The impact of neighborhood deprivation on patients' unscheduled out-of-hours healthcare seeking behavior: a cross-sectional study

Background: The use of unscheduled out of hours medical care is related to the social status of the patient. However, the social variance in the patient's preference for a hospital based versus a primary care based facility, and the impact of specific patient characteristics such as the travel distance to both types of facilities is unclear. This study aims to determine the social gradient in emergency care seeking behavior (consulting the emergency department (ED) in a hospital or the community-based Primary Care Center (PCC)) taking into account patient characteristics including the geographical distance from the patient's home to both services. Methods: A cross-sectional study, including 7,723 patients seeking out-of-hours care during 16 weekends and 2 public holidays was set up in all EDs and PCCs in Ghent, Belgium. Information on the consulted type of service, and neighborhood deprivation level was collected, but also the exact geographical distance from the patient's home to both types of services, and if the patient has a regular GP. Results: Patients living in a socially deprived area have a higher propensity to choose a hospital-based ED than their counterparts living in more affluent neighborhoods. This social difference persists when taking into account distance to both services, having a regular GP, and being hospitalized or not. The impact of the distance between the patient's home address and the location of both types of services on the patient's choice of service is rather small. Conclusions: Initiatives aiming to lead patients more to PCC by penalizing inappropriate ED use might increase health inequity when they are not twinned with interventions improving the access to primary care services and tackling the underlying mechanisms of patients' emergency care seeking behavior. Further research exploring the impact of out-of-hours care organization (gatekeeping, payment systems, ...) and the patient's perspectives on out-of-hours care services is needed

Classification of airborne laser scanning point clouds based on binomial logistic regression analysis

This article presents a newly developed procedure for the classification of airborne laser scanning (ALS) point clouds, based on binomial logistic regression analysis. By using a feature space containing a large number of adaptable geometrical parameters, this new procedure can be applied to point clouds covering different types of topography and variable point densities. Besides, the procedure can be adapted to different user requirements. A binomial logistic model is estimated for all a priori defined classes, using a training set of manually classified points. For each point, a value is calculated defining the probability that this point belongs to a certain class. The class with the highest probability will be used for the final point classification. Besides, the use of statistical methods enables a thorough model evaluation by the implementation of well-founded inference criteria. If necessary, the interpretation of these inference analyses also enables the possible definition of more sub-classes. The use of a large number of geometrical parameters is an important advantage of this procedure in comparison with current classification algorithms. It allows more user modifications for the large variety of types of ALS point clouds, while still achieving comparable classification results. It is indeed possible to evaluate parameters as degrees of freedom and remove or add parameters as a function of the type of study area. The performance of this procedure is successfully demonstrated by classifying two different ALS point sets from an urban and a rural area. Moreover, the potential of the proposed classification procedure is explored for terrestrial data

Spatio-temporal modeling of soil characteristics for soilscape reconstruction

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The small wind turbine field lab extensive field tests for small wind turbines

This paper describes the research possibilities at the Small Wind Turbine Field Lab and the involved research groups of Ghent University, covering different aspects of a small wind energy system. In contrast to large and medium-sized wind turbines, small wind turbines are still plagued by relatively high production and purchase costs, and low reliability and energy yield. Furthermore, most of them have not been subjected to a field test program. Power-Link, the energy knowledge platform of Ghent University, has for three years operated a modest field test site for small wind turbines, that drew the attention of a lot of manufacturers of small wind turbines. In response, Ghent University decided to launch the Small Wind Turbine Field Lab (SWT Field Lab), to subject small wind turbines to more extensive field tests. Now not only the energy yield is tested, but also topics such as grid integration, structural strength, noise propagation, generator and drive train design and tower construction are studied. All of these parameters are correlated with meteorological data measured on-site

Vebimobe: correcte snelheidsinformatie voor correct rijgedrag: onderzoek naar mogelijkheden verkeersbordendatabank voor ITS-toepassingen

Onaangepaste snelheid is naast dronkenschap de hoofdoorzaak van zware ongevallen. Assistentie van de bestuurder bij het snelheidsgedrag is daarom een cruciaal hulpmiddel om ongevallen te voorkomen. De huidige navigatiesystemen geven al wel advi-serende snelheidsinformatie, maar die is niet dwingend en verre van accuraat. In het VEBIMOBE-project van het VIM (Vlaams Instituut voor Mobiliteit) wordt onderzocht hoe de Vlaamse verkeersbordendatabank zou kunnen bijdragen tot correcte snelheidsinformatie en -gedrag. Meer specifiek: er wordt nage-gaan hoe de data van de verkeersbordendatabank naar ITS-standaarden kunnen worden overgezet en hoe die data via innovatieve technieken verfijnd en geactualiseerd kunnen worden