STRUCTURAL HETEROGENEITY IN HEALTH ECONOMIC MODELLING APPROACHES FOR HCV AND HDV DIAGNOSTICS: NEED FOR HARMONIZATION
Author(s)
R. Lakshmi, MSc1, Anurag Gupta, MSc1, Aishee Ghatak, MSc1, Paridhi Sharma, MSc1, Shubham Kumar, MSc1, Tushar Srivastava, MSc2, Devian Parra-Padilla, MSc2, Aris Skandamis, MSc2;
1ConnectHEOR, Delhi, India, 2ConnectHEOR, London, United Kingdom
1ConnectHEOR, Delhi, India, 2ConnectHEOR, London, United Kingdom
OBJECTIVES: Health economic modelling (HEM) of diagnostic tests is crucial to inform value-based screening and testing policies in hepatitis C (HCV) and hepatitis D (HDV). However, variability in the methodological approaches used to assess their economic value has been reported in the literature. This study aims to review the current HEM evidence of diagnostic strategies for HCV and HDV, identify key modelling limitations, and provide methodological recommendations towards harmonization.
METHODS: A targeted literature search in PubMed (1 January 2023-1 January 2026) was conducted to identify economic evaluations of diagnostic technologies for HCV and HDV. Data were extracted on population, model structure, time horizon, outcomes, and reported methodological limitations.
RESULTS: The search identified 122 HDV and 589 HCV records, of which 4 and 31 studies met inclusion criteria, respectively. Markov cohort or hybrid decision tree-Markov models were most common (21/35; 60%), followed by dynamic transmission models (8/35; 23%) and decision tree-only models (6/35; 17%). Long-term liver disease health states (fibrosis progression, decompensated cirrhosis, hepatocellular carcinoma) were included in 83% (29/35) of studies, and 74% (26/35) adopted lifetime or long-term horizons (≥20 years). Structural limitations were common across HCV and HDV models: 66% (23/35) used simplified treatment assumptions (single treatment and fixed uptake), and 66% (23/35) reported uncertainty in the assumptions on the frequency of subjects linked to care/treatment after diagnosis. Among HCV studies specifically, key limitations included inability to capture reinfection or transmission (26/31; 84%), country-specific data limitations (20/31; 65%), and exclusion of societal costs (4/31; 13%).
CONCLUSIONS: The current HEM evidence for HCV and HDV diagnostic strategies is heterogeneous, with variation in structure, assumptions, and outcome scope. Best practice guidance on modelling approaches is required to improve the quality of evidence, reduce bias and uncertainty, and improve comparability in future studies.
METHODS: A targeted literature search in PubMed (1 January 2023-1 January 2026) was conducted to identify economic evaluations of diagnostic technologies for HCV and HDV. Data were extracted on population, model structure, time horizon, outcomes, and reported methodological limitations.
RESULTS: The search identified 122 HDV and 589 HCV records, of which 4 and 31 studies met inclusion criteria, respectively. Markov cohort or hybrid decision tree-Markov models were most common (21/35; 60%), followed by dynamic transmission models (8/35; 23%) and decision tree-only models (6/35; 17%). Long-term liver disease health states (fibrosis progression, decompensated cirrhosis, hepatocellular carcinoma) were included in 83% (29/35) of studies, and 74% (26/35) adopted lifetime or long-term horizons (≥20 years). Structural limitations were common across HCV and HDV models: 66% (23/35) used simplified treatment assumptions (single treatment and fixed uptake), and 66% (23/35) reported uncertainty in the assumptions on the frequency of subjects linked to care/treatment after diagnosis. Among HCV studies specifically, key limitations included inability to capture reinfection or transmission (26/31; 84%), country-specific data limitations (20/31; 65%), and exclusion of societal costs (4/31; 13%).
CONCLUSIONS: The current HEM evidence for HCV and HDV diagnostic strategies is heterogeneous, with variation in structure, assumptions, and outcome scope. Best practice guidance on modelling approaches is required to improve the quality of evidence, reduce bias and uncertainty, and improve comparability in future studies.
Conference/Value in Health Info
2026-05, ISPOR 2026, Philadelphia, PA, USA
Value in Health, Volume 29, Issue S6
Code
EE22
Topic
Economic Evaluation
Disease
SDC: Infectious Disease (non-vaccine)