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Objectives To assess the incidence of head injury and predictors of complication across the care continuum. Design Retrospective cohort study using data from a research network. We calculated the incidence of overall head injury in a longitudinal cohort covering 1-year interval (31369 patient-years), and the incidence of complicated head injury in a longitudinal cohort covering 10 years interval (220 352 patient-ears). Incidence rates were calculated per 1000 patient-years with 95% CI using the Mid-P exact test. We calculated ORs to assess potential risk factors for a complicated head injury. Setting A practice-based research network covering a population of >30 000 patients. Participants All patients listed in practices within the research network during the years 2005–2014. Main outcome measures Incidence of (complicated) head injury and predictors for clinical complications. Results The incidence of overall head injury was 22.1 per 1000 person-years and the incidence of a complicated course following head injury was 0.16 per 1000 personyears. The following determinants were risk factors for a complicated course: high energy trauma, bicycle accident, traffic accident in general, use of anticoagulants, alcohol intoxication, age above 60 years and low Glasgow Coma Scale at initial presentation. A complicated course was very unlikely when the patients' first encounter with a healthcare professional was in primary care (OR 0.03, 95% CI 0.01 to 0.07). Conclusions Complication after head injury are rarely seen in general practice. Patients who do experience complications are often easily identifiable as requiring specialist care. A more reserved referral policy for general practice may be desirable, suggesting that current guidelines are too defensive. [ABSTRACT FROM AUTHOR]

The hydrodynamic flows in the Singapore regional waters (SRW) are the result of a complex mix of tide, seasonal and meteorological effects. The study of non-tidal effects or sea level anomalies (SLA) in this region has shown that it is possible to model some of these anomalies. The present study addresses the non-tidal barotropic water levels and currents in the Singapore region in detail. This analysis includes a multi-scale approach, and addresses amongst others hydrodynamic model grid resolution and the importance of resolving non-linear tide–surge interaction. The results show that the water level and current anomalies phenomena in a complex region like SRW can be effectively modelled using an approach combining non-tidal barotropic and multi-scale numerical modelling. A detailed investigation of the levels of non-linear tide–surge interaction is carried out by simulating SLA events in the Singapore regional waters during North-East (positive SLA) and South-West (negative SLA) monsoons based on ECMWF numerical weather forcing conditions. The results of combining both approaches suggest that the finer grid resolution improves the accuracy of water level and current anomalies simulations. Furthermore, the results also indicate that for the simulations of non-tidal barotropic flows in this area, non-linear tide–surge interaction is important and should be taken into account. Finally, the behaviour of the non-tidal barotropic flow in the region and its changes with tidal variation in the shallow region of the Singapore Strait is now much better understood. Therefore, for reliable operational forecast of sea level, the inclusion of non-linear tide–surge interaction should be required in numerical models to reproduce both tides and surges with improved accuracy in this region. [ABSTRACT FROM AUTHOR]

An overview of Composite Modelling (CM) is presented, as elaborated in the EU/HYDRALAB joint research project Composite Modelling of the Interactions Between Beaches and Structures. An introduction and a review of the main literature on CM in the hydraulic community are given. In Section 3, the case studies of CM of the seven partners participating in this project are discussed. The focus is on the methodologies used and their impact on the modelling approach, rather than the results of the experiments per se. A further section presents reflections on key elements in CM, as they emerged in the various case studies. The related subject of Good Modelling Practice is summarized in Section 5. Then guidelines are given on how to decide if CM may be beneficial, and how to set up a CM experiment. It is concluded that CM in the hydraulic community is still in its infancy but involves challenging research with significant potential. [ABSTRACT FROM AUTHOR]

The present paper describes the analysis and modeling of the South China Sea (SCS) temperature cycle on a seasonal scale. It investigates the possibility to model this cycle in a consistent way while not taking into account tidal forcing and associated tidal mixing and exchange. This is motivated by the possibility to significantly increase the model’s computational efficiency when neglecting tides. The goal is to develop a flexible and efficient tool for seasonal scenario analysis and to generate transport boundary forcing for local models. Given the significant spatial extent of the SCS basin and the focus on seasonal time scales, synoptic remote sensing is an ideal tool in this analysis. Remote sensing is used to assess the seasonal temperature cycle to identify the relevant driving forces and is a valuable source of input data for modeling. Model simulations are performed using a three-dimensional baroclinic-reduced depth model, driven by monthly mean sea surface anomaly boundary forcing, monthly mean lateral temperature, and salinity forcing obtained from the World Ocean Atlas 2001 climatology, six hourly meteorological forcing from the European Center for Medium range Weather Forecasting ERA-40 dataset, and remotely sensed sea surface temperature (SST) data. A sensitivity analysis of model forcing and coefficients is performed. The model results are quantitatively assessed against climatological temperature profiles using a goodness-of-fit norm. In the deep regions, the model results are in good agreement with this validation data. In the shallow regions, discrepancies are found. To improve the agreement there, we apply a SST nudging method at the free water surface. This considerably improves the model’s vertical temperature representation in the shallow regions. Based on the model validation against climatological in situ and SST data, we conclude that the seasonal temperature cycle for the deep SCS basin can be represented to a good degree. For shallow regions, the absence of tidal mixing and exchange has a clear impact on the model’s temperature representation. This effect on the large-scale temperature cycle can be compensated to a good degree by SST nudging for diagnostic applications. [ABSTRACT FROM AUTHOR]