Generating health technology assessment evidence for rare diseases

Objectives: Rare diseases are often heterogeneous in their progression and response to treatment, with only a small population for study. This provides challenges for evidence generation to support HTA, so novel research methods are required. Methods: Discussion with an expert panel was augmented with references and case studies to explore robust approaches for HTA evidence generation for rare disease treatments. Results: Traditional RCTs can be modified using sequential, three-stage or adaptive designs to gain more power from a small patient population or to focus trial design. However, such designs need to maintain important design aspects such as randomization and blinding and be analyzed to take account of the multiple analyses performed. N-of-1 trials use within-patient randomization to test repeat periods of treatment and control until a response is clear. Such trials could be particularly valuable for rare diseases and when prospectively planned across several patients and analyzed using Bayesian techniques, a population effect can be estimated that might be of value to HTA. When the optimal outcome is unclear in a rare disease, disease specific patient reported outcomes can elucidate impacts on patients’ functioning and wellbeing. Likewise, qualitative research can be used to elicit patients’ perspectives, with just a small number of patients. Conclusions: International consensus is needed on ways to improve evidence collection and assessment of technologies for rare diseases, which recognize the value of novel study designs and analyses in a setting where the outcomes and effects of importance are yet to be agreed.</p

How to encourage a lifelong learner? The complex relation between learning strategies and assessment in a medical curriculum

To foster lifelong learning skills, we need new didactic approaches with aligned assessment methods. Therefore, we investigated whether the outcomes of a project assignment show a different relation to learning strategies than a longitudinal knowledge-based assessment. We studied learning strategies of first year students of medicine and biomedical sciences (n = 248) and performed hierarchical regression analyses for the learning strategies and grades of the longitudinal knowledge-based test and project assignment. Scores of students, measured with the Motivated Strategies for Learning Questionnaire (Likert scale 1-7), were relatively low for critical thinking (3.53), compared to rehearsal (4.40), elaboration (4.82), organisation (4.69) and metacognitive self-regulation (4.33). Knowledge based tests showed a significant relation to elaboration (p <0.01). For the project-based assessment, we did not find a significant relation to any learning strategy (p = 0.074). Explained variance of the grades was low for all learning strategies (R-2 <0.043). Different types of assessment did not discriminate between students with high or low scores on learning strategies associated with lifelong learning. An explanation is that the curriculum is not aligned with assessment, or students do not benefit in terms of grades. We conclude that, if assessment is to drive lifelong learning skills, this is not self-evident

Problems and Promises of Health Technologies: The Role of Early Health Economic Modeling

Background: To assess whether early health economic modeling helps to distinguish those healthcare innovations that are potentially cost-effective from those that are not potentially cost-effective. We will also study what information is retrieved from the health economic models to inform further development, research and implementation decisions.Methods: We performed secondary analyses on an existing database of 32 health economic modeling assessments of 30 innovations, performed by our group. First, we explored whether the assessments could distinguish innovations with potential cost-effectiveness from innovations without potential cost-effectiveness. Second, we explored which recommendations were made regarding development, implementation and further research of the innovation. Results: Of the 30 innovations, 1 (3%) was an idea that was not yet being developed and 14 (47%) were under development. Eight (27%) innovations had finished development, and another 7 (23%) innovations were on the market. Although all assessments showed that the innovation had the potential to become cost-effective, due to improved patient outcomes, cost savings or both, differences were found in the magnitude of the potential benefits, and the likelihood of reaching this potential. The assessments informed how the innovation could be further developed or positioned to maximize its cost-effectiveness, and informed further research.Conclusion: The early health economic assessments provided insight in the potential cost-effectiveness of an innovation in its intended context, and the associated uncertainty. None of the assessments resulted in a firm ‘no-go’ recommendation, but recommendations could be provided on further research and development in order to maximize value for money

Group medical appointments for people with physical illness

This is the protocol for a review and there is no abstract. The objectives are as follows: To assess the effects of group medical appointments (GMAs) on the health status and well-being of patients with a primary physical illness as compared to one-to-one patient-clinician appointments

Priority setting for universal health coverage: We need evidence-informed deliberative processes, not just more evidence on cost-effectiveness

Priority setting of health interventions is generally considered as a valuable approach to support low- and middle-income countries (LMICs) in their strive for universal health coverage (UHC). However, present initiatives on priority setting are mainly geared towards the development of more cost-effectiveness information, and this evidence does not sufficiently support countries to make optimal choices. The reason is that priority setting is in reality a value-laden political process in which multiple criteria beyond cost-effectiveness are important, and stakeholders often justifiably disagree about the relative importance of these criteria. Here, we propose the use of ‘evidence-informed deliberative processes’ as an approach that does explicitly recognise priority setting as a political process and an intrinsically complex task. In these processes, deliberation between stakeholders is crucial to identify, reflect and learn about the meaning and importance of values, informed by evidence on these values. Such processes then result in the use of a broader range of explicit criteria that can be seen as the product of both international learning (‘core’ criteria, which include eg, cost-effectiveness, priority to the worse off, and financial protection) and learning among local stakeholders (‘contextual’ criteria). We believe that, with these evidence-informed deliberative processes in place, priority setting can provide a more meaningful contribution to achieving UHC

Ethical analysis in HTA of complex health interventions

Background: In the field of health technology assessment (HTA), there are several approaches that can be used for ethical analysis. However, there is a scarcity of literature that critically evaluates and compares the strength and weaknesses of these approaches when they are applied in practice. In this paper, we analyse the applicability of some selected approaches for addressing ethical issues in HTA in the field of complex health interventions. Complex health interventions have been the focus of methodological attention in HTA. However, the potential methodological challenges for ethical analysis are as yet unknown. Methods: Six of the most frequently described and applied ethical approaches in HTA were critically assessed against a set of five characteristics of complex health interventions: multiple and changing perspectives, indeterminate phenomena, uncertain causality, unpredictable outcomes, and ethical complexity. The assessments are based on literature and the authors’ experiences of developing, applying and assessing the approaches. Results: The Interactive, participatory HTA approach is by its nature and flexibility, applicable across most complexity characteristics. Wide Reflective Equilibrium is also flexible and its openness to different perspectives makes it better suited for complex health interventions than more rigid conventional approaches, such as Principlism and Casuistry. Approaches developed for HTA purposes are fairly applicable for complex health interventions, which one could expect because they include various ethical perspectives, such as the HTA Core Model® and the Socratic approach. Conclusion: This study shows how the applicability for addressing ethical issues in HTA of complex health interventions differs between the selected ethical approaches. Knowledge about these differences may be helpful when choosing and applying an approach for ethical analyses in HTA. We believe that the study contributes to increasing awareness and interest of the ethical aspects of complex health interventions in general

Impact of Expanding Eligibility Criteria for Cochlear Implantation - Dynamic Modeling Study

Objectives Eligibility criteria for cochlear implantation (CI) are shifting due to technological and surgical improvements. The aim of this study was to explore the impact of further expanding unilateral CI criteria in those with severe hearing loss (HL) (61-80 dBHL) in terms of number of CI recipients, costs, quality of life, and cost-effectiveness. Methods A dynamic population-based Markov model was constructed mimicking the Dutch population in three age categories over a period of 20 years. Health states included severe HL (61-80 dBHL), profound HL (&gt;81 dBHL), CI recipients, and no-CI recipients. Model parameters were based on published literature, (national) databases, expert opinion, and model calibration. Results If persons with severe HL would qualify and opt for CI similar to those with profound HL now, this would lead to a 6-7 times increase of new CI recipients and an associated increase in costs (euro550 million) and QALYs (54.000) over a 20-year period (incremental cost utility ratio: 10.771 euros/QALY [2.5-97.5 percentiles: 1.252-23.171]). One-way-sensitivity analysis indicated that model outcomes were most sensitive to regaining employment, utility associated with having a CI, and costs of surgery and testing. Conclusion Our findings suggest that expanding eligibility for CI to persons with severe HL could be a cost-effective use of resources. Clearly, however, it would require a significant increase in diagnostic, operative, and rehabilitative capacity. Our quantitative estimates can serve as a basis for a wider societal deliberation on the question whether such an increase can and should be pursued. Level of Evidence N/A Laryngoscope, 202

Priority Setting for Universal Health Coverage: We Need Evidence-Informed Deliberative Processes, Not Just More Evidence on Cost-Effectiveness

Priority setting of health interventions is generally considered as a valuable approach to support low- and middle-income countries (LMICs) in their strive for universal health coverage (UHC). However, present initiatives on priority setting are mainly geared towards the development of more cost-effectiveness information, and this evidence does not sufficiently support countries to make optimal choices. The reason is that priority setting is in reality a value-laden political process in which multiple criteria beyond cost-effectiveness are important, and stakeholders often justifiably disagree about the relative importance of these criteria. Here, we propose the use of ‘evidence-informed deliberative processes’ as an approach that does explicitly recognise priority setting as a political process and an intrinsically complex task. In these processes, deliberation between stakeholders is crucial to identify, reflect and learn about the meaning and importance of values, informed by evidence on these values. Such processes then result in the use of a broader range of explicit criteria that can be seen as the product of both international learning (‘core’ criteria, which include eg, cost-effectiveness, priority to the worse off, and financial protection) and learning among local stakeholders (‘contextual’ criteria). We believe that, with these evidence-informed deliberative processes in place, priority setting can provide a more meaningful contribution to achieving UHC

Guidance on the use of logic models in health technology assessments of complex interventions

Challenges in assessments of health technologies In recent years there have been major advances in the development of health technology assessment (HTA). However, HTA still has certain limitations when assessing technologies, which fi are complex, i.e. consist of several interacting components, target different groups or organisational levels, have multiple and variable outcomes, and/or permit a certain degree of flexibility or tailoring; fi are context-dependent, with HTA usually focusing on the technology rather than on the system within which it is used; fi perform differently depending on the way they are implemented; and/or fi have distinct effects on different individuals. Logic models are one important means of conceptualising and handling complexity in HTAs or systematic reviews (SRs) of complex technologies, as well as integrating the findings of multi-component HTAs. A logic model is described as “… a graphic description of a system … designed to identify important elements and relationships within that system”. When evaluating complex health technologies, logic models can serve an instrumental purpose at every stage of the HTA/SR process, from scoping the topic of the HTA/SR, including formulating the question and defining the intervention; conducting the HTA/SR; interpreting results and making the HTA/SR relevant for decision makers to implement in policy and practice. Purpose and scope of the guidance This guidance is targeted at commissioners, producers and users of guidelines, HTAs and SRs with an interest in using logic models as an overarching framework for their work. It aims to make the use of logic models as straightforward as possible by facilitating the systematic identification or development as well as utilisation of different types and sub-types of logic models. In principle, logic models are a useful tool in any kind of SR or HTA, as they aid with the conceptualisation of the intervention and the review question. This is particularly useful for complex technologies, where conceptualising the intervention and its implementation within a system is critical. In addition, logic models can enhance communication within the HTA/SR team and with relevant stakeholders. Three types of logic model are described: With a priori logic models the logic model is specified upfront and remains unchanged during the HTA/SR process. With iterative logic models the logic model is subject to continual modification throughout the course of an HTA/SR. The staged logic model harnesses the strengths of both a priori and iterative approaches by pre-specifying revision points at which major data inputs are anticipated. In | 6 addition, two subtypes are identified, namely logic models that seek to represent structure (system-based) and those that focus on processes or activities (process-orientated). This guidance offers direction on how to choose between distinct types and sub-types of logic models, describes each logic model type and its application in detail, and provides templates for getting started with the development of an HTA/SR-specific logic model. Development of the guidance This guidance was informed by a combination of (i) systematic searches for published examples of logic models supplemented by purposive sampling of iterative logic modelling approaches; (ii) searches for existing guidance on the use of logic models in primary research, SRs and HTAs; (iii) development of two draft templates for system-based and process-orientated logic models in an iterative process within the research team and in consultation with external methodological experts; and (iv) application of these draft templates in multiple SRs and one HTA of different complex health technologies covering technical, educational and policy interventions in environmental health, e-learning for health professionals and models of palliative care. Application of this guidance For a comprehensive integrated assessment of a complex technology we have developed a five step process, the INTEGRATE-HTA model. In Step 1 the HTA objective and the technology are defined with the support from a panel of stakeholders. A system-based logic model is developed in Step 2. It provides a structured overview of technology, the context, implementation issues, and relevant patient groups. It then frames the assessment of the effectiveness, as well as economic, ethical, legal, and socio-cultural aspects in Step 3. In Step 4 a graphical overview of the assessment results, structured by the logic model, is provided. Step 5 is a structured decision-making process informed by the HTA (and is thus not formally part of the HTA but follows it). Logic models therefore form an integral element of the INTEGRATE-HTA model but may also be useful in individual steps. This guidance starts off by offering support in identifying and, as needed, adapting existing logic models from the literature or developing an HTA-/SR-specific logic model de novo. In either case, the user will need to decide upfront whether to pursue an a priori, staged or iterative approach to logic modelling, and the guidance offers criteria on how to decide between these distinct types of logic modelling. The system-based and process-orientated logic model templates provide a starting point for the de novo development of either type of logic model. The guidance also discusses the advantages and drawbacks of adopting the system-based or process-orientated sub-type, and offers some general considerations in applying logic models, such as the variety of data sources used, transparency in reporting and necessary trade-offs between comprehensiveness and complexity of the logic model in communicating with stakeholders. For a priori logic modelling, a six-step process comprises: (1) defining the PICO elements of the HTA/SR as well as relevant aspect of context and implementation; (2) deciding on a system- vs. process-orientated logic model subtype with the former focusing on a conceptualization of the question and the latter more concerned with an explanation of the pathways from the intervention to the outcomes; (3) populating the logic model template with information obtained through literature searches, discussions within the author team and consultations with content experts; (4) asking stakeholders for input and refining the logic model accordingly; (5) repeating steps 3 and 4 until all members of the author team agree that the logic model accurately represents the framework for the specific HTA/SR; and (6) publishing the final logic model with the protocol of the HTA or SR. This logic model remains unchanged during the HTA/SR process. For iterative logic modelling, a five-step process includes: (1) creating an initial logic model as a starting point for subsequent exploration, where a logic model template is used to create an initial logic model de novo; (2) identifying data on the whole system or entire process, or on individual components of the model, where data may come from stakeholders, the review team, ongoing primary research or the published literature; (3) making 7 | changes to the initial logic model repeatedly and at any point of the review and documenting these changes carefully; (4) creating a new numbered version of the logic model, where changes are considered substantive or stepwise; and (5) recording a definitive version of the logic model for the purpose of publication within the final HTA/SR report. It is recognised that this version of the logic model is only definitive with regard to the specific project timeframe and may well be subject to subsequent modification by the HTA/SR team, or indeed by other teams. For staged logic modelling, a four-step process consists of: (1) developing an initial logic model, using one of the templates and various mechanisms to populate them, in particular input from stakeholders and literature searches; (2) pre-specifying points within the HTA/SR process at which significant inputs, defined in terms of quantity or importance, are likely to have an impact on the structure and content of the HTA/SR and thus the logic model; (3) revisiting the logic model at the pre-specified review and revision points, and creating new and clearly labelled versions, documenting how and based on which data sources changes were made; and (4) presenting selected versions of the logic model, as a minimum the initial and the final logic models, in the HTA/ SR report. Conclusions Logic models are an important tool when conducting HTAs or SRs of complex health technologies, as they enhance transparency on underlying assumptions and help understand complexity by depicting the entire system, its parts and the interactions between intervention and outcomes; they also play a key role in integrating across different parts of a multi-component HTA. Nonetheless, logic models are not a panacea in addressing or resolving complexity and each type shows its specific strengths and limitations. This guidance provides a stateof-the-art overview of current practices in the use of logic models within HTAs and SRs. By providing templates for generating a logic model de novo, it aims to make the process of logic model development and application as straightforward as possible. Certain types and sub-types of logic models are more or less suitable depending on the technology concerned and the HTA/SR question addressed and approach pursued. This guidance offers a series of considerations on how to choose between a priori, iterative and staged logic models, illustrated with example applications of each type