Pharmacoeconomic analysis of paliperidone palmitate for treating schizophrenia in Greece

BACKGROUND: Patients having chronic schizophrenia with frequent relapses and hospitalizations represent a great challenge, both clinically and financially. Risperidone long-acting injection (RIS-LAI) has been the main LAI atypical antipsychotic treatment in Greece. Paliperidone palmitate (PP-LAI) has recently been approved. It is dosed monthly, as opposed to biweekly for RIS-LAI, but such advantages have not yet been analysed in terms of economic evaluation. PURPOSE: To compare costs and outcomes of PP-LAI versus RIS-LAI in Greece. METHODS: A cost-utility analysis was performed using a previously validated decision tree to model clinical pathways and costs over 1 year for stable patients started on either medication. Rates were taken from the literature. A local expert panel provided feedback on treatment patterns. All direct costs incurred by the national healthcare system were obtained from the literature and standard price lists; all were inflated to 2011 costs. Patient outcomes analyzed included average days with stable disease, numbers of hospitalizations, emergency room visits, and quality-adjusted life-years (QALYs). RESULTS: The total annual healthcare cost with PP-LAI was €3529; patients experienced 325 days in remission and 0.840 QALY; 28% were hospitalized and 15% received emergency room treatment. With RIS-LAI, the cost was €3695, patients experienced 318.6 days in remission and 0.815 QALY; 33% were hospitalized and 17% received emergency room treatment. Thus, PP-LAI dominated RIS-LAI. Results were generally robust in sensitivity analyses with PP-LAI dominating in 74.6% of simulations. Results were sensitive to the price of PP-LAI. CONCLUSIONS: PP-LAI appears to be a cost-effective option for treating chronic schizophrenia in Greece compared with RIS-LAI since it results in savings to the health care system along with better patient outcomes

Από τον Έρασμο της Αναγέννησης στα Ευρωπαϊκά προγράμματα Erasmus του 21ου αιώνα

Η ανάγκη να δημιουργηθεί στην Ευρώπη ένας κώδικας εκπαιδευτικών αξιών και στρατηγικών, ώστε η παρεχόμενη εκπαίδευση να έχει κάποιες συγκεκριμένες και κοινές προδιαγραφές για τους φοιτητές και νέους και κατ’ επέκταση έναν κοινό προσανατολισμό, τη διαμόρφωση δηλαδή Ευρωπαίων πολιτών, να ενσωματωθεί κοινώς στο εκπαιδευτικό σύστημα κάθε χώρας μια Ευρωπαϊκή Διάσταση της Εκπαίδευσης και κατ’ επέκταση η Ευρωπαϊκή ενοποίηση οδήγησε στην ανάπτυξη του προγράμματος Erasmus, το οποίο εν καιρώ έγινε ευρέως αποδεκτό. Παράλληλα, προκύπτει ότι μια εκπαίδευση με Ευρωπαϊκά χαρακτηριστικά, όπως είναι αυτή που παρέχεται από το Erasmus, είναι ανάλογη με τις συνεχείς εξελίξεις στην αγορά εργασίας, τόσο στους παραγωγικούς τομείς όσο και γενικότερα στην οικονομική οργάνωση της χώρας. Παρουσιάζει ακόμα μια ωφελιμότητα ως προς την κατάρτιση της νεολαίας και τον προσανατολισμό της σε αντικείμενα με τα οποία πρόκειται ν’ ασχοληθεί στο μέλλον, ενώ συνδέεται άμεσα με την παρεχόμενη εκπαίδευση στα Ανώτατα Εκπαιδευτικά Ιδρύματα. Η παρούσα έρευνα έχει ως σκοπό να διερευνήσει κατά πόσο οι ερωτώμενοι γνωρίζουν τα προγράμματα Erasmus, από που προήλθε το όνομά τους , ποια προσθήκη στο χώρο της Παιδείας έκανε ο Έρασμος και να περιγράψει το κατά πόσο το πρόγραμμα Erasmus προωθεί την Ευρωπαϊκή Διάσταση της Εκπαίδευσης και την Ευρωπαϊκή Ενοποίηση και κινητοποιεί το χώρο της Ανώτατης Εκπαίδευσης.The need for the creation in Europe of a code of educational values and strategies, so that the education provided has some specific and common specifications for students and young persons and by extension a common orientation, i. e. the formation of European citizens, simply put to incorporate into the educational system of each country a European Dimension of Education and consequently the European integration has led to the development of the Erasmus program, which in time became widely accepted. At the same time, we conclude that an education with European characteristics like the one provided by Erasmus, is corresponding to the constant developments in the labor market, both in productive areas and in the economic organization of the country more generally. It also presents a usefulness regarding the education of young persons and their orientation towards the subjects they are going to occupy themselves in the future, while it is directly linked to the education provided by Higher Education Institutions. The present research aims to study whether participants know the Erasmus programs, where their name comes from, which was the addition of Erasmus in the Educational Sector and to describe whether the Erasmus program promotes the European Dimension of Education and European Integration and motivates the area of Higher Education

Pharmacoeconomic analysis of paliperidone palmitate for treating schizophrenia in Greece

Abstract Background Patients having chronic schizophrenia with frequent relapses and hospitalizations represent a great challenge, both clinically and financially. Risperidone long-acting injection (RIS-LAI) has been the main LAI atypical antipsychotic treatment in Greece. Paliperidone palmitate (PP-LAI) has recently been approved. It is dosed monthly, as opposed to biweekly for RIS-LAI, but such advantages have not yet been analysed in terms of economic evaluation. Purpose To compare costs and outcomes of PP-LAI versus RIS-LAI in Greece. Methods A cost-utility analysis was performed using a previously validated decision tree to model clinical pathways and costs over 1 year for stable patients started on either medication. Rates were taken from the literature. A local expert panel provided feedback on treatment patterns. All direct costs incurred by the national healthcare system were obtained from the literature and standard price lists; all were inflated to 2011 costs. Patient outcomes analyzed included average days with stable disease, numbers of hospitalizations, emergency room visits, and quality-adjusted life-years (QALYs). Results The total annual healthcare cost with PP-LAI was €3529; patients experienced 325 days in remission and 0.840 QALY; 28% were hospitalized and 15% received emergency room treatment. With RIS-LAI, the cost was €3695, patients experienced 318.6 days in remission and 0.815 QALY; 33% were hospitalized and 17% received emergency room treatment. Thus, PP-LAI dominated RIS-LAI. Results were generally robust in sensitivity analyses with PP-LAI dominating in 74.6% of simulations. Results were sensitive to the price of PP-LAI. Conclusions PP-LAI appears to be a cost-effective option for treating chronic schizophrenia in Greece compared with RIS-LAI since it results in savings to the health care system along with better patient outcomes.</p

Pharmacoeconomic analysis of paliperidone palmitate for treating schizophrenia in Greece

Abstract Background Patients having chronic schizophrenia with frequent relapses and hospitalizations represent a great challenge, both clinically and financially. Risperidone long-acting injection (RIS-LAI) has been the main LAI atypical antipsychotic treatment in Greece. Paliperidone palmitate (PP-LAI) has recently been approved. It is dosed monthly, as opposed to biweekly for RIS-LAI, but such advantages have not yet been analysed in terms of economic evaluation. Purpose To compare costs and outcomes of PP-LAI versus RIS-LAI in Greece. Methods A cost-utility analysis was performed using a previously validated decision tree to model clinical pathways and costs over 1 year for stable patients started on either medication. Rates were taken from the literature. A local expert panel provided feedback on treatment patterns. All direct costs incurred by the national healthcare system were obtained from the literature and standard price lists; all were inflated to 2011 costs. Patient outcomes analyzed included average days with stable disease, numbers of hospitalizations, emergency room visits, and quality-adjusted life-years (QALYs). Results The total annual healthcare cost with PP-LAI was €3529; patients experienced 325 days in remission and 0.840 QALY; 28% were hospitalized and 15% received emergency room treatment. With RIS-LAI, the cost was €3695, patients experienced 318.6 days in remission and 0.815 QALY; 33% were hospitalized and 17% received emergency room treatment. Thus, PP-LAI dominated RIS-LAI. Results were generally robust in sensitivity analyses with PP-LAI dominating in 74.6% of simulations. Results were sensitive to the price of PP-LAI. Conclusions PP-LAI appears to be a cost-effective option for treating chronic schizophrenia in Greece compared with RIS-LAI since it results in savings to the health care system along with better patient outcomes

5G-PPP Technology Board:AI and ML – Enablers for Beyond 5G Networks

This white paper on AI and ML as enablers of beyond 5G (B5G) networks is based on contributions from 5G PPP projects that research, implement and validate 5G and B5G network systems. The white paper introduces the main relevant mechanisms in Artificial Intelligence (AI) and Machine Learning (ML), currently investigated and exploited for 5G and B5G networks. A family of neural networks is presented which are, generally speaking, non-linear statistical data modelling and decision-making tools. They are typically used to model complex relationships between input and output parameters of a system or to find patterns in data. Feed-forward neural networks, deep neural networks, recurrent neural networks, and convolutional neural networks belong to this family. Reinforcement learning is concerned about how intelligent agents must take actions in order to maximize a collective reward, e.g., to improve a property of the system. Deep reinforcement learning combines deep neural networks and has the benefit that is can operate on non-structured data. Hybrid solutions are presented such as combined analytical and machine learning modelling as well as expert knowledge aided machine learning. Finally, other specific methods are presented, such as generative adversarial networks and unsupervised learning and clustering. In the sequel the white paper elaborates on use case and optimisation problems that are being tackled with AI/ML, partitioned in three major areas namely, i) Network Planning, ii) Network Diagnostics/Insights, and iii) Network Optimisation and Control. In Network Planning, attention is given to AI/ML assisted approaches to guide planning solutions. As B5G networks become increasingly complex and multi-dimensional, parallel layers of connectivity are considered a trend towards disaggregated deployments in which a base station is distributed over a set of separate physical network elements which ends up in the growing number of services and network slices that need to be operated. This climbing complexity renders traditional approaches in network planning obsolete and calls for their replacement with automated methods that can use AI/ML to guide planning decisions. In this respect two solutions are discussed, first the network element placement problem is introduced which aims at improvements in the identification of optimum constellation of base stations each located to provide best network performance taking into account various parameters, e.g. coverage, user equipment (UE) density and mobility patterns (estimates), required hardware and cabling, and overall cost. The second problem considered in this regard is the dimensioning considerations for C-RAN clusters, in which employing ML-based algorithms to provide optimal allocation of baseband unit (BBU) functions (to the appropriate servers hosted by the central unit (CU)) to provide the expected gains is addressed. In Network Diagnostics, attention is given to the tools that can autonomously inspect the network state and trigger alarms when necessary. The contributions are divided into network characteristics forecasts solutions, precise user localizations methods, and security incident identification and forecast. The application of AI/ML methods in high-resolution synthesising and efficient forecasting of mobile traffic; QoE inference and QoS improvement by forecasting techniques; service level agreement (SLA) prediction in multi-tenant environments; and complex event recognition and forecasting are among network characteristics forecasts methods discussed. On high-precision user localization, AI-assisted sensor fusion and line-of-sight (LoS)/non-line-of-sight (NLoS) discrimination, and 5G localization based on soft information and sequential autoencoding are introduced. And finally, on forecasting security incidents, after a short introduction on modern attacks in mobile networks, ML-based network traffic inspection and real-time detection of distributed denial-of-service (DDoS) attacks are briefly examined. In regard to the Network Optimisation and Control, attention is given to the different network segments, including radio access, transport/fronthaul (FH)/backhaul (BH), virtualisation infrastructure, end-to-end 5G PPP Technology Board AI/ML for Networks 3 (E2E) network slicing, security, and application functions. Among application of AI/ML in radio access, the slicing in multi-tenant networks, radio resource provisioning and traffic steering, user association, demand-driven power allocation, joint MAC scheduling (across several gNBs), and propagation channel estimation and modelling are discussed. Moreover, these solutions are categorised (based on the application time-scale) into real-time, near-real-time, and non-real-time groups. On transport and FH/BH networks, AI/ML algorithms on triggering path computations, traffic management (using programmable switches), dynamic load balancing, efficient per-flow scheduling, and optimal FH/BH functional splitting are introduced. Moreover, federated learning across MEC and NFV orchestrators, resource allocation for service function chaining, and dynamic resource allocation in NFV infrastructure are among introduced AI/ML applications for virtualisation infrastructure. In the context of E2E slicing, several applications such as automated E2E service assurance, resource reservation (proactively in E2E slice) and resource allocation (jointly with slice-based demand prediction), slice isolation, and slice optimisation are presented. In regard to the network security, the application of AI/ML techniques in responding to the attack incidents are discussed for two cases, i.e. in moving target defence for network slice protection, and in self-protection against app-layer DDoS attacks. And finally, on the AI/ML applications in optimisation of application functions, the dash prefetching optimization and Q-learning applications in federated scenarios are presented.The white paper continues with the discussions on the application of AI/ML in the 5G and B5G network architectures. In this context the AI/ML based solutions pertaining to autonomous slice management, control and orchestration, cross-layer optimisation framework, anomaly detection, and management analytics, as well as aspects in AI/ML-as-a-service in network management and orchestration, and enablement of ML for the verticals' domain are presented. This is followed by topics on management of ML models and functions, namely the ML model lifecycle management, e.g., training, monitoring, evaluation, configuration and interface management of ML models. Furthermore, the white paper investigates the standardisation activities on the enablement of AI/ML in networks, including the definition of network data analytics function (NDAF) by 3GPP, the definition of an architecture that helps address challenges in network automation and optimization using AI and the categories of use cases where AI may benefit network operation and management by ETSI ENI, and finally the O-RAN definition of non-real-time and near-real-time RAN controllers to support ML-based management and intelligent RAN optimisation. Additionally, the white paper identifies the challenges in view of privacy and trust in AI/ML-based networks and potential solutions by introducing privacy preserving mechanisms and the zero-trust management approach are introduced. The availability of reliable data-sets as a crucial prerequisite to efficiency of AI/ML algorithms is discussed and the white paper concludes with a brief overview of AI/ML-based KPI validation and system troubleshooting. In summary the findings of this white paper conclude with the identification of several areas (research and development work) for further attention in order to enhance future network return-on-investment (ROI): (a) building standardized interfaces to access relevant and actionable data, (b) exploring ways of using AI to optimize customer experience, (c) running early trials with new customer segments to identify AI opportunities, (d) examining use of AI and automation for network operations, including planning and optimization, (e) ensuring early adoption of new solutions for AI and automation to facilitate introduction of new use cases, and (f) establish/launch an open repository for network data-sets that can be used for training and benchmarking algorithms by all