Literature DB >> 27803594

A time-and-motion approach to micro-costing of high-throughput genomic assays.

S Costa1, D A Regier2, B Meissner3, I Cromwell1, S Ben-Neriah3, E Chavez3, S Hung3, C Steidl4, D W Scott5, M A Marra6, S J Peacock7, J M Connors5.   

Abstract

BACKGROUND: Genomic technologies are increasingly used to guide clinical decision-making in cancer control. Economic evidence about the cost-effectiveness of genomic technologies is limited, in part because of a lack of published comprehensive cost estimates. In the present micro-costing study, we used a time-and-motion approach to derive cost estimates for 3 genomic assays and processes-digital gene expression profiling (gep), fluorescence in situ hybridization (fish), and targeted capture sequencing, including bioinformatics analysis-in the context of lymphoma patient management.
METHODS: The setting for the study was the Department of Lymphoid Cancer Research laboratory at the BC Cancer Agency in Vancouver, British Columbia. Mean per-case hands-on time and resource measurements were determined from a series of direct observations of each assay. Per-case cost estimates were calculated using a bottom-up costing approach, with labour, capital and equipment, supplies and reagents, and overhead costs included.
RESULTS: The most labour-intensive assay was found to be fish at 258.2 minutes per case, followed by targeted capture sequencing (124.1 minutes per case) and digital gep (14.9 minutes per case). Based on a historical case throughput of 180 cases annually, the mean per-case cost (2014 Canadian dollars) was estimated to be $1,029.16 for targeted capture sequencing and bioinformatics analysis, $596.60 for fish, and $898.35 for digital gep with an 807-gene code set.
CONCLUSIONS: With the growing emphasis on personalized approaches to cancer management, the need for economic evaluations of high-throughput genomic assays is increasing. Through economic modelling and budget-impact analyses, the cost estimates presented here can be used to inform priority-setting decisions about the implementation of such assays in clinical practice.

Entities:  

Keywords:  Personalized medicine; economic evaluations; genomic technology; micro-costing; time-and-motion analyses

Year:  2016        PMID: 27803594      PMCID: PMC5081006          DOI: 10.3747/co.23.2987

Source DB:  PubMed          Journal:  Curr Oncol        ISSN: 1198-0052            Impact factor:   3.677


  31 in total

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Authors:  Ronald Bayer; Sandro Galea
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2.  A comparison of self-reported and observational work sampling techniques for measuring time in nursing tasks.

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3.  Determining cell-of-origin subtypes of diffuse large B-cell lymphoma using gene expression in formalin-fixed paraffin-embedded tissue.

Authors:  David W Scott; George W Wright; P Mickey Williams; Chih-Jian Lih; William Walsh; Elaine S Jaffe; Andreas Rosenwald; Elias Campo; Wing C Chan; Joseph M Connors; Erlend B Smeland; Anja Mottok; Rita M Braziel; German Ott; Jan Delabie; Raymond R Tubbs; James R Cook; Dennis D Weisenburger; Timothy C Greiner; Betty J Glinsmann-Gibson; Kai Fu; Louis M Staudt; Randy D Gascoyne; Lisa M Rimsza
Journal:  Blood       Date:  2014-01-07       Impact factor: 22.113

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Authors:  Marcelo Lopetegui; Po-Yin Yen; Albert Lai; Joseph Jeffries; Peter Embi; Philip Payne
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Authors:  S A Finkler; J R Knickman; G Hendrickson; M Lipkin; W G Thompson
Journal:  Health Serv Res       Date:  1993-12       Impact factor: 3.402

6.  A 36-hospital time and motion study: how do medical-surgical nurses spend their time?

Authors:  Ann Hendrich; Marilyn P Chow; Boguslaw A Skierczynski; Zhenqiang Lu
Journal:  Perm J       Date:  2008

7.  Equivalence of two healthcare costing methods: bottom-up and top-down.

Authors:  Michael K Chapko; Chuan-Fen Liu; Mark Perkins; Yu-Fang Li; John C Fortney; Matthew L Maciejewski
Journal:  Health Econ       Date:  2009-10       Impact factor: 3.046

8.  Genomic sequencing: assessing the health care system, policy, and big-data implications.

Authors:  Kathryn A Phillips; Julia R Trosman; Robin K Kelley; Mark J Pletcher; Michael P Douglas; Christine B Weldon
Journal:  Health Aff (Millwood)       Date:  2014-07       Impact factor: 6.301

9.  Predicting treatment outcome in classical Hodgkin lymphoma: genomic advances.

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10.  Cost Effectiveness of Sequencing 34 Cancer-Associated Genes as an Aid for Treatment Selection in Patients with Metastatic Melanoma.

Authors:  Yonghong Li; Lance A Bare; Richard A Bender; John J Sninsky; Leslie S Wilson; James J Devlin; Frederic M Waldman
Journal:  Mol Diagn Ther       Date:  2015-06       Impact factor: 4.074

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1.  The cost and cost trajectory of whole-genome analysis guiding treatment of patients with advanced cancers.

Authors:  Deirdre Weymann; Janessa Laskin; Robyn Roscoe; Kasmintan A Schrader; Stephen Chia; Stephen Yip; Winson Y Cheung; Karen A Gelmon; Aly Karsan; Daniel J Renouf; Marco Marra; Dean A Regier
Journal:  Mol Genet Genomic Med       Date:  2017-03-12       Impact factor: 2.183

2.  A Micro-Costing Study of Screening for Lynch Syndrome-Associated Pathogenic Variants in an Unselected Endometrial Cancer Population: Cheap as NGS Chips?

Authors:  Neil A J Ryan; Niall J Davison; Katherine Payne; Anne Cole; D Gareth Evans; Emma J Crosbie
Journal:  Front Oncol       Date:  2019-02-26       Impact factor: 6.244

3.  Estimating the costs of genomic sequencing in cancer control.

Authors:  Louisa G Gordon; Nicole M White; Thomas M Elliott; Katia Nones; Anthony G Beckhouse; Astrid J Rodriguez-Acevedo; Penelope M Webb; Xing J Lee; Nicholas Graves; Deborah J Schofield
Journal:  BMC Health Serv Res       Date:  2020-06-03       Impact factor: 2.655

4.  Cost-Effectiveness of Molecularly Guided Treatment in Diffuse Large B-Cell Lymphoma (DLBCL) in Patients under 60.

Authors:  Dean A Regier; Brandon Chan; Sarah Costa; David W Scott; Christian Steidl; Joseph M Connors; Aly Karsan; Marco A Marra; Robert Kridel; Ian Cromwell; Samantha Pollard
Journal:  Cancers (Basel)       Date:  2022-02-12       Impact factor: 6.639

5.  Costs of Next-Generation Sequencing Assays in Non-Small Cell Lung Cancer: A Micro-Costing Study.

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6.  Health economic evidence for the use of molecular biomarker tests in hematological malignancies: A systematic review.

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Review 7.  Value-based genomics.

Authors:  Jun Gong; Kathy Pan; Marwan Fakih; Sumanta Pal; Ravi Salgia
Journal:  Oncotarget       Date:  2018-01-30
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