A Cross-Sectional Survey on Psychological Health and Influencing Factors Among 2628 Asymptomatic and Mild COVID-19 Patients in Fangcang Shelter Hospital

Introduction

Coronavirus disease 2019 (COVID-19), caused by the novel coronavirus SARS-CoV-2, is marked by rapid transmission, multiple routes of spread, high infectivity, and susceptibility among the population.^1,2 The global response to the COVID-19 pandemic has underscored not only its widespread impact, but has also exposed a significant surge in psychological strain among individuals.^3,4 A survey conducted during the pandemic revealed increased levels of mild depression, generalized anxiety, sleep problems, and psychological stress disorders among pregnant women, significantly higher than pre-pandemic levels.⁵ Moreover, residents in epidemic hotspots, especially women and young people, have reported severe insomnia.⁶

Reflecting on previous respiratory outbreaks and the psychological aftermath of viral epidemics, it is crucial for governments to establish robust mental health services to mitigate mental disorder risks. Early in the pandemic, various countries, including China, implemented measures such as mandatory quarantine and social distancing.⁷ China, facing challenges like limited medical resources and high population density, established Fangcang Shelter Hospitals (FSH) in cities to manage asymptomatic and mild COVID-19 patients.^8,9 Currently, only three cross-sectional studies focused on patients in FSH have indicated a notable prevalence of anxiety, depressive disorders, insomnia, and stress disorders among them, with higher occurrence rates in comparison to patients undergoing home isolation.^10–12 These studies shed light on the psychological challenges within FSH during the early stages of the COVID-19 outbreak, spanning from January 2020 to 2021. However, there remains a dearth of research examining the psychological status and associated influencing factors among patients undergoing isolation treatment in FSH in China, occurring just prior to the government’s relaxation of COVID-19 control measures, after October 2022.

This study specifically aims to investigate the prevalence of insomnia, depression, anxiety, and stress disorders among patients in FSH in Zhengzhou during the COVID-19 epidemic, with a particular focus on the psychological health risks among women and individuals with higher education. And identify potential factors influencing these psychological conditions, exploring whether and how these factors exacerbate or alleviate the mental health of the patients. Additionally, we examine the impact of electronic device usage on mental health. The findings of this study are intended to provide critical insights into psychological impacts during major disease outbreaks and offer valuable guidance for implementing effective psychological interventions in such contexts.

Materials and Methods

Study Sample and Data

A total of 13,640 patients with COVID-19 over 14 years old who were treated in FSH of Zhengzhou City, Henan Province, China, were included in the investigation scope from 30 October to 6 December 2022. It should be noted that to ensure the safety of elderly individuals (>65 years old), pregnant women, infants, postpartum women, lactating women, patients with severe underlying diseases, severe COVID-19 patients, and other patients requiring immediate medical attention were arranged for treatment and observation at designated hospitals by the health authorities. Therefore, these special populations were not included in our study scope.

A cross-sectional survey was conducted using an online questionnaire survey by scanning a WeChat two-dimensional code, and participants had the right to withdraw from the study at any time. We meticulously adhered to the Chinese version of the STROBE cross-sectional reporting guidelines, referencing and implementing them to ensure a systematic and transparent presentation of our research methods, thereby contributing to the overall rigor and reliability of our study.¹³ Convenience sampling was adopted as our preferred method to minimize direct contact with patients, demonstrating notable advantages in terms of time efficiency, cost-effectiveness, and logistical feasibility within the specific constraints of our research context. While we acknowledge potential limitations, we firmly believe that convenience sampling represents the most pragmatic choice in this particular scenario.

Inclusion and Exclusion Criteria Inclusion Criteria

All COVID-19 patients participating in this study were diagnosed based on the Diagnosis and Treatment Plan for Novel Coronavirus Infection in China.¹⁴

Inclusion criteria: (1) Positive results in consecutive nucleic acid tests. (2) Presence of respiratory tract infection as the main clinical manifestation, such as fever, cough, sore throat, runny nose, etc. (3) Asymptomatic with only positive nucleic acid test results; (4) 65>Age>14 years old.

Exclusion Criteria: (1) Participants who did not have a smartphone or were unable to use a smartphone to complete the survey. (2) Participants who were unwilling to cooperate with the survey. (3) Participants with persistent high fever (>3 days), or cough and dyspnea, respiratory rate < 30 breaths/minute, resting state, oxygen saturation > 93% while breathing ambient air. (4) Participants with severe pneumonia or shock, respiratory rate ≥ 30 breaths/minute, resting state, oxygen saturation ≤ 93% while breathing ambient air, arterial partial pressure of oxygen/oxygen concentration ≤ 300 mmHg, progressive worsening of clinical symptoms, and CT scan showing severe lung infection with > 50% lesion progression within 24–48 h.

Theoretical Framework

This study meticulously adhered to ethical standards, including strict data confidentiality and ensuring that all participants were fully informed about the research’s purpose and provided their informed consent prior to participation. For participants under the age of 18, informed consent was obtained from their parents or legal guardians, safeguarding the rights and welfare of these minor participants. Conducted in strict compliance with the ethical principles outlined in the 1964 Declaration of Helsinki and its subsequent amendments, the study also received ethical clearance from the Medical Ethics Committee of the First Affiliated Hospital of Henan University of Chinese Medicine (Ethics number: 2022HL-458-01).

Survey Method

The self-designed questionnaire was finalized after expert consultation (involving personal basic information) and pre-survey, and was conducted by trained qualified investigators. The questionnaire consisted of five sections: socio-demographic characteristics, the Chinese versions of Insomnia Severity Index (ISI),¹⁵ 9-item Patient Health Questionnaire (PHQ-9),¹⁶ Generalized Anxiety Disorder-7 (GAD-7),¹⁷ and Impact of Event Scale-Revised (IES-R).¹⁸ Investigators personally assisted patients in completing the questionnaires by scanning a QR code using participants smartphones, providing individual guidance throughout the process, with each questionnaire requiring approximately 5 to 15 minutes for completion. Investigators diligently verify on-site that patients have personally completed the survey questionnaire by meticulously cross-referencing essential details such as the patient’s name, gender, and age. For participants who were unable to self-administer the questionnaire due to physiological or educational reasons, the investigators conducted a face-to-face interview and recorded their responses item by item.

To ensure a sample size sufficient to detect effects on psychological health issues, we conducted a detailed sample size calculation based on previous study data. Using PASS software and considering the highest prevalence rate of depression (54.4%), we calculated that a minimum of 1565 participants was needed. Taking into account a potential dropout rate, we increased the total sample size to more than 1957. The final sample size obtained in this study markedly exceeded the initially calculated minimum requirement, thereby substantially reinforcing the reliability and validity of our research findings.

Demographic and Clinical Characteristics

The final confirmed basic data were self-designed and compiled by the researchers, including gender, age, marital status, occupation, education level, presence of underlying diseases (such as hypertension, diabetes, etc.), duration of illness, nucleic acid test results, total daily sleep duration, total daily electronic device usage duration, and other relevant information. The flowchart as shown in Figure 1 illustrated the process of enrolling respondents and outlines the study procedures. It provided a visual representation of the sequential steps involved in participant recruitment, screening, and the progression through various stages of the research.

Figure 1 The flowchart of the enrollment of respondents and study procedures.

Research Instruments

ISI was used to assess the participants’ sleep quality over the past two weeks. Higher scores indicate more severe insomnia. The scoring range is from 0 to 28, with scores of 0–7 indicating no insomnia, 8–14 indicating mild insomnia, 15–21 indicating moderate insomnia, and 22–28 indicating severe insomnia.

PHQ-9 was used to assess depressive symptoms over the past two weeks. It is one of the recommended depression screening tools by the World Health Organization, based on the diagnostic criteria for major depressive episodes in the DSM-IV. The questionnaire consists of nine items, each scored from 0 to 3, with a total score range of 0–27. Scores of 0–4 represent “no depression” 5–9 represent “mild depression” 10–14 represent “moderate depression” and scores above 15 represent “severe depression”.

GAD-7 scale was used to assess anxiety levels. It is a concise and effective self-rated anxiety scale consisting of seven items, with scoring and rating criteria similar to PHQ-9.

IES-R was used to assess stress disorders related to COVID-19 infection. This scale consists of 11 items, with a total score range of 0–88. The scoring criteria are as follows: 0–8 indicates no post-traumatic stress disorder (PTSD), 9–25 indicates mild PTSD, 26–43 indicates moderate PTSD, and 44–88 indicates severe PTSD.

The Chinese versions of ISI, PHQ-9, GAD-7, and IES-R have been validated and demonstrated good psychometric properties. The validation process involved comprehensive assessments of reliability and validity, affirming the dependability and effectiveness of these scales in measuring pertinent psychological constructs. During the COVID-19 pandemic, our study within FSH faced significant constraints, limiting extensive psychometric testing of environmental factors. Prioritizing rapid data collection, we utilized descriptive analysis to provide valuable insights, albeit less detailed than full psychometric testing. Recognizing this as a study limitation, future research should aim for more in-depth testing. Expert consensus in psychometrics confirms the practical challenges of conducting comprehensive testing in emergency scenarios, validating our methodology under these circumstances.

Quality Control of the Study

To ensure the transparency of our analysis process, we implemented the following measures to avoid duplications or fraud in the online survey. Firstly, technical measures were employed to ensure that each participant could submit only one questionnaire. Logical consistency checks were incorporated into the questionnaire design to identify and eliminate inconsistent or anomalous responses. Additionally, we monitored participant IP addresses to prevent multiple submissions by the same user. Secondly, we analyzed participants who did not complete the questionnaire to identify potential non-response biases. By comparing the characteristics of participants who completed the questionnaire with those who did not, we checked for significant differences that might affect the interpretation of our research findings. Thirdly, to assess time-related biases, we compared responses from early participants to those from later participants. This helped to reveal any trends or changes that might have occurred during the survey period, which could impact the quality and content of the responses. Lastly, to evaluate potential biases arising from the measurement method, we used diverse formats and scales in our questionnaire design. Furthermore, we assessed common method bias in our data using statistical methods like the Harman’s single-factor test.

During the data collection process, it was identified that 175 responses were duplicates, as determined by cross-referencing the hospital numbers of the respondents. To ensure the integrity and accuracy of our data, these duplicate entries were excluded from the analysis. This decision was made to maintain the validity of the dataset, as each entry is intended to represent an individual patient’s response. In this context, applying methods such as mean or median imputation to handle these duplicates was deemed inappropriate, as they were not cases of missing data but rather instances of repetition. Our approach aligns with the objective to provide a clear and accurate representation of the surveyed population, thereby ensuring that the results and conclusions drawn from this study are based on reliable and singular patient responses.

Statistical Analysis

In conducting our statistical analysis, we chose methods best suited to the nature of our data and our research objectives. We imported the raw data into SPSS Statistics 27.0 (IBM, USA) for comprehensive analysis. Given the categorical nature of our data, we presented it as frequencies and percentages. To determine correlations between variables such as insomnia, depression, anxiety, and stress disorders severity, we employed Spearman correlation analysis, a non-parametric method ideal for ordinal data. For assessing differences in psychological impacts within FSH, t-tests and Dunnett’s multiple comparison analysis were utilized, which are robust methods for comparing means across groups. Finally, a multiple logistic regression analysis was employed to identify independent risk factors for psychological conditions, providing odds ratios with 95% confidence intervals. This method is particularly effective in studies like ours where the outcome is binary. Each statistical procedure was carefully chosen to ensure the most accurate and reliable analysis of our data, adhering to the highest standards of research rigor.

Results

General Information

There were 2803 COVID-19 patients completed an electronic survey questionnaire, which accounting for 20.55% of the total number of people. After excluding unqualified questionnaires, a total of 2628 valid questionnaires were obtained, resulting in an effective rate of 93.76%. Among the valid questionnaires, there were 1593 male participants (60.62%) and 1035 female participants (39.38%). The average age was 32.37±13.83 years (Table 1).

Table 1 Analysis of General Condition of Patients

Insomnia, Depression, Anxiety, and Stress Disorders Levels of COVID-19 Patients

Among the 2628 cases of COVID-19 infection, 880 cases (33.49%) experienced varying degrees of insomnia, Among them, 40.07% and 33.06% of individuals reported insomnia due to continuous positive nucleic acid testing, inability to leave the centralized isolation facility in a timely manner, and concerns about COVID-19 infection. Additionally, 24.09%, 44.25%, and 11.19% of individuals attributed their insomnia to excessive lighting in the isolation area, noisy environment, and regulations regarding sleep schedules, respectively. A total of 941 patients (35.80%) experienced varying degrees of depressive disorder, Among the patients, 834 cases (31.74%) experienced varying degrees of anxiety, Furthermore, 1145 patients (43.57%) exhibited varying degrees of psychological stress disorders (Table 1).

Analysis of Different Variables with Scores on the ISI, PHQ-9, GAD-7, and IES-R

Those patients of female, higher education levels, underlying medical conditions, shorter daily sleep duration, and longer total usage of electronic devices, had relatively higher scores on the ISI (OR values of 0.069, 0.202, 0.121, −0.385, and 0.100, respectively), PHQ-9 (OR values of 0.070, 0.240, 0.117, −0.205, 0.163, respectively), IES-R (OR values of 0.123, 0.203, 0.120, −0.202, and 0.081, respectively), and GAD-7 (OR values of 0.123, 0.203, 0.120, −0.202, and 0.081, respectively), with statistically significant differences (p<0.01) (Table 2). On the other hand, younger patients had higher scores on the PHQ-9 and IES-R (OR values of −0.041 and 0.080, respectively), with statistically significant differences (p<0.01) (Table 2). Moreover, patients with continuous positive nucleic acid testing results were more likely to experience insomnia (OR values of 0.056), with statistically significant differences (p<0.01) (Table 2).

Table 2 Comparison and Correlation Analysis of ISI, PHQ-9, GAD-7, and IES-R Scores Under Different Variables

Results of the Analysis Between Different Factors in FSH with ISI, PHQ-9, GAD-7, and IES-R

The Dunnett multiple comparison analysis of the different influencing factors in FSH showed the following results. The sleep schedule management regulations had the greatest impact for ISI and IES-R Scores (p<0.01), while noise had a relatively smaller impact compared to other factors (p<0.01). The impact of lighting exposure was the greatest impact for GAD-7 and IES-R Scores (p<0.01), while environmental factors had a relatively smaller impact compared to other factors (p<0.01) (Table 3).

Table 3 Multiple Comparisons of ISI, PHQ-9, GAD-7, and IES-R Scores Under Different Influencing Factors

Results of Logistic Regression Analysis on Different Variables with ISI, PHQ-9, GAD-7, and IES-R Scores

Logistic regression analysis was performed to analyze the data. The results showed that insomnia was associated with “education level” “sleep duration” “presence of underlying diseases” “duration of electronic device use until falling asleep” and “factors within FSH” (p<0.05 and Exp(B) values within the 95% confidence interval). Among them, “higher education level” “factors within FSH” and “duration of electronic device use” were risk factors. (OR values of 1.296, 8.190, and 1.279, respectively) (p<0.01), while “Longer sleep duration” and “absence of underlying diseases” were protective factors (OR values of 0.443 and 0.550, respectively) (p<0.01) (Figure 2A).

Figure 2 Logistic regression analysis of different factors and mental health. Abbreviations: OR, Odds ratio; 95% CI, 95% confidence interval. Notes: (A) Logistic regression analysis of different factors and depression. (B) Logistic regression analysis of different factors and sleep quality. (C) Logistic regression analysis of different factors and anxiety. (D) Logistic regression analysis of different factors and stress disorder.

Depressive symptoms was associated with “gender” “presence of underlying diseases” “education level” “total duration of electronic device use” “sleep duration” and “factors within FSH” (p<0.05 and Exp(B) values within the 95% confidence interval). Among them, “longer duration of electronic device use” “higher education level” and “factors within the field hospital” were risk factors (OR values of 1.359, 1.463, and 5.156, respectively) (p<0.05), while “longer sleep duration” “absence of underlying diseases” and “male gender” were protective factors (OR values of 0.672, 0.434, and 0.785) (p<0.05) (Figure 2B).

Anxiety symptoms was associated with “gender” “presence of underlying diseases” “education level” “sleep duration” and “duration of electronic device use until falling asleep” (p<0.05 and Exp(B) values within the 95% confidence interval). Among them, “longer duration of electronic device use” and “higher education level” were risk factors (OR values of 1.204 and 1.506, respectively) (p<0.01), while “longer sleep duration” “absence of underlying diseases” and “male gender” were protective factors (OR values of 0.691, 0.535, and 0.629, respectively) (p<0.05) (Figure 2C).

Stress disorders was associated with “age” “gender” “presence of underlying diseases” “factors within the field hospital” “education level” and “sleep duration” (p<0.05 and Exp(B) values within the 95% confidence interval). Among them, “higher education level” “age” and “factors within the field hospital” were risk factors (OR values of 1.389, 1.213, and 3.903, respectively) (p<0.01), while “Sleep duration” “absence of underlying diseases” and “male gender” were protective factors (OR values of 0.751, 0.577, and 0.749, respectively) (p<0.01) (Figure 2D).

Discussion

The establishment of FSH is a major initiative in China’s public health prevention and control measures.¹⁹ Patients in FSH are free to move around, and the government provides all necessary living and medical supplies. By admitting asymptomatic and mild COVID-19 patients to FSH, the rapid spread of the virus in the community could be effectively slowed down, which markedly relieving the pressure on the healthcare system and ensuring the safety of vulnerable patients.²⁰ However, since FSH are public areas, factors such as lighting, noisy, and sleep management regulations inevitably have an impact on the sleep quality, anxiety, depressive disorder, and stress disorders of some individuals.²¹ Therefore, quieter spaces should be provided for individuals experiencing severe insomnia, anxiety, and depressive symptoms in FSH.

In this study, we meticulously analyzed the psychological health status of 2803 COVID-19 patients, with a focus on the prevalence and severity of insomnia, depression, anxiety, and stress disorders. Our findings highlight the amplified mental health challenges in the specialized environment of FSH, where environmental stressors such as inadequate lighting, persistent noise, and rigid sleep management regulations significantly exacerbate mental health conditions.

Analyzing the relationship between various demographic and behavioral factors and their impact on mental health, as measured by the ISI, PHQ-9, GAD-7, and IES-R, we found that females, individuals with higher education, those with underlying medical conditions, shorter sleep durations, and extended electronic device usage exhibited higher scores across these mental health indices. This pattern aligns with existing research, highlighting the disproportionate impact of the pandemic on specific populations. In FSH, limited recreational activities lead patients to heavily rely on electronic devices for work, study, and social interaction. However, prolonged use of these devices can cause various mental health issues, including insomnia and disrupted sleep quality due to overexposure to blue light, and increased anxiety and stress disorders from excessive social media use and constant news updates.^22–24

During the COVID-19 pandemic, women experienced higher rates of insomnia, anxiety, depression, and stress disorders, attributed to physiological and hormonal differences such as menstrual cycles, pregnancy, and menopause.^25–27 Additionally, their increased responsibilities for family health and safety added to their stress disorders and anxiety.^28,29 Individuals with higher education levels also reported higher incidences of these mental health issues, possibly due to information overload and heightened concern about the pandemic’s risks.^30–32 Previous studies have shown that insomnia prevalence generally increases with age, but a study during the pandemic in France found higher rates of insomnia among young people, attributed to increased social isolation and economic difficulties.^33,34 Additionally, chronic diseases like hypertension, diabetes, and mental disorders are closely linked to poorer mental health outcomes, underscoring the importance of holistic management of both physical and mental health in these patients.^35–37 Moreover, noise and lighting issues in FSH significantly exacerbated patients’ psychological health problems, consistent with the findings of Stansfeld et al in similar environments.³⁸ However, our study further reveals the roles of factors such as education level and electronic device usage in influencing mental health, which have been less explored in previous research.

In response to these insights, we advocate for a multifaceted approach to enhance mental health care in FSH and similar healthcare settings. This includes improving environmental conditions, such as better lighting and noise control, refining sleep management policies, and providing targeted psychological support and interventions for high-risk groups, especially women and individuals with higher educational levels. Additionally, our study emphasizes the need for healthcare professionals and public health authorities to be mindful of the potential adverse effects of prolonged electronic device usage on mental health, especially during pandemics. Tailored guidelines and recommendations for healthier electronic device usage are crucial.

It is important to acknowledge the limitations inherent in our study. The concentration of our sample population in FSH may not fully represent the diverse experiences of all COVID-19 patients, especially those with severe symptoms or undergoing home isolation. The reliance on online survey methods, while reducing COVID-19 transmission risk, might introduce selection bias and limit the generalizability of our findings. Moreover, the lack of a control group limits our capacity to isolate the specific impacts attributable to FSH environment. Given these limitations, future research should aim for a more inclusive demographic and employ varied data collection methods. This would enable a more comprehensive understanding of the pandemic’s extensive impact on mental health across different patient populations and settings.

Conclusion

This study reveals that the psychological health status of patients in Fangcang shelter hospitals is influenced by factors such as gender, education level, duration of illness, underlying diseases, usage of electronic devices, light exposure, noise, and sleep management regulations. These insights underscore the necessity of implementing targeted psychological support and intervention strategies within healthcare settings, particularly in temporary medical facilities. Paying attention to environmental factors and managing patients’ lifestyles, alongside medical treatment, is crucial in alleviating the psychological distress experienced by COVID-19 patients. Moreover, our study serves as an essential resource for healthcare providers to understand and address the diverse psychological challenges faced by patients during such unprecedented health crises. Future research should continue to explore a broader population and the effectiveness of psychological health strategies in different medical settings, guiding the development of more effective mental health interventions.