covid 19 and mental health research paper

The impact of mental health and the COVID-19 pandemic on employability and learning outcomes: evidence from Taiwanese University students

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• Published: 28 August 2024
• Volume 5 , article number  216 , ( 2024 )

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• Yi-Chih Lee   ORCID: orcid.org/0009-0006-2565-4918 1  

In response to the emergence of COVID-19, schools were forced to adopt online learning, which later transitioned into a hybrid mode of teaching. However, these changes in the teaching and learning mode may have an adverse effect on mental health, thereby affecting learning outcomes. Therefore, providing immediate resource support for disadvantaged groups may improve students’ learning outcomes. This study investigated the impact of mental health on employability, learning outcomes in the context of blended learning, and the support provided by school resources among college students. We then analyzed survey data from university students and examined the associations among mental health, employability, learning outcomes before and after blended learning, frequency of seeking counseling, and school resource support. The research findings indicate that as the severity of mental health worsened, participants perceived lower learning ability for their future careers. Moreover, during the pandemic, there were variations in learning outcomes for students exposed to blended learning, but it was found that female students demonstrated better learning outcomes. It was also determined that it is beneficial for disadvantaged students to promptly apply for school resource support, as such support can contribute to improved learning effectiveness. Establishing mental health prevention mechanisms and providing school and external resources in a timely manner are the best solutions for helping students learn.

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1 Introduction

Mental health is an essential component of individual and collective health and well-being, representing people’s ability to exercise their human rights, not just the absence of disease [ 1 ]. However, under the impact of COVID-19, the global mental health condition has continued to deteriorate [ 1 ]. One in seven children and adolescents aged 10–19 years experience mental health issues worldwide, and suicide is the fourth leading cause of death among adolescents aged 15–29 years [ 2 ]. Individuals with mental health problems often face human rights violations and discrimination, with severe cases leading to premature death.

Mental health issues are prevalent in all countries, with varying prevalence rates based on sex and age [ 1 ]. Moreover, mental health significantly impacts individuals’ performance at school and work, their relationships with family and friends, and their ability to participate in society [ 3 ]. However, not all individuals with mental health issues experience low levels of mental well-being [ 1 ].

Many students with mental health issues pursue higher education, which may involve various pressures in their lives, including academic and social concerns [ 4 ]. Thus, there is a great need for mental health services and support for these students. A prepandemic survey conducted in Canada in 2019 reported that among more than 50,000 university students from 58 institutions, 68.9% experienced extreme anxiety, and 51.6% had experienced depression in the previous 12 months [ 5 ]. During the COVID-19 pandemic in the United States in 2021, a survey of more than 30,000 university students from 41 schools reported that 50.8% of the students experienced moderate psychological distress and 22% experienced severe psychological distress [ 6 ].

During the COVID-19 pandemic, researchers reported that Thai university students exhibited the highest levels of anxiety, whereas Taiwanese students presented the lowest levels of negative psychological responses [ 7 ]. Another study indicated that many Malaysian university students faced mental health issues during the pandemic [ 8 ]. In Taiwan, one out of every four university students sought psychological counseling [ 9 ].

Students choose to pursue higher education with the expectation that higher qualifications will lead to better job opportunities in the future. Learning workplace skills supports individuals in their daily activities in the workplace and ensures that they will be productive and fulfill the requisite job responsibilities. Workplace skills are generally categorized into two types: soft skills, which include communication, teamwork, critical thinking, among others, and hard skills, such as information technology, data analysis, and other skills that are often acquired through formal education [ 10 ]. Therefore, during their university studies, students need to not only focus on their academic courses but also develop their workplace skills. However, university students may encounter challenges with respect to their work performance due to mental health issues [ 11 ].

The first COVID-19 case in 2019 marked the beginning of the pandemic [ 12 ], a crisis that caused significant harm to people’s mental health. In the first year of the pandemic, the global incidence of depression and anxiety increased by 25%, and the number of people with mental health issues rose by nearly 1 billion [ 1 ]. Because of the pandemic, many university students suddenly had to shift from in-person learning to online learning, and the research indicates that this transition had a negative impact on academic performance [ 13 ].

According to a report by the United Nations [ 14 ], the COVID-19 pandemic created multiple stressors that led to increased or intensified anxiety and distress among individuals who previously experienced few to no such symptoms. Some individuals developed mental health issues, while those already suffering from mental health conditions saw their conditions worsen. During the COVID-19 pandemic, the stress arising from changes in academic responsibilities and daily life also exacerbated the mental health of university students [ 15 , 16 ]. A study of college students revealed that respondents reported increased anxiety due to the COVID-19 pandemic, citing concerns about family members contracting the virus, financial pressures, and academic disruptions [ 17 ].

Social status reflects individuals' education and occupation, influences their thoughts and attitudes, and consequently impacts their behavior [ 18 ]. People may seek relative social prestige, a higher economic class, and power in society to highlight their social status and achieve psychological satisfaction [ 19 , 20 , 21 ]. Education not only allows individuals to build their social networks but it also reflects their social standing [ 22 ]. Social status is observed publicly through a person's social mobility, thereby influencing other people’s beliefs about the individual’s level of intelligence. This motivation for status is often magnified into economic inequalities among people from different social backgrounds [ 23 ] given that social status affects individuals in the process of acquiring tangible resources and intangible social influence.

According to the United Nations, vulnerable groups include indigenous populations; ethnic, religious, and linguistic minorities; immigrants, refugees, asylum seekers, and internally displaced persons; individuals living in extreme poverty; women; and LGBTQI individuals [ 24 ]. In Taiwan, the Ministry of Education classifies university students as vulnerable if they fall into one of the two categories, namely, the culturally disadvantaged, which includes new immigrants and their children; and the economically disadvantaged, which includes students from low- and middle- to low-income households, students with disabilities, students whose parents have disabilities, indigenous students, students from families with special circumstances, and vulnerable students who receive financial assistance [ 25 , 26 ]. These classifications also constitute the criteria for defining vulnerable students in this study.

In Taiwan, according to statistics from 2022, the tuition fees at private universities were approximately twice as high as those at public universities, and yet, approximately 40% of university students attend public universities, whereas 60% attend private universities. Public universities receive more resources, but many vulnerable students can only attend private universities and face starting salaries approximately TWD 2,000 lower than their counterparts from public universities after graduation [ 27 ]. In Taiwan, educational attainment significantly influences employment opportunities. Students with lower social status may choose to continue their university studies to have better job prospects, but this decision often entails higher tuition fees and, in some cases, student loans, as there are relatively few educational resources. After entering the workforce, they may receive lower starting salaries or struggle to find good jobs and thus fall into the trap of the poverty cycle [ 27 ].

Vulnerable students at Taiwanese universities can access various resources provided by the Ministry of Education, including tuition assistance, living subsidies, emergency relief grants, accommodation benefits, scholarships, academic counseling funds, peer support funds, career counseling, accommodation and transportation subsidies for those from remote areas attending interviews, etc. [ 25 , 26 , 28 ]. Additionally, universities raise funds to assist these students. In this study, the higher education SPROUT project refers to the external resources provided by the Ministry of Education to support vulnerable students.

Research has shown that being in a marginalized racial group, having low socioeconomic status, being unemployed, and having similar factors are risk factors for negative mental health in adults after disasters, particularly in the postpandemic context [ 29 ]. As a consequence, there is increasing concern about the impact of COVID-19 on the mental health of vulnerable populations [ 30 ], as the economic repercussions may lead to students losing internship opportunities and low-income students experiencing delays in graduation [ 31 ]. Therefore, economically disadvantaged students who faced multiple impacts during the COVID-19 period are in greater need of support from the government, schools, and society [ 32 ].

Previous studies have explored the impact of the mental health of workers on their work performance [ 33 , 34 ], and have confirmed a link between poor mental health and reduced work capacity. Additionally, many studies have investigated the effects of the COVID-19 pandemic on students' mental health and learning outcomes [ 35 , 36 , 37 ]. Paz et al. [ 35 ] synthesize 47 articles and conclude that the COVID-19 outbreak had negative effects on medical students' mental health that resulted in increased levels of stress, depression, and anxiety as well as emotional and behavioral changes. Koh and Daniel [ 36 ] analyze 36 articles on teaching strategies implemented by higher education institutions during the pandemic and report that designing replicable online classes, providing online practical skills training, ensuring integrity in online assessments, and implementing student engagement strategies improve the quality of online learning. Meo et al.'s research suggests that isolation among university students led to emotional distancing from family, colleagues, and friends, resulting in decreased overall work performance and reduced study time, and hence, negative effects on learning outcomes [ 37 ].

However, despite numerous studies exploring the impact of mental health factors and COVID-19 on education and the recognized need to support disadvantaged students affected by the pandemic [ 38 ], there is a paucity of empirical research that comprehensively examines the effects of employability and learning outcomes among students with varying mental health statuses and socioeconomic backgrounds during the pandemic. Furthermore, investigations into the effectiveness of providing appropriate resource support to students in need under the adverse conditions of the pandemic are limited. Therefore, this study significantly contributes to filling this research gap.

This study had several objectives. First, it aimed to investigate the impact of mental health on employability among university students. The second objective was to analyze whether students with different mental health statuses before and after the pandemic were affected differently by various teaching methods with respect to their learning outcomes. The third objective was to explore whether students with different mental health statuses, genders, and social statuses, especially those from vulnerable backgrounds, experienced learning difficulties during the pandemic. Finally, the study aimed to determine whether providing timely resources and support to vulnerable students during the pandemic contributed to improved learning outcomes.

2.1 Samples

This study used secondary data from different databases. The data source was a private university in Taoyuan, Taiwan, which conducted surveys on mental health, employability, learning outcomes before and after the COVID-19 pandemic, and use of scholarships among daytime students. The purpose of administering the psychological health questionnaire was to proactively provide counseling and assistance for high-risk students to support their academic pursuits. The employability survey was aimed at assessing students' individual capabilities and strengths in the workplace. Both questionnaires were administered by the school's psychologists, who personally explained the survey objectives to the students. The age and gender variables used in the study were based on the information provided by the students on their enrollment forms.

Learning outcomes were presented based on the course assessments conducted at Taiwan University from 2021 to 2022, including both in-person and blended teaching approaches in response to the COVID-19 pandemic. In this study, blended teaching refers to a method in which instructors combine online and offline instruction via video conferencing tools or cloud-based smart classrooms as a novel approach for delivering instruction. School-supported financial aid refers to scholarships provided to students with lower social status who voluntarily participated in the higher education SPROUT project and engaged in school activities to receive awards and financial assistance. Survey participants consented and voluntarily completed the questionnaires after being informed of the survey’s purpose.

Before the data analysis, all database information was de-identified. The research project was classified as minimal risk, and the risks to participants were not greater than those encountered by nonparticipants. Following an assessment by the review committee, the project was deemed exempt from review, and an exemption certificate was issued. This project was certified for exemption from the Human Research Ethics Committee at National Cheng Kung University (HREC (Exempt)112–502). The study was performed in accordance with the ethical standards of the 1964 Declaration of Helsinki and its later amendments and comparable ethical standards.

2.2 Measures

The psychological health variables were measured via the Ko Depression Inventory (KDI) [ 39 ], which consists of 26 items. This inventory categorizes depressive symptoms into five major domains: mood (e.g., irritability), physical activity (e.g., decreased or increased appetite), behavioral ability (e.g., psychomotor retardation or agitation), cognitive thinking (e.g., pessimism), and motivation (e.g., loss of interest). The options are presented on a 4-point scale ranging from 0 (not feeling low or rarely feeling low) to 3 (feeling low and depressed all the time, unable to improve, and intolerably distressed). Summing the scores of all items, a total score of 0–9 indicates no depressive symptoms, a score of 10–20 suggests mild to moderate depression, and a score of 21 or above indicates a state of high-level depression. The inventory exhibits good reliability and validity [ 39 ].

The employability survey questionnaire adopted the UCAN Higher Education Employment Competency questionnaire developed by the Taiwan Ministry of Education [ 40 ]. Generic workplace competency refers to the skills required for various professions and are measured across eight categories: communication and expression, continuous learning, interpersonal interaction, teamwork, problem-solving, innovation, work responsibility and discipline, and information technology application. The questionnaire consists of a total of 54 items, with a score of 1 indicating, "I am unable to complete this task and find it difficult to learn," and a score of 5 indicating, "I can perform this task exceptionally well".

2.3 Data analysis

Data analyses were performed via the IBM Statistical Package for the Social Sciences (SPSS) version 18 (Chicago: SPSS Inc.) [ 41 ]. This study used different statistical analyses based on different variables and different situations. The variable analysis in this study was conducted using means, standard deviations, chi-square tests, ANOVA, the Scheffe test, Welch's ANOVA, the Games–Howell test, independent sample t tests, paired sample t tests, and regression analysis. A p -value less than 0.05 indicates statistical significance.

The survey collected data from 2592 students whose average age was 22.9 (SD ± 0.8) years. Table 1 provides the students’ mental health conditions and their basic characteristics, employability, and learning outcomes. The participants underwent a psychological health assessment, and the majority of the students reported having no depressive symptoms, whereas approximately 15.3% indicated that they experienced severe depressive states.

As the severity of their psychological health worsened, the participants perceived that they had weaker workplace competency. There was no significant difference among individuals with different psychological health conditions in the context of face-to-face learning, but learning outcomes varied in the blended learning context. A comparison of learning outcomes before and during the COVID-19 pandemic revealed that students with severe depression experienced the sharpest decline in learning outcomes (75.0 vs. 73.6; p = 0.019). Moreover, during the pandemic, students with severe depression sought counseling more frequently.

A cross-analysis of mental health status and gender revealed that, in the blended learning context, individuals with good mental health, especially women, exhibited better learning outcomes than did men (78.8 ± 12.4 vs. 72.3 ± 14.3; p < 0.001), had fewer absences (14.7 ± 24.1 vs. 25.2 ± 38.3; p < 0.001), and failed fewer subjects (0.9 ± 1.9 vs. 1.4 ± 2.4; p < 0.001). Among those with mild to moderate depression, women also exhibited better learning outcomes than did men (80.2 ± 12.9 vs. 74.0 ± 14.0; p < 0.001), had fewer absences (13.7 ± 28.9 vs. 23.7 ± 35.9; p < 0.001), and failed fewer subjects (0.7 ± 1.7 vs. 1.3 ± 2.5; p < 0.001). Even among those with severe depression, women had better learning outcomes than did men (75.9 ± 14.3 vs. 72.1 ± 14.5; p = 0.031), but there were no statistically significant differences in absences or failed subjects between the genders.

Overall, in the blended learning context, disadvantaged students had lower learning outcomes than did non-disadvantaged students (73.1 ± 16.3 vs. 75.1 ± 13.7; p = 0.048). When the cross-analysis is based on mental health and disadvantaged status, in the blended teaching context, disadvantaged students with good mental health had more absences than did the non-disadvantaged students (33.1 ± 47.0 vs. 20.9 ± 33.1; p = 0.005), while the remaining classifications exhibited no difference. Among the students with mild to moderate depression, the disadvantaged group had poorer learning outcomes than did the non-disadvantaged the group (73.6 ± 15.5 vs. 76.5 ± 13.7; p = 0.037), had more absences (27.7 ± 40.0 vs. 19.2 ± 32.9; p = 0.028), and they also failed more subjects (1.6 ± 3.0 vs. 1.0 ± 2.1; p = 0.041). Among those with severe depression, there were no statistically significant differences between disadvantaged and non-disadvantaged students in terms of learning outcomes, absences, or failed subjects.

With respect to disadvantaged groups, further analysis on the basis of mental health status and gender revealed that among mentally healthy individuals, women had better learning outcomes (77.1 ± 16.1 vs. 69.4 ± 18.0; p = 0.020) and fewer absences (19.3 ± 30.7 vs. 40.2 ± 52.3; p = 0.005). In the case of students with mild to moderate depression, gender was statistically significant only for learning outcomes, with women outperforming men (78.8 ± 15.9 vs. 71.7 ± 15.0; p = 0.029). However, no statistically significant differences between genders were observed among students with severe depression.

Vulnerable students with different mental health statuses who participated in the higher education SPROUT project exhibited improved learning outcomes and failed fewer subjects, as shown in Table  2 . Students who received more financial aid had higher academic achievement and failed fewer subjects. Table 3 shows that students with various mental health statuses experienced better learning outcomes after joining the higher education SPROUT project.

4 Discussion

This study reveals the impact of mental health on the employability of Taiwanese university students, as well as their learning outcomes under different teaching strategies and varied participation in financial aid programs. This study specifically explored these relationships with respect to vulnerable students and gender. This is the first study in Taiwan to integrate mental health status, workplace and professional competency, and support from assistance programs among university students during the COVID-19 pandemic.

This study investigated students with different mental health issues and examined their perceptions regarding whether they had sufficient knowledge and skills to successfully complete work tasks or improve their personal performance. The results indicated that students with good mental health had the best performance, whereas those with severe depression rated their personal ability as the lowest among the three groups. Students with severe depression perceived that they were less proficient in various skills, including expressing their thoughts effectively for others to understand and comprehending information conveyed by others; efficiently planning and managing their time with a growth mindset; adopting appropriate ways to interact with others on the basis of different situations; actively participating in team tasks; having positive interactions with team members to achieve goals; identifying and systematically solving problems; proposing effective methods to improve systems or processes; understanding and executing personal tasks within the organization while adhering to ethical, regulatory, and integrity requirements; and utilizing information technology to manage information effectively. These findings are consistent with previous studies that reported that students with suicidal ideation had lower problem-solving ability than did those without such thoughts [ 42 ]. This suggests that individuals with better mental health are more likely to exhibit personal efficacy and autonomy as well as better ability to interact with others and realize their potential [ 43 ].

Additionally, this study revealed that during the COVID-19 pandemic, students continued to seek psychological counseling from their schools. Before the pandemic, there were no statistically significant difference in learning outcomes based on students' mental states when they were attending in-person classes. However, after the pandemic, the transition to virtual learning had varied effects on students’ mental states. Specifically, students with mild to moderate depression had better learning outcomes. Research has indicated that individuals who experienced social anxiety before COVID-19 tend to prefer virtual learning [ 44 ], which could explain why the students in this study with mild to moderate depression exhibited improved learning outcomes, as long as they were not further affected by external factors. Conversely, another study noted that some students mentioned that during the pandemic, anxiety and stress motivated them to work harder in their studies, but most students believed that high-risk depressive symptoms were associated with lower academic performance [ 45 ]. This study also revealed that the pandemic led to lower learning outcomes among students with severe depression and caused further setbacks in their grades.

In the context of blended learning, gender had a stronger influence on learning outcomes than did mental health status. Compared with men, women outperformed men academically, and among students with mild to moderate depression and those with normal mental health, women received fewer failing grades and had fewer absences. Among the groups with good mental health and severe depression, there were no differences in learning outcomes between non-disadvantaged and disadvantaged students. However, in the mild to moderate depression group, vulnerable students had lower outcomes, with more failing grades and absences. Regarding the impact of gender on learning outcomes during the COVID-19 period, there is no consensus in the current research [ 46 ]. A South African study reported that girls lagged behind boys in reading performance [ 47 ], whereas other studies reported that girls academically outperformed boys [ 48 , 49 ]. In this study, female students had better learning outcomes in the blended learning context. However, some research suggests that the pandemic may have led vulnerable families to allocate valuable educational resources to boys, thereby affecting girls' educational performances [ 50 ]; however, this study did not find any differences in resource allocation, but rather, it revealed better learning outcomes among girls.

With respect to social status, this study analyzed the possibility for individuals to gradually rise from their disadvantaged position by participating in the higher education SPROUT project and by acquiring resources and transforming them into personal capabilities through the learning process. Students with lower social status do not have an inferior learning nature or diminished abilities. The main obstacles that hinder their learning process are economic and cultural. For example, they may need to spend more time working to meet their living expenses, which limits their ability to fully and freely spend time and economic resources on showcasing their talents. However, for students with lower social status, actively pursuing external offerings, such as participating in school scholarship assistance programs, seeking academic guidance, engaging in competitions, and obtaining the certifications required for employment, can provide these students with additional economic resources. This not only helps them meet their basic living needs but also reduces the time spent on work, allowing them to focus more on academic learning.

The results of this study show that disadvantaged students with different mental health conditions can improve their learning outcomes by participating in the higher education SPROUT project. Such students can significantly reduce the number of failed courses, absences, and leaves of absence. By effectively transferring their time resources from work to learning and acquiring multiple professional skills, they are more likely to obtain job interview opportunities in the future. This, in turn, will enable them to effectively transform their social status.

5 Limitations

This study is limited to data from a single university; thus, it reflects only the performance of students within that specific institution. However, the results align with similar findings in studies conducted in different countries. A limitation is that gender minorities, such as nonbinary individuals, were excluded from this study. Furthermore, the scale used in this study is not a depression and employability questionnaire but rather is specifically designed for the COVID-19 pandemic. Another limitation of this research is the inability to obtain reliability and validity data for the questionnaire items. In addition, the survey is an ongoing test that was initiated before the outbreak, and the timing may have had some influence that this study did not take into account. Therefore future research should examine different time points to study the changes in mental health and students' learning outcomes.

6 Conclusion

Overall, our study revealed that the mental health status of university students influenced their employability and learning outcomes in the context of blended learning. During the pandemic, students from vulnerable backgrounds exhibited lower learning outcomes than did those from nonvulnerable backgrounds. Furthermore, this study found that female students outperformed male students across all groups. During the pandemic, students with poorer mental health sought school counseling and guidance at a higher rate. In situations with limited resources, participating in school support programs helped to improve the learning outcomes of vulnerable students with different mental health statuses. Therefore, in the long run, establishing mental health prevention mechanisms and providing immediate access to schools and external resources are the optimal solutions for supporting student learning.

Data availability

The relevant data can be requested from the author via email.

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Global prevalence of mental health issues among the general population during the coronavirus disease-2019 pandemic: a systematic review and meta-analysis

• Surapon Nochaiwong   ORCID: orcid.org/0000-0003-1100-7171 1 , 2 ,
• Chidchanok Ruengorn   ORCID: orcid.org/0000-0001-7927-1425 1 , 2 ,
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• Tinakon Wongpakaran   ORCID: orcid.org/0000-0002-9062-3468 6  

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To provide a contemporary global prevalence of mental health issues among the general population amid the coronavirus disease-2019 (COVID-19) pandemic. We searched electronic databases, preprint databases, grey literature, and unpublished studies from January 1, 2020, to June 16, 2020 (updated on July 11, 2020), with no language restrictions. Observational studies using validated measurement tools and reporting data on mental health issues among the general population were screened to identify all relevant studies. We have included information from 32 different countries and 398,771 participants. The pooled prevalence of mental health issues amid the COVID-19 pandemic varied widely across countries and regions and was higher than previous reports before the COVID-19 outbreak began. The global prevalence estimate was 28.0% for depression; 26.9% for anxiety; 24.1% for post-traumatic stress symptoms; 36.5% for stress; 50.0% for psychological distress; and 27.6% for sleep problems. Data are limited for other aspects of mental health issues. Our findings highlight the disparities between countries in terms of the poverty impacts of COVID-19, preparedness of countries to respond, and economic vulnerabilities that impact the prevalence of mental health problems. Research on the social and economic burden is needed to better manage mental health problems during and after epidemics or pandemics. Systematic review registration : PROSPERO CRD 42020177120.

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Introduction.

After the World Health Organisation (WHO) declared the rapid worldwide spread of coronavirus disease-2019 (COVID-19) to be a pandemic, there has been a dramatic rise in the prevalence of mental health problems both nationally and globally 1 , 2 , 3 . Early international evidence and reviews have reported the psychological effects of the COVID-19 outbreak on patients and healthcare workers, particularly those in direct contact with affected patients 4 , 5 , 6 , 7 , 8 . Besides patients with COVID-19, negative emotions and psychosocial distress may occur among the general population due to the wider social impact and public health and governmental response, including strict infection control, quarantine, physical distancing, and national lockdowns 2 , 9 , 10 .

Amid the COVID-19 pandemic, several mental health and psychosocial problems, for instance, depressive symptoms, anxiety, stress, post-traumatic stress symptoms (PTSS), sleep problems, and other psychological conditions are of increasing concern and likely to be significant 5 , 10 , 11 . Public psychological consequences can arise through direct effects of the COVID-19 pandemic that are sequelae related to fear of contagion and perception of danger 2 . However, financial and economic issues also contribute to mental health problems among the general population in terms of indirect effects 12 , 13 . Indeed, economic shutdowns have disrupted economies worldwide, particularly in countries with larger domestic outbreaks, low health system preparedness, and high economic vulnerability 14 , 15 , 16 .

The COVID-19 pandemic may affect the mental health of the general population differently based on national health and governmental policies implemented and the public resilience and social norms of each country. Unfortunately, little is known about the global prevalence of mental health problems in the general population during the COVID-19 pandemic. Previous systematic reviews have been limited by the number of participants included, and attention has been focussed on particular conditions and countries, with the majority of studies being conducted in mainland China 5 , 8 , 11 , 17 , 18 . To the best of our knowledge, evidence on mental health problems among the general population worldwide has not been comprehensively documented in the current COVID-19 pandemic. Therefore, a systematic review and meta-analysis at a global level is needed to provide robust and contemporary evidence to inform public health policies and long-term responses to the COVID-19 pandemic.

As such, we have performed a rigorous systematic review and meta-analysis of all available observational studies to shed light on the effects of the global COVID-19 pandemic on mental health problems among the general population. We aimed to: (1) summarise the prevalence of mental health problems nationally and globally, and (2) describe the prevalence of mental health problems by each WHO region, World Bank income group, and the global index and economic indices responses to the COVID-19 pandemic.

This systematic review and meta-analysis was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines 19 and reported in line with the Meta-analysis of Observational Studies in Epidemiology statement (Appendix, Table S1 ) 20 . The pre-specified protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO: CRD42020177120).

Search strategy

We searched electronic databases in collaboration with an experienced medical librarian using an iterative process. PubMed, Medline, Embase, PsycINFO, Web of Science, Scopus, CINAHL, and the Cochrane Library were used to identify all relevant abstracts. As the WHO declared the COVID-19 outbreak to be a public health emergency of international concern on January 30, 2020, we limited the search from January 1, 2020, to June 16, 2020, without any language restrictions. The main keywords used in the search strategy included “coronavirus” or “COVID-19” or “SARS-CoV-2”, AND “mental health” or “psychosocial problems” or “depression” or “anxiety” or “stress” or “distress” or “post-traumatic stress symptoms” or “suicide” or “insomnia” or “sleep problems” (search strategy for each database is provided in the Appendix, Table S2 ). Relevant articles were also identified from the reference lists of the included studies and previous systematic reviews. To updated and provide comprehensive, evidence-based data during the COVID-19 pandemic, grey literature from Google Scholar and the preprint reports from medRxiv, bioRxiv, and PsyArXiv were supplemented to the bibliographic database searches. A targeted manual search of grey literature and unpublished studies was performed through to July 11, 2020.

Study selection and data screening

We included observational studies (cross-sectional, case–control, or cohort) that (1) reported the occurrence or provided sufficient data to estimate the prevalence of mental health problems among the general population, and (2) used validated measurement tools for mental health assessment. The pre-specified protocol was amended to permit the inclusion of studies the recruited participants aged 12 years or older and college students as many colleges and universities were closed due to national lockdowns. We excluded studies that (1) were case series/case reports, reviews, or studies with small sample sizes (less than 50 participants); (2) included participants who had currently confirmed with the COVID-19 infection; and (3) surveyed individuals under hospital-based settings. If studies had overlapping participants and survey periods, then the study with the most detailed and relevant information was used.

Eligible titles and abstracts of articles identified by the literature search were screened independently by two reviewers (SN and CR). Then, potentially relevant full-text articles were assessed against the selection criteria for the final set of included studies. Potentially eligible articles that were not written in English were translated before the full-text appraisal. Any disagreement was resolved by discussion.

The primary outcomes were key parameters that reflect the global mental health status during the COVID-19 pandemic, including depression, anxiety, PTSS, stress, psychological distress, and sleep problems (insomnia or poor sleep). To deliver more evidence regarding the psychological consequences, secondary outcomes of interest included psychological symptoms, suicidal ideation, suicide attempts, loneliness, somatic symptoms, wellbeing, alcohol drinking problems, obsessive–compulsive symptoms, panic disorder, phobia anxiety, and adjustment disorder.

Data extraction and risk of bias assessment

Two reviewers (SN and YR) independently extracted the pre-specified data using a standardised approach to gather information on the study characteristics (the first author’s name, study design [cross-sectional survey, longitudinal survey, case–control, or cohort], study country, article type [published article, short report/letters/correspondence, or preprint reporting data], the data collection period), participant characteristics (mean or median age of the study population, the proportion of females, proportion of unemployment, history of mental illness, financial problems, and quarantine status [never, past, or current]), and predefined outcomes of interest (including assessment outcome definitions, measurement tool, and diagnostic cut-off criteria). For international studies, data were extracted based on the estimates within each country. For studies that had incomplete data or unclear information, the corresponding author was contacted by email for further clarification. The final set of data was cross-checked by the two reviewers (RA and CP), and discrepancies were addressed through a discussion.

Two reviewers (SN and CR) independently assessed and appraised the methodological quality of the included studies using the Hoy and colleagues Risk of Bias Tool-10 items 21 . A score of 1 (no) or 0 (yes) was assigned to each item. The higher the score, the greater the overall risk of bias of the study, with scores ranging from 0 to 10. The included studies were then categorised as having a low (0–3 points), moderate (4–6 points), or high (7 or 10 points) risk of bias. A pair of reviewers (RA and CP) assessed the risk of bias of each study. Any disagreements were resolved by discussion.

Data synthesis and statistical methods

A two-tailed P value of less than 0.05 was considered statistically significant. We used Stata software version 16.0 (StataCorp, College Station, TX, USA) for all analyses and generated forest plots of the summary pooled prevalence. Inter-rater agreements between reviewers for the study selection and risk of bias assessment were tested using the kappa (κ) coefficient of agreement 22 . Based on the crude information data, we recalculated and estimated the unadjusted prevalence of mental health and psychological problems using the crude numerators and denominators reported by each of the included studies. Unadjusted pooled prevalence with corresponding 95% confidence intervals (CIs) was reported for each WHO regions (Africa, America, South-East Asia, Europe, Eastern Mediterranean, and Western Pacific) and World Bank income group (low-, lower-middle-, upper-middle-, and high-income).

We employed the variance of the study-specific prevalence using the Freeman–Tukey double arcsine methods for transforming the crude data before pooling the effect estimates with a random-effect model to account for the effects of studies with extreme (small or large) prevalence estimates 23 . Heterogeneity was evaluated using the Cochran’s Q test, with a p value of less than 0.10 24 . The degree of inconsistency was quantified using I 2 values, in which a value greater than 60–70% indicated the presence of substantial heterogeneity 25 .

Pre-planned subgroup analyses were performed based on the participant (i.e., age, the proportion of female sex, the proportion of unemployment, history of mental illness, financial problems, and quarantine status) and study characteristics (article type, study design, data collection, and sample size). To explore the inequality and poverty impacts across countries, subgroup analyses based on the global index and economic indices responses to the COVID-19 pandemic were performed, including (1) human development index (HDI) 2018 (low, medium, high, and very high) 26 ; (2) gender inequality index 2018 (below vs above world average [0.439]) 27 ; (3) the COVID-19-government response stringency index during the survey (less- [less than 75%], moderate- [75–85%], and very stringent [more than 85%]) according to the Oxford COVID-19 Government Response Tracker reports 28 ; (4) the preparedness of countries in terms of hospital beds per 10,000 people, 2010–2018 (low, medium–low, medium, medium–high, and high) 15 ; (5) the preparedness of countries in terms of current health expenditure (% of gross domestic product [GDP] 2016; low, medium–low, medium, medium–high, and high) 15 ; (6) estimated percent change of real GDP growth based on the International Monetary Fund, April 2020 (below vs above world average [− 3.0]) 29 ; (7) the resilience of countries’ business environment based on the 2020 global resilience index reports (first-, second-, third-, and fourth-quartile) 30 ; and (8) immediate economic vulnerability in terms of inbound tourism expenditure (% of GDP 2016–2018; low, medium–low, medium, medium–high, and high) 15 .

To address the robustness of our findings, we conducted a sensitivity analysis by restricting the analysis to studies with a low risk of bias (Hoy and Colleagues-Tool, 0–3 points). Furthermore, a random-effects univariate meta-regression analysis was used to explore the effect of participant and study characteristics, and the global index and economic indices responses to the COVID-19 pandemic as described above on the prevalence estimates.

The visual inspection of funnel plots was performed when there was sufficient data and tested for asymmetry using the Begg’s and Egger’s tests for each specific. A P value of less than 0.10 was considered to indicate statistical publication bias 31 , 32 . If the publication bias was detected by the Begg’s and Egger’s regression test, the trim and fill method was then performed to calibrate for publication bias 33 .

Initially, the search strategy retrieved 4642 records. From these, 2682 duplicate records were removed, and 1960 records remained. Based on the title and abstract screening, we identified 498 articles that seemed to be relevant to the study question (the κ statistic for agreement between reviewers was 0.81). Of these, 107 studies fulfilled the study selection criteria and were included in the meta-analysis (Appendix, Figure S1 ). The inter-rater agreement between reviewers on the study selection and data extraction was 0.86 and 0.75, respectively. The reference list of all included studies in this review is provided in the Appendix, Table S3 .

Characteristics of included studies

In total, 398,771 participants from 32 different countries were included. The mean age was 33.5 ± 9.5 years, and the proportion of female sex was 60.9% (range, 16.0–51.6%). Table 1 summarises the characteristics of all the included studies according to World Bank income group, the global index of COVID-19 pandemic preparedness, and economic vulnerability indices. The included studies were conducted in the Africa (2 studies 34 , 35 [1.9%], n = 723), America (12 studies 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 [11.2%], n = 18,440), South-East Asia (10 studies 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 [9.4%], n = 11,953), Europe (27 studies 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 [25.2%], n = 148,430), Eastern Mediterranean (12 studies 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 [11.2%], n = 23,396), and Western Pacific WHO regions (44 studies 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 [41.1%], n = 195,829). Most of the included studies were cross-sectional (96 studies, 89.7%), used an online-based survey (101 studies, 95.3%), conducted in mainland China (34 studies, 31.8%), and were conducted in countries with upper-middle (49 studies, 45.8%) and high-incomes (44 studies, 41.1%). Detailed characteristics of the 107 included studies, measurement tools for evaluating the mental health status and psychological consequences, and the diagnostic cut-off criteria are described in Appendix, Table S4 . Of the 107 included studies, 76 (71.0%) had a low risk, 31 (29.0%) had a moderate risk, and no studies had a high risk of bias (Appendix, Table S5 ).

Global prevalence of mental health issues among the general population amid the COVID-19 pandemic

Table 2 presents a summary of the results of the prevalence of mental health problems among the general population amid the COVID-19 pandemic by WHO region and World Bank country groups. With substantial heterogeneity, the global prevalence was 28.0% (95% CI 25.0–31.2) for depression (75 studies 34 , 35 , 36 , 37 , 38 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 57 , 58 , 60 , 61 , 64 , 66 , 67 , 68 , 69 , 70 , 71 , 73 , 74 , 75 , 76 , 77 , 80 , 81 , 82 , 83 , 87 , 88 , 91 , 93 , 96 , 97 , 99 , 101 , 104 , 105 , 106 , 107 , 108 , 109 , 112 , 113 , 114 , 116 , 117 , 119 , 120 , 122 , 124 , 125 , 126 , 127 , 129 , 130 , 131 , 132 , 133 , 134 , 136 , 138 , 139 , 140 , n = 280,607, Fig.  1 ); 26.9% (95% CI 24.0–30.0) for anxiety (75 studies 35 , 37 , 38 , 40 , 42 , 43 , 44 , 46 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 57 , 58 , 60 , 61 , 64 , 66 , 67 , 68 , 69 , 71 , 73 , 74 , 75 , 76 , 77 , 80 , 81 , 82 , 83 , 87 , 88 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 104 , 105 , 107 , 108 , 109 , 112 , 113 , 114 , 115 , 116 , 117 , 119 , 120 , 122 , 124 , 125 , 126 , 129 , 130 , 131 , 132 , 133 , 134 , 136 , 138 , 139 , 140 , n = 284,813, Fig.  2 ); 24.1% (95% CI 17.0–32.0) for PTSS (28 studies 35 , 44 , 56 , 59 , 62 , 64 , 66 , 69 , 75 , 78 , 80 , 81 , 82 , 89 , 90 , 91 , 106 , 109 , 110 , 111 , 119 , 123 , 124 , 125 , 127 , 131 , 135 , 138 , n = 56,447, Fig.  3 ); 36.5% (95% CI 30.0–43.3) for stress (22 studies 37 , 50 , 51 , 52 , 53 , 54 , 57 , 58 , 71 , 73 , 75 , 76 , 80 , 114 , 117 , 119 , 120 , 122 , 125 , 129 , 131 , 136 , n = 110,849, Fig.  4 ); 50.0% (95% CI 41.8–58.2) for psychological distress (18 studies 39 , 47 , 52 , 59 , 63 , 65 , 70 , 72 , 78 , 79 , 85 , 86 , 88 , 102 , 110 , 118 , 121 , 128 , n = 81,815, Fig.  5 ); and 27.6% (95% CI 19.8–36.1) for sleep problems (15 studies 35 , 53 , 58 , 80 , 84 , 103 , 106 , 107 , 109 , 119 , 120 , 125 , 134 , 136 , 137 , n = 99,534, Fig.  6 ). The prevalence of mental health problems based on different countries varied (Appendix, Table S6 ), from 14.5% (South Africa) to 63.3% (Brazil) for depressive symptoms; from 7.7% (Vietnam) to 49.9% (Mexico) for anxiety; from 10.5% (United Kingdom) to 52.0% (Egypt) for PTSS; from 19.7% (Portugal) to 72.8% (Thailand) for stress; from 23.9% (China) to Jordan (92.9%) for psychological distress; from 9.2% (Italy) to 53.9% (Thailand) for sleep problems.

Pooled prevalence of depression among the general population amid the COVID-19 pandemic. COVID-19 coronavirus disease 2019, CI confidence interval, df degree of freedom, NA not applicable. References are listed according to WHO region in the appendix, Table S3 .

Pooled prevalence of anxiety among the general population amid the COVID-19 pandemic. COVID-19 coronavirus disease 2019, CI confidence interval, df degree of freedom, NA not applicable. References are listed according to WHO region in the appendix, Table S3 .

Pooled prevalence of PTSS among the general population amid the COVID-19 pandemic. COVID-19 coronavirus disease 2019, CI confidence interval, df degree of freedom, NA not applicable, PTSS post-traumatic stress symptoms. References are listed according to WHO region in the appendix, Table S3 .

Pooled prevalence of stress among the general population amid the COVID-19 pandemic. COVID-19 coronavirus disease 2019, CI confidence interval, df degree of freedom, NA not applicable. References are listed according to WHO region in the appendix, Table S3 .

Pooled prevalence of psychological distress among the general population amid the COVID-19 pandemic. COVID-19 coronavirus disease 2019, CI confidence interval, df degree of freedom, NA not applicable. References are listed according to WHO region in the appendix, Table S3 .

Pooled prevalence of sleep problems among the general population amid the COVID-19 pandemic. COVID-19 coronavirus disease 2019, CI confidence interval, df degree of freedom, NA not applicable. References are listed according to WHO region in the appendix, Table S3 .

With respect to the small number of included studies and high degree of heterogeneity, the pooled secondary outcome prevalence estimates are presented in Appendix, Table S7 . The global prevalence was 16.4% (95% CI 4.8–33.1) for suicide ideation (4 studies 36 , 41 , 53 , 124 , n = 17,554); 53.8% (95% CI 42.4–63.2) for loneliness (3 studies 41 , 44 , 45 , n = 2921); 30.7% (95% CI 2.1–73.3) for somatic symptoms (3 studies 53 , 69 , 134 , n = 7230); 28.6% (95% CI 9.2–53.6) for low wellbeing (3 studies 53 , 68 , 97 , n = 15,737); 50.5% (95% CI 49.2–51.7) for alcohol drinking problems (2 studies 97 , 114 , n = 6145); 6.4% (95% CI 5.5–7.4) for obsessive–compulsive symptoms (2 studies 73 , 134 , n = 2535); 25.7% (95% CI 23.7–27.8) for panic disorder (1 study 74 , n = 1753); 2.4% (95% CI 1.6–3.4) for phobia anxiety (1 study 134 , n = 1255); 22.8% (95% CI 22.1–23.4) for adjustment disorder (1 study 80 , n = 18,147); and 1.2% (95% CI 1.0–1.4) for suicide attempts (1 study 36 , n = 10,625).

Subgroup analyses, sensitivity analyses, meta-regression analyses, and publication bias

In the subgroup analyses (Appendix, Table S8 , Table S9 , Table S10 , Table S1 , Table S12 ), the prevalence of mental health problems was higher in countries with a low to medium HDI (for depression, anxiety, PTSS, and psychological distress), high HDI (for sleep problems), high gender inequality index (for depression and PTSS), very stringent government response index (for PTSS and stress), less stringent government response index (for sleep problems), low to medium hospital beds per 10,000 people (for depression, anxiety, PTSS, stress, psychological distress, and sleep problems), low to medium current health expenditure (for depression, PTSS, and psychological distress), estimated percent change of real GDP growth 2020 below − 3.0 (for psychological distress), low resilience (fourth-quartile) of business environment (for depression, anxiety, and PTSS), medium resilience (second-quartile) of business environment (for psychological distress, and sleep problems), high economic vulnerability-inbound tourism expenditure (for psychological distress, sleep problems), article type-short communication/letter/correspondence (for stress), cross-sectional survey (for PTSS and psychological distress), longitudinal survey (for anxiety and stress), non-mainland China (for depression, anxiety, and psychological distress), sample size of less than 1000 (for psychological distress), sample size of more than 5000 (for PTSS), proportion of females more than 60% (for stress and sleep problems), and measurement tools (for depression, anxiety, stress, and sleep problems). However, several pre-planned subgroup analyses based on participant characteristics and secondary outcomes reported could not be performed due to limited data in the included studies.

Findings from the sensitivity analysis were almost identical to the main analysis (Appendix, Table S14 ). The pooled prevalence by restricting the analysis to studies with a low risk of bias was 28.6% (95% CI 25.1–32.3) for depression, 27.4% (95% CI 24.1–30.8) for anxiety, 30.2% (95% CI 20.3–41.1) for PTSS, 40.1% (95% CI 32.5–47.9) for stress, 45.4% (95% CI 32.0–59.2) for psychological distress, and 27.7% (95% CI 19.4–36.9) for sleep problems.

On the basis of univariate meta-regression, the analysis was suitable for the primary outcomes (Appendix, Table S15 ). The increased prevalence of mental health problems was associated with the WHO region (for depression, anxiety, and psychological distress), female gender inequality index (for depression and anxiety), the COVID-19-government response stringency index during the survey (for sleep problems), hospital beds per 10,000 people (for depression and anxiety), immediate economic vulnerability-inbound tourism expenditure (for sleep problems), study design (cross-sectional vs longitudinal survey; for stress), surveyed country (mainland China vs non-mainland China; for depression and psychological distress), and risk of bias (for PTSS).

The visual inspection of the funnel plots, and the p values tested for asymmetry using the Begg’s and Egger’s tests for each prevalence outcome, indicated no evidence of publication bias related to the sample size (Appendix, Table S16 , and Figure S2 ).

This study is, to the best of our knowledge, the first systematic review and meta-analysis on the overall global prevalence of mental health problems and psychosocial consequences among the general population amid the COVID-19 pandemic. Overall, our findings indicate wide variability in the prevalence of mental health problems and psychosocial consequences across countries, particularly in relation to different regions, the global index of COVID-19 pandemic preparedness, inequalities, and economic vulnerabilities indices.

Two reports examined the global prevalence of common mental health disorders among adults prior to the COVID-19 outbreak. The first study was based on 174 surveys across 63 countries from 1980 to 2013. The estimated lifetime prevalence was 29.1% for all mental disorders, 9.6% for mood disorders, 12.9% for anxiety disorders, and 3.4% for substance use disorder 141 . Another report which was conducted as part of the Global Health Estimates by WHO in 2015, showed that the global estimates of depression and anxiety were 4.4% and 3.6% (more common among females than males), respectively 142 . Despite the different methodological methods used, our findings show that the pooled prevalence of mental health problems during the COVID-19 pandemic is higher than before the outbreak.

Previous studies on the prevalence of mental health problems during the COVID-19 pandemic have had substantial heterogeneity. Three systematic reviews reported the prevalence of depression, anxiety, and stress among the general population (mainly in mainland China). The first of these by Salari et al. 11 , was based on 17 included studies (from ten different countries in Asia, Europe, and the Middle East), the pooled prevalence of depression, anxiety, and stress were 33.7% (95% CI 27.5–40.6), 31.9% (95% CI 27.5–36.7), and 29.6% (95% CI 24.3–35.4), respectively. A review by Luo et al. 8 , which included 36 studies from seven different countries, reported a similar overall prevalence of 27% (95% CI 22–33) for depression and 32% (95% CI 25–39) for anxiety. However, a review by Ren et al. 17 , which focussed on only the Chinese population (8 included studies), found that the pooled prevalence was 29% (95% CI 16–42) and 24% (95% CI 16–32), respectively. Nevertheless, previous systematic reviews have been mainly on investigating the prevalence of PTSS, psychological distress, and sleep problems among the patients or healthcare workers that are limited to the general population during the COVID-19 pandemic. With regard to the general population, a review by Cénat et al. 143 , found that the pooled prevalence of PTSS, psychological distress, and insomnia were 22.4% (95% CI 7.6–50.3; 9 included studies), 10.2% (95% CI 4.6–21.0; 10 included studies), and 16.5% (95% CI 8.4–29.7; 8 included studies), respectively.

In this systematic review and meta-analysis, we updated and summarised the global prevalence of mental health problems and psychosocial consequences during the COVID-19 pandemic using information from 32 different countries, and 398,771 participants. A range of problems, including depression, anxiety, PTSS, stress, psychological distress, and sleep problems were reported. The global prevalence of our findings was in line with the previous reviews mentioned above in terms of depression (28.0%; 95% CI 25.0–31.2), anxiety (26.9%; 95% CI 24.0–30.0), and stress (36.5%; 95% CI 30.0–43.3). Interestingly, our findings highlight the poverty impacts of COVID-19 in terms of inequalities, the preparedness of countries to respond, and economic vulnerabilities on the prevalence of mental health problems across countries. For instance, our results suggest that countries with a low or medium HDI had a higher prevalence of depression and anxiety compared to countries with a high or very high HDI (Appendix, Table S8 , and Table S9 ). The prevalence of depression was higher among countries with a gender inequality index of 0.439 or greater (39.6% [95% CI 30.3–49.3] vs 26.2% [95% CI 23.1–29.3]; P  = 0.020; Appendix, Table S8 ). Likewise, the prevalence of depression and anxiety was higher among countries with low hospital beds per 10,000 people (Appendix, Table S8 , and Table S9 ). Our findings suggest that the poverty impacts of COVID-19 are likely to be quite significant and related to the subsequent risk of mental health problems and psychosocial consequences. Although we performed a comprehensive review by incorporating articles published together with preprint reports, there was only limited data available on Africa, low-income groups, and secondary outcomes of interest (psychological distress, suicide ideation, suicide attempts, loneliness, somatic symptoms, wellbeing, alcohol drinking problems, obsessive–compulsive symptoms, panic disorder, phobia anxiety, and adjustment disorder).

Strengths and limitations of this review

From a methodological point of view, we used a rigorous and comprehensive approach to establish an up-to-date overview of the evidence-based information on the global prevalence of mental health problems amid the COVID-19 pandemic, with no language restrictions. The systematic literature search was extensive, comprising published peer-reviewed articles and preprints reporting data to present all relevant literature, minimise bias, and up to date evidence. Our findings expanded and addressed the limitations of the previous systematic reviews, such as having a small sample size and number of included studies, considered more aspects of mental health circumstance, and the generalisability of evidence at a global level 5 , 6 , 11 , 17 , 18 . To address biases from different measurement tools of assessment and the cultural norms across countries, we summarised the prevalence of mental health problems and psychosocial consequences using a random-effects model to estimate the pooled data with a more conservative approach. Lastly, the sensitivity analyses were consistent with the main findings, suggesting the robustness of our findings. As such, our data can be generalised to individuals in the countries where the included studies were conducted.

There were several limitations to this systematic review and meta-analysis. First, despite an advanced comprehensive search approach, data for some geographical regions according to the WHO regions and World Bank income groups, for instance, the Africa region, as well as the countries in the low-income group, were limited. Moreover, the reporting of key specific outcomes, such as suicide attempts and ideation, alcohol drinking or drug-dependence problems, and stigma towards COVID-19 infection were also limited. Second, a subgroup analysis based on participant characteristics (that is, age, sex, unemployment, history of mental illness, financial problems, and quarantine status), could not be performed as not all of the included studies reported this data. Therefore, the global prevalence of mental health problems and psychosocial consequences amid the COVID-19 pandemic cannot be established. Third, it should be noted that different methods, for example, face-to-face interviews or paper-based questionnaires, may lead to different prevalence estimates across the general population. Due to physical distancing, the included studies in this review mostly used online surveys, which can be prone to information bias and might affect the prevalence estimates of our findings. Fourth, a high degree of heterogeneity between the included studies was found in all outcomes of interest. Even though we performed a set of subgroup analyses concerning the participant characteristics, study characteristics, the global index, and economic indices responses to the COVID-19 pandemic, substantial heterogeneity persisted. However, the univariate meta-regression analysis suggested that the WHO region, gender inequality index, COVID-19-government response stringency index during the survey, hospital beds, immediate economic vulnerability (inbound tourism expenditure), study design, surveyed country (mainland China vs non-mainland China), and risk of bias were associated with an increased prevalence of mental health problems and psychosocial consequences amid the COVID-19 pandemic. Finally, we underline that the diagnostic cut-off criteria used were not uniform across the measurement tools in this review, and misclassification remains possible. The genuine variation in global mental health circumstances across countries cannot be explained by our analyses. Indeed, such variation might be predisposed by social and cultural norms, public resilience, education, ethnic differences, and environmental differences among individual study populations.

Implications for public health and research

Despite the limitations of our findings, this review provides the best available evidence that can inform the epidemiology of public mental health, implement targeted initiatives, improving screening, and reduce the long-term consequences of the COVID-19 pandemic, particularly among low-income countries, or those with high inequalities, low preparedness, and high economic vulnerability. Our findings could be improved by further standardised methods and measurement tools of assessment. There is a need for individual country-level data on the mental health problems and psychosocial consequences after the COVID-19 pandemic to track and monitor public health responses. There are a number network longitudinal surveys being conducted in different countries that aim to improve our understanding of the long-term effects of the COVID-19 pandemic 144 . To promote mental wellbeing, such initiatives could also be advocated for by public health officials and governments to increase awareness and provide timely proactive interventions in routine practice.

Conclusions

In conclusion, this systematic review and meta-analysis provides a more comprehensive global overview and evidence of the prevalence of mental health problems among the general population amid the COVID-19 pandemic. The results of this study reveal that the mental health problems and psychosocial consequences amid the COVID-19 pandemic are a global burden, with differences between countries and regions observed. Moreover, equality and poverty impacts were found to be factors in the prevalence of mental health problems. Studies on the long-term effects of the COVID-19 pandemic on the mental health status among the general population at a global level is needed. Given the high burden of mental health problems during the COVID-19 pandemic, an improvement of screening systems and prevention, prompt multidisciplinary management, and research on the social and economic burden of the pandemic, are crucial.

Data sharing

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

The authors thank the research assistances and all staff of Pharmacoepidemiology and Statistics Research Center (PESRC), Chiang Mai, Thailand. This work reported in this manuscript was partially supported by a grant by the Chiang Mai University, Thailand. The funder of the study had no role in the study design collection, analysis, or interpretation of the data, or writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit it for publication.

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S.N. conceived the study and, together with C.R., K.T., R.A., C.P., and Y.R. developed the protocol. S.N. and C.R. did the literature search, selected the studies. S.N. and Y.R. extracted the relevant information. S.N. synthesised the data. S.N. wrote the first draft of the paper. K.T., B.H., N.W., and T.W. critically revised successive drafts of the paper. All authors approved the final draft of the manuscript. SN is the guarantor of the study.

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Nochaiwong, S., Ruengorn, C., Thavorn, K. et al. Global prevalence of mental health issues among the general population during the coronavirus disease-2019 pandemic: a systematic review and meta-analysis. Sci Rep 11 , 10173 (2021). https://doi.org/10.1038/s41598-021-89700-8

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Mental Health and COVID-19: Early evidence of the pandemic’s impact: Scientific brief, 2 March 2022

The COVID-19 pandemic has had a severe impact on the mental health and wellbeing of people around the world while also raising concerns of increased suicidal behaviour. In addition access to mental health services has been severely impeded. However, no comprehensive summary of the current data on these impacts has until now been made widely available.

This scientific brief is based on evidence from research commissioned by WHO, including an umbrella review of systematic reviews and meta-analyses and an update to a living systematic review. Informed by these reviews, the scientific brief provides a comprehensive overview of current evidence about:

• the impact of the COVID-19 pandemic on the prevalence of mental health symptoms and mental disorders
• the impact of the COVID-19 pandemic on prevalence of suicidal thoughts and behaviours
• the risk of infection, severe illness and death from COVID-19 for people living with mental disorders
• the impact of the COVID-19 pandemic on mental health services
• the effectiveness of psychological interventions adapted to the COVID-19 pandemic to prevent or reduce mental health problems and/or maintain access to mental health services

• Open access
• Published: 14 January 2022

COVID-19 impact on mental health

• Jingyu Cui 1 ,
• Jingwei Lu 1 ,
• Yijia Weng 1 ,
• Grace Y. Yi 1 , 2 &
• Wenqing He 1  

BMC Medical Research Methodology volume  22 , Article number:  15 ( 2022 ) Cite this article

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The coronavirus disease 2019 (COVID-19) pandemic has posed a significant influence on public mental health. Current efforts focus on alleviating the impacts of the disease on public health and the economy, with the psychological effects due to COVID-19 relatively ignored. In this research, we are interested in exploring the quantitative characterization of the pandemic impact on public mental health by studying an online survey dataset of the United States.

The analyses are conducted based on a large scale of online mental health-related survey study in the United States, conducted over 12 consecutive weeks from April 23, 2020 to July 21, 2020. We are interested in examining the risk factors that have a significant impact on mental health as well as in their estimated effects over time. We employ the multiple imputation by chained equations (MICE) method to deal with missing values and take logistic regression with the least absolute shrinkage and selection operator (Lasso) method to identify risk factors for mental health.

Our analysis shows that risk predictors for an individual to experience mental health issues include the pandemic situation of the State where the individual resides, age, gender, race, marital status, health conditions, the number of household members, employment status, the level of confidence of the future food affordability, availability of health insurance, mortgage status, and the information of kids enrolling in school. The effects of most of the predictors seem to change over time though the degree varies for different risk factors. The effects of risk factors, such as States and gender show noticeable change over time, whereas the factor age exhibits seemingly unchanged effects over time.

Conclusions

The analysis results unveil evidence-based findings to identify the groups who are psychologically vulnerable to the COVID-19 pandemic. This study provides helpful evidence for assisting healthcare providers and policymakers to take steps for mitigating the pandemic effects on public mental health, especially in boosting public health care, improving public confidence in future food conditions, and creating more job opportunities.

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This article does not report the results of a health care intervention on human participants.

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Since the outbreak of the COVID-19 pandemic, people's lifestyle has been changed significantly. However, no sufficient resources have been available to attenuate the pandemic effects on mental health and well-being [ 1 ]. Various studies have been conducted to investigate how the COVID-19 pandemic may affect people psychologically. For example, Cao et al. [ 2 ] conducted a survey on college students in China and showed that more than 24% of the students were experiencing anxiety. Spoorthy et al. [ 3 ] investigated the mental health problems faced by healthcare workers during the COVID-19 pandemic.

While those studies provided descriptive results by summarizing the information obtained from the questionnaire, it is unclear how the impact of COVID-19 changes over time; what factors are relevant to describe the impact of the pandemic; and how the severity of the mental health issues is quantitatively associated with the risk factors. In this paper, we examine these questions and aim to provide some quantitative insights. Our explorations are carried out using a large scale online public survey study conducted by the U.S. Census Bureau [ 4 ]. The data include twelve data sets each collected in a 1-week window over 12 consecutive weeks from April 23, 2020 to July 21, 2020. Different data sets contain the measurements from different participants on the same questions. Among the 12 data sets, the smallest one contains 41,996 subjects and the largest one has 132,961 participants. We treat the survey in each week as an independent study. We are interested in assessing how the effects of the associated risk factors may change over time by applying the same method to each of the 12 data sets separately.

The survey includes multiple questions perceived to be relevant to describing the impact of the pandemic on the public. To quantitatively identify the risk factors for impacting the mental health by the pandemic, we engage the penalized logistic regression method, with the least absolute shrinkage and selection operator (Lasso) penalty [ 5 ]. However, a direct application of the Lasso method is not possible due to the presence of missing observations. To handle missing values, we employ the multiple imputation by chained equations (MICE) method (e.g., [ 6 , 7 ]). Further, survey data commonly involve measurement error due to recall bias, the inability of providing precise descriptions of some answers, and reporting errors. It is imperative to address this issue when pre-processing the data. To this end, we combine the levels of those highly related categorical variables to mitigate the measurement error effects.

Original survey data

The data used in this project are from phase 1 of the Household Pulse Survey conducted by the U.S. Census Bureau [ 4 ] from April 23, 2020 to July 21, 2020 for 12 consecutive weeks, giving rise to 12 data sets each for a week. The survey aims to study the pandemic impacts on the households across the United States from social and economic perspectives. The survey contains 50 questions ranging from education, employment, food sufficiency, health, housing, social security benefits, household spending, stimulus payments, to transportation. The participants of the survey come from all the 50 states plus Washington, D.C., United States, aging from 18 to 88. The gender ratio (the ratio of males to females) remains fairly stable ranging between 0.6 and 0.7 over the 12 weeks. Figure S1 in the Supplementary Material shows the curves of the number of cumulative confirmed cases for all the states which are grouped into four categories of the severity of the pandemic, derived from the data from the Centers for Disease Control and Prevention [ 8 ]. Table 1 lists the state members for each category, together with the total number of participants over the 12 weeks and the corresponding percentage for each category. It is seen that the majority (72.5%) of the participants of the survey come from the states with mild pandemic and the least proportion (2.3%) of subjects are from the states with a serious pandemic.

Pre-processing the data to reduce errors

Among the initial 50 questions, nine questions, such as “ Where did you get free groceries or free meals ” and “ How often is the Internet available to children for educational purposes ”, are excluded because they are not perceived as sustainable factors on affecting mental health. Measurement error is typically involved in survey data. Prior to a formal analysis of the data, we implement a pre-processing procedure to mitigate the measurement error effects by combining questions to create new variables, or collapsing levels of variables to form binary variables.

Information on mental health is collected via four questions concerning anxiety , worry , loss of interest , and feeling down . Each question is a four-level Likert item [ 9 ] with values 1, 2, 3 and 4, showing the degree of each aspect for the past 7 days prior to the survey time. In contrast to Twenge and Joiner [ 10 ] who combined the measurements of the first two questions anxiety and worry to indicate the anxiety level and the last two questions loss of interest and feeling down to show the depression level, we define a single binary response to reflect the mental health status of an individual by combing measurements of the four variables. The response variable takes value 1 if the average of the scores of the four variables is greater than 2.5, and 0 otherwise, where the threshold 2.5 is the median value for each question. This binary response gives a synthetic way to indicate the mental health status which is easier thaeach question. This binary response gives a synthetic wayn examining measurements of multiple variables.

Two variables describe the loss of work: Wrkloss indicates whether an individual in the household experiences a loss of employment income since March 13, 2020; Expctloss indicates if the individual expects a member in the household to experience a loss of employment income in the next 4 weeks because of the COVID-19 pandemic. These two variables are combined to form a single indicator which is denoted Wrkloss , with value 1 if at least one of these two events happens. Two ordinal variables, Prifoodsuf and Curfoodsuf , are used to describe the food sufficiency status before the pandemic and at present, respectively. The Foodcon.change variable is constructed by comparing the current and the previous food sufficiency status to form a binary variable, taking 1 if the current food sufficiency status is no worse than the food status before the pandemic, and 0 otherwise. Variable Med.delay.notget is combined from two indicator variables Delay (indicating if medical care is delayed) and Notget (indicating if the medical care is not received), taking value 1 if either medical care is delayed or no medical care is received, and 0 otherwise. Predictor Mort.prob is combined from one binary variable and an ordinal variable, taking 1 if a participant does not pay last month’s rent or mortgage or does not have enough confidence in paying the next rent or mortgage payment on time, and 0 otherwise. In addition, three ordinal variables, Emppay , Healins, and Schoolenroll , are modified by collapsing their levels to form binary categories. Emppay has value 1 if he/she gets paid for the time he/she is not working, and 0 otherwise. Healins has value 1 if the individual is currently covered by the health insurance, and 0 otherwise. Schoolenroll has value 1 if there is a child in the household enrolled in school, and 0 otherwise. Except for the variables discussed above, the remaining variables are kept as in the original form.

The final data include the binary response (indicating the mental health status of an individual) and 25 predictors measuring various aspects of individuals. To be specific, nine predictors show basic information: State , Age , Male , Rhispanic , Race , Educ , MS (marital status), Numper (the number of people in the household), and Numkid (the number of people under 18 in the household); five variables concern the income and employment: Income , Wrkloss , Anywork , Kindwork , and Emppay ; five variables are related to food: Foodcon.change , Freefood , Tspndfood , Tspndprpd , and Foodconf ; three variables pertain to health and insurance: Hlthstatus , Healins , and Med.delay.notget ; one variable, Mort.prob , is for mortgage and housing; and two variables, Schoolenroll and Ttch_Hrs , reflect child education. The variable dictionary for the pre-processed data is shown in Table 2 .

Missing observations

In the data sets, 17 covariates together with the response variable have missing observations. To provide a quick and intuitive sense of the missingness proportions for different variables over the 12 data sets, we combine those data sets by individual variable to form a single pooled data set. Then we calculate the missingness proportion for each variable by dividing the number of missing observations in the variable by the total number of subjects in the pooled data set. We display in Fig. 1 the missingness rates for those 17 risk factors and the response variable (mental health status) for the pooled data. The risk factors having the three highest missingness rates are the variables Ttch_hrs , Schoolenroll and Emppay , and the corresponding missingness rates are 76.7%, 66.9% and 60.5%, respectively. Five variables incur higher than 30% missingness proportions, and the missingness proportion for 12 risk factors is larger than 5%. The missingness proportion for the response variable is about 8.6%.

The missingness rates for the 17 risk factors and the response of the pooled data

Missing values present a challenge for data analysis and model fitting. One may perform the so-called complete data analysis by deleting those subjects with missing observations or the so-called available data analysis by using all available data, and then repeating a standard analysis procedure. Such analyses are easy to implement, however, biased results are expected if the missing completely at random (MCAR) mechanism is not true. Here we consider a broader setting where missing data do not necessarily follow the MCAR but follow the missing at random (MAR) mechanism. We employ the MICE method which is developed under the MAR mechanism and applies to various types of variables such as continuous, binary, nominal, and ordinal variables subject to missingness. A detailed discussion on this method was provided by van Buuren et al. [ 11 ].

Here we employ the MICE method to accommodate missing observations that are present in both the predictors and the response. Following the suggestion of Allison [ 12 ], we choose to do five imputations for the data in each week by employing the same algorithm with different random seeds. The implementation is conducted in R (version 3.6.1) with the R package: Multivariate Imputation by Chained Equation (mice). The details on the R code are presented in the code availability in the Declarations section .

To empirically assess the imputation results, we take the data in week 6 as an example and compare the five imputed data sets to the original data by displaying their distribution using the R function density for the continuous variables; the results are reported in Figure S2 in the Supplementary Material . It is seen that the distributions of the 5 imputed data sets for the three continuous variables, Tspndfood , Tspndprpd , and Ttch_hrs , are fairly similar to that of the original data. Further, in Tables S1, S2, and S3 in the Supplementary Material , we report the proportions of different levels for the categorical variables for both the imputed and original data, showing the similarity in the distributions of the imputed data and of the original data.

Model building and inference

We intend to employ logistic regression with the Lasso penalty to analyze the data that contain a binary response and potentially related predictors or covariates. First, we introduce the basic notation and discuss the method in general terms. For i  = 1, …, n , let Y i represent the binary response with value 1 indicating that the mental health problem occurs for subject i and 0 otherwise. Let X ij denote the j th covariate for subject i , where j  = 1, …, p , and p is the number of predictors. Write X i  = ( X i 1 ,  X i 2 , …,  X ip ) T and let π i  =  P ( Y i  = 1|  X i ).

Consider the logistic regression model

where β  = ( β 1 , …, β p ) T denotes the vector of regression parameters. Consequently, the log-likelihood function for β is given by

To select the predictors associated with the dichotomous response, we employ the Lasso method. The Lasso estimates are the values that maximize the penalized log-likelihood function obtained by adding an L 1 penalty to the expression (2):

where λ is the tuning parameter. The 10-fold cross-validation is employed to obtain a proper value for the tuning parameter and the one-standard-error rule [ 13 ] is applied to pick the most parsimonious model within one standard error of the minimum cross-validation misclassification rate (e.g., [ 14 ]).

Model fitting and variable selection

The Lasso logistic regression is applied to each of the five imputed data sets for each week. The predictors corresponding to the nonzero coefficient estimates are considered the risk factors selected, which may be different across five imputed data sets for each of the 12 weeks. To explore in a full spectrum, we start with two extreme models, called the full model by including the union of all the selected risk factors by the Lasso logistic regression, and the reduced model by including only the common factors selected for all five imputed data sets in any week. The full model includes all the 25 predictors in the original data, and the reduced model contains 11 predictors: Age , Male , MS , Numkid , Wrkloss , Anywork , Foodconf , Hlthstatus , Healins , Med.delay.notget , and Mort.prob . We expect the predictors in the final model to form a set in-between the sets of the predictors for the reduced mode and the full model . Now, the problem is how to find the final model using the reduced and full models . To this end, we carry out the following four steps.

In Step 1, we fit logistic regression with predictors in the full model and in the reduced model , respectively, to each of the five surrogate data sets for each of the 12 weeks.

In Step 2, the estimates and standard errors of the model coefficients for a given week are obtained using the algorithm described by Allison [ 12 ]. To be specific, let M  = 5 be the number of surrogate data sets for the original incomplete data. Let β j be the j th component of the model parameter vector β . For k  = 1, …, M , let \({\hat{\beta}}_j^{(k)}\) denote the estimate of the model parameter β j obtained from fitting the k th surrogate data set in a week and let \({S}_j^{(k)}\) be its associated standard error. Then the point estimate of β j is given by the average of those estimates of β j derived from the M imputed data sets:

To determine the variability associated with \({\hat{\beta}}_j\) , one needs to incorporate both the within imputation variance, denoted V w , and the between imputation variance, denoted V b . According to Rubin’s rule [ 6 ], the total variance associated with the multiple imputation estimate \({\hat{\beta}}_j\) is given by \(Var\left({\hat{\beta}}_j\right)={V}_w+\left(1+\frac{1}{M}\right){V}_b\) , where \({V}_w=\frac{1}{M}\sum_{k=1}^M{\left\{{S}_j^{(k)}\right\}}^2\) , and the between imputation variance, \({V}_b=\frac{1}{M-1}\sum_{k=1}^M{\left\{{\hat{\beta}}_j^{(k)}-{\hat{\beta}}_j\right\}}^2\) , is inflated by a factor \(\frac{1}{M}\) . As a result, the standard error associated with \({\hat{\beta}}_j\) is given by \(se\left({\hat{\beta}}_j\right)=\sqrt{Var\left({\hat{\beta}}_j\right)}\) , i.e.,

We report in Tables S4 and S5 in the Supplementary Material the estimated results of the covariate effects obtained, respectively, from the full and reduced models for the data in 12 weeks, where the covariates marked with an asterisk are statistically significant with p-values smaller than 0.05 for more than 6 weeks. It is found that in addition to those covariates included in the reduced model, fitting the full model also shows that five additional covariates, State , Rhispanic , Race , Numper , and Schoolenroll, are statistically significant for more than 6 weeks’ data. Table S 5 shows that almost all the covariates in the reduced model are statistically significant, with all the p-values derived from the data in 12 weeks smaller than 0.05.

Consequently, in Step 3, we take the 11 significant risk factors from the reduced model , and the 5 additional partially significant covariates suggested by fitting the full model , State , Rhispanic , Race , Numper , and Schoolenroll, to form the list of risk factors for the final model.

In Step 4, we construct the final model  using the model form (1) to include the selected variables in Step 3 as predictors, where dummy variables are used to express categorical variables State , Race , MS , Foodconf , and Hlthstatus with levels more than two, yielding 28 variables in the model. The final model is then given by

where β j is the regression coefficients for j  = 0, 1, …, 28, and the subscript i is suppressed in π and the covariates for ease of exposition.

Then, we fit the final logistic model (6) to each of the imputed data sets for each of the 12 weeks; in the same manner as indicated by (4) and (5), we obtain the point estimates of the model parameters and the associated standard errors. To have a visual display, we plot in Fig. 2 the estimates of the coefficients for all the factors in the final model for 12 weeks; to precisely show the estimates, we report in Table 3  the point estimates for the covariate effects obtained from the final model , where we further calculate the average of the 12 estimates for each covariate and report the results in the last column. The associated standard errors and the p-values are deferred to Table S6 in the Supplementary Material . The results suggest that the factors Numper, Healins and Schoolenroll are only significant in some of 12 weeks, while other factors in the final models are significant in all 12 weeks.

The estimates of the coefficients for all the factors in the final model are displayed against the week number

Figure 2 shows that the absolute values of coefficient estimates for some levels of variables Foodconf and Hlthstatus are greater than 1 (in Fig.  2 K and L). The coefficient estimates of Med.delay.notget over 12 weeks are between 0.5 and 1 (in Fig. 2 N). Other variables have coefficient estimates between -0.5 and 0.5.

To have an overall sense of the estimates of the predictor effects in the final model, we now utilize the averages reported in the last column of Table 3 to estimate the relative change in the odds of having mental issues with one unit increase in a predictor from its baseline while keeping other predictors unchanged, yet leaving the associated variability uncharacterized. Let \({\overline{\hat{\beta}}}_j\) represent the average of those estimates of the covariate effect β j over the 12 weeks for j  = 1, …, 28, which is a sensible estimate of β j , because the arithmetic average preserves the consistency if all the estimators obtained for the 12 weeks are consistent for β j . Using \({\overline{\hat{\beta}}}_j\) is advantageous in offering us a single estimate of β j with generally expected smaller variability than those estimates obtained from each of the 12 weeks. If \({\overline{\hat{\beta}}}_j\) is negative, then \(1-{\exp}\left({\overline{\hat{\beta}}}_j\right)\) shows an estimate of the decrease in the odds of having mental issues relative to the baseline; if \({\overline{\hat{\beta}}}_j\) is positive, then \({\exp}\left({\overline{\hat{\beta}}}_j\right)-1\) suggests an estimate of the increase in the odds of having mental issues relative to the baseline.

To be specific, for the variable State with large daily increases of cases as the baseline, people from mild pandemic States exhibit an estimate of 1 −   exp  (−0.139) ≈ 13% decrease in the odds of having mental issues; people from the States with moderate daily increases show an estimate of 1 −   exp  (−0.053) ≈ 5.16% degrease in the odds; people from serious pandemic States are generally associated with an estimate of 1 −   exp  (−0.039) ≈ 3.82% decrease in the odds.

For Age and Gender , their averages of the estimates over the 12 weeks are -0.030 and -0.228, respectively, implying that one unit increase in Age is associated with about an estimate of 1 −   exp  (−0.030) ≈ 2.96% decrease in the odds of occurrence of mental health problems; and being a male relative to a female is associated with an estimate of 1 −   exp  (−0.228) ≈ 20.39% decrease in the odds of having mental health issues. Similarly, the 12-week estimated effects of Rhispanic indicate that the origin of Hispanic, Latino or Spanish is associated with a smaller odds of having mental issues than others. The 12-week mean of the coefficient estimates of Rhispanic is -0.172, leading to an estimate of the odds of mental health problem occurrence being reduced by around 1 −   exp  (−0.172) ≈ 15.80%.

For the variable Race with White as the baseline, the 12-week mean of coefficient estimates for Black (Race2) and Asian (Race3) are -0.446 and -0.262, respectively, yielding an estimate of the odds of occurrence of mental health issues for Black and Asian to be, respectively, 1 −   exp  (−0.446) ≈ 35.98% and 1 −   exp  (−0.262) ≈ 23.05% less than White .

For MS (marital status) with now married as the baseline, an estimate of the increase in the odds of having mental issues relative to the baseline, is exp (0.206) − 1≈22.88%, exp (0.236) − 1≈26.62%, exp (0.242) − 1≈27.38%, and exp (0.181) − 1≈19.84%, respectively, for people who are widowed (MS2), divorced (MS3), separated (MS4) , or never married (MS5).

For predictors Numper and Numkid , the averages of the estimates suggest that the increase of the number of people and kids in the household is associated with the decrease of the odds of having mental issues. Specifically, one person increase in the household is associated with an estimate of 1 −   exp  (−0.024)≈2.37% decrease in odds, and one more kid in the household is associated with an estimate of 1 −   exp  (−0.106)≈10.06% decrease in the odds.

For the work-related factors Wrkloss and Anywork , the results shown in the last column in Table 3 indicate that experiencing a loss of employment income since March 13, 2020 is associated with an estimate of exp (0.352) − 1≈42.19% increase in the odds of having mental issues, and doing any work during the last 7 days is associated with an estimate of 1 −   exp  (−0.141)≈13.15% decrease in the odds.

The 12-week results of Foodconf in Table 3 show that, with the not at all confident on the future food affordability as the baseline, an increase in the confidence of food affordability is negatively associated with the odds of having mental issues. On average of 12 weeks, shown in the last column in Table 3 , the more confident in the food affordability, the less the odds of having mental issues. For example, the person who is very confident (Foodconf4) in the food affordability for the next four weeks demonstrates an estimate of 1 −   exp  (−1.348)≈74.02% decrease in the odds of having mental issues, relative to the person who is not at all confident .

With excellent health conditions as the baseline, the estimates of Hlthstatus in Table 3 say that the worse the self-evaluated health condition, the larger the odds of having mental issues. Considering the worst level of health condition poor (Hlthstatus5) as an example, the average of the estimates over the 12 weeks yields that people in poor health conditions have an estimate of the odds of having mental issues exp (2.021)≈7.55 times higher than people of excellent health conditions. For other health-related predictors, Healins and Med.delay.notget , people who are currently covered by health insurance are associated with an estimate of 1 −   exp  (−0.083)≈7.96% decrease in the odds of mental issues occurrence, and people who do not get medical care or have delayed medical care are associated with an estimate of exp (0.684) − 1≈98.18% increase in the odds.

For Mort.prob and Schoolenroll , people having rental or mortgage problems are associated with an estimate of exp (0.232) − 1≈26.15% increase in the odds of having mental health problems, and people whose household has kids enrolled in school are associated with an estimate of exp (0.109) − 1≈11.52% increase in the odds of having mental issues.

In summary, the factors in the final model associated with a reduction in the odds of having mental health issues include: States not having large daily increases of cases, older in age, being male, having a Hispanic, Latino or Spanish origin, being non-White, more people or kids in the household, having job during the last 7 days, having confidence in the food affordability in the future, and being covered by insurance. The factors in the final model associated with the increase in the odds of getting mental issues are: not married, experiencing loss of job, poor self-evaluations on health conditions, having problems in getting medical care and mortgage, and having kids enrolled in school.

In this paper we investigate the impact of the COVID-19 pandemic on the public mental health using an online survey data set from the United States. Prior to the analysis, we pre-process the data by combining some levels of certain variables in the hope to ameliorate the effects of the errors that are often induced in survey data, including recall bias, reporting error, uncertainty in providing a precise assessment of the situation, inability to decide a right scale to a question, and inconsistency in the answers to the same question that is phrased differently [ 15 ]. In addition, some variables are quite similar or even identical in nature, thus, combining them can help alleviate unwanted noise. Further, we employ multiple imputation to account for the missingness effects, and use the penalized logistic regression with the Lasso penalty to select important risk factors for mental health.

While this study offers us quantitative evidence how the COVID-19 pandemic can psychologically challenge the public, several limitations need to be pointed out. Firstly, the online survey data were designed to assess the pandemic impact from the social and economic perspectives, and they may not contain enough necessary factors related to mental health issues. In addition, the interaction effects between the predictors are not considered in our analysis, which may restrict the capacity of the model. Secondly, while the choice of M  = 5 in our analysis follows the suggestion of Allison [ 12 ], it would be interesting to study how the variability may be incurred by setting different values for M .

Thirdly, though it is easy to see that the data exhibit arbitrary missingness patterns, or the so-called intermittent missing data patterns , it is difficult to tell what exactly the underlying missing data mechanism is, as in many other missing data problems [ 16 ]. Though the multiple imputation method is useful for handling missing data with the MAR mechanism [ 16 ], its performance can be considerably impacted by different proportions of missing values. Efforts of accounting for missingness effects do not always come to be rewarding. In the presence of excessive missing observations, the multiple imputation method, like any other method, can fail to yield sensible results even if the MAR mechanism is true. In such instances, one needs to be cautious to interpret the analysis results and be aware of potentially induced biases due to a high proportion of missing information.

Finally, in the analysis, we define the response variable to be binary by combining the information collected from four questions about mental health. While this approach gives a simple way to indicate the mental health status and is similarly taken by other authors (e.g., [ 10 ]), it is heuristic, as pointed out by a referee. It is thereby interesting to take the original four categorial variables as outcomes and conduct multivariate analysis to examine how those outcomes are associated with the covariates with missingness effects accommodated. Such analyses would be more sophisticated and require extra care to facilitate the association structures among the multiple response variables. Further, the yielded results may be less intuitive to interpret than those derived from using a single response variable.

The analysis results unveil evidence-based findings to identify the groups who are psychologically vulnerable to the COVID-19 pandemic. This study provides helpful evidence to assist healthcare providers and policymakers to take steps for mitigating the pandemic effects on public mental health, especially in boosting public health care, improving public confidence in future food conditions, and creating more job opportunities.

Availability of data and materials

The data sets analyzed here are available in the Bureau of the Census, Household Pulse Survey Public Use File (PUF) repository [ 4 ], https://www.census.gov/programs-surveys/household-pulse-survey/datasets.html .

Abbreviations

Coronavirus disease 2019

multiple imputations by chained equations

least absolute shrinkage and selection operator

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Acknowledgements

The authors thank the reviewers for their helpful comments on the initial submission. The research was partially supported by the grants of the Discovery Grants Program and the Emerging Infectious Disease Modeling Program from the Natural Sciences and Engineering Research Council of Canada. Yi is Canada Research Chair in Data Science (Tier 1). Her research was undertaken, in part, thanks to funding from the Canada Research Chairs program. The grants provide support to JC, JL and YW to conduct the study.

Code availability

All the computation in this study is conducted in R (version 3.6.1) and the R code is posted in GitHub at: https://github.com/JingyuCui639/R-code-for-COVID-19-Impact-on-Mental-Health-over-Time [ 17 ].

The research was partially supported by the grants of the Discovery Grants Program and the Emerging Infectious Disease Modeling Program from the Natural Sciences and Engineering Research Council of Canada. Yi is Canada Research Chair in Data Science (Tier 1). Her research was undertaken, in part, thanks to funding from the Canada Research Chairs program. The grants provide support to JC, JL and YW to conduct the analyses.

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Jingyu Cui, Jingwei Lu, Yijia Weng, Grace Y. Yi & Wenqing He

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JC leads the project; YW identifies the data; JC, JL, and YW jointly analyze the data and prepare the initial draft. Professors WH and GYY offer ideas and discussions for the project; GYY writes the manuscript. All authors have read and approved the manuscript.

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Cui, J., Lu, J., Weng, Y. et al. COVID-19 impact on mental health. BMC Med Res Methodol 22 , 15 (2022). https://doi.org/10.1186/s12874-021-01411-w

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• logistic regression
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Effects of the COVID-19 pandemic on mental health, anxiety, and depression

• Ida Kupcova 1 ,
• Lubos Danisovic 1 ,
• Martin Klein 2 &
• Stefan Harsanyi 1  

BMC Psychology volume  11 , Article number:  108 ( 2023 ) Cite this article

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The COVID-19 pandemic affected everyone around the globe. Depending on the country, there have been different restrictive epidemiologic measures and also different long-term repercussions. Morbidity and mortality of COVID-19 affected the mental state of every human being. However, social separation and isolation due to the restrictive measures considerably increased this impact. According to the World Health Organization (WHO), anxiety and depression prevalence increased by 25% globally. In this study, we aimed to examine the lasting effects of the COVID-19 pandemic on the general population.

A cross-sectional study using an anonymous online-based 45-question online survey was conducted at Comenius University in Bratislava. The questionnaire comprised five general questions and two assessment tools the Zung Self-Rating Anxiety Scale (SAS) and the Zung Self-Rating Depression Scale (SDS). The results of the Self-Rating Scales were statistically examined in association with sex, age, and level of education.

A total of 205 anonymous subjects participated in this study, and no responses were excluded. In the study group, 78 (38.05%) participants were male, and 127 (61.69%) were female. A higher tendency to anxiety was exhibited by female participants (p = 0.012) and the age group under 30 years of age (p = 0.042). The level of education has been identified as a significant factor for changes in mental state, as participants with higher levels of education tended to be in a worse mental state (p = 0.006).

Conclusions

Summarizing two years of the COVID-19 pandemic, the mental state of people with higher levels of education tended to feel worse, while females and younger adults felt more anxiety.

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Introduction

The first mention of the novel coronavirus came in 2019, when this variant was discovered in the city of Wuhan, China, and became the first ever documented coronavirus pandemic [ 1 , 2 , 3 ]. At this time there was only a sliver of fear rising all over the globe. However, in March 2020, after the declaration of a global pandemic by the World Health Organization (WHO), the situation changed dramatically [ 4 ]. Answering this, yet an unknown threat thrust many countries into a psycho-socio-economic whirlwind [ 5 , 6 ]. Various measures taken by governments to control the spread of the virus presented the worldwide population with a series of new challenges to which it had to adjust [ 7 , 8 ]. Lockdowns, closed schools, losing employment or businesses, and rising deaths not only in nursing homes came to be a new reality [ 9 , 10 , 11 ]. Lack of scientific information on the novel coronavirus and its effects on the human body, its fast spread, the absence of effective causal treatment, and the restrictions which harmed people´s social life, financial situation and other areas of everyday life lead to long-term living conditions with increased stress levels and low predictability over which people had little control [ 12 ].

Risks of changes in the mental state of the population came mainly from external risk factors, including prolonged lockdowns, social isolation, inadequate or misinterpreted information, loss of income, and acute relationship with the rising death toll. According to the World Health Organization (WHO), since the outbreak of the COVID-19 pandemic, anxiety and depression prevalence increased by 25% globally [ 13 ]. Unemployment specifically has been proven to be also a predictor of suicidal behavior [ 14 , 15 , 16 , 17 , 18 ]. These risk factors then interact with individual psychological factors leading to psychopathologies such as threat appraisal, attentional bias to threat stimuli over neutral stimuli, avoidance, fear learning, impaired safety learning, impaired fear extinction due to habituation, intolerance of uncertainty, and psychological inflexibility. The threat responses are mediated by the limbic system and insula and mitigated by the pre-frontal cortex, which has also been reported in neuroimaging studies, with reduced insula thickness corresponding to more severe anxiety and amygdala volume correlated to anhedonia as a symptom of depression [ 19 , 20 , 21 , 22 , 23 ]. Speaking in psychological terms, the pandemic disturbed our core belief, that we are safe in our communities, cities, countries, or even the world. The lost sense of agency and confidence regarding our future diminished the sense of worth, identity, and meaningfulness of our lives and eroded security-enhancing relationships [ 24 ].

Slovakia introduced harsh public health measures in the first wave of the pandemic, but relaxed these measures during the summer, accompanied by a failure to develop effective find, test, trace, isolate and support systems. Due to this, the country experienced a steep growth in new COVID-19 cases in September 2020, which lead to the erosion of public´s trust in the government´s management of the situation [ 25 ]. As a means to control the second wave of the pandemic, the Slovak government decided to perform nationwide antigen testing over two weekends in November 2020, which was internationally perceived as a very controversial step, moreover, it failed to prevent further lockdowns [ 26 ]. In addition, there was a sharp rise in the unemployment rate since 2020, which continued until July 2020, when it gradually eased [ 27 ]. Pre-pandemic, every 9th citizen of Slovakia suffered from a mental health disorder, according to National Statistics Office in 2017, the majority being affective and anxiety disorders. A group of authors created a web questionnaire aimed at psychiatrists, psychologists, and their patients after the first wave of the COVID-19 pandemic in Slovakia. The results showed that 86.6% of respondents perceived the pathological effect of the pandemic on their mental status, 54.1% of whom were already treated for affective or anxiety disorders [ 28 ].

In this study, we aimed to examine the lasting effects of the COVID-19 pandemic on the general population. This study aimed to assess the symptoms of anxiety and depression in the general public of Slovakia. After the end of epidemiologic restrictive measures (from March to May 2022), we introduced an anonymous online questionnaire using adapted versions of Zung Self-Rating Anxiety Scale (SAS) and Zung Self-Rating Depression Scale (SDS) [ 29 , 30 ]. We focused on the general public because only a portion of people who experience psychological distress seek professional help. We sought to establish, whether during the pandemic the population showed a tendency to adapt to the situation or whether the anxiety and depression symptoms tended to be present even after months of better epidemiologic situation, vaccine availability, and studies putting its effects under review [ 31 , 32 , 33 , 34 ].

Materials and Methods

This study utilized a voluntary and anonymous online self-administered questionnaire, where the collected data cannot be linked to a specific respondent. This study did not process any personal data. The questionnaire consisted of 45 questions. The first three were open-ended questions about participants’ sex, age (date of birth was not recorded), and education. Followed by 2 questions aimed at mental health and changes in the will to live. Further 20 and 20 questions consisted of the Zung SAS and Zung SDS, respectively. Every question in SAS and SDS is scored from 1 to 4 points on a Likert-style scale. The scoring system is introduced in Fig.  1 . Questions were presented in the Slovak language, with emphasis on maintaining test integrity, so, if possible, literal translations were made from English to Slovak. The questionnaire was created and designed in Google Forms®. Data collection was carried out from March 2022 to May 2022. The study was aimed at the general population of Slovakia in times of difficult epidemiologic and social situations due to the high prevalence and incidence of COVID-19 cases during lockdowns and social distancing measures. Because of the character of this web-based study, the optimal distribution of respondents could not be achieved.

Categories of Zung SAS and SDS scores with clinical interpretation

During the course of this study, 205 respondents answered the anonymous questionnaire in full and were included in the study. All respondents were over 18 years of age. The data was later exported from Google Forms® as an Excel spreadsheet. Coding and analysis were carried out using IBM SPSS Statistics version 26 (IBM SPSS Statistics for Windows, Version 26.0, Armonk, NY, USA). Subject groups were created based on sex, age, and education level. First, sex due to differences in emotional expression. Second, age was a risk factor due to perceived stress and fear of the disease. Last, education due to different approaches to information. In these groups four factors were studied: (1) changes in mental state; (2) affected will to live, or frequent thoughts about death; (3) result of SAS; (4) result of SDS. For SAS, no subject in the study group scored anxiety levels of “severe” or “extreme”. Similarly for SDS, no subject depression levels reached “moderate” or “severe”. Pearson’s chi-squared test(χ2) was used to analyze the association between the subject groups and studied factors. The results were considered significant if the p-value was less than 0.05.

Ethical permission was obtained from the local ethics committee (Reference number: ULBGaKG-02/2022). This study was performed in line with the principles of the Declaration of Helsinki. All methods were carried out following the institutional guidelines. Due to the anonymous design of the study and by the institutional requirements, written informed consent for participation was not required for this study.

In the study, out of 205 subjects in the study group, 127 (62%) were female and 78 (38%) were male. The average age in the study group was 35.78 years of age (range 19–71 years), with a median of 34 years. In the age group under 30 years of age were 34 (16.6%) subjects, while 162 (79%) were in the range from 31 to 49 and 9 (0.4%) were over 50 years old. 48 (23.4%) participants achieved an education level of lower or higher secondary and 157 (76.6%) finished university or higher. All answers of study participants were included in the study, nothing was excluded.

In Tables  1 and 2 , we can see the distribution of changes in mental state and will to live as stated in the questionnaire. In Table  1 we can see a disproportion in education level and mental state, where participants with higher education tended to feel worse much more than those with lower levels of education. Changes based on sex and age did not show any statistically significant results.

In Table  2 . we can see, that decreased will to live and frequent thoughts about death were only marginally present in the study group, which suggests that coping mechanisms play a huge role in adaptation to such events (e.g. the global pandemic). There is also a possibility that living in times of better epidemiologic situations makes people more likely to forget about the bad past.

Anxiety and depression levels as seen in Tables  3 and 4 were different, where female participants and the age group under 30 years of age tended to feel more anxiety than other groups. No significant changes in depression levels based on sex, age, and education were found.

Compared to the estimated global prevalence of depression in 2017 (3.44%), in 2021 it was approximately 7 times higher (25%) [ 14 ]. Our study did not prove an increase in depression, while anxiety levels and changes in the mental state did prove elevated. No significant changes in depression levels go in hand with the unaffected will to live and infrequent thoughts about death, which were important findings, that did not supplement our primary hypothesis that the fear of death caused by COVID-19 or accompanying infections would enhance personal distress and depression, leading to decreases in studied factors. These results are drawn from our limited sample size and uneven demographic distribution. Suicide ideations rose from 5% pre-pandemic to 10.81% during the pandemic [ 35 ]. In our study, 9.3% of participants experienced thoughts about death and since we did not specifically ask if they thought about suicide, our results only partially correlate with suicidal ideations. However, as these subjects exhibited only moderate levels of anxiety and mild levels of depression, the rise of suicide ideations seems unlikely. The rise in suicidal ideations seemed to be especially true for the general population with no pre-existing psychiatric conditions in the first months of the pandemic [ 36 ]. The policies implemented by countries to contain the pandemic also took a toll on the population´s mental health, as it was reported, that more stringent policies, mainly the social distancing and perceived government´s handling of the pandemic, were related to worse psychological outcomes [ 37 ]. The effects of lockdowns are far-fetched and the increases in mental health challenges, well-being, and quality of life will require a long time to be understood, as Onyeaka et al. conclude [ 10 ]. These effects are not unforeseen, as the global population suffered from life-altering changes in the structure and accessibility of education or healthcare, fluctuations in prices and food insecurity, as well as the inevitable depression of the global economy [ 38 ].

The loneliness associated with enforced social distancing leads to an increase in depression, anxiety, and posttraumatic stress in children in adolescents, with possible long-term sequelae [ 39 ]. The increase in adolescent self-injury was 27.6% during the pandemic [ 40 ]. Similar findings were described in the middle-aged and elderly population, in which both depression and anxiety prevalence rose at the beginning of the pandemic, during the pandemic, with depression persisting later in the pandemic, while the anxiety-related disorders tended to subside [ 41 ]. Medical professionals represented another specific at-risk group, with reported anxiety and depression rates of 24.94% and 24.83% respectively [ 42 ]. The dynamic of psychopathology related to the COVID-19 pandemic is not clear, with studies reporting a return to normal later in 2020, while others describe increased distress later in the pandemic [ 20 , 43 ].

Concerning the general population, authors from Spain reported that lockdowns and COVID-19 were associated with depression and anxiety [ 44 ]. In January 2022 Zhao et al., reported an elevation in hoarding behavior due to fear of COVID-19, while this process was moderated by education and income levels, however, less in the general population if compared to students [ 45 ]. Higher education levels and better access to information could improve persons’ fear of the unknown, however, this fact was not consistent with our expectations in this study, as participants with university education tended to feel worse than participants with lower education. A study on adolescents and their perceived stress in the Czech Republic concluded that girls are more affected by lockdowns. The strongest predictor was loneliness, while having someone to talk to, scored the lowest [ 46 ]. Garbóczy et al. reported elevated perceived stress levels and health anxiety in 1289 Hungarian and international students, also affected by disengagement from home and inadequate coping strategies [ 47 ]. Wathelet et al. conducted a study on French University students confined during the pandemic with alarming results of a high prevalence of mental health issues in the study group [ 48 ]. Our study indicated similar results, as participants in the age group under 30 years of age tended to feel more anxious than others.

In conclusion, we can say that this pandemic changed the lives of many. Many of us, our family members, friends, and colleagues, experienced life-altering events and complicated situations unseen for decades. Our decisions and actions fueled the progress in medicine, while they also continue to impact society on all levels. The long-term effects on adolescents are yet to be seen, while effects of pain, fear, and isolation on the general population are already presenting themselves.

The limitations of this study were numerous and as this was a web-based study, the optimal distribution of respondents could not be achieved, due to the snowball sampling strategy. The main limitation was the small sample size and uneven demographic distribution of respondents, which could impact the representativeness of the studied population and increase the margin of error. Similarly, the limited number of older participants could significantly impact the reported results, as age was an important risk factor and thus an important stressor. The questionnaire omitted the presence of COVID-19-unrelated life-changing events or stressors, and also did not account for any preexisting condition or risk factor that may have affected the outcome of the used assessment scales.

Data Availability

The datasets generated and analyzed during the current study are not publicly available due to compliance with institutional guidelines but they are available from the corresponding author (SH) on a reasonable request.

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We would like to provide our appreciation and thanks to all the respondents in this study.

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Ida Kupcova, Lubos Danisovic & Stefan Harsanyi

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IK and SH have produced the study design. All authors contributed to the manuscript writing, revising, and editing. LD and MK have done data management and extraction, SH did the data analysis. Drafting and interpretation of the manuscript were made by all authors. All authors read and approved the final manuscript.

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Kupcova, I., Danisovic, L., Klein, M. et al. Effects of the COVID-19 pandemic on mental health, anxiety, and depression. BMC Psychol 11 , 108 (2023). https://doi.org/10.1186/s40359-023-01130-5

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Research Article

Impact of COVID-19 pandemic on mental health: An international study

Roles Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

¶ ‡ ATG, MK and AK designed and implemented the study together. AK and MK should be considered joint senior authors.

Affiliation Division of Clinical Psychology & Intervention Science, Department of Psychology, University of Basel, Basel, Switzerland

Roles Data curation, Formal analysis, Writing – original draft, Writing – review & editing

Affiliation Department of Health Sciences, European University Cyprus, Nicosia, Cyprus

Roles Investigation, Resources, Writing – review & editing

Affiliation Psychological Laboratory, Faculty of Public Health and Social Welfare, Riga Stradiņš University, Riga, Latvia

Affiliation Kore University Behavioral Lab (KUBeLab), Faculty of Human and Social Sciences, Kore University of Enna, Enna, Italy

Affiliation Department of Social Sciences, School of Humanities and Social Sciences, University of Nicosia, Nicosia, Cyprus

Affiliation Department of Nursing, Cyprus University of Technology, Limassol, Cyprus

Affiliation Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus

Affiliation Department of Psychological Counseling and Guidance, Faculty of Education, Hasan Kalyoncu University, Gaziantep, Turkey

Affiliation The Nethersole School of Nursing, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong

Affiliation Department of Psychology, Fundación Universitaria Konrad Lorenz, Bogotà, Columbia

Roles Conceptualization, Investigation, Resources, Writing – review & editing

Affiliation Faculty of Psychology, University of La Sabana, Chía, Columbia

Affiliation School of Applied Psychology, University College Cork, Cork, Ireland

Affiliation School of Psychology, University College Dublin, Dublin, Ireland

Affiliation Medical University Innsbruck, Innsbruck, Austria

Affiliation Department of Psychology, Babeş-Bolyai University (UBB), Cluj-Napoca, Romania

Affiliation Instituto Superior de Psicologia Aplicada (ISPA), Instituto Universitário; APPsyCI—Applied Psychology Research Center Capabilities & Inclusion, Lisboa, Portugal

Affiliation Faculdade de Psicologia, Alameda da Universidade, Universidade de Lisboa, Lisboa, Portugal

Affiliation LIP/PC2S, Université Grenoble Alpes, Grenoble, France

Affiliation Department of Biomedicine, Biotechnology and Public Health, University of Cadiz, Cadiz, Spain

Affiliation Instituto ACT, Madrid, Spain

Affiliation Department of Psychology, European University of Madrid, Madrid, Spain

Affiliation Department of Psychology and Sociology, University of Zaragoza, Zaragoza, Spain

Affiliation Vadaskert Child and Adolescent Psychiatric Hospital, Budapest, Hungary

Affiliation Private Pratice, Poland

Affiliation Department of Psychology, University of Jyväskylä, Jyväskylä, Finland

Affiliation Clinic for Psychiatry, Clinical Center of Montenegro, Podgorica, Montenegro

Affiliation Ljubljana University Medical Centre, Ljubljana, Slovania

Affiliation Département de Psychologie, Université du Québec à Trois-Rivières, Trois-Rivières, Canada

Affiliation Department of Psychiatry and Behavioral Science, Duke University, Durham, North Carolina, United States of America

Roles Conceptualization, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing

Affiliation Department of Psychology, University of Cyprus, Nicosia, Cyprus

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• Andrew T. Gloster, 
• Demetris Lamnisos, 
• Jelena Lubenko, 
• Giovambattista Presti, 
• Valeria Squatrito, 
• Marios Constantinou, 
• Christiana Nicolaou, 
• Savvas Papacostas, 
• Gökçen Aydın, 

• Published: December 31, 2020
• https://doi.org/10.1371/journal.pone.0244809
• Reader Comments

The COVID-19 pandemic triggered vast governmental lockdowns. The impact of these lockdowns on mental health is inadequately understood. On the one hand such drastic changes in daily routines could be detrimental to mental health. On the other hand, it might not be experienced negatively, especially because the entire population was affected.

The aim of this study was to determine mental health outcomes during pandemic induced lockdowns and to examine known predictors of mental health outcomes. We therefore surveyed n = 9,565 people from 78 countries and 18 languages. Outcomes assessed were stress, depression, affect, and wellbeing. Predictors included country, sociodemographic factors, lockdown characteristics, social factors, and psychological factors.

Results indicated that on average about 10% of the sample was languishing from low levels of mental health and about 50% had only moderate mental health. Importantly, three consistent predictors of mental health emerged: social support, education level, and psychologically flexible (vs. rigid) responding. Poorer outcomes were most strongly predicted by a worsening of finances and not having access to basic supplies.

Conclusions

These results suggest that on whole, respondents were moderately mentally healthy at the time of a population-wide lockdown. The highest level of mental health difficulties were found in approximately 10% of the population. Findings suggest that public health initiatives should target people without social support and those whose finances worsen as a result of the lockdown. Interventions that promote psychological flexibility may mitigate the impact of the pandemic.

Citation: Gloster AT, Lamnisos D, Lubenko J, Presti G, Squatrito V, Constantinou M, et al. (2020) Impact of COVID-19 pandemic on mental health: An international study. PLoS ONE 15(12): e0244809. https://doi.org/10.1371/journal.pone.0244809

Editor: Joel Msafiri Francis, University of the Witwatersrand, SOUTH AFRICA

Received: October 3, 2020; Accepted: December 16, 2020; Published: December 31, 2020

Copyright: © 2020 Gloster et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: This work was supported by grants from the Swiss National Science Foundation awarded to Andrew T. Gloster (PP00P1_ 163716/1 & PP00P1_190082). The funder provided support in the form of salaries for authors [ATG], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section. One of the authors is employed by a commercial affiliation: Private Pratice, Poland. This affiliation provided support in the form of salaries for authors [BK], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: One of the authors is employed by a commercial affiliation: Private Pratice, Poland. This affiliation provided support in the form of salaries for author BK, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. This does not alter our adherence to PLOS ONE policies on sharing data and materials. No other authors have competing interests to declare.

Introduction

The COVID-19 global pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) virus triggered governmentally mandated lockdowns, social distancing, quarantines and other measures in the interest of public health. The mandated lockdowns abruptly and dramatically altered people’s daily routines, work, travel, and leisure activities to a degree unexperienced by most people living outside of war zones. Simultaneously, the highly contagious, yet invisible virus transformed previously neutral situations to perceived potentially dangerous ones: social interaction, touching one’s face, going to a concert, shaking someone’s hand, and even hugging grandparents. Given these changes and looming threat, increases in anxiety and depression can be expected [ 1 ]. Indeed, common psychological reactions to previous quarantines include post-traumatic symptoms, confusion, and anger [ 2 ], though these data stem from quarantines of specific regions or a subgroup of exposed people, such as medical professionals. It therefore remains an empirical question whether such patterns are consistent when entire populations across the globe are simultaneously affected.

For most people, it stands to reason that governmentally mandated lockdowns decrease their activity levels and the number of stimuli experienced compared to pre-lockdown levels. The impact of reducing activities, stimuli and routines on the population is unknown, but various analogue situations can be used to make predictions, like death of a spouse [ 3 ]; hearing loss [ 4 ]; job loss [ 5 ]; long duration expeditions [ 6 ]; poor acculturation [ 7 ]; and even ageing when combined with loneliness [ 8 ]. Each of these situations is associated with increases in psychological distress. This reduction of stimulations may lead to boredom and reductions in reinforcement, which has been associated with depression [ 9 ]. The sum total of these literatures, and some evidence from country specific studies on COVID-19 suggests that for some people, the mental distress in the form of stress, depression, and negative affect are likely reactions to the lockdown; therefore, people’s wellbeing is likely to suffer. Indeed, increased loneliness, social isolation, and living alone are associated with increased mortality [ 10 ]–the exact effect that mandated lockdown and social distancing rules aimed to counteract.

Alternately, the planned slowing down of daily routines can be beneficial. For example, vacations and weekends are highly sought-after–if not always achieved–periods of relaxation and stress reduction [ 11 ]. Likewise, some religious and spiritual traditions encourage simplicity, mindfulness, and solitude with the goal of increasing wellbeing [ 12 ]. It is therefore conceivable that for some people the lockdown could offer a reprieve from daily hassles and stress and even lead to increases in wellbeing. It is therefore equally important to identify protective factors that can buffer against the negative effects of the lockdown.

Although nearly all people around the globe have been subject to some form of lockdown measures to contain the COVID-19 response, variations exist with respect to how each person is confined, even within a single country. For instance, during the COVID-19 pandemic some people were allowed to go to work, whereas others were required to work exclusively from home. For various reasons, some people had difficulty obtaining some basic supplies. Further, some were thrust into the situation of taking care of others (e.g., children, due to closing of schools). Finally, some people lost income as a result of the lockdown, and this is a known risk-factor for poor mental health [ 13 , 14 ]. Finally, a lockdown may be experienced differently the longer it continues and potentially when in confined spaces [ 2 ]. All of these lockdown-specific features may have an impact on one’s mental health, but to date it remains inadequately explored.

As the risk of the pandemic continues, it is important to understand to what degree the virus-induced uncertainty and the lockdown-induced changes in daily routines impact stress, depression, affect, and wellbeing. Towards this end, it is important to identify factors that can mitigate potential negative psychological effects of pandemics and lockdowns. Various social and psychological factors have been identified in other contexts that may also help build resilience in large-scale pandemics such as COVID-19. On the social level, one such candidate is social support, which has repeatedly been found to positively impact mental health and wellbeing [ 15 – 18 ]. Another social factor is the family climate and family functioning, which clearly impacts people’s mental health [ 19 , 20 ]. Psychological factors such as mindfulness and psychologically flexible response styles (as opposed to rigid and avoidant response styles) are behavioral repertoires that have previously been shown to buffer the impact of stress and facilitate wellbeing [ 21 – 24 ].

Given the scope of the COVID-19 pandemic, it is crucial to better understand how a pandemic and associated lockdowns impact on mental health. Thus, the aim of this study was to determine mental health outcomes and to examine known predictors of outcomes to identify psychological processes and contextual factors that can be used in developing public health interventions. It can be assumed, but remains untested, that those with risks in social-demographic factors, living conditions, social factors and psychological factors have more severe reactions to the lockdown. We therefore tested whether outcomes of stress, depression, affect, and wellbeing were predicted by country of residence, social demographic characteristics, COVID-19 lockdown related predictors, social predictors, and psychological predictors.

Participants

The inclusion criteria were ≥18 years of age and ability to read one of the 18 languages (English, Greek, German, French, Spanish, Turkish, Dutch, Latvian, Italian, Portuguese, Finnish, Slovenian, Polish, Romanian, Hong Kong, Hungarian, Montenegrin, & Persian.). There were no exclusion criteria. People from all countries were eligible to participate.

Ethics approval was obtained from the Cyprus National Bioethics Committee (ref.: EEBK EΠ 2020.01.60) followed by site approvals from different research teams involved in data collection. All participants provided written informed consent prior to completing the survey (computer-based, e.g., by clicking “yes”).

A population based cross-sectional study was conducted in order to explore how people across the world reacted to the COVID-19. The anonymous online survey was distributed using a range of methods. Universities emailed the online survey to students and academic staff and also posted the survey link to their websites. In addition, and in order to broaden the sample to older age groups and to those with different socio-demographic characteristics, the survey was disseminated in local press (e.g., newspapers, newsletters, radio stations), in social media (e.g., Facebook, Twitter, etc.), in professional networks, local hospitals and health centers and professional groups’ email lists (e.g., medical doctors, teachers, engineers, psychologists, government workers), and to social institutions in the countries (e.g., churches, schools, cities/townships, clubs, etc.).

Data were collected for two months between 07th April and 07th June 2020. The majority of countries where data were collected had declared a state of emergency for COVID-19 during this time.

Well validated and established measures were used to assess constructs. When measures did not already exist in a language, they were subject to forward and backward translation procedures. Well-validated measures of predictors and outcomes and items measuring COVID-19 related characteristics were selected after a consensus agreement among the members of this study.

Respondents’ countries were coded and entered as predictors.

Socio-demographic status.

Participants responded to questions related to their socio-demographic characteristics including their age, gender, country of residence, marital status, employment status, educational level, whether they have children as well as their living situation.

Lockdown variables.

Participants responded to questions related to lockdown including length of lockdown, whether they need to leave home for work, any change in their finances, whether they were able to obtain basic supplies, the amount of their living space confined in during the lockdown. They were also asked whether they, their partner, or a significant other was diagnosed with COVID-19.

Social factors.

Social factors were measured using the Brief Assessment of Family Functioning Scale (BAFFS; [ 25 ]) and the Oslo Social Support Scale (OSSS; [ 26 ]). The BAFFS items are summed to produce a single score with higher scores indicating worse family functioning. The OSSS items are summed up and provide three levels types of social support: low (scored 3–8), moderate (scored 9–11) and high (scored 12–14).

Psychological factors.

Psychological factors including mindfulness and psychological flexibility. Mindfulness was measured using the Cognitive Affective Mindfulness Scale (CAMS; [ 27 ]). The CAMS produces a single score with higher scores indicating better mindfulness qualities. Psychological flexibility (e.g., hold one’s thoughts lightly, be accepting of one’s experiences, engage in what is important to them despite challenging situations) was measured using the Psyflex scale [ 28 ]. The Psyflex produces a single score with higher scores indicating better psychological flexibility qualities.

Stress was measured using the Perceived Stress Scale (PSS; [ 29 ]). The PSS assesses an individual’s appraisal of how stressful situations in their life are. Items ask about people’s feelings and thoughts during the last month. A total score is produced, with higher scores indicating greater overall distress.

Depression.

Depressive symptomatology was assessed using two items from the disengagement subscale of the Multidimensional State Boredom Scale (MSBS; [ 30 ]). These items assessed wanting to do pleasurable things but not finding anything appealing (i.e., boredom), as well as wasting time. Based on concepts of reinforcement deprivation (i.e., lack of access to or engagement with positive stimuli) that is known to contribute to depression, we added an item that measured how rewarding or pleasurable people found the activities that they were engaging in (i.e., reinforcement). Higher scores indicated higher depressive symptomatology.

Positive affect/ negative affect.

The Positive And Negative Affect Scale (PANAS) was used to measure affect [ 31 ]. The original version of the questionnaire was used with five additional items: bored, confused, angry, frustrated and lonely. All items were scored on a 5-point Likert type scale, ranging from 1 = very little/not at all to 5 = extremely and summed up so that higher scores in the positive-related items indicating higher positive affect and higher scores in the negative-related items indicating higher negative affect. In order to capture additional dimensions of negative affect believed to be relevant to the COVID-19 lockdowns, we additionally added five items: bored, confused, angry, frustrated, lonely.

Wellbeing was assessed using the Mental Health Continuum Short Form (MHC-SF; [ 32 ]); which assesses three aspects of wellbeing: emotional, psychological, and social. The MHC-SF produces a total score and scores for each of the three aspects of wellbeing. The MHC-SF can also be scored to produce categories of languishing (i.e., low levels of emotional, psychological, and social well-being), flourishing (i.e., high levels of emotional psychological and social well-being almost every day), and moderately mentally healthy (in between languishing and flourishing).

Statistical analysis

The mean and standard deviation was calculated for dependent variables that follow the normal distribution while the median and interquartile range (IQR) were computed for non-normally distributed data. Bivariable association between an outcome variable and each predictor was investigated with ANOVA test for categorical predictor and univariable linear regression for numerical predictor. Linear mixed-effect model with random effect for country was performed to consider simultaneously several predictors in the same model and to account for the variation in outcome variable between countries. Four separate linear mixed-effect models were used for each outcome variable, one for each set of socio-demographic, lockdown, social and psychosocial predictors and multicollinearity for each set of predictors was investigated with the variation inflation criterion (VIF). Standardized regression coefficients were computed as effect size indices to measure the strength of the association between predictor variables and outcome variables. The comparison between the country mean and overall mean for each outcome variable was estimated though a linear regression model with dependent variable the mean centering outcome and predictor the country. Cohen’s d effect size of the standardize difference between country mean and the overall mean was computed as a measure of the magnitude of the difference between the two means.

The whole sample was used in linear mixed-effect models while for the comparison of country mean to the overall mean was used the sample from countries with sample size ≥100. The R packages lme4 and effect sizes were used for fitting the linear mixed effect model and to compute the standardized regression coefficients of the linear mixed effect models [ 33 ]. Significance test and confidence intervals were calculated at a significance level of 0.05. The following cut-off values were used for the evaluation of the effect sizes: ‘tiny’ ≤0.05, ‘very small’ from 0.05 to ≤0.10, ‘small’ from 0.10 to ≤ 0.20, ‘medium’ from 0.20 to ≤ 0.30, ‘large’ from 0.30 to ≤ 0.40 and ‘very large’ > 0.40 [ 34 ].

Descriptive

Participants were n = 9,565 people from 78 countries. See supporting information for a participation flowchart ( S1 Appendix ). The countries with the largest samples were: Latvia (n = 1285), Italy (n = 962), Cyprus (n = 957), Turkey (n = 702), Switzerland (n = 550), Hong Kong (n = 516), Colombia (n = 485), Ireland (n = 414), Austria (n = 368), Romania (n = 339), Portugal (n = 334), France (n = 313), Spain (n = 296), Germany (n = 279), Hungary (n = 273), Greece (n = 270), USA (n = 268), Finland (n = 157), Montenegro (n = 147), Poland (n = 135), United Kingdom (n = 100), Slovenia (n = 77), and Canada (n = 60). The remaining countries are listed in the supporting information ( S1 Table ).

Outcome variables

The means, standard deviations, and where appropriate percentage of participants within categories of the five outcome variables can be seen in Table 1 .

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https://doi.org/10.1371/journal.pone.0244809.t001

Predictor variables

A full list of countries can be found in the supporting information ( S1 Table ).

The mean age was 36.9 (13.3) years. A majority of participants were female (77.7%), approximately a fifth male (22.0%), and small minority identified as other (0.3%). More than half of the respondents were either in a relationship (25.7%) or married (36.1%), almost a third were single (30.8%), and the rest were either divorced (5%), widower (1.1%) or other (1.3%). Participants indicated that they lived: alone (14.6%), with both parents (20.8%), one parent (5.1%), with their own family including partner and children (54.1%), or with friends or roommates (5.5%). Less than half of respondents had children (40.8%). Approximately half of the participants were working full time (53.4%), almost a fifth were working part-time (17.5%), 23.2% were unemployed and a small minority were either on parental leave (2.2%) or retired (3.7%).

COVID-19 lockdown variables.

At the time of responding, participants were in lockdown or self-isolation for a median of 5.0 (3.0 IQR) weeks. Most people indicated that they had not been infected with COVID-19 (88.0%), a small minority indicated they had been infected (1.4%) and the rest had symptoms but were unsure (10.6%). Similar patterns were seen with reported infection rates of partners (no: 92.2%, yes: 0.7%, unsure: 7.1%) and of people close to them (no: 86.0%; yes: 5.6%; unsure: 8.4%). With respect to leaving the house for work, almost half (47.7%) indicated that this never occurred, 7.7% indicated leaving only once, whereas an almost equal number indicated leaving a couple times per week (23.7%) or more than three times per week (21.0%). Nearly all participants indicated they were able to obtain all the basic supplies they needed (93.5%). Participants reported having a median inner living space of 90.0 square meters (80.0 IQR) and median outdoor space of 20.0 square meters (192.1 IQR). Finally, with respect to finances, more than half indicated that their financial situation remained about the same (57.9%), a minority indicated it improved (8.9%), and a third reported that their finances had gotten worse (33.3%).

Social and psychological predictors.

Mean values of the other predictors (i.e., social predictors and psychological predictors) can be seen in Table 1 .

Multivariate analyses

Results of multivariate analyses for the outcome of stress can be seen in Table 2 . The largest protective factor against stress was social support (high support vs low support (-3.35, 95%CI, -3.39 to -2.92), with a very large effect size). Positive predictors of stress with large effect sizes were being female (2.42, 95%CI, 2.07 to 2.77) and worsening of finances (2.32, 95%CI, 1.68 to 2.96), whereas psychological flexibility buffered this response (-0.65, 95%CI, -0.69 to -0.62). Higher education levels were also associated with lower levels of stress, with a large effect size (see Table 2 ). Moderate effect sizes for predictors associated with less stress were older age (-0.13, 95%CI, -0.14, -0.11) and mindfulness (-0.69, 95%CI, -0.74, -0.64). Moderate effect sizes of predictors associated with more stress were worse family functioning (0.98, 95%CI, 0.90, 1.06) and not being able to obtain all basic supplies (1.82 95%CI, 1.12, 2.52).

https://doi.org/10.1371/journal.pone.0244809.t002

Differences in reported levels of stress across countries were largely negligible, with the exception of two countries that reported higher levels of stress (Hong Kong (2.85, 95%CI, 2.22, 3.49) and Turkey (2.47, 95%CI, 1.93, 3.02)) and two that reported lower levels of stress (Portugal (-2.50, 95%CI, -3.29, -1.71) and Montenegro (-3.30, 95%CI, -4.49, -2.11)) than the average stress level across all countries. See supporting information for information on each country ( S2 – S6 Tables).

Results of multivariate analyses for the outcome of depression can be seen in Table 3 . The strongest predictor of depression was social support, such that high (-1.30, 95%CI, -1.44, -1.16) and medium levels (-0.73, 95%CI, -0.85, -0.62) of social support were protective against depression (relative to low levels) with a very large and large effect sizes, respectively. The only other large effect size was for psychological flexibility, which also served in a protective manner (-0.20, 95%CI, -0.22, -0.19). Moderate effect sizes of predictors associated with less depression symptoms were also observed for higher education levels (see Table 3 ). Moderate effect sizes of predictors associated with more depression were worse family functioning (0.29, 95%CI, 0.27, 0.32) and not being able to obtain all basic supplies (0.49, 95%CI, 0.27, 0.70).

https://doi.org/10.1371/journal.pone.0244809.t003

The amount of depression symptoms reported on average within countries was similar for most countries with the exception of one country with lower reported levels than average with a large effect size (Austria (-0.71, 95%CI, -0.95, -0.47)) and one with higher levels than average with a large effect size (USA (0.85, 95%CI, 0.58, 1.13)). See supporting information for information on each country ( S2 – S6 Tables).

Results of multivariate analyses for the outcome of affect can be seen in Table 4 . With respect to positive affect, social support (high support vs low support (5.69, 95%CI, 5.23, 6.16) and psychological flexibility (0.77, 95%CI, 0.74, 0.81) were both predictors with very large effect sizes. Interestingly, those who left their house more than three times per week had higher levels of positive affect than those that did not leave their house for work (1.68, 95%CI, 1.18, 2.17), with a medium effect size. Higher education levels were associated with higher levels of positive affect with a medium to large effect size (see Table 4 , PANAS-Positive).

https://doi.org/10.1371/journal.pone.0244809.t004

The amount of positive affect reported on average within countries was similar for most countries with the exception of one country with lower reported levels than average with a large effect size (Finland (-2.96, 95%CI, -4.19, -1.73)) and one with higher reported levels than average with a large effect size (Portugal (2.96, 95%CI, 2.12, 3.80)). See supporting information for information on each country ( S2 – S6 Tables).

With respect to negative affect, social support (high support vs low support (-2.74, 95%CI, -3.2, -2.29) and psychological flexibility (-0.62, 95%CI, -0.66, -0.58) were again the strongest associated predictors, with large effects. Higher education levels were also associated with lower levels of negative affect, with a medium effect (see Table 4 , PANAS-Negative). Higher levels of negative affect were noted, with medium effect sizes, for the predictors: worsening of finances (1.75, 95%CI, 1.10, 2.40) and not being able to obtain all basic supplies (1.6, 95%CI, 0.89, 2.31).

The amount of negative affect reported on average within countries was similar for most countries with the exception of few countries with lower reported negative affect levels than average with a very large effect sizes (Switzerland (-4.96, 95%CI, -5.91, -4.01), Germany (-4.70, 95%CI, -6.03, -3.37) & Austria (-6.49, 95%CI, -7.65, -5.33)) and one with a large effect size (Montenegro (-3.56, 95%CI, -5.39, -1.73). The average amount of negative affect was higher than average in two countries, with very large effects size (Turkey (5.75, 95%CI, 4.92, 6.59) & Finland (7.57, 95%CI, 5.80, 9.34)). See supporting information for information on each country ( S2 – S6 Tables).

Results of multivariate analyses for the outcome of wellbeing can be seen in Table 5 . Once again, social support (high support vs low support (13.20, 95%CI, 12.39, 14.01)) and psychological flexibility (1.42, 95%CI, 1.34, 1.49) were the predictors with the largest effect sizes (very large) on wellbeing. Higher education levels were associated with higher levels of wellbeing with a medium to large effect sizes (see Table 5 ). Medium negative effect sizes were noted for family functioning (-1.98, 95%CI, -2.12, -1.83) and inability to obtain all basic supplies (-3.27, 95%CI, -4.67, -1.87). Two medium positive effect sizes were observed: mindfulness (0.95, 95%CI, 0.86–1.04) and living with friends/roommates ((3.04, 95%CI, 1.59, 4.48), relative to living alone).

https://doi.org/10.1371/journal.pone.0244809.t005

The level of wellbeing reported on average within countries was similar for most countries with the exception of three countries with higher levels with large effect sizes (Austria (4.95, 95%CI, 3.55, 6.34), Finland (5.24, 95%CI, 3.10, 7.38), & Portugal (4.59, 95%CI, 3.12, 6.05)) and two countries with lower levels of wellbeing than average with large (Italy (-4.36, 95%CI, -11.06, 2.35)) and very large effect sizes (Hong Kong (-6.84, 95%CI, -8.02, -5.66)). See supporting information for information on each country ( S2 – S6 Tables).

The COVID-19 is the largest pandemic in modern history. This study assessed nearly 10,000 participants across many countries to examine the impact of the pandemic and resultant governmental lockdown measures on mental health. During the height of the lockdown, the pandemic was experienced as at least moderately stressful for most people, and 11% reported the highest levels of stress. Symptoms of depression were also high, including 25% of the sample indicating that the things they did were not reinforcing, 33% reporting high levels of boredom, and nearly 50% indicating they wasted a lot of time. Consistent with symptoms of stress and depression, 10% of participants were psychologically languishing. These results suggest that there is a subgroup of people who are especially suffering and that in about 50% of the respondents’ levels of mental health was only moderate. Previous studies have found that along with low levels, even moderate levels of mental health (which consists of only moderate levels of emotional, psychological, and social well-being) are associated with increased subsequent disability, productivity loss, and healthcare use [ 35 – 37 ]. Not everyone was suffering, however, as evidenced by the nearly 40% of participants who reported levels of mental health consistent with flourishing. The present results, while serious, do not point to more severe reactions observed in previous samples of selective quarantined individuals or groups [ 2 ]. Perhaps the previously reported distress in these groups is prevented when an entire country or world is in lockdown so that the feeling emerges that “everyone is in it together”.

Importantly, a handful of predictors emerged that consistently predicted all outcomes: Social support, education level, finances, access to basic needs, and the ability to respond psychologically flexible. The consistency of results examining predictors is noteworthy, both in terms of the consistently strong predictors (e.g., social support, education, psychological flexibly, as well as loss of income and lack of access to necessities) and in terms of the other predictors that were either not predictive or only weakly so. All predictors were chosen based on theoretical ties to the outcomes, previous findings, and studies on quarantines [ 2 ].

A novel finding was that people who left their house three or more times per week reported more positive affect than those that left their house less often. It is possible that these people experienced more variation, which contributed to positive affect. It is also possible they experienced a greater sense of normality. Future studies are encouraged to further investigate possible mechanisms through which this result unfolds.

Overall, these patterns did not differ substantially between countries. Although some differences did emerge, they were mostly inconsistent across outcomes. Three countries fared worse on two outcomes each: Hong Kong (stress & wellbeing); Turkey (stress & negative affect); and Finland (lower positive affect and higher negative affect)–though participants in Finland also reported higher levels of wellbeing than average. Two countries had more favorable outcomes than the average levels across all countries: Portugal (lower stress and higher wellbeing) and Austria (lower depression and higher wellbeing). The differences observed are likely due to a combination of chance, sampling, nation specific responses to the COVID-19 pandemic, cultural differences, and other factors playing out in the countries (e.g., political unrest [ 38 ]). If replicated, future studies are encouraged to examine possible mechanisms of these outcomes.

This study provides valuable insights on several levels. First, it documents the mental health outcomes across a broad sample during the COVID-19 global pandemic. Second, it informs about the conditions and resilience factors (social support, education, and psychological flexibility) and risk factors (loss of income and inability to get basic supplies) that affect mental health outcomes. Third, these factors can be used in future public health responses are being made, including those that require large scale lockdowns or quarantines. That is, public health officials should direct resources to identifying and supporting people with poor social support, income loss, and potentially lower levels of education and provide a strategy to mitigate special risks in these subpopulations. The importance of social support needs to be made clear to the public and to the degree possible mechanisms that can contribute to social support should be supported. Further, psychological flexibility is a trainable set of skills that has repeatedly been shown to ameliorate suffering [ 22 , 39 ]; and can be widely distributed with modern technological intervention tools such as digital, internet, or virtual means [ 40 ]. We do not claim, however, that psychological flexibility is the only factor that can be used for interventions. Instead, it is a recognized transdiagnostic factor assessed in this study and one that is feasible to be targeted and modified by interventions and prevention [ 41 – 43 ].

This study is limited by several important factors. First, the results are based on cross sectional analysis and correlations. As such, causation cannot be inferred and any delayed impact of the pandemic and lockdown on peoples’ mental health was not captured. Second, all results of this survey were obtained via self-report questionnaires, which can be subject to retrospective response bias. Third, although the sample was large and based on varied recruitment sources, it was not representative of the population and undersampled people who suffered most from the pandemic (i.e., front line health care professionals, people in intensive care, etc.) or people without internet access, etc. Finally, the country-specific incidence rates and lockdown measures differed across countries. These were not assessed, but future studies are encouraged to investigate how such factors impact mental health outcomes.

These limitations notwithstanding, based on nearly 10,000 international participants, this study found that approximately 10% of the population was languishing during or shortly after the lockdown period. These finding have implications for public health initiatives. First, officials are urged to attend to, find, and target people who have little social support and/ or whose finances have worsened as a result of the measures. Second, public health interventions are further urged to target psychological processes such as psychological flexibility in general to potentially help buffer other risk factors for mental health. Likewise, availability of social support and information about where to get support and remain connected are needed. These recommendations should become part of public health initiatives designed to promote mental health in general, and should equally be considered when lockdowns or physical distancing are prescribed during a pandemic.

Supporting information

S1 table. list of all countries included in the data set..

https://doi.org/10.1371/journal.pone.0244809.s001

S2 Table. Geodemographic predictors for Perceived Stress Scale.

https://doi.org/10.1371/journal.pone.0244809.s002

S3 Table. Geodemographic predictors for MSBS–depression.

https://doi.org/10.1371/journal.pone.0244809.s003

S4 Table. Geodemographic predictors for PANAS positive.

https://doi.org/10.1371/journal.pone.0244809.s004

S5 Table. Geodemographic predictors for PANAS negative.

https://doi.org/10.1371/journal.pone.0244809.s005

S6 Table. Geodemographic predictors for MHCSF—mental health continuum.

https://doi.org/10.1371/journal.pone.0244809.s006

S1 Appendix. Participation flowchart.

https://doi.org/10.1371/journal.pone.0244809.s007

Acknowledgments

We wish to thank the following people for their work in helping to implement the study: Spyros Demosthenous, Christiana Karashali, Diamanto Rovania (University of Cyprus); Maria Antoniade (European University of Cyprus); Ioanna Menoikou (Cyprus University of Technology); Elias Ioannou (University of Nicosia); Sonja Borner, Victoria Firsching-Block, Alexander Fenn (University of Basel); Cristīne Šneidere, Ingrīda Trups-Kalne, Lolita Vansovica, Sandra Feldmane, (Riga Stradiņš University); David Nilsson (Lund University); Miguel A. Segura-Vargas (Fundación Universitaria Konrad Lorenz); Claudia Lenuţa Rus, Catalina Otoiu, Cristina Vajaean (Babes-Bolyai University). We further wish to thank Fabio Coviello and Sonja Borner (University of Basel) for their help in preparing the manuscript.

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• Volume 10, Issue 12
• Impact of the COVID-19 pandemic on mental health and well-being of communities: an exploratory qualitative study protocol
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• http://orcid.org/0000-0003-0180-0213 Anam Shahil Feroz 1 , 2 ,
• Naureen Akber Ali 3 ,
• Noshaba Akber Ali 1 ,
• Ridah Feroz 4 ,
• Salima Nazim Meghani 1 ,
• Sarah Saleem 1
• 1 Community Health Sciences , Aga Khan University , Karachi , Pakistan
• 2 Institute of Health Policy, Management and Evaluation , University of Toronto , Toronto , Ontario , Canada
• 3 School of Nursing and Midwifery , Aga Khan University , Karachi , Pakistan
• 4 Aga Khan University Institute for Educational Development , Karachi , Pakistan
• Correspondence to Ms Anam Shahil Feroz; anam.sahyl{at}gmail.com

Introduction The COVID-19 pandemic has certainly resulted in an increased level of anxiety and fear in communities in terms of disease management and infection spread. Due to fear and social stigma linked with COVID-19, many individuals in the community hide their disease and do not access healthcare facilities in a timely manner. In addition, with the widespread use of social media, rumours, myths and inaccurate information about the virus are spreading rapidly, leading to intensified irritability, fearfulness, insomnia, oppositional behaviours and somatic complaints. Considering the relevance of all these factors, we aim to explore the perceptions and attitudes of community members towards COVID-19 and its impact on their daily lives and mental well-being.

Methods and analysis This formative research will employ an exploratory qualitative research design using semistructured interviews and a purposive sampling approach. The data collection methods for this formative research will include indepth interviews with community members. The study will be conducted in the Karimabad Federal B Area and in the Garden (East and West) community settings in Karachi, Pakistan. The community members of these areas have been selected purposively for the interview. Study data will be analysed thematically using NVivo V.12 Plus software.

Ethics and dissemination Ethical approval for this study has been obtained from the Aga Khan University Ethical Review Committee (2020-4825-10599). The results of the study will be disseminated to the scientific community and to the research subjects participating in the study. The findings will help us explore the perceptions and attitudes of different community members towards the COVID-19 pandemic and its impact on their daily lives and mental well-being.

• mental health
• public health

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/ .

https://doi.org/10.1136/bmjopen-2020-041641

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Strengths and limitations of this study

The mental health impact of the COVID-19 pandemic is likely to last much longer than the physical health impact, and this study is positioned well to explore the perceptions and attitudes of community members towards the pandemic and its impact on their daily lives and mental well-being.

This study will guide the development of context-specific innovative mental health programmes to support communities in the future.

One limitation is that to minimise the risk of infection all study respondents will be interviewed online over Zoom and hence the authors will not have the opportunity to build rapport with the respondents or obtain non-verbal cues during interviews.

The COVID-19 pandemic has affected almost 180 countries since it was first detected in Wuhan, China in December 2019. 1 2 The COVID-19 outbreak has been declared a public health emergency of international concern by the WHO. 3 The WHO estimates the global mortality to be about 3.4% 4 ; however, death rates vary between countries and across age groups. 5 In Pakistan, a total of 10 880 cases and 228 deaths due to COVID-19 infection have been reported to date. 6

The worldwide COVID-19 pandemic has not only incurred massive challenges to the global supply chains and healthcare systems but also has a detrimental effect on the overall health of individuals. 7 The pandemic has led to lockdowns and has created destructive impact on the societies at large. Most company employees, including daily wage workers, have been prohibited from going to their workplaces or have been asked to work from home, which has caused job-related insecurities and financial crises in the communities. 8 Educational institutions and training centres have also been closed, which resulted in children losing their routine of going to schools, studying and socialising with their peers. Delay in examinations is likewise a huge stressor for students. 8 Alongside this, parents have been struggling with creating a structured milieu for their children. 9 COVID-19 has hindered the normal routine life of every individual, be it children, teenagers, adults or the elderly. The crisis is engendering burden throughout populations and communities, particularly in developing countries such as Pakistan which face major challenges due to fragile healthcare systems and poor economic structures. 10

The COVID-19 pandemic has certainly resulted in an increased level of anxiety and fear in communities in terms of disease management and infection spread. 8 Further, the highly contagious nature of COVID-19 has also escalated confusion, fear and panic among community residents. Moreover, social distancing is often an unpleasant experience for community members and for patients as it adds to mental suffering, particularly in the local setting where get-togethers with friends and families are a major source of entertainment. 9 Recent studies also showed that individuals who are following social distancing rules experience loneliness, causing a substantial level of distress in the form of anxiety, stress, anger, misperception and post-traumatic stress symptoms. 8 11 Separation from family members, loss of autonomy, insecurity over disease status, inadequate supplies, inadequate information, financial loss, frustration, stigma and boredom are all major stressors that can create drastic impact on an individual’s life. 11 Due to fear and social stigma linked with COVID-19, many individuals in the community hide their disease and do not access healthcare facilities in a timely manner. 12 With the widespread use of social media, 13 rumours, myths and inaccurate information about COVID-19 are also spreading rapidly, not only among adults but are also carried on to children, leading to intensified irritability, fearfulness, insomnia, oppositional behaviours and somatic complaints. 9 The psychological symptoms associated with COVID-19 at the community level are also manifested as anxiety-driven panic buying, resulting in exhaustion of resources from the market. 14 Some level of panic also dwells in the community due to the unavailability of essential protective equipment, particularly masks and sanitisers. 15 Similarly, mental health issues, including depression, anxiety, panic attacks, psychotic symptoms and even suicide, were reported during the early severe acute respiratory syndrome outbreak. 16 17 COVID-19 is likely posing a similar risk throughout the world. 12

The fear of transmitting the disease or a family member falling ill is a probable mental function of human nature, but at some point the psychological fear of the disease generates more anxiety than the disease itself. Therefore, mental health problems are likely to increase among community residents during an epidemic situation. Considering the relevance of all these factors, we aim to explore the perceptions and attitudes towards COVID-19 among community residents and the impact of these perceptions and attitude on their daily lives and mental well-being.

Methods and analysis

Study design.

This study will employ an exploratory qualitative research design using semistructured interviews and a purposive sampling approach. The data collection methods for this formative research will include indepth interviews (IDIs) with community members. The IDIs aim to explore perceptions of community members towards COVID-19 and its impact on their mental well-being.

Study setting and study participants

The study will be conducted in two communities in Karachi City: Karimabad Federal B Area Block 3 Gulberg Town, and Garden East and Garden West. Karimabad is a neighbourhood in the Karachi Central District of Karachi, Pakistan, situated in the south of Gulberg Town bordering Liaquatabad, Gharibabad and Federal B Area. The population of this neighbourhood is predominantly Ismailis. People living here belong mostly to the middle class to the lower middle class. It is also known for its wholesale market of sports goods and stationery. Garden is an upmarket neighbourhood in the Karachi South District of Karachi, Pakistan, subdivided into two neighbourhoods: Garden East and Garden West. It is the residential area around the Karachi Zoological Gardens; hence, it is popularly known as the ‘Garden’ area. The population of Garden used to be primarily Ismailis and Goan Catholics but has seen an increasing number of Memons, Pashtuns and Baloch. These areas have been selected purposively because the few members of these communities are already known to one of the coinvestigators. The coinvestigator will serve as a gatekeeper for providing entrance to the community for the purpose of this study. Adult community members of different ages and both genders will be interviewed from both sites, as mentioned in table 1 . Interview participants will be selected following the eligibility criteria.

• View inline

Study participants for indepth interviews

IDIs with community members

We will conduct IDIs with community members to explore the perceptions and attitudes of community members towards COVID-19 and its effects on their daily lives and mental well-being. IDI participants will be identified via the community WhatsApp group, and will be invited for an interview via a WhatsApp message or email. Consent will be taken over email or WhatsApp before the interview begins, where they will agree that the interview can be audio-recorded and that written notes can be taken. The interviews will be conducted either in Urdu or in English language, and each interview will last around 40–50 min. Study participants will be assured that their information will remain confidential and that no identifying features will be mentioned on the transcript. The major themes will include a general discussion about participants’ knowledge and perceptions about the COVID-19 pandemic, perceptions on safety measures, and perceived challenges in the current situation and its impact on their mental well-being. We anticipate that 24–30 interviews will be conducted, but we will cease interviews once data saturation has been achieved. Data saturation is the point when no new themes emerge from the additional interviews. Data collection will occur concurrently with data analysis to determine data saturation point. The audio recordings will be transcribed by a transcriptionist within 24 hours of the interviews.

An interview guide for IDIs is shown in online supplemental annex 1 .

Supplemental material

Eligibility criteria.

The following are the criteria for inclusion and exclusion of study participants:

Inclusion criteria

Residents of Garden (East and West) and Karimabad Federal B Area of Karachi who have not contracted the disease.

Exclusion criteria

Those who refuse to participate in the study.

Those who have experienced COVID-19 and are undergoing treatment.

Those who are suspected for COVID-19 and have been isolated/quarantined.

Family members of COVID-19-positive cases.

Data collection procedure

A semistructured interview guide has been developed for community members. The initial questions on the guide will help to explore participants’ perceptions and attitudes towards COVID-19. Additional questions on the guide will assess the impact of these perceptions and attitude on the daily lives and mental health and well-being of community residents. All semistructured interviews will be conducted online via Zoom or WhatsApp. Interviews will be scheduled at the participant’s convenient day and time. Interviews are anticipated to begin on 1 December 2020.

Patient and public involvement

No patients were involved.

Data analysis

We will transcribe and translate collected data into English language by listening to the audio recordings in order to conduct a thematic analysis. NVivo V.12 Plus software will be used to import, organise and explore data for analysis. Two independent researchers will read the transcripts at various times to develop familiarity and clarification with the data. We will employ an iterative process which will help us to label data and generate new categories to identify emergent themes. The recorded text will be divided into shortened units and labelled as a ‘code’ without losing the main essence of the research study. Subsequently, codes will be analysed and merged into comparable categories. Lastly, the same categories will be grouped into subthemes and final themes. To ensure inter-rater reliability, two independent investigators will perform the coding, category creation and thematic analyses. Discrepancies between the two investigators will be resolved through consensus meetings to reduce researcher bias.

Ethics and dissemination

Study participants will be asked to provide informed, written consent prior to participation in the study. The informed consent form can be submitted by the participant via WhatsApp or email. Participants who are unable to write their names will be asked to provide a thumbprint to symbolise their consent to participate. Ethical approval for this study has been obtained from the Aga Khan University Ethical Review Committee (2020-4825-10599). The study results will be disseminated to the scientific community and to the research subjects participating in the study. The findings will help us explore the perceptions and attitudes of different community members towards the COVID-19 pandemic and its impact on their daily lives and mental well-being.

The findings of this study will help us to explore the perceptions and attitudes towards the COVID-19 pandemic and its impact on the daily lives and mental well-being of individuals in the community. Besides, an indepth understanding of the needs of the community will be identified, which will help us develop context-specific innovative mental health programmes to support communities in the future. The study will provide insights into how communities are managing their lives under such a difficult situation.

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Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

• Data supplement 1

ASF and NAA are joint first authors.

Contributors ASF and NAA conceived the study. ASF, NAA, RF, NA, SNM and SS contributed to the development of the study design and final protocols for sample selection and interviews. ASF and NAA contributed to writing the manuscript. All authors reviewed and approved the final version of the paper.

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests None declared.

Patient consent for publication Not required.

Provenance and peer review Not commissioned; externally peer reviewed

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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Mental Health Research During the COVID-19 Pandemic: Focuses and Trends

Affiliations.

• 1 Law School, Changsha University, Changsha, China.
• 2 Department of Psychology, University of Toronto St. George, Toronto, ON, Canada.
• 3 Centre for Mental Health and Education, Central South University, Changsha, China.
• PMID: 35958839
• PMCID: PMC9360762
• DOI: 10.3389/fpubh.2022.895121

Background: The COVID-19 pandemic has profoundly influenced the world. In wave after wave, many countries suffered from the pandemic, which caused social instability, hindered global growth, and harmed mental health. Although research has been published on various mental health issues during the pandemic, some profound effects on mental health are difficult to observe and study thoroughly in the short term. The impact of the pandemic on mental health is still at a nascent stage of research. Based on the existing literature, we used bibliometric tools to conduct an overall analysis of mental health research during the COVID-19 pandemic.

Method: Researchers from universities, hospitals, communities, and medical institutions around the world used questionnaire surveys, telephone-based surveys, online surveys, cross-sectional surveys, systematic reviews and meta-analyses, and systematic umbrella reviews as their research methods. Papers from the three academic databases, Web of Science (WOS), ProQuest Academic Database (ProQuest), and China National Knowledge Infrastructure (CNKI), were included. Their previous research results were systematically collected, sorted, and translated and CiteSpace 5.1 and VOSviewers 1.6.13 were used to conduct a bibliometric analysis of them.

Result: Authors with papers in this field are generally from the USA, the People's Republic of China, the UK, South Korea, Singapore, and Australia. Huazhong University of Science and Technology, Hong Kong Polytechnic University, and Shanghai Jiao Tong University are the top three institutions in terms of the production of research papers on the subject. The University of Toronto, Columbia University, and the University of Melbourne played an important role in the research of mental health problems during the COVID-19 pandemic. The numbers of related research papers in the USA and China are significantly larger than those in the other countries, while co-occurrence centrality indexes in Germany, Italy, England, and Canada may be higher.

Conclusion: We found that the most mentioned keywords in the study of mental health research during the COVID-19 pandemic can be divided into three categories: keywords that represent specific groups of people, that describe influences and symptoms, and that are related to public health policies. The most-cited issues were about medical staff, isolation, psychological symptoms, telehealth, social media, and loneliness. Protection of the youth and health workers and telemedicine research are expected to gain importance in the future.

Keywords: COVID-19; bibliometric analysis; focuses; keyword clustering; mental health; trends.

Copyright © 2022 Liang, Sun and Tan.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The volume of COVID-19 and…

The volume of COVID-19 and mental health-related articles in 2020–2022.

Country or region co-occurrence.

Author co-occurrence.

Author co-occurrence groups.

Institutions' co-occurrence.

Keyword clustering.

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The COVID-19 pandemic has had a huge impact on public health around the globe in terms of both physical and mental health, and the mental health implications of the pandemic may continue long after the physical health consequences have resolved. This research area aims to contribute to our understanding of the COVID-19 pandemics implications for mental health, building on a robust literature on how environmental crises, such as SARS or natural disasters, can lead to mental health challenges, including loneliness, acute stress, anxiety, and depression. The social distancing aspects of the COVID-19 pandemic may have particularly significant effects on mental health. Understanding how mental health evolves as a result of this serious global pandemic will inform prevention and treatment strategies moving forward, including allocation of resources to those most in need. Critically, these data can also serve as evidence-based information for public health organizations and the public as a whole.

Understanding the Mental Health Implications of a Pandemic

Introduction

The world is entering into a new phase with COVID-19 spreading rapidly. People will be studying various consequences of the COVID-19 pandemic and mental and behavioral health should be a core part of that effort. There is a robust literature on how environmental crises, such as SARS or natural disasters, can lead to mental health challenges, including loneliness, acute stress, anxiety, and depression. The social distancing aspects of the current pandemic may have particularly significant effects on mental health. Understanding how mental health evolves as a result of this serious global outbreak will inform prevention and treatment strategies moving forward, including allocation of resources to those most in need. Critically, these data can also serve as evidence-based information for public health organizations and the public as a whole.

The data will be leveraged to address many questions, such as:

• Which individuals are at greatest risk for high levels of mental health distress during a pandemic?
• As individuals spend more time inside and isolated, how does their mental health distress evolve?
• How do different behaviors (such as media consumption) relate to mental health? 

Read more about how our experts are measuring mental distress amid a pandemic.  

We have been working to ensure that measurement of mental health measures is a key part of large-scale national and international data collections relative to COVID-19.

Read more about conducting research studies on mental health during the pandemic. 

Mental Health Resources

See our resources guide here.

Members of the COVID-19 Mental Health Measurement Working Group

• M. Daniele Fallin, JHSPH
• Calliope Holingue, Kennedy Krieger Institute, JHSPH
• Renee M. Johnson, JHSPH
• Luke Kalb, Kennedy Krieger Institute, JHSPH
• Frauke Kreuter, University of Maryland, University of Mannheim
• Courtney Nordeck, JHSPH
• Kira Riehm, JHSPH
• Emily J. Smail, JHSPH
• Elizabeth Stuart, JHSPH
• Johannes Thrul, JHSPH
• Cindy Veldhuis, Columbia University School of Nursing

The Johns Hopkins COVID-19 Mental Health Measurement Working Group developed key questions to add to existing large domestic and international surveys to measure the mental health impact of the pandemic.

Transforming the understanding and treatment of mental illnesses.

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COVID-19 and Mental Health

What is covid-19.

COVID-19 is a disease caused by a virus named SARS-CoV-2. COVID-19 most often affects the lungs and respiratory system, but it can also affect other parts of the body. Some people develop post-COVID conditions, also called  Long COVID  . These symptoms can include neurological symptoms such as difficulty thinking or concentrating, sleep problems, and depression or anxiety.

Why is NIMH studying COVID-19 and mental health?

Both SARS-CoV-2 and the COVID-19 pandemic have significantly affected the mental health of adults and children. Many people experienced symptoms of  anxiety ,  depression , and substance use disorder during the pandemic. Data also suggest that people are more likely to develop mental illnesses or disorders in the months following COVID-19 infection. People with Long COVID may experience many symptoms related to brain function and mental health  .

While the COVID-19 pandemic has had widespread mental health impacts, some people are more likely to be affected than others. This includes people from racial and ethnic minority groups, mothers and pregnant people, people with financial and housing insecurity, children, people with disabilities, people with preexisting mental illnesses or substance use problems, and health care workers. 

How is NIMH research addressing this critical topic?

NIMH is supporting research to understand and address the impacts of the pandemic on mental health. This includes research to understand how COVID-19 affects people with existing mental illnesses across their entire lifespan. NIMH also supports research to help meet people’s mental health needs during the pandemic and beyond. This includes research focused on making mental health services more accessible through telehealth, digital tools, and community-based interventions.

NIMH is also working to understand the unique impacts of the pandemic on specific groups of people, including people in underserved communities and children. For example, NIMH supports research investigating how pandemic-related factors, such as school disruptions, may influence children’s brain, cognitive, social, and emotional development.

Where can I learn more about COVID-19 and mental health?

• NIMH video: Mental Illnesses and COVID-19 Risks
• NIMH Director’s Messages about COVID-19
• NIMH events about COVID-19
• NIMH news about COVID-19

Where can I learn more about Long COVID and COVID-19?

• NIH page on Long COVID 
• NIH RECOVER Initiative  
• CDC COVID-19 resources 

How can I find help for mental health concerns?

If you have concerns about your mental health, talk to a primary care provider. They can refer you to a qualified mental health professional, such as a psychologist, psychiatrist, or clinical social worker, who can help you figure out the next steps. Find tips for talking with a health care provider about your mental health.

You can learn more about getting help on the NIMH website. You can also learn about finding support  and locating mental health services  in your area on the Substance Abuse and Mental Health Services Administration (SAMHSA) website.

Last Reviewed:  May 2024

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COVID-19 and mental health: A review of the existing literature

• • Subsyndromal mental health concerns are a common response to the COVID-19 outbreak.
• • These responses affect both the general public and healthcare workers.
• • Depressive and anxiety symptoms have been reported in 16–28% of subjects screened.
• • Novel methods of consultation, such as online services, can be helpful for these patients.
• • There is a need for further long-term research in this area, especially from other countries

The COVID-19 pandemic is a major health crisis affecting several nations, with over 720,000 cases and 33,000 confirmed deaths reported to date. Such widespread outbreaks are associated with adverse mental health consequences. Keeping this in mind, existing literature on the COVID-19 outbreak pertinent to mental health was retrieved via a literature search of the PubMed database. Published articles were classified according to their overall themes and summarized. Preliminary evidence suggests that symptoms of anxiety and depression (16–28%) and self-reported stress (8%) are common psychological reactions to the COVID-19 pandemic, and may be associated with disturbed sleep. A number of individual and structural variables moderate this risk. In planning services for such populations, both the needs of the concerned people and the necessary preventive guidelines must be taken into account. The available literature has emerged from only a few of the affected countries, and may not reflect the experience of persons living in other parts of the world. In conclusion, subsyndromal mental health problems are a common response to the COVID-19 pandemic. There is a need for more representative research from other affected countries, particularly in vulnerable populations.

1. Introduction

Originating as a cluster of unexplained cases of pneumonia in Wuhan, China, novel coronavirus disease – officially designated as COVID-19 by the World Health Organization – has reached the level of a pandemic, affecting countries all across the world. To date (March 30 th , 2020), over 720,000 confirmed cases and 33,000 deaths attributable to this disease have been reported. In the wake of this global health crisis, stringent public health measures have been implemented to curtail the spread of COVID-19 ( Adhikari et al., 2020 ).

Widespread outbreaks of infectious disease, such as COVID-19, are associated with psychological distress and symptoms of mental illness ( Bao et al., 2020 ). Psychiatrists across the world should be aware of these manifestations, their correlates, and strategies to manage them that encompass both the needs of specific populations ( Yang et al., 2020 ) and the precautionary measures necessary to contain the spread of COVID-19 ( Liu et al., 2020a ). They should also be aware of lacunae in the existing literature, which may need to be filled in over time through more widespread clinical experience and research.

With the above objectives in mind, the current review was designed to summarize the existing literature addressing mental health concerns related to the COVID-19 pandemic.

2. Methodology

2.1. search methodology and article selection.

The current article is a narrative review of the existing literature on mental health symptoms and interventions relevant to the COVID-19 pandemic. A search of the PubMed electronic database was undertaken using the search terms “novel coronavirus”, “COVID-19”, “nCoV”, “mental health”, “psychiatry”, “psychology”, “anxiety”, “depression” and “stress” in various permutations and combinations. A total of 47 citations were retrieved using this method. On reviewing the above citations, 19 articles were excluded: 3 because they were available only in the Chinese language, and 16 because they dealt with other aspects of the COVID-19 outbreak, such as drug therapy, animal models, public health and preventive measures, and organization of health care systems. A careful review of these 16 articles revealed no material relevant to mental health.

2.2. Methodological and thematic analysis of selected articles

The remaining 28 articles were included in this review. Of these 28 articles, only a minority (n = 4) could be genuinely labelled as “original research”. All these four studies were cross-sectional and observational in design. The remaining 24 articles consisted of letters to the editor (n = 16) and editorials or commentary related to mental health and COVID-19 (n = 8).

As it was not possible to conduct a formal systematic review or meta-analysis given the nature of the above publications, it was instead decided to conduct a narrative review, giving priority to the few observational studies available and briefly summarizing the salient themes from the other publication types. Five broad themes were identified across the 26 publications, and were used to organize the review: (a) observational studies reporting on mental health symptoms in particular populations, (b) commentary and correspondence broadly addressing the psychological impact of COVID-19 on the population, (c) commentary and correspondence addressing the impact of COVID-19 on healthcare workers, (d) commentary and correspondence specifically related to high-risk or vulnerable populations, and (e) commentary and correspondence related to methods of delivering mental health care during the COVID-19 outbreak.

The majority of published articles (18/28 of all articles; 64.3%) and all the observational studies (4/4; 100%) were from Chinese centres. There were two publications each from Iran and Canada; one each from Brazil, Singapore, India and Japan; and two publications with no specified country of origin.

3.1. Observational studies on mental health problems related to COVID-19

Four studies, all from Chinese centres, examined the frequency of specific mental health-related variables in persons affected by the COVID-19 outbreak ( Wang et al., 2020 ; Xiao et al., 2020a ; Li et al., 2020 ; Xiao et al., 2020b ). Their results are summarized in the Table below ( Table 1 ).

Observational studies of mental health concerns related to COVID-19.

As seen in the above results, only one study has provided rough estimates of the frequencies of individual mental health symptoms, with anxiety being the commonest. Anxiety was associated with impaired sleep in both studies examining this link ( Xiao et al., 2020a , b ). In the population-based study, female gender, being a student, having symptoms suggestive of COVID-19, and poor perceived health were associated with higher rates of anxiety and depression; on the other hand, the availability of accurate information and the use of specific preventive measures, such as hand-washing, seemed to mitigate these effects ( Wang et al., 2020 ). No descriptive studies of this sort could be retrieved from other countries.

3.2. Literature addressing the mental health impact of COVID-19 on the general population

Eight publications, including commentaries (n = 4) and correspondence (n = 5) addressed the potential mental health impact of COVID-19 on the general population, based on literature from previous disease outbreaks or specified theoretical models. There was greater geographical diversity in this group of publications, with papers originating from China, Canada, Iran, Japan, Singapore and Brazil.

Two of these papers examined the likely impact of the COVID-19 pandemic in specific countries. One of these, from Iran ( Zandifar and Badrfam, 2020 ) highlighted the role of unpredictability, uncertainty, seriousness of the disease, misinformation and social isolation in contributing to stress and mental morbidity. The authors highlighted the need for both mental health services, particularly for vulnerable populations, and the strengthening of social capital to reduce the adverse psychological impact of the outbreak. Another, from Japan ( Shigemura et al., 2020 ), emphasised the economic impact of COVID-19 and its effects on well-being, as well as the likely high levels of fear and panic behaviour, such as hoarding and stockpiling of resources, in the general population. This paper also identified populations at higher risk of adverse mental health outcomes, including patients with COVID-19 and their families, individuals with existing physical or psychiatric morbidity, and healthcare workers.

Of the remaining papers, one pointed out that the wide scope and spread of COVID-19 could lead to a true mental health crisis, especially in countries with high case loads ( Dong and Bouey, 2020 ) which would require both large-scale psychosocial crisis interventions, and the incorporation of mental health care in disaster management plans in the future. In a related report ( Duan and Zhu, 2020 ) it was pointed out that while Western countries have incorporated psychological interventions into their protocols for disease outbreaks, this has not yet happened in countries such as China, leading to the emergence and persistence of stress-related disorders in affected persons. This paper also offered suggestions for the development of intervention strategies, which will be summarized in section 3 .5 below. In contrast, Bao et al. (2020) highlighted the services that were already being provided in China, and also provided a list of strategies for the general public to minimize outbreak-related stress: (1) assessment of the accuracy of information, (2) enhancing social support, (3) reducing the stigma associated with the disease, (4) maintaining as normal a life as feasible while adhering to safety measures, (5) use of available psychosocial services, particularly online services, when needed. Such methods, in their opinion, would empower society to handle the COVID-19 outbreak in an adaptive manner. Similar strategies were reiterated in a paper from Singapore ( Ho et al., 2020 ) which also discussed the role of improved screening for mental disorders, improving links between community and hospital services, and providing accurate information to the general public in order to minimize maladaptive responses such as “panic” and paranoia regarding the disease and its transmission. Finally, a brief review paper ( Lima et al., 2020 ) highlighted the role of anxiety as the dominant emotional response to an outbreak, and the need for adequate training of healthcare personnel and the optimal use of technological advances to deliver mental health care.

In contrast to the above literature on practical considerations, two papers from Canada ( Asmundson and Taylor, 2020a , b ) have discussed the mental health impact of COVID-19 from the point of view of health anxiety . Health anxiety, which arises from the misinterpretation of perceived bodily sensations and changes, can be protective in everyday life. However, during an outbreak of infectious disease, particularly in the presence of inaccurate or exaggerated information from the media, health anxiety can become excessive. At an individual level, this can manifest as maladaptive behaviours (repeated medical consultations, avoiding health care even if genuinely ill, hoarding particular items); at a broader societal level, it can lead to mistrust of public authorities and scapegoating of particular populations or groups. The authors underline the need for evidence-based research into health anxiety and its determinants, so that valid individual- and population-level strategies can be developed to minimize it in the face of the COVID-19 pandemic and future outbreaks of a similar nature.

3.3. Literature addressing the mental health impact of COVID-19 on healthcare workers

As discussed briefly in section 3.1, healthcare workers are at a significant risk of adverse mental health outcomes during the COVID-19 outbreak. Reasons for this include long working hours, risk of infection, shortages of protective equipment, loneliness, physical fatigue, and separation from families ( Kang et al., 2020 ).

Excluding observational studies, three papers, all from Chinese centres, have addressed this topic. One of these vividly illustrates the gap between planned services at a given hospital and the actual needs of healthcare workers ( Chen et al., 2020 ). This centre had developed a three-pronged approach to address the mental health of their staff: development of an intervention team which would design online materials, implementation of a psychological assistance hotline, and group activities for stress reduction. However, this programme met with reluctance from the healthcare workers themselves. After direct interaction with the workers, this programme was redesigned to include the provision of a rest area, care for basic physical needs such as food, training on the care of COVID-19 patients, information on protective measures, leisure activities, and periodic visits to the rest area by a counsellor. This resulted in greater satisfaction among healthcare workers, and highlights the need for ongoing feedback and modification of such programmes if they are not acceptable to the workers themselves. Liu et al. (2020b) pointed out that mental health professionals may need to work especially closely with those working in critical care units, to minimize stress levels and reduce the risk of depression, while Kang et al. (2020) noted the positive impact of telephone helplines for healthcare workers to specifically address mental health problems. To date, no literature pertaining to healthcare workers from other countries has been published.

3.4. Literature related to the mental health risks of COVID-19 in vulnerable populations

Seven publications (correspondence, n = 6; commentary, n = 1) have identified particular populations that may be more vulnerable to the mental health impact of the COVID-19 pandemic, and some of these have provided suggestions regarding interventions and service provision. The vulnerable groups identified by these authors include older adults ( Yang et al., 2020 ), the homeless ( Tsai and Wilson, 2020 ), migrant workers ( Liem et al., 2020 ), the mentally ill (Yao et al., 2020a; Zhu et al., 2020 ), pregnant women ( Rashidi Fakari and Simbar, 2020 ) and Chinese students studying overseas ( Zhai and Du, 2020 ).

Of particular interest to practicing psychiatrists are the two reports from China (Yao et al., 2020, Zhu et al., 2020 ) regarding COVID-19 and patients with pre-existing psychiatric illness. To date, a single outbreak of COVID-19, affecting around 50 patients and 30 staff, has been reported in a psychiatric hospital, and this was contained by strict quarantine. Reasons for this may have included overcrowding, lack of general medical facilities in psychiatric hospitals, lack of knowledge among mental health professionals, and difficulty in obtaining the cooperation of patients for preventive measures, especially those suffering from psychotic disorders ( Zhu et al., 2020 ). Conversely, patients with pre-existing mental disorders may be at higher risk of relapse or new episodes of their disorder due to the stress associted with the COVID-19 outbreak (Yao et al., 2020a). During this period, it is crucial that psychiatrists familiarize themselves with screening and triage procedures, and work closely with physicians and public health specialists to minimize the risks that their patients face ( Zhu et al., 2020 ).

With regards to the other populations listed above, specific issues raised include the high rates of pre-existing depressive symptoms in the elderly and their lack of access to mental health services ( Yang et al., 2020 ); the fears of involuntary admission or imprisonment among the homeless which may act as a barrier to mental health care ( Tsai and Wilson, 2020 ); the need for outreach and social support among migrant worker populations to reduce the risk of common mental disorders ( Liem et al., 2020 ); the relationship between COVID-19 – related stress and anxiety and adverse maternal and neonatal outcomes ( Rashidi Fakari and Simbar, 2020 ); and the potential discrimination and stigmatization faced by Chinese students overseas during the pandemic, leading to anxiety and stress-related disorders ( Zhai and Du, 2020 ). In all these cases, close collaboration between psychiatrists and specialities from other branch of medicine, as well as with local authorities and health workers in the community, is essential.

3.5. Therapeutic interventions and strategies

Five papers (correspondence, n = 2; commentary, n = 3) have directly addressed the use of specific strategies to deliver mental health care to persons affected by the COVID-19 epidemic ( Duan and Zhu, 2020 ; Liu et al., 2020a ; Xiao, 2020 ; Zhou et al., 2020 ; Yao et al., 2020b ). In addition, a paper from India has discussed the importance of psychiatrists during the COVID-19 pandemic in general terms. This paper identified six important roles for the psychiatrist: a) education of the public about the common psychological effects of a pandemic, b) motivating the public to adopt strategies for disease prevention and health promotion, c) integrating their services with available health care, d) teaching problem-solving strategies to cope with the current crisis, e) empowering patients with COVID-19 and their caregivers, and f) provision of mental health care to healthcare workers ( Banerjee, 2020 ).

With reference to more specific therapeutic strategies, proposals include the development of teams of specialists qualified to address emotional distress ( Duan and Zhu, 2020 ); the training of community health personnel in basic aspects of mental health care ( Duan and Zhu, 2020 ); the use of online surveys to assess the scope of mental health problems ( Liu et al., 2020b ); the development of online materials for mental health education ( Liu et al., 2020a ); the provision of online counselling and self-help services ( Liu et al., 2020b ); the use of structured letters as a form of asynchronous telepsychiatry consultation ( Xiao, 2020 ); the development of synchronous telemedicine services for diagnostic purposes as well as counselling ( Zhou et al., 2020 ); and the need to make online mental health services accessible to individuals from lower socioeconomic strata ( Yao et al., 2020b ). Such strategies offer the hope of providing mental health services in an easily accessible manner without any increase in infection risk. However, they depend crucially on the availability of trained manpower and infrastructure, and it is not known to what extent these approaches will be accepted by the general public. Moreover, they have not yet been tested or validated in the respective target populations.

4. Conclusions and further directions

Though there are few large-scale observational studies available in this field to date, it is clear that the COVID-19 pandemic has led to a vigorous and multifaceted response from psychiatrists and allied professionals, and that mental health is clearly being taken into consideration at multiple levels – in the general population, among healthcare workers, and in vulnerable populations. Though the quality of evidence in the available literature is relatively low, it still contains numerous valuable observations and suggestions for all professionals working in this field, whether they are associated with psychiatric or general hospitals or working in the community. As the number of patients affected by this pandemic continues to increase, the psychiatric profession – particularly in Asian countries – faces both a challenge and an opportunity; the challenge of addressing the numerous barriers and limitations identified in the above literature, but also the opportunity to implement those suggestions or recommendations which are feasible at a local or regional level. The long-term mental health impact of COVID-19 may take weeks or months to become fully apparent, and managing this impact requires concerted effort not just from psychiatrists but from the health care system at large ( Maunder, 2009 ). There is a need for further research, even in the form of preliminary or pilot studies, to assess the scope of this pandemic in other countries, particularly in those where mental health infrastructure is less developed and the impact is likely to be more severe ( Duan and Zhu, 2020 ). Researchers should also attempt to assess the impact of COVID-19 on other vulnerable populations, such as children and adolescents, those in remote or rural areas who face barriers in accessing health care, and those belonging to lower socio-economic strate. Further, there is a need to develop mental health interventions which are time-limited, culturally sensitive, and can be taught to healthcare workers and volunteers. Once developed, such interventions should be tested, so that information regarding effective therapeutic strategies can be widely disseminated among those working in this field.

Disclosure of funding sources

Not applicable.

Financial disclosure

The author has no sources of funding or other financial disclosures concerning the above article.

Declaration of Competing Interest

The authors declare that there are no conflicts of interest.

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Study highlights mental health benefits of COVID-19 vaccination

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In a recent study published in JAMA Psychiatry , a team of scientists from the United Kingdom (U.K.) investigated whether coronavirus disease 2019 (COVID-19) was associated with mental health illnesses and whether the association was modified based on COVID-19 vaccination status among the general population, as well as among patients who were hospitalized due to the disease.

Numerous studies on hospitalized COVID-19 patients as well as non-hospitalized individuals who experienced milder forms of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection have shown that COVID-19 is associated with the subsequent development of mental health illnesses.

These include mental health impairments such as depression and anxiety, as well as more severe forms such as psychotic disorders.

While microvascular alterations and persistent inflammation due to SARS-CoV-2 infection are some of the potential physiological mechanisms linked to mental health illnesses after COVID-19, psychosocial causes such as anxiety about the disease and the outcomes post-COVID-19 have also been implicated.

Furthermore, although the rapid development of COVID-19 vaccines was instrumental in limiting the transmission and morbidity of SARS-CoV-2 infections, the long-term implications of these rapidly developed vaccines on other post-COVID-19 outcomes, including mental health issues, remain unclear.

About the study

In the present study, the researchers used electronic health records of over 18 million individuals in the U.K. to examine associations between COVID-19 diagnoses and the subsequent development of mental health illnesses before the availability of the vaccine and in vaccinated and unvaccinated individuals after COVID-19 vaccine rollouts.

The associations were also examined separately based on sex, age, disease severity, ethnicity, previous SARS-CoV-2 infections, and history of mental health illnesses.

The mental health illness outcomes examined in the study were depression, generalized anxiety disorders, addiction, eating disorders, self-harm, and post-traumatic stress disorder, as well as serious illnesses such as bipolar disorder, schizoaffective disorder, schizophrenia, and psychotic depression.

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Covariates such as sex, age, ethnicity, smoking status, comorbidities, deprivation, employment in health care, and residence in a care home were included as potential cofounders. Three cohorts were included in the study, with the follow-up for the pre-vaccine cohort stretching from early January 2020 to mid-December 2021.

Since the vaccine became available for all adults on June 18 th, 2021, the follow-up for the vaccinated cohort began around then, or two weeks after the second dose of the COVID-19 vaccine, and ended in mid-December 2021, which was the end of the study. For the unvaccinated cohort, the follow-up began 12 weeks after the vaccine became available.

COVID-19 diagnoses were confirmed based on laboratory tests, irrespective of the manifestation of symptoms.

The follow-up period for the pre-vaccine cohort overlapped with the period of circulation of the wild-type and Alpha variants of SARS-CoV-2, while the follow-ups for the vaccinated and unvaccinated cohorts were during the period of circulation of the Delta variant.

Confirmed diagnoses of COVID-19 were the exposure in the study, and the measured outcomes were comparisons of adjusted hazard ratios for the incidence of various mental health illnesses before and after the availability of the COVID-19 vaccine and between unvaccinated and vaccinated individuals.

The study found that the incidence of mental health illnesses was higher for close to a year after COVID-19 among individuals who were not vaccinated against SARS-CoV-2 infections.

The findings showed that the mental health illness incidence rates were significantly higher in the four weeks following the onset of the COVID-19 pandemic as compared to before the pandemic.

However, the incidence rates were relatively lower among the vaccinated cohort. Furthermore, the incidence rates for mental health illnesses remained higher for close to seven months in individuals who got COVID-19 before the vaccinations became available, especially among those hospitalized for acute SARS-CoV-2 infections.

The subgroup analyses showed that the association between COVID-19 and the incidence of mental health illnesses was stronger among men, older adults, and those with a history of mental health illnesses. However, the association did not vary significantly between ethnic groups.

While COVID-19 vaccines were found to mitigate the impact of SARS-CoV-2 infections on subsequent mental health status, the study showed that a history of mental health illnesses influenced vaccine uptake.

This finding highlighted the importance of actively encouraging individuals with existing mental health illnesses to get vaccinated.

The high incidence of mental health illnesses associated with COVID-19 before the availability of the vaccine also potentially reflects the greater levels of uncertainty and concern surrounding COVID-19 outcomes and the effectiveness of treatment options in the early stages of the pandemic.

Conclusions

Overall, the study found that the COVID-19 vaccine mitigated the incidence of mental health illnesses, and unvaccinated individuals were at a higher risk of developing mental health difficulties after COVID-19.

The association was stronger among men and older adults, as well as among people with a history of mental health issues, highlighting the need for encouraging vaccine uptake.

Walker, V. M., Patalay, P., Ignacio, J., Denholm, R., Forbes, H., Stafford, J., Moltrecht, B., Palmer, T., Walker, A., Thompson, E. J., Taylor, K., Cezard, G., Elsie, Wei, Y., Arab, A., Knight, R., Fisher, L., Massey, J., Davy, S., & Mehrkar, A. (2024). COVID-19 and Mental Illnesses in Vaccinated and Unvaccinated People. JAMA Psychiatry . doi : 10.1001/jamapsychiatry.2024.2339 . https://jamanetwork.com/journals/jamapsychiatry/fullarticle/2822342

Posted in: Medical Science News | Medical Research News | Medical Condition News

Tags: Addiction , Anxiety , Bipolar Disorder , Coronavirus , Coronavirus Disease COVID-19 , covid-19 , Depression , Health Care , Inflammation , Laboratory , Mental Health , Pandemic , Post-Traumatic Stress Disorder , Psychiatry , Respiratory , SARS , SARS-CoV-2 , Schizoaffective Disorder , Schizophrenia , Severe Acute Respiratory , Severe Acute Respiratory Syndrome , Smoking , Stress , Syndrome , Vaccine

Dr. Chinta Sidharthan

Chinta Sidharthan is a writer based in Bangalore, India. Her academic background is in evolutionary biology and genetics, and she has extensive experience in scientific research, teaching, science writing, and herpetology. Chinta holds a Ph.D. in evolutionary biology from the Indian Institute of Science and is passionate about science education, writing, animals, wildlife, and conservation. For her doctoral research, she explored the origins and diversification of blindsnakes in India, as a part of which she did extensive fieldwork in the jungles of southern India. She has received the Canadian Governor General’s bronze medal and Bangalore University gold medal for academic excellence and published her research in high-impact journals.

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