Concerns related to COVID-19 in South Korea : What are associated individual and regional factors?

doi:10.21203/rs.3.rs-1947568/v1

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Concerns related to COVID-19 in South Korea : What are associated individual and regional factors?

https://doi.org/10.21203/rs.3.rs-1947568/v1

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Background: COVID-19 is raising new concerns previously non-existent. It is important to identify risk groups to avoid potential problems that may arise from this.

Methods: Data from the 2020 Korea Community Health Survey and official government database were collected for this analysis and are nationally representative. The final study population was 225,689 (men: 102,358; 45·4%, mean age: 54·9). Multilevel regression analysis was employed to evaluate concerns related to COVID-19 and its related individual- and area-level factors.

Results: 4·86% of the variability in the scores of concerns related to COVID-19 was accounted for by district areas. Individuals who lived in the most deprived area showed a higher coefficient of 0·123 (p-value: 0·070) compared to the least deprived area. In addition, concern type 3 (“Concerns of being blamed by others of infection”) showed a higher coefficient when individuals lived in the most deprived area (Q4 coefficient: 0·088, p-value: 0·006).

Conclusion: People in deprived areas had inferior coping strategies; thus, they are more likely to be a risk population for current or future psychological problems. Proper strategies on the population are needed to build risk perception with adequate information about COVID-19.

COVID-19

Concerns related to COVID-19

Area deprivation

Concerns related to COVID-19 was accounted 4.86% by area characteristics.
Individuals lived in most deprived area had higher concerns of COVID-19.
Aged 19-49 lived in most deprived area had higher concerns of COVID-19.
“Concerns of being blamed by others of infection” was the most influential.

Coronavirus disease 2019 (COVID-19) originated from Wuhan, Hubei Province, People's Republic of China, at the end of 2019.[1] So far (January 2022), over 304 million confirmed cases and over 5·4 million deaths had been reported, where most regions showed an increased incidence of new cases and deaths.[2] In South Korea, 692,174 confirmed cases and 6,310 deaths were reported as of January 16, 2022, which is an unprecedented daily incidence rate.[3]

Infectious disease outbreaks affect a population psychologically, cause social reactions, and restrict certain behaviors usually allowed before the outbreak.[4, 5] Especially, COVID-19 has unprecedently suffered humanity while infringing on personal freedoms and provoking financial loss due to the government’s strict quarantine policies.[5–7] Consequently, policies such as lockdown, social distancing, and its economic consequences such as unemployment and disruption of services have affected individuals’ mental health and increased the risk of suicide.[5–10]

People have prejudice and fear of spreading the infection since there is a terror that one might transmit the virus to others.[11–13] Moreover, anxiety and fear related to infection may lead to any kind of discrimination.[11–13] Accordingly, response to fear and intolerance of unclear consequences from the infection, including health, economic aspect, and social discrimination, leads people to have negative societal behaviors.[11, 14]

Therefore, understanding concerns about COVID-19 is important as it gives evidence of how people consider the current pandemic situation, prompting authorities to mitigate the impact.[5–7] It is particularly important to identify the population who are more likely to have concerns related to the pandemic as they are vulnerable to developing negative mental health conditions or already are. Given that, measuring factors associated with concerns about COVID-19 is important, as it gives evidence for allocating resources and assistance of mental health care. Consequently, it can minimize the burdens of mental health arising from COVID-19.

First, we assessed the COVID-19 infection rate that accommodates our eligible data (Community Health Survey data, 2020) up to date. Second, we investigated whether individual (socio-demographic, economic, and health-related factors) and regional (Area deprivation score, infection rate of region, type of region) factors were associated with concerns related to COVID-19. We primarily focused on area deprivation as a main independent variable to concerns of COVID-19, given the established results that poor social and economic environment was associated with vulnerability of the infection.[15–21] Third, we further employed subgroup analysis to reveal whether specific characteristics differentiated the association between area deprivation and the concerns. Finally, we analyzed whether the type of concern was differentially associated with the area deprivation.

Data source and study population

We conducted a cross-sectional study with data obtained from the 2020 Korea Community Health Survey (KCHS), which is an anonymized and nationwide health interview initiated by the Korea Disease Control and Prevention Agency (KCDA), aimed to establish a standardized community survey to help develop health projects for all local districts. The 2020 KCHS was conducted from October 16, 2020 to December 31, 2020. Detailed information on the study design and aims of the KCHS has been previously reported.[22] Trained interviewers conducted one-to-one interviews with individuals aged 19 or older across the 255 local districts, which are all administrative districts in South Korea, yielding about 230,000 representative population. In the 2020 KCHS, new questionnaires regarding COVID-19 were added, including types of concerns related to COVID-19. In our study, we excluded 474 individuals who refused to answer at least one question regarding concerns related to COVID-19, and 3,106 who had at least one missing value of covariates considered in our study, accordingly 225,689 remained as the final study population (Fig. 1).

(Fig. 1)

We deployed data of cumulated infection rate of COVID-19 regional from the initial occurrence date (January 20, 2020) to the last survey date of KCHS (December 31, 2020), from open data source of Public Data Portal managed by the Ministry of the Interior and Safety (https://www.data.go.kr/en/index.do). Thus, our study reflects the COVID-19 experience of the study population in KCHS as possible.

We also used 2015 Korean population census data to calculate area deprivation index (ADI) in the context of material and social deprivation.[23] We tried to use up-to-date census data as possible to calculate ADI, where might have some changes in status of each area between 2015 and 2020.

All personal information in this data was de-identified before its distribution; therefore, the institutional review board of Yonsei University confirmed that this study is eligible for exemption from full institutional review board review.

Variables

Dependent variable: Concerns related to COVID-19 (0–16 score)

Concerns related to COVID-19 questionnaires are as follows: (1) “I am concerned about getting infected with COVID-19,”(response rate: 99.9%; 229,202/229,269) (2) “I am concerned about dying if I get infected with COVID-19,” (response rate: 99.9%; 229,077/229,269) (3) “I am concerned about being criticized or disadvantaged by others around me if I get infected with COVID-19,” (response rate: 99.9%; 229,007/229,269) (4) “I am concerned about my family members who are vulnerable (e.g. elderly, infant, or patient) getting infected with COVID-19,” (response rate: 99.9%; 229,192/229,269) and (5) “I am concerned about the financial loss to my family and I caused by COVID-19 pandemic” (response rate: 99.9%; 229,145/229,269). Of the five types of concern related to COVID-19, we excluded the fourth type since its questionnaire only targeted individuals who live with a vulnerable family member (e.g. elderly, infant, or patient); thus, it did not align with our study objectives. Each question is rated on a 5-point scale from 1 (most concerned) to 5 (least concerned). We manually recorded the scales from 0 (least concerned) to 4 (most concerned). Accordingly, the total score ranges from 0 to 16, with a higher score indicating greater concerns related to COVID-19.

Main independent variable: Area deprivation index (ADI)

ADI is a summary measure used to indicate the level of material and social deprivation of a geographical area and consists of a number of standardized and weighted variables.[24] Previous research has proven that the ADI can be useful for uncovering geographically-based differences in a community’s health.[25] Specifically, it has been reported that different levels of ADI were associated with the infection rate of COVID-19.[15–21] The ADI used in our study was calculated based on the 2015 Korean population census data.[23] A total of eleven variables were utilized for the overall degree of AD across 13 regional states and four metropolitan cities, which are the entire geographical area of South Korea. These variables are (1) proportion of people aged 25–64 with no high school diploma, (2) proportion of households not owning their own house, (3) proportion of households living in a monthly/yearly rental house, (4) proportion of households with overcrowded living conditions (> 1 person/room), (5) proportion of the population aged 65 or over, (6) proportion of households with a woman as head of the household, (7) proportion of separated, divorced, or widowed individuals aged ≥ 15 years, (8) proportion of households living below the minimum housing standard (house without separate kitchen, bathroom, hot-water supply system, and heating apparatus), (9) proportion of households without a motor vehicle, (10) proportion of people living alone, and (11) proportion of the population with occupational lower social class. These occupations include a) agriculture, forestry, and fishing workers; b) device, operation, and assembly workers; and c) simple labor workers.

Each variable was standardized using a Z-score, combined to calculate the district-specific deprivation score, and linked with a participant’s residential area code. We manually classified the ADI into quartile groups: Quartile 1 (Least deprived, 1–25%, z-score < -3·67); Quartile 2 (26–50%, -3·67 < z-score ≤ -0·15); Quartile 3 (51–75%, -0·15 < z-score ≤ 3·61); Quartile 4 (Most deprived, 76–100%, 3·61 < z-score).

Covariates

We incorporated certain variables into the analysis as possible covariates to the aspect of individual- and area-level. Included individual-level covariates were sex, age groups (19–29, 30–39, 40–49, 50–59, 60–69, or ≥ 70), monthly household income (< ₩2,000,000, ₩2,000,000–2,999,999, ₩3,000,000–3,999,999, ₩4,000,000–4,999,999, or ≥ ₩ 5,000,000; ₩1,000 = around $0·921), education (primary school graduated or below, middle school graduated, high school graduated, or college graduated or above), marital status (single, married living together, or separated, divorced, or bereaved), subject health status (good, fair, or bad), smoking status (every day, occasionally, past, or never), alcohol drinking status (more than 4 times/a week, 2–3 times/a week, 2–4 times/a month, once or less than once/a month, or never), diabetes (no, or yes), high blood pressure (no, or yes), depressive symptom (Patient Health Quastionnaire 9 items (PHQ-9) score; ranges from 0 to 27), and daily sleep hours. Included area-level covariates were COVID-19 infection rate by region and region type (capital city, metropolitan areas, or others).

Statistical analysis

We estimated the regression coefficient using multilevel regression model with individual-level factors nested within 255 district areas in South Korea (area-level). Since the dependent variable was continuous (ranges 0–16) with normal distribution, we fitted the model with the identity link. Multilevel modeling begins with analyzing a null model. The null model includes distinct types of variance of the dependent variable, such as within-area and between-area variances.[26]

To test between-area variability, we calculated the intraclass correlation coefficient (ICC). The ICC is the ratio between the between-area variance and the sum of both within-area and between-area variances. In other words, the ICC reports on the amount of variation unexplained by any predictors in the model that can be attributed to the grouping variable, as compared to the overall unexplained variance (within and between variance). A high ICC indicates that between-area variance is not negligible, and thus a multilevel model should be employed to explain the inter-area dynamics. ICC equation is expressed as follows:

$$\text{I}\text{C}\text{C}=\frac{{\sigma }_{{u}_{0}}^{2}}{{\sigma }_{{u}_{0}}^{2}+{\sigma }_{e}^{2}}$$

where ${\sigma }_{{u}_{0}}^{2}$ is the variance of the level-2 (area-level) residuals and ${\sigma }_{e}^{2}$ is the variance of the level-1 (individual-level) residuals.

After examining the crude association (null model), we included area-level deprivation (Model 1). We then entered individual-level characteristics to the null model in Model 2. To follow, we included both individual- and area-level characteristics in Model 3. The multilevel equation is expressed as follows:

Level 1 (Individual level)

$${Y}_{ij}={\beta }_{0j}+{\beta }_{1j}{X}_{ij}+{e}_{ij}$$

Level 2 (Area level)

$${\beta }_{0j}={\gamma }_{00}+{\gamma }_{01}{Z}_{j}+{\mu }_{0j}{X}_{ij}+{e}_{ij}$$

$${\beta }_{1j}={\gamma }_{10}+{\mu }_{1j}$$

Here, ${Y}_{ij}$ represents the value of the dependent variable of the $i$th individual in area $j$, while ${X}_{ij}$ and ${Z}_{j}$ indicate the independent variables at different levels. To explain, ${X}_{ij}$ contains values about the individuals in area $j$; ${Z}_{j}$ includes values about the areas. ${\beta }_{0j}$ and ${\beta }_{1j}$ are the individual-level intercept and slope, respectively, in area $j$. ${e}_{ij}$ indicates the error term at the individual-level (i.e., within-area variance). ${\gamma }_{00}$ denotes the average of the dependent variable ${Y}_{ij}$, controlling for the area-level variables ${Z}_{j}$; ${\gamma }_{01}$ is the slope of the area-level variables ${Z}_{j}$; and γ${\gamma }_{10}$ indicates the overall value of the slope at the individual-level, controlling for the area-level variables ${Z}_{j}$. Lastly, ${\mu }_{0j}$ and ${\mu }_{1j}$ are error terms at the area-level (i.e., between-area variance).

All the statistical tests were two-tailed, and a p-value of < 0·05 was considered to be significant. The analyses were performed using Stata (15.1, StataCorp LLC, College Station, TX).

COVID-19 infection rate per 1,000 varied by regions of South Korea from 0·059–9·499 (Fig. 2). ADI ranges from − 13·1 to 16·7. There seems to be a tendency for areas with lower ADI to have a higher rate of COVID-19.

(Fig. 2)

The mean score of concerns related to COVID-19 among 225,689 of the study population was 11·3 with a 3·2 standard deviation (SD) (Table 1). The proportion of men was 45·4%. The mean score of PHQ-9 was 1·96 with a 2·95 SD. The mean COVID-19 infection rate per 1,000 of regions was 0·39 with a 0·79 SD. More people lived in the least (89,316 individuals; 39·6%) or most deprived areas (73,047 individuals; 32·4%).

(Table 1)

The random effect covariance was 0·508 (standard error: 0·048, p-value < 0·001) in the null model, and ICC value was 0·486, indicating that 4·86% of the variability in the scores of concerns related to COVID-19 was accounted for by district areas (Table 2). Model 3 considered both individual- and area-level characteristics presents the best fitting with the lowest − 2 Log-likelihood and Akaike Information Criterion. In model 2, individuals living in more deprived area had higher coefficient of concerns related to COVID-19 referenced with the least deprived area (Q4 coefficient: 0·484, p-value < 0·001; Q3 coefficient: 0·348, p-value: 0·014; Q2 coefficient: 0·273, p-value: 0·031). Even though it was marginally significant in Model 3, those who lived in the most deprived area showed a higher coefficient of 0·123 (p-value: 0·070) and a similar tendency with those living in more deprived areas. Women were more likely to have concerns related to COVID-19. Those aged thirties or over fifties had greater concerns relative to those aged 19–29. Those whose monthly income was higher were more likely to have lesser concerns. Higher education level was associated with lesser concerns. Self-rated health status was negatively associated with the concerns. Depressive symptom was correlated with the concerns (coefficient: 0·039, p-value < 0·001). However, COVID-19 infection rate was not correlated with an individual’s concerns about COVID-19 (coefficient: -0·032, p-value < 0·532).

(Table 2)

With regards to stratification by sex, age group, and household income group in the association between ADI and concerns related to COVID-19, notable results were observed in those aged 19–29, 30–39, 40–49 with significantly greater coefficients among those who lived in a more deprived area (Table 3).

(Table 3)

Concern type 3 related to COVID-19 (“Concerns of being blamed by others of infection”) showed a higher coefficient when an individual lived in a more deprived area (Q4 coefficient: 0·088, p-value: 0·006) (Table 4). The result was in line with the respective analysis where the included population did not answer a corresponding question (Supplementary Table 1).

(Table 4)

The current study confirmed that living in a more deprived area was associated with greater concerns related to COVID-19. While explaining 4·86% of the concerns in area-level, which is somewhat noticeable where the majority of previous research populated in South Korea indicated little explanation of regional variation to health-related outcomes (around 3%).[27–30] Other characteristics of individuals that had significant associations with the concern, which are in line with existing research, are; women,[31] older,[31–33] lower income,[34] lower education,[31] perceived health status,[32] and current depressive symptom.[32, 35] The association between ADI and the concern related to COVID-19 differed by age groups of 19–29, 30–39, and 40–49, which are the economically active population. The concern of being blamed by others for infection was sorely associated with area deprivation.

Understandably, living in a more deprived area was associated with greater concerns of COVID-19 since the more deprived areas had a higher rate of poorer mental health, thus higher vulnerability to concerns related to COVID-19. Environmental factors of an individual’s dwelling area such as demographic profile, economic status, educational status, housing status, employment rate are critical factors to suicide, anxiety, and depression since they are indirectly associated with each other.[36–38] Indeed, areas with greater deprivation in South Korea showed higher rates of suicide and mental illness such as depression and anxiety, moderated by a higher proportion of the elderly and poorer economic status.[39, 40] Furthermore, those who already had mental illness showed a greater risk of developing its severity.[41]

Moreover, those who live in more deprived areas would be more likely to have a lower educational and socio-economical level; thus, they may have less opportunity to acquire appropriate information about COVID-19 in proper time. Therefore, people living in deprived areas have fewer COVID-19 preventive behaviors and greater fear of COVID-19. It is important to acquire relevant and updated information about COVID-19, as it guides an individual to practice preventive behaviors, such as hand hygiene, avoiding close contact with the sick, staying home when suspicious of being ill, using a facemask, cleaning and disinfecting contact objects and surfaces.[35, 42] Accordingly, relevant information about COVID-19 played an important role in reducing the fear of COVID-19 while preventive behaviors were mediated.[35]

Those aged 19–49 living in deprived areas had greater concerns due to COVID-19. This is interpreted that they are socially or economically active but environmentally aggravated, thus more likely to be frightened of the negative consequences related to COVID-19, such as being infected, blamed by others due to infection of COVID-19, financially disadvantaged, or death.

Regarding the contribution of the variability of area-level factors on an individual’s concerns on COVID-19, our study’s results indicate how deprived areas may indirectly affect individuals’ fears of COVID-19. Even though the infection rate of COVID-19 has been reported to be higher in developed areas (where they are more crowded and have a higher rate of the floating population), concern on COVID-19 was more prominent in the population living in a more deprived area. This is because individuals living in the deprived area would have lesser information on COVID-19 and trust in the government’s risk management, thus more fear of the infection.[43] Especially, our study result showed those living in the most deprived areas had greater concerns of being blamed by others for infection. This indicates that they were less likely to worry about getting infected, getting financially disadvantaged, or death as a result of the infection, but more likely to be concerned about creating relational problems in their society. Fear of interpersonal relationships can later lead to a feeling of social isolation and further psychological problems.[44–46] This is especially distinctive during the COVID-19 outbreak.[45, 46] Accordingly, coping strategies toward those populations living in deprived areas are needed to prevent future psychological problems or manage existing problems. Firstly, there is a government role in building risk perception on populations living in deprived areas who lack access to appropriate information on COVID-19. Secondly, the government has the urgent duty to improve the population’s trust about ongoing regulations since they are criticized for being a one-size-fits-all policy; social distancing (avoiding mass gathering and non-essential social engagement, without restricting the movement of people who are not patients or contacts), restricting operating hours on business or convenience facilities (restaurant, entertainment facilities, recreational facilities, fitness centers, concert halls), and COVID-19 PASS system (restricting person who doesn’t get vaccinated to use public facilities).[47–49]

This study should be interpreted while considering its limitations. First, some unmeasured underlying factors have their impact on concerns related to COVID-19, such as accessibility of health care resources, whether there is a vicinity infected with COVID-19, or whether vaccinated. Second, due to the study’s cross-sectional nature, we could not reflect frequent changes in the COVID-19 infection rate and involved government regulations. There should be variations in individuals’ concerns on COVID-19 that highly depend on fluctuating COVID-19 infection rates and the government’s control over it. There would be umeasured effect of COVID-19 lockdown and containment strategies on individuals concerns related to COVID-19 that were not considered in our study. Moreover, cross-sectional design precludes the causal inference among variables. Third, although we utilized up-to-date nationally representative data, it does not yet fully reflect the ongoing COVID-19 outbreak where the most severe condition lies. However, the study result can be generalizable for Korean population upto early state of COVID-19 period. With regards to the limitations, we suggest future research to elaborate a potential mechanism that explains causal inferences among prominent variables related to ongoing COVID-19 situation and its concerns. Also, future research should consider the effect of government regulations of COVID-19 on individuals’ behavioral changes and concerns.

Individual concerns related to COVID-19 are associated with area deprivation, which is noteworthy. Those living in deprived areas had inferior coping strategies; thus, they are more likely to be a risk population for future or current psychological problems. Therefore, proper strategies for the population are needed to build risk perception with adequate information about COVID-19 required by the government.

Funding: This study was supported by a faculty research grant of Yonsei University College of Medicine for (6-2021-0112).

Author Contribution

Doo Woong Lee: Conceptualization; Methodology; Formal analysis; Data curation; Writing – original draft; Writing – review and editing; Visualization

Jieun Jang: Conceptualization; Visualization;

Jaeyong Shin: Conceptualization; Validation; Supervision

All authors gave final approval

Conflict of Interest Disclosures: None

Source of funding: This study was supported by a faculty research grant of Yonsei University College of Medicine for (6-2021-0112).

Zhu N, Zhang D, Wang W, Li X, Yang B, Song J, et al. A novel coronavirus from patients with pneumonia in china, 2019. New England journal of medicine 2020. DOI.
World Health Organization. Covid-19 weekly epidemiological update, edition 74, 11 january 2022 2022.
Ministry of Health and Welfare. Coronavirus (covid-19), republic of korea 2022.
Taylor S. The psychology of pandemics: Preparing for the next global outbreak of infectious disease: Cambridge Scholars Publishing; 2019.
Cullen W, Gulati G, Kelly BD. Mental health in the covid-19 pandemic. QJM: An International Journal of Medicine 2020;113(5):311–312. DOI.
Kumar A, Nayar KR. Covid 19 and its mental health consequences. Vol 30: Taylor & Francis; 2021:1–2.
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. The British Journal of Psychiatry 2021;218(6):326–333. DOI.
Holmes EA, O'Connor RC, Perry VH, Tracey I, Wessely S, Arseneault L, et al. Multidisciplinary research priorities for the covid-19 pandemic: A call for action for mental health science. The Lancet Psychiatry 2020;7(6):547–560. DOI.
Moutier C. Suicide prevention in the covid-19 era: Transforming threat into opportunity. JAMA psychiatry 2021;78(4):433–438. DOI.
McKibbin W, Fernando R. The economic impact of covid-19. Economics in the Time of COVID-19 2020;45(10.1162). DOI.
Usher K, Durkin J, Bhullar N. The covid-19 pandemic and mental health impacts. International Journal of Mental Health Nursing 2020;29(3):315. DOI.
Ren S-Y, Gao R-D, Chen Y-L. Fear can be more harmful than the severe acute respiratory syndrome coronavirus 2 in controlling the corona virus disease 2019 epidemic. World journal of clinical cases 2020;8(4):652. DOI.
Perrin PC, McCabe OL, Everly GS, Links JM. Preparing for an influenza pandemic: Mental health considerations. Prehospital and disaster medicine 2009;24(3):223–230. DOI.
Rubin GJ, Wessely S. The psychological effects of quarantining a city. Bmj 2020;368. DOI.
Hubbard G, Daas Cd, Johnston M, Murchie P, Thompson CW, Dixon D. Are rurality, area deprivation, access to outside space, and green space associated with mental health during the covid-19 pandemic? A cross sectional study (charis-e). International Journal of Environmental Research and Public Health 2021;18(8):3869. DOI.
Morrissey K, Spooner F, Salter J, Shaddick G. Area level deprivation and monthly covid-19 cases: The impact of government policy in england. Social Science & Medicine 2021;289:114413. DOI.
Hatef E, Chang H-Y, Kitchen C, Weiner JP, Kharrazi H. Assessing the impact of neighborhood socioeconomic characteristics on covid-19 prevalence across seven states in the united states. Frontiers in public health 2020;8. DOI.
Lewis NM, Friedrichs M, Wagstaff S, Sage K, LaCross N, Bui D, et al. Disparities in covid-19 incidence, hospitalizations, and testing, by area-level deprivation—utah, march 3–july 9, 2020. Morbidity and Mortality Weekly Report 2020;69(38):1369. DOI.
KC M, Oral E, Straif-Bourgeois S, Rung AL, Peters ES. The effect of area deprivation on covid-19 risk in louisiana. Plos one 2020;15(12):e0243028. DOI.
Bach-Mortensen AM, Degli Esposti M. Is area deprivation associated with greater impacts of covid-19 in care homes across england? A preliminary analysis of covid-19 outbreaks and deaths. J Epidemiol Community Health 2021;75(7):624–627. DOI.
Feehan AK, Denstel KD, Katzmarzyk PT, Velasco C, Burton JH, Price-Haywood EG, et al. Community versus individual risk of sars-cov-2 infection in two municipalities of louisiana, USA: An assessment of area deprivation index (adi) paired with seroprevalence data over time. PLOS ONE 2021;16(11):e0260164. DOI: 10.1371/journal.pone.0260164.
Kang YW, Ko YS, Kim YJ, Sung KM, Kim HJ, Choi HY, et al. Korea community health survey data profiles. Osong public health and research perspectives 2015;6(3):211–217. DOI.
MicroData Integrated Service (MDIS). 2021.
Jarman B, Townsend P, Carstairs V. Deprivation indices. BMJ: British Medical Journal 1991;303(6801):523. DOI.
Carstairs V. Deprivation indices: Their interpretation and use in relation to health. J Epidemiol Community Health 1995;49 Suppl 2:S3-8. DOI: 10.1136/jech.49.suppl_2.s3.
Snijders TA, Bosker RJ. Multilevel analysis: An introduction to basic and advanced multilevel modeling: sage; 2011.
Lee JH, Heo T-Y. A study of effect on the smoking status using multilevel logistic model. The Korean Journal of Applied Statistics 2014;27(1):89–102. DOI.
Park YM, Kim Y. A spatially filtered multilevel model to account for spatial dependency: Application to self-rated health status in south korea. International journal of health geographics 2014;13(1):1–10. DOI.
Jung M. Covariation in community-and individual-based community capacity and health behavior: A multilevel analysis of populations in seoul, south korea. The health care manager 2012;31(4):308–319. DOI.
Kong J, Cho S-i. Effect of tobacco outlet density on quit attempts in korea: A multi-level analysis of the 2015 korean community health survey. Epidemiology and Health 2021;43. DOI.
Rahman MA, Hoque N, Alif SM, Salehin M, Islam SMS, Banik B, et al. Factors associated with psychological distress, fear and coping strategies during the covid-19 pandemic in australia. Globalization and Health 2020;16(1):95. DOI: 10.1186/s12992-020-00624-w.
Ahorsu DK, Lin C-Y, Pakpour AH. The association between health status and insomnia, mental health, and preventive behaviors: The mediating role of fear of covid-19. Gerontology and Geriatric Medicine 2020;6:2333721420966081. DOI: 10.1177/2333721420966081.
Nagarkar A. Challenges and concerns for older adults in india regarding the covid-19 pandemic. Journal of gerontological social work 2020;63(4):259–261. DOI.
Sandín B, Valiente RM, García-Escalera J, Campagne DM, Chorot P. Psychological impact of the covid-19 pandemic: Negative and positive effects in spanish population during the mandatory national quarantine. J Psychopathol Clin Psychol 2020;25:1–21. DOI.
Chang K-C, Strong C, Pakpour AH, Griffiths MD, Lin C-Y. Factors related to preventive covid-19 infection behaviors among people with mental illness. Journal of the Formosan Medical Association 2020;119(12):1772–1780. DOI: https://doi.org/10.1016/j.jfma.2020.07.032.
Walters K, Breeze E, Wilkinson P, Price GM, Bulpitt CJ, Fletcher A. Local area deprivation and urban–rural differences in anxiety and depression among people older than 75 years in britain. American journal of public health 2004;94(10):1768–1774. DOI.
Rehkopf DH, Buka SL. The association between suicide and the socio-economic characteristics of geographical areas: A systematic review. Psychological medicine 2006;36(2):145–157. DOI.
O’Farrell I, Corcoran P, Perry I. The area level association between suicide, deprivation, social fragmentation and population density in the republic of ireland: A national study. Social psychiatry and psychiatric epidemiology 2016;51(6):839–847. DOI.
Cheong K-S, Choi M-H, Cho B-M, Yoon T-H, Kim C-H, Kim Y-M, et al. Suicide rate differences by sex, age, and urbanicity, and related regional factors in korea. Journal of Preventive Medicine and Public Health 2012;45(2):70. DOI.
Kim C, Chang EJ, Kim C-y. Regional differences in the effects of social relations on depression among korean elderly and the moderating effect of living alone. Journal of Preventive Medicine and Public Health 2021;54(6):441. DOI.
Pfefferbaum B, North CS. Mental health and the covid-19 pandemic. New England Journal of Medicine 2020;383(6):510–512. DOI.
Basch CH, Hillyer GC, Meleo-Erwin ZC, Jaime C, Mohlman J, Basch CE. Preventive behaviors conveyed on youtube to mitigate transmission of covid-19: Cross-sectional study. JMIR public health and surveillance 2020;6(2):e18807. DOI.
Ye M, Lyu Z. Trust, risk perception, and covid-19 infections: Evidence from multilevel analyses of combined original dataset in china. Social Science & Medicine 2020;265:113517. DOI.
Leigh-Hunt N, Bagguley D, Bash K, Turner V, Turnbull S, Valtorta N, et al. An overview of systematic reviews on the public health consequences of social isolation and loneliness. Public health 2017;152:157–171. DOI.
Pietrabissa G, Simpson SG. Psychological consequences of social isolation during covid-19 outbreak. Frontiers in Psychology 2020;11:2201. DOI.
Rogers JP, Chesney E, Oliver D, Pollak TA, McGuire P, Fusar-Poli P, et al. Psychiatric and neuropsychiatric presentations associated with severe coronavirus infections: A systematic review and meta-analysis with comparison to the covid-19 pandemic. The Lancet Psychiatry 2020;7(7):611–627. DOI.
Ministry of Health and Welfare. All about korea`s response to covid-19. 2020. DOI.
George Mason University Korea. 'Living with covid-19' scheme. 2022. DOI.
Seoul Metropolitan Government. [covid-19] steady return to pre-pandemic life (starting nov. 1). 2021. DOI.

Tables 1 to 4 are available in the Supplementary Files section

No competing interests reported.

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Concerns related to COVID-19 in South Korea : What are associated individual and regional factors?

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Abstract

Figures

Highlights

Introduction

Methods

Data source and study population

Variables

Dependent variable: Concerns related to COVID-19 (0–16 score)

Main independent variable: Area deprivation index (ADI)

Covariates

Statistical analysis

Results

Discussion

Conclusion

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1