Economic Evaluation of Medical Devices

There are a number of challenges in the economic evaluation of medical devices (MDs). They are typically less regulated than pharmaceuticals, and the clinical evidence requirements for market authorization are generally lower. There are also specific characteristics of MDs, such as the device–user interaction (learning curve), the incremental nature of innovation, the dynamic nature of pricing, and the broader organizational impact. Therefore, a number of initiatives need to be taken in order to facilitate the economic evaluation of MDs. First, the regulatory processes for MDs need to be strengthened and more closely aligned to the needs of economic evaluation. Second, the methods of economic evaluation need to be enhanced by improving the analysis of the available clinical data, establishing high-quality clinical registries, and better recognizing MDs’ specific characteristics. Third, the market entry and diffusion of MDs need to be better managed by understanding the key influences on MD diffusion and linking diffusion with cost-effectiveness evidence through the use of performance-based risksharing arrangements

Should Health Technology Assessment be more patient-centric? If so, how?

Health technology assessment (HTA) methods and processes have been criticized for not being sufficiently ‘patient centric’. For example, Perfetto [1] argued that a proposed approach for assessing the value of health care interventions had not sufficiently incorporated a patient perspective and suggested that it represented a ‘missed opportunity’. A similar point was made about the other ‘value assessment frameworks’ developed recently in the United States [2]. In addition, Slejko et al. [3] proposed some key elements of a ‘patient informed’ reference case for conducting economic evaluations, which would supplement reference cases outlined by groups such as the second Panel on Cost-Effectiveness in Health and Medicine [4], by including consideration of elements such as convenience in receiving care, effects on the patient’s family, examination of whether quality of life instruments include the most relevant domains and a model structure for the economic evaluation that adequately reflects the patient’s journey through the various treatment options. While the case to consider the patient perspective is strong, the way in which it should be incorporated in HTA is not obvious. Most HTA analysts would argue that HTAs, with the possible exception of some that are undertaken to support the development of clinical guidelines, or initiatives such as shared (clinical) decision-making, are conducted for those making decisions about the allocation of health care resources for a given population. This population may be the enrollees of a given health plan or, in the case of national health services or national insurance schemes, the whole population of a given country. The population would include patients who currently have the disease of interest, their families, those who have been patients in the past and those who may contract the disease in the future, as well as those past, present and future sufferers of other diseases. Of course, there may be considerable similarity between the perspective of patients currently suffering from a disease and the population at large, but this is not necessarily the case [5, 6, 7]. Therefore, making HTA more patient centric may not be as simple as it appears. The case for considering the patient perspective may differ by type of health care system [8]. For example, in a private insurance-based system, where a substantial proportion of the payments may be made directly by the patients, one might expect more consideration of the patient perspective than in a publicly funded national health service. The empirical evidence generated in the recent years shows that there is greater awareness about the importance of patients’ views in HTA, but there is no common understanding of what “patient-centric HTA” actually means [9]. Here, we attempt to shed further light on the issue by conceptualizing “patient centricity” in two ways: (a) encouraging patients’ engagement in HTA process and (b) enlarging the scope of evidence in HTA to include patients’ outcomes and preferences (HTA methods). We discuss the opportunities and challenges of each, by providing some recent examples from different countries. Finally, we discuss some additional ways to make HTA more patient centric

Characterization of bread wheat cultivars (Triticum aestivum L.) by glutenin proteins

Glutenin polymers composed of HMW and LMW subunits are important contributors to the wheat end-use properties. Twenty-six winter wheat cultivars differing in bread processing quality were collected at the experimental fields of the Agricultural Institute Osijek, Croatia and Institute of the Field and Vegetable Crops Novi Sad, Serbia, in 2008/2009 season. The HMW glutenins composition and glutenin proteins content were determined by SDS-PAGE and RP-HPLC, respectively, with aim to determine the relationship between glutenin protein fractions and wheat quality properties. Significant differences were found between Croatian and Serbian cultivars in several quality attributes (GI, WA, DDT, DS and R/EXT) as well as in the content of total glutenins and LMW glutenins and GLI/GLU ratio. The dominant HMW subunits in analyzed cultivars were 2*, 7 + 9/7 + 8 and 5 + 10. Principal component analysis (PCA) confirmed the presence of association between HMW glutenins composition and GI, dough E, R and R/EXT, while the glutenins quantitative data showed pronounced relation with P, DDT, DS, E, R and R/EXT. GLI/GLU ratio had the opposite effect on these parameters

High prevalence of extrapyramidal signs and symptoms in a group of Italian dental technicians

This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

Evaluating complex health financing interventions: using mixed methods to inform further implementation of a novel PBI intervention in rural Malawi

Background: Gaps remain in understanding how performance-based incentive (PBI) programs affect quality of care and service quantity, whether programs are cost effective and how programs could be tailored to meet client and provider needs while remaining operationally viable. In 2014, Malawi’s Ministry of Health launched the Service Delivery Integration-PBI (SSDI-PBI) program. The program is unique in that no portion of performance bonuses are paid to individual health workers, and it shifts responsibility for infrastructure and equipment procurement from facility staff to implementing partners. This protocol outlines an approach that analyzes processes and outcomes, considers expected and unexpected consequences of the program and frames the program’s outputs relative to its costs. Findings from this evaluation will inform the intended future scale-up of PBI in Malawi. Methods/design: This study employs a prospective controlled before-and-after triangulation design to assess effects of the PBI program by analyzing quantitative and qualitative data from intervention and control facilities. Guided by a theoretical framework, the evaluation consists of four main components: service provision, health worker motivation, implementation processes and costing. Quality and access outcomes are assessed along four dimensions: (1) structural elements (related to equipment, drugs, staff); (2) process elements (providers’ compliance with standards); (3) outputs (service utilization); (4) experiential elements (experiences of service delivery). The costing component includes costs related to start-up, ongoing management, and the cost of incentives themselves. The cost analysis considers costs incurred within the Ministry of Health, funders, and the implementing agency. The evaluation relies on primary data (including interviews and surveys) and secondary data (including costing and health management information system data). Discussion: Through the lens of a PBI program, we illustrate how complex interventions can be evaluated via not only primary, mixed-methods data collection, but also through a wealth of secondary data from program implementers (including monitoring, evaluation and financial data), and the health system (including service utilization and service readiness data). We also highlight the importance of crafting a theory and using theory to inform the nature of data collected. Finally, we highlight the need to be responsive to stakeholders in order to enhance a study’s relevance

Does the approach to economic evaluation in health care depend on culture, values, and institutional context?

There is a fairly strong case to argue that culture, values, and institutional context have an influence on the use of HTA and economic evaluation in health care, either directly, or indirectly through the impact on the organization of the health care system. Therefore, this observation may be useful both in explaining the different approaches to the use of economic evaluation between countries and in determining the most appropriate use of economic evaluation for other countries yet to adopt the approach

The flare likelihood and region eruption forecasting (FLARECAST) project: flare forecasting in the big data & machine learning era

The European Union funded the FLARECAST project, that ran from January 2015 until February 2018. FLARECAST had a research-to-operations (R2O) focus, and accordingly introduced several innovations into the discipline of solar flare forecasting. FLARECAST innovations were: first, the treatment of hundreds of physical properties viewed as promising flare predictors on equal footing, extending multiple previous works; second, the use of fourteen (14) different machine learning techniques, also on equal footing, to optimize the immense Big Data parameter space created by these many predictors; third, the establishment of a robust, three-pronged communication effort oriented toward policy makers, space-weather stakeholders and the wider public. FLARECAST pledged to make all its data, codes and infrastructure openly available worldwide. The combined use of 170+ properties (a total of 209 predictors are now available) in multiple machine-learning algorithms, some of which were designed exclusively for the project, gave rise to changing sets of best-performing predictors for the forecasting of different flaring levels, at least for major flares. At the same time, FLARECAST reaffirmed the importance of rigorous training and testing practices to avoid overly optimistic pre-operational prediction performance. In addition, the project has (a) tested new and revisited physically intuitive flare predictors and (b) provided meaningful clues toward the transition from flares to eruptive flares, namely, events associated with coronal mass ejections (CMEs). These leads, along with the FLARECAST data, algorithms and infrastructure, could help facilitate integrated space-weather forecasting efforts that take steps to avoid effort duplication. In spite of being one of the most intensive and systematic flare forecasting efforts to-date, FLARECAST has not managed to convincingly lift the barrier of stochasticity in solar flare occurrence and forecasting: solar flare prediction thus remains inherently probabilistic