How physical multimorbidity affected the mental health and healthcare utilization of patients with severe mental illness during the COVID-19 pandemic and earthquakes

Background: People with severe mental illness (SMI) commonly have chronic physical illnesses (CPI) and multimorbidity (CPM). The study aimed to investigate the association between mental health, healthcare utilization, and physical comorbidities in SMI patients affected by the double disasters in Croatia (two earthquakes and the COVID-19 pandemic), and identify predictors of CPI and CPM in this population.

Method: A prospective study was conducted among 90 SMI patients at two-time points: May/June, 2020 (first COVID-19 wave and earthquake) and December, 2020/January, 2021 (second COVID-19 wave and earthquake).

Results: At the first study point, the CPM group showed significantly higher severity of insomnia (p=.003; mean ISI scores (SD): 5.60(4.360), 9.47(6.802), and 10.83(8.026) for no-comorbidity group, CPI and CPM respectively), while CPI group showed higher perceived stress levels (p=.026; mean PSS scores (SD): 18.21(6.882), 22.32(4.619) and 21.67(3.343) for no-comorbidity group, CPI and CPM respectively) compared to the no-comorbidity group. CPI (5/18) and CPM (10/23) groups visited other specialized non-mental health care services significantly more frequently than those without comorbidity (7/46) (χ2 = 6.557, p=.038). A lower score of perceived social support by friends predicted CPI (OR=0.549; 95% CI:0.349-0.864; p=.010, corrected p=.04), but a higher score on alexithymia subscale “difficulty identifying feelings” predicted CPM (OR=1.235; 95% CI:1.071-1.424; p=.002, corrected p=.004).

Conclusion: People with SMI and CPI are especially vulnerable during the COVID-19 pandemic and disasters, as it affects their mental and physical health, leading to increased healthcare utilization. An integrated approach to treating SMI and CPI is necessary for the emergency health response.

1 Introduction

Over the last several decades, researchers have consistently reported the high prevalence of chronic physical illnesses (CPI) and multimorbidity (CPM) (≥2 CPI) in individuals diagnosed with severe mental illness (SMI) (1–5). Previous studies have discovered that individuals with SMI have a life expectancy of 20 years less than those without, primarily due to co-existing CPI and limited access to physical healthcare (4, 5).

Multiple factors have been suggested to explain multimorbidity in patients with SMI, including the duration of SMI, poor response to treatment, the presence of negative and cognitive symptoms that may lead to a sedentary lifestyle and unhealthy eating habits, high smoking rates, and medication side effects (1, 4–7). However, individuals experiencing first-episode psychosis exhibit elevated levels of CPI/CPM, even prior to the onset of symptoms (1, 8, 9), which may indicate a shared vulnerability to stressors. Indeed, altered stress response which is common for many (psycho) somatic disorders (10, 11), was also found among patients with psychotic disorders, indicated by higher perceived levels of distress, alterations of cortisol levels, and subsequent altered immune response in response to daily hassles, and this was the case even after treatment (12, 13). An impaired stress response in SMI and CPI/CPM may result from different factors (14), including early trauma (4, 10, 11, 15–17), the inability to recognize emotional states or to cope with negative emotions (18, 19) and alexithymia (20–22). As SMI patients often have lowered or disturbed emotional perception, emotional expression, and alexithymia (23, 24), they may more vulnerable to stress, especially if they have co-morbid somatic disorders (25).

During the COVID-19 pandemic, it has been found that individuals with SMI had a higher incidence of coronavirus disease (COVID-19), a higher risk of developing complicated forms of COVID-19, and higher mortality rates from COVID-19 compared to the general population largely attributed to the presence of comorbidities (26–28). At the same time, psychiatric services across the globe were reduced to emergency care only in many countries during the COVID-19 pandemic, leading to a significant reduction in access to care for those in need of healthcare services worldwide (29). In parallel, the findings from the study examining the direct effects of the COVID-19 pandemic on the delivery of general inpatient care in Croatia, demonstrated a 21% reduction in overall hospital admissions across the hospital network during the pandemic in 2020 (30), reflecting a broader decline in healthcare utilization. This substantial reduction in hospital admissions appears to have been driven by a combination of factors, including enforced lockdowns and quarantines, systemic reorganization of hospital operations, constraints in the availability of the medical workforce, and increased public hesitancy to seek inpatient care (30). While the pandemic spread, Croatia experienced two major earthquakes on March 22^nd and December 29^th, 2020, leading to additional challenges for both mental health service users and care providers (31, 32).

In this study, we first aimed to examine the associations between CPI and CPM, the severity of mental health problems and the use of health services by persons with SMI in double disasters (pandemic and earthquakes). We hypothesized that a higher number of physical comorbidities in persons with SMI was associated with increased severity of mental health problems and higher use of health services during the COVID-19 pandemic and earthquakes. Secondly, we aimed to identify predictors of CPI and CPM (defined as ≥2CPI) among persons with SMI.

2 Materials and methods

2.1 Study overview

The study was conducted at the Department of Psychiatry and Psychological Medicine, University Hospital Centre (UHC) Zagreb from May, 2020 until January, 2021 as part of a larger hybrid effectiveness-implementation trial (the RECOVER-E project (LaRge-scalE implementation of COmmunity based mental health care for people with seVere and Enduring mental ill health in EuRopE); see KBC ZAGREB (33–35). The study reported herein entails a prospective survey administered at two-time points during the COVID-19 pandemic. The first measurement was conducted during the first pandemic wave (April-July, 2020) (36) and following the Zagreb earthquake. The second measurement was conducted during the second pandemic wave (September, 2020–February, 2021) (36) and following the Petrinja (central Croatia) earthquake (December 29th, 2020). The detailed circumstances of the study are described elsewhere (32).

2.2 Participants

Participants with SMI (schizophrenia and other psychotic disorders (F20-F29), bipolar disorder (F31), or major depressive disorder (F32, F33); according to ICD-10 (International Classification of Diseases 10th Revision)), were recruited consecutively at UHC Zagreb from December, 2018 if they met the eligibility criteria, agreed to participate, and gave informed consent. Other inclusion and exclusion criteria are described elsewhere (32, 34). Data on participants were used in accordance with the regulations on personal data in clinical research according to Resolution No. 52 of July 24^th, 2008. OJ n.190 of August 14^th, 2008, and in accordance with the regulations of General Data Protection Regulation EU, 2016/679, i.e., each participant was assigned a three-digit number that represents an identification mark on all questionnaires, and all data are marked with the assigned code.

All participants previously involved in the RECOVER-E project were offered to participate in this additional research on the impact of the COVID-19 pandemic and the earthquake on mental health. The Ethics Committee of UHC Zagreb approved the project (July 18^th, 2018, Class: 8.1-18/149-2, Number: 02/21 AG), as well as this extension of the research related to the COVID-19 pandemic and the earthquake (May 4^th, 2020).

2.3 Tools and measures

Patients were interviewed by researchers via telephone-administered surveys at two-time points: (i) from May to June, 2020 and (ii) from December, 2020 to January, 2021.

2.3.1 Socio-demographic data

Socio-demographic data included age, sex, marital status, education, current employment, household size, and structure.

2.3.2 Medical data and utilization of services

Medical data was obtained from available medical records, and by interview and it contained information about whether a participant had been in quarantine or contracted COVID-19 at the moment of the survey, the leading psychiatric diagnosis, prescribed medications, and the presence of co-morbid chronic physical illness (CPI and CPM). CPI was defined as a non-mental health condition requiring medical treatment. In line with our previous studies, chronicity was operationalized as a duration of illness or ongoing somatic therapy of at least six months (6, 37). CPM was defined as having two or more CPIs. Utilization of available medical services (emergency room (ER), general practitioner (GP), psychiatrist, and other specialties) during the COVID-19 pandemic was reported by the participants.

2.3.3 Severity of mental health problems

We defined mental health problems among patients with SMI by the presence of symptoms of insomnia, depression, anxiety, severe stress, and alexithymia.

● Insomnia severity was assessed using the Insomnia Severity Index (ISI), a seven-item questionnaire that assesses the quality of sleep in the past two weeks. A 5-point Likert scale (0–4) is used to rate each item, according to the severity of the problem. The total score ranges from 0 to 28, and higher scores indicate greater sleep difficulties (38).

● The Depression Anxiety Stress Scale-21 (DASS-21) was used for assessing symptoms of depression, anxiety, and stress (39). DASS-21 has 21 items rated from 0 (did not apply to me at all) to 3 (applied to me almost completely or most of the time).

● The Perceived Stress Scale (PSS-10) is a 10-item questionnaire originally, and it is the most widely used psychological instrument for measuring the perception of stress. It consists of 10 items, using Likert’s s Scale for scoring. The total score ranges from 0 to 40, with higher scores indicating higher perceived stress (40).

● The Toronto Alexithymia Scale (TAS-20) is the most commonly used measure of the alexithymia construct (41). It is a 20-item scale; each item was rated on a five-point Likert-type scale, ranging from “strongly disagree” (scored 1) to “strongly agree” (scored 5), with scores ranging from 20 to 100. Higher total scores indicated more alexithymia. TAS-20 has three subscales that assess difficulty identifying feelings (DIF), difficulty describing feelings (DDF) to others, and externally oriented thinking (EOT).

Listed questionnaires regarding mental health problems were used in this study at two-time points, except BRCS and TAS-20, which were used only once.

2.3.4 Coping mechanisms

Coping mechanisms were assessed at first assessment using Brief Resilient Coping Scale (BRCS). Participants circle the answers on a five-point scale (from 1 = strongly disagree to 5 = strongly agree) and the total sum score ranges from 4 to 20. A higher score indicates high resilient coping, and a lower one indicates low resilient coping (42).

2.3.5 Perceived social support

Perceived social support was assessed using The Multidimensional Scale of Perceived Social Support (MSPSS), is a 12-item scale designed to measure perceived social support from three sources: family, friends, and a significant other (43). There are four items for each source of social support. On a scale from 1 (I do not agree at all) to 7 (I completely agree), the participant indicates the degree that best suits real life. Subscales (support from family, friends, and others) are scored separately. Only the results from the first study point were considered.

2.4 Statistical analysis

Descriptive analysis was used for sample description. For the primary outcome analysis (the associations of the number of comorbidities (none, CPI, and CPM) and the severity of mental health problems), ANOVA was used to assess the differences in mental health problems between the three clinical groups (CPI, CMP, and no-comorbidity groups) in the two study points. For the secondary outcome analysis (the associations of the number of comorbidities (none, CPI, and CPM) and the utilization of mental health services), Pearson χ2 was used to assess the difference in health care services utilization among the three clinical groups (CPI, CMP, and no-comorbidity groups). For the exploratory outcome analysis, multinomial regression was used to predict the presence of somatic comorbidities (CPI and CPM) from age, sex, level of education, living conditions, alexithymia and stress coping mechanisms. The results were interpreted at the 5% significance level (α = 0.05). The statistical program STATA/IC 15.1 Stata Corp LLC was used for statistical analysis (see STATA/IC). To correct for multiple testing, we applied Holm-Bonferroni sequential correction (44) using the Excel calculator (45).

3 Results

We enrolled a sample of 90 outpatients diagnosed with SMI. Sociodemographic and medical data at baseline are presented in Table 1. Within the sample, we found that CPI were present in 42/90 (46.7%) of patients, CPM in 23/90 (25.6%) and 48/90 (53.3%) without physical comorbidity. The most prevalent CPI were diseases of the musculoskeletal system and connective tissue, endocrine, nutritional and metabolic diseases, and autoimmune diseases.

Table 1

Table 1. Participants’ sociodemographic and medical data.

3.1 Associations of CPI and CPM with mental health problems

In the first time point, the three clinical groups (CPI, CMP, and no-comorbidity groups) differed in insomnia severity (mean ISI scores (SD): 5.60 (4.360), 9.47(6.802) and 10.83 (8.026) for no-comorbidity group, CPI and CPM respectively, p=.002, corrected 0.004) with post-hoc test showing significantly higher insomnia severity in patients with CPM (p=.003) compared to patients with no-comorbidity. Stress levels were also different between the groups (mean PSS scores (SD): 18.21 (6.882), 22.32 (4.619) and 21.67 (3.343) for no-comorbidity group, CPI and CPM respectively, p=.01, corrected p=.01), with CPI (p=.026) patients showing significantly higher levels of stress compared to no-comorbidity patients. In the second assessment, these differences were not significant anymore (Table 2). This was mainly due to the levels of the severity of insomnia and perceived stress rising among patients with no-comorbidities as well, close to the levels of the two other groups.

Table 2

Table 2. Differences in mental health problems between the three clinical groups (no comorbidity, CPI, CPM) in the two study points.

3.2 Utilization of medical services according to number of comorbidities

Data on utilization of medical services are presented in Table 3. There was no significant difference between the three groups (patients with no comorbidities, CPI and CPM) in the first assessment, except for the frequency of visits to specialized care doctors other than mental healthcare, where patients with CPI (5 out of 18) and CPM (10 out of 23) visited other specialized care doctors more frequently than those with no-comorbidity (7 out of 46) (χ2 = 6.557, p=.038). This difference was not found to persist in the second assessment.

Table 3

Table 3. Utilization of medical services during the two study points in patients with SMI according to the number of co-morbidities.

At the first assessment, none of the participants reported having contracted COVID-19. However, one was self-isolated because of contact with an infected person, whereas at the second assessment, one participant was infected, and three self-isolated.

3.3 Predictors of CPI and CPM in persons with SMI

CPI was predicted only by low perceived social support by friends (OR = 0.549; 95% CI 0.349-0.864; p=.010, corrected p=.04), indicating that those who had more perceived social support by friends were less likely to have CPI. CPM was significantly predicted by older age (OR= 1.066; 95% CI 1.007-1.130; p=.029), but also by alexithymia low scores of subscales ‘‘difficulty describing feelings’’ (OR =0.745; 95% CI 0.573-0.969; p=.028), but both significances were lost after correction for multiple testing (both corrected p=.084). However, after correction for multiple testing, CPM was also predicted by higher scores ‘‘difficulty identifying feelings’’ (OR =1.235; 95% CI 1.071-1.424; p=.002, corrected p=.004). Details are shown in Table 4.

Table 4

Table 4. Predictors of chronic physical illness (CPI) and chronic physical multimorbidity (CPM) in patients with SMI.

4 Discussion

We aimed to analyze the associations of the number of comorbidities in patients with SMI and severity of mental health problems and utilization of health services when experiencing double disasters. As we found that almost 50% of SMI patients in our sample have comorbidities, these results are compatible with available reports showing higher prevalence of physical multimorbidity in patients with schizophrenia spectrum compared to the general population, with some studies reporting rates as high as 60-80% (46). At the first time point, the patients with physical comorbidity showed significantly higher severity of insomnia and perceived stress levels compared to the no-comorbidity group, which is consistent with our hypothesis. Since early reports indicated that COVID-19 occurs in severe forms, especially for people with poor physical health, high cardiovascular risk, and obesity (47), it could have caused more distress in this specific population (48, 49). Interestingly, by the time of the second study point, the differences were lost, likely because the perceived levels of stress and insomnia increased among those without physical comorbidities as well. Concordantly, patients with CPI and CPM visited other specialized (non-mental health care) doctors significantly more frequently than those without physical comorbidities in the same study point. This may reflect the possible deterioration of their somatic conditions during the COVID-19 pandemic (50), which is in line with reports showing that the general population’s health was deteriorating at that time (51, 52). These differences in healthcare utilization were lost over time due to the increased utilization of somatic specialist care among patients without comorbidities. Again, this aligns with the studies in the general study population (53). Broader changes in the healthcare system during the pandemic (30, 54) —particularly those related to access to services as explained in the introduction section—should be considered when interpreting these findings, as they likely influenced participants’ healthcare-seeking behaviors and, consequently, the overall study outcomes.

In our study, CPM in SMI patients was significantly predicted by alexithymia scores, while CPI was predicted by low social support from their friends.

Alexithymia has not been extensively researched in persons with SMI, even though emotion regulation is considered a well-known problem in the management of chronic diseases (55) and may affect immune function, according to some studies (18, 19). We found that CPM was significantly predicted by the alexithymia subscales score of more ‘‘difficulty identifying feelings’’. This is concordant with the results showing that alexithymia may increase proneness to somatic and psychiatric disorders in addition to genetic factors, possibly through the disruptions of homeostasis in autonomic, endocrine, and immune functions and increase proneness to chronic distress (25). Patients with alexithymia have limited ability to adapt to stressful situations and tend to engage in unhealthy behaviors, including poor nutrition, excessive alcohol consumption, reduced physical activity, and sedentary lifestyles (20–22) which may all contribute to the development of illnesses. Indeed, difficulty identifying feelings has also been associated with an increased risk of uncontrolled hypertension and Crohn’s disease, possibly due to impaired emotional regulation and increased physiological reactivity to stress (56, 57). Furthermore, due to their difficulty in identifying feelings, patients with alexithymia may have difficulty maintaining close relationships with others and utilizing social supports appropriately to protect themselves from potentially pathological influences of stressful events (22, 58). Indeed, we found that perceived low social support from friends further increases the risk of developing comorbidities. This is in line with many reports indicating that poor social support can further compromise patients’ mental and physical health (59), possibly by compromising immune systems, increasing inflammation, and increasing their vulnerability to chronic physical illnesses and multimorbidity (60). However, we also found that the two alexithymia subscales indicate associations with CPM in opposite directions. While most researchers agree that ‘difficulty identifying feelings’ and ‘difficulty describing feelings’ are salient features of alexithymia, some have suggested that the ‘externally oriented thinking’ subscale differs significantly from ‘difficulty identifying feelings’ and ‘difficulty describing feelings’ (61, 62), which may explain the absence of the associations of the total alexithymia scores and CPI/CPM in our findings. Given the pandemic’s impact on the prevalence of various psychopathologies, notably the increased rates of anxiety and depression, the identification of high-risk groups helps to properly support these individuals (63, 64).

4.1 Limitations

There are several important limitations that should be taken into account. Firstly, the assessments relied on self-reporting and may therefore potentially cause over- or under-reporting. The self-reported measures have limited accuracy compared to an interview conducted by a psychiatrist, introducing a possibility for bias. Thus, the discrepancy could be attributed to inadequate detection or recording of mental health problems and physical illnesses. To reduce this potential bias, we utilized available information from medical documentation to complement self-reported data and enhance the reliability. Secondly, the longer-term effects of the COVID-19 pandemic are not addressed, as some of the effects of the disruption of healthcare services may become evident only after a longer period. The third limitation was our sample’s heterogeneity concerning different diagnoses considered SMIs. The fourth limitation of the study pertains to the inability to ascertain whether the observed differences in physical healthcare utilization among participants were driven by individual-level behavioral factors or by systemic constraints in service availability.

Finally, the sample size of participants was rather small and not powered, since the present study was conceived only after the COVID-19 outbreak and post hoc power analyses are discouraged (65), which poses limitations for statistical power and generalizability.

5 Conclusion

In this study, we confirmed that the presence of comorbid physical illnesses and multimorbidity are common problems that often coexist in the SMI population. The coexistence of comorbid physical illnesses in individuals with SMI in the context of the COVID-19 pandemic and earthquakes may have further affected their mental health by increasing the perceived level of stress and insomnia, which may have led to chronic distress and worsening of their somatic disorders during the COVID-19 pandemic and earthquakes as reflected by the higher utilization of specialized non-mental health care. This calls for an integrated liaison approach in treating persons with SMI, now in the context of pandemics (29) and emergencies, coupling psychosocial interventions that reduce distress together with somatic care.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving humans were approved by The Ethics Committee of University Hospital Centre Zagreb. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

SL: Conceptualization, Data curation, Investigation, Methodology, Project administration, Writing – original draft, Writing – review & editing. ISF: Conceptualization, Writing – original draft, Writing – review & editing, Methodology, Supervision. SM: Data curation, Investigation, Methodology, Project administration, Writing – review & editing. LS-Z: Funding acquisition, Resources, Writing – review & editing. FB: Funding acquisition, Resources, Writing – review & editing. FLM: Data curation, Writing – review & editing. MRK: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing – original draft, Writing – review & editing.

Funding

The author(s) declare that financial support was received for the research and/or publication of this article. The project RECOVER-E was funded by the European Union’s Horizon, 2020 research and innovation program under grant agreement No, 779362. Open access publication fees were funded through University of Zagreb Support, Number: 10106-24-1560; Project Functional Recovery After First Psychotic Episode 2: 5-Year Longitudinal Follow-Up.

Acknowledgments

We are grateful to all participants in the RECOVER-E project, for their time and devotion to participate in this research.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declare that no Generative AI was used in the creation of this manuscript.

Publisher’s note

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References

1. Šimunović Filipčić I, Ivezić E, Jakšić N, Mayer N, Grah M, Rojnić Kuzman M, et al. Gender differences in early onset of chronic physical multimorbidities in schizophrenia spectrum disorder: Do women suffer more? Early Interv Psychiatry. (2020) 14:418–27. doi: 10.1111/eip.12867

PubMed Abstract | Crossref Full Text | Google Scholar

2. Filipčić I, Šimunović Filipčić I, Grošić V, Bakija I, Šago D, Benjak T, et al. Patterns of chronic physical multimorbidity in psychiatric and general population. J Psychosom Res. (2018) 114:72–80. doi: 10.1016/j.jpsychores.2018.09.011

PubMed Abstract | Crossref Full Text | Google Scholar

3. Halstead S, Cao C, Høgnason Mohr G, Ebdrup BH, Pillinger T, McCutcheon RA, et al. Prevalence of multimorbidity in people with and without severe mental illness: a systematic review and meta-analysis. Lancet Psychiatry [Internet]. (2024) 11:431–42. doi: 10.1016/S2215-0366(24)00091-9

PubMed Abstract | Crossref Full Text | Google Scholar

4. Firth J, Siddiqi N, Koyanagi A, Siskind D, Rosenbaum S, Galletly C, et al. The Lancet Psychiatry Commission: a blueprint for protecting physical health in people with mental illness. Lancet Psychiatry. (2019) 6:675–712. doi: 10.1016/S2215-0366(19)30132-4

PubMed Abstract | Crossref Full Text | Google Scholar

5. Thornicroft G, Sunkel C, Alikhon Aliev A, Baker S, Brohan E, El Chammay R, et al. The Lancet Commission on ending stigma and discrimination in mental health. Lancet (London England). (2022) 400:1438–80. doi: 10.1016/S0140-6736(22)01470-2

PubMed Abstract | Crossref Full Text | Google Scholar

6. Filipcic I, Simunovic Filipcic I, Ivezic E, Matic K, Tunjic Vukadinovic N, Vuk Pisk S, et al. Chronic physical illnesses in patients with schizophrenia spectrum disorders are independently associated with higher rates of psychiatric rehospitalization; a cross-sectional study in Croatia. Eur Psychiatry. (2017) 43:73–80. doi: 10.1016/j.eurpsy.2017.02.484

PubMed Abstract | Crossref Full Text | Google Scholar

7. Šimunović Filipčić I, Bajić Ž, and Filipčić I. Effects of chronic physical illness on treatment outcomes among patients with schizophrenia. Curr Opin Psychiatry. (2019) 32:451–60. doi: 10.1097/YCO.0000000000000532

PubMed Abstract | Crossref Full Text | Google Scholar

8. Sorensen HJ, Nielsen PR, Benros ME, Pedersen CB, and Mortensen PB. Somatic diseases and conditions before the first diagnosis of schizophrenia: A nationwide population-based cohort study in more than 900–000 individuals. Schizophr Bull. (2015) 41:513–21. doi: 10.1093/schbul/sbu110

PubMed Abstract | Crossref Full Text | Google Scholar

9. Šimunović Filipčić I, Bajić Ž, and Filipčić I. The onset and accumulation of physical multimorbidity in severe and common mental disorders. Curr Opin Psychiatry. (2020) 33(5):484–90. doi: 10.1097/YCO.0000000000000635

PubMed Abstract | Crossref Full Text | Google Scholar

10. Ruby E, Polito S, McMahon K, Gorovitz M, Corcoran C, and Malaspina D. Pathways associating childhood trauma to the neurobiology of schizophrenia. Front Psychol Behav Sci. (2014) 3:1–17.

PubMed Abstract | Google Scholar

11. Riley G, Perrin M, Vaez-Azizi LM, Ruby E, Goetz RR, Dracxler R, et al. Telomere length and early trauma in schizophrenia. Schizophr Res. (2018) 199:426–30. doi: 10.1016/j.schres.2018.02.059

PubMed Abstract | Crossref Full Text | Google Scholar

12. Mizrahi R, Addington J, Rusjan PM, Suridjan I, Ng A, Boileau I, et al. Increased stress-induced dopamine release in psychosis. Biol Psychiatry. (2012) 71:561–7. doi: 10.1016/j.biopsych.2011.10.009

PubMed Abstract | Crossref Full Text | Google Scholar

13. Rossini Gajšak L, Vogrinc Ž, Čelić Ružić M, Bošnjak Kuharić D, Bošković M, Koričančić Makar A, et al. Salivary cortisol response to psychosocial stress in patients with first-episode psychosis. Croat Med J. (2021) 62:80–9. doi: 10.3325/cmj.2021.62.80

PubMed Abstract | Crossref Full Text | Google Scholar

14. Selye H. Stress and the general adaptation syndrome. BMJ. (1950) 1:1383–92. doi: 10.1136/bmj.1.4667.1383

PubMed Abstract | Crossref Full Text | Google Scholar

15. Pillinger T, Beck K, Gobjila C, Donocik JG, Jauhar S, and Howes OD. Impaired glucose homeostasis in first-episode schizophrenia: A systematic review and meta-analysis. JAMA Psychiatry. (2017) 74:261–9. doi: 10.1001/jamapsychiatry.2016.3803

PubMed Abstract | Crossref Full Text | Google Scholar

16. Rajkumar AP, Horsdal HT, Wimberley T, Cohen D, Mors O, Børglum AD, et al. Endogenous and antipsychotic-related risks for diabetes mellitus in young people with schizophrenia: A danish population-based cohort study. Am J Psychiatry. (2017) 174:686–94. doi: 10.1176/appi.ajp.2016.16040442

PubMed Abstract | Crossref Full Text | Google Scholar

17. Spitzer C, Vogel M, Barnow S, Freyberger HJ, and Grabe HJ. Psychopathology and alexithymia in severe mental illness: the impact of trauma and posttraumatic stress symptoms. Eur Arch Psychiatry Clin Neurosci. (2007) 257:191–6. doi: 10.1007/s00406-006-0669-z

PubMed Abstract | Crossref Full Text | Google Scholar

18. Guilbaud O, Perrin C, Curt F, Chaouat G, Dugré-Le Bigre C, Strebler M, et al. Decreased immune response in alexithymic women: A one-year longitudinal study. Front Psychiatry. (2021) 12:756031. doi: 10.3389/fpsyt.2021.756031

PubMed Abstract | Crossref Full Text | Google Scholar

19. Uher T. Alexithymia and immune dysregulation: A critical review. Act Nerv Super (Praha). (2010) 52:40–4. doi: 10.1007/BF03379564

Crossref Full Text | Google Scholar

20. Lumley MA, Neely LC, and Burger AJ. The assessment of alexithymia in medical settings: implications for understanding and treating health problems. J Pers Assess. (2007) 89:230–46. doi: 10.1080/00223890701629698

PubMed Abstract | Crossref Full Text | Google Scholar

21. Lumley MA, Stettner L, and Wehmer F. How are alexithymia and physical illness linked? A review and critique of pathways. J Psychosom Res. (1996) 41:505–18. doi: 10.1016/S0022-3999(96)00222-X

PubMed Abstract | Crossref Full Text | Google Scholar

22. Kojima M. Alexithymia as a prognostic risk factor for health problems: a brief review of epidemiological studies. Biopsychosoc Med. (2012) 6:21. doi: 10.1186/1751-0759-6-21

PubMed Abstract | Crossref Full Text | Google Scholar

23. Bosnjak Kuharic D, Makaric P, Kekin I, Lukacevic Lovrencic I, Savic A, Ostojic D, et al. Differences in facial emotional recognition between patients with the first-episode psychosis, multi-episode schizophrenia, and healthy controls. J Int Neuropsychol Soc. (2019) 25:165–73. doi: 10.1017/S1355617718001029

PubMed Abstract | Crossref Full Text | Google Scholar

24. Tremeau F and Antonius D. Review: emotion identification deficits are associated with functional impairments in people with schizophrenia. Evid Based Ment Health. (2012) 15:106. doi: 10.1136/ebmental-2012-100880

PubMed Abstract | Crossref Full Text | Google Scholar

25. Taylor GJ, Bagby RM, Parker JDA, and Grotstein J. Disorders of Affect Regulation: Alexithymia in medical and psychiatric illness Vol. 7. Cambridge (UK): Cambridge University Press (1997) p. 240–0.

Google Scholar

26. Wang Q, Xu R, and Volkow ND. Increased risk of COVID-19 infection and mortality in people with mental disorders: analysis from electronic health records in the United States. World Psychiatry. (2021) 20:124–30. doi: 10.1002/wps.20806

PubMed Abstract | Crossref Full Text | Google Scholar

27. Taquet M, Luciano S, Geddes JR, and Harrison PJ. Bidirectional associations between COVID-19 and psychiatric disorder: retrospective cohort studies of 62–354 COVID-19 cases in the USA. Lancet Psychiatry. (2021) 8:130–40. doi: 10.1016/S2215-0366(20)30462-4

PubMed Abstract | Crossref Full Text | Google Scholar

28. Nemani K, Li C, Olfson M, Blessing EM, Razavian N, Chen J, et al. Association of psychiatric disorders with mortality among patients with COVID-19. JAMA Psychiatry. (2021) 78:380–6. doi: 10.1001/jamapsychiatry.2020.4442

PubMed Abstract | Crossref Full Text | Google Scholar

29. Rojnic Kuzman M, Vahip S, Fiorillo A, Beezhold J, Pinto da Costa M, Skugarevsky O, et al. Mental health services during the first wave of the COVID-19 pandemic in Europe: Results from the EPA Ambassadors Survey and implications for clinical practice. Eur Psychiatry. (2021) 64:e41. doi: 10.1192/j.eurpsy.2021.2215

PubMed Abstract | Crossref Full Text | Google Scholar

30. Kalanj K, Marshall R, Karol K, Tiljak MK, and Orešković S. The impact of COVID-19 on hospital admissions in Croatia. Front Public Heal [Internet]. (2021) 9:720948. doi: 10.3389/fpubh.2021.720948

PubMed Abstract | Crossref Full Text | Google Scholar

31. Medved S, Imširagić AS, Salopek I, Puljić D, Handl H, Kovač M, et al. Case series: managing severe mental illness in disaster situation: the Croatian experience after 2020 earthquake. Front Psychiatry. (2021) 12:795661. doi: 10.3389/fpsyt.2021.795661

PubMed Abstract | Crossref Full Text | Google Scholar

32. Levaj S, Medved S, Grubišin J, Tomašić L, Brozić K, Shields-Zeeman L, et al. The impact of the COVID-19 pandemic and earthquake on mental health of persons with severe mental illness: A survey study among people receiving community mental health care versus treatment as usual in Croatia. Int J Soc Psychiatry. (2023) 69(3):653–63. doi: 10.1177/00207640221130966

PubMed Abstract | Crossref Full Text | Google Scholar

33. KBC ZAGREB. RECOVER-E projekt. (2022). Available online at: https://www.kbc-zagreb.hr/recover-e-projekt.aspx. (Accessed November 1, 2025).

Google Scholar

34. Shields-Zeeman L, Petrea I, Smit F, Walters BH, Dedovic J, Kuzman MR, et al. Towards community-based and recovery-oriented care for severe mental disorders in Southern and Eastern Europe: aims and design of a multi-country implementation and evaluation study (RECOVER-E). Int J Ment Health Syst. (2020) 14:30. doi: 10.1186/s13033-020-00361-y

PubMed Abstract | Crossref Full Text | Google Scholar

35. Wijnen BFM, Smit F, Uhernik AI, Istvanovic A, Dedovic J, Dinolova R, et al. Sustainability of community-based specialized mental health services in five european countries: protocol for five randomized controlled trial-based health-economic evaluations embedded in the RECOVER-E program. JMIR Res Protoc. (2020) 9:e17454. doi: 10.2196/17454

PubMed Abstract | Crossref Full Text | Google Scholar

36. Vilibic-Cavlek T, Stevanovic V, Brlek-Gorski D, Ferencak I, Ferenc T, Ujevic-Bosnjak M, et al. Emerging trends in the epidemiology of COVID-19: the Croatian “One health” Perspective. Viruses. (2021) 13(12):2354. doi: 10.3390/v13122354

PubMed Abstract | Crossref Full Text | Google Scholar

37. Šimunović Filipčić I, Filipčić I, Glamuzina L, Devčić S, Bajić Ž, Braš M, et al. The effect of chronic physical illnesses on psychiatric hospital admission in patients with recurrent major depression. Psychiatry Res. (2019) 272:602–8. doi: 10.1016/j.psychres.2018.12.178

PubMed Abstract | Crossref Full Text | Google Scholar

38. Morin CM, Belleville G, Bélanger L, and Ivers H. The Insomnia Severity Index: psychometric indicators to detect insomnia cases and evaluate treatment response. Sleep. (2011) 34:601–8. doi: 10.1093/sleep/34.5.601

PubMed Abstract | Crossref Full Text | Google Scholar

39. Lovibond PF and Lovibond SH. The structure of negative emotional states: comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventories. Behav Res Ther. (1995) 33:335–43. doi: 10.1016/0005-7967(94)00075-U

PubMed Abstract | Crossref Full Text | Google Scholar

40. Cohen S, Kamarck T, and Mermelstein R. A global measure of perceived stress. J Health Soc Behav. (1983) 24:385–96. doi: 10.2307/2136404

Crossref Full Text | Google Scholar

41. Bagby RM, Parker JD, and Taylor GJ. The twenty-item Toronto Alexithymia Scale–I. Item selection and cross-validation of the factor structure. J Psychosom Res. (1994) 38:23–32. doi: 10.1016/0022-3999(94)90005-1

PubMed Abstract | Crossref Full Text | Google Scholar

42. Smith BW, Dalen J, Wiggins K, Tooley E, Christopher P, and Bernard J. The brief resilience scale: assessing the ability to bounce back. Int J Behav Med. (2008) 15:194–200. doi: 10.1080/10705500802222972

PubMed Abstract | Crossref Full Text | Google Scholar

43. Zimet GD, Powell SS, Farley GK, Werkman S, and Berkoff KA. Psychometric characteristics of the multidimensional scale of perceived social support. J Pers Assess. (1990) 55:610–7. doi: 10.1207/s15327752jpa5503&4_17

PubMed Abstract | Crossref Full Text | Google Scholar

44. Holm S. A simple sequentially rejective multiple test procedure. Scand J Stat. (1979) 6:65–70.

Google Scholar

45. Gaetano J. Holm-Bonferroni sequential correction: an Excel calculator [Internet]. Version 10. (2013). doi: 10.13140/RG.2.1.4466.9927

Crossref Full Text | Google Scholar

46. Hert M DE, Correll CU, Bobes J, Cetkovich-Bakmas M, Cohen D, Asai I, et al. Physical illness in patients with severe mental disorders. I. Prevalence, impact of medications and disparities in health care. World Psychiatry. (2011) 10:52–77. doi: 10.1002/j.2051-5545.2011.tb00014.x

PubMed Abstract | Crossref Full Text | Google Scholar

47. Correll C, Solmi M, Vernose N, and Al E. Prevalence, incidence and mortality from cardiovascular disease in patients with pooled and specific severe mental illness: a large-scale meta-analysis of 3,211,768 patients and 113,383,368 controls. World Psychiatry. (2017) 16:163–80. doi: 10.1002/wps.20420

PubMed Abstract | Crossref Full Text | Google Scholar

48. Vardavas CI, Mathioudakis AG, Nikitara K, Stamatelopoulos K, Georgiopoulos G, Phalkey R, et al. Prognostic factors for mortality, intensive care unit and hospital admission due to SARS-CoV-2: a systematic review and meta-analysis of cohort studies in Europe. Eur Respir Rev. (2022) 31(166):220098. doi: 10.1183/16000617.0098-2022

PubMed Abstract | Crossref Full Text | Google Scholar

49. Fiorillo A and Gorwood P. The consequences of the COVID-19 pandemic on mental health and implications for clinical practice. Eur Psychiatry. (2020) 63:e32. doi: 10.1192/j.eurpsy.2020.35

PubMed Abstract | Crossref Full Text | Google Scholar

50. Boyer L, Fond G, Pauly V, Orléans V, Auquier P, Solmi M, et al. Impact of the COVID-19 pandemic on non-COVID-19 hospital mortality in patients with schizophrenia: a nationwide population-based cohort study. Mol Psychiatry. (2022) 27:5186–94. doi: 10.1038/s41380-022-01803-4

PubMed Abstract | Crossref Full Text | Google Scholar

51. Bodilsen J, Nielsen PB, Søgaard M, Dalager-Pedersen M, Speiser LOZ, Yndigegn T, et al. Hospital admission and mortality rates for non-covid diseases in Denmark during covid-19 pandemic: nationwide population based cohort study. BMJ. (2021) 373:n1135. doi: 10.1136/bmj.n1135

PubMed Abstract | Crossref Full Text | Google Scholar

52. Mogharab V, Ostovar M, Ruszkowski J, Hussain SZM, Shrestha R, Yaqoob U, et al. Global burden of the COVID-19 associated patient-related delay in emergency healthcare: a panel of systematic review and meta-analyses. Global Health. (2022) 18:58. doi: 10.1186/s12992-022-00836-2

PubMed Abstract | Crossref Full Text | Google Scholar

53. Moynihan R, Sanders S, Michaleff ZA, Scott AM, Clark J, To EJ, et al. Impact of COVID-19 pandemic on utilization of healthcare services: a systematic review. BMJ Open. (2021) 11:e045343. doi: 10.1136/bmjopen-2020-045343

PubMed Abstract | Crossref Full Text | Google Scholar

54. Džakula A, Banadinović M, Lovrenčić IL, Vajagić M, Dimova A, Rohova M, et al. A comparison of health system responses to COVID-19 in Bulgaria, Croatia and Romania in 2020. Health Policy. (2022) 126:456–64. doi: 10.1016/j.healthpol.2022.02.003

PubMed Abstract | Crossref Full Text | Google Scholar

55. de Ridder D, Geenen R, Kuijer R, and van Middendorp H. Psychological adjustment to chronic disease. Lancet (London England). (2008) 372:246–55. doi: 10.1016/S0140-6736(08)61078-8

PubMed Abstract | Crossref Full Text | Google Scholar

56. Martino G, Caputo A, Schwarz P, Bellone F, Fries W, Quattropani MC, et al. Alexithymia and inflammatory bowel disease: A systematic review. Front Psychol. (2020) 11:1763. doi: 10.3389/fpsyg.2020.01763

PubMed Abstract | Crossref Full Text | Google Scholar

57. Casagrande M, Mingarelli A, Guarino A, Favieri F, Boncompagni I, Germanò R, et al. Alexithymia: A facet of uncontrolled hypertension. Int J Psychophysiol. (2019) 146:180–9. doi: 10.1016/j.ijpsycho.2019.09.006

PubMed Abstract | Crossref Full Text | Google Scholar

58. Kojima M, Senda Y, Nagaya T, Tokudome S, and Furukawa TA. Alexithymia, depression and social support among Japanese workers. Psychother Psychosom. (2003) 72:307–14. doi: 10.1159/000073027

PubMed Abstract | Crossref Full Text | Google Scholar

59. Thoits PA. Mechanisms linking social ties and support to physical and mental health. J Health Soc Behav. (2011) 52:145–61. doi: 10.1177/0022146510395592

PubMed Abstract | Crossref Full Text | Google Scholar

60. Druss B and Walker E. Mental disorders and medical comorbidity. The Synthesis Project: Research Synthesis Report Number 21. Princeton, NJ: Robert Wood Johnson Foundation (2011).

Google Scholar

61. Vanheule S, Meganck R, and Desmet M. Alexithymia, social detachment and cognitive processing. Psychiatry Res. (2011) 190:49–51. doi: 10.1016/j.psychres.2010.06.032

PubMed Abstract | Crossref Full Text | Google Scholar

62. Veirman E, Van Ryckeghem DML, Verleysen G, De Paepe AL, and Crombez G. What do alexithymia items measure? A discriminant content validity study of the Toronto-alexithymia-scale-20. PeerJ. (2021) 9:e11639. doi: 10.7717/peerj.11639

PubMed Abstract | Crossref Full Text | Google Scholar

63. Clemente-Suárez VJ, Martínez-González MB, Benitez-Agudelo JC, Navarro-Jiménez E, Beltran-Velasco AI, Ruisoto P, et al. The impact of the COVID-19 pandemic on mental disorders. A critical review. Int J Environ Res Public Health. (2021) 8(19):10041. doi: 10.3390/ijerph181910041

PubMed Abstract | Crossref Full Text | Google Scholar

64. Kılınçel Ş, Kılınçel O, Muratdağı G, Aydın A, and Usta MB. Factors affecting the anxiety levels of adolescents in home-quarantine during COVID-19 pandemic in Turkey. Asia Pac Psychiatry. (2021) 13:e12406. doi: 10.1111/appy.12406

PubMed Abstract | Crossref Full Text | Google Scholar

65. Hoenig JM and Heisey DM. The abuse of power. Am Stat. (2001) 55:19–24. doi: 10.1198/000313001300339897

Crossref Full Text | Google Scholar