To estimate the value of genomic sequencing for complex pediatric neurological disorders of suspected genetic origin.
A discrete choice experiment (DCE) was undertaken to elicit societal preferences and values. A Bayesian D-efficient and explicit partial profile design was used. The design included 72 choice tasks, split across six blocks, with eight attributes (three overlapping per choice task) and three alternatives. Choice data were analyzed using a panel error component mixed logit model and a latent class model. Preference heterogeneity according to personal socioeconomic, demographic, and attitudinal characteristics was explored using linear and fractional logistic regressions.
In total, 820 members of the Australian public were recruited. Statistically significant preferences were identified across all eight DCE attributes. We estimated that society on average would be willing to pay AU$5650 more (95% confidence interval [CI]: AU$5500 to $5800) (US$3955 [95% CI: US$3850 to $4060]) for genomic sequencing relative to standard care. Preference heterogeneity was identified for some personal characteristics.
On average, society highly values all diagnostic, process, clinical, and nonclinical components of personal utility. To ensure fair prioritization of genomics, decision makers need to consider the wide range of risks and benefits associated with genomic information.
Subscribe to Journal
Get full journal access for 1 year
only $41.58 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Thevenon J, Duffourd Y, Masurel‐Paulet A, et al. Diagnostic odyssey in severe neurodevelopmental disorders: toward clinical whole-exome sequencing as a first-line diagnostic test. Clin Genet. 2016;89:700–707.
Vissers L, van Nimwegen KJM, Schieving JH, et al. A clinical utility study of exome sequencing versus conventional genetic testing in pediatric neurology. Genet Med. 2017;19:1055–1063.
Srivastava S, Love-Nichols JA, Dies KA, et al. Meta-analysis and multidisciplinary consensus statement: exome sequencing is a first-tier clinical diagnostic test for individuals with neurodevelopmental disorders. Genet Med. 2019;21:2413–2421.
Srivastava S, Cohen JS, Vernon H, et al. Clinical whole exome sequencing in child neurology practice. Ann Neurol. 2014;76:473–483.
Soden SE, Saunders CJ, Willig LK, et al. Effectiveness of exome and genome sequencing guided by acuity of illness for diagnosis of neurodevelopmental disorders. Sci Transl Med. 2014;6:265ra168.
Lewis C, Skirton H, Jones R. Living without a diagnosis: the parental experience. Genet Test Mol Biomarkers. 2010;14:807–815.
Carmichael N, Tsipis J, Windmueller G, Mandel L, Estrella E. “Is it going to hurt?”: the impact of the diagnostic odyssey on children and their families. J Genet Couns. 2015;24:325–335.
Clark MM, Stark Z, Farnaes L, et al. Meta-analysis of the diagnostic and clinical utility of genome and exome sequencing and chromosomal microarray in children with suspected genetic diseases. NPJ Genom Med. 2018;3:16.
Kohler JN, Turbitt E, Biesecker BB. Personal utility in genomic testing: a systematic literature review. Eur J Hum Genet. 2017;25:662–668.
Feero WG, Wicklund C, Veenstra DL. The economics of genomic medicine: insights from the IOM Roundtable on Translating Genomic-Based Research for Health. JAMA. 2013;309:1235–1236.
Regier DA, Peacock SJ, Pataky R, et al. Societal preferences for the return of incidental findings from clinical genomic sequencing: a discrete-choice experiment. CMAJ. 2015;187:E190–E197.
Esquivel-Sada D, Nguyen MT. Diagnosis of rare diseases under focus: impacts for Canadian patients. J Community Genet. 2018;9:37–50.
Lewis C, Sanderson S, Hill M, et al. Parents’ motivations, concerns and understanding of genome sequencing: a qualitative interview study. Eur J Hum Genet. 2020;28:874–884.
Grosse SD, Rasmussen SA. Exome sequencing: value is in the eye of the beholder. Genet Med. 2020;22:280–282.
Phillips KA, Deverka PA, Marshall DA, et al. Methodological issues in assessing the economic value of next-generation sequencing tests: many challenges and not enough solutions. Value Health. 2018;21:1033–1042.
Regier DA, Weymann D, Buchanan J, Marshall DA, Wordsworth S. Valuation of health and nonhealth outcomes from next-generation sequencing: approaches, challenges, and solutions. Value Health. 2018;21:1043–1047.
Regier DA, Friedman JM, Makela N, Ryan M, Marra CA. Valuing the benefit of diagnostic testing for genetic causes of idiopathic developmental disability: willingness to pay from families of affected children. Clin Genet. 2009;75:514–521.
Buchanan J, Wordsworth S, Schuh A. Patients’ preferences for genomic diagnostic testing in chronic lymphocytic leukaemia: a discrete choice experiment. Patient. 2016;9:525–536.
Marshall DA, MacDonald KV, Heidenreich S, et al. The value of diagnostic testing for parents of children with rare genetic diseases. Genet Med. 2019;21:2798–2806.
Goranitis I, Best S, Christodoulou J, Stark Z, Boughtwood T. The personal utility and uptake of genomic sequencing in pediatric and adult conditions: eliciting societal preferences with three discrete choice experiments. Genet Med. 2020;22:1311–1319.
Grosse SD, Wordsworth S, Payne K. Economic methods for valuing the outcomes of genetic testing: beyond cost-effectiveness analysis. Genet Med. 2008;10:648–654.
Soekhai V, de Bekker-Grob EW, Ellis AR, Vass CM. Discrete choice experiments in health economics: past, present and future. PharmacoEconomics. 2019;37:201–226.
Hensher DA, Rose JM, Greene WH. Applied choice analysis. 2nd ed. Cambridge: Cambridge University Press; 2015.
Johnson FR, Lancsar E, Marshall D, et al. Constructing experimental designs for discrete-choice experiments: report of the ISPOR conjoint analysis experimental design good research practices task force. Value Health. 2013;16:3–13.
Coast J, Horrocks S. Developing attributes and levels for discrete choice experiments using qualitative methods. J Health Serv Res Policy. 2007;12:25–30.
Best S, Stark Z, Phillips P, et al. Clinical genomic testing: what matters to key stakeholders? Eur J Hum Genet. 2020;28:866–873.
ChoiceMetrics. User manual & reference guide. Ngene 1.2 ed. Sydney, Australia; 2018.
Bliemer MC, Collins AT. On determining priors for the generation of efficient stated choice experimental designs. J Choice Model. 2016;21:10–14.
Kessels R, Jones B, Goos P. Bayesian optimal designs for discrete choice experiments with partial profiles. J Choice Model. 2011;4:52–74.
Rose JM, Bliemer MC. Sample size requirements for stated choice experiments. Transportation. 2013;40:1021–1041.
Brazier J, Ratcliffe J, Saloman J, Tsuchiya A. Measuring and valuing health benefits for economic evaluation. Oxford: Oxford University Press; 2017.
Australian Bureau of Statistics (ABS). Census QuickStats. 2016. https://quickstats.censusdata.abs.gov.au/census_services/getproduct/census/2016/quickstat/036. Accessed August 2019.
Small KA, Rosen HS. Applied welfare economics with discrete choice models. Econometrica. 1981;49:105–130.
Ryan M, Gerard K, Amaya-Amaya M. Using discrete choice experiments to value health and health care. Dordrecht, Netherlands: Springer; 2007.
Greene WH, Hensher DA. A latent class model for discrete choice analysis: contrasts with mixed logit. Transp Res B Methodol. 2003;37:681–698.
Regier DA, Veenstra DL, Basu A, Carlson JJ. Demand for precision medicine: a discrete-choice experiment and external validation study. PharmacoEconomics. 2020;38:57–68.
Weymann D, Veenstra DL, Jarvik GP, Regier DA. Patient preferences for massively parallel sequencing genetic testing of colorectal cancer risk: a discrete choice experiment. Eur J Hum Genet. 2018;26:1257–1265.
Marshall DA, Deal K, Bombard Y, Leighl N, MacDonald KV, Trudeau M. How do women trade-off benefits and risks in chemotherapy treatment decisions based on gene expression profiling for early-stage breast cancer? A discrete choice experiment. BMJ Open. 2016;6:e010981.
Evans JR, Mathur A. The value of online surveys. Internet Res. 2005;15:195–219.
Quaife M, Terris-Prestholt F, Di Tanna GL, Vickerman P. How well do discrete choice experiments predict health choices? A systematic review and meta-analysis of external validity. Eur J Health Econ. 2018;19:1053–1066.
Australian Genomics Health Alliance is funded by a National Health and Medical Research Council (NHMRC) grant (grant reference number 1113531) and the Australian Government’s Medical Research Future Fund (MRFF). The research conducted at the Murdoch Children’s Research Institute was supported by the Victorian Government’s Operational Infrastructure Support Program. This work represents independent research and the views expressed are those of the authors and not necessarily those of the NHMRC or MRFF.
The authors declare no conflicts of interest.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Goranitis, I., Best, S., Stark, Z. et al. The value of genomic sequencing in complex pediatric neurological disorders: a discrete choice experiment. Genet Med (2020). https://doi.org/10.1038/s41436-020-00949-2
- personal utility
- neurodevelopmental disorders