Higher Subjective Burden in Psychiatric Compared to Somatic Healthcare Workers in Germany During the first wave of the COVID-19 Pandemic.

The COVID-19 pandemic has posed unprecedented challenges for health care workers (HCWs) worldwide. While the adverse effects of the pandemic on the well-being of HCWs in general have now been established, little is known about the impact on HCWs of psychiatric hospitals (PHCWs). PHCWs are of special interest, given that they faced both an increase in infection rates among psychiatric patients as well as in mental strain of the general public due to consequences of the pandemic. The aim of the present study was to investigate how the pandemic affected PHCWs as well as possible differences between PHCWs and other health care workers (OHCWs) in Germany during the first wave of the pandemic. We conducted a country-wide anonymous online survey early during the first pandemic wave between April 15th and May 1st, 2020, to assess different aspects of subjective burden and perceived stress using 5-point Likert-scale questions. We analysed data of 1530 PHCWs and 2114 OHCWs and showed that PHCWs reported higher subjective burden and stress compared to OHCWs (p<0.001). Overall, nurses from both groups of HCWs showed higher ratings in subjective burden and stress than physicians. These higher ratings for subjective burden were even more pronounced for nurses working in psychiatric hospitals. Future research is needed to investigate the causes for PHCWs' increased stress and subjective burden, especially when taking into account the long-term effects of the pandemic, which may lead to further challenges and an ever-increasing workload, especially for PHCWs.

Prevalence of COVID-19 and Psychotropic Drug Treatment in Psychiatric In-patients in Germany in 2020: Results from a Nationwide Pilot Survey.

INTRODUCTION: In patients with a pre-existing mental disorder, an increased risk for a first manifestation of a psychiatric disorder in COVID-19 patients, a more severe course of COVID-19 and an increased mortality have been described. Conversely, observations of lower COVID-19 incidences in psychiatric in-patients suggested protective effects of psychiatric treatment and/or psychotropic drugs against COVID-19. METHODS: A retrospective multi-center study was conducted in 24 German psychiatric university hospitals. Between April and December 2020 (the first and partly second wave of COVID-19), the effects of COVID-19 were assessed on psychiatric in-patient care, the incidence and course of a SARS-CoV-2 infection, and treatment with psychotropic drugs. RESULTS: Patients (n=36,322) were admitted to the hospitals. Mandatory SARS-CoV-2 tests before/during admission were reported by 23 hospitals (95.8%), while 18 (75%) conducted regular testing during the hospital stay. Two hundred thirty-two (0.6%) patients were tested SARS-CoV-2-positive. Thirty-seven (16%) patients were receiving medical treatment for COVID-19 at the psychiatric hospital, ten (4.3%) were transferred to an intermediate/intensive care unit, and three (1.3%) died. The most common prescription for SARS-CoV-2-positive patients was for second-generation antipsychotics (n=79, 28.2%) and antidepressants (SSRIs (n=38, 13.5%), mirtazapine (n=36, 12.9%) and SNRIs (n=29, 10.4%)). DISCUSSION: Contrary to previous studies, our results showed a low number of infections and mortality in SARS-CoV-2-positive psychiatric patients. Several preventive measures seem effective to protect this vulnerable group. Our observations are compatible with the hypothesis of a protective effect of psychotropic drugs against COVID-19 as the overall mortality and need for specific medical treatment was low.

Can minimal clinically important differences in patient reported outcome measures be predicted by machine learning in patients with total knee or hip arthroplasty? A systematic review.

OBJECTIVES: To systematically review studies using machine learning (ML) algorithms to predict whether patients undergoing total knee or total hip arthroplasty achieve an improvement as high or higher than the minimal clinically important differences (MCID) in patient reported outcome measures (PROMs) (classification problem). METHODS: Studies were eligible to be included in the review if they collected PROMs both pre- and postintervention, reported the method of MCID calculation and applied ML. ML was defined as a family of models which automatically learn from data when selecting features, identifying nonlinear relations or interactions. Predictive performance must have been assessed using common metrics. Studies were searched on MEDLINE, PubMed Central, Web of Science Core Collection, Google Scholar and Cochrane Library. Study selection and risk of bias assessment (ROB) was conducted by two independent researchers. RESULTS: 517 studies were eligible for title and abstract screening. After screening title and abstract, 18 studies qualified for full-text screening. Finally, six studies were included. The most commonly applied ML algorithms were random forest and gradient boosting. Overall, eleven different ML algorithms have been applied in all papers. All studies reported at least fair predictive performance, with two reporting excellent performance. Sample size varied widely across studies, with 587 to 34,110 individuals observed. PROMs also varied widely across studies, with sixteen applied to TKA and six applied to THA. There was no single PROM utilized commonly in all studies. All studies calculated MCIDs for PROMs based on anchor-based or distribution-based methods or referred to literature which did so. Five studies reported variable importance for their models. Two studies were at high risk of bias. DISCUSSION: No ML model was identified to perform best at the problem stated, nor can any PROM said to be best predictable. Reporting standards must be improved to reduce risk of bias and improve comparability to other studies.

Subjective burden and perspectives of German healthcare workers during the COVID-19 pandemic.

Healthcare workers (HCW) face tremendous challenges during the COVID-19 pandemic. Little is known about the subjective burden, views, and COVID-19 infection status of HCWs. The aim of this work was to evaluate the subjective burden, the perception of the information policies, and the agreement on structural measures in a large cohort of German HCW during the COVID-19 pandemic. This country-wide anonymous online survey was carried out from April 15th until May 1st, 2020. 25 content-related questions regarding the subjective burden and other dimensions were evaluated. We evaluated different dimensions of subjective burden, stress, and perspectives using 5-point Likert-scale questions. Moreover, the individual COVID-19 infection status, the amount of people infected in circle of friends and acquaintances and the hours working overtime were assessed. A total of 3669 HCWs provided sufficient responses for analyses. 2.8% of HCWs reported to have been tested positive for COVID-19. Nurses reported in principle higher ratings on all questions of subjective burden and stress than doctors and other hospital staff. Doctors (3.6%) and nurses (3.1%) were more likely to be tested positive for COVID-19 than other hospital staff (0.6%, Chi (2) 2 = 17.39, p < 0.0005). HCWs who worked in a COVID-19 environment reported higher levels of subjective burden and stress compared to all other participants. Working in a COVID-19 environment increased the likelihood to be tested positive for COVID-19 (4.8% vs. 2.3%, Chi (1) 2 = 12.62, p < 0.0005) and the severity of the subjective burden. During the COVID-19 pandemic, nurses experience more stress than doctors. Overall, German HCWs showed high scores of agreement with the measures taken by the hospitals.

MBT3D: Deep learning based multi-object tracker for bumblebee 3D flight path estimation.

This work presents the Multi-Bees-Tracker (MBT3D) algorithm, a Python framework implementing a deep association tracker for Tracking-By-Detection, to address the challenging task of tracking flight paths of bumblebees in a social group. While tracking algorithms for bumblebees exist, they often come with intensive restrictions, such as the need for sufficient lighting, high contrast between the animal and background, absence of occlusion, significant user input, etc. Tracking flight paths of bumblebees in a social group is challenging. They suddenly adjust movements and change their appearance during different wing beat states while exhibiting significant similarities in their individual appearance. The MBT3D tracker, developed in this research, is an adaptation of an existing ant tracking algorithm for bumblebee tracking. It incorporates an offline trained appearance descriptor along with a Kalman Filter for appearance and motion matching. Different detector architectures for upstream detections (You Only Look Once (YOLOv5), Faster Region Proposal Convolutional Neural Network (Faster R-CNN), and RetinaNet) are investigated in a comparative study to optimize performance. The detection models were trained on a dataset containing 11359 labeled bumblebee images. YOLOv5 reaches an Average Precision of AP = 53, 8%, Faster R-CNN achieves AP = 45, 3% and RetinaNet AP = 38, 4% on the bumblebee validation dataset, which consists of 1323 labeled bumblebee images. The tracker's appearance model is trained on 144 samples. The tracker (with Faster R-CNN detections) reaches a Multiple Object Tracking Accuracy MOTA = 93, 5% and a Multiple Object Tracking Precision MOTP = 75, 6% on a validation dataset containing 2000 images, competing with state-of-the-art computer vision methods. The framework allows reliable tracking of different bumblebees in the same video stream with rarely occurring identity switches (IDS). MBT3D has much lower IDS than other commonly used algorithms, with one of the lowest false positive rates, competing with state-of-the-art animal tracking algorithms. The developed framework reconstructs the 3-dimensional (3D) flight paths of the bumblebees by triangulation. It also handles and compares two alternative stereo camera pairs if desired.