Posttraumatic Stress Symptoms of Health Care Workers during the Corona Virus Disease 2019 (COVID‐19)

Abstract

Corona Virus Disease 2019 (COVID‐19) outbreak has attracted worldwide attention. The COVID‐19 outbreak is unique in its rapid transmission and results in heavy stress for the front‐line health care workers (HCWs). The current study aimed to exam posttraumatic stress symptoms (PTSS) of HCWs fighting for the COVID‐19 and to evaluate their sleep quality after one‐month stressful suffering. 377 HCWs working in different provinces of China participated in the survey between February 1st and 5th. The demographic information was collected firstly. Posttraumatic Stress Disorder Checklist for DSM‐5 (PCL‐5) and The Pittsburgh Sleep Quality Index (PSQI) were selected to measure PTSS and sleep quality. Results showed that one month after the outbreak, the prevalence of PTSS was 3.8% in HCWs. Female HCWs were more vulnerable to PTSS with hazard ratio of 2.136 (95% CI= 1.388‐3.286). HCWs with higher exposure level also significantly rated more hyper‐arousal symptoms (hazard ratio= 4.026, 95% CI= 1.233‐13.140). There was a significant difference of sleep quality between participants with and without PTSS (Z value= 6.014, p<0.001) and among different groups with various contact frequency (Chi‐square=7.307, p=0.026). Path analysis showed that there was a significant indirect effect from exposure level to PTSS through sleep quality (coefficient =1.750, 95% CI of Boostroop test = 0.543‐2.998). In summary, targeted interventions on sleep contribute to the mental recovery during the outbreak of COVID‐19. Understanding the mental health response after a public health emergency might help HCWs and communities prepare for a population's response to disaster.

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doi: 10.1002/cpp.2477
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Liu Weizhi (Orcid ID: 0000-0002-8775-6665)
Title:Posttraumatic Stress Symptoms of Health Care Workers during the Corona Virus
Disease 2019 (COVID-19)
Qianlan Yin + , Zhuoer Sun+, Tuanjie Liu + , Xiong Ni +, Xuanfeng Deng , Yanpu Jia, Zhilei
Shang ,Yaoguang Zhou, Weizhi Liu
+ these authors contributed equally to this work.
Author Contributions
QY,ZS contributed to the writing of this article and part of statistical analysis. WL leaded the
whole study, including putting forward this study and carrying out the study, and was the
corresponding author. TL, XN contributed to revise this article and part of statistical analysis.
XD,YJ,ZS contributed to perform the investigation and collection of all data. YZ helped to
collect the data.
We are all accountable for all aspects of the work in ensuring that questions related to the
accuracy or integrity of any part of the work are appropriately investigated and resolved.
Acknowledgments
The authors would like to acknowledge the HCWs who participated in the study and
appreciate all the endeavor of the workers contributing to the fight for the COVID-19. We
also pay tribute to the cooperation of affiliated hospital of Navy Medical University and
involved hospital.

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Abstract
Corona Virus Disease 2019 (COVID-19) outbreak has attracted worldwide attention. The
COVID-19 outbreak is unique in its rapid transmission and results in heavy stress for the
front-line health care workers (HCWs). The current study aimed to exam posttraumatic stress
symptoms (PTSS) of HCWs fighting for the COVID-19 and to evaluate their sleep quality
after one-month stressful suffering. 377 HCWs working in different provinces of China
participated in the survey between February 1st and 5th. The demographic information was
collected firstly. Posttraumatic Stress Disorder Checklist for DSM-5 (PCL-5) and The
Pittsburgh Sleep Quality Index (PSQI) were selected to measure PTSS and sleep quality.
Results showed that one month after the outbreak, the prevalence of PTSS was 3.8% in
HCWs. Female HCWs were more vulnerable to PTSS with hazard ratio of 2.136 (95% CI=
1.388-3.286). HCWs with higher exposure level also significantly rated more hyper-arousal
symptoms (hazard ratio= 4.026, 95% CI= 1.233-13.140). There was a significant difference
of sleep quality between participants with and without PTSS (Z value= 6.014, p<0.001) and
among different groups with various contact frequency (Chi-square=7.307, p=0.026). Path
analysis showed that there was a significant indirect effect from exposure level to PTSS
through sleep quality (coefficient =1.750, 95% CI of Boostroop test = 0.543-2.998). In
summary, targeted interventions on sleep contribute to the mental recovery during the
outbreak of COVID-19. Understanding the mental health response after a public health
emergency might help HCWs and communities prepare for a population’s response to
disaster.
Key words: COVID-19; PTSD; Health care workers (HCWs); Sleep Quality; Psychological
guidance
Abbreviations
Corona Virus Disease 2019 (COVID-19); Health Care Workers (HCWs); Post traumatic
stress symptoms (PTSS); World Health Organization (WHO); Confidence Interval (CI);
Severe Acute Respiratory Syndromes(SARS)

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Introduction
On Feb 11, 2020, this novel coronavirus was officially named as the Corona Virus
Disease 2019 (COVID-19) by the World Health Organization (WHO). It is more likely to
affect older males with comorbidities and can result in severe and even fatal respiratory
diseases such as acute respiratory distress syndrome(Chen, 2020). About one month after the
first confirmed case, the number of confirmed cases soared up to more than 20,000 in the
mainland China. By April 14, 2020, the COVID-19 have hit 211 countries and regions in the
world, and confirmed cases reach to 1,883,183. Therefore, due to its rapid transmission, the
COVID-19 outbreak not only raises public health concerns but also causes tremendous
psychological distress(Xiang et al., 2020), particularly among health care workers (HCWs)
who are the very core of any public health response and form the crucible base which must
not melt in the heat of crisis.
It is alarmed that HCWs are vulnerable to the sustained psychological impacts of the
epidemic(Kang, Li, Hu, Chen, & Yang, 2020). HCWs who are at the first line of battle under
the influence of high fatality and intense media coverage, will undoubtedly show the emotion
of fear, anxiety, anger, and frustration(Lu, Shu, Chang, & Lung, 2004;Maunder, Hunter,
Vincent, Bennett, & Mazzulli, 2003). A survey of psychological impacts of 2003 SARS
outbreak on HCWs in Taiwai showed social functioning and immediate mental health were
among the worst subscales after caring for patients with SARS(Chen et al., 2007). These
results highlighted the emotional and occupational vulnerability of HCWs. Worsely, the
outbreak of COVID-19 was at the corner of the Spring festival, one of the biggest festivals in
a year for Chinese people to enjoy the happiness of family reunion and relieve from a year’s
hardworking. However, COVID-19 terminated the holiday and HCWs have to work
continually. Undoubtedly, at the harsh moment, HCWs will weather not only physical but
also mental sufferings. According to previous studies about the PHEIC (Public Health
Emergency of International Concerns), angers, fears and anxiety might be at peak of the
outbreak(Robertson, Hershenfield, Grace, & Stewart, 2004), yet they would decrease along
with the stability of the spreading of the virus(Wu, Chan, & Ma, 2005a). Nevertheless, most
of survivors and aid personnel experiencing a stress reaction in the disaster had the poor
performance on the mood, cognition, physical health, and interpersonal relationships for
several days or weeks(Raphel, Singh, Bradbury, & Lambert, 1983;Grimm, Hulse, Preiss, &
Schmidt, 2012; Kaya, Sungur, & Burhanettin, 2001; Xu, Dong, Hu, Song, & Liang, 2005). As
reported in recent investigations of COVID-19 in China, most people were preoccupied with
the thoughts of COVID-19 and had paranoia about infecting virus, among who 18.2%
reported sleep difficulties and enhanced the perception of mental health care need(Huang &
Zhao, 2020). Worringly, a study of the immediate psychological impact of COVID-19 on
HCWs showed, 29.8%, 13.5% and 24.1% HCWs reported stress, depression and anxiety
symptoms(Lua, Wang, Linc, & Li, 2020). Hereby, there are reasons to believe HCWs will
experience more troubles confronting with such tough situation and it is urgent to focus on
the mental health of HCWs during the COVID-19 outbreak.
Post-traumatic stress disorder (PTSD) is a state of psychological unbalance following an
exposure to traumatic events, with which people often re-experience traumatic events,
demonstrate avoidance behavior, and become irritable(Blake, 1995). The reported occurrence
rate of PTSD features for SARS survivors was in the middle of the range, which was

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investigated in previous samples of other medical diseases(Hawryluck, Gold, Robinson,
Pogorski, & Styra, 2004; Wu, Chan, & Ma, 2005a). Nevertheless, subsequent empirical
researches consistently demonstrate substantial rates of subclinical posttraumatic stress
symptoms (PTSS) were more discerning. Post-traumatic Stress symptoms (PTSS) follows
traumatic occurrences outside the range of common human experience such as violent
physical assaults, torture, accidents, rape or natural disasters and is characterized by a typical
symptom pattern of intrusions, persistence of trauma, relevant stimuli avoidance, emotional
numbing and physiological hyper-arousal(Deja, Denke, Weber-Carstens, & Schr Der, 2006).
Zhang et al. (2006) investigated the occurrence of PTSS in three groups of people (patients,
HCWs, and the public) related to SARS showing the incidence of PTSS respectively were
55.1%, 25.8% and 31.18%(Zhang et al., 2006). Also, those respondents who had been
isolated, worked in high-risk workplaces such as SARS wards, or had friends or close
relatives who contacted SARS were two to three times more likely to develop high levels of
PTSS than those who were not exposed to the virus(Wu, Chan, & Ma, 2005b). An
investigation in a regional general hospital reported a total of 177 out of 661 participants
(27%) had psychiatric symptoms (35% of physicians and 25% of nurses), and approximately
20% of the participants were diagnosed with PTSD after 2 months of the epidemic
outbreak(Chan & Yiong, 2004). More notably, HCWs, after a long period of the horrified
situation, are subjective to the early PTSS (Brondolo, Eftekharzadeh, Clifton, Schwartz, &
Delahanty, 2017; Lazarus, 2014). At the first period of the epidemic, HCWs exposed to high
level of unfamiliarity and uncontrollability about the virus, which, literally documented,
could in turn affect their likelihoods for PTSS and developing PTSD(Marshall, Bryant, Amsel,
Suh, & Neria, 2007; Schlenger et al., 2002). However, a paucity of studies caught the
detection of PTSS of HCWs at the fastigium of epidemic outbreak. These explorations could
avail to the future retrospective studies. Therefore, we focused on the PTSS in HCWs
immediately after the outbreak of COVID-19 and investigated the potential related factors to
PTSD in the present study.
Importantly, evidence suggests that sleep is not simply a secondary symptom of PTSD,
but rather is a risk factor for worsening symptoms of the disorder(Schlenger et al., 2002;
Wright et al., 2011). As PTSD endures for many years, factors such as expectations of
disturbed sleep, conditioning to environmental stimuli, and psychiatric and medical
comorbidity likely confound the relationship, yet investigations during the early aftermath of
trauma reduces it to some extent(Mellman, Pigeon, Nowell, & Nolan, 2007). To the best of
our knowledge, sleep disorders and exhaustion tolled on the HCWs at the early stage of the
SARS outbreak (approximately one month later). It was estimated that 10.67% HCWs had
the problem to fall asleep, while 40.31% of them were extremely overwhelmed and even
fainted (Yang, 2004). Being burn-out and allotting all time to care the patients, HCWs are
also under the condition of sleep deprivation and miss their opportunity for restorative
break(Geiger-Brown et al., 2012). Notably, research suggests that poor sleep quality is a risk
factor for PTSD (Spoormaker & Montgomery, 2008). For example, insomnia 1 month after
exposure to a traumatic event predicts the development of PTSD 6 weeks (Mellman, 1995), 6
months (Harvey, 1998), and 1 year post exposure(Koren, Arnon, Lavie, & Klein, 2002). A
limited number of laboratory studies during periods closer to the time of trauma exposure
have also generated findings that an index of REM sleep continuity, the average duration of

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uninterrupted segments of REM sleep (REM segment duration), was significantly reduced in
the group who subsequently showed PTSD(Mellman, Bustamante, David, & Fins, 2002;
Mellman, Bustamante, Fins, Pigeon, & Nolan, 2002). However, even though the association
between sleep quality and PTSD is clearly established, the bulk of related researches only
focuses on the epidemiology and disease process or organizational issues. Relatively little
work has sought to investigate mechanisms in sleep and PTSS during the early aftermath of
trauma. Moreover, previous studies have not directed the mechanism at first-line HCWs
under the pressure from the epidemic.
Current study
Hence, this cross-sectional study was to document and describe the PTSS of the HCWs
combating for the COVID-19; to evaluate the staff’s sleep quality after one-month stressful
suffering; to explore mechanisms in sleep and PTSS during the early aftermath of trauma.
The findings of this study will offer better psychological guidance to HCWs inside or outside
China dealing with COVID-19 or other similar diseases/disasters, as well as be important in
planning for future outbreaks of emerging diseases.
Methods
Participants
A cross-sectional online survey was conducted using snowball sampling through e-mail,
Wechat, shares, and on-line website to the contacts of the investigators in different provinces
of China between February 1st and 5th, which was the one-month time point of the
comprehensive epidemic defensive work and the fastigium of the COVID-19 outbreak. All
participants voluntarily completed the scale. The participants were also explained that the
survey results would be used exclusively for research purposes, that the information they
would provide would not be leaked. The survey began with an informed consent procedure
that included information on research purposes and confidentiality. The questionnaire can be
completed anonymously. In this study, the inclusion criteria were: 1-Health care workers;
2-Among 18-60 years old (the age range for incumbent HCWs); 3-Understanding the
question literally; 4-No diagnosed mental disorders like depression, anxiety disorder, PTSD
and so on. Finally, 377 HCW participants met all the criteria and the average time for their
responding was 257.7±150 seconds. With 6 HCWs whose response time below 100s
excluded, thereby, the valid rate was 98.41%. Figure 1 shows each step of the enrolment. This
project was approved by the Ethics Committee of the Navy Medical University.
Measures
Demographic characteristics
The demographic information was collected by asking for their personal information (current
location, occupation, gender, education background, age and occupations), exposure history
(whether they have contacted suspected or confirmed patients of COVID-19 and their contact
frequencies), and quarantined status (whether being quarantined in home or other safety
places). According to the exposure history, we categorized the traumatic exposure into 3 level:
HCWs without contact with potentially infected people were thought to be experiencing low
exposure level; having contacted the potential patients but not in a persistent exposure to

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virus rated as under middle level; HCWs working in Wuhan, the center of the epidemic, were
deemed to be highly frequently contacted with the virus and were at high-exposure-level risk.
The PTSD Checklist for DSM-5 (PCL-5)
The PTSD Checklist for DSM-5 (PCL-5), which was developed by an American PTSD
research center following the Diagnostic and Statistical Manual of Mental Disorders-V
(DSM-5). According to Weathers et al. , exposure to an epidemic was a traumatic event
(Weathers, 2013). Thus, in present study, Participants of epidemic-hit cities were selected and
were instructed to rate how much they were bothered by the outbreak of COVID-19 in the
last month upon answering PCL-5. This scale includes 20 items for evaluating four clusters
of PTSS, including intrusive symptoms (Criterion B: questions 1-5), avoidance symptoms
(Criterion C: questions 6-7), negative alteration in cognition and mood (Criterion D:
questions 8-14) and hyper-arousal symptoms (Criterion E: questions 15-20). Respondents
were asked to rate how bothered they have been by each of 20 items in the past month on a
5-point Likert scale ranging from 0-4. Items are summed to provide a total severity score
(range = 0-80). The total PCL, Criterion B, Criterion C, Criterion D and Criterion E had
Cronbach's alpha coefficients of 0.906, 0.798, 0.791, 0.750, and 0.829 respectively, thereby
indicating high internal consistency. Each item rated as 2 = "Moderately" or “higher” was
treated as a symptom endorsed. The PCL-5 can determine a provisional prediction according
to summing all 20 items and using cut-point score of 33 for PTSS appears to be a reasonable
based upon current psychometric work, considering the goals of the assessment and the
population being assessed and giving a probable differentiation (Weathers, 2013).
Pittsburgh Sleep Quality Index
The Pittsburgh Sleep Quality Index (PSQI) is a self-administrated questionnaire which
assesses sleep quality and disturbances over a 1-month time interval(Buysse, Iii, Monk,
Berman, & Kupfer,1989). Chinese vision of PSQI was also proved to be valid(C. &
Tang,1996). It is indicated that the use of “component” scores in the PSQI is also empirical
and clinical(Buysse, Sonia, Edinger, Lichstein, & Morin, 2006; Harvey, Kathleen, Whitaker,
Damian, & Harvinder, 2008). Therefore, we selected 4 questions from PSQI in the present
study to save time for HCWs, including subjective sleep quality by asking “how good is your
sleep quality”, sleep disturbances by asking “do you have easy waking during sleep and early
waking in the morning”, sleep latency by asking “do you have difficulty in starting sleep” and
sleep duration with asking “what’s you actual sleep time recently”. Each question weighted
equally on a 0-3 scale, respectively present different choices. In items for subjective sleep
quality, “0-3” was equal to “very good to very bad”; in term of sleep disturbance, it was equal
to “no-more than 3 times a week”; in sleep latency it was equal to “no-more than 3 times a
week” and then in sleep duration “more than 7 hours to less than 5”. The scores of 4
questions were summed to yield a global PSQI score; higher scores indicate worse sleep
quality. Cronbach's alpha coefficients of the global score was 0.793.
Statistical analysis
Statistical analysis was performed using SPSS 19.0, Release Version 19.0 (2010, Armonk,
NY) for Windows. The differences of PTSS in characteristic of HCWs were compared using

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Chi-square tests for dichotomous variables, which were characterized using frequency
statistics to examine differences in key variables as a function of sociodemographic
characteristics (i.e., age and sex). Sleep quality summed by the four components of PSQI was
coded as a continuous variable whose mean values and SDs were calculated. Non-parametric
test was performed in comparing the group differences of sleep quality, which was not
normally distributed, to describe its interactions with exposure level or the severity of PTSS.
Furthermore, the total score of PCL-5, which was normally distributed and represented the
severity of PTSS, was evaluated by Hierarchical multiple regression models to examine the
relationships among these variables and potential predictors of PTSS. Furthermore, mediation
analyses were conducted to determine the potential mediating effect on the relationship
between PTSS and epidemic exposures based the regression results from Hierarchical
multiple regression. Specifically, path analyses via a structural equation modeling framework
and bootstrap estimation were performed with SPSS Amos, Release Version 20.0(2010,
Armonk, NY), and statistical tests were two-sided with ɑ=0.05. We hypothesized that sleep
quality could be a potential mediator. Hence, firstly, a model planned to exam bidirectional
influences of PTSS and exposure level were regressed on their respective baseline levels as
well as each other at baseline. Second, sleep quality at baseline was indicated by a global
PSQI score and was predicted by baseline PTSS and exposure level. Given significant
correlations between PTSS and gender, analyses were adjusted for gender; sleep quality then
predicted change in exposure level and PTSS adjusting for their respective baseline levels.
Results
Description of sample
The demographics information was showed in Table 1. A total of 371 HCWs were recruited
in this survey, among which 143(38.5%) were males and 228(61.5%) were females. The
average age was 35.30±9.48 and the average level of education was the bachelor, indicating
the participants might be mainly from the middle-age group with rich work experiences. Most
of these participants (96.8%) were not distributed in Wuhan, where the epidemic situation
was not as serious as that of Wu Han but also in the high alert and tight sense. 18.1% of the
HCWs were doctors; 71.2% were nurses and 10.2% were other HCW jobs like the medical
technicians. 245 (66%) of our cohort were quarantined at home and safe at this moment
(according to the national mandate, all people should strictly maintain the social distance at
home and stop regular outsider activities) while 126(34%) were still working at the first line
and undertaking the risk of infection. Frequency of contact with COVID-19 patients in the
daily working environment represented the exposure level. 288 (77.6%), 71(19.1%) and
12(3.2%) HCWs were belong to low, middle and high exposure group, respectively.
Prevalence of PTSS in HCWs
The outcome data from the PCL-5 scale were showed in Table 2. 3.8% (14 in 371)
HCWs’ total score of PCL-5 was over 33, which indicated the diagnosis of PTSS according
to the criterion. The intrusive symptoms assessed by Criterion B were more common one
month after the epidemic (with the positive rate of 44.5%) compared with other PTSS (12%
HCWs met Criterion C; 16.4% met Criterion D and 16.2 met Criterion E) . Female HCWs
were more vulnerable to PTSS and significantly showed more intrusive symptoms than males
with hazard ratio of 2.136 (95% confidence interval= 1.388-3.286; p=0.010). Moreover,
HCWs with higher exposure level also significantly rated more hyper-arousal symptoms of

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the criterion E. HCWs working in Wuhan had an over-fourfold greater risk of showing
hyper-arousal symptoms (hazard ratio= 4.026, 95% confidence interval= 1.233-13.140;
p=0.021). However, there was no significant difference of PTSS in the groups of different
education background or occupations.
Sleep quality of HCWs
Sleep quality was assessed by four questions including sleep satisfaction, sleep
disturbance, sleep latency and sleep duration. As shown in Table 3, 2.7% HCWs scored “very
bad” for sleep satisfaction; 11.3% reported frequent sleep disturbance; 6.7% complained
having difficulty falling asleep and 6.2% had less than 6-hour sleeping time. Hence, sleep
disturbance was more common than other sleep problems in HCWs. HCWs with high
exposure level significantly had more sleep disturbance and longer sleep latency, indicating
the poorer sleep quality than those with less exposure. Notably, HCWs categorized by the
total score of PCL-5 into two groups showed significant between-group differences in all the
components of sleep quality. Based on the overall sleep quality, the sum of the four questions,
there was a significant difference in the two group- one without-PTSS and the other
with-PTSS (Z value= 6.014, p<0.001), and also among different groups with frequency of
contact (Chi-square=7.307, p=0.026). As illustrated in the Figure 2, HCWs with PTSS had
poorer overall sleep quality. In the group with PTSS, there was no significant differences in
sleep quality of HCWs with different exposure level (Chi square =0.391, p=0.823).
Nonetheless, in the group without PTSS, the higher exposure level seemed to relate to poorer
sleep quality, yet the difference was neither significant (Chi square =5.519, p=0.063). Hence,
there was no significant interaction effect between exposure level and sleep quality on the
potential diagnosis of PTSS.
Association among exposure, sleep quality and PTSS
Correlation analysis results were demonstrated in Table 4, suggesting the significantly
close interrelation in sleep quality components and clusters of PTSS (all the P value were less
than 0.01.). Exposure level was significant positive associated sleep satisfaction (p=0.018),
sleep disturbance(p=0.002), and sleep latency (p=0.001), however, it was only significantly
correlated to hyper-arousal symptom clusters of PTSS (p=0.003). Based on the above
analysis, further exploration was conducted in explaining the role of different variables in the
genesis of PTSS. As showed in Table 5, PTSS assessed by the total score of PCL-5, was
regressed on the demographic variables in Model 1 and only gender was a significant
predictor indicating female HCWs were reported more PTSS. In model 2, exposure level was
also a significant related factor to PTSS and gender remained associated to PTSS. However,
after the entering of sleep quality, only sleep quality turned significant related to PTSS.
Hence, sleep quality was more strongly associated with PTSS, which was also proved by the
results of mediation analysis showed in Figure 3. The total score of PCL-5 was entered into
the mediation model as a dependent variable, with sleep quality as a possible mediator,
exposure level as a predictor, and gender as a covariate. There were significant direct effects
whereby higher exposure level was associated with worse sleep quality (coefficient =1.03,
p=0.006) and sleep quality predicted PTSS (coefficient =1.700, p<0.001). There was a
significant indirect effect from exposure level to PTSS through sleep quality (coefficient
=1.750, 95% CI of Boostroop test = 0.543-2.998). Coefficient of the total effect was 2.399
with P value at 0.0143. However, the direct effect from exposure level to PTSS was not

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significant, indicating that sleep quality completely mediated the relationship between
exposure level and PTSS in this model (Figure 3). Moreover, it was not possible to infer
causality from the aforementioned analysis given the concurrent measurement of sleep and
PTSS. And we reran the model in the reverse direction to substantiate our conclusions from
the mediation analysis. In this analysis, PTSS was entered as a predictor. This mediation
model did not fit.
Discussion
The COVID-19 was declared to be a PHEIC by the World Health Organization. Worldwide,
as millions of people stay at home to minimize transmission of the virus. On the contrary,
HCWs leave for clinics and hospitals, putting themselves at high risk of contracting
COVID-19, which is worthy respect but at the meantime also aggravate the mental risk.
Therefore, the present study firstly investigated the prevalence of PTSS or PTSS one month
after traumatic exposure and sleep quality of HCWs, aiming at understanding the mental
health of HCWs on the backdrop of such a PHEIC and helping medical workers and
communities with targeted psychological interventions.
The total prevalence of PTSS was 3.8% in the current 371 participants along with
different prevalence in single clusters of PTSS including intrusive symptoms (44.5%),
avoidance symptoms (12.7%), negative alteration in cognition and mood symptoms (16.4%)
and hyper-arousal symptoms (16.2%). HCWs experiencing high-level exposure were more
vulnerable to having PTSS (with the prevalence reaching 8.3%), who also significantly
presented more hyper-arousal symptoms. Referred to a study conducted in 2003 about the
3-month follow-up investigation of PTSS in 476 SARS survivors (with 131 responded at the
both l month and 3 months) , 13 health workers (6 at 1 month and 7 at 3 months) had the
diagnosis of PTSD, and HCWs showed higher scores at the subscale of hyperarousal
symptoms than other survivors(Wu, Chan, & Ma, 2005a). Although using a different measure
assessing PTSS, our result was consistent with the study in 2003, suggesting HCWs were
vulnerable to have PTSS, especially the hyper-arousal symptoms. Moreover, our recent
published research reported the prevalence of PTSS after COVID epidemic for hardest-hit
areas in China reached 7% less than that of the HCWs in the same areas(with the prevalence
reaching 8.3%)(Liu et al., 2020). As speculated, the prevalence of 3.8% in the overall HCWs
might be lower than the previous studies, given the time-point of our study and the backdrop
against the early-stage of COVID epidemic. Hence, the follow-up studies are needed and
should focus on deterring the development of PTSS. In fact, figures from China’s National
Health Commission show that more than 3,300 HCWs have been infected as of the early
March and by the end of February at least 22 died according to local media. In Italy, 20% of
responding HCWs were infected, and some have died. Reports from medical staff describe
physical and mental exhaustion, the torment of difficult triage decisions, and the pain of
losing patient and colleagues, all in addition to the infection risk(Lancet, 2020). Given that,
HCWs at higher risk of being infected were in the more nervous and alerted status than usual.
Meanwhile, HCWs might report more difficulty in falling asleep (longer sleep latent) and
short sleep duration compared the public in hardest-hit areas (according to our previous study,
8.4% of the public were unable to feel asleep frequently and 4.2% of them have less than
5-hour sleep time). Besides, combined with the results of sleep quality, more sleep

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disturbance and longer sleep latency in HCWs of high exposure responded to the
hyper-arousal symptoms, which is a consequence of having heightened anxiety and arousal
responses and in the long term may result in difficulty falling or staying asleep, or even a bad
sleep pattern(Pace-Schott, Germain, & Milad, 2015). To date, medical workers all over the
world have been facing enormous pressure, including a high risk of infection and inadequate
protection from contamination, overwork, frustration, discrimination, isolation, patients with
negative emotions, a lack of contact with their families, and exhaustion(Kang, 2020; Zhou et
al., 2020). These distresses have proved to exert an obvious impact on their sleep quality and
elicit a lasting effect on their mental health such as long-term PTSD(Lancee, 2008; Liu, Zhou,
& Shi, 2008). Therefore, protecting the mental health of these medical workers by reducing
exposure risk would be important.
It is important to note the sleep quality statistically mediated the association between
exposure level and PTSS. Consistent with previous researches, poor sleep quality has been
linked to both the onset and maintenance of PTSD(Babson & Feldner, 2010; Harvey, Jones, &
Schmidt, 2003; Koren et al.,2002). Current theory suggests the develop of PTSD is in a
dynamic process. That is, poor sleep quality may result in a context in which individuals are
more prone to avoid trauma reminders. Accordingly, when reminders cannot be avoided,
individuals with elevated levels of anxiety and alertness lead to the genesis of PTSS.
However, to the best of our knowledge, less information is available about the role of sleep
quality playing in the relationship between exposure experience and PTSS, especially in the
public emergency involving a various and unavoidable exposure. Therefore, this study further
explored the obtained preliminary results, showing that the mediated role of sleep quality
played in the association between exposure level and PTSS. With gender as the covariate
-considering gender was the only significant demographic predictors for PTSS in the
regression, sleep quality was a completely mediator, which signaled that the poor sleep
quality might be a major reason for different exposure level developing PTSS in HCWs on
the backdrop of the epidemic. Notably, this kind of exposure, specified by the contact
frequency of the confirmed or suspected patients, related to the genesis of the PTSS, mainly
depended on its relation to sleep quality. This finding raises the possibility that HCWs with
poor sleep quality, who were most frequently contacted with patients infected by COVID-19,
suffered from more PTSS. Moreover, such findings provided preliminary evidences that the
association of higher exposure level with reduced sleep quality could comprise a significant
predictor and effective intervention point for PTSS.
Limitations
Despite several important results of this study, there are some limitations should be warrant.
Firstly, the small sample size of HCWs, especially those working at the first line. Due to most
HCWs in Wuhan have abided to the strict quarantine standard and devoted to the
overwhelming works, it was difficult for them to participate in or finish the questionnaires.
There was no definite number of the large population of HCWs from the frontline, but we
assured the actual statue of PTSS in HCWs could be more serious than our results as the
limited number of HCWs in the hardest-hit area sent their reports. Secondly, other potentially
significant associated factors for PTSS were not fully discussed in the current study such as
genders，married status, infection of family members/friends/colleagues, life stress, which
could be intertwined with the exposure level on the effect of trauma on PTSS, and coping

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styles, personality, many of which have potential confounding effects on sleep quality and
PTSS. Besides, considering the design was a cross-sectional study, results gleaning from the
mediating analysis on the role of sleep quality played in the traumatic experiences and PTSS
should be interpreted cautiously. Thirdly, the measures of stress symptoms and sleep quality
used in this study may be vulnerable to inherent bias because of their online self-reports.
Attentionally, this research did not include a structured diagnostic interview, and thus was not
able to evaluate comparability of the PCL-5 scores with those based on clinicians’ ratings or
evaluate diagnostic utility of the PCL-5 against a clinical PTSD diagnosis. Moreover, referred
to the researches, the cut-off at 33 of PCL-5 total score could imply for PTSS, nevertheless,
the cut-off score was not reified and still required appropriate concerns about the populations
and purposes for validity. A dynamic measure and obviation could be more objective and
helpful for diagnosis of PTSD in the future researches.
Implications
These results of current studies have important implications. Firstly, our findings
demonstrated a significant psychosocial impact of COVID-19 outbreak on HCWs combating
at the front line, when knowledge of the diseases was limited and the number of confirmed
and suspected patients was rapidly increasing. The diagnosis and prevalence of PTSS in our
participants, especially those working in the epic-center of the epidemic, highlights the need
for greater supports to HCWs during such crises with psychological interventions to address
psychosocial distress and concerns. Secondly, low sleep quality resulted from stress and
overload of work have crippling effect on HCWs including woeful psychological
consequences like PTSS. Hence, as recommended, more shifts and breaktime should be
guaranteed for HCWs by redeploying the hospital personal arrangements, despatching more
health workers to the epic center where the patients are gregarious and so on. Through the
immediate intervention of the sleep quality, traumas with high exposure level could remain
less psychological distress and PTSD could be never developed in to chronic. As seen in
China, most general hospitals have established a shift-system to allow front-line medical
workers to rest and to take turns in high-pressured roles; online platforms with medical
advice have been provided to share information on how to decrease the risk of transmission
between the patients in medical setting; psychological invention teams have been set up and
assistance hotline teams composed of volunteers provided telephone guidance. Hundreds of
medical workers are receiving these interventions, with good response, and their provision is
expanding to more people and hospitals. As a whole, understanding the mental health
response after a public health emergency might help HCWs and communities prepare for a
population’s response to disaster.
Conclusion
In sum, HCWs showed PTSS after one month of the COVID-19 outbreak, and 3.8% of them
could be at high risk of PTSS. Gender, exposure level (contact frequency of COVID-19
patients), and sleep quality were all significantly associated with PTSS, with sleep quality
playing an important role in the development of PTSS after epidemic. Importantly, the
influencing factors identified in this study may be extremely effective in defining the high
risk HCWs group in the similar influenza epidemics and offer hints on how HCWs can cope
with future epidemics. HCWs deserve more attention and multifaceted psychological
guidance, which are urgently needed after the COVID-19 outbreak.

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Data Access
Data obtained for the study will not be made available to others.
Declaration of conflict of interest
The authors declare that they have no conflict of interests.
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Figure 1 Sampling frame

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Figure 2. Group differences on the summed scores of PSQI
Note: PTSS was implied by the PCL-5 by summing all 20 items and using a cut-point score
of 33.

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Figure 3. Sleep quality mediates the relationship between exposure and PTSS
Note: The mediated model with direct and indirect effects symbolized by the straight-line
arrows and total effect marked by a curving arrow.

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Table 1. Demographic characteristic of participated HCWs
Demographics
Samples
Percentage (%)
Gender
Male
143
38.5
Female
228
61.5
Education
Senior high school or below
10
2.7
Academy or bachelor
271
73.0
Master or above
90
24.3
Ages
20-30 years
142
38.3
31-40 years
116
31.3
Occupation
above 40 years
Doctors
Nurse
Others
113
67
264
40
30.5
18.1
71.2
10.2
Location
Wu Han
12
3.2
Other cities
359
96.8
Quarantined
Not Quarantined
126
34.0
Quarantined
245
66.0
Exposure level
Low
288
77.6
Middle
71
19.1
High
12
3.2

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Table 2. Prevalence of PTSS in HCWs
Criterion
B
C
D
E
Total
Total
Positive rate (%)
44.5
12.7
16.4
16.2
3.8
Gender
Male (%)
32.7
8.8
15.0
14.3
2.0
Female (%)
50.9
14.8
17.0
17.0
4.8
Chi-square
12.092*
*
2.899
0.262
0.478
1.885
Education
Senior high school
or below (%)
20.0
20.0
10.0
40.0
0.0
Academy or
bachelor (%)
44.7
10.9
15.6
14.5
4.0
Master or above
(%)
43.5
16.3
18.5
17.4
3.3
Chi-square
2.401
2.373
0.700
4.870
0.502
Ages
20-30 years (%)
46.3
12.2
19.0
20.4
3.4
31-40 years (%)
40.2
13.7
16.2
12.8
4.3
above 40 years (%)
44.2
11.5
12.4
13.3
3.5
Occupation
Chi-square
Doctor
Nurse
Other
Chi-square
0.996
37.5
44.7
47.8
1.086
0.259
7.5
12.9
14.9
1.285
2.089
12.5
17.8
13.4
1.250
3.644
17.5
15.5
17.9
0.282
0.152
0
4.2
7.5
3.427
Exposure level
Low (%)
43.0
11.0
16.2
13.4
2.7
Middle (%)
47.3
16.2
14.9
21.6
6.8
High (%)
41.7
25.0
25.0
41.7
8.3
Chi-square
0.474
3.257
0.783
9.121**
3.390
Note: ** means P value less than 0.01

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Table 3. Group differences on the four components of PSQI
sleep
satisfaction
sleep
disturbance
sleep
latency
sleep duration
Total
Mean±SD
Percentage(%)
0
1
2
3
0.91±0.782
31.5
46.9
18.9
2.7
0.61±0.948
55.5
16.7
16.4
11.3
0.82±1.068
64.7
15.4
13.2
6.7
1.03±0.915
33.4
34.8
25.6
6.2
exposure level
Lower
0.87±0.748
0.54±0.879
0.76±1.047
1±0.915
Middle
1±0.876
0.74±1.061
0.93±1.064
1.15±0.902
High
1.33±0.888
1.42±1.379
1.75±1.215
1.25±0.965
Chi-square
5.289
7.343*
11.375**
2.567
PTSS
Without-PTSS
0.85±0.727
0.54±0.877
0.75±1.013
0.98±0.879
With-PTSS
2.43±0.646
2.43±0.938
2.64±0.842
2.5±0.519
Z value
5.826***
5.923***
5.485***
5.407***
Note: Each item of the four components of PSQI weighted equally on a 0-3 scale; higher
scores indicate a worse degree. * means P value less than 0.01; ** means P value less than
0.01; *** means P value less than 0.001

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Table 4 Correlations among exposure level, sleep quality components and PTSS.
1
2
3
4
5
6
7
8
9
1.exposure
1.000
2.sleep
satisfaction
.123*
1.000
3.sleep
disturbance
.158**
.529**
1.000
4.sleep latency
.169**
.618**
.595**
1.000
5.sleep duration
0.080
.527**
.357**
.358**
1.000
6.Criterion B
0.030
.313**
.229**
.198**
.157**
1.000
7.Criterion C
0.096
.202**
.218**
.186**
.123*
.328**
1.000
8.Criterion D
0.020
.275**
.197**
.216**
.145**
.232**
.334**
1.000
9.Criterion E
.154**
.361**
.375**
.399**
.170**
.285**
.339**
.477**
1.000
Note: * means p < 0.05 ;** means P< 0.01.

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Table 5. Regression analysis with the total score of PCL-5 for HCWs as the outcome variable
Model
B
SE
Beta
t
P value
R
square
1
Constant
8.697
3.048
2.853
0.005
0.021
Gender
2.752
1.017
0.140
2.707
0.007
Age
-0.033
0.052
-0.033
-0.629
0.530
Education
0.727
1.048
0.036
0.694
0.488
2
Constant
8.387
3.030
2.768
0.006
0.037
Gender
2.802
1.010
0.143
2.774
0.006
Age
-0.046
0.052
-0.045
-0.881
0.379
Education
0.785
1.041
0.039
0.753
0.452
exposure
2.409
0.974
0.128
2.473
0.014
3
Constant
3.035
2.631
1.154
0.249
0.298
Gender
1.754
0.868
0.089
2.02
0.044
Age
-0.028
0.045
-0.028
-0.636
0.525
Education
0.779
0.890
0.039
0.875
0.382
exposure
0.686
0.846
0.036
0.811
0.418
sleep quality
1.700
0.146
0.522
11.649
0.000
Note: Dependent variable was the total score of PCL-5