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      The personal utility and uptake of genomic sequencing in pediatric and adult conditions: eliciting societal preferences with three discrete choice experiments

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          Abstract

          Purpose

          To estimate the personal utility and uptake of genomic sequencing (GS) across pediatric and adult-onset genetic conditions.

          Methods

          Three discrete choice experiment (DCE) surveys were designed and administered to separate representative samples of the Australian public. Bayesian D-efficient explicit partial profile designs were used. Choice data were analyzed using a panel error component random parameter logit model.

          Results

          Overall, 1913 participants completed the pediatric ( n = 533), symptomatic adult ( n = 700) and at-risk adult ( n = 680) surveys. The willingness-to-pay for GS information in pediatric conditions was estimated at $5470–$15,250 (US$3830–$10,675) depending on the benefits of genomic information. Uptake ranged between 60% and 81%. For symptomatic adults, the value of GS was estimated at $1573–$8102 (US$1100–$5671) and uptake at 34–82%. For at-risk adults, GS was valued at $2036–$5004 (US$1425–$3503) and uptake was predicted at 35–61%.

          Conclusion

          There is substantial personal utility in GS, particularly for pediatric conditions. Personal utility increased as the perceived benefits of genomic information increased. The clinical and regulatory context, and individuals’ sociodemographic and attitudinal characteristics influenced the value and uptake of GS. Society values highly the diagnostic, clinical, and nonclinical benefits of GS. The personal utility of GS should be considered in health-care decision-making.

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          Most cited references24

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          Implementing genomic medicine in the clinic: the future is here

          Although the potential for genomics to contribute to clinical care has long been anticipated, the pace of defining the risks and benefits of incorporating genomic findings into medical practice has been relatively slow. Several institutions have recently begun genomic medicine programs, encountering many of the same obstacles and developing the same solutions, often independently. Recognizing that successful early experiences can inform subsequent efforts, the National Human Genome Research Institute brought together a number of these groups to describe their ongoing projects and challenges, identify common infrastructure and research needs, and outline an implementation framework for investigating and introducing similar programs elsewhere. Chief among the challenges were limited evidence and consensus on which genomic variants were medically relevant; lack of reimbursement for genomically driven interventions; and burden to patients and clinicians of assaying, reporting, intervening, and following up genomic findings. Key infrastructure needs included an openly accessible knowledge base capturing sequence variants and their phenotypic associations and a framework for defining and cataloging clinically actionable variants. Multiple institutions are actively engaged in using genomic information in clinical care. Much of this work is being done in isolation and would benefit from more structured collaboration and sharing of best practices. Genet Med 2013:15(4):258–267
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            Applied Welfare Economics with Discrete Choice Models

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              Developing attributes and levels for discrete choice experiments using qualitative methods.

              The rigour with which the first two stages of discrete choice experiments (attribute development and the choice of levels of these attributes) are generally conducted is questionable. This paper provides a case study describing how attributes and their levels were developed for a study of access to dermatology specialist services for non-urgent skin conditions. Semi-structured interviews were conducted with 19 dermatology patients with non-urgent skin conditions. Informants were purposively sampled for maximum variation and interviews continued until all attributes were fully and clearly defined. An iterative approach was used with data collection and analysis proceeding concurrently. The interviews and parallel analysis generated three iterations. The first iteration comprised early exploratory work with expertise and waiting time emerging as important to informants. The second iteration continued to emphasize these attributes, but individualized care and convenience were added. By the end of the third iteration all attributes were fully elaborated. Qualitative methods enabled attributes to be defined. There was clear tension between the aim in qualitative work to explore and describe, and the reductiveness needed to encapsulate the different aspects of the service within a minimum number of attributes for use in the discrete choice modelling. Improved reporting of the methods of attribute development in all discrete choice experiments is required.
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                Author and article information

                Contributors
                ilias.goranitis@unimelb.edu.au
                Journal
                Genet Med
                Genet. Med
                Genetics in Medicine
                Nature Publishing Group US (New York )
                1098-3600
                1530-0366
                6 May 2020
                6 May 2020
                2020
                : 22
                : 8
                : 1311-1319
                Affiliations
                [1 ]ISNI 0000 0001 2179 088X, GRID grid.1008.9, Health Economics Unit, Centre for Health Policy, Melbourne School of Population and Global Health, , University of Melbourne, ; Melbourne, Australia
                [2 ]Australian Genomics Health Alliance, Melbourne, Australia
                [3 ]ISNI 0000 0000 9442 535X, GRID grid.1058.c, Murdoch Children’s Research Institute, ; Melbourne, Australia
                [4 ]ISNI 0000 0001 2158 5405, GRID grid.1004.5, Australian Institute of Health Innovation, , Macquarie University, ; Sydney, Australia
                [5 ]ISNI 0000 0001 2179 088X, GRID grid.1008.9, Department of Paediatrics, , University of Melbourne, ; Melbourne, Australia
                Author information
                http://orcid.org/0000-0001-7946-8324
                Article
                809
                10.1038/s41436-020-0809-2
                7394876
                32371919
                fab9b71e-ace9-4938-a499-41711aa3e6e1
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, and provide a link to the Creative Commons license. You do not have permission under this license to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

                History
                : 28 November 2019
                : 1 April 2020
                : 2 April 2020
                Categories
                Article
                Custom metadata
                © American College of Medical Genetics and Genomics 2020

                Genetics
                utility,preferences,genetic conditions,next-generation sequencing,uptake
                Genetics
                utility, preferences, genetic conditions, next-generation sequencing, uptake

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