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Cost-effectiveness frameworks for comparing genome and exome sequencing versus conventional diagnostic pathways: A scoping review and recommended methods

期刊

GENETICS IN MEDICINE
卷 24, 期 10, 页码 2014-2027

出版社

ELSEVIER SCIENCE INC
DOI: 10.1016/j.gim.2022.06.004

关键词

Cost-effectiveness analysis; Decision modeling; Economic evaluation; Exome sequencing; Genome sequencing

资金

  1. National Human Genome Research Institute (NHGRI)
  2. National Institute on Minority Health and Health Disparities
  3. National Cancer Institute [U01 HG006485, U01 HG007301, U01 HG007292, U01 HG006487, U01 HG009610, U01 HG009599, U24HG007307]
  4. NHGRI [U01HG007292, K99HG011491, 1R01HG009694-01]

向作者/读者索取更多资源

This study aims to develop conceptual frameworks for model-based cost-effectiveness analyses of GS/ES. Through a scoping review and expert discussions, specific considerations were provided for different clinical scenarios to guide the design of model-based CEAs.
Purpose: Methodological challenges have limited economic evaluations of genome sequencing (GS) and exome sequencing (ES). Our objective was to develop conceptual frameworks for model-based cost-effectiveness analyses (CEAs) of diagnostic GS/ES. Methods: We conducted a scoping review of economic analyses to develop and iterate with experts a set of conceptual CEA frameworks for GS/ES for prenatal testing, early diagnosis in pediatrics, diagnosis of delayed-onset disorders in pediatrics, genetic testing in cancer, screening of newborns, and general population screening. Results: Reflecting on 57 studies meeting inclusion criteria, we recommend the following considerations for each clinical scenario. For prenatal testing, performing comparative analyses of costs of ES strategies and postpartum care, as well as genetic diagnoses and pregnancy outcomes. For early diagnosis in pediatrics, modeling quality-adjusted life years (QALYs) and costs over >= 20 years for rapid turnaround GS/ES. For hereditary cancer syndrome testing, modeling cumulative costs and QALYs for the individual tested and first/second/third-degree relatives. For tumor profiling, not restricting to treatment uptake or response and including QALYs and costs of downstream outcomes. For screening, modeling lifetime costs and QALYs and considering consequences of low penetrance and GS/ES reanalysis. Conclusion: Our frameworks can guide the design of model-based CEAs and ultimately foster robust evidence for the economic value of GS/ES. (C) 2022 by American College of Medical Genetics and Genomics. Published by Elsevier Inc.

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