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SYSTEMATIC REVIEW
published: 13 March 2019
doi: 10.3389/fpsyg.2019.00284
Frontiers in Psychology | www.frontiersin.org 1March 2019 | Volume 10 | Article 284
Edited by:
Renato Pisanti,
University Niccolò Cusano, Italy
Reviewed by:
Krystyna Golonka,
Jagiellonian University, Poland
Cristina Queiros,
Universidade do Porto, Portugal
*Correspondence:
Anthony Montgomery
monty5429@hotmail.com
Specialty section:
This article was submitted to
Organizational Psychology,
a section of the journal
Frontiers in Psychology
Received: 05 October 2018
Accepted: 29 January 2019
Published: 13 March 2019
Citation:
Koutsimani P, Montgomery A and
Georganta K (2019) The Relationship
Between Burnout, Depression, and
Anxiety: A Systematic Review and
Meta-Analysis.
Front. Psychol. 10:284.
doi: 10.3389/fpsyg.2019.00284
The Relationship Between Burnout,
Depression, and Anxiety: A
Systematic Review and
Meta-Analysis
Panagiota Koutsimani, Anthony Montgomery*and Katerina Georganta
Department of Educational & Social Policy, School of Social Sciences, Humanities and Arts, University of Macedonia,
Thessaloniki, Greece
Background: Burnout is a psychological syndrome characterized by emotional
exhaustion, feelings of cynicism and reduced personal accomplishment. In the past
years there has been disagreement on whether burnout and depression are the same or
different constructs, as they appear to share some common features (e.g., loss of interest
and impaired concentration). However, the results so far are inconclusive and researchers
disagree with regard to the degree to which we should expect such overlap. The aim
of this systematic review and meta-analysis is to examine the relationship between
burnout and depression. Additionally, given that burnout is the result of chronic stress
and that working environments can often trigger anxious reactions, we also investigated
the relationship between burnout and anxiety.
Method: We searched the online databases SCOPUS, Web of Science, MEDLINE
(PubMed), and Google Scholar for studies examining the relationship between burnout
and depression and burnout and anxiety, which were published between January 2007
and August 2018. Inclusion criteria were used for all studies and included both cross-
sectional and longitudinal designs, published and unpublished research articles, full-text
articles, articles written in the English language, studies that present the effects sizes of
their findings and that used reliable research tools.
Results: Our results showed a significant association between burnout and depression
(r=0.520, SE =0.012, 95% CI =0.492, 0.547) and burnout and anxiety (r=
0.460, SE =0.014, 95% CI =0.421, 0.497). However, moderation analysis for both
burnout–depression and burnout–anxiety relationships revealed that the studies in which
either the MBI test was used or were rated as having better quality showed lower
effect sizes.
Conclusions: Our research aims to clarify the relationship between burnout–depression
and burnout–anxiety relationships. Our findings revealed no conclusive overlap between
burnout and depression and burnout and anxiety, indicating that they are different and
robust constructs. Future studies should focus on utilizing more longitudinal designs in
order to assess the causal relationships between these variables.
Keywords: burnout, depression, anxiety, meta-analysis, systematic review
Koutsimani et al. Burnout, Depression, Anxiety, Meta-Analysis
INTRODUCTION
One of the most common psychological symptoms modern
people increasingly experience is burnout, i.e., the outcome of
chronic, work-related stress (Melamed et al., 2006). Burnout
descriptions can be found in the historical record and they
appear to be apparent across different times and cultures (reports
of burnout feelings can be found from the Old Testament to
Shakespeare’s writings) (Kaschka et al., 2011). However, it was
not until the mid 1970s that researchers have started investigating
burnout feelings. In particular, two independent researchers,
Herbert Freudenberger, a psychiatrist, and Christina Maslach,
a social psychologist, were the first researchers who began
examining burnout. Specifically, Freudenberger (1974) was the
first to describe the concept of staff burnout. The basic elements
of his definition of burnout described these experiences as to fail,
wear out, or become exhausted by making excessive demands
on energy, strength or resources, and can still be seen in the
modern definitions of job burnout. Maslach et al. (1996) defined
burnout as the experience of exhaustion, where the individuals
who suffer from it become cynical toward the value of their
occupation and doubt their ability to perform. According to
Maslach et al. (1996), burnout is composed of three dimensions
i.e., exhaustion, cynicism, and lack of professional efficacy. In
more particular, exhaustion refers to feelings of stress, specifically
chronic fatigue resulting from excessive work demands. The
second dimension, depersonalization or cynicism, refers to an
apathetic or a detached attitude toward work in general and the
people with whom one works; leading to the loss of interest in
work, and feeling that work has lost its meaning. Finally, lack
of professional efficacy refers to reduced feelings of efficiency,
successful attainment, and accomplishment both in one’s job and
the organization.
As Maslach and Leiter (2016) later highlighted, burnout is
the result of prolonged interpersonal stressors at work. Research
has shown that burnout is related to reduced performance
in the workplace (Ruotsalainen et al., 2015) often leading to
several forms of withdrawal, such as absenteeism and intention
to leave the job (Alarcon, 2011; Kim and Kao, 2014). To
put it in other words, it is the worker’s inability or lack
of resources to meet with the demands that are associated
with the job tasks (Weber and Jaekel-Reinhard, 2000; Maslach
et al., 2001; Bianchi et al., 2015a). It has been argued, for
instance, that burnout is not only associated with difficulties
related to the working environment, but also other factors,
such as learned helplessness, learning theory, environmental
and/or personality factors (for a review see Kaschka et al.,
2011). To quote Bühler’s and Land’s (2003) question “why
under the same working conditions one individual burns out,
whereas another shows no symptoms at all?” we need to keep
in mind that burnout is in fact a response to stressful events
(Cherniss, 1980) and how each individual responds to such
events depends on how he/she evaluates them (Sarason, 1972;
Lazarus and Folkman, 1984); therefore, a person’s reaction to a
work stressor might range from minor to significant stimulation.
In other words, while there are employees who report that
they experience burnout, there are others who do not, although
they all work within the same working environment. A possible
mechanism that differentiates employees’ reaction to a stressful
working environment is personality characteristics. Personality
can either be a coping mechanism which allows individuals to
acquire/conserve resources and protect themselves from deviant
behavior (Ghorpade et al., 2007) or it can make someone more
susceptible and vulnerable to stressors. Two crucial psychological
phenomena which are related with personality, are depression
and anxiety. As Middeldorp et al. (2006) mention neuroticism,
i.e., emotional instability and proneness to anxiety (Eysenck and
Rachman, 2013), and low extraversion are positively correlated
with both depression and anxiety. Indeed, emotional stability
has been shown to be negatively related to the core component
of burnout, i.e., emotional exhaustion, and depersonalization
and positively related to personal accomplishment (Ghorpade
et al., 2007), whereas extroversion has been found to be
negatively related to emotional exhaustion and positively related
to personal accomplishment (Ghorpade et al., 2007). That is to
say, individuals who are more extroverted and more emotionally
stable, are less likely to develop burnout and vice versa. However,
the question as to what degree burnout is differentiated from
depression and anxiety, or whether they complement each other,
remains unanswered; and this question is crucial as burnout
might be falsely labeled as depression and/or anxiety disorders,
leading to inappropriate treatment techniques.
Burnout and Depression
There is disagreement among researchers who study burnout as
to whether there is an overlap between burnout and depression
(Bianchi et al., 2015a). As Freudenberger (1974) mentions,
people who suffer from burnout look and act as if they were
depressed. Indeed, we cannot overlook the fact that some
of the burnout symptoms appear to resemble the ones of
depression; as it is characterized by anhedonia, i.e., the loss of
interest or pleasure, depressed mood, fatigue or loss of energy,
impaired concentration, and feelings of worthlessness, decreased
or increased appetite, sleep problems (hypersomnia or insomnia)
and suicidal ideation (American Psychiatric Association, 2013).
However, despite its severity and resemblance to depression
characteristics, burnout is not mentioned in DSM-V and still no
diagnostic criteria exist for identifying it (Bakusic et al., 2017).
It is worth noting that in clinical practice, exhausted employees
are being diagnosed with burnout and frequently, in order for the
clinicians to proceed with their treatment, they turn to alternative
diagnoses like the ones of depression or exhaustion (Kaschka
et al., 2011). Yet, the question is still an open one, to what degree
can we differentiate burnout from depression and anxiety?
Bianchi and Brisson (2017), for instance, examined to what
extent individuals with burnout and depression attribute their
feelings to their job. What the researchers found was that the
number of the participants who attributed their burnout feelings
to their job was proportional to the ones who attributed their
depressive symptoms to their job as well, indicating that there
might be an overlap between burnout and depression in relation
to their antecedents. Many studies have also shown that there
is a positive correlation between burnout and depression (Glass
and McKnight, 1996; Schaufeli and Enzmann, 1998; Bianchi
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Koutsimani et al. Burnout, Depression, Anxiety, Meta-Analysis
et al., 2013, 2014, 2015b; Bianchi and Laurent, 2015). Indeed,
as Bianchi et al. (2015a) mention in their systematic review,
it has been found that inventories that assess burnout, and
more specifically the subscale of emotional exhaustion–the core
component of burnout–are positively correlated with depressive
symptoms (Takai et al., 2009; Bianchi et al., 2013; Ahola et al.,
2014). Several researchers have argued that because studies have
found a consistent medium to high correlation between the two
concepts, this might suggest an overlap and that burnout might
not be a distinct psychological phenomenon but a dimension
of depression (Bianchi et al., 2015b). Additionally in terms of
consequences, in a recent study by Bianchi et al. (2018a) it was
observed that both burnout and depression were associated not
only with the increased recall of negative words, but also with
the decreased recall of positive words. The authors concluded
that burnout and depression overlap with each other and this
overlap extends also to emotional memory. It is worth noting,
and regarding the diagnostic differentiation between burnout and
depression, in their review Kaschka et al. (2011) mention that
correlations between burnout and depression appear frequently
among relevant studies, showing that either there is an overlap
between burnout and depression, or burnout probably might
be a risk factor of developing depression. As it regards to the
similarity of the two constructs at a biological level, in their
systematic review, Bakusic et al. (2017) found that burnout
and depression appear to share a common biological basis. In
particular, according to the researchers’, the epigenetics studies
so far appear to advocate toward a probable mediator, i.e., DNA
methylation, which might act as a biomarker of stress-related
mental disorders, such as depression, burnout and chronic
stress. Therefore, we can observe that besides the psychological
common characteristics these two constructs appear to share,
they also seem to share a common biological basis.
On the other hand, not all researchers seem to agree
with the above notion. Although burnout and depression
appear to share some common features (e.g., loss of energy),
several researchers believe that burnout and depression are
two separate constructs (Ahola and Hakanen, 2007) and that
emotional exhaustion is not related to depression (Schaufeli
and Enzmann, 1998). There are quite a few studies which
have shown that burnout and depression do not overlap with
each other and that burnout is differentiated from depression
(Bakker et al., 2000; Schaufeli et al., 2001; Toker and Biron,
2012). Furthermore, one major factor that appears to distinguish
burnout from depression is the fact that burnout is work related
and situation specific, whereas depression is context free and
pervasive (Freudenberger and Richelson, 1980; Maslach et al.,
2001; Iacovides et al., 2003). That is, burnout is specifically related
to someone’s working environment, while depression can show
up regardless of the circumstances of the environment (e.g., social
or family environment). Nevertheless, it should be noted that this
distinction might not be very accurate as depression at its first
stages might be domain specific (Rydmark et al., 2006). Thus, it
is plausible that depression might start as work-related stress or it
might evolve as burnout, as this work-related stress gets stronger.
The existing literature is still inconclusive as to whether
burnout and depression are the same or different constructs
and, although most of the research studies are cross-sectional,
longitudinal studies also provide mixed results (McKnight and
Glass, 1995; Hakanen and Schaufeli, 2012). As Bianchi et al.
(2015b) note, the aim of most longitudinal studies is not to
examine the casual relationship between the two variables, but
they are designed in order to predict whether burnout can predict
depression or vice versa. All in all, despite the majority of the
research studies that examine the relationship between burnout
and depression, we are not still able to answer whether the two
phenomena are the same or different constructs. By conducting
the present meta-analysis, we aim to provide more clarification
concerning this relationship. Additionally, by knowing if burnout
in its essence falls under the umbrella of depression diagnosis, it
would provide valuable information as to whether it should be
included in the diagnostic criteria of depression or it should be
integrated as a different diagnostic entity.
Burnout and Anxiety
One other factor that appears to be related with burnout, but is
not as frequently investigated in relation to it as depression, is
anxiety (Sun et al., 2012). Anxiety is a common psychological
condition which acts as a protective factor against threatening
situations (Cole, 2014). However, prolonged anxiety might
result in psychological distress affecting an individual’s everyday
functioning (Cole, 2014). According to Ahmed et al. (2009),
anxiety is “a psychological and physiologic state characterized
by cognitive, somatic, emotional, and behavioral components.”
Nevertheless, although anxiety is considered a general reaction
to threatening situations, it is divided into two related constructs;
trait and state anxiety (Turnipseed, 1998). In particular, trait
anxiety is an individual’s stable characteristic and the degree
to which he/she perceives stressful situations as threatening,
i.e., a person’s proneness to anxiety (Spielberger, 1966). On the
other hand, state anxiety is the individual’s reaction toward a
situation after having appraised it as threatening (Spielberger,
1966). That is, an individual’s proneness to anxiety reflects trait
anxiety, whereas state anxiety is the reaction after a situation
has been appraised as threatening. Some researchers suggest that
occupational stress might in fact be a risk factor for anxiety
symptoms (DiGiacomo and Adamson, 2001; Sun et al., 2012).
For example, in the study of Vasilopoulos (2012) the participants
who reported high social anxiety levels reported high burnout
levels as well. Additionally, Mark and Smith (2012) found that job
demands, extrinsic effort, and over-commitment were associated
with increased anxiety levels. Similarly, Ding et al. (2014) found
that emotional exhaustion and cynicism were positively related to
anxiety symptoms, whereas professional efficacy was negatively
related to anxiety symptoms. That is, the more emotionally
exhausted, cynical, and less efficient toward his/her work an
individual feels, the more anxious he/she will be. Turnipseed
(1998) also found that burnout and anxiety symptoms are
significantly correlated with each other, with the strongest link
existing between anxiety and emotional exhaustion. According
to Turnipseed (1998), this interaction between work situations
and individuals’ personalities –as mentioned earlier– creates a
state of anxiety and, by extension, contributes to burnout onset.
However, to our knowledge it is still unclear the exact relationship
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Koutsimani et al. Burnout, Depression, Anxiety, Meta-Analysis
between burnout and anxiety. Specifically, are people with higher
trait anxiety more prone to developing burnout or do burnout
feelings compound anxiety symptoms? Furthermore, is there an
overlap between burnout and anxiety?
Objectives
Overall, the evidence regarding the relation between burnout
and depression and burnout and anxiety are still inconclusive.
The purpose of this systematic review and meta-analysis is
to investigate and clarify the association between the above
variables. Our goal is to clarify the existing evidence and have
a better understanding of the relationship between burnout and
depression and burnout and anxiety.
Research Questions
Our research questions were the following:
1. Is there an overlap between burnout and depression?
2. Is there a potential moderator underlying the relationship
between burnout and depression?
3. Is there an overlap between burnout and anxiety?
4. Is there a potential moderator underlying the relationship
between burnout and anxiety?
METHODS
Systematic Review Protocol
Before we began our database search, firstly we searched
PROSPERO’s database for possible registered protocol reviews
that might have been conducting the same meta-analysis. As no
such protocol review was found at PROSPERO’s database, we
wrote and registered a systematic protocol review in which we
stated our purpose with the current meta-analysis, our eligibility
criteria and our search strategy. After the registration of our
systematic protocol review (CRD42018090505), we continued
with the database search. Specifically, selection procedure, study
identification, and critical appraisal of the research studies was
conducted according to the checklist presented in the Preferred
Reporting Items for Systematic Reviews and Meta-analyses
(PRISMA) statement (Moher et al., 2009; see Figures 1,2),
Supplementary Data Sheet S1. For burnout and depression, 67
papers were identified which resulted in 69 studies for analysis.
For burnout and anxiety 34 papers were identified which resulted
in 36 studies for analysis.
Search Strategy
We searched the online databases SCOPUS, Web of Science,
MEDLINE (PubMed) and Google Scholar for research published
between January 2007 and August 2018. The combinations of
the key words we used were the following: burnout, depression,
anxiety. Additionally, we used MeSH terms with the term
“burnout” being the major topic of the meta-analysis and
our search was formed as follows: burnout/depression [majr]
AND burnout/anxiety [majr]; burnout/depression [majr] OR
burnout/anxiety [majr]. After we completed the electronic
database search, a manual scoping of the cited studies by all
articles found was also done in case some of them did not show
up in our search.
Our eligibility criteria included; (i) all types of studies, both
cross-sectional and longitudinal, (ii) published and unpublished
research articles, (iii) full-text articles, (iv) research articles
written in the English language, (v) studies that present the
effects sizes of their results and (vi) studies that used reliable
research tools. Additionally, all studies had to describe the types
of methods they used in order to assess burnout, depression and
anxiety. Regarding the type of the populations used in the studies,
we included studies that examined employed individuals and
professional athletes as well.
Furthermore, we categorized the research studies into five
moderators, depending on the type of the assessment tools
that were used and the type of the studies (cross-sectional or
longitudinal) in which they were utilized. Specifically, and after
we conducted frequencies analyses, it was found that the most
widely used tools for assessing our variables of interest were
the Maslach Burnout Inventory (MBI) (Maslach et al., 2006) for
assessing burnout, the Patient Health Questionnaire (PHQ) for
assessing depression (Spitzer et al., 1994; Kroenke et al., 2001)
and the Hospital Anxiety Depression Scale (HADS) (Zigmond
and Snaith, 1983) for assessing anxiety. Consequently, the three
moderator variables that were created were: (i) the MBI vs. Non-
MBI studies, (ii) the PHQ vs. Non-PHQ studies, and (iii) the
HADS vs. Non-HADS studies. The fourth moderator was the
type of the study, i.e., cross-sectional or longitudinal. This way
we were able to examine whether the assessment tools and/or the
type of the studies had different effect on the results or not. Lastly,
the fifth moderator was occupation.
Quality Assessment
Quality assessment was performed based on the Quality
Assessment Tool for Observational Cohort and Cross-Sectional
Studies (Feng et al., 2014). The tool contains 14 criteria and
the evaluator is asked to answer whether the study in question
meets the criterion. The possible answers are Yes, No, Cannot
Determine, Not applicable, and Not Reported. A score of >11
corresponds to good quality, 7–10 to fair quality and <7 to poor
quality. Of the 67 studies measuring burnout and depression
that were included in the meta-analysis 28 were rated by two
independent evaluators as fair and 40 as good (one paper
contained 2 studies - each one of the two studies was evaluated
differently). Of the 34 studies measuring burnout and anxiety
that were included in the meta-analysis 15 were rated by two
independent evaluators as fair and 19 as good.
Analysis
All analyses were guided by Lipsey and Wilson (2001) and
conducted using Comprehensive Meta-Analysis software (Lipsey
and Wilson, 2001; Borenstein et al., 2005). In deriving effect
sizes and confidence intervals, random-effects models were used.
Random-effects models assume variation in effect sizes between
studies, and this is due to both sampling error and true random
variance arising from differences between studies in terms of
their procedures and settings (as opposed to only sampling error
stipulated in a fixed effect model). In comparison to fixed-effects
models, then, random-effects models are generally considered to
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Koutsimani et al. Burnout, Depression, Anxiety, Meta-Analysis
Records idenfied through database
searching
(n = 3884)
Screening
Included Eligibility Iden fica on
Records excluded (duplicates,
not peer reviewed papers)
(n = 858)
No fit the inclusion criteria
(e.g. no depression, no
burnout, maternal burnout,
students, review, qualitave
etc. (n = 2921)
Studies included in quantave
synthesis (meta-analysis)
(n = 67)
Records screened
(n = 3026)
Records excluded (language
restricons)
(n = 17)
Records excluded (no full
text)
(n = 21)
Records screened
(n = 3009)
Records screened
(n = 2988)
FIGURE 1 | Flow diagram for burnout and depression.
be preferable and allow generalization beyond the set of studies
examined to future studies (Schmidt et al., 2009).
The summary statistic reported is the weighted r. Cohen
provided rules of thumb for interpreting these effect sizes,
suggesting that an r of 0.10, represents a “small” effect size,
0.30 represents a “medium” effect size and 0.50 represents
a “large” effect size (Cohen, 1992). However, researchers
have suggested that the indiscriminate use of Cohen’s
generic small, medium, and large effect size values to
characterize effect sizes in domains in which normative
values do not apply is inappropriate and misleading (Lipsey
et al., 2012). Therefore, it is important that effect sizes are
grounded in the context by assessing their contribution
to knowledge.
Moderation Analysis
The following moderators were examined as possible reasons
for heterogeneity; burnout measure (MBI vs. Non-MBI
measurement of burnout), the emotional exhaustion dimension
of the MBI vs. the other two dimensions vs. the dimensions
of the Non-MBI scales (Emotional exhaustion vs. Non-
Emotional exhaustion scales vs. other burnout scales), the
depression measure (PHQ vs. Non-PHQ), the anxiety measure
(HADS vs. Non-HADS), the type of study (Cross-sectional
vs. Longitudinal), the occupational status (Healthcare vs.
Educational vs. Other professionals), and their quality as
described above (Fair vs. Good quality). The selection of the
above measurements as moderators was decided after taking
into consideration the frequency in which they were used in
the studies.
Moderation was assessed by calculating the degree of
inconsistency in the observed relationship across studies (I2).
This index is interpreted as the percentage of total variation
across studies due to “true” heterogeneity rather than sampling
error (Higgins et al., 2003). As I2increases, the level of true
heterogeneity increases (0 to 100%). Values of 25, 50, and
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Koutsimani et al. Burnout, Depression, Anxiety, Meta-Analysis
Records idenfied through database
searching
(n = 2309)
Screening
Included Eligibility Iden fica on
Records excluded (duplicates,
not peer reviewed papers)
(n = 290)
No fit the inclusion criteria
(e.g. no depression, no
burnout, maternal burnout,
students, review, qualitave
etc. (n = 1970)
Studies included in quantave
synthesis (meta-analysis)
(n = 34)
Records screened
(n = 2019)
Records excluded (language
restricons)
(n = 10)
Records excluded (no full
text)
(n = 5)
Records screened
(n = 2009)
Records screened
(n = 2004)
FIGURE 2 | Flow diagram for burnout and anxiety.
75% have been identified as low, medium, and high levels
of heterogeneity.
RESULTS
Studies Retrieved for the Meta-Analysis
As it regards the number of records that were originally
identified, concerning burnout, and depression a total of 3,884
records were found. After refining the search results, 3,026
records were screened, 21 of them were excluded as they were
not full-texts, 17 were excluded due to language restrictions (non-
English), and 2,921 because they didn’t fit the inclusion criteria
(e.g., no depression, no burnout, maternal burnout, students,
review, qualitative etc.) or were excluded because the appropriate
statistics were not provided. In total 67 papers (69 studies) were
included in the meta-analysis (see Figure 1).
Concerning burnout and anxiety, 2,309 records were
identified. After refining the results, 2,019 available records
were screened; 10 of them were excluded due to non-use of the
English language, 5 were not full-texts and 1,970 were excluded
because they didn’t fit the inclusion criteria (e.g., no depression,
no burnout, maternal burnout, students, review, qualitative
etc.) or because the appropriate statistics were not provided. In
total 34 papers (36 studies) were eligible for the meta-analysis
(see Figure 2).
Study Selection and Characteristics
Tables 1,2provide a detailed summary of all the studies that were
included in the meta-analysis for both depression and anxiety,
respectively. In total 101 studies were included in this review; 67
studies for burnout and depression and 34 studies for burnout
and anxiety. Table 3 provides a list of all the questionnaires
used in the studies included in the meta-analysis (includes
the abbreviations).
Concerning the publication year of the studies about burnout
and depression, 43.3% of them were published during 2018 (until
August), followed by 13.4% of them which were published in
2016 and 11.9% in 2015; 7.5% of the studies were published in
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Koutsimani et al. Burnout, Depression, Anxiety, Meta-Analysis
TABLE 1 | Studies measuring burnout and depression included in the meta-analysis (69 studies).
Studies (in alphabetical order) nBurnout measure Depression measure Design
1Ahola et al., 2014 1,964 MBI-HS BDI-SF Longitudinal
2Bakir et al., 2010 377 MBI BDI Cross-sectional
3Bauernhofer et al., 2018 103 MBI-GS BDI Cross-sectional
4Bianchi and Brisson, 2017 468 SMBM PHQ9 Cross-sectional
5Bianchi and Laurent, 2015 54 MBI BDI-II Cross-sectional
6Bianchi and Laurent, 2015 54 BM BDI-II Cross-sectional
7Bianchi and Schonfeld, 2016 323 SMBM PHQ9 Cross-sectional
8Bianchi and Schonfeld, 2018 911 MBI-GS PHQ-8 Cross-sectional
9Bianchi et al., 2013 1,658 MBI BDI-II Cross-sectional
10 Bianchi et al., 2014 5,575 MBI PHQ-9 Cross-sectional
11 Bianchi et al., 2015b 627 MBI PHQ-9 Longitudinal
12 Bianchi et al., 2016a 1,046 SMBM PHQ-9 Cross-sectional
13 Bianchi et al., 2016b 184 SMBM PHQ-9 Cross-sectional
14 Bianchi et al., 2018a 1,056 SMBM PHQ-9 Cross-sectional
15 Bianchi et al., 2018b 222 SMBM PHQ-9 Cross-sectional
16 Bianchi et al., 2018c 1,015 SMBM PHQ-9 Cross-sectional
17 Capone and Petrillo, 2018 285 MBI-GS CES-D Cross-sectional
18 Cardozo et al., 2012 212 MBI-HS HSCL-25 Longitudinal
19 Choi et al., 2018 386 MBI-GS PHQ Cross-sectional
20 Choi et al., 2018 ProQOL PHQ Cross-sectional
21 da Silva Valente et al., 2018 1,046 MBI PHQ-9 Cross-sectional
22 De Stefano et al., 2018 26 MBI BDI Cross-sectional
23 Duan-Porter et al., 2018 281 OLBI PHQ-9 Longitudinal
24 Favrod et al., 2018 208 MBI HADS Cross-sectional
25 Fong et al., 2016 312 CBI HADS Longitudinal
26 Garrouste-Orgeas et al., 2015 1,534 MBI CES-D Cross-sectional
27 Grover et al., 2018 445 MBI PHQ-9 Cross-sectional
28 Hakanen et al., 2008 2,555 MBI BDI Longitudinal
29 Hakanen and Schaufeli, 2012 1,964 MBI BDI Longitudinal
30 Hemsworth et al., 2018 273 ProQOL DASS-21 Cross-sectional
31 Hintsa et al., 2014 3,283 MBI-GS BDI Cross-sectional
32 Idris and Dollard, 2014 117 MBI PHQ-9 Longitudinal
33 Johnson et al., 2017 323 MBI DASS-21 Cross-sectional
34 Karaoglu et al., 2015 74 MBI HADS Cross-sectional
35 Lebensohn et al., 2013 168 MBI CES-D Cross-sectional
36 Lee et al., 2018 464 MBI-GS HADS Cross-sectional
37 Lobo, 2018 10 MBI HADS Cross-sectional
38 Malmberg-Gavelin et al., 2018 119 SMBQ HADS Cross-sectional
39 Mather et al., 2016 5,093 PBM SCID Cross-sectional
40 Melchers et al., 2015 944 MBI-GS BDI-II Cross-sectional
41 Metlaine et al., 2018 140 MBI HADS Cross-sectional
42 Moore and Schellinger, 2018 62 PQLS CES-D Cross-sectional
43 Mosing et al., 2018 10,120 MBI-GS SCL-90 Cross-sectional
44 Mutkins et al., 2011 80 MBI DASS-21 Cross-sectional
45 Oe et al., 2018 158 MBI HADS Cross-sectional
46 Penz et al., 2018 412 MBI PHQ-9 Cross-sectional
47 Pereira-Lima and Loureiro, 2015 400 BSI PHQ-4 Cross-sectional
48 Peterson et al., 2008 3,719 OLBI HADS Cross-sectional
49 Plieger et al., 2015 755 MBI-GS BDI-II Cross-sectional
(Continued)
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TABLE 1 | Continued
Studies (in alphabetical order) nBurnout measure Depression measure Design
50 Richardson et al., 2018 119 CBI IDAS-II Cross-sectional
51 Rogers et al., 2014 349 CBI PHQ Cross-sectional
52 Samios, 2017 69 ProQOL DASS-21 Cross-sectional
53 Santa Maria et al., 2018 811 CBI PHQ-2 Cross-sectional
54 Schiller et al., 2018 51 SMBM HADS Cross-sectional
55 Schonfeld and Bianchi, 2016 1,386 SMBM PHQ-9 Cross-sectional
56 Silva et al., 2018 100 BSI PHQ-9 Cross-sectional
57 Steinhardt et al., 2011 267 MBI CES-D Cross-sectional
58 Takai et al., 2009 84 PBM BDI-II Cross-sectional
59 Talih et al., 2016 118 BM PHQ-9 Cross-sectional
60 Talih et al., 2018 91 BM PHQ-9 Cross-sectional
61 Toker and Biron, 2012 1,632 SMBM PHQ Longitudinal
62 Tourigny et al., 2010 550 MBI CES-D Cross-sectional
63 Trockel et al., 2018 250 PFI PROMIS Cross-sectional
64 Tzeletopoulou et al., 2018 72 MBI CES-D Cross-sectional
65 van Dam, 2016 113 MBI SCL-90 Cross-sectional
66 Vasconcelos et al., 2018 91 MBI-HS BDI Cross-sectional
67 Weigl et al., 2016 313 MBI STDS Cross-sectional
68 Wurm et al., 2016 5,897 HBI MDI Cross-sectional
69 Yeh et al., 2018 172 OBI EPDS Cross-sectional
2014, 5.6% in 2017, 4.5% in 2012, 15.6% were published in each
of the following years: 2008, 2010, 2011, and 2013 (3.9% each),
and 1.5% in 2009. In relation to publication year of the studies
about burnout and anxiety, 52.9% were published in 2018 (until
August), 11.8% studies were published in 2015, 11.8% in 2016,
5.8% in 2014 and 5.8% 2017; 11.6% were published during the
years of 2007, 2010, 2011, and 2012 (2.9% each).
Regarding the studies relating to burnout and depression, the
overall sample size for the 67 studies was 84,169 participants,
30,942 (37%) men, and 49,898 (59%) women (three studies did
not include gender characteristics). Of the studies that measured
burnout, 55% of them used the MBI and variations of it (i.e.,
MBI-GS, MBI-HS), 14.5% used the SMBM test, 5.8% used the
CBI test, 4.3% used the BM, and 4.3% the ProQOL tests, 8.7%
of them used the BSI, the OLBI and the PBM tests, and 7% used
other measures of burnout (HBI, OBI, PFI, PQLS, and SMBQ); in
two studies burnout was measured with two tests, MBI and PBM
and MBI and ProQOL test.
Most of the studies (36.1%) used the PHQ to measure
depression, 20.2 % of them used the BDI, 14.5% used the HADS,
another 10.1% of the studies used the CES-D, 5.8% used the
DASS-21, 2.9% used the SCL-90 and, lastly, 1.4% used the EPDS,
HSCL-25, IDAS-II, MDI, PROMIS, SCID, and STDS tests.
Respectively, in the studies relating to burnout and anxiety,
the overall sample size for the 34 studies were 40,751 participants,
15,561 (38%) were men, and 23,915 (59%) were women (in two
studies gender characteristics were not included). Concerning the
burnout tool which was used across the studies, 63.9% of them
used the MBI test and its variations (i.e., MBI-GS, MBI-HS), 8.3%
used the BM test, 5.6% used the BSI, ProQOL and SMBM tests
and 2.8% used the BSI, CBI, OLBI, PBM, PFI, and SMBQ tests. In
two studies burnout was measured with two tests, MBI and PBM,
and MBI and ProQOL test.
In relation to the measurement of anxiety, 30.6% of the studies
used the HADS, 11.1% used the GAD-7, 8.3% used the STAI
and the SAS, 5.6% of the studies used the DASS-21 and 1-item
self-constructed test, and 2.8% of the studies used the GHQ-28,
HAM-A, HSCL-25, IPIP, JAS, PHQ-4, POMS, PROMIS, SCID,
SCL-90, and SSAI tests.
Concerning the design of the studies, 87% of them examining
the burnout and depression relationship utilized a cross-sectional
design, and 13% were longitudinal; 97.2% of the studies
measuring burnout and anxiety utilized a cross-sectional design
and 2.8% were longitudinal.
Main Meta-Analysis: Association Between
Burnout and Depression
Overall results indicated a significant effect (r=0.520, SE =
0.012, 95% CI =0.492, 0.547). The confidence intervals around
the effect sizes for each study are presented in the forest plot
(see Figure 3).
Main Meta-Analysis: Association Between
Burnout and Anxiety
Overall results indicated a significant effect (r=0.460, SE =
0.014, 95% CI =0.421, 0.497). The confidence intervals around
the effect sizes for each study are presented in the forest plot
(see Figure 4).
Sub-group Analysis: Measure and Context
The meta-analysis indicated significant heterogeneity with the I-
squared =98.432 and I-squared =95.367 for both depression
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Koutsimani et al. Burnout, Depression, Anxiety, Meta-Analysis
TABLE 2 | Studies measuring burnout and anxiety included in the meta-analysis (36 studies).
Studies (in alphabetical order) nBurnout measure Depression measure Design
1Andreassen et al., 2018 988 MBI-GS GHQ-28 Cross-sectional
2Bianchi and Laurent, 2015 54 MBI HADS Cross-sectional
3Bianchi and Laurent, 2015 54 BM HADS Cross-sectional
4Bianchi and Schonfeld, 2018 911 MBI-GS Self-constr. Cross-sectional
5Cardozo et al., 2012 212 MBI-HS HSCL-25 Longitudinal
6Choi et al., 2018 386 MBI-GS GAD-7 Cross-sectional
7Choi et al., 2018 386 ProQOL GAD-7 Cross-sectional
8Craiovan, 2015 60 CBI HAM-A Cross-sectional
9De Stefano et al., 2018 26 MBI STAI Cross-sectional
10 Demir, 2018 335 BSI-SV IPIP Cross-sectional
11 Diestel and Schmidt, 2010 324 MBI STAI Cross-sectional
12 Ding et al., 2014 1,243 MBI-GS SAS Cross-sectional
13 Favrod et al., 2018 208 MBI HADS Cross-sectional
14 Gallego-Alberto et al., 2018 101 MBI POMS Cross-sectional
15 Gillet et al., 2018 521 SMBM JAS Cross-sectional
16 Hemsworth et al., 2018 273 ProQOL DASS-21 Cross-sectional
17 Karaoglu et al., 2015 74 MBI HADS Cross-sectional
18 Katkat, 2015 336 MBI SSAI Cross-sectional
19 Lee et al., 2018 464 MBI-GS HADS Cross-sectional
20 Lobo, 2018 10 MBI HADS Cross-sectional
21 Malmberg-Gavelin et al., 2018 119 SMBQ HADS Cross-sectional
22 Mather et al., 2016 5,093 PBM SCID Cross-sectional
23 Metlaine et al., 2018 140 MBI HADS Cross-sectional
24 Mutkins et al., 2011 80 MBI DASS-21 Cross-sectional
25 Oe et al., 2018 158 MBI HADS Cross-sectional
26 Pereira-Lima and Loureiro, 2015 400 BSI PHQ-4 Cross-sectional
27 Peterson et al., 2008 3,719 OLBI HADS Cross-sectional
28 Schiller et al., 2018 51 SMBM HADS Cross-sectional
29 Shi et al., 2018 696 MBI-GS Self-constr. Cross-sectional
30 Talih et al., 2016 118 BM GAD-7 Cross-sectional
31 Talih et al., 2018 91 BM GAD-7 Cross-sectional
32 Trockel et al., 2018 250 PFI PROMIS Cross-sectional
33 van Dam, 2016 113 MBI SCL-90 Cross-sectional
34 Yazicioglu and Kizanlikli, 2019 284 MBI STAI Cross-sectional
35 Zhou et al., 2016 1,274 MBI SAS Cross-sectional
36 Zhou et al., 2018 1,354 MBI SAS Cross-sectional
and anxiety respectively, suggesting that moderation analysis
was appropriate.
In terms of context, the depression studies that used the
MBI reported lower effect sizes (r=0.472, SE =0.011, 95%
CI =0.441, 0.503) in comparison with other scales (r=
0.622, SE =0.042, 95% CI =0.564, 0.675). Likewise, anxiety
studies that used the MBI reported slightly lower effect sizes
as well (r=0.451, SE =0.011, 95% CI =0.406, 0.493) in
comparison with other scales (r=0.482, SE =0.029, 95%
CI =0.408, 0.549).
Concerning the burnout dimension, in the burnout—
depression relationship the effect sizes of the emotional
exhaustion dimension were higher (r=0.508, SE =0.012, 95%
CI =0.467, 0.546) comparing to the other dimensions of the
MBI test (r=0.409, SE =0.006, 95% CI =0.380, 0.437),
but lower compared to the other burnout measurements that
report total burnout scores (r=0.749, SE =0.136, 95% CI
=0.643, 0.827) and those that report scores from individual
subscales (r=0.608, SE =0.005, 95% CI =0.574, 0.639).
With respect to the burnout—anxiety relationship, the effect sizes
of the emotional exhaustion dimension were slightly higher (r
=0.472, SE =0.012, 95% CI =0.417, 0.524), compared to
the other dimensions of the MBI test (r=0.426, SE =0.017,
95% CI =0.369, 0.479) and slightly lower compared to the
other burnout measurements that report total scores (r=0.494,
SE =0.060, 95% CI =0.318, 0.637) and those that report
scores from individual subscales (r=0.499, SE =0.052, 95%
CI =0.379, 0.602).
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Koutsimani et al. Burnout, Depression, Anxiety, Meta-Analysis
TABLE 3 | Questionnaires used for measuring burnout, depression and anxiety in
the studies included in the meta-analysis.
Short name Name
BURNOUT
BSI Burnout Syndrome Inventory
CBI Copenhagen Burnout Inventory
HBI Hamburg Burnout Inventory
MBI Maslach Burnout Inventory
OBI Occupational Burnout Inventory
OLBI Oldenburg Burnout Inventory
PBM Pines Burnout Measure
PFI Professional Fulfillment Index
PQLS Professional Quality of Life Scale
ProQOL Professional Quality of Life
SMBM Shirom–Melamed Burnout Measure
SMBQ Shirom-Melamed Burnout Questionnaire
DEPRESSION
BDI Beck Depression Inventory
CES-D Center for Epidemiologic Studies Depression Scale
DASS-21 Depression Anxiety Stress Scales
EDPS Edinburgh Postnatal Depression Scale
HADS Hospital Anxiety and Depression Scale
HSCL-25 Hopkins Symptom Checklist-25
IDAS-II Inventory of Depression and Anxiety Symptoms-II
MDI Major Depression Inventory
PHQ Patient Health Questionnaire
PROMIS Patient-Reported Outcomes Measurement Information System
SCID Structured Clinical Interview for DSM-IV Disorders
SCL-90 Symptom Checklist
STDS State-TraitDepressionScales
ANXIETY
– 1 Item Self-Constructed
DASS-21 Depression Anxiety Stress Scales
GAD-7 Generalized Anxiety Disorder-7
GHQ-28 General Health Questionnaire-28
HADS Hospital Anxiety and Depression Scale
HAM-A Hamilton Anxiety Rating Scale
HSCL-25 Hopkins Symptom Checklist-25
IPIP International Personality Item Pool
JAS Job-Anxiety-Scale
PHQ-4 Patient Health Questionnaire-4
POMS Profile of Moods State
PROMIS Patient-Reported Outcomes Measurement Information System
SAS Zung Self-Rating Anxiety Scale
SCID Structured Clinical Interview for DSM-IV Disorders
SCL-90 Symptom Checklist
SSAI Spielberger State Anxiety Inventory
STAI State and Trait Anxiety Scales
Additionally, the studies that used the PHQ scale reported
higher effect sizes (r=0.628, SE =0.040, 95% CI =0.565, 0.684)
in comparison with other scales (r=0.481, SE =0.009, 95% CI
=0.453, 0.507). Likewise, in relation to anxiety, studies that used
the HADS scale reported higher effect sizes as well (r=0.507, SE
=0.023, 95% CI =0.448, 0.562) in comparison with other scales
(r=0.437, SE =0.016, 95% CI =0.387, 0.484).
With respect to the design of the studies (cross-sectional or
longitudinal), concerning the burnout—depression relationship,
the cross-sectional studies reported higher effect sizes (r=0.526,
SE =0.022, 95% CI =0.488, 0.562) comparing to the longitudinal
ones (r=0.505, SE =0.009, 95% CI =0.466, 0.543). Concerning
the burnout—anxiety relationship, sub-group analysis regarding
the design of the studies was not conducted as there was only one
longitudinal study in the meta-analysis.
As it regards the occupational status, and specifically the
burnout—depression relationship, educational staff reported
higher effect sizes (r=0.679, SE =0.049, 95% CI =0.609,
0.738) comparing to healthcare workers (r=0.495, SE =0.008,
95% CI =0.466, 0.524) and the general employed population
(r=0.449, SE =0.020, 95% CI =0.399, 0.496). Regarding the
burnout—anxiety relationship, healthcare professionals reported
slightly lower effect sizes (r=0.436, SE =0.010, 95% CI =0.396,
0.475) comparing to the general employed population (r=0.492,
SE =0.035, 95% CI =0.418, 0.559). Sub-group analysis with
the educational staff was not conducted as there were only two
studies in which the participants fitted in the occupational status.
Lastly, with respect to the quality of the studies (see section
Quality Assessment), concerning the burnout—depression
relationship, the studies with fair quality reported slightly higher
effect sizes (r=0.565, SE =0.032, 95% CI =0.515, 0.610)
comparing to the good quality studies (r=0.488, SE =0.009,
95% CI =0.456, 0.518). Concerning the burnout—anxiety
relationship, the studies with fair quality reported slightly higher
effect sizes (r=0.466, SE =0.009, 95% CI =0.418, 0.511)
comparing to the good quality studies (r=0.453, SE =0.018,
95% CI =0.402, 0.502).
Publication Bias
In order to assess publication bias (the “file-drawer” problem)
we adopted a number of strategies. We examined the fail-safe
number (fail-safe N) for each effect size. We also inspected funnel
plots (a scatterplot of effect sizes against the reciprocal of its
standard error).
Rosenthal (1979) recommends that the fail-safe number
should be >5 k +10, where k equals the number of observed
effect sizes (Rosenthal, 1979). In the present analysis the
classic fail-safe Nis 8,603 and 5,932 for the burnout—
depression relationship and the burnout—anxiety relationships,
respectively. Rosenthal’s method has been critiqued on the
grounds that it fails to take into account the bias in the “file
drawer” of unpublished studies, and thus can give misleading
results (Scargle, 1999). Therefore, we also calculated Orwin’s fail-
safe N, which was equal to 944 (depression) and 288 (anxiety)
(using 0.10 as a criterion for a trivial correlation).
In terms of publication bias, the funnel plots (see Figures 5,
6) indicate a degree of asymmetry. However, funnel plots are
not a good way to investigate publication bias per se, as there
can be a number of reasons for asymmetrical funnel plots
(also called small study effects), which are due to heterogeneity,
reporting bias and poor methodological design (Sterne et al.,
2011; Sedgwick, 2013).
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Koutsimani et al. Burnout, Depression, Anxiety, Meta-Analysis
FIGURE 5 | Funnel plot of Standard Error for burnout and depression.
FIGURE 6 | Funnel plot of Standard Error for burnout and anxiety.
DISCUSSION
Summary of Main Findings
During the last decade, research regarding the relationship of
burnout and depression, and burnout and anxiety, has grown.
As we observed from our database search on the studies that
measure the aforementioned relationships, the research in this
field of area has increased in recent years, with the majority
of the studies being conducted during the last year (43.5
and 52.8% for the burnout—depression and burnout—anxiety
relationships, respectively). The interest on clarifying these
relationships appears to be growing stronger and by conducting
the present meta-analysis we wanted to clarify whether there is an
overlap between burnout and depression, and an overlap between
burnout and anxiety. Overall, burnout research is growing—
particularly when it comes to small-scale occupational studies,
but the research tends to be varied, and applies a range of
different instruments to measure burnout (Eurofound, 2018).
It is possible that employees who have been diagnosed with a
depressive and/or an anxiety disorder might also suffer from
burnout (Eurofound, 2018). Indicatively, Maske et al. (2016)
found that 59% of individuals who have been diagnosed with
burnout they were also diagnosed with an anxiety disorder,
58% with an affective disorder, i.e., depression or a depressive
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Koutsimani et al. Burnout, Depression, Anxiety, Meta-Analysis
episode and 27% with a somatoform disorder. In other words,
the similarities between burnout and depression and burnout
and anxiety might lead to false diagnosis or it is possible that
burnout might be overlooked on the account of these similarities,
resulting in false treatments of the individuals who suffer from it.
Regarding the burnout-depression relationship, i.e., whether
there is an overlap between burnout and depression, the results
of our meta-analysis showed that there is an association between
these two variables. Although burnout and depression are
associated with each other, the effect size is not so strong that
it would suggest they are the same construct. In other words,
burnout and depression are more likely to be two different
constructs rather than one. Additionally, as the MBI test was the
one which was used in more of the half of the studies (55.1%),
we examined the burnout–depression relationship in terms of
context. According to our results, the studies that used the MBI
test reported a lower association between burnout and depression
compared to the studies that used other burnout measures, where
the association between burnout and depression was higher.
Concerning the burnout—anxiety relationship, i.e., whether
there is an overlap between burnout and anxiety, our results
indicated a relationship between the two variables as well. In
particular, although there appears to be an association between
burnout and anxiety, this association is not so strong that
it indicates an overlap between the two variables. This result
indicates that although burnout is associated with anxiety, they
are in fact different constructs. This finding can help us also
answer the question of why some people develop burnout while
others do not (Bühler and Land, 2003). According to our results,
it is possible that individuals who are more prone to experiencing
higher levels of anxiety (trait anxiety) are also more likely to
develop burnout as well. As it regards the burnout-anxiety
relationship in terms of context, likewise with the burnout-
depression relationship, we found that in the studies that used
the MBI test (63.9%) the effect between burnout and anxiety
was lower compared to the ones that used a different burnout
measure. However, it should be noted that there was variability in
the inventories that were used in the research studies for assessing
anxiety. It is possible that the effect sizes between burnout and
anxiety would differ if there was a common widely used tool for
assessing anxiety.
Overall, our results suggest that the studies that used
measures other than the MBI burnout tool could potentially
be artificially inflating the association between burnout and
depression and burnout and anxiety as well. Burnout is an
occupationally-specific dysphoria that is distinct from depression
as a broadly based mental illness (Maslach et al., 2001). Maslach
and Leiter (2016) have argued that while studies confirm that
burnout and depression are not independent, claiming that they
are simply the same mental illness is not supported by the
accumulated evidence.
Another interesting finding of our meta-analysis was that the
majority of the research studies that measured the relationship
between burnout and depression, and burnout and anxiety,
utilized cross- sectional designs (87% and 97% of the studies
for depression and anxiety respectively).We noticed that there
was a lack of longitudinal designs examining the burnout–
depression and the burnout—anxiety relationship. Moreover,
most of the longitudinal designs that were eligible for our meta-
analysis did not examine directly the association between these
two relationships, but they were focused mostly on whether
burnout predicts depression or anxiety, or the opposite. As
Bianchi et al. (2015b) aptly note, most longitudinal studies are
not designed to examine casual relationships, but they mainly
aim to investigate whether burnout can predict depression or
vice versa. Therefore, although the burnout—depression and the
burnout—anxiety relationships are found to be related, we are
still not able to know whether these relationships are casual.
Future studies need to focus more on utilizing longitudinal
designs which will mostly aim at examining the causality of
these relationships.
Overall, according to our results burnout and depression
and burnout and anxiety appear to be different constructs
that share some common characteristics and they probably
develop in tandem, rather they fall into the same category with
different names being used to describe them. However, further
studies examining the psychosocial and neurobiological basis
of these constructs are needed as well as their relationship
with other illnesses (e.g., physical problems), as this field of
research area is under investigated (Kaschka et al., 2011). It
is worth noting that in their review Kaschka et al. (2011)
mention that there appears to be a connection between
burnout and cardiovascular musculoskeletal and cutaneous
diseases and even with type II diabetes mellitus; and as
burnout increases, the somatic co-morbidity appear to increase
as well. Interestingly, a meta-analysis by Salvagioni et al.
(2017) showed that burnout is a predictor of 12 somatic
diseases, among which are; coronary heart disease, headaches,
respiratory diseases and mortality under the age of 45
years old. Consequently, we can understand that burnout
can have multifactorial psychological and somatic effects
upon individuals.
Another field of research area, that would contribute further
to the clarification of the association of these constructs, is the
biological studies examining the neurobiological mechanisms
behind burnout, depression, and anxiety. To this date such
studies are scarce, however, researchers that have examined
burnout, depression and anxiety at a biological level showed that
these constructs appear to be similar. In particular, Korczak et al.
(2010) found that neuroendocrine changes in individuals who
suffer from burnout do not differ from the ones that suffer from
depression or other stress related disorders; a finding that seems
to be in accordance with Bakusic et al. (2017) review, as the
authors suggest that burnout and depression share a common
biological basis. Nevertheless, according to our meta-analysis
results, burnout and depression and burnout and anxiety appear
to be different rather the same constructs. These findings appear
to be very crucial regarding medical diagnosis. Interestingly, only
two European countries, i.e., Italy and Latvia, have recognized
and classified burnout as an “occupational disease” (Eurofound,
2018), by distinguishing burnout from depression and anxiety,
this will lead to more optimized practical implications to all
experts who study burnout and work-stressors in general, and
build more focused treatment plans.
All in all, we believe that our meta-analysis findings have
helped toward the clarification of the relationship between
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Koutsimani et al. Burnout, Depression, Anxiety, Meta-Analysis
burnout and depression, and burnout and anxiety. Additionally,
another finding that emerged is that further studies need to be
conducted which will be focusing on these two relationships
and their behavioral, psychosomatic, and biological patterns as
well. The findings of these studies will lead to the elucidation
of the relationship of these constructs. Furthermore, it is worth
mentioning that according to Eurofound (2018), during the
past decade only a few countries across Europe have conducted
surveys which focus exclusively on burnout and other European
countries have mainly examined burnout related constructs,
such as work stress and/or work-related exhaustion. Hence,
we can realize that, besides the growing interest regarding the
examination of both occupational work stressors and exhaustion
and the clarification between burnout and depression and
burnout and anxiety, more relevant studies are still lacking.
Limitations
Our meta-analysis has some limitations. Firstly, we searched only
for research studies that were conducted during the past decade.
We cannot be certain if our results would be different if we were
to include earlier studies as well. A second limitation is that the
studies that did not provide appropriate statistical results were
not included in the meta-analysis, therefore, again, we cannot
be certain whether the inclusion of these studies would change
our results. The problems of finding appropriate data to conduct
analyses and the reluctance/inability of authors to provide such
data when directly contacted has been identified within the
literature as a significant barrier to conducting comprehensive
meta-analysis (Hardwicke and Ioannidis, 2018). Lastly, a third
limitation of our meta- analysis is that, although our database
search was conducted through four well–known databases, we
still cannot be certain whether all the studies that examined
the burnout–depression and burnout–anxiety relationships are
reported; a limitation which is also known as the “file–drawer
problem” (Rosenthal, 1979). That is, we are not able to know
if/and to what extent there was a selective publication bias and
whether there were studies that remained unpublished due to
non–statistically significant results. Hence, it is possible that
studies in which these two relationships were examined but did
not provide statistically significant results remained unpublished
and, therefore, they were not included in our meta-analysis.
Conclusions
In conclusion, our results showed that while there is statistical
relationship between burnout and depression and burnout and
anxiety, and while they are interconnected, they are not the
same constructs. However, future studies examining these two
relationships are still required in order to be able to draw safer
conclusions. More longitudinal studies that focus on the causality
of the burnout-depression and burnout—anxiety relationships
are needed, as they will be able to clarify these two relationships.
By conducting this meta-analysis, we aimed to examine the
association between burnout and depression and burnout and
anxiety. With our results we hope to inform potential effective
interventions for treating burnout symptoms; by knowing the
nature of a problem this can lead to more targeted solutions.
The modern workplace is characterized by significant
proportions of people who feel exhausted, suffer from
health problems, may be taking antidepressants or other
medication, which can all contribute to feelings of diminished
efficacy. The confluence of the aforementioned highlights the
importance of clarifying the relationship between burnout and
depression/anxiety, so as to avoid a one-dimensional approach
to worker well-being.
AUTHOR CONTRIBUTIONS
PK developed and designed the methodology, conducted data
collection, applied statistical techniques to analyze and synthesize
the study data, prepared the published work, specifically writing
the initial draft and acquired the financial support for the
project leading to this publication. AM formulated the research
goals and aims, developed and designed the methodology,
applied statistical techniques to analyze and synthesize the study
data, provided the analysis tool and prepared the published
work, specifically with critical reviews, editing, and revisions.
KG conducted data collection, applied statistical techniques to
analyze and synthesize study data and prepared the published
work, specifically with critical reviews, editing and revisions.
ACKNOWLEDGMENTS
This research is co-financed by Greece and the European
Union (European Social Fund- ESF) through the Operational
Programme Human Resources Development, Education and
Lifelong Learning in the context of the project Strengthening
Human Resources Research Potential via Doctorate Research
(MIS-5000432), implemented by the State Scholarships
Foundation (IKY).
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fpsyg.
2019.00284/full#supplementary-material
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