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Open Access
BMC Public Health
The impact ofinformation sources
onCOVID-19 vaccine hesitancy andresistance
insub-Saharan Africa
Uchechukwu L. Osuagwu1,2,3* , Khathutshelo P. Mashige2, Godwin Ovenseri‑Ogbomo4 ,
Esther Awazzi Envuladu5, Emmanuel Kwasi Abu6 , Chundung Asabe Miner7, Chikasirimobi G. Timothy8,
Bernadine N. Ekpenyong2,9 , Raymond Langsi10, Onyekachukwu M. Amiebenomo11,12,
Richard Oloruntoba13 , Piwuna Christopher Goson14, Deborah Donald Charwe15 , Tanko Ishaya16 and
Kingsley E. Agho2,3,17
Abstract
Background Vaccination remains the most powerful weapon against the emergence of new variants of coronavirus
(COVID‑19). However, false information about COVID‑19 vaccines through various platforms including social media
remains a major threat to global public health. This study examined the impact of information sources on COVID‑19
vaccine hesitancy and resistance in sub‑Saharan Africa (SSA).
Methods A validated web‑based cross‑sectional study was conducted from 14 March to 16 May 2021, and was
administered in both French and English to 2572 participants aged 18 years and over. Data on sociodemographic
characteristics, medical and vaccination history, and the information sources (mainstream media and social media)
used by the participants during the pandemic were obtained. There were three main outcomes: The vaccinated
group were those who responded in the affirmation (Yes) to the question of whether they have been vaccinated
against COVID‑19. Those who responded ‘not sure’ or ‘no’ to the question were then asked if they were willing to be
vaccinated when the vaccine became available in their home countries. The responses to this follow‑up question
were used to derive the second and third outcome variables of ‘vaccine hesitancy’ and ‘vaccine resistance’, respec‑
tively. A series of logistic regression analyses were used to examine the impact of information sources on the three
main outcomes.
Results The prevalence of COVID‑19 vaccine hesitancy among the participants was lowest among newspaper read‑
ers (42%) and highest among TV (72%) and social media users (73%). The prevalence of COVID‑19 vaccine‑resistance
was also lowest among newspaper readers (37%) but highest among social media users (87%). Multivariate analyses
revealed that compared to those who did not use these information sources, SSA participants who relied on the radio
(aOR 0.83, 95%CI = 0.70, 0.99), TV (aOR 0.80, 95%CI = 0.65, 0.97) and social media (aOR 0.79, 95%CI = 0.65, 0.97) for
information during the pandemic were less likely to be hesitant towards taking the vaccines. However, social media
users (aOR 2.13, 95%CI = 1.62, 2.80), those who watched TV (aOR 1.40, 95%CI =1.08, 1.80), relied on healthcare workers
(HCWs: aOR 1.32, 95%CI = 1.07, 1.63) and families/friends (aOR 1.31, 95%CI = 1.06, 1.61) for COVID‑19 related informa‑
tion during the pandemic were more likely to resist taking the COVID vaccines in this study. Participants who relied
*Correspondence:
Uchechukwu L. Osuagwu
l.osuagwu@westernsydney.edu.au
Full list of author information is available at the end of the article
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Page 2 of 16
Osuagwuetal. BMC Public Health (2023) 23:38
on the newspaper for information during the pandemic were less likely to resist the vaccines (aOR 0.77, 95%CI = 0.62,
0.95) compared to non‑readers of a newspaper.
Conclusion We found that all six information sources except radio were strong predictors of the resistance towards
COVID‑19 vaccination. Further research on how these channels can be used to improve the availability of reliable
healthcare information is needed. Investments in these resources will protect people and empower them to make
appropriate choices about their health.
Keywords Coronavirus, Facebook, Media, Africa, Television, Misinformation, Survey, Radio, Healthcare workers,
Lockdown
Background
e COVID-19 pandemic has significantly impacted eco-
nomic, health and living conditions on the African con-
tinent and elsewhere [1, 2]. e impact on individuals,
families and communities across Africa has been unprec-
edented. While the global economic loss is still unfold-
ing, it is projected to be quite huge particularly in African
countries [3]. e risk of COVID-19 resurgence remains
high in several African countries due to poor adherence
to public health measures, mass gatherings, low testing
and low vaccination rates [4]. is resurgence creates
more demands on an already depleted and struggling
healthcare system thereby leaving many of the citizens
in a dilemma. Governments are also overburdened with
balancing the provision of care regarding the presence of
other viral infections and diseases that have sprung up
again due to all attention being diverted to the COVID-
19 pandemic as is seen in countries like the Democratic
Republic of Congo (Ebola), Lassa fever in Guinea, Libe-
ria, Kenya (Rift valley Fever), Nigeria and Sierra Leone,
Republic of Guinea (Marburg virus disease), among other
African countries [5–8]. Furthermore, residents have
purchased and stored some medications commonly used
for treating other infectious diseases causing scarcity, and
rising costs due to an increase in demand [9].
Vaccination remains the most powerful weapon against
the emergence of new variants [10] as well as reaching
herd immunity [11]. However, compared with the rich
European and North-American countries, COVID-19
vaccination remains very low among African countries
with only 11% of the adult population fully vaccinated
[10]. is lack of adequate and complete vaccination of
the populace, among other factors, is brought about by
the state of the economy in African countries. Most Afri-
can countries are in the low-middle income strata. High
income economies, purchase and hoard vaccines imme-
diately or even before they are mass produced by pay-
ing pharmaceutical companies huge deposits for these
vaccines before production which affects the vaccine
distribution globally. is also limits effective control of
the widely spreading disease, particularly among African
countries and thus the emergence of various variants of
the virus as seen in South Africa (omicron), Brazil (delta)
and India [12]. is act of hoarding vaccines could be
directly attributable to the non-achievement of disease
control and its resurgence in other variants in low-mid-
dle-income countries. As such the inability to attain com-
munity immunity globally since people are still travelling,
more so, with most of these countries lowering their
guard on the earlier preventive measures [12].
e African continent has witnessed four waves of
COVID-19 over the last 2 years and has improved its
capacity to manage COVID-19 cases [10]. e supply of
COVID-19 vaccines across the region has also increased
with approximately 672 million doses distributed across
the region, mostly facilitated by COVAX (65%) and the
rest through bilateral deals (29%) and the African Union’s
Vaccines Acquisition. Despite this improvement, there
are concerns that the rapid spread of ‘false or misleading
information’ in digital and physical environments causes
confusion and risk-taking behaviours that can harm
health and lead to mistrust in health authorities and
undermine the public health response [13]. For instance,
in Pakistan, vaccine hesitance and resistance fuelled
by fear of the unknown, country of manufacture of the
vaccine, religious and cultural ideologies, have made it
almost impossible to reach the people [14]. Yet, despite
the widespread concern about the potential impacts of
misinformation on vaccination, little is known about the
magnitudes of those impacts nor their differential effects
across various countries in sub-Saharan Africa (SSA).
Exposure time to COVID-19-related news increased
over time during the pandemic [15] and more exposures
to the news have direct implications on people’s actions
such that receiving timely and informative communi-
cation during a time of uncertainties promotes public
cooperation [16]. Infodemic affects the hesitance and
resistance to uptake of new products across the mar-
ket, and it becomes worse in a pandemic as seen with
the coronavirus disease and its management and sup-
posed consequences [13]. Vaccine hesitancy (reluc-
tance to receive vaccines) is one of the top ten threats to
global health [17] and this is fuelled by health informa-
tion obtained from the news media, internet and social
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Osuagwuetal. BMC Public Health (2023) 23:38
media platforms [18–21]. Vaccine hesitancy is also high
among certain population groups [22, 23] probably due
to the previous medical experiment amongst these popu-
lation groups [24] and poor messaging [25]. Misinforma-
tion regarding the benefits, medicinal composition, and
adverse effects of vaccination, limits patient understand-
ing and overall buy-in [18]. Although access to technol-
ogy has improved during the pandemic, and the use of
social media has increased [18], there are concerns about
the spread of misinformation across different social net-
works propagated via the contemporary anti-vaccination
movement, to fuel vaccine hesitancy [26, 27]. is has
the potential to compromise public confidence in the
COVID-19 vaccine for the prevention of the disease [28].
However, where social media platforms were used to
propagate healthy messages, by nurses and doctors, a sig-
nificant improvement in compliance with public health
messages and subsequent COVID-19 infections has been
reported [21].
Sources of vaccination information have different
effects on people’s coping appraisal of COVID-19 vac-
cination [20]. Unlike mainstream media, social media
such as Facebook, Twitter, Instagram, WhatsApp, and
Pinterest allow individuals to rapidly create and share
content globally without editorial oversight [29, 30].
ese are complex and fluid ecosystems, in which anti-
vaccination viewpoints can be amplified and represented
as mainstream, and vaccine-hesitant parents can encoun-
ter compelling narratives from other parents dissuading
vaccination [31]. Misinformation and unsubstantiated
rumours regarding COVID-19 and potential vaccina-
tion against SARS-CoV-2 have already begun emerging
on social media platforms, threatening to erode pub-
lic confidence as the vaccines are rolled out in African
countries [32]. Information spread through social media
directly or indirectly increases hesitancy toward COVID-
19 vaccination, while the opposite effect was observed for
institutional websites [27]. Since social media platforms
may self-select content streams, contributing to ideo-
logical isolation, owners must ensure that social media
platforms provide access to accurate information on the
safety and efficacy of vaccinations [29].
e uptake of COVID-19 vaccination in SSA may
be impeded by the rapid spread of misinformation on
social media leading to belief in false rumours about
the pandemic [29], which has been associated with poor
health-seeking behaviour [33, 34]. e recent mixed
international messages about the efficacy of the differ-
ent COVID-19 vaccines, their side effects beyond the
local and systemic effects [35, 36] and the lack of clarity
regarding the required dosage [37] may further reduce
the confidence of African populations in the safety of the
vaccines [21]. In addition, the halting of the AstraZeneca
vaccine in South Africa, which showed less protection
against the new variant SARS-CoV-2 that can evade key
antibodies [21], may have contributed to lower people’s
confidence in the vaccine efficacy. Healthcare workers are
among the most trusted experts [38–40].
Intensive global efforts for continued physical distanc-
ing and isolation to curb the spread of new strains of
SARS-CoV-2 may intensify the use of social media as
individuals try to remain connected while apart [41]. In a
randomized controlled trial to understand the impact of
social media in the United States, researchers found that
messages spread by nurses and doctors on social media
led to a significant reduction in holiday travel and subse-
quent COVID-19 infections [21]. erefore, identifying,
understanding, and addressing how information sources
affect vaccine acceptance [42], hesitancy and resistance
[43] is potentially important to increase vaccine uptake.
erefore, this study was designed to, a) determine
the proportions of SSA participants that were depend-
ent on the different sources of information (social media
and mainstream media sources) for COVID-19-related
information; b), profile individuals who use the main-
stream media outlets (TV and radio, newspaper) to
obtain COVID-19 related information by identifying
the key socio-demographic, and health-related fac-
tors that are associated with the different information
sources; and c), determine the sources of information
about the COVID-19 pandemic among vaccine-hesitant
and resistant individuals across SSA countries as well as
identify the association between sources of information
and vaccine hesitancy. By identifying the distinguishing
characteristics, public health officials may be better able
to target a sub-population at greater risk of exposure
to misinformation about the COVID-19 vaccine. Find-
ings will also offer a greater understanding of how pub-
lic health officials can effectively tailor health behaviour
messaging to align with the socio-demographic profiles
of vaccine-hesitant or resistant individuals, while also
considering their consumption of COVID-19 informa-
tion and the predominant sources. In addition, the study
findings will help to provide steps on how social media
may be used to improve health literacy and build public
trust in vaccination.
Materials andmethods
Survey design
is was a cross-sectional study that recruited partici-
pants across SSA countries between March 14 and May
16, 2021. e questionnaire was initially developed and
used for a similar study [44]. e questionnaire was tested
for the internal validity of the items, and Cronbach’s
alpha coefficient score ranged from 0.70 and 0.74, indi-
cating satisfactory consistency [44]. e questionnaire
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Osuagwuetal. BMC Public Health (2023) 23:38
was adapted with minor modifications to suit this study’s
objective and was made available in English and French
languages to allow for residents residing in the Anglo-
phone and Francophone SSA countries to participate.
is was also necessary to increase the reach of the sur-
vey, one of the past study limitations [33, 34]. Moreover,
a pilot study was conducted on 10 participants who were
not included in the final study and were not part of the
research group to ensure clarity and understanding as
well as to determine the duration of completing the ques-
tionnaire before dissemination. e final questionnaire is
presented as Supplementary Table S1.
Participants
Eligible participants were adults of SSA origin, living in
or outside of Africa, aged 18 years and older, who were
able to provide informed consent at the time of this
study. Since this was an online survey, it is possible that
participants were those who had access to the internet
and those who were on their respective social media
platforms and used them. Participants were excluded if
they were not from SSA countries, were younger than
18 years, were unable to provide informed consent, and
participated in the initial pilot study. e supplementary
Fig. S1 shows the distribution of the participants by their
countries of origin.
Using a snowball sampling technique, participants
were recruited online after the survey was created in sur-
vey monkey (SurveyMonkey Inc., San Mateo, California,
USA, www. surve ymonk ey. com) and was administered
in two languages. An e-link to the survey was dissemi-
nated via emails and posted on social media platforms
(Facebook and WhatsApp). e distribution of the sur-
vey was strongly reliant on the snowballing or chain-
referral approach using virtual networks to reach the
population who used social media and other online for-
mats, thus saving time and cost for data collection [45,
46]. Authors were also encouraged to share the e-link of
the survey through personal emails and social network
groups in their respective countries. e use of an online
survey ensured that a large spectrum of prospective par-
ticipants across SSA could be reached in limited time and
resources.
e sample size calculation was based on a single pop-
ulation proportion formula by the World Health Organi-
zation (WHO) as well as previous studies [33, 34, 47].
Assuming a 20% attrition rate for a proportion of 50% of
the population and using the desired precision of 2% and
the 5% significance level for a two-sided test to detect sta-
tistical differences between groups at 80% power, a sam-
ple size of 2502 was considered adequate for this study
aims.
Dependent variables
e main outcomes were the three COVID-19 vaccine
indicators of the participants. e vaccinated group
was formed by those who responded in the affirmation
(Yes) to the question of whether they have been vac-
cinated against COVID-19. ose who responded ‘not
sure’ or ‘no’ to the question were then asked if they were
willing to be vaccinated when the vaccine became avail-
able in their home countries. e responses to these
follow-up questions were used to derive the second and
third outcome variables of ‘vaccine hesitancy’ and ‘vac-
cine resistance’, respectively, similar to a previous study
[48]. In this study, vaccine acceptance refers to a position
ranging from passive acceptance to active demand [42],
whereas hesitancy and resistance, respectively, were used
to define the reluctance to receive vaccines (i.e. positions
of being unsure about taking a vaccine) and being abso-
lutely against taking a vaccine [43].
Exposure variables
e exposure variables were derived from the question
of how the participants obtained information on the
COVID-19 vaccine. e participants responded ‘yes’
or ‘no’ to whether they obtained the information from
the mainstream media (Radio, Television, Newspaper),
Social media (such as Facebook, WhatsApp, Twitter) or
healthcare workers (HCWs), or family and friends.
Independent variables
e questionnaire included demographic data (age
group, sex, country of origin, religion, marital status,
educational level, employment status, occupational sta-
tus), health indicator factors (smoking status, presence of
pre-existing conditions including diabetes, lung disease,
heart disease, hypertension, obesity, asthma) and previ-
ous immunisations/vaccines history. ese constituted
the independent variables.
Statistical analysis
Analyses were performed using STATA/MP version 14
(Stata Corp, College Station, TX, USA) and categori-
cal data are shown as counts and percentages. e pro-
portion of participants who used each of the sources of
information was conducted using cross-tabulation. e
proportion of participants who used each of the sources
of information was conducted using cross-tabulation.
e associations between sources of information and
vaccine hesitancy and resistance were determined in a
series of logistic regression analyses that included sources
of information as exposure variables after controlling for
demographic factors, and health indicator factors. ere
is no unique statistical test for multicollinearity for binary
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Osuagwuetal. BMC Public Health (2023) 23:38
logistic regression but in our analysis, we treat the binary
outcome variables as a continuous variable and used the
“Logit” command and then ‘collin’ command in Stata to
determine multicollinearity including Variance Inflation
Factors (VIF) because collinearity is driven by the char-
acteristic of the independent variables and no the type of
regression used [49] and the VIF < 4 was considered suit-
able [50]. e odds ratios with 95% confidence intervals
(CI) were calculated to assess the adjusted odds of expo-
sure and independence variables.
Ethical consideration
is self-administered web-based cross-sectional study
was approved by the Humanities and Social Sciences
Research Ethics Committee (HSSREC 00002504/2021) of
the University of KwaZulu-Natal, Durban, South Africa.
e study adhered to the principles of the 1967 Helsinki
declaration (as modified in Fortaleza 2013) for research
involving human subjects. Before the study, an explana-
tion detailing the nature and purpose of the study was
provided to all participants using an online preamble.
Informed consent was obtained from the participants
who were required to answer either a ‘yes’ or ‘no’ to a
question on whether they were willing to voluntarily par-
ticipate in the survey. e confidentiality of participant
responses was assured, and anonymity was maintained.
Participation in the study was voluntary without any
incentive, inducement, or obligation from the research-
ers. To ensure that only one response per participant was
included in the study, participants were instructed not to
take part in the survey more than once, and during analy-
sis, we also restricted the data by the IP address of the
participants.
Results
e socio-demographic characteristics of the 2572 par-
ticipants who took part in this study are reported in
Table1. Of these participants, 1390 were males (54%),
mostly educated (80% of the participants had completed
a bachelor’s or higher education degree), about one-third
were aged 18-28 years (929, 36.1%), and more than half of
them were not married (1440, 56.0%) and resided in West
African countries (1446, 56.2%). About 80% of the par-
ticipants were employed in non-healthcare sectors and of
health indicators, there were few smokers (177, 6.9%) and
people who reported that they had a pre-existing condi-
tion (880, 34.2%).
Television and social media were the main sources of
information for more than two-thirds (n = 1897 and 1879,
respectively) of the participants in this study during the
pandemic, while less than half relied on the newspaper
(n = 1067, 41.5%) for such information (Table1). is was
consistent across regions, age groups and gender. More
than half of the Central African participants reported
that they sought COVID-19-related information from
HCWs, whereas East African participants relied less on
this source of information. Fifty-five percent of those
with a pre-existing health condition and those that had
previous vaccination reported that they relied on HCWs
for COVID-19-related information.
Percentage ofvaccine acceptance, hesitance,
andresistance bytheinformation sources
e proportion of COVID-19 vaccinated, hesitant and
resistant participants at the time of this study was 14.9,
17.8, and 67.3%, respectively. Figure1 displays the pro-
portion of participants who reported COVID-19 hesi-
tancy and resistance, across the different media sources
used by the participants during the pandemic. A total
of 17% of mainstream listeners and 13% of social media
users were vaccinated at the time of this study. Irrespec-
tive of the participants’ source of information during the
pandemic, the proportion who resisted the vaccine was
significantly higher and ranged from 37% among news-
paper readers to 85% among social media users. In com-
parison, the proportion who were hesitant to take the
vaccine ranged from 42% among newspaper readers to
73% among those who watched TV during the pandemic.
e Chi-square test found significant associations
between the participants’ vaccination status and their
reliance on social media (p < 0.0001), TV (p = 0.004),
HCWs (p < 0.0001) and friends/families (p = 0.001) for
COVID-19-related information, during the pandemic.
Socio‑demographic, andhealth indicators associated
withCOVID‑19‑related information sources
e full set of findings from the multinomial logistic
regression analyses for the characteristics of those that
relied on the various sources of information during the
pandemic, after adjusting for the potential cofounders, is
presented in Table2. In this study, reliance on the main-
stream media for information during the pandemic was
more likely to be observed among Central and Southern
African participants, whereas social media was less likely
to be used for COVID-19 information retrieval in those
with primary education (aORs = 0.36, 95%C I = 0.20, 0.62)
and non-Christians (aORs = 0.74, 95%CI = 0.56, 0.97).
Central African participants and those who worked
in health sectors were more likely to rely on HCWs for
COVID-19-related information as compared to West
African participants and those who worked in non-
healthcare sectors, during the pandemic. Compared with
males, female participants were less likely to listen to the
radio, watch TV and read the newspaper but more likely
to rely on friends and family (aOR = 1.23, 95%CI = 1.05,
1.45), for COVID-19-related information, during the
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Osuagwuetal. BMC Public Health (2023) 23:38
Table 1 Distribution (n, %) of the socio‑demographic characteristics of the participants and their main sources of COVID‑19 related
information during the pandemic
HCW Healthcare workers
a Items have some missing responses
b Includes widowed, divorced and never married people. Postgraduate degree includes Masters /PhD
Variables All Radio TV Newspaper Social media HCW Family/friends
n, % 2572 (100) 1449 (56.3) 1897(73.8) 1067 (41.5) 1879 (73.1) 1289 (50.1) 1215 (47.2)
Demography
Age category in yearsa
18‑28 929 (36.1) 497 (54.0) 656 (70.6) 347 (37.4) 682 (73.4) 437 (47.0) 461 (49.6)
29‑38 720 (28.0) 415 (57.6) 532 (73.9) 293 (40.7) 523 (72.6) 363 (50.4) 321 (44.6)
39‑48 502 (19.5) 293 (58.4) 390 (77.7) 212 (42.2) 364 (72.5) 271 (54.0) 228 (45.4)
49+346 (13.5) 201 (58.1) 271 (78.3) 177 (51.2) 265 (76.6) 178 (51.4) 164 (47.4)
Sex
Males 1390 (54.0) 829 (59.6) 1047 (75.3) 629 (45.2) 1028 (74.0) 690 (49.6) 623 (44.8)
Females 1182 (46.0) 620 (52.4) 850 (71.9) 438 (37.1) 851 (72.0) 599 (50.7) 592 (50.1)
SSA region of origina
West Africa 1446 (56.2) 800 (55.3) 1054 (72.9) 597 (41.3) 1077 (74.5) 755 (52.0) 668 (46.2)
East Africa 124 (4.8) 50 (40.3) 82 (66.1) 48 (38.7) 96 (77.4) 48 (38.7) 45 (36.3)
Central Africa 314 (12.2) 184 (58.6) 251 (79.9) 145 (46.2) 225 (71.7) 176 (56.1) 162 (51.6)
Southern Africa 667 (25.9) 409 (61.3) 500 (75.0) 269 (40.3) 472 (70.8) 303 (45.4) 332 (49.8)
Marital status
Married 1132 (44.0) 648 (57.2) 866 (76.5) 472 (41.7) 821 (72.5) 590 (52.0) 505 (44.6)
Not marriedb1440 (56.0) 801 (55.6) 1031 (71.6) 595 (41.3) 1058 (73.5) 699 (49.0) 710 (49.3)
Highest level of education
Postgraduate degree 757 (29.4) 406 (53.6) 598 (79.0) 335 (44.3) 567 (74.9) 378 (49.9) 349 (46.1)
Bachelor’s degree 1309 (50.9) 750 (57.3) 955 (73.0) 551 (42.1) 969 (74.0) 707 (54.0) 614 (46.9)
Secondary 448 (17.4) 262 (58.5) 312 (69.6) 158 (35.3) 314 (70.1) 181 (40.4) 234 (52.2)
Primary or less 58 (2.3) 31 (53.5) 32 (55.2) 23 (39.7) 29 (50.0) 23 (39.7) 18 (31.0)
Employment status
Employed/self employed 1890 (73.5) 1095 (57.9) 1428 (75.6) 827 (43.8) 1393 (73.7) 991 (52.4) 872 (46.1)
Unemployed/retired 682 (26.5) 354 (51.9) 469 (68.8) 240 (35.2) 486 (71.3) 298 (43.7) 343 (50.3)
Religion
Christianity 2301 (89.5) 1324 (57.5) 1736 (75.4) 957 (41.6) 1699 (73.8) 1170 (50.9) 1112 (48.0)
Others 271 (10.5) 125 (46.1) 161 (59.4) 110 (40.6) 180 (66.4) 119 (43.9) 103 (38.0)
Occupation
Non‑healthcare sector 1771 (68.9) 1017 (57.4) 1314 (74.2) 760 (42.9) 1301 (73.5) 801 (45.0) 908 (51.3)
Healthcare sector 801 (31.1) 432 (53.9) 583 (72.8) 307 (38.3) 578 (72.2) 488 (60.9) 307 (38.3)
Health indicators
Smoking status
Ex‑smoker 160 (6.2) 82 (51.3) 108 (67.5) 66 (41.3) 118 (73.8) 70 (44.0) 63 (39.4)
Current smoker 177 (6.9) 114 (64.4) 132 (74.6) 65 (36.7) 133 (75.1) 75 (42.4) 102 (57.6)
Non‑smoker 2235 (86.9) 1253 (56.1) 1657 (74.1) 936 (41.9) 1628 (72.8) 1144 (51.0) 1050 (47.0)
Any pre‑existing condition
No 1692 (65.8) 1184 (55.0) 1568 (72.9) 880 (40.9) 1555 (72.3) 1056 (49.0) 1008 (46.9)
Yes 880 (34.2) 265 (63.0) 329 (78 .2) 187 (44.4) 324 (77.0) 233 (55.0) 207 (49.2)
History of previous vaccination
No 1692 (65.8) 910 (53.8) 1229 (72.6) 661 (39.1) 1237 (73.1) 803 (47.0) 793 (46.9)
Yes 880 (34.2) 539 (61.3) 668 (75.9) 406 (46.1) 642 (72.9) 486 (55.0) 422 (47.9)
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Page 7 of 16
Osuagwuetal. BMC Public Health (2023) 23:38
pandemic. Current smokers were also more likely to rely
on friends and family (aOR = 1.97, 95%CI = 1.26, 3.10),
while those with primary or no education as well as non-
Christians were less likely to rely on social media for
information, during the pandemic.
Associations betweenCOVID‑19 vaccine hesitancy,
resistance, andsources ofinformation used byparticipants
inSSA duringthepandemic
e aORs and their 95%CI for factors associated with
vaccine hesitancy and vaccine resistance are presented in
Tables3 and 4, respectively. After adjusting for the poten-
tial confounders, in this study, participants who listened
to the radio, those who watched TV, and social media
users, during the pandemic, were less likely to report
COVID-19 vaccine hesitancy. As shown in Table4, age
(29-38 years), SSA region of origin (East Africa), educa-
tional level (primary education or less), religion and occu-
pation of the participants were associated with resistance
towards COVID-19 vaccination. Except for those who
listened to the radio, reliance on other media sources for
COVID-19-related information was significantly asso-
ciated with vaccine resistance, with the strongest asso-
ciation found among social media users (aOR = 2.13
95%CI = 1.62, 2.80) Table4. Also, those who watched TV
and people who relied on HCWs and friends/family for
COVID-19-related information were more likely to resist
COVID-19 vaccination, whereas reading the newspaper
reduced the likelihood of vaccine hesitancy (aOR = 0.77,
95%CI 0.62, 0.95) among the participants.
e forest plots showing the adjusted odd ratios for
the association between the media sources used by the
participants in SSA countries during the pandemic and
vaccine hesitancy and resistance are shown in Figs.2 and
3, respectively. Figure2 shows that COVID-19 vaccine
hesitancy was significantly associated with four of the
six media sources examined in this study. Reliance on
HCWs, social media and traditional sources (TV and
radio) for COVID-19-related information during the
pandemic reduced the odds of COVID-19 vaccine hesi-
tancy by 27, 21, 20 and 17%, respectively.
ere was a strong association between the use of
social media and resistance towards COVID-19 vacci-
nation (aOR = 2.13, 95%CI 1.62, 2.80) as seen in Fig. 3.
Other factors such as watching TV and reliance on
friends/families for information related to COVID-19
were also associated with COVID-19 vaccine resistance
among the participants. ose who relied on the newspa-
pers for information during the pandemic were less likely
to be resistant towards taking the COVID-19 vaccines
compared to those who did not (Fig.3).
Discussion
is study was undertaken to determine the role of differ-
ent information sources on COVID-19 vaccine hesitancy
and resistance in SSA. Consistent across age groups, gen-
der and regions, television and Facebook, were the main
sources of up-to-date information for participants in SSA
during the pandemic. However, information from these
sources, particularly those obtained from social media
platforms, can be misleading, and as shown in the pre-
sent study, social media users were twice more likely to
resist the COVID-19 vaccines compared with non-users.
ose who relied on the TV, HCWs, friends, and family
members for their up-to-date information had a higher
likelihood of vaccine resistance than their counterparts.
In contrast, the odds for vaccine resistance were signifi-
cantly reduced among those who reported that the news-
paper was their main source of information during the
pandemic.
Fig. 1 Prevalence of COVID‑19 vaccination, hesitancy, resistance by information sources in sub‑Saharan Africa, during the pandemic (n = 2572)
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Page 8 of 16
Osuagwuetal. BMC Public Health (2023) 23:38
Although the finding of a strong independent associa-
tion between social media use and vaccine resistance was
contrary to previous studies on smaller samples in Saudi
Arabia [51, 52], this is important considering the wide
utilisation of Facebook as the main source of information
by many participants during the pandemic. A Facebook
IQ survey revealed that more than 95 million people in
SSAs access Facebook, with 97% of these doing so on
handheld and mobile devices each month. erefore,
these popular sources of information (Television and
Facebook) must be used to convey reliable, science-based
information about COVID-19 vaccines and future pan-
demics to the millions of SSA people.
Smokers and females were more likely to rely on fam-
ily and friends for COVID-19-related information, but
less likely to rely on mainstream media (such as TV) than
their male counterparts. ere was a lower likelihood for
non-Christians and those with lower education to rely
on social media for information during the lockdown. Of
the information sources, reliance on social media showed
the strongest association with COVID vaccine hesitancy
and resistance. After adjusting for potential covariates,
information sources played a significant role in vaccine
hesitancy and resistance among SSAs. ose who relied
on information obtained from watching TV and family/
friends were more likely to resist the COVID vaccine
when compared to those who did not rely on those media
sources. Listening to the radio and obtaining information
from HCWs had a positive influence on intent towards
vaccination because it reduced their likelihood of being
resistant and hesitant towards COVID-19 vaccination.
e negative influence of TV and social media use on
COVID-19 vaccination reported in this study was not
surprising as some emerging anti-vaccine television and
Table 2 Adjusted odd ratios (AORs) of factors associated with information sources used by participants in sub‑Saharan Africa during
the pandemic
Condence intervals (CI) that does not include 1.00 are signicant variables
Postgraduate degree includes Masters /PhD
HCW Healthcare workers
Variables Radio Television Newspaper Social media HCW Family/Friends
Demography AORs [95% CI] AORs [95% CI] AORs [95% CI] AORs [95% CI] AORs [95% CI] AORs [95% CI]
Sex
Males Reference Reference Reference Reference
Females 0.72 [0.81, 0.84] 0.81 [0.68, 0.98] 0.73 [0.62, 0.86] – – 1.23 [1.05, 1.45]
SSA region of origin
West Africa Reference Reference Reference Reference Reference Reference
East Africa 0.53 [0.37, 0.78] 0.74 [0.50, 1.10] 0.88 [0.60, 1.29] 1.18[0.76, 1.83] 0.56 [0.38, 0.82] 0.66 [0.45, 0.97]
Central Africa 1.16 [0.90, 1.50] 1.69 [1.24, 2.29] 1.20 [0.93, 1.54] 0.92[0.70, 1.22] 1.37 [1.07, 1.77] 1.12 [0.87, 1.44]
Southern Africa 1.49 [1.22, 1.81] 1.44 [1.14, 1.81] 1.11 [0.91, 1.36] 0.89 [0.72, 1.11] 0.89 [0.73, 1.08] 1.03 [0.84, 1.27]
Highest level of education
Postgraduate degree Reference Reference Reference Reference Reference
Bachelor’s degree 0.71 [0.57, 0.88] 0.97 [0.81, 1.17] 0.95 [0.77, 1.17] 1.20 [1.00, 1.45] 1.01 [0.84, 1.21]
Secondary 0.53 [0.40, 0.70] 0.73 [0.55, 0.96] 0.82 [0.62, 1.08] 0.86 [0.67, 1.11] 0.96 [0.74, 1.24]
Primary or less 0.34 [0.19, 0.61] 0.96 [0.54, 1.69] 0.36 [0.20, 0.62] 0.83 [0.47, 1.46] 0.44 [0.25, 0.80]
Employment status
Employed/self employed Reference Reference
Unemployed/retired 0.72 [0.60, 0.88] 0.72 [0.59, 0.89]
Religion
Christianity Reference Reference Reference
Others 0.57 [0.44, 0.74] 0.45 [0.34, 0.59] 0.74 [0.56, 0.97] 0.65 [0.50, 0.85]
Occupation
Non‑healthcare sector Reference Reference Reference Reference
Healthcare sector 0.82 [0.69, 0.99] 0.71 [0.59, 0.86] 1.81 [1.51, 2.17] 0.58 [0.48, 0.69]
Smoking status
Ex‑smoker Reference
Current smoker 1.97 [1.26, 3.10]
Non‑smoker 1.35 [0.96, 1.89]
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Page 9 of 16
Osuagwuetal. BMC Public Health (2023) 23:38
Table 3 Adjusted odd ratios for factors associated with media sources and vaccine hesitancy among participants in sub‑Saharan
Africa during the pandemic
Condence intervals (CI) that does not include 1.00 are signicant variables
Postgraduate degree includes Masters /PhD
HCW Healthcare workers
Variables Total Radio TV Newspaper Social media HCWs Family/
Friends
Demography AORs [95% CI] AORs [95% CI] AORs [95% CI] AORs [95% CI] AORs [95% CI] AORs [95% CI] AORs [95% CI]
Age category in years
18–28 Reference Reference Reference Reference Reference Reference Reference
29–38 0.85 [0.66, 1.10] 0.86 [0.66, 1.11] 0.85 [0.66, 1.10] 0.85 [0.66, 1.10] 0.85 [0.66, 1.10] 0.84[0.65, 1.09] 0.85[0.66, 1.10]
39–48 0.88 [0.64, 1.19] 0.88 [0.65, 1.20] 0.88 [0.67, 1.99) 0.88 [0.64, 1.19] 0.87[0.64, 1.19] 0.88[0.65, 1.20] 0.88 [0.65, 1.20]
49+0.86 [0.61, 1.20] 0.86 [0.61, 1.21] 0.86 [0.61, 1.21] 0.85 [0.60, 1.19] 0.86[0.61, 1.21] 0.86 [0.61, 1.21] 0.86 [0.61, 1.21]
Sex
Males Reference Reference Reference Reference Reference Reference Reference
Females 0.83 [0.70, 0.99] 0.82 [0.69, 0.98] 0.83 [0.69, 0.99] 0.84 [0.70, 0.99] 0.83[0.70, 0.99] 0.84 [0.70, 0.99] 0.84 [0.70, 1.00]
SSA Region of Origin
West Africa Reference Reference Reference Reference Reference Reference Reference
East Africa 1.10 [0.73, 1.64] 1.07 [0.71, 1.60] 1.08 [0.72, 1.62] 1.10[0.73, 1.64] 1.10[0.74, 1.65] 1.06 [0.71, 1.58] 1.08[0.72, 1.62]
Central Africa 0.86 [0.66, 1.13] 0.87 [0.66, 1.13] 0.88 [0.67, 1.15] 0.86 [0.66, 1.12] 0.86 [0.66, 1.12] 0.88[0.68, 1.16] 0.87 [0.66, 1.13]
Southern Africa 1.24 [0.98, 1.56] 1.26 [1.00, 1.59] 1.26 [1.00, 1.58] 1.23[0.98, 1.55] 1.23 [0.97, 1.54] 1.23[0.98, 1.55] 1.24 [0.98, 1.56]
Marital Status
Married Reference Reference Reference Reference Reference Reference Reference
Not married 0.73 [0.58, 0.90] 0.73 [0.58, 0.90] 0.73 [0.58, 0.90] 0.72 [0.58, 0.90] 0.73[0.59, 0.91] 0.73 [0.58, 0.90] 0.73 [0.59, 0.91]
Highest level of education
Postgraduate Degree Reference Reference Reference Reference Reference Reference Reference
Bachelor’s degree 0.89 [0.72, 1.10] 0.90 [0.73, 1.11] 0.88 [0.71, 1.09] 0.89[0.72, 1.10] 0.89 [0.72, 1.09] 0.90[0.73, 1.12] 0.89 [0.72, 1.10]
Secondary 0.84 [0.61, 1.16] 0.85 [0.62, 1.18] 0.83 [0.60, 1.14] 0.85[0.61, 1.17] 0.83 [0.60, 1.44] 0.84 [0.61, 1.16] 0.84 [0.61, 1.16]
Primary or less 0.59 [0.32, 1.12] 0.61 [0.32, 1.14] 0.57 [0.30, 1.07] 0.59 [0.32, 1.12] 0.56[0.30, 1.06] 0.58[0.31, 1.10] 0.58 [0.31, 1.09]
Employment status
Employed/self employed Reference Reference Reference Reference Reference Reference Reference
Unemployed/retired 1.28 [1.00, 1.63] 1.26 [0.99, 1.61] 1.26 [0.99, 1.61] 1.28 [1.01, 1.64] 1.28 [1.00, 1.63] 1.27 [0.99, 1.61] 1.28 [1.00, 1.63]
Religion
Christianity Reference Reference Reference Reference Reference Reference Reference
Others 1.29 [0.96, 1.73] 1.26 [0.94, 1.69] 1.24 [0.93, 1.67] 1.29 [0.96, 1.73] 1.27[0.95, 1.71] 1.28 [0.95, 1.71] 1.28 [0.95, 1.71]
Occupation
Non‑healthcare sector Reference Reference Reference Reference Reference Reference Reference
Healthcare sector 0.59 [0.48, 0.72] 0.58 [0.48, 0.71] 0.58 [0.48, 0.71] 0.59 [0.48, 0.72] 0.58[0.48, 0.71] 0.61[0.50, 0.75] 0.58 [0.47, 0.71]
Smoking status
Ex‑smoker Reference Reference Reference Reference Reference Reference Reference
Current smoker 0.88 [0.54, 1.42] 0.90 [0.55, 1.45] 0.90 [0.55, 1.45] 0.88 [0.54, 1.42] 0.89 [0.55, 1.43] 0.88[0.54, 1.42] 0.90 [0.56, 1.45]
Non‑smoker 1.04 [0.73, 1.50] 1.06 [0.74, 1.52] 1.07 [0.74, 1.53] 1.04 [0.72, 1.49] 1.04[0.73, 1.50] 1.06[0.74, 1.53] 1.05[0.73, 1.52]
Any pre‑existing condition
No Reference Reference Reference Reference Reference Reference Reference
Yes 0.81 [0.64, 1.03] 0.82 [0.65, 1.04] 0.82 [0.64, 1.04] 0.81 [0.64, 1.03] 0.82 [0.64, 1.04] 0.82[0.65, 1.05] 0.81[0.64, 1.03]
Previous vaccine as a child
No Reference Reference Reference Reference Reference Reference Reference
Yes 0.90 [0.75, 1.08] 0.91 [0.76, 1.09] 0.90 [0.75, 1.08] 0.84 [0.75, 1.07] 0.90 [0.75, 1.08] 0.91[0.76, 1.10] 0.90 [0.75, 1.08]
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Page 10 of 16
Osuagwuetal. BMC Public Health (2023) 23:38
Table 4 Adjusted odd ratios for factors associated with media sources and vaccine resistance among participants in sub‑Saharan
Africa during the pandemic
Condence intervals (CI) that does not include 1.00 are signicant variables
Postgraduate degree includes Masters /PhD
HCW Healthcare workers
Variables Total Radio TV Newspaper Social media HCWs Family/
Friends
Demography AORs [95% CI] AORs [95% CI] AORs [95% CI] AORs [95% CI] AORs [95% CI] AORs [95% CI] AORs [95% CI]
Age category in years
18‑28 Reference Reference Reference Reference Reference Reference Reference
29‑38 1.58 [1.16, 2.15] 1.58 [1.16, 2.15] 1.58 [1.16, 2.15] 1.59 [1.17, 2.17] 1.60 [1.17, 2.19] 1.59 [1.17, 2.17] 1.58 [1.16, 2.15]
39‑48 1.13 [0.78, 1.66] 1.13 [0.77, 1.66] 1.13 [0.77, 1.65] 1.15 [0.78, 1.68] 1.15 [0.78, 1.68] 1.13 [0.77, 1.65] 1.13 [0.77, 1.66]
49+1.30 [0.86, 1.96] 1.30 [0.86, 1.96] 1.29 [0.85,1.95] 1.34 [0.89, 2.04] 1.29 [0.85, 1.95] 1.30 [0.86, 1.97] 1.29 [0.85, 1.96]
Sex
Males Reference Reference Reference Reference Reference Reference Reference
Females 1.11[0.89, 1.37] 1.11 [0.90, 1.37] 1.12 [0.91, 1.39] 1.09 [0.88, 1.35] 1.12 [0.90, 1.38] 1.10[0.89, 1.37] 1.09 [0.88, 1.35]
SSA Region of Origin
West Africa Reference Reference Reference Reference Reference Reference Reference
East Africa 1.65 [1.07, 2.53] 1.65[1.07, 2.54] 1.69 [1.10, 2.59] 1.64 [1.07, 2.53] 1.63 [1.06, 2.51] 1.71[1.11, 2.63] 1.70 [1.10, 2.61]
Central Africa 0.73 [0.52, 1.04] 0.73 [0.52, 1.04] 0.72 [0.51, 1.02] 0.74 [0.52, 1.05] 0.75 [0.53, 1.07] 0.72 [0.51, 1.02] 0.73 [0.51, 1.03]
Southern Africa 1.02 [0.77, 1.33] 1.01[0.77, 1.33] 0.99 [0.75, 1.31] 1.03 [0.78, 1.35] 1.05 [0.79, 1.38] 1.02[0.78, 1.32] 1.01 [0.77, 1.33]
Marital Status
Married Reference Reference Reference Reference Reference Reference Reference
Not married 1.20 [0.92, 1.55] 1.19[0.92, 1.55] 1.20 [0.92, 1.56] 1.22 [0.94, 1.59] 1.17 [0.90, 1.52] 1.19 [0.91, 1.55] 1.19[0.91, 155]
Highest level of education
Postgraduate Degree Reference Reference Reference Reference Reference Reference Reference
Bachelor’s degree 0.86 [0.67, 1.11] 0.86[0.67, 1.11] 0.88 [0.68, 1.13] 0.86[0.67, 1.11] 0.87[0.68, 1.13] 0.85[0.66, 1.10] 0.87 [0.67, 1.11]
Secondary 0.86 [0.58, 1.26] 0.86 [0.58, 1.26] 0.88 [0.60, 1.30] 0.84[0.58, 1.24] 0.89[0.61, 1.32] 0.86[0.59, 1.26] 0.86 [0.58, 1.26]
Primary or less 0.27 [0.08, 0.91] 0.27[0.08, 0.91] 0.29 [0.09, 0.98] 0.27[0.08, 0.91] 0.30[0.09, 1.02] 0.28 [0.08, 0.92] 0.28[0.09, 0.95]
Employment status
Employed/self employed Reference Reference Reference Reference Reference Reference Reference
Unemployed/retired 0.84 [0.63, 1.13] 0.84[0.63, 1.13] 0.85 [0.64, 1.14] 0.83 [0.62, 1.11] 0.85 [0.63, 1.14] 0.85 [0.63, 1.14] 0.84[0.63, 1.13]
Religion
Christianity Reference Reference Reference Reference Reference Reference Reference
Others 0.57 [0.38, 0.84] 0.57[0.38, 0.84] 0.60 [0.40, 0.89] 0.56[0.38, 0.84] 0.60 [0.40, 0.88] 0.58 [0.39, 0.85] 0.59 [0.40, 0.87]
Occupation
Non‑healthcare sector Reference Reference Reference Reference Reference Reference Reference
Healthcare sector 0.64 [0.50, 0.82] 0.64[0.50, 0.82] 0.65 [0.51, 0.83] 0.63[0.49, 0.81] 0.65[0.51, 0.83] 0.62 [0.48, 0.79] 0.66 [0.52, 0.85]
Smoking status
Ex‑smoker Reference Reference Reference Reference Reference Reference Reference
Current smoker 1.65 [0.92, 2.96] 1.65 [0.92, 2.96] 1.61 [0.90, 2.90] 1.64 [0.91, 2.94] 1.62 [0.90, 2.91] 1.65 [0.92, 2.96] 1.58 [0.88, 2.83]
Non smoker 1.29 [0.81, 2.05] 1.29 [0.81, 2.04] 1.25 [0.79, 1.99] 1.31 [0.82, 2.07] 1.30 [0.82, 2.06] 1.27 [0.80, 2.01] 1.26[0.79, 2.00]
Any pre‑existing condition
No Reference Reference Reference Reference Reference Reference Reference
Yes 0.97 [0.72, 1.30] 0.97 [0.72, 1.30] 0.95 [0.71, 1.28] 0.97 [0.72, 1.31] 0.93[0.69, 1.26] 0.95[0.71, 1.28] 0.96 [0.71, 1.29]
Previous vaccine as a child
No Reference Reference Reference Reference Reference Reference Reference
Yes 0.82 [0.66, 1.03] 0.82[0.66, 1.03] 0.82 [0.65, 1.02] 0.84 [0.67, 1.05] 0.82 [0.65, 1.03] 0.81 [0.64, 1.01] 0.82 [0.65, 1.03]
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Page 11 of 16
Osuagwuetal. BMC Public Health (2023) 23:38
Fig. 2 Forest plot of association between main information sources and vaccine hesitancy and resistance among the participants in sub‑Saharan
Africa, during the pandemic
Fig. 3 Forest plot of association between main information sources and vaccine resistance among the participants in sub‑Saharan Africa, during
the pandemic
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Page 12 of 16
Osuagwuetal. BMC Public Health (2023) 23:38
social media campaigns are responsible for generating
and perpetuating vaccine hesitancy and resistance. e
high prevalence of inaccurate and negative informa-
tion on social media regarding COVID-19 may predict a
greater likelihood of negative vaccine intent in this case
as well [53, 54]. In addition, social media is generally
unregulated and has enabled people with anti-vaccine
beliefs to generate and disseminate information freely
[55]. e findings of this study are consistent with a pre-
vious study which found that, relative to social media and
the internet, there was a positive association between
reliance on traditional news sources and intention to
uptake a COVID-19 vaccine in the United States [56].
Another previous work also highlighted the role of nega-
tive information on social media in shaping individual
perceptions regarding human papillomavirus (HPV) vac-
cination intent [57].
Central and Southern African participants showed
greater reliance on mainstream media for COVID-19-re-
lated information, particularly watching TV, and this
increased their likelihood of not taking the vaccine. is
finding could, in part, be related to the nature of lock-
downs in different sub-Saharan countries. For instance,
South Africa went into Level 5 (hard lockdown) quite
early in the pandemic (March 2020), and residents were
mostly confined to their homes, watching TV [58]. Reli-
ance on social media platforms for COVID-19-related
information was associated with higher educational lev-
els, which agreed with a study from South Africa [58]
which found that education-related inequalities were
visible in the use of COVID-19 preventive measures in
South Africa.
e finding that the participants with pre-existing
medical conditions or those who had a prior history of
vaccinations were more reliant on HCWs for COVID-
19-related information during the pandemic suggests
that HCWs are trusted to have a better understanding of
COVID-19 information, and as such, they can be a source
of essential care and information in future pandemics. In
a previous study, participants rated health information
from doctors and other health workers as highly reli-
able [59]. is assertion is supported by a recent study
that showed that HCWs are essential front liners, work-
ing to ensure the health of older adults and those with
chronic conditions or disabilities during the COVID-19
pandemic [60]. e high vaccination and low hesitancy
rates reported among participants who relied on HCWs
for information were consistent with a previous study,
which showed that HCWs have adequate information
on vaccines and have the ability and confidence to com-
municate such information effectively [61]. is finding
supports the idea that HCWs, can positively influence
the use of vaccines and have the potential to impact
COVID-19 vaccination in SSA. However, recent litera-
ture has also warned of the inadequate capacity of HCWs
to deal with anti-vaccine messages on social media [62].
One interesting finding of this paper is the resistant
effect of information derived from HCW reported by
participants. Studies among Africans have shown that
HCWs themselves are resistant to the vaccine with their
information being obtained from unreliable sources such
as social media, friends and family [63, 64]. Safety con-
cerns, insufficient or inaccurate information, lack of trust
in the government’s capacity to manage, and personal
beliefs are factors that have been reported to influence
the acceptance or resistance of HCWs to the vaccine
[65–67]. e likelihood of such health workers passing
on information to the populace with content that may be
tainted with their own beliefs and inaccuracies can con-
tribute to making those who interact with them resistant
to the vaccine.
Females were less likely to listen to the radio, watch TV
and read newspapers but more likely to rely on friends
and family, and this increased their likelihood of vac-
cine hesitancy. is finding may suggest that women
expressed interest in COVID-19 issues with their friends
and family (leaving very little room for individual pro-
active decision-making) while men were significantly
more likely than women to get such information from
the radio, TV and newspapers. e study also showed
differences in behaviour, such that the less educated,
non-Christians were not more reliant on social media
platforms for information during the pandemic than
their counterparts. For those who were more likely to be
resistant (such as those who watched TV and those who
relied on their families and friends for information), addi-
tional vaccine promotional efforts would be required.
Limitations andstrengths
Some limitations should be considered when interpreting
the findings of this study. First, this was a cross-sectional
study, and as such, we cannot determine causation. Sec-
ond, like previous studies conducted during COVID-19 in
SSA [34, 47, 68, 69], we utilized an internet-based meth-
odology which was the only reliable means to disseminate
information at the time of this study. e survey was dis-
tributed electronically using social media platforms and
emails because it was difficult to physically access some
participants in some places due to the protective meas-
ures still in place at the time of the study. is method of
soliciting participants may have inadvertently excluded
some potential participants whose opinions differed,
such as those without internet access and people living
in rural areas, where internet penetration remains rela-
tively low [70]. ird, the survey was presented in English
and French and thus inadvertently excluding non-English
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Page 13 of 16
Osuagwuetal. BMC Public Health (2023) 23:38
and non-French speaking countries in SSA from partici-
pating. Fourth, although the study showed satisfactory
internal validity, its generalization or transferability to
all SSA countries may be limited. Notwithstanding these
limitations, this was the first study from the SSA region
to provide insight into some of the impacts of informa-
tion sources on the acceptance of COVID-19 vaccines
which has been a worry to the international community.
Although this topic is commonplace as reliance on online
information sources is expected to happen during pan-
demics, no study has demonstrated the impacts of these
sources of information on COVID vaccination in the way
the present study did, including the use of a robust analy-
sis to control for potential confounders during the anal-
ysis and reduce the possibility of a bias. is makes our
study a unique one since it provided the first documented
evidence from SSA showing the impacts of the lockdown
on the behaviour of ordinary citizens.
Implications ofour ndings
is study provides an understanding of how the expo-
sure of SSAs to various media sources during the pan-
demic, influences their attitude toward the COVID-19
vaccination program. Our focus on COVID-19 vaccine
hesitancy and resistance is important because of the need
to stem the pandemic by vaccinating enough people in
the face of the recent rise in infections [11]. e findings
are important because people’s negative attitudes toward
vaccination in general, and their hesitancy or resistance
to the COVID-19 vaccine, is a growing public health
problem. is study provides insight into how the vari-
ous media outlets commonly used by the participants liv-
ing in different SSAs regions to obtain COVID-19-related
information affect their attitude towards vaccine uptake.
is finding underlines the importance of media expo-
sure, suggesting that the media can be used to improve
vaccine literacy across the region [71]. In addition, this
study contributes to our understanding of the interplay
between SSA regions and media exposure during the
pandemic. For example, the study found greater reli-
ance on the mainstream media for COVID-19-related
information among those from Central and Southern
Africa, which negatively influenced vaccine uptake. is
insight has important practical implications by inform-
ing us about the dynamics of individuals’ attitudes and
would help researchers understand the underlying fac-
tors that influence the acceptance of vaccination during
a pandemic. is study will help public health and health
promotion officers in various SSA countries design more
effective communications and interventions.
Furthermore, the very low vaccination rate observed in
this study raises the concern of vaccine nationalism with
challenges of vaccine inequity in low and middle-income
countries which was shown to be counterproductive
during the pandemic [5, 12, 72]. High-income countries
prioritized investment in the stock of vaccinations over
immediate capacity building and delivery of such life-
saving vaccines by healthcare systems. ese lessons are
important in tackling future pandemics. Although vacci-
nations are the only effective means of tackling viral dis-
eases, prior studies have demonstrated that many people
do not believe in their safety and effectiveness [14]. ere
is also the possibility that previously eradicated infections
may re-emerge in some regions. People need to be edu-
cated about vaccines, their safety and their efficacy. e
media can be used to boost people’s confidence in taking
the vaccine [14, 73, 74].
Conclusions
e findings of this study suggest that healthcare organi-
zations and governments of SSA fight misinformation
by providing factual messages countries need to utilise
social media platforms, television, and healthcare work-
ers to provide reliable information to influence vaccine
hesitancy and encourage uptake of the COVID-19 vac-
cination. Failure to access and apply reliable healthcare
information, whether for the public or health workers,
has always been a major cause of avoidable deaths. More
research and investment are needed to improve the avail-
ability of reliable healthcare information, protect people
from misinformation, and empower people with educa-
tion on how to identify misinformation. e ongoing
trajectory of misinformation - from vaccine hesitancy
to previous infectious diseases to COVID-19 –calls for
global action as the ‘infodemic’ of the next public health
emergency may be worse than the current COVID
infodemic.
Abbreviations
COVID‑19 Coronavirus disease
SSA Sub–Saharan Africa
OR Odds ratio
AOR Adjusted odds ratio
CI Confidence interval
TV Television
HCW Healthcare worker
WHO World Health Organization
Supplementary Information
The online version contains supplementary material available at https:// doi.
org/ 10. 1186/ s12889‑ 022‑ 14972‑2.
Additional le1: Supplementary TableS1. Sample of Survey Item.
Additional le2: Figure S1. Country of origin of respondents.
Acknowledgements
None.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 14 of 16
Osuagwuetal. BMC Public Health (2023) 23:38
Authors’ contributions
All authors were involved in the conceptualization of the study; K.E.A, U.L.O.,
and K.P.M performed the methodology; Software, K.E.A., U.L.O.; Validation, T.I.,
R.O., E.E., B.N.E., O.A., K.P.M., E.K.A., M.C. and T.C.; Formal Analysis, K.E.A., and U.L.O.;
Investigation, all authors; Resources, all authors; Data Curation, K.E.A., O.M.A,
and U.L.O.; Writing – Original Draft Preparation, P.C.G., G.O., R.O.; E.E., U.L.O., E.A.;
Writing – Review & Editing, K.P.M., G.O., O.A., E.A., K.E.A., K.P.M., R.L., D.D.C., and
M.C.; Visualization, K.P.M., and K .E.A.; Supervision, K.E.A., U.L.O., T.I, B.N.E, K.P.M;
Project Administration, K.E.A., U.L.O. and P.C.G.. All authors reviewed the manu‑
script, read and agreed to the published version of the manuscript.
Funding
This research did not receive any specific grant from funding agencies in the
public, commercial, or not‑for‑profit sectors.
Availability of data and materials
The dataset supporting the conclusions of this article is included within the
article (and its additional files). Data is also available on request from the cor‑
responding author OUL.
Declarations
Ethics approval and consent to participate
The study was conducted following the Declaration of Helsinki involving
human subjects and was approved by the Humanities and Social Sciences
Research Ethics Committee (approval #: HSSREC 00002504/2021) of the
University of KwaZulu‑Natal, Durban, South Africa. Informed consent was
obtained from all participants involved in the study.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1 Bathurst Rural Clinical School (BRCS), School of Medicine, Western Sydney
University Bathurst, Bathurst, NSW 2795, Australia. 2 African Vision Research
Institute, Discipline of Optometry, Westville Campus, University of KwaZulu‑
Natal, Durban 3629, South Africa. 3 Translational Health Research Institute (THRI),
Western Sydney University, Campbeltown, NSW 2560, Australia. 4 Department
of Optometry, University of the Highlands and Islands, Inverness IV2 3JH, UK.
5 Department of Community Medicine, College of Health Sciences, University
of Jos, Jos 930003, Nigeria. 6 Department of Optometry and Vision Science,
School of Allied Health Sciences, College of Health and Allied Sciences, Uni‑
versity of Cape Coast, Cape Coast 00233, Ghana. 7 Department of Community
Medicine, College of Health Sciences, University of Jos, Jos 930003, Nigeria.
8 Department of Optometry, Faculty of Health sciences, Mzuzu University, P. Bag
201 Luwinga 2,, Mzuzu, Malawi. 9 Department of Public Health, Faculty of Allied
Medical Sciences, College of Medical Sciences, University of Calabar, Cross
River State, Calabar 540271, Nigeria. 10 Health Division, University of Bamenda,
Bambili, P. O. Box 39, Cameroon. 11 Department of Optometry, Faculty of Life
Sciences, University of Benin, Benin, Nigeria. 12 School of Optometry and Vision
Sciences, College of Biomedical Sciences, Cardiff CF24 4HQ, UK. 13 School
of Management and Marketing, Curtin Business School, Curtin University,
Bentley, WA 6151, Australia. 14 Department of Psychiatry, College of Health
Sciences, University of Jos, Jos, Nigeria. 15 Tanzania Food and Nutrition Center, P.
O. Box 977, Dar es Salaam, Tanzania. 16 Department of Computer Science, Uni‑
versity of Jos, Jos 930003, Nigeria. 17 School of Health Sciences, Western Sydney
University, Campbelltown, NSW 2560, Australia.
Received: 22 February 2022 Accepted: 29 December 2022
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