Presentation (CTI)

OBJECTIVES: Gross domestic product (GDP)-adjusted healthcare cross-country cost transfer methods are emerging as a solution to data gaps in economic evaluations. This pilot study evaluated the predictive performance of a GDP-adjusted framework for healthcare cost estimation among countries in the Middle East and North Africa (MENA).
METHODS: An Excel-based workflow was developed to implement the GDP-adjusted cost transfer framework proposed by Zrubka et al. (2022). The method applies purchasing power parity (PPP)-adjusted GDP per capita and a fixed transfer coefficient (β = 0.281) derived from that same published literature, with a ±50% uncertainty threshold. Unit cost data were obtained from private-sector healthcare datasets in Qatar and Saudi Arabia. All costs were standardized to 2025 US dollars and harmonized to a common costing year using inflation adjustment, from a private payer perspective. The framework was applied to nine healthcare services, including laboratory tests, specialist consultations, and imaging services. External validation compared transferred estimates against observed United Arab Emirates (UAE) costs. Model performance was assessed using mean absolute percentage error (MAPE) and service-level predictive accuracy.
RESULTS: The GDP-adjusted cost transfer framework yielded a mean absolute percentage error (MAPE) of 36.6% across nine diagnostic services. Overall, 77.8% (7/9) of estimates fell within the ±50% uncertainty range. Performance varied by service type. The largest deviations were observed for brain MRI with contrast (−56.7%) and renal function testing (+53.0%), indicating under- and overestimation in imaging and laboratory services. Closer agreement with UAE costs was seen for general practitioner consultations (−7.7%), hepatic function testing (+27.6%), and specialist outpatient consultations (−29.9%).
CONCLUSIONS: GDP-adjusted healthcare cost transfer is a promising solution for addressing cost data gaps in MENA. Moderate predictive performance was observed, although variability highlights remaining limitations. Further research is needed to validate the approach across broader settings, service categories, and data sources to improve transferability and accuracy for economic evaluation.