Skip to main content

How (not) to mobilize health workers in the fight against vaccine hesitancy: Experimental evidence from Germany’s AstraZeneca controversy

Abstract

Background

COVID-19 vaccine hesistancy is a serious policy issue in Germany as vaccinations have stagnated at low levels compared to most other European countries. In this context, we study whether and how health workers can be leveraged to promote the COVID-19 vaccination campaign.

Methods

We employed an information experiment with health workers in Germany to quantify how access to information related to (i) AstraZeneca’s vaccine safety, (ii) misinformation, (iii) individual health risks, and (iv) public health risks can sway health workers’ recommendations for any of the following vaccines: AstraZeneca, Johnson & Johnson, Moderna, Pfizer/BioNTech, Sinopharm, and Sputnik-V. The information experiment was conducted as a randomized controlled trial with four treatment arms and was embedded in an online survey.

Results

Health workers reduce their willingness to recommend four out of six vaccines once they learn about different statements of European and German health authorities with respect to the safety of the AstraZeneca vaccine. Consistent with the discussion on AstraZeneca’s safety focusing on possible side effects among younger women, we find that especially female health workers become less likely to recommend the majority of COVID-19 vaccines. Lastly, we show that health workers vaccine recommendations are not affected by misinformation and appeals to individual or public health.

Conclusion

In order to mobilize health workers in the fight against vaccine hesitancy, information campaigns need to be tailor-made for the target audience. In particular, health workers react to different types of information than the general public. As with the general public, we provide suggestive evidence that health workers require unambigious messages from drug authorities in order to support vaccination efforts. We believe that a more coordinated and coherent approach of public authorities can reduce the amount of mixed signals that health workers receive and therefore contribute to health workers engagement in the outroll of mass COVID-19 vaccination campaigns.

Trial registration

The trial was registered retrospectively with the repository of the Open Science Framework (OSF) under the number osf.io/qa4n2.

Peer Review reports

Introduction

The success of COVID-19 vaccination campaigns depends on the fast and widespread uptake of the vaccines among the general public. With different types of vaccines becoming increasingly available in developed countries, the policy focus is shifting toward demand-side constraints.

A particular concern relates to widespread COVID-19 vaccine hesitancy [14] as it is estimated that herd immunity can only be reached if between 55 percent to 85 percent of the population is vaccinated against COVID-19 [5, 6]. To address vaccine hesitancy and increase vaccination rates, governments frequently rely on information campaigns that deploy various individuals (health experts, celebrities, religious leaders), channels (media, health centres, religious institutions), and topics (the risks of the virus and the safety of the vaccines). Whether these campaigns are effective will depend on issues of access and trust. For instance, some people might not be reached through conventional media outreach campaigns, others might distrust the government and therefore disregard vaccinations [712].

In this context, health workers such as nurses, paramedics, and doctors are important actors. They have direct access to patients, relevant experience, and are often considered highly trustworthy [13]. In this way they are in a position to provide informal advice that can influence their patients, friends, family and the wider public to take-up COVID-19 vaccines [14].

Yet little is known about the type of vaccine recommendations that health workers give. Informal advice might differ from public recommendations, for a number of reasons. First, health workers are a highly selective population that (i) has an intrinsic interest in health topics, (ii) received more extensive training on the benefits and risks of vaccines, and (iii) is at high risk of catching COVID due to their work on the frontline [15]. Second, health workers - like anyone else - are exposed to multiple sources of information when forming their own opinions. As such, they can be affected by misinformation, selective information processing, and the public debate in general.

This paper aims to assess to what extent health workers are responsive to different types of health-related information. More specifically, our study asks several policy-relevant questions: Which COVID-19 vaccines do health workers prefer? How are vaccine recommendations of health workers influenced by information provision? Does information provision alter beliefs and intended behaviour beyond vaccine recommendations?

To provide associative empirical evidence on our research questions, we employ an information treatment experiment that alters the information set. The setting for our experiment is Germany. Between April and May 2021, we recruited via Facebook and Instagram 3,318 health workers for an online survey. As part of the survey health workers were randomized into one of five experimental groups and subsequently asked to state their willingness to recommend any of the following six vaccines: AstraZeneca, Johnson & Johnson, Moderna, Pfizer/BioNTech, Sinopharm, and Sputnik-V.

This paper proceeds in six sections. Country context section provides background information on the German context. Research design section describes our data. Main results section outlines the empirical strategy, presents the main results, and explores robustness checks. Discussion section discusses extensions to the main findings. Conclusion section offers concluding thoughts.

Country context

The online survey was conducted during Germany’s “third wave” of COVID-19 infections that had about 22,000 new COVID-19 cases per day.Footnote 1 At the time of the survey four vaccines were officially approved by European and German authorities for adults above 18 years: two vector (AstraZeneca, Johnson & Johnson) and two mRNA (Moderna, Pfizer/BioNTech) vaccines.Footnote 2 By mid-April 2021 about 6 percent of the general population in Germany were fully vaccinated against COVID-19 and another 9.8 percent of the population had already received the first injection [16].Footnote 3 In mid-April 2021 the majority of these people were vaccinated with Pfizer/BioNTech (about 67 percent) followed by AstraZeneca (about 22.5 percent), Moderna (about 9 percent), and Johnson & Johnson (about 0.9 percent).

COVID-19 related vaccine hesitancy is a relevant policy issue in Germany and is vaccine-specific. In general preferences for mRNA vaccines are stronger than those for vector vaccines. According to a survey conducted in March/April 2021 by Germany’s Robert Koch Institute about 23.2 percent (44.3 percent) of respondents declared that they were undecided or against being vaccinated with a mRNA (vector) vaccine [16].

The gap in acceptance rates between mRNA and vector vaccines is largely driven by views on the vaccines that are most commonly available in Germany – AstraZeneca and Pfizer/BioNTech. While acceptance rates are positively correlated with official vaccine efficacy rates (82 percent for AstraZeneca vs. 95 percent for Pfizer/BioNTech)Footnote 4, a number of context-specific factors need to be considered. First, BioNTech is locally-based and the marketing of the vaccine in Germany emphasizes pride and trust into a ‘German vaccine’. Second, messages of national public health authorities were particular ambiguous with respect to AstraZeneca which increased distrust into the safety of the vaccine.Footnote 5

Research design

Data, sample, and measures

We collected data during April and May 2021. Respondents were recruited via advertisements on Facebook and Instagram which linked to an online survey hosted on Unipark. Respondents did not receive any monetary incentives to participate in the survey.

As described in Table 1 the survey started with a short explanation concerning its purpose, highlighted the target group (health workers), and asked for informed consent (step 1). The survey took about 25 minutes to complete. 4,921 respondents finished steps 1 to 4 (see Table 1).

Table 1 Description of intervention arms

In step 2 of the survey, we collected information on the respondent’s socio-demographic and professional background. Likewise, we gathered data on the respondents own health, (i) whether the respondent had already received a COVID-19 vaccine, and (ii) whether the respondent already had an appointment for a vaccination (if not yet vaccinated).

The information treatment (step 3) was conducted with respondents who self-identified as health worker and who already received at least 1 COVID-19 vaccination. In total, 3,318 respondents (about 67 percent of the survey sample) matched these criteria.

In step 4 of the survey information on our principal outcome variables were collected. Respondents were asked to what extent they would recommend to a friend/family member of their own age and gender any of the following vaccines: AstraZeneca, Johnson & Johnson, Moderna, Pfizer/BioNTech, Sinopharm, and Sputnik V. Respondents had to state their preference for each of these vaccines using a 7-point Likert scale that ranged from 1 (not likely to recommend) to 7 (very likely to recommend). The order in which the recommendation for a particular vaccine was elicited was randomized.

Furthermore, step 4 gathered information on respondents’ trust into different government institutions and about respondents’ predictions regarding the COVID-19 situation in Germany in October 2021 (about six months after the survey).

Intervention

Information treatments were conducted at step 3 of the online questionnaire. Once respondents reached step 3 a text was shown to them that consisted of about 2 paragraphs (about 4-5 sentences) with a specific theme. Respondents were randomized at the individual level into one of five groups for which the exact wording is depicted in Table 4 in the Appendix. The five groups can be summarized as follows:

Treatment 1: Scientific AstraZeneca (AZ) debate Subjects were exposed to the arguments and debate that led to well-established German drug regulators halting vaccinations with AstraZeneca, with the European Medicine Agency (EMA) later reiterating the advantages and benefits of the vaccine.

Treatment 2: Misinformation Subjects received information which highlighted arguments typically used by vaccine opponents.

Treatment 3: Own health Subjects received scientific information about the possible negative health consequences of a COVID-19 infection. It was highlighted that vaccinations could reduce the likelihood that the subject suffered severe diseases.

Treatment 4: Public health Subjects were informed about the rapid spread of COVID-19 in Germany. The aggressive transmission and the possible severe consequences for other people’s health - in particular the elderly - were highlighted.

Control condition: We implemented a ‘passive’ control group design (Haarland et al.: Designing information provision experiments, forthcoming). Subjects received no additional information before being asked about their vaccine recommendations.

To facilitate the effectiveness and reliability of the experiment several precautionary design decisions were taken. To increase the understanding of treatment messages, the text that appeared on the website was displayed in an easy-to-read format by adjusting spaces, highlighting particular words, and adjusted to be properly displayed on any possible device. Moreover, to minimize concerns about experimenter demand effects the wording and language used in each treatment was neutral and tried to not involve any suggestive expressions. Likewise, the treatments avoided complicated expressions that some respondents might not have understood. Lastly, the survey (step 1) informed respondents that the research project has no commercial interests, that data is stored anonymously, and that the research project strictly complies with European and German GDPR regulations.

Descriptive statistics

Table 2 depicts descriptive statistics regarding our sample of health workers with Table 5 in the Appendix containing a detailed description of key variables.

Table 2 Summary statistics

The analytical sample consists of 3,318 respondents. The average respondent is 37 years old and female (about 75 percent). The majority of respondents are nurses (about 61 percent) with a minority of respondents being medical doctors (about 4 percent), and paramedics (about 10 percent). Moreover, Table 2 shows the majority of respondents filled-out the survey via smartphone (about 81 percent) and were recruited via ads on Facebook (about 53 percent) and Instragram (about 47 percent). Furthermore, as shown Table 6 in the Appendix vaccinated health workers tend to be relatively younger, more female, and more risk averse compared to health workers that were not yet vaccined at the time of the survey.

Furthermore, most health workers had been vaccinated with Pfizer/BionTech (about 64 percent), followed by AstraZeneca (about 29 percent), and Johnson & Johnson (about 7 percent).

To assess the willingness of health workers to recommend any of the six vaccines in the absence of our information treatments, we provide in Fig. 1 descriptive statistics by gender for the control group. Among both men and women, the Pfizer/BioNTech vaccine was strongly recommended by almost all health workers, and the likelihood of recommending Moderna was also high. The willingness to recommend AstraZeneca and Johnson & Johnson was at a medium level. Finally, Sinopharm and Sputnik-V were least likely to be recommended. Men were slightly more likely to recommend five out of six vaccines. This effect was particularly visible for AstraZeneca, possibly due to the ongoing public debate in Germany that emphasises rare side effects of the vaccine among young women [19].

Fig. 1
figure 1

Willingness to recommend vaccines to others (control group only)

Furthermore, we provide results from balance tests in Table 7 in the Appendix. Out of the 126 comparisons (14 variables times 9 subgroup comparisons), only one is statistically significant at the five percent level (person has a completed college degree the comparison between T1 and the control group). Overall, we find that the randomization worked very well.Footnote 6

Main results

Empirical specification

We estimate treatment effects by OLS based on the following regression model:

$$ Y_{is}=\alpha_{s}+ \sum_{r=1}^{r=4} \gamma_{r} T_{r} + X^{'}_{is}\beta + \epsilon_{is} $$
(1)

where Yis refers to the outcome variable (willingness to recommend vaccine) for individual i in province s, αs indicate fixed effects for province s, and X refers to respondent-specific control variables.Footnote 7Trj are dummy variables indicating treatment arm r{1,2,3,4}. Robust standard errors are used.

Results

Our main results are depicted in Table 3. Regarding the four information treatments we find the following: First, providing health workers with information on the reasoning and decisions of German and European drug regulating bodies with respect to AstraZeneca (T1) reduces health workers’ willingness to recommend AstraZeneca by 0.68 units. Second, obtaining information as part of T1 does not only affect recommendations with respect to AstraZeneca but creates negative spillovers to all other drugs that were less common/not yet approved at the time of the survey. While the willingness to recommend Johnson & Johnson, Sinopharm and Sputnik-V decreases, we observe an increase (albeit statistically insignificant) in recommending the vaccine of Pfizer/BioNTech.Footnote 8 In addition, we observe a modest effect of exposing health workers to the arguments used by vaccine opponents and conspiracy theorists (T2). It appears that being exposed to these arguments rather reinforces health workers’ willingness to recommend vaccination. Moreover, our treatment arms that provide information on the relationship between COVID-19 and the respondent’s own or the public’s health (T3 & T4) have largely no effect on health workers’ willingness to recommend any of the six vaccines.

Table 3 Impact of treatments on willigness to recommend (OLS)

Robustness checks

In order to assess, whether our previous findings are sensitive to our preferred empirical specification we show in the Appendix results from estimations that (i) include additional covariates (Table 8), (ii) alternative clustering of standard errors (Table 9), (iii) adjusting standard errors for multiple hypothesis testing (Table 10) following the procedure of [20, 21], and (iv) and a sample that focuses on nurses and therapists and therefore excludes medical doctors and administrative workers (Tables 11). Overall, our previous results remain.

Discussion

The information treatment T1 seems to have contributed to reducing the willingness to recommend AstraZeneca and other less well-established COVID-19 vaccines. In the following, we consider possible mechanisms behind our finding and examine how in particular T1 affects health workers’ recommendations and beliefs.

Gender

There are various reasons for why men and women might react differently to our information treatments. For instance, women tend to be slightly less likely to recommend COVID-19 vaccines and more likely to have joined demonstrations of vaccine opponents/conspiracy theories [22, 23], while possible side-effects of COVID-19 vaccines were more frequently discussed with respect to thrombosis among younger women [24].

Our results with respect to gender are summarized in Fig. 2 and Tables 12 and 13 in the online Appendix. For both, men and women, we find evidence for a negative impact of T1 on the willingness to recommend AstraZeneca. Likewise, for both genders, we observe negative spillovers of T1 to all less common/not yet approved vaccines. By and large, spillover effects appear to be more present among women; in particular with respect to the observed substitution effect away from AstraZeneca towards Pfizer/BioNTech.

Fig. 2
figure 2

Impact of ITs on willingness to recommend vaccines to others (OLS). Note: Bars indicate 95% percentile intervals

Economic preferences

It is well established that economic preferences such as risk, patience, and altruism can drive individual decision making. As part of our survey (step 2), we collected unincentivized preference measures based on survey items from the Global Preference Survey module [25]. In Fig. 3 below and Table 14 in the online Appendix, we investigate whether these preferences mediate the impact of T1. Therefore, we shed light on whether information related to AstraZeneca had a particularly strong impact among risk-averse health workers or those who are more patient, and less altruistic. By and large, we find that economic preferences neither influence the willingness to recommend a vaccine nor do these preferences mediate the impact of T1.

Fig. 3
figure 3

Impact of T1 and economic preferences on willingness to recommend vaccines to others (OLS). Note: Bars indicate 95% percentile intervals

Exposure

Health workers might react differently to information treatments and in particular to T1 depending on their individual exposure to COVID-19 risks and vaccines. In Table 15, we investigate whether respondents who belong to a high-risk group process T1 differently. Furthermore, we analyze whether the associative nature of the T1 relationship differs depending on whether a health worker was vaccinated with AstraZeneca vs. Pfizer/BioNTech.

As shown in Fig. 4 below and Table 15 in the online Appendix, we do not observe that a person’s COVID-19 risk status changes recommendations or the role of T1 (interaction effect). Regarding a person’s own COVID-19 vaccine history, we find that health workers who were vaccinated with AstraZeneca (Pfizer/BioNTech) were more likely to recommend the vaccine they were vaccinated with (panel A & B). Furthermore, we observe that particularly health workers who were vaccinated with AstraZeneca increase their willingness to recommend Pfizer/BioNTech once receiving the information from T1.

Fig. 4
figure 4

Impact of T1 and exposure on willingness to recommend vaccines to others (OLS). Note: Bars indicate 95% percentile intervals

Beliefs

Our experiment possibly alters the information set of health workers. Information can affect beliefs and (perceived) constraints which in turn can affect people’s choices. In this section we examine to what extent our information treatments have an impact on health workers’ satisfaction with government institutions and predictions about the future state of the world.

In Table 16 in the online Appendix we show the impact of our information treatments on health workers’ satisfaction with different government institutions at the state and national level (columns 1 to 4). In addition, we elicit satisfaction with Germany’s minister of health (at the time of the experiment), Mr. Jens Spahn, who featured very prominently in Germany’s COVID-19 strategy.

We find that three out of four information treatments (T2 & T3 & T4) seem to have no effect on satisfaction levels with the government. In contrast, we observe a sizeable positive impact of T1 on satisfaction with state-level institutions, such as the government and the ministry of health. While our data does not allow to disentangle mechanisms further, we believe that the results illustrate that the public and scientifically led discussion about the COVID-19 vaccine of AstraZeneca has re-ensured health workers that the government has a strict and reliable quality control mechanism in place. Moreover, the results suggest that the impact of T1 on vaccination recommendations is not driven by reductions in governmental trust; a channel highlighted in related contexts [26, 27].

In Table 17 in the online Appendix, we investigate whether our information treatments affected health worker’s attitudes and perception about the near future (reference period referred to October 2021; about six months after the survey). In columns 1 to 3, we focus on health workers’ predictions about how life in Germany will look like, while in columns 4-5 we look into future health-related behaviour in terms of the willingness to download COVID-19 apps (column 4) and refresh their own DPT vaccination when it is due.

Overall, we do not find that our information treatments affected health workers’ views of the future and their own predicted health behaviour.

Conclusion

In this paper, we examine whether health workers’ views on COVID-19 vaccines can be swayed by information provision. Employing an information treatment experiment with four treatment arms and one control condition, we investigate whether health workers’ vaccine recommendations change once they learn from information related to (i) conspiracy theories, (ii) scientific discussions of drug authorities on AstraZeneca, (iii) risk of COVID-19 for their own health, and (iv) risk of COVID-19 for the public’s health. The experiment was conducted as part of an online survey with 3,318 health workers in April/May 2021 in Germany.

Our findings suggest that health workers’ willingness to recommend COVID-19 vaccines strongly depends on the messages conveyed by public drug regulators. More specifically, we find that the mixed messages that were conveyed by German and European authorities with respect to the vaccine of AstraZeneca decreased health workers’ acceptance of the vaccine. Moreover, we observe negative spillover effects to other in Germany less established vaccines such as Johnson & Johnson, Sinopharm, and Sputnik-V. Therefore, health workers became less likely to recommend those latter vaccines once learning about statements from the public drug authorities related to AstraZeneca. In contrast, we do not find that health workers’ vaccine recommendations are affected by misin- formation and arguments related to their own/the public’s health.

We believe that our experiment contributes to the existing literature in important ways. First, we relate to the literature that examines the impact of information provision on vaccination intentions and outcomes. While the literature traditionally had focused on routine vaccinations such as those for measles and influenza [2830], a number of recent studies focused on the effect of information on attitudes towards COVID-19 vaccines [7, 3133]. Second, we relate to the literature that studies the impact of information on a broader set of COVID-19 related outcomes such as stigmatization [34, 35] and beliefs about its risk factors and contagiousness [26, 3638]. In contrast to both strands of the literature, our study does not focus on vaccine intentions among the general population but on vaccine recommendations of health workers. Consequently, our study closes an important gap in the existing literature by shedding light on how to best mobilize health workers in the global fight against vaccine hesitancy.

In this context we believe that our study entails important policy implications for the design of health campaigns. First, our results underscore that information campaigns need to be tailor-made with the specific target audience in mind. While information campaigns related to misinformation and/or appeals to individual/societal benefits had been shown to affect COVID-19 vaccination attitudes among the general population, we illustrate that such information does not affect health workers. Health workers, however, are nonetheless highly responsive to the information environment; in our setting information from public health authorities.

Second, we think that the negative impact of our AstraZeneca information treatment on the willingness to recommend several approved COVID-19 vaccines shows that public health authorities should coordinate public health messages more closely. The example that we selected for our study refers to the case in which within a very short time period (three days) the recommendation for the general public was revised two times (from being advisable, to being suspended, to being advisable again). While the public’s trust into the AstraZeneca vaccine never recovered from the related temporary suspension, we illustrate related impacts among health professionals.

Third, we highlight that policy makers need to carefully consider the consequences of emergency drug approval processes. While an early approval can save many lifes, it comes with the risk of revising recommendations and guidelines several times, which ultimately can substantially slow down vaccination campaigns over the medium to long-run.

Appendix

Table 4 Description of intervention arms
Table 5 Description of main variables
Table 6 Balance table: Respondent characteristics
Table 7 Balance Table: Respondent characteristics by treatment arm
Table 8 Impact of treatments on willingness to recommend COVID-19 vaccines (OLS): Extended controls
Table 9 Impact of treatments on willingness to recommend COVID-19 vaccines (OLS): Alternative clustering
Table 10 Adjusted p-values for multiple hypothesis testing
Table 11 Impact of treatments on willingness to recommend COVID-19 vaccines (OLS): Sample without doctors
Table 12 Women: Impact of treatments on willingness to recommend COVID-19 vaccines (OLS)
Table 13 Men: Impact of treatments on willingness to recommend COVID-19 vaccines (OLS)
Table 14 Role of economic preferences on treatment effects (OLS)
Table 15 Role of health experience on treatment effects (OLS)
Table 16 Impact on satisfaction with government institutions (OLS)
Table 17 Impact on perceptions of future (behaviour) (OLS)

Availability of data and materials

All data used in this study are available at https://osf.io/6fnye/. A copy of the materials used in this study, as displayed to respondents, can be obtained from the authors upon request. Source data are provided with this paper.

Furthermore, the statistical code to run all data cleaning steps and the analysis for this study is available at https://osf.io/6fnye/.

Notes

  1. The “first wave” was in March/April 2020 with about 6,000 new cases per day, while the “second wave” was in October 2020 until January 2021 with up to 32,000 new cases per day. The situation improved in February to March 2021 with cases substantially rising again in April 2021.

  2. Vaccines were officially approved in Germany in the following order: 1. Pfizer/BioNTech on 21 December 2020, 2. Moderna on 6 January 2021, 3. AstraZeneca on 29 January 2021, and 4. Johnson & Johnson on 11 March 2021.

  3. Since health workers belonged to priority groups 1 & 2 they gained preferential access to COVID-19 vaccines. In mid-April 2021 about 32.4 percent of nurses were fully vaccinated and another 44.6 percent of nurses had already a first injection [16]. From the start of the vaccination campaign in December 2020 until today health workers have not been mandated by the government or employers to become vaccinated. In many cases, however, employers (hospitals, practices) have asked their staff to become vaccinated against COVID-19.

  4. Efficacy rates refer to the ALPHA variant of SARS-CoV-2.

  5. While in the case of Pfizer/BioNTech vaccinations were recommended for the age group 18 years and older consistently since December 2020, official recommendations for AstraZeneca changed multiple times. Germany’s Permanent Vaccine Commission (Ständige Impfkommission) initially suggested on 4 February 2021 to ideally use the vaccine for the age group 18 to 64 only [17], it revised the recommendation on 12 March 2021 to include everyone older than 17 years [18], and ultimately suggested on 1 April 2021 that the vaccine is best suited for individuals older than 59 years [19].

  6. While most of the literature on information experiments assumes estimated treatment effects to be causal once covariate balance is achieved, we consider estimates to be associative in nature. There are two reasons for this: First, randomization might still have resulted in imbalances in unobservables. Second, in our context effects are more difficult to interpret as causal given the circumstance that health workers had been exposed for already many months to different COVID-19 vaccine-related information prior to our intervention. Thus, it is likely that the information provided in the treatments was not new to the group of health workers, and the experimental manipulation likely made only a piece of specific information more salient. For the experiment, we derive a minimum detectable effect size (MDE) of about 6.6 percentage points. Calculations assume a statistical power of 80 percent at a significance level of 5 percent.

  7. X comprises the following variables: age, gender, education, vaccine order, social media platform, and device type.

  8. A possible concern with respect to the impact of T1 on the willingness to recommend the vaccine of AstraZeneca relates to experimenter demand effects. We believe that the observed spillover effects provide credible evidence on the circumstance that health workers’ reactions to ambigious signals with respect to a vaccine are not AstraZeneca specific and do neither reflect experimenter demand effects nor social desirability bias.

References

  1. Anderson RM, Vegvari C, Truscott J, Collyer BS. Challenges in creating herd immunity to sars-cov-2 infection by mass vaccination. Lancet. 2020; 396:1614–6.

    CAS  Article  Google Scholar 

  2. Lazarus JV, Ratzan SC, Palayew A, Gostin LO, Larson HJ, Rabin K, Kimball S, El-Mohandes A. A global survey of potential acceptance of a covid-19 vaccine. Nat Med. 2021; 27:225–8.

    CAS  Article  Google Scholar 

  3. Sallam M. Covid-19 vaccine hesitancy worldwide: A concise systematic review of vaccine acceptance rates. Vaccines. 2021; 16:160.

    Article  Google Scholar 

  4. Troiano G, Nardi A. Vaccine hesitancy in the era of covid-19. Public Health. 2021; 194:245–51.

    CAS  Article  Google Scholar 

  5. Kwok KO, Lai F, Wei WI, Wong SYS, Tang JW. Herd immunity—estimating the level required to halt the covid-19 epidemics in affected countries. J Infect. 2020; 80:32–3.

    Article  Google Scholar 

  6. Sanche S. High contagiousness and rapid spread of severe acute respiratory syndrome coronavirus. Emerg Infect Dis. 2020; 26:1470–7.

    CAS  Article  Google Scholar 

  7. Freeman D, Loe BS, Yu L-M, Freeman J, Chadwick A, Vaccari C, Shanyinde M, Harris V, Waite F, Rosebrock L, Petit A, Vanderslott S, Lewandowsky S, Larkin M, Innocenti S, Pollard AJ, McShane H, Lambe S. Effects of different types of written vaccination information on covid-19 vaccine hesitancy in the uk (oceans-iii): a single-blind, parallel-group, randomised controlled trial. Lancet Public Health. 2021; 6(6):E416–E427.

    Article  Google Scholar 

  8. Lin C, Tu P, Beitsch LM. Confidence and receptivity for covid-19 vaccines: A rapid systematic review. Vaccines. 2021; 9:16.

    CAS  Article  Google Scholar 

  9. Nature. Nature covid vaccine confidence requires radical transparency. Nature. 2020; 586.

  10. COVID vaccine confidence requires radical transparency. Nature. 2020; 586(7827):8. https://doi.org/10.1038/d41586-020-02738-y.

  11. Razai MS, Chaudhry U, Umar AR, Ooerholt K, Bauld L, Majeed A. Covid-19 vaccination hesitancy. BMJ (Clin Res ed.) 2021; 373:n1138. https://doi.org/10.1136/bmj.n1138.

    Google Scholar 

  12. Romer D, Jamieson KH. Conspiracy theories as barriers to controlling the spread of COVID-19 in the U.S. Soc Sci Med. 1982; 263:113356. https://doi.org/10.1016/j.socscimed.2020.113356.

    Article  Google Scholar 

  13. WHO. Health Workers in Focus: Policies and Practices for Successful Public Response to COVID-19 Vaccination. Copenhagen: World Health Organization Regional Office for Europe, UN City; 2021.

    Google Scholar 

  14. WHO. The Role of Community Health Workers in COVID-19 Vaccination Implementation Support Guide. Geneva: World Health Organization; 2021.

    Google Scholar 

  15. Gholami M, Fawad I, Shadan S, Rowaiee R, Ghanem H, Khamis AH. Covid-19 and healthcare workers: A systematic review and meta-analysis. Int J Infect Dis. 2021;104(335-336). https://doi.org/10.1016/j.ijid.2021.01.013.

  16. RKI. COVID-19 Impfquoten-Monitoring in Deutschland (COVIMO) – 3. Report (Kurzbericht). Berlin: Robert Koch Institute; 2021.

    Google Scholar 

  17. STIKO. Beschluss der stiko zur 2. aktualisierung der covid-19- impfempfehlung und die dazugehörige wissenschaftliche begründung. Epidemiologisches Bull. 2021; 5:3–79.

    Google Scholar 

  18. STIKO. Stiko: 3. aktualisierung der covid-19- impfempfehlung. Epidemiologisches Bull. 2021; 12:13–25.

    Google Scholar 

  19. STIKO. Beschluss der stiko zur 4. aktualisierung der covid-19-impfempfehlung und die dazugehörige wissenschaftliche begründung. Epidemiologisches Bull. 2021; 16:3–8.

    Google Scholar 

  20. Romano J, Wolf M. Stepwise multiple testing as formalized data snooping. Econometrica. 2005; 77(3):1237–82.

    Article  Google Scholar 

  21. Romano J, Wolf M. Effcient computation of adjusted p-values for resampling-based stepdown multiple testing. Probab Lett. 2016; 113:38–40.

    Article  Google Scholar 

  22. Koos S. Die “querdenker” wer nimmt an corona-protesten teil und warum?Forschungsbericht Univ Konstanz. 2021. http://nbn-resolving.de/urn:nbn:de:bsz:352-2-bnrddxo8opad0.

  23. Nachtwey O, Schäfer R, Frei N. Politische soziologie der corona-proteste. Forschungsbericht Universität Basel. Switzerland: Faculty of Sociology, University of Basel; 2020.

    Google Scholar 

  24. Dyer O. Covid-19: Ema defends astrazeneca vaccine as germany and canada halt rollouts. Br Med J. 2021;373(883). https://doi.org/10.1136/bmj.n883.

  25. Falk A, Becker A, Dohmen T, Enke B, Huffman D, Sunde U. Global Evidence on Economic Preferences. Quart J Econ. 2018; 133(4):1645–92.

    Article  Google Scholar 

  26. Akesson J, Ashworth-Hayes S, Hahn R, Metcalfe RD, Rasooly I. Fatalism, beliefs, and behaviors during the covid-19 pandemic. NBER Working Paper no. 27245. 2020. https://doi.org/10.3386/w27245.

  27. Merkley E, Loewen PJ. Anti-intellectualism and the mass public’s response to the covid-19 pandemic. Nat Hum Behav. 2021; 5:706–715.

    Article  Google Scholar 

  28. Alsan M, Eichmeyer S. Persuasion in medicine: Messaging to increase vaccine demand. NBER Technical Report. 2021.

  29. Nyhan B, Richey S, Freed GL. Effective messages in vaccine promotion: a randomized trial. Vaccine. 2015; 133(4):835–42.

    Google Scholar 

  30. Nyhan B, Reifler J. Does correcting myths about the flu vaccine work? an experimental evaluation of the effects of corrective information. Vaccine. 2015; 33(3):459–64.

    Article  Google Scholar 

  31. Kerr JR, Freeman ALJ, Marteau TM, van der Linden S. Effect of information about covid-19 vaccine effectiveness and side effects on behavioural intentions: Two online experiments. Vaccines. 2021; 9(4):1–22.

    Article  Google Scholar 

  32. Loomba S, de Figueiredo A, Piatek SJ, de Graaf K, Larson HJ. Measuring the impact of covid-19 vaccine misinformation on vaccination intent in the uk and usa. Nat Hum Behav. 2021; 5:337–48.

    Article  Google Scholar 

  33. Schwarzinger M, Watson V, Arwidson P, Alla F, Luchini S. Covid-19 vaccine hesitancy in a representative working-age population in france: A survey experiment based on vaccine characteristics. Lancet Publ Health. 2021; 6:210–21.

    Article  Google Scholar 

  34. Harell A, Lieberman E. How information about race-based health disparities affects policy preferences: Evidence from a survey experiment about the covid-19 pandemic in the united states. Soc Sci Med. 2021; 277(May):1–10.

    Google Scholar 

  35. Islam A, Pakrashi D, Vlassopoulos M, Wang LC. Stigma and misconceptions in the time of the covid-19 pandemic: A field experiment in india. Soc Sci Med. 2021; 113966.

  36. Faia E, Fuster A, Pezone V, Zafar B. Biases in information selection and processing: Survey evidence from the pandemic. NBER Technical Report. 2021.

  37. Fetzer T, Hensel L, Hermle J, Roth C. Coronavirus perceptions and economic anxiety. Rev Econ Stat. 2021; 103(5):968–978.

    Article  Google Scholar 

  38. Kim HK, Ahn J, Atkinson L, Kahlor LA. Effects of covid-19 misinformation on information seeking, avoidance, and processing: a multicountry comparative study. Sci Commun. 2020; 42(5):586–615.

    Article  Google Scholar 

Download references

Acknowledgements

We would like to thank the research groups at BNITM, GESIS, and GIGA. The paper benefited from discussions with Jessica Daikeler, Renate Hartwig and Bernd Weiß.

Funding

The authors acknowledge institutional funding from GESIS. Besides providing funding for the online survey, GESIS was not involved in the development, implementation, analysis, and interpretation of the data. Open Access funding enabled and organized by Projekt DEAL.

Author information

Authors and Affiliations

Authors

Contributions

All authors contributed to the questionnaire design, with C.B. implementing and monitoring the online survey. H.S. clarified the need for ethical review via GESIS. J.P. and H.S. designed the statistical analyses. J.P. performed all model calculations and inference and created all figures for publication. J.P. and H.S. performed image selection. J.P. and H.S. wrote the final manuscript with input from all authors. All authors contributed to the interpretation of the results. S.P. and H.S. supervised the project. The authors read and approved the final manuscript.

Corresponding author

Correspondence to Jan Priebe.

Ethics declarations

Ethics approval and consent to participate

GESIS requires ethical reviews only in the case of highly invasive interventions and/or data collection. Consequently, the need for an ethics approval was waived.

Study subjects: Before the start of the online survey all participants were informed about the purpose of the study, the GESIS, and were asked to provide their informed consent that the data can be stored (anonymized), analyzed, and passed on (anonymized). Only participants who provided informed consent received the online questionnaire. The authors confirm that all methods were carried out in accordance with relevant guidelines and regulations.

Consent for publication

Not applicable.

Competing interests

All authors declare that they have no competing interests. In addition, one author (Christoph Beuthner) declares that he currently holds a small number of stocks of two companies involved in the supply of COVID-19 vaccines (BioNTech, Moderna). Christoph Beuthner declares that the stocks did not affect his work and contribution in any kind.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The views expressed in this paper are those of the authors alone and do not present the views of BNITM, GESIS, and GIGA.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visithttp://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Priebe, J., Silber, H., Beuthner, C. et al. How (not) to mobilize health workers in the fight against vaccine hesitancy: Experimental evidence from Germany’s AstraZeneca controversy. BMC Public Health 22, 516 (2022). https://doi.org/10.1186/s12889-022-12725-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12889-022-12725-9

Keywords

  • COVID-19
  • Misinformation
  • Vaccine hesitancy
  • Germany