Abstract
The coronavirus disease 2019 (COVID-19), a dual threat to public physical and mental health, prompted an investigation into the psychological well-being of residents in low- to medium-risk areas of China during the initial stages of the pandemic. We administered WeChat-based questionnaire surveys and employed chi-square tests and multiple logistic regression to analyze correlations between residents’ age, gender, education, symptoms, COVID-19 close contact history, information sources, and anxiety, depression, and attitudes toward lockdown measures. We received 10,433 valid questionnaires, revealing 26% anxiety and 19.5% depression. Support for lockdown measures reached 98.2%. Factors such as female gender, self-diagnosed pneumonia symptoms, close contact history, and higher education levels increased anxiety risk. Having a doctorate posed a severe anxiety risk, at 4.5 times (P = .019, 95% CI 1.29–15.73). Older age acted as a protective factor, reducing severe anxiety risk to 0.98 and 0.22 times (P < .001, 95% CI 0.14–0.34). Females with a master degree or below and those receiving COVID-19 information from multiple channels faced higher depression risk. Pneumonia symptoms were a risk for all anxiety and depression degrees. Attitudes toward lockdown measures had no significant impact on psychological status, nor did any of the analyzed factors affect residents’ overall attitude toward lockdown. Our findings underscore the need for increased psychological counseling, particularly for young females with lower educational backgrounds or self-suspected infection symptoms, to mitigate mild to moderate anxiety and depression in future epidemics or pandemics. The public, especially those of working age with doctorates or higher education, bears the highest risk of severe anxiety. Lockdown measures enjoy strong support in low- to medium-risk areas of China.
Keywords: anxiety, COVID-19, depression, lockdown, low- and medium-risk areas, residents
1. Introduction
The coronavirus disease 2019 (COVID-19), a global pandemic for approximately 4 years, was first identified in China. The surge in COVID-19 cases in early 2020 resulted from its high contagion and increased travel during the Chinese New Year.[1] In only 50 days, from January 20, 2020 to March 11, 2020, it evolved into a pandemic affecting many countries.[2–5] Both domestic and international governments closely monitored the pandemic, particularly the implementation of nationwide lockdowns.
Nationwide lockdowns tended to induce anxiety, depression, and other emotions in the population. With the declaration of lockdown measures, residents faced restrictions on activities, leading to social isolation, canceled travel plans, information overload from the media, and panic buying of essential goods. Simultaneously, individuals reliant on daily wages encountered survival challenges. The escalating threat of the pandemic contributed to a national atmosphere of anxiety and depression, especially during the Chinese Spring Festival in 2019, a crucial time for family reunions and extensive travel for the Chinese population. Lockdowns, however, curtailed travel, impacting the traditional festivities. Anxiety and depression often accumulate in isolated individuals facing threats to their families from diseases.[6–9]
This study aimed to investigate the psychological status of residents in low-to medium-risk areas of COVID-19 at the onset of lockdown management during the Chinese Spring Festival when no comprehensive nucleic acid testing was conducted. During the questionnaire distribution period, <10% of the 2854 county-level cities in China were considered high-risk areas (the risk classification method of regions used in China is shown in Table 1). Therefore, we believe our results not only depict the mental health of Sichuan residents but also reflect the psychological status of most Chinese residents during this unique period. Many areas had not experienced an epidemic in the past 4 years, making the results valuable for managing these places and future epidemics.
Table 1.
The criterion to classify COVID-19 risk by area.
| Area with no COVID-19 case (low-risk area) | No COVID-19 cases in the administrative region, the last case of was cured and doesn’t get infected within 14 d, or the last new case was transferred to another administrative region and no new case appear within 14 d |
| Area with sporadic COVID-19 case (medium-risk area) | Only isolated or sporadic COVID-19 cases in this administrative region |
| Community outbreak area and local epidemic area (high-risk area) | The number of COVID-19 cases in this administrative region increased significantly, with clusters, outbreaks, or sustained transmission, and the incidence within 1 wk was higher than 3/100,000 |
CI = confidence interval, COVID-19 = coronavirus disease 2019.
2. Methods
2.1. Ethical considerations
Ethics approval was obtained from the Medical Ethics Committee of MianYang Central Hospital (approval no: P2020045).
2.2. Settings and participants
This study utilized a mobile phone app-based questionnaire survey. Eligible participants were residents aged 18 or older following MianYang Central Hospital official WeChat account. Exclusions comprised hospitalized/discharged patients within 3 months, out-patients visited in 3 months and those residing in high-risk areas. Followers of our official WeChat included: hospital visitors and their families; health checkup residents; and residents accustomed to reading health knowledge on the WeChat public account. Participants were given the option to reject or agree at the beginning of the survey. The questionnaire was conducted from February 18, 2020 to March 11, 2020.
2.3. Questionnaire design
An anonymous questionnaire was used to obtain basic information (sex, age, and education). Anxiety and depression were assessed using the 7-item Generalized Anxiety Disorder Scale and the 9-item Patient Health Questionnaire, respectively.[9,10] Attitude toward lockdown was divided into 5 groups: completely support, support majority measures (at least 85%), support general measures (at least 50%), support minority measures, and against all measures. Educational background was categorized as elementary, secondary, bachelor, master, or PhD.
COVID-19 close contact history, (individuals who had close contact with COVID-19-infected patients or came back from Wuhan in 2 weeks), the presence of COVID-19 symptoms, and COVID-19 information channels were also collected. We noted that symptoms of COVID-19 were different from COVID-19 infection, todays we already know that patients may not have any obvious symptoms at the initial stage after COVID-19 infection, the common symptoms are fever, dry cough, fatigue, muscle pain, headache, sore throat and so on which are nonspecific symptoms and common in many other diseases; however, during the very beginning of the pandemic and strict management, symptoms, such as fever and headache were likely to induce fear of falling sick or dying, helplessness, and stigma, probably resulting in anxiety and depression.[7,11] Therefore, whether there were symptoms of COVID-19 depended on the resident self-diagnosis in this study. We did not list specific symptom choices in the questionnaire, and the residents wrote down the symptoms they had.
Information about COVID-19 were updated every day and were distributed to residents via different channels. Information overload showed detrimental effects on individual mental health[12,13] and led to further anxiety and depression.[14,15] Thus, we surveyed residents’ information channels and divided them into 4 groups: 4 channels, 3 channels, 2 channels and 1 channel. The 4 channels included state medias such as China Central Television, China Daily, People Daily, Xinhua Daily, China National Radio, Party Organ, Central Government Organ, and local official TV or radio; medical and health institutions, such as Center for Disease Control, hospital, community health station and basic health institutions; nongovernmental web, such as Sohu net, Sina net, surging net, Netease net and ifeng (Phoenix) net, and other channels.
A pilot test was conducted in 40 residents following the MianYang Central Hospital official WeChat account. A questionnaire was revised before this survey.
2.4. Statistical analysis
Data were analyzed using IBM SPSS Statistics version 22.0 software (IBM Corp., Armonk, NY, USA) and Hosmer–Lemeshow Test. Univariate analysis employed Chi-squared test, and multivariate analysis used multiple logistic regression. Statistical significance was set at P < .05.
3. Results
A total of 10,894 residents were invited from February 18 to March 11, 2020, with 10,433 (96.76%) providing valid and analyzable questionnaires.
3.1. Demographic characteristics
Table 2 shows participants’ general characteristics, including gender, age, education, COVID-19 close contact history, symptoms, attitudes toward lockdown, and information channels.
Table 2.
General characteristics of participants (n = 10,433).
| Demographic variables | Characteristics | Number of respondents | Percent |
|---|---|---|---|
| Gender | Female | 5959 | 57.1% |
| Male | 4474 | 42.9% | |
| Age | 18–30 yr | 2944 | 28.2% |
| 30–40 yr | 3359 | 32.2% | |
| 40–50 yr | 2137 | 20.5% | |
| 50–60 yr | 1201 | 11.5% | |
| 60 yr | 792 | 7.6% | |
| Education | Elementary | 827 | 7.9% |
| Secondary | 2961 | 28.4% | |
| Bachelor | 6112 | 58.6% | |
| Master | 448 | 4.3% | |
| Doctor | 85 | 0.8% | |
| Close contact history with COVID-19 | Yes | 129 | 1.2% |
| No | 10,304 | 98.8% | |
| Pneumonia-related symptoms | Yes | 952 | 9.1% |
| No | 9481 | 90.9% | |
| Attitude toward lockdown measures | Completely supportive | 8733 | 83.7% |
| Majority supportive | 1390 | 13.3% | |
| General supportive | 228 | 2.2% | |
| minority supportive | 59 | 0.6% | |
| Opposed | 23 | 0.2% | |
| Depression | Normal | 8396 | 80.5% |
| Mild | 1631 | 15.6% | |
| Moderate | 272 | 2.6% | |
| Severe | 84 | 0.8% | |
| Extremely severe | 50 | 0.5% | |
| Anxiety | Normal | 7723 | 74.0% |
| Mild | 2417 | 23.2% | |
| Moderate | 181 | 1.7% | |
| Severe | 112 | 1.1% | |
| Number of information channels | 1 | 4783 | 45.8% |
| 2 | 2766 | 26.5% | |
| 3 | 1887 | 18.1% | |
| 4 | 997 | 9.6% |
CI = confidence interval, COVID-19 = coronavirus disease 2019.
Of the 10,433 participants, 129 (1.2%) had a history of close contact with COVID-19, either returning from Wuhan or being in contact with COVID-19 patients within 2 weeks. A follow-up investigation 3 weeks later revealed that 78 of these 129 individuals were diagnosed as COVID-19 patients or suspected cases.
The total frequency of information sources for the 10,433 participants across 4 categories was 20,674. State media was accessed by 9056 participants, medical and health institutions by 1746, various nongovernmental websites by 5967, and other channels by 3905. About 54.2% of residents obtained information from more than one source, with state media and nongovernmental websites being the most common channels.
3.2. Univariate analysis
Table 3 presents univariate analysis results of demographic characteristics impacting psychological status and attitudes toward lockdown measures.
Table 3.
Association between psychological status, attitudes to lockdown management and gender, age group, degree, epidemiological history, pneumonia-related symptoms, number of message channels during the middle COVID-19 epidemic, univariate analysis (n = 10,433).
| Depression | Anxiety | Attitude to lockdown measures | ||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Factors | No | Mild | Mode-rate | Severe | Extremely severe | X2 | P value | No | Mild | Mode-rate | Severe | X2 | P value | Completely | Majority | General | Minority | Opposed | X2 | P value |
| Gender | 10.483 | .033 | 27.648 | .000 | 56.694 | 0.000 | ||||||||||||||
| Female | 4733 | 977 | 168 | 49 | 32 | 4303 | 1467 | 123 | 66 | 5125 | 681 | 118 | 27 | 8 | ||||||
| Male | 3663 | 654 | 104 | 35 | 18 | 3420 | 950 | 58 | 46 | 3608 | 709 | 110 | 32 | 15 | ||||||
| Age Group | 130.063 | .000 | 31.710 | .002 | 84.045 | 0.000 | ||||||||||||||
| 18–30 y | 2223 | 522 | 133 | 36 | 30 | 2131 | 697 | 69 | 47 | 2328 | 485 | 97 | 23 | 11 | ||||||
| 30–40 y | 2734 | 521 | 66 | 29 | 9 | 2496 | 774 | 55 | 34 | 2862 | 408 | 63 | 19 | 7 | ||||||
| 40–50 y | 1787 | 297 | 36 | 7 | 10 | 1618 | 474 | 31 | 14 | 1848 | 248 | 31 | 9 | 1 | ||||||
| 50–60 y | 1005 | 164 | 25 | 7 | 0 | 880 | 301 | 10 | 10 | 1032 | 148 | 16 | 2 | 3 | ||||||
| >60 y | 647 | 127 | 12 | 5 | 1 | 598 | 171 | 16 | 7 | 663 | 101 | 21 | 6 | 1 | ||||||
| Degree | 46.251 | .000 | 26.828 | .008 | 93.107 | 0.000 | ||||||||||||||
| Elementary | 718 | 92 | 9 | 3 | 5 | 646 | 164 | 9 | 8 | 718 | 92 | 9 | 3 | 5 | ||||||
| High school | 2396 | 446 | 74 | 25 | 20 | 2161 | 706 | 62 | 32 | 2396 | 446 | 74 | 25 | 20 | ||||||
| Bachelor | 4863 | 1001 | 175 | 53 | 20 | 4514 | 1433 | 101 | 64 | 4863 | 1001 | 175 | 53 | 20 | ||||||
| Master | 349 | 82 | 11 | 3 | 3 | 333 | 103 | 8 | 4 | 349 | 82 | 11 | 3 | 3 | ||||||
| Doctor | 70 | 10 | 3 | 0 | 2 | 69 | 11 | 1 | 4 | 70 | 10 | 3 | 0 | 2 | ||||||
| Close contact history | 12.363 | .015 | 25.153 | .000 | 10.243 | 0.037 | ||||||||||||||
| Yes | 90 | 31 | 7 | 1 | 0 | 76 | 43 | 8 | 2 | 106 | 18 | 1 | 3 | 1 | ||||||
| No | 8306 | 1600 | 265 | 83 | 50 | 7647 | 2374 | 173 | 110 | 8627 | 1372 | 227 | 56 | 22 | ||||||
| Pneumonia symptoms | 25.153 | .000 | 119.673 | .000 | 13.583 | 0.009 | ||||||||||||||
| Yes | 638 | 229 | 55 | 19 | 11 | 578 | 315 | 30 | 29 | 761 | 150 | 30 | 9 | 2 | ||||||
| No | 7758 | 1402 | 217 | 65 | 39 | 7145 | 2102 | 151 | 83 | 7972 | 1240 | 198 | 50 | 21 | ||||||
| Number of information channels | 21.004 | .050 | 10.916 | .282 | 11.821 | 0.460 | ||||||||||||||
| 1 | 3919 | 676 | 123 | 40 | 25 | 3578 | 1061 | 86 | 58 | 3998 | 626 | 116 | 28 | 15 | ||||||
| 2 | 2199 | 465 | 74 | 19 | 9 | 2023 | 676 | 47 | 20 | 2288 | 409 | 54 | 12 | 3 | ||||||
| 3 | 1494 | 311 | 53 | 19 | 10 | 1380 | 450 | 35 | 22 | 1581 | 254 | 37 | 12 | 3 | ||||||
| 4 | 784 | 179 | 22 | 6 | 6 | 742 | 230 | 13 | 12 | 866 | 101 | 21 | 7 | 2 | ||||||
| Total | 8396 | 1631 | 272 | 84 | 50 | 7723 | 2417 | 181 | 112 | 8733 | 1390 | 228 | 59 | 23 | ||||||
CI = confidence interval, COVID-19 = coronavirus disease 2019.
The incidence of depression and anxiety varied between men and women, with depression increasing to 20.6% among women from overall 19.5%. The incidence of anxiety was higher in women at 27.8%. Lockdown support was expressed by 86.0% of men and 80.6% of women, and within the supportive population, 86.0% of women and 80.0% of men strongly supported lockdown. Single-factor Chi-Square test results indicated statistically significant differences in anxiety, depression, and attitudes toward lockdown measures between men and women (χ2 = 27.648, P = .000; χ2 = 10.483, P = .033; χ2 = 56.694, P = .000, respectively).
Psychological status and attitudes toward lockdown varied across age groups. Individuals aged 18 to 30 not only showed higher rates of depression and anxiety but were also more likely to experience moderate to (extremely) severe levels of depression and anxiety compared to other age groups. The distribution of depression and anxiety levels among the 5 age groups differed significantly (χ2 = 130.063, P = .000; χ2 = 31.710, P = .002). Attitudinal differences among the 5 age groups were also significant (χ2 = 84.045, P = .000), with the 18 to 30 age group having the highest proportion of those who did not support or opposed (34 residents [23 did not support, 11 opposed] out of 2944 (1.15%), followed by the ≥ 60 years age group).
Different educational backgrounds correlated with distinct mental statuses and attitudes toward lockdown measures. Significant differences in depression status were observed among the 5 educational background groups (χ2 = 46.251, P = .000), with no linear correlation trend. Anxiety incidence decreased with higher educational levels, showing a significant difference among the 5 educational backgrounds (χ2 = 26.828, P = .008). Support for lockdown measures decreased with increasing education level, except for the doctor group (χ2 = 93.107, P = .000).
129 of 10,433 residents had a close contact history with COVID-19, the incidence of depression and anxiety increasing to 30.2% and 41.1% from overall 19.5% and 26.0%, respectively (χ2 = 25.153, P = .015; χ2 = 12.363, P = .015). 125 of the 129 residents (97.0%) were less supportive to lockdown measures compared to residents without close contact history (99.2%) (χ2 = 10.243, P = .037).
Similarly, 952 of 10,433 residents who believed they had pneumonia symptoms developed higher depression and anxiety incidence. The main symptoms noted were fever, cough, sputum, sore throat, tightness of breath, chest pain when coughing, muscle pain, diarrhea, nausea, and vomiting. The severity of anxiety and depression differed significantly in patients with or without pneumonia symptoms (χ2 = 119.673, P = .000; χ2 = 25.153, P = .000, respectively). The difference in attitude between people with and without pneumonia symptoms was statistically significant (χ2 = 13.583, P = .009).
In total, 54.2% of residents obtained news from more than one source. Among the 10,433 participants, the total frequency of those receiving information from 4 categories was 20,674: 9056 participants obtained COVID-19-related information through state media, 1746 through medical and health institutions, 5967 from various nongovernmental web sources, and 3905 through other channels. The most common way for residents to obtain information is through state media and the nongovernmental web. We classified residents into 4 groups: 4 channels, 3 channels, 2 channels, and 1 channel. Single-factor chi-square test shows depression is different in the 4 groups (χ2 = 21.004, P = .050), with no significant differences in terms of anxiety and attitude towards lockdown measures among the 4 groups (χ2 = 10.916, P = .282; χ2 = 11.821, P = .460, respectively).
Sex, age group, educational background, and close contact history affected participants’ psychological status and attitudes toward lockdown in univariate analysis (all P < .05). The number of media channels had a significant impact on depression.
3.3. Multivariate analysis of demographic characteristics affecting psychological status and attitude to lockdown measures
Table 4 shows the multiple logistic regression analysis of anxiety. Factors were compared between residents with no anxiety and those with mild, moderate, and severe anxiety. Females faced a 1.25 times higher risk of anxiety than males (P < .001, 95% confidence interval [CI] 1.14–1.36).Increasing age offered protection against moderate anxiety and severe anxiety (P = .037 and 0.004, respectively). High school graduates had a 2.03 times higher risk of moderate anxiety (P = .05, 95% CI 1.00–4.13). Those with a doctorate degree faced a 4.5 times higher risk of severe anxiety (P = .019, 95% CI 1.29–15.73). The risk of anxiety generally decreased with higher education but without statistical significance. The number of information channels was not a significant factor for total anxiety or the 3 subgroups. Lack of close contact history was protective for anxiety, mild anxiety, and moderate anxiety at 0.62 times (P = .09, 95% CI 0.43–0.89), 0.65 times (P = .029, 95% CI 0.44–0.96), and 0.27 times (P = .01, 95% CI 0.13–0.58), respectively. In 112 severely anxious residents, close contact history was no longer a significant factor (P = .963). Absence of pneumonia symptoms was protective against anxiety, with a risk of 0.51 times for total people (P < .001, 95% CI 0.44–0.59) and 3 subgroups (mild: P < .001, odds ratio [OR]: 0.54, 95% CI 0.47–0.63; moderate: P < .001, OR 0.42, 95% CI 0.28–0.63; severe: P < .001, OR 0.22, 95% CI 0.14–0.34).
Table 4.
Multiple logistic regression analysis of anxiety.
| Anxiety VS no anxiety | Mild anxiety vs no anxiety | Moderate anxiety vs no anxiety | Severe anxiety vs no anxiety | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Factors | OR (95%CI) | Wald | P | OR (95%CI) | Wald | P | OR (95%CI) | Wald | P | OR (95%CI) | Wald | P |
| Age | 1.00 (0.99,1.00) | 1.487 | .223 | 0.99 (0.95,1.00) | 0.119 | .730 | 0.99 (0.98,1.00) | 4.337 | .037 | 0.98 (0.96,0.99) | 8.348 | .004 |
| Female | 1.25 (1.14,1.36) | 22.821 | <.001 | 1.23 (1.12,1.35) | 18.141 | <.001 | 1.64 (1.19,2.25) | 9.201 | .002 | 1.16 (0.79,1.71) | 0.598 | .439 |
| High school | 1.31 (1.09,1.57) | 7.979 | .005 | 1.27 (1.05,1.54) | 6.107 | .014 | 2.03 (1.00,4.13) | 3.859 | .050 | 1.13 (0.51,2.46) | 0.087 | .769 |
| Bachelor | 1.22 (1.02,1.45) | 4.690 | .030 | 1.21 (1.01,1.46) | 4.158 | .041 | 1.50 (0.75,3.02) | 1.322 | .250 | 1.00 (0.47,2.11) | 5.830 | .994 |
| Master | 1.20 (0.92,1.58) | 1.770 | .183 | 1.19 (0.90,1.58) | 1.516 | .218 | 1.66 (0.63,4.38) | 1.056 | .304 | 0.88 (0.26,2.96) | 0.046 | .831 |
| Doctor | 0.83 (0.47,1.48) | 0.381 | .537 | 0.63 (0.33,1.23) | 1.828 | .176 | 1.04 (0.13,8.45) | 0.001 | .972 | 4.50 (1.29,15.73) | 5.547 | .019 |
| Number of information channels | 1.02 (0.98,1.07) | 0.775 | .379 | 1.03 (0.98,1.08) | 1.326 | .249 | 0.95 (0.81,1.10) | 0.517 | .472 | 0.96 (0.79,1.16) | 0.211 | .646 |
| Attitude to lockdown measures | 1.04 (0.95,1.14) | 0.833 | .361 | 1.04 (0.95,1.14) | 0.652 | .419 | 0.97 (0.74,1.29) | 0.036 | .849 | 1.29 (0.83,1.99) | 1.313 | .252 |
| NO pneumonia-related symptoms | 0.51 (0.44,0.59) | 89.185 | <.001 | 0.54 (0.47,0.63) | 65.915 | <.001 | 0.42 (0.28,0.63) | 17.457 | <.001 | 0.22 (0.14,0.34) | 46.589 | <.001 |
| Close contact history with COVID-19 | 0.62 (0.43,0.89) | 6.759 | .009 | 0.65 (0.44,0.96) | 4.755 | .029 | 0.27 (0.13,0.58) | 11.088 | .001 | 0.97 (0.23,4.13) | 0.002 | .963 |
CI = confidence interval, COVID-19 = coronavirus disease 2019.
Table 5 shows the multiple logistic regression analysis of depression. Females faced a 1.14 times higher risk of mild depression (P = .016, 95% CI 1.03–1.28). Increasing age was protective across all 5 depression groups (total: P < .001, OR 0.89, 95% CI 0.85–0.93; mild: P < .008, OR 0.94, 95% CI 0.91–1.00; moderate: P < .001, OR 0.97, 95% CI 0.96–0.98; severe: P < .001, OR: 0.96, 95% CI 0.94–0.98; extremely severe: P < .001, OR 0.93, 95% CI 0.91–0.96). Higher educational degrees (except a doctorate degree) correlated with increased depression risk. High school, bachelor, and master graduates faced risks of 1.51 times (P < .001, 95% CI 1.21–1.89), 1.54 times (P = .001, 95% CI 1.25–1.91) and 1.72 times (P = .001, 95% CI 1.27–2.33). High school and bachelor had a moderate depression risk of 2.37 times (P = .016, 95% CI 1.18–4.77) and 2.50 times (P < .008, 95% CI 1.26–4.93) respectively. More COVID-19 informational channels posed a risk for depression and mild depression, with odds of 1.06 times (P = .029, 95% CI 1.01–1.11) and 1.08 times (P = .005, 95% CI 1.02–1.14). Lack of symptoms was protective against depression (P < .001, OR: 0.45, 95% CI 0.38–0.52). No statistical attitude differences toward lockdown were found in people with depression. Additionally, Table 6 indicates no statistical attitude difference toward lockdown management across all 9 factors.
Table 5.
Multiple logistic regression analysis of depression.
| Depression VS no depression | Mild depression vs no depression | Moderate depression vs no depression | Severe depression VS no depression | Extremely severe depression VS no depression | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Factors | OR (95%CI) | Wald | P | OR (95%CI) | Wald | P | OR (95%CI) | Wald | P | OR (95%CI) | Wald | P | OR (95%CI) | Wald | P |
| Age | 0.89 (0.85, 0.93) | 29.951 | <.001 | 0.94 (0.99, 1.00) | 7.085 | .008 | 0.97 (0.96,0.98) | 33.460 | <.001 | 0.96 (0.94,0.98) | 25.148 | <.001 | 0.93 (0.91,0.96) | 20.113 | <.001 |
| Female | 1.15 (1.04,1.27) | 7.339 | .007 | 1.14 (1.03,1.28) | 5.816 | .016 | 1.18 (0.92,1.52) | 1.657 | .198 | 1.01 (0.65,1.57) | 0.102 | .749 | 1.32 (0.74,2.38) | 0.879 | .348 |
| High school | 1.51 (1.21,1.89) | 13.232 | <.001 | 1.43 (1.13,1.82) | 8.648 | .003 | 2.37 (1.18,4.77) | 5.847 | .016 | 2.42 (0.73, 8.06) | 2.077 | .150 | 1.07 (0.40,2.88) | 0.017 | .897 |
| bachelor | 1.54 (1.25,1.91) | 15.877 | .001 | 1.50 (1.19,1.89) | 11.842 | <.001 | 2.50 (1.26,4.93) | 6.957 | .008 | 2.26 (0.70,7.30) | 1.845 | .174 | 0.44 (0.16,1.19) | 2.609 | .106 |
| master | 1.72 (1.27,2.33) | 12.090 | .001 | 1.72 (1.24,2.39) | 10.570 | <.001 | 2.22 (0.91,5.44) | 3.046 | .081 | 1.82 (0.36,9.11) | 0.529 | .467 | 0.98 (0.23,4.17) | 0.054 | .982 |
| doctor | 1.34 (0.73,2.43) | 0.905 | .341 | 1.07 (0.53,2.15) | 0.032 | .859 | 3.03 (0.79,11.61) | 2.617 | .106 | 6.59 (0.02,15.89) | 0.056 | .981 | 3.53 (0.65,19.14) | 2.140 | .144 |
| Number of information channels | 1.06 (1.01,1.11) | 4.770 | .029 | 1.08 (1.02,1.14) | 7.834 | .005 | 0.96 (0.84,1.08) | 0.528 | .468 | 0.96 (0.77,1.19) | 0.167 | .683 | 1.05 (0.79,1.39) | 0.124 | .725 |
| Attitude to lockdown measures | 1.00 (1 0.91,1.10) | 0.0190 | .965 | 0.99 (0.90,1.10) | 0.011 | .915 | 1.10 (0.86,1.42) | 0.589 | .442 | 0.81 (0.57,1.16) | 1.306 | .253 | 1.36 (0.68,2.73) | 0.761 | .383 |
| NO pneumonia-related symptoms | 0.45 (0.38,0.52) | 115.242 | <.001 | 0.50 (0.43,0.59) | 67.070 | <.001 | 0.31 (0.23,0.43) | 52.452 | <.001 | 0.26 (0.15,0.44) | 25.148 | <.001 | 0.23 (0.12,0.45) | 17.833 | <.001 |
| NO close contact history | 0.75 (0.51,1.11) | 2.052 | .152 | 0.72 (0.47,1.10) | 2.350 | .125 | x0..63 (0.28,1.42) | 1.229 | .268 | 1.39 (0.19,10.34) | 0.102 | .749 | 2.52 (0.11,20.58) | 0.053 | .988 |
Table 6.
Multiple logistic regression analysis of attitude to lockdown measures.
| Support vs against | |||
|---|---|---|---|
| Factors | OR (95%CI) | Wald | P |
| Age | 0.99 (0.0.98,1.01) | 0.574 | .449 |
| Female | 1.35 (0.87,2.09) | 1.83 | .18 |
| High school | 1.22 (1.,1.57) | 7.979 | .342 |
| Bachelor | 1.15 (1.06,1.87) | 1.76 | .501 |
| Master | 1.26 (0.62,3.51) | 0.93 | .211 |
| Doctor | 2.34 (1.71,2.88) | 1.027 | .672 |
| Number of information channels | 1.13 (0.89,1.42) | 0.997 | .318 |
| NO pneumonia-related symptoms | 1.53 (0.78,3.00) | 1.55 | .213 |
| NO close contact history | * (*) | 6.759 | .998 |
Decimal, indescribable.
4. Discussion
During the first month of the COVID-19 pandemic, we assessed the psychological state and attitudes toward lockdown in low- to medium-risk areas of China using 10,433 valid questionnaires. Our findings, highlighting significant associations between demographic characteristics and anxiety, depression, and attitudes toward lockdown, underscore the importance of recognizing specific groups’ psychological statuses at the pandemic onset.
Our survey provides detailed insights into the impact of educational levels on psychological well-being. The incidence of anxiety decreases with higher educational levels. Among residents experiencing depression or mild depression, bachelor and master degrees pose a risk compared to elementary and high school education, with risk values increasing with higher educational levels. A doctorate degree is a significant risk factor for severe anxiety. These results align with Le et al’s findings[16] on higher stress levels in Vietnamese individuals with postgraduate education during COVID-19. However, Wang et al[17] no correlation between anxiety and educational levels, while master degrees or above were depression risk factors. Gloster et al,[18] in a survey of 9565 people from 78 countries and 18 languages, found higher educational backgrounds associated with fewer depression symptoms. Upon reanalyzing the doctor population, we found an average age of 39.30 years, with 65 doctors over 30 years old likely in more critical positions and experiencing higher work-related stress. Younger doctors may face anxiety due to delayed graduation or difficulty finding desired jobs amid the pandemic.
Young women exhibit a higher likelihood of psychological abnormalities. Our findings indicate that increasing age acts as a protective factor against moderate to severe anxiety and all levels of depression, likely due to greater experience with public health emergencies as age increases. In contrast, females are only at risk for mild to moderate psychological abnormalities, with no significant gender differences observed in (extremely) severe anxiety and depression. Other studies have similarly reported that being older and male are protective factors for psychological well-being.[16,19–21] It is noteworthy that anxiety and depression did not hinder compliance with strict management measures, aligning with the cooperative attitude toward lockdown measures during that period. Another plausible explanation is that national solidarity against the epidemic instilled confidence in the swift control of COVID-19.
State media emerged as the most popular information channel during the COVID-19 breakout in China, followed by non-governmental websites and social media. This contradicts findings from Liu et al[22] in 2021, where social media was reported as the predominant method for COVID-19 information. The discrepancy may be attributed to concerns about false information on social media during the pandemic.
High misdiagnosis rates in self-diagnosis highlight the need for increased psychological counseling for anxiety and depression. Residents with a history of close contact with COVID-19, despite not being risk factors for severe anxiety and depression, received timely medical care during strict isolation. This underscores the importance of timely symptomatic treatment and mental health care during public health emergencies.
To the best of our knowledge, this is the largest investigation into the residents’ psychological status during the first quarter of COVID-19 in 2020. Questionnaires were distributed 1 month after the epidemic declaration and 20 days after the national implementation of partial lockdown measures, providing firsthand evidence of the psychological impacts of COVID-19 and attitudes toward lockdown measures during that Spring Festival. Wang et al[23] reported a survey involving 600 individuals from the general population in China, but the respondents’ geographical areas varied greatly, overlooking the impact of different regional COVID-19 risks on residents’ psychological well-being. Other mental health studies on COVID-19 were cross-sectional and overlooked the geographical factor.[17] Some surveys focused on health professionals or specific age groups, lacking in-depth analysis to identify risk or protective factors for mental health.[17,24–26] We believe our results are not only meaningful for the COVID-19 pandemic but also serve as an example of the psychological state of the general population during other public health emergencies in the future.
Our study has several strengths. This study analyzed the relationships between the mentioned factors and psychological state, as well as their impact on each psychological subgrade. Mild anxiety and mild depression are treatable through self-regulation before hospital interventions. In addition, we categorized residents’ attitudes toward lockdown management into 5 groups, analyzed the influence of each factor on attitude, and explored the relationship between attitudes and psychological state. Despite all results being negative, it suggests that lockdown has no significant impact on residents’ psychological state.
This study also has some limitations. First, we did not consider unemployment, suspension, living alone, divorce, and other factors because the prolonged duration of the unexpected COVID-19 epidemic was not foreseen. Second, after almost 4 years, many studies have demonstrated that the mentioned factors affect patients’ anxiety and depression across different countries and occupational groups, but our detailed findings may compensate for some shortcomings. Finally, our questionnaires were distributed to WeChat users. While WeChat is widely used by residents throughout China, some individuals, such as children aged 1 to 12 years, do not use WeChat. Residents in remote mountainous areas are also less likely to use WeChat, potentially biasing the results.
5. Conclusion
Due to the COVID-19 outbreak during China Spring Festival and the implementation of lockdown measures, our findings emphasize the need for more psychological counseling for young female residents, especially those with lower educational backgrounds or experiencing infection-related symptoms through self-suspicion. This is crucial to alleviate their mild and moderate anxiety and depression in future epidemics or pandemics. The same applies to the public with a doctorate or higher degree, particularly those of working age, as they exhibit the highest risk of severe anxiety. Multivariate analysis indicated that all demographic characteristics did not influence residents’ attitudes toward lockdown.
Author contributions
Conceptualization: Xiaobo Du.
Data curation: Yao Liao, Min Liao.
Formal analysis: Xiaobo Du, Yuwei Yang.
Investigation: Yao Liao, Xiaobo Du, Min Liao, Zuhong Zhou.
Methodology: Yao Liao, Xiaobo Du.
Project administration: Xiaobo Du, Zuhong Zhou.
Software: Zuhong Zhou.
Supervision: Xiaobo Du, Min Liao, Zuhong Zhou.
Validation: Yao Liao.
Writing – original draft: Yao Liao.
Writing – review & editing: Xiaobo Du, Zuhong Zhou.
Abbreviations:
- CI
- confidence interval
- COVID-19
- coronavirus disease 2019
- OR
- odds ratio
YL and ML contributed equally to this work.
The authors have no funding and conflicts of interest to disclose.
The datasets generated during and/or analyzed during the current study are not publicly available, but are available from the corresponding author on reasonable request.
How to cite this article: Liao Y, Liao M, Yang Y, Zhou Z, Du X. Psychological status of residents at the onset of nationwide COVID-19 lockdown in low- and medium-risk areas of China. Medicine 2024;103:10(e37391).
Contributor Information
Yao Liao, Email: minluar@qq.com.
Min Liao, Email: minluar@qq.com.
Yuwei Yang, Email: yyw318@vip.163.com.
Zuhong Zhou, Email: 499493896@qq.com.
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