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. 2021 Jul 4;21(4):e2716. doi: 10.1002/pa.2716

Silent cries behind closed doors: An online empirical assessment of fear of COVID‐19, situational depression, and quality of life among Pakistani citizens

Muhammad Umair Ashraf 1,✉, Saqlain Raza 2, Asfa Ashraf 3, Waqas Mehmood 4, Ataul Karim Patwary 5
PMCID: PMC8420565  PMID: 34512182

Abstract

The present study aimed to investigate the effects of fear of COVID‐19 and situational depression on the quality of life (QOL) of Pakistani citizens. An online cross‐sectional survey was conducted on Pakistani citizens via the snowball sampling technique. A total of 377 respondents (256 males and 121 females) participated in this study from August to October 2020. Adapted scales were validated using confirmatory factor analysis, and partial least squares structural equation modelling (PLS‐SEM) was applied to the data to test the hypothesised model. The study's findings showed a negative relationship between fear of COVID‐19 and QOL. Likewise, a reciprocal relationship was found between situational depression and quality of life. The results indicate that fear of COVID‐19 and situational depression during the pandemic have affected the lives of Pakistani citizens. The findings are particularly relevant for improving the QOL by limiting the information received from media and social networks. There is a need to control these mediums and promote community‐based interventions to provide accurate knowledge regarding COVID‐19. Fear of COVID‐19 and situational depression may be reduced in this way. Based on the current findings, psychotherapy and counselling programmes must be planned to minimise the adverse effects of fear of COVID‐19 and depression on the QOL of citizens due to the ongoing progression of the pandemic.

Keywords: depression, fear of COVID‐19, Pakistani citizens, quality of life

1. INTRODUCTION

Wuhan Municipal Health Commission (2020) reported desperate respiratory fatal disease comprising unknown grounds, known as the Coronavirus Disease 2019 (COVID‐19) in December 2019 in Wuhan, a city in Hubei Province, China (Dogan et al., 2020; Fareed et al., 2020; Iqbal et al., 2020; Shahzad, Shahzad, Fareed, et al., 2020; Shahzad, Shahzad, Iqbal, et al., 2020; Shakoor et al., 2020). The disease spread rapidly that within 3 months of identification, a total number of 81,846 cases were reported; of these 81,846 cases, 3287 people were dead by 25 March 2020. Globally, the disease spread from Wuhan to 193 countries (Huang & Zhao, 2020; Wang et al., 2020). According to the World Health Organization (WHO) (2020) identified 69,176 incidents in Italy, 42,058 instances in Spain, 53,588 instances in the United States, and 4,541,392 cases in Pakistan till date. The most common indications are fever, myalgia, dry cough, and dyspnea, which usually appear within 2–14 days after the COVID‐19 exposure. However, with the high infection statistics and comparatively high death rates, many people have been disquieted by the disease (Shahzad, Shahzad, Iqbal, et al., 2020; Zhao et al., 2020). Vast literature and Health Care Practitioners (HCPs) have indicated that COVID‐19 can be transmitted through social interactions and transferred from human to human. This invisible virus has caused many negative feelings and fear, which can lead to psychological problems such as depressed feelings comprising sadness, a lack of concentration, appetite change, disturbed sleep, and thoughts of death (Ma et al., 2020).

Furthermore, the Centers for Disease and Prevention (2020) stated that in addition to the severity and life‐threatening nature of COVID‐19, the fear of COVID‐19 itself can be harmful. The onset of COVID‐19 and the pandemic's natural surroundings creates fears throughout the world, leading to social stigmatisation in some cases. Contagious diseases are associated with several phenomena, such as fear of becoming a victim of the disease. The high death statistics being broadcasted on news channels and other mass media sources lead to psychological complications, including stigmatisation and a loss of stability and well‐being (Ahorsu et al., 2020). While many countries are endeavouring to produce vaccines for COVID‐19, little consideration is being paid to the socio‐psychological impacts of COVID‐19 on the general population (Hamza Shuja, Aqeel, Jaffar, & Ahmed, 2020). Kang et al. (2020) and Jones et al. (2017) associated high rates of infection, poor protection against contagion, being overworked and uncomfortable, social isolation, and social distancing with mental health problems, that is, stress and situational depression, insomnia, disowning, aggression, and fear of COVID‐19.

Among the South Asian countries, Pakistan is one of the affected countries that are experiencing many serious societal and economic concerns. Mamun and Ullah (2020) found that psychological problems associated with fear of COVID‐19 lead to suicidal intentions, which have been reported in Pakistan and neighbouring countries such as India and Bangladesh. These cases could be caused by financial recession, fear of being infected with COVID‐19, and high reported death statistics (Goyal et al., 2020; Kumar & Pinky, 2020; Mamun & Griffiths, 2020). Furthermore, Mowbray (2020) found that the frequency of depression among the general population has increased by 7% and identified some determinants that increase the risk that a citizen will suffer from situational depression. These determinants include a poor socioeconomic background, being female, interpersonal conflict, family violence, overwhelming usage of social media, and inadequate social support (Torales et al., 2020; Usher et al., 2020).

Many governments decided to follow the lockdown strategy to control the spread of COVID‐19 (Dogan et al., 2020; Minai et al., 2021; Shahzad, Shahzad, Fareed, et al., 2020; Shakoor et al., 2020). During a lockdown, many people from the low‐income group such as daily earning labourers and drifting labourers get infected because they work far from their homes and family in overcrowded territories to afford essential life conveniences. Fighting against COVID‐19 and thoughts of fulfilling basic needs can sometimes lead to contradictory outcomes, leading to severe depression and anxiety among the people whose quality of life (QOL) is severely affected (Pennycook et al., 2020).

WHO (2020) defines ‘quality of life’ as a person's self‐perception regarding their locus in the environment of beliefs and value structure in which they live and in association with their vision, aspirations, standards, and apprehensions. It is a wide‐ranging construct affected in a complicated way by the person's physical well‐being, psychological stability, liberation level, social companionships, and their association with the prominent aspects of the environment in which they live (Vahedi, 2010). An et al. (2020) describe the protection model QOL in the light of COVID‐19 as the interaction between protective moral support, social support, and a stable socioeconomic status (SES) and upsetting determiners, that is, disease or mental health problems. Clinically, the hopelessness, insomnia, and cognitive impairment result from these problems are influencing the overall QOL among citizens (Voruganti et al., 1998). Based on the above arguments, this work attempts to fill the gap in the QOL literature by;

  • investigate the effects of fear of COVID‐19 and situational depression on the quality of life of Pakistani citizens.

As such, there is a dearth of literature on the empirical examination of fear of death due to COVID‐19. The severity and alarming consequences of COVID‐19 have developed high fear among the population worldwide, and lockdown experiences have led to psychological problems, that is, depression among the population. These two constructs are invisible but prevalent; therefore, researchers have described fear of COVID‐19 and situational depression as influencing people's lives behind closed doors. Since the sufferers rarely discuss these issues with others, researchers have coined the situation as silent cries behind closed doors. These issues have directly influenced the QOL of the general Pakistani population. The present study is among the pioneer to focus on fear of COVID‐19, situational depression, and their impacts on the QOL of Pakistani citizens because this issue has not been adequately addressed in the existing literature. Therefore, this study seeks to examine the QOL of Pakistanis during the COVID‐19 pandemic outbreak in order to provide socio‐psychological support to Pakistani citizens and promote their psychological well‐being and mental health.

1.1. COVID‐19 outbreak in Pakistan: An overall scenario

A government official confirmed two first cases of COVID‐19 on 26 February 2020 in Pakistan. The first case was of a student at the University of Karachi, Pakistan, while the second case was from a federal territory. Three new cases were diagnosed 1 week later. By 26 March 2020, a total of 1179 has been reported. In other nearby countries, the numbers of cases and deaths were 8,267,623 cases and 123,097 deaths for India, 11,737 cases and 38 deaths for Maldives, 419,000 cases and 6049 deaths for Bangladesh, and 13,227 cases and 30 deaths for Sri Lanka (The COVID‐19 Alerts, 2020). According to The News International, during the year 2019, Pakistan ranked 152nd among 189 countries in Human Development Index (HDI). Alarmingly, Pakistan ranked lower than other South Asian countries. The lockdown during the pandemic situation has severely destroyed Pakistan's economy, reducing the citizens' living standards. To date, the COVID‐19 health advisory platform updated that 322,452 cases of COVID‐19 have been detected including 6654 deaths and 306,640 cases are recovered form last 24 hours on 18 October 2020 in Pakistan. Citizens living below the poverty line and the middle class groups who earn daily wages do not follow the standard operating procedures (SOPs) recommended by the health department because they claim that if they remain at home, they would have died of hunger. Previous studies in the same context were conducted to investigate Pakistan's preparation for Coronavirus (Ahmad et al., 2020) as well the mental health and psychological aspects of COVID‐19 (Mukhtar, 2020). Meanwhile, the media has provided an overwhelming amount of information such that everyone already knows about the COVID‐19 symptoms. Preventive measures like wearing a mask, using hand sanitiser, wearing gloves, and maintaining social distance can protect people from getting infected with COVID‐19.

Figure 1 depicts the COVID‐19 statistics in Pakistan from April 2020 to October 2020 (Government of Pakistan, 2020). The highest death statistics were reported in June, followed by a declined from July onwards leading to the lowest statistics in October.

FIGURE 1.

FIGURE 1

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Covid‐19 status in Pakistan. Source: Government of Pakistan

1.2. Theoretical and hypothetical model

According to Ferriss (2004), there is no specific theoretical background to provide an assessment of QOL. However, the concept of neo‐Malthusian theory provides some foundations regarding the factors influencing QOL as it states that demographic changes lead to dysfunction due to the deprivation of social needs, that is, food and jobs, which affect the dynamics of QOL. Furthermore, Goldschmidt's (2006) hypothetical model states that an increase in the size of economic activities reduces poverty. In view of other concerns regarding the development of the social system, Marx and by Durkheim laid the foundation of the structural function theory, which specifies the specific necessities such as involvement in the environment, biological necessities of humankind, the social system to survive, and the necessity of group life, while the absence of these necessities would have a significant influence on QOL (Ferriss, 2004). Due to the dearth of studies employing Maslow's (1943) hierarchy of needs and theory of human motivation (1943), the current study becomes among the pioneer studies that use this theory to fill this gap.

Maslow (1943) argued in his theory of human motivation that human beings are motivated towards specific needs that are superior over others. When the first need is fulfilled, the person will move to fulfilling another need. According to the hierarchy of needs theory, there are five stages of human survival. In the first stage, a person struggles for his basic needs, which are his physiological needs such as air, food, water, home, clothes, warmth, sex, and sleep. According to Maslow, if these needs are not fulfilled, the human body cannot work properly. The second stage is security needs, consisting of order, sureness, independence from fear, safety against grievance, health and well‐being, and control of their life. These needs are fulfilled through family relations and society. Currently, many people are experiencing various socio‐psychological issues due to the COVID‐19 outbreaks, such as a lack of basic needs, fear of the disease, joblessness, depression, and other psychological disorders. These situations have changed the ability to fulfil basic needs from previously. Huang and Zhao (2020) stated that fear and compromised physical health significantly influence people's mental health. Fear of COVID‐19 is transformed into depression and anxiety when one gets infected. Prior studies show that people's emotional reaction comprises fear and uncertainty in terms of negative social behaviour, which directly influence their QOL (Mowbray, 2020; Torales et al., 2020). Fear of COVID‐19 develops due to perceived threat to life, isolation, and fear of compromised physical health. These progress into severe feelings of depression and stress among the people who have experienced or been exposed to COVID‐19 cases (Dong & Zheng, 2020; Jeong et al., 2016; Liu, 2020). These experiences and exposure seem to be more severe in developing countries like Pakistan. Based on this discussion, the following hypothesis is proposed:

There is a significantly negative relationship between fear of COVID‐19 and the quality of life of the citizens of Pakistan.

The third stage is love and a sense of belonging. People seek feelings of interconnectedness encompassing affection, dependence, and affiliation as part of particular groups, that is, family, peers, and work, while fourth stage is esteem needs that consist of two types, namely self‐esteem or self‐respect and social repercussion or status. After satisfying all the above discussed stages, a person will reach the last stage of self‐actualisation, which is self‐satisfaction, in search of particular growth. The preceding study of Lodhi et al. (2019) stated that QOL is an all‐encompassing line of attack that can be explained in various manners but there is a considerable agreement that QOL is a multi‐mechanism phenomena determined through the subjective and objective well‐being of a human being. It is influenced by multiple factors such as age, gender, employability, and SES. According to Hunt and Mckenna (1992), the prerequisites of QOL are eating, drinking, good sleep, sex, averting pain, warmth, security, absence of depression, steadiness, love, physical connection, closeness, interconnecting, shared experiences, working for collective targets, curiosity, exploration of the world, consent, respect, a sense of usefulness, self‐esteem, skills, authority, freedom, and self‐actualisation. Depression statistics were not well understood in Pakistan in the past, but WHO considers depression as the main factor in developing fear. It is the primary cause of incapacity; these statistics lie between 28% to 63% among the Pakistani population. One of the probable causes of depression is social adversity in semi‐periphery countries like Pakistan (Figure 2). Depression can affect the social and psychological well‐being of a person, and hence, there is a need to investigate and address this critical issue to promote and develop initiatives to reduce depression (The Nation News, 2020).

There is a significantly negative relationship between situational depression and quality of life among the citizens of Pakistan.

FIGURE 2.

FIGURE 2

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Research model

2. RESEARCH METHOD

2.1. Data and sampling procedure

Cross‐sectional research was conducted right after a lockdown was effected in Pakistan. The researchers decided to collect online responses using the snowball sampling technique, aligned with the online assessment of very recent studies (An et al., 2020; Doshi et al., 2020; Zhang & Ma, 2020). The data collection depended on the authors' social relations with the local citizens living in Pakistan. A single page draft placard was dispatched to WhatsApp, Facebook, Instagram, and Twitter groups. The placard enclosed a brief introduction regarding the background, objective, methods, deliberate nature of the contribution, statement of secrecy, and steps for filling the questionnaire along with the link and fast response. Citizens with a Pakistani nationality and aged 18 years or above who were willing to participate in this research were directed to fill the questionnaire via a Google form. From August to October 2020, 396 responses were received. Of these 396 responses, 19 responses were incomplete. Henceforth, the sample size of the present study is N = 377.

2.2. Measures

To test the study's hypotheses, questionnaires were adapted from several existing studies. Respondents reported QOL based on two factors, namely fear of COVID‐19 and situational depression. The researchers divided the questionnaire into three parts, which are: a) Socio‐demographic profile, b) Predictor constructs (Fear of COVID‐19 and Situational Depression), and c) Outcome construct (Quality of life). The researchers used questions on age, gender, and level of education to obtain the respondents' demographic profile, while fear of COVID‐19 and situational depression were used as the predictor constructs. The fear of COVID‐19 scale (FCV‐19S) by Doshi et al. (2020), consisting of seven items, was used to measure respondents' fear of COVID‐19. This instrument has been used in previous studies, and it is a cross‐cultural instrument because the symptoms of COVID‐19 are similar in all the countries affected by this disease. Depression was measured using Goldberg's (1993) depression scale consisting of 18 items. The motivation behind using Goldberg's scale is that it is one of the most used scales for measuring depression worldwide. In the next section, the researchers used the WHO quality of life scale (WHOQOL‐BREF), comprising 26 items, to measure the physical, psychological, social, and environmental domains of life (WHO, 2004). This scale was recently used in the prior studies of Lodhi et al. (2019) and An et al. (2020). All the scales were measured using a 5‐point Likert scale ranging from 1 = strongly disagree to 5 = strongly agree.

2.3. Data analysis

SPSS version 25.0 was used to obtain the descriptive statistics and detect multivariate outliers. As suggested Lynch’s (2013), Mahalanobis distance was employed to detect the outliers, and a significant level was considered in an acceptable range. Common Method Bias (CMB) was checked by performing Harman’s single‐factor test. No bias were found as a single factor explained only 35.734%, which is less than a threshold of 50% (Podsakoff & Organ, 1986). Further, Smart PLS‐3 was used to perform the measurement and structural models to test the study's hypotheses (Hair et al., 2017).

Table 1 summarises the socio‐demographic profile of the respondents who participated in the study. The majority of the respondents (201 respondents, 53.3%) were 18–25 years old. In comparison, the majority of the respondents (4081 respondents, 56.4%) in Huang and Zhao's (2020) study were above 35 years old. In this study, 256 respondents (67.9%) were males, while in Martínez‐Lorca et al.'s (2020) study, 497 respondents (82%) were females. Most of this study's respondents (299, 79.3%) were single, whereas in An et al.'s (2020) study, 710 respondents (64.4%) were married. In terms on education, 301 respondents (79.8%) in this study stated that they obtained a bachelor's/master's degree. In comparison, Ma et al. (2020) found 336 respondents (43.6%) were obtained their bachelor's/master's level of education in their study. A total of 323 respondents (85.7%) in this study were from urban areas. Similarly, 152 (89.4%) respondents in the study of Xiao et al. (2020) were urban residents. In this study, 256 (67.9%) respondents reported earning 50,000PKR–100,000PKR per month. These results provide a basis to understand the relationships between the independent variables (fear of COVID‐19 and depression) and the dependent variable (QOL) concerning the socio‐demographic aspects.

TABLE 1.

Socio‐demographic profile of the subjects

Items Categories F (%)
Age 18–25 201 (53.3)
26–32 99 (26.3)
>32 77 (20.4)
Gender Male 256 (67.9)
Female 121 (32.1)
Marital status Single 299 (79.3)
Married 78 (20.7)
Level of education Matriculation‐Intermediate 54 (14.3)
Bachelors‐Masters 301 (79.8)
Other 22 (5.8)
Place of residence Rural 54 (14.3)
Urban 323 (85.7)
Family income 50,000PKR‐10,000PKR 256 (67.9)
100,001PKR‐150,000PKR 80 (21.2)
>150,000PKRS 41 (10.9)
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Note: N = 377, f = frequency, % = percentage.

2.4. Multicollinearity

As part of the diagnostic test, multicollinearity was checked to evaluate the inter‐correlation among variables. In checking for multicollinearity, the variance inflation factor (VIF) and tolerance values were considered to detect misleading results (Marsh et al., 2004). As shown in Table 2, the highest correlation value between the variables is 0.583, and the highest VIF value is 1.099. These results indicate no collinearity issues exist as VIF values are below the threshold level (Table 3, Hair et al., 2017; Yu et al., 2015).

TABLE 2.

Means, SD, and zero‐order correlation of the study variables

Variables MEAN SD 1 2 3
1 FOC 3.6419 1.03395 1
2 DEP 3.3058 0.85874 0.300** 1
3 QOL 2.0705 0.81513 −0.281** −0.583** 1
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Note: M, Means; SD, standard deviation, ** two‐tailed significant correlation at the 0.01 level.

Abbreviations: DEP, depression; FOC, fear of Covid‐19; QOL, quality of life.

TABLE 3.

Collinearity assessment

Coefficient Tolerance VIF
FOC 0.910 1.099
DEP 0.910 1.099
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Dependent variable, that is, quality of life; DEP, depression; FOC, fear of Covid‐19.

3. RESULTS

3.1. Measurement model

The two steps procedures were applied to evaluate the models: the measurement model and structural model (Henseler et al., 2009). The measurement model of the study was examined using average variance extracted (AVE), Cronbach's alpha (α), composite reliability (CR), and discriminant validity. For the indicators' outer loadings, DEP 8, 12, 14, and 18 and QOL1, 2, 3, 4, 5, 6, 7, and 8 recorded items loadings below 0.40 and hence, were dropped (Hair et al., 2014).

Discriminant validity was also assessed by evaluating the loading, cross‐loading, and heterotrait‐monotrait (HTMT) ratio criterion. As shown in Table 4, the prime factor items have higher loadings than other items in other variables. Moreover, as recommended by Henseler et al. (2015), the HTMT values should be less than 0.85 to ensure discriminant validity. Based on this criterion, all the values are less than 0.85 (see Table 5).

TABLE 4.

Convergent validity with loading, cross‐loading, AVE, CR, and α

Variables Items FOC DEP QOL a CR AVE
FOC FCV1 0.816 0.203 −0.213 0.928 0.939 0.688
FCV2 0.839 0.214 −0.185
FCV3 0.838 0.210 −0.216
FCV4 0.875 0.437 −0.421
FCV5 0.850 0.359 −0.321
FCV6 0.795 0.291 −0.230
FCV7 0.792 0.177 −0.144
DEP DEP1 0.232 0.731 −0.577 0.925 0.935 0.508
DEP10 0.198 0.721 −0.408
DEP11 0.212 0.706 −0.439
DEP13 0.207 0.676 −0.536
DEP15 0.265 0.624 −0.403
DEP16 0.318 0.765 −0.675
DEP17 0.324 0.672 −0.460
DEP2 0.274 0.680 −0.491
DEP3 0.252 0.656 −0.405
DEP4 0.345 0.789 −0.564
DEP5 0.309 0.755 −0.534
DEP6 0.164 0.628 −0.400
DEP7 0.210 0.771 −0.550
DEP9 0.243 0.779 −0.517
QOL QOL10 −0.240 −0.471 0.721 0.970 0.973 0.672
QOL11 −0.291 −0.548 0.807
QOL12 −0.296 −0.580 0.831
QOL13 −0.283 −0.596 0.863
QOL14 −0.280 −0.641 0.878
QOL15 −0.271 −0.502 0.773
QOL16 −0.347 −0.646 0.868
QOL17 −0.336 −0.606 0.872
QOL18 −0.225 −0.548 0.834
QOL19 −0.303 −0.659 0.886
QOL20 −0.336 −0.659 0.847
QOL21 −0.237 −0.432 0.672
QOL22 −0.185 −0.544 0.786
QOL23 −0.252 −0.594 0.846
QOL24 −0.233 −0.641 0.888
QOL25 −0.286 −0.630 0.862
QOL26 −0.272 −0.654 0.899
QOL9 −0.169 −0.441 0.526
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Abbreviations: AVE, average variance extracted; CR, composite reliability; DEP, depression; FOC, fear of Covid‐19; QOL, quality of life; α, Cronbach's alpha.

TABLE 5.

HTMT ratio criterion

DEP FOC QOL
DEP
FOC 0.347
QOL 0.735 0.311
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Abbreviations: DEP, depression; FOC, fear of Covid‐19; QOL, quality of life.

Table 6 shows that the standardised root means residual (SRMR) is 0.07, which is lower than 0.08, thus indicating a good model fit (Hair et al., 2017). Cross‐validated redundancies (Q 2) are more significant than zero (Figure 3), showing the model has adequate predictive relevance (Hair et al., 2017). The coefficient of determination (R 2) is 0.512, indicating that the exogenous variables have substantial effects on the endogenous variable (Hair et al., 2017).

TABLE 6.

Quality of the model and fit indices

Variables R2 Q2 SRMR
Quality of life 0.512 0.318 0.07
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FIGURE 3.

FIGURE 3

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Measurement model

3.2. Structural model

In the second step, as recommended by Dijkstra and Henseler (2015), consistent bootstrapping was applied to 5000 samples for the reflective measurement model to test the study's hypotheses. Table 7 shows that fear of COVID‐19 has a significant negative relationship with QOL (β = −0.088, t = 2.181, p > 0.05), which supports H1, while depression has a significant negative relationship with QOL (β = −0.679, t = 20.135, p > 0.05), which supports H2 (Figure 4).

TABLE 7.

Hypothesis testing

95% confidence interval bias corrected
Hypotheses Relationship Beta value SE t‐value p‐value Decision LL UL
H1 FOC ‐> QOL −0.088 0.040 2.181 0.029 Supported −0.166 −0.010
H2 DEP ‐> QOL −0.679 0.034 20.135 0.000 Supported −0.739 −0.603
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Note: N = 377, *p ≤ 0.001 or t ≥ 3.29, **p ≤ 0.01 or t ≥ 2.58, ***p ≤ 0.05 or t ≥ 1.96, β, path coefficient; DEP, depression; FOC, fear of Covid‐19; LL, lower limit; QOL, quality of life; UL, upper limit.

FIGURE 4.

FIGURE 4

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Structural model

4. DISCUSSION AND CONCLUSION

This study is significant because the novel COVID‐19 has psychologically and physically shaken the world; consequently, people are suffering from fear of COVID‐19, situational depression, and their impact on QOL (Ahorsu et al., 2020; Ahuja et al., 2020). The present study has been conducted to examine the relationships between fear of COVID‐19, situational depression, and QOL among Pakistani citizens. The results show that fear of COVID‐19 and situational depression negatively affect people's QOL. The finding indicates an inversely proportional relationship between fear of COVID‐19 and QOL, where a higher level of fear of COVID‐19 prescribes a poor QOL. Thus, H1 is supported. This finding is consistent with the studies of Naeem (2021), Roy et al. (2020), Doshi et al. (2020) and Ahuja et al. (2020), which stated that pandemics are periodic phenomena during which citizens face different challenges. Pandemics also disturb the psychological well‐being of the population. Fear of COVID‐19 also affects people's behaviour in supporting and helping each other. Zhang and Ma (2020) explained that increased social well‐being could improve psychological well‐being and QOL. Subsequently, this study also investigated the relationship between situational depression and QOL. The result shows a negative link between situational depression and QOL, thus supporting H2. This result is in line with the studies of Pennycook et al. (2020), Ahuja et al. (2020), Zhang and Ma (2020) and An et al. (2020), which stated that COVID‐19 has affected people's lives, especially the marginalised groups.

4.1. Theoretical implications

This study makes several theoretical contributions. First, it has empirically established the links between fear of COVID‐19, situational depression, and the QOL of Pakistani citizens, which were ignored in previous literature. This study enriches the theory of human motivation (1943) by understanding the socio‐psychological factors, such as a lack of basic needs, safety, belongingness and love needs, social needs, and self‐actualisation as. This study's findings confirm that these factors develop the fear of COVID‐19, depression, and their impact on QOL during the current COVID‐19 outbreak. These findings enrich the existing body of literature and support the theoretical underpinning. Moreover, this is a pioneer study that examined the fear of COVID‐19, situational depression, and their impact on the QOL of Pakistani citizens by empirically testing the underpinning theory.

4.2. Practical implications and recommendations

The study's findings mirror the phrase ‘silent cries behind closed doors’ because people were living inside their homes to conform with the government's orders and maintaining social distancing for the safety of themselves and others. This study concludes that fear of COVID‐19 and depression regarding COVID‐19 have severely influenced the QOL of Pakistanis. Moreover, the spread of COVID‐19 has become a health threat globally. Consequently, the degree of depression, fear, and uncertainty among people is still increasing. Further, some groups of people such as healthcare workers, the sick, and older people are seriously affected. In addition, the QOL, livelihood, and routine habits of the general public have been impaired due to the strict application of interventions, especially quarantines and lockdowns. In such circumstances, depression, hopelessness, isolation, anxiety, suicidal thoughts/behaviour, mental disorder, bad mood, frustration, domestic violence, and other stress symptoms have also risen. Moreover, it is challenging to predict the direction the present pandemic will take.

This pandemic has presented a lesson on how to plan and cope efficiently with future crises. This situation can be better handled with efficient cooperation between the state, regional and local governments. There is a need to practise social distancing and encourage public health interventions by coordinating the society and local governments. The study's findings should be implemented at both micro and macro level as the findings provide knowledge regarding the basis of QOL during this pandemic. People need proper and authentic guidance, and they should spend less time searching for news about COVID‐19 from the media. In addition, one should avoid obtaining unnecessary knowledge about the symptoms and consequences of COVID‐19. Likewise, people can improve their QOL by limiting sharing fake information on social media and media. Based on current findings and health crises, the government and health authorities should arrange regular screenings to target depression and launch preventive measures to eradicate the risk of depression by providing exact knowledge and awareness as well as financial and psychological support. Also, online psychotherapy and counselling programmes must be planned to reduce the adverse effects of fear of COVID‐19 and situational depression because the pandemic is still ongoing. In essence, during lockdowns and strict SOP policies by the government, sociologists and psychologists must play their role by providing online assistance to people and help them come out of current mental health crises. Therefore, the outcomes of this study are novel and valuable for Pakistani citizens and foreigners trying to attain a good QOL in troubling times. Based on the whole scenario, this study suggests that more studies should be conducted to investigate the perceived socioeconomic problems and high inflation rates during COVID‐19, which are affecting people's QOL. Also, future studies should focus on the impact pre and post COVID‐19 because the pandemic is still ongoing, as well as researchers should analyse whether social distancing and lockdowns affect people's health both physically and mentally.

ACKNOWLEDGEMENT

The authors are grateful to the editor and anonymous referees of the journal for their extremely useful suggestions to improve the quality of the paper.

Biographies

Dr. Muhammad Umair Ashraf is Ph.D. in Sociology from the School of Government, Universiti Utara Malaysia. His research interests focus on juvenile delinquency, parental behavior, parenting styles, peer pressure, aggressive behavior, mental health, violent behavior, Big‐Five personality traits, academic performance, religiosity, cultural values, family socio‐economic status, socialization, and youth development. He has published in various international and national journals. He is also involved as a member of the editorial review board in the South Asian Journal of Social Sciences and Humanities. In addition, he has expertise in data analysis such as SPSS‐25, Partial Least Squares Structural Equation Modeling (PLS‐SEM), AMOS and G*POWER Analysis.

Dr. Saqlain Raza is an Assistant Professor of Management at Shaheed Zulfikar Ali Bhutto Institute of Science and Technology (SZABIST) Karachi. He received his PhD in Management from Universiti Utara Malaysia (AACSB & AMBA). His research interests focus on entrepreneurial competencies, entrepreneurship education, digital social innovation and individual characteristics. He publishes research articles in internationally recognised journals and regularly participates in academic events.

Ms. Asfa Ashraf is an M.Phil. Scholar from Bahauddin Zakariya University. Her research interest areas are related to parenting, personality development, mental health, depression, aggressive behavior, academic performance, religiosity, and child development. She has expertise in different statistical analysis such as SPSS, G*Power. She has published a number of research papers in both national and international journals. Currently she is associated with Bahauddin Zakariya University in the department of Sociology as a visiting lecturer.

Waqas Mehmood is a PhD candidate at School of Economics, Finance and Banking, Universiti Utara Malaysia. His area of interest is Corporate Finance, Investment Finance, specifically in Initial public offerings. He is also a reviewer of few international refereed journals including Pacific Accounting Review, Global Business Review, South Asian Journal of Business Studies, Journal of Financial Economic Policy. Nankai Business Review International and others. He has published in a number of scholarly journals such as Pacific Accounting Review, Review of Behavioral Finance, Global Business Review, International Journal of Business and Society, South Asian Journal of Business Studies, International Journal of Islamic and Middle Eastern Finance and Management and others.

Ataul Karim Patwary is a PhD candidate at School of tourism, hospitality and event Management, Universiti Utara Malaysia. He earned his Bachelor of Hospitality Management (Hon.) and Masters by research from Universiti Utara Malaysia. His research interest includes organizational studies in hospitality management, consumers' behaviour, tourists' behaviour, green innovation and environmental impacts of the tourism industry. He has been considered as reviewer and editor in several journals and conferences. He has good command in SPSS, PLS‐SEM and related quantitative techniques.

Ashraf, M. U. , Raza, S. , Ashraf, A. , Mehmood, W. , & Patwary, A. K. (2021). Silent cries behind closed doors: An online empirical assessment of fear of COVID‐19, situational depression, and quality of life among Pakistani citizens. Journal of Public Affairs, 21(4), e2716. 10.1002/pa.2716

DATA AVAILABILITY STATEMENT

The data set is available on request.

REFERENCES

  1. Ahmad, T. , Khan, M. , Khan, F. M. , & Hui, J. (2020). Are we ready for the new fatal Coronavirus: Scenario of Pakistan? Human Vaccines & Immunotherapeutics, 16(3), 736–738. 10.1080/21645515.2020.1724000 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Ahorsu, D. K. , Lin, C. Y. , Imani, V. , Saffari, M. , Griffiths, M. D. , & Pakpour, A. H. (2020). The fear of COVID‐19 scale: Development and initial validation. International Journal of Mental Health and Addiction, 1–9. 10.1007/s11469-020-00270-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Ahuja, P. , Syal, G. , & Kaur, A. (2020). Psychological stress: Repercussions of COVID‐19 on gender. Journal of Public Affairs, e2533. 10.1002/pa.2533 [DOI] [Google Scholar]
  4. An, Y. , Yang, Y. , Wang, A. , Li, Y. , Zhang, Q. , Cheung, T. , Ungvari, G. S. , Qin, M. Z. , An, F. R. , & Xiang, Y. T. (2020). Prevalence of depression and its impact on quality of life among frontline nurses in emergency departments during the COVID‐19 outbreak. Journal of Affective Disorders, 276, 312–315. 10.1016/j.jad.2020.06.047 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Centers for Disease and Prevention (2020). Retrieved from: http://www.cdc.gov/.
  6. Dijkstra, T. K. , & Henseler, J. (2015). Consistent partial least squares path modeling. MIS Quarterly, 39(2), 297–316. [Google Scholar]
  7. Dogan, B. , Jebli, M. B. , Shahzad, K. , Farooq, T. H. , & Shahzad, U. (2020). Investigating the effects of meteorological parameters on COVID‐19: Case study of New Jersey, United States. Environmental Research, 191, 110148. 10.1016/j.envres.2020.110148 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Dong, M. , & Zheng, J. (2020). Letter to the editor: Headline stress disorder caused by Netnews during the outbreak of COVID‐19. Health Expectations: An International Journal of Public Participation in Health Care and Health Policy, 23(2), 259–260. 10.1111/hex.13055 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Doshi, D. , Karunakar, P. , Sukhabogi, J. R. , Prasanna, J. S. , & Mahajan, S. V. (2020). Assessing coronavirus fear in Indian population using the fear of COVID‐19 scale. International Journal of Mental Health and Addiction, 1–9. 10.1007/s11469-020-00332-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Fareed, Z. , Iqbal, N. , Shahzad, F. , Shah, S. G. M. , Zulfiqar, B. , Shahzad, K. , Hashmi, S. H. , & Shahzad, U. (2020). Co‐variance nexus between COVID‐19 mortality, humidity, and air quality index in Wuhan, China: New insights from partial and multiple wavelet coherence. Air Quality, Atmosphere & Health, 13, 673–682. 10.1007/s11869-020-00847-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Ferriss, A. L. (2004). The quality‐of‐life concept in sociology. The American Sociologist, 35(3), 37–51. [Google Scholar]
  12. Goldberg, I. K. (1993). Goldberg Depression Scale. Retrieved from http://www.nashp.org/Files/Texas_Goldberg_Depression_Scale.pdf
  13. Goldschmidt, N. (2006). A cultural approach to economics. Intereconomics, 41(4), 176–182. 10.1007/s10272-006-0188-1 [DOI] [Google Scholar]
  14. Government of Pakistan . (2020). Retrieved from www.covid.gov.pk/
  15. Goyal, K. , Chauhan, P. , Chhikara, K. , Gupta, P. , & Singh, M. P. (2020). Fear of COVID 2019: First suicidal case in India! Asian Journal of Psychiatry, 49, 101989. 10.1016/j.ajp.2020.101989 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Hair, J. , Hollingsworth, C. L. , Randolph, A. B. , & Chong, A. Y. L. (2017). An updated and expanded assessment of PLS‐SEM in information systems research. Industrial Management & Data Systems, 117(3), 442–458. 10.1108/IMDS-04-2016-0130 [DOI] [Google Scholar]
  17. Hair, J. F. , Black, W. C. , Babin, B. J. , & Anderson, R. E. (2014). Multivariate data analysis: Pearson new international edition (7th ed.). Pearson Higher Ed. [Google Scholar]
  18. Hamza Shuja, K. , Aqeel, M. , Jaffar, A. , & Ahmed, A. (2020). COVID‐19 pandemic and impending global mental health implications. Psychiatria Danubina, 32(1), 32–35. 10.24869/psyd.2020.32 [DOI] [PubMed] [Google Scholar]
  19. Henseler, J. , Ringle, C. M. , & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance‐based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. 10.1007/s11747-014-0403-8 [DOI] [Google Scholar]
  20. Henseler, J. , Ringle, C. M. , & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In Sinkovics R. R. & Ghauri P. N. (Eds.), New challenges to international marketing (Advances in international marketing, Vol. 20) (pp. 277–319). Emerald Group Publishing Limited. 10.1108/S1474-7979(2009)0000020014 [DOI] [Google Scholar]
  21. Huang, Y. , & Zhao, N. (2020). Generalized anxiety disorder, depressive symptoms, and sleep quality during COVID‐19 outbreak in China: A web‐based cross‐sectional survey. Psychiatry Research, 288, 112954. 10.1016/j.psychres.2020.112954 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Hunt, S. M. , & McKenna, S. P. (1992). The QLDS: A scale for the measurement of quality of life in depression. Health Policy, 22(3), 307–319. 10.1016/0168-8510(92)90004-U [DOI] [PubMed] [Google Scholar]
  23. Iqbal, N. , Fareed, Z. , Shahzad, F. , He, X. , Shahzad, U. , & Lina, M. (2020). The nexus between COVID‐19, temperature and exchange rate in Wuhan city: New findings from partial and multiple wavelet coherence. Science of the Total Environment, 729, 138916. 10.1016/j.scitotenv.2020.138916 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Jeong, H. , Woo, H. , Song, Y. J. , Ki, M. , Min, J. , Cho, J. , & Chae, J. H. (2016). Mental health status of people isolated due to Middle East Respiratory Syndrome. Epidemiology and health, 38, e2016048. 10.4178/epih.e2016048 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Jones, N. M. , Thompson, R. R. , Schetter, C. D. , & Silver, R. C. (2017). Distress and rumor exposure on social media during a campus lockdown. Proceedings of the National Academy of Sciences of the United States of America, 114(44), 11663–11668. 10.1073/pnas.1708518114 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Kang, L. , Li, Y. , Hu, S. , Chen, M. , Yang, C. , Yang, B. X. , Wang, Y. , Hu, J. , Lai, J. , Ma, X. , Chen, J. , Guan, L. , Wang, G. , Ma, H. , & Liu, Z. (2020). The mental health of medical workers in Wuhan, China dealing with the 2019 novel coronavirus. The Lancet Psychiatry, 7(3), e14. 10.1016/S2215-0366(20)30047-X [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Kumar, B. , & Pinky, S. D. (2020). Addressing economic and health challenges of COVID‐19 in Bangladesh: Preparation and response. Journal of Public Affairs, e2556. 10.1002/pa.2556 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Liu, K. (2020). How I faced my coronavirus anxiety. Science, 367(6484), 1398–1398. 10.1126/science.367.6484.1398 [DOI] [PubMed] [Google Scholar]
  29. Lodhi, F. S. , Montazeri, A. , Nedjat, S. , Mahmoodi, M. , Farooq, U. , Yaseri, M. , Kasaeian, A. , & Holakouie‐Naieni, K. (2019). Assessing the quality of life among the Pakistani general population and their associated factors using the World Health Organization's quality of life instrument (WHOQOL‐BREF): A population based cross‐sectional study. Health and Quality of Life Outcomes, 17(1), 9. 10.1186/s12955-018-1065-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Lynch, S. M. (2013). Using statistics in social research: A concise approach. Springer Science & Business Media. 10.1007/978-1-4614-8573-5 [DOI] [Google Scholar]
  31. Ma, Y. F. , Li, W. , Deng, H. B. , Wang, L. , Wang, Y. , Wang, P. H. , Bo, H.‐X. , Cao, J. , Wang, Y. , Zhu, L.‐Y. , Yang, Y. , Cheung, T. , Ng, C. H. , Wu, X. , & Xiang, Y.‐T. (2020). Prevalence of depression and its association with quality of life in clinically stable patients with COVID‐19. Journal of Affective Disorders, 275, 145–148. 10.1016/j.jad.2020.06.033 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Mamun, M. A. , & Griffiths, M. D. (2020). First COVID‐19 suicide case in Bangladesh due to fear of COVID‐19 and xenophobia: Possible suicide prevention strategies. Asian Journal of Psychiatry, 51, 102073. 10.1016/j.ajp.2020.102073 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Mamun, M. A. , & Ullah, I. (2020). COVID‐19 suicides in Pakistan, dying off not COVID‐19 fear but poverty?–The forthcoming economic challenges for a developing country. Brain, Behavior,and Immunity, 87, 165–168. 10.1016/j.bbi.2020.05.028 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Marsh, H. W. , Dowson, M. , Pietsch, J. , & Walker, R. (2004). Why multicollinearity matters: A re‐examination of relations between self‐efficacy, self‐concept, and achievement. Journal of Educational Psychology, 96(3), 518–522. 10.1037/0022-0663.96.3.518 [DOI] [Google Scholar]
  35. Martínez‐Lorca, M. , Martínez‐Lorca, A. , Criado‐Álvarez, J. J. , & Armesilla, M. D. C. (2020). The fear of COVID‐19 scale: Validation in Spanish university students. Psychiatry Research, 293, 113350. 10.1016/j.psychres.2020.113350 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Maslow, A. H. (1943). A theory of human motivation. Psychological Review, 50(4), 370–396. [Google Scholar]
  37. Minai, M. S. , Raza, S. , & Segaf, S. (2021). Post COVID‐19: Strategic digital entrepreneurship in Malaysia. In Modeling economic growth in contemporary Malaysia (pp. 71–79). Emerald Publishing Limited. 10.1108/978-1-80043-806-420211007 [DOI] [Google Scholar]
  38. Mowbray, H. (2020). Letter from China: Covid‐19 on the grapevine, on the internet, and in commerce. BMJ, 368, m643. 10.1136/bmj.m643 [DOI] [PubMed] [Google Scholar]
  39. Mukhtar, M. S. (2020). Mental health and psychosocial aspects of coronavirus outbreak in Pakistan: Psychological intervention for public mental health crisis. Asian Journal of Psychiatry, 51, 102069. 10.1016/j.ajp.2020.102069 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Naeem, M. (2021). Do social media platforms develop consumer panic buying during the fear of Covid‐19 pandemic. Journal of Retailing and Consumer Services, 58, 102226. 10.1016/j.jretconser.2020.102226 [DOI] [Google Scholar]
  41. Pennycook, G. , McPhetres, J. , Zhang, Y. , Lu, J. G. , & Rand, D. G. (2020). Fighting COVID‐19 misinformation on social media: Experimental evidence for a scalable accuracy‐nudge intervention. Psychological Science, 31(7), 770–780. 10.1177/0956797620939054 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Podsakoff, P. M. , & Organ, D. W. (1986). Self‐reports in organizational research: Problems and prospects. Journal of Management, 12(4), 531–544. [Google Scholar]
  43. Roy, D. , Tripathy, S. , Kar, S. K. , Sharma, N. , Verma, S. K. , & Kaushal, V. (2020). Study of knowledge, attitude, anxiety & perceived mental healthcare need in Indian population during COVID‐19 pandemic. Asian Journal of Psychiatry, 51, 102083. 10.1016/j.ajp.2020.102083 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Shahzad, F. , Shahzad, U. , Fareed, Z. , Iqbal, N. , Hashmi, S. H. , & Ahmad, F. (2020). Asymmetric nexus between temperature and COVID‐19 in the top ten affected provinces of China: A current application of quantile‐on‐quantile approach. Science of the Total Environment, 736, 139115. 10.1016/j.scitotenv.2020.139115 [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Shahzad, K. , Shahzad, U. , Iqbal, N. , Shahzad, F. , & Fareed, Z. (2020). Effects of climatological parameters on the outbreak spread of COVID‐19 in highly affected regions of Spain. Environmental Science and Pollution Research, 27(31), 39657–39666. 10.1007/s11356-020-10551-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Shakoor, A. , Chen, X. , Farooq, T. H. , Shahzad, U. , Ashraf, F. , Rehman, A. , Sahar, N. . , & Yan, W. (2020). Fluctuations in environmental pollutants and air quality during the lockdown in the USA and China: Two sides of COVID‐19 pandemic. Air Quality, Atmosphere & Health, 13(11), 1335–1342. 10.1007/s11869-020-00888-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. The COVID‐19 alerts . (2020). Retrieved from https://www.google.com/search?q=covid19+statistics+in+South+asia&oq=covid19+statistics+in+South+asia&aqs=chrome..69i5j0i22i3i457.9729j0j7&sourceid=chrome&ie=UTF-8
  48. The Nation News . (2020). Retrieved form https://nation.com.pk/08-Oct-2020/depression-anxietyundefined-or-imaginary
  49. Torales, J. , O'Higgins, M. , Castaldelli‐Maia, J. M. , & Ventriglio, A. (2020). The outbreak of COVID‐19 coronavirus and its impact on global mental health. International Journal of Social Psychiatry, 66(4), 317–320. 10.1177/0020764020915212 [DOI] [PubMed] [Google Scholar]
  50. Usher, K. , Bhullar, N. , Durkin, J. , Gyamfi, N. , & Jackson, D. (2020). Family violence and COVID‐19: Increased vulnerability and reduced options for support. International Journal of Mental Health Nursing, 29, 549–552. 10.1111/inm.12735 [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Vahedi, S. (2010). World Health Organization Quality‐of‐Life Scale (WHOQOL‐BREF): Analyses of their item response theory properties based on the graded responses model. Iranian Journal of Psychiatry, 5(4), 140–153. [PMC free article] [PubMed] [Google Scholar]
  52. Voruganti, L. , Heslegrave, R. , Awad, A. G. , & Seeman, M. V. (1998). Quality of life measurement in schizophrenia: Reconciling the quest for subjectivity with the question of reliability. Psychological Medicine, 28(1), 165–172. 10.1017/S0033291797005874 [DOI] [PubMed] [Google Scholar]
  53. Wang, D. , Hu, B. , Hu, C. , Zhu, F. , Liu, X. , Zhang, J. , Wang, B. , Xiang, H. , Cheng, Z. , Xiong, Y. , Zhao, Y. , Li, Y. , Wang, X. , & Peng, Z. (2020). Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus–infected pneumonia in Wuhan, China. JAMA, 323(11), 1061–1069. 10.1001/jama.2020.1585 [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. World Health Organization . (2004). The world health organization quality of life (WHOQOL)‐BREF (No. WHO/HIS/HIS Rev.2012.02). World Health Organization.
  55. World Health Organization . (2020). Novel coronavirus (2019‐nCoV). Situation Report, 28.
  56. Wuhan Municipal Health Commission . (2020, January 20). Report of novel coronavirus‐infected pneumonia in China. Retrieved from http://wjw.wuhan.gov.cn/front/web/showDetail/2020012009077
  57. Xiao, H. , Zhang, Y. , Kong, D. , Li, S. , & Yang, N. (2020). Social capital and sleep quality in individuals who self‐isolated for 14 days during the coronavirus disease 2019 (COVID‐19) outbreak in January 2020 in China. Medical Science Monitor: International Medical Journal Of Experimental And Clinical Research, 26, e923921‐1–e923921‐8. 10.12659/MSM.923921 [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Yu, H. , Jiang, S. , & Land, K. C. (2015). Multicollinearity in hierarchical linear models. Social Science Research, 53, 118–136. 10.1016/j.ssresearch.2015.04.008 [DOI] [PubMed] [Google Scholar]
  59. Zhang, Y. , & Ma, Z. F. (2020). Impact of the COVID‐19 pandemic on mental health and quality of life among local residents in Liaoning Province, China: A cross‐sectional study. International Journal of Environmental Research and Public Health, 17(7), 2381. 10.3390/ijerph17072381 [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Zhao, S. , Lin, Q. , Ran, J. , Musa, S. S. , Yang, G. , Wang, W. , Lou, Y. , Gao, D. , Yang, L. , He, D. , & Wang, M. H. (2020). Preliminary estimation of the basic reproduction number of novel coronavirus (2019‐nCoV) in China, from 2019 to 2020: A data‐driven analysis in the early phase of the outbreak. International Journal of Infectious Diseases, 92, 214–217. 10.1016/j.ijid.2020.01.050 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data set is available on request.

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