Abstract

Objectives

Mathematical models inform hepatitis C virus (HCV) microelimination strategies in high-risk groups, but their reliability depends on underlying methodological choices. We systematically appraised the modeling methods used in economic evaluations of HCV microelimination published between 2015 and 2026.

Methods

Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (International Prospective Register of Systematic Reviews: CRD420251185978), we searched PubMed/MEDLINE, Embase, and Web of Science for original studies using mathematical models to assess the cost-effectiveness of HCV interventions in high-risk populations. Using a structured extraction tool aligned with modeling best practices, we evaluated model conceptualization, structure, uncertainty handling, and validation. Reporting quality was assessed using the Consolidated Health Economic Evaluation Reporting Standards 2022 checklist.

Results

Fifty-five studies met the inclusion criteria. Most relied on aggregate-level frameworks, comprising dynamic compartmental models (n = 25) and static Markov/decision-tree structures (n = 18), whereas 12 used individual-based models. People who inject drugs were the primary cohort in 30 evaluations. A pervasive limitation across the literature was the omission of endogenous behavioral feedback, with only 2 models allowing risk behaviors or service uptake to dynamically respond to intervention outcomes. Approximately half of the models (n = 28) synthesized parameters without a formal calibration process. Although parameter uncertainty was universally explored, external validation against real-world data was uncommon (n = 12) and, under our operational definition of distributional equity characterization, no study reported this assessment.

Conclusion

Current economic models for HCV microelimination are technically sophisticated but frequently rely on structural simplifications—particularly the lack of dynamic behavioral feedback and network-based transmission—that may limit their real-world policy utility. Future efforts should prioritize data-driven network structures, rigorous external validation, and equity-informative frameworks to better support global elimination targets.