Next Article in Journal
Experiences in the Decision-Making Regarding the Place of Care of the Elderly: A Systematic Review
Next Article in Special Issue
Problems and Needs Persist for Oklahoma City Bombing Survivors Many Years Later
Previous Article in Journal
Sooo Sweeet! Presence of Long Vowels in Brand Names Lead to Expectations of Sweetness
Previous Article in Special Issue
The Symptom Structure of Postdisaster Major Depression: Convergence of Evidence from 11 Disaster Studies Using Consistent Methods
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Behavioral Sciences
Volume 11
Issue 2
10.3390/bs11020013
behavsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Factors Associated with Symptoms of Depression and Psychological Distress during the COVID-19 Pandemic

by
Yuqi Guo
1,
Omar T. Sims
2,3,4,5,*,
Weidi Qin
6 and
Fan Yang
7,8,*
1
School of Social Work, College of Health and Human Services, University of North Carolina Charlotte, Charlotte, NC 28223, USA
2
Department of Social Work, College of Arts and Sciences, University of Alabama at Birmingham, Birmingham, AL 35294, USA
3
Department of Health Behavior, School of Public Health, University of Alabama at Birmingham, Birmingham, AL 35294, USA
4
Center for AIDS Research, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA
5
Integrative Center for Aging Research, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA
6
Jack, Joseph and Morton Mandel School of Applied Social Sciences, Case Western Reserve University, Cleveland, OH 44106, USA
7
Social Welfare Program, School of Public Administration, Dongbei University of Finance and Economics, Dalian 102488, China
8
Institute of Quantitative & Technological Economies, Chinese Academy of Social Sciences, Beijing 102488, China
*
Authors to whom correspondence should be addressed.
Behav. Sci. 2021, 11(2), 13; https://0-doi-org.brum.beds.ac.uk/10.3390/bs11020013
Submission received: 18 December 2020 / Revised: 17 January 2021 / Accepted: 19 January 2021 / Published: 21 January 2021
(This article belongs to the Special Issue Disasters, Diagnosis, and Distress: Multiple Perspectives, Populations, and Methodologies)
Versions Notes

Abstract

:
The objective of this study was to examine factors associated with symptoms of depression and psychological distress during the COVID-19 pandemic in China. Convenience sampling and snowball sampling were used to recruit a sample of adults in China (n = 2130) from 14 February 2020 to 3 March 2020 to complete an online survey. Linear regression was used to examine the predictors of symptoms of depression and psychological distress. Living in a non-urban area and the number of confirmed cases in their city of residence were positively associated with symptoms of depression. Female gender, not being married, practicing social distancing, the amount of time spent daily on social media searching for and reading information on COVID-19, the number of confirmed cases of COVID-19 in their city of residence, and having confirmed or suspected cases of COVID-19 in personal networks were positively associated with psychological distress. Social distancing is a widely used public health approach for population-wide virus-containment of COVID-19. However, reductions in population-wide psychological well-being are inadvertent consequences of social distancing. There is an emerging need to negate factors that increase adverse mental health vulnerabilities during the COVID-19 pandemic.

1. Introduction

The novel coronavirus disease 2019 (COVID-19) emerged as a cluster of unexplained cases of pneumonia in Wuhan, China but rapidly spread throughout the world and emerged as a global pandemic [1]. As of 14 December 2020, China had 95,064 confirmed cases of COVID-19 and 4758 confirmed deaths related to COVID-19 [2]. Worldwide, there were 71,051,805 confirmed cases of COVID-19 and 1,608,648 confirmed deaths related to COVID-19 [2].
COVID-19′s R0 of 5.7 (i.e., the average number of people who will acquire an infection from one person with COVID-19) [3] has hastened the global pandemic, led to a massive international public health crisis, and hindered public health efforts to control the pandemic. Those infected with COVID-19 experience a wide range of symptoms including but not limited to fever, cough, shortness of breath, fatigue, muscle or body aches, and new loss of taste or smell [4]. Chronic kidney disease, hypertension, chronic obstructive pulmonary disease (COPD), obesity, type 2 diabetes mellitus (T2DM), and primary immunodeficiency diseases (PIDs) are a few underlying medical conditions associated with elevated risks for COVID-19 disease severity and death [5,6].
An infectious disease pandemic can lead to an iatrogenic pandemic of panic or pandemic-related psychological distress in the general population often characterized by fear, nervousness, fright, excessive worry, and other anxiety and panic behavior, and the recent COVID-19 pandemic is no exception [7]. There are many factors related to COVID-19 that can alter and negatively impact psychological well-being in the general population. For example, fear of acquiring the infection, shortage of personal protective equipment (PPE), limited resources for testing and treatment, economic and financial loss, and repeated school and work closures may lead to psychological distress [8,9,10,11].
The first nationwide large-scale survey on psychological distress that was conducted early in the epidemic in China found that nearly 35% of the general population experienced psychological distress due to COVID-19 [12]. In an iterative study a month later, 54% of the general population in China rated the psychological impact of COVID-19 as moderate to severe, 28.8% reported moderate to severe symptoms of anxiety, 16.5% reported moderate to severe symptoms of depression, and 8.1% reported moderate to severe levels of stress [13]. Similarly, a cross-sectional epidemiological study reported that 29% and 37% of Chinese adults experienced symptoms of anxiety and depression, respectively [14]. A recent meta-analysis of 19 studies conducted in China estimated that the prevalence of stress related to the COVID-19 pandemic was 48%, and prevalence estimates of depression and anxiety related to the COVID-19 pandemic were 27% and 22%, respectively [15]. Furthermore, increased symptoms of depression, anxiety, and psychological distress during the COVID-19 pandemic have been observed in several other countries (e.g., Italy, Turkey, and Saudi Arabia) [16,17,18,19].
However, more research on the mental health consequences of COVID-19 is needed. The number of published studies on the biological and clinical consequences of COVID-19 dwarfs the number of studies that provide mental health characterizations and consequences of COVID-19. Equally important, more empirical data on factors associated with symptoms of depression and psychological distress are needed. Given the universality of the COVID-19 pandemic, this knowledge would be advantageous for mental health professionals in and outside of China who provide mental health care and treatment to those in clinical and public health settings. To fill these respective knowledge gaps, the objective of this study is to examine factors associated with symptoms of depression and psychological distress during the COVID-19 pandemic among adults living in China.
To achieve the study objective, the study specifically considered multi-level factors guided by Bronfenbrenner’s Ecological Systems Theory (EST) [20]. EST is a widely used theoretical model that considers ways in which interrelated and multi-level individual, community, and environmental factors influence health and mental health [21,22]. In particular, EST has previously been used to examine protective factors and risk factors that impact smoking behaviors, weight-related behaviors, obesity, and health-related microaggressions [23,24,25,26]. EST proposes that an individual’s health and mental health status are enmeshed in and affected by a variety of surrounding micro-, exo-, meso-, and macrosystems. The microsystem includes individual level factors (e.g., sex, age, and health); the exosystem includes family level factors (e.g., location and family support); and the mesosystem and macrosystem include indirect environmental level (e.g., neighborhood and community services) and social and culture level factors (e.g., social media), respectively.
For the current study, individual factors (e.g., age, gender, education, marital status, income, self-rated general health, and social distancing behaviors), community factors (e.g., residential area and living arrangement), and environmental factors (e.g., time spent daily on social media reading searching for and reading information on COVID-19, number of confirmed cases of COVID-19 in their city of residence, and confirmed or suspected cases of COVID-19 in personal networks) were used to guide the analysis. The first hypothesis for this study is that symptoms of depression and psychological distress are elevated during the COVID-19 pandemic. The second hypothesis is that living alone, not practicing social distancing, the amount of time spent daily on social media searching for and reading information on COVID-19, and the number of confirmed cases of COVID-19 in their city of residence are positively associated with increases in symptoms of depression and psychological distress.

2. Materials and Methods

2.1. Study Design

Convenience sampling and snowball sampling were used to recruit a sample of Chinese adults (≥18 years old) in China. A recruitment announcement for study participation was emailed on an institutional listserv to those employed by and affiliated with the Dongbei University of Finance and Economics (Dalian, China). The email explained the purpose of the study and that participation was voluntary, and the email provided a weblink to the online survey in Wenjuanxing (the Chinese version of Qualtrics). Similarly, recruitment announcements for study participation were posted on Wechat and Weibo (Chinese versions of Twitter). Those who participated were asked to “retweet” the announcement to others in their social media networks for potential participation. Participants who fully completed survey questions from 14 February 2020 to 3 March 2020 (n = 2130) were included in the present study. Participants provided informed consent and were informed that their responses would remain anonymous. The study was conducted according to the guidelines of the Declaration of Helsinki and was approved by the Dongbei University of Finance and Economics.

2.2. Outcomes

Symptoms of depression and psychological distress: the primary outcomes of interest were predictors of symptoms of depression and psychological distress. Symptoms of depression include but are not limited to depressed mood most of the day, diminished interest in pleasurable activities, insomnia or hypersomnia, significant weight loss, and fatigue [27]. Psychological distress is a discomforting, emotional state often caused by a specific stressor or demand [28]. The Center for Epidemiologic Studies Depression Scale (CES-D-10) was used to measure symptoms of depression [29,30]. The CES-D-10 is a short, 10-item self-report Likert scale that assesses symptoms of depression in the past week (e.g., felt depressed, felt fearful, felt lonely, and sleep was restless) (0 = “rarely or none of the time” to 4 = “all of the time”). The total score range of the CES-D-10 is 0 to 30. Higher scores on the CES-D-10 suggest greater severity of symptoms of depression, and a score ≥ 10 is indicative of significant symptoms of depression. Psychometric properties of the CES-D-10 have been previously validated for use among Chinese adults [31,32,33]. In the current study, the CES-D-10′s Cronbach’s alpha was 0.83. The Kessler Psychological Distress Scale (K6) was used to measure psychological distress [34]. Using a 5-point Likert scale (1 = “none of the time” to 5 = “all of the time”), the 6-item scale measures psychological distress based on a framework that includes assesses emotional, behavioral, and psychophysiological manifestations (e.g., nervousness, hopelessness, and worthlessness) [35]. Higher scores on the K6 are indicative of higher psychological distress. In the current study, the K6′s Cronbach’s alpha was 0.84.

2.3. Predictors

Individual factors: the individual factors included self-reported age, gender, education, marital status, income (i.e., CNY ¥0–10,000, 10,001–30,000, 30,001–50,000, 50,001–100,000, and >100,000 in Chinese currency), social distancing, and self-ratings of general health. The following questions, adapted from social distancing recommendations published by the Centers for Disease Control and Prevention (CDC) [36], were used to assess participants’ social distancing behaviors: “Since the COVID-19 outbreak, have you (1) avoided social gatherings, (2) avoided contacting people not living in your household, and (3) self-isolated at home?” Participants who endorsed yes on all three questions were defined as engaging in social distancing. For self-ratings of general health, participants were asked to self-rate their general health (e.g., 0 = poor, 1 = fair, 2 = good, 3 = very good, and 4 = excellent).
Community factors: the community factors included self-reported residential area (urban or non-urban area) and living arrangement (lived alone or lived with someone).
Environmental factors: the environmental factors included the amount of time spent daily on social media searching for and reading information on COVID-19, the number of confirmed cases of COVID-19 in their city of residence, and confirmed or suspected cases of COVID-19 in personal networks. The following question was used to assess the amount of time spent daily on social media searching for and reading information on COVID-19: “How much time do you spend daily on social media searching for and reading information on COVID-19 (e.g., news coverage, updates, protection knowledge, and online discussions)?” (never to >1 h). The following question was used to ask participants about the number of confirmed cases of COVID-19 in their city of residence: “There are how many confirmed cases of COVID-19 in your city of residence?” (none to >300). Last, the following question was used to ask participants if there were confirmed or suspected cases of COVID-19 in personal networks: “Are there confirmed or suspected cases of COVID in your personal network?” (yes or no).

2.4. Statistical Analysis

Measures of central tendency and frequency distributions were used to characterize the sample. Linear regression, using ordinary least squares (OLS), was used to examine the predictors of symptoms of depression and psychological distress. Because age was positively skewed, age was log transformed for the analysis. The amount of time spent daily on social media searching for and reading information on COVID-19 and the number of confirmed COVID-19 cases in their city of residence were treated as quasi-interval variables in the OLS regression models. Statistical assumptions were checked ahead of running OLS regression models. No violations of normal distribution of continuous variables were detected (skewness ≤ |1.43| and kurtosis ≤ |3|) [37]. Linear regression models were without multicollinearity (variance inflation factors (VIFs) ranged from 0.36 to 0.99) [38]. All statistical analyses were conducted in Stata 14.

3. Results

3.1. Sample Characteristics

The mean (SD) age of participants was 28.43 (10.46) (Table 1). The majority of participants were female (54.84%), had an education beyond high school (78.45%), were not married (68.64%), and had an income between ¥0 to 10,000 (56.76%). The mean self-rating of general health was 4.35 (0.75) (i.e., very good). The majority of participants practiced social distancing (95.63%) and lived in non-urban area (52.39%). More than one-third (34.04%) spent 16–30 min daily on social media searching for and reading information on COVID-19; more than one-third (39.01%) reported 10–100 confirmed cases of COVID-19 in their city of residence; and a little more than one-tenth (13.29%) reported having confirmed or suspected cases of COVID-19 in personal networks. Mean symptoms of depression (e.g., CES-D-10) and psychological distress (e.g., K6) scores were 14.65 (5.49) and 7.31 (4.71), respectively.

3.2. Multivariate Analysis

Predictors of Symptoms of Depression

Living in a non-urban area (t = 3.02, p = 0.000) and the number of confirmed cases in their city of residence (t = 2.01, p = 0.04) were positively associated with symptoms of depression (Table 2). Practicing social distancing (t = −2.21, p = 0.021) was negatively associated with symptoms of depression.

3.3. Predictors of Psychological Distress

Female gender (t = 3.09, p < 0.00), not being married (t = 2.43, p = 0.01), practicing social distancing (t = 2.73, p = 0.00), amount of time spent daily on social media searching for and reading information on COVID-19 (t = 3.73, p < 0.00), the number of confirmed cases of COVID-19 in their city of residence (t = 2.57, p = 0.01), and having confirmed or suspected cases of COVID-19 in personal networks (t = 3.31, p = 0.00) were positively associated with psychological distress (Table 3). An education level greater than high school (t = −2.13, p = 0.03) was negatively associated with psychological distress.

4. Discussion

This study examined the factors associated with symptoms of depression and psychological distress during the COVID-19 pandemic among adults living in China. Several notable findings emerged from the analysis. First, symptoms of depression and psychological distress were elevated during the COVID-19 pandemic. Higher education was a protective factor against increases in psychological distress, the number of confirmed cases of COVID-19 in city of residence was a risk factor for increases in symptoms of depression and psychological distress, and confirmed or suspected cases of COVID-19 in personal networks was a risk factor for increases in psychological distress [19,39,40]. However, living in a non-urban area was a risk factor for increases in symptoms of depression. The findings suggest that the COVID-19 pandemic, the number of cases of COVID-19, and positivity within personal networks all have broad deleterious effects on psychological well-being, while higher levels of education may be protective of psychological well-being. Similar findings have been found in other studies conducted in Italy [16,39]. On the contrary, living in a non-urban area was not associated with increases in symptoms of depression in a study conducted in Turkey [19]. Living in an urban area was a risk factor for symptoms of depression. That study’s sample was largely urban (81%), whereas a little more than half of the sample from the present study was non-urban (52%). However, the discrepancy in this opposing finding is unclear. Future studies are encouraged to further investigate associations between residential area and depression during the COVID-19 pandemic. Compared to urban areas, non-urban areas typically have greater and more complex issues related to availability and accessibility of local mental health services. It is plausible that the COVID-19 pandemic has further compounded these respective issues in a population with preexisting vulnerabilities. Public health efforts are needed to combat and reduce the psychological consequences of COVID-19 in the general population and to eliminate geographic disparities in psychological well-being within the general population.
Second, social distancing was independently associated with increases in psychological distress. The primary purpose of social distancing is virus-containment, and the primary endpoints are reductions in COVID-19 transmission. Due to the highly contagious nature of COVID-19, social distancing is a public health necessity. However, long durations of social distancing, frustration and boredom with social distancing, avoidance of those who may test positive, limited to no physical contact with social and familial networks, and limited or inadequate access to basic supplies all have the potential to create and increase psychological distress [41,42]. Psychological distress is an unintended aftereffect of social distancing. Public health and clinical intervention models are needed not only to mitigate the mental health consequences of COVID-19 but also to concurrently mitigate the psychological aftereffect of social distancing. Even with the advent of an effective COVID-19 vaccine, it is probable that reliance on virus-containment strategies such as social distancing will continue for the foreseeable future—at least until widespread accessibility and availability of a COVID-19 vaccine and herd immunity are both achieved. The advent of a COVID-19 vaccine may loosen some social distancing parameters, but it will not fully eliminate the need for social distancing or its aftereffects.
Conversely, the association between social distancing and decreases in symptoms of depression was somewhat unexpected—mostly because studies from several countries have demonstrated that the COVID-19 pandemic has led to considerable increases in symptoms of depression [43,44,45,46,47,48]. However, after conducting an in-depth literature search, participant compliance with COVID-19 precautionary measures may explain the association. In a recent and large survey internet-based cohort study (n = 1642) conducted in Europe, a higher compliance with recommended precautionary measures to prevent the spread of COVID-19 (e.g., hand hygiene, wearing a mask regardless of the presence or absence of symptoms, covering the mouth when coughing and sneezing, and washing hands immediately after coughing or sneezing) predicted lower depression scores, and similar findings were reported elsewhere [13,49]. Unfortunately, the current study did not assess or include variables related to compliance with COVID-19 precautionary measures, and the social distancing variable was too broad to serve as a proxy. Associations between compliance with COVID-19 precautionary measures and symptoms of depression are understudied, and future studies are encouraged to consider assessment and inclusion of compliance with COVID-19 precautionary measures when examining factors associated with symptoms of depression. If the finding is replicated, iterative studies are encouraged to elucidate the association.
Fourth, the amount of time spent daily on social media searching for and reading information on COVID-19 was associated with increases in psychological distress. A previous study reported that people spend more time on social media during the COVID-19 pandemic compared to social media usage before the pandemic [50] and that people who excessively use social media for COVID-19 information are more likely to experience increases in feelings of fear and in symptoms of anxiety [40,50]. Social media is an easily accessible platform for information dissemination and acquisition, particularly during periods of social distancing and stay-at-home orders. On the other hand, anxiety associated with social isolation has the potential to lead to compulsive social media usage and over usage has the potential to further amplify anxiety at the population, community, and individual levels [11,51]. This can lead to a cycle that may be difficult for many to evade [11]. Equally important, overreliance on social media increases the likelihood of receipt of false rumors, hearsay, and inaccurate information [52]. Though the social media source was not collected in the present study, it is plausible that a significant number of participants may have acquired information on COVID-19 from people in their social media networks or from nonauthoritative sources. To minimize psychological distress in the general population, efforts are needed to educate the general public on the necessity of being discriminant with COVID-19 news sources and the benefits of relying on social media posts and information from reliable sources, such as the Ministry of Health of the People’s Republic of China, the Chinese Center for Disease Control and Prevention (CCDC), and the World Health Organization (WHO).
This study had notable limitations and strengths. This study relied heavily on analysis of self-report data, and bias is inherent in self-report data. The cross-sectional nature of the study did not allow for examination of cause–effect relationships. The sample was largely young, and non-probability sampling limited the representativeness of the study population. A one-time email on an institutional listserv and postings on social media platforms for participant recruitment did not allow for capture and quantification of a non-response rate. A single study is unable to identify and include all factors that may be associated with study outcomes. As such, it is probable that there are other factors associated with symptoms of depression and psychological distress that were not included in the study. The study was geographically limited to China. More studies in and outside of China and in developed and underdeveloped countries are needed to fully characterize the adverse impact of the COVID-19 pandemic on psychological well-being. Studies that examine longitudinal changes in psychological well-being during the COVID-19 pandemic are needed as well. Notwithstanding, some generalizations of study findings outside of China are possible given the global widespread and negative impact of the COVID-19 pandemic, particularly among countries with a higher prevalence of COVID-19-related deaths than China (e.g., the Brazil, France, India, Russia, South Africa, Spain, the United Kingdom, and the United States). The sampling strategy successfully recruited a large number of participants (n = 2130) in less than 3 weeks. Widely used scales with validated psychometric properties were employed to assess study outcomes.

5. Conclusions

Social distancing is a widely used public health approach for population-wide virus-containment of COVID-19. However, reductions in population-wide psychological well-being are inadvertent consequences of social distancing. There is an emerging need to negate factors that increase adverse mental health vulnerabilities during the COVID-19 pandemic.

Author Contributions

Conceptualization, Y.G.; formal analysis, W.Q.; methodology, Y.G., W.Q., and F.Y.; supervision, O.T.S.; writing—original draft, Y.G., W.Q., F.Y., and O.T.S.; writing—review and editing, O.T.S. and Y.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and was approved by Dongbei University of Finance and Economics (Number: 2020001).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data is available on request to the corresponding author (F.Y.).

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Wang, D.; Hu, B.; Hu, C.; Zhu, F.; Liu, X.; Zhang, J.; Wang, B.; Xiang, H.; Cheng, Z.; Xiong, Y.; et al. Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus–Infected Pneumonia in Wuhan, China. JAMA 2020, 323, 1061–1069. [Google Scholar] [CrossRef] [PubMed]
  2. World Health Organization. WHO Coronavirus Disease (COVID-19) Dashboard. Available online: https://covid19.who.int/ (accessed on 20 January 2021).
  3. Sanche, S.; Lin, Y.T.; Xu, C.; Romero-Severson, E.; Hengartner, N.; Ke, R. High Contagiousness and Rapid Spread of Severe Acute Respiratory Syndrome Coronavirus. Emerg. Infect. Dis. 2020, 26, 1470–1477. [Google Scholar] [CrossRef] [PubMed]
  4. Menni, C.; Valdes, A.; Freydin, M.B.; Ganesh, S.; Moustafa, J.E.S.; Visconti, A.; Hysi, P.; Bowyer, E.; Mangino, M.; Falchi, M.; et al. Loss of smell and taste in combination with other symptoms is a strong predictor of COVID-19 infection. Nat. Med. 2020. [Google Scholar] [CrossRef]
  5. Lippi, G.; Wong, J.; Henry, B.M. Hypertension and its severity or mortality in Coronavirus Disease 2019 (COVID-19): A pooled analysis. Pol. Arch. Intern. Med. 2020, 130, 304–309. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  6. Lippi, G.; Henry, B.M. Chronic obstructive pulmonary disease is associated with severe coronavirus disease 2019 (COVID-19). Respir. Med. 2020, 167, 105941. [Google Scholar] [CrossRef]
  7. Bonneux, L.; Van Damme, W. An iatrogenic pandemic of panic. BMJ 2006, 332, 786–788. [Google Scholar] [CrossRef] [Green Version]
  8. Galea, S.; Merchant, R.M.; Lurie, N. The Mental Health Consequences of COVID-19 and Physical Distancing: The need for prevention and early intervention. JAMA Intern. Med. 2020, 180, 817–818. [Google Scholar] [CrossRef] [Green Version]
  9. McGowan, M.L.; Norris, A.H.; Bessett, D. Care Churn—Why Keeping Clinic Doors Open Isn’t Enough to Ensure Access to Abortion. N. Engl. J. Med. 2020, 383, 508–510. [Google Scholar] [CrossRef]
  10. Rajkumar, R.P. COVID-19 and mental health: A review of the existing literature. Asian J. Psychiatry 2020, 52, 102066. [Google Scholar] [CrossRef]
  11. Holmes, E.A.; O’Connor, R.C.; Perry, V.H.; Tracey, I.; Wessely, S.; Arseneault, L.; Ballard, C.; Christensen, H.; Silver, R.C.; Everall, I.; et al. Multidisciplinary research priorities for the COVID-19 pandemic: A call for action for mental health science. Lancet Psychiatry 2020, 7, 547–560. [Google Scholar] [CrossRef]
  12. Qiu, J.; Shen, B.; Zhao, M.; Wang, Z.; Xie, B.; Xu, Y. A nationwide survey of psychological distress among Chinese people in the COVID-19 epidemic: Implications and policy recommendations. Gen. Psychiatry 2020, 33, e100213. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Wang, C.; Pan, R.; Wan, X.; Tan, Y.; Xu, L.; Ho, C.; Ho, R. Immediate Psychological Responses and Associated Factors during the Initial Stage of the 2019 Coronavirus Disease (COVID-19) Epidemic among the General Population in China. Int. J. Environ. Res. Public Health 2020, 17, 1729. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Ahmed, M.Z.; Ahmed, O.; Aibao, Z.; Hanbin, S.; Siyu, L.; Ahmad, A. Epidemic of COVID-19 in China and associated Psychological Problems. Asian J. Psychiatry 2020, 51, 102092. [Google Scholar] [CrossRef] [PubMed]
  15. Salari, N.; Hosseinian-Far, A.; Jalali, R.; Vaisi-Raygani, A.; Rasoulpoor, S.; Mohammadi, M.; Rasoulpoor, S.; Khaledi-Paveh, B. Prevalence of stress, anxiety, depression among the general population during the COVID-19 pandemic: A systematic review and meta-analysis. Glob. Health 2020, 16, 57. [Google Scholar] [CrossRef] [PubMed]
  16. Roma, P.; Monaro, M.; Colasanti, M.; Ricci, E.; Biondi, S.; Di Domenico, A.; Verrocchio, M.C.; Napoli, C.; Ferracuti, S.; Mazza, C. A 2-Month Follow-Up Study of Psychological Distress among Italian People during the COVID-19 Lockdown. Int. J. Environ. Res. Public Health 2020, 17, 8180. [Google Scholar] [CrossRef] [PubMed]
  17. Fiorillo, A.; Sampogna, G.; Giallonardo, V.; Del Vecchio, V.; Luciano, M.; Albert, U.; Carmassi, C.; Carrà, G.; Cirulli, F.; Dell’Osso, B.; et al. Effects of the lockdown on the mental health of the general population during the COVID-19 pandemic in Italy: Results from the COMET Collaborative Network. Eur. Psychiatry 2020, 63, 1–28. [Google Scholar] [CrossRef] [Green Version]
  18. Alkhamees, A.A.; Alrashed, S.A.; Alzunaydi, A.A.; Almohimeed, A.S.; Aljohani, M.S. The psychological impact of COVID-19 pandemic on the general population of Saudi Arabia. Compr. Psychiatry 2020, 102, 152192. [Google Scholar] [CrossRef]
  19. Özdin, S.; Özdin, Ş.B. Levels and predictors of anxiety, depression and health anxiety during COVID-19 pandemic in Turkish society: The importance of gender. Int. J. Soc. Psychiatry 2020, 66, 504–511. [Google Scholar] [CrossRef]
  20. Bronfenbrenner, U. Ecological systems theory. Encycl. Psychol. 2000, 3, 129–133. [Google Scholar] [CrossRef]
  21. Atilola, O. Child mental-health policy development in sub-Saharan Africa: Broadening the perspectives using Bronfenbrenner’s ecological model. Health Promot. Int. 2014, 32, 380–391. [Google Scholar] [CrossRef] [Green Version]
  22. Eriksson, M.; Ghazinour, M.; Hammarström, A. Different uses of Bronfenbrenner’s ecological theory in public mental health research: What is their value for guiding public mental health policy and practice? Soc. Theory Health 2018, 16, 414–433. [Google Scholar] [CrossRef] [Green Version]
  23. Polfuss, M.; Dobson, C.; Sawin, K.J.; Klingbeil, C.G. The Influence of a Developmental Disability on the Child’s Weight-Related Behaviors: A Parent’s Perspective. J. Pediatr. Nurs. 2019, 47, 121–130. [Google Scholar] [CrossRef] [PubMed]
  24. Wiium, N.; Wold, B. An Ecological System Approach to Adolescent Smoking Behavior. J. Youth Adolesc. 2008, 38, 1351–1363. [Google Scholar] [CrossRef] [PubMed]
  25. Zhou, N.; Cheah, C.S.L. Ecological risk model of childhood obesity in Chinese immigrant children. Appetite 2015, 90, 99–107. [Google Scholar] [CrossRef] [Green Version]
  26. Manyam, S.; Panjwani, S.; Parker, J.M. HIV Microaggressions across Bronfenbrenner’s Ecological Systems: A Social Justice Perspective. Rehabil. Res. Policy Educ. 2019, 33, 245–259. [Google Scholar] [CrossRef]
  27. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, 5th ed.; (DSM-5); American Psychiatric Association: Washington, DC, USA, 2013; p. 5. [Google Scholar]
  28. Ridner, S.H. Psychological distress: Concept analysis. J. Adv. Nurs. 2004, 45, 536–545. [Google Scholar] [CrossRef]
  29. Kohout, F.J.; Berkman, L.F.; Evans, D.A.; Cornoni-Huntley, J. Two Shorter Forms of the CES-D Depression Symptoms Index. J. Aging Health 1993, 5, 179–193. [Google Scholar] [CrossRef]
  30. Andresen, E.M.; Malmgren, J.A.; Carter, W.B.; Patrick, D.L. Screening for Depression in Well Older Adults: Evaluation of a Short Form of the CES-D. Am. J. Prev. Med. 1994, 10, 77–84. [Google Scholar] [CrossRef]
  31. Cheng, S.-T.; Chan, A.C.M. The Center for Epidemiologic Studies Depression Scale in older Chinese: Thresholds for long and short forms. Int. J. Geriatr. Psychiatry 2005, 20, 465–470. [Google Scholar] [CrossRef]
  32. Jiang, L.; Wang, Y.; Zhang, Y.; Li, R.; Wu, H.; Li, C.; Wu, Y.; Tao, Q. The Reliability and Validity of the Center for Epidemiologic Studies Depression Scale (CES-D) for Chinese University Students. Front. Psychiatry 2019, 10, 315. [Google Scholar] [CrossRef] [Green Version]
  33. Zhang, J.; Sun, W.; Kong, Y.; Wang, C. Reliability and validity of the Center for Epidemiological Studies Depression Scale in 2 special adult samples from rural China. Compr. Psychiatry 2012, 53, 1243–1251. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  34. Kessler, R.C.; Andrews, G.; Colpe, L.J.; Hiripi, E.; Mroczek, D.K.; Normand, S.-L.T.; Walters, E.E.; Zaslavsky, A.M. Short screening scales to monitor population prevalences and trends in non-specific psychological distress. Psychol. Med. 2002, 32, 959–976. [Google Scholar] [CrossRef] [PubMed]
  35. Kessler, R.C.; Barker, P.R.; Colpe, L.J.; Epstein, J.F.; Gfroerer, J.C.; Hiripi, E.; Howes, M.J.; Normand, S.-L.T.; Manderscheid, R.W.; Walters, E.E.; et al. Screening for Serious Mental Illness in the General Population. Arch. Gen. Psychiatry 2003, 60, 184–189. [Google Scholar] [CrossRef] [PubMed]
  36. Centers for Disease Control and Prevention. Coronavirus (COVID-19). Available online: https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/prevention.html (accessed on 20 January 2021).
  37. Curran, P.J.; West, S.G.; Finch, J.F. The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis. Psychol. Methods 1996, 1, 16–29. [Google Scholar] [CrossRef]
  38. Lomax, R.; Hahs-Vaughn, D. An Introduction to Statistical Concepts; Routledge: New York, NY, USA, 2012. [Google Scholar]
  39. Mazza, C.; Ricci, E.; Biondi, S.; Colasanti, M.; Ferracuti, S.; Napoli, C.; Roma, P. A Nationwide Survey of Psychological Distress among Italian People during the COVID-19 Pandemic: Immediate Psychological Responses and Associated Factors. Int. J. Environ. Res. Public Health 2020, 17, 3165. [Google Scholar] [CrossRef] [PubMed]
  40. Zanin, G.M.; Gentile, E.; Parisi, A.; Spasiano, D. A Preliminary Evaluation of the Public Risk Perception Related to the COVID-19 Health Emergency in Italy. Int. J. Environ. Res. Public Health 2020, 17, 3024. [Google Scholar] [CrossRef] [PubMed]
  41. Brooks, S.K.; Webster, R.K.; Smith, L.E.; Woodland, L.; Wessely, S.; Greenberg, N.; Rubin, G.J. The psychological impact of quarantine and how to reduce it: Rapid review of the evidence. Lancet 2020, 395, 912–920. [Google Scholar] [CrossRef] [Green Version]
  42. Venkatesh, A.; Edirappuli, S. Social distancing in covid-19: What are the mental health implications? BMJ 2020, 369, m1379. [Google Scholar] [CrossRef] [Green Version]
  43. Shevlin, M.; McBride, O.; Murphy, J.; Miller, J.G.; Hartman, T.K.; Levita, L.; Mason, L.; Martinez, A.P.; McKay, R.; Stocks, T.V.A.; et al. Anxiety, depression, traumatic stress and COVID-19-related anxiety in the UK general population during the COVID-19 pandemic. BJPsych Open 2020, 6, 1–9. [Google Scholar] [CrossRef]
  44. Bäuerle, A.; Teufel, M.; Musche, V.; Weismüller, B.; Kohler, H.; Hetkamp, M.; Dörrie, N.; Schweda, A.; Skoda, E.-M. Increased generalized anxiety, depression and distress during the COVID-19 pandemic: A cross-sectional study in Germany. J. Public Health 2020, 42, 672–678. [Google Scholar] [CrossRef]
  45. Cénat, J.M.; Blais-Rochette, C.; Kokou-Kpolou, C.K.; Noorishad, P.-G.; Mukunzi, J.N.; Mcintee, S.-E.; Dalexis, R.D.; Goulet, M.-A.; Labelle, P.R. Prevalence of symptoms of depression, anxiety, insomnia, posttraumatic stress disorder, and psychological distress among populations affected by the COVID-19 pandemic: A systematic review and meta-analysis. Psychiatry Res. 2021, 295, 113599. [Google Scholar] [CrossRef]
  46. Wu, T.; Jia, X.; Shi, H.; Niu, J.; Yin, X.; Xie, J.; Wang, X. Prevalence of mental health problems during the COVID-19 pandemic: A systematic review and meta-analysis. J. Affect. Disord. 2021, 281, 91–98. [Google Scholar] [CrossRef]
  47. Alamri, H.S.; Algarni, A.; Shehata, S.F.; Al Bshabshe, A.; AlShehri, N.N.; AlAsiri, A.M.; Hussain, A.H.; Alalmay, A.Y.; AlShehri, E.A.; AlQarni, Y.; et al. Prevalence of Depression, Anxiety, and Stress among the General Population in Saudi Arabia during Covid-19 Pandemic. Int. J. Environ. Res. Public Health 2020, 17, 9183. [Google Scholar] [CrossRef]
  48. De Boni, R.; Balanzá-Martínez, V.; Mota, J.C.; Cardoso, T.D.A.; Ballester, P.; Atienza-Carbonell, B.; Bastos, F.I.; Kapczinski, F. Depression, Anxiety, and Lifestyle Among Essential Workers: A Web Survey from Brazil and Spain During the COVID-19 Pandemic. J. Med. Internet Res. 2020, 22, e22835. [Google Scholar] [CrossRef]
  49. Solomou, I.; Constantinidou, F. 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 2020, 17, 4924. [Google Scholar] [CrossRef]
  50. Boursier, V.; Gioia, F.; Musetti, A.; Schimmenti, A. Facing Loneliness and Anxiety During the COVID-19 Isolation: The Role of Excessive Social Media Use in a Sample of Italian Adults. Front. Psychiatry 2020, 11, 586222. [Google Scholar] [CrossRef]
  51. Wang, C.; Pan, R.; Wan, X.; Tan, Y.; Xu, L.; McIntyre, R.S.; Choo, F.N.; Tran, B.; Ho, R.; Sharma, V.K.; et al. A longitudinal study on the mental health of general population during the COVID-19 epidemic in China. Brain Behav. Immun. 2020, 87, 40–48. [Google Scholar] [CrossRef]
  52. Drouin, M.; McDaniel, B.T.; Pater, J.; Toscos, T. How Parents and Their Children Used Social Media and Technology at the Beginning of the COVID-19 Pandemic and Associations with Anxiety. Cyberpsychol. Behav. Soc. Netw. 2020, 23, 727–736. [Google Scholar] [CrossRef]
Table
Table 1. Sample characteristics (n = 2130).
Table 1. Sample characteristics (n = 2130).
VariablesFrequency/M, (SD)
Individual factors
 Age28.43 (10.46)
 Gender
  Male962 (45.16)
  Female1168 (54.84)
 Education
  ≤High school459 (21.55)
  >High school1671 (78.45)
 Marital status
  Married668 (31.36)
  Not married1462 (68.64)
 Income (Chinese currency, CNY ¥)
  ¥0–10,0001209 (56.76)
  ¥10,001–30,000264 (12.39)
  ¥30,001–50,000247 (11.60)
  ¥50,001–100,000235 (11.03)
  >¥100,000175 (8.22)
 Self-rated general health4.35 (0.75)
 Practiced social distancing
  No93 (4.37)
  Yes2037 (95.63)
Community factors
 Residential area
  Urban1014 (47.61)
  Non-urban1116 (52.39)
 Living arrangement
  Lived with someone2059 (96.67)
  Lived alone71 (3.33)
Environmental factors
 Amount of time spent daily on social media searching for and reading information on COVID-19
  Never21 (0.99)
  <15 min261 (12.25)
  16–30 min529 (24.84)
  31–45 min725 (34.04)
  56–60 min348 (16.34)
  >1 h246 (11.55)
 Number of confirmed cases of COVID-19 in their city of residence
  None224 (10.52)
  <10673 (31.60)
  10–100831 (39.01)
  101–200230 (10.80)
  201–30062 (2.91)
  >300110 (5.16)
 Confirmed or suspected cases of COVID-19 in personal networks
  No1847 (86.71)
  Yes283 (13.29)
Mental health outcomes
 Symptoms of depression (CES-D-10)14.65 (5.49)
 Psychological distress (K6)7.31 (4.71)
Table
Table 2. Linear regression predicting symptoms of depression (n = 2130).
Table 2. Linear regression predicting symptoms of depression (n = 2130).
PredictorsSymptoms of Depression
Individual factorsb (SE)tp-values
 Age−1.12 (0.68)−1.640.10
 Female (ref: male)0.02 (0.24)0.120.90
 High school or above (ref: below High school)−0.58 (0.29)−1.960.05
 Non-married (ref: married)0.62 (0.45)1.370.17
 Income (ref: ≤ ¥10,000, Chinese currency, CNY ¥)
  ¥10,001–30,0000.09 (0.39)0.230.81
  ¥30,001–50,0000.21 (0.41)0.520.60
  ¥50,001–100,000−0.78 (0.42)−1.850.06
  ≥¥100,001−0.77 (0.49)−1.560.12
 Self-rated general health0.04 (0.15)0.280.77
 Practiced social distancing (no)−1.37 (0.59)−2.310.02
Community factors
 Non-urban (ref: urban)0.76 (0.25)3.020.00
 Lived alone (ref: lived with someone)0.48 (0.66)0.730.46
Environmental factors
 Amount of time spent daily on social media searching for and reading information on COVID-190.00 (0.10)0.050.96
 Number of confirmed cases of COVID-19 in their city of residence0.21 (0.10)2.010.04
 Confirmed or suspected cases of COVID-19 in personal networks (ref: no)1.76 (1.04)1.680.09
Model statistics
 F (df) 5.06
 Overall significance<0.00
 R20.035
Significant variables were highlighted in bold.
Table
Table 3. Linear regression predicting psychological distress (n = 2130).
Table 3. Linear regression predicting psychological distress (n = 2130).
PredictorsPsychological Distress
Individual factorsb (SE)tp-values
 Age (log transformed)−0.88 (0.58)−1.520.12
 Female (ref: male)0.63 (0.20)3.090.00
 High school or above (ref: below High school)−0.53 (0.25)−2.130.03
 Non-married (ref: married)0.94 (0.39)2.430.01
 Income (ref: ≤ ¥10,000, Chinese currency, CNY ¥)
  ¥10,001–30,0000.41 (0.34)1.230.22
  ¥30,001–50,0000.42 (0.35)1.190.23
  ¥50,001–100,0000.08 (0.36)0.220.82
  ≥¥100,0010.07 (0.42)0.170.86
 Self-rated general health0.05 (0.13)0.420.67
 Practiced social distancing (no)1.38 (0.21)2.730.00
Community factors
 Non-Urban (ref: urban)0.25 (0.21)0.160.24
 Lived alone (ref: lived with someone)−2.26 (0.56)−0.470.63
Environmental factors
 Amount of time spent daily on social media searching for and reading information on COVID-190.32 (0.08)3.730.00
 Number of confirmed cases of COVID-19 in their city of residence0.23 (0.09)2.570.01
 Confirmed or suspected cases of COVID-19 in personal networks (ref: no)2.96 (0.89)3.310.00
Model statistics
 F (df)5.19
 Overall significance<0.00
 R20.036
Significant variables were highlighted in bold.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Guo, Y.; Sims, O.T.; Qin, W.; Yang, F. Factors Associated with Symptoms of Depression and Psychological Distress during the COVID-19 Pandemic. Behav. Sci. 2021, 11, 13. https://0-doi-org.brum.beds.ac.uk/10.3390/bs11020013

AMA Style

Guo Y, Sims OT, Qin W, Yang F. Factors Associated with Symptoms of Depression and Psychological Distress during the COVID-19 Pandemic. Behavioral Sciences. 2021; 11(2):13. https://0-doi-org.brum.beds.ac.uk/10.3390/bs11020013

Chicago/Turabian Style

Guo, Yuqi, Omar T. Sims, Weidi Qin, and Fan Yang. 2021. "Factors Associated with Symptoms of Depression and Psychological Distress during the COVID-19 Pandemic" Behavioral Sciences 11, no. 2: 13. https://0-doi-org.brum.beds.ac.uk/10.3390/bs11020013

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop