A Review of Economic Models Submitted to NICE’s Technology Appraisal Programme, for Treatments of T1DM & T2DM

• Published in: Frontiers in pharmacology Vol. 13; p. 887298
• Main Authors: Daly, Marie-Josée, Elvidge, Jamie, Chantler, Tracey, Dawoud, Dalia
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 11.05.2022
• Subjects: artificial intelligence; health technology assessment; national institute for health and care excellence; Pharmacology; type 1 diabetes mellitus; type 2 diabetes mellitus; health technology assessment; national institute for health and care excellence; type 2 diabetes mellitus; type 1 diabetes mellitus; artificial intelligence
• ISSN: 1663-9812; 1663-9812
• DOI: 10.3389/fphar.2022.887298

Background: In the UK, 4.7 million people are currently living with diabetes. This is projected to increase to 5 million by 2025. The direct and indirect costs of T1DM and T2DM are rising, and direct costs already account for approximately 10% of the National Health Service (NHS) budget. Objective: The aim of this review is to assess the economic models used in the context of NICE’s Technology Appraisals (TA) Programme of T1DM and T2DM treatments, as well as to examine their compliance with the American Diabetes Association’s (ADA) guidelines on computer modelling. Methods: A review of the economic models used in NICE’s TA programme of T1DM and T2DM treatments was undertaken. Relevant TAs were identified through searching the NICE website for published appraisals completed up to April 2021. The review also examined the associated Evidence Review Group (ERG) reports and Final Appraisal Documents (FAD), which are publicly accessible. ERG reports were scrutinised to identify major issues pertaining to the economic modelling. The FAD documents were then examined to assess how these issues reflected on NICE recommendations. Results: Overall, 10 TAs pertaining to treatments of T1DM and T2DM were identified. Two TAs were excluded as they did not use economic models. Seven of the 8 included TAs related to a novel class of oral antidiabetic drugs (OADs), gliflozins, and one to continuous subcutaneous insulin infusion (CSII) devices. There is a lack of recent, robust data informing risk equations to enable the derivation of transition probabilities. Despite uncertainty surrounding its clinical relevance, bodyweight/BMI is a key driver in many T2DM-models. HbA1c’s reliability as a predictor of hard outcomes is uncertain, chiefly for macrovascular complications. The external validity of T1DM is even less clear. There is an inevitable trade-off between the sophistication of models’ design, their transparency and practicality. Conclusion: Economic models are essential tools to support decision-making in relation to market access and ascertain diabetes technologies’ cost effectiveness. However, key structural and methodological issues exist. Models’ shortcomings should be acknowledged and contextualised within the framework of technology appraisals. Diabetes medications and other technologies should also be subject to regular and consistent re-appraisal to inform disinvestment decisions. Artificial intelligence could potentially enhance models’ transparency and practicality.