer COVID-19 vaccine

I've researched autism for more than a decade. Specifically, I've investigated how some antibodies in expecting mothers could complicate fetal development and lead to the condition. Through all my research and that of my colleagues, one thing is clear: Vaccines are not the cause of autism. And yet, that connection is on the tip of many tongues.

None of the claims have proven to be true when it comes to autism, and there's no reason to think they are any more valid with the Covid-19 vaccines.

Unfortunately, the fabricated link between autism and vaccines has made all vaccines suspect in the eyes of some skeptics. Now that Covid-19 vaccines are finally rolling out, the disinformation is clouding the science and adding fuel to the vaccine hesitancy fire.

A recent Pew Research Center poll reports that 39 percent of people say they definitely or probably wouldn't get a coronavirus vaccination. This endangers more than the people who don't get shots; we need a large though as-yet-undetermined percentage of people to be vaccinated before we see a slowdown in the virus's spread and with it the indirect protection known as herd immunity. Meanwhile, vulnerable groups whose immune systems are too compromised to be vaccinated are unprotected.

"Vaccine scares" have existed ever since the first smallpox vaccine was developed. Religious beliefs and distrust in medicine dissuaded some from inoculations; others believed they violated their personal liberty. Legally mandating vaccines in the mid-19 century galvanized these objectors into anti-vaccine movements , members of which claimed the right to make their own decisions about their children's bodies and their own.

Opinion Toxic Christian ideology is infecting the Covid debate. And that's bad for everyone.

The autism variant of these historical conspiracy theories started in 1998 with a report in a prestigious medical journal suggesting that 12 children developed autism shortly after they received the measles, mumps and rubella, or MMR, vaccine. But the findings were plagued with problems : The research of the lead scientist was funded by a lawyer suing a vaccine manufacturer , while the researcher himself held a patent for a new MMR vaccine . He altered the children's medical histories to boot. Since then, scores of medical research findings have invalidated the report, and the researcher's license was revoked .

Yet anti-vaxxers continue to cling to this infamous mythology, resulting in U.S. outbreaks of life-threatening diseases, such as whooping cough and measles , thought to be well-controlled and even eradicated. Today, so-called vaccine truthers continue to claim that vaccines overwhelm the infant immune system, that natural immunization is better than vaccination and that vaccines themselves contain toxins or actually give you the disease.

None of the claims have proven to be true when it comes to autism, and there's no reason to think they are any more valid with the Covid-19 vaccines. With the stakes so high, it's important to understand just how and why vaccine doubters are wrong.

It's true that the Covid-19 vaccine went through an unprecedentedly rapid process — for which we should all be grateful, given the urgency. And while there's concern that the Covid-19 vaccines were rushed and that that means they haven't been properly vetted or that their safety is otherwise in question, it's simply not the case.

Opinion We want to hear what you THINK. Please submit a letter to the editor.

A chief reason for the speedy turnaround was a decision the federal government made to expedite delivery of the vaccine — which has nothing to do with the scientific validity of the drug itself. The government allowed the drugmakers to mass-produce the vaccine while still conducting clinical trials. This was a gamble: If the Food and Drug Administration deemed the vaccines not safe and effective, those doses would be no better than trash. But it's a bet that seems to have paid off.

Another concern stems from the talk that the medical technology involved is "novel." Other vaccines, like that for the flu, use forms of inactivated or weakened viruses. In contrast, the Covid-19 vaccines by Pfizer-BioNTech and Moderna deliver a small snippet of messenger RNA into the body. Messenger RNA, or mRNA, is a genetic coding material the body uses as instructions to make specific proteins. Once a protein is made, it is displayed on the surface of the cell. The body then recognizes the foreign protein and develops an immune response to fend off future infection.

It's the first time such a vaccine technique has been authorized, but that doesn't mean it's unknown . In fact, RNA-based platforms to deliver vaccines have been researched since the 1990s . Having this technology and know-how in place allowed for speedy development during a pandemic and should be applauded.

The coronavirus vaccines do have side effects — but that doesn't mean they're harmful. It actually means they're working. We know from Pfizer's clinical trials that short-term side effects occurred within 24 to 48 hours, especially after the second dose. Sixteen percent of people ages 18 to 55 and 11 percent of people over 55 reported fevers after the second dose . Even more people reported having fatigue, headaches and joint pain. (The Covid-19 vaccine hasn't yet been approved for children under 16.)

While such symptoms can be unpleasant, they are transient and not dangerous. They don't mean you're sick with Covid-19; they mean the vaccine has triggered your immune response to create the "bodyguards" that fight future Covid-19 infection.

A very small number of people have suffered from allergic reactions after vaccination that require medical attention, such as rashes, shortness of breath, racing heart, puffy eyes and lightheadedness. These people received standard medical treatment for allergies and were released from the hospital within a short time. Although that may sound scary, clinics are equipped to deal with such reactions in real time .

In contrast to the hyped-up concerns about what the Covid-19 vaccine might do, we have incontrovertible evidence about the harm that the virus itself really does.

What about long-term effects? At this point, there's no reason to worry about those, either. While we don't have a full two years of safety data to confirm the lack of unexpected long-term side effects, severe or extreme side effects have appeared within weeks rather than years of previous vaccines' being given. It has been over 14 weeks since the completion of the second dose in Pfizer clinical trials , while the nation has been vaccinating for more than two weeks and we have not seen those responses, though monitoring systems are in place to follow up after vaccination.

I've also heard of concerns that the vaccine may cause cancer in the long term, particularly from anti-vaxxers worried about what other ingredients in the vaccines can do. First, unlike non-mRNA-based vaccines , Covid-19 vaccines don't contain other components. Second, mRNA-based vaccines can't make changes to the human genome and therefore are extremely unlikely to induce new genetic mutations in the cells of the kind that lead to cancer.

In contrast to the hyped-up concerns about what the Covid-19 vaccine might do, we have incontrovertible evidence about the harm that the virus itself really does. So far in the U.S., 345,000 people have died, while countless others are still suffering from health complications. Millions of people have lost their jobs and businesses and homes. It is our responsibility as a society not to believe misinformation so that we may leave 2020 behind us.

Lior Brimberg, PhD, is an assistant professor at the Feinstein Institutes for Medical Research. Her research focuses on the role of the in utero environment in autism.

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The Morning Newsletter

Vaccine Persuasion

Many vaccine skeptics have changed their minds.

By David Leonhardt

When the Kaiser Family Foundation conducted a poll at the start of the year and asked American adults whether they planned to get vaccinated, 23 percent said no.

But a significant portion of that group — about one quarter of it — has since decided to receive a shot. The Kaiser pollsters recently followed up and asked these converts what led them to change their minds . The answers are important, because they offer insight into how the millions of still unvaccinated Americans might be persuaded to get shots, too.

First, a little background: A few weeks ago, it seemed plausible that Covid-19 might be in permanent retreat, at least in communities with high vaccination rates. But the Delta variant has changed the situation. The number of cases is rising in all 50 states .

Although vaccinated people remain almost guaranteed to avoid serious symptoms, Delta has put the unvaccinated at greater risk of contracting the virus — and, by extension, of hospitalization and death. The Covid death rate in recent days has been significantly higher in states with low vaccination rates than in those with higher rates:

(For more detailed state-level charts, see this piece by my colleagues Lauren Leatherby and Amy Schoenfeld Walker. The same pattern is evident at the county level, as the health policy expert Charles Gaba has been explaining on Twitter.)

Nationwide, more than 99 percent of recent deaths have occurred among unvaccinated people, and more than 97 percent of recent hospitalizations have occurred among the unvaccinated, according to the C.D.C. “Look,” President Biden said on Friday, “the only pandemic we have is among the unvaccinated.”

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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:

Debate on the report “COVID-19 vaccines: ethical, legal and practical considerations”

Parliamentary assembly of the council of europe (pace).

Mr Rik Daems, President of the Parliamentary Assembly of the Council of Europe,

Ms Jennifer De Temmerman, Rapporteur of the Resolution,

Excellencies, honourable members of the Parliamentary Assembly of the Council of Europe,

Thank you for inviting me to join you today.

Let me start by commending the Parliamentary Assembly for your commitment to keeping the COVID-19 pandemic response at the top of national agendas.

I also commend the report under discussion today for its emphasis on international cooperation for the fair and equitable distribution of vaccines, which was echoed in the Council of Europe’s recent statement.

And I commend the resolution you will discuss today, which recognizes COVID-19 vaccines as a global public good.

It is in times of crisis such as these that our higher principles are most important.

This pandemic has tested us like never before, and now, even as we have developed vaccines in record time, it is testing us again.

Vaccine equity is not just a moral imperative. Ending this pandemic depends upon it.

This fundamental principle is one that many European governments and the European Commission recognized with their support of the Access to COVID-19 Tools Accelerator.

For the last nine months, this landmark partnership has been laying the groundwork for the equitable distribution and deployment of life-saving tools.

We have new rapid tests that provide results in less than 30 minutes, which are being rolled out soon.

We have identified dexamethasone to treat severe disease, which is being stockpiled for use in low and lower-middle income countries.

And the development and approval of safe and effective vaccines less than a year after the emergence of this new virus is a stunning scientific achievement. It gives us all a much-needed source of hope.

One vaccine now has WHO emergency use listing, and three are authorized for emergency use by stringent regulatory authorities.

WHO is working to expedite the regulatory review of several other vaccines for emergency use listing, in collaboration with national governments and regional bodies such as the European Medicines Agency.

COVAX has now secured two billion doses from five producers, with options on more than one billion more doses for 2021 and early 2022. We expect COVAX to make its first deliveries next month.

In short, COVAX is ready to deliver what it was created for.

I want to thank our partners Gavi and CEPI for their exceptional work to bring us to this point.

Together, we have overcome scientific barriers, legal barriers, logistical barriers and regulatory barriers.

But even as the first vaccines begin to be deployed, the promise of equitable access is at serious risk.

We now face the real danger that even as vaccines bring hope to those in wealthy countries, much of the world could be left behind.

Some countries and companies are making bilateral deals, going around COVAX, driving up prices and attempting to jump to the front of the queue.

COVID-19 vaccines are now being administered in 50 countries around the world, nearly all of which are wealthy nations. Seventy-five percent of doses have been deployed in only ten countries.

It is understandable that governments want to prioritize vaccinating their own health workers and older people first.

But it is not right that younger, healthier adults in rich countries are vaccinated before health workers and older people in poorer countries. I hope you will understand this.

The situation is compounded by the fact that most manufacturers have prioritized regulatory approval in rich countries, rather than submitting full dossiers to WHO for Emergency Use Listing.

We must work together to prioritize those most at risk of severe disease and death, in all countries.

The emergence of rapidly-spreading variants makes the speedy and equitable rollout of vaccines all the more important.

A me-first approach leaves the world’s poorest and most vulnerable people at risk.

It is also self-defeating. These actions will only prolong the pandemic, the restrictions needed to contain it, and the human and economic suffering.

A study published this week by the International Chamber of Commerce Research Foundation found that vaccine nationalism could cost the global economy up to 9.2 trillion US dollars, and almost half of that – 4.5 trillion dollars – would be incurred in the wealthiest economies.

Prompt and equitable dose sharing is critical if we are to overcome this pandemic.

While many European countries have made generous financial contributions to COVAX, funds to complete the purchase of the two billion dose target are still needed.

It is just as important that COVAX receives timely donations of extra doses of vaccine that so many countries have secured.

This is another critical means by which COVAX can equitably allocate vaccine doses to protect additional populations.

To put it bluntly: many countries have bought more vaccine than they need. It is critical that COVAX receives those extra doses soon, not the leftovers many months from now.

Lives depend on it.

We need urgent action from governments, vaccine producers and the global community to walk the talk on vaccine equity.

My request to all countries is to act in solidarity. Only by working together can we bring this pandemic to an end.

I have five critical actions countries must take:

First, to prioritize: We need to protect the COVAX Facility and ensure it can work as envisaged. WHO must be provided with vaccine data at the same time as other regulators, so that provision of vaccines to all countries can be sped up;

Second, to act in fairness: Excess doses should be shared, or countries should suspend their rights to access COVAX doses, once they have vaccinated their health workers and older people, to allow other countries to do the same;

Third, to be accountable: All partners must live up to the promises they have made and do everything possible to increase volumes of approved vaccines, including through increased production, technology transfer, and licensing;

Fourth, to be ready: All countries need to ensure that the regulatory and logistical mechanisms are in place to roll out and scale up tests, treatments and vaccines, and ensure that no dose is wasted;

And fifth, to be transparent: We call on all countries with bilateral contracts – and control of supply – to be transparent on these contracts with COVAX, including on volumes, pricing and delivery dates.

Parliaments have a critical role to play, in both advocacy and community engagement, in keeping government accountable, in countering misinformation, and in allocating adequate budgets for policy priorities.

Finally, I’d like to note that WHO’s Emergency Committee, convened under the International Health Regulations, has determined that requiring proof of vaccination for international travellers does not make sense at the current time.

Travellers are not considered a high-risk group, nor is there any evidence that vaccines reduce transmission.

Colleagues and honourable members, 2021 can be and should be a year of renewed hope, when we overcome the acute phase of the pandemic.

Together, we must ensure that vaccination of health workers and older people is underway in all countries within the first 100 days of this year.

We have 74 days left. Time is short, and the stakes could not be higher.

Every moment counts.

I wish you a fruitful discussion, and thank you so much for inviting me, it’s an honour to join you.

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Persuasive messaging to increase COVID-19 vaccine uptake intentions

Affiliations.

1 Yale Institute for Global Health, New Haven, CT, USA; Department of Internal Medicine, Section of Infectious Diseases, Yale School of Medicine, New Haven, CT, USA.
2 Institution for Social and Policy Studies, Yale University, New Haven, CT, USA; Center for the Study of American Politics, Yale University, New Haven, CT, USA.
3 Institution for Social and Policy Studies, Yale University, New Haven, CT, USA; Center for the Study of American Politics, Yale University, New Haven, CT, USA; Department of Political Science, Yale University, New Haven, CT, USA.
4 Yale Institute for Global Health, New Haven, CT, USA; Department of Internal Medicine, Section of Infectious Diseases, Yale School of Medicine, New Haven, CT, USA; Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA; Yale School of Nursing, West Haven, CT, USA.
5 Institution for Social and Policy Studies, Yale University, New Haven, CT, USA; Center for the Study of American Politics, Yale University, New Haven, CT, USA; Department of Political Science, Yale University, New Haven, CT, USA. Electronic address: [email protected].
PMID: 34774363
PMCID: PMC8531257
DOI: 10.1016/j.vaccine.2021.10.039

Widespread vaccination remains the best option for controlling the spread of COVID-19 and ending the pandemic. Despite the considerable disruption the virus has caused to people's lives, many people are still hesitant to receive a vaccine. Without high rates of uptake, however, the pandemic is likely to be prolonged. Here we use two survey experiments to study how persuasive messaging affects COVID-19 vaccine uptake intentions. In the first experiment, we test a large number of treatment messages. One subgroup of messages draws on the idea that mass vaccination is a collective action problem and highlighting the prosocial benefit of vaccination or the reputational costs that one might incur if one chooses not to vaccinate. Another subgroup of messages built on contemporary concerns about the pandemic, like issues of restricting personal freedom or economic security. We find that persuasive messaging that invokes prosocial vaccination and social image concerns is effective at increasing intended uptake and also the willingness to persuade others and judgments of non-vaccinators. We replicate this result on a nationally representative sample of Americans and observe that prosocial messaging is robust across subgroups, including those who are most hesitant about vaccines generally. The experiments demonstrate how persuasive messaging can induce individuals to be more likely to vaccinate and also create spillover effects to persuade others to do so as well. The first experiment in this study was registered at clinicaltrials.gov and can be found under the ID number NCT04460703 . This study was registered at Open Science Framework (OSF) at: https://osf.io/qu8nb/?view_only=82f06ecad77f4e54b02e8581a65047d7.

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Conflict of interest statement

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Experiment 1. Messages that frame…

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Effectiveness of COVID‐19 vaccines: findings from real world studies

David a henry.

1 Institute for Evidence-Based Healthcare, Bond University, Gold Coast QLD

2 Gold Coast University Hospital and Health Service, Gold Coast QLD

Mark A Jones

3 University of Queensland, Brisbane QLD

Paulina Stehlik

Paul p glasziou.

Community‐based studies in five countries show consistent strong benefits from early rollouts of COVID‐19 vaccines

By the beginning of June 2021, almost 11% of the world’s population had received at least one dose of a coronavirus disease 2019 (COVID‐19) vaccine. 1 This represents an extraordinary scientific and logistic achievement — in 18 months, researchers, manufacturers and governments collaborated to produce and distribute vaccines that appear effective and acceptably safe in preventing COVID‐19 and its complications. 2 , 3

The initial randomised trials confirmed immunological responses and generated unbiased evidence of vaccine efficacy. They were conducted in selected populations with limited numbers of participants in high risk groups, such as older people and those with serious underlying medical conditions. 2 , 3 They provided sparse information on the impact of vaccination on transmission of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), were too small to quantify rare but serious harms, and did not take account of the logistic obstacles encountered during the community‐wide rollout of new vaccines. While large cluster randomised trials could address some of these concerns, 4 large observational studies have used large linked routinely collected population datasets in five countries to address important knowledge gaps. 5 , 6 , 7 , 8 , 9

This article reviews findings from the initial real world studies and stresses that researchers in Australia currently do not have timely access to the linked Commonwealth and state datasets needed to perform such analyses.

Real world studies

In five countries (Israel, England, Scotland, Sweden and the United States) researchers have analysed routinely collected data to report the early outcomes of community‐wide vaccination programs with three of the first vaccines to reach market: the BNT162b2 mRNA (Pfizer–BioNTech), mRNA‐1273 (Moderna) and ChAdOx1 adenoviral vector (Oxford–AstraZeneca) vaccines. 5 , 6 , 7 , 8 , 9

At the time of writing, two of the articles (from the US and Sweden ) have not yet been peer reviewed, so details reported here may change after revisions to these reports. 8 , 9 There is a rapidly growing literature on the community impact of COVID‐19 and it has provided very consistent evidence of substantial vaccine effectiveness with the original (Wuhan) viral strain and the Alpha variant. An important focus of future work will be the effectiveness of existing vaccines against emerging viral variants.

The vaccination programs against COVID‐19 commenced in December 2020 in the study countries, so follow‐up is limited. Most of the investigators used rigorous designs and statistical methods to analyse linked routinely collected person‐level data from large community‐wide databases that tracked outcomes in vaccinated and unvaccinated individuals ( Box ). Importantly, allocation to vaccines was not by randomisation, and vaccinated and unvaccinated populations differed in respect of factors that were associated with both the probability of vaccination and with the severe outcomes of COVID‐19. Information that featured in most studies included demographic details, a vaccine register, results of laboratory polymerase chain reaction (PCR) testing, records of hospitalisation and death, and some geographic measures of social deprivation. In addition, the Israeli, US and Scottish studies included linkage to clinical records from which to quantify comorbidities. 5 , 6 , 8 The Israeli study included information on previous adherence to influenza vaccination schedules. 5

Characteristics of five real world community‐based studies of effectiveness of SARS‐CoV‐2 vaccines

	Dagan 2021	Bernal 2021	Vasileiou 2021	Bjork 2021	Pawlowski 2021
Country	Israel	England	Scotland	Sweden	United States
Vaccine	BNT162b2 (1 or 2 doses)	BNT162b2 (2 doses) or ChAdOx1 (1 dose)	BNT162b2 or ChAdOx1 (1 dose)	BNT16b2 (1 or 2 doses)	BNT162b2 or mRNA‐1273 (2 doses)
Study design	Target trial emulation using 1:1 individual matching of vaccinated and unvaccinated participants	Hybrid of test‐negative case–control followed by cohort analysis of PCR‐positive individuals	Controlled cohort study	Controlled cohort study	Controlled cohort study with 1:1 individual matching of vaccinated and unvaccinated participants
Source population	Aged ≥ 16 years: 1 503 216 vaccinated; 1 655 920 unvaccinated enrolled with single state‐mandated health care provider	Aged ≥ 70 years; > 7.5 million enrolled with NHS UK	Aged ≥ 15 years: 1 137 775 vaccinated; 3 271 836 unvaccinated enrolled with NHS UK	Aged 18‐64 years: 26 587 vaccinated; 779 154 unvaccinated enrolled with single regional health service	Aged ≥ 18 years: 249 708 enrolled with single non‐profit health care provider who had PCR test for SARS‐CoV‐2
Numbers analysed	596 618 vaccinated; 596 618 matched unvaccinated controls	44 590 cases (PCR‐positive) and 112 340 controls in case–control study; 1846 vaccinated and 8096 unvaccinated in follow‐up study	Same as source population	Same as source population	31 069 vaccinated; 31 069 unvaccinated
Analysis methods	Kaplan–Meier analysis	Logistic regression analysis	Time‐dependent Cox regression and Poisson regression adjusting for time at risk	Incidence rate ratios	Kaplan–Meier analysis
Study endpoints included in analyses ( )	Infections (10 561); hospitalisations (369); deaths (41)	Infections (32 832); hospitalisations (1859); deaths (1228)	Hospitalisations (7914)	Infections (4228); deaths (36)	Infections (924); hospitalisations (224)
Confounder adjustments	1:1 matching on day of vaccination on seven features: age, sex, place, ethnicity, past influenza vaccine, pregnancy, number of pre‐existing medical conditions	Adjusted for five features: age, sex, ethnicity, NHS region, deprivation	Adjusted for five features: age, sex, deprivation score, number of prior SARS‐CoV‐2 PCR tests, number of medical conditions	Adjusted for age and sex	Propensity‐matched based on sex, age, ethnicity, location and number of prior SARS‐CoV‐2 PCR tests
Check on bias due to healthy vaccinee effect	Yes, calibrated to check no effect in first 14 days	Yes, used immediate post vaccination period as reference	No, and significant benefit noted before day 14	No, did not evaluate endpoints before day 14	No, and significant benefit noted before day 14
Vaccine effectiveness: selected measures (95% CI)	Days 14–20: infection, 46% (40–51%); hospitalisation, 74% (56–86%); death, 72% (19–100%)	Days 28–34 (BNT162b2): infection, 61% (51–69%); hospitalisation 43% (33–52%); death, 51% (37–62%)	Days 28–34 (BNT162b2): hospitalisation 86% (76‐91%)	Day 14+: infection, 42% (14–63%); death not calculated	Day 14+: infection, 75% (67–81%); hospitalisation 60% (14–79%)
	Day 7+ after second dose: infection 92% (88–95%); hospitalisation, 87% (55–100%)	Days 28–34 (ChAdOx1): infection, 60% (41–73%); hospitalisation 37% (3–59%)	Days 28–34 (ChAdOx1): hospitalisation 94% (73–99%)	Day 7+ after second dose: infection, 86% (72–94%); death not calculated	Day 36+ (2 doses only); infection 89% (68–97%)
Viral variants of concern	Alpha variant was common during the study	Alpha variant was dominant during the study	Alpha variant was common during the study	Alpha variant was common during the study	No mention of variants

BNT162b2 =Pfizer–BioNTech mRNA vaccine; ChAdOx1 = Oxford–AstraZeneca adenoviral vector vaccine; mRNA‐1273 = Moderna mRNA vaccine; NHS = National Health Service; PCR = polymerase chain reaction.

Study designs and adjustments for confounding

The studies used different approaches to adjust for confounding ( Box ). The most advanced design was used to analyse the linked data from members of the Clalit Health Services integrated health care organisation in Israel, which covers around 4.7 million people. 5 The investigators extracted data on matched cohorts of vaccinees and non‐vaccinated controls and analysed study endpoints using rules that emulated the steps taken in a randomised trial. 10 These steps minimised selection or measurement biases and controlled for potential confounders through precise 1:1 matching of vaccinated and non‐vaccinated subjects across seven domains. The investigators took the additional step of calibrating their statistical model against the results of the pivotal phase 3 randomised trial, which found no benefit during the first 2 weeks after vaccination. 2 In contrast, this observational study found lower rates of infection in the first 2 weeks after vaccination, which remained after matching for age and sex — illustrating the potential for confounding. Only after full matching on seven factors was this source of bias eliminated. 5

In England, investigators linked data from a national vaccine register to laboratory PCR swab results, emergency department admissions, demographic and ethnicity data, care home status, and deaths in participants aged 70 years and over ( Box ). 7 The first part was a test‐negative case–control design, which compared vaccination status in those who received a positive PCR swab result with contemporaneous controls who returned a negative result. That both cases and controls had been tested for SARS‐CoV‐2 should have controlled for clinical and behavioural factors that influence the probability of having a test. The second part of the study followed participants aged 80 years and over with a positive PCR test result and analysed them according to vaccination status. The investigators calculated adjusted hazard ratios for death up to and beyond 14 days from the first vaccine dose.

A study in Scotland used an unmatched cohort design comparing hospital admission for COVID‐19 in people who received either the Pfizer–BioNTech or Oxford–AstraZeneca vaccines with an unvaccinated control group. 6 The Oxford–AstraZeneca vaccine was given later to an older population. The study adjusted for age and sex, frequency of prior PCR tests and clinical risk groups extracted from linked health records. The statistical model generated unexpectedly strong protective effects of the vaccines on hospitalisation rates in the first 2 weeks after vaccination, indicating possible bias due to a healthy vaccinee effect.

In the US, researchers working within the Mayo Clinic health system used postcode and propensity scores (based on age, sex, race, ethnicity and records of PCR testing) to match a cohort of individuals who received the Pfizer–BioNTech or Moderna mRNA vaccine with unvaccinated controls, to measure impact on infections and hospitalisations. 8

A simple unmatched cohort design using linkage of routinely collected administrative data measured infection rates in a cohort who received the Pfizer–BioNTech vaccine in a single county in Sweden. 9 The unvaccinated population acted as controls ( Box ). Confounding adjustments in this study were limited to age and sex.

The Box summarises the results of these studies. All included at least one mRNA vaccine and the reductions in infections and hospitalisations were consistent and large. Two studies reported on mortality and the reductions were substantial, although based on small numbers of deaths in Israel. 5 , 7 The studies did not directly compare vaccines, but the Oxford–AstraZeneca vaccine appeared to perform as well as the mRNA vaccines in reducing hospitalisations.

Other approaches to estimating vaccine effectiveness

In the UK, over 600 000 volunteers using a COVID‐19 symptom mobile phone app recorded adverse events after vaccination with either the Pfizer–BioNTech or Oxford–AstraZeneca vaccine. 11 Based on post‐vaccination self‐reports of infections and after adjustment for age, sex, obesity and comorbidities, they estimated effectiveness rates of 60–70% beyond 21 days after administration of either vaccine.

Three studies measured the effectiveness of COVID‐19 vaccines in care home, health care and other frontline workers in the UK, Israel and the US. 12 , 13 , 14 These projects enrolled smaller numbers of participants than the community‐based studies but used similar designs and adjustment techniques. Importantly, workers in these settings undergo routine PCR testing for SARS‐CoV‐2, which enabled detection of asymptomatic infections. These studies also found large protective effects and a potential to reduce viral transmission. The latter possibility has been investigated directly in a study conducted in Scotland that showed that 14 days or more after health care workers received a second dose of vaccine, their household members had a 54% lower rate of COVID‐19 than individuals who shared households with non‐vaccinated health care workers. 15

Conclusions

We can draw important conclusions from these non‐randomised studies of vaccine effectiveness. Most importantly, the currently available COVID‐19 vaccines appear to be very effective in preventing severe complications and deaths from COVID‐19 in adults of all ages. Recent real world studies confirm that substantial protection extends to the Delta variant of SARS‐CoV‐2, although this requires two vaccine doses. 16 , 17 Follow‐up periods in all studies are relatively short, and these reports do not provide information on rare but serious adverse events, such as cerebral venous thrombosis. The use of sophisticated trial emulation methods in the Israeli study 5 replicated some key features of the pivotal randomised trial of the Pfizer–BioNTech vaccine, 2 particularly by controlling for an early healthy cohort effect that confounded the incompletely adjusted endpoint analyses. This design should prove useful in enabling direct head‐to‐head comparisons of effectiveness and safety of vaccines, the duration of their protective effects, the degree to which vaccines prevent transmission of viral variants, and the impact of vaccines on so‐called long COVID.

These studies exemplify the value of advanced analyses of large multiply linked routinely collected community datasets. This resource is not yet readily available to researchers in Australia due to continued lack of agreement on the governance of linked state and Commonwealth datasets. 18 While Australia’s current low rates of community transmission of SARS‐CoV‐2 reduce the feasibility of observational studies of vaccine effectiveness, the available data can provide important information on potential harms of vaccines. With continuing questions about the comparative safety of vaccines, the emergence of viral variants, the long term effects of COVID‐19 and the likelihood of future epidemics, it is essential that Australia urgently removes barriers to allowing prequalified researchers to safely access the linked de‐identified population datasets that are needed to expeditiously conduct the types of studies reviewed here.

Competing interests

No relevant disclosures.

Not commissioned; externally peer reviewed.

The unedited version of this article was published as a preprint on mja.com.au on 20 May 2021.

Here Are Arguments That Can Help Overcome COVID-19 Vaccine Hesitancy

August 2024

Scientific arguments to use when talking to COVID-19 vaccine sceptics

Photo: Sam Moqadam / Unsplash

Recent reports on certain COVID-19 vaccines , coupled with the decision by some states and authorities to restrict their use within certain age groups, have prompted mistrust , notably decelerating the rhythm of vaccination.

According to the most recent CIS survey, some 6.5% of Spanish society will refuse the vaccine when their turn comes around, and 5% are still hesitant or have not yet decided what they will do. This percentage has also increased following recent reports.

After reports from the European Medicines Agency on rare cases of blood clots among recipients of one of the vaccines, the number of people failing to turn up for their vaccination appointments or directly refusing the vaccine, explicitly motivated by the issues raised and the potential associated risks, has multiplied. In the Community of Madrid, to give just one example, figures show that the number of people refusing to be vaccinated jumped from 3% in late March to over 60% , owing to recent reports and to decisions made in mid April regarding the administration of the vaccine in some age groups.

"Information mismanagement and decisions based on unscientific, anti-statistical criteria have dealt a heavy blow to vaccine confidence. Caution is a fundamental aspect and we need to remain alert and investigate each case, but we also have to be aware that all drugs have secondary effects and that these vaccines are no different from the thousands of other drugs that we have taken before and that we take every day", says Salvador Macip i Maresma, doctor and lecturer at the UOC Faculty of Health Sciences , Head of the Mechanisms of Cancer and Ageing Laboratory at Leicester University and science writer, who considers that the issue has been exaggerated.

Arguments of anti-vaxxers

The following are some of the arguments based on individual liberties , secondary effects or risk, used by people to turn down the vaccine or raise doubts about the campaign. "While getting vaccinated or not is an individual issue, it is also a community one, as not getting vaccinated affects society and those around us, so it is partly a civic matter ", says Macip.

In this regard, Manuel Armayones, psychologist, lecturer in Psychology and Education Science, and researcher with the UOC eHealth Center , points out that agreeing to get the vaccine is an altruistic move, benefiting not only oneself but also wider society . "By getting vaccinated, we are contributing to building a safer society, one that cares for the wellbeing of all citizens , including its most fragile and vulnerable members, such as old people, and people who, for whatsoever reason, cannot be vaccinated", says Armayones.

Taking the information in context, we see that among vulnerable groups, such as the elderly, who are more prone to health problems and complications, deaths in nursing homes fell from 700 a week in January and February, to just two a week in March, according to IMSERSO records , a drop that can be attributed to mass vaccination in nursing homes over the first few months of the year.

Another misgiving of anti-vaxxers is the speed with which the vaccines have been developed, overlooking the massive financial and professional investment made over the past year.

"Europe is using very innovative vaccines , but that does not mean that they are new or that they were invented last year. RNA vaccines were first put forward 20 or 30 years ago and a lot of research has gone into developing them since then. What's more, clinical trials were already underway before the pandemic hit , but the results have been expedited because of the pandemic and the huge investment that has been put into them," says Macip.

Increased motivation

Another of the fundamental aspects towards gaining social acceptance and massive COVID-19 vaccination is human behaviour. In other words, we need to create an environment that favours our goal. For this to be possible, three essential factors need to concur, i.e. people must be able to receive the vaccine , they must be motivated to do so and they must receive a "signal" to do so .

"We have the capacity to be vaccinated, because everybody will be offered vaccination free of charge and because, as a group, we are fortunate to have skilled professionals and a healthcare system that provides the time and place in which to do it. On the other hand, the third component required for vaccination to take place, i.e. the signal, is transmitted to each person individually, by means of a call or message. If this is not possible for whatsoever reason, each Autonomous Community is equipped with the means to let everybody to know when they are due for vaccination", says Armayones.

Armayones also agrees with other investigators around the world that the main challenge to mass vaccination is motivating people , particularly right now, when we are being bombarded with disinformation in the media and particularly social media. He adds that, that in order for the message to be effective, it must be easily understood and adapted to each social group , and that people's doubts must be listened to and clear answers offered in response. Another strategy for increasing motivation is to recruit influential persons as role models, to persuade people who may still be hesitant by their example.

In this way, information must be used in a balanced manner, using verifiable facts to help people understand the enormous benefits of COVID-19 vaccines compared to the risks of failure to take action. "Unfortunately, there will always be a percentage of sceptics who won't listen to reason, but if we provide scientifically validated data in a clear, graphic manner, and establish reliable channels of communication , via social media and platforms, this percentage will be minimal and will not be an issue. However, if the percentage increases, the social risk is far more serious," says Macip.

Keys to overcoming doubts

In this regard, the best tools for winning over vaccine sceptics and other people with qualms about possible risks are based on transparency of information and a positive focus on the current vaccination situation and the benefits of herd immunity to society.

"We need to highlight the positive aspects of vaccination compared to the negative effects of not being vaccinated, basically the high death rate and the null possibilities of economic and social recovery , when vaccination is part of the solution. The fact is that vaccination offers benefits not only to the recipient as an individual, but also to society as a whole", says Armayones, who suggests looking at the flip side as a bleak alternative: what would happen if scientists had not developed vaccines against the pandemic? How much longer would society have been able to hold out without a much harder global crash than the one we are already living through, particularly in certain sectors?

Likewise, we have to emphasize the importance of accurate, verifiable information, easily understood by the general public, to explain the data in context, instead of extraordinary occurrences, such as the rare incidence of blood clots when certain therapies are used, but also using real figures, such as the percentage of people do get blood clots, compared to the adverse effects of much more commonly-used drugs or the number of people that the vaccine is saving from dying of SARS-CoV-2.

"In the field of public health, we need to explain the benefits that society as a whole will be seeing thanks to increased vaccination and herd immunity, such as the dramatic reduction in deaths in nursing homes since the vaccine campaign was rolled out, or the fact that a higher level of immunization among the population will allow us to set and meet goals in regard to gradually recovering activities we miss so badly, in relation to work, culture and leisure . By seeding hope among the population and highlighting the huge collective effort involved in the vaccination campaign, most people will accept it as working towards a common goal, " he adds.

Thus, the dual combination of transparency and mass vaccination will prove that the vaccines are working and get most people to see how important they are. " Once we have the information, the example of the real effect of the vaccine in society will be more important , as people see the situation improving. Thus, little by little, we will gain the trust of more people and the number of sceptics will drop ", says Macip.

Ten points to use when talking to anti-vaxxers

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