Statistics Question

1. Develop tables for each of the statistical test in your study.

2. Apply Linear regression, correlation, descriptive statistics and hypothesis testing to your primary data.

3. Each table must be labeled with a review of the results underneath the table.

My topic is To assess the factors that impact mental health in the United States?

Data

The data for this study was collected through an online survey distributed to healthcare workers in China. The survey asked about the healthcare workers’ exposure to the coronavirus, mental health status, and satisfaction with their mental health care; The secondary data sources used for this study were articles from the Chinese media, scientific papers, and online surveys.

Research questions for the SMAC project

1. Does age affect mental health in the United States?

2. Does genetics have an impact on mental health in the United States?

3. Does identity have an impact on mental health in the United States?

4. Do income levels affect an individual’s mental health in the United States?

5. Does marital status affect an individual’s mental health in the United States?

Data Analysis
Demographics
Prior to evaluating the survey findings, it is important to understand the population that
actually completed the survey. Several demographic questions were asked of the respondents
such as gender, age, years of service, employment status, field, and ethnicity. Figures 13-18
reflect the breakdown of the 219 surveys evaluated. It was revealed that a very strong cross
sectional randomize sample of the population was obtained.
Figure 13 – Male vs. Female
Figure 14 – Age
Figure 15 – Years of Service
Figure 16 – Employment Status
Figure 17 – Field
Figure 18 – Ethnicity
Descriptive Statistics
The mean, median, mode, and standard deviation of each of the X variables were
developed and are reflected in Figure 19. The mean is the statistical average, the median is the
middle value, and the mode is the response most given. The standard deviation is the spread
from the mean. The higher the deviation, the further from the mean the variable is. The
variables were compared to the hypothesis variable of Motivation (X1) which the researchers
predicted to be the strongest independent variable.
Figure 19 – Mean Median, Mode, and Standard Deviation of the Independent Variables.
Independent Variables
Q2.
X1. Motivation
Q3.
X2. Attitude
Q4.
X3. Promotion
Q5.
X4. Nepotism
Q6.
X5. Morale
Q7.
X6. Politics
Q8.
X1. Motivation
Q9.
X7. Leadership
Q10.
X8. Ethics
Q11.
X9. Corporate Finance
Q12.
X10. Culture
Descriptive Statistics
Mean
Median
3.19069767
3
4.74883721
5
2.44651163
2
3.66046512
4
3.50232558
4
2.64186047
3
4.11162791
4
3.40930233
4
4.1627907
4
3.89302326
4
3.54418605
4
Mode
4
5
2
4
4
2
4
4
4
4
4
Standard Deviation
1.182389066
0.465828622
1.011926274
0.952649904
1.080301181
1.075259707
0.884236114
1.072122089
0.746650857
0.866132058
1.02156704
The variables with the highest means were Attitude (X2), followed by Ethics (X8), and
Motivation (X1, Q8). The variables with the lowest means were Promotion (X4), Politics (X6),
Motivation (X1, Q2). Attitude (X2) had the lowest standard deviation followed by Ethics (X8),
and Morale (X5). Attitude (X2) was the only variable with a mode of 5 and Promotion (X3) was
the only one with a mode of 2. Seven of the variables had modes of 4 with means ranging from
3.1 to 4.1, and standard deviations ranging from 0.8 to 1.1.
As it relates to Motivation (X1), the mean was higher for males (3.3) than for females
(3.0). The standard deviation for males was 1.1 compared to females at 1.24. The mode for
males was 4 while for females it was 2 (Appendix B).
The mean for Motivation (X1) for “age” was highest for ages 50-59 at 3.52 and lowest
for ages 30-39 at 3.02. The standard deviation was lowest for ages 60-over and was trailed very
closely by ages 50-59. The highest standard deviation went to ages 40-49. The mode was a 4
for all groups (Appendix C).
The mean for Motivation (X1) for “years of service” was highest for those working over
20 years with 3.57 and lowest for those working 15-20 years with 2.96. The standard deviation
for those working over 20 years was the lowest at 1.01 and highest for those working 5-9 years at
1.3. The mode was a 4 for all groups except those working from 5-9 years which was 2.
(Appendix D).
For “ethnicity”, the mode for Motivation (X1) was highest for Native Americans at 3.5
and lowest for African Americans at 2.5. Native Americans showed the lowest standard
deviation at 0.7 and Asians demonstrated the highest at 1.34. The mode varied with Asian and
Caucasian scoring 4, Hispanic/Latino and Other 3, and African American showing 2 (Appendix
E).
Correlation Analysis
The results from the correlation analysis are reflected in Figure 20. There were several
variables that had strong positive correlation factors. There were three variables that had strong
relationships with Decision making; Motivation (X1), Promotion (X3) and Leadership (X7).
Two variables had a relationship factor above 60%; Motivation (X1) with the strongest
relationship at .65 and Leadership (X7) combined with Motivation (X1) at .64. Leadership had
the strongest relationships among the variables with several scoring above 50%.
The variables that had negative correlation factors with Decision making were Nepotism
(X4), Politics (X6), and Corporate finance (X9).
Figure 20 – Correlation Analysis Results
Y.
Decision
Making
X1.
Motivation
X2.
Attitude
X3.
Promotion
X4.
Nepotism
X5.
Morale
X6.
Politics
X1.
Motivat
ion
X7.
Leadership
X8.
Ethics
X9.
Corporate
Finance
Y. Decision
Making
1.0000
X1. Motivation
0.6513
1.0000
X2. Attitude
0.0339
0.0704
1.0000
X3. Promotion
0.5950
0.4948
-0.0088
1.0000
X4. Nepotism
-0.2278
-0.1953
0.0070
-0.1910
1.0000
X5. Morale
0.4364
0.5466
0.1497
0.4436
-0.0651
1.0000
X6. Politics
-0.2814
-0.2952
0.0435
-0.1659
0.2731
-0.1340
1.0000
X1. Motivation
0.3641
0.3282
0.1024
0.3514
-0.0879
0.4302
-0.3264
1.0000
X7. Leadership
0.5722
0.6422
0.0477
0.4811
-0.1607
0.5600
-0.3060
0.5135
1.0000
X8. Ethics
0.0606
0.0758
0.1181
0.1013
-0.0730
0.2342
0.0497
0.2555
0.1265
1.0000
X9. Corporate
Finance
-0.0129
-0.0028
0.0952
-0.0359
0.1087
0.0227
0.0289
-0.0026
0.0071
0.0921
1.0000
X10. Culture
0.4408
0.5056
0.0038
0.3425
-0.0973
0.4498
-0.1068
0.3359
0.5210
0.0977
-0.0026
Hypothesis Testing
Two types of hypothesis testing were conducted to determine if the Null Hypothesis (Ho)
should be accepted or rejected. The first test was ANOVA, standing for analysis-of-variance.
The ANOVA test assisted in identifying factors that influence a given data set. After the
ANOVA test is performed, the analyst is able to perform further analysis on the variables that
X10.
Culture
1.0000
are statistically contributing to the data set’s variability. With ANOVA testing, the probability
value (P value) and F critical value are reviewed. If the P value if less than 0.05, the null
hypothesis (Ho) is rejected in favor of the alternate hypothesis (Ha). Also, if the F critical value
is less than the F value, the same conclusion can be drawn. Figure 21 reflects the results of the
ANOVA test on the set of the X variables in this research. The P value is significantly less than
0.05 and the F critical is substantially less than the F value.
Figure 21 – ANOVA Testing on all Independent Variables
Anova: Single Factor
SUMMARY
Groups
X1. Motivation
X2. Attitude
X3. Promotion
X4. Nepotism
X5. Morale
X6. Politics
X1. Motivation
X7. Leadership
X8. Ethics
X9. Corporate Finance
X10. Culture
Count
215
215
215
215
215
215
215
215
215
215
215
Sum
686
1021
526
787
753
568
884
733
895
837
762
Average
3.190698
4.748837
2.446512
3.660465
3.502326
2.64186
4.111628
3.409302
4.162791
3.893023
3.544186
Variance
1.398044
0.216996
1.023995
0.907542
1.167051
1.156183
0.781874
1.149446
0.557488
0.750185
1.043599
ANOVA
Source of Variation
Between Groups
Within Groups
Total
SS
955.7607
2172.614
3128.375
df
MS
95.57607
0.922946
F
103.5555
10
2354
2364
P-value
3.1E-178
F crit
1.834715
Further ANOVA testing was conducted to determine if the X1 variable of Motivation
impacted the Y variable of Decision making. Figure 22 reveals that both X1 variable questions
were used in the statistical analysis yielded very strong results. Again, the P value was
significantly less than 0.05 and the F critical was substantially less than the F value.
Figure 22 – ANOVA Testing on Independent Variable Motivation
Anova: Single Factor
SUMMARY
Groups
X1. Motivation. Q2
X1. Motivation. Q8
Count
215
215
Sum
686
884
Average
Variance
3.190697674
4.111627907
1.398044
0.781874
ANOVA
Source of Variation
Between Groups
Within Groups
Total
SS
91.17209
466.5023
557.6744
df
1
428
429
MS
F
P-value
F crit
91.17209302
1.089958705
83.6473
2.4E-18
3.86328
The next test of hypothesis is the two-tailed test that was used to compare the null
hypothesis (Ho) to the alternative hypothesis (Ha) as it relates to how males responded versus
females. Again, the P value is analyzed for its significance. As reflected in Figure 23, the P
value for each independent X variable is greater than 0.05 meaning there is little statistical
difference in the way males responded versus females.
Figure 23 – Two Tail Test of Hypothesis; Male vs. Female
Two Tail Test of Hypothesis: Male vs. Female
X Factor
P-Value
Meaning
X1. Motivation
0.076488955
p value is > 0.05
X2. Attitude
0.07820858
p value is > 0.05
X3. Promotion
0.980322169
p value is > 0.05
X4. Nepotism
0.875623409
p value is > 0.05
X5. Morale
0.841457823
p value is > 0.05
X6. Politics
0.836675542
p value is > 0.05
X1. Motivation
0.977609365
p value is > 0.05
X7. Leadership
0.268470688
p value is > 0.05
X8. Ethics
0.54783813
p value is > 0.05
X9. Corporate Finance
0.366110225
p value is > 0.05
X10. Culture
0.193487239
p value is > 0.05
Reject Ho?
No
No
No
No
No
No
No
No
No
No
No
Regression Analysis
Linear and multiple regression analysis were conducted on the independent X variables to
determine or predict the significance of the relationship on the dependent Y variable. Three
areas were focused on; Multiple R, R square, and the P value. Multiple R is an index reflecting
relationships between the variables. The index ranges from 0 to 1. The closer to 1 the variable is,
the stronger the relationship between the variables is. R square determines the percentage of
change or variation in the Y variable. The closer R Square is to 1, the greater the impact the
independent variable has on the dependent variable. The effects of the P value have been
previously stated.
Figure 24 – Linear Regression Summary Chart for X Variables
Independent
Variable
X1. Motivation
X2. Attitude
X3. Promotion
X4. Nepotism
X5. Morale
X6. Politics
X1. Motivation
X7. Leadership
X8. Ethics
X9. Corporate
Finance
X10. Culture
Multiple R
0.651310143
0.033914499
0.594980451
0.227757952
0.436401408
0.281354331
0.364053647
0.572215723
0.060592784
R2
0.424204902
0.001150193
0.354001737
0.051873685
0.190446188
0.07916026
0.132535058
0.327430833
0.003671486
P Value
2.4507E-27
0.620933677
5.60744E-22
0.000766973
2.07645E-11
2.83547E-05
3.86864E-08
4.26197E-20
0.376643352
Level of Significance
Significant
Not Significant
Significant
Significant
Significant
Significant
Significant
Significant
Not Significant
0.012885216
0.440757334
0.000166029
0.194267028
0.851001498
1.24276E-11
Not Significant
Significant
Figure 24 is a summary chart reflecting the results of the linear regression analysis to
determine the significance of the independent variables. There were three X variables that had
no significance on Decision making; Attitude (X2), Ethics (X8), Corporate finance (X9). All
had P values greater than 0.05, and low Multiple R and R square ratings.
The factors having the highest Multiple Rs were Motivation (X1) at .65, Promotion (X3)
at .59 and Leadership (X7) at .57. These variables also had the lowest P values in the same
order of significance. Nepotism, (X4), Morale (X5), Politics (X6), and Culture (X10) all had
significant P values. However, the Multiple Rs and R squared for these variables were low.
A multiple regression analysis was conducted that eliminated the three factors that were
not significant. This “sifting out” process allows for a stronger analysis. Figure 25 reflects very
significant P values for Motivation (X1) and Promotion (X3). Furthermore, the Multiple R and
R squared indexes are significant. Also, recall the relationship between the Significant F and F
value, which in this case is also substantial.
Figure 25 – Multiple Regression with only Significant X Variables
Regression Statistics
Multiple R
0.742599714
R Square
0.551454336
Adjusted R
Square
0.534035087
Standard Error
0.716534884
Observations
215
ANOVA
df
Regression
Residual
Total
8
206
214
SS
130.0303675
105.7649813
235.7953488
MS
16.25379594
0.51342224
F
31.65775591
t Stat
2.57956693
5.137414088
5.488662055
P-value
0.010588404
6.44447E-07
1.18287E-07
0.268208778
0.051562147
-1.11019606
0.436672285
1.053480177
Standard
Error
0.378869425
0.060784287
0.059190522
X6. Politics
Coefficients
0.977319041
0.31227405
0.324876774
0.060388336
0.026352802
0.054319699
X1. Motivation
0.043290986
0.068748189
0.629703657
0.529586274
X7. Leadership
0.122371316
0.069774972
1.753799565
0.080951995
X10. Culture
0.081399854
0.05945558
1.369086868
0.172463005
Intercept
X1. Motivation
X3. Promotion
X4. Nepotism
X5. Morale
0.054394299
0.060349152
0.662806381
0.293355061
Significance
F
4.53127E-32
Lower 95%
0.230360305
0.192434995
0.208179899
0.167629232
0.145333968
0.155976875
0.092249274
0.015193295
0.035819594
Upper 95%
1.724277777
0.432113105
0.441573648
0.04685256
0.092628364
0.047337477
0.178831246
0.259935927
0.198619301
Lower 95.0%
0.230360305
0.192434995
0.208179899
0.167629232
0.145333968
0.155976875
0.092249274
0.015193295
0.035819594
Upper 95.0%
1.724277777
0.432113105
0.441573648
0.04685256
0.092628364
0.047337477
0.178831246
0.259935927
0.198619301
Lastly, a multiple regression analysis was conducted using just the two Motivation (X1)
questions (Q2, Q8). Figure 26 reflects once again very strong P values, Multiple R, and
Significant F values.
Figure 26. Multiple Regression using only Motivation Variables
SUMMARY
OUTPUT
Regression Statistics
0.67046780
Multiple R
9
0.44952708
R Square
4
Adjusted R
0.44433394
Square
3
0.78246994
Standard Error
4
Observations
215
ANOVA
df
Regression
2
Residual
212
Total
214
Intercept
X1. Motivation.
Q2
X1. Motivation.
Q8
Coefficients
0.68479538
9
0.52913567
0.19997955
SS
105.996395
5
129.798953
4
235.795348
8
Standard
Error
0.26290039
4
0.04788978
8
0.06403760
4
MS
52.9981977
4
0.61225921
4
F
86.5616989
2
Significance
F
3.29618E28
t Stat
2.60477125
3
11.0490291
9
P-value
0.00984442
5
1.03923E22
0.00204117
6
Lower 95%
0.16656167
2
0.43473450
7
0.07374753
8
3.12284559
Upper 95%
1.20302910
6
0.62353683
3
0.32621156
1
Lower
95.0%
0.16656167
2
0.43473450
7
0.07374753
8
Upper
95.0%
1.20302910
6
0.62353683
3
0.32621156
1
Research Findings
The descriptive statistics indicted that the researcher’s prediction of the strongest
variable, Motivation (X1), had an average mean of 3.6, a mode of 4, and an average standard
deviation of .99. This can be interpreted to signify that the respondents tended to agree that
Motivation is connected to Decision making with little deviation from the overall average
respondent. Males felt stronger than females, though not by much. It appeared that as people
aged, they tended to feel stronger about this concept as well although tenure did not yield any
significant differences in outcomes. Although Native Americans felt the strongest, the
responses were only 1.3% of the total and therefore immaterial. The Asian and Caucasian
communities felt stronger than the African American community, which tended to disagree the
most with low deviation.
A strong relationship and correlation was established between the independent variable
of Motivation and Decision making. The correlation analysis indicated that Motivation (X1)
clearly had the strongest positive relationship with Decision making amongst all of the
variables evaluated. The test of hypothesis revealed a probability value of substantial
proportions indicating that the null hypothesis Ho should be rejected in favor of the alternative
hypothesis Ha.
The significance of the relationship was further validated through the regression
analysis, which resulted in Motivation having the strongest linear relationship and lowest
probability values. When the variables that had little impact, such as a person’s attitude,
organizational ethics, and corporate finance, were removed from the equation, Motivation
clearly separated itself from the pack with by far the strongest probability value. Furthermore,
when Motivation was solely compared to Decision making, the result was significant.
Conclusion
This research study focused on assessing the factors that impacted decision making in the
workplace. Applying the scientific method to the collected primary data and supporting all
findings with the appropriate literature review led to the conclusion that motivation is the
strongest factor that impacts decision making in an organization. These findings fit the high
performance cycle theory that focused on the relationship between motivation, performance and
decision making. It is important to note that leadership and promotion also have a high impact
on decision making and should be considered for future research testing as shown in Figure 27.
Figure 27 – Impact of Variables
Variable
X1
X3
X7
X5
X6
X10
X4
X2
X8
X9
Research Question
Impact
Does Motivation impact decision making in business?
Do Promotions impact decision making in business?
Does Leadership impact decision making in business?
Does Morale impact decision making in business?
Does Politics impact decision making in business?
Does Culture impact decision making in business?
Does Nepotism impact decision making in business?
Does Attitude impact decision making in business
Do Ethics impact decision making in business?
Does Corporate Finance impact decision making in business?
High
High
High
Medium
Medium
Medium
Low
None
None
None
It was also determined that morale, politics, and culture had a medium impact on decision
making and based on the research findings, play an important role on employees’ feelings
regarding organization decisions made in a fair way.
Nepotism surprisingly had a low impact on the decision making process but it is still
subject to further research because of its potential impact on employee’s morale. Attitude,
ethics, and corporate finance did not have an impact on the decision making process, although
the literature review emphasized the role they play in an organization’s operations and
performance.
Recommendations
Based on the research findings, the authors of this research can recommend that
organizations would be wise to focus attention and resources on Motivation, Promotion, and
Leadership. The researchers can assume that organizations that improve the management and
development of these three variables will be better perceived as an organization where
employees feel that the decision making process is done fairly.
Encouraging employees to get involved in program implementation, but most
importantly, understanding the mission statement of the company and reason for existence, can
promote motivation. Organizations can also improve their efforts on creating a culture of
employee engagement and development which will result in professional growth and drive. It is
important to note that motivated employees will tend to perform better in an organization and
will demonstrate characteristics of high morale and job satisfaction.
Additionally, it is vital for an organization to carefully monitor the employee’s perception
of the organization’s promotion process. The wrong perception can negatively impact
employees’ views about the decision making process and affect their performance. Establishing
transparency and genuine equality will serve as the support employees need to function without a
sense of biased treatment and unfair practices.
Lastly, high performing employees will also tend to take a leadership role in the
organization, so it is important for companies to offer the appropriate leadership development
programs to maximize the positive impact these program could have on the work environment
and overall operations.
Original Investigation | Psychiatry
Factors Associated With Mental Health Outcomes Among Health Care Workers
Exposed to Coronavirus Disease 2019
Jianbo Lai, MSc; Simeng Ma, MSc; Ying Wang, MSc; Zhongxiang Cai, MD; Jianbo Hu, MSc; Ning Wei, MD; Jiang Wu, MD; Hui Du, MD; Tingting Chen, MD; Ruiting Li, MD;
Huawei Tan, MD; Lijun Kang, MSc; Lihua Yao, MD; Manli Huang, MD; Huafen Wang, BD; Gaohua Wang, MD; Zhongchun Liu, MD; Shaohua Hu, MD
Abstract
Key Points
IMPORTANCE Health care workers exposed to coronavirus disease 2019 (COVID-19) could be
psychologically stressed.
Question What factors are associated
with mental health outcomes among
health care workers in China who are
OBJECTIVE To assess the magnitude of mental health outcomes and associated factors among
health care workers treating patients exposed to COVID-19 in China.
treating patients with coronavirus
disease 2019 (COVID-19)?
Findings In this cross-sectional study of
DESIGN, SETTINGS, AND PARTICIPANTS This cross-sectional, survey-based, region-stratified
1257 health care workers in 34 hospitals
study collected demographic data and mental health measurements from 1257 health care workers
equipped with fever clinics or wards for
in 34 hospitals from January 29, 2020, to February 3, 2020, in China. Health care workers in hospitals
patients with COVID-19 in multiple
equipped with fever clinics or wards for patients with COVID-19 were eligible.
regions of China, a considerable
proportion of health care workers
MAIN OUTCOMES AND MEASURES The degree of symptoms of depression, anxiety, insomnia, and
reported experiencing symptoms of
distress was assessed by the Chinese versions of the 9-item Patient Health Questionnaire, the 7-item
depression, anxiety, insomnia, and
Generalized Anxiety Disorder scale, the 7-item Insomnia Severity Index, and the 22-item Impact of
distress, especially women, nurses,
Event Scale–Revised, respectively. Multivariable logistic regression analysis was performed to
those in Wuhan, and front-line health
identify factors associated with mental health outcomes.
care workers directly engaged in
diagnosing, treating, or providing
RESULTS A total of 1257 of 1830 contacted individuals completed the survey, with a participation
nursing care to patients with suspected
rate of 68.7%. A total of 813 (64.7%) were aged 26 to 40 years, and 964 (76.7%) were women. Of all
or confirmed COVID-19.
participants, 764 (60.8%) were nurses, and 493 (39.2%) were physicians; 760 (60.5%) worked in
hospitals in Wuhan, and 522 (41.5%) were frontline health care workers. A considerable proportion of
participants reported symptoms of depression (634 [50.4%]), anxiety (560 [44.6%]), insomnia (427
[34.0%]), and distress (899 [71.5%]). Nurses, women, frontline health care workers, and those
working in Wuhan, China, reported more severe degrees of all measurements of mental health
symptoms than other health care workers (eg, median [IQR] Patient Health Questionnaire scores
among physicians vs nurses: 4.0 [1.0-7.0] vs 5.0 [2.0-8.0]; P = .007; median [interquartile range
{IQR}] Generalized Anxiety Disorder scale scores among men vs women: 2.0 [0-6.0] vs 4.0 [1.0-7.0];
Meaning These findings suggest that,
among Chinese health care workers
exposed to COVID-19, women, nurses,
those in Wuhan, and front-line health
care workers have a high risk of
developing unfavorable mental health
outcomes and may need psychological
support or interventions.
P < .001; median [IQR] Insomnia Severity Index scores among frontline vs second-line workers: 6.0 [2.0-11.0] vs 4.0 [1.0-8.0]; P < .001; median [IQR] Impact of Event Scale–Revised scores among those in Wuhan vs those in Hubei outside Wuhan and those outside Hubei: 21.0 [8.5-34.5] vs 18.0 [6.028.0] in Hubei outside Wuhan and 15.0 [4.0-26.0] outside Hubei; P < .001). Multivariable logistic regression analysis showed participants from outside Hubei province were associated with lower risk of experiencing symptoms of distress compared with those in Wuhan (odds ratio [OR], 0.62; 95% + Invited Commentary + Supplemental content Author affiliations and article information are listed at the end of this article. CI, 0.43-0.88; P = .008). Frontline health care workers engaged in direct diagnosis, treatment, and care of patients with COVID-19 were associated with a higher risk of symptoms of depression (OR, 1.52; 95% CI, 1.11-2.09; P = .01), anxiety (OR, 1.57; 95% CI, 1.22-2.02; P < .001), insomnia (OR, 2.97; 95% CI, 1.92-4.60; P < .001), and distress (OR, 1.60; 95% CI, 1.25-2.04; P < .001). (continued) Open Access. This is an open access article distributed under the terms of the CC-BY License. JAMA Network Open. 2020;3(3):e203976. doi:10.1001/jamanetworkopen.2020.3976 (Reprinted) Downloaded From: https://jamanetwork.com/ on 10/18/2022 March 23, 2020 1/12 JAMA Network Open | Psychiatry Mental Health Outcomes Among Health Care Workers Exposed to COVID-19 Abstract (continued) CONCLUSIONS AND RELEVANCE In this survey of heath care workers in hospitals equipped with fever clinics or wards for patients with COVID-19 in Wuhan and other regions in China, participants reported experiencing psychological burden, especially nurses, women, those in Wuhan, and frontline health care workers directly engaged in the diagnosis, treatment, and care for patients with COVID-19. JAMA Network Open. 2020;3(3):e203976. doi:10.1001/jamanetworkopen.2020.3976 Introduction Since the end of December 2019, the Chinese city of Wuhan has reported a novel pneumonia caused by coronavirus disease 2019 (COVID-19), which is spreading domestically and internationally.1 The virus has been named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this report, we will refer to the disease, COVID-19. According to data released by the National Health Commission of China, the number of confirmed cases in mainland China has increased to 80 151 as of March 2, 2020,2 and confirmed cases have been reported in more than a dozen other countries. Moreover, person-to-person transmission has been recorded outside mainland China.3 On January 30, 2020, the World Health Organization held an emergency meeting and declared the global COVID-19 outbreak a public health emergency of international concern.4 Facing this critical situation, health care workers on the front line who are directly involved in the diagnosis, treatment, and care of patients with COVID-19 are at risk of developing psychological distress and other mental health symptoms. The ever-increasing number of confirmed and suspected cases, overwhelming workload, depletion of personal protection equipment, widespread media coverage, lack of specific drugs, and feelings of being inadequately supported may all contribute to the mental burden of these health care workers. Previous studies have reported adverse psychological reactions to the 2003 SARS outbreak among health care workers.5-8 Studies showed that those health care workers feared contagion and infection of their family, friends, and colleagues,5 felt uncertainty and stigmatization,5,6 reported reluctance to work or contemplating resignation,6 and reported experiencing high levels of stress, anxiety, and depression symptoms,7 which could have long-term psychological implications.7 Similar concerns about the mental health, psychological adjustment, and recovery of health care workers treating and caring for patients with COVID-19 are now arising. Psychological assistance services, including telephone-, internet-, and application-based counseling or intervention, have been widely deployed by local and national mental health institutions in response to the COVID-19 outbreak. On February 2, 2020, the State Council of China announced that it was setting up nationwide psychological assistance hotlines to help during the epidemic situation.9 However, evidence-based evaluations and mental health interventions targeting front-line health care workers are relatively scarce. To address this gap, the aim of current study was to evaluate mental health outcomes among health care workers treating patients with COVID-19 by quantifying the magnitude of symptoms of depression, anxiety, insomnia, and distress and by analyzing potential risk factors associated with these symptoms. Participants from Wuhan city (the capital of Hubei province) and other areas inside and outside Hubei province in China were enrolled in this survey to compare interregional differences. This study aimed to provide an assessment of the mental health burden of Chinese health care workers, which can serve as important evidence to direct the promotion of mental wellbeing among health care workers. JAMA Network Open. 2020;3(3):e203976. doi:10.1001/jamanetworkopen.2020.3976 (Reprinted) Downloaded From: https://jamanetwork.com/ on 10/18/2022 March 23, 2020 2/12 JAMA Network Open | Psychiatry Mental Health Outcomes Among Health Care Workers Exposed to COVID-19 Methods Study Design This study followed the American Association for Public Opinion Research (AAPOR) reporting guideline. Approval from the clinical research ethics committee of Renmin Hospital of Wuhan University was received before the initiation of this study. Verbal informed consent was provided by all survey participants prior to their enrollment. Participants were allowed to terminate the survey at any time they desired. The survey was anonymous, and confidentiality of information was assured. The study is a cross-sectional, hospital-based survey conducted via a region-stratified, 2-stage cluster sampling from January 29, 2020, to February 3, 2020. During this period, the total confirmed cases of COVID-19 exceeded 10 000 in China. To compare the interregional differences of mental health outcomes among health care workers in China, samples were stratified by their geographic location (ie, Wuhan, other regions inside Hubei province, and regions outside Hubei province). Because Wuhan was most severely affected, more hospitals in Wuhan were sampled. Hospitals equipped with fever clinics or wards for COVID-19 were eligible to participate in this survey. A total of 20 hospitals in Wuhan (10 designated by the local government to treat COVID-19 and 10 nondesignated), 7 hospitals in other regions of Hubei province, and 7 hospitals from 7 other provinces with a high incidence of COVID-19 (1 hospital from each province) were included. In total, 34 hospitals were involved. Milestone events during the outbreak of COVID-19 and the duration of this study are presented in the eFigure in the Supplement. Participants One clinical department was randomly sampled from each selected hospital, and all health care workers in this department were asked to participate in this study. The target sample size of participants was determined using the formula N = Zα2P(1 − P) / d2, in which α = 0.05 and Zα = 1.96, and the estimated acceptable margin of error for proportion d was 0.1. The proportion of health care workers with psychological comorbidities was estimated at 35%, based on a previous study of the SARS outbreak.7 To allow for subgroup analyses, we amplified the sample size by 50% with a goal of at least 1070 completed questionnaires from participants. Outcomes and Covariates We focused on symptoms of depression, anxiety, insomnia, and distress for all participants, using Chinese versions of validated measurement tools.10-13 Accordingly, the 9-item Patient Health Questionnaire (PHQ-9; range, 0-27),10 the 7-item Generalized Anxiety Disorder (GAD-7) scale (range, 0-21),11 the 7-item Insomnia Severity Index (ISI; range, 0-28),12 and the 22-item Impact of Event Scale–Revised (IES-R; range, 0-88)13 were used to assess the severity of symptoms of depression, anxiety, insomnia, and distress, respectively. The total scores of these measurement tools were interpreted as follows: PHQ-9, normal (0-4), mild (5-9), moderate (10-14), and severe (15-21) depression; GAD-7, normal (0-4), mild (5-9), moderate (10-14), and severe (15-21) anxiety; ISI, normal (0-7), subthreshold (8-14), moderate (15-21), and severe (22-28) insomnia; and IES-R, normal (0-8), mild (9-25), moderate (26-43), and severe (44-88) distress. These categories were based on values established in the literature.10-13 The cutoff score for detecting symptoms of major depression, anxiety, insomnia, and distress were 10, 7,14 15, and 26, respectively. Participants who had scores greater than the cutoff threshold were characterized as having severe symptoms. Demographic data were self-reported by the participants, including occupation (physician or nurse), sex (male or female), age (18-25, 26-30, 31-40, or >40 years), marital status, educational level
(ⱕundergraduate or ⱖpostgraduate), technical title (junior, intermediate, or senior), geographic
location (Wuhan, Hubei province outside Wuhan, or outside Hubei province), place of residence
(urban or rural), and type of hospital (secondary or tertiary). The different technical titles of
respondents refer to the professional titles certificated by the hospital. Participants were asked
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Mental Health Outcomes Among Health Care Workers Exposed to COVID-19
whether they were directly engaged in clinical activities of diagnosing, treating, or providing nursing
care to patients with elevated temperature or patients with confirmed COVID-19. Those who
responded yes were defined as frontline workers, and those who answered no were defined as secondline workers.
Statistical Analysis
Data analysis was performed using SPSS statistical software version 20.0 (IBM Corp). The
significance level was set at α = .05, and all tests were 2-tailed. The original scores of the 4
measurement tools were not normally distributed and so are presented as medians with interquartile
ranges (IQRs). The ranked data, which were derived from the counts of each level for symptoms of
depression, anxiety, insomnia, and distress, are presented as numbers and percentages. The
nonparametric Mann-Whitney U test and Kruskal-Wallis test were applied to compare the severity of
each symptom between 2 or more groups. To determine potential risk factors for symptoms of
depression, anxiety, insomnia, and distress in participants, multivariable logistic regression analysis
was performed, and the associations between risk factors and outcomes are presented as odds ratios
(ORs) and 95% CIs, after adjustment for confounders, including sex, age, marital status, educational
level, technical title, place of residence, working position (first-line or second-line), and type of
hospital.
Results
Demographic Characteristics
In the study, among the 1830 health care workers (702 [38.4%] physicians and 1128 [61.6%] nurses)
asked to participate, 1257 respondents (68.7%) completed the survey. The occupational and
geographic data of nonrespondents were similar to those of respondents (eTable 1 in the
Supplement). Of the 1257 responding participants, 493 (39.2%) were physicians, and 764 (60.8%)
were nurses. The response rates for physicians and nurses were 70.2% and 67.7%, respectively. Of
the participants, 760 (60.5%) worked in Wuhan, 261 (20.8%) worked in Hubei province outside
Wuhan, and 236 (18.8%) worked outside Hubei province. Most participants were women (964
[76.7%]), were aged 26 to 40 years (813 [64.7%]), were married, widowed, or divorced (839
[66.7%]), had an educational level of undergraduate or less (953 [75.8%]), had a junior technical title
(699 [55.6%]), and worked in tertiary hospitals (933 [74.2%]). A total of 522 participants (41.5%)
were frontline health care workers directly engaged in diagnosing, treating, or caring for patients
with or suspected to have COVID-19. Nearly all participants (1220 [97.1%]) lived in urban areas
(Table 1).
Severity of Measurements and Associated Factors
A considerable proportion of participants had symptoms of depression (634 [50.4%]), anxiety (560
[44.6%]), insomnia (427 [34.0%]), and distress (899 [71.5%]). Nurses, women, frontline workers,
and those in Wuhan reported experiencing more severe symptom levels of depression, anxiety,
insomnia, and distress (eg, severe depression among physicians vs nurses: 24 [4.9%] vs 54 [7.1%];
P = .01; severe anxiety among men vs women: 10 [3.4%] vs 56 [5.8%]; P = .001; severe insomnia
among frontline workers vs second-line workers: 9 [1.7%] vs 3 [0.4%]; P < .001; severe distress among workers in Wuhan vs Hubei outside Wuhan and outside Hubei: 96 [12.6%] vs 19 [7.2%] among those in Hubei outside Wuhan and 17 [7.2%] among those outside Hubei; P < .001) (Table 2). Compared with those working in tertiary hospitals, participants working in secondary hospitals were more likely to report severe symptoms of depression (53 [5.6%] vs 25 [7.7%]; P = .003), anxiety (48 [5.1%] vs 18 [5.5%]; P = .046), and insomnia (10 [1.0%] vs 2 [0.6%]; P = .02) but not distress (Table 2). JAMA Network Open. 2020;3(3):e203976. doi:10.1001/jamanetworkopen.2020.3976 (Reprinted) Downloaded From: https://jamanetwork.com/ on 10/18/2022 March 23, 2020 4/12 JAMA Network Open | Psychiatry Mental Health Outcomes Among Health Care Workers Exposed to COVID-19 Scores of Measurements and Associated Factors The median (IQR) scores on the PHQ-9 for depression, the GAD-7 for anxiety, the ISI for insomnia, and the IES-R for distress for all respondents were 5.0 (2.0-8.0), 4.0 (1.0-7.0), 5.0 (2.0-9.0), and 20.0 (7.0-31.0), respectively. Similar to findings in severity of symptoms, participants who were nurses, women, frontline health care workers, and working in Wuhan had higher scores in all 4 scales compared with those who were physicians, men, second-line health care workers, and working in Hubei province outside Wuhan or outside Hubei province (eg, median [IQR] PHQ-9 scores among physicians vs nurses: 4.0 [1.0-7.0] vs 5.0 [2.0-8.0]; P = .007; median [IQR] GAD-7 scores among men vs women: 2.0 [0-6.0] vs 4.0 [1.0-7.0]; P < .001; median [IQR] ISI scores among frontline vs secondline workers: 6.0 [2.0-11.0] vs 4.0 [1.0-8.0]; P < .001; median [IQR] IES-R scores among those in Wuhan vs those in Hubei outside Wuhan and those outside Hubei: 21.0 [8.5-34.5] vs 18.0 [6.0-28.0] in Hubei outside Wuhan and 15.0 [4.0-26.0] outside Hubei; P < .001) (Table 3). Compared with health care workers in tertiary hospitals, those in secondary hospitals reported higher scores on scales measuring symptoms of depression, anxiety, and insomnia (median [IQR] PHQ-9 score, 4.0 [1.0-7.0] vs 5.0 [2.0-9.0]; P < .001; median [IQR] GAD-7 score, 3.0 [0-7.0] vs 4.0 [1.0-7.0]; P = .005; median [IQR] ISI score, 4.0 [2.0-9.0] vs 6.0 [2.0-10.0]; P = .008). There were no differences in hospital status for scores of distress (median [IQR] IES-R score: workers in tertiary hospitals, 19.0 [7.0-32.0]; workers in secondary hospitals, 20.0 [6.0-31.0]; P = .46). However, frontline health care Table 1. Demographic and Occupational Characteristics of Responders No. (%) Occupation Location Characteristic Total Physician Nurse Wuhan Hubei province outside Wuhan Outside Hubei province Overall 1257 (100) 493 (39.2) 764 (60.8) 760 (60.5) 261 (20.8) 236 (18.8) Men 293 (23.3) 223 (45.2) 70 (9.2) 146 (19.2) 52 (19.9) 95 (40.3) Women 964 (76.7) 270 (54.8) 694 (90.8) 614 (80.8) 209 (80.1) 141 (59.7) 18-25 198 (15.8) 10 (2.0) 188 (24.6) 162 (21.3) 32 (12.3) 4 (1.7) 26-30 407 (32.4) 126 (25.6) 281 (36.8) 258 (33.9) 111 (42.5) 38 (16.1) 31-40 406 (32.3) 200 (40.6) 206 (27.0) 224 (29.5) 71 (27.2) 111 (47.0) >40
246 (19.5)
157 (31.8)
89 (11.6)
116 (15.3)
47 (18.0)
83 (35.2)
Sex
Age, y
Marriage status
Unmarried
418 (33.3)
87 (17.6)
331 (43.3)
314 (41.3)
66 (25.3)
38 (16.1)
Marrieda
839 (66.7)
406 (82.4)
433 (56.7)
446 (58.7)
195 (74.7)
198 (83.9)
≤Undergraduate
953 (75.8)
217 (44.0)
736 (96.3)
611 (80.4)
238 (91.2)
104 (44.1)
≥Postgraduate
304 (24.2)
276 (56.0)
28 (3.7)
149 (19.6)
23 (8.8)
132 (55.9)
Junior
699 (55.6)
153 (31.0)
546 (71.5)
481 (63.3)
169 (64.8)
49 (20.8)
Intermediate
378 (30.1)
187 (37.9)
191 (25.0)
221 (29.1)
61 (23.4)
96 (40.7)
Senior
180 (14.3)
153 (31.1)
27 (3.5)
58 (7.6)
31 (11.8)
91 (38.5)
Urban
1220 (97.1)
474 (96.1)
746 (97.6)
751 (98.8)
247 (94.6)
222 (94.1)
Rural
37 (2.9)
19 (3.9)
18 (2.4)
9 (1.2)
14 (5.4)
14 (5.9)
Frontline
522 (41.5)
176 (35.7)
346 (45.3)
390 (51.3)
72 (27.6)
60 (25.4)
Second-line
735 (58.5)
317 (64.3)
418 (54.7)
370 (48.7)
189 (72.4)
176 (74.6)
Tertiary
933 (74.2)
369 (74.8)
564 (73.8)
538 (70.8)
218 (83.5)
177 (75.0)
Secondary
324 (25.8)
124 (25.2)
200 (26.2)
222 (29.2)
43 (16.5)
59 (25.0)
Education level
Technical title
Place of residence
Working position
Type of hospital
a
Married category included widowed and divorced participants.
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448 (35.6)
108 (8.6)
78 (6.2)
Mild
Moderate
Severe
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406 (32.3)
88 (7.0)
66 (5.3)
Mild
Moderate
Severe
24 (4.9)
85 (6.8)
12 (1.0)
Moderate
Severe
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459 (36.5)
308 (24.5)
132 (10.5)
Mild
Moderate
Severe
43 (8.7)
120 (24.3)
167 (33.9)
163 (33.1)
89 (11.6)
188 (24.6)
292 (38.2)
195 (25.5)
8 (1.0)
61 (8.0)
223 (29.2)
472 (61.8)
43 (5.6)
54 (7.1)
263 (34.4)
404 (52.9)
54 (7.1)
64 (8.4)
291 (38.1)
355 (46.5)
.01