Contents lists available at ScienceDirectPsychiatry Resear

Contents lists available at ScienceDirect
Psychiatry Research
journal homepage: www.elsevier.com/locate/psychres
Factors associated with depression, anxiety, and PTSD
symptomatology
during the COVID-19 pandemic: Clinical implications for U.S.
young adult
mental health
Cindy H. Liu (PhD)a,c,d,⁎ , Emily Zhang (MA)a,c, Ga Tin Fifi
Wong (BA)a,c, Sunah Hyun (PhD)a,c,
Hyeouk “Chris” Hahm (PhD)b,c
a Department of Newborn Medicine, Brigham and Women's
Hospital, Boston, MA, USA
b Department of Psychiatry, Brigham and Women's Hospital,
Boston, MA, USA
c School of Social Work, Boston University, Boston, MA, USA
d Harvard Medical School
A R T I C L E I N F O
Keywords:
Psychological stress, Loneliness
University health services
Social support
Ethnicity
COVID-19
Depression

Anxiety
PTSD
A B S T R A C T
This study sought to identify factors associated with depression,
anxiety, and PTSD symptomatology in U.S.
young adults (18-30 years) during the COVID-19 pandemic.
This cross-sectional online study assessed 898
participants from April 13, 2020 to May 19, 2020,
approximately one month after the U.S. declared a state of
emergency due to COVID-19 and prior to the initial lifting of
restrictions across 50 U.S. states. Respondents
reported high levels of depression (43.3%, PHQ-8 scores ≥ 10),
high anxiety scores (45.4%, GAD-7 scores ≥
10), and high levels of PTSD symptoms (31.8%, PCL-C scores ≥
45). High levels of loneliness, high levels of
COVID-19-specific worry, and low distress tolerance were
significantly associated with clinical levels of de-
pression, anxiety, and PTSD symptoms. Resilience was
associated with low levels of depression and anxiety
symptoms but not PTSD. Most respondents had high levels of
social support; social support from family, but not
from partner or peers, was associated with low levels of
depression and PTSD. Compared to Whites, Asian
Americans were less likely to report high levels across mental
health symptoms, and Hispanic/Latinos were less
likely to report high levels of anxiety. These factors provide
initial guidance regarding the clinical management
for COVID-19-related mental health problems.
1. Introduction
The COVID-19 pandemic that has upended the lives of
individuals
worldwide escalated in the U.S. beginning in March of 2020.

Although
research on acute and widescale stressors (e.g., natural
disasters), de-
monstrates severe implications for mental health (Kessler et al.,
2008),
there is no precedent for understanding the mental health effects
due to
COVID-19, as prospective studies investigating the effects of a
pan-
demic are virtually non-existent. In particular, the identification
of risk
factors associated with depression, anxiety, and post-traumatic
stress
disorder (PTSD) among U.S. young adults (18-30 years) during
the
pandemic is urgently needed. Comprising more than one-third
of the
current U.S. workforce, young adults (often referred to as
“Millennials”
and “Generation Z”) will be a dominant workforce group for the
next
decade, and our societal functioning depends on how they
emerge from
the pandemic. Understanding their health and well-being now is
crucial
as it sets the stage for later outcomes.
Certain risk and protective factors are likely to be implicated in
pandemic-related mental health. COVID-19-related worry (e.g.,
main-
taining employment, getting tested for coronavirus) may be
linked to
mental health symptoms. The early weeks of the pandemic saw
rapid
changes in daily routines, with students moving following

university
closures and attending classes remotely, and for other young
adults,
transitioning to remote work or experiencing loss of work.
These dis-
ruptions may put an already vulnerable group at greater risk for
mental
health challenges (Conrad, 2020). Furthermore, loneliness may
be
particularly prevalent and devastating during the pandemic
given di-
rectives for social distancing and isolation. Those under the age
of 25
already show elevated levels of loneliness (Domagala-Krecioch
and
Majerek, 2013), and the pandemic may exacerbate these
feelings. De-
spite the critical role that social support plays in mitigating the
risks to
mental health problems, directives on social distancing may
impede on
https://doi.org/10.1016/j.psychres.2020.113172
Received 28 April 2020; Received in revised form 30 May
2020; Accepted 30 May 2020
⁎ Corresponding author.
E-mail address: [email protected] (C.H. Liu).
Psychiatry Research 290 (2020) 113172
Available online 01 June 2020
0165-1781/ © 2020 Elsevier B.V. All rights reserved.
T

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one's typical means for obtaining such support.
Individual resilience, which refers to one's ability to cope with
stress, and distress tolerance, which describes one's ability to
manage
and tolerate emotional distress, may be salient characteristics
that
protect against the mental health symptoms that follow major
stressors.
Individual resilience is a significant protective factor for
depression,
PTSD, and general health after natural disasters (Kukihara et
al., 2014).
Findings have generally demonstrated distress tolerance to be
asso-
ciated with lower symptoms of depression and PTSD following
torna-
does (Cohen et al., 2016). However, the extent to which these
factors
are associated with mental health sympto ms during a pandemic
is un-
known.
This study sought to identify potential factors that contribute to
mental health outcomes among young adults during the COVID-
19

pandemic. The CARES 2020 Project (COVID-19 Adult
Resilience
Experiences Study, www.cares2020.com) was launched to track
the
health and well-being of young adults in the U.S. across
multiple time
points in 2020 and 2021. This present analysis assessed
depression,
anxiety, and PTSD symptomatology, and psychological
experiences
including distress tolerance, resilience, social support, and
loneliness.
We included depression and anxiety as these are common
mental health
symptoms among young adults (Blazer et al., 1994; Chen et al.,
2019;
Eisenberg et al., 2007; Liu et al., 2019; Mojtabai et al., 2016) .
We as-
sessed PTSD symptoms given documented high rates of trauma
by
young adulthood (Costello et al., 2002; Reynolds et al., 2016;
Vrana and
Lauterbach, 1994); a concern was that the pandemic would
either
create and/or exacerbate symptoms related to prior trauma
(Breslau et al., 2008, 1999; Brunet et al., 2001). New items that
as-
sessed COVID-19-specific concerns were also included. The
objective of
this work is to identify salient psychosocial risks for mental
health
symptoms and to prioritize intervention targets for addressing
mental
health symptoms among young adults.
2. Methods

2.1. Study population
This present cross-sectional study assessed potential risk and
pro-
tective factors for mental health outcomes based on preliminary
CARES
2020 data obtained from Wave 1 data collection (N = 898)
conducted
from April 13, 2020 to May 19, 2020, approximately one month
after
the U.S. declared a state of emergency due to COVID-19 and
prior to the
initial lifting of restrictions across 50 U.S. states. Eligible
participants
were young adults aged 18 to 30 years currently living in the
U.S. or
receiving education from a U.S. institution. Participants were
recruited
online via email list serves, social media, and word of mouth
(i.e., list
serves and Facebook groups for school organizations or clubs,
alumni
groups, classes, churches). This took place initially through
organiza-
tions from the New England area before additional list serves
from
other regions of the U.S. (Midwest, South, and West) were
targeted.
Respondents were asked to complete a 30-minute online
Qualtrics
survey regarding COVID-19-related experiences, risk and
resilience,
and physical and mental health outcomes. To ensure data
quality,
human verification and attention checks were implemented

throughout
the survey; the data were further inspected visually for response
irre-
gularities indicative of bots. Participants were compensated via
raffle in
which one out of every 10 participants received a $25 gift card.
All
procedures were approved by the Institutional Review Board at
Boston
University.
2.2. Measures
Binary scores were created after calculating the mean or sum of
each measure. Rather than relying on the sample characteristics
to
categorize our data (e.g., mean, median, tertile or quartile s plit),
the
determination of the cutoff score was based on standard cutoffs
from
previous research; when a standard was not available, scale
response
descriptors to determine the cutoffs.
2.2.1. Risk and protective factors
Psychological resilience was measured using the 10-item
Connor-
Davidson Resilience Scale (CD-RISC-10, Connor and Davidson,
2003),
which assesses one's ability to cope with adverse experiences.
Partici-
pants indicated how they felt in the past month on a 5-point
scale, with
0 indicating “not true at all” and 4 indicating “true nearly all

the time.”
Sum scores were recoded dichotomously into “high resilience”
and “low
resilience” with a cutoff score of 30 or greater. This cutoff
score char-
acterizes responses that tended to be “often true” and “true
nearly all
the time,” with those endorsing a score ≥30 considered to be at
“very
high risk with mental disorders” (Andrews and Slade, 2001;
Kessler and
Mroczek, 1992).
The Distress Tolerance Scale is a 15-item measure that assesses
participants’ abilities to withstand and cope with emotional
distress
(Simons and Gaher, 2005). Respondents rated personal attitudes
to-
wards feelings of emotional distress on a 5-point scale, ranging
from 1
(“strongly agree”) to 5 (“strongly disagree”), with higher
ratings in-
dicating greater distress tolerance. A global mean score of
distress tol-
erance was calculated. We considered the scale descriptors and
fol-
lowed the cutoffs used for the CD-RISC, which was also a 5-
point scale.
As such, scores were dichotomously recoded so that global
mean scores
less than 4 indicated “low distress tolerance” and scores of 4-to-
5 in-
dicated “high distress tolerance.”
Perceived social support was measured using the
Multidimensional

Scale of Perceived Social Support (MSPSS, Zimet et al., 1988),
in which
participants rated perceived emotional support using a 7-point
Likert
scale ranging from 1 (“very strongly disagree”) to 7 (“very
strongly
agree”). This measure includes three subscales assessing
perceived
support quality from family, friends, and partners. Because
mean scores
greater than 5 reflected responses indicating “mildly agree,”
“strongly
agree,” and “very strongly agree,” each subscale mean scores
were re-
coded so that scores 5 or greater referred to “high perceive d
social
support,” and scores below 5 were referred to as “low perceived
social
support.”
Instrumental support was assessed through a 4-item subscale of
the
Two-Way Social Support Scale (Shakespeare-Finch and Obst,
2011).
Participants indicated the extent of they received instrumental
support
based on a 6-point Likert scale ranging from 0 (“not at all”) to 5
(“al-
ways”). Items were summed to create a total score with a
possible range
of 0 to 20. Given scale descriptors, a cutoff score with a sum of
16 or
greater indicated “high instrumental support,” whereas scores
lower
than 16 indicated “low instrumental support.”

Loneliness was measured using an adapted 3-item version of the
UCLA Loneliness Scale Short Form (Hughes et al., 2004).
Participants
rated lack of companionship, feelings of being left out, and
isolation
from others on a scale of 1-to-3, with 1 as “hardly ever,” 2 as
“some of
the time,” and 3 as “often.” A sum score for loneliness was
calculated
with a total possible range of 3 to 9 and recoded dichotomously;
a
cutoff score of 6 or greater indicated “high loneliness” as used
in prior
studies (Lowthian et al., 2016; Tymoszuk et al., 2019).
Severity of COVID-19 pandemic-related worry was assessed
using a
newly developed measure consisting of 6 items, which included
the
following concerns: “Having enough groceries during city
lockdowns/
social distancing protocols”, “obtaining a COVID-19 test if I
become
sick”, “getting treated for COVID-19 if I contract it”, “keeping
in touch
with loved ones during social distancing protocols”,
“maintaining em-
ployment during the subsequent economic downturn”, and
“having
enough money to pay for rent and buy basic necessities.”
Participants
were asked to indicate their level of worry for each item on a
scale of 1
to 5, with 1 being “not worried at all,” and 5 being “very
worried.” Sum
scores were calculated with a total possible range of 6 to 30 and

re-
coded into a dichotomous variable with a cutoff score of 24 or
greater
as “highly worried.” Cronbach's alpha for measure items was
.70,
C.H. Liu, et al. Psychiatry Research 290 (2020) 113172
2
http://www.cares2020.com
indicating good reliability.
2.2.2. Mental health outcomes
Depression was assessed with the 8-item version of the Patient
Health Questionnaire (PHQ-8, Kroenke et al., 2009) which
assessed
frequency of depressive symptoms in the past two weeks on a
scale of 0
(“not at all”) to 3 (“nearly every day”). Sum scores of the PHQ-
8 had a
total possible range of 0 to 24 and were recoded dichotomously
based
on a cutoff score of 10 or higher (Wu et al., 2019).
Anxiety was assessed with the Generalized Anxiety Disorder
Scale
(GAD-7, Spitzer et al., 2006) a widely used measure assessing
the fre-
quency of anxiety symptoms in the past two weeks on a scale of
0 to 3,
with 0 being “not at all” and 3 being “nearly every day.” Sum
scores

ranged from 0 to 21. Following the convention of other studies
(Plummer et al., 2016), responses were recoded dichotomously
based
on a cutoff score of 10 or higher to determine elevated anxiety.
The PTSD Checklist—Civilian Version (PCL-C), a validated
17-item
measure, was administered to assess PTSD symptoms (Weathers
et al.,
1993). Participants indicated how much they were bothered by
pro-
blems and experiences in response to stressful life events in the
past
month, with 1 as “not at all” and 5 as “extremely.” Sum scores
of the 17
items were calculated and created into a dichotomous variable
with a
cutoff score of 45 or greater, based on the psychometric
properties for
the measure and as suggested by the National Center for PTSD
(Blanchard et al., 1996).
2.2.3. Statistical analyses
The variables were normally distributed, with predictors
indicating
acceptable levels of collinearity (VIF < 5). To identify potential
risk
and protective factors of mental health symptoms, three logistic
re-
gression models were performed to examine depression,
anxiety, and
PTSD symptoms as primary outcomes. Resilience, distress
tolerance,
perceived social support, instrumental social support,
loneliness, and

COVID-19-specific worry were entered as predictors in
unadjusted
models. Age, gender, income, and race were entered in each of
the three
adjusted models. All variables were binary with exception to
age and
income, which were continuous. Two-tailed p-values were used.
To
guard against Type I error, Bonferroni-adjustments were made
to con-
sider the 8 predictors and 4 covariates used in each model (.05/
12=.004). Our results and interpretations are therefore based on
a
significance set at p<.004 (note that the significance in the
tables re-
main unadjusted to provide more rather than less information to
the
reader). All analyses were performed using SPSS 25.0.
3. Results
Table 1 shows demographic characteristics of our participants
and
descriptive data on all predictors and outcomes. The sample was
ra-
cially and ethnically diverse, with 59.6% White, 21.2% Asian,
5.3%
Black, 6.0% Hispanic/Latino, 0.1% AI/NA, 6.2% mixed race,
and 1.4%
indicating another race. The majority of respondents were
women
(81.3%), U.S.-born (86.3%), employed (66.7%), students
(61.3%), and
those who earned less than $50,000 per year (82.1%). Among
those
identifying as students, 89.7% were enrolled as full-time and

7.3% were
international students. Overall, participants scored as having
high
loneliness (61.5%), low resilience (72.0%), and low distress
tolerance
(74.1%). At the same time, the majority of respondents reported
having
high levels of social support (family, partners, peer, and
instrumental).
Finally, 43.3% of our sample had high levels of depression
(PHQ-8
scores ≥ 10), 45.4% had high anxiety scores (GAD-7 scores ≥
10) and
31.8% had high levels of PTSD symptoms (PCL-C scores ≥ 45).
Table 2 displays the associations between predictors and mental
health outcomes in each of the three models adjusted for the
age,
gender, race, and income. The results described here pertain
only to
significance set at p<.004 with Bonferroni corrections.
Predictors that
were significantly associated with depression, anxiety, and
PTSD
Table 1
Demographic characteristics and variable descriptives from
Wave 1 of CARES
2020.
Factors Means (range) or %
Age (years) 24.5 (18.0 – 30.9)
18-21 28.6 %
22-26 34.7 %
26-30 36.6 %

Gender
Men 14.1 %
Women 81.3 %
Other gender 4.6 %
Race
White 59.6 %
Asian 21.2 %
Black 5.3 %
Hispanic or Latinx 6.0 %
American Indian/Native American 0.1 %
Mixed 6.2 %
Other 1.4 %
U.S.-born
Yes 86.3 %
No 13.7 %
Employed
Yes 66.7 %
No 33.3 %
Individual Income (USD/year)
No income 11.8 %
< $25,000 45.9 %
$25,000 - $49,999 24.4 %
$50,000 – $74,999 11.6 %
$75,000 – $99,999 2.6 %
$100,000 – $124,999 2.1 %
$125,000 – $149,999 0.3 %
$150,000 - $174,999 0.3 %
$175,000 - $199,999 0.6 %
$200,000 - $249,999 0.2 %
≥$250,000 0.2 %

Student
Yes 61.3 %
No 38.7 %
Student Enrollment Status (students only)
Full time 89.7 %
Part time 8.7 %
Other 1.6 %
International Student
Yes 7.3 %
No 92.7 %
Loneliness (LS-SF) 6.1 (3.0 – 9.0)
<6 38.5 %
≥6 61.5 %
COVID-19-specific worry 15.9 (6.0 – 30.0)
<24 89.9 %
≥24 10.1 %
Resilience (CD-RISC-10) 26.0 (4 – 40)
<30 72.0 %
≥30 28.0 %
Distress tolerance (DTS) 3.3 (1.0 – 5.0)
<4 74.1 %
≥4 25.9 %
Family social support (MSPSS) 5.1 (1.0 – 7.0)
<5 37.3 %
≥5 62.7 %
Partner social support (MSPSS) 5.6 (1.0 – 7.0)
<5 26.3 %
≥5 73.7 %

Peer social support (MSPSS) 5.7 (1.0 - 7.0)
<5 16.9 %
≥5 83.1 %
Instrumental social support (2-Way SSS) 16.6 (1.0 – 20.0)
<16 30.1 %
≥16 69.9 %
Depression (PHQ-8) 9.0 (0 – 24.0)
<10 56.7 %
≥10 43.3 %
Anxiety (GAD-7) 9.4 (0 - 21.0)
<10 54.6 %
(continued on next page)
3
included loneliness (OR range = 1.98 – 2.72), COVID-19-
specific worry
(OR range = 2.87 – 5.05), and distress tolerance (OR range =
0.22 –
0.42). Specifically, those who endorsed high levels of loneliness
and
worries about COVID-19 and low levels of distress tolerance
were more
likely to score above the clinical cutoffs for depression,
anxiety, and
PTSD. Those with high levels of resilience were less likely to
score

above the cutoff for depression and anxiety. Those with high
levels of
family support were less likely to score above the clinical cutoff
for
depression and PTSD (OR = 0.46 and 0.44, respectively).
Instrumental
support was negatively associated with depression. No
associations
were obtained between support from partners and friends.
In analyses of associations between covariates and outcomes,
age
and income were not associated with depression, anxiety, or
PTSD.
With regard to gender, men who identified as transgender were
more
likely to report high levels of PTSD (OR = 4.20, CI = 1.62 –
10.89,
p=.003); no differences were observed between men and
women. Asian
Americans compared to Whites were less likely to report high
levels of
depression (OR = 0.50, CI = 0.33 – 0.76, p=.001) and PTSD
(OR = 0.40, CI = 0.25 – 0.64, p<.001). Asians Americans and
Hispanic/Latinos were less likely to report high levels of
anxiety
(OR = 0.35, CI = 0.24 – 0.53, p<.001, OR = 0.35, CI = 0.18 –
0.68,
p=.00, respectively).
4. Discussion
Our findings highlight major psychological challenges faced by
young adults during the initial weeks of the COVID-19
pandemic. At

least one-third of young adults reported having clinically
elevated le-
vels of depression (43.3%), anxiety (45.4%), and PTSD
symptoms
(31.8%). The rates of depression, anxiety, and PTSD in our
study are
considerably higher compared to prior studies that have used the
same
cut points (PHQ-8 ≥ 10; GAD-7 ≥ 10; and PCL-C ≥ 45). For
instance,
PHQ-8 data collected from a study on U.S. adults in 2006
yielded a
prevalence of 6.2% among 18-24-year-olds and a prevalence of
13.1%
among 25-34-year-olds (Kroenke et al., 2009). Studies using the
GAD-7
showed the following rates among similar groups: U.S. primary
care
patients (23.0%; Spitzer et al., 2006), U.S. college students
(21.0%;
Martin et al., 2014), and U.S. non-veteran community college
students
(17.4%; Fortney et al., 2016). Finally, studies using a cutoff of
≥ 45 on
the PCL-C to assess PTSD in trauma survivors showed the
following
rates: U.S. patients following hospital discharge from traumatic
ortho-
pedic injury after one year (22.0%; Archer et al., 2016) and
survivors
from the Wenchuan, China earthquake also after one year
(26.3%;
Zhang et al., 2011). The high rates from our sample may reflect
ongoing
distress, as we measured the symptoms in the weeks following
the

government directives for closures. Young adults may have
been par-
ticularly distressed in managing school or work responsibilities
during
this time while having no sense of certainty regarding the
pandemic's
end. As well, the high rate of mental health concerns among
study
participants may be partially attributable to the specific
characteristics
of our sample; given that the study was launched on the East
Coast, our
young adult respondents may have been located at pandemic
“hot
spots,” with proximity to a greater number of COVID-19 cases
poten-
tially being an added stressor for our sample.
Strikingly, the majority of respondents reported feeling lonely
during the first two months of the pandemic, as well as having
low
resilience and low ability to tolerate distress. However, the
majority
reported having social support from family, partners, and peers,
as well
as instrumental support during this time. We note that the
absolute
rates of low perceived social support seem problematic. For
instance,
approximately 37% of respondents reported low family support.
These
Table 1 (continued)
Factors Means (range) or %

≥10 45.4 %
PTSD (PCL-C) 38.3 (17.0 – 85.0)
<45 68.2 %
≥45 31.8 %
N = 898
Table 2
Odds ratios and confidence intervals for mental health outcomes
from Wave 1 of CARES 2020.
Factors PHQ-8 – DepressionAdjusted ORa(95% CI) GAD-7 –
AnxietyAdjusted ORa(95% CI) PTSD AdjustedAdjusted
ORa(95% CI)
Loneliness (LS-SF)
<6 1.0 1.0 1.0
≥6 2.72 (1.92 – 3.87) ⁎ ⁎ ⁎ 1.98 (1.41 – 2.77) ⁎ ⁎ ⁎ 2.31 (1.55
– 3.43) ⁎ ⁎ ⁎
COVID-19-specific worry
<24 1.0 1.0 1.0
≥24 2.87 (1.67 – 4.94) ⁎ ⁎ ⁎ 4.12 (2.33 – 7.29) ⁎ ⁎ ⁎ 5.05 (2.92
– 874) ⁎ ⁎ ⁎
Resilience (CD-RISC-10)
<30 1.0 1.0 1.0
≥30 0.56 (0.38 – 0.83) ⁎ ⁎ 0.44 (0.30 – 0.64) ⁎ ⁎ ⁎ 0.70 (0.46 –
1.07)
Distress tolerance (DTS)
<4 1.0 1.0 1.0
≥4 0.36 (0.24 – 0.54) ⁎ ⁎ ⁎ 0.42 (0.28 – 0.62) ⁎ ⁎ ⁎ 0.22 (0.13
– 0.37) ⁎ ⁎ ⁎
Family social support (MSPSS)

<5 1.0 1.0 1.0
≥5 0.46 (0.32 – 0.66) ⁎ ⁎ ⁎ 0.64 (0.44 – 0.91)* 0.44 (0.30 –
0.64)⁎ ⁎ ⁎
Partner social support (MSPSS)
<5 1.0 1.0 1.0
≥5 1.26 (0.84 – 1.88) 1.32 (0.89 – 1.96) 1.00 (0.66 – 1.52)
Peer social support (MSPSS)
<5 1.0 1.0 1.0
≥5 1.05 (0.68 – 1.62) 1.27 (0.83 – 1.96) 0.88 (0.56 – 1.39)
Instrumental social support (2-Way SSS)
<16 1.0 1.0 1.0
≥16 0.60 (0.41 – 0.86)⁎ ⁎ 0.67 (0.46 – 0.96)* 0.63 (0.43 –
0.93)*
N = 898
⁎ p<.05
⁎ ⁎ p<.01
⁎ ⁎ ⁎ p<.001 (two-tailed, without Bonferroni adjustment),
a Adjusted covariates include age, race, gender, individual
income
4
findings highlight major psychological challenges currently
faced by
young adults during the initial weeks of the COVID-19
pandemic.
Our study also identified factors associated with clinical levels

of
depression, anxiety, and PTSD symptoms. High loneliness and
low
distress tolerance levels were consistently associated with high
levels of
depression, anxiety, and PTSD. High levels of resilience were
associated
with low anxiety. Social support from family was associated
with low
levels of depression and PTSD symptoms, whereas support from
part-
ners or friends was not associated with any mental health
outcomes.
High levels of instrumental support were associated with low
levels of
depression.
Our data is consistent with findings demonstrating loneliness as
a
risk factor for mental health (Banerjee et al., 2020; Hawkley
and
Cacioppo, 2010; Okruszek et al., 2020); this is particularly
salient with
government directives for social distancing and isolation.
Feeling cut off
from social groups may lead one to feel vulnerable and
pessimistic
about one's circumstances, altogether producing negative mood
states
and anxiety (Muyan et al., 2016) that are further heightened
during a
pandemic. The high levels of reported loneliness in our sample
and its
association with depression, anxiety, and PTSD symptoms
underscore
the severity of experiences of young adults during the

pandemic.
Distress tolerance, or one's ability to manage and tolerate
emotional
distress, was strongly associated low levels of depressive and
anxiety,
and PTSD symptoms; individual resilience was associated with
low le-
vels of depression and anxiety symptoms, but not PTSD.
Individual
resilience, which encompasses personal competence and trust in
one's
instincts (Connor and Davidson, 2003), has been associated with
low
levels of depression, anxiety, and PTSD symptomatology after
disasters
(Blackmon et al., 2017). One's perceived ability to tolerate
negative or
aversive emotional and/or physical states may be more
protective than
the personal qualities that comprise psychological resilience,
especially
for those experiencing symptoms of PTSD during a pandemic.
The
pandemic is worldwide stressor without a foreseeable endpoint,
and the
effects of the pandemic cannot be controlled by a single
individual.
Furthermore, the pandemic simultaneously impacts various
domains
(e.g., financial, relational, and health) with this stress
potentially ex-
acerbating the sensations associated with PTSD symptoms. As
such,
psychological resilience that is typically associated with
overcoming

setbacks may not be sufficient for protecting against PTSD
symptoms
within the first several weeks of a widespread pandemic.
Interventions
that target distress tolerance, such as mindfulness-based
interventions,
may be more effective than cognitive interventions targeting
core be-
liefs about the self especially for those with PTSD symptoms
(Nila et al.,
2016). Longitudinal approaches would help to examine this
possibility
further.
Emotional support from family but not from friends and
significant
others was associated with low levels of depression and PTSD.
Friends
and significant others may have or are perceived to have less
capacity
to validate other's emotional experiences during a pandemic,
con-
sidering that they may be young adults who are experiencing
similar
struggles. Emotional support provided by family may be more
stable
and coupled with the provision of material resources that young
adults
may still receive from parents. Our findings are consistent with
prior
work showing that family support but not friend and partner
support
mediates the effects of stress on health (Lee et al., 2018).
Family sup-
port may be more meaningful in providing reassurance to young
adults,

considering the possible concrete needs during the pandemic.
Instrumental support, or tangible assistance, may be an
important
factor for the mental health of young adults during the
immediate
weeks of the COVID-19 pandemic onset given that many were
faced
with acute disruptions, such as unemployment, financial stress,
and
relocation following university campus closures. However,
instru-
mental support was not significantly associated with any of the
out-
comes after adjusting the p-value to .004. Additional research is
needed
to clarify the respective roles on both emotional and instrument
support
given variations in their potential effects on depression,
anxiety, and
PTSD.
Our newly developed COVID-19-related worry measure
uniquely
predicted mental health symptoms, underscoring how the
specific fea-
tures of this pandemic give rise to acute stress. The stress
resulting from
lifestyle changes due to features of COVID-19 itself may lead
to greater
mental health concerns distinct from the endorsement of other
risks.
Our analyses showed that the six items in our measure were
reliable,
and the total subscale score was significantly associated with

the
symptoms assessed in this study; however, additional work is
required
to determine the validity of this measure.
In general, Asian Americans were less likely to report high
levels of
mental health symptoms compared to Whites, with
Hispanic/Latinx
respondents also being less likely to report high anxiety. Asian
and
Latinx immigrants compared to those who are born in the U.S.
are less
likely to endorse psychological distress (Dey and Lucas, 2006;
Takeuchi et al., 2007). It is possible that other experiences such
as
ethnic identity, social networking, and family cohesion serve as
a pro-
tective factor for mental health, especially for non-U.S.-born
partici-
pants (Leong et al., 2013). The under-recognition of distress
symptoms
may also be possible among ethnic minorities (Liu et al., 2020).
Al-
though our sample size of gender minorities was small, men
who
identified as transgender were more likely to report a high level
of
PTSD symptoms, consistent with prior research (Reisner et al.,
2016;
Shipherd et al., 2011). Greater attention to gender differences in
mental
health symptoms as well as a deeper study regarding the
specific ex-
periences faced by racial/ethnic and gender minorities during
pan-

demic is warranted.
The cross-sectional design limits our ability to infer causality
in-
volved in leading to mental health problems. We used a
convenience
sample, and caution must be taken in the generalizability of our
find-
ings to the broader population of young adults in the U.S. given
the
uneven sampling of subgroups. The reliance of self-report itself
has
limitations, such that it may be prone to misinterpretation.
Future
analyses with the anticipated waves of data collection will
enable us to
examine the association of our predictors to outcome measures
of
mental health and to adjust for additional confounds. As well,
we will
have an opportunity to examine potential moderation effects to
un-
derstand whether outcomes vary by circumstances or individual
char-
acteristics, such as socioeconomic capital, social support type,
distress
tolerance, and resilience.
To our knowledge, our study is the first prospective cohort
study to
assess mental health outcomes and risk and resilience factors in
U.S.
young adults during the first several weeks of the COVID-19
pandemic.
In our study, one in three U.S. young adults reported clinical
cut-off

symptoms of depression, anxiety, and PTSD as well as high
levels of
loneliness. We present new evidence that signifies the roles of
lone-
liness, distress tolerance, family support, and COVID-19-related
worry
on mental health outcomes during the first month of the
COVID-19
pandemic. Mental health interventions should incorporate these
con-
structs to help mediate the impact of COVID-19 on adverse
mental
health status among U.S. young adults.
CRediT authorship contribution statement
Cindy H. Liu: Conceptualization, Methodology, Formal
analysis,
Investigation, Writing - original draft, Writing - review &
editing,
Project administration, Supervision, Funding acquisition. Emily
Zhang:
Data curation, Writing - original draft, Writing - review &
editing,
Project administration. Ga Tin Fifi Wong: Data curation,
Writing -
original draft, Project administration. Sunah Hyun: Writing -
review &
editing. Hyeouk “Chris” Hahm: Conceptualization, Writing -
review &
editing, Supervision, Funding acquisition.
Declaration of Competing Interest
There are no conflicts of interest to declare.

5
Acknowledgments
Support for this manuscript was provided through the National
Science Foundation (2027553) award (to C.H.L. and H.C.H.), a
Mary A.
Tynan Faculty Fellowship and a NIMH K23 MH 107714-01 A1
award
(to C.H.L.), as well as a T32 MH 16259-39 award (to. S.H.).
Supplementary materials
Supplementary material associated with this article can be
found, in
the online version, at doi:10.1016/j.psychres.2020.113172.
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with depression, anxiety, and PTSD symptomatology during the
COVID-19 pandemic: Clinical implications for U.S. young adult
mental healthIntroductionMethodsStudy
populationMeasuresRisk and protective factorsMental health
outcomesStatistical analysesResultsDiscussionCRediT
authorship contribution statementDeclaration of Competing
Interestmk:H1_13Acknowledgmentsmk:H1_15Supplementary
materialsReferences
Competency
Relate one's moral framework to notable ethical theories on the
topic of justice.
Instructions
The topic of justice manifests itself in a variety of ways, and is
often discussed in broad terms. What does justice mean to you?
In this assessment you will address the subject of justice and
related ethical theories. In a properly formatted, researched
paper, you need to address the following questions:
· What does justice mean to you?
· What do you believe is a good foundation for justice?
· What is Rawls’ foundation of justice and how does it relate to
what justice means to you?
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Contents lists available at ScienceDirect
Psychiatry Research
journal homepage: www.elsevier.com/locate/psychres
Review article
PTSD symptoms in healthcare workers facing the three
coronavirus
outbreaks: What can we expect after the COVID-19 pandemic
Claudia Carmassia, Claudia Foghia, Valerio Dell'Ostea,b,⁎ ,
Annalisa Cordonea,
Carlo Antonio Bertellonia, Eric Buic, Liliana Dell'Ossoa
a Department of Clinical and Experimental Medicine,
University of Pisa, Pisa, Italy
b Department of Biotechnology Chemistry and Pharmacy,
University of Siena, Siena, Italy
c Department of Psychiatry, Massachusetts General Hospital,
Harvard Medical School, Boston, MA, USA
A R T I C L E I N F O
Keywords:
Corona
Mental health

Nurses
Physicians
Psychological distress
Stress
A B S T R A C T
The COronaVIrus Disease-19 (COVID-19) pandemic has
highlighted the critical need to focus on its impact on
the mental health of Healthcare Workers (HCWs) involved in
the response to this emergency. It has been con-
sistently shown that a high proportion of HCWs is at greater
risk for developing Posttraumatic Stress Disorder
(PTSD) and Posttraumatic Stress Symptoms (PTSS). The
present study systematic reviewed studies conducted in
the context of the three major Coronavirus outbreaks of the last
two decades to investigate risk and resilience
factors for PTSD and PTSS in HCWs. Nineteen studies on the
SARS 2003 outbreak, two on the MERS 2012
outbreak and three on the COVID-19 ongoing outbreak were
included. Some variables were found to be of
particular relevance as risk factors as well as resilience factors,
including exposure level, working role, years of
work experience, social and work support, job organization,
quarantine, age, gender, marital status, and coping
styles. It will be critical to account for these factors when
planning effective intervention strategies, to enhance
the resilience and reduce the risk of adverse mental health
outcomes among HCWs facing the current COVID-19
pandemic.
1. Introduction
The outbreak of Corona Virus Disease-19 (COVID) that
emerged in
December 2019 in Wuhan (China), quickly spread outside of

China,
leading the World Health Organization (WHO) Emergency
Committee
to declare a Public Health Emergency of International Concern
(PHEIC)
on January 30th 2020 (Nishiura, 2020), and a pandemic on
March 11,
2020. The SARS-CoV2 – the virus responsible for COVID-19 –
was
isolated by 7th January 2020, and belongs to the same viral
family as
the coronavirus syndrome (SARS-CoV) and the Middle East
respiratory
coronavirus syndrome (MERS-CoV). Both of these coronavirus-
based
respiratory syndromes infected over 10,000 cases in the past
two dec-
ades, with overall mortality rates as high as 11% and 35%,
respectively
(Peeri et al., al.,2020; de Wit et al., 2016; Leung et al., 2004;
WHO, 2004). Compared to the Severe Acute Respiratory
Syndrome
(SARS) and the Middle East Respiratory Syndrome (MERS), the
Corona
Virus Disease-19 (COVID-19) has a greater transmission rate
but a
lower, though still significant, fatality rate (Peeri et al., 2020;
Huang et al., 2020). To date, with more than 14 million infected
worldwide and a spread that is far from being contained,
investigating
the psychological impact of this pandemic on healthcare
workers
(HCWs) including physicians and nurses, has become
increasingly im-
portant.

In the last two decades, first responders’ mental health
outcomes has
been the focus of increasing attention, particularly in the
aftermath of
September 11 2001, terrorist attacks that shed light on the risks
they
are exposed to when operating in emergency settings, as they
may be
affected by physical and mental disorders, such as burnout and
post-
traumatic stress disorder (PTSD) (Perlman et al., 2011;
Carmassi et al.,
2016, 2018; Martin et al., 2017). The DSM-5 (APA, 2013)
indicates that
"experiencing repeated or extreme exposure to aversive details
of the trau-
matic event(s)" can be considered as potentially traumatic
events (cri-
terion A4: e.g. first responders collecting human remains, police
officers
repeatedly exposed to details of child abuse).
Healthcare Workers (HCWs) in emergency care settings are
parti-
cularly at risk for PTSD because of the highly stressful work-
related
situations they are exposed to, that include: management of
critical
medical situations, caring for severely traumatized people,
frequent
witnessing of death and trauma, operating in crowded settings,
inter-
rupted circadian rhythms due to shift work) (Figley, 1995;
Crabbe et al.,

Received 1 May 2020; Received in revised form 18 July 2020;
Accepted 18 July 2020
⁎ Corresponding author at: Department of Clinical and
Experimental Medicine, University of Pisa, Via Roma 67, 56100
Pisa, Italy.
E-mail address: [email protected] (V. Dell'Oste).
Psychiatry Research 292 (2020) 113312
Available online 20 July 2020
0165-1781/ © 2020 Elsevier B.V. All rights reserved.
T
http://www.sciencedirect.com/science/journal/01651781
https://www.elsevier.com/locate/psychres
mailto:[email protected]
http://crossmark.crossref.org/dialog/?doi=10.1016/j.psychres.20
20.113312&domain=pdf
2004; Cieslak et al., 2014; Berger et al., 2012; Hegg-Deloye et
al., 2013;
Garbern et al., 2016). PTSD rates have been reported to range
from 10
to about 20% (Grevin, 1996; Clohessy and Ehlers, 1999;
Robertson and
Perry, 2010; DeLucia et al., 2019), with even higher PTSD rates
(8% to
30%) among Intensive Care Unit (ICU) staff, (Mealer et al.,
2009;

Karanikola et al., 2015; Machado et al., 2018).
Although most individuals prove to be resilient after being
exposed
to a traumatic event (Bonanno et al., 2007), several risk factors
may
compromise the effectiveness of adaptation, including prior
psychiatric
history, female sex, lack of social support (Brewin et al., 1999;
Ozer et al., 2003; Carmassi et al., 2020a, 2020b), having young
children
(Yehuda et al., 2015; Bryant 2019); experiencing feelings of
help-
lessness during the trauma or intensity of emotions when
exposed (i.e.,
anger, peritraumatic distress) (Vance et al., 2018; Carmassi et
al.,
2017). On the other hand, resilience, defined as the capacity to
react to
stress in a healthy way through which goals are achieved at a
minimal
psychological and physical cost (Epstein and Krasner, 2013),
plays a
key role in mitigating the impact of traumatic events and hence
redu-
cing PTSS, enhancing at the same time the quality of care
(Wrenn et al.,
2011; Ager et al., 2012; Haber et al., 2013; McGarry et al.,
2013;
Craun and Bourke, 2014; Hamid and Musa, 2017; Colville et al.,
2017;
Cleary et al., 2018; Winkel et al., 2019).
This interplay of risk and resilience factors becomes even more
complex and challenging when applied in the context of an
infectious

epidemic. This statement is first supported by the fact that, as
previous
studies outlined, during epidemics a high percentage of HCWs,
(up to 1
in 6 of those providing care to affected patients), develops
significant
stress symptoms (Lu et al., 2006; McAlonan et al., 2007) It is
worth
considering that in epidemic contexts HCWs are first in line
facing the
clinical challenges intrinsically linked to the course of the
disease while
under the constant personal threat of being infected or
representing a
source of infection.
The current COVID-19 pandemic is characterized by some
relevant
features that increase the risk for PTSD among HCWs
addressing the
emergency, such as the unprecedented numbers of critically ill
patients,
with an often unpredictable course of the disease, high mortality
rates
and lack of effective treatment, or treatment guidelines (Wang,
2020;
Peeri et al., 2020). Thus, the burden of the current outbreak on
healthcare providers deserves the closest attention, as it is
extremely
likely that health care workers involved in the diagnosis,
treatment and
care of patients with COVID-19 are at risk of developing
psychological
distress and other mental health symptoms (Bao et al., 2020; Lai
et al.,
2020; Carmassi et al., 2020c)

The aim of the present paper is therefore to systematically
review
the studies investigating the potential risk and resilience factors
for the
development of PTSD symptoms in HCWs who faced the two
major
Coronavirus outbreaks that occurred worldwide in the last two
decades,
namely the SARS and the MERS, as well as the ongoing
COVID-19
pandemic, in order to outline effective measures to reduce the
HCWs’
psychiatric burden during the current crisis affecting healthcare
sys-
tems all over the world.
2. Methods
2.1. Search strategy
We reviewed articles indexed in the electronic database PubMed
until 20th April 2020. No time limit was set in regard to the
year of
publication. The search terms were combined with the Boolean
op-
erator as follows: “(Post-traumatic stress OR Post-traumatic
stress dis-
order OR Post-traumatic stress symptoms OR PTSD OR PTSS)
AND
(Severe Acute Respiratory Syndrome OR SARS OR Middle East
Respiratory Syndrome OR MERS OR Corona Virus Disease 19
OR
COVID-19 OR Coronavirus)”. Furthermore, relevant articles
were ex-
tracted from the references section of the manuscripts found in

the
initial search, to complete our search.
2.2. Eligibility criteria
We included articles that met the following inclusion criteria:
ori-
ginal studies on humans investigating possible risk and/or
resilience
factors for PTSD symptoms in HCWs facing the coronavirus
outbreaks
of SARS, MERS and COVID-19. Articles in print or published
ahead of
print were accepted. The exclusion criteria were: (a) studies
involving
general population samples that did not consider a sub-sample
of
HCWs; (b) studies examining other mental health symptoms but
not
PTSS; (c) studies assessing PTSS but not considering potential
risk and
resilience factors; (d) literature reviews; (e) full text not
available; (f)
not available in English.
2.3. Study selection
The first author screened each study for eligibility by reading
the
title and abstract. Any uncertainties about eligibility were
clarified
through discussion among all authors. Decisions for inclusion or
ex-
clusion are summarized in a flowchart according to PRISMA re-
commendations, usually used to conduct meta-analyses and
systematic

reviews of randomized clinical trials, but that have also been
used for
other types of systematic reviews such as our present one
(Moher et al.,
2009).
3. Results
3.1. Process of study selection
The study selection process is outlined in a flow-chart (Fig. 1).
The
electronic database search returned 263 publications. Following
a
preliminary screening of the titles and abstracts, 47 articles
were con-
sidered of potential relevance, their eligibility was assessed by
means of
a full text examination. Twenty-four of these studies, published
be-
tween 2004 and 2020, were included in this review. The main
reasons
for study exclusion were: the absence of a HCW sample or sub-
sample,
the lack of data regarding PTSS and/or about possible risk or
resilience
factors related to psychopathology.
3.2. Characteristics of included studies
The key characteristics of the studies included are summarized
in
Table 1. All retrieved studies were published between January
2004
and April 2020. Nineteen studies were on the SARS 2003
outbreak, two

on the MERS 2012 outbreak, and three on the ongoing Covid-19
out-
break. Nine studies were on a mixed population in which HCWs
re-
presented a sub-sample (Bai et al., 2004; Chong et al., 2004;
Kwek et al., 2006; Reynolds et al., 2007; Lancee et al., 2008;
Wu et al.,
2009; Mak et al., 2010; Wing and Leung, 2012; Li et al., 2020)
while all
other studies included HCWs only. Finally, five studies
included spe-
cifically survivors from the infection (Kwek et al., 2006; Lee et
al.,
2007; Mak et al., 2010; Wing and Leung, 2012; Ho et al., 2005).
3.3. PTSD and PTSS risk factors in HCWs facing the
coronavirus outbreaks
3.3.1. Level of exposure
Ten studies (Chong et al., 2004; Maunder et al., 2004; Lin et
al.,
2007; Su et al., 2007; Styra et al., 2008; Wu et al., 2009; Lee et
al.,
2018; Lai et al., 2020; Kang et al., 2020; Jung et al., 2020)
highlighted
the role of exposure level, such as working in high-risk wards
or in
front-line settings during the Coronavirus outbreaks, as the
major risk
factor for developing PTSS and PTSD. Particularly, they
pointed out the
relevance of perceived threat for health and life and the
experienced
feelings of vulnerability as mediating factors. Most of these
studies re-

ported on the 2003 SARS outbreak. Lin et al. (2007) showed
higher
rates of PTSD (21,7%) among 66 emergency department staff
compared
to 26 HCWs of non-emergency departments (i.e., psychiatric
ward,
C. Carmassi, et al. Psychiatry Research 292 (2020) 113312
2
13%). Wu et al. (2009) investigated a sample of 549 HCWs in
Beijing
(China), including administrative staff, finding 2 to 3 times
higher PTSS
rates among respondents who worked in high-risk locations and
per-
ceived high SARS-related risks, beside an increased risk for
subsequent
alcohol abuse/dependence. This latter resulted significantly
related
with hyper-arousal symptoms. A further study in Toronto (Styra
et al.,
2008) confirmed the impact of operating in a high-risk unit, and
first
reported that caring for only one SARS patient was related to a
higher
risk than caring for multiple SARS patients. A recent study on
147
nurses who worked in MERS units during the outbreak found
higher
PTSD rates among emergency HCWs than among non-
emergency ones
(Jung et al., 2020). To date, two studies have explored this issue

in the
COVID-19 pandemic. Li et al. (2020) found among 526 nurses,
that
those who worked on the frontline appeared to be less prone to
de-
veloping PTSS compared to second-line ones; conversely
Kang et al. (2020) in a large study on 994 HCWs in Wuhan
reported the
exposure level to infected people, more broadly including
colleagues,
relatives or friends, to be a risk factor for mental health
problems, in-
cluding PTSS.
3.3.2. Occupational role
Five studies, four on the SARS epidemic and one on the
COVID-19
pandemic, highlighted the occupational role as a major risk
factor for
PTSD or PTSS in Coronavirus outbreaks. Maunder et al. (2004)
found
on a sample of 1557 HCWs collected in Toronto, higher PTSS
rates
among nurses and explained this finding by means of the longer
contact
and higher exposure to patients of the nursing staff. A study on
96
emergency HCWs, assessed six months after the 2003 SARS
outbreak,
revealed a greater burden of PTSS among nurses than among
physicians
(Tham et al., 2004). A further study by Phua et al. (2005)
confirmed
this finding in a sample of 99 HCWs. Finally, a most recent
study on

1257 hospital physicians and nurses caring for COVID-19
patients
reached the same conclusion (Lai et al., 2020).
3.3.3. Age and gender
Three studies on the SARS outbreak and one on the COVID-19
pandemic reported that younger HCWs had a greater risk of
developing
PTSS (Sim et al., 2004; Su et al., 2007; Wu et al., 2009). From
a wider
perspective, further studies pointed out an association between
fewer
years of work experience and an increased PTSS risk in HCWs,
as de-
scribed in two SARS studies (Chong et al., 2004; Lancee et al.,
2008)
and in one COVID-19 study (Lai et al., 2020). As far as gender
is
concerned, while one recent study on COVID-19 reported a
higher risk
for the female HCWs, a previous study involving 1257 HCWs in
a ter-
tiary hospital affected by SARS found an increased risk of PTSS
among
males (Chong et al., 2004).
3.3.4. Marital status
Three studies focused on the relevance of marital status, two of
which referred to the SARS outbreaks and one to the current
COVID-19
pandemic. Chan and Huak (2004) in a study on 661 HCWs in
Singapore
showed that those who were not married were more adversely

affected
than married ones. In contrast, a further study in Singapore
(Sim et al.,
2004) found a positive association between post-traumatic
morbidities
and being married. Likewise, a recent case control study on
HCWs fa-
cing the COVID-19 pandemic showed that married, divorced or
wi-
dowed operators reported higher scores in vicarious
traumatization
symptoms compared to unmarried HCWs (Li et al., 2020).
3.3.5. Quarantine, isolation and stigma
Three SARS studies on Chinese hospital staff members (Bai et
al.,
2004; Reynolds et al., 2007; Wu et al., 2009) and one on the
MERS
outbreak (Lee et al., 2018) consistently reported high levels of
PTSS
among HCWs who had been quarantined. More specifically,
Bai et al. (2004) examining 338 HCWs in an East Taiwan
hospital found
that 5% of them suffered from acute stress disorder, with
quarantine
being the most frequently associated factor, and a further 20%
felt
stigmatized and rejected in their neighborhood because of their
hospital
work, with also 9% reporting reluctance to work and/or
considering
quitting their job. Similar findings emerged from a Canadian
SARS
study on 1057 subjects (Reynolds et al., 2007), in which
quarantined

HCWs reported more PTSS than non-HCWs quarantined
individuals.
Moreover, in a study on MERS outbreak, Lee et al. (2018)
assessed PTSS
experienced by 359 university HCWs who cared for infected
patients,
observing that quarantined HCWs had a higher risk of
developing PTSS
which persisted over time, particularly sleep and numbness-
related
symptoms. More in general, social isolation and separation from
family
was found to be associated with higher rates of PTSS in SARS
outbreak,
as well as having friends or close relatives with the infection
(Maunder et al., 2004; Chong et al., 2004; Wu et al., 2009).
3.3.6. Previous psychiatric disorders
Three studies on SARS have stressed the presence of previous
psy-
chiatric disorders as a risk factor for the development of PTSS
Fig. 1. PRISMA flowchart of studies selection process.
3
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y
E
m
er
ge
n
cy
H
C
W
s
(d
oc
to
rs
n
=
3
8
;
n
u
rs
es

n
=
5
8
)
Im
p
ac
t
of
E
ve
n
ts
Sc
al
e
IE
S
sc
or
e
≥
2
6
in
1
3

.2
%
d
oc
to
rs
an
d
2
0
.7
%
n
u
rs
es
;
G
en
er
al
H
ea
lt
h
Q
u
es
ti

on
n
ai
re
-2
8
≥
5
in
1
5
.8
%
d
oc
to
rs
an
d
2
0
.7
%
n
u
rs
es
R

is
k
fa
ct
or
s:
n
u
rs
es
H
o
et
al
.
(2
0
0
5
)
SA
R
S
cr
os
s-
se
ct
io
n
al

st
u
d
y
8
2
H
C
W
s
n
ot
in
fe
ct
ed
an
d
9
7
H
C
W
s
w
h
o
re
co
ve

re
d
fr
om
SA
R
S
Im
p
ac
t
of
E
ve
n
ts
Sc
al
e
(C
h
in
es
e
ve
rs
io
n
)

H
C
W
s
re
co
ve
re
d
re
p
or
te
d
h
ig
h
P
T
SS
in
tr
u
si
on
sy
m
p
to
m
s

an
d
m
or
e
co
n
ce
rn
s
ab
ou
t
ot
h
er
h
ea
lt
h
p
ro
bl
em
s
an
d
d
is
cr
im

in
at
io
n
.
H
C
W
s
n
ot
in
fe
ct
ed
h
ad
st
ro
n
ge
r
fe
ar
re
la
te
d
to

in
fe
ct
io
n
th
an
H
C
W
s
re
co
ve
re
d
;
eq
u
al
co
n
ce
rn
ab
ou
t
in

fe
ct
in
g
ot
h
er
s
(e
sp
ec
ia
ll
y
fa
m
il
y
m
em
be
rs
)
th
an
be
in
g
se
lf
-
in

fe
ct
ed
em
er
ge
d
R
is
k
fa
ct
or
s:
be
in
g
H
C
W
s
su
rv
iv
or
s
P
h
u
a
et

al
.
(2
0
0
5
)
SA
R
S
cr
os
s-
se
ct
io
n
al
st
u
d
y
9
9
H
C
V
s
(d
oc

to
rs
n
=
4
1
;
n
u
rs
e
n
=
5
8
)
Im
p
ac
t
of
E
ve
n
ts
Sc
al
e

1
7
.7
%
IE
S
>
2
6
;
R
is
k
Fa
ct
or
:
n
u
rs
es
R
es
il
ie
n
ce
fa
ct

or
s:
p
os
it
iv
e
co
p
in
g
st
yl
es
(h
u
m
or
an
d
p
la
n
n
in
g)
K
w
ek
et

al
.
(2
0
0
6
)
SA
R
S
cr
os
s-
se
ct
io
n
al
st
u
d
y
6
3
H
C
W
s
SA
R
S

su
rv
iv
or
s
Im
p
ac
t
of
E
ve
n
ts
Sc
al
e
4
1
%
sc
or
ed
in
d
ic
at
iv
e

of
P
T
SD
;
3
0
%
li
ke
ly
an
xi
et
y
an
d
d
ep
re
ss
io
n
.
R
is
k
fa

ct
or
:
be
in
g
H
C
W
su
rv
iv
or
s
M
au
n
d
er
et
al
.
(2
0
0
6
)
SA
R
S

cr
os
s-
se
ct
io
n
al
st
u
d
y
7
6
9
H
C
W
s
(S
A
R
S
an
d
n
o-
SA
R
S

u
n
it
s)
Im
p
ac
t
of
E
ve
n
ts
Sc
al
e
SA
R
S
u
n
it
H
C
W
s
re
p
or
te
d

h
ig
h
er
P
T
SS
,
bu
rn
ou
t,
an
d
p
sy
ch
ol
og
ic
al
d
is
tr
es
s
ra
th

er
th
an
n
o-
SA
R
S
u
n
it
H
C
W
s.
SA
R
S
u
n
it
H
C
W
s
m
or
e
re
d
u
ce

d
p
at
ie
n
t
co
n
ta
ct
an
d
w
or
k
h
ou
rs
.
R
is
k
fa
ct
or
s:
m
al
ad
ap

ti
ve
co
p
in
g
st
ra
te
gi
es
(a
vo
id
an
ce
,
h
os
ti
le
co
n
fr
on
ta
ti
on
,

an
d
se
lf
-
bl
am
e)
.
R
es
il
ie
n
ce
fa
ct
or
s:
tr
ai
n
in
g,
Su
p
p
or
t

fr
om
fa
m
il
y/
su
p
er
vi
so
rs
/c
ol
le
ag
u
es
,
w
or
k
or
ga
n
iz
at
io
n
Le

e
et
al
.
(2
0
0
7
)
SA
R
S
co
h
or
t
st
u
d
y
SA
R
S
su
rv
iv
or
s
(n
on
–H

C
W
s
n
=
4
9
;
H
C
W
s
n
=
3
0
)
Im
p
ac
t
of
E
ve
n
t
Sc
al
e–
R

ev
is
ed
P
ar
ti
ci
p
an
ts
w
it
h
at
le
as
t
m
od
er
at
e
P
T
SS
re
p
or
te
d

3
2
.2
%
In
tr
u
si
on
,
2
0
.0
%
A
vo
id
an
ce
,
an
d
2
2
.2
%
H
yp

er
ar
ou
sa
l.
H
C
W
SA
R
S
su
rv
iv
or
s
w
er
e
m
or
e
d
is
tr
es
se
d
th
an
n
on

–H
C
W
on
e
ye
ar
af
te
r
th
e
ou
tb
re
ak
.
R
is
k
fa
ct
or
s:
be
in
g
H
C

W
su
rv
iv
or
s.
Li
n
et
al
.
(2
0
0
7
)
SA
R
S
cr
os
s-
se
ct
io
n
al
st
u
d

y
6
6
em
er
ge
n
cy
H
C
W
s
an
d
2
6
n
o-
em
er
ge
n
cy
H
C
W
s

D
av
id
so
n
T
ra
u
m
a
Sc
al
e-
C
h
in
es
e
ve
rs
io
n
(D
T
S-
C
)
E
m
er
ge

n
cy
H
C
W
s
re
p
or
te
d
>
D
T
S-
C
sc
or
es
th
an
n
o-
em
er
ge
n
cy
H

C
W
s;
2
1
,7
%
em
er
ge
n
cy
H
C
W
s
an
d
1
3
%
n
o-
em
er
ge
n
cy

H
C
W
s
re
p
or
te
d
D
T
S-
C
>
4
0
(s
u
sp
ec
te
d
P
T
SD
).
R
is
k
fa
ct
or
:
le

ve
l
of
ex
p
os
u
re
R
ey
n
ol
d
s
et
al
.
(2
0
0
7
)
SA
R
S
cr
os
s-
se

ct
io
n
al
st
u
d
y
1
0
5
7
qu
ar
an
ti
n
ed
su
bj
ec
ts
(H
C
W
s
n
=
2

6
9
)
Im
p
ac
t
of
E
ve
n
ts
Sc
al
e
–
R
ev
is
ed
1
4
.6
%
IE
S-
R
≥

2
0
;
qu
ar
an
ti
n
ed
H
C
W
s
ex
p
er
ie
n
ce
d
gr
ea
te
r
P
T
SS
th
an
qu

ar
an
ti
n
ed
n
o-
H
C
W
s
R
is
k
fa
ct
or
s:
qu
ar
an
ti
n
e
Su
et
al
.
(2

0
0
7
)
SA
R
S
p
ro
sp
ec
ti
ve
an
d
p
er
io
d
ic
fo
ll
ow
-u
p
st
u
d
y

1
0
2
H
C
W
s
(7
0
SA
R
S
an
d
3
2
n
o-
SA
R
S
H
C
W
s)
D
av
id
so
n

T
ra
u
m
a
Sc
al
e-
C
h
in
es
e
ve
rs
io
n
(D
T
S-
C
)
SA
R
S
u
n
it
H
C
W
s
re

p
or
te
d
h
ig
h
er
D
ep
re
ss
io
n
(3
8
.5
%
vs
.
3
.1
%
)
in
so
m
n
ia
(3
7

%
vs
.
9
.7
%
)
an
d
P
T
SS
(3
3
%
vs
.
1
8
.7
%
,
bu
t
n
ot
si
gn

ifi
ca
n
t)
.
R
is
k
fa
ct
or
s:
le
ve
l
of
ex
p
os
u
re
La
n
ce
e
et
al
.
(2
0

0
8
)
SA
R
S
cr
os
s-
se
ct
io
n
al
st
u
d
y
1
3
9
h
os
p
it
al
st
aff
(H
C

W
s
n
=
1
0
3
;
cl
er
ic
al
st
aff
n
=
1
3
;
O
th
er
n
=
2
1
)

St
ru
ct
u
re
d
C
li
n
ic
al
In
te
rv
ie
w
fo
r
D
SM
-I
V
;
C
li
n
ic
ia
n
-A
d
m

in
is
te
re
d
P
T
SD
Sc
al
e
3
0
%
li
fe
ti
m
e
p
re
va
le
n
ce
of
d
ep
re

ss
iv
e,
an
xi
et
y,
or
su
bs
ta
n
ce
u
se
d
ia
gn
os
is
.
5
%
n
ew
p
sy
ch

ia
tr
ic
d
is
or
d
er
s
af
te
r
ou
tb
re
ak
R
is
k
fa
ct
or
s:
p
re
vi
ou
s
p
sy

ch
ia
tr
ic
d
is
or
d
er
,
<
ye
ar
s
of
w
or
k
ex
p
er
ie
n
ce
(c
on
ti
nu

ed
on
ne
xt
pa
ge
)
4
T
ab
le
1
(c
on
ti
nu
ed
)
St
u
d
y
O

u
tb
re
ak
T
yp
e
Sa
m
p
le
P
T
SS
/P
T
SD
m
ea
su
re
s
M
ai
n
ge
n
er

al
fi
n
d
in
gs
M
ai
n
ri
sk
an
d
re
si
li
en
ce
fa
ct
or
s
R
es
il
ie
n
ce
fa
ct

or
s:
tr
ai
n
in
g
an
d
su
p
er
vi
so
r/
co
ll
ea
gu
es
su
p
p
or
t.
St
yr
a
et
al

.
(2
0
0
8
)
SA
R
S
cr
os
s-
se
ct
io
n
al
st
u
d
y
SA
R
S
u
n
it
s
H
C
W
s

(n
=
1
6
0
)
an
d
n
o-
SA
R
S
u
n
it
s
H
C
W
s
(n
=
8
8
)
Im
p
ac
t
of

E
ve
n
t
Sc
al
e—
R
ev
is
ed
H
C
W
s
ta
ki
n
g
ca
re
of
on
ly
on
e
SA

R
S
p
at
ie
n
t
h
ad
h
ig
h
er
P
T
SS
le
ve
ls
th
an
th
os
e
ta
ki
n
g
ca
re

of
n
on
e
or
m
or
e
th
an
tw
o
SA
R
S
p
at
ie
n
ts
R
is
k
fa
ct
or
:
le
ve
l

of
ex
p
os
u
re
W
u
et
al
.
(2
0
0
9
)
SA
R
S
cr
os
s-
se
ct
io
n
al
st
u
d

y
5
4
9
h
os
p
it
al
st
aff
(2
1
%
d
oc
to
rs
,
3
8
%
n
u
rs
es
,
2
2

%
te
ch
n
ic
ia
n
s;
2
0
%
ad
m
in
is
tr
at
iv
e
an
d
ot
h
er
s)
Im
p
ac
t
of

E
ve
n
t
Sc
al
e—
R
ev
is
ed
A
bo
u
t
1
0
%
IE
S-
R
≥
2
0
.
R
is

k
fa
ct
or
s:
le
ve
lo
f
ex
p
os
u
re
;y
ou
n
ge
r
ag
e;
qu
ar
an
ti
n
e/
is
ol
at

io
n
(q
u
ar
an
ti
n
e,
h
av
in
g
fr
ie
n
d
s
or
cl
os
e
re
la
ti
ve
s
in
fe
ct

ed
).
R
es
il
ie
n
ce
fa
ct
or
:
co
p
in
g
st
ra
te
gi
es
(a
lt
ru
is
ti
c
ac
ce
p
ta
n

ce
of
w
or
k-
re
la
te
d
ri
sk
s)
M
ak
et
al
.
(2
0
1
0
)
SA
R
S
re
tr
os
p
ec
ti

ve
co
h
or
t
st
u
d
y
9
0
SA
R
S
su
rv
iv
or
s
(3
0
%
H
C
W
s)
St
ru
ct
u

re
d
C
li
n
ic
al
In
te
rv
ie
w
fo
r
th
e
D
SM
-I
V
;
Im
p
ac
t
of
E
ve
n

ts
Sc
al
e–
R
ev
is
ed
4
7
.8
%
P
T
SD
in
th
e
af
te
rm
at
h
of
SA
R
S.
2

5
.6
%
st
il
l
su
ff
er
s
P
T
SD
3
0
-m
on
th
s
p
os
t-
SA
R
S
R
is
k
fa
ct

or
s:
be
in
g
H
C
W
s
su
rv
iv
or
s
(b
u
t
la
rg
e
p
ro
p
or
ti
on
of
th
e
H
C
W
s

w
er
e
fe
m
al
e,
an
d
th
is
co
u
ld
aff
ec
t
re
su
lt
s)
.
W
in
g
an
d
Le
u

n
g
(2
0
1
2
)
SA
R
S
ca
se
-c
on
tr
ol
st
u
d
y
2
3
3
SA
R
S
su
rv
iv
or

s
C
h
in
es
e
bi
li
n
gu
al
ve
rs
io
n
of
th
e
Se
m
i-
St
ru
ct
u
re
d
C
li
n
ic

al
In
te
rv
ie
w
(S
C
ID
-I
)
Im
p
ac
t
of
E
ve
n
t
Sc
al
e-
re
vi
se
d
5
0

%
SA
R
S
su
rv
iv
or
s
a
li
fe
ti
m
e
p
sy
ch
ia
tr
ic
d
is
or
d
er
(d
ep
re
ss
io

n
,
P
T
SD
,
so
m
at
of
or
m
p
ai
n
d
is
or
d
er
,
p
an
ic
d
is
or
d
er
)
R
is

k
fa
ct
or
:
be
in
g
H
C
W
s
su
rv
iv
or
s
Le
e
et
al
.
(2
0
1
8
)
M
E
R
S

co
h
or
t
st
u
d
y
3
5
9
H
C
W
s
(M
E
R
S
an
d
n
o-
M
E
R
S
u
n
it
)
Im

p
ac
t
of
E
ve
n
ts
Sc
al
e–
R
ev
is
ed
5
1
%
H
C
W
s
re
p
or
te
d
IE
S>

2
5
(M
E
R
S
u
n
it
s
>
n
o-
M
E
R
S
u
n
it
s)
in
th
e
fi
rs
t
m
on
th
of

M
E
R
S
ou
tb
re
ak
.
A
ft
er
on
e
m
on
th
:
qu
ar
an
ti
n
ed
M
E
R
S

u
n
it
s
H
C
W
s
sh
ow
ed
h
ig
h
er
sl
ee
p
an
d
n
u
m
bn
es
s
sc
or
es
;
M

E
R
S
u
n
it
s
H
C
W
s
sh
ow
ed
h
ig
h
er
in
tr
u
si
on
sy
m
p
to
m
s
R
is
k

fa
ct
or
s:
le
ve
l
of
ex
p
os
u
re
,
qu
ar
an
ti
n
e
Ju
n
g
et
al
.
(2
0
2

0
)
M
E
R
S
cr
os
s-
se
ct
io
n
al
st
u
d
y
1
4
7
H
C
W
s
(n
u
rs
es
of
M

E
R
S
u
n
it
s)
Im
p
ac
t
of
E
ve
n
t
Sc
al
e–
R
ev
is
ed
K
or
ea
n
ve
rs
io

n
5
7
.1
%
P
T
SD
(2
5
.1
%
fu
ll
P
T
SD
an
d
3
2
.0
%
p
ar
ti
al
P
T

SD
).
P
T
SD
w
as
as
so
ci
at
ed
w
it
h
tu
rn
ov
er
in
te
n
ti
on
R
is
k
fa
ct
or

s:
le
ve
l
of
ex
p
os
u
re
(e
m
er
ge
n
cy
H
C
W
s
>
n
o-
em
eg
en
cy

H
C
W
s)
,
p
re
vi
ou
s
p
sy
ch
ia
tr
ic
d
is
or
d
er
s
R
es
il
ie
n
ce
fa
ct
or

:
su
p
er
vi
so
r
su
p
p
or
t
K
an
g
et
al
.
(2
0
2
0
)
C
O
V
ID
-1
9
cr

os
s-
se
ct
io
n
al
st
u
d
y
9
9
4
W
u
h
an
H
C
W
s
(d
oc
to
rs
an
d
n
u

rs
es
)
Im
p
ac
t
of
E
ve
n
t
Sc
al
e-
R
ev
is
ed
R
eg
ar
d
in
g
m
en
ta
l

h
ea
lt
h
p
ro
bl
em
(i
n
cl
u
d
in
g
P
T
SS
),
3
6
.9
%
h
ad
su
b-
th
re
sh
ol
d

d
is
tu
rb
an
ce
s,
3
4
.4
%
m
il
d
d
is
tu
rb
an
ce
s,
2
2
.4
%
m
od
er

at
e
d
is
tu
rb
an
ce
s,
an
d
6
.2
%
se
ve
re
d
is
tu
rb
an
ce
.
R
is
k
fa
ct
or

s:
le
ve
l
of
ex
p
os
u
re
(t
o
p
eo
p
le
ar
ou
n
d
th
em
w
h
o
w
er
e

in
fe
ct
ed
,
in
cl
u
d
in
g
fa
m
il
y/
co
ll
eg
u
es
/f
ri
en
d
s)
.
R
es
il
ie

n
ce
fa
ct
or
s:
co
p
in
g
st
ra
te
gi
es
(b
ei
n
g
m
ot
iv
at
ed
to
le
ar
n
th
e
n

ec
es
sa
ry
sk
il
ls
to
re
sp
on
d
to
d
iv
er
se
ch
al
le
n
ge
s)
La
i
et
al
.

(2
0
2
0
)
C
O
V
ID
-1
9
cr
os
s-
se
ct
io
n
al
st
u
d
y
1
2
5
7
H
C
W
s

(d
oc
to
rs
n
=
4
9
3
,
n
u
rs
es
n
=
7
6
4
)
Im
p
ac
t
of
E
ve

n
t
Sc
al
e–
R
ev
is
ed
7
1
.5
%
re
p
or
te
d
m
il
d
to
se
ve
re
P
T
SS
(3
6

.5
%
m
il
d
,
2
4
.5
%
m
od
er
at
e,
1
0
.5
se
ve
re
).
R
is
k
fa
ct
or
s:

le
ve
l
of
ex
p
os
u
re
,
n
u
rs
es
,
fe
m
al
e,
fe
w
er
ye
ar
s
of
w
or
k

ex
p
er
ie
n
ce
.
Li
et
al
.
(2
0
2
0
)
C
O
V
ID
-1
9
ca
se
-c
on
tr
ol
st

u
d
y
2
1
4
ge
n
er
al
p
u
bl
ic
an
d
5
2
6
H
C
W
s
(2
3
4
fr
on
t-
li

n
e
n
u
rs
es
,
2
9
2
n
on
-
fr
on
t-
li
n
e
n
u
rs
es
)
V
ic
ar
io
u
s
tr
au

m
at
iz
at
io
n
qu
es
ti
on
n
ai
re
(b
as
ed
on
se
ve
ra
l
qu
es
ti
on
n
ai
re
s,

in
cl
u
d
in
g
IE
S-
R
)
V
ic
ar
io
u
s
tr
au
m
at
iz
at
io
n
w
as
si
gn
ifi
ca
n

tl
y
lo
w
er
in
fr
on
t-
li
n
e
n
u
rs
es
th
an
n
on
-f
ro
n
t-
li
n
e
on
es
an

d
ge
n
er
al
p
u
bl
ic
(n
o
d
iff
er
en
ce
be
tw
ee
n
n
on
-f
ro
n
t-
li
n
e

n
u
rs
es
an
d
ge
n
er
al
p
u
bl
ic
)
R
is
k
fa
ct
or
s:
le
ve
l
of
ex
p
os

u
re
,
m
ar
it
al
st
at
u
s.
H
C
W
s:
h
ea
lt
h
ca
re
w
or
ke
rs
.
5

(Sim et al., 2004; Su et al., 2007; Lancee et al., 2008).
Accordingly,
Su et al. (2007) on a sample of 70 nurses from two SARS units
and 32
nurses from two non-SARS units found highlighted a previous
history of
mood disorders as a major risk factor for PTSS. One study on
MERS
outbreak confirmed this finding (Jung et al., 2020).
3.4. PTSD and PTSS resilience factors of in HCWs facing the
three
coronavirus outbreaks
3.4.1. Family and social support
Two studies on the SARS outbreak highlighte d the support of
family
and friends as having a major role in protecting from PTSS
development
(Chan and Huak, 2004; Su et al., 2007). In particular, Su et al.
(2007)
investigating 102 nurses found that strong social and family
support
protected against acute stress, with a positive impact on their
global
functioning as a function of time.
3.4.2. Supervisors and colleagues support
Three researches concerning the SARS outbreak (Chan and
Huak, 2004; Maunder et al., 2006; Lancee et al., 2008) and one
on the
MERS (Jung et al., 2020), reported a protective role of the

support from
supervisors/colleagues. Particularly, Lancee et al. (2008), in
139 HCWs
in Canada, showed feeling well supported while working as a
resilience
factor also in the long-term. Jung et al. (2020) noticed that
manage-
ment strategies based on supervisors’ support proved helpful in
order to
reduce PTSS in 147 nurses in three isolation hospitals in South
Korea
during the MERS outbreak.
3.4.3. Training
The perception of being adequately trained was identified as a
po-
tentially protective factor in two studies on the SARS (Maunder
et al.,
2006; Lancee et al., 2008)., Comparing 769 Canadian HCWs
displaced
in 9 hospitals that treated SARS patients and 4 hospitals that
did not,
from 13 to 26 months after the outbreak, Maunder et al. (2006)
sug-
gested the importance of supportive interventions in preventing
PTSD
and PTSS with particular impact on maladaptive coping styles.
3.4.4. Work organization
The same authors reported that working in structured units and
the
perceived safety of the working environment are further factors
which
seem to enhance the resilience of HCWs, in line with findings

of another
study by Su et al., 2007). Moreover, it has also been observed
that a
clear communication of directives and precautionary measures
to be
adopted was related to a better outcome with regard to PTSS
(Chan and
Huak, 2004).
3.4.5. Coping strategies
In five studies on the SARS outbreak (Chan and Huak 2004;
Sim et al., 2004; Phua et al., 2005; Su et al., 2007; Wu et al.,
2009),
positive coping strategies were reported to be a protective factor
against
the development of PTSD psychopathology. Particularly, in a
study
carried out in Singapore on 41 physicians and 58 nurses,
Phua et al. (2005) reported an association between the use of
humor
and planning as coping strategies, and lower rates of PTSD.
Other
protective coping styles included: the altruistic acceptance of
work-re-
lated risks (Wu et al., 2009), the ability to talk to someone
about their
experiences, and the presence of religious beliefs (Chan and
Huak
2004). Accordingly, Maunder et al. (2006) found that
maladaptive
coping strategies, such as avoidance, hostile confrontation and
self-
blame, resulted in worse outcomes in terms of PTSS and
Sim et al. (2004) reported that less venting, humor and
acceptance were

associated to higher levels of PTSS. Consistently, positive
coping stra-
tegies, such as motivation to learning different skills, have been
in-
dicated as resilience factors also in HMWs dealing with the
current
COVID-19 pandemic (Kang et al., 2020).
3.5. HCWs survivors to coronavirus outbreaks
Five studies focusing on HCWs who survived the SARS
infection
highlighted this population as particularly “at risk” for PTSD.
Kwek et al. (2006) in a sample of SARS survivors at 3 months
post-
discharge found that HCWs were more affected by PTSS than
non-
HCWs. Lee et al. (2007) examined a sample of 96 Hong Kong
SARS
survivors divided into sub-samples of HCWs and non-HCWs,
found that
while PTSS levels were similar in the two sub-samples at the
peak of the
outbreak, HCWs compared to non-HCWs, reported significantly
higher
PTSS one year after discharge, suggesting a lack of recovery as
a
function of time, among HCW SARS survivors. In line with
this, a later
study among 233 Chinese SARS survivors also reported a
higher risk of
PTSD among HCW compared to non-HCW (Wing and Leung,
2012).
Furthermore, a study conducted on a sample of 90 Hong Kong
SARS
survivors at 30 months after the outbreak (Mak et al., 2010)

showed
that being a HCW was significantly associated with PTSD
development,
despite the authors hypothesizing that this finding could be
gender-
biased because the majority of the sample was made up of
female
HCWs. Finally, Ho et al. (2005) in 97 HCWs in Hong Kong
found a
positive correlation between the presence of pronounced SARS-
related
fears and PTSS burden, particularly intrusion symptoms; in
addition
HCWs who had recovered from SARS appeared to be more
concerned
about death, discrimination and quarantine than those who had
not
been infected.
4. Discussion
To the best of our knowledge we conducted the first review ad-
dressing PTSD and PTSS risk and resilience factors in HCWs
who were
involved in the three major recent Coronavirus outbreaks,
namely the
SARS, the MERS and the current COVID-19, which have
affected the
worldwide population in the last two decades. Converging data
suggest
a high risk for PTSD development among emergency HCWs,
with stu-
dies consistently outlining several risk factors that are enhanced
in the
case of these highly lethal outbreaks, such as: the frequent
unpredict-

ability of daily caseloads, having to frequently manage patients
and
their families’ expectations in unexpected critical
cases/situations
(Mealer et al., 2009; Czaja et al., 2012; Iranmanesh et al., 2013;
Fjeldheim et al., 2014). In the context of an outbreak emergency
such as
the COVID-19 crisis, difficulties are further heightened by the
rapidly
increasing flow of critical patients requiring increased medical
atten-
tion, the decision-making burden and high daily fatality rates,
and the
constant updates of hospital procedures following advances in
knowl-
edge about the disease, that creates another burden for HCWs
who need
to keep up to date. Further, patients medical management
requires tight
physical isolation, to protect patients and HCWs because of the
ex-
tremely high risk of contamination (Petrie et al., 2018; Berger
et al.,
2012; Brooks et al., 2019). Occupational role, marital status,
age and
gender, quarantine, stigma, previous psychiatric disorders,
isolation
and being survivors of the same outbreak also emerged as robust
risk
factors for PTSS. In parallel, the literature highlighted a number
of
resilience factors, such as support, training, prompt work
organization
and good coping strategies.
The majority of studies included in our review focused on the

2003
SARS outbreak; fewer data were available on the MERS, and
the studies
on COVID-19 are only emerging at the time of writing. All
these studies
reported a high risk for adverse psychological reactions,
particularly
PTSS and PTSD among HCWs, suggesting the proximity to
“ground zero”
as a primary risk factor (Kwek et al., 2006; Lee et al., 2018).
HCWs’ fear
of contagion and infection of their family, friends and
colleagues,
feelings of uncertainty, stigmatization and rejection in their
neighbor-
hood because of their hospital work were also reported. Studies
also
reported the reluctance to work and/or considering quitting their
job,
as well as high levels of stress, anxiety and depression
symptoms, which
could have long-term psychological implications (Maunder et
al., 2003;
6
Bai et al., 2004; Lee et al., 2007; Wu et al., 2009). The self-
perceived
high risk for contagion might be the most important aspect
related to
the front-line activities, with for example Su et al. (2007),
failing to find

any significant difference between HCWs in SARS vs. non-
SARS units in
PTSD prevalence rate. This suggests that not only HCWs
working within
the SARS units, but also those working outside them and facing
un-
certainty because of the displacement, might develop PTSS
during the
outbreak. In this regard, in the ongoing COVID-19 pandemic,
the lack of
personal protection devices represents a critical issue.
Interestingly, however, some authors found first-line exposure
to
have a protective effect. Styra et al. (2008) reported that HCWs
working
in SARS high risk units, as expected, experienced greater
distress than
HCWs displaced in other departments such as the psychiatric
one, but
contrary to expectations HCWs caring for many SARS patients
while
working in high-risk units emerged as being less distressed.
This finding
suggests that experience in treating SARS patients may be a
mediating
factor that could be amenable to intervention in future
outbreaks. This
is in line with more recent findings from a COVID-19 study,
according
to which PTSS severity of non-front-line nurses was greater
than that of
front-line nurses, who showed stronger psychological
endurance. The
authors argue that this finding may be explained considering
that front-

line nurses were voluntarily selected and provided with
sufficient
psychological preparation. Moreover, the selected front-line
nurses
were mainly middle-level backbone staff with working
experience and
psychological capacity (Li et al., 2020).
Hence, there is evidence that perceived adequacy of training re -
presents a protective factor against adverse outcomes of
traumatic ex-
posure (Maunder et al., 2006; Lancee et al., 2008). Similarly,
other
factors concerning positive working organization, such as
working in
structured units, a sense of protection of environment (Maunder
et al.,
2006; Su et al., 2007) and clear communication of directives
and of
precautionary measures (Chan and Huak, 2004), have proven to
be
protective factors against the development of PTSS in HCWs. In
parti-
cular, Chan and Huak (2004) explored the important role in
preventing
PTSS of clear and prompt communicati on of directives and
information
about the disease, of providing precautionary measures, such as
Per-
sonal Protective Equipment (PPE), and of the support of a
supervisor/
head of department, colleagues and family. The support from
family
and friends as well as that from supervisors and colleagues has
been
shown to represent an important resilience factor against the

develop-
ment of PTSS, as widely demonstrated in the literature (Chan
and
Huak, 2004; Maunder et al., 2006; Su et al., 2007; Lancee et al.,
2008).
Nevertheless, this matter deserves further consideration since in
this
peculiar clinical setting the implications of the contagion risk
often lead
to self-isolation, with subsequent decreased social support.
Some important individual risk and resilience factors for PTSS
have
also been reported among HCWs facing a coronavirus outbreak.
First,
female gender. Despite the fact that the majority of the studies
corro-
borate the preventive role of professional training as to PTSD
onset up
to the point of flattening of the gender gap which is commonly
observed
in PTSD reports, most of the studies on HCWs dealing with
Coronavirus
outbreaks tend to show a higher incidence of PTSD among
women.
Females, in fact, were shown to be most affected by PTSS in
three SARS
studies (Lee et al., 2007; Reynolds et al., 2007; Lai et al.,
2020), as well
as younger HCWs or HCWs with fewer years of work
experience
(Reynolds et al., 2007; Lancee et al., 2008). Moreover, nurses
proved to
be more affected by PTSS than other HCWs (Tham et al., 2004;
Maunder et al., 2004). Although this has been explained as
related to

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