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Background Evidence suggests reduced survival rates following Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection in people with preexisting mental disorders, especially psychotic disorders, before the broad introduction of vaccines. It remains unknown whether this elevated mortality risk persisted at later phases of the pandemic and when accounting for the confounding effect of vaccination uptake and clinically recorded physical comorbidities. Methods and findings We used data from Czech national health registers to identify first-ever serologically confirmed SARS-CoV-2 infections in 5 epochs related to different phases of the pandemic: 1st March 2020 to 30th September 2020, 1st October 2020 to 26th December 2020, 27th December 2020 to 31st March 2021, 1st April 2021 to 31st October 2021, and 1st November 2021 to 29th February 2022. In these people, we ascertained cases of mental disorders using 2 approaches: (1) per the International Classification of Diseases 10th Revision (ICD-10) diagnostic codes for substance use, psychotic, affective, and anxiety disorders; and (2) per ICD-10 diagnostic codes for the above mental disorders coupled with a prescription for anxiolytics/hypnotics/sedatives, antidepressants, antipsychotics, or stimulants per the Anatomical Therapeutic Chemical (ATC) classification codes. We matched individuals with preexisting mental disorders with counterparts who had no recorded mental disorders on age, sex, month and year of infection, vaccination status, and the Charlson Comorbidity Index (CCI). We assessed deaths with Coronavirus Disease 2019 (COVID-19) and from all-causes in the time period of 28 and 60 days following the infection using stratified Cox proportional hazards models, adjusting for matching variables and additional confounders. The number of individuals in matched-cohorts ranged from 1,328 in epoch 1 to 854,079 in epoch 5. The proportion of females ranged from 34.98% in people diagnosed with substance use disorders in epoch 3 to 71.16% in individuals diagnosed and treated with anxiety disorders in epoch 5. The mean age ranged from 40.97 years (standard deviation [SD] = 15.69 years) in individuals diagnosed with substance use disorders in epoch 5 to 56.04 years (SD = 18.37 years) in people diagnosed with psychotic disorders in epoch 2. People diagnosed with or diagnosed and treated for psychotic disorders had a consistently elevated risk of dying with COVID-19 in epochs 2, 3, 4, and 5, with adjusted hazard ratios (aHRs) ranging from 1.46 [95% confidence intervals (CIs), 1.18, 1.79] to 1.93 [95% CIs, 1.12, 3.32]. This patient group demonstrated also a consistently elevated risk of all-cause mortality in epochs 2, 3, 4, and 5 (aHR from 1.43 [95% CIs, 1.23, 1.66] to 1.99 [95% CIs, 1.25, 3.16]). The models could not be reliably fit for psychotic disorders in epoch 1. People diagnosed with substance use disorders had an increased risk of all-cause mortality 28 days postinfection in epoch 3, 4, and 5 (aHR from 1.30 [95% CIs, 1.14, 1.47] to 1.59 [95% CIs, 1.19, 2.12]) and 60 days postinfection in epoch 2, 3, 4, and 5 (aHR from 1.22 [95% CIs, 1.08, 1.38] to 1.52 [95% CIs, 1.16, 1.98]). Cases ascertained based on diagnosis of substance use disorders and treatment had increased risk of all-cause mortality in epoch 2, 3, 4, and 5 (aHR from 1.22 [95% CIs, 1.03, 1.43] to 1.91 [95% CIs, 1.25, 2.91]). The models could not be reliably fit for substance use disorders in epoch 1. In contrast to these, people diagnosed with anxiety disorders had a decreased risk of death with COVID-19 in epoch 2, 3, and 5 (aHR from 0.78 [95% CIs, 0.69, 0.88] to 0.89 [95% CIs, 0.81, 0.98]) and all-cause mortality in epoch 2, 3, 4, and 5 (aHR from 0.83 [95% CIs, 0.77, 0.90] to 0.88 [95% CIs, 0.83, 0.93]). People diagnosed and treated for affective disorders had a decreased risk of both death with COVID-19 and from all-causes in epoch 3 (aHR from 0.87 [95% CIs, 0.79, 0.96] to 0.90 [95% CIs, 0.83, 0.99]), but demonstrated broadly null effects in other epochs. Given the unavailability of data on a number of potentially influential confounders, particularly body mass index, tobacco smoking status, and socioeconomic status, part of the detected associations might be due to residual confounding. Conclusions People with preexisting psychotic, and, less robustly, substance use disorders demonstrated a persistently elevated risk of death following SARS-CoV-2 infection throughout the pandemic. While it cannot be ruled out that part of the detected associations is due to residual confounding, this excess mortality cannot be fully explained by lower vaccination uptake and more clinically recorded physical comorbidities in these patient groups., Author(s): Tomás Formánek 1,2,*, Libor Potocár 1,3, Katrin Wolfova 4,5, Hana Melicharová 6, Karolína Mladá 1,7, Anna Wiedemann 2, Danni Chen 8, Pavel Mohr 9,10, Petr Winkler 1,11, Peter B. [...]

Objective: The COVID-19 pandemic has had a significant impact on pupils and staff in English schools. This study aimed to provide an in-depth understanding of the challenges schools faced and the processes they implemented to protect the mental wellbeing of students and staff in the later stages of the pandemic, focusing on January-June 2022. Design: Qualitative study. Setting: Primary and secondary schools in England from April to September 2022. Method: Ten semi-structured interviews were conducted using Zoom with six primary and four secondary school headteachers (or other members of the senior leadership team) in England. Results: Mental wellbeing programming for students and staff was scaled up in both primary and secondary schools amid concerns that the COVID-19 pandemic would impact on mental health. Headteachers perceived changes in the behaviour of students, including increased dysregulation, and staff struggling with fatigue and a sense of being devalued as professionals. Schools scaled up the supports they offered to students and staff, but challenges remain in maintaining such increased support due to the perceived additional needs of staff and students within the context of funding constraints. Despite initial concerns that there would be tension between promoting mental wellbeing and academic catch-up among students, headteachers identified work addressing these two priorities as synergistic. Conclusion: Mental wellbeing impacts of the pandemic on pupils and staff required increased provision of support programmes. Ongoing efforts to monitor the wellbeing needs of students and staff is required. Formal and informal methods to enhance the mental health for pupils and staff should continue into the recovery period.

BackgroundIn magnetic resonance (MR)-guided thermal therapy, respiratory motion can cause a significant temperature error in MR thermometry and reduce the efficiency of the treatment. A respiratory motion simulator is necessary for the development of new MR imaging (MRI) and motion compensation techniques.PurposeThe purpose of this study is to develop a low-cost and simple MR-compatible respiratory motion simulator to support proof-of-concept studies of MR monitoring approaches with respiratory-induced abdominal organ motion.MethodsThe phantom motion system integrates pneumatic control via an actuator subsystem located outside the MRI and coupled via plastic tubing to a compressible bag for distention and retraction within the MRI safe motion subsystem and phantom positioned within the MRI scanner. Performance of the respiratory motion simulator was evaluated with a real-time gradient echo MRI pulse sequence.ResultsThe motion simulator can produce respiratory rates in the range of 8-16 breaths/min. Our experiments showed the consistent periodic motion of the phantom during MRI acquisition in the range of 3.7-9 mm with 16 breaths/min. The operation of the simulator did not cause interference with MRI acquisition.ConclusionsIn this study, we have demonstrated the ability of the motion simulator to generate controlled respiratory motion of a phantom. The low-cost MR-compatible respiratory motion simulator can be easily constructed from off-the-shelf and 3D-printed parts based on open-source 3D models and instructions. This could lower the barriers to the development of new MRI techniques with motion compensation.