Literature DB >> 32240764

Impact on mental health and perceptions of psychological care among medical and nursing staff in Wuhan during the 2019 novel coronavirus disease outbreak: A cross-sectional study.

Lijun Kang1, Simeng Ma1, Min Chen2, Jun Yang2, Ying Wang1, Ruiting Li1, Lihua Yao1, Hanping Bai1, Zhongxiang Cai3, Bing Xiang Yang4, Shaohua Hu5, Kerang Zhang6, Gaohua Wang1, Ci Ma7, Zhongchun Liu8.   

Abstract

The severe 2019 outbreak of novel coronavirus disease (COVID-19), which was first reported in Wuhan, would be expected to impact the mental health of local medical and nursing staff and thus lead them to seek help. However, those outcomes have yet to be established using epidemiological data. To explore the mental health status of medical and nursing staff and the efficacy, or lack thereof, of critically connecting psychological needs to receiving psychological care, we conducted a quantitative study. This is the first paper on the mental health of medical and nursing staff in Wuhan. Notably, among 994 medical and nursing staff working in Wuhan, 36.9% had subthreshold mental health disturbances (mean PHQ-9: 2.4), 34.4% had mild disturbances (mean PHQ-9: 5.4), 22.4% had moderate disturbances (mean PHQ-9: 9.0), and 6.2% had severe disturbance (mean PHQ-9: 15.1) in the immediate wake of the viral epidemic. The noted burden fell particularly heavily on young women. Of all participants, 36.3% had accessed psychological materials (such as books on mental health), 50.4% had accessed psychological resources available through media (such as online push messages on mental health self-help coping methods), and 17.5% had participated in counseling or psychotherapy. Trends in levels of psychological distress and factors such as exposure to infected people and psychological assistance were identified. Although staff accessed limited mental healthcare services, distressed staff nonetheless saw these services as important resources to alleviate acute mental health disturbances and improve their physical health perceptions. These findings emphasize the importance of being prepared to support frontline workers through mental health interventions at times of widespread crisis.
Copyright © 2020 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  2019 novel coronavirus disease; Exposure; Medical and nursing staff; Mental health; Mental healthcare

Mesh:

Year:  2020        PMID: 32240764      PMCID: PMC7118532          DOI: 10.1016/j.bbi.2020.03.028

Source DB:  PubMed          Journal:  Brain Behav Immun        ISSN: 0889-1591            Impact factor:   7.217


Introduction

In November 2019, a novel coronavirus disease (COVID-19) was first reported and then became widespread within Wuhan, the capital city of Hubei Province of China (Chan et al., 2020). The disease rapidly spread throughout China and elsewhere, becoming a global health emergency (WHO, 2020). The mental health of medical and nursing staff has been greatly challenged during the immediate wake of the viral epidemic (Chong et al., 2004, Wu et al., 2009). In battling the sudden emergence of severe acute respiratory syndrome (SARS), psychological distress among medical staff appeared gradually: fear and anxiety appeared immediately and decreased in the early stages of the epidemic, but depression, psychophysiological symptoms and posttraumatic stress symptoms appeared later and lasted for a long time, leading to profound impacts (Chong et al., 2004, Wu et al., 2009). Being isolated, working in high-risk positions, and having contact with infected people are common causes of trauma (Wu et al., 2009, Maunder et al., 2003). These factors may have impacted medical and nursing staff in Wuhan, leading to mental health problems. The experience of medical staff responding to SARS shows that the effects on medical staff members’ mental health have not only short-term but also long-term impacts and that the value of effective support and training is meaningful (Maunder et al., 2006). Efficient and comprehensive actions should be taken in a timely fashion to protect the mental health of medical staff. The Chinese government has made various efforts to reduce the pressure on medical and nursing staff in China, such as sending more medical and nursing staff to reduce work intensity, adopting strict infection control, providing personal protective equipment and offering practical guidance. Based on previous responses to Middle East respiratory syndrome (MERS), medical staff tend to believe that such measures help protect their mental health (Khalid et al., 2016). In addition, to reduce the psychological damage of COVID-19 among medical and nursing staff, mental health workers in Wuhan are also taking action by establishing psychological intervention teams and providing a range of psychological services, including providing psychological brochures, counseling and psychotherapy (Kang et al., 2020). At the same time, television news and online media are also disseminating information about coping strategies for psychological self-help. However, evidence-based mental health services are preferable, and it is necessary to assess the quality of mental health services (Aarons et al., 2012). Therefore, we explore the mental health status of medical and nursing staff in Wuhan, the efficacy of the psychological care accessed, and their psychological care needs.

Methods

Participant

We recruited doctors or nurses working in Wuhan to participate in this survey from January 29, 2020, to February 4, 2020. This study was approved by the Clinical Research Ethics Committee of Renmin Hospital of Wuhan University (WDRY2020-K004). Data were collected through Wenjuanxing (www.wjx.cn) with an anonymous, self-rated questionnaire that was distributed to all workstations over the internet. All subjects provided informed consent electronically prior to registration. The informed consent page presented two options (yes/no). Only subjects who chose yes were taken to the questionnaire page, and subjects could quit the process at any time.

Questionnaire

The questionnaire consists of six parts: basic demographic data, mental health assessment, risks of direct and indirect exposure to COVID-19, mental healthcare services accessed, psychological needs, and self-perceived health status compared to that before the COVID-19 outbreak.

Demographic data

Basic demographic data include occupation (doctor or nurse), gender (male or female), age (years), marital status (unmarried, married or divorced), educational level (undergraduate or lower, postgraduate or higher), technical title (primary, intermediate, or senior), and department (divided into high-exposure departments and non-high-exposure departments according to the possibility of exposure to confirmed patients; high-exposure departments included the fever clinic, emergency department, general isolation ward, and intensive care unit).

Mental health assessment

We used four scales to assess the mental health status of medical and nursing staff. The 9-item Patient Health Questionnaire (PHQ-9), the 7-item Generalized Anxiety Disorder (GAD-7), the 7-item Insomnia Severity Index (ISI) and the 22-item Impact of Event Scale-Revised (IES-R) were used to evaluate depression, anxiety, insomnia and distress, respectively. The PHQ-9 is a self-report measure used to assess the severity of depression, with the total scores categorized as follows: minimal/no depression (0–4), mild depression (5–9), moderate depression (10–14), or severe depression (15–21) (Kocalevent et al., 2013). The GAD-7 is a self-rated scale to evaluate the severity of anxiety and has good reliability and validity. The total scores are categorized as follows: minimal/no anxiety (0–4), mild anxiety (5–9), moderate anxiety (10–14), or severe anxiety (15–21) (Löwe et al., 2008). The ISI is a measure of insomnia severity that has been shown to be valid and reliable. The total scores are categorized as follows: normal (0–7), subthreshold (8–14), moderate insomnia (15–21), or severe insomnia (22–28) (Morin et al., 2011). The IES-R is a self-report measure used to assess the response to a specific stressful life event and has extensive reliability and validity. The event used for this questionnaire was the occurrence of COVID-19. The total scores are categorized as follows: subclinical (0–8), mild distress (9–25), moderate distress (26–43), and severe distress (44–88) (Daniel and Weiss, 2007).

Exposure to COVID-19

Exposure to COVID-19 was determined with the following questions asked to medical and nursing staff: Have you been diagnosed with COVID-19? Do you manage patients diagnosed with COVID-19? Has your family been diagnosed with COVID-19? Have your friends been diagnosed? Have your neighbors (people living in the same community who may or may not know each other) been diagnosed? Then, participants were asked whether there was anyone living with them with suspected symptoms. The answer to each question was yes or no.

Accessed mental healthcare services

The following question was used to determine which psychological services the subject had accessed. Have you ever received the following services: psychological materials (leaflets, brochures and books provided by mental health workers and distributed to staff in the hospital), psychological resources available through media (psychological assistance methods and techniques provided by psychologists through online media or TV news or various online platforms) (Supplementary material), and counseling or psychotherapy (including individual or group therapy)?

Meeting psychological care needs

Three areas were assessed regarding the psychological services that participants hoped to receive in the future: what kind of mental health service content were participants most interested in (including knowledge of psychology, ways to alleviate their own psychological reactions, ways to help others alleviate their psychological reactions, or ways to seek help from psychologists or psychiatrists); what kind of resources were most anticipated (including psychological materials, psychological resources available through media, group psychotherapy, individual counseling and psychotherapy, uninterested or other); and who participants would prefer to receive care from (including psychologists or psychiatrists, family or relatives, friends or colleagues, do not need help, or other).

Self-perceived health status

Health status was determined by asking participants to compare their current health status to their health status before the outbreak of COVID-19: How do you perceive your current health status compared to your health status before the outbreak? (answer options included getting better, almost unchanged, worse, or much worse).

Statistical analysis

Data analysis was performed using IBM SPSS Statistics for Windows (Version 23.0) and Mplus (version 7.4). Descriptive analysis was used to describe the general data and currently accessed psychological services. For count data, frequencies and percentages were used. The k-means clustering method was used to cluster the PHQ-9, GAD-7, ISI, and IES-R scores (Ball, 1967). With the Euclidean square root distance as the measurement index, the patients were divided into 4 groups by the Ward method. According to this grouping, exposure to COVID-19 and the current state of mental healthcare services were compared. The chi-square test was used to compare the data for different categorical variables. A structural equation model (SEM) was constructed via Mplus to explore the relationship among the four components, namely, exposure, accessed mental healthcare services, mental health status (PHQ-9, GAD-7, ISI, and IES-R scores) and self-perceived health status compared to that before the COVID-19 outbreak. The estimation method used weighted least squares with mean and variance adjustment test statistics (Distefano and Morgan, 2014). We used a Monte Carlo method with 1000 guided resamplings to construct a confidence interval for the estimation effect (Bauer et al., 2006). In SEM, several criteria, such as root mean square error of approximation (RMSEA) values < 0.08 and comparative fit index (CFI) and Tucker-Lewis index (TLI) values >0.90, indicate acceptable models (Hu and Bentler, 1998). P values < 0.05 indicated that a difference was statistically significant.

Results

Demographic characteristics

In total, 994 participants, including 183 (18.4%) doctors and 811 (81.6%) nurses, completed the survey. A total of 31.1% worked in high-risk departments. The participants tended to be female (85.5%), be aged 25 to 40 years (63.4%), be married (56.9%), have an educational level of undergraduate or less (85%), and have a junior technical title (66.3%), as shown in Table 1 .
Table 1

Demographic characteristics.

VariablesNumberPercentage (%)
Total994100
Gender
Male14414.5
Female85085.5
Age
18–2521421.5
~3033934.1
~4029129.3
~5011411.5
>50363.6
Marriage
Unmarried or divorce42843.1
Married56656.9
Education level
Undergraduate or less84585.0
Postgraduate or more14915.0
Technical title
Junior65966.3
Intermediate27828.0
Senior575.7
Occupation
Doctor18318.4
Nurse81181.6
Department
High risk30931.1
Ordinary68568.9
Demographic characteristics.

Accessed mental healthcare services

Of all participants, 36.3% had received psychological materials, 50.4% had obtained psychological resources available through media, and 17.5% had participated in group psychological counseling, as shown in Table 2 .
Table 2

Resources of mental healthcare services.

VariablesNumberPercentage (%)
Psychological materialsNo63363.7
Yes36136.3
Psychological resources available through mediaNo49349.6
Yes50150.4
Counseling or psychotherapyNo82082.5
Yes17417.5
Resources of mental healthcare services.

Cluster analysis of mental health states

According to the PHQ-9, GAD-7, ISI, and IES-R scores, the 994 participants were divided into 4 groups. Thirty-six percent of the medical staff had subthreshold mental health disturbances (mean PHQ-9: 2.4, GAD-7: 1.5, ISI: 2.8, IES-R: 6.1), 34.4% had mild disturbances (mean PHQ-9: 5.4, GAD-7: 4.6, ISI: 6.0, IES-R: 22.9), 22.4% had moderate disturbances (mean PHQ-9: 9.0, GAD-7: 8.2, ISI: 10.4, IES-R: 39.9), and 6.2% had severe disturbances (mean PHQ-9: 15.1, GAD-7: 15.1, ISI: 15.6, IES-R: 60.0). There were significant differences in the PHQ-9, GAD-7, ISI, and IES-R scores among the four groups, as shown in Table 3 .
Table 3

Cluster analysis grouping.

Variables1234P-value
Number/percentage (%)367 (36.9)342 (34.4)223 (22.4)62 (6.2)
PHQ-9 M (SD)2.4 (3.0)5.4 (3.4)9.0 (3.9)15.1 (5.2)<0.001
GAD-7 M (SD)1.5 (2.4)4.6 (2.9)8.2 (3.6)15.1 (4.3)<0.001
ISI M (SD)2.8 (3.0)6.0 (4.0)10.4 (4.8)15.6 (5.2)<0.001
IES-R M (SD)6.1 (4.4)22.9 (4.8)39.9 (5.4)60.0 (9.8)<0.001
Cluster analysis grouping.

Differences among clusters

In contrast, there were no significant differences in demographic data among the four groups, as shown in Table 4 .
Table 4

Comparison of demographic characteristics between different clusters.

Cluster (n (Percentage (%)))1234TotalP-value
Age18–2581 (22.1)74 (21.6)42 (18.8)17 (27.4)214 (21.5)0.101
~30135 (36.8)123 (36.0)68 (30.5)13 (21.0)339 (34.1)
~40106 (28.9)96 (28.1)65 (29.1)24 (38.7)291 (29.3)
~5035 (9.5)35 (10.2)37 (16.6)7 (11.3)114 (11.5)
>5010 (2.7)14 (4.1)11 (4.9)1 (1.6)36 (3.6)
GenderMale62 (16.9)39 (11.4)31 (13.9)12 (19.4)144 (14.5)0.133
Female305 (83.1)303 (88.6)192 (86.1)50 (80.6)850 (85.5)
MarriageUnmarried and divorce161 (43.9)157 (45.9)81 (36.3)29 (46.8)428 (43.1)0.127
Married206 (56.1)185 (54.1)142 (63.7)33 (53.2)566 (56.9)
Education levelUndergraduate or less306 (83.4)297 (86.8)186 (83.4)56 (90.3)845 (85.0)0.322
Postgraduate or more61 (16.6)45 (13.2)37 (16.6)6 (9.7)149 (15.0)
OccupationDoctor76 (20.7)56 (16.4)42 (18.8)9 (14.5)183 (18.4)0.409
Nurse291 (79.3)286 (83.6)181 (81.2)53 (85.5)811 (81.6)
DepartmentHigh risk109 (29.7)98 (28.7)75 (33.6)27 (43.5)309 (31.1)0.092
Ordinary258 (70.3)244 (71.3)148 (66.4)35 (56.5)685 (68.9)
Technical titleJunior252 (68.7)234 (68.4)137 (61.4)36 (58.1)659 (66.3)0.307
Intermediate93 (25.3)89 (26.0)73 (32.7)23 (37.1)278 (28.0)
Senior22 (6.0)19 (5.6)13 (5.8)3 (4.8)57 (5.7)
Comparison of demographic characteristics between different clusters. For medical and nursing staff, exposure to people around them who were infected varied among the different groups. The group with subthreshold mental health disturbances had contact with fewer people confirmed or suspected to be infected with the virus. Each group with a higher level of distress had a more extensive scope of exposure. There were also significant differences in mental healthcare services among the four groups; those with severe disturbances had accessed fewer psychological materials and psychological resources available through media. In addition, the perception of current health status compared to that before the outbreak of COVID-19 was also different among the groups, as shown in Table 5 .
Table 5

Comparison of characteristics between different clusters.

Cluster (n (Percentage (%)))1234TotalP-value
Risk factors for exposure
Patient infectedNo210 (57.2)176 (51.5)92 (41.3)15 (24.2)493 (49.6)<0.001
Yes157 (42.8)166 (48.5)131 (58.7)47 (75.8)501 (50.4)
Own infectionNo361 (98.4)340 (99.4)215 (96.4)59 (95.2)975 (98.1)0.023
Yes6 (1.6)2 (0.6)8 (3.6)3 (4.8)19 (1.9)
Family infectionNo359 (97.8)336 (98.2)208 (93.3)60 (96.8)963 (96.9)0.005
Yes8 (2.2)6 (1.8)15 (6.7)2 (3.2)31 (3.1)
Colleague infectionNo170 (46.3)149 (43.6)69 (30.9)18 (29.0)406 (40.8)<0.001
Yes197 (53.7)193 (56.4)154 (69.1)44 (71.0)588 (59.2)
Friend infectionNo305 (83.1)280 (81.9)174 (78.0)32 (51.6)791 (79.6)<0.001
Yes62 (16.9)62 (18.1)49 (22.0)30 (48.4)203 (20.4)
Neighbor infectionNo295 (80.4)273 (79.8)157 (70.4)36 (58.1)761 (76.6)<0.001
Yes72 (19.6)69 (20.2)66 (29.6)26 (41.9)233 (23.4)
Co-residents with suspected symptomsYes48 (13.1)69 (20.2)63 (28.3)19 (30.6)199 (20.0)<0.001
No319 (86.9)273 (79.8)160 (71.7)43 (69.4)795 (80.0)
Self-perceived health status compared to before COVID-19 outbreak
Self-perceived health statusBetter32 (8.7)7 (2.0)5 (2.2)1 (1.6)45 (4.5)<0.001
Almost unchanged296 (80.7)240 (70.2)99 (44.4)16 (25.8)651 (65.5)
Worse38 (10.4)94 (27.5)99 (44.4)28 (45.2)259 (26.1)
Much worse1 (0.3)1 (0.3)20 (9.0)17 (27.4)39 (3.9)
Resources of mental healthcare services
Psychological materialsNo215 (58.6)216 (63.2)151 (67.7)51 (82.3)633 (63.7)0.002
Yes152 (41.4)126 (36.8)72 (32.3)11 (17.7)361 (36.3)
Psychological publicity of the mediaNo168 (45.8)161 (47.1)125 (56.1)39 (62.9)493 (49.6)0.011
Yes199 (54.2)181 (52.9)98 (43.9)23 (37.1)501 (50.4)
Counseling or psychotherapyNo301 (82.0)276 (80.7)194 (87.0)49 (79.0)820 (82.5)0.216
Yes66 (18.0)66 (19.3)29 (13.0)13 (21.0)174 (17.5)
Comparison of characteristics between different clusters.

Role of mental healthcare services accessed

We established an SEM of the associations between the four areas. First, exposure as a risk factor for mental health, including the confirmed diagnosis of patients, the participants’ themselves, family, friends, colleagues, neighbors, and coresidents with suspected symptoms, was analyzed in the previous step. Second, the mental healthcare services accessed consisted of psychological materials and psychological resources available through media. Third, mental health consisted of the PHQ-9, GAD-7, ISI, and IES-R scores. The fourth area was the subjective feelings of the staff regarding whether their physical conditions were worse than before the epidemic. The chi-square test of model fit yielded a value of 129.1, with degrees of freedom = 72, P-value = 0.000, RMSEA = 0.028, CFI = 0.978, and TLI = 0.973, indicating a good fit. The results showed that the risk factors of exposure affected mental health and that mental health affected subjective physical health perceptions. Mental healthcare services only partially mediated the relationship between exposure risks and mental health. Mental healthcare services regulated the relationship between the risk of exposure and subjective physical health perceptions by affecting mental health. The results are shown in Fig. 1 and Table 6 .
Fig. 1

In this model, the solid line represents a significant relationship between the two, while the dotted line represents the relationship is not significant.

Table 6

Direct and indirect effects in SEM.

Direct or indirect effects pathwayEstimatestandard errorP-value95% confidence interval
Exposure → mental health5.3471.130<0.0013.831, 8.184
Mental healthcare → mental health−0.8680.2720.001−1.385, −0.289
Exposure → mental healthcare−0.3200.1700.059−0.734, −0.040
Mental health → physical health0.1310.016<0.0010.098, 0.159
Exposure → physical health0.1200.2480.628−0.340, 0.675
Mental healthcare → physical health−0.0080.0590.887−0.115, 0.105
Exposure → mental healthcare → mental health0.2780.1330.0360.016, 0.565
Exposure → mental healthcare → physical health0.0030.0220.903−0.043, 0.047
Exposure → mental health → physical health0.6980.167<0.0010.475, 1.103
Exposure → mental healthcare → mental health → physical health0.0360.0180.0400.002, 0.072
Mental healthcare → mental health → physical health−0.1130.0370.002−0.184, −0.038
In this model, the solid line represents a significant relationship between the two, while the dotted line represents the relationship is not significant. Direct and indirect effects in SEM.

Psychological care needs of medical and nursing staff

In terms of the content of interest, namely, psychological care, medical and nursing staff with subthreshold disturbances most wanted to obtain skills to help alleviate others’ psychological distress, whereas other medical and nursing staff most wanted to obtain self-help skills. Medical and nursing staff with higher levels of mental health problems were more interested in skills for self-rescue and showed more urgent desires to seek help from psychotherapists and psychiatrists. Medical and nursing staff differed in terms of how they wanted to obtain services based on their levels of mental health problems. Medical and nursing staff with subthreshold and mild disturbances preferred to obtain such services from media sources, while staff with heavier burdens wanted to seek services directly from professionals. Apart from medical and nursing staff with subthreshold disturbances who did not think they needed help from others, the other workers saw a greater need to obtain help from professionals than from close family and friends. The results are shown in Table 7 .
Table 7

Mental Healthcare Services among Medical Staff.

Cluster (n (Percentage (%)))1234TotalP-value
Content of interest
Knowledge of psychologyNo181 (49.3)169 (49.4)133 (59.6)42 (67.7)525 (52.8)0.004
Yes186 (50.7)173 (50.6)90 (40.4)20 (32.3)469 (47.2)
Skills for self-rescueNo149 (40.6)91 (26.6)38 (17.0)10 (16.1)288 (29.0)<0.001
Yes218 (59.4)251 (73.4)185 (83.0)52 (83.9)706 (71.0)
Skills for help others alleviate psychological distressNo131 (35.7)117 (34.2)100 (44.8)33 (53.2)381 (38.3)0.004
Yes236 (64.3)225 (65.8)123 (55.2)29 (46.8)613 (61.7)
Seek help from psychologists or psychiatristsNo272 (74.1)227 (66.4)133 (59.6)31 (50.0)663 (66.7)<0.001
Yes95 (25.9)115 (33.6)90 (40.4)31 (50.0)331 (33.3)
Resources
Psychological materials88 (24.0)63 (18.4)28 (12.6)6 (9.7)185 (18.6)<0.001
Psychological resources available through media96 (26.2)86 (25.1)53 (23.8)7 (11.3)242 (24.3)
Group psychotherapy52 (14.2)56 (16.4)47 (21.1)15 (24.2)170 (17.1)
Individual counseling and psychotherapy39 (10.6)67 (19.6)57 (25.6)27 (43.5)190 (19.1)
Uninterested79 (21.5)64 (18.7)34 (15.2)6 (9.7)183 (18.4)
Others13 (3.5)6 (1.8)4 (1.8)1 (1.6)24 (2.4)
Prefer to receive care from
Psychologists or psychiatrists117 (31.9)139 (40.6)103 (46.2)41 (66.1)400 (40.2)<0.001
Family or relatives52 (14.2)53 (15.5)28 (12.6)6 (9.7)139 (14.0)
Friends or colleagues37 (10.1)57 (16.7)40 (17.9)12 (19.4)146 (14.7)
Do not need help154 (42.0)89 (26.0)49 (22.0)2 (3.2)294 (29.6)
Others7 (1.9)4 (1.2)3 (1.3)1 (1.6)15 (1.5)
Mental Healthcare Services among Medical Staff.

Discussion

This is the first mental health investigation in the wake of the coronavirus epidemic in Wuhan, China that aims, in part, to explore the demand for mental healthcare services in this context. When cities are struck by deadly, large-scale disasters of various types, the characteristics of mental health problems that arise can differ across different periods (Shioyama et al., 2000). We therefore chose to survey a set of people (health care providers) in the discrete window of time soon after the initiation of a chaotic event (the outbreak of coronavirus infections). To conduct a comprehensive analysis, we used multiple different scales to evaluate the mental health of medical staff. Our study has revealed the limits in the availability of mental healthcare services provided by psychologists and psychiatrists and thus the limits in access points for psychological care for distressed individuals, including less personalized sources of support such as publication-style psychological materials and psychological resources available from media. These latter methods can nonetheless contribute positively to alleviating mental health problems and physical discomfort caused by risk factors such as the exposure of close contacts to COVID-19. Such exposure is known to be mentally injurious in epidemic settings: when the SARS epidemic hit, not only did the direct exposure of the work environment affect the mental health of medical staff, but the infection of friends or close relatives generated psychological trauma (Wu et al., 2009). We found that subthreshold and mild mental health disturbances accounted for a large proportion of disturbances. People with such levels of disturbances may be more likely than those with more severe disturbances to take action and be motivated to learn the necessary skills and to adapt in productive ways to respond to diverse challenges. These skills have been shown in previous retrospective studies to be protective for later mental health (Maunder et al., 2006). In addition, we note that people with subthreshold and mild mental health disturbances want to find ways to better help others, which is beneficial for health care teams. In terms of physiology, positive coping has been seen to increase immune function when victimized subjects report high mental demands, leading to a better state of response (Sakami et al., 2004). However, there are negative consequences of stimulation caused by pressure, as acute psychological stress is known to activate the sympathetic adrenal medulla system and hypothalamus-pituitary adrenal axis, and this two-component stress response impacts physical and mental health and has disease consequences (Turner et al., 2020). In summary, continuous mental healthcare services are necessary even for subthreshold and mild psychological reactions during this epidemic to attenuate the possibility of escalating complications. Multiple features were found for the group of untreated clinical personnel who had serious psychological problems. First, compared to less severely affected groups, they had accessed fewer printed psychological advice materials (e.g., office brochures) and had accessed less psychological guidance publicized through digital media. Second, they were more likely to desire personalized, one-on-one counseling as a therapy option. One might speculate a cause-and-effect relationship wherein more frequent exposure of the other groups to the noted materials in some way protected them from reaching the most severely impacted category, but our cross-sectional results are, by nature, correlational. This study limitation does not detract, however, from the importance of widely implementing prevention and monitoring strategies; mildly to moderately impacted personnel expressed interest in having access to psychological guidance materials, which provides evidence of the importance of prevention strategies. The number of people suffering from mental health impacts after a major event is often greater than the number of people who are physically injured, and mental health effects may last longer. Nonetheless, mental health attracts far fewer personnel for planning and resources (Allsopp et al., 2019). Thus, the Lancet Global Mental Health Commission’s observation that the use of nonprofessionals and digital technologies can provide a range of mental health interventions may indicate an opportunity (Patel et al., 2018). Our data are consistent with a model in which psychological advice and guidance in print resources and disseminated in the media can provide a level of protection for medical and nursing staff, improving mental health by reducing the stress impacts caused by high risk of infection. Clearly, there is a role, nonetheless, for therapist-driven sessions, as previous research showed that a convenient group course intervention for doctors reduced depersonalization, improved views on the meaning of work, and achieved sustained results (West et al., 2014). We anticipate similar benefit for COVID-19 staff in Wuhan based on our findings contained herein. Interestingly, previous studies on medical staff and other infectious agents have repeatedly emphasized that mental health impacts are related to department and occupation (Hawryluck et al., 2004, Wu et al., 2009). Health care workers with professional knowledge about differences in the relative exposure patterns and transmission of different infectious diseases could gain some degree of comfort and control over their situations (Chowell et al., 2015). For example, over the decades, although hepatitis viruses and HIV have often caused lethal infections, radiologists, pathologists and nurses knew that their risk of exposure was low as long as they exercised caution in their contact with bodily fluids. The situation has been different in Wuhan due to the pernicious characteristics of COVID-19. Many infected individuals exhibit minimal or no symptoms while contagious, for example, early in the course of infection (Bai et al., 2020). These individuals may thus visit a variety of different hospital departments in an infectious but asymptomatic state, unknowingly spreading the disease directly through aerosolized droplets or indirectly through skin contact with handled surfaces. These features of the infectivity of coronavirus involve a substantial risk of exposure for medical workers, regardless of their hospital department, job title or building location; thus, any worker – whether doctor or nurse, specialist or generalist – is at substantial risk. The resultant stress due to concerns about infection risk thus indiscriminately affects large numbers of personnel. There is a need to better recognize mental health needs as an important component of mobilizing a large-scale therapeutic response to sudden city-scale crisis scenarios. A large rapid response team in crisis situations should include mental healthcare workers. Local medical and nursing staff at the epicenter of a crisis are pivotal to the overall response, and care for these caregivers – whether through face-to-face counseling or comparable support through digital platforms such as cell phone interfaces – is essential in efforts to extend their immediate efficiency and to better protect their mental health in the long term. Our research also has some limitations. First, compared with face-to-face interviews, self-reporting has certain limitations. Second, the study is cross-sectional and does not track the efficacy of psychological services. Due to changes in posttraumatic mental health, dynamic observation is necessary. A randomized prospective study could better determine correlation and causation. Third, a larger sample size is needed to verify the results. In summary, the results demonstrate that a strikingly large portion of health care providers in virus-plagued Wuhan are suffering from mental health disturbances. They would benefit from greater availability of personalized mental health care from psychotherapists and psychiatrists, wherein different mental health groups could focus on providing specialized mental healthcare services. Among the steps needed to better prepare for future infectious disease outbreaks would be a greater investment in the mental health tools in society’s medical arsenal to protect and care for future medical and nursing staff who find themselves unexpectedly on the dangerous front lines of disease response.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
  12 in total

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Authors:  Daniel J Bauer; Kristopher J Preacher; Karen M Gil
Journal:  Psychol Methods       Date:  2006-06

Review 2.  The Lancet Commission on global mental health and sustainable development.

Authors:  Vikram Patel; Shekhar Saxena; Crick Lund; Graham Thornicroft; Florence Baingana; Paul Bolton; Dan Chisholm; Pamela Y Collins; Janice L Cooper; Julian Eaton; Helen Herrman; Mohammad M Herzallah; Yueqin Huang; Mark J D Jordans; Arthur Kleinman; Maria Elena Medina-Mora; Ellen Morgan; Unaiza Niaz; Olayinka Omigbodun; Martin Prince; Atif Rahman; Benedetto Saraceno; Bidyut K Sarkar; Mary De Silva; Ilina Singh; Dan J Stein; Charlene Sunkel; JÜrgen UnÜtzer
Journal:  Lancet       Date:  2018-10-09       Impact factor: 79.321

3.  The Insomnia Severity Index: psychometric indicators to detect insomnia cases and evaluate treatment response.

Authors:  Charles M Morin; Geneviève Belleville; Lynda Bélanger; Hans Ivers
Journal:  Sleep       Date:  2011-05-01       Impact factor: 5.849

4.  Validation and standardization of the Generalized Anxiety Disorder Screener (GAD-7) in the general population.

Authors:  Bernd Löwe; Oliver Decker; Stefanie Müller; Elmar Brähler; Dieter Schellberg; Wolfgang Herzog; Philipp Yorck Herzberg
Journal:  Med Care       Date:  2008-03       Impact factor: 2.983

5.  The psychological impact of the SARS epidemic on hospital employees in China: exposure, risk perception, and altruistic acceptance of risk.

Authors:  Ping Wu; Yunyun Fang; Zhiqiang Guan; Bin Fan; Junhui Kong; Zhongling Yao; Xinhua Liu; Cordelia J Fuller; Ezra Susser; Jin Lu; Christina W Hoven
Journal:  Can J Psychiatry       Date:  2009-05       Impact factor: 4.356

6.  The immediate psychological and occupational impact of the 2003 SARS outbreak in a teaching hospital.

Authors:  Robert Maunder; Jonathan Hunter; Leslie Vincent; Jocelyn Bennett; Nathalie Peladeau; Molyn Leszcz; Joel Sadavoy; Lieve M Verhaeghe; Rosalie Steinberg; Tony Mazzulli
Journal:  CMAJ       Date:  2003-05-13       Impact factor: 8.262

7.  Positive coping up- and down-regulates in vitro cytokine productions from T cells dependent on stress levels.

Authors:  Shotaro Sakami; Masaharu Maeda; Takayuki Maruoka; Akinori Nakata; Gen Komaki; Noriyuki Kawamura
Journal:  Psychother Psychosom       Date:  2004 Jul-Aug       Impact factor: 17.659

8.  Healthcare Workers Emotions, Perceived Stressors and Coping Strategies During a MERS-CoV Outbreak.

Authors:  Imran Khalid; Tabindeh J Khalid; Mohammed R Qabajah; Aletta G Barnard; Ismael A Qushmaq
Journal:  Clin Med Res       Date:  2016-02-04

9.  The organizational social context of mental health services and clinician attitudes toward evidence-based practice: a United States national study.

Authors:  Gregory A Aarons; Charles Glisson; Phillip D Green; Kimberly Hoagwood; Kelly J Kelleher; John A Landsverk; John R Weisz; Bruce Chorpita; Robert Gibbons; Charles Glisson; Evelyn Polk Green; Kimberly Hoagwood; Peter S Jensen; Kelly Kelleher; John Landsverk; Stephen Mayberg; Jeanne Miranda; Lawrence Palinkas; Sonja Schoenwald
Journal:  Implement Sci       Date:  2012-06-22       Impact factor: 7.327

10.  A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster.

Authors:  Jasper Fuk-Woo Chan; Shuofeng Yuan; Kin-Hang Kok; Kelvin Kai-Wang To; Hin Chu; Jin Yang; Fanfan Xing; Jieling Liu; Cyril Chik-Yan Yip; Rosana Wing-Shan Poon; Hoi-Wah Tsoi; Simon Kam-Fai Lo; Kwok-Hung Chan; Vincent Kwok-Man Poon; Wan-Mui Chan; Jonathan Daniel Ip; Jian-Piao Cai; Vincent Chi-Chung Cheng; Honglin Chen; Christopher Kim-Ming Hui; Kwok-Yung Yuen
Journal:  Lancet       Date:  2020-01-24       Impact factor: 79.321

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  371 in total

1.  Mental health interventions for healthcare workers during the first wave of COVID-19 pandemic in Spain.

Authors:  Amador Priede; Inés López-Álvarez; Diego Carracedo-Sanchidrián; César González-Blanch
Journal:  Rev Psiquiatr Salud Ment       Date:  2021-02-04       Impact factor: 3.318

2.  Adaptation of evidence-based suicide prevention strategies during and after the COVID-19 pandemic.

Authors:  Danuta Wasserman; Miriam Iosue; Anika Wuestefeld; Vladimir Carli
Journal:  World Psychiatry       Date:  2020-10       Impact factor: 49.548

3.  Mental Health of Communities during the COVID-19 Pandemic.

Authors:  Daniel Vigo; Scott Patten; Kathleen Pajer; Michael Krausz; Steven Taylor; Brian Rush; Giuseppe Raviola; Shekhar Saxena; Graham Thornicroft; Lakshmi N Yatham
Journal:  Can J Psychiatry       Date:  2020-05-11       Impact factor: 4.356

4.  Concerns, Perceived Impact, Preparedness in Coronavirus Disease (COVID-19) Pandemic and Health Outcomes among Italian Physicians: A Cross-Sectional Study.

Authors:  Igor Portoghese; Federico Meloni; Maura Galletta; Ilenia Piras; Ernesto D'Aloja; Gabriele Finco; Marcello Campagna
Journal:  J Prim Care Community Health       Date:  2021 Jan-Dec

5.  The Multifaceted Impact of COVID-19 on the Female Academic Emergency Physician: A National Conversation.

Authors:  Devjani Das; Michelle D Lall; Laura Walker; Valerie Dobiesz; Penelope Lema; Pooja Agrawal
Journal:  AEM Educ Train       Date:  2020-10-21

6.  The association between witnessing patient death and mental health outcomes in frontline COVID-19 healthcare workers.

Authors:  Mariela Mosheva; Raz Gross; Nimrod Hertz-Palmor; Ilanit Hasson-Ohayon; Rachel Kaplan; Rony Cleper; Yitshak Kreiss; Doron Gothelf; Itai M Pessach
Journal:  Depress Anxiety       Date:  2021-02-05       Impact factor: 6.505

Review 7.  Psychomorbidity, Resilience, and Exacerbating and Protective Factors During the SARS-CoV-2 Pandemic.

Authors:  Donya Gilan; Nikolaus Röthke; Manpreet Blessin; Angela Kunzler; Jutta Stoffers-Winterling; Markus Müssig; Kenneth S L Yuen; Oliver Tüscher; Johannes Thrul; Frauke Kreuter; Philipp Sprengholz; Cornelia Betsch; Rolf Dieter Stieglitz; Klaus Lieb
Journal:  Dtsch Arztebl Int       Date:  2020-09-18       Impact factor: 5.594

8.  Are We Coping Well with COVID-19?: A Study on Its Psycho-Social Impact on Front-line Healthcare Workers.

Authors:  Tinashe Maduke; James Dorroh; Ambarish Bhat; Armin Krvavac; Hariharan Regunath
Journal:  Mo Med       Date:  2021 Jan-Feb

9.  The Assessment of Knowledge, Behaviors, and Anxiety Levels of the Orthodontists about COVID-19 Pandemic.

Authors:  Hanife Nuray Yilmaz; Elvan Onem Ozbilen
Journal:  Turk J Orthod       Date:  2020-12-01

10.  Mental health interventions for healthcare workers during the first wave of COVID-19 pandemic in Spain.

Authors:  Amador Priede; Inés López-Álvarez; Diego Carracedo-Sanchidrián; César González-Blanch
Journal:  Rev Psiquiatr Salud Ment (Engl Ed)       Date:  2021 Apr-Jun
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