The Impact of COVID-19 on Nurses' Mental Healthcare and
Sustainability by Data Analysis
Xinran Liu
1,*
, Xueming Wang
2
, Songcheng Zhuo
3
and Fangce Zhang
4
1
The High School Affliated to Renmin University of China, Beijing, 100080, China
2
School of Education, Johns Hopkins University, Baltimore, 21287, U.S.A.
3
College of Liberal Arts & Sciences, University of Illinois at Urbana Champaign, Urbana, IL 61801, U.S.A.
4
College of Arts & Sciences, Rutgers University, Piscataway, NJ 08854, U.S.A.
2
vivianwang2016@gmail.com,
3
zsc980909@gmail.com,
4
fangtse.chang@gmail.com
Keywords: COVID-19 Pandemic, Sustainability Science, Mental Healthcare, Frontline Nurses, Big Data Analysis.
Abstract: COVID-19 has profoundly impacted everyone’s lives with many publications investigating different aspects
of this pandemic, yet limited literature focused on the mental health of frontline nurses with a small sample
size. The published dataset in Hu et al.’s studies, with collected frontline nurses’ mental health conditions and
sociodemographic characteristics in Wuhan, February 13, 2020, were chosen to be the original dataset. In this
paper, three responses-depression, anxiety, and fear-are selected, and 17 socio-demographic covariates are
chosen. The results were obtained by applying linear regression, logistic regression, ordinal regression,
corresponding model selection, and diagnostics in R. It revealed that Wuhan as origin, working wards
changed, and willingness to participate in the frontline are the factors associated with anxiety; child-rearing,
monthly household income, and etc. to participate in the frontline are the factors associated with depression;
age, sex, education, average working hours/shift, Wuhan as origin, and etc. are the factors associated with
fear.
1 INTRODUCTION
COVID-19 has profoundly changed the world in the
past year; the pandemic has impacted every side of
life, from medical care to businesses and schools. A
lot of publications were posted after the pandemic
concerning different aspects of this pandemic.
During this challenging time, those health
workers, who would require hugely increased
workload associated with unusual risk, are therefore
needed to be concerned. This problem, however, is
often overlooked as the major focus is always the
patients rather than the health workers. In comparison
to the vastly available literature on COVID-19, there
are merely around 500 pieces of research on the
mental health of frontline nurses on Pubmed.
One phenomenon is unique in China. As more
patients arose in Wuhan, teams from other cities in
China-such as Shanghai, Guangdong, and Tianjin-
formed the Chinese Emergency Medical Teams to
support hospitals in infected areas. The team had
professional doctors, nurses, and paramedics from
governments, charities, and international
organizations. From January the 24th, approximately
nineteen thousand nurses-respectively in 346 medical
teams-arrived at Wuhan. Approximately 8000
medical workers also work in the square-cabin
hospital built for all the positive covid patients to stay
(WHO 2021).
The final project takes to the research question of
"to what extent does the pandemic, COVID-19,
influence the mental health of frontline nurses?" and
"any difference (in factors) between anxiety,
depression, and fear?" The study population of this
research is frontline nurses in Wuhan.
This research aims to call attention to the mental
health of frontline nurses in China, especially the
healthcare workers' mental health during the
pandemic. As there are not many studies on the
mental health of Chinese healthcare workers during
the pandemic, this research would like to focus on the
impact of COVID-19 on nurses' mental health. Once
the factors that could cause the mental health problem
the most are known, the government could intervene
and prevent more serious mental health problems and
decrease unnecessary mental consumption in nurses.
Liu, X., Wang, X., Zhuo, S. and Zhang, F.
The Impact of COVID-19 on Nurses’ Mental Healthcare and Sustainability by Data Analysis.
DOI: 10.5220/0011241200003438
In Proceedings of the 1st International Conference on Health Big Data and Intelligent Healthcare (ICHIH 2022), pages 181-193
ISBN: 978-989-758-596-8
Copyright
c
2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
181
2 BACKGROUND AND
LITERATURA REVIEW
Nurses remain at the forefront of patient care and are
known to work in a psychologically and physically
demanding work environment, which exposes them
to a higher risk of developing negative mental states
such as depression, anxiety, and stress. Many
researchers have conducted surveys and studies
revealed that nurses are generally susceptible to
negative mental health states, which could worsen
under pandemic situations.
Previous studies have suggested that medical
workers are more likely to have depressive symptoms
and mental health problems (P<0.05) due to higher
job stresses and found that more than half of the
Chinese nurses had depressive symptoms that
adversely affected the quality of life and quality of
care in 2009 (Chen, Zhong, Hua, Nie, & Wang, Gao,
Pan, Sun, Wu, Wang, & Wang 2012). Moreover, a
meta-analysis of 244 papers indicated that the mental
health level of Chinese nurses decreased steadily
from 1998 to 2016 (Xin, Jiang, & Xin 2019). The
mental health problems of nurses are prevalent
throughout the world as well, and several studies
found that depression, anxiety, and stress are also
prevalent among Portugal, Australia, and England
nurses (Seabra, Lopes, Calado, & Capelas, Maharaj,
Lees, & Lal 2019, Mark, & Smith 2012).
In these studies, various covariates have been
identified by multivariate logistic regression analysis
as positively associated with depressive symptoms,
including lower job rank, less sleeping hours, higher
over-commitment, worse nurse-patient relationship,
and higher education background, while social
support, rewards, and skill discretion were negatively
associated with mental health problems.
A global pandemic is likely to influence the
mental health status of many people as research has
demonstrated that individuals tend to be more
depressed during a pandemic. Dr. Tzeng’s team
points out that the SARS cohort had a 2.8-fold
increased risk of overall psychiatric disorders when
compared to the control cohort, which means that
SARS has a long-term mental health effect on people
(Tzeng 2020). Dr. Ettman shows that the prevalence
of depressive symptoms in the US was more than 3-
fold higher during COVID-19 compared with before
the COVID-19 pandemic (Ettman, Abdalla, Cohen,
Sampson, Vivier, & Galea 2020). Although SARS
and COVID-19 are two different pandemics, they
share some similarities: for instance, both of them are
caused by two similar, but different, coronaviruses.
Further, as more studies are related to nurses'
mental health in China and other countries during the
pandemic, they demonstrated increases in mental
health problems of nurses worldwide. For instance,
Dr. Havaei found a rise in anxiety and depression
among the nurses in BC, Canada (Havaei, Smith,
Oudyk, & Potter 2021). Dr. Liu also noticed more
mental health problems of nurses in China. (Liu, Wu,
Shi, Ma, Ma, Teng, Zeng 2020). Furthermore, some
countries, like South Korea, Japan, and Canada, all
had news about nurses striking and quitting their jobs
because of pandemic stress and mental health
problems (Kim, Gillis, Gillis 2021). However, there
is no news about that in China. Oppositely, Dr. Hu
detected that even if the nurses in Wuhan hospitals
had some mental health problems, they still expressed
their willingness to work (Hu, Kong, Li, Han, Zhang,
Zhu, Zhu 2020).
Moreover, previous studies on mental health
problems suggested some differences between China
and other countries in the world. The awareness of
mental health problems is still not enough in China.
So, people with mental health problems would not
seek professional help (Wong, & Li 2014). When Dr.
Wong compared British and Hong Kong on
identifying mental health problems, he found that the
British had a higher level of awareness of mental
problems than Hong Kong (Loo, Wong, & Furnham
2012).
In China, much of the mental health studies are on
teenagers, women, and teachers; relatively few
studies are focused on nurses, unlike other countries
that already had much research on nurses' mental
health. And the reasons that caused the mental health
problem for nurses are also different. Dr. Roelen
discovered that other countries are mainly because of
job demands (Roelen, van Hoffen, Waage, Schaufeli,
Twisk, Bjorvatn, Pallesen 2018), while Dr. Xin found
that, besides job demands, Chinese nurses would also
have mental health problems due to the patient-to-
nurse ratio (Xin, Jiang, & Xin 2019). In general,
nurses in China had a higher chance of getting mental
health problems than in other countries.
This research improved the limitation of existing
studies on the following points. Firstly, this research
is more comprehensive with a large sample size of
2014 and considers more variables, while existing
studies are generally on the small sample size of
fewer than one thousand data. For instance, Dr.
Kameg’s research has only collected 151 cases.
Secondly, previous studies focused on numerous
different variables, which are blurred and difficult to
understand by readers. Dr. Hu focused on a more
general range, such as self-efficacy, burnout
ICHIH 2022 - International Conference on Health Big Data and Intelligent Healthcare
182
resilience, perceived social support, etc (Hu, Kong,
Li, Han, Zhang, Zhu, Zhu 2020). However, this
research focused specifically on three response
variables: anxiety, depression, and fear. Finally, this
study is also more specifically focused on frontline
workers in Wuhan when covid-19 just began,
whereas most studies available right now focused on
the general population or nurses in other places. For
instance, Dr. Ettman’s research focused on the
general population in the US (Ettman, Abdalla,
Cohen, Sampson, Vivier, & Galea 2020).
3 METHODS
Data collection happened in two Wuhan’s hospitals in
China. The three divisions in three separated
locations of one of the public tertiary hospitals all
participated in the process of data collection. Two of
the divisions only accept COVID-19 patients between
January 13, 2020, and February 13, 2020, with around
1860 beds and 2000 nurses. The other hospital also
only received COVID-19
patients from February 3,
2020, with approximately 1000 beds and 600 nurses
(Hu, Kong, Li, Han, Zhang, Zhu, Zhu 2020). All
frontline nurses, except the head nurses and the
directors of nursing, in these two hospitals taking care
of COVID-19 patients are encouraged to take an
online self-developed questionnaire survey that was
sent to them in WeChat on February 13, 2020. This
dataset was found online with the article, "Frontline
nurses' burnout, anxiety, depression, and fear statuses
and their associated factors during the COVID-19
outbreak in Wuhan, China: A large-scale cross-
sectional study", by Deying Hu, Yue Kong, et al (Hu,
Kong, Li, Han, Zhang, Zhu, Zhu 2020).
The sample size of the dataset is 2014 rows and
190 columns; for this research, 20 columns that are
relevant to the research topic are used: 17 covariates
and 3 responses (Hu, Kong, Li, Han, Zhang, Zhu, Zhu
2020).
Data screening has proceeded to accomplish the
goal of this research. Initially, the sociodemographic
covariates include identity background, working
experience and background, personal confidence and
beliefs, and willingness. This research selected all the
numerical variables that correspond with this study,
excluding covariates that are too diversified, such as
the original and current clinical department. This will
not bring inaccuracy to this research since whether
there is a transfer between original and current
department is recorded with binomial responses. Only
completed cases are used.
In this research, the responses are anxiety,
depression, and fear, categorized already by the
publisher. Response variables include the following:
Zung's Self-Rating Anxiety Scale (SAS) checked out
for emotional and physical symptoms of anxiety
ranging from 25 to 100, where 50–59 is mild, 60–69
is moderate, and ≥70 is severe anxiety (Zung 1971).
Zung's Self-Rating Scale (SDS) checked out for
emotional, psychomotor, psychological, and
physiological imbalance also arranged between 25
and 100, where 53–62 is mild, 63–72 is moderate, and
≥73 is severe depression (Zung 1965). Fear Scale for
Healthcare Professionals (FS-HPs) checked out the
fear for nurses and their family and friends to get
infected and fear towards death. It ranged from 8 to
40, where ≤19 is mild, 20–29 is moderate, and 30–40
is severe fear. Table 1 showed the description of
covariates from EDA.
For the analysis methods, first, EDA was used to
find the mean, standard deviation of the covariates
and responses (see table 1). Next, multiple linear
regression was conducted, where covariates are used
to predict the outcomes of the three responses. Then,
this research used log transformation to fit a better
model and make the response variables as normally
disturbed as possible. Then, stepwise selection, AIC,
and BIC were applied to find the best fit and most
significant variables. For both AIC and BIC, the
smaller, the better. Moreover, since anxiety,
depression, and fear were a group of ordered variables
ranging none, mild, moderate to severe mental health
problems, ordinal logistic regression was performed
to determine whether the relative ordering between
the covariates is significant (Zung 1965, Zung 1971).
Lastly, this research also conducted model
diagnostics for each full model with the residuals vs.
fitted plot, normal QQ plot, and R-squared to see how
well the model fitted. All codes of data analysis,
tables, and graphs are finished in R software.
The Impact of COVID-19 on Nurses’ Mental Healthcare and Sustainability by Data Analysis
183
Table 1: Description and parameters of the covariates (Hu, Kong, Li, Han, Zhang, Zhu, Zhu 2020).
Sociodemographic variables
Mean (SD) n (%)
Ag
e 30.99
(
-6.17
)
Sex
Male 260 (12.9%)
Female 1752 (87.1%)
M
arital status
Marrie
d
1230 (61.1%)
Other marital status 784
(
38.9%
)
Had one or more
children
Yes 1100 (54.6%)
No 914
(
45.4%
)
Education
Di
p
loma or lowe
r
441
(
21.9%
)
Bachelor's degree or highe
r
1573 (78.1%)
Pro
f
essional title
Junio
r
1495 (74.2%)
Intermediate and senio
r
519
(
25.8%
)
Monthly household
income
(USD/month)
≤1440 1109
(
55.1%
)
>1440 905 (44.9%)
Clinical
experience
(month)
107.76 (78.09)
Working duration as frontline nurse during the COVID-19
outbreak (days)
20.72 (12.94)
Average working
hours/ shift
6.57 (1.90)
Wuhan as origin
working place
Yes 1324 (65.7%)
No 690
(
34.3%
)
Original position
Bedside nurse 1818 (90.3%)
Head nurse or nurse director (including vice-director) 196 (9.7%)
Position in Wuhan
Bedside nurse 1894 (94.0%)
Head nurse or nurse director
(
includin
g
vice-director
)
120
(
6.0%
)
Working wards
chan
g
ed
Yes 747 (37.1%)
No 1267
(
62.9%
)
Prior training about caring patients with infectious disease
Yes 1654
(
82.1%
)
No 360 (17.9%)
Prior ex
p
erience about carin
g
p
atients with in
f
ectious disease
Yes 785 (39.0%)
No 1229
(
61.0%
)
Willin
g
ness to
p
artici
p
ate in
f
rontline durin
g
COVID-19 outbrea
k
Yes 1950 (96.8%)
No 64
(
3.2%
)
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4 RESULTS
As linear regression was conducted on the dataset, the
result is displayed in Table 2 with its confidence
interval and P-value. Applying the threshold of
p<0.05, the p-value that fitted the range was highly
significant. Among seventeen covariates, ten in
anxiety, nine in depression, and eight in fear display
high significance. The AIC BIC model selection
results displayed high similarities between the three
responses. Model diagnostics are then conducted.
According to the outcomes of diagnostic, the model
generally obeyed the four assumptions of linear
regression: no outlier is found (residuals vs.
leverage), residuals are normally distributed (Q-Q
plot), Homoscedasticity of residuals is displayed
(residuals vs. fitted).
For anxiety, the nine covariates are sex, child-
rearing, monthly household income, frontline
working duration, Wuhan as origin, position in
Wuhan, working wards changed, prior training,
willingness to participate in the frontline. The model
after AIC selection left eleven covariates, while two
covariates (whether Wuhan is the original working
place and the willingness to participate in the
frontline) were left after BIC selection.
For depression, the significant variables were age,
child-rearing, monthly household income, Wuhan as
origin, position in Wuhan, working wards changed,
prior training, and willingness to participate
in the frontline. Then, in AIC, thirteen covariates
were left, and in BIC, the same two covariates as that
in anxiety were left.
For fear, the eight significant covariates were age,
sex, education, average working hours/shift, Wuhan
as origin, original position, prior experience, and
willingness to participate in the frontline. The models
of fear after BIC (see table 3) and AIC (see table 4)
selection displayed a
relatively more different pattern
from anxiety and depression. Among eleven
covariates of the AIC model, only six overlapped with
that of either anxiety or depression; among two
covariates, Wuhan as origin and sex, sex stands out as
a difference.
Table 2: Linear Regression Model.
anxiet
y
de
p
ression
f
ea
r
Predictors Estimates CI (95%) P-Value Estimates CI (95%) P-Value Estimates CI (95%) P-Value
Interce
p
t 51.9 45.1
–
58.7 <0.001 51.8 44.9
–
58.6 <0.001 32.8 28.3
–
37.3 <0.001
A
g
e (
y
ear) 0.1 -0.1
–
0.3 0.294 0.2 0.0
–
0.4 0.046 -0.2 -0.3
–
-0.0 0.030
Sex 1.6 0.1
–
3.1 0.031 2.1 0.6
–
3.5 0.007 2.6 1.7
–
3.6 <0.001
Marital Status -1.4 -3.1 – 0.3 0.114 -1.4 -3.1 – 0.3 0.099 1.1 -0.0 – 2.2 0.057
Child Rearing 2.4 0.7 – 4.1 0.007 2.4 0.7 – 4.2 0.006 -0.1 -1.3 – 1.0 0.828
Education 0.5 -0.8 – 1.7 0.450 0.1 -1.2 – 1.3 0.894 0.9 0.1 – 1.7 0.034
Professional Title 0.5 -1.0
–
1.9 0.545 1.1 -0.4
–
2.6 0.151 0.0 -1.0
–
1.0 0.962
Monthly Household
Income
-1.7 -2.8 – -0.7 0.001 -2.4 -3.4 – -1.4 <0.001 0.2 -0.5 – 0.9 0.573
Clinical Ex
p
erience -0.0 -0.0
–
0.0 0.195 -0.0 -0.0
–
0.0 0.179 0.0 -0.0
–
0.0 0.180
Frontline Working
Duration
0.0 0.0 – 0.1 0.035 0.0 -0.0 – 0.1 0.064 0.0 -0.0 – 0.0 0.235
Average Working
Hours
Shift
-0.0 -0.3 – 0.3 0.982 -0.1 -0.3 – 0.2 0.593 -0.3 -0.5 – -0.2 <0.001
Wuhan as Origin 3.4 2.3 – 4.5 <0.001 3.4 2.3 – 4.5 <0.001 3.3 2.6 – 4.1 <0.001
The Impact of COVID-19 on Nurses’ Mental Healthcare and Sustainability by Data Analysis
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Original Position -2.0 -4.5 – 0.5 0.110 -1.7 -4.2 – 0.8 0.187 -2.3 -4.0 – -0.7 0.006
Position in Wuhan 3.1 0.1 – 6.1 0.041 3.2 0.2 – 6.2 0.038 -0.5 -2.5 – 1.5 0.621
Working Wards
Chan
g
ed
1.3 0.2 – 2.3 0.018 1.6 0.5 – 2.6 0.004 -0.1 -0.8 – 0.6 0.856
Prior Traning -2.2 -3.5 – -0.9 0.001 -2.4 -3.7 – -1.1 <0.001 -0.6 -1.5 – 0.2 0.150
Prior Experience 0.1 -0.9 – 1.2 0.848 0.2 -0.9 – 1.2 0.740 -1.2 -1.9 – -0.5 0.001
Willingness to
Participate in
Frontline
-10.8 -13.5 – -8.1 <0.001 -10.4 -13.1 – -7.7 <0.001 -2.1 -3.9 – -0.3 0.024
Observations 2014 2014 2014
R
2
/ R
2
adjusted 0.085 / 0.077 0.090 / 0.082 0.124 / 0.117
Table 3: Linear Regression BIC Model.
anxiety depression
f
ea
r
Predictors
E
stimates CI (95%)
P
-Value
E
stimates CI (95%)
P
-Value
E
stimates CI (95%)
P
-Value
Intercept 55.77 52.97
–
58.58 <0.001 58.08 55.24
–
60.92 <0.001 25.06 24.10
–
26.02 <0.001
Wuhan as
Origin
3.83 2.83 – 4.83 <0.001 3.69 2.67 – 4.71 <0.001 3.72 3.05 – 4.39 <0.001
Willingness to
Participate in
Frontline
-
10.84
-13.55 – -
8.12
<0.001 -
10.34
-13.09 – -
7.59
<0.001
Sex 3.33 2.38
–
4.29 <0.001
Observations 2014 2014 2014
R
2
/ R
2
adjusted 0.060 / 0.059 0.054 / 0.053 0.082 / 0.081
Table 4: Linear Regression AIC Model.
anxiety depression
f
ea
r
P
redictors
E
stimatesCI (95%)
P
-Value
E
stimatesCI (95%)
P
-Value
E
stimatesCI (95%)
P
-Value
Intercept 55.7 52.4
–
59.0 <0.001 54.1 49.0
–
59.1 <0.001 33.3 29.0
–
37.5 <0.001
Sex 1.4 -0.0
–
2.9 0.052 1.9 0.4
–
3.3 0.011 2.6 1.7
–
3.6 <0.001
Marital
Status
-1.4 -3.0 – 0.3 0.111 -1.4 -3.1 – 0.3 0.103 1.0 0.3 – 1.8 0.010
Child
Rearing
2.2 0.6 – 3.8 0.008 2.4 0.7 – 4.2 0.006
Monthly
Household
Income
-1.8 -2.8 – -0.7 0.001 -2.4 -3.5 – -1.4 <0.001
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Frontline
Working
Duration
0.0 0.0 – 0.1 0.043 0.0 -0.0 – 0.1 0.077
Wuhan as
Origin
3.6 2.5 – 4.6 <0.001 3.4 2.3 – 4.5 <0.001 3.3 2.7 – 4.0 <0.001
Original
Position
-2.3 -4.7 – 0.1 0.055 -1.8 -4.3 – 0.7 0.154 -2.6 -3.7 – -1.4 <0.001
Position in
Wuhan
3.1 0.2 – 6.1 0.037 3.2 0.2 – 6.2 0.036
Working
Wards
Changed
1.2 0.2 – 2.2 0.015 1.5 0.5 – 2.5 0.003
Prior
Traning
-2.1 -3.4 – -0.9 0.001 -2.3 -3.6 – -1.1 <0.001 -0.7 -1.5 – 0.2 0.128
Willingness
to
Participate
in Frontline
-10.9 -13.6 – -8.2 <0.001 -10.4 -13.1 – -7.7 <0.001 -2.1 -3.9 – -0.3 0.023
Age (year) 0.1 -0.0
–
0.2 0.106 -0.2 -0.3
–
-0.0 0.022
Professional
Title
1.3 -0.2 – 2.7 0.081
Education 0.9 0.1
–
1.7 0.023
Clinical
Experience
0.0 -0.0 – 0.0 0.139
Average
Working
Hours
Shift
-0.3 -0.5 – -0.2 <0.001
Prior
Experience
-1.2 -1.9 – -0.5 0.001
Observations 2014 2014 2014
R
2
/
R
2
adjuste
d
0.084 / 0.079 0.089 / 0.083 0.123 / 0.119
Since the R-square for linear regression is 0.0852,
far from 1, which meant it is not the best fit, log
transformation was applied to the response variables
for the remedy diagnosed from the linear regression.
Table 5 showed the association between the
covariates and mental health problems when log
transformation is conducted. In anxiety, 8 of the
covariates were observed to have p-value < 0.05: sex,
child-rearing, monthly household income, frontline
working duration, Wuhan as origin, working wards
changed, prior training, willingness to participate in
the frontline. Since there were too many variables,
stepwise selections were conducted to find the most
significant factors related to anxiety. BIC (see table
6) and AIC (see table 7) showed only two covariates,
Wuhan as origin and willingness to participate in the
frontline that are the same and significant.
The Impact of COVID-19 on Nurses’ Mental Healthcare and Sustainability by Data Analysis
187
Table 5: Log Transformation Model.
anxiety depression
f
ea
r
Predictors Estimates CI (95%) P-Value Estimates CI (95%) P-Value Estimates CI (95%) P-Value
Intercept 3.9 3.8
–
4.1 <0.001 3.9 3.8
–
4.1 <0.001 3.5 3.3
–
3.7 <0.001
Age (year) 0.0 -0.0 – 0.0 0.259 0.0 -0.0 – 0.0 0.051 -0.0 -0.0 – -0.0 0.044
Sex 0.0 0.0 – 0.1 0.018 0.0 0.0 – 0.1 0.002 0.1 0.1 – 0.2 <0.001
Marital Status -0.0 -0.1 – 0.0 0.103 -0.0 -0.1 – 0.0 0.109 0.0 0.0 – 0.1 0.049
Child Rearing 0.1 0.0
–
0.1 0.004 0.0 0.0
–
0.1 0.008 -0.0 -0.1
–
0.0 0.648
Education 0.0 -0.0 – 0.0 0.514 0.0 -0.0 – 0.0 0.693 0.0 0.0 – 0.1 0.026
Professional Title 0.0 -0.0 – 0.0 0.486 0.0 -0.0 – 0.1 0.172 -0.0 -0.0 – 0.0 0.700
Monthly Household
Income
-0.0 -0.1 – -0.0 0.002 -0.0 -0.1 – -0.0 <0.001 0.0 -0.0 – 0.0 0.725
Clinical Experience -0.0 -0.0 – 0.0 0.152 -0.0 -0.0 – 0.0 0.186 0.0 -0.0 – 0.0 0.317
Frontline Working
Duration
0.0 0.0 – 0.0 0.044 0.0 -0.0 – 0.0 0.127 0.0 -0.0 – 0.0 0.285
Average Working
Hours
Shift
-0.0 -0.0 – 0.0 0.857 -0.0 -0.0 – 0.0 0.529 -0.0 -0.0 – -0.0 0.001
Wuhan as Origin 0.1 0.0
–
0.1 <0.001 0.1 0.0
–
0.1 <0.001 0.1 0.1
–
0.2 <0.001
Original Position -0.0 -0.1 – 0.0 0.096 -0.0 -0.1 – 0.0 0.119 -0.1 -0.2 – -0.0 0.012
Position in Wuhan 0.1 -0.0 – 0.1 0.070 0.1 0.0 – 0.1 0.043 -0.0 -0.1 – 0.1 0.571
Working Wards
Changed
0.0 0.0 – 0.0 0.012 0.0 0.0 – 0.1 0.004 0.0 -0.0 – 0.0 0.924
Prior Traning -0.0 -0.1 – -0.0 0.002 -0.0 -0.1 – -0.0 <0.001 -0.0 -0.1 – 0.0 0.329
Prior Experience 0.0 -0.0
–
0.0 0.740 0.0 -0.0
–
0.0 0.719 -0.1 -0.1
–
-0.0 <0.001
Willingness to
Participate in
Frontline
-0.2 -0.3 – -0.1 <0.001 -0.2 -0.3 – -0.1 <0.001 -0.1 -0.1 – 0.0 0.162
Observations 2014 2014 2014
R
2
/ R
2
adjusted 0.081 / 0.073 0.083 / 0.075 0.110 / 0.102
Table 6: Log Transformation BIC Model.
anxiety depression
f
ea
r
Predictors Estimates CI (95%) P-Value
E
stimates CI (95%) P-Value Estimates CI (95%) P-Value
Intercept 4.0 3.9
–
4.1 <0.001 3.9 3.8
–
3.9 <0.001 3.2 3.2
–
3.2 <0.001
Wuhan as Origin 0.1 0.1
–
0.1 <0.001 0.1 0.1
–
0.1 <0.001 0.1 0.1
–
0.2 <0.001
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Willingness to
Participate in
Frontline
-0.2 -0.3 – -0.1 <0.001
Sex 0.1 0.1
–
0.2 <0.001
Observations 2014 2014 2014
R
2
/ R
2
adjusted 0.055 / 0.055 0.028 / 0.027 0.071 / 0.070
Table 7: Log Transformation AIC Model.
anxiety depression
f
ea
r
Predictors Estimates CI (95%)
P-
Value
Estimates CI (95%)
P-
Value
Estimates CI (95%)
P-
Value
Intercept 4.0 3.9
–
4.1 <0.001 3.9 3.8
–
4.1 <0.001 3.3 3.2
–
3.4 <0.001
Sex 0.0 0.0
–
0.1 0.035 0.0 0.0
–
0.1 0.002 0.1 0.1
–
0.2 <0.001
Marital Status -0.0 -0.1
–
0.0 0.098 -0.0 -0.1
–
0.0 0.119 0.0 0.0
–
0.1 0.015
Child Rearing 0.0 0.0
–
0.1 0.006 0.0 0.0
–
0.1 0.010
Monthly Household
Income
-0.0 -0.1 – -
0.0
0.001 -0.0 -0.1 – -
0.0
<0.001
Frontline Working
Duration
0.0 -0.0 – 0.0 0.055 0.0 -0.0 – 0.0 0.138
Wuhan as Origin 0.1 0.1
–
0.1 <0.001 0.1 0.1
–
0.1 <0.001 0.1 0.1
–
0.1 <0.001
Original Position -0.1 -0.1 – -
0.0
0.041 -0.0 -0.1 – 0.0 0.085 -0.1 -0.1 – -
0.0
<0.001
Position in Wuhan 0.1 -0.0
–
0.1 0.063 0.1 -0.0
–
0.1 0.052
Working Wards Changed 0.0 0.0
–
0.0 0.010 0.0 0.0
–
0.1 0.004
Prior Traning -0.0 -0.1 – -
0.0
0.002 -0.0 -0.1 – -
0.0
<0.001
Willingness to
Participate in Frontline
-0.2 -0.3 – -
0.1
<0.001 -0.2 -0.3 – -
0.1
<0.001
Age (year) 0.0 -0.0 – 0.0 0.059 -0.0 -0.0 – -
0.0
0.024
Clinical Experience -0.0 -0.0
–
0.0 0.102
Education 0.0 0.0
–
0.1 0.019
Average Working Hours
Shift
-0.0 -0.0 – -
0.0
<0.001
Prior Experience -0.1 -0.1 – -
0.0
<0.001
Observations 2014 2014 2014
R
2
/ R
2
adjusted 0.079 / 0.074 0.082 / 0.076 0.107 / 0.103
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Depression also had eight covariates that were
observed to be significant: sex, child-rearing,
monthly household income, Wuhan as origin,
position in Wuhan, working wards changed, prior
training, and willingness to participate in the
frontline. As there were too many variables, BIC and
AIC were applied. In AIC, except for the position in
Wuhan, other variables were the same as the full
model. BIC only selected Wuhan as the origin.
For fear, age, sex, education, average working
hours/shift, Wuhan as origin, original position, prior
experience, and willingness to participate in the
frontline were related to fear significantly. When AIC
and BIC selections were used, AIC selected the same
eight covariates, where BIC selected only two, and
they were sex and Wuhan as the origin. All detailed
AIC and BIC models and results can be found in the
table 7 as the selections were supporting the full
models.
When diagnosing the full log transformation
model, the residuals vs. fitted plot for anxiety and
depression seemed linear with no distinctive pattern
and homoscedasticity. The normal QQ plot showed
the models were mostly normal for anxiety and
depression. However, the R-squared here for anxiety
and depression were 0.0807 and 0.0830, respectively,
which was far from 1, so these models were not a
good fit. For fear, the plot was not linear and had
some patterns. The normal QQ plot also still showed
the model was not normally distributed and had some
normal error as the plot showed light-tailed. More
remedies were needed here.
Ordinal logistic regression was selected to fit a
better model since anxiety, depression, and fear are
categorical variables with an order. Table 8 showed
the association between the covariates and mental
health problems with ordinal logistic regression. Two
of the covariates in anxiety were significant: Wuhan
as origin, working wards changed. Depression also
had six covariates that were observed to be
significant: child-rearing, monthly household
income, Wuhan as origin, working wards changed,
prior training, and willingness to participate in the
frontline. The covariates related to fear significantly
were age, sex, education, average working
hours/shift, Wuhan as origin, original position, and
prior experience.
Table 8: Ordinal Logistic Regression Model.
anxiety depression
f
ea
r
Predictors
Odds
R
atios
CI (95%) P-Value
Odds
R
atios
CI (95%) P-Value
Odds
R
atios
CI (95%) P-Value
mild anxiety|moderate
anxiety
0.5 0.5 – 0.5 0.317
moderate anxiety|no
anxiety
0.9 0.7 – 1.1 0.834
no anxiety|severe anxiety 43.0 31.6
–
58.5 <0.001
Age (year) 1.0 1.0
–
1.0 0.961 1.0 1.0
–
1.0 0.825 1.0 0.9
–
1.0 0.048
Sex 0.9 0.7
–
1.2 0.666 1.1 0.8
–
1.4 0.659 1.8 1.4
–
2.3 <0.001
Marital Status 1.0 0.7
–
1.4 0.951 1.1 0.8
–
1.5 0.574 1.4 1.0
–
1.9 0.066
Child Rearing 0.9 0.7
–
1.3 0.720 0.6 0.5
–
0.9 0.006 1.0 0.7
–
1.4 0.996
Education 1.2 0.9
–
1.5 0.200 1.0 0.8
–
1.2 0.806 1.3 1.1
–
1.7 0.017
Professional Title 1.2 1.0
–
1.6 0.105 1.0 0.8
–
1.3 0.846 1.1 0.8
–
1.5 0.441
Monthly Household
Income
1.1 0.9 – 1.4 0.234 1.3 1.1 – 1.6 0.007 1.0 0.8 – 1.2 0.773
Clinical Experience 1.0 1.0
–
1.0 0.832 1.0 1.0
–
1.0 0.810 1.0 1.0
–
1.0 0.169
Frontline Working
Duration
1.0 1.0 – 1.0 0.509 1.0 1.0 – 1.0 0.100 1.0 1.0 – 1.0 0.232
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Average Working Hours
Shift
1.0 1.0 – 1.1 0.381 1.0 0.9 – 1.0 0.445 0.9 0.9 – 1.0 <0.001
Wuhan as Origin 0.7 0.5
–
0.8 <0.001 0.6 0.5
–
0.7 <0.001 2.3 1.9
–
2.9 <0.001
Original Position 1.1 0.7
–
1.8 0.620 1.2 0.8
–
2.0 0.404 0.6 0.4
–
1.0 0.045
Position in Wuhan 1.1 0.6
–
1.9 0.792 0.6 0.4
–
1.1 0.121 0.9 0.5
–
1.5 0.689
Working Wards
Changed
0.8 0.7 – 1.0 0.026 0.8 0.6 – 0.9 0.008 1.0 0.8 – 1.3 0.722
Prior Traning 1.0 0.8
–
1.3 0.760 1.3 1.0
–
1.7 0.028 0.9 0.7
–
1.2 0.488
Prior Experience 0.9 0.8
–
1.1 0.457 0.9 0.8
–
1.2 0.593 0.8 0.6
–
0.9 0.009
Willingness to
Participate in Frontline
1.2 0.7 – 1.9 0.581 1.7 1.1 – 2.7 0.023 0.6 0.3 – 1.1 0.117
mild
depression|moderate
depression
0.7 0.7 – 0.7 0.584
moderate depression|no
depression
1.1 0.8 – 1.4 0.889
no depression|severe
depression
140.2 102.3 – 192.0 <0.001
mild fear|moderate fear 0.0 0.0
–
0.1 <0.001
moderate fear|severe
fear
0.3 0.3 – 0.5 0.123
Observations 2014 2014 2014
R
2
Nagelkerke 0.017 0.043 0.115
5 DISCUSSION
In this study, we observed that Wuhan as origin,
working wards changed, and willingness to
participate in the frontline were the factors associated
with anxiety as they appeared in all models. Wuhan
as origin, change in working wards was positively
associated with anxiety, whereas willingness to
participate in the frontline was negatively associated
with anxiety. Depression was dependent on the
factors of child-rearing, monthly household income,
Wuhan as origin, working wards changed, prior
training, and willingness to participate in the
frontline. Other than monthly household income,
prior training, and willingness to participate in the
frontline were negatively correlated to depression;
other factors were positively related to depression.
The covariates associated with fear were age, sex,
education, average working hours/shift, Wuhan as
origin, original position, and prior experience. All
factors are positively correlated to fear, except for
age, and prior experience were negatively related to
fear.
When comparing the three mental health
problems, Wuhan as origin appeared in all three
mental health problems; other factors were all
different. Anxiety and depression are all correlated to
working wards changed. Other factors are not the
same between depression and fear.
Our findings are consistent with previous studies.
Ettman et al’s study conducted in America
(Prevalence of Depression Symptoms in US Adults
Before and During the COVID-19 Pandemic)
demonstrated that lower income, having less than
$5000 in savings, and having exposure to more
stressors were associated with greater risk of
depression symptoms during COVID-19 (Ettman,
Abdalla, Cohen, Sampson, Vivier, & Galea 2020).
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191
While this study pointed out that income is negatively
associated with the level of depression in general
during the pandemic, we limit the range to nurses and
demonstrate that nurses are more prone to have
depression symptoms when they earn less. Another
study conducted by Lai et al’s team indicated that
health care workers in Wuhan reported high rates of
symptoms of depression (Lai, Ma, Wang, Cai, Hu,
Wei, Hu 2019). Our study, based on its conclusion,
primarily focused on identifying the factors that are
related to this problem.
One of the strengths of our study is that our study
uses a dataset that was collected in February, when
the pandemic was severest in Wuhan. Hence, our
conclusion could maximally reflect the correlation
between depression and the pandemic on medical
workers. Compared to previous studies, our study has
a larger sample size with nurses not only from Wuhan
but also from other cities of China. Therefore, our
sample is less biased and could reflect a broader range
of medical workers.
However, there are certain limitations of our
study. For example, our study only focuses on the
mental status of nurses in Wuhan in February 2020,
and does not perform a time series analysis as the data
is only collected once. Also, the data about mental
health are self-evaluated and reported, hence the
results may not be very precise. Moreover, we did not
proceed further with subgroup analyses as that would
entail too many details and blur our main results.
Besides, we only specifically focus on the influence
of COVID-19 on Chinese’s nurses’ mental health, for
there is no other dataset of this sample size and
diverse response available from other countries, and
it would be hard to compare data from different
studies using different measuring standards of mental
health. Hence, there are certain spatial and temporal
limitations of our study.
6 CONCLUSIONS
Our results observed that Wuhan as origin, working
wards changed, and willingness to participate in the
frontline were associated with anxiety. Child-rearing,
monthly household income, Wuhan as origin,
working wards changed, prior training, and
willingness to participate in the frontline are
correlated to depression. Fear is depended on the
factors of age, sex, education, average working
hours/shift, Wuhan as origin, original position, and
prior experience. The three mental health problems
are all related to Wuhan as origin. Between anxiety
and depression, there is one factor that is the same,
working wards changed. These are the factors that
could cause problems for nurses’ mental health
problems. The best part of our researches that made
differences from other studies is that the population
that we concentrated on are the frontline nurses
during Covid-19, while other studies aim at general
nurses. Also, the dataset we found includes the nurses
from Wuhan and the nurses from other cities in
China, a relatively large sample size for us to use.
With the factors in our results, the Chinese
government could watch out for these factors and find
a way to change or avoid them in order for the nurses
to stay healthy psychologically during the pandemic
period. Most importantly, our research wants more
attention to mental health problems in China,
especially to the nurses, who have higher possibilities
of mental diseases. Only when the nurses are mentally
healthy, they could cure more patients. In the future,
we would like to conduct similar research on the
nurses at the hospital in other cities or
even outside of
China if the datasets are published. Moreover, it could
be great to follow up on the nurses throughout the
pandemic to see if the factors would change since it
could be significant to observe the status of nurses’
mental health all the time in order for them to work in
their best condition.
REFERENCES
Chen, C., Zhong, B., Hua, Y., Nie, F., & Wang, X. (n.d.).
Correlation between Depressive Symptom and Job
Stress among Nurses from First-Class Hospitals in
Wuhan-Journal of Environmental & Occupational
Medicine201208. Retrieved 2021-07-22, from
https://en.cnki.com.cn/Article en/CJFDTotal-
LDYX201208008.htm
Ettman, C. K., Abdalla, S. M., Cohen, G. H., Sampson, L.,
Vivier, P. M., & Galea, S. (2020, September).
Prevalence of Depression Symptoms in US Adults
Before and During the COVID-19 Pandemic. JAMA
Network Open, 3(9), e2019686. Retrieved 2021-07-25,
from https://jamanetwork.com/journals/
jamanetworkopen/fullarticle/2770146 doi:
10.1001/jamanetworkopen.2020 .19686
Gao, Y.-Q., Pan, B.-C., Sun, W., Wu, H., Wang, J.-N., &
Wang, L. (2012). Depressive symptoms among Chinese
nurses: prevalence and the associated factors. Journal of
Advanced Nursing, 68(5), 1166–1175. Retrieved
202107-22, from
https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1365
-2648 .2011.05832.x ( eprint:
https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1365
2648.2011.05832.x) doi: 10.1111/j.1365-
2648.2011.05832.x
ICHIH 2022 - International Conference on Health Big Data and Intelligent Healthcare
192
Gillis, L. (n.d.-a). Nurses across Canada are quitting their
jobs and leaving because of pandemic stress | The Star.
Retrieved 2021-07-22, from https://www.thestar.com/
news/canada/2021/04/13/nurses-across-canada-are-
quitting-their-jobs-and-leaving-because-of-pandemic-
stress.html?rf
Gillis, L. (n.d.-b). Over 50% of nurses at COVID-19
hospital wards in Japan have considered quitting | The
Japan Times. Retrieved 2021-0722, from
https://www.japantimes.co.jp/news/2021/05/23/nation
al/nurses-consider-quitting-covid-19-strain/
Havaei, F., Smith, P., Oudyk, J., & Potter, G. G. (2021,
May). The impact of the COVID-19 pandemic on
mental health of nurses in British Columbia, Canada
using trends analysis across three time points: The
impact of COVID-19 on nurse mental health. Annals of
Epidemiology, S1047–2797(21)00087–9. doi:
10.1016/j.annepidem .2021.05.004
Hu, D., Kong, Y., Li, W., Han, Q., Zhang, X., Zhu, L. X., .
. . Zhu, J. (2020, July). Frontline nurses’ burnout,
anxiety, depression, and fear statuses and their
associated factors during the COVID-19 outbreak in
Wuhan, China: A large-scale cross-sectional study.
EClinicalMedicine, 24, 100424. Retrieved 2021-07-22,
from https://www
.sciencedirect.com/science/article/pii/S258953702030
1681 doi: 10.1016/ j.eclinm.2020.100424
Kim, E.-y. (2021, January). Nurses at Covid-19 hospitals
quit over pay gap with dispatched ones. Retrieved
2021-07-25, from http://www.koreabiomed.com/news/
articleView.html?idxno=10108 (Section: Hospital)
Lai, J., Ma, S., Wang, Y., Cai, Z., Hu, J., Wei, N., . . . Hu,
S. (n.d.). Factors Associated With Mental Health
Outcomes Among Health Care Workers Exposed to
Coronavirus Disease 2019, 12.
Liu, Z., Wu, J., Shi, X., Ma, Y., Ma, X., Teng, Z., . . . Zeng,
Y. (2020, October). Mental Health Status of Healthcare
Workers in China for COVID-19 Epidemic. Annals of
Global Health, 86(1), 128. Retrieved 2021-07-25, from
https://
annalsofglobalhealth.org/articles/10.5334/aogh.3005/
doi: 10.5334/aogh.3005
Loo, P.-W., Wong, S., & Furnham, A. (2012, June). Mental
health literacy: A cross-cultural study from Britain,
Hong Kong and Malaysia. Asia-Pacific Psychiatry:
Official Journal of the Pacific Rim College of
Psychiatrists, 4(2), 113–125. doi:10.1111/j.1758-
5872.2012.00198.x
Maharaj, S., Lees, T., & Lal, S. (2019, January). Prevalence
and Risk Factors of Depression, Anxiety, and Stress in
a Cohort of Australian Nurses. International Journal of
Environmental Research and Public Health, 16(1), 61.
Retrieved 2021-07-25, from
https://www.mdpi.com/1660-4601/16/1/61 (Number: 1
Publisher: Multidisciplinary Digital Publishing
Institute) doi: 10.3390/ijerph16010061
Mark, G., & Smith, A. P. (2012). Occupational stress, job
characteristics, coping, and the mental health of nurses.
British Journal of Health Psychology, 17(3), 505–521.
Retrieved 2021-07-25, from https://bpspsychub
.onlinelibrary.wiley.com/doi/abs/10.1111/j.2044-
8287.2011.02051.x ( eprint:
https://bpspsychub.onlinelibrary.wiley.com/doi/pdf/10
.1111/j.20448287.2011.02051.x) doi: 10.1111/j.2044-
8287.2011.02051.x
Roelen, C. A. M., van Hoffen, M. F. A., Waage, S.,
Schaufeli, W. B., Twisk, J. W. R., Bjorvatn, B., . . .
Pallesen, S. (2018, February). Psychosocial work
environment and mental health-related long-term
sickness absence among nurses. International Archives
of Occupational and Environmental Health, 91(2), 195–
203. Retrieved 2021-07-22, from
https://doi.org/10.1007/s00420-017-1268-1 doi:
10.1007/ s00420-017-1268-1
Seabra, P. R. C., Lopes, J. M. d. O., Calado, M. E., &
Capelas, M. L. (2019, July). A national survey of the
nurses’ mental health - The case of Portugal. Nursing
Forum, 54(3), 425–433. doi: 10.1111/nuf.12350
Tzeng, N.-S. (2020). What could we learn from SARS
when facing the mental health issues related to the
COVID-19 outbreak? A nationwide cohort study in
Taiwan. ,9.
WHO, . (n.d.). Chinese Emergency Medical Teams support
COVID-19 response. Retrieved 2021-07-22, from
https://www.who.int/china/news/feature-stories/detail/
chinese-emergency-medical-teams-support-covid-19-
response
Wong, D. F. K., & Li, J. C. M. (2014, June). Cultural
Influence on Shanghai Chinese People’s Help-Seeking
for Mental Health Problems: Implications for Social
Work Practice. The British Journal of Social Work,
44(4), 868–885. Retrieved 2021-0725, from
https://doi.org/10.1093/bjsw/bcs180 doi:
10.1093/bjsw/bcs180
Xin, S., Jiang, W., & Xin, Z. (2019). Changes in Chinese
nurses’ mental health during 1998–2016: A cross-
temporal meta-analysis. Stress and Health, 35(5), 665–
674. Retrieved 2021-07-25, from
https://onlinelibrary.wiley.com/doi/abs/10.1002/
smi.2907 (eprint:
https://onlinelibrary.wiley.com/doi/pdf/10.1002/smi.2
907) doi: 10.1002/smi.2907
Zung, W. W. (1965, January). A SELF-RATING
DEPRESSION SCALE. Archives of General
Psychiatry, 12, 63–70. doi:
10.1001/archpsyc.1965.01720310065008
Zung, W. W. (1971, December). A rating instrument for
anxiety disorders. Psychosomatics, 12(6), 371–379.
doi: 10.1016/S0033-3182(71)71479-0
The Impact of COVID-19 on Nurses’ Mental Healthcare and Sustainability by Data Analysis
193
