Pooled prevalence, risk factors and barriers to care for hepatitis C virus (HCV) in adults with serious mental illness (SMI): an updated systematic review and meta-analysis – protocol

Vollständiger Abstract

Worum geht es in dieser Arbeit?

Introduction The hepatitis C virus (HCV) is a significant healthcare burden across the globe and is a major cause of liver malignancies. However, patients with serious mental illness (SMI) living with HCV are disproportionately affected and underserved within the current care framework. A previous meta-analysis on this topic yielded a prevalence of 8%, which is considerably higher than that observed in the broader population. Since the original review was published in early 2022, an update is required to incorporate data published during and after the COVID-19 pandemic and to evaluate its effect on overall prevalence and care. Methodology This systematic review and meta-analysis will replicate the methodology of the original review. To ensure a thorough investigation, a systematic search strategy will be implemented across PubMed, Google Scholar, Scopus, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Embase and Web of Science to include studies published from 2 July 2020 to 22 June 2026. The review will include prospective observational and retrospective cross-sectional studies conducted among adults aged over 18 years with a verified SMI diagnosis and laboratory-confirmed HCV status. A dual-reviewer protocol will be employed to evaluate all retrieved titles, abstracts and full-text manuscripts to ensure unbiased selection. Any disagreements will be adjudicated by a third senior investigator. Methodological rigour will be assessed using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Prevalence Studies Quality Assessment Tool for Systematic Reviews of Observational Studies. This validated instrument will ensure transparency and standardised quality assessment and will be supplemented by a customised 3-point global assessment. A Generalised Linear Mixed Model (GLMM) with a logit transformation will be used for the meta-analytical synthesis to effectively account for expected heterogeneity and extreme proportions, while the degree of inconsistency across studies will be quantified using the I 2 statistic. Ethics and dissemination Formal ethical clearance is not required for this study, as it relies entirely on the synthesis of secondary data from published articles. PROSPERO registration number CRD420261331799.