How do employment tax credits work? : an analysis of the German inheritance tax

Employment tax credit programs have been repeatedly used during economic crises, although their usefulness is empirically contestable. The objective of this paper is to quantify the tax effects of employment tax credit programs. A recent revision of the German inheritance tax law provides an eminent opportunity to analyze the effects caused by such a preferential treatment. The tax liability depends on a company’s future employment expenses. Hence, we use micro-level data of realized business transfers from the German Inheritance Tax Statistic and combine them with a simulation of the future development of employment over the relevant time-horizon. We identify the magnitude of tax reductions granted to business transfers under a preferential treatment. Further, we demonstrate that these reductions are considerably larger in times of economic growth. Our findings also suggest that employment tax credits have pro-cyclical effects and specifically foster transfers between unrelated parties. Finally, the preferential treatment of business transfers does not provide incentives to increase employment

The effects of a single bout of exercise on motor memory interference in the trained and untrained hemisphere

Increasing evidence suggests that cardiovascular exercise has positive effects on motor memory consolidation. In this study, we investigated whether a single session of high-intensity interval training (HIIT) mitigates the effects of practicing an interfering motor task. Furthermore, learning and interference effects were assessed in the actively trained and untrained limb as it is known that unilateral motor learning can cause bilateral adaptations.Subjects performed a ballistic training and then the HIIT either before (HIIT_before) or after (HIIT_after) practicing an interfering accuracy task (AT). The control group (No_HIIT) did not participate in the HIIT but rested instead. Performance in the ballistic task (BT) was tested before and after the ballistic training, after the exercise and practice of the AT and 24 h later. After ballistic training, all groups showed comparable increases in performance in the trained and untrained limb. Despite the practice of the AT, HIIT_before maintained their BT performance after the high-intensity interval training whereas HIIT_after (trend) & No_HIIT showed prominent interference effects. After 24 h, HIIT_before still did not show any interference effects but further improved ballistic motor performance. HIIT_after counteracted the interference resulting in a comparable BT performance after 24 h than directly after the ballistic training while No_HIIT had a significantly lower BT performance in the retention test. The results were similar in the trained and untrained limb. The current results imply that a single session of cardiovascular exercise can prevent motor interference in the trained and untrained hemisphere. Overall learning was best, and interference least, when HIIT was performed before the interfering motor task

Modelling blood flow in patients with heart valve disease using deep learning: A computationally efficient method to expand diagnostic capabilities in clinical routine

Introduction: The computational modelling of blood flow is known to provide vital hemodynamic parameters for diagnosis and treatment-support for patients with valvular heart disease. However, most diagnosis/treatment-support solutions based on flow modelling proposed utilize time- and resource-intensive computational fluid dynamics (CFD) and are therefore difficult to implement into clinical practice. In contrast, deep learning (DL) algorithms provide results quickly with little need for computational power. Thus, modelling blood flow with DL instead of CFD may substantially enhances the usability of flow modelling-based diagnosis/treatment support in clinical routine. In this study, we propose a DL-based approach to compute pressure and wall-shear-stress (WSS) in the aorta and aortic valve of patients with aortic stenosis (AS). Methods: A total of 103 individual surface models of the aorta and aortic valve were constructed from computed tomography data of AS patients. Based on these surface models, a total of 267 patient-specific, steady-state CFD simulations of aortic flow under various flow rates were performed. Using this simulation data, an artificial neural network (ANN) was trained to compute spatially resolved pressure and WSS using a centerline-based representation. An unseen test subset of 23 cases was used to compare both methods. Results: ANN and CFD-based computations agreed well with a median relative difference between both methods of 6.0% for pressure and 4.9% for wall-shear-stress. Demonstrating the ability of DL to compute clinically relevant hemodynamic parameters for AS patients, this work presents a possible solution to facilitate the introduction of modelling-based treatment support into clinical practice

Towards improving the accuracy of aortic transvalvular pressure gradients: rethinking Bernoulli

The transvalvular pressure gradient (TPG) is commonly estimated using the Bernoulli equation. However, the method is known to be inaccurate. Therefore, an adjusted Bernoulli model for accurate TPG assessment was developed and evaluated. Numerical simulations were used to calculate TPGCFD in patient-specific geometries of aortic stenosis as ground truth. Geometries, aortic valve areas (AVA), and flow rates were derived from computed tomography scans. Simulations were divided in a training data set (135 cases) and a test data set (36 cases). The training data was used to fit an adjusted Bernoulli model as a function of AVA and flow rate. The model-predicted TPGModel was evaluated using the test data set and also compared against the common Bernoulli equation (TPGB). TPGB and TPGModel both correlated well with TPGCFD (r > 0.94), but significantly overestimated it. The average difference between TPGModel and TPGCFD was much lower: 3.3 mmHg vs. 17.3 mmHg between TPGB and TPGCFD. Also, the standard error of estimate was lower for the adjusted model: SEEModel = 5.3 mmHg vs. SEEB = 22.3 mmHg. The adjusted model's performance was more accurate than that of the conventional Bernoulli equation. The model might help to improve non-invasive assessment of TPG. Graphical abstract Processing pipeline for the definition of an adjusted Bernoulli model for the assessment of transvalvular pressure gradient. Using CT image data, the patient specific geometry of the stenosed AVs were reconstructed. Using this segmentation, the AVA as well as the volume flow rate was calculated and used for model definition. This novel model was compared against classical approaches on a test data set, which was not used for the model definition

Special orthopaedic geriatrics (SOG) - a new multiprofessional care model for elderly patients in elective orthopaedic surgery: a study protocol for a prospective randomized controlled trial of a multimodal intervention in frail patients with hip and knee replacement

Background Due to demographic change, the number of older people in Germany and worldwide will continue to rise in the coming decades. As a result, the number of elderly and frail patients undergoing total hip and knee arthroplasty is projected to increase significantly in the coming years. In order to reduce risk of complications and improve postoperative outcome, it can be beneficial to optimally prepare geriatric patients before orthopaedic surgery and to provide perioperative care by a multiprofessional orthogeriatric team. The aim of this comprehensive interventional study is to assess wether multimorbid patients can benefit from the new care model of special orthopaedic geriatrics (SOG) in elective total hip and knee arthroplasty. Methods The SOG study is a registered, monocentric, prospective, randomized controlled trial (RCT) funded by the German Federal Joint Committee (GBA). This parallel group RCT with a total of 310 patients is intended to investigate the specially developed multimodal care model for orthogeriatric patients with total hip and knee arthroplasty (intervention group), which already begins preoperatively, in comparison to the usual orthopaedic care without orthogeriatric co-management (control group). Patients ≥70 years of age with multimorbidity or generally patients ≥80 years of age due to increased vulnerability with indication for elective primary total hip and knee arthroplasty can be included in the study. Exclusion criteria are age < 70 years, previous bony surgery or tumor in the area of the joint to be treated, infection and increased need for care (care level ≥ 4). The primary outcome is mobility measured by the Short Physical Performance Battery (SPPB). Secondary outcomes are morbidity, mortality, postoperative complications, delirium, cognition, mood, frailty, (instrumental) activities of daily living, malnutrition, pain, polypharmacy, and patient reported outcome measures. Tertiary outcomes are length of hospital stay, readmission rate, reoperation rate, transfusion rate, and time to rehabilitation. The study data will be collected preoperative, postoperative day 1 to 7, 4 to 6 weeks and 3 months after surgery. Discussion Studies have shown that orthogeriatric co-management models in the treatment of hip fractures lead to significantly reduced morbidity and mortality rates. However, there are hardly any data available on the elective orthopaedic care of geriatric patients, especially in total hip and knee arthroplasty. In contrast to the care of trauma patients, optimal preoperative intervention is usually possible