• Users Online: 73
  • Home
  • Print this page
  • Email this page
Home Current issue Archives Ahead of print Search Subscribe Instructions Submit article About us Editorial board Contacts Login 

 Table of Contents  
ORIGINAL ARTICLE
Year : 2022  |  Volume : 43  |  Issue : 2  |  Page : 116-121

Predictors of coronavirus disease 2019 anxiety and coping among different age groups of Egyptian population


1 Department of Geriatrics and Gerontology, Ain Shams University Hospitals, Egypt
2 Department of Psychiatry, Kasr Al Ainy Hospitals, Faculty of Medicine, Cairo University, Cairo, Egypt
3 Department of Statistics, Faculty of Commerce, Kafr El Sheikh University, Kafr El Sheikh, Egypt
4 Department of Psychology, Faculty of Art, Kafr El Sheikh University, Kafr El Sheikh; Main Center for Sustainable Development, Tanta University, Tanta, Egypt
5 Department of Geriatrics and Gerontology, Faculty of Medicine, Ain Shams University Abbasiya, Cairo, Egypt

Date of Submission16-Jan-2022
Date of Decision26-Jan-2022
Date of Acceptance13-Feb-2022
Date of Web Publication24-Jun-2022

Correspondence Address:
PhD Omaima R.E Madkor
The Martyr Engineer Ahmed Shawky Hospital for Elderly Medicine, Ain Shams University Hospitals, Abbasiya, Cairo 1156
Egypt
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ejpsy.ejpsy_1_22

Rights and Permissions
  Abstract 


Objectives To determine predictors of anxiety and coping during coronavirus disease 2019 (COVID-19) pandemic among different age groups of Egyptian population.
Patients and methods A cross-sectional study of 500 participants who completed an online questionnaire for anxiety and coping the following tools: COVID-19 Coping Responses Inventory, Coping Responses Inventory, COVID-19 Life Events−Anxiety Inventory, and the Arabic version of State-Trait Anxiety Inventory. Correlation between different variables, anxiety and coping related to COVID-19 was evaluated by Pearson’s correlation coefficient. Regression analysis was performed to evaluate predictors for anxiety and coping.
Results COVID-19 Life Events (C-19L) is the most important in predicting COVID-19 related Anxiety (C-19A) as it has B value of 0.568 followed by level of education (B=0.217), and, monthly family income (B=0.140).
Conclusion This study shows the impact of sociodemographic factors in predicting COVID-19 anxiety and coping among various sectors in the Egyptian community.

Keywords: aged, anxiety, coping, coronavirus disease 2019


How to cite this article:
Madkor OR, Zayet DK, Abdelhady DH, AbdulJawad PM, Elsorady KE. Predictors of coronavirus disease 2019 anxiety and coping among different age groups of Egyptian population. Egypt J Psychiatr 2022;43:116-21

How to cite this URL:
Madkor OR, Zayet DK, Abdelhady DH, AbdulJawad PM, Elsorady KE. Predictors of coronavirus disease 2019 anxiety and coping among different age groups of Egyptian population. Egypt J Psychiatr [serial online] 2022 [cited 2022 Aug 18];43:116-21. Available from: http://new.ejpsy.eg.net/text.asp?2022/43/2/116/348145




  Introduction Top


Coronavirus disease 2019 (COVID-19) is related to higher levels of anxiety and distress (Rosen et al., 2020). It could be assessed by different tools such as the State-Trait Anxiety Inventory (STAI) for adults (Spielberger, 1983). Various sociodemographic factors could be related to anxiety during the pandemic (Choi et al., 2020). Coping is defined as the behaviors and thoughts of a person toward various stressful situations (Folkman and Moskowitz, 2004). There are different methods to monitor coping strategies using various coping scales such as Coping Strategies Questionnaire and Ways of Coping Questionnaire (Aust et al., 2016). The study aims to identify predictors of anxiety and coping among various sectors in the Egyptian community.


  Patients and methods Top


The data for this study were taken from COVID-19 Life Events−Anxiety Inventory (C-19LAI) Development, Reliability, and Validity Study on Egyptian Population (Madkor et al., 2021).

Sample and setting

This research was conducted in various governorates. Within Egypt using an online survey in a convenience sample. We used Google forms. Moreover, an offline survey was conducted for those who had no access to the online survey.

Ethics approval and consent to participate

Ethical approval for the study was granted by the Tanta University Ethical Committee, with approval Code 34394. Informed written consent for taking part in the study was obtained from each participant.

Tools

The survey included Four tools. Coping Responses Inventory (CRI) (Moos and Moos, 1988) has been designed and validated before (Moos et al., 1990) and translated and validated into Arabic before (Madkor, 2007). It showed acceptable reliability (α=0.927) in the total sample in our study (see supplementary material; Table S1). CRI comprises eight subscales (logical analysis, positive reappraisal, seek guidance and support, take problem-solving action, cognitive avoidance, acceptance/resignation, seek alternative rewards, and emotional discharge). The first set of four subscales measure approach coping, and the second set measures avoidance coping. The first two subscales in each set reflect cognitive coping, and the second two reflect behavioral coping.

COVID-19 Coping Responses Inventory (C-19C) is composed of four subscales (50 items) that measure different types of specific responses about coping with COVID-19 activities (eight items), behavioral phenomena (11 items), direct dealing with the problem (11 items), and positive reappraisal (20 items).

C-19C reflects method of coping domains (behavioral or cognitive).

Behavioral coping reflects efforts to master COVID-19 or to avoid thinking about it. Cognitive coping reflects cognitive attempts with COVID-19.

Respondents rate their reliance on each of 50 items on a four-point scale varying from ‘not at all’ to ‘fairly often.’

We selected and categorized the items according to the method of coping with the help of a conceptual framework. Moreover, a pilot study was conducted for this aim, and then we did content analysis and expansion of the item pool to cover additional responses.

We established specific criteria to classify items and excluded items that were nonspecific, redundant, or too infrequent or complex. We rewrote words (see supplementary material for sample items in English; Table S3).

We studied validity and reliability of the inventory. Its Psychometric proprieties are shown in the supplementary material (Table S2). In brief, the Cronbach’s alpha of internal consistency was 0.775 and each value of item-total correlation was in the range of 0.169–0.682. The total item degree was positive and significant (P<0.01). The four subscales show acceptable internal consistencies that range from 0.75 to 0.94.

In addition to the CRI and C-19C, we administered C-19LAI (Madkor et al., 2021) and S-STAI (Abdel-khalek, 1983). C-19LAI shows acceptable reliability and moderate validity. The COVID-19 anxiety scale (C-19A) correlates in a positive, moderate, and significant way (P≤0.01) with the Arabic S-STAI (Abdel-khalek, 1983) (r=0.407). It has been used in several studies and shows high internal consistency reliability and high construct validity.

Procedure and data analysis

The first phase of the study was designing a tool specifically to assess C-19C. For this phase, a pilot study was conducted online in a convenience sample of 33 Egyptians, with 78% females, 18 years old and above (from 8/4 to 10/4/2020). Based on the data obtained from this pilot study and in line with the theoretical framework, an item pool of C-19C was developed.

The second phase included the Psychometric testing of C-19C . It was conducted in a total sample of 500 participants throughout the time-period from April, 21 to May, 16 including a well-prepared battery for C-19C and CRI.

Descriptive statistics were used to summarize information of the total sample. We computed Cronbach’s alpha for internal consistency and reliability. Pearson correlations were used to test item-total correlation as internal consistency validity for the aforementioned two inventories (C-19C and CRI) and concurrent validity between C-19C and other variables.

Pearson’s correlation coefficient (r) was conducted to assess correlations between variables. Significance was adopted at P value less than 0.05 for interpretation of results of tests of significance.

Stepwise regression analysis was conducted to evaluate the risk factors for anxiety and coping during COVID-19 pandemic.

The explanatory power of the overall regression model (R2) was reported using the F test of significance. Once this was significant, individual explanatory power testing of various independent variables was conducted and then we did a comparison of their relative importance.

All statistical analyses were performed with the SPSS 23 (SPSS, IBM Corp. Released 2017. IBM SPSS Statistics for Windows, Version 25.0. Armonk, NY: IBM Corp).


  Results Top


A total of 500 individuals completed the questionnaire online, comprising 182 males and 318 females. The most responsive governates were Cairo (n=158 participants) followed by Gharbia (n=116), and Giza (n=85). Overall, 260 (52%) participants were less than 40 years, 170 (34%) were between 40 and 59 years, and 70 (14%) participants were 60 years and older. Overall, 34.4% of the sample worked in the health sector. Moreover, 259 (51.8%) individuals lived with an elderly person. In addition, 134 (26.8%) participants presented with chronic illnesses, and only 11 (2.2%) participants presented with physical disability. Overall, 4.2% of the sample was diagnosed with a psychiatric disorder.

Stepwise regression analysis was done to evaluate the risk factors for anxiety during COVID-19 pandemic. Our results showed that age is an important risk factor of anxiety. The increase in age was accompanied by increase in anxiety in both scales, anxiety related to COVID-19 scale (P<0.01, beta=0.324) and Spielberger scale (P<0.05, beta=0.093), which makes the geriatric group the most vulnerable age group. Moreover, increase in age is a risk factor for predicting deterioration of coping responses as shown in [Table 1], which again confirms the vulnerability of the Geriatric population during this crucial era.
Table 1 Results of simple linear regression analyses of age in predicting Coping Responses Inventory as dependent variable in participants (N=500)

Click here to view


Other demographic variables such as monthly income, level of education, and occupation have shown to be important risk factors that share in the prediction of anxiety and proper coping mechanisms. The decrease in the monthly income was associated with an increase in the level of anxiety related to COVID-19 and decrease in the coping responses, as shown in [Table 2] and [Table 3]. Moreover, results showed that education can predict −0.217% of anxiety related to COVID-19, which means that individuals with a higher level of education were less prone to anxiety, as shown in [Table 2].
Table 2 Results of stepwise regression analyses of monthly family income, level of education, and life events in predicting C-19A as dependent variable in participants (N=500)

Click here to view
Table 3 Results of stepwise liner regression analyses of monthly family income and level of occupation as independent variables and COVID-19 Coping Responses Inventory as a dependent variable in participants (N=500)

Click here to view


Moreover, negative life events that occurred during COVID such as morbidity, mortality, social, financial, and occupational negative events had strong contributes and can highly predict the increase in the level of anxiety (R2=0.568), and again, this was shown in its direct positive correlation with both anxiety scales. Moreover, the predictive regression analysis showed that emotional discharge and seeking alternative rewards can significantly explain anxiety related to COVID. Overall, 0.314% of anxiety related to COVID-19 was predicted through emotional discharge, whereas 0.194% predicted via seeking alternative rewards (P≤0.01).

Anxiety related to COVID-19 showed a significant positive correlation (P≤0.01) with Spielberger anxiety scale (r=0.407) and life event scale (r=0.666). Our results also showed that anxiety negatively affects the coping strategies and vice versa. This was evident through the negative significant correlation between anxiety related to COVID-19 on one side and coping inventory scale (P≤0.01, r=0.119) and two of its subscales, which are seeking support/information (P≤0.05, r=−0.96) and acceptance/resignation subscales (P≤0.01, r=−0.197) on the other side.

This significant negative correlation between anxiety and coping was also more evident between the two scales related to the pandemic: anxiety related to COVID-19 and coping with COVID-19 (P≤0.05, r=0.098), as shown in [Table 4]. Anxiety shows a significant decrease with the increase in the two subscales of coping: (a) positive reappraisal way of coping such as arranging priorities, re-evaluation of family relations, etc. (P≤0.01, r=−0.139) and (b) direct dealing with the problem such as following the measures of safety and social distance (r=−0.093). On the contrary, there is a positive correlation between anxiety related to COVID-19 and negative behaviors related to COVID-19 (r=0.124), such as insisting on gatherings, hypersomnia, or substance abuse, which reflect the negative way of coping during the pandemic.
Table 4 Correlations of variables in the sample (N=500)

Click here to view



  Discussion Top


COVID-19 and its consequences resulted in many physical and Psychological health concerns. Its negative effect on the mental health in different communities is growing more and more with the rapid spread of the virus. A recent meta-analysis study that included 17 studies with a sample size of 63 439 showed that the prevalence of anxiety has reached 31.9% (Salari et al., 2020). Thus, it is essential to predict the different risk factors of anxiety during the pandemic to try to protect the most vulnerable groups and decrease the prevalence of anxiety and other Psychological disorders among these groups. In our study, there were a number of demographic factors that were predictive of anxiety during COVID-19 pandemic, including age, monthly income, and education. It was found that the mental health state of different age groups was not the same during the pandemic. It appeared that elder people were more likely to experience anxiety. Our results showed that increase in age is a risk factor in increasing anxiety and deterioration of the coping mechanisms.

This was consistent with a study done in Germany on 15 308 participants, which showed that COVID-19-related fear was more evident in elderly participants. They suggested that the high individual risk of infection in elderly and its possible complication justifies the intense COVID-19-related fear (Schweda et al., 2021). Another study showed that the prevalence of anxiety in the elderly was 4.95% before the COVID-19 pandemic and 10.10% during the outbreak which showed that there was a double increase in anxiety among the elderly population (Wang et al., 2020). Another important factor that may contribute in increasing anxiety among geriatrics was the loneliness and separation from other family members. Studies showed that loneliness during periods of lockdown during the outbreak was an additional important factor for both depression and anxiety (Jaspal and Breakwell, 2020), but its effect on the elderly population in particular needs further research.

However, the relation between increased anxiety and increased age has not appeared in a study done in Canada on 8267. They found that the highest levels of anxiety according to GAD-7 scales were evident among young people aged less than or equal to 25 years and lowest among the elderly population aged more than 60 years old (Nwachukwu et al., 2020). This was shown also in another study in which females aged 18–29 years were at a higher risk for increased anxiety (Solomou and Constantinidou, 2020). This may be contributed in part to the differences in Psychological support services available for the elderly population between different countries.

In our study, the results showed that decrease monthly income increases the risk of anxiety. Any pandemic can create survival uncertainty owing to the health complications, unemployment, and different economic sequalae, and as a developing country, these factors are very important in Egypt especially in the presence of millions of temporary workers. Overall, 45.4% of the sample reported that their monthly income is only sufficient for basic needs, whereas 18.4% reported their need for help. This means that further decrease in the monthly income during the pandemic and the periods of lockdown is considered a real threat for the basic human needs for this part of the Egyptian community. This was consistent with an American study on 514 participants, which raised the importance of detecting economic anxiety and determining the more vulnerable and affected groups (Mann et al., 2020).

Moreover, education was one of the predictors of anxiety in this study. The significant differences in state anxiety among people with different education levels showed that people with higher education showed better management of anxiety and Psychological stress better than lower literacy. One of the important results shown to us was the negative significant correlation between anxiety scale and certain coping mechanisms such as acceptance/resignation, seeking information and support, in addition to direct dealing with the problem. Coping mechanisms are considered as cornerstones in facing anxiety in the presence of different inevitable negative life events in the form of increased mortality, morbidity, financial, and social burdens during the outbreak. Different studies denoted that even simple coping behaviors such as balanced diet, avoiding overindulgent of knowing updates about the outbreak may help in protection against anxiety (Fullana et al., 2020). Moreover, strategies such as maintaining social contacts, the concept of self-efficacy, acceptance of negative emotions, and seeking enough information about simple steps of management and how to obtain medical advice when needed may be associated with decrease in Psychological burden (Petzold et al., 2020). There are some limitations in our study, which included the need for a larger sample of elderly people for further detailed assessment of the geriatric vulnerability for anxiety. Moreover, we need more statistical data about anxiety and coping in the medical staff, which represented about 34% and also about the caregivers of the elderly population in our sample.


  Conclusion Top


To develop a predictive model for anxiety and coping, a reliable and valid tool is needed. The C-19C inventory has been developed in this study. It demonstrated preliminary reliability evidence, good internal consistency, and convergent validity. There is a need to test C-19C (Arabic version) in a larger sample of elderly in both normal and psychiatric patients, to use it in a full-scale clinical research. That is why, we must establish as a community and health providers a therapeutic target based on coping responses with the most vulnerable groups to protect them.

As is shown in [Table 1], the predictive regression coefficient demonstrates that age can explain CRI significantly (P<0.01) and by considering R2 coefficient, 0.07% of CRI is predicted through age.

As shown in [Table 2], monthly family income, level of education, and C-19L could predict C-19A. [Table 2] shows that monthly family income has a beta value that is predictive of C-19A (B coeff.=−2.973, P<0.01). It means further increase in monthly family income resulted in decreased C-19A among individuals. Level of education was a predictor (B coeff.=−7.866, P<0.01). C-19L was a predictor (B coeff.=2.132, P<0.01). According to R2 value (0.568) the model explains about 57% of the fitted data in the regression model, and This value indicates that 57% of the variance in C-19A can be predicted from the three variables. According to the standardized coefficient β, it seems that the variable C-19L is the most important in predicting C-19A as it has the largest B value (0.568), then level of education (0.217), and finally, monthly family income (0.140).

As shown in [Table 3], We can conclude that According to R2 value (0.07) the model explains about 7% of the fitted data in the regression model, and This value indicates that 7% of the variance in C-19C scores can be predicted from the three variables.

Acknowledgements

The authors would like to thank the participants who took part in this study.

Authors’ contribution Details: O.R.E.M. contributed to coordinating and facilitating investigators’ roles. O.R.E.M., K.E.E., P.M.A.J., and D.H.A. contributed to the concept and design of the study. O.R.E.M. and P.M.A.J. contributed to the designing of new battery. O.R.E.M. contributed to translation of new battery. D.H.A., K.E.E., P.M.A.J., and O.R.E.M. contributed to the statistical analysis. P.M.A.J., O.R.E.M., D.H.A., and K.E.E. contributed to the data collection. K.E.E., P.M.A.J., D.K.Z., and D.H.A. contributed to the proofreading of the article. D.K.Z., K.E.E., and O.R.E.M. contributed to the article write up. All authors have read and approved the manuscript.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.









[19]

 
  References Top

1.
Abdel-khalek AM (1983). Manual for the state-trait anxiety inventory. Alexandria: University Knowledge Press.  Back to cited text no. 1
    
2.
Aust H, Rüsch D, Schuster M, Sturm T, Brehm F, Nestoriuc Y (2016). Coping strategies in anxious surgical patients. BMC Health Serv Res 16:1.  Back to cited text no. 2
    
3.
Choi E, Hui B, Wan E (2020). Depression and anxiety in Hong Kong during COVID-19. Int J Environ Res Public Health 17:3740.  Back to cited text no. 3
    
4.
Folkman S, Moskowitz J (2004). Coping: pitfalls and promise. Annu Rev Psychol 55:745–774.  Back to cited text no. 4
    
5.
Fullana M, Hidalgo-Mazzei D, Vieta E, Radua J (2020). Coping behaviors associated with decreased anxiety and depressive symptoms during the COVID-19 pandemic and lockdown. J Affect Disord 275:80–81.  Back to cited text no. 5
    
6.
Jaspal R, Breakwell G (2020). Socio-economic inequalities in social network, loneliness and mental health during the COVID-19 pandemic. Int J Soc Psychiatry 68:155–165.  Back to cited text no. 6
    
7.
Madkor O (2007). Behavioral disturbances. Cairo: Egyptian General Book Authority.  Back to cited text no. 7
    
8.
Madkor O, Elsorady K, Abdelhady D, AbdulJawad P, Aly El Gabry D (2021). COVID-19 Life Events-Anxiety Inventory (C-19LAI): development, reliability, and validity study on Egyptian population. Middle East Curr Psychiatry 28:1.  Back to cited text no. 8
    
9.
Mann F, Krueger R, Vohs K (2020). Personal economic anxiety in response to COVID-19. Pers Individ Dif 167:110233.  Back to cited text no. 9
    
10.
Moos RH, Moos BS (1988). Coping responses inventory manual. Palo Alto, CA: Center for Health Care Evaluation, Department of Veteran Affairs and Stanford University Medical Centers.  Back to cited text no. 10
    
11.
Moos R, Brennan P, Fondacaro M, Moos B (1990). Approach and avoidance coping responses among older problem and nonproblem drinkers. Psychol Aging 5:31–40.  Back to cited text no. 11
    
12.
Nwachukwu I, Nkire N, Shalaby R, Hrabok M, Vuong W, Gusnowski A et al. (2020). COVID-19 pandemic: age-related differences in measures of stress, anxiety and depression in Canada. Int J Environ Res Public Health 17:6366.  Back to cited text no. 12
    
13.
Petzold M, Bendau A, Plag J, Pyrkosch L, Mascarell Maricic L, Betzler F et al. (2020). Risk, resilience, psychological distress, and anxiety at the beginning of the COVID‐19 pandemic in Germany. Brain Behav 10:9.  Back to cited text no. 13
    
14.
Rosen Z, Weinberger-Litman SL, Rosenzweig C, Rosmarin DH, Muennig P, Carmody ER, Litman L. (2020). Anxiety and distress among the first community quarantined in the US due to COVID-19. Psychological implications for the unfolding crisis. doi: 10.31234/osf.io/7eq8c.  Back to cited text no. 14
    
15.
Salari N, Hosseinian-Far A, Jalali R, Vaisi-Raygani A, Rasoulpoor S, Mohammadi M et al. (2020). Prevalence of stress, anxiety, depression among the general population during the COVID-19 pandemic: a systematic review and meta-analysis. Glob Health 16:1.  Back to cited text no. 15
    
16.
Schweda A, Weismüller B, Bäuerle A, Dörrie N, Musche V, Fink M et al. (2021). Phenotyping mental health: age, community size, and depression differently modulate COVID-19-related fear and generalized anxiety. Compr Psychiatry 104:152218.  Back to cited text no. 16
    
17.
Solomou I, Constantinidou F (2020). Prevalence and predictors of anxiety and depression symptoms during the COVID-19 pandemic and compliance with precautionary measures: age and sex matter. Int J Environ Res Public Health 17:4924.  Back to cited text no. 17
    
18.
Spielberger C (1983). State-Trait Anxiety Inventory: Bibliography (2nd ed.). Palo Alto, CA: Consulting Psychologists Press. Manual for the State-Trait Anxiety Inventory, 14:72–76.  Back to cited text no. 18
    
19.
Wang ZH, Qi SG, Zhang H, Mao PX, He YL, Li J et al. (2020). Impact of the COVID-19 epidemic on anxiety among the elderly in community. Zhonghua Yi Xue Za Zhi 100:3179–3185.  Back to cited text no. 19
    



 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4]



 

Top
 
 
  Search
 
Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
Access Statistics
Email Alert *
Add to My List *
* Registration required (free)

 
  In this article
Abstract
Introduction
Patients and methods
Results
Discussion
Conclusion
References
Article Tables

 Article Access Statistics
    Viewed107    
    Printed8    
    Emailed0    
    PDF Downloaded15    
    Comments [Add]    

Recommend this journal


[TAG2]
[TAG3]
[TAG4]