A Review of Health Economic Studies Comparing Traditional and Massively Parallel Sequencing Diagnostic Pathways for Suspected Genetic Disorders

Patrick Fahr (), James Buchanan and Sarah Wordsworth
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Patrick Fahr: University of Oxford
James Buchanan: University of Oxford
Sarah Wordsworth: University of Oxford

PharmacoEconomics, 2020, vol. 38, issue 2, No 3, 143-158

Abstract: Abstract Genetic disorders are clinically diverse and genetically heterogeneous, and are traditionally diagnosed based on an iterative phenotype-guided genetic assessment. However, such diagnostic approaches are long (diagnostic odysseys are common), misdiagnoses occur frequently, and diagnostic rates are low. Massively parallel sequencing (MPS) technologies may improve diagnostic rates and reduce the time to diagnosis for patients with suspected genetic disorders; however, MPS technologies are expensive and the health economic evidence base to support their use is limited. Several studies have compared the costs of traditional and MPS diagnostic pathways for patients with suspected genetic disorders, however costing methods and diagnostic scenarios are heterogeneous across studies. We conducted a literature review to identify and summarise information on these costing methods and diagnostic scenarios. Relevant studies were identified in MEDLINE, EMBASE, EconLit, University of York Centre for Reviews and Dissemination and the Cochrane Library, from 2010 to 2018. Twenty-four articles were included in the review. We observed considerable heterogeneity across studies with respect to the selection of items of resource use used to derive total diagnostic pathway cost estimates. We also observed structural differences in the diagnostic scenarios used to compare the traditional and MPS diagnostic pathways. There is a need for guidelines on the costing of diagnostic pathways to encourage the use of consistent methods. More micro-costing studies that evaluate diagnostic service delivery are also required. Greater homogeneity in costing approaches would facilitate more reliable comparisons between studies and improve the transferability of cost estimates across countries.

Date: 2020
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DOI: 10.1007/s40273-019-00856-8

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