Evaluation of the effect of the COVID-19 pandemic on depression, anxiety and psychological resilience in patients with primary immunodeficiency

Abstract

Background

Primary immunodeficiencies (PIDs) are a group of diseases that develop as a result of primary or congenital malfunction of the immune system and progress with chronic and/or recurrent bacterial, fungal, protozoal and/or viral infections. In this study, we aimed to examine the effects of the COVID-19 pandemic on depression, anxiety levels and psychological resilience in patients with PID and to compare them with those in controls.

Methods

Seventy patients, aged 18–65 years, who were being followed up with a diagnosis of PID and 69 people as healthy control group, participated in our study. The participants were evaluated cross-sectionally once; sociodemographic data form, Hamilton Depression Rating Scale (HAM-D), Hamilton Anxiety Rating Scale (HAM-A), Resilience Scale for Adults (RSA), and COVID-19 Evaluation form were administered to the participants.

Results

HAM-A and HAM-D scores were significantly higher in PID patients compared to controls (HAM-D: 5.5 vs. 3.0, p < 0.001; HAM-A: 6.0 vs. 4.0, p = 0.008). RSA was significantly lower in the patient group (RSA total: 122.5 vs. 136.0, p < 0.001), and pandemic-related risk perception was higher (PRPS: 33.9 vs. 28.3, p < 0.001). Sleep, appetite, and attention-related disturbances were also more common in the patient group. Multivariate regression analyses revealed that PID diagnosis was an independent predictor of increased depression severity (HAM-D), lower psychological resilience (RSA), and greater pandemic-related risk perception. Female sex was independently associated with higher anxiety severity (HAM-A). A personal psychiatric history and greater number of comorbidities were also significant predictors of psychological vulnerability, particularly in relation to depression and anxiety.

Conclusion

Given the observed associations between PID and increased levels of depression, anxiety, and reduced psychological resilience during the pandemic, clinicians may consider heightened vigilance for psychological symptoms in this population during times of public health crisis.

Background

In December 2019, cases of pneumonia attributed to the novel coronavirus (COVID-19, SARS-CoV-2) began to be reported in Wuhan, and following the rapid global spread of the virus, this situation escalated into a global health threat, leading the World Health Organization to classify it as a pandemic in March 2020 [1, 2]. While COVID-19 infection can be asymptomatic, it can also lead to pneumonia, heart and respiratory failure and death [3, 4]. The severity and mortality risk of SARS-CoV-2 infection are associated with factors such as advanced age, primary or secondary immunodeficiency, preexisting lung disease, cardiovascular disease, diabetes mellitus, hypertension and malignancy [5,6,7]. The severity of SARS-CoV-2 infection is linked to the viral infection load and the host’s immune response [8, 9].

In addition to its physical effects, COVID-19 is also associated with negative effects on mental health [10]. Owing to the pathogenicity of the virus, its rate of spread, and its high mortality rate, COVID-19 can affect the mental health of individuals in various groups of society, from infected patients to healthcare workers, families, children, students, individuals with psychiatric disorders, individuals with chronic diseases and people working in nonhealth fields [11,12,13]. The majority of studies examining psychiatric disorders during the COVID-19 pandemic indicate that individuals affected by this process experience psychological complaints such as stress, depression, emotional distress, mood fluctuation, irritability, insomnia, attention deficit, posttraumatic stress disorder and irritability [14, 15].

The COVID-19 pandemic has the potential to profoundly impact daily life, social relationships and habits, leading to increases in anxiety, depression, and other psychiatric disorders [16]. Additionally, individuals respond to life challenges and stressors in a wide range of ways. Coping with difficulties, adapting positively, and even emerging more strongly from adversities are assessed within the framework of ‘psychological resilience’ [17]. High psychological resilience significantly reduces the likelihood of developing a psychiatric disease [18]. Moreover, individuals with high psychological resilience also experience faster recovery from potential psychiatric disorders [19]. During the pandemic period, psychological resilience levels have been observed to be significantly lower compared to community data under normal circumstances [20, 21]. There is a significant relationship between low levels of psychological resilience and more severe depression, anxiety and suicidal thoughts [20].

Primary immunodeficiencies (PIDs) represent a broad group of disorders, primarily of genetic origin, characterized by the absence, deficiency, or malfunction of certain components of the immune system, encompassing over 430 different disorders [22]. In developed countries, the prevalence of PID varies between 0.01% and 0.0001% in the general population [23]. PIDs are chronic and/or recurrent diseases that present with bacterial, fungal, protozoal and viral infections [24]. The mainstay of treatment for PID patients involves the administration of intravenous immunoglobulin (IVIG) or subcutaneous immunoglobulin (SCIG) [25]. Individuals with PID are considered a high-risk group for COVID-19 infection because of their increased susceptibility to severe infections, driven by the risk of irregular inflammatory responses or cytokine storms [26].

Compared to other high-risk groups such as cancer, asthma, diabetes, or HIV/AIDS patients, individuals with PID may face unique psychosocial stressors. These include long-term dependency on immunoglobulin therapy, a chronic course with unpredictable infections, limitations in socialization due to increased susceptibility to infectious diseases, and a lack of public awareness or understanding of their condition [27,28,29]. These factors can lead to a heightened perception of vulnerability, chronic isolation, and stigmatization, all of which may exacerbate psychological distress during pandemics [30]. Furthermore, unlike acquired immunodeficiencies such as HIV/AIDS, PID often affects individuals from childhood, shaping long-term coping strategies and mental health outcomes [27]. Notably, children with PID have been shown to exhibit higher rates of psychiatric diagnoses, including depression and anxiety, compared to those with asthma, despite similar disease burden [29]. Adults with PID also report lower health-related quality of life than patients with cancer, asthma, or diabetes, particularly in the domains of emotional well-being and social functioning [27, 28]. These findings underscore the distinct psychological challenges faced by individuals with PID beyond those encountered in other chronic illness populations.

Although both PID and HIV/AIDS are characterized by immune system dysfunction and increased infection risk, they differ in important ways that may shape psychological outcomes. PID is typically congenital and presents in childhood, requiring lifelong management with immunoglobulin therapy [22], whereas HIV/AIDS is acquired later in life and managed with antiretroviral treatment [31]. Public health awareness and social support frameworks for HIV/AIDS are more developed globally, while PIDs remain relatively under-recognized and less understood by the general public [32, 33]. Studies have shown that individuals living with HIV experienced elevated levels of depression and anxiety during the COVID-19 pandemic, often due to intersecting medical and social vulnerabilities [34, 35]. Comparative data on psychological outcomes between HIV and PID populations remain limited, highlighting a gap in the literature that future research could address.

There are a limited number of studies on the mental status of PID patients during the pandemic. In a study conducted by Cekic et al., anxiety about contracting COVID-19 was significantly greater in the patient group than in the control group [36]. Another study revealed that 42.3% of PID patients were in the high-risk group for anxiety or depression on the basis of the scale results [37]. In a study conducted by Çölkesen et al., chronic disease groups, including PID patients, were compared among themselves and with healthcare workers, and the group with the highest disease-related fear scale values and anxiety scores was PID patients after healthcare workers [38].

The types and severity of mental health problems may differ between people with different health conditions and social roles due to differences in infection risk and lifestyle impacts [39]. Understanding the prevalence of psychiatric disorders that develop during the COVID-19 pandemic and the psychological effects of the pandemic is critically important for comprehending the burden of these processes on various patient groups and for implementing effective measures. Understanding and managing the psychological symptoms triggered by the pandemic in PID patients is crucial. It not only helps to achieve better treatment outcomes but also prepares us to handle similar situations more effectively in the future. This study aims to evaluate the impact of the COVID-19 pandemic on depression, anxiety and psychological resilience in PID patients.

Materıals and methods

Study population

A total of 70 PID patients who applied to the Psychiatry Department Outpatient Clinic and/or the Pediatric Immunology Department Outpatient Clinic at Bursa Uludağ University Faculty of Medicine between September 1, 2021, and December 25, 2021, were included in our study. The control group consisted of 69 individuals without significant psychiatric complaints and without a PID diagnosis. The study commenced with research approval from the Scientific Research Platform of the Turkish Ministry of Health and ethical committee approval from the Bursa Uludağ University Faculty of Medicine (decision number 2021-9/9). All procedures were conducted in accordance with the Declaration of Helsinki and local laws and regulations. All volunteers who agreed to participate in the study were informed both verbally and through written materials about the purpose and process of the study and their consent was obtained.

The inclusion criteria for the study for the patient group were being between the ages of 18–65, being literate, and having received a diagnosis of PID. The inclusion criteria for the control group in the study were being between the ages of 18–65, being literate, and not having received a diagnosis of PID. The exclusion criteria for the study were being outside the age range of 18–65 years, having a psychiatric diagnosis that significantly impairs judgment and reality assessment, and having substance use disorders.

Psychological measures/instrumentation

The patients who participated in the study and the control group were seen cross-sectionally once. All participants in both the patient and control groups were evaluated by a psychiatrist prior to study inclusion. Each individual underwent a semi-structured clinical interview based on DSM-5 diagnostic criteria to screen for the presence of current psychiatric disorders. Participants with active substance use disorders or severe psychiatric conditions impairing judgment or participation were excluded. Symptom severity for depression and anxiety was subsequently assessed using the Hamilton Depression Rating Scale (HAM-D) and the Hamilton Anxiety Rating Scale (HAM-A), both administered by the same psychiatrist. In addition, participants completed the Resilience Scale for Adults (RSA), the Sociodemographic Data Form, and the COVID-19 Evaluation Form as self-report instruments.

Sociodemographic data form

A researcher-developed questionnaire was used to obtain the sociodemographic characteristics, smoking and alcohol habits, medical history, psychiatric illness and psychotropic drug use history, family history of psychiatric illness and psychotropic drug use, and COVID-19 vaccination history of the individuals included in the study.

Resilience scale for adults (RSA)

A validity and reliability study of the scale developed by Friborg et al. [40] was conducted in Turkish by Basım and Çetin in 2011 [41]. The scale, consisting of 33 items rated on a 5-point Likert scale, assesses planned future, social competence, social resources, family cohesion, structured style and perception of self. It is believed that as the scores obtained from the scale increase, psychological resilience also increases.

Hamilton depression rating scale (HAM-D)

This scale, which was originally developed by Hamilton and transformed into a structured form by Williams [42], is designed to measure the severity of depression in patients. It is administered by an interviewer, and in this study, a 17-item version of the questionnaire was utilized. Scores ranging from 0 to 7 indicate no depression, scores between 8 and 15 suggest mild depression, scores between 16 and 28 indicate moderate depression, and scores of 29 and above indicate severe depression. A Turkish validity and reliability of the scale were established by Akdemir et al. in 1996 [43].

Hamilton anxiety rating scale (HAM-A)

This scale, developed by Hamilton, is used to assess the severity of psychic and somatic anxiety in patients [44]. It is an interviewer-administered scale consisting of 13 items. It is believed that as the score increases, the level of anxiety also increases. Scores of 5 points and below are considered within normal limits, scores between 5 and 14 indicate mild anxiety, and scores of 14 and above are indicative of significant anxiety. A Turkish validity and reliability study was conducted by Yazıcı et al. in 1998 [45].

COVID-19 evaluation form

This questionnaire was developed by Aşut [46] to evaluate responses to the COVID-19 pandemic and assess the level of impact experienced during the pandemic. The questionnaire includes inquiries about whether participants themselves had contracted COVID-19 and whether they received hospital treatment during the infection process (PPIL-1) (1: Not contracted; 2: Contracted, not hospitalized; 3: Contracted, hospitalized), whether at least one of their close relatives was diagnosed with COVID-19 (PPIL-2) (1: Yes; 2: No), the amount of time spent on social media after the pandemic (PPIL-3), their sleep duration (PPIL-4), sleep quality (PPIL-5), appetite levels (PPIL-6), and changes related to their ability to maintain focus (PPIL-7). These questions are consolidated under the title of Pandemic Psychosocial Impact Level (PPIL), and each question is assessed individually. Responses to questions 3, 4, 5, 6, and 7 are categorized as follows: “1: Significantly increased; 2: Slightly increased; 3: Unchanged; 4: Slightly decreased; 5: Significantly decreased.

COVID-19 diagnosis and hospitalization history were confirmed through the national health portal (e-Nabız) for all participants.

The Pandemic Risk Perception Scale (PRPS), developed to assess participants’ stress levels related to the COVID-19 pandemic, their perceived risk situations, anxieties and fears, and their consideration of discontinuing treatments, is prepared in a 5-point Likert scale format, consisting of 10 questions. Higher scores on this scale are interpreted as indicating a higher level of risk perception related to the COVID-19 pandemic.

The reliability of the PRPS was assessed through the examination of its internal consistency via Cronbach’s α coefficient, which was determined to be 0.847. The scale is considered to exhibit high reliability in this regard. The validity of the scale was examined in terms of structural and criterion validity. An examination of the HAM-A scale, which serves a similar purpose, revealed a statistically significant positive correlation between the PRPS score and HAM-A score (r = 0.639, p < 0.001). The PRPS is believed to demonstrate criterion validity in this respect. Structural validity, on the other hand, was evaluated in relation to the total number of comorbidities. The expectation was that the total number of comorbidities would be positively correlated with the PRPS. The analysis revealed a statistically significant positive correlation between the total number of comorbidities and the PRPS (r = 0.274, p = 0.001). The PRPS is considered to demonstrate structural validity in this regard.

Statistical analysis

The normality of the distribution of the variables was assessed via the Shapiro-Wilk test. In cases where a normal distribution was confirmed, comparisons between the two groups were conducted via t-tests. When a normal distribution could not be assumed, the Mann-Whitney U test was employed. For comparisons involving more than two groups and due to the data’s lack of normal distribution, the Kruskal-Wallis test was utilized, with post-hoc comparisons performed via the Dunn-Bonferroni correction. Descriptive statistics are presented as mean ± standard deviation in cases where a normal distribution was established, and as median (minimum: maximum) values in cases where a normal distribution could not be assumed. For categorical variables, group comparisons were conducted via the Pearson Chi-Square test, Fisher’s Exact test, and Fisher-Freeman-Halton test as appropriate. Categorical variables are expressed as n(%). Relationships between variables were examined using the Pearson correlation coefficient and Spearman rank correlation coefficient. The reliability of the PRPS was analyzed via Cronbach’s alpha (α) coefficient. Statistical analyses were performed using SPSS v22.0 software, and a significance level of p = 0.05 was employed. Data with fewer than four observations were excluded from the statistical comparisons.

Effect sizes (Cohen’s d) were calculated for between-group comparisons of continuous variables to complement p-values and better illustrate the magnitude of observed differences. Effect sizes were interpreted according to conventional thresholds: 0.2 as small, 0.5 as medium, and 0.8 or above as large.

In addition, four separate multivariate linear regression models were constructed to examine the independent associations between sociodemographic and clinical factors and psychological outcomes: HAM-D, HAM-A, RSA, and PRPS. Each model included group status (PID vs. control), age, sex, marital status, monthly income level, personal history of psychiatric referral, family history of psychiatric disorders, and number of comorbid conditions as independent variables. Model assumptions (linearity, normality, and homoscedasticity) were tested and met. Descriptive and bivariate analyses were conducted using SPSS v22.0, while multivariate regression analyses were performed using Python (v3.10, statsmodels module). A significance level of p < 0.05 was considered for all analyses.

Results

A total of 139 individuals, including 70 patients with PID and 69 healthy controls participated in the study. When the patient and control groups were compared in terms of sociodemographic characteristics, no statistically significant difference was found. The sociodemographic characteristics of the patient and control groups are presented in Table 1.

Table 1 Comparison of control and patient groups in terms of sociodemographic characteristics

In the evaluated cross-section, 31.4% of the patients (n = 22) and 23.2% of the controls (n = 16) were using psychotropic drugs. Additionally, the presence of psychotropic drug use in the family was found in 37.1% of the patients (n = 26) and 36.2% of the controls (n = 25). There were no significant differences observed between the patient and control groups in terms of history of psychiatric referrals (p = 0.172), psychiatric diagnosis (p = 0.490), psychotropic drug use (p = 0.276), family history of psychiatric referrals (p = 0.772), family history of psychiatric diagnosis (p = 0.235) and family history of psychotropic drug use (p = 0.911).

In the patient group, median age at diagnosis was 23 years (range:4–64), and the median duration of treatment was 93 months (range:3-330). A total of 74.3% of the patients (n = 52) received IVIG treatment, whereas 25.7% (n = 18) received SCIG treatment. Among the patients, 94% (n = 66) were diagnosed with primary antibody deficiency syndrome, 4% (n = 3) with immune dysregulation, and 2% (n = 1) with syndromic combined immunodeficiency. Regarding primary antibody deficiencies, diagnoses included common variable immunodeficiency (CVID) (n = 34), B-cell linker protein (BLNK) deficiency (n = 1), IgG subclass deficiency (n = 7), IgG subclass and IgM deficiency (n = 2), specific antibody deficiency (n = 1), PIK3CD deficiency (n = 1), hypogammaglobulinemia (n = 9), agammaglobulinemia (n = 4), hyper IgM syndrome (n = 6), and partial IgA deficiency (n = 1). For immunodysregulation, the diagnoses included lipopolysaccharide-responsive beige-like anchor protein (LRBA) deficiency (n = 2) and adenosine deaminase II (ADA II) deficiency (n = 1). Among those with syndromic combined immunodeficiencies, one patient was diagnosed with Wiskott-Aldrich syndrome (n = 1).

When the details and comparisons of COVID-19 vaccination status was analyzed %20 (n = 14) of the patient group and %2.9 (n = 2) of the control group had not received any COVID-19 vaccination (p < 0.001).

The comparison of the patient and control groups in terms of HAM-D, HAM-A, RSA and PRPS scores is presented in Table 2. The distributions of HAM-D, HAM-A, PRPS, and RSA scores between groups are presented in Fig. 1.

Table 2 Comparison of the control and patient groups in terms of HAM-D, HAM-A, RSA and PRPS scores

Fig. 1

Distribution of HAM-D, HAM-A, PRPS, and RSA scores for the patient and the control groups

To assess whether the observed group differences in HAM-D, HAM-A, RSA, and PRPS remained significant after adjusting for potential confounding variables, multivariate linear regression analyses were conducted for each outcome.

In the regression model predicting HAM-D scores, PID group status and personal psychiatric history were found to be significant independent predictors. Compared to controls, PID patients had significantly higher HAM-D scores (β = − 3.03, p < 0.001). Additionally, a history of psychiatric referral was associated with increased HAM-D scores (β = 2.50, p = 0.005). The overall model was statistically significant (F(8,130) = 6.088, p < 0.001) and explained 27.3% of the variance (R² = 0.273).

The regression model for HAM-A revealed that female sex and number of comorbid conditions were significant predictors of increased anxiety levels. Women scored significantly higher on the HAM-A (β = − 2.21, p = 0.006), and each additional comorbid condition was associated with a 1.31-point increase in anxiety scores (β = 1.31, p = 0.001). The model accounted for 28.5% of the variance in HAM-A scores (R² = 0.285, F(8,130) = 6.493, p < 0.001).

In the model assessing RSA, PID diagnosis was independently associated with lower resilience (β = − 12.23, p < 0.001). Higher monthly income was linked to greater resilience (β = 10.15, p < 0.001), whereas having a family history of psychiatric disorders (β = − 7.99, p = 0.007) and a higher number of comorbidities (β = − 4.11, p = 0.004) were associated with decreased RSA scores. The model was statistically significant (F(8,130) = 8.168, p < 0.001) with an R² value of 0.335.

Multivariate regression analysis for PRPS identified PID group status (β = − 5.35, p < 0.001), female sex (β = − 3.65, p = 0.002), and personal psychiatric history (β = 3.46, p = 0.006) as significant predictors. Patients with PID reported significantly higher PRPS scores than controls. The model explained 30.1% of the variance in PRPS scores (R² = 0.301, F(8,130) = 6.994, p < 0.001).

Considering the COVID-19 infection status (PPIL-1), 77.1% (n = 54) of the patients in the patient group did not contract COVID-19 infection, 12.9% (n = 9) had contracted the infection and received inpatient treatment, and 10% (n = 7) had contracted the infection but did not require inpatient treatment. In the control group, 65.2% (n = 45) of individuals did not contract COVID-19 infection, while 34.8% (n = 24) had contracted the infection, and none of them required inpatient treatment due to COVID-19 infection. A significant difference was found between the two groups in terms of contracting COVID-19 infection (p < 0.001). When the COVID-19 infection status of their close relatives (PPIL-2) was examined, 61.4% (n = 43) of the patients in the patient group had at least one close relative who had contracted COVID-19, whereas 92.8% (n = 64) in the control group had at least one close relative with a history of COVID-19 infection. In this regard, a significant difference was found between the two groups (p < 0.001). The values of sleep duration during the pandemic (PPIL-4), appetite level during the pandemic (PPIL-6), and attention level during the pandemic (PPIL-7) were significantly greater in the patient group than in the control group (p = 0.025; p = 0.001; p = 0.003, respectively). There was no significant difference between the two groups regarding social media usage during the pandemic (PPIL-3) or sleep quality during the pandemic (PPIL-5).

Comorbidities related to the respiratory system were observed in 50% of the patients (n = 35) and in 13% of the controls (n = 9). There was a significant difference between the two groups in terms of comorbidities related to the respiratory system (p < 0.001). There was no significant difference between two groups in terms of hematological (p = 0.116), endocrinological (p = 0.220), gastrointestinal (p = 0.563), dermatological (p = 0.745), musculoskeletal system (p = 1.00), renal (p = 0.496), cardiovascular (p = 0.222), rheumatological (p = 1.00), neurological (p = 0.326) or malignant (p = 0.620) comorbidities.

In the patient group, the total HAM-D and HAM-A scores were significantly greater in female patients than in male patients (p = 0.029, Cohen’s d = 0.431; p = 0.035, Cohen’s d = 0.541, respectively), indicating moderate effect sizes. In the control group, when the relationships between scales and sex were examined, HAM-A total and PRPS scores were significantly higher in females than in males (p = 0.031; p < 0.001, respectively).

When the relationship between patients’ marital status and scale scores was examined, HAM-D, HAM-A, and PRPS scores were significantly higher in married patients (n = 34) than in unmarried patients (n = 36) (p = 0.07; p = 0.016; p = 0.041, respectively).

When the relationships between age and scale scores in the patient group were examined, a statistically significant positive correlation was detected between age and HAM-D, HAM-A, and PRPS scores in this group (r = 0.314, p = 0.008; r = 0.369, p = 0.002; r = 0.294, p = 0.014, respectively).

When the relationships between history of psychiatric referrals and scale scores and subscale scores were examined among patients, HAM-D, HAM-A, and PRPS scores were greater in patients with a history of psychiatric referrals (n = 28) than in those without such a history (n = 42), and a significant difference was found (p = 0.024, Cohen’s d = 0.566; p = 0.025, Cohen’s d = 0.519; p < 0.001, Cohen’s d = 0.779, respectively), indicating moderate to large effect sizes. In the control group, when individuals with a history of psychiatric referrals (n = 20) were compared to those without such a history (n = 49), HAM-D and HAM-A values were greater (p = 0.001; p = 0.050, respectively), and RSA scores were lower (p = 0.033). In the control group, there was no significant difference in the PRPS results between individuals with a history of psychiatric referrals and those without such a history (p = 0.285).

In the patient group, 43 patients had no history of psychiatric referrals or diagnoses, whereas 19 patients were followed up with diagnoses of depressive disorders and 8 patients with anxiety disorders. In the control group, 49 individuals had no history of psychiatric referrals, whereas 14 individuals were followed up with diagnoses of depressive disorders and 6 individuals with anxiety disorders. The comparison of scale and subscale scores based on psychiatric diagnoses for patients and the control group is presented in Table 3.

Table 3 The comparison of scale scores based on psychiatric diagnoses for patients and the control group

In the patient group, 22 individuals used psychotropic drugs and 48 individuals did not use them. In the control group, 16 individuals were using psychotropic drugs, and 53 individuals were not. The relationships between the use of psychotropic drugs and the scores of the scales and subscales in both the patient and control groups are presented in Table 4.

Table 4 The relationship between the use of psychotropic drugs and the scores of the scales in patient and control groups

When examining the relationship between family history of psychiatric referrals and the scores of the scales, in the patient group, no significant relationship was observed. However, in the control group, individuals with a family history of psychiatric referrals (n = 24) had significantly higher HAM-D scores (p < 0.027) and significantly lower RSA scores (p < 0.001).

There was no significant difference between the vaccination status and scores of the scales in the patient group (HAM-D: p = 0.213; HAM-A: p = 0.210; RSA: p = 0.255; PRPS: p = 0.327). The relationships between the COVID-19 infection statuses (PPIL-1) of the patient and control groups and the scores of the scales are presented in Table 5.

Table 5 Relationships between the COVID-19 infection statuses (PPIL-1) of the patient and control groups and the scores of the scales

The detailed relationships between the scales and subscales in the patient and control groups are shown in detail in Table 6.

Table 6 Relationships between the scales and subscales in the patient and control groups

A statistically significant positive correlation was found between the total number of comorbidities and HAM-D, HAM-A psychic, HAM-A somatic, HAM-A total, and PRPS total scores (r = 0.323, p = 0.006; r = 0.387, p = 0.001; r = 0.446, p < 0.001; r = 0.444, p < 0.001; r = 0.284, p = 0.017, respectively). There was a significant negative correlation between the number of comorbidities and total RSA score (r=-0.315, p = 0.008).

When the scale scores between the treatment methods in the patient group were compared, there was no significant difference between the IVIG and SCIG treatments (HAM-D: p = 0.212; HAM-A: p = 0.731; RSA: p = 0.743; PRPS: p = 0.118).

Conclusıon

In our study, depression and anxiety levels, psychological resilience, social media usage, sleep quality and duration, appetite, ability to maintain attention, and specific fears and anxieties related to the COVID-19 pandemic were evaluated with HAM-D, HAM-A, RSA, and PRPS in 70 patients diagnosed with PID and compared with 69 controls. In the patient group, HAM-D, HAM-A (psychic and somatic subscales and total scores), and the scores of the PRPS scale, which assesses fears and concerns related to the pandemic, were significantly higher than those in the control group. All subdimensions of the RSA scores and the total RSA scores were significantly lower in the patient group than in the control group. Negative changes in sleep duration, appetite levels, and attention levels during the pandemic period were also considered to be significantly greater in the patient group than in the control group. Patients diagnosed with depression in the past or present had higher HAM-D, HAM-A, and PRPS scores compared to those without a psychiatric diagnosis. Additionally, patients using psychotropic drugs had significantly higher HAM-D, HAM-A, and PRPS values compared to those not using psychotropic drugs.

Due to their susceptibility to opportunistic infections, frequent occurrence of recurrent or chronic infections, presence of comorbidities, and the need for regular treatment, PIDs are associated with decreased quality of life and increased risk of mental health challenges [47]. In the literature, there have been few studies examining the impact of the COVID-19 pandemic on the mental health of PID patients. The negative impact of psychiatric disorders on the mortality and morbidity of PID patients [47] makes it imperative to conduct research and interventions to improve the mental and physical well-being of these patients during the pandemic. Our study is the first known research to investigate the relationship between psychological resilience and pandemic-related physiological symptoms and mental health in PID patients, in addition to the impact of the COVID-19 pandemic on depression and anxiety levels.

In a study conducted by Cekic et al. during the pandemic, anxiety levels of PID patients were significantly greater than those of the control group [36]. In a study evaluating 158 PID patients in the early pandemic period, 42.3% of the patients were in the high-risk group for depression and anxiety disorders [37]. In another study comparing the levels of depression and anxiety in PID patients with other chronic diseases and risk groups during the pandemic, that PID patients represented the second-highest group in terms of anxiety after healthcare workers and the second-highest group in terms of depression after cancer patients [38]. In a large-scale study conducted on patients with IgA deficiency, depression, anxiety disorders, suicide attempts, and self-harming behaviors were significantly greater in PID patients compared to individuals without immunodeficiency [48].

In our study, psychic, somatic, and HAM-A and HAM-D scores were higher in the patient group than in the control group. The negative effects of the pandemic on mental health have become more pronounced for individuals with chronic illnesses. Factors such as uncertainty about one’s health, irregularity in clinical follow-up, and interruptions in the treatment process can exacerbate the psychological impact of the COVID-19 pandemic in these groups [49]. Furthermore, multivariate regression analyses revealed that PID diagnosis was an independent predictor of increased HAM-D score, even after adjusting for sociodemographic and clinical variables. However, PID diagnosis was not independently associated with HAM-A in the multivariate model, suggesting that the observed higher anxiety levels among patients might be influenced by additional factors such as sex and comorbidities.

In the study by Cekic et al., female patients had higher levels of anxiety than male patients did, along with more concerns related to contracting SARS-CoV-2 infection or transmitting it to their close contacts due to socialization-related viral exposure [36]. Another study also revealed a stronger relationship between PID and psychiatric disorders in females than males [48]. In our study, HAM-D and HAM-A scores were significantly higher in female patients than in male patients, whereas in the control group, HAM-A scores were higher in females. This pattern may be related to the possibility that the pandemic and its associated restrictions disproportionately impacted women compared to men. In the general population, the COVID-19 pandemic has had a greater impact on women in terms of anxiety disorders and depression [50]. However, multivariate regression analysis showed that female sex remained an independent predictor only for increased HAM-A score, while its association with HAM-D was no longer statistically significant after adjusting for covariates. This suggests that the observed gender differences in depression may be partially explained by other confounding variables such as psychiatric referral history and comorbidities.

In a study conducted with Fear of Illness and Virus Evaluation (FIVE), a scale that evaluates concerns about exposure and illness related to the SARS-CoV-2 virus, PID patients received the highest scores after healthcare workers did [38]. In another study conducted with 511 PID patients, patients were asked how worried they were about the possibility of their relatives or themselves experiencing COVID-19 infection; 30.9% of the patients reported that they were extremely worried and 33.3% reported that they were very worried. In the same study, when asked whether anxiety due to the pandemic affected patients’ daily lives and functionality, 16.8% answered constantly and 22.3% answered very often [51]. In our study, items related to feeling at risk regarding viral infection, experiencing extra stress in daily life, fear of becoming sick, thoughts of not surviving if infected, concerns about transmitting the infection to others, and anxiety about potential future similar pandemic processes, which are essentially similar in function to the FIVE scale, were included in the PRPS. The total PRPS scores in the patient group were significantly higher than those in the control group. Given that total PRPS scores exhibited changes in the same direction and were significantly correlated with HAM-D and HAM-A total scores, the impact of the pandemic on mental health is clearly evident. Moreover, multivariate regression analysis confirmed that PID diagnosis was an independent predictor of increased PRPS score, even after adjusting for sociodemographic and clinical factors, further emphasizing the psychological vulnerability of this group during health crises.

In a study conducted in 2019, the rate of depression in PID patients was found to be higher compared to the general population, and it was stated that the development of depression was associated with comorbidities [47]. Studies have shown that the presence of comorbidities in individuals with chronic diseases shows a positive relationship with anxiety levels [52]. In our study, depression and anxiety scores were significantly higher in the patient group compared to the control group, and there was a significant positive relationship between the increase in the number of comorbidities and HAM-D, HAM-A and PRPS scores. Furthermore, in multivariate regression analysis, the number of comorbid conditions was independently associated with increased HAM-A scores and decreased psychological resilience, although its association with HAM-D did not remain statistically significant after adjustment. This suggests that comorbidities may contribute more prominently to anxiety and resilience outcomes than to depressive symptoms when other factors are taken into account. Comorbidities in PID patients, combined with immune system dysfunction and susceptibility to infections, may be associated with increased psychological vulnerability during the pandemic.

During the pandemic, the prevalence of posttraumatic stress disorder in the general population has been reported to range from 4 to 41%, with a 7% prevalence of major depression [53]. Factors that can increase susceptibility to psychiatric disorders include female sex, low socioeconomic status, interpersonal relationship problems, low psychological resilience, and inadequate social support, as determined by the study mentioned above [53]. In our study, HAM-D scores in the patient group were significantly higher in female patients than in male patients, and in line with the studies mentioned above, a significant inverse relationship was found between psychological resilience and depression and anxiety levels. High psychological resilience is known to be a protective factor that can prevent the development of psychiatric disorders and stands out as such in crisis situations. The relationships between psychological resilience and depression and anxiety levels are not surprising.

In a review that examined the psychological issues associated with the COVID-19 pandemic and included 43 studies, it was observed that individuals with preexisting psychiatric disorders experienced an exacerbation of their symptoms during the pandemic. Compared with the general population, there was a decrease in subjective well-being and an increase in anxiety levels [30]. In our study, among the patient group, individuals with a history of psychiatric referrals had significantly higher HAM-D, HAM-A, and PRPS scores than those without a history of psychiatric referrals. In the control group, HAM-D and HAM-A scores were also significantly higher in individuals with a history of psychiatric referrals than in those without, and RSA scores were significantly lower. Unlike the patient group, there was no significant difference in PRPS scores between individuals with and without a history of psychiatric referrals in the control group. This lack of difference in the PRPS scores in the control group was thought to be due to these patients not being as at risk for contracting COVID-19 as PID patients were. Supporting these findings, multivariate regression analysis demonstrated that a history of psychiatric referrals was independently associated with increased HAM-D scores and elevated pandemic-related risk perception, although its relationship with HAM-A did not remain significant after adjustment for other variables.

Many studies have reported that sleep-related problems and sleep disorders are frequently observed during the COVID-19 pandemic [14, 15]. Wu et al. conducted a meta-analysis revealing a high prevalence of sleep disorders in individuals with chronic illnesses and other at-risk groups [54]. In their study during the pandemic, Stanton et al. noted that negative changes related to sleep were closely associated with depression [55]. Consistent with the aforementioned studies, our examination of responses to a question assessing sleep duration (PPIL-4) during the pandemic indicated that sleep duration was significantly lower in the patient group than in the control group. In our study, a significant relationship was observed between a reduction in sleep duration (PPIL-4) and an increase in HAM-A and HAM-D scores, indicating that a decrease in sleep duration and worsening sleep quality (PPIL-5) were significantly associated with higher anxiety and depression scores. Given the close relationship between changes in sleep patterns and anxiety and depressive disorders, these findings are not surprising.

Studies examining psychiatric disorders observed during the COVID-19 pandemic indicate that individuals affected by this period experience mental complaints such as stress, irritability, insomnia, attention deficits and irritability [14, 15]. When we look at the PPIL-7, which assesses the ability to maintain focus during the pandemic in our study, it is evident that a decrease in attention during the pandemic was significantly greater in the patient group compared to the control group. It is believed that elevated levels of depression and anxiety contribute to this finding. When the relationships with the HAM-A and HAM-D scores were examined, an increase in these scales was significantly associated with complaints related to attention in the same direction.

In a study assessing 215 patients hospitalized for SARS-CoV-2 infection, 57% of patients presented symptoms of psychiatric disorders after discharge. When patients with psychiatric symptoms were examined, posttraumatic stress disorder was found in 34%, anxiety disorders in 24%, and depression in 42% of the patients. The rate of psychiatric disorders was 78% in patients with a history of psychiatric referrals, whereas it was 42% in those without such a history, indicating that a history of psychiatric referral is a strong predictor of psychiatric symptom development after discharge [56]. Similar to Wang et al.’s study [56], our study revealed significantly higher anxiety levels, as assessed by HAM-A, in the patient group who had contracted SARS-CoV-2 and received inpatient treatment (n = 7) than in those who had experienced the infection without hospitalization (n = 9). Although the sample sizes are quite small, this finding is considered significant. It is believed that hospitalization in the patient group, especially in cases related to COVID-19 hospitalizations, may be more traumatic, as factors that increase the level of stress such as isolation and lack of accompaniment are more evident.

In a study conducted by Heath et al. with 33 individuals diagnosed with PID, individuals with a family history of psychiatric disorders had higher depression scores. Unhealthy eating habits, low sleep quality, a subjective evaluation of one’s health as ‘poor,’ and a family history of mental disorders such as depression or anxiety were found to be associated with elevated anxiety levels [57]. In our own study, we also observed a significant decrease in the appetite and sleep duration of the patients compared with those of the control group, although there was no significant difference in sleep quality.

When studies conducted during the pandemic period were examined, studies reported a greater prevalence of psychiatric disorders such as depression, anxiety, and posttraumatic stress disorder in younger individuals compared to older individuals [10, 58]. This phenomenon may be attributed to factors such as a heavier workload at a younger age, a younger age distribution within the productive population and increased viral exposure in the working environment. Chew et al. reported that there was an increase in psychiatric disorders and symptoms such as anxiety, depression and insomnia with increasing age during the pandemic period [59]. In our study, we observed a significant positive correlation between age and HAM-D, HAM-A, and PRPS scores. It is speculated that factors such as the potential for more severe progression of SARS-CoV-2 infection in older individuals and the increasing number of comorbidities with increasing age may have contributed to the observed trend in our study.

In our study, depression and anxiety scores of married patients were greater than those of single patients. Although some studies have reported that being married has positive effects on quality of life [60], it is surprising that such a difference was found between married and unmarried patients in our study. Long-term quarantine practices and social lifestyle changes due to the pandemic process have led to married individuals to spend more time with their families. People with healthy and stable family relationships reported that their communication with family members improved [61]. The opposite may be true for people who have problems in their relationships and unhealthy marriages. The data obtained in our study should be reviewed by evaluating marital harmony and satisfaction and the relationships between them should be examined. However, it should be noted that in multivariate regression analyses, marital status was not found to be an independent predictor of depression or anxiety severity after adjusting for other sociodemographic and clinical variables. This suggests that the observed bivariate differences may be confounded by factors such as sex, psychiatric history, or comorbid conditions.

One of the findings of our study is that, in terms of experiencing a COVID-19 infection (PPIL-1) and having close contacts who have had a COVID-19 infection (PPIL-2), the control group was significantly more common than the patients were. This finding may have developed because of the high awareness of patients and their relatives, who are in a vulnerable position in terms of infection, and their more serious implementation of precautions such as isolation and hygiene. Since there is no tool to assess preventive measures against infection, further evidence is needed to substantiate these findings.

Psychological resilience can be defined as the capacity to return to a previous state of mental well-being following traumatic or stress-inducing events in life [62]. Psychological resilience can also be defined as the ability to recover from periods of change, depression or illnesses; the capacity to rapidly recover from challenging situations; and the ability to adapt [63]. Social and economic challenges caused by the COVID-19 pandemic have increased concerns about the development of psychiatric disorders; however people have different abilities to cope with difficulties and trauma, and this is where psychological resilience comes into play.

In a study conducted with 1004 people in the USA in the early period of the pandemic, it was observed that psychological resilience levels were lower than the societal average compared to the pre-pandemic period, and this low level was explained by situations such as the sudden onset of the pandemic process and social isolation. Lower levels of psychological resilience were found to be associated with increases in depression and anxiety levels. Furthermore, a significant inverse relationship was found between concerns related to COVID-19 and psychological resilience [20]. In another study, low psychological resilience was reported to be linked to an increase in stress during the pandemic [21]. In our study, all subdimension scores and total RSA scores were significantly lower in the patient group compared to the control group. Additionally, a significant inverse relationship was observed between the subdimension scores and the total RSA and HAM-D scores in the patient group. Furthermore, in the patient group, all subdimension scores and total scores of RSA, with the exception of the structured style, were inversely associated with HAM-A scores. A significant inverse relationship was also found between the scores of the PRPS scale, which aimed to measure concerns and attitudes related to the pandemic, and the scores of all subdimensions and the total scores of the RSA, except for the structural style. These findings were further supported by multivariate regression analysis, which identified PID diagnosis as an independent predictor of lower psychological resilience, even after controlling for other clinical and sociodemographic variables.

In this study, multivariate regression analyses were conducted to evaluate whether the observed group differences in psychological outcomes were independent of potential confounding factors. Even after adjusting for sociodemographic variables such as age, sex, marital status, and psychiatric history, the associations between PID diagnosis and higher depression, anxiety, and risk perception scores—as well as lower resilience—remained significant. These findings strengthen the internal validity of the results and suggest that the psychological burden in individuals with PID is not solely attributable to background characteristics.

This study has several limitations. First, it was conducted in a single center, which may limit the generalizability of the findings. Second, the sample size—although adequate for the statistical methods used—was relatively small and may have limited power to detect subtle associations. Third, while the study included various subtypes of PIDs, the number of patients in each subgroup was not sufficient to allow for detailed comparative analyses. Fourth, due to the cross-sectional nature of this study, no causal relationships can be inferred between clinical or psychological variables. The observed associations reflect correlations rather than directional or mechanistic effects. Fifth, this study was conducted one year after the onset of the COVID-19 pandemic, which allows for the assessment of relatively long-term pandemic effects. Lastly, although standardized psychometric scales (HAM-D, HAM-A) were used, structured clinical interviews were not employed. Nevertheless, all psychiatric evaluations and scale administrations were performed by trained psychiatrists using semi-structured interviews based on DSM-5 criteria.

Chronic diseases create lifelong challenges, especially in times of crisis and uncertainty. Patients are often faced with the need to cope with physical challenges, emotional fluctuations, lifestyle adjustments, and potential adverse outcomes. PIDs are chronic conditions that require individuals to adapt to long-term treatment and ongoing self-management. With the additional challenges brought about by the global COVID-19 pandemic, the burden on these patients has further increased. Therefore, understanding the psychological and clinical challenges faced by individuals with PID during such periods is essential not only for effective patient care but also for preparedness in future public health emergencies. In this context, there is a need for longitudinal studies with larger sample sizes to better capture long-term outcomes.

Given the elevated levels of depression, anxiety, and reduced psychological resilience observed in patients with PID during the pandemic, clinicians may consider incorporating routine psychological screening into standard care protocols for this population. Moreover, these findings highlight the importance of integrating mental health strategies into public health preparedness plans, particularly for immunocompromised groups. This could inform future guidelines and policy frameworks aimed at enhancing psychosocial support during pandemics and other large-scale health crises.