Social Psychiatry and Psychiatric Epidemiology https://doi.org/10.1007/s00127-023-02438-8 RESEARCH Results of the COVID‑19 mental health international for the health professionals (COMET‑HP) study: depression, suicidal tendencies and conspiracism Konstantinos N. Fountoulakis1 · Grigorios N. Karakatsoulis1 · Seri Abraham2,3,4 · Kristina Adorjan5 · Helal Uddin Ahmed6 · Renato D. Alarcón7,8 · Kiyomi Arai9 · Sani Salihu Auwal10,11 · Julio Bobes15,16 · Teresa Bobes‑Bascaran17,18 · Julie Bourgin‑Duchesnay19 · Cristina Ana Bredicean20 · Laurynas Bukelskis21 · Akaki Burkadze22,23 · Indira Indiana Cabrera Abud24 · Ruby Castilla‑Puentes25 · Marcelo Cetkovich26,27 · Hector Colon‑Rivera28 · Ricardo Corral29,30 · Carla Cortez‑Vergara31 · Piirika Crepin32 · Domenico de Berardis33,34,35 · Sergio Zamora Delgado36 · David de Lucena37 · Avinash de Sousa38,39 · Ramona di Stefano40 · Seetal Dodd12,13,41 · Livia Priyanka Elek42 · Anna Elissa43 · Berta Erdelyi‑Hamza42 · Gamze Erzin44 · Martin J. Etchevers45 · Peter Falkai5 · Adriana Farcas46 · Ilya Fedotov47 · Viktoriia Filatova48 · Nikolaos K. Fountoulakis49 · Iryna Frankova50 · Francesco Franza51,52 · Pedro Frias53 · Tatiana Galako54 · Cristian J. Garay45 · Leticia Garcia‑Álvarez18 · Paz García‑Portilla15,55 · Xenia Gonda42 · Tomasz M. Gondek56 · Daniela Morera González57 · Hilary Gould58 · Paolo Grandinetti33 · Arturo Grau36,59 · Violeta Groudeva60 · Michal Hagin61 · Takayuki Harada62 · Tasdik M. Hasan63,64 · Nurul Azreen Hashim65 · Jan Hilbig21 · Sahadat Hossain66 · Rossitza Iakimova67 · Mona Ibrahim68 · Felicia Iftene69 · Yulia Ignatenko70 · Matias Irarrazaval71 · Zaliha Ismail72 · Jamila Ismayilova73 · Asaf Jacobs74,75 · Miro Jakovljević76 · Nenad Jakšić14 · Afzal Javed77,78,79 · Helin Yilmaz Kafali80 · Sagar Karia38 · Olga Kazakova81 · Doaa Khalifa68 · Olena Khaustova50 · Steve Koh58 · Svetlana Kopishinskaia82,83 · Korneliia Kosenko84 · Sotirios A. Koupidis85 · Illes Kovacs42 · Barbara Kulig42 · Alisha Lalljee39 · Justine Liewig19 · Abdul Majid86 · Evgeniia Malashonkova19 · Khamelia Malik43 · Najma Iqbal Malik87 · Gulay Mammadzada88 · Bilvesh Mandalia39 · Donatella Marazziti89,90,91 · Darko Marčinko14,76 · Stephanie Martinez58 · Eimantas Matiekus21 · Gabriela Mejia58 · Roha Saeed Memon92 · Xarah Elenne Meza Martínez93 · Dalia Mickevičiūtė94 · Roumen Milev69 · Muftau Mohammed95 · Alejandro Molina‑López96 · Petr Morozov97 · Nuru Suleiman Muhammad98 · Filip Mustač14 · Mika S. Naor99 · Amira Nassieb68 · Alvydas Navickas21 · Tarek Okasha68 · Milena Pandova67 · Anca‑Livia Panfil100 · Liliya Panteleeva101 · Ion Papava20 · Mikaella E. Patsali102,103 · Alexey Pavlichenko71 · Bojana Pejuskovic104,105 · Mariana Pinto da Costa106 · Mikhail Popkov107 · Dina Popovic108 · Nor Jannah Nasution Raduan65 · Francisca Vargas Ramírez36,59 · Elmars Rancans109,110 · Salmi Razali65 · Federico Rebok111,112 · Anna Rewekant113 · Elena Ninoska Reyes Flores114 · María Teresa Rivera‑Encinas115 · Pilar A. Saiz15 · Manuel Sánchez de Carmona116 · David Saucedo Martínez117 · Jo Anne Saw65 · Görkem Saygili118 · Patricia Schneidereit119 · Bhumika Shah120 · Tomohiro Shirasaka121 · Ketevan Silagadze22 · Satti Sitanggang122 · Oleg Skugarevsky123 · Anna Spikina124 · Sridevi Sira Mahalingappa125 · Maria Stoyanova67 · Anna Szczegielniak126 · Simona Claudia Tamasan100 · Giuseppe Tavormina52,127,128 · Maurilio Giuseppe Maria Tavormina52 · Pavlos N. Theodorakis129 · Mauricio Tohen130 · Eva‑Maria Tsapakis131,132 · Dina Tukhvatullina133 · Irfan Ullah134 · Ratnaraj Vaidya135 · Johann M. Vega‑Dienstmaier136 · Jelena Vrublevska109,110,140 · Olivera Vukovic103,137 · Olga Vysotska138 · Natalia Widiasih43 · Anna Yashikhina82,139 · Panagiotis E. Prezerakos140 · Michael Berk12,13 · Sarah Levaj14 · Daria Smirnova82,141 Received: 11 September 2022 / Accepted: 2 February 2023 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany 2023 Abstract Introduction The current study aimed to investigate the rates of anxiety, clinical depression, and suicidality and their changes in health professionals during the COVID-19 outbreak. Extended author information available on the last page of the article 13 Vol.:(0123456789) Social Psychiatry and Psychiatric Epidemiology Materials and methods The data came from the larger COMET-G study. The study sample includes 12,792 health professionals from 40 countries (62.40% women aged 39.76 ± 11.70; 36.81% men aged 35.91 ± 11.00 and 0.78% non-binary gender aged 35.15 ± 13.03). Distress and clinical depression were identified with the use of a previously developed cut-off and algorithm, respectively. Statistical analysis Descriptive statistics were calculated. Chi-square tests, multiple forward stepwise linear regression analyses, and Factorial Analysis of Variance (ANOVA) tested relations among variables. Results Clinical depression was detected in 13.16% with male doctors and ‘non-binary genders’ having the lowest rates (7.89 and 5.88% respectively) and ‘non-binary gender’ nurses and administrative staff had the highest (37.50%); distress was present in 15.19%. A significant percentage reported a deterioration in mental state, family dynamics, and everyday lifestyle. Persons with a history of mental disorders had higher rates of current depression (24.64% vs. 9.62%; p < 0.0001). Suicidal tendencies were at least doubled in terms of RASS scores. Approximately one-third of participants were accepting (at least to a moderate degree) a non-bizarre conspiracy. The highest Relative Risk (RR) to develop clinical depression was associated with a history of Bipolar disorder (RR = 4.23). Conclusions The current study reported findings in health care professionals similar in magnitude and quality to those reported earlier in the general population although rates of clinical depression, suicidal tendencies, and adherence to conspiracy theories were much lower. However, the general model of factors interplay seems to be the same and this could be of practical utility since many of these factors are modifiable. Keywords COVID-19 · Health professionals · Depression · Suicidality · Mental health · Conspiracy theories · Mental disorders · Psychiatry · Anxiety Introduction There are many reports in the literature suggesting that health professionals are at particular risk to experience a deterioration of their mental health during the COVID-19 pandemic [23]. Clinical depression, sleep disorders, and post-traumatic stress disorder (PTSD) was also reported both in the general population as well as in health care professionals (HCP) [55, 97]. Thus, while the COVID-19 pandemic started as an epidemic of an infectious agent, it soon gained a wider content and included all effects on all aspects of human life by this condition, even the overwhelming burst of information of questionable reliability and validity (‘infodemic’) [10]. The abuse of the terms ‘trauma’ and ‘PTSD’ is such an example. The vast majority of studies reported a ‘tsunami’-scale impact on mental health. It is highly possible that this could be an exaggeration [96]. In addition, changes to social behavior, as well as working conditions, daily habits, and routines have imposed secondary stress. Higher levels of anxiety, stress, and depressive feelings have been reported, but it seems that this depends on the temporal situation and the specific events; the response is by no means homogenous [37, 85, 96, 115], [75, 104]. Apart from the effect of the virus itself, in addition, changes in social behavior, as well as in working conditions, daily habits, and routine are expected to impose further stress, especially with the expectation of an upcoming economic crisis and possible unemployment. In this frame, mental health has gained a central position as an area that is expected to be affected by 13 the pandemic because of its threatening nature as well as because of the profound impact on the everyday life of people. Especially concerning the later, it has been suggested that lockdowns triggered feelings of loneliness, irritableness, restlessness, and nervousness in the general population [90]. Especially the expectation of an upcoming economic crisis and possible unemployment were stressful factors. Conspiracy theories and maladaptive behaviors were also prevalent, compromising the public defense against the outbreak. The issue of increased suicidality as a consequence of extreme stress and depression has been raised again [25, 84]. At the end of the day, although are several empirical data papers, their methodology varies, it is very difficult to make comparisons among countries and it is also difficult to arrive at universally valid conclusions [55, 97]. Additionally, the literature is full of opinion papers, viewpoints, perspectives, guidelines, and narrations of activities to cope with the pandemic. These borrow from previous experiences with different pandemics and utilize common sense, but, as a result, they often obscure rather than clarify the landscape. The role of mass and social media has been discussed but remains poorly understood in empirical terms. An early meta-analysis reported high rates of anxiety (25%) and depression (28%) in the general population [86] while a second one reported that 29.6% of people experienced stress, 31.9% anxiety and 33.7% depression [92]. Not only do we need more reliable and valid data, but we also need to identify risk and protective factors to be able to recommend measures that will eventually improve public Social Psychiatry and Psychiatric Epidemiology health by preventing the adverse impact on mental health and simultaneously improve health-related behaviors [4, 47, 73, 75]. The aim of the current study was to investigate the rates of anxiety, clinical depression, and suicidality and their changes in health professionals aged 18–69 internationally, during the COVID-19 pandemic. The secondary aims were to investigate their relations with several personal, interpersonal/social, and lifestyle variables. The aim also included the investigation of the spreading of conspiracy theories concerning the COVID-19 outbreak and their relationship with mental health in this specific population group. Materials and methods Methods The protocol used is available in the webappendix; each question was given an ID code; these ID codes were used throughout the results for increased accuracy. According to a previously developed method, [39, 40, 42] the cut-off score of 23/24 for the Center for Epidemiological Studies-Depression (CES-D) scale and a derived algorithm were used to identify cases of clinical depression. This algorithm utilized the weighted scores of selected CES-D items to arrive at the diagnosis of clinical depression, and has already been validated. Cases identified by only either method were considered cases of distress (false positive cases in terms of depression), while cases identified by both the cut-off and the algorithm were considered as clinical depression. The State-Trate Anxiety Inventory-State form (STAI-S) [100] and the Risk for Assessment of Suicidality Scale (RASS) [42] were used to assess anxiety and suicidality respectively. The data were collected online and anonymously from April 2020 through March 2021, covering periods of full implementation of lockdowns as well as of relaxations of measures in countries around the world. Announcements and advertisements were done on the social media and through news sites, but no other organized effort had been undertaken. The first page included a declaration of consent which everybody accepted by continuing with the participation. Approval was initially given by the Ethics Committee of the Faculty of Medicine, Aristotle University of Thessaloniki, Greece, and locally concerning each participating country. Materials The data came from the larger COMET-G study [41]. The study population was self-selected. It was not possible to apply post-stratification on the sample as it was done in a previous study [40], because this would mean that we would utilize a similar methodology across many different countries and the population data needed were not available for all. Statistical analysis • Chi-square tests were used for the comparison of fre- quencies when categorical variables were present and for the post hoc analysis of the results a Bonferroni-corrected method of pair-wise comparisons was utilized [69]. • Factorial Analysis of Variance (ANOVA) was used to test for the main effect as well as the interaction among grouping variables concerning continuous variables. The Scheffe test was used as the post-hoc test. • Multiple forward stepwise linear regression analysis (MFSLRA) was performed to investigate which variables could function as predictors and contribute to the development of others (e.g. clinical depression). To correct for multiple comparisons, the level of p<0.001 was accepted as the level of significance for ANOVA and MFSLRA results (but not for post-hoc tests) Results Demographics From the 55,589 responses from 40 countries (Table 1) of the COMET study, 23.01% reported they were working in the health field. Thus, the study sample of the current paper includes 12,792 health professionals (N = 7983–62.40% women aged 39.76 ± 11.70; N = 4709–36.81% men aged 35.91 ± 11.00 and N = 100–0.78% non-binary gender aged 35.15 ± 13.03). The contribution of each country and the gender and age composition are shown in Table 1. The sex-by-specific occupation composition is shown in Table 2. The sociodemographic characteristics are shown in webtables 1,2,3,4,5,6,7 of the appendix. Details concerning various sociodemographic variables (marital status, education, work, etc. are shown in the webappendix, in webtables 1,2,3,4,5,6,7,8,9). History of health Moderate or bad somatic health was reported by 16.70% and the presence of a chronic medical somatic condition was reported by 21.29%. Detailed results are shown in webtable 8. Being either relatives or caretakers of vulnerable persons was reported by 46.88% (webtable 9). 13 Social Psychiatry and Psychiatric Epidemiology Table 1 List of participating countries by sex, with number of subjects and mean age Country Argentina Australia Azerbaijan Bangladesh Belarus Brazil Bulgaria Canada Chile Croatia Egypt France Georgia Germany Greece Honduras Hungary India Indonesia Israel Italy Japan Kyrgyz Republic Latvia Lithuania Malaysia Mexico Nigeria Pakistan Peru Poland Portugal Romania Russia Serbia Spain Turkey Ukraine UK USA Total Men Women Non-binary gender Age Age Age N % Mean SD N % Mean SD N % Mean SD N % 439 21 70 1681 200 86 202 142 86 1041 24 64 48 15 624 74 146 3044 909 28 257 182 614 1036 271 311 447 752 575 56 286 16 293 3825 152 330 95 306 55 124 18,927 20.14 30.43 19.89 55.42 18.30 40.19 26.47 27.73 26.71 35.91 14.55 24.33 11.59 25.00 18.26 33.48 19.13 61.01 27.68 19.44 26.22 70.00 27.76 39.72 21.54 32.29 25.03 65.22 28.24 36.13 18.58 18.82 20.22 38.50 25.08 31.82 27.38 21.07 34.38 30.32 34.05 44.53 33.67 36.20 24.09 38.62 31.36 14.39 8.05 10.33 5.24 12.46 13.06 14.49 7.89 11.46 5.48 11.11 9.97 16 17.29 26.00 27.42 0.00 8.88 42.57 39.57 42.32 39.66 37.89 32.06 34.87 34.00 32.05 41.36 31.59 30.49 38.97 41.22 41.71 38.87 45.26 40.16 39.24 38.18 25.83 23.45 38.72 33.65 42.34 46.77 31.74 41.84 48.52 25.05 39.09 44.56 37.78 35.80 14.00 15.08 11.84 11.82 15.53 9.04 13.98 9.87 11.09 11.95 11.97 11.42 13.56 14.17 11.10 14.58 14.64 12.75 11.71 14.74 7.55 4.42 14.03 11.24 13.77 14.21 12.25 11.77 13.53 7.36 13.13 11.95 14.51 13.61 0.73 0.00 0.57 0.63 0.00 0.47 0.39 0.59 0.62 0.79 0.00 0.76 0.48 0.00 0.64 0.00 0.00 0.56 0.52 0.00 0.61 0.00 1.67 0.08 0.32 7.68 0.39 0.35 0.79 0.00 0.91 1.18 0.83 2.66 0.17 0.39 0.86 0.96 0.00 2.93 1.11 37.44 15.49 15.43 11.70 14.18 14.70 6.82 18.58 10.58 7.17 11.95 8.94 12.06 18.24 16.17 11.61 14.16 12.38 13.62 12.08 16.13 7.46 6.35 15.80 11.54 18.22 14.45 12.03 11.94 14.85 6.26 15.38 11.12 15.47 13.29 79.13 69.57 79.55 43.95 81.70 59.35 73.13 71.68 72.67 63.30 85.45 74.90 87.92 75.00 81.10 66.52 80.87 38.42 71.80 80.56 73.16 30.00 70.57 60.20 78.14 60.02 74.58 34.43 70.97 63.87 80.51 80.00 78.95 58.85 74.75 67.79 71.76 77.96 65.63 66.75 64.85 40.60 32.63 37.71 23.98 39.15 28.80 42.24 40.76 41.73 37.38 38.98 30.77 48.93 36.55 28.19 44.60 33.51 33.64 48.79 43.10 45.31 36.38 48.18 39.34 41.95 36.84 30.30 25.46 43.80 33.46 43.31 47.54 30.34 39.16 51.49 25.03 38.42 43.53 37.50 34.90 1725 48 280 1333 893 127 558 367 234 1835 141 197 364 45 2772 147 617 1917 2358 116 717 78 1561 1570 983 578 1332 397 1445 99 1239 68 1144 5847 453 703 249 1132 105 273 36,047 2180 69 352 3033 1093 214 763 512 322 2899 165 263 414 60 3418 221 763 4989 3284 144 980 260 2212 2608 1258 963 1786 1153 2036 155 1539 85 1449 9936 606 1037 347 1452 160 409 55,589 3.92 0.12 0.63 5.46 1.97 0.38 1.37 0.92 0.58 5.22 0.30 0.47 0.74 0.11 6.15 0.40 1.37 8.97 5.91 0.26 1.76 0.47 3.98 4.69 2.26 1.73 3.21 2.07 3.66 0.28 2.77 0.15 2.61 17.87 1.09 1.87 0.62 2.61 0.29 0.74 100.00 In terms of mental health history and self-harm, 8.59% had a prior history of an anxiety disorder, 10.93% of depression, 0.71% of Bipolar disorder, 0.42% of psychosis, and 2.90% of other mental disorder. Any mental disorder history 13 2 19 1 3 3 2 23 2 2 22 28 17 6 37 2 4 74 7 4 16 14 1 12 264 1 4 3 14 12 615 31.00 46.33 42.50 44.26 17.79 16.26 13.75 27.50 33.50 10.61 6.36 29.59 6.68 28.36 28.00 7.86 11.62 42.17 21.14 33.57 48.00 40.75 39.03 22.86 31.75 24.75 12.60 18.38 12.89 12.66 4.78 7.97 10.93 31.21 38.00 51.58 27.64 58.00 50.00 21.33 35.93 14.67 28.00 31.64 9.78 13.15 15.45 10.87 13.11 0.58 17.88 was present in 23.58%. At least once, 17.20% had hurt themselves in the past and 8.20% had attempted at least once in the past. The detailed rates by sex and country are shown in webtable 10. Social Psychiatry and Psychiatric Epidemiology Table 2 Sex-by-occupation composition and rates of clinical depression and distress % Age Mean Administrative staff in hospital (4.10%) Women 64.35 40.29 Men 34.10 38.01 Non-binary gender 1.54 39.00 Total Doctor (42.66%) Women 70.70 39.88 Men 28.67 40.21 Non-binary gender 0.63 37.35 Total Nurse (10.89%) Women 87.22 41.11 Men 12.20 34.99 Non-binary gender 0.58 32.00 Total Other healthcare profession (36.21%) Women 44.97 38.77 Men 54.36 33.34 Non-binary gender 0.68 36.35 Total Other hospital staff (6.14%) Women 60.05 39.35 Men 37.63 34.40 Non-binary gender 2.32 28.61 Total Total study sample Women 62.40 39.76 Men 36.81 35.91 Non-binary gender 0.78 35.15 Total 100.00 38.31 Distress % Clinical Distress plus clinidepression % cal depression (%) 11.14 10.83 12.42 12.28 13.56 37.50 13.10 17.37 15.82 37.50 17.15 29.64 29.38 75.00 30.25 11.84 13.42 15.25 15.36 14.03 35.29 15.11 15.23 7.89 5.88 13.07 30.60 21.91 41.18 28.17 11.90 11.25 12.74 14.49 16.67 0.00 14.67 16.65 11.31 37.50 16.12 31.14 27.98 37.50 30.79 11.31 7.87 11.57 15.69 14.54 29.03 15.16 14.81 9.60 16.13 11.99 30.50 24.15 45.16 27.15 11.78 12.43 9.58 17.38 18.84 33.33 18.30 15.45 14.38 22.22 15.21 32.83 33.22 55.56 33.51 11.70 11.00 13.03 11.61 15.30 14.68 30.30 15.19 15.44 9.63 17.17 13.31 30.75 24.31 47.47 28.50 SD Family Present mental health In terms of family status, 57.86% were married, 60.02% had at least one child and only 11.42% were living alone. The responses suggested an increased need for communication with family members in 41.79%, an increased need for emotional support in 30.26%, fewer conflicts in 37.36% and increased conflicts within families for 17.77%, an improvement of the quality of relationships in 25.62%, while in most cases (90.18%) there was maintenance of basic daily routine at least somehow (webtable 11). During lockdowns 80.81% continued to work, while 47.65% expected their economic situation to worsen because of the COVID-19 outbreak (webtable 12). Concerning mental health, data 47.15% reported an increase in anxiety, and 39.95% reported a worsening in depressive affect. Suicidal thoughts were increased in 10.48%. Overall, current clinical depression was present in 13.16% of the study sample (unweighted average) with male doctors and ‘non-binary genders’ having the lowest rates (7.89 and 5.88%, respectively) and ‘non-binary gender’ nurses and administrative staff having the highest (37.50%). In detail, the results are shown in Table 2. However, after taking into consideration the expected rates of current clinical depression in the population, men with positive history had the highest Relative Risk (RR = 6.47) while the lowest 13 Social Psychiatry and Psychiatric Epidemiology was observed in women without a history of mental disorder (RR = 1.81). Additionally, distress was present in 15.19%, with the highest rates being for ‘non-binary genders’ (> 30%) and the lowest for female administrative staff (12.28%). The complete rates by sex and occupation are shown in webtable 17. Suicidal tendencies doubled according to RASS subscales scores (webtable 17) and in comparison to what is expected [42]. Persons with a history of mental disorders had higher rates of current clinical depression (24.64% vs. 9.62%, chisquare test = 454.90; df = 1; p < 0.0001) (webtable 14). In persons without mental health history, the RR ranged from 2.14 to 2.90. In persons with a mental health history, the RR was highest in those with a psychotic history (Bipolar disorder RR = 9.04; Psychosis RR = 8.08) and ranged between 3.82 and 6.40 for non-psychotic history, with the lowest RR in persons with a history of ‘other mental disorder’, in comparison to the expected prevalence of depression (3% for men and 6% for women). Of women with clinical depression, half were new cases (without any past history of mental disorder) while this was true for two thirds of men. Taking into consideration that the pandemic increased the risk by definition, the risk to develop clinical depression during the pandemic when having a previous history was highest for bipolar disorder (RR = 4.23), while the previous history of self-harm or suicidal attempts did not increase the risk (Table 3). The mean scale scores were 43.52 ± 11.99 for the STAIS, 19.36 ± 8.17 for the CES-D, and 70.23 ± 134.74 for the Intention subscale of the RASS. The complete results by sex and country are shown in webtable 17. Table 3 Relative Risk (RR) to develop clinical depression vs. participants with no mental health history and no history of self-harm or suicidal acts History No previous history at all Any mental disorder Anxiety Clinical depression Bipolar disorder Psychosis Other Only history of self-harm/attempt 13 Risk to develop clinical depression When alone When history of self-harm/ attempt is also present % RR % RR 9.62 31.81 19.93 28.81 40.66 36.36 17.20 17.89 1.00 3.31 2.07 2.99 4.23 3.78 1.79 1.86 29.87 29.49 30.46 30.78 30.93 30.52 1.0 3.11 3.07 3.17 3.20 3.22 3.17 From the total sample, 5.17% reported that they often thought much or very much about committing suicide if they had the chance. Men and women had similar rates (5.76% vs. 4.69%) but those self-identified as ‘non-binary gender’ had much higher rates (17.00%). In subjects with a history of psychotic disorder or self-harm/attempt the rate was 15.45% while in those with a history of non-psychotic disorder, it was 9.76%. In persons free of any mental disorder or selfharm/attempt history the rate was as low as 2.09%. This means that the RR for the manifestation of at least moderate suicidal thoughts was equal to 7.4 for psychotic history and 3.7 for non-psychotic history. In those identified as ‘nonbinary gender’, the RR was approximately equal to 3.5. Beliefs in conspiracy theories Approximately one third of responders accepted at least a moderate degree some non-bizarre conspiracy theory. The acceptance of inflated death rates was 44.24% while that of the 5G antenna theory was 20.81%. Doctors had the lowest rates, but impressively, 37.6% of doctors and 50.86% of nurses reported they were believing in the deliberate inflation of death rates by governments and 14.75% and 27.22% respectively were accepting the 5G theory. In detail, the responses by sex and country are shown in webtable 21. Modeling of mental health changes during the pandemic The presence of any mental health history acted as a risk factor for the development of current clinical depression with all chi-square tests being significant at p < 0.001. Interestingly a history of self-harm or suicidality emerged as a risk factor even for persons without reporting mental health history. In persons with only a history of self-harm or suicidality, 17.89% developed clinical depression. The combination of both self-harm and suicidal attempts history with specific mental health history revealed that subjects without any such history at all had the lowest rate or current clinical depression (9.62%), while the presence of previous self-harm/attempts increased the risk in subjects with past anxiety (29.49%) and other mental disorders (30.52%), but not clinical depression (30.46%), Bipolar disorder (30.78%) and psychoses (30.93%). The highest relative risk (RR) was calculated for history of Bipolar disorder but history of selfharm/attempt played no role (RR = 4.23). All RR values are shown in Table 2 and webtable 22. After taking into consideration that the annual incidence of depression is 0.3% [66], the calculated risk because of the pandemic for the health professionals population to develop clinical depression is RR = 30 Fig. 1. The presence of a chronic somatic condition acted as a significant but weak risk factor for the development of Social Psychiatry and Psychiatric Epidemiology Fig. 1 Map of the 40 participating countries clinical depression (Chi-square = 14.61, df = 1, p < 0.001; In terms of rates, 15.35% of those with a chronic somatic condition manifested clinical depression vs. 12.56% of those without (RR = 1.22). The results of the MFSLRA suggested that a significant number of variables acted either as risk or as protective factors (Table 4, Fig. 2, webtable 23). These factors explained 16.1% of the change in anxiety, 11.6% of change in depressive affect, 19.1% of the development of distress or clinical depression, and 5.1% of change in suicidal thoughts. The individual contribution of each predictor separately was very small (many b coefficients were very close to zero). If we consider a more or less linear continuum from fear to anxiety to depressive emotions to clinical depression and eventually to suicidality, the model which can be derived suggests there is a core of variables (Fig. 2, webfigure 1) that exert a stable either adverse or protective effect throughout the course of the development of the mental state. Factorial ANOVA with the scores of STAI-S, CES-D and RASS as continuous variables and sex and being a doctor or a nurse as grouping variables was always significant for sex (p < 0.0001). For doctors only the interaction with sex is significant (Wilks = 0.996, F = 4.86, df = 10, error df = 25,562, p < 0.0001), with ‘non-binary genders’ having higher psychopathology. For nurses it was significant both independently (Wilks = 0.999, F = 7.768, df = 10, error df = 25,562, p = 0.009) as well as in interaction with sex (Wilks = 0.998, F = 2.618, df = 10, error df = 25,562, p = 0.004). Overall nurses had lower psychopathological scores than the rest. The Scheffe post-hoc tests (at p < 0.05) revealed that most groups defined by sex and occupation differed from each other in a complex and difficult-to-explain matrix. Conspiracy theories manifest a complex behavior with some of them exerting a protective effect at certain phases (Fig. 2), but their overall impact was lower in comparison to the general population. The mean scores of responses to questions pertaining to different conspiracy beliefs by history of any mental disorder and current clinical depression are shown in Table 5 and webtable 24. Factorial ANOVA suggested that history of any mental disorder and current clinical depression as well as their interaction were significant factors concerning the belief in conspiracy theories (Table 5). The results of post-hoc tests are shown in webtable 25. They suggest that persons with a history of mental disorder have lower overall tendency in believing in both the threatening and the reassuring conspiracy theories. Not believing in any conspiracy theory had a different composition with history of having any mental disorder being the determining factor leading to lower adaption of conspiracy theories and with depression acting at a second level and further decreasing this tendency. These findings were consistent across disorders and conspiracy theories. Discussion This large international study in a convenient sample of 12,792 health professionals from 40 countries detected clinical depression in 13.31% (unweighted average) with men and ‘non-binary genders’ doctors having the lowest rates (7.89% and 5.88% respectively) and ‘non-binary gender’ nurses and administrative staff having the highest (37.50%). Distress was present in 15.19%, with the highest rates being for the ‘non-binary gender’ (> 30%) and the lowest for female administrative staff (12.28%). A significant percentage reported a deterioration in mental state, family dynamics and everyday lifestyle. Persons with a history of mental disorders had higher rates of current clinical depression 13 13 Table 4 Results of four separate Multiple Forward Stepwise Linear Regression Analysis (MFSLRA) with change in anxiety (F21), change in depressive affect (G21), change in suicidal thoughts (O11) and the development of distress or clinical depression as dependent variables Change in anxiety (F21) R2 = 0.161; F (25, 12,620) = 97.286 p < < 0.0001; SE of est: 0.809 b t p b SE t p Development of distress or clinical depression R2 = 0.191; F(31,12,611) = 93.466 p < < 0.0001; SE of est: 0.640 b SE t p – 0.87 0.06 – 14.49 < 0.001 – 0.75 0.06 – 11.95 < 0.001 0.59 0.06 0.00 < 0.001 0.001 0.00 – 0.04 0.01 – 3.02 < 0.001 0.00 0.00 – 4.00 0.02 3.88 0.00 3.23 – 0.02 0.01 – 2.23 0.00 3.67 0.026 – 0.02 0.01 – 2.42 0.04 14.31 0.016 0.10 0.01 14.02 < 0.001 0.11 0.01 15.11 < 0.001 – 0.02 0.01 – 3.15 0.10 0.01 13.90 < 0.001 0.08 0.01 10.55 < 0.001 – 0.09 0.01 – 15.30 – 0.04 0.02 – 2.60 – 0.03 0.12 0.11 0.02 0.01 0.01 0.01 0.01 – 4.53 12.27 12.78 2.63 0.009 – 0.04 0.01 – 2.98 < 0.001 – 0.04 < 0.001 0.15 < 0.001 0.10 0.009 0.02 0.01 0.01 0.01 0.01 – 5.21 15.33 11.46 2.22 – 0.14 0.03 – 5.37 – 0.10 0.02 – 4.14 < 0.001 – 0.12 0.03 – 4.39 < 0.001 – 0.13 0.02 – 5.45 – 0.20 0.04 – 4.50 < 0.001 – 0.05 0.02 – 2.66 – 0.10 0.01 – 12.09 – 0.08 0.01 – 11.22 < 0.001 – 0.05 0.01 – 6.41 < 0.001 – 0.04 0.01 – 6.35 0.02 < 0.001 0.02 0.01 3.89 0.01 3.21 Change in suicidal thoughts (O11) R2 = 0.051; F (31,12,614) = 22.282 p < < 0.0001; SE of est: 0.770 b SE t p < 0.001 0.44 0.06 7.24 < 0.001 0.003 0.06 < 0.001 0.00 – 0.02 0.02 0.02 0.00 0.01 0.01 3.98 – 3.79 – 2.99 2.02 < 0.001 < 0.001 0.003 0.044 0.002 – 0.02 0.01 – 2.58 0.010 < 0.001 – 0.02 0.01 – 3.29 0.001 0.003 0.04 0.01 2.57 < 0.001 0.04 < 0.001 – 0.02 < 0.001 – 0.10 0.026 0.03 0.01 7.37 0.01 -3.24 0.01 – 14.79 0.01 4.99 < 0.001 0.06 0.001 – 0.06 < 0.001 – 0.05 < 0.001 – 0.05 0.01 0.01 0.01 0.01 < 0.001 0.20 < 0.001 0.36 0.37 0.44 0.15 0.008 0.17 0.14 0.02 9.37 0.02 18.61 0.09 4.26 0.07 6.36 0.03 4.46 0.02 9.63 0.02 5.65 < 0.001 0.05 < 0.001 0.07 < 0.001 < 0.001 – 0.19 < 0.001 < 0.001 0.05 < 0.001 0.11 0.03 2.03 0.02 2.95 0.042 0.003 0.08 – 2.28 0.023 < 0.001 0.06 < 0.001 0.05 0.01 0.01 < 0.001 0.02 0.01 2.08 < 0.001 – 0.01 0.01 – 2.24 0.001 8.69 9.92 8.30 – 6.67 – 5.81 – 6.22 0.010 0.02 2.48 0.03 3.87 < 0.001 < 0.001 < 0.001 < 0.001 0.013 < 0.001 0.037 0.025 Social Psychiatry and Psychiatric Epidemiology Intercept Demographics Sex (A1)- ‘non-binary gender’ was not included Age (A2) Number of persons in household (A5) Education level (A7) Work and finance Continue to work during lockdown (A11) Change in economic situation (E7) Health Condition of general health (B1) Presence of a chronic medical condition (B2) Family/social Being a carer of a person belonging to a vulnerable group (B4) Conflicts within family (E3) Change in quality of relationships within family (E4) Keeping a basic routine during lockdown (E5) Changes in religiousness/spirituality (P1) Mental health history History of anxiety (B5) History of depression (B5) History of psychosis (B5) History of bipolar disorder (B5) History of other mental disorder (B5) History of self-harm (O12) History of suicidal attempt (O13) The effect of the pandemic Fears of getting COVID-19 (C1) Fears that a member of the family will get COVID-19 and die (C3) Time spent outside of house during lockdown (D1) SE Change in depressive affect (G21) R2 = 0.116; F (25, 12,620) = 66.742 p < < 0.0001; SE of est: 0.827 Change in anxiety (F21) R2 = 0.161; F (25, 12,620) = 97.286 p < < 0.0001; SE of est: 0.809 Currently locked up in the house (D2) Satisfaction by availability of information (D4) Beliefs in conspiracy theories The vaccine was ready before the virus broke out and they conceal it (J1) COVID-19 was created in a laboratory as a biochemical weapon (J2) COVID-19 is the result of 5G technology antenna (J3) COVID-19 appeared accidentally from human contact with animals (J4) COVID-19 has much lower mortality rate but there is terrorinducing propaganda (J5) COVID-19 is a creation of the world’s powerful leaders to create a global economic crisis (J6) COVID-19 is a sign of divine power to destroy our planet (J7) Occupation (A10) Doctor Nurse Other Hospital staff Other healthcare professional Administrative staff The predictors are shown in the left column Change in depressive affect (G21) R2 = 0.116; F (25, 12,620) = 66.742 p < < 0.0001; SE of est: 0.827 b SE 0.06 0.01 8.09 – 0.03 0.01 – 4.03 < 0.001 0.06 0.01 7.88 < 0.001 < 0.001 0.04 0.01 5.15 < 0.001 0.03 < 0.001 0.03 t p b SE Development of distress or clinical depression R2 = 0.191; F(31,12,611) = 93.466 p < < 0.0001; SE of est: 0.640 t 0.01 4.41 – 0.03 0.01 – 3.19 0.01 0.04 0.01 4.43 < 0.001 0.07 0.02 2.89 0.004 0.01 2.03 p b SE t p Change in suicidal thoughts (O11) R2 = 0.051; F (31,12,614) = 22.282 p < < 0.0001; SE of est: 0.770 b SE t p 0.02 0.01 3.05 – 0.06 0.01 – 8.12 0.002 < 0.001 4.38 < 0.001 – 0.02 0.01 – 2.97 0.003 – 0.03 0.01 – 4.09 < 0.001 – 0.02 0.01 – 2.91 0.004 0.001 0.05 0.01 0.01 0.01 0.01 6.71 2.42 < 0.001 0.016 – 0.02 0.01 – 3.75 < 0.001 0.02 0.01 2.43 0.015 – 0.02 0.01 – 2.15 0.031 0.04 0.01 6.12 0.042 – 0.04 0.01 – 3.08 < 0.001 0.02 0.01 2.99 0.003 0.002 – 0.05 0.02 – 2.06 0.06 0.03 2.13 0.040 0.033 Social Psychiatry and Psychiatric Epidemiology Table 4 (continued) 13 Social Psychiatry and Psychiatric Epidemiology Fig. 2 The model which was previously developed in the general population and was proven valid also in the population of health professionals. It includes multiple vulnerabilities representing the mechanism through which the COVID-19 outbreak in combination a great number of factors could lead to clinical depression through stress, and eventually to suicidality. A number of variables act as risk factors (red) or as protective factors (green), while some of them change direction of action depending on the phase (green/ red). Three core clusters emerge (delineated with the doted lines) (24.64%) while persons without any such history had the lowest rate or current clinical depression (9.62%). The highest rate was for the history of Bipolar disorder (40.66%; RR = 4.23). In those with a chronic somatic condition, the rate of clinical depression was 15.35% vs. 12.56% in those without (RR = 1.22). Believing in conspiracy theories was significant with at least one-third of cases accepting at least to a moderate degree a non-bizarre conspiracy. The model developed suggested that a significant number of variables acted either as risk or as protective factors, explaining 19.1% of the development of distress or clinical depression, but their individual contribution was very small. Conspiracy theories manifested a complex behavior with some of them exerting a protective effect at certain phases. Current clinical depression acted as a risk factor and past history acted as a protective for the development of such beliefs. The overall levels of clinical depression were lower than the rates reported in the literature, probably because of the stringent criteria of the algorithm in the current study. The large heterogeneity among countries probably reflects different phases of the pandemic in each country during the data collection. Rates of depression and mental health deterioration, in general, are probably higher in those that actually suffered from COVID-19 [29]. Other studies reported that half or more of health care professionals might suffer from depression. [7, 24, 32, 48, 76, 81, 82, 103, 117],Mira et al. 2020; [111, 112, 118]. Our results are identical to two reports [26, 52]. Meta-analyses suggested that depression rates range from 27% to 36% [45, 102, 109, 116] which is two to three times higher in comparison to our findings. In comparison, it has been reported that more than two-thirds of the general population experienced at least severe distress 13 [18, 31, 50, 62, 80, 83, 110], and high levels of suicidality [19]. Furthermore, our findings are in accord with a recently published meta-analysis that reported much lower depression rates in the general population [21]. An important observation is that while the rate of clinical depression was much higher in persons with a history of a mental disorder the proportion of depressed persons without such a history is much higher than expected, taking into consideration that the annual incidence of depression is 0.3% [66]. This might mean that the pandemic posed a RR = 30 on the population of health professionals to develop clinical depression. The multivariable analysis of the data allowed the current paper to confirm a staged model previously proposed concerning the effect of the pandemic on mental health (Fig. 2). This model had been developed concerning the general population and it seems that in principle it is valid for health professionals, although with some differences, especially concerning the attenuating effect of conspiracy theories and religiousness/spirituality. According to it, with the onset of the pandemic, its psychological impact and the development of severe anxiety and distress were determined by several sociodemographic and interpersonal variables including age, fears specific to the pandemic, the quality of relationships within the family, keeping a basic daily routine, change in the economic situation, history of any mental disorder and being afraid that him/herself or a family member will get COVID-19 and die. Similar findings concerning the effects of these factors have been reported in the literature [7, 17, 32, 35, 36, 46, 56, 57, 65, 67, 70, 80, 95, 98, 107, 111–113], Garre-Olmo et al. 2021; [89], but until now their detailed contribution had not been identified and no comprehensive model had been Current clinical depression History of any mental dis Reassuring conspiracy theories J1 Means of response scores by clinical depression and history No No Yes Yes All Grps Yes No Yes No 0.80 J5 Threatening conspiracy theories J7 J2 J3 No believing in conspiracy theories J6 J4 Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD 0.61 0.81 0.94 1.06 1.11 1.02 1.10 1.23 1.21 1.31 1.20 1.32 1.28 1.41 1.26 1.29 1.25 1.28 1.25 0.57 0.41 0.57 0.58 0.95 1.01 0.89 0.99 1.05 1.28 1.24 0.97 1.28 1.31 1.43 1.24 1.15 1.24 1.29 1.28 0.55 0.39 0.56 0.63 0.89 0.96 0.85 0.95 1.08 1.16 1.17 0.92 1.20 1.22 1.44 1.25 1.19 1.24 1.31 1.30 1.76 2.01 1.69 1.87 1.73 1.21 1.21 1.19 1.23 1.21 Wilks F Effect df Error df p 0.986 0.988 0.997 26.54 23.06 5.51 7 7 7 12,780 12,780 12,780 < 0.001 < 0.001 < 0.001 ANOVA results Clinical depression History of any mental disorder Clinical depression* history of any mental disorder Social Psychiatry and Psychiatric Epidemiology Table 5 Means of responses (from –2 to + 2) to all conspiracy theories by current clinical depression and history of any mental disorder and ANOVA results 13 Social Psychiatry and Psychiatric Epidemiology developed. On the other hand, several factors not assessed by the current study, including the level of training, whether the person worked in the frontline against COVID or in an ICU, etc. [8, 54, 67, 111, 112], Mira et al. 2020; [7, 20, 24, 48, 51, 52, 71, 82, 95, 101, 103, 107] were reported as contributing in the development of clinical depression. The current paper suggests that from all health occupations, nurses might be at a higher risk to develop severe stress and clinical depression, and this is in accord with the literature [33, 48, 51, 58, 118]. Previous reports on the role of temperament are in accord with this [74] At the pandemic onset, we might not have imagined the important role and the impact of conspiracy theories, which are largely social media driven. They are currently widely accepted as being important since the literature strongly supports their relationship with anxiety and depression [22, 28]. According to the results of the current study, approximately one-third of responders accepted at least to a moderate degree a non-bizarre conspiracy theory, and this was true both for ‘threatening’ as well as for ‘reassuring’ theories. The acceptance of inflated death rates was 44.24% while that of the 5G antenna theory was 20.81%. Doctors had the lowest rates, but impressively, 37.6% of doctors and 50.86% of nurses reported they were believing in the deliberate inflation of death rates by governments, and 14.75% and 27.22% respectively were accepting the 5G theory. Interestingly, believing in conspiracy theories pertaining to COVID-19 was lower in comparison to the general population and played an attenuated role in the development of anxiety and depression, however, these beliefs seem to be an important factor even among doctors. The high rates of believing in conspiracy theories are in accord with findings from various countries [1, 64, 91, 108] and are a worrying manifestation. Conspiracy beliefs – especially those regarding science, medicine, and health-related topics – are widespread [78], are widely distributed in social media [1, 11] and they challenge the capacity of the average person to distill and assess the content [30, 34]. They exert a well-documented adverse effect on health behaviors, especially vaccination [2, 3, 14–16, 43, 49, 59, 63, 72, 88, 91, 93, 99, 105]. There seems to be some relationship between believing in bizarre conspiracy theories and psychotic tendencies or a history of psychotic disorders [60]. As was found in the general population, current clinical depression and past history of mental disorders are both critical factors related to believing in conspiracy theories. Our results could mean that the critical factor which increases belief is the presence of current clinical depression, while the past history acts at a second level. As correlation does not imply causation, conspiracy theories could be either the cause of clinical depression, a copying mechanism against clinical depression, or a marker of maladaptive psychological patterns of cognitive appraisal. 13 After taking into consideration the complete model, and especially the relationship to past mental health history, the authors propose that the beliefs in conspiracy theories are a copying mechanism against stress. The finding of the relationship between current clinical depression and believing in conspiracies is in accord with the literature [28, 44, 106], One explanation could be found in the theory concerning ‘Depressive Realism’ [5, 6], Alloy et al. 1981; [12, 68, 77] which suggests that depressive persons are more able than others to realistically interpret the world, however, this higher ability leads to pessimism. At the most extreme end, when the emergence of suicidal thinking is possible, the family environment and family responsibilities and care act either as risk or protective factors, depending on their quality, while religiosity/ spirituality and all beliefs in conspiracy theories act as protective factors, except for one which includes religious content. These results are in accord with the reports in the literature [9, 56, 57, 61, 65, 79, 113]. A difficult-to-answer question is how many of the cases detected by questionnaires and sophisticated algorithms correspond to real major clinical depression. The underlying neurobiology is opaque and maybe much diagnosed clinical depression might simply be an extreme form of a normal adjustment reaction [53]. However, there is no better way to psychometrically achieve higher validity and the algorithm we utilized is the best available method. The impressive increase in new cases of clinical depression (9.62% of persons without any history of mental disorders developed depression) which was found in our sample is in accord with the literature [87]. However, a large part of clinical depressions emerged from a previous mental health history. Of the 13.15% with current clinical depression, 7.35% were new cases while 5.80% had previous history. This suggests that almost beyond doubt true clinical depression increased by 30% (in the extreme scenario that none of cases without previous history was a case of true clinical depression. This extremely positive scenario also suggests that maybe relapses expected to occur in the next several years occurred earlier. Concerning those without a previous history of mental disorder, it is expected that much of the adverse effects on mental health will rapidly attenuate with the end of the pandemic [27] but enduring effects will impact some vulnerable populations. So far studies investigating the long-term outcome and the long-term impact of the pandemic on mental health display equivocal findings [13, 114]. Especially sociability and the sense of belonging could be important factors determining mental health and health-related behaviors [15], and these factors seem to correspond to specific vulnerabilities seen especially in western cultures. Social Psychiatry and Psychiatric Epidemiology Conclusion participated in interpreting the data and developing further stages and the final version of the paper. The current paper reports high rates of clinical depression, distress, and suicidal thoughts among the population of health workers during the pandemic, with a high prevalence of beliefs in conspiracy theories. For the development of clinical depression, general health status, previous mental health history, self-harm and suicidal attempts, family responsibility, economic change, and age acted as risk factors while keeping a daily routine, religiousness/spirituality, and belief in conspiracy theories were acting mostly as protective factors. These findings, although they should be closely monitored longitudinally, support previous suggestions by other authors concerning the need for a proactive intervention to protect the mental health of the general population but more specifically of vulnerable groups [38, 94] Funding None. Strengths and limitations The strengths of the current paper include the large number of persons who filled out the questionnaire and the large bulk of information obtained, as well as the detailed way of post-stratification of the study sample. The major limitation was that the data were obtained anonymously online through the self-selection of the responders. Additionally, the assessment included only the cross-sectional application of self-report scales, although the advanced algorithm used for the diagnosis of clinical depression corrected the problem to a certain degree. However, what is included under the umbrella of ‘clinical depression’ in the stressful times of the pandemic remains a matter of debate. Also, the lack of baseline data concerning the mental health of a similar study sample before the pandemic is also a problem. Finally, a limitation would be that data from different countries were pooled together and with a rather large difference in numbers among countries. So the interpretation of findings should bear in mind the possible bias induced by different cultural backgrounds. However, one should have also in mind that backgrounds are not so different as one might think since the online survey of a specific professional population poses requirements that lead to similarities rather than differences among the populations from different countries. Each of these countries was also undergoing a different phase of the pandemic at each time point and phases were also different across individuals. This means that the results should be interpreted. Author contributions All authors contributed equally to the paper. KNF and DS conceived and designed the study. The other authors participated formulating the final protocol, designing and supervising the data collection and creating the final dataset. KNF and DS did the data analysis and wrote the first draft of the paper. All authors Data availability statement Raw data are available upon request to the principal investigator. Declarations Conflict of interest None pertaining to the current paper. References 1. 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Zerbini G, Ebigbo A, Reicherts P, Kunz M, Messman H (2020) Psychosocial burden of healthcare professionals in times of COVID-19 - a survey conducted at the University Hospital Augsburg. Ger Med Sci. https://doi.org/10.3205/000281 Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Authors and Affiliations Konstantinos N. Fountoulakis1 · Grigorios N. Karakatsoulis1 · Seri Abraham2,3,4 · Kristina Adorjan5 · Helal Uddin Ahmed6 · Renato D. Alarcón7,8 · Kiyomi Arai9 · Sani Salihu Auwal10,11 · Julio Bobes15,16 · Teresa Bobes‑Bascaran17,18 · Julie Bourgin‑Duchesnay19 · Cristina Ana Bredicean20 · Laurynas Bukelskis21 · Akaki Burkadze22,23 · Indira Indiana Cabrera Abud24 · Ruby Castilla‑Puentes25 · Marcelo Cetkovich26,27 · Hector Colon‑Rivera28 · Ricardo Corral29,30 · Carla Cortez‑Vergara31 · Piirika Crepin32 · Domenico de Berardis33,34,35 · Sergio Zamora Delgado36 · David de Lucena37 · Avinash de Sousa38,39 · Ramona di Stefano40 · Seetal Dodd12,13,41 · Livia Priyanka Elek42 · Anna Elissa43 · Berta Erdelyi‑Hamza42 · Gamze Erzin44 · Martin J. Etchevers45 · Peter Falkai5 · Adriana Farcas46 · Ilya Fedotov47 · Viktoriia Filatova48 · Nikolaos K. Fountoulakis49 · Iryna Frankova50 · Francesco Franza51,52 · Pedro Frias53 · Tatiana Galako54 · Cristian J. Garay45 · Leticia Garcia‑Álvarez18 · Paz García‑Portilla15,55 · Xenia Gonda42 · Tomasz M. Gondek56 · Daniela Morera González57 · Hilary Gould58 · Paolo Grandinetti33 · Arturo Grau36,59 · Violeta Groudeva60 · Michal Hagin61 · Takayuki Harada62 · Tasdik M. Hasan63,64 · Nurul Azreen Hashim65 · Jan Hilbig21 · Sahadat Hossain66 · Rossitza Iakimova67 · Mona Ibrahim68 · Felicia Iftene69 · Yulia Ignatenko70 · Matias Irarrazaval71 · Zaliha Ismail72 · Jamila Ismayilova73 · Asaf Jacobs74,75 · Miro Jakovljević76 · Nenad Jakšić14 · Afzal Javed77,78,79 · Helin Yilmaz Kafali80 · Sagar Karia38 · Olga Kazakova81 · Doaa Khalifa68 · Olena Khaustova50 · Steve Koh58 · Svetlana Kopishinskaia82,83 · Korneliia Kosenko84 · Sotirios A. Koupidis85 · Illes Kovacs42 · Barbara Kulig42 · Alisha Lalljee39 · Justine Liewig19 · Abdul Majid86 · Evgeniia Malashonkova19 · Khamelia Malik43 · Najma Iqbal Malik87 · Gulay Mammadzada88 · Bilvesh Mandalia39 · Donatella Marazziti89,90,91 · Darko Marčinko14,76 · Stephanie Martinez58 · Eimantas Matiekus21 · Gabriela Mejia58 · Roha Saeed Memon92 · Xarah Elenne Meza Martínez93 · Dalia Mickevičiūtė94 · Roumen Milev69 · Muftau Mohammed95 · Alejandro Molina‑López96 · Petr Morozov97 · Nuru Suleiman Muhammad98 · Filip Mustač14 · Mika S. Naor99 · Amira Nassieb68 · Alvydas Navickas21 · Tarek Okasha68 · Milena Pandova67 · Anca‑Livia Panfil100 · Liliya Panteleeva101 · Ion Papava20 · Mikaella E. Patsali102,103 · Alexey Pavlichenko71 · Bojana Pejuskovic104,105 · Mariana Pinto da Costa106 · Mikhail Popkov107 · Dina Popovic108 · Nor Jannah Nasution Raduan65 · Francisca Vargas Ramírez36,59 · Elmars Rancans109,110 · Salmi Razali65 · Federico Rebok111,112 · Anna Rewekant113 · Elena Ninoska Reyes Flores114 · María Teresa Rivera‑Encinas115 · Pilar A. Saiz15 · Manuel Sánchez de Carmona116 · David Saucedo Martínez117 · Jo Anne Saw65 · Görkem Saygili118 · Patricia Schneidereit119 · Bhumika Shah120 · Tomohiro Shirasaka121 · Ketevan Silagadze22 · Satti Sitanggang122 · Oleg Skugarevsky123 · Anna Spikina124 · Sridevi Sira Mahalingappa125 · Maria Stoyanova67 · Anna Szczegielniak126 · Simona Claudia Tamasan100 · Giuseppe Tavormina52,127,128 · Maurilio Giuseppe Maria Tavormina52 · Pavlos N. Theodorakis129 · Mauricio Tohen130 · Eva‑Maria Tsapakis131,132 · Dina Tukhvatullina133 · Irfan Ullah134 · Ratnaraj Vaidya135 · Johann M. Vega‑Dienstmaier136 · Jelena Vrublevska109,110,140 · Olivera Vukovic103,137 · Olga Vysotska138 · Natalia Widiasih43 · Anna Yashikhina82,139 · Panagiotis E. Prezerakos140 · Michael Berk12,13 · Sarah Levaj14 · Daria Smirnova82,141 * Grigorios N. Karakatsoulis gregkarakatsoulis@gmail.com Kristina Adorjan Kristina.Adorjan@med.uni-muenchen.de Konstantinos N. Fountoulakis Kostasfountoulakis@gmail.com Helal Uddin Ahmed soton73@gmail.com Seri Abraham seri.abraham@nhs.net Renato D. Alarcón renato.alarcon@upch.pe; alarcon.renato@mayo.edu 13 Social Psychiatry and Psychiatric Epidemiology Kiyomi Arai k_arai@shinshu-u.ac.jp Peter Falkai Peter.Falkai@med.uni-muenchen.de Sani Salihu Auwal auwal01@yahoo.com Adriana Farcas 6amf@queensu.ca Julio Bobes bobes@uniovi.es Ilya Fedotov ilyafdtv11@gmail.com Teresa Bobes-Bascaran bobesmaria@uniovi.es Viktoriia Filatova filatovaviktoria@mail.ru Julie Bourgin-Duchesnay julie.bourgin@gmail.com Nikolaos K. Fountoulakis nikolasfountoulakis@gmail.com Cristina Ana Bredicean brediceancristina@gmail.com Iryna Frankova iryna.frankova@gmail.com Laurynas Bukelskis bukelskis@gmail.com Francesco Franza franza.francesco@virgilio.it Akaki Burkadze dr.burkadze@gmail.com Pedro Frias friaspn@gmail.com Indira Indiana Cabrera Abud indira_ica@hotmail.com Tatiana Galako tatiana-galako@yandex.ru Ruby Castilla-Puentes rcastil4@its.jnj.com Cristian J. Garay cristiangaray@psi.uba.ar Marcelo Cetkovich mcetkovich@ineco.org.ar Leticia Garcia-Álvarez lettti@gmail.com Hector Colon-Rivera hectorcolonriveramd@gmail.com Paz García-Portilla albert@uniovi.es Ricardo Corral rcorral33@gmail.com Xenia Gonda kendermagos@yahoo.com Carla Cortez-Vergara carla.cortez.v@upch.pe Tomasz M. Gondek tomaszmgondek@gmail.com Piirika Crepin piirika.crepin@gmail.com Daniela Morera González danielamorera@gmail.com Domenico de Berardis domenico.deberardis@aslteramo.it Hilary Gould hgould@health.ucsd.edu Sergio Zamora Delgado szamora@calvomackenna.cl Paolo Grandinetti grandinetti.paolo@gmail.com David de Lucena alienista@ufc.br; dvdlucena@gmail.com Arturo Grau doctorgrau@yahoo.com Avinash de Sousa avinashdes888@gmail.com Violeta Groudeva violetagroudeva@gmail.com Ramona di Stefano ramonadist@gmail.com Michal Hagin michal.hagin@gmail.com Seetal Dodd seetaldodd@gmail.com Takayuki Harada tkharada77@gmail.com Livia Priyanka Elek elek.livia.priyanka@gmail.com Tasdik M. Hasan tasdikhdip@yahoo.com Anna Elissa annaelissa.md@gmail.com Nurul Azreen Hashim azreen@uitm.edu.my Berta Erdelyi-Hamza hamzaberta@gmail.com Jan Hilbig hilbig.jan@gmail.com Gamze Erzin gamze.erzin@gmail.com Sahadat Hossain sahadat.hossain@juniv.edu Martin J. Etchevers martinjetchevers@gmail.com Rossitza Iakimova rosica.iakimova@abv.bg 13 Social Psychiatry and Psychiatric Epidemiology Mona Ibrahim monaawaad99@gmail.com Najma Iqbal Malik Najma.iqbal@uos.edu.pk; najmamalik@gmail.com Felicia Iftene iftenef@providencecare.ca Gulay Mammadzada gulay.mammadzada@gmail.com Yulia Ignatenko hvala_korolevna@rambler.ru Bilvesh Mandalia bilveshmandalia@gmail.com Matias Irarrazaval matias.irarrazaval@minsal.cl Donatella Marazziti dmarazzi@psico.med.unipi.it Zaliha Ismail zaliha78@uitm.edu.my Darko Marčinko predstojnik.psi@kbc-zagreb.hr Jamila Ismayilova ismayilova.d@gmail.com Stephanie Martinez stm032@health.ucsd.edu Asaf Jacobs asafjacobs@gmail.com Eimantas Matiekus eimantasmatiekus@yahoo.com Miro Jakovljević jakovljevic.miro@yahoo.com Gabriela Mejia ggmejia@health.ucsd.edu Nenad Jakšić nenad_jaksic@yahoo.com Roha Saeed Memon memon.roha@gmail.com Afzal Javed afzalj@gmail.com Xarah Elenne Meza Martínez xarahmeza22@gmail.com Helin Yilmaz Kafali helinyilmaz136@gmail.com Dalia Mickevičiūtė dalia.mickeviciute@gmail.com Sagar Karia kariabhai117@gmail.com Roumen Milev roumen.milev@queensu.ca Olga Kazakova olga.kazakova.md@gmail.com Muftau Mohammed miftahmuhammad101@gmail.com Doaa Khalifa doaakhalifa72@gmail.com Alejandro Molina-López doctor.alex.psiquiatra@gmail.com Olena Khaustova 7974247@gmail.com Petr Morozov prof.morozov@gmail.com Steve Koh shkoh@ucsd.edu Nuru Suleiman Muhammad nurusulemuhammad@gmail.com Svetlana Kopishinskaia kopishinskaya@gmail.com Filip Mustač filip.mustac@gmail.com Korneliia Kosenko sun2003@ukr.net Mika S. Naor Mikanaor@mail.tau.ac.il Sotirios A. Koupidis sotirioskoupidis@yahoo.gr Amira Nassieb amiraelbatrawy@gmail.com Illes Kovacs kovilles@gmail.com Alvydas Navickas alvydas.navickas@mf.vu.lt Barbara Kulig kulig.barbara@hotmail.com Tarek Okasha tarek.okasha@gmail.com Alisha Lalljee alishalalljee@gmail.com Milena Pandova milena.pandova@gmail.com Justine Liewig j.liewig@gmail.com Anca-Livia Panfil anca.livia.panfil@gmail.com Abdul Majid maajid72@gmail.com Liliya Panteleeva p.lilya12@gmail.com Evgeniia Malashonkova e.malashonkova@gh-nord-essonne.fr Ion Papava papava.ion@umft.ro Khamelia Malik khameliapsi@gmail.com Mikaella E. Patsali mikaellapatsali@gmail.com 13 Social Psychiatry and Psychiatric Epidemiology Alexey Pavlichenko apavlichenko76@gmail.com Maria Stoyanova mb_milenkova@yahoo.com Bojana Pejuskovic bpejuskovic@hotmail.com Anna Szczegielniak anna.szczegielniak@gmail.com Mariana Pinto da Costa mariana.pintodacosta@gmail.com Simona Claudia Tamasan simona_tamasan@yahoo.com Mikhail Popkov mihailpopkovanat@gmail.com Giuseppe Tavormina dr.tavormina.g@libero.it Dina Popovic popovic.dina@gmail.com Maurilio Giuseppe Maria Tavormina mtavormina@virgilio.it Nor Jannah Nasution Raduan norjannah@uitm.edu.my Pavlos N. Theodorakis theodorakisp@who.int Francisca Vargas Ramírez fvargas.ra@gmail.com Mauricio Tohen MTohen@salud.unm.edu Elmars Rancans erancans@latnet.lv Eva-Maria Tsapakis emtsapakis@doctors.org.uk Salmi Razali drsalmi@uitm.edu.my Dina Tukhvatullina d.tukhvatullina@smd20.qmul.ac.uk Federico Rebok federicorebok@gmail.com Irfan Ullah Irfanullahecp2@gmail.com Anna Rewekant reweana@gmail.com Ratnaraj Vaidya r.vaidya@newcastle.ac.uk Elena Ninoska Reyes Flores elena.reyes@unah.edu.hn Johann M. Vega-Dienstmaier johann.vega.d@upch.pe; johannvega@yahoo.com María Teresa Rivera-Encinas mriverae@usmp.pe Jelena Vrublevska vrublevskaja@inbox.lv Pilar A. Saiz frank@uniovi.es Olivera Vukovic olivukovic@gmail.com Manuel Sánchez de Carmona msanchezdecarmona@mac.com Olga Vysotska uafmed@gmail.com David Saucedo Martínez davidsaucedomartinez1@gmail.com Natalia Widiasih widiasih_1973@yahoo.com Jo Anne Saw annejosaw@uitm.edu.my Anna Yashikhina akvaraul@mail.ru Görkem Saygili gsaygili@gmail.com Panagiotis E. Prezerakos prezerpot@gmail.com Patricia Schneidereit p.schneidereit@klinikum-weissenhof.de Michael Berk michael.berk@deakin.edu.au Bhumika Shah bhumikarshah47@gmail.com Sarah Levaj sarahbjedov@gmail.com Tomohiro Shirasaka shirasaka.t@gmail.com Daria Smirnova daria.smirnova.md.phd@gmail.com Ketevan Silagadze ketevani26@gmail.com 1 3rd Department of Psychiatry, School of Medicine, Aristotle University of Thessaloniki Greece, Thessaloniki, Greece Satti Sitanggang sattiradja_96@yahoo.co.id 2 Pennine Care NHS Foundation Trust, Ashton-under-Lyne, UK Oleg Skugarevsky skugarevsky@gmail.com 3 Manchester Metropolitan University, Manchester, UK 4 Core Psychiatry Training, Health Education England North West, Manchester, UK 5 Department of Psychiatry, Ludiwig-Maximilians-University, Munich, Germany Anna Spikina a-spikina@yandex.ru Sridevi Sira Mahalingappa sridevi.siramahalingappa@nhs.net 13 Social Psychiatry and Psychiatric Epidemiology 6 7 8 Child Adolescent and Family Psychiatry, National Institute of Mental Health, Dhaka, Bangladesh Section of Psychiatry and Mental Health, Universidad Peruana Cayetano Heredia, Facultad de Medicina Alberto Hurtado, Lima, Peru Department of Psychiatry and Psychology, Mayo Clinic School of Medicine, Rochester, MN, USA 30 University of Buenos Aires, Buenos Aires, Argentina 31 Universidad Peruana Cayetano Heredia, Clínica AngloAmericana, Lima, Perú 32 Sanitaire and Social Union for Accompaniment and Prevention, Center of Ambulatory Psychiatry of Narbonne and Lezigan, Narbonne, France 33 Department of Mental Health, Psychiatric Service of Diagnosis and Treatment, Hospital “G. Mazzini”, ASL Teramo, Teramo, Italy 9 School of Medicine and Health Science, Institute of Health Science Shinshu University, Matsumoto, Japan 10 Department of Psychiatry, Bayero University, Kano, Nigeria 34 School of Nursing, University of L’Aquila, L’Aquila, Italy 11 Aminu Kano Teaching Hospital, Kano, Nigeria 35 12 IMPACT–the Institute for Mental and Physical Health and Clinical Translation, Deakin University, School of Medicine, Barwon Health, Geelong, Australia Department of Neuroscience and Imaging, School of Psychiatry, University of Chieti, Chieti, Italy 36 Child and Adolescent Psychiatry Department, Hospital Luis Calvo Mackenna, Santiago, Chile 37 Departamento de Fisiología E Farmacología, Universidade Federal Do Ceará, Fortaleza, Ceará, Brazil 38 Department of Psychiatry, Lokmanya Tilak Municipal Medical College, Mumbai, India 39 Desousa Foundation, Mumbai, India 40 Department of Biotechnological and Applied Clinical Sciences, Section of Psychiatry, University of L’Aquila, L’Aquila, Italy 41 University Hospital Geelong, Barwon Health, Geelong, VIC, Australia 42 Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary 43 Department of Psychiatry, Faculty of Medicine, Universitas Indonesia, Cipto Mangunkusumo National Referral Hospital, Jakarta, Indonesia 44 Psychiatry Department, Ankara Dışkapı Training and Research Hospital, Ankara, Turkey 45 Faculty of Psychology, University of Buenos Aires (UBA), Buenos Aires, Argentina 46 Centre of Neuroscience, Queen’s University, Kingston, ON, Canada 47 Department of Psychiatry and Narcology, Ryazan State Medical University N.a. Academician I.P. Pavlov, Ryazan, Russia 13 14 Orygen The National Centre of Excellence in Youth Mental Health, Centre for Youth Mental Health, Florey Institute for Neuroscience and Mental Health and the Department of Psychiatry, The University of Melbourne, Melbourne, Australia Department of Psychiatry and Psychological Medicine, University Hospital Centre Zagreb, Zagreb, Croatia 15 Psychiatry Area, Department of Medicine, University of Oviedo, ISPA, INEUROPA. CIBERSAM, Oviedo, Spain 16 Department of Psychiatry, Hospital Universitario Central de Asturias, ISPA, INEUROPA. CIBERSAM, Oviedo, Spain 17 Mental Health Center of La Corredoria, ISPA, INEUROPA. CIBERSAM, Oviedo, Spain 18 Department of Psychology, University of Oviedo, ISPA, INEUROPA. CIBERSAM, Oviedo, Spain 19 Division of Child and Adolescent Psychiatry, Department of Psychiatry, Groupe Hospitalier Nord Essonne, Orsay, France 20 Department of Neuroscience, Discipline of Psychiatry, “Victor Babes” University of Medicine and Pharmacy, Timisoara, Romania 21 Clinic of Psychiatry, Institute of Clinical Medicine, Medical Faculty, Vilnius University, Vilnius, Lithuania 22 Mental Hub, Tbilisi, Georgia 23 NGO Healthcare Research and Quality Agency, Tbilisi, Georgia 48 24 State Budgetary Institution of the Rostov Region “Psychoneurological Dispensary”, Rostov-On-Don, Russia Hospital San Juan de Dios Hospital, Guadalajara, Mexico 49 25 Janssen Research and Development, Johnson and Johnson, American Society of Hispanic Psychiatry and WARMI Women Mental Health, Cincinnati, OH, USA Faculty of Medicine, Medical University of Sofia, Sofia Center, Bulgaria 50 Institute of Translational and Cognitive Neuroscience (INCyT), INECO Foundation, Favaloro University, Buenos Aires, Argentina Medical Psychology, Psychosomatic Medicine and Psychotherapy Department, Bogomolets National Medical University, Kiev, Ukraine 51 Villa Dei Pini Psychiatric Rehabilitation Center, Avellino, Italy National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina 52 Psychiatric Studies Centre, Provaglio d’Iseo, Italy APM Board Certified in General Psychiatry and Neurology, Addiction Psychiatry, and Addiction Medicine, UPMC, DDAP, Philadelphia, USA 53 Hospital Magalhães Lemos, Porto, Portugal 54 Department of Psychiatry, Medical Psychology and Drug Abuse, Kyrgyz State Medical Academy, Bishkek, Kyrgyz Republic 26 27 28 29 Department of Teaching and Research, Hospital Borda, Buenos Aires, Argentina 13 Social Psychiatry and Psychiatric Epidemiology 55 Mental Health Center of La Ería, ISPA, INEUROPA. CIBERSAM, Oviedo, Spain 56 Specialty Training Section, Polish Psychiatric Association, Wroclaw, Poland 82 International Centre for Education and Research in Neuropsychiatry (ICERN), Samara State Medical University, Samara, Russia 83 Kirov State Medical University, Kirov, Russia Drug Abuse and Psychology Department, Odessa National Medical University, Odessa, Ukraine 57 Instituto Nacional de Psiquiatría Ramón De La Fuente Muñiz, Mexico City, Mexico 84 58 Department of Psychiatry, University of California San Diego, San Diego, USA 85 59 Universidad Diego Portales, Santiago, Chile Occupational and Environmental Health Sector, Public Health Policy Department, School of Public Health, University of West Attica, Athens, Greece 60 Department of Diagnostic Imaging, University Hospital Saint Ekaterina, Sofia, Bulgaria 86 Department of Psychiatry, SKIMS Medical College, Srinagar, India 61 Forensic Psychiatry Unit, Abarbanel Mental Health Center, Bat Yam, Israel 87 Department of Psychology, University of Sargodha, Sargodha, Pakistan 62 Faculty of Human Sciences, Education Bureau of the Laboratory Schools, University of Tsukuba, Tokyo, Japan 88 Department of Psychiatry, Azerbaijan Medical University, Baku, Azerbaijan 89 Department of Clinical and Experimental Medicine, Section of Psychiatry, University of Pisa, Pisa, Italy 90 Unicamillus, Saint Camillus International University of Health Sciences, Rome, Italy 91 Brain Research Foundation Onus, Lucca, Italy 92 Department of Public Health and Informatics, Jahangirnagar University, Dhaka, Bangladesh Dow Medical College, Dow University of Health Sciences, Karachi, Pakistan 93 Second Psychiatric Clinic, University Hospital for Active Treatment in Neurology and Psychiatry “Saint Naum”, Sofia, Bulgaria Postgraduate Program in Psychiatry, National Autonomous University of Honduras, Tegucigalpa, Honduras 94 Private Outpatient Clinics “JSC InMedica Klinika”, Vilnius, Lithuania 63 Department of Primary Care and Mental Health, University of Liverpool, Liverpool, UK 64 Public Health Foundation, Dhaka, Bangladesh 65 Department of Psychiatry, Faculty of Medicine, Universiti Teknologi MARA, Sungai Buloh, Selangor, Malaysia 66 67 68 Faculty of Medicine, Okasha Institute of Psychiatry, Ain Shams University, Cairo, Egypt 95 Department of Clinical Services, Federal Neuropsychiatric Hospital, Kaduna, Nigeria 69 Department of Psychiatry, Queens University, Kingston, ON, Canada 96 General Office for the Psychiatric Services of the Ministry of Health, Mexico City, Mexico 70 Mental Health Clinic No 1 N.a. N.A. Alexeev of Moscow Healthcare Department, Education Center, Moscow, Russia 97 71 Ministry of Health, Millenium Institute for Research in Depression and Personality, Santiago, Chile Department of Postgraduate Education, Russian National Research Medical University N.a. N.I. Pirogov, Moscow, Russia 98 Department of Public Health Medicine, Faculty of Medicine, Universiti Teknologi MARA, Sungai Buloh, Selangor, Malaysia Department of Community Medicine, Ahmadu Bello University Teaching Hospital, Zaria, Nigeria 99 Sackler School of Medicine New York State American Program, Tel Aviv University, Tel Aviv-Yafo, Israel 72 73 National Mental Health Center of the Ministry of Health of the Republic of Azerbaijan, Baku, Azerbaijan 100 Compartment of Liaison Psychiatry, “Pius Brinzeu” County Emergency Clinical Hospital, Timisoara, Romania 74 Department of Psychiatry, Westchester Medical Center Health System, Valhalla, NY, USA 101 75 New York Medical College, Valhalla, NY, USA Department of Medical Psychology, Psychiatry and Psychotherapy, Kyrgyz-Russian Slavic University, Bishkek, Kyrgyz Republic 76 School of Medicine, University of Zagreb, Zagreb, Croatia 102 77 School of Social Sciences, Hellenic Open University, Patras, Greece Institute of Applied Health Research, University of Birmingham, Birmingham, UK 103 78 Department of Internal Medicine, Nicosia General Hospital, Nicosia, Cyprus Warwick Medical School, University of Warwick, Coventry, UK 104 79 Faculty of Medicine, University of Belgrade, Belgrade, Serbia Psychiatric Research Centre, Fountain House, Lahore, Pakistan 105 80 Clinical Department for Crisis and Affective Disorders, Institute of Mental Health, Belgrade, Serbia Child Psychiatry Department, Ankara City Hospital, Ankara, Turkey 106 81 Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK Faculty of Medicine, Lund University, Malmö, Sweden 13 Social Psychiatry and Psychiatric Epidemiology 107 Department of the Introduction to Internal Medicine and Family Medicine, International Higher School of Medicine, Bishkek, Kyrgyz Republic 108 Abarbanel Mental Health Center, Bat-Yam, Israel 109 Department of Psychiatry and Narcology, Riga Stradins University, Riga, Latvia 110 Riga Centre of Psychiatry and Narcology, Riga, Latvia 111 112 126 Department of Psychiatric Rehabilitation, Department of Psychiatry and Psychotherapy, Faculty of Medical Sciences in Katowice, Medical University of Silesia, Katowice, Poland 127 European Depression Association and Italian Association on Depression, Brussels, Belgium 128 Servicio de Emergencia, Acute Inpatient Unit, Hospital Moyano, Buenos Aires, Argentina Bedforshire Center for Mental Health Research, in association with the University of Cambridge, Cambridge, UK 129 Argentine Institute of Clinical Psychiatry (IAPC), Buenos Aires, Argentina Health Policy, WHO Regional Office for Europe, Copenhagen, Denmark 130 Department of Psychiatry and Behavioral Sciences, School of Medicine, University of New Mexico, Albuquerque, NM, USA 113 General Psychiatry Unit I, Greater Poland Neuropsychiatric Center, Kościan, Poland 114 Department of Psychiatry, National Autonomous University of Honduras, Tegucigalpa, Honduras 131 Agios Charalambos Mental Health Clinic, Heraklion, Crete, Greece 115 Centro de Investigación en Salud Pública, Facultad de Medicina, Universidad de San Martín de Porres, Instituto Nacional de Salud Mental “Honorio Delgado – Hideyo Noguchi”, Lima, Perú 132 1st Department of Academic Psychiatry, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece 133 Centre for Global Public Health, Institute of Population Health Sciences, Queen Mary University of London, London, UK 134 Kabir Medical College, Gandhara University, Peshawar, Pakistan 135 Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, UK 136 Facultad de Medicina Alberto Hurtado, Universidad Peruana Cayetano Heredia, Lima, Perú 137 Klinik Für Allgemeine Psychiatrie Und Psychotherapie Ost, Psychiatrische Institutsambulanz, Klinikum Am Weissenhof, Weissenhof, Germany Institute of Public Health, Riga Stradins University, Riga, Latvia 138 Department for Research and Education, Institute of Mental Health, Belgrade, Serbia 120 DY Patil Medical College, Navi Mumbai, India 139 121 Department of Psychiatry, Teine Keijinkai Medical Center, Sapporo, Japan Educational and Research Center–Ukrainian Family Medicine Training Center, Bogomolets National Medical University, Kiev, Ukraine 122 Psychiatric Unit, Pambalah Batung General Hospital, South Kalimantan, Amuntai, Indonesia 140 123 Department of Psychiatry, Narcology, Psychotherapy and Clinical Psychology, Samara State Medical University, Samara, Russia Department of Psychiatry and Medical Psychology, Belarusian State Medical University, Minsk, Belarus 141 124 Department of Nursing, University of Peloponnese, Laboratory of Integrated Health Care, Tripoli, Greece Saint Petersburg Psychoneurological Dispensary No2, Saint Petersburg, Russia 125 Derbyshire Healthcare NHS Foundation Trust, The Liasion Team, Royal Derby Hospital, Derby, Derbyshire, UK 116 117 118 119 Faculty of Health Sciences, Anahuac University, Mexico City, Mexico Department of Psychiatry, Escuela Nacional de Medicina, TEC de Monterrey. Servicio de Geriatría. Hospital Universitario “José Eleuterio González” UANL, Monterrey, Nuevo León, México Assistant Professor at Cognitive Science and Artificial Intelligence Department, Tilburg University, Tilburg, The Netherlands 13