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Perceived Severity of the Coronavirus Disease 2019:An International Comparative Analysis
Margherita AngioniCarlo Bo University of Urbino, Italy
R zvan Mihai B canuă ăUniversity of Bucharest, Romania
Fabio MussoCarlo Bo University of Urbino, Italy
Abstract
The Coronavirus disease 2019 (COVID-19) which started in China in December 2019, has rapidly spread all over the world. Italy was the first Europeancountry to experience the outbreak in mid-February 2020. The virus has been spreading at different speed and timing in other European states, includingRomania, which declared a state of emergency on 16th March. As there are no vaccines available, governments had toface the emergency byimplementing lockdown in order to reduce the number of infections. The aim of this paper is to analyse the severity perceived by citizens regardingCOVID-19 infection, through a comparative analysis between Romanians and Italians. Drawing on the theories of Health Behaviour, the perceivedseverity was measured through 8 items, subsequently reduced through an exploratory factorial analysis that allowed to identify two factors defined as“Emotional reaction” and “Perceived consequences”. For each of the two factors, the correlation was measured both with the demographic variables(gender, age, level of education) and with other variables considered relevant (possibility of home working, perceived level of information on preventivemeasures, and self-reported adoption of preventive behaviour). The citizens who answered the online questionnaire were 1126 in Romania and 742 inItaly. Although the two countries were in different stages of the infection, and with different political actions implemented by the two governments, resultsshowed numerous similarities in severity perception. Practical implications emerged for designing intervention programs by local and nationalgovernments.
Keywords: COVID-19, Perceived severity, Emotional reaction, Perceived consequences, Romanians, Italians
Riassunto. Gravità percepita dell’epidemia da Coronavirus. Un’analisi comparativa internazionale
La malattia da coronavirus (COVID-19) iniziata in Cina a dicembre 2019, si è rapidamente diffusa in tutto il mondo, con l’Italia che è stato il primo Paeseeuropeo a registrare un focolaio a metà febbraio 2020. Il virus si è diffuso con velocità e tempi differenti negli altri Stati europei, fra cui la Romania che hadichiarato lo stato di emergenza il 16 marzo. Non essendo disponibile un vaccino, i governi hanno dovuto fronteggiare l’emergenza attuando il cosiddettolockdown per ridurre il più possibile i contagi. Lo scopo di questo articolo è quello di analizzare la gravità percepita dai cittadini riguardo all’infezione daCOVID-19, attraverso una analisi comparativa fra la popolazione rumena e quella italiana. Attingendo dalle teorie comportamentali relative alla salute(Health Behaviour Theories), è stata misurata la gravità percepita attraverso 8 elementi, ridotti poi a due in seguito a una analisi esplorativa fattoriale: la“reazione emotiva” e le “conseguenze percepite”. Per ciascuno dei due è stata misurata la correlazione sia con variabili demografiche (genere, età,istruzione) sia con altre variabili rilevanti (possibilità di lavorare da casa, percezione del livello di informazione sulle misure preventive e adozione dicomportamenti di prevenzione). Gli intervistati, attraverso un questionario on-line, sono stati 1126 in la Romania e 742 in Italia. Nonostante i due Paesifossero in stadi differenti di diffusione del contagio e con differenti azioni pubbliche messe in atto, i risultati hanno evidenziato numerose similitudini. Nesono derivate significative implicazioni pratiche per politiche di intervento sia a livello locale che nazionale.
Parole chiave: COVID-19, gravità percepita, reazione emotiva, conseguenze percepite, rumeni, italiani
DOI: 10.32049/RTSA.2020.2.18
1. Introduction
Initially described as a pneumonia-like disease with unknown origins by the Chinese
authorities who informed World Health Organisation China Country Office on 31st
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December 2019 (WHO, 2020a), the Coronavirus disease (COVID-19) has been officially
classified as a pandemic on 11th March 2020 (WHO, 2020b).
Caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), as it was
latter named by the World Health Organisation (WHO, 2020c), the pandemic has its origins
in the city of Wuhan from the People’s Republic of China (Lu, Stratton and Tang, 2020),
early studies linking its origins with a local wet market (Wang, Tang and Wei, 2020; Wu et
al., 2020). Preliminary results indicate that the virus has a natural origin (Andersen et al.,
2020) and likely to have been transmitted from bats (Lillie et al., 2020; Xu et al., 2020; Ji et
al., 2020), which were linked in the past with several other severe acute respiratory
syndromes (Muller et al., 2005; de Wit et al., 2016; Bootz et al., 2017).
Due to its novelty, much is still unknown about its symptoms and means of transmission,
however several emergency warning signs have been identified so far (CDC, 2020; WHO,
2020d), as well as the incubation period estimated at around 5-6 days and up to 14 days
(WHO, 2020e). Although the WHO estimated a mortality rate of 2% (WHO, 2020f),
individuals over 60 or suffering from affections like diabetes, cancer, or hypertension are
considered at high-risk. Without an available vaccine in sight, people are advised to
implement social distancing and self-quarantine if they suspect they had been in contact
with an infected person and several countries decided to restrict movement in order to limit
the spread of the disease. Nonetheless, whether the individuals will respect the authority’s
recommendations depends on a large number of factors, including perceived severity.
Having presented the present context of the pandemic, we believe that the perceived
severity will prove to be a key unit of measurement in determining the level of
responsibility manifested by the individuals (Fishbein et al., 2001).
The aim of this paper is to analyse and explain the severity perceived by people facing
Covid-19 infections, comparing the difference in terms of emotional reaction and perceived
consequences between Romanians and Italians. In particular, the research intends to
investigate whether and how, the gender, age, level of education, the possibility of working
from home, the perceived level of information on preventive measures, the self-reported
adoption of preventive behaviour, and the current state of health, impact on perceived
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severity.
Worth noting is that the study was done in two countries at different stages of the
pandemic, with Italy reaching its pandemic peak during the data gathering and Romania
expecting to reach this point around the end of April (Voiculescu, 2020). We also feel
obliged to mention that Romania did not face a nearly similar number of deaths (Ministerul
Sănătăţii, 2020) compared with Italy (Italian Ministry of Health, 2020), when the two-time
frames of the disease evolution are compared. These results are mostly attributed both to the
fact that when the epidemic was discovered in Italy (21st February 2020), its severity was
still unknown, and to the fact that in Romania very strict social distancing measures were
imposed by the Romanian authorities as early as 16th March when an emergency situation
was declared (Romanian Parliament, 2020).
The structure of the rest of the paper is as follows: the next section illustrates the
theoretical framework focusing on the concept of perceived severity in the main behavioural
theories, section 3 presents data and the research method performed, and section 4 discusses
the results obtained by comparison between Romania and Italy.
2. Study background: the concept of Perceived severity in Health Behaviour Theories
Perceived severity is defined as the psycho-social construct of the individual’s perceived
likelihood to face a negative end-result depending on the actions taken at a specific moment
(Rosenstock, 1974).
The origins of the perceived severity (also encountered as “perceived seriousness” in the
literature) seem to be with the development of the Health Belief Model (HBM) in the 1960s
in the United States, which aimed to understand the (lack of) efficacy for public healthcare
programs (Janz and Becker, 1984). The model contains four dimensions: Perceived
susceptibility (the perceived likelihood of facing negative effects on one’s health), Perceived
severity (the construct covers the individual assumptions about the risk’s associated with the
illness and includes the probability of facing medical as well as social negative outcomes),
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Perceived benefits (capturing the gained benefits of following the recommended course of
action and the likelihood of accepting it in relation with the understood danger), and
Perceived barriers (the “costs” in regard with following the recommendation or health
procedure, be it medical side effects, or other inconveniences).
The HBM is based on behavioural psychology, namely the conceptualization of two
variables (Maiman and Becker, 1974; Janz and Becker, 1984): the importance of the
outcome, and belief that a particular action will have a desirable end-result. According to
this model, the probability of individuals choosing a specific course of action is reliant on
the dimensions presented above, but also takes into consideration the cues to action
(Rosenstock, 1974), which appears to be a structural necessity in the decision making
process and its intensity highly dependent with individual context. Later, studies also
showed that motivation is influenced interdependently by perceived probability and
perceived severity (Weinstein, 2000), which are normally hard to observe by the
researchers.
Furthermore, optimistic biases must also be taken into consideration when discussing
health risks appraisals (Weinstein and Klein, 2015; Druică, Cosma and Ianole-Călin, 2020),
with individuals holding the erroneous assumption that the likelihood of negative events is
higher among others than themselves.
Protection motivation theory (PMT) is another framework developed for understanding
human behaviour correlated with protective measure, the dimension of perceived severity
playing a crucial role. Developed in 1975 by the American psychologist Ronald Rogers, the
PMT is based on the assumption that the: «three crucial components of a fear appeal to be
(a) the magnitude of noxiousness of a depicted event; (b) the probability of that event’s
occurrence; and (c) the efficacy of a protective response» (Rogers, 1975), thus, the decided
action is correlated with the perceived severity of the situation or behaviour, the
vulnerability of the individual and the degree of counter-action which aims to ameliorate the
situation. However, as Rogers notes: «fear appeals have been found to differ in their interest
value, seriousness, importance and amount of concern elicited» (Rogers, 1975).
A third framework which worth to be mentioned is that developed in the 1990s by Kim
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Witte. Based on the existent literature, including the one cited above, Witte (1992) argues
that there are several reasons why fear appeals, «understood as persuasive messages
designed to scare people by describing the terrible things that will happen to them if they do
not do what the message recommends». He fails to provide consistent results and forwards
several reasons for this: a terminological misunderstanding when two distinct terms are
concerned, a lack of clarity when interpreting in reactions of the subject, and a lack of
consistent in-depth analysis between threat and efficacy.
Another important aspect highlighted by the theoretical models is that demographic and
socio-psychological variables may influence perceptions and, thus, indirectly influence
health-related behaviour (Glanz, Rimer and Viswanath, 2008). Several studies in perceived
severity (Kasmaei et al., 2014; Constant et al., 2005; Nau et al., 2005; Hunt et al., 1980)
showed that variables as age, gender, educational level, and current health status have (or do
not have) an indirect effect on behaviour by influencing the perception of severity.
In this study, in addition to the aforementioned demographic and socio-psychological
variables, we felt it was important to take into consideration some variables that are lacking
in the literature. Given the peculiarity of the pandemic situation, we believed that the
possibility to work from home, the perceived level of information on preventive measures,
and the self-reported adoption of preventive behaviour were useful for measuring perceived
severity. In more detail, the possibility to work from home could give individuals the belief
of continuing to live an almost normal life as the time that people must mandatorily spend at
home is occupied with daily commitments. The level of information on preventive measures
is an important factor for individuals because it can affect the management of the mood and
fear of people in an emergency that they are not used to. Finally, the self-reported adoption
of preventive behaviours in a situation of perceived seriousness is crucial for understanding
the reaction of citizens, their sense of responsibility and their respect for the rules imposed
by governments.
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3. Data and research method
In this section, the research method is illustrated. In order to provide a clear explanation
of the statistical analysis performed, the pre-processing of the data and the methodology are
shown step by step.
3.1 Data pre-processing
Data collection was carried out through the administration of an online questionnaire,
adopting a convenience sampling methodology (Kitchenham and Pfleeger, 2002) via
Facebook and LinkedIn, and via e-mail and WhatsApp, from 14th March 2020 to 9th April
2020. This methodology, also known as availability sampling (Leiner, 2016), is frequently
adopted for online questionnaires as it is fast, maximises the time-cost trade off and
increases the size of samples, being respondents readily available (Schmidt and Hollensen,
2006). According to Heckathorn (2011), as the sample expands – through social networks in
our case – it reaches an equilibrium that is independent of the convenience sample from
which it started. Therefore, if the sample size reaches a large enough threshold value, it does
not matter if the initial sample was non-random (Heckathorn, 2011).
1126 responses came from Romanians and 742 from Italians.
The questionnaire consisted of 15 questions: three for demographic aspects (Gender, Age,
Level of education); eight for Perceived Severity (SEV1, SEV2, SEV3, SEV4, SEV5,
SEV6, SEV7, SEV8) measured with a 7 point Likert type scale in terms of level of
disagreement/agreement; one for the Possibility to work from home; one for Perceived level
of information on preventive measures; one for Self-reported adoption of preventive
behaviour, and one for the Health status.
Table 1 shows the range of values and the measurement scales of these variables.
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Variables Range of values Measurement scale
Demographic
Gender Male, Female Nominal
Age 14, 15, 16, 17, ... Ordinal
Level of education
Primary or secondary education,University or post-university education
Ordinal
Perceivedseverity(SEV)
1. The thought of getting infected withCoronavirus scares me
1 – 7 (Likert scale) Interval
2. When I think about Coronavirus, my heart beats faster
1 – 7 (Likert scale) Interval
3. I am afraid even to think of Coronavirus
1 – 7 (Likert scale) Interval
4. The problems that I would experience as a result of getting ill from Coronavirus would last for a long time
1 – 7 (Likert scale) Interval
5. Getting sick from Coronavirus would threaten my relationship with important people in my life (boyfriend/girlfriend, team- mates, or parents)
1 – 7 (Likert scale) Interval
6. Getting sick from Coronavirus would threaten my work performance
1 – 7 (Likert scale) Interval
7. If I suffered from Coronavirus my whole life would change
1 – 7 (Likert scale) Interval
8. If I got ill from Coronavirus I would suffer consequences years fromnow
1 – 7 (Likert scale) Interval
Possibility to work from home Yes, No Nominal
Perceived level of information on preventive measures
1 – 10 (Likert scale) Interval
Self-reported adoption of preventive behaviour
1 – 10 (Likert scale) Interval
Health StatusLower than other people,The same as other people,Better than other people
Ordinal
Tab. 1 - Range of values and measurement scales of the variables
3.2 Hypotheses and methodology
Following data pre-processing, the statistical analysis was performed. After extracting the
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subsets for Romania and Italy, the graphs of item distributions to compare the two countries
were analysed. The visual inspection suggested that there were differences between
countries in each case. However, since the visual inspection was not enough, the Wilcoxon
Rank Sum Test was used to check whether the differences were statistically significant. This
non-parametric test was chosen due to the fact that the distributions were not normally
distributed. The hypotheses are presented below:
H1 There is a statistically significant difference between countries in the response to the sentence«The thought of getting infected with Coronavirus scares me» (SEV1)
H2 There is a statistically significant difference between countries in the response to sentence«When I think about Coronavirus, my heart beats faster» (SEV2)
H3 There is a statistically significant difference between countries in the response to the sentence«I am afraid even to think of Coronavirus» (SEV3)
H4 There is a statistically significant difference between countries in the response to the sentence«The problems that I would experience as a result of getting ill from Coronavirus would last fora long time» (SEV4)
H5 There is a statistically significant difference between countries in the response to the sentence“Getting sick from Coronavirus would threaten my relationship with important people in mylife (boyfriend/girlfriend, team-mates, or parents)” (SEV5)
H6 There is a statistically significant difference between countries in the response to the sentence«Getting sick from Coronavirus would threaten my work performance» (SEV6)
H7 There is a statistically significant difference between countries in the response to the sentence«If I suffered from Coronavirus my whole life would change» (SEV7)
H8 There is a statistically significant difference between countries in the response to the sentence«If I got ill from Coronavirus I would suffer consequences years from now» (SEV8)
In order to identify whether the 8 variables that measure Perceived severity (SEV1,
SEV2, SEV3, SEV4, SEV5, SEV6, SEV7, SEV8) could be reduced to fewer variables, a
correlational analysis was performed. Since the correlations between the elements were
high, pursuing data reduction made sense. Thus, subsequently, a Parallel Analysis (PA) with
promax rotation was conducted to identify the number of components to be extracted.
After identifying two factors (Emotional reaction and Perceived consequences) that
emerged from the Exploratory Factorial Analysis (EFA), and analysing the differences
between factors within each country, further possible correlations were investigated. In more
detail, the Wilcoxon Rank Sum Test was used to investigate the correlation, in each country,
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between factor scores and the variables “Gender”, “Level of education”, and “Possibility to
work from home”. The hypotheses are shown below:
H09 The emotional reaction depends on the gender of Romanian respondents
H10 The emotional reaction depends on the level of education of Romanian respondents
H11 The emotional reaction depends on the possibility to work from home of Romanianrespondents
H12 The emotional reaction depends on the gender of Italian respondents
H13 The emotional reaction depends on the level of education of Italian respondents
H14 The emotional reaction depends on the possibility to work from home of Italian respondents
H15 The perceived consequences depends on the gender of Romanian respondents
H16 The perceived consequences depends on the level of education of Romanian respondents
H17 The perceived consequences depends on the possibility to work from home of Romanianrespondents
H18 The perceived consequences depends on the gender of Italian respondents
H19 The perceived consequences depends on the level of education of Italian respondents
H20 The perceived consequences depends on the possibility to work from home of Italianrespondents
The Spearman’s Rank Correlation Coefficient was used to investigate the correlation, in
each country, between factor scores and the variables “Age”, “Perceived level of
information on preventive measures”, and “Self-reported adoption of preventive behaviour”.
The hypotheses are presented below:
H21 The emotional reaction depends on the age of Romanian respondents
H22 The emotional reaction depends on the perceived level of information on preventive measuresof Romanian respondents
H23 The emotional reaction depends on the self-reported adoption of preventive behaviour ofRomanian respondents
H24 The emotional reaction depends on the age of Italian respondents
H25 The emotional reaction depends on the perceived level of information on preventive measuresof Italian respondents
H26 The emotional reaction depends on the self-reported adoption of preventive behaviour ofItalian respondents
H27 The perceived consequences depends on the age of Romanian respondents
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H28 The perceived consequences depends on the perceived level of information on preventivemeasures of Romanian respondents
H29 The perceived consequences depends on the self-reported adoption of preventive behaviour ofRomanian respondents
H30 The perceived consequences depends on the age of Italian respondents
H31 The perceived consequences depends on the perceived level of information on preventivemeasures of Italian respondents
H32 The perceived consequences depends on the self reported adoption of preventive behaviour ofItalian respondents
Lastly, after a preliminary visual inspection, the non-parametric Kruskal-Wallis Rank
Sum Test and the Bonferroni Post-hoc Test were used to analyse the correlation between the
factor scores and the variable “Health status”. The hypotheses are the following:
H33 There is a statistically significant difference across the health status categories of Romanianrespondents and the emotional reaction
H34 There is a statistically significant difference across the health status categories of Italianrespondents and the emotional reaction
H35 There is a statistically significant difference across the health status categories of Romanianrespondents and the perceived consequences
H36 There is a statistically significant difference across the health status categories of Italianrespondents and the perceived consequences
The hypotheses are summarized in Figure 1.
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Fig. 1 – Conceptual model
4. Findings and discussion
Data analysis was performed with R software, version 3.4.3, considering a 0.05
significance level for all analyses.
4.1 Sample profile
Table 2 shows the main frequencies of the sample. The sample consists of 24.51% male
and 75.49% female for Romania, and 38.14% male and 61.86% female for Italy. Data about
Age were collected as continuous variable. The age of respondents is between 16 and 82
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years old for Romanians and between 14 and 79 years old for Italians. Regarding the level
of education, both the majority of Romanians and Italians are highly educated (67.76% and
65.09% respectively).
Romania Italyn. % n. %
GenderMale 276 24.51 283 38.14Female 850 75.49 459 61.86
Min Max Min MaxAge 16 82 14 79
n. % n. %Level of educationPrimary or secondary education 363 32.24 259 34.91University or post-university education 763 67.76 483 65.09
Tab. 2 - Sample profile
4.2 Descriptive statistic
Table 3 shows the minimum, first quartile, median, mean, third quartile, and maximum
for each of the eight items of perceived severity (SEV).
Min 1st Qu. Median Mean 3rd Qu. Max
SEV1 1.000 2.000 4.000 3.908 6.000 7.000
SEV2 1.000 1.000 2.000 2.823 4.000 7.000
SEV3 1.000 1.000 2.000 3.001 4.000 7.000
SEV4 1.000 2.000 3.000 3.369 5.000 7.000
SEV5 1.000 2.000 4.000 3.911 6.000 7.000
SEV6 1.000 2.000 4.000 4.055 6.000 7.000
SEV7 1.000 1.000 3.000 3.114 4.000 7.000
SEV8 1.000 1.000 2.000 2.730 4.000 7.000
Tab. 3 - Descriptive statistic
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4.3 Perceived severity plots and Wilcoxon Rank Sum Test
The Kernel density plots in Figure 2 compare Romania and Italy in terms of Perceived
severity distributions. The Kernel density curve allows observing the shape of distribution
more closely, considering a total area equal to one. This visual inspection suggests that there
are differences between countries in each item.
As can be observed, Italians seem to be more worried than Romanians about the
contraction of the Coronavirus (SEV1, SEV2, SEV3). Distributions relating to threats to
relationships with important people, effects on work performance, and duration of
consequences (SEV4, SEV5, SEV6, SEV7, SEV8) are asymmetric for both countries, but
with very different peaks.
Fig. 2 - Perceived severity plots - Romania and Italy
However, as mentioned above, the visual inspection is never enough, therefore we use the
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Wilcoxon Rank Sum Test to check whether the differences are statistically significant (Table
4).
Hypotheses Items W p-value Statisticalsignificance
H1 SEV1 358744 .000 acceptedH2 SEV2 376872 .000 acceptedH3 SEV3 250681 .000 acceptedH4 SEV4 391956 .022 acceptedH5 SEV5 423132 .632 rejectedH6 SEV6 460568 .000 acceptedH7 SEV7 383114 .002 acceptedH8 SEV8 456962 .000 accepted
Tab. 4 - Wilcoxon Rank Sum Test
Results in Table 4 show that the only item with no country differences is SEV5 which
corresponds to the statement “Getting sick from Coronavirus would threaten my relationship with
important people in my life (boyfriend/girlfriend, team- mates, or parents)” (H5, p-value = .632),
thus H5 is rejected. All other hypotheses (H1, H2, H3, H4, H6, H7, H8, p-value < .05) are
accepted. This means that there are statistically significant differences between Romanians
and Italians for the items SEV1 (“The thought of getting infected with Coronavirus scares
me”), SEV2 (“When I think about Coronavirus, my heart beats faster”), SEV3 (“I am afraid
even to think of Coronavirus”), SEV4 (“The problem that I would experience as a result of
getting ill from Coronavirus would last for a long time”), SEV6 (“Getting sick from
Coronavirus would threaten my work performance”), SEV7 (“If I suffered from
Coronavirus my whole life would change”), and SEV8 (“If I got ill from Coronavirus I
would suffer consequences years from now”).
4.4 Advanced analysis
The aim of the advanced analysis is to identify whether the 8 variables measuring
Perceived severity can be reduces to a lower number of variables. We begin with a
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correlational analysis (Table 5).
ROMANIA ITALY
SEV1 SEV2 SEV3 SEV4 SEV5 SEV6 SEV7 SEV8 SEV1 SEV2 SEV3 SEV4 SEV5 SEV6 SEV7 SEV8
SEV1 1.00 SEV1 1.00
SEV2 .723 1.00 SEV2 .699 1.00
SEV3 .674 .810 1.00 SEV3 .832 .773 1.00
SEV4 .535 .538 .512 1.00 SEV4 .547 .470 .513 1.00
SEV5 .390 .362 .375 .444 1.00 SEV5 .390 .361 .411 .405 1.00
SEV6 .326 .279 .273 .411 .566 1.00 SEV6 .291 .244 .313 .423 .522 1.00
SEV7 .460 .459 .470 .554 .605 .546 1.00 SEV7 .422 .360 .437 .528 .575 .646 1.00
SEV8 .356 .370 .387 .575 .431 .385 .647 1.00 SEV8 .399 .379 .402 .569 .459 .512 .710 1.00
Tab. 5 - Correlational analysis
The correlations among items are high, so pursuing data reduction makes sense.
In order to measure the internal consistency, that is, how closely related a set of items are
as a group, the Cronbach's Alpha is measured. By measuring the internal consistency of our
data we find a high Cronbach’s Alpha values (= .88 for each country) that cannot be
increased by dropping items. As known, a high value for Alpha does not imply that the
measure is unidimensional, so we use the Exploratory Factor Analysis that is a good method
to check dimensionality.
4.4.1 Parallel analysis: Emotional reaction and Perceived consequences
After examining the Kaiser's rule (eigenvalues >1) and producing the scree plots, we
performed the Parallel analysis both for Romania and Italy. As the data is not normally
distributed, we extracted the factors using the principal axis method and the promax
rotation.
In both cases, we set the cut-off for factor loadings as .400. The reliability of both models
is good: TLI = .926 for Romania and TLI = .955 for Italy; the RMSEA indices are
around .90, slightly higher than the recommended value. Lastly, the cumulative variance
explained in the case of Romania is 61%, and in the case of Italy is nearly 65%.
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Table 6 shows the results of Parallel analysis both for Romania and Italy.
ROMANIA ITALY
PA1 PA2 PA1 PA2
SEV1 .737 SEV1 .888
SEV2 .991 SEV2 .835
SEV3 .885 SEV3 .965
SEV4 .474 SEV4 .445
SEV5 .728 SEV5 .573
SEV6 .730 SEV6 .795
SEV7 .839 SEV7 .937
SEV8 .654 SEV8 .749
Tab. 6 - Results of Parallel analysis
Given the items in each factor, we label factor 1 as “Emotional reaction” (SEV1, SEV2,
SEV3), and factor 2 as “Perceived consequences” (SEV4, SEV5, SEV6, SEV7, SEV8).
Figure 3 shows the plots of the new factors by comparing Romania and Italy. The Kernel
density plots regarding Emotional reaction and Perceived consequences show a different
distribution for Romanians and Italians. In the first plot, the data distributions for the two
countries seem quite similar, however a small peak for Italy is highlighted. This means that,
on average, the emotional reaction of the respondents of the two countries is mainly similar:
both the majority of the Romanian and Italian respondents, indeed, responded with low
scores that correspond to a greater level of disagreement with the proposed statements.
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Fig. 3 - Emotional reaction and Perceived consequences between Romania and Italy
In the second plot, distributions are very different with a strong peak for Romania. In this
case, the responses of the Italian respondents regarding the perceived consequences are
distributed more homogeneously than those of the Romanian respondents. The latter,
indeed, tend to be more optimistic.
The Kernel density plots in Figure 4 allow us to better observe the differences between
Emotional reaction and Perceived consequences within each country.
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Fig. 4 - Emotional reaction and Perceived consequences within Romania and Italy
By comparing the factor scores within countries, the first plot in Figure 3 highlights a
peak for Perceived consequences compared to Emotional reaction for Romania’s data.
Conversely, in the second plot, Emotional reaction shows a higher peak compared to
Perceived consequences for Italy’s data. This suggests that the emotional reaction of most
Romanians is not of concern, however even those who are concerned are anyway optimistic
about the future consequences of the virus. The opinion of Italians is different, indeed, even
those who declare that they are not worried, perceive in a more negative way the
consequences of a possible contraction of the COVID-19 infection. This result may depend
on the different stage of contagion that the countries were experiencing at the time of the
interview.
4.4.2 Non-parametric analysis: Wilcoxon Rank Sum Test and Spearman’s Rank Correlation
Coefficient
In order to identify any correlations, in this section the differences between the new
factors Emotional reaction and Perceived consequences within each country are analysed.
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We use the Wilcoxon Rank Sum Test to investigate the correlation (hypotheses from H9
to H20), in Romania and in Italy, between factor scores and the variables “Gender”, “Level
of education”, and “Possibility to work from home” (Table 7).
Emotional reaction Perceived consequencesRomania Italy Romania Italy
H p-value Sig. H p-value Sig. H p-value Sig. H p-value Sig.Gender H9 .004 H12 .000 H15 .000 H18 .000
Level of education H10 .880 X H13 .209 X H16 .268 X H19 .685 X
Work from home H11 .733 X H14 .791 X H17 .149 X H20 .746 X
= accepted; X= rejected
Tab. 7 - Wilcoxon Rank Sum Test
The results in Table 7 highlight that the only statistically significant relation is with
Gender (H9 p-value =.004; H12 p-value =.000; H18 p-value =.000) for both countries. This
means that neither the Emotional reaction nor Perceived consequences depend on the level
of education (H10, H13, H16, H19) and on the possibility to work from home (H11, H14,
H17, H20).
Figure 5 shows that, both for Emotional reaction and Perceived consequences, women
score higher than men. Although gender difference has been widely demonstrated in the
medical and socio-psychological literature (Hess et al., 2000; Ross and Bird, 1994), in the
particular case of Covid-19 this result is unexpected as data currently made known by all
countries show that men are more affected by the virus than women (Chen et al., 2020; ISS,
2020).
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Fig. 5 - Emotional reaction, perceived consequences, and gender in each country
Now, we test whether factors scores are correlated with the “Age”, the “Perceived level
of information on preventive measures” (Information), and the “Self-reported adoption of
preventive behaviour” (Prevention) (Table 8) using the Spearman’s Rank Correlation
Coefficient.
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Emotional reaction Perceived consequencesRomania Italy Romania Italy
H p-value Sig. H p-value Sig. H p-value Sig. H p-value Sig.Age H21 .026 H24 .887 X H27 .986 X H30 .006
Information H22 .025 H25 .016 H28 .000 H31 .163 X
Prevention H23 .179 X H26 .954 X H29 .029 H32 .012
= accepted; X= rejected
Tab. 8 - Spearman’s Rank Correlation Coefficient
As shown in Table 8, Age is positively related to Emotional reaction in Romania (H21,
rho = .0663), but it is unrelated in Italy (H24). For what concerns the Perceived
consequences, Age is unrelated in Romania (H27), but is negatively related in Italy (H28,
rho = -.1012).
The higher the level of Information related to preventive measures, the lower the
Emotional reaction in both countries (H22, rho = -.0668; H25, rho = -.0885). Regarding the
Perceived consequences, Information on preventive measures records a negative correlation
in Romania (H28, rho = -.1190), but it is uncorrelated in Italy (H31).
Lastly, the Self-reported level of adoption does not seem related to Emotional reaction
(H23 and H26). However, the higher the score for Perceived consequences, the higher the
score for Self-reported preventive behaviour adoption (H29, rho =.0649; H32, rho = .0920).
4.4.3 Health status
In the questionnaire, the Health status was investigated with the question: “How do you
assess your health status?”. Respondents could choose between “Lower than others people”,
“The same as other people”, or “Better than other people”.
From a preliminary visual inspection of the data (Figure 6), it is possible to note that
respondents with a health status lower than others tend to score higher at the Emotional
reaction. The same applies to Romania with respect to Perceived consequences. In Italy,
however, those with perceived similar health status as others tend to score higher at
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perceived consequences.
Fig. 6 - Health status plots – Romania and Italy
This is an unexpected result, but we know that visual inspection is not reliable, so we
conduct statistical testing. Thus, in order to test whether there are differences, we apply the
non-parametric Kruskal-Wallis Rank Sum Test.
Emotional reaction Perceived consequences
Romania Italy Romania Italy
χ2 4,9948 5,7891 15,767 11,826
df 2 2 2 2
p-value .082 .055 .000 .003
H H33 = X H34 = X H35 = H36 =
= accepted; X= rejected
Tab. 9 - Kruskal-Wallis Rank Sum Test for Health status – Romania and Italy
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The results in Table 9 show that there are no statistically significant differences across
Health status categories in terms of Emotional reaction (H33, p-value = .082 for Romania,
and H34, p-value = .055 for Italy). Therefore, we only accept hypotheses relating to
Perceived consequences (H35 and H36).
Lastly, the following Bonferroni post-hoc test will clarify which categories are different
(Table 10).
Perceived consequences
Romania Italy
1 2 1 2
2 .077 - 2 1.000 -
3 .001 .015 3 .470 .002
Tab. 10 - Bonferroni post-hoc test – Romania and Italy
As shown in Table 10, in Romania the differences appear between category 2 (same level
of health status as others) and 3 (better than others) as the p-value is .015. Also, there is a
significant difference between category 1 (lower than others) and category 3 (p-value
= .001).
We conclude that the lower than health status, the higher the perceived consequences of
infection but there are no significant differences between the perception of those with low
health status (category 1) and those in the second category, with self-reported health status
the same as other people (p-value = .077).
For what concerns the perceived consequences in Italy, the post-hoc test shows that the
only statistically significant difference occurs between categories 2 and 3 (p-value = .002).
Those in category 2 scoring higher at perceived consequences than those in category 3.
5. Conclusions, limits, implications, and future research
Perception of severity, one of the fundamental factors of the Health Belief Model
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developed in 1966 with the purpose to analyse health-promoting behaviour, has long been
studied in the literature (Rosenstock, 1974; Janz and Becker, 1984; Weinstein, 2000;
Weinstein and Klein, 2015).
In this research, we have chosen to investigate the severity perceived by the population
during the first phase of COVID-19, which was spreading worldwide. In this pandemic
period, the aim was to analyse the perception of severity as a factor affecting people’s
behaviour in relation to a series of variables, specifically gender, age, level of education, the
possibility to work from home, the perceived level of information on preventive measures,
and the self-reported adoption of preventive behaviours. The analysis was carried out by
comparing the data collected simultaneously in two different European countries: Romania
and Italy. The results obtained demonstrate several similarities between the two populations.
Although the data provided worldwide on the characteristics of COVID-19 patients tell
us that the subjects most exposed to infection are men, in both countries the perceived
severity both in terms of emotional reaction and perceived consequences records higher
scores for women. This is certainly a fact that governments must take into account in their
political and management decisions. Indeed, if it is true that mortality is more than double in
the male gender, it is equally true that women are more exposed to the risk of being
infected. Indeed, women in almost all the cultures of the world have always been the ones
who run the house (Plant, 1997; Levinson, 2011): they buy groceries and take care of
children and the elderly (Angioni and Musso, 2020). During this pandemic, supermarkets
remain a fundamental place to go for food purchasing but at the same time, they are also the
place where the risk of contagion is greatly elevated. In the same way, the risk of contagion
is perceived as high even in contact with children - whom the television stations have often
reported as possible healthy carriers of the disease - and with the elderly, the main victims of
the virus. In this context, therefore, proper communication campaigns by public authorities
should have been taken into account of these issues, particularly highlighting that women,
despite having a lower predisposition of being infected, are more exposed to risks
depending on behaviours and contacts occurred in daily activities.
Another interesting fact concerns age. While in Italy this factor does not seem to be
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related to the emotional reaction, in Romania as the age increases the emotional aspect of
perceived severity also increases, according to what previous studies reported about the
relationship between age and optimism (Chowdhury et al., 2014). On the contrary, the age
of Romanians does not impact on the perceived consequences, while in Italy it is young
people who perceive the negative consequences of the infection more. It should be noted
that in Italy, from the outset, the provisions given by the government focused on the social
distancing referred in particular to the relationship between grandparents and grandchildren.
Young people have certainly suffered from this ban, amplifying the unease of the
population. The communication focused on the necessary sacrifice, but the benefits of these
sacrifices were probably not clearly communicated. In this area too, the practical
implications suggest that public authorities' communication campaigns should be clearly
addressed to population groups, making clearer the responsibility of individuals in ensuring
collective behavior that could counter the spread of the epidemic.
Among the results obtained, it worth to be noted that, in this particular case, unlike the
existing literature (Barron, Gamboa and Rodriguez-Lesmes, 2019; Ophir, 2019), the level of
education does not affect the perceived severity in any of the countries analysed.
Communication once again becomes a central issue in the management of perceived
severity as a determining factor of behaviour when analysing the relationship with the
perceived level of information on preventive measures. The results show that in both
countries as the perception of information on preventive measures increases, the emotional
reaction of citizens decreases. In Romania, moreover, a high level of information lowered
the perceived consequences (this result must be interpreted as positive indication since the
related questions of items were placed using pessimistic terms). Thus, governments need to
ensure updated scientific information and clear and transparent communication about their
decision making. In this context, also mass media have a role, providing a connection
between daily events and public perceptions (Lippmann, 1922). As Cohen (1963), and
subsequently McCombs and Shaw (1972) stated in their agenda-setting theory, «press may
not be successful much of the time in telling people what to think, but it has unexpected
success in telling its readers what to think about. The world will look different to different
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people, depending on the map drawn for them by writers, editors, and publishers of the
paper they read».
Both Romanian and Italian citizens claim to adopt adequate prevention behaviour.
Indeed, the more the perception of the consequences from COVID-19 increases, the greater
the commitment made by citizens to respect the anti-contagion rules. So, in this first phase,
the prevention campaigns carried out by governments seem to have hit the target. However,
it must be remembered that if the road to a return to normality is long, citizens are going to
suffer increasingly. The risk is to go towards a relaxation of the measures that could once
again raise the spread curve of the virus, frustrating all the sacrifices made so far. The
suggestion is to implement sustainability-oriented policies (Musso, Esposito and Angioni,
2019), with new communication strategies, in order to keep in the long term citizens’
attention on the importance of the adoption of preventive measures also in the following
phases.
The main limitation of this research is that the data were collected in two countries that
are at different times and speeds of the spread of the infection. However, the results studied
demonstrate numerous similarities in the perception of severity by Romanian and Italian
citizens.
Furthermore, from the results that emerged, a fundamental aspect that we are keen to
suggest for future research is to consider in depth the communication campaigns carried out
by governments, including press conferences, legislative acts, and posts on the institutional
profiles of social networks and analyse how these affect the perceived severity while
studying citizens’ reactions and comments.
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