DATA ACCESSIBILITY AND METHODOLOGICAL HETEROGENEITY IN ESTIMATING THE BURDEN OF RARE DISEASES ACROSS FOUR CENTRAL AND EASTERN EUROPEAN COUNTRIES: A CROSS-COUNTRY COMPARISON
Author(s)
Radka Štorchová, MSc, PhD1, Katarína Breciková, MSc, PhD2, Juliana Grand Mullerova, MSc2, Gleb Donin, MSc, PhD1, Dominik Grega, PharmD, PhD3, Martin Visnansky, MBA, MSc, PharmD, PhD3, Ivana Šarkanová, MSc, PhD2.
1Department of Biomedical Technology, Czech Technical University in Prague, Kladno, Czech Republic, 2CEEOR, Bratislava, Slovakia, 3Faculty of Pharmacy, Masaryk University, Brno, Czech Republic.
1Department of Biomedical Technology, Czech Technical University in Prague, Kladno, Czech Republic, 2CEEOR, Bratislava, Slovakia, 3Faculty of Pharmacy, Masaryk University, Brno, Czech Republic.
OBJECTIVES: To compare the feasibility and methodological limitations of estimating the societal burden of selected rare diseases in the Czech Republic (CZ), Slovakia (SK), Hungary (HU), and Poland (PL), focusing on data accessibility, granularity, disease identification, and indirect cost methodology.
METHODS: A societal perspective was applied, including healthcare costs, indirect morbidity costs, and mortality-related productivity losses, expressed in 2023 EUR. The analysis was shaped by country-specific data availability. CZ used health insurance claims covering approximately 40% of the population in 2020-2022, with patients identified through repeated outpatient contacts or hospitalization. SK used National Health Information Center data for 2020-2023, although public outputs are mainly aggregated and hospital-focused. HU used National Health Insurance Fund claims for 2020-2023 with claims-based case definitions. PL relied on publicly available aggregated open data, with disease identification based on 3- or 4-digit ICD-10 codes. Indirect morbidity costs were estimated using a time-use approach in CZ, SK, and HU, though with different valuation scopes; PL used an absenteeism approach based on sick-leave days. Mortality costs used the human capital approach.
RESULTS: Data accessibility and structure differed substantially across countries. CZ provided the strongest basis for claims-based analysis, but detailed data required formal authorization. SK had relevant national data infrastructure, but public outputs were limited for outpatient and cost components. HU enabled payer-based analysis, although individually authorized therapies may be underrepresented. PL offered the most transparent public access route, but only through aggregated ICD-based data, limiting patient-level cohort construction and cost decomposition. Key heterogeneity arose from data granularity, case definition, and indirect cost scope.
CONCLUSIONS: Cross-country rare disease burden estimation in CEE is limited less by disease epidemiology than by differences in data access, granularity, and methodology. Future studies should report these limitations transparently and harmonize case definitions and cost approaches where feasible, to separate true burden differences from methodological artefacts.
METHODS: A societal perspective was applied, including healthcare costs, indirect morbidity costs, and mortality-related productivity losses, expressed in 2023 EUR. The analysis was shaped by country-specific data availability. CZ used health insurance claims covering approximately 40% of the population in 2020-2022, with patients identified through repeated outpatient contacts or hospitalization. SK used National Health Information Center data for 2020-2023, although public outputs are mainly aggregated and hospital-focused. HU used National Health Insurance Fund claims for 2020-2023 with claims-based case definitions. PL relied on publicly available aggregated open data, with disease identification based on 3- or 4-digit ICD-10 codes. Indirect morbidity costs were estimated using a time-use approach in CZ, SK, and HU, though with different valuation scopes; PL used an absenteeism approach based on sick-leave days. Mortality costs used the human capital approach.
RESULTS: Data accessibility and structure differed substantially across countries. CZ provided the strongest basis for claims-based analysis, but detailed data required formal authorization. SK had relevant national data infrastructure, but public outputs were limited for outpatient and cost components. HU enabled payer-based analysis, although individually authorized therapies may be underrepresented. PL offered the most transparent public access route, but only through aggregated ICD-based data, limiting patient-level cohort construction and cost decomposition. Key heterogeneity arose from data granularity, case definition, and indirect cost scope.
CONCLUSIONS: Cross-country rare disease burden estimation in CEE is limited less by disease epidemiology than by differences in data access, granularity, and methodology. Future studies should report these limitations transparently and harmonize case definitions and cost approaches where feasible, to separate true burden differences from methodological artefacts.
Conference/Value in Health Info
2026-11, ISPOR Europe 2026, Vienna, Austria
Value in Health, Volume 29, Issue 12S
Code
EE408
Topic
Economic Evaluation
Topic Subcategory
Cost/Cost of Illness/Resource Use Studies
Disease
No Additional Disease & Conditions/Specialized Treatment Areas, Rare & Orphan Diseases