Abstract
Background
The COVID-19 pandemic has brought about significant social changes that could potentially affect mental health. This study aimed to examine the association between specific periods of the pandemic and depressive symptoms in South Korean adults.
Methods
We used cross-sectional data from 1,376,438 participants involved in the 2019–2024 Korean Community Health Survey. The pandemic period was categorized into pre-COVID-19 (2019), intra-COVID-19 (2020–2022), and post-COVID-19 (2023–2024) periods. Depressive episodes were assessed using the Patient Health Questionnaire-9. Multivariate logistic regression was used to estimate the odds of depressive episodes across the time periods.
Results
Compared to the pre-pandemic period, individuals assessed during the intra-COVID-19 period had higher odds of depressive episodes (odds ratio [OR] = 1.23; 95% confidence interval [CI]: 1.19–1.27). The odds further increased in the post-COVID-19 period (OR = 1.41; 95% CI: 1.36–1.45). The association was greatest among adults aged 20–39 years old.
Conclusions
Depressive symptoms were more common during and after the pandemic, particularly among younger adults. These findings emphasize the need for long-term mental health support in the post-COVID-19 pandemic era.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12889-026-27134-5.
Keywords: COVID-19, Pandemic, Depressive episodes, Mental Health, Patient Health Questionnaire-9
Background
With rapid changes in healthcare utilization, service delivery, and clinical organizational financing triggered by the COVID-19 pandemic, substantial differences have emerged between the pre- and post-COVID-19 eras [1]. The pandemic, along with physical distancing measures and travel restrictions, has profoundly affected global health. The constraints that were introduced hindered in-person consultations and interfered with multiple forms of treatment administration [2]. Furthermore, the pandemic prompted major lifestyle changes, such as stay-at-home orders and social distancing, along with a shift in healthcare delivery models, most notably an increased reliance on telemedicine [3]. These transformations placed a considerable burden on healthcare systems and often limited the availability of essential medical services. In many cases, hospitals were deliberately avoided by the general public due to fear of infection, a pattern similar to that observed during the severe acute respiratory syndrome (SARS) outbreak [4]. This avoidance and disruption led to an increase in unmet medical needs, which in turn resulted in a deterioration in overall health status among many individuals.
Post-acute sequelae of SARS-coronovirus-2 (SARS-CoV-2) infection, commonly referred to as ‘long COVID,’ have emerged as a significant public health concern. Long COVID encompasses symptoms that emerge, persist, or recur after acute COVID-19, such as fatigue and respiratory issues [5]. It is typically characterized by the persistence of symptoms for more than four weeks after the initial infection [6]. Long COVID has been reported to affect nearly every organ system, presenting with various symptoms, including cognitive impairment and memory loss [7]. Many individuals report a troubling array of persistent symptoms, including fatigue, insomnia, altered smell and taste, depression, and anxiety [8]. The European Center for Disease Prevention and Control reported that the prevalence of anxiety, depression, and post-traumatic stress disorder in patients with post-COVID-19 syndrome recruited from community settings was 17.2%, 17.3%, and 20.6%, respectively [9]. Mental health conditions, such as depression and anxiety, worsened during the pandemic, particularly in the context of delayed or unmet medical care [4]. Even conservative estimates suggest that long COVID affects a substantial proportion of the population. For instance, assuming a prevalence rate of 12% among individuals experiencing three or more symptoms, approximately 9.6 million people in the United States may have developed long COVID, which is nearly 10 times the number of COVID-19 related deaths [10]. Despite ongoing public health interventions, the global impact of COVID-19 remains substantial [11]. The pathophysiology of long COVID appears to be multifactorial and continues to be an area of active research. Current treatment goals focus on symptom management, functional improvement, and enhancement of the quality of life, often requiring multidisciplinary care [12].
In South Korea, nationwide public health measures—including extensive testing, contact tracing, and social distancing—were rapidly implemented to contain viral spread. While effective in reducing infection rates, these measures also had notable adverse effects on mental health. National surveys reported an increase in depressive symptoms during the pandemic, with approximately 18.8% of adults exhibiting clinically significant depression [13]. Analyses using National Health Insurance data further indicated a rise in psychiatric disorders and heightened anxiety and depression compared with the pre-pandemic period [14]. Given these findings, it is crucial to examine how the pandemic has affected mental health in the Korean population. This study hypothesized that individuals assessed during and after the intra-COVID-19 period would exhibit higher odds of experiencing depression than those assessed prior to the pandemic. Accordingly, this study examined the association between the COVID-19 pandemic period and depressive symptoms using a nationally representative sample of adults in South Korea.
Methods
Data and study participants
This study utilized data from the 2019–2024 waves of the Korean Community Health Survey (KCHS). The KCHS, a large-scale population-based survey initiated in 2008 by the Korea Centers for Disease Control and Prevention (KCDC), is designed to monitor health-related behaviors and public health trends across South Korea’s communities. The survey is conducted annually through direct interviews with adults aged 19 years and above spanning 255 administrative districts. A two-stage stratified and systematic sampling design is used to ensure a representative cross-section of the national population [15]. The KCHS protocol was reviewed and approved by the KCDC Institutional Review Board (IRB No. 2016–10-01-P-A) [16]. To maintain confidentiality, all identifying information is removed before data release, and informed consent procedures are waived because of the use of anonymized datasets. For a comprehensive explanation of the study methodology, readers are referred to earlier publications [17].
Of the initial 1,382,875 individuals who participated in the 2019–2024 waves, 6,473 were excluded due to missing responses to the Patient Health Questionnaire-9 (PHQ-9). An additional 1,948 individuals were excluded from the analysis because of incomplete information on socioeconomic variables. This resulted in 1,377,454 individuals for consideration. From this subset, participants lacking data on key health-related variables including regular physical activity, diabetes status, hypertension, perceived stress levels, and self-rated health were excluded. After these exclusions, the final analytical sample comprised 1,376,438 individuals (Fig. 1).
Fig. 1.
Flowchart of participant selection
Variables
The primary independent variable was COVID-19 status. South Korea reported its first COVID-19 case on February 19, 2020, while the government officially declared the pandemic over on May 11, 2023 [18, 19]. Accordingly, this study compared depressive episodes among participants across three pandemic timelines: pre-COVID-19 (2019), intra-COVID-19 (2020–2022), and post-COVID-19 (2023–2024) periods. These categories reflect changes in the societal and policy context rather than the complete absence of SARS-CoV-2 transmission.
This study examined various demographic, socioeconomic, and health-related characteristics. Demographic characteristics included age (19 to 39, 40 to 59, and 60 or older) and gender. These age groups were selected to reflect major life-course stages (young adulthood, midlife/working age, and older adulthood), which differ meaningfully in social roles and health profiles. Socioeconomic variables included educational level (middle school or below, high school, and college or above), region (urban or rural), and occupation. Residential areas were classified as urban or rural, based on their designation as areas in metropolitan cities and therefore urban, or rural otherwise. Occupations were organized according to the Korean Standard Classification of Occupations and further simplified into four groups: white collar (office and administrative positions), pink collar (service and sales roles), blue collar (agricultural, forestry, fishing, and military work), and unemployed individuals [20].
Health-related characteristics included smoking status, alcohol consumption, physical activity, self-rated health, and perceived stress. Alcohol consumption was divided into three categories: heavy drinkers (those consuming alcohol two or more times per week), moderate drinkers (once a week or less), and light drinkers (approximately once a month). Individuals who smoked cigarettes were classified as smokers. Self-assessed health status was measured by asking participants to rate their general health as very good, good, average, poor, or very poor. Responses were categorized as high (“very good” or “good”), middle (“average”), and low (“poor” or “very poor”), respectively. Perceived stress was assessed by asking how much stress participants usually feel in daily life, with response options of very much, much, a little, or hardly any. These were categorized as high (“very much” or “much”), middle (“a little”), and low (“hardly any”). Physical activity was considered regular if participants engaged in at least 75 min of vigorous-intensity aerobic exercise per week or 150 min of moderate-intensity activity, in line with World Health Organization (WHO) guidelines [21].
Depressive episode was included as the primary dependent variable. Depressive episode was assessed using the PHQ-9, a standardized screening tool for depressive symptoms [22]. The PHQ-9 includes nine questions, with each item scored on a scale of 0 to 3, producing a cumulative score ranging from 0 to 27. Higher scores reflect a greater severity of depressive symptoms. In line with prior research, individuals with a PHQ-9 score above 10 were classified as experiencing depressive episode, whereas those with a score of 10 or below were considered as not depressed [23].
Statistical analysis
Descriptive analyses were conducted using the chi-square test to compare the general characteristics of the study sample. Multivariate logistic regression models were employed to assess the relationship between the COVID-19 pandemic periods and depressive symptoms, including an interaction term between age group and pandemic period to evaluate potential effect modification. Multivariate logistic regression analyses used a stratified sampling (k strata) and a clustering variable (primary sampling units) provided by the KCHS. Results were obtained from the weighted sample. These models controlled for potential confounders including demographic, socioeconomic, and health-related variables. The results are presented as odds ratios (ORs) with 95% confidence intervals (CIs). Statistical significance was defined as a p-value less than 0.05. All analyses were performed using SAS software (version 9.4; SAS Institute Inc., Cary, NC, USA).
Results
Table 1 presents the general characteristics of the study population, which included 1,376,438 participants. A total of 227,107 individuals from the pre-COVID-19 (2019) period were compared with 687,112 individuals from the intra-COVID-19 (2020–2022) period and 462,219 individuals from the post-COVID-19 (2023–2024) period, according to their demographic, socioeconomic, and health-related variables, including depressive episodes. Among the 227,107 individuals in the pre-COVID-19 period, 2.5% (n = 5,778) had a depressive episode, whereas the remaining 97.5% (n = 221,329) did not. This proportion increased to 2.7% (n = 18,295) during the intra-COVID-19 period and further increased to 3.1% (n = 14,552) in the post-COVID-19 period.
Table 1.
General characteristics of the participants (n = 1,376,438)
| Variables | Pre-COVID-19 | Intra-COVID-19 | Post-COVID-19 | P-value | |||
|---|---|---|---|---|---|---|---|
| 227,107 | 687,112 | 462,219 | |||||
| Depressive episodes | <.0001 | ||||||
| Yes | 5,778 | (2.5) | 18,295 | (2.7) | 14,552 | (3.1) | |
| No | 221,329 | (97.5) | 668,817 | (97.3) | 447,667 | (96.9) | |
| Gender | <.0001 | ||||||
| Male | 101,783 | (44.8) | 313,153 | (45.6) | 211,348 | (45.7) | |
| Female | 125,324 | (55.2) | 373,959 | (54.4) | 250,871 | (54.3) | |
| Age (years) | <.0001 | ||||||
| 20–39 | 49,815 | (21.9) | 149,148 | (21.7) | 90,930 | (19.7) | |
| 40–59 | 79,546 | (35.0) | 236,021 | (34.3) | 152,265 | (32.9) | |
| ≥ 60 | 97,746 | (43.0) | 301,943 | (43.9) | 219,024 | (47.4) | |
| Region | 0.4975 | ||||||
| Urban | 66,880 | (29.5) | 201,850 | (29.4) | 135,493 | (29.3) | |
| Rural | 160,227 | (70.6) | 485,262 | (70.6) | 326,726 | (70.7) | |
| Educational level | <.0001 | ||||||
| Middle school or below | 79,968 | (35.2) | 221,831 | (32.3) | 142,228 | (30.8) | |
| High school | 65,068 | (28.7) | 199,703 | (29.1) | 133,859 | (29.0) | |
| College or above | 82,071 | (36.1) | 265,578 | (38.7) | 186,132 | (40.3) | |
| Occupational classification | <.0001 | ||||||
| White-collar | 42,710 | (18.8) | 138,078 | (20.1) | 98,188 | (21.2) | |
| Blue-collar | 68,614 | (30.2) | 201,248 | (29.3) | 136,797 | (29.6) | |
| Pink-collar | 29,913 | (13.2) | 87,430 | (12.7) | 61,349 | (13.3) | |
| None | 85,870 | (37.8) | 260,356 | (37.9) | 165,885 | (35.9) | |
| Marital status | <.0001 | ||||||
| Married | 151,129 | (66.5) | 431,486 | (62.8) | 290,163 | (62.8) | |
| Separated or divorced | 40,831 | (18.0) | 136,974 | (19.9) | 94,896 | (20.5) | |
| Unmarried | 35,147 | (15.5) | 118,652 | (17.3) | 77,160 | (16.7) | |
| Self-Reported Health Status | <.0001 | ||||||
| High | 77,340 | (34.1) | 295,203 | (43.0) | 177,357 | (38.4) | |
| Middle | 101,662 | (44.8) | 280,109 | (40.8) | 195,484 | (42.3) | |
| Low | 48,105 | (21.2) | 111,800 | (16.3) | 89,378 | (19.3) | |
| Stress Level | <.0001 | ||||||
| High | 50,218 | (22.1) | 148,416 | (21.6) | 98,735 | (21.4) | |
| Middle | 121,311 | (53.4) | 364,392 | (53.0) | 242,250 | (52.4) | |
| Low | 55,578 | (24.5) | 174,304 | (25.4) | 121,234 | (26.2) | |
| Physical Activity | <.0001 | ||||||
| Yes | 52,794 | (23.2) | 135,714 | (19.8) | 105,710 | (22.9) | |
| No | 174,313 | (76.8) | 551,398 | (80.2) | 356,509 | (77.1) | |
| Alcohol consumption status | <.0001 | ||||||
| Heavy drinking | 46,818 | (20.6) | 123,960 | (18.0) | 86,271 | (18.7) | |
| Moderate drinking | 43,752 | (19.3) | 121,272 | (17.6) | 85,541 | (18.5) | |
| Light drinking | 136,537 | (60.1) | 441,880 | (64.3) | 290,407 | (62.8) | |
| Smoking | 0.0004 | ||||||
| Yes | 37,640 | (16.6) | 111,840 | (16.3) | 76,238 | (16.5) | |
| No | 189,467 | (83.4) | 575,272 | (83.7) | 385,981 | (83.5) | |
Table 2 presents the results concerning the association between the COVID-19 pandemic and depressive episodes among South Korean adults as adjusted for the relevant covariates. Individuals assessed during the intra-COVID-19 period exhibited significantly higher odds of depressive episodes than those assessed in the pre-COVID-19 period. (OR = 1.23, 95% CI: 1.19–1.27). The likelihood of having depressive episodes further increased in the post-COVID-19 period (OR = 1.41, 95% CI: 1.36–1.45). In addition, self-reported stress levels were associated with the higher odds of depressive episodes. Compared to those participants with low stress, those with moderate stress had higher odds of depressive episodes (OR = 1.83, 95% CI: 1.75–1.91), while those individuals reporting high stress levels were at a substantially greater risk (OR = 13.29, 95% CI: 12.74–13.86). Men were less likely to report depressive symptoms than women (OR = 0.73, 95% CI: 0.71–0.75). Lastly, compared with individuals without an occupation, those who were employed showed lower odds of depressive episodes (white-collar: OR = 0.51, 95% CI: 0.49–0.53; blue-collar: OR = 0.43, 95% CI: 0.42–0.45; pink-collar: OR = 0.54, 95% CI: 0.52–0.56).
Table 2.
Factors associated with depressive episodes
| Variables | Depressive episodes | ||||
|---|---|---|---|---|---|
| AOR | 95% CI | P-value | |||
| COVID-19 | |||||
| Post-COVID-19 | 1.48 | (1.41 | – | 1.55) | <.0001 |
| Intra-COVID-19 | 1.27 | (1.21 | – | 1.33) | <.0001 |
| Pre-COVID-19 | 1.00 | ||||
| Gender | |||||
| Male | 0.68 | (0.65 | – | 0.70) | <.0001 |
| Female | 1.00 | ||||
| Age (years) | |||||
| 20–39 | 1.78 | (1.67 | – | 1.89) | <.0001 |
| 40–59 | 1.10 | (1.05 | – | 1.16) | <.0001 |
| ≥ 60 | 1.00 | ||||
| Region | |||||
| Urban | 1.05 | (1.02 | – | 1.08) | 0.004 |
| Rural | 1.00 | ||||
| Educational level | |||||
| Middle school or below | 1.63 | (1.54 | – | 1.72) | <.0001 |
| High school | 1.33 | (1.27 | – | 1.39) | <.0001 |
| College or above | 1.00 | ||||
| Occupational classification | |||||
| White-collar | 0.54 | (0.52 | – | 0.57) | <.0001 |
| Blue-collar | 0.48 | (0.45 | – | 0.50) | <.0001 |
| Pink-collar | 0.59 | (0.56 | – | 0.62) | <.0001 |
| None | 1.00 | ||||
| Marital status | |||||
| Married | 0.57 | (0.55 | – | 0.60) | <.0001 |
| Separated or divorced | 1.10 | (1.04 | – | 1.17) | 0.001 |
| Unmarried | 1.00 | ||||
| Self-reported health status | |||||
| High | 0.12 | (0.11 | – | 0.12) | <.0001 |
| Middle | 0.27 | (0.27 | – | 0.28) | <.0001 |
| Low | 1.00 | ||||
| Stress level | |||||
| High | 13.8 | (12.98 | – | 14.6) | <.0001 |
| Middle | 1.78 | (1.67 | – | 1.89) | <.0001 |
| Low | 1.00 | ||||
| Physical activity | |||||
| Yes | 0.98 | (0.94 | – | 1.02) | 0.383 |
| No | 1.00 | ||||
| Alcohol consumption | |||||
| Heavy drinking | 1.18 | (1.12 | – | 1.23) | <.0001 |
| Moderate drinking | 0.93 | (0.89 | – | 0.97) | <.0001 |
| Light drinking | 1.00 | ||||
| Smoking | |||||
| Yes | 1.54 | (1.47 | – | 1.60) | <.0001 |
| No | 1.00 | ||||
CI Confidence interval, AOR Adjusted odds ratio, PHQ-9 Patient Health Questionnaire-9
Table 3 presents the results of the subgroup analysis examining the association between the COVID-19 pandemic and depressive episodes across the different age groups. In all three age groups, those in the intra-COVID-19 period demonstrated increased odds of experiencing depressive episodes, with the highest odds observed among those in the post-COVID-19 period, compared to those in the pre-COVID-19 period. This trend was consistent across all three age groups, demonstrating a similar pattern of depressive symptoms throughout the population. However, the magnitude of the effect varied with age. The association between the COVID-19 period and depressive episodes was most evident among individuals aged 20–39 years, whereas it was least pronounced among those aged 60 years and older. All associations were statistically significant. In addition, an interaction analysis between age group and COVID-19 period indicated significant effect modification: all interaction terms between age group and COVID-19 period were statistically significant (Supplementary Table 1).
Table 3.
Subgroup analysis representing odds ratio for depressive episodes stratified by age groups
| Variables | AOR | 95% CI | AOR | 95% CI | AOR | ||||
|---|---|---|---|---|---|---|---|---|---|
| Post COVID-19 | Intra COVID-19 | Pre COVID-19 | |||||||
| Age (years) | |||||||||
| 20–39 | 1.62 | (1.48 | – | 1.77) | 1.29 | (1.18 | – | 1.41) | 1.00 |
| 40–59 | 1.58 | (1.45 | – | 1.73) | 1.37 | (1.26 | – | 1.49) | 1.00 |
| ≥ 60 | 1.26 | (1.18 | – | 1.34) | 1.14 | (1.07 | – | 1.22) | 1.00 |
CI Confidence interval, AOR Adjusted Odds ratio
Discussion
This study investigated the relationship between the COVID-19 pandemic and depressive symptoms. Our findings indicated that individuals assessed during and after the COVID-19 period had significantly higher odds of experiencing depressive episodes than those assessed before the pandemic, with higher odds observed in the post-COVID-19 period compared to the intra-COVID-19 period. These findings imply that the psychological burden of the pandemic not only persisted, but may have worsened beyond the acute crisis phase, adversely affecting mental health.
Notably, in age-stratified analyses, the association between the COVID-19 period and depressive episodes appeared stronger among individuals aged 20–39 years, suggesting that younger adults may have exhibited higher odds of depressive symptoms than older age groups. Our findings are consistent with previous research, which has demonstrated that anxiety and depression are widespread among young adults and have increased significantly compared with the pre-COVID-19 period [24, 25]. Younger adults aged 18–39 years are reported to have experienced greater increases in high levels of anxiety symptoms (from 9% pre-COVID-19 to 21% post-COVID-19) and moderate-to-severe depressive symptoms (from 9% pre-COVID-19 to 39% post-COVID-19) than any other adult age group [24]. These increases reflect the collective effects of the pandemic, whether due to infection itself or the consequences of social restrictions and other disruptions [26]. This finding aligns with those of other studies indicating that young adults experienced the highest levels of loneliness during the pandemic [27]. These age differences may reflect variations in social roles, economic vulnerability, and coping resources across life stages. Younger adults were more likely to experience employment instability, educational disruption, and reduced social contact, all of which may have intensified psychological distress [28]. In contrast, older adults may have benefited from greater life stability or more established support networks, potentially buffering against the same stressors [29]. Consistent with these patterns, the significant interaction effect further suggests that the impact of the pandemic on depressive episodes differed by age group.
The mental health consequences observed during and after the pandemic may be attributed not only to the direct biological effects of the virus but also to a complex interplay of psychological stressors. Pervasive fear of contagion, uncertainty, social isolation, and prolonged feelings of loneliness have consistently been highlighted as major contributors to poor psychological outcomes [30]. These stressors are prevalent even among noninfected individuals, indicating a widespread population-level mental health burden. In this context, the growing recognition of long COVID as involving post-acute sequelae of SARS-CoV-2 infection is especially relevant. Regardless of disease severity, many individuals continue to experience symptoms such as fatigue and cognitive difficulties, including memory and concentration loss [31]. These symptoms, which fall under the umbrella of long COVID, are often not attributed to other known medical conditions and may persist for weeks or even month following recovery [32]. Long COVID has affected millions of people globally and significantly increased the burden on healthcare and social support systems [33]. Persistent symptoms may severely impair quality of life, and patients with long COVID may already have compromised mental health [9]. Beyond these clinical consequences, younger adults were likely exposed to pandemic-related stressors such as employment instability, financial strain, and social isolation, which have been associated with increased psychological distress in prior studies [34]. Furthermore, the adverse effects of long COVID were most pronounced among individuals from lower-income households, who reported greater limitations in daily activities and higher psychological distress than those with higher socioeconomic status [35]. Consistent with this, Korean data indicate that lower household income and limited social support were associated with poorer mental health outcomes during the pandemic [36], underscoring the intersection of socioeconomic disadvantage and psychological vulnerability. Taken together, these findings highlight significant associations between the COVID-19 pandemic—particularly long COVID—and poorer mental health, quality of life, and social functioning. Targeted mental health interventions and support systems are urgently needed to mitigate the lasting psychological consequences of the pandemic, particularly among young adults and socially disadvantaged populations.
This study has several limitations. First, although the KCHS is a representative nationwide survey, the use of secondary, self-reported data may introduce measurement error and reporting bias. Depressive episode scores were derived from self-administered questionnaires, which may be influenced by participants’ subjective interpretation of items, recall bias, or social desirability tendencies. Consequently, depressive symptoms could have been underreported or overestimated, potentially leading to misclassification and attenuation of the observed associations. Second, some relevant variables that could affect the target association may not have been included because of data unavailability. Third, because of the cross-sectional design, our findings should be interpreted as associations rather than causal effects; therefore, causal language should be avoided when describing the results. Lastly, although we defined 2023–2024 as the post-COVID-19 period based on the government’s end-of-pandemic declaration, this grouping may mask heterogeneity within 2023–2024, as residual transmission, public health measures, and broader social and economic conditions continued to evolve; therefore, the post-period findings should be interpreted as average associations across these years. Despite these limitations, a major strength of our study is the use of a nationally representative dataset obtained through multistage stratified sampling of adults in South Korea. The analysis included more than 1,000,000 participants, providing robust statistical power and enhancing the generalizability of the findings. Depressive episodes were assessed using the PHQ-9 scale, which has been validated for its reliability and accuracy in evaluating depressive symptoms, rather than relying on a single-item question [37].
Our findings suggest that individuals assessed during and after the intra-COVID-19 period had higher odds of experiencing depressive episodes than those assessed before the pandemic. Age-stratified analyses and interaction testing suggested that the mental health impact of the pandemic was not uniform, with some age groups experiencing disproportionately larger increases in depressive episodes. Consequently, healthcare services and policies should prioritize the identification and management of pandemic-related mental health conditions, including long-term psychological sequelae. Given the possibility of future pandemics, strengthening preparedness efforts—including early detection of mental health symptoms and the development of targeted, mental health–focused intervention strategies—will be essential. Future research using longitudinal designs is needed to strengthen causal inference regarding pandemic-related changes in depressive episodes.
Supplementary Information
Acknowledgements
Not applicable.
Abbreviations
- OR
Odds ratio
- AOR
Adjusted odds ratio
- CI
Confidence interval
- SARS
Severe acute respiratory syndrome
- SARS-CoV-2
SARS-coronovirus-2
- KCHS
Korea Centers for Disease Control and Prevention
- IRB
Institutional review board
- PHQ-9
Patient Health Questionnaire-9
- WHO
World Health Organization
Authors’ contributions
Wonseok Jeong conceived and designed the study, developed the methodology, and conducted the formal analysis. Wonseok Jeong and Wonjeong Jeong drafted the initial version of the manuscript and contributed to subsequent drafting and revision. Wonjeong Jeong is the corresponding author and manuscript supervisor. All the authors have read and approved the final version of the manuscript.
Funding
This work was supported by a grant from the National Cancer Center Grant (2511622–2). The funding sources were not involved in interventions related to study design and data interpretation.
Data availability
The data supporting the findings of this study are openly available from the Korea Community Health Survey at https://chs.kdca.go.kr/chs/index.do.
Declarations
Ethics approval and consent to participate
The Korean Community Health Survey (KCHS) received approval from the Korea Centers for Disease Control and Prevention (KCDC) IRB (2016–10-01-P-A) in 2016. Since 2017, the need for ethics approval for the KCHS has been waived by the KCDC IRB, as the research does not fall under human subject research based on the enforcement rule of the Bioethics and Safety Act. All the patients provided written informed consent to participate in the study.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data supporting the findings of this study are openly available from the Korea Community Health Survey at https://chs.kdca.go.kr/chs/index.do.