Prevalence, risk, and protective factors of self-stigma for people living with depression: A systematic review and meta-analysis

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Journal of Aective Disorders 332 (2023) 327–340
Available online 13 April 2023
0165-0327/© 2023 Elsevier B.V. All rights reserved.
Review article
Prevalence, risk, and protective factors of self-stigma for people living with
depression: A systematic review and meta-analysis
Nan Du
a
, Eddie S.K. Chong
a
, Dannuo Wei
a
, Zewei Liu
b
, Zexuan Mu
a
, Shuyu Deng
a
,
Yu-Te Huang
a
,
*
a
Department of Social Work and Social Administration, the University of Hong Kong, Hong Kong, China
b
Department of Social Work, The Chinese University of Hong Kong, Hong Kong, China
ARTICLE INFO
Keywords:
Depression
Self-stigma
Prevalence
Risk factor
Protective factor
meta-analysis
ABSTRACT
Background: People living with depression are subjected to widespread stigmatization worldwide. Self-stigma
may negatively affect patients' treatment, recovery, and psychological well-being. This review aims to summa-
rize and synthesize the evidence on the prevalence, risk, and protective factors of depression self-stigma.
Methods: Four online databases, PubMed, PsycINFO, Web of Science, and Embase, were searched to identify
eligible studies. Fifty-six studies involving a total of 11,549 samples were included in the nal analysis. Four
reviewers independently screened the literature, extracted data, and assessed the risk of bias in eligible studies.
Pearson's r was chosen as the effect size metric of risk and protective factors.
Results: The results showed that the global prevalence of depression self-stigma was 29 %. Levels of self-stigma
varied across regions, but this difference was not signicant. Two demographic factors were identied: ethnicity
(r =0.10, p <0.05) and having a partner/married (r = − 0.22, p <0.001). Five risk factors were identied:
depression severity (r =0.33, p <0.01), public stigma (r =0.44, p <0.001), treatment stigma (r =0.46, p <
0.001), perceived stigma (r =0.37, p <0.001), and enacted stigma (r =0.71, p <0.001). Five protective factors
were identied: quality of life (r = − 0.38, p <0.001), social relationship (r = − 0.26, p <0.05), self-esteem (r =
−0.46, p <0.001), extroversion (r = − 0.32, p <0.001), and social functioning (r = − 0.49, p <0.001).
Limitations: Heterogeneity was observed in some of the results. Causality cannot be inferred due to the pre-
dominance of cross-sectional designs among the included literature.
Conclusions: Risk and protective factors of depression self-stigma exist across many dimensions. Future research
should examine the inner mechanisms and effectiveness of interventions to reduce stigma.
1. Introduction
Worldwide, the prevalence of depression is rising, making depression
the leading cause of disability and death (Rishi et al., 2021; Wang et al.,
2017). Relapse and residual symptoms, such as impaired social func-
tioning among people living with depression, continue to burden soci-
eties and individuals even following an individual's clinical recovery
(L´
epine and Briley, 2011). In addition to the illness burden, they often
face stereotypes, prejudice, and discrimination from the public (Corri-
gan et al., 2004; Cox et al., 2012). Stigmatization creates and perpetu-
ates barriers to accessing medical and psychological counseling services
since they may avoid seeking help to escape stigmatizing labels (Barry
and McGinty, 2014; Corrigan and Miller, 2004). Stigma and its in-
uences have been highlighted as a pressing public health problem
(Henderson et al., 2013).
Stigma is an “attribute that is deeply discrediting” and a “spoiled
social identity” (Goffman, 1963, pp. 3–5). Public stigma attached to
people living with depression refers to others' negative attitudes; it
causes several difculties, such as unemployment, poor social relation-
ships, and inefcient medical support (Barry and McGinty, 2014; Boyd
et al., 2010; Doyle and Molix, 2014; Link and Phelan, 2001; Roeloffs
et al., 2003; Sharac et al., 2010). At an individual level, stigma manifests
itself in several ways, e.g., perceived stigma, enacted stigma, treatment
stigma, and self-stigma (also known as internalized stigma) (Fox et al.,
2018). Perceived stigma refers to the awareness of stereotypes held by
the public about people living with mental illnesses (Pattyn et al., 2014).
Enacted stigma (i.e., experienced stigma) and treatment stigma (i.e.,
negative attitudes and beliefs about receiving treatment) are also
* Corresponding author at: the University of Hong Kong, Rm515, Jockey Club Tower, Centennial Campus, Hong Kong, China.
E-mail address: yuhuang@hku.hk (Y.-T. Huang).
Contents lists available at ScienceDirect
Journal of Affective Disorders
journal homepage: www.elsevier.com/locate/jad
https://doi.org/10.1016/j.jad.2023.04.013
Received 30 November 2022; Received in revised form 27 March 2023; Accepted 7 April 2023

Journal of Aective Disorders 332 (2023) 327–340
328
external stressors that can undermine treatment initiation and adher-
ence and may trigger psychological and physiological stress responses
impacting mental and physical health (Kanter et al., 2008; Link and
Phelan, 2006; Sirey et al., 2001). However, if the individual does not
agree with the stigmatizing perceptions and behaviors, they may not
progress to the internalization of stigma (Hansson et al., 2014; Moore
et al., 2018). Compared with the experiences and perceptions of public
stigma, self-stigma has a lasting and profound effect on the individual
(Fan et al., 2022). According to Corrigan and Watson's (2002) concep-
tualization, self-stigma is the response from people living with depres-
sion to themselves after assimilating and internalizing stigmatizing
beliefs towards depression and generating prejudice and discrimination
towards the self.
Considerable evidence suggests that stigma manifests itself differ-
ently in different types of mental illness (Corrigan et al., 2000). People
with psychotic disorders, such as schizophrenia, are routinely viewed as
dangerous and unpredictable (Angermeyer et al., 2011). However,
people living with depression are frequently stereotyped as incurable,
weak, and uncommunicative (Jorm et al., 2005; Woon et al., 2020).
Thus, the internalized stigmatizing perceptions of people living with
depression may involve feeling unlovable, awed, and inadequate
(McGinn, 2000). They may also blame themselves and feel guilty for not
having the strength to overcome it or feel ashamed of their illness and
the way it affects their life (Wright et al., 2011).
Self-stigma is associated with a range of negative effects, such as
hindering treatment and recovery, aggravating depressive symptoms
(Freidl et al., 2008), reducing help-seeking and treatment adherence
(Aromaa et al., 2011; Rüsch et al., 2010; Sedlackova et al., 2015;
Warner, 2010), increasing the risk of suicidal behavior (Oexle et al.,
2017), that further harm the individual's self-esteem, self-efcacy, and
empowerment (Boyd et al., 2014; Corrigan et al., 2006; Maharjan and
Panthee, 2019). Self-stigma may further compromise various aspects of
the individual's daily life (Aromaa et al., 2011; Rüsch et al., 2010;
Uebelacker et al., 2012). People internalizing stigma may compromise
their life goals and appear withdrawn in their work and social re-
lationships (Corrigan and Miller, 2004; Link et al., 2004).
Regional discrepancies in the levels of self-stigma (Yang et al., 2007)
have prompted researchers to adopt a socio-cultural lens, suggesting
that self-stigma is not an inherent and xed condition but rather de-
velops in part as a result of different contextual factors (Corrigan and
Watson, 2002; Krendl and Pescosolido, 2020; Major and O'Brien, 2005).
A study comparing self-stigma related to mental illness in Japan and
Australia showed Japanese evidenced a higher level of self-stigma than
Australians; although living conditions in these two countries are com-
parable, the disparity in self-stigma might be attributable to differences
in mental health care systems and cultural beliefs towards mental illness
(Grifths et al., 2006). Knowing the socio-cultural determinants of self-
stigma, mental health practitioners should consider cultures and public
health policies when designing interventions to reduce stigma and
developing different strategies.
Beyond these regional variations, other multi-level factors can
determine the degree to which a person internalizes stigma towards
depression (Busby Grant et al., 2016; Gonz´
alez-Sanguino and Mu˜
noz,
2022; Young and Ng, 2016). Identifying and assessing risk and protec-
tive factors at all levels of the socio-ecological framework are critical
since exposure to multiple risk factors and a lack of protective factors
could substantially intensify the harms of self-stigma (Mrazek and
Haggerty, 1994). According to Dekovi´
c (1999), risk factors identied by
cross-sectional studies refer to the conditions associated with higher
odds of problematic outcomes, while protective factors are associated
with higher odds of more benecial outcomes. Three studies have
reviewed the correlates of self-stigma: Livingston and Boyd et al. (2010)
reviewed 45 studies concerning people with mental illnesses; Lepouriel
(2021) reviewed 56 studies of people living with bipolar disorder;
Dubreucq et al. (2020) reviewed 241 studies on cross-sectional corre-
lates of self-stigma among a range of mental illnesses. Taken together,
these reviews concluded that self-stigma was correlated with de-
mographic factors (e.g., gender, age, and education), illness-related
factors (e.g., severity of symptoms, illness duration, insight, and treat-
ment setting), psychosocial factors (e.g., self-esteem, quality of life, and
social support), environment/stigma-related factors (e.g., levels of
public stigma and perceived stigma), and cultural factors (e.g., loss of
face, history of traditional treatment). However, these studies did not
synthesize the evidence or examine the magnitude of the risk and pro-
tective factors. It remains unclear which factors are particularly salient
to developing self-stigma among people living with depression.
To our knowledge, no study has provided data on the global preva-
lence of self-stigma in depression nor examined geographical differences
in self-stigma and its risk and protective factors specic to people living
with depression. Hence, this systematic review and meta-analysis aims
to 1) review and synthesize the evidence on the prevalence of depression
self-stigma by continents; 2) identify risk and protective factors of
depression self-stigma; and 3) identify limitations of the current evi-
dence and research gaps to guide future research.
2. Methods
This review followed the Preferred Reporting Items for Systematic
Reviews and Meta-Analyses (PRISMA) guidelines (Moher et al., 2009).
The protocol has been registered at PROSPERO (ID: CRD42022323657).
2.1. Search strategy and inclusion criteria
Peer-reviewed studies were identied through systematic searches of
four online databases: PubMed, PsycINFO, Web of Science, and Embase.
We performed an initial search up to February 2022 and a second-round
search in August 2022. Two sets of keywords were used in combination
and adapted according to the requirements of each database. We also
reviewed each located article's references to identify eligible studies
from other sources. English articles involving depression samples,
assessing self-stigma or its correlates with a valid measure, and
providing quantitative data on prevalence and/or correlates of self-
stigma were included. Appendix 1 shows the details of keywords for
searching and inclusion criteria.
2.2. Data extraction and quality assessment
Four researchers (ND, ZL, ZM, and SD) independently extracted data
from eligible studies using a standard data coding framework: author,
publication year, study site, study design, sample size, assessment tools,
average age, gender ratio, average illness duration in years, self-stigma
measures, other outcome variables and measures, and main ndings.
Discrepancies in data extraction were resolved by discussion among the
four researchers.
The quality of studies was assessed using an 11-item scale for cross-
sectional studies provided by the Agency for Healthcare Research and
Quality (AHRQ) (Rostom et al., 2004). Items include quality of data
source, inclusion and exclusion criteria, test/retest of primary outcome
measurements, confounder control, missing data, response rate, and
follow-up. The four researchers independently performed the quality
assessment for included studies, and discrepancies were resolved by
discussion. Appendix 2 shows the results of the quality assessment.
2.3. Data analysis
Data synthesis was completed by the software R with ‘meta’ and
‘metafor’ packages (Balduzzi et al., 2019). The prevalence of depression
self-stigma indicates the proportion of the sample experiencing depres-
sion self-stigma at moderate to high levels (Maharjan and Panthee,
2019; Young and Ng, 2016). Specically, scores above the midpoint on
commonly used self-stigma scales, e.g., the Internalized Stigma of
Mental Illness (ISMI) scale and Depression Self-Stigma Scale (DSSS/
N. Du et al.

Journal of Aective Disorders 332 (2023) 327–340
329
SSDS), represent moderate to high levels of self-stigma (Barney et al.,
2010; Hammer and Toland, 2017). Most included studies provided mean
values of self-stigma measured by ISMI. Thus, we performed subgroup
analyses to examine the differences in mean values across geographic
areas only for studies that measured depression self-stigma using ISMI.
Although is no cutoff for ISMI, many studies suggested that a mean score
of 2.5 or higher (in the range of 0–4) reects a moderate to high level of
self-stigma. Percentages with a 95 % frequency condence interval (CI)
and the mean value with a standard deviation of the self-sigma mea-
surement were calculated by pooling specic estimates. We employed
random effects models for data synthesis, given that various covariates
could inuence the effect sizes of included studies. Random effect
models are preferred when study cohorts are expected to be different, or
outcome variables among studies are not identical (Barili et al., 2018).
Pearson's r was chosen as the effect size metric of correlates (i.e., risk
and protective factors) as this is typically used for estimating the
correlational coefcient between variables. Other outputs (e.g., chi-
square, t and F, d-values, and Odds Ratio) were transferred into Pear-
son's r (Borenstein et al., 2021). According to Rupinski and Dunlap
(1996), Spearman's r can be converted to Pearson's r using the formula: r
=2sin (r
s
×
π
/ 6). The transformation of the effect size was conducted
on the website psychometrica. According to Cohen (1988), the magni-
tudes of effect size for Pearson's r are as follows: 0.1 to 0.3 represents a
small effect; 0.3 to 0.5 represents a moderate effect; 0.5 and higher
represents a strong effect.
The I
2
statistic was used to determine the proportion of total varia-
tion between study estimates due to heterogeneity. Studies with an I
2
value <25 %, ~50 %, and ~75 % were considered to have low, mod-
erate, and high heterogeneities, respectively (Higgins et al., 2003). For
pooled effect sizes with moderate and higher levels of heterogeneity, we
further conducted a moderation analysis to investigate whether the
excess heterogeneity could be explained by the moderator. We used
Egger's test to test publication bias with p <0.05 indicating statistically
signicant publication bias (Egger et al., 1997).
3. Results
3.1. Search process
Of the 4168 records identied through the selected databases and
two records from other resources (obtained by reviewing citations of the
included studies), 2139 were excluded after screening their titles and
abstracts. Two hundred and seven studies were included for full-text
screening, of which 151 were excluded for multiple reasons. Fig. 1
shows the study identication and selection process, and Table 1 sum-
marizes the included studies.
3.2. Study characteristics
As shown in Table 2, most included studies were cross-sectional
studies. About 40 % included samples with multiple diagnoses of
mental disorders (e.g., schizophrenia, bipolar disorder, anxiety disor-
der). Twenty studies compared levels of self-stigma across different di-
agnoses, with 12 reporting signicant differences among clinical
subgroups. Six studies showed higher self-stigma scores in schizophrenia
than in depression (Chang et al., 2016; Grover et al., 2017; Holubova
et al., 2016a, 2018; Kehyayan et al., 2021; Krajewski et al., 2013). Six
studies showed higher self-stigma in depression than in schizophrenia
Fig. 1. PRISMA ow diagram of study identication and selection process.
N. Du et al.

Journal of Aective Disorders 332 (2023) 327–340
330
Table 1
Results of the systematic review.
Study Site Design Study
sample
Size Assessment
tools
Mean
age
(SD)
Gender
(female
%)
Patient
status
Self-
stigma
measure
Risk factors Protective factors
Abo-Rass
et al.
(2019)
Israel Cross-
sectional
Only
depression
160 DSM-5 54.8 70.6 NR SSMIS Older age Higher self-esteem
Abo-Rass
et al.
(2020)
Israel Cross-
sectional
Only
depression
160 DSM-5 54.8 70.6 NR SSMIS – Lower levels of
health related
quality of life
Alemayehu
et al.
(2020)
Ethiopia Cross-
sectional
Only
depression
415 DSM* 36
(10.1)
53.0 Outpatient ISMI Being single,
longer illness
duration, suicidal
attempt, lower
treatment
adherence
Social support,
higher levels of
quality of life
Ayar et al.
(2021)
Turkey Cross-
sectional
Multiple
diagnoses
136 NR NR NR Outpatient ISMI – Solution-focused
thinking, higher
social
functionality
Barney et al.
(2010)
Australia Cross-
sectional
Only
depression
1312 PHQ9 50.9 60.7 NR SSDS Perceived social
distance, greater
depressive
symptoms,
younger age
Higher self-esteem
Bharat et al.
(2020)
U.S. Cross-
sectional
Only
depression
230 DSM-4 32.4
(12.5)
72.2 NR DSSS Greater depressive
symptoms
Subjective social
status
Borecki et al.
(2010)
Poland Cross-
sectional
Only
depression
72 BDI 54 (6) 59.7 Inpatient DSQ Personality
(neuroticism)
Personality
(extroversion)
Busby Grant
et al.
(2016)
Australia Cross-
sectional
Multiple
diagnoses
350 CES-D 22.2
(6.7)
68.0 NR SSDS Greater depressive
symptoms
–
Chang et al.
(2016)
Taiwan Cross-
sectional
Multiple
diagnoses
98 DSM-4 47.6
(11.3)
70.4 Outpatient ISMI – –
Conner et al.
(2010)
U.S. Cross-
sectional
Only
depression
248 PHQ9 72
(7.8)
84.0 NR ISMI Older African
Americans
Better attitudes
towards
psychiatric
services
Conner et al.
(2015)
U.S. Intervention Only
depression
19 PHQ9 67 (5) 63.0 NR ISMI – Contact with peer
educator
Conner et al.
(2018)
U.S. Intervention Only
depression
21 PHQ9 65
(3.6)
57.0 NR ISMI Younger age Contact with peer
educator
Drapalski
et al.
(2013)
U.S. Cross-
sectional
Multiple
diagnoses
24 NR NR NR Outpatient ISMI Greater symptom
severity
Higher self-
esteem, higher
self-efcacy, and
recovery
orientation
Dubreucq
et al.
(2020)
France Cross-
sectional
Multiple
diagnoses
27 DSM-5 NR 32.2 Outpatient ISMI Psychiatric
comorbidities,
suicide attempt,
insight, and
clinical severity
Satisfaction with
interpersonal,
familial, and
intimate
relationships,
wellbeing or
treatment
adherence, and
personal recovery
Fawzi et al.
(2016)
Egypt Longitudinal Only
depression
196 MDI 50.3
(14.5)
59.2 Outpatient ISMI – Accepted
psychiatric
referral
Gaudiano
and Miller
(2013)
U.S. Cross-
sectional
Only
depression
55 BDI 40.1
(13.7)
56.4 Inpatient DSSS Greater
depression
severity, higher
treatment stigma,
negative
medication beliefs
–
Goepfert
et al.
(2019)
Germany Intervention Only
depression
180 ICD-10 38.8 58.9 Outpatient SSDS/
SSMIS
Watching lms
about negative
aspects of
depression
–
Gonz´
alez-
Sanguino
and Mu˜
noz
(2022)
Spain Cross-
sectional
Multiple
diagnoses
36 GHQ-28 NR 54.4 Mixed ISMI – Better general
health, clinical
status, self-esteem,
frequency of
talking about
psychological
problems, quality
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N. Du et al.

Journal of Aective Disorders 332 (2023) 327–340
331
Table 1 (continued )
Study Site Design Study
sample
Size Assessment
tools
Mean
age
(SD)
Gender
(female
%)
Patient
status
Self-
stigma
measure
Risk factors Protective factors
of life, social
relationships,
personality
(extraversion and
agreeableness)
G¨
opfert et al.
(2019)
Germany Cross-
sectional
Only
depression
730 PHQ9 37.8
(13.1)
77.7 Mixed SSMIS Stereotype
awareness
Higher self-esteem
Grambal
et al.
(2016)
Czech
Republic
Cross-
sectional
Multiple
diagnoses
33 ICD-10 45.5
(11.3)
45.0 Outpatient ISMI Number of
hospitalizations,
greater illness
severity
With a partner
Grover et al.
(2017)
India Cross-
sectional
Multiple
diagnoses
352 DSM-4 40.8
(12.4)
55.0 Outpatient ISMI Longer illness
duration and
treatment, greater
depressive
severity
Hammer and
Vogel
(2010)
U.S. Intervention Only
depression
1379 CES-D 29.4
(10.2)
0.0 NR SSOSH – Reading male-
sensitive brochure
Hammer and
Toland
(2017)
U.S. Cross-
sectional
Only
depression
758 NR 41.5
(14.1)
85.1 NR ISMI – –
Holubova
et al.
(2016a)
Czech
Republic
Cross-
sectional
Multiple
diagnoses
80 ICD-10 52.2
(13.6)
73.6 Outpatient ISMI – Better life quality
Holubova
et al.
(2016b)
Czech
Republic
Cross-
sectional
Only
depression
81 ICD-10 52.1
(13.6)
77.0 Outpatient ISMI – Employment,
better life quality
Holubova
et al.
(2018)
Czech
Republic
Cross-
sectional
Multiple
diagnoses
81 ICD-10 52.7
(12.3)
74.1 Outpatient ISMI Negative coping Positive coping
Kamaradova
et al.
(2016)
Czech
Republic
Cross-
sectional
Multiple
diagnoses
57 ICD-10 NR NR Outpatient ISMI Antidepressant
dosage
Higher levels of
education, having
partners, better
adherence.
Kamis¸ et al.
(2019)
Turkey Cross-
sectional
Only
depression
173 DSM-4 53.2
(19.4)
69.4 Outpatient ISMI/
SSDS
Greater symptom
severity
–
Kanter et al.
(2008)
U.S. Cross-
sectional
Only
depression
391 CES-D 30.7
(9.3)
71.2 NR DSSS Greater symptom
severity,
stigmatizing
experiences,
secrecy, treatment
stigma, public
stigma
–
Kehyayan
et al.
(2021)
Qatar Cross-
sectional
Multiple
diagnoses
154 DSM-4/5;
ICD-10
NR NR Outpatient ISMI – –
Khan et al.
(2015)
Pakistan Cross-
sectional
Only
depression
38 DSM-4 359
(12.2)
55.0 Outpatient ISMI Female –
Krajewski
et al.
(2013)
6
European
countries
Cross-
sectional
Multiple
diagnoses
189 NR NR NR Outpatient ISMI – The number of
social contacts
Lanfredi et al.
(2015)
19
European
countries
Cross-
sectional
Only
depression
516 DSM-4 46.6
(15.3)
68.0 Outpatient ISMI – Higher social
capital and higher
empowerment
Mahalik and
Di Bianca
(2021)
U.S. Cross-
sectional
Only
depression
101 PHQ-2 37
(12.6)
0.0 NR SSOSH Self-reliance,
higher levels of
emotional control
–
Manos et al.
(2009)
U.S. Cross-
sectional
Only
depression
163 CES-D 38
(13.8)
66.5 NR DSSS Greater
depression
severity
–
Picco et al.
(2016)
Singapore Cross-
sectional
Multiple
diagnoses
74 ICD-9 NR NR Outpatient ISMI Female, Chinese
ethnicity,
separated/
divorced/
widowed,
hospitalized
individuals
Education level,
employment
Picco et al.
(2017)
Singapore Cross-
sectional
Multiple
diagnoses
74 ICD-9 42.2
(10.8)
51.4 Outpatient ISMI Greater depressive
symptoms
Higher self-
esteem, quality of
life, social
functioning.
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N. Du et al.

Journal of Aective Disorders 332 (2023) 327–340
332
(Grambal et al., 2016; Picco et al., 2016; Sedie et al., 2021; Szcze´
sniak
et al., 2018; Tanabe et al., 2016; Tanriverdi et al., 2020). Regarding the
assessment of depression, more than half of the studies used the Diag-
nostic and Statistical Manual of Mental Disorders (DSM) and the Inter-
national Classication of Diseases (ICD). About 60 % used the ISMI to
measure self-stigma. In terms of sample characteristics, more samples
were female and outpatient. Relatively more studies were conducted in
Asia, Europe, and North America. The total number of study participants
with depression was 11,549, and sample sizes in each study ranged from
14 to 1379.
3.3. Prevalence of depression self-stigma
The pooled prevalence of self-stigma for depression was 29 %,
meaning that 29 % of people living with depression showed moderate to
high levels of self-stigma. The pooled prevalence of self-stigma in studies
with patients with depression only was 34 %, higher than in the sub-
group where the sample included multiple mental disorders (23 %).
However, this difference was not statistically signicant (see Fig. 2).
Table 1 (continued )
Study Site Design Study
sample
Size Assessment
tools
Mean
age
(SD)
Gender
(female
%)
Patient
status
Self-
stigma
measure
Risk factors Protective factors
Prasko et al.
(2016)
Czech
Republic
Cross-
sectional
Only
depression
72 ICD-10 41.5
(13.3)
70.8 Outpatient ISMI Comorbid
personality
disorder
Treatment efcacy
Ran et al.
(2018)
China Cross-
sectional
Multiple
diagnoses
182 ICD-10 NR 63.7 NR ISMI – Income earner,
family income
Rusch et al.
(2008)
U.S. Cross-
sectional
Only
depression
92 CES-D 36.1
(14.3)
79.1 NR DSSS Previous
treatment, greater
depressive
symptoms,
stigmatizing
attitudes and
experiences
–
Sahoo et al.
(2018)
India Cross-
sectional
Only
depression
107 DSM-4 40.2
(11.5)
69.2 Outpatient ISMI Younger age –
Sarkin et al.
(2015)
U.S. Cross-
sectional
Multiple
diagnoses
433 NR 42.2
(13.5)
NR Outpatient SS – –
Sedie et al.
(2021)
Croatia Cross-
sectional
Multiple
diagnoses
53 NR NR NR Outpatient ISMI Impairment,
discrimination,
greater depression
severity
Higher levels of
empowerment,
resilience, better
recovery, having a
person of trust
Sedlackova
et al.
(2015)
Czech
Republic
Cross-
sectional
Only
depression
72 ICD-10 49.0
(12.6)
48.6 Outpatient ISMI – Current adherence
to treatment
Shimotsu and
Horikawa
(2016)
Japan Cross-
sectional
Only
depression
110 ICD-10 45.7
(12.7)
51.0 Outpatient DSS Dysfunctional
cognitive
schemata
Higher self-esteem
Szcze´
sniak
et al.
(2018)
Poland Cross-
sectional
Multiple
diagnoses
44 ICD-10 48.5
(13.8)
66.0 Mixed ISMI Illness duration –
Tanabe et al.
(2016)
Japan Cross-
sectional
Multiple
diagnoses
14 BDI NR NR Outpatient ISMI Greater symptom
severity
Higher self-
esteem,
empowerment
Tanriverdi
et al.
(2020)
Turkey Cross-
sectional
Multiple
diagnoses
40 DSM-4 NR 26 Inpatient ISMI – –
Werner et al.
(2009)
Israel Cross-
sectional
Only
depression
54 DSM-5 74.1
(7.1)
77.8 Outpatient ISMI Younger age Higher levels of
education, higher
income
Wong-
Anuchit
et al.
(2016)
Thailand Cross-
sectional
Multiple
diagnoses
55 NR NR NR Outpatient ISMI – Higher self-esteem
Woon et al.
(2020)
Malaysia Cross-
sectional
Only
depression
99 DSM-4 46.4
(12.3)
63.6 Outpatient ISMI Intact insight –
Yen et al.
(2005a)
Taiwan Cross-
sectional
Only
depression
247 DSM-4 43.9
(14.3)
62.3 Outpatient SSAS – –
Yen et al.
(2005b)
Taiwan Cross-
sectional
Only
depression
247 DSM-4 43.9
(14.3)
60.0 Outpatient SSAS Greater depressive
symptoms
Higher levels of
education
Yen et al.
(2009a)
Taiwan Longitudinal Only
depression
174 DSM-4 43.9
(14.3)
62.3 Outpatient SSAS – –
Yen et al.
(2009b)
Taiwan Cross-
sectional
Only
depression
229 DSM-4 43.8
(14.2)
61.6 Outpatient SSAS – Better quality of
life
Young et al.
(2020)
Hong
Kong
Intervention Only
depression
62 DSM-5 55.2
(10.4)
84.0 Outpatient ISMI – Cognitive
behavior therapy
Note. DSM-4/5 =Diagnostic and Statistical Manual of Mental Disorders, 4/5th Edition; DSM* =version of DSM not reported; ICD-9/10 =International Classication
of Diseases, 9/10th Edition; CES-D =Center for Epidemiological Studies Depression Scale; MDI =Major Depression Inventory; PHQ-2/9 =Patient Health Ques-
tionnaire (2/9 Questions); GHQ-28 =General Health Questionnaire-28 Items; ISMI =Internalized Stigma of Mental Illness Scale; SSMIS =Self-Stigma of Mental Illness
Scale; SSAS =Self-Stigma Assessment Scale; DSS =Devaluation-Discrimination Scale; SS =Stigma Scale; SSOSH =Self-Stigma of Seeking Help Scale; DSSS =
Depression Self-Stigma Scale; DSQ =Depression Stigma Questionnaire; SSDS =Self-Stigma of Depression Scale; NR =Not reported.
N. Du et al.

Journal of Aective Disorders 332 (2023) 327–340
333
3.4. Regional levels of depression self-stigma
The pooled mean of depression self-stigma measured by ISMI was
2.26, which is lower than the suggested cutoff for a moderate to high
level of self-stigma. Stratied by geographical regions, a higher mean of
depression self-stigma was found in Africa (2.41) and Asia (2.31), fol-
lowed by North America (2.21) and Europe (2.20). However, these
regional differences were not signicant (see Fig. 3).
3.5. Demographic factors
Table 3 summarizes demographic correlates. Six factors were iden-
tied, and three factors were signicantly correlated with self-stigma.
Male gender and non-White ethnicity/race were positively and
modestly associated with self-stigma while having a partner or being
married was negatively associated with self-stigma.
3.6. Risk and protective factors
Table 4 summarizes the risk and protective factors of depression self-
stigma. Six risk factors were identied, and ve were signicantly
correlated with self-stigma. Depression severity was positively and
moderately associated with self-stigma. Regarding the stigma-related
factors, public stigma, treatment stigma, and perceived stigma were
positively and moderately associated with self-stigma. Enacted stigma
showed a particularly strong correlation with self-stigma.
Seven protective factors were identied, ve of which were signi-
cantly correlated with self-stigma. No clinical factors were found to be
signicantly associated with self-stigma. Two domains of well-being
were protective of stigma internalization; higher general quality of life
and social relationships were associated with lower self-stigma. In
addition, the psychosocial factors of self-esteem, extroverted personality
traits, and social functioning were signicantly associated with self-
stigma. Appendix 2 shows the forest plots for all risk and protective
factors.
3.7. Sensitivity analysis
First, after omitting outliers, the results and heterogeneities of
pooled frequency, pooled mean, and pooled effect size of depression
severity did not show signicant changes, indicating that removing the
results of any one study did not affect the pooled meta-analytic effect
sizes. We also conducted moderation analysis on the results with high
levels of heterogeneities (I
2
>70 %), including pooled frequency, pooled
mean, and depression severity. The results show that I
2
was reduced
when accounting for sample type (depression only versus multiple di-
agnoses), gender ratio (female>50 % versus female<50 %), study site
(Africa versus Asia versus North America versus Europe), and study
quality (high versus moderate). With the study site as the moderator, I
2
for pooled prevalence and pooled mean in Europe reduced to 0 % and
35.2 %, respectively. With gender ratio as the moderator, I
2
for subgroup
depression severity (female sample >50 %) reduced to 0 %.
3.8. Publication bias
Pooled frequency, pooled mean, and depression severity were
analyzed for publication bias using Egger's test. No publication bias was
found in these results (all Egger's tests p >0.05). According to the funnel
plot for depression severity (see Appendix 4), a weak publication bias
was detected (standard error versus Fisher's z transformed correlation).
Generally, publication bias does not appear to be a concern for this
study.
4. Discussion
This study reviewed existing studies to identify factors involved in
depression self-stigma. While extensive attention has been paid to
stigma among people with mental ill-health, this study contributes to
this large body of literature by classifying these factors and discerning
the nature of their relationships with self-stigma. The meta-analysis
further uncovered the patterns of stigma internalization stratied by
geographic parameters and highlights the need for additional effort to
mitigate the depression self-stigma in populations and communities that
are particularly prone to the adverse effects of self-stigma. Identifying
risk and protective factors can inform clinical and community-based
programs to mitigate the pathway of stigma internalization among
people managing the challenges of depression.
Twelve studies reported signicant differences among clinical sub-
groups. Six studies showed higher self-stigma scores in patients with
schizophrenia than in depression. These results are consistent with
Dubreucq et al.'s (2021) meta-analysis reporting a lower global preva-
lence of self-stigma in depression than in schizophrenia (35.8 %). This
might be because patients with schizophrenia face stronger public
prejudice and discrimination and thus become particularly vulnerable to
stigma internalization (Corrigan et al., 2012). Unlike schizophrenia,
depression is less likely to be seen as a signal of danger or unpredict-
ability. However, six studies showed that patients with depression
scored higher on self-stigma than patients with schizophrenia spectrum
disorder. There were also studies reporting no signicant differences in
Table 2
Characteristics of the included studies.
Characteristic n %
Study design
Cross-sectional 49 87.5 %
Intervention 5 8.9 %
Longitudinal 2 3.6 %
Study sample
Only depression 34 60.7 %
Multiple diagnoses 22 39.3 %
Assessment tools
DSM-4/5 20 35.7 %
ICD-9/10 14 25.0 %
Others 16 28.6 %
Not report 6 10.7 %
Measures (self-stigma)
ISMI 35 62.5 %
DSSS/SSDS 7 12.5 %
Others 14 25.0 %
Gender rate
Female % ≥50 % 40 71.4 %
Female % <50 % 6 10.7 %
Not report 10 17.9 %
Patient status
Outpatient 35 62.5 %
Inpatient 3 5.4 %
Mixed 3 5.4 %
Not report 13 26.8 %
Study sites
Asia 20 35.7 %
Europe 19 33.9 %
North America 13 23.2 %
Africa 2 3.6 %
Australia 2 3.6 %
Quality rating
Moderate 38 67.9 %
High 18 32.1 %
N. Du et al.

Journal of Aective Disorders 332 (2023) 327–340
334
the self-stigma level among the clinical groups (Ayar et al., 2021;
Drapalski et al., 2013; Dubreucq et al., 2020; Gonz´
alez-Sanguino and
Mu˜
noz, 2022; Kamaradova et al., 2016; Picco et al., 2017; Ran et al.,
2018; Wong-Anuchit et al., 2016). Diagnosis itself may be a distal
correlate of self-stigma, while contextual and lived experiences may
more heavily inuence its development. Visiting a psychiatrist, for
example, may threaten a patient's self-image (Holubova et al., 2016c),
suggesting varying self-stigma levels in different contexts and lives.
The meta-analysis shows that the mean score of depression self-
stigma was higher in study participants in Africa, Asia, and North
America than in Europe, although these differences were not statistically
signicant. This variation could be attributed to socio-cultural factors. It
has been suggested that mental illness is more stigmatized in poorer
areas (Robinson and Henderson, 2019). How the public perceives
mental illness is closely related to the rate of self-stigma (Krajewski
et al., 2013). Studies have found that Europeans and North Americans
report better awareness and literacy about mental illness and have more
access to medical and health resources (Gonz´
alez-Sanguino and Mu˜
noz,
2022). Socially oriented services in Europe, such as mental health sup-
port from nongovernmental sectors, also help reduce self-stigma among
users (Szcze´
sniak et al., 2018). It is worth noting that the degree of social
tolerance is not entirely dependent on the degree of social development
and the economy. Wong-Anuchit et al. (2016) explained that the low
level of self-stigma in Thailand may be connected to the Buddhist belief
of Thum-Jai, which involves the mixture of “being accepting, patient,
understanding, reasonable, and responsible.” Thus, social tolerance and
the level of public stigma are also inuenced by cultural beliefs.
Cultural beliefs might also account for the geographical variations in
self-stigma among people living with mental illness. Under a specic
cultural inuence, individuals are likely to endorse public stigma and
develop self-stigma (Young and Ng, 2016). People endorsing collec-
tivism tend to construct their self-concepts on social relationships close
to them and are more likely to assign heavier weight to negative ste-
reotypes from others and internalize them than people with individu-
alistic orientations (Fung et al., 2007). Yu et al. (2021) thus posited that
the more collectivistic a culture, the stronger the correlation between
experienced and perceived stigma with self-stigma. In contrast, people
attached to individualistic cultures exhibit a stronger level of indepen-
dent self-construal in making determinations according to their own
values (Li et al., 2018). The thought that “my self-view would not change
just because of my mental illness” may protect individualistic in-
dividuals from stigma internalization (Li et al., 2022). This may also
explain why people from collectivistic regions (Africa and Asia) have
higher levels of self-stigma than those from individualistic regions
(Europe and North America) (Vu et al., 2017).
In this review, we identied six demographic factors commonly
measured in previous studies. The meta-analysis further indicated that
men tended to internalize stigma more than women (8/12 studies). In
addition, six of 12 studies found that lower education was associated
with higher self-stigma. Evidence also suggests that African Americans/
ethnic minorities report higher self-stigma than their white/Caucasian
counterparts. Lower income and lacking a partner or being unmarried
are associated with higher self-stigma. However, in the studies included
in this review, these socio-demographic factors were neither consis-
tently nor strongly correlated with self-stigma.
Seven clinical factors were identied as being involved in stigma
internalization. Most studies reported a positive association between the
severity of depression/illness duration and self-stigma. These two clin-
ical factors indicate that individuals might face worse consequences of
depression and are exposed to a pervasively stigmatizing environment
(Grover et al., 2017; Sahoo et al., 2018; Szcze´
sniak et al., 2018), which
could aggravate their self-stigma. The level of self-stigma is also asso-
ciated with a younger age of onset and a more negative attitude towards
treatment and is positively associated with comorbidity and illness
insight. Increased insight enables individuals to recognize the symptoms
and their impact, which may motivate them to seek and receive treat-
ment. However, people who assume personal responsibility for their
depression may be more likely to blame themselves (Mak and Wu,
2006). Yen et al. (2005a) also found no associations between self-stigma
and the three dimensions of insight (i.e., awareness of illness, attribu-
tion, and need for treatment). Thus, the impact of illness insight on self-
stigma among people living with depression warrants further
investigation.
This review also highlights a wide range of contributors to self-
stigma, covering stigma-related factors, well-being, and psychosocial
factors. First, public stigma and enacted stigma were positively corre-
lated to self-stigma. Treatment stigma and perceived stigma were robust
predictors of self-stigma among patients with depression. This reveals
that one major effect of public stigma is to lead individuals who expe-
rience prejudice or discrimination to accept stereotypes about people
Fig. 2. Forest plot of prevalence.
Note. Case =the number of participants in the depressed sample with moderate to high levels of self-stigma; Total =Total sample size; CI =condence interval.
N. Du et al.

Journal of Aective Disorders 332 (2023) 327–340
335
living with depression and feel demoralized, negative about themselves,
and socially withdrawn (Drapalski et al., 2013). Other included studies
reported that exposure to news with stigmatizing content, information
about depression, and restrictions in daily activities were all related to a
higher level of depression self-stigma. The above evidence illustrates the
contribution of enacted stigma to self-stigma.
Second, on an individual level, quality of life was commonly exam-
ined and found to be negatively related to self-stigma. Various domains
of life quality, including environmental quality, physical health, and
psychological health, were all negatively correlated with self-stigma
(Lanfredi et al., 2015; Picco et al., 2017). In addition, self-esteem was
strongly and negatively correlated with self-stigma. While most studies
conceptualized self-esteem as a consequence of self-stigma, one study
suggested that low self-esteem predicted self-stigma among people with
mental illness (Livingston and Boyd, 2010). The relationship between
self-stigma and low self-esteem might be explained by heightened
sensitivity to stigmatizing beliefs. Low self-esteem is also considered a
symptom of depression (Silverstone and Salsali, 2003).
Moreover, higher social status and greater social capital were
negatively related to self-stigma. Social status represents how in-
dividuals assess themselves in relation to others in their communities
and countries (Bharat et al., 2020). Social capital comprises perceived
social support, social network, and interpersonal trust (Lanfredi et al.,
2015). These social resources could counteract stigma internalization by
fostering a sense of empowerment and reducing stigmatizing stress
(Lanfredi et al., 2015). Specically, depression self-stigma was
Fig. 3. Forest plot of mean in different areas.
Note. SD =standard deviation. Holubova et al. (2016a, 2016b) contain two studies with the same sample and results.
N. Du et al.

Journal of Aective Disorders 332 (2023) 327–340
336
negatively associated with social support, social relationship, social
network, and interpersonal trust and positively associated with social
distance. Diverse sources of social relationships and social support are
important to enable individuals to cope with prejudice and discrimina-
tion (Lanfredi et al., 2015). In particular, Kamaradova et al. (2016)
observed that patients may interpret difculties in maintaining a rela-
tionship as the price to be paid for their illness and may contribute to
high self-stigma. Barney et al. (2010) proposed that seeking support
from others (e.g., colleagues, friends, and family) was related to a lower
level of self-stigma.
In addition, other psychosocial risk factors associated with self-
stigma cover emotional control, self-reliance, social inadequacy, and
dysfunctional attitude (i.e., achievement, self-control, and dependency).
Psychosocial protective factors include empowerment, social func-
tioning, and problem appraisal. In light of the current ndings, self-
stigma experienced by patients with depression is related to both
external factors and personal characteristics (Borecki et al., 2010). It is
reported that individual personality could inuence the outcome of
stigma internalization (Margeti´
c et al., 2010). Specically, the level of
depression self-stigma is positively related to neuroticism and negatively
related to extraversion and agreeableness. Another personal feature,
resilience, which means “a positive personality characteristic that en-
hances individual adaption” has been noted as a protective factor in
reducing depression self-stigma (Sedie et al., 2021).
4.1. Implications
This review identied that being non-white and not having a partner
or being married were correlated with self-stigma. These results suggest
that socio-demographic factors cannot be ignored in the analytical
framework of stigma internalization in depression. More importantly,
future research could examine the underlying mechanism involved in
such observations. In addition, the variation of self-stigma across
geographic areas is marginally signicant (p =0.06). This small degree
of geographic variation of self-stigma may suggest a need to examine
contextual and institutional factors, such as public health policies and
mental health systems. Globalization, uneven geographical de-
velopments, and cultural diversity (Gopalkrishnan, 2018), may prevent
making simple geographical/cultural comparisons of self-stigma levels
for depression. It is misleading and an oversimplication to assume that
individuals from collectivist countries have higher levels of self-stigma.
Nowadays, whether an individual internalizes stigma also depends on
other factors, such as personal cultural orientation, quality of life, social
capital, and personal traits. This nding thus highlights the importance
of a systemic and intersectional lens for understanding the issue of self-
stigma (Dubke and Corrigan, 2021).
Findings from this review can inform de-stigmatization practices and
interventions to improve clinical and psychosocial outcomes for people
living with depression. Education and contact-oriented interventions
should not be limited to improving knowledge about depression and
interpersonal interactions (Mittal et al., 2012). More attention should be
paid to other psychosocial issues, such as helping people living with
depression foster their self-esteem, self-efcacy, and awareness of their
personality traits and improve their quality of life. Policymakers could
also enhance public awareness of depression and treatment of mental
illnesses. In this way, public and treatment stigma might be reduced,
along with less resistance to psychiatric hospitals and mental health
services. Lastly, this study also highlights the promising directions for
intervention and research to examine the relative efcacy of distinct
approaches to addressing the self-stigma of people living with
depression.
4.2. Limitations
This review has several limitations. First, we adopted the random-
effects model for meta-analysis. However, the measurements of self-
Table 3
Summary of the pooled effect sizes of demographic factors.
Factors k n r 95 % CI Q I
2
Gender (male)* 12 2881 0.06 [0.00; 0.11] 21.17 48
%
Ethnicity/race (non-
White)*
4 1093 0.10 [0.01; 0.19] 5.42 45
%
Age 16 4939 −0.04 [−0.11;
0.02]
67.63 78
%
Education 12 3027 −0.13 [−0.40;
0.16]
193.95 94
%
With a partner/
married***
4 6,06 −0.22 [−0.30;
−0.14]
2.75 0 %
Income 2 236 −0.14 [−0.50;
0.26]
6.90 86
%
Note. k =number of studies, r =pooled effect sizes of Pearson's r, Q =Cochran's
Q tests of heterogeneity, CI =condence interval, I
2
=the percentage of vari-
ation across studies.
*
p <0.05.
***
p <0.001.
Table 4
Summary of the pooled effect sizes of risk and protective factors.
Category Factors k n r 95 % CI Q I
2
Risk factors
Clinical Depression severity (symptoms)*** 25 6215 0.33 [0.24; 0.41] 250.79 90 %
Illness duration 4 1121 0.04 [−0.16; 0.24] 34.43 91 %
Stigma-related Public stigma*** 5 824 0.44 [0.30; 0.56] 20.56 81 %
Treatment stigma*** 4 718 0.46 [0.40; 0.51] 0.59 0 %
Perceived stigma*** 6 3268 0.37 [0.15; 0.54] 137.22 96 %
Enacted stigma*** 3 646 0.71 [0.67; 0.74] 0.57 0 %
Protective factors
Clinical Age at onset 2 459 −0.15 [−0.39; 0.11] 5.77 83 %
Treatment attitude 4 549 −0.09 [−0.36; 0.20] 21.81 86 %
Well-being Quality of life*** 4 691 −0.38 [−0.48; −0.27] 5.93 49 %
Social relationship* 3 339 −0.26 [−0.46; −0.03] 6.63 70 %
Psychological Self-esteem*** 9 3125 −0.46 [−0.57; −0.33] 128.41 94 %
Personality (extroversion)*** 2 108 −0.32 [−0.48; −0.14] 0.03 0 %
Social functioning*** 2 210 −0.49 [−0.62; −0.34] 1.71 88 %
Note. k =number of studies, r =pooled effect sizes of Pearson's r, Q =Cochran's Q tests of heterogeneity, CI =condence interval, I
2
=the percentage of variation
across studies.
*
p <0.05.
***
p <0.001.
N. Du et al.

Journal of Aective Disorders 332 (2023) 327–340
337
stigma and other outcome variables were different, which may have
inuenced the results and their levels of heterogeneity. Second, the
majority of the included studies were cross-sectional, thus examining
correlational rather than causal relationships. Third, only qualitative
studies were analyzed in the systematic review, while factors identied
in these qualitative studies could not be captured in the meta-analysis.
Finally, language bias resulted from only including articles published
in English. Ideally, literature reviews should not exclude studies on the
basis of the language in which they are written (Catalogue of Bias
Collaboration et al., 2017). Moreover, most studies were from Asia and
Europe. No South American studies and only a few studies conducted in
Africa and Australia were included. This may have affected the results of
meta-analyses, e.g., the prevalence of depression self-stigma may not be
representative of the true global level.
5. Conclusion
To our knowledge, this is the rst study to systematically review the
prevalence, geographical differences, risk, and protective factors of self-
stigma among people living with depression. The ndings indicate that
people living with depression are at considerable risk of internalizing
negative stereotypes and discrimination associated with depression. An
array of risk and protective factors were identied. Prominent risk fac-
tors include depression severity and stigma-related factors. Promising
protective factors include quality of life and self-esteem. This study
contributes to expanding the research frameworks and intervention
models for depression self-stigma.
Role of the funding source
This study does not have any funding support.
CRediT authorship contribution statement
Nan Du conceptualized and designed the study, screened studies,
extracted data, assessed quality of studies, analyzed data, wrote the
initial draft of the manuscript, and revised manuscript.
Eddie S.K Chong conceptualized and designed the study and revised
manuscript.
Dannuo Wei analyzed data.
Zewei Liu screened studies, extracted data, and assessed quality of
studies.
Zexuan Mu screened studies, extracted data, and assessed quality of
studies.
Shuyu Deng screened studies, extracted data, and assessed quality of
studies.
Yu-Te Huang conceptualized and designed the study, revised
manuscript, supervised the research team.
Conict of interest
There is no conict of interest to report for this submission.
Acknowledgements
We would like to thank Professor Eric Blyth for editing and proof-
reading the manuscript.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.jad.2023.04.013.
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