Structural characteristics and contractual terms of specialist palliative homecare in Germany

Background Multi-professional specialist palliative homecare (SPHC) teams care for palliative patients with complex symptoms. In Germany, the SPHC directive regulates care provision, but model contracts for each federal state are heterogeneous regarding staff requirements, cooperation with other healthcare providers, and financial reimbursement. The structural characteristics of SPHC teams also vary. Aim We provide a structured overview of the existing model contracts, as well as a nationwide assessment of SPHC teams and their structural characteristics. Furthermore, we explore whether these characteristics serve to find specifc patterns of SPHC team models, based on empirical data. Methods This study is part of the multi-methods research project “SAVOIR”, funded by the German Innovations Fund. Most model contracts are publicly available. Structural characteristics (e.g. number, professions, and affiliations of team members, and external cooperation) were assessed via an online database (“Wegweiser Hospiz- und Palliativversorgung”) based on voluntary information obtained from SPHC teams. All the data were updated by phone during the assessment process. Data were descriptively analysed regarding staff, cooperation requirements, and reimbursement schemes, while latent class analysis (LCA) was used to identify structural team models. Results Model contracts have heterogeneous contract partners and terms related to staff requirements (number and qualifications) and cooperation with other services. Fourteen reimbursement schemes were available, all combining different payment models. Of the 283 SPHC teams, 196 provided structural characteristics. Teams reported between one and 298 members (mean: 30.3, median: 18), mainly nurses and physicians, while 37.8% had a psychosocial professional as a team member. Most teams were composed of nurses and physicians employed in different settings; for example, staff was employed by the team, in private practices/nursing services, or in hospitals. Latent class analysis identified four structural team models, based on the team size, team members’ affiliation, and care organisation. Conclusion Both the contractual terms and teams’ structural characteristics vary substantially, and this must be considered when analysing patient data from SPHC. The identified patterns of team models can form a starting point from which to analyse different forms of care provision and their impact on care quality

Separation – integration – and now …? - An historical perspective on the relationship between German management accounting and financial accounting

German accounting has traditionally followed a dual ledger approach with strictly separated internal cost accounting, as the basis for management information, and external financial accounting focusing on creditor protection and based on the commercial law. However, the increased adoption of integrated accounting system implies a significant change in the relationship between financial and management accounting systems. We use Hegelian dialectic to trace the historical development of German accounting from separated systems towards antithetical propositions of full integration, and the emergence of partial integration as the synthesis of this transformation process. For this reason, our paper provides a comprehensive analysis of the literature on the relationship between financial and management accounting in Germany. On this basis, we elaborate how financial accounting in Germany has been shaped by its economic context and legislation, and how financial accounting – accompanied by institutional pressures – in turn influenced management accounting. We argue that the changing relationship between management and financial accounting in the German context illustrates how current accounting practice is shaped not only by its environment, but also by its historical path. Based on this reasoning, we discuss several avenues for future research

Modelling the dependence and internal structure of storm events for continuous rainfall simulation

Gyasi-Agyei, Y ORCiD: 0000-0002-2671-1180Pair-copula construction methodology has been explored to model the dependence structure between net storm event depth (R), maximum wet periods’ depth (M), and the total wet periods’ duration (L), noting that the total storm event depth is RT = R + M. Random variable R was used instead of RT in order to avoid physical boundary effects due to the condition of RTPM. The flexibility of pair-copula construction allowed the examination of 11 bivariate copulas at the three bivariate stages of the three-dimensional (3D) copula. For 21 years of hourly rainfall data from Cook County, Illinois, USA, examined, three different copulas were found suitable for the bivariate stages. For the internal storm event structure, a Geometric distribution was used to model the net event duration, defined as the difference between the total duration (D) and L. A two-parameter Poisson model was adopted for modelling the distribution of the L wet periods within D, and the first-order autoregressive Lognormal model was applied for the distribution of RT over the L wet periods. Incorporation of an inter-event (I) sub-model completed the continuous rainfall simulation scheme. The strong seasonality in the marginal and dependence model parameters was captured using first harmonic Fourier series, thus, reducing the number of parameters. Polynomial functions were fitted to the internal storm event model parameters which did not exhibit seasonal variability. Four hundred simulation runs were carried out in order to verify the developed model. Kolmogorov–Smirnov (KS) tests found the hypothesis that the observed and simulated storm event quantiles come from the same distribution cannot be rejected at the 5% significance level in nearly all cases. Gross statistics (dry probability, mean, variance, skewness, autocorrelations, and the intensity–duration–frequency(IDF) curves) of the continuous rainfall time series at several aggregation levels were very well preserved by the developed model

Modelling the dependence and internal structure of storm events for continuous rainfall simulation

Pair-copula construction methodology has been explored to model the dependence structure between net storm event depth (R), maximum wet periods’ depth (M), and the total wet periods’ duration (L), noting that the total storm event depth is RT = R + M. Random variable R was used instead of RT in order to avoid physical boundary effects due to the condition of RTPM. The flexibility of pair-copula construction allowed the examination of 11 bivariate copulas at the three bivariate stages of the three-dimensional (3D) copula. For 21 years of hourly rainfall data from Cook County, Illinois, USA, examined, three different copulas were found suitable for the bivariate stages. For the internal storm event structure, a Geometric distribution was used to model the net event duration, defined as the difference between the total duration (D) and L. A two-parameter Poisson model was adopted for modelling the distribution of the L wet periods within D, and the first-order autoregressive Lognormal model was applied for the distribution of RT over the L wet periods. Incorporation of an inter-event (I) sub-model completed the continuous rainfall simulation scheme. The strong seasonality in the marginal and dependence model parameters was captured using first harmonic Fourier series, thus, reducing the number of parameters. Polynomial functions were fitted to the internal storm event model parameters which did not exhibit seasonal variability. Four hundred simulation runs were carried out in order to verify the developed model. Kolmogorov–Smirnov (KS) tests found the hypothesis that the observed and simulated storm event quantiles come from the same distribution cannot be rejected at the 5% significance level in nearly all cases. Gross statistics (dry probability, mean, variance, skewness, autocorrelations, and the intensity–duration–frequency(IDF) curves) of the continuous rainfall time series at several aggregation levels were very well preserved by the developed model