Disease-modifying dementia treatments (DMDTs) target amyloid beta or tau proteins and have the potential to change disease progression, representing a step change in the management and treatment of Alzheimer’s disease. Given the novel mechanism of action and impact on health care, the NICE Health Technology Assessment Innovation Laboratory (HTA Lab) sought to identify and contextualize the key issues for future appraisals.

We reviewed published assessment reports of DMDTs from international HTA agencies and conducted a scoping review of published economic models of pharmacological treatments to understand the challenges associated with evaluating the cost-effectiveness of dementia treatments. The HTA Lab held an engagement workshop with 27 external stakeholders, including expert clinicians, implementation partners, health economists, and representatives from international agencies to discuss and confirm the key issues and considerations likely to emerge during an appraisal of DMDTs.

Key clinical and cost-effectiveness issues were identified and discussed. We concluded that consideration needs to be given to the diagnostic methods to identify the DMDT-eligible population in the UK, the validity of the surrogate outcomes used in the DMDT clinical trials, treatment effectiveness in different populations, and the incidence of DMDT-associated adverse events. Economic considerations include the type of economic model used in the appraisal, modeling the natural history of the disease, paucity of quality-of-life data in the treatment population, the inclusion of societal impact, treatment duration, stopping rules, and long-term effectiveness beyond the clinical trials.

DMDTs could have the potential to transform Alzheimer’s disease care. With multiple treatments on the horizon, the appropriateness and acceptability of the new mechanism of action underpinning these treatments should be considered. We have identified areas of uncertainty that are likely to arise during an appraisal process, to facilitate the timely approval of these medicines and patient access.

Economic evaluation using decision analytical models (DAMs) plays a limited role in shaping healthcare resource optimization and reimbursement decisions in the Middle East. This review aimed to systematically examine economic evaluation studies focusing on DAMs of medicines in the Middle East, defining methodological characteristics and appraising the quality of the identified models.

Six databases were searched (MEDLINE, Embase, EconLit, Web of Science, the Global Health Cost-Effectiveness Analysis Registry, and the Global Index Medicus) from 1998 to September 2023 to identify published DAMs of medicines in the Middle East. Studies meeting the inclusion criteria—full economic evaluations of medicines using a model-based method in the Middle East—were included. Data were extracted and tabulated to include study characteristics and methodological specifications. The results were analyzed narratively. The Philips checklist was used to assess the quality of the studies.

Sixty-two DAM studies of medicines were identified from nine Middle Eastern countries, the majority of which (76%) were conducted in Iran, Turkey, and Saudi Arabia. The cost effectiveness of medications for non-communicable diseases was explored in 70 percent of the models. Cost-effectiveness thresholds based on gross domestic product were commonly used. International sources provided data on intervention effectiveness and health outcomes, while national sources were mainly used for the costs of resource use. Most models incorporated an assessment of parameter uncertainty, whereas other types of uncertainty were not explored. Studies from high-income countries were generally of higher quality than those from middle-income countries.

The number of published DAMs was low considering the available medicines and disease burden. Key aspects of high quality DAMs regarding model structure, input sources, and uncertainty assessment were not consistently fulfilled. Recommendations for future studies and policies included strengthening existing health economic capacities, establishing country-specific health technology assessment systems, and initiating collaborations to generate national cost and outcome data.

There is an increasing number of policy and guidance documents on the use and acceptability of real-world evidence (RWE) to support regulatory and health technology assessment (HTA) decision-making. The Innovative Health Initiative Integration of Heterogeneous Data and Evidence towards Regulatory and HTA Acceptance (IDERHA) partnership is undertaking a global landscape review of these documents to understand where there is consensus and divergence, and where further policy development is needed.

A literature search of the MEDLINE and Embase databases was performed, in addition to handsearching the websites of specific HTA and regulatory organizations. All policies, standards, frameworks, and guidance documents on requirements for acceptable RWE data use published from 2017 were included. Two reviewers independently extracted data using a standard data extraction form that was pilot tested before use. Any discrepancies between the reviewers were resolved by consensus. Extracted data are currently being analyzed by researchers with regulatory or HTA expertise. A workshop held in October 2023 sought input from experts on analysis plans.

The initial literature search yielded 3,184 results. After screening against the inclusion criteria, a total of 87 documents were selected for full-text review (21 HTA and 62 regulatory documents). Of these, 32 were identified as key documents and prioritized for initial review. Key themes in the documents, including transparency, data collection, study design, and data quality, were identified and validated in a workshop with five regulatory or HTA experts. Data extraction is ongoing for the remaining documents and any further themes identified will be added. Any gaps and areas of divergence will be identified, so they can be addressed by future IDERHA work.

This review assessed the increasingly complex global landscape of regulatory and HTA policies and guidance on the use of RWE. Through the exploration of similarities, differences, and gaps in these policies, this work will extend the current understanding of best practice and identify areas that need development of further guidance.

Progress and innovation in artificial intelligence (AI)-based healthcare interventions continue to develop rapidly. However, there are limitations in the published health economic evaluations (HEEs) of AI interventions, including limited reporting on characteristics and development of algorithms. We developed an extension to the existing Consolidated Health Economic Evaluation Reporting Standards (CHEERS) to improve consistency, transparency, and reliability of the reporting of HEEs of AI interventions.

The Delphi method was used, following a prespecified study protocol. A steering group with expert oversight was formed to guide the development process. A long list of potential items was defined based on two recent systematic reviews of HEEs of AI-based interventions. The steering group identified and invited 119 experts to the three-stage survey. Participants were asked to score each item on a nine-point Likert scale, and they were also able to provide free-text comments. The final checklist was piloted on a random sample of nine HEEs of AI-based interventions.

Three stages of the Delphi survey were completed by 58, 42, and 31 multidisciplinary respondents, respectively, including HTA specialists, health economists, AI experts, and patient representatives. The CHEERS-AI extension includes 18 AI-specific reporting items. Ten are entirely new items, including considerations about user autonomy, validation of the AI component, and AI-specific uncertainty. In addition, elaborations on eight existing CHEERS items were added to emphasize important AI-specific nuances. Some participants highlighted that CHEERS-AI can provide key benefits; for example, it could clarify the misconception that the predictive algorithms supporting AI-driven healthcare interventions are available for use without cost.

CHEERS-AI can aid in improved reporting quality for researchers, editors, and reviewers conducting or assessing HEEs of AI interventions.

Health technology assessment (HTA) is growing in low- and middle-income countries (LMICs) to ensure optimal use of limited resources. However, the impact of HTAs on decision making in LMICs has been limited. The study aimed to provide an overview of Turkiye’s progress since establishing the first HTA agency in 2012.

The web sites of three national HTA agencies in Turkiye were searched for HTA guidelines and national HTA reports. The HTA guidelines were assessed by two researchers independently against the key principles of HTA developed by Drummond et al., and the HTA reports against the national guidelines.

The study included one HTA guideline and eight national HTA reports. The guideline included very limited technical guidance. Compliance with the principles was poor to moderate, and significant methodological limitations were identified. The reports were inconsistent regarding the scope and the HTA assessment criteria. The link between HTA findings, HTA decision making, and health policies were not clear.

The inconsistencies between the reports and the methodological limitations demonstrate the need for national HTA guidelines. Improving the characteristics of the HTA might impact implementation. Among the key issues is transparency regarding priority setting, the HTA process, and decision making.

Establishing and adopting national HTA guidelines at international standards is needed. Involving external scientific committees and health economists in the HTA processes might help ensure that the key principles of HTA are followed. The study findings might be helpful for countries that are developing their HTA systems.