Time-Series Trends of Depressive Levels of Korean Adults During the 2020 COVID-19 Pandemic in South Korea

Article information

Psychiatry Investig. 2023;20(2):101-108
Publication date (electronic) : 2023 February 23
doi : https://doi.org/10.30773/pi.2022.0178
1Department of Social Welfare, Daegu University, Gyeongsan, Republic of Korea
2Department of Social Welfare, Cheongju University, Cheongju, Republic of Korea
3Department of Psychology, Keimyung University, Daegu, Republic of Korea
4Department of Social Welfare, Sangji University, Wonju, Republic of Korea
5Department of Psychology, Kangwon National University, Chuncheon, Republic of Korea
6Department of Psychiatry, National Medical Center, Seoul, Republic of Korea
7Department of Social Welfare, Nambu University, Gwangju, Republic of Korea
8Department of Psychiatry, Kyung Hee University College of Medicine, Seoul, Republic of Korea
Correspondence: Sunju Sohn, PhD Department of Social Welfare, Cheongju University, 298 Daeseong-ro, Cheongwon-gu, Cheongju 28503, Republic of Korea Tel: +82-43-229-8298, Fax: +82-43-229-8233, E-mail: ssohn124@cju.ac.kr
Received 2022 July 7; Revised 2022 September 4; Accepted 2022 December 5.



This study aimed to observe the changes in people’s depressive levels over 9 months since the coronavirus disease of 2019 (COVID-19) outbreak as well as to identify the predictors of people’s depressive levels including COVID-19 infection fear in the context of South Korea in 2020.


For these purposes, four cross-sectional surveys were periodically implemented from March to December 2020. We randomly recruited 6,142 Korean adults (aged 19 to 70) by using a quota survey. Along with descriptive analysis, which included a one-way analysis of variance and correlations, multiple regression models were built to identify the predictors of people’s depressive levels during the pandemic.


Overall, people’s depressive levels and fear of COVID-19 infection gradually increased since the COVID-19 outbreak. In addition to demographic variables (i.e., being a female, young age, unemployed, and living alone) and the duration of the pandemic, people’s COVID-19 infection fear was associated with their depressive levels.


To ameliorate these rising mental health issues, access to mental health services should be secured and expanded, particularly for individuals who present greater vulnerabilities due to socioeconomic characteristics that may affect their mental health.


The coronavirus disease of 2019 (COVID-19) pandemic situation is a stressor because it evokes excessive and continuous distress in people and thus impacts society as a whole [1]. While stress responses are physiological and psychological reactions to deal with emergency situations or crisis, which is quite normal under everyday conditions, excessive stress responses can result in people exaggerating the seriousness of the pandemic. Adverse effects from these stressors are largely associated with diverse mental health and behavioral issues such as depression, anxiety, psychosomatic preoccupations, insomnia, substance use, and domestic violence [2], as well as maladaptive behaviors, emotional distress, and defensive responses [3]. Particularly relevant stressors include exposures to infected sources, infected family members, loss of loved ones, physical distancing, and quarantine, which are critical triggers that elevate people’s stress levels [2]. Prolonged confinement or quarantine, insufficient supplies, difficulty securing medical care and medications, as well as secondary adversities such as economic/financial loss are also critical stressors during the pandemic [4]. In particular, a few studies have emphasized the long-term effects of people’s responses to infectious diseases on mental health outcomes. As one of the responses to infectious diseases, fear of COVID-19 was associated with people’s mental health outcomes such as depression and anxiety [1,4,5]. However, few studies have longitudinally observed the relationship between fear of COVID-19 and mental health issues. Thus, this study particularly focused on this relationship from a long-term perspective.

Since the COVID-19 outbreak, findings from many crosssectional studies suggested that elevated psychological mental health problems stemming from the COVID-19 pandemic have become a serious worldwide concern. Several studies have been published regarding various mental health issues such as stress and fear [1,4], anxiety [6-18], depression [4,5,7,11-13,16,17,19], loneliness [1], and suicidal ideation [9,20-22]. Specifically, depression has become one of the most concerning mental health issues during the COVID-19 pandemic. For example, Ettman et al. [19] reported that about 27.8% of 1,441 adults in the U.S. were at risk of depression. In contrast, prior to the COVID-19 outbreak, only 2.4% experienced moderate depressive symptoms, according to the survey conducted in 2019 [16]. The risk of depression among 3,094 adults in the U.K. was 31.6% after the COVID-19 outbreak [11]. Findings from both the U.S. and U.K. indicated that approximately one out of three people would require professional help.

In the early stage of the pandemic, Wang et al. [18] studied 1,210 Chinese adults and reported that 16.5% of the respondents showed moderate-to-severe depressive symptoms, and women tended to show higher depressive levels than men. In a sample of 1,212 people in Sweden, 30.0% of the respondents reported significant levels of depression and poor self-rated overall health [23]. In Greece, about 22.8% of 3,209 respondents reported to be at high risk of depression during COVID-19 [5]. In the Philippines, 16.9% of 1,879 people from the general population showed moderate-to-severe depressive symptoms [24]. In Saudi Arabia, in a sample of 1,160 respondents of the general population, 28.3% reported moderate-to-severe depressive symptoms [25].

Like other countries, people in South Korea have also experienced mental health issues during the COVID-19 pandemic. Compared to the findings from the Korean Community Health Survey (KCHS) in 2018, people’s depression levels have increased approximately 2.4 times after the COVID-19 outbreak in 2020. Based on a national sample of 2,063 adults (aged 19 to 70), the average Patient Health Quesionnaire-9 (PHQ-9) score was 5.52 (standard deviation [SD]=5.49) in December 2020, with females showing higher levels of depression than men. Additionally, 20.0% of the respondents were at high risk of depression, which is about 4.86 times higher than prior to the COVID-19 outbreak [26]. Although the prevalence of individuals at high risk of depression in South Korea was lower than those of the U.K. (31.6%) [11], the U.S. (27.8%) [19], and Greece (22.8%) [5], it was higher than other East-Asian countries such as Japan (17.3%) [17] and China (14.8%) [12].

A limitation of the abovementioned cross-sectional studies is that they were conducted in the early stages of the pandemic, except for the studies in South Korea, the U.K., Japan, and Spain. Therefore, there is limited information regarding changes across time during the development of the COVID-19 pandemic. From previous experiences pertaining to epidemics, such as with severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), and a novel influenza A (H1N1), it has been noted that in some cases, the psychological effects from infectious diseases can deteriorate over periods of months and years post outbreak [27-29]. Hence, it is necessary to conduct prospective studies that examine the psychological impacts over time. A longitudinal study including a national sample of 3,077 adults in the U.K. showed that over the course of 2.25 months from baseline to follow-up (wave 1 from March 31 to April 9; wave 2 from April 10 to 27; and wave 3 from April 28 to May 11, 2020) [20], suicidal ideation increased while depressive symptoms and levels of loneliness remained as before without any change. Additionally, females, young adults (18–29 years), and those with pre-existing mental health issues showed serious mental health outcomes.

Another study conducted in Japan reported that, among 2,078 respondents there was a significant increase in the people experiencing severe psychological distress (SPD) from 9.3% to 11.3% between the early phase of the study period (February 25–27, 2020) and community-transmission phase (follow-up during April 1–6, 2020) [30]. In particular, people in the lowest income group and those with respiratory diseases were approximately twice as likely to develop SPD than their counterparts [30]. Another longitudinal study (n=4,724) in Spain found that symptomatic scores of depression increased from 44.1% to 46.4% between March and April in 2020 [31].

During the pandemic, mental health professionals need to address current and emergent mental health issues, to create services plans to meet people’s needs, and to effectively and timely deliver the adequate services. Therefore, researchers play a key role in finding and disseminating important knowledge related to the psychological impact of COVID-19. For this reason, this study investigated the changes in depressive levels over the course of 9 months and identified predictors of people’s depressive levels. In particular, this study focused on the influence of people’s COVID-19 infection fear on their depressive levels while considering the COVID-19 pandemic.


Study sample and data collection

Four cross-sectional surveysa were conducted in 2020. A total of 6,142 adults (aged 19 to 70) were randomly recruited by using a cluster sampling. The different samples participated in self-administrated online surveys in March (n=1,014), May (n=1,002), September (n=2,063), and December (n=2,063) 2020.b These four periods of data collection enabled us to compare the periodic changes in people’s depressive levels and their COVID-19 infection fear since the COVID-19 outbreak. All four surveys were approved by the Institutional Review Board (IRB) of Kangwon National University in South Korea (KWNUIRB-2020-03-004-001; KWNUIRB-2020-03-004-003).


The Korean version of PHQ-9 was used to measure the sample’s level of depression [32-34]. The PHQ-9 including nine items was administered as a self-report measure using a 4-point scale (0=not at all to 3=nearly every day). Higher scores indicated higher depressive levels; the total PHQ-9 scores over 10 indicated moderate or severe depression (cut-off ≥10) [32-34]. The total scores of PHQ-9 were used for statistical analysis (ranged 0 to 27). The PHQ-9 in this study showed a high internal consistency at the four survey points (overall Cronbach’s α=0.905).

The covariates were comprised of three blocks to explain the variance of PHQ-9: 1) survey points, 2) demographic variables, and 3) COVID-19 infection fear. As categorical variables, the survey points were assigned by the month of each survey, such as March (time 1; reference), May (time 2), September (time 3), and December (time 4) in 2020. Five demographic variables included gender, age, employment, family types, and regions. Gender was defined as a binary variable: male and female. Age was gauged as years at the point of each survey. Unemployment was coded as 1; others were coded as 0. The sample’s family type was categorized into four attributes: 1) living alone (reference), 2) living with only a partner, 3) living with a partner and child, and 4) other family/co-residence composition. Regions were divided into three attributes: 1) Seoul Capital area (reference), 2) other metropolitan (urban) areas, and 3) provinces (Table 1).

Demographic characteristic of the sample

The “COVID-19 infection fear” scale was originally developed and validated by a group of multidisciplinary mental health specialists and researchers (i.e., psychiatry, social welfare, clinical psychology, and nursing) for measuring fear that was directly related to the COVID-19 situation and in consideration of the Korean culture and situations, which distinguished the construct validity from other COVID-19 fear-related measures. Prior to the surveys, the face and content validity of COVID-19 infection fear scale were obtained through several reviews and agreements of 13 mental health specialists and researchers from the multidisciplinary field. The COVID-19 infection fear scale consisted of nine itemsc including: I am afraid that 1) “I may get infected by coronavirus,” 2) “My family members might get infected by coronavirus,” 3) “I may be infected with coronavirus and pass it onto my family members or others,” 4) “I may be infected with coronavirus and will harm others, such as my colleagues at work or school shutdown,” 5) “If I and/or my family get infected, we will be separated/quarantined for treatment,” 6) “I may not be able to receive proper medical treatment,” 7) “I will be stigmatized as a confirmed person of coronavirus infection,” 8) “My community will be stigmatized,” and 9) “I may lose a job or have economic difficulties.” Each item was answered using a 4-point rating (0=very disagree, 1=disagree, 2=agree, and 3=very agree). Higher scores indicated higher levels of fear on COVID-19 infection. The average scores of the COVID-9 infection fear scale were used in the statistical analysis (ranged 0 to 3).d Although the COVID-19 infection fear scale was urgently developed at the early stage of the pandemic, it showed high internal consistency at all survey points (overall Cronbach’s α=0.928) (Table 2).

Trends of PHQ-9 and COVID-19 infection fear

Statistical analysis

Descriptive analysis was conducted to explore the variability and distribution of collected data; a one-way analysis of variance (ANOVA) was performed to compare the changes in people’s depressive levels (i.e., PHQ-9) and their COVID-19 infection fear. The internal consistency of PHQ-9 and COVID-19 infection fear scale (i.e., Cronbach’s α) was also tested. In addition, the bivariate correlations between COVID-infection fear and PHQ-9 were tested by times. Finally, multiple regression models were built to identify the predictors of people’s depressive levels.


Demographic variables

Table 1 shows the sample’s characteristics. Nearly half of the respondents were females (49.27%, n=3,026) and the average age was 44.52 years old (SD=13.43). About 8.60% of the sample (n=528) were unemployed at the time of survey. 13.20% of the sample (n=811) lived alone; 13.32% (n=818) were married without a child; and 44.38% (n=2,726) lived with a partner and at least one child. Geographically, 32.61% of the sample (n=2,003) resided in Seoul Capital area; 36.86% (n=2,264) lived in other metropolitan (urban) areas; and 30.53% (n=1,875) lived in provinces. There were no significant differences of demographic attributes across four survey points.

Trends of PHQ-9 and COVID-19 infection fear

Table 2 shows the time-series trends of people’s depressive levels and COVID-19 infection fear in 2020. Overall, the mean of total PHQ-9 score was 5.50 (SD=5.51). The means of PHQ-9 by survey points were continuously changed throughout 2020. Specifically, the means of PHQ-9 in March (time 1), May (time 2), September (time 3), and December (time 4) were 5.10 (SD=5.26), 5.12 (SD=5.44), 5.86 (SD=5.66), and 5.52 (SD=5.49), respectively. The overall trends of PHQ-9 gradually increased from March (time 1) to December (time 4) (F[3, 6138]=6.282, p<0.001). As a result of the post hoc tests, the means of PHQ-9 increased by 0.76 between March (time 1) and September (time 3) (p<0.001) and elevated by 0.74 between May (time 2) and September (time 3) (p<0.001). In addition, the prevalence of depression (total PHQ-9 scores ≥10) significantly increased by times (χ2[3]=11.079, p=0.011). The prevalence of depression in March and December were 17.5% (n=177) and 20.0% (n=412), respectively.

Similar to the trends of PHQ-9, COVID-19 infection fear has increased since the COVID-19 outbreak (F[3, 6138]= 16.531, p<0.001). The overall means of average COVID-19 infection fear score was 1.74 (SD=0.71) in 2020. Although the means of COVID-19 infection fear slightly decreased by 0.14 (p<0.001) between March (time 1) and May (time 2), they increased again after May (time 2). Between May (time 2) and September (time 3), they increased by 0.17 (p<0.001). Compared to May (time 2), COVID-19 infection fear in December (time 4) increased by 0.18 (p<0.001). Mostly, both PHQ-9 and COVID-19 infection fear gradually increased over 9 months after the COVID-19 outbreak. In particular, they significantly increased after May, 2020 (time 2).

On the other hand, COVID-19 infection fear and PHQ-9 were positively correlated regardless of the survey points (r=0.424, p<0.001). The correlations between COVID-19 infection fear and PHQ-9 were to become stronger from March (time 1; r=0.389, p<0.001) to December (time 4; r=0.416, p< 0.001). In particular, the strength of correlations rapidly increased between March (time 1; r=0.389, p<0.001) and May (time 2; r=0.451, p<0.001).

Multiple regression results of PHQ-9

Table 3 presents the predictors of the PHQ-9, especially in the context of the prolonged pandemic. Model 1, only including the four survey points as the covariates, shows significant changes of PHQ-9 during the pandemic (F[3, 6138]=6.282, p<0.001; R2=0.003). Compared to March (time 1), PHQ-9 in September (time 3) and December (time 4) increased by 0.757 (b=0.757, t=3.588, β=0.065, p<0.001) and 0.425 (b=0.425, t= 2.015, β=0.044, p=0.044), respectively. Like the results of oneway ANOVA (Table 2), these results showed a gradual increase in people’s depressive symptoms as the pandemic persisted.

Regression models of PHQ-9

In Model 2, the demographic variables and the COVID-19 infection fear were added to Model 1 (F[12, 6129]=129.734, p<0.001). The inputted variables in Model 2 explained approximately 20.3% of the variance of PHQ-9 (R2=0.203). Similar to Model 1, there was a significant increase in PHQ-9 between March (time 1) and September (time 3) in 2020, after controlling for the demographic variables and COVID-19 infection fear (b=0.575, t=3.002, β=0.049, p=0.003).

As well, the PHQ-9 was significantly associated with the demographic variables such as gender, age, employment, and family type. Specifically, females showed 0.724 higher scores of PHQ-9 than males (b=0.724, t=5.684, β=0.066, p<0.001). As the sample’s age increased by one year, the PHQ-9 scores decreased by 0.027 (b=-0.027, t=-5.062, β=-0.067, p<0.001). Unemployed people showed 1.184 higher PHQ-9 scores than their counterparts (b=1.184, t=5.212, β=0.060, p<0.001). In addition, people living alone showed higher PHQ-9 scores than those in any other family types. For example, people who lived only with a partner showed 1.126 lower scores of the PHQ-9 than those who lived alone (b=-1.126, t=-4.487, β=-0.069, p<0.001). Additionally, people who lived with a partner and a child(ren) showed 1.189 lower scores than those who lived alone (b=-1.189, t=-5.858, β=-0.107, p<0.001). Finally, the level of COVID-19 infection fear was more strongly associated with PHQ-9 than other covariates, such as the survey points and demographic variables. As the sample’s average scores of COVID-19 infection fear increased by one-point, total PHQ-9 scores increased by 3.232 (b=3.232, t=35.790, β= 0.416, p<0.001). These results suggested the influence of people’s fear of infectious diseases on depressive levels in the context of the ongoing pandemic.


Among various issues related the COVID-19 pandemic, this study focused on the changes of people’s depressive levels through analyzing four consecutive surveys that were collected in 2020. To summarize study findings on changes in depressive levels and COVID-19 infection fear among Korean adults over 9 months after the COVID-19 outbreak in 2020, people’s depressive levels were more severe among women than men. We may speculate that given the high composition of female employees in South Korea, higher depressive levels and greater fear of infection may be more prominent among individuals who may need to be compatible with occupation as well as caregiver roles. Further, individuals with economic hardships including unemployment were at higher risk for continuously depressive levels. In the context of a pandemic, people’s COVID-19 infection fear had a significant influence on their depressive level. These findings are consistent with the recent studies on psychological effect of the COVID-19 [4,7,11,19].

Since the beginning of the COVID-19, we have experienced the second and third wave during the 2.5 years of the prolonged pandemic to this moment. Specifically, there was a gradual increase of people’s depressive levels between March and December in 2020 based on the analyses of time-series trends in the depressive levels using periodically collected data during the COVID-19 pandemic. Demographically, being a female, younger in age, and being unemployed were associated with people’s depressive levels in the ongoing pandemic. In addition, people who lived alone were comparably more vulnerable than any other population, as they reported significantly higher PHQ-9 scores than those who lived with someone. Presumably, this may be because, if infected, they may lack social support to deal with their illness and related difficulties, not to mention experiencing fear related to appropriate or timely access to the healthcare system. Given that the prevalence of depression is increasing over time, this may indicate that the high-risk group ratio is also on the rise. Therefore, it is necessary to plan a depression prevention program for targeted groups and to strengthen preemptive psychological support by local governments, centering on regional trauma centers.

Lastly, our findings suggest that people’s fear of infectious diseases was crucial to addressing mental health issues in the context of a long-term pandemic, as COVID-19 infection fear was more strongly associated with people’s depressive levels than other predictors (e.g., survey points and demographic characteristics). To reduce and preemptively prevent people’s depression, approaches aimed at lowering the fear of infection in the context of the COVID-19 pandemic should be carefully considered. Furthermore, efforts at the national level are necessary to improve the knowledge related to COVID-19 and health promotion practices. When an infectious disease spreads, the public experiences confusion and fear, resulting in a strong demand for information from the media [35]. To prevent the spread of fear and anxiety about the disease due to exaggerated reports and unverified articles in the media, reliable information should be delivered in a timely manner. Most importantly, access to mental health services should be secured particularly for individuals who present greater vulnerabilities due to socioeconomic factors that may affect their mental health.

Despite the meaningful findings, our study includes a few limitations that should be considered in future studies. The COVID-19 infection fear scale, which was the critical predictor of PHQ-9 in this study and was developed by our multidisciplinary mental health professionals, is awaiting publication for testing its measurement validity. Additionally, because this study used a trend data that involved recruited different samples at four survey points within a year, it was impossible to test the actual changes at the individual level. Fortunately, we began building a panel data since December, 2020 (time 4). Thus, we expect to estimate more accurately the changes in individuals’ depressive levels with a longer time window by using advanced statistical analyses (e.g., fixed effects model, latent growth model, etc.).



Each survey recruited new participants; the four surveys were not panel data but trend data.


The survey periods and sampling errors at 95 confidential level by times are as follows: 1) time 1 (March 17–30, 2020; ±3.1%), 2) time 2 (May 25–31, 2020; ±3.1%), time 3 (September 10–21, 2020; ±2.2%), and time 4 (December 1–10, 2020; ±2.2%).


In the early stage of COVID-19 pandemic, item (6) and (9) were neither appropriate and nor collected at the first survey (March, 2020; time 1).


The average score of 7 items were used at the first survey (March; time 1); the average score of 9 items were utilized for the rest of surveys. Thus, the range of COVID-19 fear scale in the statistical analysis was 0 to 3 regardless of the survey points.


Availability of Data and Material

The datasets generated or analyzed during the study are not publicly available due to ongoing data collection but existing data may be available from the corresponding author on reasonable request.

Conflicts of Interest

The authors have no potential conflicts of interest to disclose.

Author Contributions

Conceptualization: Seok-Joo Kim, Sunju Sohn. Data curation: Seok-Joo Kim, Heeguk Kim, Jong-Sun Lee. Investigation: Seok-Joo Kim, Sunju Sohn, Yun-Kyeung Choi, Jinhee Hyun, Heeguk Kim, So Hee Lee, Yu-Ri Lee, Jong-Woo Paik. Supervision: Jinhee Hyun, So Hee Lee, Jong-Woo Paik. Writing—original draft: Seok-Joo Kim, Sunju Sohn. Writing—review & editing: Sunju Sohn, Yun-Kyeung Cho, Jong-Sun Lee, Yu-Ri Lee.

Funding Statement



1. Wang J, Wang JX, Yang GS. The psychological impact of COVID-19 on Chinese individuals. Yonsei Med J 2020;61:438–440.
2. Pfefferbaum B, North CS. Mental health and the Covid-19 pandemic. N Engl J Med 2020;83:510–512.
3. Taylor S. The psychology of pandemics: preparing for the next global outbreak of infectious disease Newcastle: Cambridge Scholars Publishing; 2019.
4. Brooks SK, Webster RK, Smith LE, Woodland L, Wessely S, Greenberg N, et al. The psychological impact of quarantine and how to reduce it: rapid review of the evidence. Lancet 2020;395:912–920.
5. Parlapani E, Holeva V, Voitsidis P, Blekas A, Gliatas I, Porfyri GN, et al. Psychological and behavioral responses to the COVID-19 pandemic in Greece. Front Psychiatry 2020;11:821.
6. Barzilay R, Moore TM, Greenberg DM, DiDomenico GE, Brown LA, White LK, et al. Resilience, COVID-19-related stress, anxiety and depression during the pandemic in a large population enriched for healthcare providers. Transl Psychiatry 2020;10:291.
7. Bäuerle A, Steinbach J, Schweda A, Beckord J, Hetkamp M, Weismüller B, et al. Mental health burden of the COVID-19 outbreak in Germany: predictors of mental health impairment. J Prim Care Community Health 2020;11:2150132720953682.
8. Choi EPH, Hui BPH, Wan EYF. Depression and anxiety in Hong Kong during COVID-19. Int J Environ Res Public Health 2020;17:3740.
9. Czeisler MÉ, Lane RI, Petrosky E, Wiley JF, Christensen A, Njai R, et al. Mental health, substance use, and suicidal ideation during the COVID-19 pandemic—United States, June 24–30, 2020. MMWR Morb Mortal Wkly Rep 2020;69:1049–1057.
10. Huang Y, Zhao N. Generalized anxiety disorder, depressive symptoms and sleep quality during COVID-19 outbreak in China: a web-based cross-sectional survey. Psychiatry Res 2020;288:112954.
11. Jia R, Ayling K, Chalder T, Massey A, Broadbent E, Coupland C, et al. Mental health in the UK during the COVID-19 pandemic: cross-sectional analyses from a community cohort study. BMJ Open 2020;10e040620.
12. Lai J, Ma S, Wang Y, Cai Z, Hu J, Wei N, et al. Factors associated with mental health outcomes among health care workers exposed to coronavirus disease 2019. JAMA Netw Open 2020;3e203976.
13. Li J, Yang Z, Qiu H, Wang Y, Jian L, Ji J, et al. Anxiety and depression among general population in China at the peak of the COVID-19 epidemic. World Psychiatry 2020;19:249–250.
14. Makhanova A, Shepherd MA. Behavioral immune system linked to responses to the threat of COVID-19. Pers Individ Dif 2020;167:110221.
15. Rubin GJ, Wessely S. The psychological effects of quarantining a city. BMJ 2020;368:m313.
16. Terlizzi EP, Villarroel MA. Symptoms of generalized anxiety disorder among adults: United States, 2019. NCHS Data Brief 2020;:1–8.
17. Ueda M, Stickley A, Sueki H, Matsubayashi T. Mental health status of the general population during the COVID-19 pandemic: a cross-sectional national survey in Japan. medRxiv [Preprint]. 2020 [cited 2022 Feb 2]. Available at: https://doi.org/10.1101/2020.04.28.20082453.
18. Wang C, Pan R, Wan X, Tan Y, Xu L, Ho CS, et al. 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.
19. Ettman CK, Abdalla SM, Cohen GH, Sampson L, Vivier PM, Galea S. Prevalence of depression symptoms in US adults before and during the COVID-19 pandemic. JAMA Netw Open 2020;3e2019686.
20. O’Connor RC, Wetherall K, Cleare S, McClelland H, Melson AJ, Niedzwiedz CL, et al. Mental health and well-being during the COVID-19 pandemic: longitudinal analyses of adults in the UK COVID-19 mental health & wellbeing study. Br J Psychiatry 2021;218:326–333.
21. Radeloff D, Papsdorf R, Uhlig K, Vasilache A, Putnam K, von Klitzing K. Trends in suicide rates during the COVID-19 pandemic restrictions in a major German city. Epidemiol Psychiatr Sci 2021;30e16.
22. Thakur V, Jain A. COVID 2019-suicides: a global psychological pandemic. Brain Behav Immun 2020;88:952–953.
23. McCracken LM, Badinlou F, Buhrman M, Brocki KC. Psychological impact of COVID-19 in the Swedish population: depression, anxiety, and insomnia and their associations to risk and vulnerability factors. Eur Psychiatry 2020;63e81.
24. Tee ML, Tee CA, Anlacan JP, Aligam KJG, Reyes PWC, Kuruchittham V, et al. Psychological impact of COVID-19 pandemic in the Philippines. J Affect Disord 2020;277:379–391.
25. Alkhamees AA, Alrashed SA, Alzunaydi AA, Almohimeed AS, Aljohani MS. The psychological impact of COVID-19 pandemic on the general population of Saudi Arabia. Compr Psychiatry 2020;102:152192.
26. Korea Disease Control and Prevention Agency. Korea community health at a glance 2020: Korea community health survey (KCHS) Cheongju: Korea Disease Control and Prevention Agency; 2021.
27. Cowling BJ, Lau LL, Wu P, Wong HW, Fang VJ, Riley S, et al. Entry screening to delay local transmission of 2009 pandemic influenza A (H1N1). BMC Infect Dis 2010;10:82.
28. Gardner PJ, Moallef P. Psychological impact on SARS survivors: critical review of the English language literature. Can Psychol 2015;56:123–135.
29. Shin J, Park HY, Kim JL, Lee JJ, Lee H, Lee SH, et al. Psychiatric morbidity of survivors one year after the outbreak of Middle East respiratory syndrome in Korea, 2015. J Korean Neuropsychiatr Assoc 2019;58:245–251.
30. Kikuchi H, Machida M, Nakamura I, Saito R, Odagiri Y, Kojima T, et al. Changes in psychological distress during the COVID-19 pandemic in Japan: a longitudinal study. J Epidemiol 2020;30:522–528.
31. Planchuelo-Gómez Á, Odriozola-González P, Irurtia MJ, de Luis-García R. Longitudinal evaluation of the psychological impact of the COVID-19 crisis in Spain. J Affect Disord 2020;277:842–849.
32. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med 2001;16:606–613.
33. Park SJ, Choi HR, Choi JH, Kim KW, Hong JP. Reliability and validity of the Korean version of the patient health questionnaire-9 (PHQ-9). Anxiety and Mood 2010;6:119–124.
34. Spitzer RL, Kroenke K, Williams JB. Validation and utility of a self-report version of PRIME-MD: the PHQ primary care study. JAMA 1999;282:1737–1744.
35. Lee YR, Lee JY, Park IH, Kim M, Jhon M, Kim JW, et al. The relationships among media usage regarding COVID-19, knowledge about infection, and anxiety: structural model analysis. J Korean Med Sci 2020;35e426.

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Table 1.

Demographic characteristic of the sample

Time 1 (March) (N=1,014) Time 2 (May) (N=1,002) Time 2 (September) (N=2,063) Time 4 (December) (N=2,063) Total (N=6,142)
Female 498 (49.11) 490 (48.90) 1,019 (49.39) 1,019 (49.39) 3,026 (49.27)
Age (yr) 44.37±13.39 44.31±13.62 44.61±13.48 44.61±13.32 44.52±13.43
Unemployed 70 (6.90) 105 (10.48) 219 (10.62) 134 (6.50) 528 (8.60)
Family types
Living alone 113 (11.14) 137 (13.67) 253 (12.26) 308 (14.93) 811 (13.20)
Living only with a partner 133 (13.12) 123 (12.28) 262 (12.70) 300 (14.54) 818 (13.32)
Living with a partner and child 490 (48.32) 440 (43.91) 907 (43.97) 889 (43.09) 2,726 (44.38)
Others 278 (27.42) 302 (30.14) 641 (31.07) 566 (27.44) 1,787 (29.09)
Seoul Capital area 228 (22.49) 223 (22.26) 776 (37.62) 776 (37.62) 2,003 (32.61)
Other metropolitan areas 514 (50.69) 510 (50.90) 620 (30.05) 620 (30.05) 2,264 (36.86)
Providences 272 (26.82) 269 (26.85) 667 (32.33) 667 (32.33) 1,875 (30.53)

Values are presented as mean±standard deviation or number (%).

Table 2.

Trends of PHQ-9 and COVID-19 infection fear

Time Month of 2020
Time 11a) March Time 22a) May Time 33a) September Time 44a) December Total
COVID-19 prevalencea)
Cumulative cases (N) 9,6611b) 11,4682b) 23,0443b) 40,0894b) 40,0864b)
Cumulative deaths (N) 1581b) 2702b) 3853b) 5644b) 5644b)
Sample: N (%) 1,014 (16.51) 1,002 (16.31) 2,063 (33.59) 2,063 (33.59) 6,142 (100.00)
Total PHQ-9 scoresb)
Cronbach’s α 0.897 0.909 0.905 0.906 0.905
Mean (SD) 5.10 (5.26) 5.12 (5.44) 5.86 (5.66) 5.52 (5.49) 5.50 (5.51)
One-Way ANOVA F(3, 6138)=6.282, p<0.001
Post hoc tests (Scheffe) (Time 3–Time 1)=0.76*, (Time 3–Time 2)=0.74*
Non-depression: N (%) 837 (82.5) 816 (81.4) 1,607 (77.9) 1,651 (80.0) 4,911 (80.0)
Depressionc): N (%) 177 (17.5) 186 (18.6) 456 (22.1) 412 (20.0) 1,231 (20.0)
χ2 test χ2(3)=11.079, p=0.011
Average COVID-19 infection fear scoresd)
Cronbach’s α 0.927 0.933 0.930 0.938 0.928
Mean (SD) 1.73 (0.72) 1.60 (0.73) 1.77 (0.70) 1.78 (0.69) 1.74 (0.71)
One-Way ANOVA F(3, 6138)=16.531, p<0.001
Post hoc tests (Scheffe) (Time 2–Time 1)=-0.14*, (Time 3–Time 2)=0.17*, (Time 4–Time 2)=0.18*
Average COVID-19 infection fear scores and total PHQ-9 scores: Pearson’s r 0.389* 0.451* 0.432* 0.416* 0.424*

Korea Disease Control and Prevention Agency (www.kdca.go.kr);


ranged 0–27; 9 items;


total PHQ-9 scores ≥10;


ranged 0–3; 7 items at time 1; 9 items at time 2, 3, and 4;


survey period (March 17–30, 2020);


March 30, 2020;


survey period (May 25–31, 2020);


May 31, 2020;


survey period (September 10–21, 2020);


September 21, 2020;


survey period (December 1–10, 2020);


December 10, 2020.



PHQ-9, Patient Health Quesionnaire-9; COVID-19, coronavirus disease of 2019; N, number; SD, standard deviation; ANOVA, analysis of variance

Table 3.

Regression models of PHQ-9

Model 1
Model 2
b SE t β p b SE t β p
Intercept 5.098 0.173 29.490 <0.001 0.540 0.403 1.340 0.180
Time 1 (March, 2020; reference)
Time 2 (May, 2020) 0.021 0.245 0.086 0.001 0.931 0.375 0.220 1.704 0.025 0.088
Time 3 (September, 2020) 0.757 0.211 3.588 0.065 <0.001 0.575 0.191 3.002 0.049 0.003
Time 4 (December, 2020) 0.425 0.211 2.015 0.044 0.044 0.232 0.191 1.214 0.020 0.225
Female (=1)* 0.724 0.127 5.684 0.066 <0.001
Age (year) -0.027 0.005 -5.062 -0.067 <0.001
Unemployed (=1) 1.184 0.227 5.212 0.060 <0.001
Living alone (reference)
Living only with a partner -1.126 0.251 -4.487 -0.069 <0.001
Living with a partner and child -1.189 0.203 -5.858 -0.107 <0.001
Others -0.676 0.212 -3.193 -0.056 <0.001
Seoul Capital area (reference)
Other metropolitan areas -0.092 0.155 -0.592 -0.008 0.554
Providences -0.160 0.159 -1.017 -0.013 0.314
COVID-19 infection fear (average scores; 0–3) 3.232 0.090 35.790 0.416 <0.001
Model fits F(3, 6138)=6.282, p<0.001 F(12, 6129)=129.734, p<0.001
Δ Model fits - ΔF(9, 6129)=170.365, p<0.001
R2/ΔR2 0.003/- 0.203/0.199

Dependent variable: total scores of PHQ-9 (ranged 0–27), N=6,142.


Female=1 vs. Male=0;

Unemployed=1 vs. Employed=0.

PHQ-9, Patient Health Quesionnaire-9; SE, standard error; N, number