Real World Cost-of-Illness Evidence in Hepatitis C Virus: A systematic review.

Background: The introduction of direct-acting antivirals (DAAs) represents a potential clinical cure for hepatitis C virus (HCV) infection. Identification of costs associated with different stages of untreated disease through cost-of-illness (COI) evaluation helps inform policy decisions and cost-effectiveness analyses (CEAs). This study's objective was to review published real-world costs for patients with HCV to estimate the COI across different stages of disease progression.

Methods: A literature search of EMBASE, Scopus, and PubMed from January 1, 2010 to August 31, 2019 was conducted to identify real-world evidence related to HCV. Data extraction included citation details, population, study type, costing method used, currency and inflation adjustments, and disease-specific costs. Standardized costing method categories (sum all medical, sum diagnosis specific, matching, regression, other incremental, and other total) were assigned. The risk of bias was assessed at the outcome level for influence on costs attributable to HCV.

Results: The search strategy identified 278 studies, with 31 included in the final review after inclusion and exclusion criteria were applied. Retrospective cohorts (77%) and cross-sectional analyses (16%) were most frequently encountered. Sum Diagnosis Specific was the most common costing method (39%), followed by Regression (32%). Of the 31 studies analyzed, 35% included costs that would be included in a societal model. Costs were identified for various stages and complications related to HCV disease progression. Several studies included were determined to have a high (48%) or moderate risk (42%) of bias related to COI estimates.

Conclusion: Cost estimates for formal, informal, and non-health care services were identified in this review, but several challenges still exist in fully quantifying HCV burden. Future modeling studies including cost inputs should critically evaluate the risk of bias based on costing methods and data sources.