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Engaging hepatitis C infected patients in cost-effectiveness analyses: A literature review.

Joseph T MattinglyEleanor M PerfettoSophia L Johnson
Published in: Hepatology (Baltimore, Md.) (2017)
Cost-effectiveness analyses (CEAs) of hepatitis C virus (HCV) treatment strategies have become common, but few appear to include patient engagement or the patient perspective. The objectives of the current study were to (1) identify published HCV CEA studies that include patient input and (2) derive insights on patient-informed variable and outcome selection to build a framework for future economic analyses of HCV. A literature search was conducted using SCOPUS, EMBASE, and PubMed from January 1, 2012 to May 28, 2017. Terms sought included a combination of "incremental cost-effectiveness ratio" OR "economic evaluation" OR "cost effectiveness analysis" OR "cost utility analysis" OR "budget impact analysis" OR "cost benefit analysis" AND "hepatitis C". A total of 1,040 articles were identified in the search and seven articles were selected for further evaluation after abstracts and the full text of eligible articles were screened. One economic evaluation used direct patient engagement to account for patient preferences in the final model. The study endpoints identified included a variety of clinical, social, psychological, and economic outcomes. Costs primarily focused on productivity loss, missed work, out-of-pocket treatment costs, and indirect costs to family or friends supporting the patient. Conclusion: To date, the inclusion of the patient voice through patient engagement as part of methods in cost-effectiveness research in existing published studies has been limited. Future CEA studies should consider how patient engagement may impact economic models and their implementation into practice. (Hepatology 2018;67:774-781).
Keyphrases
  • case report
  • hepatitis c virus
  • healthcare
  • primary care
  • mental health
  • climate change
  • human immunodeficiency virus
  • physical activity
  • weight loss
  • decision making
  • hiv infected
  • data analysis