Availability of Effective Evidence-Based Symptomatic Treatments for Cluster Headache in the eU Countries— A Survey of the European Headache Alliance and European Headache Federation

Aims: To assess the reimbursement options and accessibility of three effective medicines for cluster headache (CH) (subcutaneous sumatriptan, oxygen, and zolmitriptan spray) across the European Union (EU). Methods: A brief survey investigating the availability of symptomatic treatments for CH was sent by email in January 2017 to at least one headache specialist for every single country in the EU. Results: The questionnaire was completed by 26 headache specialists (93% of the EU countries, representing 99.75% of the European population) and by 10 CH patients representative of patient organizations. Oxygen was reimbursable for 63% of the CH population. Oxygen device was reimbursable for 50% of the CH EU population. Subcutaneous sumatriptan was reimbursable for 66% and was accessible without restrictions for 45% of the CH EU population. Zolmitriptan spray was reimbursable for 23.7% and accessible without restrictions for 30.9% of the CH EU population. Conclusion: Only 47% of the EU population had unrestricted access to effective CH treatments, with unacceptable inequalities between eastern countries and the rest of Europe. Headache societies and patient associations should pressure European and national health authorities to improve the availability of effective symptomatic treatments for CH © 2020. by Quintessence Publishing Co Inc. All Rights Reserved

Static analysis of PE files using neural network techniques for a pocket tool

The continuous growth in the number of malware instances has posed a serious challenge to the security of computer systems; hence, malware detection is a key factor in securing various devices, from personal devices to large servers. Static analysis allows for the extraction of multiple file characteristics belonging to different categories of information without incurring the overhead of dynamic analysis and the risks associated with it. In this paper, we present a methodology to classify Portable Executable (PE) files as malware or non-malware by exploiting the technology of neural networks, adapting it to the collected data to obtain better results. The aim of our methodology is to create a pocket tool, i.e., a tool that can be used even on devices with limited available resources. Hence our tests were conducted entirely using a personal computer with only 16GB of RAM. After a careful analysis of the techniques at our disposal and a selection of the most relevant information, we reduced the amount of resources used, both in terms of time and space, while maintaining a high accuracy of 93%

Cross-national health care database utilization between Spain and France: results from the EPICHRONIC study assessing the prevalence of type 2 diabetes mellitus

Guillaume Moulis,1–3,* Berta Ibañez,4–6,* Aurore Palmaro,2,3 Felipe Aizpuru,6–8 Eduardo Millan,6,8 Maryse Lapeyre-Mestre,2,3,9 Laurent Sailler,1–3 Koldo Cambra5,6,10 1Department of Internal Medicine, Toulouse University Hospital, Toulouse, France; 2UMR1027 INSERM, University of Toulouse, Toulouse, France; 3Clinical Investigation Center 1436, Toulouse University Hospital, Toulouse, France; 4Navarrabiomed, Health Department, Public University of Navarra, Pamplona, Spain; 5IdiSNA, Pamplona, Spain; 6Health Service Research on Chronic Patients Network (REDISSEC), Pamplona, Spain; 7Research Unit Araba (BioAraba), Osakidetza-Basque Health Department, Vitoria-Gasteiz, Spain; 8Healthcare Services Sub-directorate, Osakidetza-Basque Health Service, Araba, Spain; 9Department of Medical and Clinical Pharmacology, Toulouse University Hospital, Toulouse, France; 10Institute of Public Health and Labour Health of Navarra, Pamplona, Spain *These authors contributed equally to this work Aim: The EPICHRONIC (EPIdemiology of CHRONIC diseases) project investigated the possibility of developing common procedures for French and Spanish electronic health care databases to enable large-scale pharmacoepidemiological studies on chronic diseases. A feasibility study assessed the prevalence of type 2 diabetes mellitus (T2DM) in Navarre and the Basque Country (Spain) and the Midi-Pyrénées region (France). Patients and methods: We described and compared database structures and the availability of hospital, outpatient, and drug-dispensing data from 5.9 million inhabitants. Due to differences in database structures and recorded data, we could not develop a common procedure to estimate T2DM prevalence, but identified an algorithm specific to each database. Patients were identified using primary care diagnosis codes previously validated in Spanish databases and a combination of primary care diagnosis codes, hospital diagnosis codes, and data on exposure to oral antidiabetic drugs from the French database. Results: Spanish and French databases (the latter termed Système National d’Information Inter-Régimes de l’Assurance Maladie [SNIIRAM]) included demographic, primary care diagnoses, hospital diagnoses, and outpatient drug-dispensing data. Diagnoses were encoded using the International Classification of Primary Care (version 2) and the International Classification of Diseases, version 9 and version 10 (ICD-9 and ICD-10) in the Spanish databases, whereas the SNIIRAM contained ICD-10 codes. All data were anonymized before transferring to researchers. T2DM prevalence in the population over 20 years was estimated to be 6.6–7.0% in the Spanish regions and 6.3% in the Midi-Pyrénées region with ~2% higher estimates for males in the three regions. Conclusion: Tailored procedures can be designed to estimate the prevalence of T2DM in population-based studies from Spanish and French electronic health care records. Keywords: epidemiology, pharmacoepidemiology, electronic health care database, cross-national study, population-based study, type 2 diabetes mellitu