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A media intervention applying debunking versus non-debunking content
to combat vaccine misinformation in elderly in the Netherlands: A digital
randomised trial
Hamza Yousuf
a,1
, Sander van der Linden
b,1
, Luke Bredius
a
, G.A. (Ted) van Essen
c
,
Govert Sweep
a
, Zohar Preminger
a
, Eric van Gorp
d
, Erik Scherder
e
, Jagat Narula
f
,
Leonard Hofstra
a,
*
a
Department of Cardiology, Amsterdam UMC, VU University medical center, De Boelelaan 1117 1118, 1081 HV Amsterdam, the Netherlands
b
Department of Psychology, School of Biology, University of Cambridge, Cambridge, UK
c
Dutch Influenza Foundation, Amersfoort, the Netherlands
d
Department of Viroscience, Erasmus MC, Rotterdam, the Netherlands
e
Department of Clinical Neuropsychology, VU University, Amsterdam, the Netherlands
f
Mount Sinai, St. Luke's Hospital, New York, NY, USA
ARTICLE INFO
Article History:
Received 26 February 2021
Revised 13 April 2021
Accepted 16 April 2021
Available online xxx
ABSTRACT
Background: As several COVID-19 vaccines are rolled-out globally, it has become important to develop an
effective strategy for vaccine acceptance, especially in high-risk groups, such as elderly. Vaccine misconcep-
tion was declared by WHO as one of the top 10 health issues in 2019. Here we test the effectiveness of apply-
ing debunking to combat vaccine misinformation, and reduce vaccine hesitancy.
Methods: Participants were recruited via a daily news show on Dutch Television, targeted to elderly viewers.
The study was conducted in 980 elderly citizens during the October 2020 National Influenza Vaccination
Campaign. Borrowing from the recent literature in behavioural science and psychology we conducted a two-
arm randomized blinded parallel study, in which participants were allocated to exposure to a video contain-
ing social norms, vaccine information plus debunking of vaccination myths (intervention group, n= 505) or a
video only containing vaccine information plus social norm (control group, n= 475). Participants who viewed
either of the video's and completed both a pre- and post-intervention survey on vaccination trust and knowl-
edge, were included in the analysis. The main outcomes of this study were improvement on vaccine knowl-
edge and awareness.
Findings: Participants were recruited from the 13th of October 2020 till the 16th of October 2020 and could
immediately participate in the pre-intervention survey. Subsequently, eligible participants were randomly
assigned to an interventional video and the follow-up survey, distributed through email on the 18th of Octo-
ber 2020, and available for participation till the 24th of October 2020. We found that exposure to the video
with addition of debunking strategies on top of social norm modelling and information resulted in substan-
tially stronger rejection of vaccination misconceptions, including the belief that: (1) vaccinations can cause
Autism Spectrum Disorders; (2) vaccinations weaken the immune system; (3) influenza vaccination would
hamper the COVID-19 vaccine efficacy. Additionally, we observed that exposure to debunking in the inter-
vention resulted in enhanced trust in government.
Interpretation: Utilizing debunking in media campaigns on top of vaccine information and social norm
modeling is an effective means to combat misinformation and distrust associated with vaccination in elderly,
and could help maximize grounds for the acceptance of vaccines, including the COVID-19 vaccines.
Funding: Dutch Influenza Foundation.
© 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/by/4.0/)
Keywords:
Vaccines
Misinformation
Public health
Media psychology
Debunking
Media intervention
* Corresponding author.
E-mail address: l.hofstra@amsterdamumc.nl (L. Hofstra).
1
Both the authors contributed equally to this work.
https://doi.org/10.1016/j.eclinm.2021.100881
2589-5370/© 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
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eclinm.2021.100881
EClinicalMedicine 000 (2021) 100881
Contents lists available at ScienceDirect
EClinicalMedicine
journal homepage: https://www.journals.elsevier.com/eclinicalmedicine
1. Introduction
At least 3 COVID-19 vaccine candidates have received emergency
use authorization (EUA) [1]. A major challenge, however, will be to
maximize acceptance of the novel vaccines which have been devel-
oped at an unprecedented pace, compared to previous vaccine devel-
opment timelines. The World Health Organization (WHO) declared
vaccine hesitancy as one of the top 10 global health threats in 2019,
as they referred to the uprising in anti-vax myths by individuals and
bots actively spreading fake news, especially on social media [2,3].
This, in turn, is fuelling distrust in science, governments and health
organizations as an infodemic [4]. This was amplified during the
COVID-19 pandemic, and could pose a challenge to public acceptance
of the COVID-19 vaccine, on top of its effects on vaccine hesitancy in
general [5]. A striking example is the renewed societal hesitancy
against measles vaccination, which has led to an increase of 31% in
measles cases reported globally [6]. This calls for effective measures
to counteract misinformation and behaviour pertaining to vaccine
hesitancy [2].
To enhance public confidence in vaccination, governments world-
wide should implement public health strategies to reduce vaccine
refusal, as the intention to accept a COVID-19 vaccine is non-optimal
and is even decreasing [7,8]. In addition, in as much as a third to half
of the population in several countries, such as Germany, France and
Japan do not plan to take a COVID-19 vaccination [9]. Optimal vacci-
nation levels are particularly important in high-risk subjects includ-
ing elderly and those with pre-existing heart or respiratory disease.
Furthermore, on top of health determinants of high-risk, social char-
acteristics play an important part in COVID-19 vulnerability. For
instance, it has been observed that the African American population
has been hit disproportionately by the COVID-19 pandemic, and
moreover seem to be at higher risk for vaccine refusal [10]. Social and
behavioural science evidence has demonstrated that debunking can
effectively mitigate misperceptions including vaccination myths, and
therefore, could help to decrease vaccine refusal [11,12]. We have
previously demonstrated that evidence-based campaigning and
entertainment-based interventions can effectively promote aware-
ness and influence behaviours on a mass scale [1315]. In the ran-
domized study presented here we aimed to test the hypothesis that a
video containing evidence-based content, social norm modelling and
debunking strategies should help to improve knowledge and aware-
ness about vaccines in targeted individuals, compared to a video con-
taining information and social norms without debunking strategies.
We tested this Public Health communication strategy during the
National Influenza Vaccination Campaign for elderly in the Nether-
lands, in October of 2020.
2. Methods
2.1. Study design
This study was designed as a randomized parallel-group blinded
study, in the Netherlands. A video intervention in 2 different versions
was randomly assigned via Qualtrics (digital randomisation) to the
participants. A follow up survey was used to measure the outcomes.
This study was reviewed by the Medical Ethical Review Board
(METC) of the Amsterdam UMC and VU University Medical Center,
and was exempted from the need for an IRB approval. Participants
were required to give informed consent for participation by ticking
the ‘I have read the information given above and agree to participate
in this study’-box in the digital informed consent form that preceded
the start of the pre-intervention survey. If participants did not pro-
vide informed consent, they were redirected to the end of the survey
and excluded for participation.
This study follows the CONSORT 2010 guidelines for reporting
randomized trials.
2.2. Participants and randomisation
Due to the restricted sampling population from viewers of the
television channel and opportunistic participation, we did not pre-
determine a sample size. Participants who filled out the diagnostic
survey (n= 2541), were assessed on eligibility (1. gave informed con-
sent, 2. provided an email address), and n= 2158 participants were
randomized with an equal allocation ratio (1:1) to a Qualtrics link
containing either Video 1 (n= 1076) without debunk strategies, or
Video 2 (n= 1082) with debunk strategies, and a questionnaire that
was identical for both conditions (Fig. 1). Of the n= 2158 randomized
participants who were invited to view an Influenza campaign video
and fill the follow-up survey, n= 748 participants were lost to follow-
up, and n= 430 participants did not complete the follow-up survey.
This approach yielded n= 475 included participants for the non-
debunking group, and n= 505 included participants for the debunk-
ing group that filled all fields of the survey. The study was performed
in a blinded fashion, in which participants were not aware of the
existence of the different videos. Since the videos were posted on our
own YouTube channel, the videos were only visible on invitation,
which minimized the possibility of a spill-over effect between
groups.
2.3. Procedures
2.3.1. Diagnostic survey development
To evaluate vaccine awareness, knowledge, misconceptions and
hesitancy in the Netherlands, we developed a survey, to evaluate (A)
demographic information (age, gender, migration background, edu-
cation, annual income, living surrounding, political view, religion,
and e-mail (11 questions), (B) governmental trust on influenza vacci-
nation (7 questions, adapted from WHO SAGE) [16], (C) vaccine
Research in context
Evidence before this study
Vaccination hesitancy is regarded as a top 10 health priority by
the WHO. Recent studies in behavioural science suggest that
debunking vaccination myths should be an effective method to
increase vaccination confidence. In addition, debunking scripts
have been developed. Here, we used these latest insights in
behavioural science to investigate the impact of debunking, on
top of information and social norm modelling, in a randomised
video intervention study in the 980 elderly citizens.
Added value of this study
The results show that adding debunking scripts on top of vac-
cine information and social norm modelling resulted in stron-
ger rejection of vaccination myths, and enhanced trust in the
COVID-19 mitigation measures by the government. These
results suggest that debunking is an effective communication
strategy in public health messaging, to enhance vaccination
confidence. Based on these results, the intervention video with
debunking was used in a widespread campaign via television.
Implications of all the available evidence
On the basis of this study and the other existing evidence, the
strategy to use evidence based health campaigning using the
latest insights in behavioural science, could serve as an effective
and relatively low cost method to convey public health
messages.
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Please cite this article as: H. Yousuf et al., A media intervention applying debunking versus non-debunking content to combat vaccine
misinformation in elderly in the Netherlands: A digital randomised trial, EClinicalMedicine (2021), https://doi.org/10.1016/j.
eclinm.2021.100881
2H. Yousuf et al. / EClinicalMedicine 00 (2021) 100881
hesitancy (10 questions, adapted from a governmental trust survey
during the H1N1 pandemic) [17], and (D) myths and knowledge
about influenza and COVID-19 (6 questions) (Supplemental data).
2.3.2. Distribution of the survey
The diagnostic survey was promoted through a daily talk show
with an average audience of 600,000 daily viewers and was made
available for participation on the digital platforms of Omroep Max
(the TV channel broadcasting the show) on the 13th of October
2020. The target audience of Omroep MAX is within the same age
group (60+) as those susceptible for influenza and SARS-CoV-2
infection complications. On an evening talk show a well-known
Dutch physician- Ted van Essen, MD, requested viewers to
participate in the vaccination survey available on Omroep MAX
website.
2.3.3. Formulation of the videos
The outcomes of the diagnostic survey were used to design videos
aiming at the gaps in understanding and misconceptions surrounding
vaccinations (Table 1; description of outcomes in the Results section).
In both videos three different TV celebrity scientists (Prof. Erik
Scherder, Prof. Dr. Eric van Gorp, Dr. Ted van Essen), the Dutch state
secretary of Health, Welfare and Sport (Paul Blokhuis), and Prof. Dr.
Leonard Hofstra (professor in Cardiology) were displayed, explaining
the different aspects of vaccination, including social norm, informa-
tion on vaccinations. Furthermore, The control video (Video 1) con-
tained only information on vaccination and social norms (Non-
debunking video) (Fig. 2). Video 2 contained all the contents of Video
1, but on top also had several Debunking fragments on vaccination
misconceptions. The length of the Non-debunking video (social
norms and information only) was 5 min and 11 s, and 6 min and 43 s
for the Debunking video (social norms, information and debunking)
(Fig. 2). A full time-stamped transcript of both videos can be found in
the Supplemental data.
2.3.4. Applied psychological theories
In both videos, we utilized Social norms theory aimed at influenc-
ing public opinion through acclaimed experts. Social norms convey
information about what others are doing (descriptive norms) as well
as prescriptions about what is desirable in society and how people
ought to behave (prescriptive norms). In particular, it has been
reported that explanation of herd immunity and the social benefits of
vaccination can induce prosocial vaccination decisions. On top of
that, in the debunking video we also applied key insights from the
cognitive psychology literature on how to effectively correct false-
hood, by providing (1) social consensus, (2) supporting evidence, (3)
consistency, (4) coherence, and (5) credibility [11,12,1821].
2.3.5. Informational content in both videos
In the videos we provided information on (1) groups at higher
risk for an influenza infection and its complications, (2) the effect of
healthy lifestyle behaviour on the immune system, (3) the safety,
effectiveness, working mechanisms and development of vaccines in
general, (4) the safety and contents of the influenza vaccine, and
that (5) GP practices have taken effective precautions against
COVID-19 to ensure safety of individuals receiving an influenza vac-
cine.
2.3.6. Content modelling social norms in both videos
We modelled social norms by presenting video graphic material
in which the scientific popular opinion leader received an influenza
vaccination, as well as by encouraging viewers to let themselves be
vaccinated against Influenza for themselves, but also for the health
protective effects for people at higher risk for complications during
an influenza infection in their social environment (e.g. their parents,
grandparents, frail individuals in their social circle).
2.3.7. Debunking content in Video 2
In the video containing debunking fragments (Video 2), we stated
in the introduction that there are a lot of misconceptions on vaccines,
Fig. 1. Aflow-chart describing the trial profile of the study.
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H. Yousuf et al. / EClinicalMedicine 00 (2021) 100881 3
that we will address these in this video, explain that they were falsely
conveyed to the public and provide scientific facts on the actual
workings. Subsequently, in Video 2 we debunked various myths that
we provide information on in both videos, including (1) the safety,
efficacy and development of vaccines in general, (2) the falsely pro-
claimed link between autism and vaccines, (3) the safety and con-
tents of the influenza vaccine.
2.3.8. Distribution of the intervention and follow-up survey
The interventional videos and the follow-up survey were distrib-
uted through email to the participants on the 18th of October 2020,
which contained a link to Qualtrics
XM
. When accessing the link, par-
ticipants were able to view either Video 1 or Video 2, based on the
condition they were randomized to. After viewing either one of the
videos, participants could directly fill the follow-up survey, which
Table 1
Demographic characteristics of participants at baseline.
Non-debunked (n= 475) Debunked (n= 505)
Characteristics Mean SD, Range Mean SD, Range P-value
Age, y 69,33 7593, [33 - 94] 69,15 7963, [30 - 95] 0,378
Gender Frequency Valid (%) Frequency Valid (%)
Male 193 40,60% 191 37,80% 0,368
Female 282 59,40% 314 62,20%
Other 0 0,00% 0 0,00%
Migration background
Yes 11 2,32% 19 3,80% 0,181
No 460 96,84% 485 96,00%
Unknown 4 0,84% 1 0,20%
Education
Elementary school 21 4,43% 18 3,56% 0,227
High school 109 23,00% 93 18,42%
Prevocational secondary education 53 11,18% 56 11,09%
Senior general secondary education 22 4,64% 22 4,36%
Preuniversity education 110 23,21% 104 20,59%
Secondary vocational education 131 27,64% 161 31,88%
Higher proffesional education 7 1,48% 13 2,57%
University education 21 4,43% 38 7,52%
Income annual
Low income 267 69,20% 283 68,20% 0,766
High income 119 30,80% 132 31,80%
Living surrounding
City/Urban environment 307 64,80% 332 65,70% 0,715
Rural environment 167 35,20% 173 34,30%
Political view
Progressive 234 49,30% 247 49,20% 0,985
Conservative 241 50,70% 255 50,80%
Religious
Yes 175 37,00% 159 31,50% 0,077
No 298 63,00% 345 68,50%
Fig. 2. The general structure of both interventional videos showing which strategies were implemented and which themes were discussed. The coloured blocks represent Debunking frag-
ments (labelled yellow; only in B. Video 2 (Debunking)), Social Norm Modelling (labelled pink), and Information (labelled green) in the respective contents of either video.
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Table 2
Overview of results for all outcomes in both groups.DThe mean difference between post and pre outcomes.
Non-Debunked Group n= 475 Debunked Group n= 505
Outcome variable Mea
n Pre-test
SD Mean
Post-test
SD DPost -Pre SD P-value
pre-post
Mean Pre-test SD Mean Post-test SD DPost -Pre SD P-value
pre-post
P-value pre-post
both groups
How committed do you
think the government
is to protect you from
influenza?
3,36 0,767 3,55 0,652 0,198 0,658 0,000 3,41 0,712 3,66 0,573 0,254 0,654 0,000 0,185
How much care and con-
cern do you think the
government has
shown about people
who may be affected
by a flu outbreak?
3,06 0,847 3,31 0,765 0,251 0,776 0,000 3,03 0,817 3,36 0,753 0,331 0,766 0,000 0,104
How open do you think
the government is with
information regarding
Influenza?
3,04 0,887 3,25 0,848 0,206 0,765 0,000 3,07 0,881 3,4 0,789 0,337 0,765 0,000 0,008
How competent do you
think the government
is in dealing with
influenza
2,91 0,869 3,2 0,823 0,291 0,755 0,000 2,94 0,816 3,29 0,823 0,345 0,761 0,000 0,271
How honest do you think
the government is with
information regarding
Influenza?
2,99 0,896 3,23 0,855 0,242 0,734 0,000 2,98 0,877 3,36 0,808 0,384 0,742 0,000 0,003
To what extent do you
believe that the actions
of the government in
response to influenza
is in your personal
interest?
3,05 0,906 3,3 0,867 0,257 0,788 0,000 3,07 0,909 3,38 0,827 0,311 0,819 0,000 0,293
To what extent do you
think the government
will protect you
against Influenza?
2,94 0,86 3,17 0,796 0,234 0,719 0,000 2,91 0,838 3,18 0,793 0,267 0,728 0,000 0,467
Do you believe vaccina-
tions can protect you
against serious
illnesses?
1,9 0,299 1,93 0,262 0,025 0,267 0,000 1,94 0,244 1,95 0,225 0,01 0,256 0,000 0,356
Are there any reasons
you can think of why
you should not get
vaccinated?
1,71 0,455 1,75 0,434 0,04 0,417 0,000 1,68 0,468 1,73 0,444 0,054 0,361 0,000 0,591
Vaccinations are impor-
tant for my health
3,38 0,723 3,49 0,763 0,118 0,58 0,000 3,38 0,695 3,5 0,655 0,119 0,547 0,000 0,980
Vaccines are effective
means to prevent
disease
3,23 0,638 3,28 0,629 0,055 0,56 0,000 3,17 0,625 3,3 0,626 0,123 0,52 0,000 0,049
Getting vaccinated is
important to the health
of others in my
community
3,32 0,676 3,45 0,629 0,137 0,555 0,000 3,33 0,672 3,43 0,62 0,097 0,585 0,000 0,275
New vaccines carry more
risks than older
vaccines
2,48 0,762 2,46 0,795 0,019 0,754 0,000 2,39 0,761 2,55 0,97 0,166 0,807 0,000 0,000
The information I receive
about vaccinations
from the vaccination
program is reliable.
3,03 0,638 3,13 0,658 0,11 0,564 0,000 3,01 0,642 3,21 0,642 0,202 0,58 0,000 0,012
(continued on next page)
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Table 2 (Continued)
Non-Debunked Group n= 475 Debunked Group n= 505
Getting vaccinations is a
good way to protect
myself from illnesses.
3,24 0,694 3,34 0,653 0,099 0,541 0,000 3,23 0,662 3,35 0,665 0,121 0,515 0,000 0,522
I am concerned about the
serious adverse effects
of vaccinations.
2,85 0,806 2,92 0,83 0,07 0,736 0,000 2,83 0,773 2,97 0,811 0,139 0,719 0,000 0,137
I do need vaccinations for
diseases that no longer
occur.
2,46 0,845 2,65 0,805 0,196 0,754 0,000 2,47 0,787 2,63 0,821 0,162 0,736 0,000 0,478
‘An influenza epidemic
(flu wave) is associated
with a strong increase
in heart attacks’
1,92 1228 2,9 1603 0,983 1729 0,000 1,89 1227 3,11 1,56 1226 1756 0,000 0,030
‘Receiving an Influenza
Vaccination (Flu shot)
is as effective as smok-
ing cessation or medi-
cation to prevent heart
attacks’
2,19 1264 2,56 1462 0,371 1636 0,000 2,05 1,22 2,65 1526 0,6 1746 0,000 0,035
‘Flu vaccination can actu-
ally lead to flu’
3,49 1366 3,67 1322 0,173 1381 0,000 3,37 1376 3,63 1,36 0,254 1361 0,000 0,356
‘Receiving an Influenza
Vaccination (Flu shot)
can lead to decline of
strength of my
immune system’
3,36 1449 3,56 1356 0,2 1431 0,000 3,14 1505 3,59 1393 0,452 1522 0,000 0,008
‘Receiving an Influenza
Vaccination (Flu shot)
can lead to decline of
effectivity of a poten-
tial COVID-19 vaccine’
2,75 1674 2,83 1716 0,082 1843 0,000 2,65 1698 3,03 1715 0,382 1775 0,000 0,010
‘Vaccines can lead to the
development of an
Autism Spectrum
Disorder’
3,14 1738 3,23 1765 0,089 1565 0,000 3,06 1751 4,06 1446 0,994 1762 0,000 0,000
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consisted of the same questions as the diagnostic survey excluding
demographic information.
2.4. Outcomes
2.4.1. Re-coding variables and data analysis
Ordinal data was collected through a 4-point Likert scale (for
questions 12, 13, 14, 15, 16, 17, 18, 21, 22, 23, 24, 25, 26, 27, 28) and a
5-point Likert scale (for questions, 29, 30, 31, 32, 33, 34). These scales
then were converted into numerical values (0, 1, 2 and 3, and 0, 1, 2,
3, and 4, respectively. Although, questions 24, 27, 31, 32, 33 and 34
were reverse coded, due to the nature of the questions.
Question 5, regarding educational level of the respondent, was re-
coded into a binary variable, as 0 for Lower Educated (consisting out
of Elementary school, Pre-vocational secondary education, and Second-
ary vocational education), and 1 for Higher Educated (consisting out
of all the remaining levels of education). Question 6, regarding annual
income of the respondent, was re-coded into a binary variable, as 0
for Lower Income (below €40.000 annual income), and 1 for Higher
Income (same or higher than €40.000 annual income). Question 8,
regarding which political party the responded would vote on, was re-
coded into a binary variable, as 0 for progressive parties, and 1 for
conservative voting preference [22].
2.5. Statistical analysis
Nominal and Binary data were analysed with the Chi-Square test
to assess differences between both arms of the intervention for dem-
ographics, as well as differences between pre-test and post-test out-
comes. For all outcomes, an ANCOVA was performed to assess the
pre-to-post differences, in which the post-test outcomes were used
as dependent variables, the pre-test outcomes were used as covari-
ates, and were compared between the two arms of the study. When
the latter was found to be statistically significant (p<.05), linear
regression was performed for outcomes that were converted into
numerical data, and a logistic regression for binary outcomes, in
which was corrected for sex, religion and political views
The result are reported as means, and mean changes (post-pre) for
each arm of the intervention, with SDs and 95% CIs. Analysis was con-
ducted using IBM SPSS Statistics for Mac, version 26.0, Armonk, NY,
IBM Corp.
2.6. Role of the funding source
This study was funded by the Dutch Influenza Foundation. The
funder had no role in study design, data collection, data analysis, data
interpretation, or writing of the report. Furthermore, the funder had
no access to collected data and was not involved in the decision to
submit the findings of this study for peer-reviewed publication. All
authors had full access to all the data in the study and had final
responsibility for the decision to submit for publication.
3. Results
The pre-intervention survey was completed by 2541 participants,
of which 2158 were eligible for participation and were randomized
to either the non-debunking Video 1, which yielded n= 475 included
participants, or the debunking Video 2, which yielded n= 505
included participants (Fig. 1). This study was performed between
October 13th and October 24th. All participants completed the post-
intervention survey after viewing either video. There was no signifi-
cant difference in baseline characteristics between those randomly
assigned to either the non-debunking or debunking intervention
(Table 1). The mean (SD) age of the group assigned to exposure to the
Non-debunking video was 69.33 (7.59), and the group assigned to
the video containing debunking was 69.15 (7.96) years; 41 and 38
percent were male participants, respectively. In the group assigned
to the non-debunking video, 69% participants had a low-income sta-
tus, 49% had a progressive political view, and 37% reported to adhere
to any religion. In the group assigned to the debunking video, 68% of
the participants had low-income status, 49% had progressive political
views and 32% reported to adhere to any religion.
Through the diagnostic survey we observed that participants that
participated in the diagnostic survey (n= 2541) generally reported
that (1) they could think of a reason to not get vaccinated, (2) they
were concerned about side effects of vaccinations, (3) new vaccines
carried more risks than older already existing vaccines, (4) they did
not know whether getting vaccinated lowered the strength of the
immune system, (5) taking an influenza vaccine might affect the
effectiveness of a potential COVID-19 vaccine, (6) vaccines can lead
to an autism spectrum disorder, (7) an influenza vaccine is not as
effective as quitting smoking on reducing the chances of a heart
attack during an influenza epidemic, (8) vaccines can protect you
against serious illnesses, and (9) vaccines for diseases that no longer
occur are not needed. Furthermore, participants reported low trust in
the government regarding handling the Influenza virus (i.e., data for
participants of the diagnostic survey that were not included in the
final analysis is not shown).
Table 2. summarizes mean outcomes measured in all participants
as well as mean change pre and post intervention. Outcomes that
were found to be significantly different between groups, underwent
regression analysis corrected for sex, religion and political views
(Table 3;Fig. 3).
The results show that participants exposed to the debunking
video were significantly more likely to reject the myth that vaccines
could lead to the development of an Autism Spectrum Disorder (odds
ratio for disagreement, 2.44 [95% CI, 1.983.01]), compared to partic-
ipants exposed to the non-debunking control video.
Participants exposed to the debunking intervention were signifi-
cantly more likely to agree that an influenza epidemic is associated
with a strong increase in heart attacks (OR 1.30 [95% CI, 1.041.62]),
and that vaccination for influenza is as effective as smoking cessation
or medication to prevent heart attacks (OR 1.26 [95% CI, 1.021.56]),
respectively, compared to participants exposed to the non-debunking
control video.
Participants exposed to the debunking intervention were signifi-
cantly more likely to reject that an influenza vaccination could result
in a weakened immune system (OR 1.27 [95% CI, 1.061.53]), and to
reject that receiving influenza vaccination could lead to lower efficacy
of COVID-19 vaccine (OR, 1.32 [95% CI, 1.051.65]), respectively, com-
pared to participants exposed to the non-debunking control video.
Participants exposed to the debunking intervention were signifi-
cantly more likely to have a more favourable view about how open
the government was (OR 1.14 [95% CI, 1.031.25]), how honest the
government was with information regarding influenza virus (OR 1.15
[95% CI, 1.051.27]), and the reliability of information on vaccines
provided by the national vaccination program (OR 1.09 [95% CI,
1.011.17]), respectively, compared to participants exposed to the
non-debunking control video.
Finally, participants exposed to the debunking intervention were
significantly more likely to agree that new vaccines do not carry
greater risks than the older vaccines (OR 1.21 [95% CI, 1.101.34]).
However, exposure to the debunking video was not associated with a
mean change in the belief concerning vaccination effectiveness on
preventing disease (OR 1.07 [95% CI, 1.001.14]; P= 0.056), respec-
tively, compared to participants exposed to the non-debunking con-
trol video.
4. Discussion
The current COVID-19 pandemic has resulted in a renewed public
interest in vaccinations as an effective tool for prevention, but has
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H. Yousuf et al. / EClinicalMedicine 00 (2021) 100881 7
also fuelled intense anti-vax sentiments in significant numbers of the
population, wordlwide [23]. The latter may pose a threat to the
efforts to curb COVID-19 pandemic. However, an increasing demand
for other vaccinations, such as the influenza vaccine, has been
observed [24]. The efficacy of influenza vaccination in a meta-analysis
of high risk individuals including patients with underlying cardiovas-
cular disease showed a 3040% reduction in cardiovascular events
[25]. This preventive effect of Influenza vaccination in high risk
groups is as good as quitting smoking or even the prescription of sta-
tins. Despite these convincing benefits, acceptance of influenza vacci-
nation in high risk groups is only about 60% [26]. This suggests that
we could still accrue a substantial health benefit with increased vac-
cination coverage even in a known disease, with a vaccine which is
known to be safe and effective, such as influenza. Even more, given
that vaccine refusal is even prominent in generally known vaccines,
most governments and health institutions foresee that a major effort
Table 3
Overview of regression analysis results of the debunking-group compared with the non-debunking group.
Regression results are shown with and without correction for Sex, Religion and Political view for each significantly different outcome between both conditions.
Variable R2 Adjusted
R2*
OR (95% CI) Lower
Bound
Upper
Bound
Adjusted
OR (95% CI)*
Lower
Bound
Upper
Bound
P-value
How open do you think the government is with
information regarding Influenza?
0,007 0,01
Condition (Non-Debunked >Debunked) 1,14 1,03 1,25 1,14 1,03 1,25 0,008
Sex (Female >Male) 0,99 0,89 1,09 0,820
Religion (Non >Religious) 1,03 0,93 1,15 0,533
Political View (Progressive >Conservative) 0,92 0,83 1,02 0,107
How honest do you think the government is with informa-
tion regarding Influenza?
0,009 0,013
Condition (Non-Debunked >Debunked) 1,15 1,05 1,27 1,15 1,05 1,27 0,003
Sex (Female >Male) 0,95 0,87 1,05 0,335
Religion (Non >Religious) 1,04 0,94 1,15 0,436
Political View (Progressive >Conservative) 0,92 0,84 1,02 0,107
Vaccines are effective means to prevent disease 0,004 0,007
Condition (Non-Debunked >Debunked) 1,07 1,00 1,15 1,07 1,00 1,14 0,056
Sex (Female >Male) 0,96 0,90 1,03 0,269
Religion (Non >Religious) 0,97 0,90 1,05 0,448
Political View (Progressive >Conservative) 0,98 0,91 1,05 0,529
New vaccines carry more risks than older vaccines 0,014 0,019
Condition (Non-Debunked >Debunked) 1,21 1,09 1,33 1,21 1,10 1,34 0,000
Sex (Female >Male) 0,96 0,87 1,07 0,459
Religion (Non >Religious) 1,12 1,00 1,24 0,044
Political View (Progressive >Conservative) 0,96 0,87 1,06 0,409
The information I receive about vaccinations from the
vaccination program is reliable
0,006 0,012
Condition (Non-Debunked >Debunked) 1,10 1,02 1,18 1,09 1,01 1,17 0,019
Sex (Female >Male) 1,09 1,02 1,18 0,018
Religion (Non >Religious) 1,02 0,94 1,10 0,713
Political View (Progressive >Conservative) 1,01 0,94 1,09 0,769
‘An influenza epidemic (flu wave) is associated with a
strong increase in heart attacks’
0,005 0,011
Condition (Non-Debunked >Debunked) 1,29 1,03 1,60 1,30 1,04 1,62 0,019
Sex (Female >Male) 0,94 0,75 1,18 0,608
Religion (Non >Religious) 1,31 1,03 1,67 0,026
Political View (Progressive >Conservative) 0,89 0,71 1,11 0,299
‘Receiving an Influenza Vaccination (Flu shot) is as effec-
tive as smoking cessation or medication to prevent heart
attacks’
0,004 0,008
Condition (Non-Debunked >Debunked) 1,25 1,01 1,55 1,26 1,02 1,56 0,034
Sex (Female >Male) 1,00 0,80 1,24 0,964
Religion (Non >Religious) 1,17 0,93 1,48 0,178
Political View (Progressive >Conservative) 1,10 0,88 1,37 0,407
‘Receiving an Influenza Vaccination (Flu shot) can lead to
decline of strength of my immune system’
0,006 0,009
Condition (Non-Debunked >Debunked) 1,26 1,05 1,52 1,27 1,06 1,53 0,011
Sex (Female >Male) 1,18 0,97 1,43 0,090
Religion (Non >Religious) 1,04 0,85 1,27 0,737
Political View (Progressive >Conservative) 0,96 0,79 1,16 0,667
‘Receiving an Influenza Vaccination (Flu shot) can lead to
decline of effectivity of a potential COVID-19 vaccine’
0,006 0,008
Condition (Non-Debunked >Debunked) 1,32 1,05 1,65 1,32 1,05 1,65 0,018
Sex (Female >Male) 1,14 0,90 1,44 0,274
Religion (Non >Religious) 0,93 0,72 1,19 0,562
Political View (Progressive >Conservative) 1,14 0,90 1,44 0,289
‘Vaccines can lead to the development of an Autism Spec-
trum Disorder’
0,067 0,076
Condition (Non-Debunked >Debunked) 2,45 1,98 3,02 2,44 1,98 3,01 0,000
Sex (Female >Male) 1,29 1,04 1,59 0,022
Religion (Non >Religious) 0,80 0,64 1,01 0,059
Political View (Progressive >Conservative) 0,94 0,76 1,17 0,578
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misinformation in elderly in the Netherlands: A digital randomised trial, EClinicalMedicine (2021), https://doi.org/10.1016/j.
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8H. Yousuf et al. / EClinicalMedicine 00 (2021) 100881
must be invested for acceptance of newer vaccines, such as for
COVID-19.
Here, we applied the latest insights in behavioural science to
decrease the risk of vaccination hesitancy. Recent work on debunking
of misinformation has demonstrated that its success relies in part on
the credibility of the communicator [11,12,18]. For this reason, we
utilized well respected scientists and doctors to provide not only the
social norm and information, but also the debunking statements in
the intervention videos. In addition, we used the recommended
debunking communication strategy by first providing the truth, fol-
lowed by warning about the myth, and reiterating the truth at end
[19,20]. The data presented here show that the debunking strategies
were effective in increasing knowledge and awareness surrounding
vaccinations, combatting vaccine misinformation, and enhanced the
trust in governmental institutions. The use of randomized trials as a
tool to investigate the effect of campaign-like interventions is
unusual, but is advocated by internationally renowned behavioural
scientists [27]. Our results also show that the debunking strategy, on
top of social norm and vaccine information, helped reject misconcep-
tion that vaccination caused autism, weakened the immune system,
and that the influenza vaccination adversely affected the efficacy of
COVID-19 vaccination. Intriguingly, exposure to the debunking video
also improved the knowledge pertaining to the effect of influenza
vaccination in preventing cardiovascular events more. These findings
suggest that juxta-positioning the debunking of myths with informa-
tion on vaccination efficacy and social norms modelling might benefit
in retaining the facts about vaccination, and that the familiarity with
authentic scientific information could help change beliefs in targeted
participants. We would recommend further research to explore the
effects of debunking vaccine misinformation on the willingness to
get vaccinated. Of importance is the fact that 69% (i.e., only shown
per condition) of the participants were classified as having a low
income, a group known to be at more risk to be hesitant to vaccina-
tion. The data presented here show that also in the lower social class,
such a media intervention is effective. The strategy of using a diag-
nostic survey first, to uncover the most prominent gaps in knowledge
and behaviour in the target audience, and use this insight for the
design of the intervention, increases the chance of creating an effec-
tive media campaign. By using existing media as carriers for the cam-
paign ensures that it can be done at a low cost.
We believe that governments around the globe could utilize this
so-called Evidence Based Campaigning strategy to maximize grounds
for vaccine acceptance, not only during the current COVID-19 pan-
demic but also for existing vaccinations, such as against measles and
influenza. In addition, the strategy could also be applicable to other
prominent public health issues.
Our study was conducted during the upslope of the second wave
of COVID-19 in the Netherlands. Therefore, the data may not be
entirely representative for communications outside a pandemic.
However, it has been previously shown that debunking is an effective
strategy to combat misinformation in general. In addition, the distri-
bution of the survey and intervention were done by a TV channel
catering to older viewers, which is indicated by the high median age
of 61 years of channel viewers. Nevertheless, it was anticipated that
the viewers of the TV channel provided a good representation of
elderly in the Netherlands, since it is the most popular medium for
Dutch elderly. Requesting online participation through email may
have resulted in bias, due to differences in tech savviness of elderly
Fig. 3. Forest-plot depicting effect of the debunking video as compared to the non-debunking video, for all outcomes that were significantly different between both conditions.
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Please cite this article as: H. Yousuf et al., A media intervention applying debunking versus non-debunking content to combat vaccine
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eclinm.2021.100881
H. Yousuf et al. / EClinicalMedicine 00 (2021) 100881 9
and tendency to respond. However, internet access in the Nether-
lands remains one of the highest in the world (98% of all households).
In addition, all outcomes are self-reported by the participant, which
could have resulted in bias related to social desirability of answers.
We minimized the possibility of recall bias, since participants were
prompted to fill the survey instantly after viewing either Video 1 or
Video 2, based on the condition they were randomized to. There was
a loss of participants between the diagnostic survey and the post-
intervention survey. However, participants were randomized after
full completion of the diagnostic survey, and being found eligible for
participation (1. provided informed consent; 2. provided a valid email
address). The probability that a participant was lost to follow up after
this step is assumed to be similar in both arms of the study. The
impact of debunking may not be generalizable to other age groups.
However, the age group above 60 is particularly vulnerable to the
complications of COVID-19, and therefore the data presented here
are still relevant. Finally, due to the nature of the study we could only
measure short term effects of the video intervention. Therefore, the
long term effects of the intervention are not clear, as effects of the
intervention might be impacted due to exposure to the vast amount
of misinformation disseminated nowadays. We would recommend to
further research the sustained effects of such interventions, as well
as, the effects of repeated exposure to content applying debunking to
combat misinformation.
Due to the execution of this study in the Netherlands, the gener-
alizability of the study results might be bound to Western or high-
income and high-to-middle income countries. Nonetheless, vaccine
hesitancy is labelled as one of the top 10 global health threats by the
WHO, and is largely fuelled by the uprise in vaccine misinformation.
We believe that utilizing the approach of this study, by identifying
knowledge and behaviour gaps, and tackling misinformation through
personalized content, applying debunking, social norm modelling,
and accurate vaccine information, remains a powerful broadly appli-
cable concept [3].
In conclusion, we demonstrated that adding debunking to a cam-
paign video, on top of vaccination information and social norm
modelling, increased its effectiveness in combating vaccination
myths, resulting in higher rejection of misinformation. We hope the
results of our work will encourage policy makers to employ debunk-
ing in their public health campaigns, including social media cam-
paigns, to positively influence the spectrum of opinions for
developing public confidence in vaccinations, including the COVID-
19 vaccine.
Funding
Dutch Influenza Foundation
Data sharing agreement
The authors agree to make the anonymized data available upon
reasonable request to the corresponding/first author.
Contributors
Dr Yousuf was responsible for literature search, figures, study
design, data collection, intervention design, data analysis, data inter-
pretation and writing; Dr van der Linden was responsible for litera-
ture search, study design, data analysis, intervention design, data
interpretation and writing; Mr Bredius was responsible for literature
search, figures, study design, intervention design, data collection,
data analysis, data interpretation and writing; Dr van Essen was
responsible for literature search and study design; Mr Sweep was
responsible for writing and intervention design; Ms Preminger was
responsible for literature search and data analysis; Dr van Gorp was
responsible for literature search, intervention design, data
interpretation and writing; Dr Scherder was responsible for literature
search, intervention design, data interpretation and writing; Dr Nar-
ula was responsible for data interpretation and writing; Dr Hofstra
was responsible for literature search, figures, study design, data col-
lection, intervention design, data analysis, data interpretation and
writing
Declaration of Competing Interest
Dr. Hofstra and Dr Yousuf report grants from Dutch Influenza
Foundation, during the conduct of the study. No other authors have
any competing interests to report.
Supplementary materials
Supplementary material associated with this article can be found,
in the online version, at doi:10.1016/j.eclinm.2021.100881.
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