The effect of depression on compliance with physical distancing during the COVID-19 pandemic

doi:10.21203/rs.3.rs-2009618/v1

Background:

This study aimed to investigate the possible factors associated with compliance with physical distancing during the early stage of the COVID-19 pandemic, focusing on depression.

Methods:

Korea Community Health Survey data of 2020 were investigated. The group with depression was subdivided according to whether or not they were concerned about COVID-19. The association between depression and compliance was analyzed using multivariate Poisson regression analysis with robust variance estimates.

Results:

The number of participants was 132,595, and the proportion of compliance with physical distancing was 86.1% in men and 89.1% in women. The prevalence of compliance was 0.939 times lower in men (95% confidence interval [CI], 0.915–0.963) and 0.953 times lower in women (95% CI, 0.938–0.969) living with depression than in participants who were not depressed. Despite its positive association with compliance, participants who were depressed and had concerns about COVID-19 were not statistically significantly different from those who were not depressed and had no concerns.

Conclusions:

Depression was associated with poor compliance, and the positive effect of concern about COVID-19 was canceled out by depression. Preparing interventions for depression is necessary in the early stages of a pandemic.

community health survey

COVID-19

depression

physical distancing

theory of planned behavior

Since January 2020, the coronavirus disease 2019 (COVID-19) has had a worldwide impact, with approximately 526.6 billion confirmed cases and 6.3 billion deaths as of May 2022 [1]. Physical distancing, which is a nonpharmaceutical quarantine measure for pandemic control, has been adopted in many countries [2]. Physical distancing, the so-called ‘behavioral vaccine’ is the only measure against unprecedented contagious disease before the development of a chemical vaccine. Physical distancing was introduced according to the situation in each country. The physical distancing strategy in Korea targeted public hygiene management, such as wearing a facial mask, washing hands, ventilation, and contact management, such as 2-week self-quarantine, limitation of public gatherings, and keeping a 2 m distance from others [3].

The purpose of physical distancing is to prevent a crisis in the healthcare system by slowing the spread of the virus. The effectiveness of physical distancing has been researched, and similar results have been reported. Physical distancing has induced a decrease in the number of contacts among people and the reproduction number of COVID-19 [4, 5]. Wearing masks and avoiding social contact are effective in mitigating COVID-19 transmission [6]. Even after mass vaccination, physical distancing plays an important role because of incomplete protection of the vaccine [7]. Furthermore, it is supposed that early non-pharmaceutical interventions decreased virus transmission during the 1918 influenza pandemic [8]. Other positive effects, such as a decreased incidence of other airborne contagious diseases such as influenza infection [9] or a decrease in greenhouse gas emissions [10] were also identified.

However, this policy has induced some side effects. One study found a positive association between anxiety levels and physical distancing among Indonesian students [11]. In 26 European countries, worsening mental health of older adults was associated with unmet needs during the COVID-19 pandemic [12]. On the other hand, educational and economical challenges include poor preparation of online lectures or assessment tools [13], increased global inflation and unemployment rates, and decreased economic growth [14, 15]. Therefore, efficient public health interventions are needed to reduce side effects.

The repeated emergence of new zoonotic infectious diseases is strongly predicted because of excessive human land use [16]. Therefore, compliance with physical distancing is a substantial factor in ensuring successful quarantine measures during the next pandemic. There are a few studies on the factors, such as belief in science or neighborhood income, associated with compliance [17, 18]. Therefore, we analyzed self-reported compliance levels with physical distancing and associated factors, especially focusing on the depression of participants, which is modifiable. Modifiable factors could be targets for improving compliance.

2.1. Study data

Raw data from the 2020 Korea Community Health Survey (CHS) were analyzed in this study. The CHS is an annual, nationwide, self-report survey that has been conducted since 2008 to evaluate the health status of Korean residents. The Korea Disease Control and Prevention Agency (KDCA) conducted the survey. The CHS participants were adults above the age of 19 who were classified by administrative district units and type of housing. The 2020 CHS was conducted from August 16, 2020, to October 31, 2020. The CHS was executed by 1:1 computer-assisted personal interviewing (CAPI) during the trained researcher’s personal visit, and quality control of data through telephone interviews was conducted for 13% of participants after the survey. In this study, we excluded the participants who did not answer to the survey questions. Institutional review board approval was waived because CHS data is publicly available for scientific research.

2.2. Measures

2.2.1. Primary independent variable: Depression

Depression was evaluated using the Patient Health Questionnaire-9 (PHQ-9), a well-known depression screening tool [19]. Participants with a PHQ-9 score of 10 were classified as depressed, and those with scores above 15 were classified as severely depressed.

2.2.2. Dependent variable: Compliance with physical distancing

Participants were questioned about physical distancing and two specific topics were addressed, namely, maintaining hygiene and avoiding contact. For example, ‘Did you cover your mouth and nose with a sleeve when sneezing or coughing?; ‘Did you ventilate your daily space, such as your home or office, more than twice a day?”; “Did you wear a mask in indoor facilities used by unspecified people (for example, hospitals, public transportation, religious facilities, restaurants, department stores)?”; and “Did you wear a mask if it was difficult to keep a distance of more than 2 m from others outdoors?” were questions related to hygiene. “Did you keep a 2 m-distance (at least 1 m) from others?” and “Did you refrain from going out, gathering, and events?” were questions related contact. The participants answered on a scale from 1 (very much) to 3 (not at all). Based on their answers to these questions, the participants were divided into two groups: good and poor compliance.

2.2.3. Other independent variables

The history of admission or quarantine due to COVID-19, confidence in the government or medical institutions, and concerns regarding COVID-19 were evaluated. Concern about the COVID-19 pandemic consisted of three types, namely, concern about contracting COVID-19 itself, concern about criticism or damage from others on testing positive for COVID-19, and concern about economic damage (difficulty in finding a jobs). The participants answered on a scale from 1 (very much) to 5 (not at all). Concerns regarding COVID-19 were divided into three subgroups: no concern, moderate concern, and severe concern.

2.2.4. Covariates

To adjust for analysis, we included sex, age, area of residence, type of family members, economic activity, level of education, economic status, drinking, smoking, physical activity, perceived stress level, and comorbidity of physician-diagnosed hypertension or type 2 diabetes mellitus as covariates. The area of residence was divided into four subgroups: Seoul, which is the capital of Korea; Daegu, which was the most severely affected area during the early pandemic; other metropolitan areas; and rural provinces. Participants were divided based on household status into three groups: one-person households, households with more than one member, and households with minor members. Participants were divided into three groups based on economic status, grade, perceived stress level, and self-reported health status. Physical activity was evaluated by whether they usually do exercise for at least 10 minutes a day except walking.

2.3. Statistical analysis

Chi-square tests were used to analyze the characteristics. Multivariate Poisson regression analysis with robust variance estimates was used to evaluate the relationship between compliance with physical distancing and depression [20]. All data are presented as prevalence ratios (PR) and 95% confidence intervals (CI). SAS software (version 9.4; SAS Institute, Cary, North Carolina, USA) was used for all analyses. Statistical significance was set at p < 0.05.

The sociodemographic and health-related characteristics of the participants, based on sex, are presented in Table 1. The total number of included study population with appropriate response was 132,595, including 60,634 men (45.7%) and 71,961 women (54.3%). The proportion of good compliance with physical distancing was 86.1% in men and 89.1% in women. In both sexes, people with depression showed a lower proportion of good compliance than those without depression.

Table 1

The health-related and socioeconomic characteristics of study population
Variables		Male (N=60634)					Female(N=71961)
		Good Compliance		Poor Compliance		p-value	Good Compliance		Poor Compliance		p-value
		N	(%)	N	(%)	p-value	N	(%)	N	(%)	p-value
Mood						<0.0001					<0.0001
	Not depressive	51390	(86.3)	8157	(13.7)		62200	(89.3)	7458	(10.7)
	Depressive	842	(77.5)	245	(22.5)		1899	(82.5)	404	(17.5)
Age						<0.0001					<0.0001
	19-29	7134	(85.1)	1251	(14.9)		7979	(86.9)	1199	(13.1)
	30-39	6852	(87.0)	1028	(13.0)		7715	(90.3)	832	(9.7)
	40-49	9387	(87.4)	1358	(12.6)		11004	(91.7)	995	(8.3)
	50-59	10449	(87.2)	1539	(12.8)		12820	(91.3)	1217	(8.7)
	60-69	9453	(86.2)	1519	(13.8)		12086	(90.4)	1277	(9.6)
	70-79	6551	(85.1)	1147	(14.9)		8693	(86.4)	1374	(13.6)
	above 80	2406	(81.1)	560	(18.9)		3802	(79.7)	968	(20.3)
Area of residence						<0.0001					<0.0001
	Seoul(Capital)	5498	(91.5)	514	(8.5)		6989	(93.1)	520	(6.9)
	Daegu	1941	(81.9)	428	(18.1)		2390	(86.8)	363	(13.2)
	Other metropolitans	20094	(87.5)	2870	(12.5)		24468	(90.3)	2642	(9.7)
	Province(rural)	24699	(84.3)	4590	(15.7)		30252	(87.5)	4337	(12.5)
Family members						<0.0001					<0.0001
	One-person household	6207	(82.5)	1314	(17.5)		10004	(84.9)	1777	(15.1)
	More than one	34496	(86.5)	5401	(13.5)		39437	(89.6)	4598	(10.4)
	With minors	11529	(87.2)	1687	(12.8)		14658	(90.8)	1487	(9.2)
Economic activity						0.7775					0.2366
	Absence	38308	(86.2)	6126	(13.8)		34453	(89.2)	4162	(10.8)
	Existence	13924	(86.0)	2276	(14.0)		29646	(88.9)	3700	(11.1)
Level of education						<0.0001					0.0275
	Below high school	26882	(84.5)	4923	(15.5)		39780	(88.1)	5350	(11.9)
	Above university	25350	(87.9)	3479	(12.1)		24319	(90.6)	2512	(9.4)
Economic status						<0.0001					0.0001
	High	25789	(85.0)	4563	(15.0)		34433	(87.8)	4765	(12.2)
	Middle	21235	(87.4)	3068	(12.6)		23753	(90.5)	2492	(9.5)
	Low	5208	(87.1)	771	(12.9)		5913	(90.7)	605	(9.3)
Alcohol Status						0.0989					<0.0001
	Never	6372	(88.5)	826	(11.5)		20738	(89.5)	2434	(10.5)
	Ever	45860	(85.8)	7576	(14.2)		43361	(88.9)	5428	(11.1)
Smoking Status						<0.0001					<0.0001
	Never	18533	(88.3)	2446	(11.7)		61411	(89.3)	7369	(10.7)
	Ever	33699	(85.0)	5956	(15.0)		2688	(84.5)	493	(15.5)
Physical Activity						0.2436					0.0681
	Low	27165	(86.0)	4435	(14.0)		42166	(88.9)	5260	(11.1)
	High	25067	(86.3)	3967	(13.7)		21933	(89.4)	2602	(10.6)
Perceived Stress level						<0.0001					<0.0001
	Low	41127	(86.7)	6299	(13.3)		49376	(89.4)	5866	(10.6)
	High	52232	(96.1)	2103	(3.9)		14723	(88.1)	1996	(11.9)
Comorbidity						0.3370					0.0084
	Absence	36194	(86.3)	5727	(13.7)		45939	(89.8)	5229	(10.2)
	Existence	16038	(85.7)	2675	(14.3)		18160	(87.3)	2633	(12.7)
Total		52232	(86.1)	8402	(13.9)		64099	(89.1)	7862	(10.9)

Table 2 shows the results of the multivariate Poisson regression models with robust variance estimates and adjustments for all potential confounders. The prevalence of good compliance with physical distancing was 0.939 times lower in men with depression and 0.953 times lower in women with depression than in participants who were not depressed. Adults in the 19–29 showed a lower prevalence of good compliance than the other age groups except for men in the > 80 years age group and women in the 70–79. Women > 80 years of age showed a 0.946 times lower prevalence than adults in their 20s. Participants who lived in Seoul, had households with more than one family member, had jobs and a high level of education, were non-drinkers and non-smokers, had low perceived stress levels, and showed a higher prevalence of good compliance in both sexes. Other factors such as economic status, level of physical activity, and comorbidity of hypertension and type 2 diabetes mellitus were not statistically significant.

Table 2

Factors associated with compliance with physical distancing using multivariate Poisson regression models with robust variance estimates
Variables		Male				Female
		Physical Distancing				Physical Distancing
		Adjusted PR	95% CI			Adjusted PR	95% CI
Mood
	Not depressive	1.000				1.000
	Depressive	0.939*	0.915	-	0.963	0.953*	0.938	-	0.969
Age
	19–29	1.000				1.000
	30–39	1.033*	1.022	-	1.045	1.035*	1.025	-	1.045
	40–49	1.047*	1.036	-	1.059	1.052*	1.043	-	1.062
	50–59	1.050*	1.039	-	1.061	1.054*	1.044	-	1.063
	60–69	1.042*	1.031	-	1.054	1.046*	1.036	-	1.056
	70–79	1.029*	1.016	-	1.042	1.007	0.995	-	1.018
	above 80	0.987	0.971	-	1.004	0.946*	0.931	-	0.960
Area of residence
	Seoul (Capital)	1.000				1.000
	Daegu	0.911*	0.895	-	0.926	0.937*	0.925	-	0.950
	Other metropolitans	0.964*	0.956	-	0.972	0.972*	0.966	-	0.979
	Province (rural)	0.937*	0.929	-	0.945	0.953*	0.946	-	0.959
Family member
	One-person household	1.000				1.000
	More than one	1.036*	1.027	-	1.046	1.022*	1.014	-	1.029
	With minors	1.034*	1.023	-	1.046	1.024*	1.015	-	1.034
Economic activity
	Absence	1.000				1.000
	Existence	1.011*	1.004	-	1.019	1.010*	1.005	-	1.015
Level of education
	Below high school	1.000				1.000
	Above university	1.029*	1.022	-	1.035	1.016*	1.011	-	1.022
Economic status
	High	1.000				1.000
	Middle	1.005	0.999	-	1.012	1.002	0.997	-	1.008
	Low	0.994	0.984	-	1.004	1.000	0.991	-	1.008
Alcohol Status
	Never	1.000				1.000
	Ever	0.977*	0.969	-	0.985	0.979*	0.974	-	0.984
Smoking Status
	Never	1.000				1.000
	Ever	0.974*	0.968	-	0.980	0.963*	0.951	-	0.975
Physical Activity
	Low	1.000				1.000
	High	1.000	0.995	-	1.006	0.999	0.994	-	1.004
Perceived Stress level
	Low	1.000				1.000
	High	0.974*	0.981	-	0.967	0.986*	0.981	-	0.992
Comorbidity
	Absence	1.000				1.000
	Existence	1.001	0.995	-	1.008	0.997	0.991	-	1.003
PR = Prevalence Ratios; CI = Confidence Interval; significance levels: * p < 0.05

The results of compliance with subdivided physical distancing are shown in Table 3. The prevalence of good compliance with hygiene-associated physical distancing among people with depression was 0.952 times lower in men and 0.967 times lower in women than in individuals who were not depressed. The prevalence of good compliance with contact-associated physical distancing among people with depression was 0.962 times lower in men and 0.982 times lower in women than in individuals who were not depressed.

Table 3

The association between depression and hygiene-related and contact-related physical distancing compliance
			Physical Distancing: Hygiene				Physical Distancing: Contact
			Adjusted PR	95% CI			Adjusted PR	95% CI
	Mood
Male		Not depressive	1.000				1.000
Male		Depressive	0.952*	0.931	-	0.973	0.967*	0.949	-	0.985
	Mood
Female		Not depressive	1.000				1.000
Female		Depressive	0.962*	0.949	-	0.975	0.982*	0.971	-	0.993
PR = Prevalence Ratios; CI = Confidence Interval; significance level: * p < 0.05

Figure 1 shows the results of the subgroup analysis of the primary independent variable (depression and compliance with physical distancing) along with other independent variables (admission or quarantine due to COVID-19, confidence in the government or medical institutions, and concerns regarding COVID-19). Both the mild and severe depression subgroups were found to have a higher prevalence of poor compliance. The moderate- and severe-concern groups showed a higher prevalence of good compliance, and there were statistically significant differences between the subgroups in both sexes. The prevalence of good compliance among those who were concerned about COVID-19 and not depressed was 1.062 times higher in men and 1.043 times higher in women than in those who were not concerned about COVID-19 and not depressed. Participants who were depressed and concerned about COVID-19 were not statistically significant in either sex (PR 1.009 in men, PR 1.005 in women). The prevalence of good compliance was higher among people who had confidence in the government or medical institutions than that among people who mistrust the government or medical institutions. Admission or quarantine due to COVID-19 was not statistically significant.

The purpose of our study was to identify possible factors associated with compliance with the COVID-19 physical distancing policy. In particular, the impact of depression on compliance with covariate adjustments was investigated. Additionally, we investigated the impact of COVID-19 on depression severity and concerns regarding COVID-19.

Several potential sociodemographic and psychological factors which are associated with compliance have been reported. Areas of low economic status in the United States, which were the least mobile before the COVID-19 pandemic, became the most mobile during the COVID-19 pandemic [21]. According to previous studies, female sex, older age, higher education level, and economic inactivity were associated with higher compliance [22, 23].

Based on the theory of planned behavior (TPB) [24] which is a health belief model for predicting behavioral intention under involuntary conditions; attitude, subjective norms, and perceived behavior control were associated with the intention to follow COVID-19 physical distancing. According to an article published in 2020, the intention to follow physical distancing among Filipinos was affected by the degree of understanding of the policy, attitudes of social group, and anxiety about COVID-19 [25]. According to another cross-sectional study among Australian adults, trust in government, perceived ability to adopt the policy, concern about isolation, and health status were associated with compliance [26]. In a cross-sectional study among Southern Ethiopian adults with chronic diseases, perceived social pressure and perceived behavior control were associated with the intention of personal preventive measures [27].

Personality characteristics, emotional status and their effects during the COVID-19 pandemic have also been researched. Fear of COVID-19, in the form of insomnia, uncomfortable feeling about COVID-19 news, palpitations, and anxiety, were positively associated with engagement with physical distancing [28]. Dark personality traits, such as psychopathy and meanness, were expected to be less effective against COVID-19 [29]. Additionally, less depressive, less anxious, and less stressful individuals were positively associated with higher perceived compliance [30].

In our study, the sociodemographic and clinical characteristics, which were positively associated with physical distancing in both sexes, were age between 30 and 60 years; living in Seoul, which had the highest number of confirmed cases during the survey period [3]; households consisting of more than one member, with children or another family member; economic activity; a college degree or higher; non-drinking or non-smoking status; and low perceived stress level. However, economic status was not associated with policy compliance, and this result is similar to that of a previous study in Korea [31].

Participants with depression showed a lower prevalence of good compliance regardless of severity and the type of physical distancing. Concerns about COVID-19, which were the attitude and subjective norms of TPB, were associated with a high prevalence of good compliance. In addition, the more concern participants had, the higher the prevalence of good compliance, with statistical significance among the no concern, moderate concern, and severe concern groups. However, the number of participants who were depressed and concerned about COVID-19 was not statistically significant. It is supposed that the positive effects of attitude and subjective norms on compliance are canceled out by depressive mood. According to a previous study on the effect of depression on medication adherence among breast cancer patients with respect to TPB [32], depression would have a direct and indirect negative relationship with the intention to follow physical distancing. The indirect effect of depression on compliance consists of the attitude, subjective norm, and perceived behavioral control of physical distancing. Thus, there are several possible mechanisms. People who are depressed would have an unfavorable intention to follow or are unable to follow physical distancing. Else, cannot expect the positive outcome of physical distancing or do not care about the expectation and atmosphere of a nearby reference group, or cannot recognize or know what to do.

Meanwhile, many previous studies have found a strong association between the prevalence of depression and COVID-19 or physical distancing including quarantine [33–35]. Further research is needed, and it is possible that depression would induce more violations of physical distancing, and physical distancing would induce a higher prevalence of depression. In addition, the effectiveness of flattening the epidemic curve through social distancing during the early stages of the COVID-19 pandemic is proved [36]. Therefore, it must be noted that the early adoption of physical distancing during the early stages of a pandemic of another unprecedented airborne infectious disease, should especially focus on people with depression for successful epidemic curve flattening. On the other hand, efforts to increase the confidence in medical institutions and the government should be accompanied by better outcomes of physical distancing.

Our study has some limitations. The study cannot confirm the cause-effect conclusion because of the cross-sectional nature of the data. CHS comprises self-reported data; therefore, participants could misunderstand the questions or make recall errors, especially for questions about compliance. In addition, these results cannot be generalized to other countries or ethnic groups. We could not investigate some important factors that could affect the association between depression and compliance, such as the number of confirmed cases, the level of understanding of policy, or proportion of people who believe in false information, alteration in physical distancing policy, and other psychiatric backgrounds.

In conclusion, some health-related and sociodemographic factors associated with compliance with physical distancing are shown in this study. Further prospective studies are required to identify the causality and mechanism of the effects of depression on compliance.

CAPI: computer-assisted personal interviewing; CHS: Community Health Survey; CI: confidence intervals; COVID-19: coronavirus disease 2019; KDCA: Korea Disease Control and Prevention Agency; PHQ-9: patient health questionnaire-9; PR: prevalence ratios; TPB: theory of planned behavior

Ethics approval and consent to participate

All methods were performed in accordance with the relevant guidelines and regulations of the Declaration of Helsinki. Institutional review board approval was waived because CHS data is publicly available for scientific research.

Consent for publication

Not applicable.

Availability of data and materials

The data analyzed in this study were obtained from the 2020 CHS, which is available to the public. All data can be requested through the CHS official website (https://chs.kdca.go.kr/chs).

Competing interests

There are no conflicts of interest to declare.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors

Authors' contributions

SIJ conceived, designed, and directed the study. JK and I Y conducted statistical analyses of the data. JK and H K interpreted data and wrote the first draft of the manuscript. ECP supervised revision of the manuscript. All authors participated sufficiently in the work and take public responsibility for the appropriate portions of the content.

Acknowledgements

We would like to thank Editage (www.editage.co.kr) for English language editing.

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No competing interests reported.

The effect of depression on compliance with physical distancing during the COVID-19 pandemic

Status:

Version 1

Abstract

Background:

Methods:

Results:

Conclusions:

Figures

1. Background

2. Methods

2.1. Study data

2.2. Measures

2.2.1. Primary independent variable: Depression

2.2.2. Dependent variable: Compliance with physical distancing

2.2.3. Other independent variables

2.2.4. Covariates

2.3. Statistical analysis

3. Results

4. Discussion

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1