INTRODUCTION:This study registered rates of specific treatment options for mental disorders as well as their long-term outcome. MATERIAL AND METHODS:The history of mental disorders was used as a proxy for diagnosis. The data came from the COMET-G study (40 countries; 54,826 subjects, 64.73 % females, 35.45±13.51 years old). The analysis included descriptive statistics, Risk Ratios, t-tests, and ANCOVA's. RESULTS:24.14 % reported a history of any mental disorder (depression >12 %, non-affective psychosis and Bipolar disorder 1 % each, >20 % self-injury, >10 % had attempted suicide, 7.17 % illegal substance abuse). Most patients were not under any kind of treatment (59.44 %) and most were not receiving treatment as recommended (e.g. 90 % of Bipolar and 2/3 of psychotic patients). No treatment at all and psychotherapy as monotherapy were consistently related to poorer outcomes. In anxiety or depression, only antidepressant monotherapy and benzodiazepines, in Bipolar disorder only antipsychotic monotherapy in males and antidepressant monotherapy in females and in non-affective psychosis antipsychotics and psychotherapy in females only, were related to good outcomes. No treatment modality was related to a good outcome in those with a history of self-harm, suicidal attempts, or illegal substance use. Only depression and treatment with antidepressants were related to metabolic syndrome. DISCUSSION:In the community, the overwhelming majority of mental patients do not receive appropriate treatment or, even worse, no treatment at all. The outcome is unfavourable for the majority and only a few selective treatment options seem to make a difference.

Communication is the cornerstone of mental healthcare. In the UK, many people who will need access to mental health services do not possess English as their first language. In this editorial, we seek to examine current policy and guidance with respect to interpreting in mental healthcare, and explore the benefits and challenges of interpretation and the ethical implications to consider. We focus on how mental health services could better engage with interpreters as cultural brokers to understand cultural expressions of distress. We conclude by suggesting an education and research agenda which could decrease ethnic disparities in mental healthcare.

BACKGROUND:The prevalence of medical illnesses is high among patients with psychiatric disorders. The current study aimed to investigate multi-comorbidity in patients with psychiatric disorders in comparison to the general population. Secondary aims were to investigate factors associated with metabolic syndrome and treatment appropriateness of mental disorders.METHODS:The sample included 54,826 subjects (64.73% females; 34.15% males; 1.11% nonbinary gender) from 40 countries (COMET-G study). The analysis was based on the registration of previous history that could serve as a fair approximation for the lifetime prevalence of various medical conditions.RESULTS:About 24.5% reported a history of somatic and 26.14% of mental disorders. Mental disorders were by far the most prevalent group of medical conditions. Comorbidity of any somatic with any mental disorder was reported by 8.21%. One-third to almost two-thirds of somatic patients were also suffering from a mental disorder depending on the severity and multicomorbidity. Bipolar and psychotic patients and to a lesser extent depressives, manifested an earlier (15-20 years) manifestation of somatic multicomorbidity, severe disability, and probably earlier death. The overwhelming majority of patients with mental disorders were not receiving treatment or were being treated in a way that was not recommended. Antipsychotics and antidepressants were not related to the development of metabolic syndrome.CONCLUSIONS:The finding that one-third to almost two-thirds of somatic patients also suffered from a mental disorder strongly suggests that psychiatry is the field with the most trans-specialty and interdisciplinary value and application points to the importance of teaching psychiatry and mental health in medical schools and also to the need for more technocratically oriented training of psychiatric residents.

Antipsychotics and severe mental illness (SMI) are associated with weight gain, and obesity increases the risks of cardiometabolic disease and premature death. These present management and liability issues for psychiatrists. Physical healthcare for people with SMI is poor, and this may partly be owing to training limitations and lack of proactiveness by psychiatrists. Ethically and legally, psychiatrists have a duty to avoid unnecessary harm and to maintain an acceptable standard of care. This would apply particularly to patients receiving compulsory treatment for their SMI owing to their vulnerability. Discrepancy between psychiatric and non-psychiatric approaches to pharmacological treatment creates ambiguity, and weight gain could demotivate antipsychotic adherence. This article explores how the Mental Health Act could be used to address these issues, and the ethical considerations, and proposes how long-acting glucagon-like peptide-1 receptor agonists could be introduced into existing psychiatric practice as a treatment option for antipsychotic-induced weight gain and obesity in SMI.

Background: The COVID-19 pandemic has brought significant mental health challenges, particularly for vulnerable populations, including non-binary gender individuals. The COMET international study aimed to investigate specific risk factors for clinical depression or distress during the pandemic, also in these special populations. Methods: Chi-square tests were used for initial screening to select only those variables which would show an initial significance. Risk Ratios (RR) were calculated, and a Multiple Backward Stepwise Linear Regression Analysis (MBSLRA) was followed with those variables given significant results at screening and with the presence of distress or depression or the lack of both of them. Results: The most important risk factors for depression were female (RR = 1.59-5.49) and non-binary gender (RR = 1.56-7.41), unemployment (RR = 1.41-6.57), not working during lockdowns (RR = 1.43-5.79), bad general health (RR = 2.74-9.98), chronic somatic disorder (RR = 1.22-5.57), history of mental disorders (depression RR = 2.31-9.47; suicide attempt RR = 2.33-9.75; psychosis RR = 2.14-10.08; Bipolar disorder RR = 2.75-12.86), smoking status (RR = 1.15-5.31) and substance use (RR = 1.77-8.01). The risk factors for distress or depression that survived MBSLRA were younger age, being widowed, living alone, bad general health, being a carer, chronic somatic disorder, not working during lockdowns, being single, self-reported history of depression, bipolar disorder, self-harm, suicide attempts and of other mental disorders, smoking, alcohol, and substance use. Conclusions: Targeted preventive interventions are crucial to safeguard the mental health of vulnerable groups, emphasizing the importance of diverse samples in future research. Limitations: Online data collection may have resulted in the underrepresentation of certain population groups.

Aims and method We conducted a cross-sectional survey to examine how undergraduate psychiatry is taught and assessed across medical schools in the UK that have at least one cohort of graduated students. Results In total, 27 medical schools completed the survey. Curriculum coverage of common mental disorders, assessment skills and mental health law was broadly consistent, although exposure to psychiatric subspecialties varied. Significant variation existed regarding the duration of psychiatry placements and availability of enrichment activities. Small-group teaching, lectures and e-learning were the most frequent teaching modalities and various professionals and lived experience educators (patient and/or carers) contributed to teaching. Objective structured clinical examinations and multiple-choice questions dominated assessments. Clinical implications Medical schools should consider increasing students’ exposure to different psychiatric subspecialties and integrating physical and mental health training to address comorbidity and promote holistic care. Future research should explore whether specific undergraduate experiences promote greater career interest and skills in psychiatry.

The global impact of the COVID-19 pandemic on mental health and substance use behaviors has sparked extensive research efforts. The COMET-G international study, organized by the Department of Medicine and the Rectorate of the Aristotle University of Thessaloniki in collaboration with the World Psychiatric Association, delved into these issues. Running from March 2020 to April 2021, the study collected responses from 55,589 individuals across 40 countries. Through a comprehensive questionnaire, participants provided insights into their mental state, attitudes toward the pandemic, and the resultant changes in their personal and daily lives. Findings revealed, among other things, significant patterns of change in substance use, with notable correlations between reduced usage and the severity of lockdown measures among non-binary individuals. Mental health history emerged as a strong predictor of substance use changes, with influences from anxiety disorders, depression, and self-harm. Additionally, family and social dynamics, including economic expectations and household composition, significantly shaped substance use behaviors during lockdowns. Given these findings, the development of comprehensive approaches targeting the adverse effects of the pandemic on individual behaviors and general welfare is crucial.

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. The data came from the larger COMET-G study. The study sample includes 12,792 health professionals from 40 countries (62.40

Introduction: There are few published empirical data on the effects of COVID-19 on mental health, and until now, there is no large international study. Material and methods: During the COVID-19 pandemic, an online questionnaire gathered data from 55,589 participants from 40 countries (64.85% females aged 35.80 +/- 13.61; 34.05% males aged 34.90 +/- 13.29 and 1.10% other aged 31.64 +/- 13.15). Distress and probable 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: Probable depression was detected in 17.80% and distress in 16.71%. 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 (31.82% vs. 13.07%). At least half of participants were accepting (at least to a moderate degree) a non-bizarre conspiracy. The highest Relative Risk (RR) to develop depression was associated with history of Bipolar disorder and self-harm/attempts (RR = 5.88). Suicidality was not increased in persons without a history of any mental disorder. Based on these results a model was developed. Conclusions: The final model revealed multiple vulnerabilities and an interplay leading from simple anxiety to probable depression and suicidality through distress. This could be of practical utility since many of these factors are modifiable. Future research and interventions should specifically focus on them. (C) 2021 Elsevier B.V. and ECNP. All rights reserved.

The topic of patients recording healthcare consultations has been previously debated in the literature, but little consideration has been given to the risks and benefits of such recordings in the context of mental health assessments and treatment. This issue is of growing importance given the increasing use of technology in healthcare and the recent increase in online healthcare services, largely accelerated by the COVID-19 pandemic. We discuss the clinical, ethical and legal considerations relevant to audio or visual recordings of mental health consultations by patients, with reference to existing UK guidance and the inclusion of a patient's perspective.

INTRODUCTION:During the COVID-19 pandemic various degrees of lockdown were applied by countries around the world. It is considered that such measures have an adverse effect on mental health but the relationship of measure intensity with the mental health effect has not been thoroughly studied. Here we report data from the larger COMET-G study pertaining to this question.MATERIAL AND METHODS:During the COVID-19 pandemic, data were gathered with an online questionnaire from 55,589 participants from 40 countries (64.85% females aged 35.80 ± 13.61; 34.05% males aged 34.90±13.29 and 1.10% other aged 31.64±13.15). Anxiety was measured with the STAI, depression with the CES-D and suicidality with the RASS. Distress and probable depression were identified with the use of a previously developed cut-off and algorithm respectively.STATISTICAL ANALYSIS:It included the calculation of Relative Risk (RR), Factorial ANOVA and Multiple backwards stepwise linear regression analysis RESULTS: Approximately two-thirds were currently living under significant restrictions due to lockdown. For both males and females the risk to develop clinical depression correlated significantly with each and every level of increasing lockdown degree (RR 1.72 and 1.90 respectively). The combined lockdown and psychiatric history increased RR to 6.88 The overall relationship of lockdown with severity of depression, though significant was small.CONCLUSIONS:The current study is the first which reports an almost linear relationship between lockdown degree and effect in mental health. Our findings, support previous suggestions concerning the need for a proactive targeted intervention to protect mental health more specifically in vulnerable groups.

BACKGROUND:During the COVID-19 pandemic as much as 40% of the global population reported deterioration in depressive mood, whereas 26% experienced increased need for emotional support. At the same time, the availability of on-site psychiatric care declined drastically because of the COVID-19 preventive social restriction measures. To address this shortfall, telepsychiatry assumes a greater role in mental health care services. Among various on-line treatment modalities, immersive virtual reality (VR) environments provide an important resource for adjusting the emotional state in people living with depression. Therefore, we reviewed the literature on VR-based interventions for depression treatment during the COVID-19 pandemic.SUBJECTS AND METHODS:We searched the PubMed and Scopus databases, as well as the Internet, for full-length articles published during the period of 2020-2022 citing a set of following key words: "virtual reality", "depression", "COVID-19", as well as their terminological synonyms and word combinations. The inclusion criteria were: 1) the primary or secondary study objectives included the treatment of depressive states or symptoms; 2) the immersive VR intervention used a head-mounted display (HMD); 3) the article presented clinical study results and/or case reports 4) the study was urged by or took place during the COVID-19-associated lockdown period.RESULTS:Overall, 904 records were retrieved using the search strategy. Remarkably, only three studies and one case report satisfied all the inclusion criteria elaborated for the review. These studies included 155 participants: representatives of healthy population (n=40), a case report of a patient with major depressive disorder (n=1), patients with cognitive impairments (n=25), and COVID-19 patients who had survived from ICU treatment (n=89). The described interventions used immersive VR scenarios, in combination with other treatment techniques, and targeted depression. The most robust effect, which the VR-based approach had demonstrated, was an immediate post-intervention improvement in mood and the reduction of depressive symptoms in healthy population. However, studies showed no significant findings in relation to both short-term effectiveness in treatment of depression and primary prevention of depressive symptoms. Also, safety issues were identified, such as: three participants developed mild adverse events (e.g., headache, "giddiness", and VR misuse behavior), and three cases of discomfort related to wearing a VR device were registered.CONCLUSIONS:There has been a lack of appropriately designed clinical trials of the VR-based interventions for depression since the onset of the COVID-19 pandemic. Moreover, all these studies had substantial limitations due to the imprecise study design, small sample size, and minor safety issues, that did not allow us making meaningful judgments and conclude regarding the efficacy of VR in the treatment of depression, taking into account those investigations we have retrieved upon the inclusion criteria of our particularistic review design. This may call for randomized, prospective studies of the short-term and long-lasting effect of VR modalities in managing negative affectivity (sadness, anxiety, anhedonia, self-guilt, ignorance) and inducing positive affectivity (feeling of happiness, joy, motivation, self-confidence, viability) in patients suffering from clinical depression.

Aims Psychiatric readmissions cause a burden on the healthcare system, incur a monetary cost and cause additional distress to acutely unwell patients. This project explores the use of the free-text of electronic patient records to predict inpatients in psychiatric hospitals at risk of readmission using predictive models generated by machine learning. Methods Free-text was extracted from the electronic patient records of patients admitted to hospitals in Birmingham and Solihull Mental Health Foundation Trust (BSMHFT) during the five years 2015–2019 inclusive. The anonymised records were obtained via the CRIS (Clinical Record Interactive Search) database. A total of 17208 records were extracted. The free-text entered by clinicians during an admission was extracted and processed using techniques of natural language processing to generate input vectors suitable to be used with machine learning algorithms. tf-idf (term frequency-inverse document frequency) vectors were used. A selection of algorithms were used to train predictive models. Two-thirds of the records were used as training data with the remainder as test data. Baseline model performance was assessed and then best-performing candidates underwent hyperparameter optimisation using five-fold cross-validation to improve performance. Bayesian optimisation was used to automate hyperparameter tuning during cross-validation. Hyperparameters were optimised on the log loss function. As the dataset was imbalanced with negative instances outnumbering positive instances to a significant degree, various techniques such as random undersampling of negative instances in the training data were used to deal with class imbalance throughout this process. Following cross-validation, the best-performing models underwent performance analysis. Models were used to make predictions on the test data. Performance was assessed using F1-measures, precision-recall curves and the average precision metric (equivalent to area under the precision-recall curve). These metrics were chosen due to their suitability in assessing models trained on imbalanced datasets. Results The best F1 score obtained was 0.233 using a Random Forest model trained using unigram tf-idf vectors of 500 token dimension. The best average precision obtained was 0.157 using a Support Vector Machine trained using unigram tf-idf vectors of 2000 token dimension. Both the above results required the use of random oversampling of positive instances to improve performance on the imbalanced dataset. Conclusion The performance indicates that the models generated are unlikely to have significant practical utility. Nevertheless, this exploratory project has produced a processed dataset with knowledge about its characteristics. This could be used for the further development of models using more complex techniques such as language modelling using neural networks.

AimsThe authors designed a simulation training programme for foundation doctors beginning psychiatry placements across a large mental health trust. The simulation training aimed to improve the confidence, competence, and well-being of foundation doctors through exposing them to realistic psychiatry scenarios and teaching clinical skills in a safe environment.MethodsFour clinical scenarios were filmed with a 360-degree camera, professional actress, and doctors working in psychiatry. The scenarios depicted the journey of a patient being admitted onto a psychiatry ward from the community. Various clinical skills were embedded into the videos including psychiatric history taking, risk assessment, managing acute distress, managing comorbid physical and mental health problems, using the Mental Health Act, and teamwork with colleagues. All videos were delivered to learners using simulation with head-mounted-displays (HMDs). Each video lasted 6–8 minutes and was accompanied by pre-briefing and de-briefing with experienced psychiatrists for a further 15–20 minutes. Participants rated their confidence regarding several skills in psychiatry on Likert scales from 1 to 5 immediately before and after the session. Wilcoxon signed rank tests were conducted to detect statistically significant differences in learner's median confidence ratings before and after the training. Free-text questions explored trainee's most and least favourite aspects of the simulation. A survey also was distributed to learners 2-months after the training to assess how it had influenced their clinical practice.Results20 foundation doctors completed the training and provided feedback. Following the simulation training, there were statistically significant improvements in foundation doctor's confidence in: completing psychiatric assessments (p < 0.01), managing physical health problems in psychiatry (p < 0.05), managing acute distress (p < 0.01), reporting information to senior colleagues (p < 0.05), and containing anxiety when communicating with patients (p < 0.05). Trainees highlighted the debriefing, group discussions, and “interactive” simulation videos as the most useful aspects of the training. Some trainees enjoyed viewing the 360-degree videos, whilst others found the HMDs difficult to use. Of the 8 trainees who completed feedback 2 months after the training, 7 (87.5%) felt that it had helped them in their current roles. All trainees agreed (37.5%) or strongly agreed (62.5%) that the simulation scenarios were closely aligned to real-life clinical encounters.ConclusionSimulation training in psychiatry using 360-degree videos and HMDs is generally well-received amongst foundation doctors. Embedding simulation training into placement induction can improve the confidence and skills of junior doctors starting psychiatry placements.

Background: Schizophrenia and antipsychotic use are associated with clinically significant weight gain and subsequent increased mortality. Despite weight loss medications (WLMs) licensed by regulatory bodies (FDA, EMA, and MHRA) being available, current psychiatric guidelines recommend off-label alternatives, which differ from non-psychiatric guidelines for obesity.Objective: Evaluate the efficacy of licensed WLMs on treating antipsychotic-induced weight gain (AIWG) and obesity in schizophrenia and psychosis (OSP).Method: A literature search was conducted using Medline, EMBASE, PsycINFO and Cochrane Library online databases for human studies using licensed WLMs to treat AIWG and OSP.Results: Three RCTs (two liraglutide, one naltrexone-bupropion), one unpublished open-label trial (naltrexone-bupropion), and seven observational studies (five liraglutide, one semaglutide, one multiple WLMs) were identified. Results for liraglutide showed statistically significant improvement in weight, BMI, waist circum-ference, HbA1c, cholesterol, and LDL readings on meta-analysis. Evidence was mixed for naltrexone-bupropion with no detailed studies conducted for setmelanotide, or stimulants.Conclusion: Evidence is strongest for liraglutide compared to other licensed WLMs. The findings, particularly the inclusion of human trial data, provide evidence for liraglutide use in treating AIWG and OSP, which would better align psychiatric practice with non-psychiatric practices around obesity. The findings also identify continued literature gaps regarding other licensed WLMs.

SummaryThe COVID-19 pandemic has brought untold tragedies. However, one outcome has been the dramatically rapid replacement of face-to-face consultations and other meetings, including clinical multidisciplinary team meetings, with telephone calls or videoconferencing. By and large this form of remote consultation has received a warm welcome from both patients and clinicians. To date, human, technological and institutional barriers may have held back the integration of such approaches in routine clinical practice, particularly in the UK. As we move into the post-pandemic phase, it is vital that academic, educational and clinical leadership builds on this positive legacy of the COVID crisis. Telepsychiatry may be but one component of ‘digital psychiatry’ but its seismic evolution in the pandemic offers a possible opportunity to embrace and develop ‘digital psychiatry’ as a whole.

AimsWe sought to develop a teaching pilot to help year 2 medical students meet the following learning outcomes: Develop a better understanding of patient and carer experiences of mental illness; Recognise and challenge unhelpful attitudes towards people with mental illness; Promote a broader understanding of cultural issues surrounding mental illness, including stigma and discrimination.Method337 medical students were invited to attend a lecture by author LQ, a documentary photographer who presented a narrative of his brother Justin's lived experience of schizophrenia (louisquail.com/big-brother-introduction). 197 students attended the session, which was recorded and made available online. Students were invited to enter a competition to win a signed copy of LQ's book, ‘Big Brother’ and asked to submit either a 500-word written reflective piece, or a creative work accompanied by a 200-word statement. 13 submissions were received, including paintings, drawings, collage, photography, and poetry, all of which were blind rated by authors SR and GB, based on originality and quality of reflection. Of the six shortlisted, three winning entries were chosen by author LQ.ResultAll reflections moved away from a technical understanding of schizophrenia, towards person-centred interpretations, with dominant themes of ‘stigma’, ‘disempowerment’, ‘understanding people as individuals’, ‘subjective experience of mental illness’, ‘inclusion’ and ‘healing power of nature’.The three prize winners (authors GY, AK and KT) used different mediums: GY painted an osprey over a chaotic collage of disordered and stigmatizing words (the osprey representing empowerment and the “reservoir for wellbeing in nature”); AK's sonnet began as an ode to the chaos of Justin's experience, but the concluding lines reframed this struggle, conveying feelings of hope and beauty; and KT's self-portrait, produced with a slow shutter-speed photograph, powerfully conveyed a sense of disorientation and disturbance. She reflected on how the stigma of mental illness affects self-perception. The talk was well-attended, and reflections were of high quality. A limitation of this pilot was that only a small proportion of students completed the reflective assignment.ConclusionInnovative teaching strategies are needed to address negative attitudes towards mental illness and psychiatry, which are prevalent amongst the medical profession. This pilot provides a model for combining carer-led, reflective, and creative elements in undergraduate psychiatry teaching, with the aim of challenging stigma. This model will be evaluated in a further study involving fifth year medical students, which will use a validated scale to measure change in students’ attitudes towards mental illness and psychiatry.