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. 2022 Dec 10;13(1):e2840. doi: 10.1002/brb3.2840

Prevalence of depression and anxiety among myasthenia gravis (MG) patients: A systematic review and meta‐analysis

Javad Nadali 1,†, Neda Ghavampour 2,†, Farzaneh Beiranvand 2, Mona Maleki Takhtegahi 2, Mohammad Eghbal Heidari 3,✉, Shahin Salarvand 4, Tina Arabzadeh 2, Omid Narimani Charan 2
PMCID: PMC9847616  PMID: 36495116

Abstract

Objective

Myasthenia gravis (MG) people experience adverse psychiatric outcomes, which may impact on their life and disturb their daily activity. Depression and anxiety are identified as significant psychiatric problems that MG people face. However, there is no sufficient epidemiological information about depression and anxiety‐based publication. Due to this limitation, the aim of this study was to review the prevalence of depression and anxiety in MG patients.

Methods

Original and international databases were searched to find papers about the estimation of anxiety and depression. Random‐effects analysis was used for calculating the proportions of anxiety and depression. For estimating anxiety and depression based the severity, instruments, type of studies, and study regions, subgroup analysis was performed.

Results

38 studies met inclusion criteria and entered study. The pooling of the prevalence of depression was found at 36%, (95% CI 28% to 45%). Also, prevalence of anxiety was found at 33%, (95% CI 25% to 42%). Prevalence of depression based on mild, moderate, and severe level was 27%, 14%, and 9%, respectively.

Conclusions

Anxiety and depression are a major concern among MG individuals. The estimation of both anxiety and depression are high even when compared to other autoimmune diseases. It seems depression and anxiety are important issues and more attention needs to be paid to these psychiatric disorders.

Keywords: anxiety, depression, MG, systematic review

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1. INTRODUCTION

Myasthenia gravis (MG) is identified as a prevalent autoimmune disease and the most common form of neuromuscular junction disease. The estimated prevalence of MG is about 20 individuals per 100,000 persons. Among gender, women are more susceptible rather than men (Cherukupally et al., 2020). It is predicted that MG affects 36,000 to 60,000 patients in the United States and more than 700,000 people worldwide annually. Age and sex are important factors in the incidence and prevalence of MG (Yamamoto et al., 2019). In MG, the transmission of nerve impulses to the muscles is disrupted. When there is no nerve–muscle connection, muscles are disrupted and cause neuromuscular disorders in these patients (Leopardi et al., 2021).

MG often occurs in adults and its common clinical manifestations include ocular, bulbar, or generalized weakness. Ocular complaints are the most common and are seen in 50% of cases. Due to the severity of symptoms such as weakness or fatigue, 30% of patients have respiratory problems and weakness and need mechanical ventilation (Hendricks et al., 2019).

Evidence showed that MG people experience psychological outcomes because of complication and recurrent symptoms. Mood disorders are the most common psychological outcomes among MG people (Kulaksizoglu, 2007; Paul et al., 2000; Qiu et al., 2010; Vitturi et al., 2021) Furthermore, about 20% of MG patients suffer from a psychiatric orpsychological disorder. Studies showed that prevalence of mental disorders and anxiety in people with MG are more than in the general population (Alanazy, 2019).

Furthermore, MG people experience different and complex treatments and invasive interventions which might impact their life and mental health. As the disease progresses, patients experience more disabilities and problems in their treatment that may reduce their quality of life and increase the incidence of mental disorders such as depression and anxiety (Yang et al., 2016). Numerous studies have reported that MG patients suffer from disability due to their symptoms, treatment, or complication (Alanazy, 2019; Qiu et al., 2010). Fatigue, drowsiness, anxiety, and depression are the most important complaints of patients (Alekseeva et al., 2019).

Psychological outcomes affect family, social, occupational, and personal aspects of patients (Parada et al., 2014). In addition, psychiatric and emotional disorders may lead to increased risk of mortality (Gavrilov et al., 2020; Kulaksizoglu, 2007).

According to studies about the prevalence of anxiety and depression in MG, the epidemiology of depression in MG varies from 6% to 76% (Kalbus et al., 2020; Sivadasan et al., 2019) and anxiety varies from 3% to 71% (Sivadasan et al., 2019; Ybarra et al., 2011). So, evidence showed that MG individuals are inclined to show high rate of depression and anxiety, and studies confirmed that anxiety and depression affect the mental health of MG people.

To sum up, many researchers have been evaluating the prevalence of depression and anxiety in MG patients and have had diverse results. For prevention and promotion of quality of life and treatment in MG patients, knowing the prevalence of depression and anxiety is necessary. Such information is essential for understanding the epidemiology of MG and is also helpful for developing strategies that help us to overcome the burdens caused by psychological manifestations of MG. Therefore, for achieving epidemiologic purposes, additional studies in different regions are necessary. According to the above statements and the absence of a systematic review or meta‐analysis of these issues, we conducted this study to determine the prevalence of depression and anxiety among MG patients all around the world.

2. MATERIALS AND METHODS

Preferred Reporting Items for Systematic Reviews and Meta‐analyses (PRISMA) were considered as guidelines for designing, performing, and writing study (Moher et al., 2009).

2.1. Search strategy

We fulfilled extensive search by searching the original databases of Web of Science, PUBMED, EMBASE, Ovid, and Scopus to find relevant and appropriate papers about estimate of anxiety and depression in MG patients. Search strategy was applied from 1960 to July 28, 2022 without any limitation on language and date. Manual searching in key journals for finding relevant articles was conducted after an initial search of databases and checking the reference list of included articles. Mesh terms, free text method, expert opinions as well as related articles and abstracts were checked to find the equivalent of search strategy terms. The terms used to search the databases were: (MG OR Myasthenia gravis) AND (depression OR Depressive disorder OR Cognitive disorder OR Anxiety OR mental disorder).

2.2. Study eligibility

Two authors (O.N. and M.E.H.) independently reviewed and retrieved articles to find out relevant study for including in the study according to titles and abstracts. Then, the full texts of papers were reviewed and studies were selected according to the inclusion criteria.

The inclusion criteria were: (1) observational studies (cohort, cross‐sectional), (2) papers which calculated or mentioned the proportions of depressive disorder or depression symptoms and anxiety, (3) an MG patient diagnosed by a physician, and 4) valid self‐reporting tools or structured diagnostic interviews for evaluating depression and anxiety. Case reports, interventional studies, magazine articles, case series, newspaper articles, or commentaries were considered as exclusion criteria. Articles that did not have complete information, had incomplete abstract or text, or did not provide sufficient and relevant data to estimate depression and anxiety were also excluded. Disagreements regarding the eligibility of studies were resolved by the third author (J,N).

2.3. Data extraction

The list of data extraction included publication year, study year, first author's name, design of study, study country, prevalence of depression and anxiety, mean age of participants, sample size, and score of Newcastle–Ottawa risk of bias. Articles that did not have the necessary information to calculate the prevalence of anxiety and depression were corresponded with the authors of the articles, and if they did not respond to the email more than three times, the studies were removed from the list of included studies.

2.4. Risk of bias (quality) assessment

Two authors investigated the quality assessment of articles. For evaluating the quality of studies, Newcastle–Ottawa risk of bias was used (Peterson et al., 2011).

2.5. Statistical analysis

STATA, version 12.0 (STATA Corporation, College Station, TX, USA) was used for data analysis. For each study, point estimates and 95% confidence intervals were calculated for the prevalence of depression and anxiety. The forest plot was also used to comprehensively represent selected studies based on consolidated estimated prevalence and 95% confidence interval. Meta regression was used to estimate the extent to which the measured covariates (year of release, sample size, and duration of disease) could explain the heterogeneity observed in prevalence estimates across studies. Publication bias was first assessed by visual inspection of the distribution of studies observed in a funnel design. Egger regression tracking (Vitturi et al., 2021) and Begg rank correlation test (Vitturi et al., 2021) were used to quantify the degree of bias shown in the funnel diagram.

3. RESULTS

3.1. Characteristics of included studies

2432 citations were reviewed based on title and abstracts; 280 retrieved by assessing full text articles. So, 38 studies remained for final analysis (Figure 1). Table 1 outlines the characteristics of studies (2, 7–9, 12, 14–17, 20–48).

FIGURE 1.

FIGURE 1

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Four‐phase PRISMA flow diagram showing the number of studies identified, screened, eligible, and included in the systematic review and meta‐analysis

TABLE 1.

Characteristics of the studies included in this meta‐analysis

First author Year Study location Sample Size Mean age Study type Instrument N of total depression N of Anxiety N of anxiety/depression N of mild depression N of moderate depression N of severe depression
Vitturi 2021 Brazil 49 18.1 Cohort HADS 14
Chanchan 2021 China 134 17.01 Cohort HDRS 71 57
Kreis 2021 Russian 73 15 Cohort BDI 44 30 14
Kalbus 2020 Ukraine 182 – Cohort BDI 139 47 18 36
Yury 2020 Switzerland 68 15.14 Cohort BDI 44 30 12 2
Bogdan 2020 Canada 179 14.49 Cohort BDI‐11 31
Annelise 2020 Brazil 39 17.13 Cross‐sectional BDI 13 5 8 3
Kiana 2020 Iran 62 13.3 Cross‐sectional HDRS 40 21 9 10
Asami 2019 Japan 103 – Cohort DSM‐IV 10 6
Ajith 2019 India 62 16.62 Cohort – 4 2
Siddig 2019 Sudan 33 – Cross‐sectional DSM‐IV 15
Hsuan‐Te 2019 Taiwan 349 6.93 Cohort – 22
Elizabeth 2019 USA 242 13.4 Cohort – 51
T. M. A 2019 Switzerland 69 14.97 Cohort BDI 14 18
Mohammed 2019 Saudi Arabia 104 16 Cross‐sectional PHQ‐9 27
Nida Fatma 2017 Turkeys 19 13 Cross‐sectional SCID‐1 4 4
Nayara, Braz 2018 Brazil 80 14.17 Cross‐sectional HADS 22 35 8 7 7
Vanya 2017 Bulgaria 97 – Cohort – 26
Jianyong 2016 China 541 9.8 Cohort – 179
Feray 2016 Turkey 30 14.9 Cross‐sectional BDI 18 17 8 7 3
Sarah 2016 Germany 200 17 Cross‐sectional HADS 38 54
Soheir 2016 Egypt 30 8.5 Cross‐sectional SCID 13 15
Stefan 2015 Australia 165 – Cross‐sectional – 26
C Freeman 2014 South Africa 30 14.9 Cross‐sectional BDI‐11 10 8
Syuichi 2013 Japan 171 – Cohort – 3
Yasushi 2011 Japan 287 17.1 Cross‐sectional BDI 39
Fikret 2013 Turkey 42 17.6 Cross‐sectional BDI 17 4
Mariana 2010 Brazil 41 13.4 Cross‐sectional MINI 18 29
Yaroslav 2010 Germany 37 20.2 Cross‐sectional 10
Qiu L 2010 China 161 – Cross‐sectional HAM‐D 94 73
Di Blasi, 2009 Italy 30 – Cross‐sectional BDI 30
Suzanne 2007 USA 100 – Cross‐sectional CES‐D 42
Lundeen 2004 USA 69 – Cross‐sectional – 38
Fisher 2003 USA 45 – Cross‐sectional BDI 15 7 5 3
Robert H 2000 USA 29 13.78 Cross‐sectional CMDI 5
T. M. 2018 Russia 73 15 Cross‐sectional BDI 44 30 14
Paradis CM 2006 USA 35 19.05 Cross‐sectional – 15
Hong 2010 China 48 18.1 Cross‐sectional HDRS 18 10 6 2
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Abbrevations: BDI, Beck Depression Inventory; CES‐D, Center for Epidemiological Studies Depression; HDRS, Hamilton Depression Rating Scale; HADS, Hospital Anxiety and Depression Scale; PHQ, Patient Health Questionnaire; SCID‐1V, Structured Clinical Interview for DSM Disorders; DSM‐IV, Diagnostic and Statistical Manual of Mental Disorders; MINI, mini international neuropsychiatric interview; HAM‐D, Hamilton Depression Rating Scale; CMDI, Chicago Multiscale Depression Inventory.

In total, 38 studies involving 4108 individuals from 20 countries were included (Figure 1). Mean duration of disease was between 8.87 and 1.95 years. The mean age of participants was 47.04 (7.96) years. Dates of publication ranged from 2000 to 2021. Eleven studies were conducted in America, 15 were conducted in Asia, 10 were conducted in Europe, and 2 were conducted in African countries. Twenty‐four studies were cross‐sectional studies and 14 were cohort studies (Table 1). According to Myasthenia Gravis Foundation of America (MGFA), 835 individuals were in stages I and II, 365 patients in stage III, and 158 people with MG were in classification of IV.

The included studies applied different types of screening tools to assess anxiety. These tools included Beck Anxiety Inventory (BAI, four studies), Hospital Anxiety and Depression Scale (HADS, three studies), ADIS‐R, Diagnostic and Statistical Manual of Mental Disorders (DSM‐IV), GAD, HARS, STAI, and Structured Clinical Interview for DSM Disorders (SCID). For screening depression, used Beck Depression Inventory (BDI, 11 studies), HADS (3 studies), Hamilton Depression Rating Scale (HDRS, 3 studies), BDI‐II (2 studies), DSM‐IV (2 studies), CES‐D, PHQ‐9, SCID, HAMD, and Chicago Multiscale Depression Inventory (CMDI).

3.2. Results of the meta‐analysis

The pooled prevalence of depression reported was found at 36% (95% CI 28% to 45%) (Figure 2). Also, prevalence of anxiety was found at 33% (95% CI 25% to 42%) (Figure 3). Significant heterogeneity was found between studies in anxiety and depression. The test I 2 were 96.18% and 91.93% in depression and anxiety estimates, respectively. The prevalence of depression by the individual studies ranged from 1% to 76% and in anxiety ranged from 3% to 71%. Sensitivity analysis indicated that no individual study affected the overall prevalence estimate by more than 0.1%.

FIGURE 2.

FIGURE 2

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Forest plot of the prevalence of depression in MG patients. The 95% confidence interval for each study is shown in the form of horizontal lines around the central mean and midpoint of the dotted line represents the mean of the overall score and the lozenge shape shows the confidence interval of the prevalence of this disorder.

FIGURE 3.

FIGURE 3

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Forest plot of the prevalence of anxiety in MG patients. The 95% confidence interval for each study is shown in the form of horizontal lines around the central mean and midpoint of the dotted line represents the mean of the overall score and the lozenge shape shows the confidence interval of the prevalence of this disorder.

3.3. Subgroup analysis

For finding the source of heterogeneity and to characterize the range of depression and anxiety, subgroup analysis was performed based on the instrument, severity, continents, duration of disease, and type of studies. Prevalence of mild, moderate, and severe depression in MG people were 27%, 14%, and 9%, respectively. Also, further analysis based on severity of depression was conducted and the estimates of minor and major depression in MG were found at 31% and 26%, respectively. Based on the continent, the highest and lowest prevalence of depression was recorded in Europe (56%) and Asia (28%), respectively. Also, the prevalence of depression was 52% and 21% based on BDI and HADS, respectively (Table 2). In the cross‐sectional and cohort studies, the pooled prevalence of depression was 40% (95% CI: 30% to 50%) and 31% (95% CI: 16% to 48%), respectively (Table 2). Also, subgroup analysis showed the highest and lowest prevalence of anxiety was recorded in America (53%) and Asia (25%), respectively. In the cross‐sectional and cohort studies, the pooled prevalence of anxiety was 40% (95% CI: 31% to 51%) and 20% (95% CI: 8% to 36%), respectively.

TABLE 2.

Results of subgroup analysis

Subgroup Prevalence (%) 95% confidence interval I 2, % p
Type of tool
Depression BDI 52 0.33–0.72 97 < .001
HDRS 52 0.39–0.65 –
BDI‐II 19 0.14–0.25 – < .001
HADS 21 0.17–0.26 – < .001
Self‐reported 14 0. 7–0.23 < .001
Severity of depression
Depression Mild 10 (Studies) 27 0.19–0.35 80 < .001
Moderate 10 (Studies) 14 0.11–0.17 70 < .001
Severe 8 (Studies) 9 0.05–0.15 68 < .001
Type of studies
Depression
Cross sectional 27 (Studies) 40 0.30–0.50 93 < .001
Cohort
11(Studies) 31 0.16–0.48 97 < 0.001
Study regions
Depression America 8 (Studies) 29 0.21–0.36 78 < .001
Europe 9 (Studies) 56 0.36–0.76 96 < .001
Asia 13 (Studies) 28 0.17–0.41 96 < .001
Africa 2 (Studies) 40 0.28‐0.52 – < .001
Duration of disease
Depression < 2 years 8 (studies) 30 0.20–0.37 98 < .001
> 2 years 11 (studies) 31 0.22–0.39 97 < .001
Type of studies
Anxiety Cross sectional 11 (Studies) 40 0.30–0.51 85 < .001
Cohort 5 (Studies) 20 0.08–0.36 95 < .001
Study Regions
Anxiety America 4 (Studies) 53 0.41–0.65 68 < .001
Europe 3 (Studies) 34 0.16–0.54 – < .001
Asia 8 (Studies) 25 0.14–0.37 94 < .001
Africa 1 (Studies) 27 0.14–0.44 – < .001
Duration of disease
Anxiety < 2 years 9 (studies) 32 0.23–0.40 98 < .001
> 2 years 11 (studies) 29 0.19–0.35 96 < .001
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3.4. Meta‐regression test

The prevalence of depression and anxiety in MG patients was not significantly related to mean age (p = .393), year of publication (p = .64), and duration of disease (p = .123).

3.5. Publication bias

This study showed that the publication bias was significant among the studies (p < .001). Sensitivity test also showed that none of the studies alone had an impressive effect on the overall prevalence of depression and anxiety (Figure 4).

FIGURE 4.

FIGURE 4

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Funnel plot to detect publication bias‐based depression. Circles show selected studies, and the area of each circle is equivalent to the weight of each study. The horizontal axis represents accuracy, and the vertical axis represents the standardized effect.

4. DISCUSSION

To the extent of our knowledge, this study is the first systematic review and meta‐analysis that investigated the prevalence of overall anxiety and depression in MG people. Thirty‐eight studies were included in this study and the total population was 4108 individuals. We estimated the proportions of depression among MG patients at 36%, (95% CI 28% to 45%), which is higher compared to other autoimmune diseases like multiple sclerosis (30.5%) (Boeschoten et al., 2017) and systemic lupus erythematosus (35.0%) (Moustafa et al., 2020). We also estimated the pooled prevalence of anxiety at 33%, (95% CI 25% to 42%) which is also higher compared to multiple sclerosis (22.1%) (Boeschoten et al., 2017) and systemic lupus erythematosus (25.8%) (Moustafa et al., 2020). According to the finding, the rate of depression is higher than other neurological diseases such as dementia (30.3%) (Orgeta et al., 2015), ALS (34%) (Heidari et al., 2021), multiple sclerosis (30.5%) (Moustafa et al., 2020), and mild cognitive impairment (32%) (Ismail et al., 2017), and elderly patients with hip fracture (23%) (Irvani et al., 2020). Also, rate of depression was lower than in Alzheimer's patients (42%) (Zhao et al., 2016).

MG mainly affects acetylcholine receptors and disturbs their mechanism (O'Connell et al., 2020). In most MG cases, the first symptoms are the weakness of extraocular muscles and ocular misalignment, which is considered as ocular MG. Within 3 years, in around 90% of cases, ocular MG develops to generalized MG (Yang et al., 2016), which is defined by symptoms of weakness and fatigue in skeletal muscles and its effects on swallowing, chewing, breathing, and talking of MG patients (Chu et al., 2019). Disabilities resulting from MG often affect patients’ quality of life and interfere with their daily routine and activity (Vitturi et al., 2021). Additionally, Instability of MG symptoms, prolonged illness, and treatment with steroids can cause psychiatric symptoms in MG patients (Yamamoto et al., 2019). Mood disorders are very common among patients with neurological conditions and MG patients are no exception. It was reported that 41% of MG patients experience mood disorders (Law et al., 2020). According to studies, depressive and anxiety symptoms are very prevalent in MG patients (Cherukupally et al., 2020; Law et al., 2020). In a cross‐sectional study by Alanazy, it was reported that about a quarter of MG patients (26.0%) experienced moderate–severe depression (Alanazy, 2019). In another study by Gavrilov et al., the prevalence of moderate–severe depression among MG patients was estimated at 20.5%. The results of a study by Braz et al. showed that around half (43.75%) of the MG patients had anxiety and 27.5% had depressive symptoms (Braz et al., 2018).

In a literature review by Law et al., it was reported that factors such as older age and disease duration can affect depression and anxiety in MG patients (Law et al., 2020), but our meta‐regression test showed no significant relation between prevalence of anxiety and depression with age and disease duration. Further research is recommended on this matter.

In our study, range of depression varied from 1% to 76% and in anxiety ranged from 3% to 71%. This might be due to the difference in methodological approach and the use of different screening tools. The most used screening tools for anxiety were BAI and HADS and for depression were BDI and HADS. The BAI is one of the most common screening tools for measuring anxiety (Bardhoshi et al., 2016). It was first developed to measure anxiety independently from depression (Toledano‐Toledano et al., 2020). BAI is a self‐report tool focusing on the physical symptoms of anxiety. It contains 21 items to measure the severity of anxiety symptoms and asks patients to rate each symptom on a four‐point scale (0 to 3). Final score can be varied from 0 to 63. Higher scores indicate more severe anxiety symptoms (Julian, 2011; Lee et al., 2018). Studies reported that the BAI is a valid and reliable instrument for assessing anxiety symptoms (Bardhoshi et al., 2016; Toledano‐Toledano et al., 2020). The HADS is a self‐report questionnaire, which was originally developed to assess depression and anxiety in non‐psychiatric patients. The HADS consisted of two sub‐scales: anxiety (HADS‐A) and depression (HADS‐D). This questionnaire has 14 items, 7 items for assessing anxiety and 7 items for assessing depression. The HADS asks individuals to rate each question on a four‐point scale (0 to 3) and the total score can range from 0 to 42, or 0 to 21 for each HADS‐A and HADS‐D (Annunziata et al., 2020; Djukanovic et al., 2017; Julian, 2011; Smarr & Keefer, 2011). Many studies with different populations reported HADS as a reliable and valid instrument to measure anxiety and depression (Bjelland et al., 2002; Bocéréan & Dupret, 2014; Cassiani‐Miranda et al., 2022; Djukanovic et al., 2017). The BDI is a 21 item self‐report questionnaire used to measure depression symptoms and severity. It is probably the most used screening tool for assessing depression in both psychiatric and non‐psychiatric populations (Moher et al., 2009; Крейс et al., 2020). The BDI has several versions including BDI‐I, BDI‐IA, BDI‐II, and BDI‐FS, which unlike others has seven items (Moher et al., 2009). The BDI consisted of 21 items to measure depression symptoms and severity, it asks patients to rate each symptom on a four‐point scale (from 0 to 3), and the final score can range between 0 and 63; higher scores indicate more severe depression symptoms. BDI‐II is a newer version designed to meet the DSM‐IV criteria for depressive disorders and includes items that measure cognitive, emotional, and physical symptoms (García‐Batista et al., 2018; Smarr & Keefer, 2011). Many studies with different types of population considered the BDI as a reliable and valid screening tool (García‐Batista et al., 2018; Lee et al., 2017; Sacco et al., 2016; Smarr & Keefer, 2011). Most of the screening tools for depression and anxiety consider somatic symptoms as an item; it can cause false results for the diagnosis of depression and anxiety in disease or conditions like MG that have somatic symptoms similar to somatic symptoms of depression and anxiety (e.g., fatigue, tiredness, etc.).

Prevalence of depression based on BDI and HADS in our results was 52% and 21%, respectively. We think that the reason for this gap is due to the difference and heterogeneity of cut points. Europe (56%) and Asia (28%) had highest and lowest prevalence of depression among continents. For anxiety, American (53%) and Asian (25%) individuals had highest and lowest prevalence in the present study.

This study is the first study that systematically reviewed the prevalence of anxiety and depression in MG patients. The search strategy, paper screening, and data extraction for this study were comprehensive. Also, PRISMA guidelines were considered for conducting study. Of course, there were some limitations in this study. First, included studies were different in design, screening tools, population origin, publication year, and setting which resulted in heterogeneity among studies. Second, some of the included studies used self‐report questionnaires to assess anxiety and depression in MG patients, which can decrease the reliability of the results. Finally, the possibility of publication bias could not be fully ignored.

5. CONCLUSION

According to our results, anxiety and depression are major problems among MG patients and the prevalence of both anxiety and depression is high even when compared to other autoimmune diseases. Symptoms such as depression and anxiety in MG patients are often overlooked or delayed in diagnosis, leading to an increase in severity of symptoms and delayed onset of treatment. Therefore, identifying the related factors and developing effective intervention strategies for MG patients are needed.

AUTHOR CONTRIBUTIONS

Javad Nadali, Mohammad Eghbal Heidari, and Neda Ghavampour: design of study, literature search, identification and selection, data extraction, quality assessment, conducting analysis, writing paper, and critical revision of paper. Mona Maleki Takhtegahi, Farzaneh Beiranvand, and Omid Narimani Charan: literature search, identification and selection, quality assessment, and critical revision of paper. Tina Arabzadeh and Shahin Salarvand: literature search, identification and selection, and critical revision of paper.

CONFLICT OF INTEREST

The authors declare no conflict of interests.

Funding information

None

PEER REVIEW

The peer review history for this article is available at https://publons.com/publon/10.1002/brb3.2840

TRANSPARENT PEER REVIEW

The peer review history for this article is available at https://publons.com/publon/10.1002/brb3.2840

ACKNOWLEDGMENTS

We would like to express our thanks and acknowledge the help of Hooman Shahsavari and Sahar Dalvand in the development of our search strategy.

Nadali, J. , Ghavampour, N. , Beiranvand, F. , Maleki Takhtegahi, M. , Heidari, M. E. , Salarvand, S. , Arabzadeh, T. , & Narimani Charan, O. (2023). Prevalence of depression and anxiety among myasthenia gravis (MG) patients: A systematic review and meta‐analysis. Brain and Behavior, 13, e2840. 10.1002/brb3.2840

Contributor Information

Javad Nadali, Email: jnadali@alumnus.tums.ac.ir.

Neda Ghavampour, Email: Ghavampourneda@gmail.com.

Farzaneh Beiranvand, Email: farzanebei1365@gmail.com.

Mona Maleki Takhtegahi, Email: mona.m2195111@gmail.com.

Mohammad Eghbal Heidari, Email: mohammad.eghbal.heydari@gmail.com.

Shahin Salarvand, Email: shsalarvand@lums.ac.ir.

Tina Arabzadeh, Email: tina.arabzadeh@yahoo.com.

Omid Narimani Charan, Email: Omid.n.ch@gmail.com.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author upon reasonable request.

ReferenceS

  1. Cherukupally, K. R. , Kodjo, K. , Ogunsakin, O. , Olayinka, O. , & Fouron, P. (2020). Comorbid depressive and anxiety symptoms in a patient with myasthenia gravis. Case Reports in Psychiatry, 2020, 8967818. 10.1155/2020/8967818 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Yamamoto, A. , Kimura, T. , Watanabe, S. , & Yoshikawa, H. (2019). Clinical characteristics of patients with myasthenia gravis accompanied by psychiatric disorders. Neurology and Clinical Neuroscience, 7(2), 65–70. 10.1111/ncn3.12267 [DOI] [Google Scholar]
  3. Leopardi, V. , Chang, Y.‐M. , Pham, A. , Luo, J. , & Garden, O. A. (2021). A systematic review of the potential implication of infectious agents in myasthenia gravis. Frontiers in Neurology, 12, 857. 10.3389/fneur.2021.618021 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Hendricks, T. M. , Bhatti, M. T , Hodge, D. O. , & Chen, J J. (2019). Incidence, epidemiology, and transformation of ocular myasthenia gravis: A population‐based study. American Journal of Ophthalmology, 205, 99–105. 10.1016/j.ajo.2019.04.017 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Vitturi, B. K. , Kim, A. I. H. , Mitre, L. P. , Pellegrinelli, A. , & Valerio, B. C. O. (2021). Social, professional and neuropsychiatric outcomes in patients with myasthenia gravis. Neurological Sciences, 42(1), 167–173. 10.1007/s10072-020-04528-w [DOI] [PubMed] [Google Scholar]
  6. Kulaksizoglu, I. B. (2007). Mood and anxiety disorders in patients with myasthenia gravis. CNS Drugs, 21(6), 473–481. 10.2165/00023210-200721060-00004 [DOI] [PubMed] [Google Scholar]
  7. Paul, R. H. , Cohen, R. A. , Goldstein, J. M. , & Gilchrist, J. M. (2000). Severity of mood, self‐evaluative, and vegetative symptoms of depression in myasthenia gravis. The Journal of Neuropsychiatry and Clinical Neurosciences, 12(4), 499–501. 10.1176/jnp.12.4.499 [DOI] [PubMed] [Google Scholar]
  8. Qiu, L. , Feng, H. Y. , Huang, X. , Mo, R. , Ou, C. Y. , Luo, C. M. , Li, Y. , & Liu, W. B. (2010). Study of incidence and correlation factors of depression, anxiety and insomnia in patients with myasthenia gravis. Zhonghua Yi Xue Za Zhi, 90(45), 3176–9. [PubMed] [Google Scholar]
  9. Alanazy, M. H. (2019). Prevalence and associated factors of depressive symptoms in patients with myasthenia gravis: A cross‐sectional study of two tertiary hospitals in Riyadh, Saudi Arabia. Behavioural Neurology, 2019, 9367453. 10.1155/2019/9367453 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Yang, Y. , Zhang, M. , Guo, J. , Ma, S. , Fan, L. , Wang, X. , Li, C. , Guo, P. , Wang, J. , Li, H. , & Li, Z. (2016). Quality of life in 188 patients with myasthenia gravis in China. The International journal of Neuroscience, 126(5), 455–462. 10.3109/00207454.2015.1038712 [DOI] [PubMed] [Google Scholar]
  11. Alanazy, M. H. (2019). Prevalence and associated factors of depressive symptoms in patients with myasthenia gravis: A cross‐sectional study of two tertiary hospitals in Riyadh, Saudi Arabia. Behavioural Neurology, 2019, 10.1155/2019/9367453 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Alekseeva, T. M. , Kreis, O. A. , Gavrilov, Y. V. , Valko, P. O. , Weber, K. P. , & Valko, Y. (2019). Impact of autoimmune comorbidity on fatigue, sleepiness and mood in myasthenia gravis. Journal of Neurology, 266(8), 2027–2034. 10.1007/s00415-019-09374-1 [DOI] [PubMed] [Google Scholar]
  13. Parada, P. , Oliva, M. , Lázaro, E. , Amayra, I. , Paz, J. F. L. , & Martínez, O. (2014). Anxiety, depression and self‐efficacy in patients with myasthenia gravis. International Journal of Psychology and Psychological Therapy, 14(1), 105–113. [Google Scholar]
  14. Gavrilov, Y. V. , Alekseeva, T. M. , Kreis, O. A. , Valko, P. O. , Weber, K. P. , & Valko, Y. (2020). Depression in myasthenia gravis: A heterogeneous and intriguing entity. Journal of Neurology, 267(6), 1802–1811. 10.1007/s00415-020-09767-7 [DOI] [PubMed] [Google Scholar]
  15. Sivadasan, A. , Alexander, M. , Aaron, S. , Mathew, V. , Nair, S. , Muthusamy, K. , Prabhakar, A. T. , Benjamin, R. N. , Shaikh, A. , & Rynjah, G. (2019). Comorbidities and long‐term outcomes in a cohort with myasthenic crisis: Experiences from a tertiary care center. Annals of Indian Academy of Neurology, 22(4), 464. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Kalbus, O. , Makarov, S. , Shastun, N. , & Somilo, O. (2020). Features of depression development in myasthenia gravis. Медичні перспективи, 25(1), 96–104. [Google Scholar]
  17. Ybarra, M. I. , Kummer, A. , Frota, E. R. C. , Oliveira, J. T. , Gomez, R. S. , & Teixeira, A. L. (2011). Psychiatric disorders in myasthenia gravis. Arquivos de Neuro‐Psiquiatria, 69(2A), 176–179. 10.1590/S0004-282X2011000200006 [DOI] [PubMed] [Google Scholar]
  18. Moher, D. , Liberati, A. , Tetzlaff, J. , & Altman, D G. (2009). Preferred reporting items for systematic reviews and meta‐analyses: The PRISMA statement. PLoS Medicine, 6(7), e1000097. 10.1371/journal.pmed.1000097 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Peterson, J. , Welch, V. , Losos, M. , & Tugwell, P. (2011). The Newcastle‐Ottawa scale (NOS) for assessing the quality of nonrandomised studies in meta‐analyses (pp. 1–12). Ottawa Hospital Research Institute. [Google Scholar]
  20. Vitturi, B. K. , Kim, A. I. H , Mitre, L. P. , Pellegrinelli, A. , & Valerio, B. C. O. (2021). Social, professional and neuropsychiatric outcomes in patients with myasthenia gravis. Neurological Sciences : Official Journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology, 42(1), 167–173. 10.1007/s10072-020-04528-w [DOI] [PubMed] [Google Scholar]
  21. Liu, C. , Li, T. , Wang, Q. , Xu, A. , & Wu, B. (2021). Post‐traumatic stress disorder symptoms after respiratory insufficiency in patients with myasthenia gravis. Psychology, Health & Medicine, 26(2), 221–7. [DOI] [PubMed] [Google Scholar]
  22. Крейс, О. , Алексеева, Т. , Гаврилов, Ю. , Валко, Ф. , & Валко, Ю. (2020). Диагностика сонливости, усталости и депрессии у пациентов с миастенией гравис. Нервно‐мышечные болезни, 10(4), 27–37. [Google Scholar]
  23. Bogdan, A. , Barnett, C. , Ali, A. , Alqwaifly, M. , Abraham, A. , Mannan, S. , Ng, E. , & Bril, V. (2020). Chronic stress, depression and personality type in patients with myasthenia gravis. European Journal of Neurology, 27(1), 204–209. 10.1111/ene.14057 [DOI] [PubMed] [Google Scholar]
  24. Ayres, A. , Winckler, P. B. , Jacinto‐Scudeiro, L. A. , Rech, R. S. , Jotz, G. P. , & Olchik, M. R. (2020). Cognitive performance in patients with myasthenia gravis: An association with glucocorticosteroid use and depression. Dementia & Neuropsychologia, 14, 315–23. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Amani, K. , Sarraf, P. , Abkhoo, A. , Tafakhori, A. , Fatehi, F. , & Bitarafan, S. (2020). Prevalence of depression among Iranian patients with myasthenia gravis during 2017–2018. Current Journal of Neurology, 19(4), 162–166. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Siddig, A. , Hussein, A. , Aladil, O. , Abbashare, H. , Abbashar, M. , Alamin, M. , & Abbasher, K. M A (2019). Prevalence of depression among sudanese patients with myasthenia gravis. Journal of the Neurological Sciences, 405, 5–6. 10.1016/j.jns.2019.10.1555 [DOI] [Google Scholar]
  27. Chu, H.‐T. , Tseng, C.‐C. , Liang, C.‐S. , Yeh, T.‐C. , Hu, L.‐Y. , Yang, A. C. , Tsai, S.‐J. , & Shen, C.‐C. (2019). Risk of depressive disorders following myasthenia gravis: A Nationwide population‐based retrospective cohort study. Frontiers in Psychiatry, 10, 481. 10.3389/fpsyt.2019.00481 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Bacci, E. D. , Coyne, K. S. , Poon, J.‐L. , Harris, L. , & Boscoe, A. N. (2019). Understanding side effects of therapy for myasthenia gravis and their impact on daily life. BMC Neurology, 19(1), 1–13. 10.1186/s12883-019-1573-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Tascilar, N. F. , Saracli, O. , Kurcer, M. A. , Ankarali, H. , & Emre, U. (2018). Is there any relationship between quality of life and polysomnographically detected sleep parameters/disorders in stable myasthenia gravis? Acta Neurologica Belgica, 118(1), 29–37. 10.1007/s13760-017-0787-6 [DOI] [PubMed] [Google Scholar]
  30. Braz, N. F. T. , Rocha, N. P. , Vieira, É. L. M. , Barbosa, I. G. , Gomez, R. S. , Kakehasi, A. M. , & Teixeira, A. L. (2018). Muscle strength and psychiatric symptoms influence health‐related quality of life in patients with myasthenia gravis. Journal of Clinical Neuroscience : Official Journal of the Neurosurgical Society of Australasia, 50, 41–44. 10.1016/j.jocn.2018.01.011 [DOI] [PubMed] [Google Scholar]
  31. Deneva, V. , Milanova, M. , Stamenova, P. , & Isakov, S. (2017). Depression and other risk factors for myasthenic crisis–A clinical study in bulgarian patients. Comptes rendus de l'Académie bulgare des Sciences, 70(4), 571–9. [Google Scholar]
  32. Zou, J. , Su, C. , Lun, X. , Liu, W. , Yang, W. , Zhong, B. , Zhu, H. , Lei, Y. , Luo, H. , & Chen, Z. (2016). Preoperative anxiety in patients with myasthenia gravis and risk for myasthenic crisis after extended transsternal thymectomy: A CONSORT study. Medicine, 95(10), e2828. 10.1097/MD.0000000000002828 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Uyaroğlu, F. G. , Bilgin, Ş. , Keleş, E. H. , & Zorlu, Y. (2016). Anxiety and depression symptoms in patients with generalized myasthenia gravis. The Journal of Tepecik Education and Research Hospital, 26(2), 97–103. [Google Scholar]
  34. Hoffmann, S. , Ramm, J. , Grittner, U. , Kohler, S. , Siedler, J. , & Meisel, A. (2016). Fatigue in myasthenia gravis: Risk factors and impact on quality of life. Brain and Behavior, 6(10), e00538. 10.1002/brb3.538 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. El‐Ghonemy, S. H. , Salem, H. H. , El‐Missiry, M. A. , El‐Ghamry, R. H. , & Mukhtar, Y. S. (2016). Myasthenia gravis and psychiatric comorbidities. Middle East Current Psychiatry, 23(2), 99–105. 10.1097/01.XME.0000481469.56478.b0 [DOI] [Google Scholar]
  36. Blum, S. , Lee, D. , Gillis, D. , Mceniery, D. F. , Reddel, S. , & Mccombe, P. (2015). Clinical features and impact of myasthenia gravis disease in Australian patients. Journal of Clinical Neuroscience, 22(7), 1164–1169. 10.1016/j.jocn.2015.01.022 [DOI] [PubMed] [Google Scholar]
  37. Freeman, C. , Lewis, I. , & Heckmann, J. M. (2014). Neuropsychiatric symptoms in patients with thymomaassociated and non‐thymoma myasthenia gravis. South African Journal of Psychiatry, 20(2), 50–3. 10.4102/sajpsychiatry.v20i2.519 [DOI] [Google Scholar]
  38. Tetsuka, S. , Fujimoto, K.‐I. , & Ikeguchi, K. (2013). Preoperative high‐dose steroid has long‐term beneficial effects for myasthenia gravis. Neurology Research International, 2013, 709480. 10.1155/2013/709480 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Suzuki, Y. , Utsugisawa, K. , Suzuki, S. , Nagane, Y. , Masuda, M. , Kabasawa, C. , Shimizu, Y. , Utsumi, H. , Uchiyama, S. , Fujihara, K. , & Suzuki, N. (2011). Factors associated with depressive state in patients with myasthenia gravis: A multicentre cross‐sectional study. BMJ Open, 1(2), e000313. 10.1136/bmjopen-2011-000313 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Aysal, F. , Karamustafalioğlu, O. , Özçelik, B. , Yilmaz, M. , Karamustafalioğlu, N. , Yumrukçal, H. , & Tankaya, O. (2013). The relationship of symptoms of anxiety and depression with disease severity and treatment modality in myasthenia gravis: A cross‐sectional study. Nöro Psikiyatri Arşivi., 50(4), 295. 10.4274/npa.y5611 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Winter, Y. , Schepelmann, K. , Spottke, A E. , Claus, D. , Grothe, C. , Schröder, R. , Heuss, D. , Vielhaber, S. , Tackenberg, B. , Mylius, V. , Reese, J.‐P. , Kiefer, R. , Schrank, B. , Oertel, W. H. , & Dodel, R. (2010). Health‐related quality of life in ALS, myasthenia gravis and facioscapulohumeral muscular dystrophy. Journal of Neurology, 257(9), 1473–1481. 10.1007/s00415-010-5549-9 [DOI] [PubMed] [Google Scholar]
  42. Di Blasi, C. , Quartuccio, M. , Pontecorvo, S. , Francia, A. , & Prencipe, M. (2009). Neuropsychological disorders and myasthenia gravis: A pilot study. Journal of the Peripheral Nervous System, 14. [Google Scholar]
  43. Stewart, S. B. , Robertson, K. R. , Johnson, K. M. , & Howard, J. F. (2007). The prevalence of depression in myasthenia gravis. Journal of Clinical Neuromuscular Disease, 8(3), 111–115. 10.1097/CND.0b013e3180316324 [DOI] [Google Scholar]
  44. Lundeen, J. , Fisher, J. , & Kothari, M. J. (2004). Frequency of anxiety in myasthenia gravis. Journal of Clinical Neuromuscular Disease, 6(1), 9–12. 10.1097/01.cnd.0000138445.13978.e4 [DOI] [PubMed] [Google Scholar]
  45. Fisher, J. , Parkinson, K. , & Kothari, M. J. (2003). Self‐reported depressive symptoms in myasthenia gravis. Journal of Clinical Neuromuscular Disease, 4(3), 105–108. 10.1097/00131402-200303000-00001 [DOI] [PubMed] [Google Scholar]
  46. Alekseeva, T. M. , Gavrilov, Y. V. , Kreis, O. A. , Valko, P. O. , Weber, K. P. , & Valko, Y. (2018). Fatigue in patients with myasthenia gravis. Journal of Neurology, 265(10), 2312–2321. 10.1007/s00415-018-8995-4 [DOI] [PubMed] [Google Scholar]
  47. Paradis, C. M. , Friedman, S. , Lazar, R. M. , & Kula, R. (1993). Anxiety disorders in a neuromuscular clinic. The American Journal of Psychiatry, 150(7), 1102–4. [DOI] [PubMed] [Google Scholar]
  48. Hong, S. (2010). Surveys on depressive disorders in patients with myasthenia gravis. Journal of Shandong Medical College, 4, 019. [Google Scholar]
  49. Boeschoten, R. E. , Braamse, A. M. J. , Beekman, A. T. F. , Cuijpers, P. , Van Oppen, P. , Dekker, J. , & Uitdehaag, B. M. J. (2017). Prevalence of depression and anxiety in multiple sclerosis: A systematic review and meta‐analysis. Journal of the Neurological Sciences, 372, 331–341. 10.1016/j.jns.2016.11.067 [DOI] [PubMed] [Google Scholar]
  50. Moustafa, A. T. , Moazzami, M. , Engel, L. , Bangert, E. , Hassanein, M. , Marzouk, S. , Kravtsenyuk, M. , Fung, W. , Eder, L. , Su, J. , Wither, J. E. , & Touma, Z. , editors. (2020). Prevalence and metric of depression and anxiety in systemic lupus erythematosus: A systematic review and meta‐analysis. Seminars in arthritis and rheumatism. Elsevier. [DOI] [PubMed] [Google Scholar]
  51. Orgeta, V. , Qazi, A. , Spector, A. , & Orrell, M. (2015). Psychological treatments for depression and anxiety in dementia and mild cognitive impairment: Systematic review and meta‐analysis. The British Journal of Psychiatry, 207(4), 293–298. 10.1192/bjp.bp.114.148130 [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Heidari, M. E. , Nadali, J. , Parouhan, A. , Azarafraz, M. , Tabatabai, S. M. , Irvani, S. S. N. , Eskandari, F. , & Gharebaghi, A. (2021). Prevalence of depressive disorder among amyotrophic lateral sclerosis (ALS) patients: Systematic review and meta‐analysis. Journal of Affective Disorders, 287, 182–190. 10.1016/j.jad.2021.03.015 [DOI] [PubMed] [Google Scholar]
  53. Ismail, Z. , Elbayoumi, H. , Fischer, C. E. , Hogan, D. B. , Millikin, C. P. , Schweizer, T. , Mortby, M. E. , Smith, E. E. , Patten, S. B. , & Fiest, K. M. (2017). Prevalence of depression in patients with mild cognitive impairment: A systematic review and meta‐analysis. JAMA Psychiatry, 74(1), 58–67. 10.1001/jamapsychiatry.2016.3162 [DOI] [PubMed] [Google Scholar]
  54. Irvani, S. S. N. , Khadem, M. , Torabi, F. , & Shahsavari, H. (2020). Prevalence of depression in elderly hip fracture: A systematic review and meta‐analysis. International Journal of Orthopaedic and Trauma Nursing, 40, 100813. [DOI] [PubMed] [Google Scholar]
  55. Zhao, Q.‐F. , Tan, L. , Wang, H.‐F. , Jiang, T. , Tan, M.‐S. , Tan, L. , Xu, W. , Li, J.‐Q. , Wang, J. , Lai, T.‐J. , & Yu, J.‐T. (2016). The prevalence of neuropsychiatric symptoms in Alzheimer's disease: Systematic review and meta‐analysis. Journal of Affective Disorders, 190, 264–271. 10.1016/j.jad.2015.09.069 [DOI] [PubMed] [Google Scholar]
  56. O'connell, K. , Ramdas, S. , & Palace, J. (2020). Management of juvenile myasthenia gravis. Frontiers in Neurology, 11, 743. 10.3389/fneur.2020.00743 [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Yang, Y. , Zhang, M. , Guo, J. , Ma, S. , Fan, L. , Wang, X. , Li, C. , Guo, P. , Wang, J. , Li, H. , & Li, Z. (2016). Quality of life in 188 patients with myasthenia gravis in China. International Journal of Neuroscience, 126(5), 455–462. 10.3109/00207454.2015.1038712 [DOI] [PubMed] [Google Scholar]
  58. Law, C. , Flaherty, C. V. , & Bandyopadhyay, S. (2020). A review of psychiatric comorbidity in myasthenia gravis. Cureus, 12(7), e9184. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Cherukupally, K. R. , Kodjo, K. , Ogunsakin, O. , Olayinka, O. , & Fouron, P. (2020). Comorbid depressive and anxiety symptoms in a patient with myasthenia gravis. Case Reports in Psychiatry, 2020, 8967818. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Braz, N. F. T. , Rocha, N. P. , Vieira, É. L. M. , Barbosa, I. G. , Gomez, R. S. , Kakehasi, A. M. , & Teixeira, A. L. (2018). Muscle strength and psychiatric symptoms influence health‐related quality of life in patients with myasthenia gravis. Journal of Clinical Neuroscience, 50, 41–44. 10.1016/j.jocn.2018.01.011 [DOI] [PubMed] [Google Scholar]
  61. Bardhoshi, G. , Duncan, K. , & Erford, B. T. (2016). Psychometric meta‐analysis of the English version of the Beck Anxiety Inventory. Journal of Counseling & Development, 94(3), 356–73. [Google Scholar]
  62. Toledano‐Toledano, F. , Moral De La Rubia, J. , Domínguez‐Guedea, M. T. , Nabors, L. A. , Barcelata‐Eguiarte, B. E. , Rocha‐Pérez, E. , Luna, D. , Leyva‐López, A. , & Rivera‐Rivera, L. (2020). Validity and reliability of the Beck Anxiety Inventory (BAI) for family caregivers of children with cancer. International Journal of Environmental Research and Public Health, 17(21), 7765. 10.3390/ijerph17217765 [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Julian, L. J. (2011). Measures of anxiety. Arthritis Care & Research, 63(0 11). [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Lee, K. , Kim, D. , & Cho, Y. (2018). Exploratory factor analysis of the Beck anxiety inventory and the Beck depression inventory‐II in a psychiatric outpatient population. Journal of Korean Medical Science, 33(16). 10.3346/jkms.2018.33.e128 [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Julian, L. J. (2011). Measures of anxiety: State‐Trait Anxiety Inventory (STAI), Beck Anxiety Inventory (BAI), and Hospital Anxiety and Depression Scale‐Anxiety (HADS‐A). Arthritis Care & Research, 63, Suppl 11(0 11), S467–72. [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Annunziata, M. A. , Muzzatti, B. , Bidoli, E. , Flaiban, C. , Bomben, F. , Piccinin, M. , Gipponi, K. M. , Mariutti, G. , Busato, S. , & Mella, S. (2020). Hospital anxiety and depression scale (HADS) accuracy in cancer patients. Supportive Care in Cancer, 28(8), 3921–3926. [DOI] [PubMed] [Google Scholar]
  67. Djukanovic, I. , Carlsson, J. , & Årestedt, K. (2017). Is the Hospital Anxiety and Depression Scale (HADS) a valid measure in a general population 65–80 years old? A psychometric evaluation study. Health and Quality of life Outcomes, 15(1), 1–10. 10.1186/s12955-017-0759-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Smarr, K. L. , & Keefer, A. L. (2011). Measures of depression and depressive symptoms: Beck depression Inventory‐II (BDI‐II), center for epidemiologic studies depression scale (CES‐D), geriatric depression scale (GDS), hospital anxiety and depression scale (HADS), and patient health Questionnaire‐9 (PHQ‐9). Arthritis Care & Research, 63(S11), S454–S66. [DOI] [PubMed] [Google Scholar]
  69. Bocéréan, C. , & Dupret, E. (2014). A validation study of the Hospital Anxiety and Depression Scale (HADS) in a large sample of French employees. BMC Psychiatry [Electronic Resource], 14(1), 1–11. 10.1186/s12888-014-0354-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Cassiani‐Miranda, C. A. , & Scoppetta, O. , & Cabanzo‐Arenas, D. F. (2022). Validity of the hospital anxiety and depression scale (HADS) in primary care patients in Colombia. General Hospital Psychiatry, 74, 102–109. [DOI] [PubMed] [Google Scholar]
  71. Bjelland, I. , Dahl, A. A. , Haug, T. T. , & Neckelmann, D. (2002). The validity of the Hospital Anxiety and Depression Scale: An updated literature review. Journal of Psychosomatic Research, 52(2), 69–77. 10.1016/S0022-3999(01)00296-3 [DOI] [PubMed] [Google Scholar]
  72. García‐Batista, Z. E. , Guerra‐Peña, K. , Cano‐Vindel, A. , Herrera‐Martínez, S. X. , & Medrano, L. A. (2018). Validity and reliability of the Beck Depression Inventory (BDI‐II) in general and hospital population of Dominican Republic. PLoS ONE, 13(6), e0199750. 10.1371/journal.pone.0199750 [DOI] [PMC free article] [PubMed] [Google Scholar]
  73. Sacco, R. , Santangelo, G. , Stamenova, S. , Bisecco, A. , Bonavita, S. , Lavorgna, L. , Trojano, L. , D'ambrosio, A. , Tedeschi, G. , & Gallo, A. (2016). Psychometric properties and validity of Beck Depression Inventory II in multiple sclerosis. European Journal of Neurology, 23(4), 744–750. 10.1111/ene.12932 [DOI] [PubMed] [Google Scholar]
  74. Lee, E.‐H. , Lee, S.‐J. , Hwang, S.‐T. , Hong, S.‐H. , & Kim, J.‐H. (2017). Reliability and validity of the Beck Depression Inventory‐II among Korean adolescents. Psychiatry Investigation, 14(1), 30. 10.4306/pi.2017.14.1.30 [DOI] [PMC free article] [PubMed] [Google Scholar]

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Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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