Exploring Depressive Symptoms Among Healthcare Professionals and the General Population During the COVID-19 Pandemic in Brazil

Abstract

Our study aimed to examine the symptoms that might play a role in the co-occurrence of 9 DSM-5 symptom criteria of major depression among Brazil's adult population and healthcare professionals after three months of detecting the new coronavirus in Brazil. We estimated regularized Gaussian graphical models for both samples and compared the network structures. Depressed mood was the most central symptom in the general population network compared to the healthcare professional network. The findings revealed some individual symptoms showed a differential association between the general population and healthcare professionals. Those symptoms may be valuable targets for future research and treatment.

Keywords

Covid-19 depression mental health network analysis

Introduction

It is well known that the fallout from the Corona Virus Disease 2019 (COVID-19) outbreak is affecting many communities and is an international public health emergency. In Brazil, the first case of COVID-19 was officially detected on February 25, 2020, in an older man from São Paulo who had returned from a trip to Italy. On May 12,177,589 cases had been confirmed (Oliveira et al., 2020). Although the country had reached the worst moment of the pandemic so far, the federal government did not impose a lockdown. Despite this, many state governors have created their criteria for dealing with the pandemic. For example, rules of social distance were strict, public events were canceled, and restaurants were closed. People were instructed to adopt protective measures, such as washing hands, using gel alcohol, and avoiding handshakes and hugs (The Lancet, 2020). However, it was only a restricted quarantine, in which only delivery and essential services worked. Since then, the country has presented a complex epidemiological scenario (The Lancet, 2020). Brazilian president Jair Bolsonaro maintains his denialist speech, minimizing the impact of the pandemic, and has returned to advocate treatments without proven effectiveness against the disease. Unfortunately, while the number of confirmed cases and deaths is growing, differences of opinion between the president, the Ministry of Health, governors, and epidemiologists remain.

Brazilians are having a disproportionately challenging time, with such diverse government guidelines, some of which contradict scientific data, increasing the fear of virus infection. Many people lost their jobs, and, under this circumstance, there was a decrease in income. In addition, some people are afraid of losing their job, increasing the risk of developing psychological symptoms such as stress, depressed mood, and sleep problems. These and other stressors may lead to great increase in mental health problems (Carvalho et al., 2020). This complex scenario poses additional challenges for epidemiological surveillance to reduce inequalities in accessing health systems and structural conditions for self-care. Research aimed at investigating the effects of COVID-19 has indicated an increase in conditions such as depression, anxiety, substance use disorders, changes in eating patterns, difficulty sleeping or concentrating, fear, and anger (Carvalho et al., 2020; Liu et al., 2020). Besides, many of these symptoms persisted after the quarantine ended (Pfefferbaum & North, 2020). A systematic review showed an expressive change in rates of anxiety (6.33% to 50.9%) and depression symptoms (14.6% to 48.3%), and stress (8.1% to 81.9%) in the general population during the COVID-19 pandemic in eight countries, including Spain, Italy, the US, Turkey, and Denmark (Xiong et al., 2020). In Brazil, Filgueiras and Stults-Kolehmainen (2020) provided an overview of the demographic and behavioral variables that predict stress, anxiety, and depression among people in quarantine. They found that men have a lesser risk for mental illness than women, and food preferences and eating habits are linked to better mental health indices. Goularte et al. (2021) recently showed a higher prevalence of depression among the general Brazilian population during the COVID pandemic: 68% of 1676 subjects had symptoms of moderate-to-severe depression, and 82% endorsed symptoms of anxiety.

Similarly, healthcare professionals are being impacted by the current pandemic. Healthcare professionals are especially vulnerable to psychological suffering and may face psychological stressors that the general population may not (Gold, 2020). Factors like concerns about infecting their families and lack of personal protective equipment have resulted in many professionals leaving their homes and sheltering elsewhere, which may further worsen their psychological well-being (Fukuti et al., 2020). According to n editorial published by Nature about 1000 Chinese healthcare professionals, roughly half showed symptoms of depression or anxiety, 35% had insomnia and 72% were distressed (Nature, 2020). Pappa et al. (2020) have reported the prevalence of depression, anxiety, and insomnia among healthcare workers. The analysis revealed a pooled prevalence of 22.8% for depression in ten studies. Most healthcare professionals reported mood and sleep disorders during the COVID-19 pandemic (Kisely et al., 2020). Not surprisingly, healthcare professionals are in a continuous state of fight-or-flight to be prepared for what is next.

Recently, dozens of studies on mental health, including the general population and healthcare professionals, have been published (Moreira et al., 2020; Schmidt et al., 2020), entailing concerns related to the psychological suffering these individuals may experience (Schmidt et al., 2020). However, most of these articles use the same methods - sum score of validated psychometric scales (Moreira et al., 2020). Nevertheless, it is hard to tell how depressive symptoms interact and may differ in these two populations. Besides that, improvements in approaches to psychopathology may provide crucial mental health indicators for the general population and healthcare professionals impacted by the pandemic.

First, results based on using a single sum score for a measure of depression can be helpful as an indicator of the psychosocial burden of symptoms. However, they discard much critical information about individual symptoms. It can be valuable to emphasize individual symptoms to consider potential causes and solutions for each symptom (Boschloo et al., 2016; Costantini et al., 2019; Fried & Nesse, 2015). Several studies have shown that the analysis of individual symptoms and their associations may offer new insights for developing more efficient treatment interventions (Fried & Nesse, 2015). On the other hand, psychological networks can be helpful to identify this kind of inter-related system of elements within a construct (Costantini et al., 2019). The theoretical premise of the psychological networks is that a mental disorder can be conceptualized as networks of mutually interacting symptoms, and this interaction can be captured in a network structure (Boschloo et al., 2016; Epskamp et al., 2018). A tightly interconnected network increases the probability of activating other symptoms (which are more pathogenic than those with fewer and weaker connections) or maybe a prognostic indicator of inadequate treatment response in depression (Borsboom & Cramer, 2013; Fried & Cramer, 2017; McElroy et al., 2018). It is also possible to determine the relative importance or influence of a symptom on the network and, consequently, provide essential clues to clinical and practical research (Opsahl et al., 2010). Understanding depression as an emergent property that may arise due to associations and vicious circles among symptoms (Hartung et al., 2019) makes it possible to adapt existing interventions to individual needs, to improve our understanding of psychological disorders for distinct groups by identifying core controllable symptoms. From this perspective, and because probably most healthcare professionals may have experienced different psychological stressors, we intend to answer two questions via network analysis: (a) to what extent healthcare professionals differ from the general population in terms of individual depressive symptoms during the first months of the pandemic in Brazil?; and (b) how these depressive symptoms are interrelated with each other among healthcare professionals and those in the general population? Thus, this study aimed to identify and to quantify the strength of associations of various depression symptoms across the general population and healthcare professionals. We estimated and compared the overall network structure. Evidencing these differences in a network of symptoms during the COVID-19 outbreak is relevant. It may offer support for developing actions and focal public policies aimed at the community and health teams.

Methods

Ethics

The Research Ethics Committee approved this study of the Faculty of Medicine of Itajubá, Brazil (#4,010,466). All volunteers gave informed consent online. The procedures were carried out under Brazilian ethical regulations and the 1964 Helsinki Declaration.

Participants and data collection

Data collection was carried out between May 11th and June 3nd, 2020, using an online electronic form prepared using the Google Forms application. Data collection started three months after sanctioning the Brazilian law, which regulates the quarantine period and specific measures against the new coronavirus. The link was sent through social media networks. The estimated time to fulfill the forms was less than 15 minutes. Participants should be 18 years and over, be in quarantine for at least 15 days (except healthcare professionals), be Brazilian or naturalized, and reside in Brazilian territory during the pandemic. Questionnaires with missing data were excluded.

For the characterization of the sample, the volunteers answered a sociodemographic questionnaire, being the following questions referred to the quarantine period: age, sex (male or female), marital status (single, married or divorced), having any family member or friend who has or had COVID-19 (yes or no), being a healthcare professional (yes or no), to present chronic disease (yes or no), to use medication daily (yes or no), to use controlled medications (anxiolytics/antidepressants; yes or no).

Measures

Patient Health Questionnaire-9 (PHQ-9). For screening depressive symptoms, we used the Patient Health Questionnaire 9-item (PHQ-9), which scores each of the 9 Major Depressive Disorder (MDD) DSM-IV criteria during the past two weeks. For each item, responses are rated on a 4-point Likert scale, ranging from 0 (“Not at all”) to 3 (“Almost every day”). PHQ-9 is suitable for screening major depressive episodes in the Brazilian population (Santos et al., 2013).

Data analysis procedure

We summarized the sociodemographic data using descriptive statistics. Estimation of the frequency (and 95% confidence interval (CI)) of each symptom presented in the PHQ-9 questionnaire was carried out by dichotomization of symptom scores at the cut-off ≥2 (occurring more than half the days during the past two weeks) (Hartung et al., 2019). Frequencies were compared using chi-squared tests (two-sided and p-value was Holm–Bonferroni corrected).

Network estimation: We estimated a Mixed Graphical Model (MGM) and treated all variables as continuous-Gaussian variables using the MGM package and compute node-wise predictability measures (Haslbeck & Waldorp, 2018). MGMs were estimated via l1-regularized (LASSO) neighborhood regression approach to ensure that spurious edge parameters were put to precisely zero, consequently exhibiting a more interpretable network (Haslbeck & Fried, 2017). The LASSO uses a tuning parameter that controls the sparsity; here defined as 0.5. Once the MGM model is estimated, it can be visualized in a network structure. Nodes represented the questionnaire items. Lines, called edges, represented the relationship between questionnaire items. The weight of the lines representing edges demonstrating the strength of association between questionnaire items and where strongly associated items were grouped as clusters (França et al., 2020).

Network description and node characteristics: We evaluated which symptoms were the most important ones in the network. It was considered that the higher the index of the centrality meant the more central the node was in the network. For that, we analyzed using node strength centrality. Node strength is the sum of all the weights connected to a given node (i.e., strength centrality measures a node total correlation with all other nodes) (Opsahl et al., 2010). We also investigated the edge stability and centrality indices by estimating network models based on the case-dropping bootstrap. This stability can be quantified using the CS-coefficient, which quantifies the maximum proportion of cases that can be dropped to retain, with 95% certainty. The CS-coefficient should not be below 0.25 and preferably above 0.5 (Epskamp et al., 2018).

Network predictability: Predictability is defined as the variance in a node explained by all other nodes in the network (i.e., how much other nodes in the network determine a node). The predictability gives us a percentage of how much a node is determined by other nodes in the network and how clinically relevant the connections are. As a predictability measure, we selected the proportion of explained variance (Haslbeck & Waldorp, 2018).

Compare differences in network structures: We statistically compared differences in network structures using the Network Comparison Test (NCT) (van Borkulo et al., 2017). The NCT is a permutation test widely used to estimate differences in the relationships between depressive symptoms across populations. We evaluated the structure of the network as (1) a whole network structure (invariant network structure), (2) the difference in the strength of a specific edge of interest (invariant edge strength), and if the level of connectivity is equal across groups (invariant global strength).

All statistical analyses were performed using R Version 3.3.1. Some analyses were based on codes from Hartung and colleagues (Hartung et al., 2019) and Haslbeck and Waldorp (2018), which are provided online.

Results

Participants

From the 1,167 participants approach for the study, 1,156 (99%) met inclusion criteria. The mean age was 37.5 years (SD = 14.0 years), distributed by 22 federal states of Brazil, with a predominance of the states of Minas Gerais (58%) and São Paulo (21%). Among the participants, 70% declared themselves female and 30% male. An estimated 42% were single, 49% were married, and 9% were divorced. Seventy-six percent of respondents reported not having chronic diseases, even though 46% have indicated using some medication daily. Of the total sample, 28% of our participants referred to having a family member or a close friend diagnosed with COVID-19. Among all participants, 34% reported themselves as a healthcare professional (n = 396), and 66% were classified as the general population (n = 760). The increase in weight during the pandemic was noticed by 47% of the general population and 49% of healthcare professionals. When comparing the period before and after the pandemic, sleep quality was rated as worse or much worse by 46% of healthcare professionals and by 40% of the general population.

Frequency of depressive symptoms

All depressive symptoms were reported more frequently in the general population than in the healthcare professionals' sample. In both groups, the three most common symptoms were energy loss, appetite change, and sleep, while suicidality was the less common symptom. However, when dichotomizing PHQ-9 items, only suicidality was significantly more common in the general population than in healthcare professionals (X² = 12.72, p > .001) (Figure 1).

Figure 1.

Relative frequency and 95% confidence intervals of PHQ-9 depressive symptoms in healthcare professionals (HP) and the general population (GP). Symptom scores were dichotomized at the cut-off ≥2 (occurring over half the days in the past two weeks).

Network models

The networks for both groups based on neighborhood regression and predictability are shown in Figure 2.

Figure 2.

The network structures of general population (left panel; n = 760) and healthcare professionals (right panel; n = 396). Ring-shaped pie charts represent predictability. Legend: 1 interest loss, 2 depressed mood, 3 sleep, 4 energy loss, 5 appetite change, 6 worthlessness, 7 trouble concentrating, 8 psychomotor issues, 9 suicidality.

Mean predictability across all nodes was 53% in the general population network (i.e., 53% of the variance of a node that the intercept model does not predict is explained by its neighbors) and 52% in the healthcare professional sample (i.e., 52% of the variance of a node that is not predicted by the intercept model is explained by its neighbors). Depressed mood (69%), worthlessness (64%), and energy loss (63%) stand out with the highest predictability in the network of the general population, while sleep (42%) and suicidality (31%) stand out with the lowest predictability estimates. On the other hand, depressed mood (68%), interest loss (61%), and energy loss (60%) had the highest predictability in healthcare professionals' network, while suicidality (24%) and appetite change (43%) had the lowest predictability. The standardized node strength centrality differed substantially between groups for sleep, appetite change, worthlessness, and trouble concentrating (as shown in Figure 3 and in the Electronic Supplementary Material 1). The CS-coefficient for edge stability indicates a moderate effect (CS = 0.52). Node strength stability performs almost the same (CS = 0.44) under subsetting cases. Node strength stability is above the recommended cut-off point (below 0.25) (Epskamp & Fried, 2016) and remains stable as the sample size decreases and remains mostly above .5. Average correlations of edge and node strength indices of networks sampled with persons dropped; the original sample are in the Electronic Supplementary Material 2.

Figure 3.

Standardized node strength centrality for PHQ-9 depressive symptoms in the general population (GP) and the healthcare professional' sample (HP).

Comparison of networks

The NCT maximum difference in any edge weight was 0.20 (p = 0.671), which means no significant difference in the overall structure. The separated global strength values of the individual networks revealed that the general population network had a global strength of 3.93, while the healthcare professional network had 3.86 (p = 0.06). It demonstrates that, although the general population had higher global strength, this difference was not significant. Two edges had significantly different weights across the networks: interest loss-psychomotor issues (p = 0.003) and sleep-trouble concentrating (Figure 2).

Discussion

In line with the questions of the current paper, network analytical approaches help identify the symptoms that are most important for understanding two groups: Brazilian adult population and healthcare professionals. We observed a tightly interconnected network with many strong edges between symptoms for both groups, being depressed mood and worthlessness the most central symptoms in the general population network compared to the healthcare professional network. A Chinese study tried to identify signs of depression and anxiety and monitor alcohol abuse and well-being via an online questionnaire. Responses were obtained from 1074 people between 14 and 68 years old. The proportion of participants with signs of severe depression was twice as high in the province of Hubei (11.4%), located in Wuhan (the initial focus of the pandemic), compared to other Chinese provinces. The results of another Chinese survey at a time when the city of Hubei had already adopted restrictive measures of social isolation showed that the proportion of people with characteristic signs of anxiety and depression was 13% and 22%, unlike those who were able to circulate and lead a life closer to normal (7% and 12%) (Lei et al., 2020).

When inspecting the healthcare professionals' network, the most central symptoms were depressed mood, energy loss, and concentration problems. Shanafelt and colleagues showed that healthcare professionals are more prone to prolonged work hours (Shanafelt et al., 2020), which may lead to the feeling of being tired, increasing concentration problems, symptoms that are directly associated with the performance of these professionals. In addition, dealing with other people's suffering could lead to a depressed mood. A study among healthcare professionals exposed to coronavirus disease showed that most of them exhibited symptoms related to depression. Around 50% of them would fulfill the criteria for a mental disorder. This study also suggested that females nurses may be particularly vulnerable (Lai et al., 2020). A pandemic scenario that causes radical changes in routine combined with a higher risk of infection is expected to increase the number of cases of psychological distress and possibly psychiatric problems.

The prevalence of depression among healthcare professionals is still uncertain in the context of the pandemic. However, some preliminary data suggest that the pandemic has already affected a high number of healthcare professionals' mental health. A recent Brazilian study showed that 38% of healthcare professionals at the forefront of combating the new coronavirus are mentally overloaded. The report, which focused on COVID-19-related mental health among 500 Brazilian healthcare professionals, also indicated that nurses and nursing technicians are among the most affected professionals. Forty-one percent of them showed symptoms of overload due to the routine with the care related to COVID-19. The study, called MENTALvid, is still receiving volunteers interested in contributing (Osório, 2020). Studies carried out in China at the beginning of the pandemic showed discharge in problems such as depression, anxiety, and burnout, especially among those who are in contact with patients infected with the new coronavirus (Greenberg, 2020; Kang et al., 2020; Lai et al., 2020). We suppose that there is no reason to suspect that the initial situation in China is quite different from that faced by other countries, like Brazil, although it is not possible to affirm whether these figures will continue during or after the pandemic. In the United States, a longitudinal study that measured problems in eight waves of data with over 5.000 individuals from March through June 2020 showed that distress increased in April 2020 and gradually decreased to levels that existed before COVID-19 (Daly & Robinson, 2020). Other studies also suggest that healthy individuals' anxiety and stress levels may have become elevated, or existing symptoms may have been exacerbated in people with established psychic disorders (Brooks et al., 2020; Vitorino et al., 2021). This fact would mean that many individuals feel psychologically stressed more than usual because of the pandemic. From a network perspective, when the symptom is activated or intensified by some external stressor, it reinforces (or activates) other symptoms, activating a cycle of symptoms, which reinforce each other, creating a dense and interconnected network (Robinaugh et al., 2020), like the one we obtained in our study. A tightly connected network in which the relationships between symptoms are sufficiently strong could maintain high activation of symptoms over time, characterizing the establishment of a psychological syndrome. However, the syndrome can only be assessed through future research with the same individuals, combined with additional measures (e.g., functional impairment, anxiety index etc.).

Specific stressors like duration of confinement, having inadequate supplies, difficulty in securing medical care and medications, being informed about the death of a close family or friend, healthcare professionals repeated exposure to extreme adverse events may lead to psychological sequelae after the COVID-19 outbreak (Pfefferbaum & North, 2020). A survey conducted in China found that several healthcare professionals were traumatized by the SARS epidemic. The study showed that psychiatric symptoms persisted even after the pandemic, and many of these healthcare professionals were diagnosed with post-traumatic stress (Wu et al., 2005). In the future, there may be an increase in symptoms associated with post-traumatic stress disorder in several countries and, consequently, an overload of the health system.

Unlike previous studies that used the sum of the scores of the self-report instrument, we also sought to identify symptoms that may be more promising for practical intervention. Concentration can be further compromised by isolation, signs of disease risk, and changes in the way and pace of work. Along with trouble concentrating, attention and cognitive resources of working memory can be impaired. Concentration problems can also directly impact memory maintenance (Oberauer, 2019). Unfortunately, so far as we know, there are no studies related to depression from the perspective of network analysis during the pandemic to compare our findings. In our study, the network analysis showed the feeling of worthlessness was less associated with healthcare professionals. Such feeling might act, at the time of the pandemic, as a protective factor against the occurrence of depressed mood. We can only speculate that this 'protective' factor may also play a role in the COVID-19 pandemic. Given the sudden outbreak of COVID-19, the changes of all aspects of everyday activities are all new relevant stressors.

A prior study using a large sample containing a set of psychiatric symptoms, including MDD, showed that depressed mood and fatigue were central in the network. In contrast, changes in weight/appetite were not (Boschloo et al., 2016). Furthermore, central symptoms directly impact other symptoms within the network and can predict the occurrence of MDD. Studies indicated that turning off a highly connected node could promote a beneficial cascade effect, whereby improvement in one symptom triggers improvements in other symptoms or the breaking of the chain of symptoms (Boschloo et al., 2016; Heeren & McNally, 2018; McNally et al., 2017). Researchers who have attempted to unpack the relationship between depressive symptoms found that the greatest node centrality strength was the loss of pleasure, self-dislike, and sadness. These symptoms were high for healthy participants sample but even higher in participants with MDD diagnosis (Hakulinen et al., 2020).

According to these recent findings within the network approach to psychopathology, it is reasonable to hypothesize that strategies to avoid or treat depressed mood, worthlessness, and concentration problems may promote a better quality of life for the general population and healthcare professionals. Note that the symptom of a change in appetite also follows a different pattern between the analyzed groups. Unfortunately, we cannot specify whether there has been an increase or decrease in appetite. However, based on the analysis of the questions about the perception of weight gain or loss, the increase in weight during the pandemic was noticed by 47% of the general population and by 49% of healthcare professionals. It makes us hypothesize that the symptom of a change in appetite leads to an increase in weight. The change in appetite has a stronger interaction with the loss of interest in doing pleasurable things in the general population than in the healthcare professionals' network, which may represent targets of therapeutic intervention.

The predictability analysis suggests that an intervention targeting the symptoms of depressed mood, worthlessness, and energy loss for the general population and the symptoms of interest loss and energy loss for the healthcare professional seems viable and valuable as a prevention strategy. Targeting the neighbor of a symptom that appears with high predictability may facilitate reductions in symptoms. On the other hand, if the predictability is low, we need to search for additional variables or intervene (Hartung et al., 2019). That is the case of suicidality. The suicidal ideation could be influenced by additional variables not included in the network (e.g., biological, environmental, or additional symptoms). A fundamental question is how to assess suicide risk in individuals who answer positively to the 9th question of the PHQ-9, considering the low predictability given that suicidal ideation was significantly different between groups with a similar central index. At this point, it is worth mentioning the article Suicide and mental health during the COVID-19 pandemic in Japan (Ueda et al., 2021). According to that, the pandemic of the new coronavirus may have had an influence on the increased suicide rate among Japanese women with less than 40 years of age throughout 2020, whose numbers increased by approximately 70% in October 2020 (IRR: 1,695, 95% CI: 1,558–1,843) compared to the last three years (2017–2019). Suicide cases may be potentially linked to the psychological implications of COVID-19. However, the reasons that can justify this increase are complex. These persons may have lost their job or have income loss, may have had to deal with adverse economic conditions, living alone, social isolation, and the social pressure to fail to adopt hygiene measures (Ueda et al., 2021).

The present study is the first one to examine depressive symptoms among Brazilians from a network analysis during the COVID-19 outbreak. Our subgroup analysis of depression symptoms based on healthcare professionals provided additional valuable insights into potential vulnerabilities. Therefore, the importance of this study is to have a photograph inside the eye of the hurricane, when death numbers due to the coronavirus were high and the rules of social distance, somehow, were strict (i.e., only essential services were working). Looking at the cut-off of depression scores and looking at what symptoms could be the best targets for interventions may improve the treatment of psychological disorders now and in the future. For instance, worthlessness may be an area that therapists can focus on because of its crucial role in the general population network. The most central symptoms in the healthcare professional network were depressed mood, energy loss, and trouble concentrating. Brazil is a country with a vast territory, with areas of difficult access and with little infrastructure (i.e., access to computers, internet etc.), which prevented a more representative application of participants. Although this fact, in response to physical distancing guidelines, data collection was being carried out remotely (i.e., via the internet). We used standardized measures that are reliable in Brazil and in several other countries to enhance the comparability between cultures (i.e., PHQ-9 scale). In addition, we believe that the aims proposed, like the explorations of potential associations between symptoms, are appropriate for this approach. Far from definitive, this study makes it possible to have an idea of what may lie ahead. More accurate information should only be known in months or years when researchers have better conditions and more time to deepen their studies on the topic.

Limitations

The results of the current study should be interpreted considering some limitations. A cross-sectional sample was used, which does not allow inspecting cross-lagged or contemporaneous relationships. We emphasize that this study represents a photograph of when the data was collected; therefore, about three months under the Brazilian announcement of exposure to the virus. After the revision process, we recognize that, perhaps, this pattern might have changed. In future studies, it is essential to explore whether a different pattern relationship might emerge at the within-subject levels combined with additional measures (e.g., functional impairment, anxiety index etc.). We cannot exclude residual confounding caused by unmeasured variables, such as social support, political issues, the level of political polarization, and the quality of the authorities' message about the virus that were not assessed. Other specific studies could focus on healthcare professionals, for instance, medical doctors, nurses, physiotherapists, technicians, and countless other professionals, to obtain more refined data.

Conclusions

This study adds to the small but growing empirical literature on the mental state of Brazilians during the COVID-19 pandemic. Here, we described how attending to specific symptoms could lead to research on causal effects among symptoms. Our findings reveal that some individual symptoms showed a differential association between the general population and healthcare professionals. Those symptoms may be valuable targets for future research and treatment. Essentially, this article also tries to shed light on the importance of seeing emotional and mental health support as being as important and vital as the use of facial masks during (and after) the COVID-19 pandemic.

Supplemental Material

sj-pdf-1-prx-10.1177_00332941211025264 - Supplemental material for Exploring Depressive Symptoms Among Healthcare Professionals and the General Population During the COVID-19 Pandemic in Brazil

Supplemental material, sj-pdf-1-prx-10.1177_00332941211025264 for Exploring Depressive Symptoms Among Healthcare Professionals and the General Population During the COVID-19 Pandemic in Brazil by Alex Bacadini França, Clarissa Trzesniak, Patrícia Waltz Schelini, Gerson Hiroshi Yoshinari Junior and Luciano Magalhães Vitorino in Psychological Reports

Supplemental Material

sj-pdf-2-prx-10.1177_00332941211025264 - Supplemental material for Exploring Depressive Symptoms Among Healthcare Professionals and the General Population During the COVID-19 Pandemic in Brazil

Supplemental material, sj-pdf-2-prx-10.1177_00332941211025264 for Exploring Depressive Symptoms Among Healthcare Professionals and the General Population During the COVID-19 Pandemic in Brazil by Alex Bacadini França, Clarissa Trzesniak, Patrícia Waltz Schelini, Gerson Hiroshi Yoshinari Junior and Luciano Magalhães Vitorino in Psychological Reports

Footnotes

Authors’ Contribution

Conceptualization, material preparation and data collection: Luciano Magalhães Vitorino, Clarissa Trzesniak and Gerson Hiroshi Yoshinari Júnior. Formal analysis, and investigation: Alex Bacadini França and Patrícia Waltz Schelini. The first draft of the manuscript was written by Alex Bacadini França and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Ethics Approval

The study was approved by the Research Ethics Committee of the Faculty of Medicine of Itajubá, Brazil (#4,010,466). All volunteers gave informed consent online. The procedures were carried out in accordance with Brazilian ethical regulations and the 1964 Helsinki Declaration

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Alex Bacadini França

Supplemental material

Supplementary material for this article is available online.

Author Biographies

Alex Bacadini França is a Postdoctoral researcher at the Psychology Department of the Federal University of São Carlos, Brazil. His main research interests focus on quantitative data analysis applied to Psychology and network analysis as a statistical and theoretical tool to model mental disorders.

Clarissa Trzesniak current research focus is on, mental health, quality of life, usability and neuroscience.

Patrícia Waltz Schelini graduated in Psychology from the Pontifical Catholic University of Campinas, Master in Psychology from the Pontifical Catholic University of Campinas and PhD in Psychology from the same University . Post-Doctorate by the University of Minho (Portugal). She is currently Associate Professor 3 at the Department of Psychology at the Federal University of São Carlos, where she teaches and develops studies on creativity, intelligence, metacognition and imaginative thinking. She has a CNPq Productivity Scholarship, she was vice-coordinator and coordinator of the Research Group on Psychological Evaluation of the National Association of Research and Graduate Studies in Psychology (2014-2018; 2019-2020).

Gerson Hiroshi Yoshinari Junior is MD PhD in Clinical Oncology, cellular therapy and stem cells by Faculdade de Medicinade Ribeirão Preto - Universidade de São Paulo, Brazil. Professor at Faculdade de Medicina de Itajubá, Brazil and Radiation Oncologist at Oncominas, Brazil.

Luciano Magalhães Vitorino current research focus is on aging, spirituality and health, mental health and quality of life.

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