Presentation (CTI)

OBJECTIVES: Limited local health-state utility data remains a major challenge for cost-effectiveness analysis in the Middle East and North Africa (MENA). This proof-of-concept study compared two pragmatic approaches for generating country-specific utility estimates for the United Arab Emirates (UAE) using a simulated asthma case example, the UAE EQ-5D-5L value set, and UAE population norms.
METHODS: Two utility adaptation methods were evaluated. Method 1 applied a population-based re-anchoring framework, whereby asthma utility estimates identified through a published systematic review and meta-analysis were adjusted to the UAE population EQ-5D-5L norm (mean 0.937), assuming transferability of disease-related utility decrements across settings. Method 2 applied the UAE EQ-5D-5L value set to simulated EQ-5D-5L health state profiles derived from published asthma distributions and aligned with Global Initiative for Asthma (GINA) 2023 control categories. Utilities were estimated using Monte Carlo simulation (n=1,000). Uncertainty was assessed using GINA-based scenario analyses.
RESULTS: Method 1 produced utility estimates of 0.930 (SD 0.089) for well-controlled asthma, 0.870 (0.089) for partly controlled asthma, and 0.690 (0.129) for uncontrolled asthma. Method 2 generated corresponding estimates of 0.915 (0.089), 0.865 (0.113), and 0.767 (0.152). Between-method differences were 0.015, 0.005, and −0.077, respectively, indicating close agreement for well-controlled and partly controlled asthma but greater divergence for uncontrolled disease. Under the GINA-based scenario, Method 2 produced lower utilities for partly controlled (0.778) and uncontrolled asthma (0.498), indicating substantial sensitivity to health-state specification.
CONCLUSIONS: This proof-of-concept study demonstrates two transparent approaches for generating interim utility estimates for MENA HTA when local data are unavailable. Both approaches produced plausible and reproducible utility estimates, although results varied according to the underlying assumptions and value set structure. Findings highlight the importance of reporting key assumptions, and the need for primary UAE EQ-5D-5L data.