Mapping and exploring health systems' response to intimate partner violence in Spain

Background: For a comprehensive health sector response to intimate partner violence (IPV), interventions should target individual and health facility levels, along with the broader health systems level which includes issues of governance, financing, planning, service delivery, monitoring and evaluation, and demand generation. This study aims to map and explore the integration of IPV response in the Spanish national health system. Methods: Information was collected on five key areas based on WHO recommendations: policy environment, protocols, training, monitoring and prevention. A systematic review of public documents was conducted to assess 39 indicators in each of Spain’s 17 regional health systems. In addition, we performed qualitative content analysis of 26 individual interviews with key informants responsible for coordinating the health sector response to IPV in Spain. Results: In 88% of the 17 autonomous regions, the laws concerning IPV included the health sector response, but the integration of IPV in regional health plans was just 41%. Despite the existence of a supportive national structure, responding to IPV still relies strongly on the will of health professionals. All seventeen regions had published comprehensive protocols to guide the health sector response to IPV, but participants recognized that responding to IPV was more complex than merely following the steps of a protocol. Published training plans existed in 43% of the regional health systems, but none had institutionalized IPV training in medical and nursing schools. Only 12% of regional health systems collected information on the quality of the IPV response, and there are many limitations to collecting information on IPV within health services, for example underreporting, fears about confidentiality, and underuse of data for monitoring purposes. Finally, preventive activities that were considered essential were not institutionalized anywhere. Conclusions: Within the Spanish health system, differences exist in terms of achievements both between regions and between the areas assessed. Progress towards integration of IPV has been notable at the level of policy, less outstanding regarding health service delivery, and very limited in terms of preventive actions.

Evaluation of an integrated system for classification, assessment and comparison of services for long-term care in Europe: the eDESDE-LTC study

Background: The harmonization of European health systems brings with it a need for tools to allow the standardized collection of information about medical care. A common coding system and standards for the description of services are needed to allow local data to be incorporated into evidence-informed policy, and to permit equity and mobility to be assessed. The aim of this project has been to design such a classification and a related tool for the coding of services for Long Term Care (DESDE-LTC), based on the European Service Mapping Schedule (ESMS). Methods: The development of DESDE-LTC followed an iterative process using nominal groups in 6 European countries. 54 researchers and stakeholders in health and social services contributed to this process. In order to classify services, we use the minimal organization unit or “Basic Stable Input of Care” (BSIC), coded by its principal function or “Main Type of Care” (MTC). The evaluation of the tool included an analysis of feasibility, consistency, ontology, inter-rater reliability, Boolean Factor Analysis, and a preliminary impact analysis (screening, scoping and appraisal). Results: DESDE-LTC includes an alpha-numerical coding system, a glossary and an assessment instrument for mapping and counting LTC. It shows high feasibility, consistency, inter-rater reliability and face, content and construct validity. DESDE-LTC is ontologically consistent. It is regarded by experts as useful and relevant for evidence-informed decision making. Conclusion: DESDE-LTC contributes to establishing a common terminology, taxonomy and coding of LTC services in a European context, and a standard procedure for data collection and international comparison.

Calmness of the Feasible Set Mapping for Linear Inequality Systems

In this paper we deal with parameterized linear inequality systems in the n-dimensional Euclidean space, whose coefficients depend continuosly on an index ranging in a compact Hausdorff space. The paper is developed in two different parametric settings: the one of only right-hand-side perturbations of the linear system, and that in which both sides of the system can be perturbed. Appealing to the backgrounds on the calmness property, and exploiting the specifics of the current linear structure, we derive different characterizations of the calmness of the feasible set mapping, and provide an operative expresion for the calmness modulus when confined to finite systems. In the paper, the role played by the Abadie constraint qualification in relation to calmness is clarified, and illustrated by different examples. We point out that this approach has the virtue of tackling the calmness property exclusively in terms of the system’s data.

Three-dimensional planar model estimation using multi-constraint knowledge based on k-means and RANSAC

Plane model extraction from three-dimensional point clouds is a necessary step in many different applications such as planar object reconstruction, indoor mapping and indoor localization. Different RANdom SAmple Consensus (RANSAC)-based methods have been proposed for this purpose in recent years. In this study, we propose a novel method-based on RANSAC called Multiplane Model Estimation, which can estimate multiple plane models simultaneously from a noisy point cloud using the knowledge extracted from a scene (or an object) in order to reconstruct it accurately. This method comprises two steps: first, it clusters the data into planar faces that preserve some constraints defined by knowledge related to the object (e.g., the angles between faces); and second, the models of the planes are estimated based on these data using a novel multi-constraint RANSAC. We performed experiments in the clustering and RANSAC stages, which showed that the proposed method performed better than state-of-the-art methods.