The value of genomic sequencing in complex pediatric neurological disorders: a discrete choice experiment

Abstract

Purpose

To estimate the value of genomic sequencing for complex pediatric neurological disorders of suspected genetic origin.

Methods

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.

Results

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.

Conclusion

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.

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References

  1. 1.

    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.

    CAS  Article  Google Scholar 

  2. 2.

    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.

    Article  Google Scholar 

  3. 3.

    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.

    Article  Google Scholar 

  4. 4.

    Srivastava S, Cohen JS, Vernon H, et al. Clinical whole exome sequencing in child neurology practice. Ann Neurol. 2014;76:473–483.

    Article  Google Scholar 

  5. 5.

    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.

    Article  Google Scholar 

  6. 6.

    Lewis C, Skirton H, Jones R. Living without a diagnosis: the parental experience. Genet Test Mol Biomarkers. 2010;14:807–815.

    Article  Google Scholar 

  7. 7.

    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.

    Article  Google Scholar 

  8. 8.

    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.

    Article  Google Scholar 

  9. 9.

    Kohler JN, Turbitt E, Biesecker BB. Personal utility in genomic testing: a systematic literature review. Eur J Hum Genet. 2017;25:662–668.

    Article  Google Scholar 

  10. 10.

    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.

    CAS  Article  Google Scholar 

  11. 11.

    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.

    Article  Google Scholar 

  12. 12.

    Esquivel-Sada D, Nguyen MT. Diagnosis of rare diseases under focus: impacts for Canadian patients. J Community Genet. 2018;9:37–50.

    Article  Google Scholar 

  13. 13.

    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.

    Article  Google Scholar 

  14. 14.

    Grosse SD, Rasmussen SA. Exome sequencing: value is in the eye of the beholder. Genet Med. 2020;22:280–282.

    Article  Google Scholar 

  15. 15.

    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.

    Article  Google Scholar 

  16. 16.

    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.

    Article  Google Scholar 

  17. 17.

    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.

    CAS  Article  Google Scholar 

  18. 18.

    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.

    Article  Google Scholar 

  19. 19.

    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.

    Article  Google Scholar 

  20. 20.

    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.

    Article  Google Scholar 

  21. 21.

    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.

    Article  Google Scholar 

  22. 22.

    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.

    Article  Google Scholar 

  23. 23.

    Hensher DA, Rose JM, Greene WH. Applied choice analysis. 2nd ed. Cambridge: Cambridge University Press; 2015.

    Google Scholar 

  24. 24.

    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.

    Article  Google Scholar 

  25. 25.

    Coast J, Horrocks S. Developing attributes and levels for discrete choice experiments using qualitative methods. J Health Serv Res Policy. 2007;12:25–30.

    Article  Google Scholar 

  26. 26.

    Best S, Stark Z, Phillips P, et al. Clinical genomic testing: what matters to key stakeholders? Eur J Hum Genet. 2020;28:866–873.

    Article  Google Scholar 

  27. 27.

    ChoiceMetrics. User manual & reference guide. Ngene 1.2 ed. Sydney, Australia; 2018.

  28. 28.

    Bliemer MC, Collins AT. On determining priors for the generation of efficient stated choice experimental designs. J Choice Model. 2016;21:10–14.

    Article  Google Scholar 

  29. 29.

    Kessels R, Jones B, Goos P. Bayesian optimal designs for discrete choice experiments with partial profiles. J Choice Model. 2011;4:52–74.

    Article  Google Scholar 

  30. 30.

    Rose JM, Bliemer MC. Sample size requirements for stated choice experiments. Transportation. 2013;40:1021–1041.

    Article  Google Scholar 

  31. 31.

    Brazier J, Ratcliffe J, Saloman J, Tsuchiya A. Measuring and valuing health benefits for economic evaluation. Oxford: Oxford University Press; 2017.

    Google Scholar 

  32. 32.

    Australian Bureau of Statistics (ABS). Census QuickStats. 2016. https://quickstats.censusdata.abs.gov.au/census_services/getproduct/census/2016/quickstat/036. Accessed August 2019.

  33. 33.

    Small KA, Rosen HS. Applied welfare economics with discrete choice models. Econometrica. 1981;49:105–130.

    Article  Google Scholar 

  34. 34.

    Ryan M, Gerard K, Amaya-Amaya M. Using discrete choice experiments to value health and health care. Dordrecht, Netherlands: Springer; 2007.

    Google Scholar 

  35. 35.

    Greene WH, Hensher DA. A latent class model for discrete choice analysis: contrasts with mixed logit. Transp Res B Methodol. 2003;37:681–698.

    Article  Google Scholar 

  36. 36.

    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.

    Article  Google Scholar 

  37. 37.

    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.

    Article  Google Scholar 

  38. 38.

    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.

    Article  Google Scholar 

  39. 39.

    Evans JR, Mathur A. The value of online surveys. Internet Res. 2005;15:195–219.

    Article  Google Scholar 

  40. 40.

    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.

    Article  Google Scholar 

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Acknowledgements

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.

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Correspondence to Ilias Goranitis PhD.

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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

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Keywords

  • genomics
  • preferences
  • personal utility
  • neurodevelopmental disorders
  • children

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