Skip to main content
SpringerLink
Log in

The Value of Genomic Testing: A Contingent Valuation Across Six Child- and Adult-Onset Genetic Conditions

  • Original Research Article
  • Published:
PharmacoEconomics Aims and scope Submit manuscript

Abstract

Objectives

The aim of this study was to elicit the willingness-to-pay (WTP) for genomic testing, using contingent valuation, among people with lived experience of genetic conditions in Australia.

Methods

Parents of children with suspected mitochondrial disorders, epileptic encephalopathy, leukodystrophy, or malformations of cortical development completed a dynamic triple-bounded dichotomous choice (DC) contingent valuation. Adult patients or parents of children with suspected genetic kidney disease or complex neurological and neurodegenerative conditions completed a payment card (PC) contingent valuation. DC data were analyzed using a multilevel interval regression and a multilevel probit model. PC data were analyzed using a Heckman selection model.

Results

In total, 360 individuals participated in the contingent valuation (CV), with 141 (39%) and 219 (61%) completing the DC and PC questions, respectively. The mean WTP for genomic testing was estimated at AU$2830 (95% confidence interval [CI] 2236–3424) based on the DC data and AU$1914 (95% CI 1532–2296) based on the PC data. The mean WTP across the six cohorts ranged from AU$1879 (genetic kidney disease) to AU$4554 (leukodystrophy).

Conclusions

Genomic testing is highly valued by people experiencing rare genetic conditions. Our findings can inform cost–benefit analyses and the prioritization of genomics into mainstream clinical care. While our WTP estimates for adult-onset genetic conditions aligned with estimates derived from discrete choice experiments (DCEs), for childhood-onset conditions our estimates were significantly lower. Research is urgently required to directly compare, and critically evaluate, the performance of CV and DCE methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  1. Schieppati A, Henter JI, Daina E, Aperia A. Why rare diseases are an important medical and social issue. Lancet. 2008;371(9629):2039–41. https://doi.org/10.1016/S0140-6736(08)60872-7.

    Article  PubMed  Google Scholar 

  2. Wakap SN, Lambert DM, Olry A, Rodwell C, Gueydan C, Lanneau V, et al. Estimating cumulative point prevalence of rare diseases: analysis of the Orphanet database. Eur J Hum Genet. 2020;28(2):165–73.

    Article  Google Scholar 

  3. Boycott KM, Vanstone MR, Bulman DE, MacKenzie AE. Rare-disease genetics in the era of next-generation sequencing: discovery to translation. Nat Rev Genet. 2013;14(10):681–91. https://doi.org/10.1038/nrg3555.

    Article  CAS  PubMed  Google Scholar 

  4. Zurynski Y, Deverell M, Dalkeith T, Johnson S, Christodoulou J, Leonard H, et al. Australian children living with rare diseases: experiences of diagnosis and perceived consequences of diagnostic delays. Orphanet J Rare Dis. 2017;12(1):68. https://doi.org/10.1186/s13023-017-0622-4.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Knight AW, Senior TP. The common problem of rare disease in general practice. Med J Aust. 2006;185(2):82–3. https://doi.org/10.5694/j.1326-5377.2006.tb00477.x.

    Article  PubMed  Google Scholar 

  6. Yaneva-Deliverska M. Rare diseases and genetic discrimination. J IMAB. 2011;17(1):116–9.

    Article  Google Scholar 

  7. Pelentsov LJ, Fielder AL, Laws TA, Esterman AJ. The supportive care needs of parents with a child with a rare disease: results of an online survey. BMC Fam Pract. 2016;17(1):88. https://doi.org/10.1186/s12875-016-0488-x.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Wu Y, Al-Janabi H, Mallett A, Quinlan C, Scheffer IE, Howell KB, et al. Parental health spillover effects of paediatric rare genetic conditions. Qual Life Res. 2020;29(9):2445–54. https://doi.org/10.1007/s11136-020-02497-3.

    Article  PubMed  Google Scholar 

  9. Cannizzo S, Lorenzoni V, Palla I, Pirri S, Trieste L, Triulzi I, et al. Rare diseases under different levels of economic analysis: current activities, challenges and perspectives. RMD Open. 2018;4(Suppl 1): e000794. https://doi.org/10.1136/rmdopen-2018-000794.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Foster MW, Mulvihill JJ, Sharp RR. Evaluating the utility of personal genomic information. Genet Med. 2009;11(8):570–4. https://doi.org/10.1097/GIM.0b013e3181a2743e.

    Article  PubMed  Google Scholar 

  11. Stark Z, Tan TY, Chong B, Brett GR, Yap P, Walsh M, et al. A prospective evaluation of whole-exome sequencing as a first-tier molecular test in infants with suspected monogenic disorders. Genet Med. 2016;18(11):1090–6.

    Article  CAS  Google Scholar 

  12. Clark MM, Stark Z, Farnaes L, Tan TY, White SM, Dimmock D, 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(1):16. https://doi.org/10.1038/s41525-018-0053-8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Bollinger JM, Scott J, Dvoskin R, Kaufman D. Public preferences regarding the return of individual genetic research results: findings from a qualitative focus group study. Genet Med. 2012;14(4):451–7.

    Article  Google Scholar 

  14. Phillips KA, Deverka PA, Marshall DA, Wordsworth S, Regier DA, Christensen KD, et al. Methodological issues in assessing the economic value of next-generation sequencing tests: many challenges and not enough solutions. Value Health. 2018;21(9):1033–42.

    Article  Google Scholar 

  15. 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(9):1043–7. https://doi.org/10.1016/j.jval.2018.06.010.

    Article  PubMed  Google Scholar 

  16. Ozdemir S, Lee JJ, Chaudhry I, Ocampo RRQ. A systematic review of discrete choice experiments and conjoint analysis on genetic testing. Patient. 2021. https://doi.org/10.1007/s40271-021-00531-1.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Marshall DA, MacDonald KV, Heidenreich S, Hartley T, Bernier FP, Gillespie MK, et al. The value of diagnostic testing for parents of children with rare genetic diseases. Genet Med. 2019;21(12):2798–806.

    Article  Google Scholar 

  18. Goranitis I, Best S, Stark Z, Boughtwood T, Christodoulou J. The value of genomic sequencing in complex pediatric neurological disorders: a discrete choice experiment. Genet Med. 2021;23(1):155–62. https://doi.org/10.1038/s41436-020-00949-2.

    Article  PubMed  Google Scholar 

  19. 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(8):1311–9. https://doi.org/10.1038/s41436-020-0809-2.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Goranitis I, Best S, Christodoulou J, Boughtwood T, Stark Z. Preferences and values for rapid genomic testing in critically ill infants and children: a discrete choice experiment. Eur J Hum Genet. 2021. https://doi.org/10.1038/s41431-021-00874-1.

    Article  PubMed  Google Scholar 

  21. Grosse SD, Wordsworth S, Payne K. Economic methods for valuing the outcomes of genetic testing: beyond cost-effectiveness analysis. Genet Med. 2008;10(9):648–54. https://doi.org/10.1097/GIM.0b013e3181837217.

    Article  PubMed  Google Scholar 

  22. Lin PJ, Cangelosi MJ, Lee DW, Neumann PJ. Willingness to pay for diagnostic technologies: a review of the contingent valuation literature. Value Health. 2013;16(5):797–805. https://doi.org/10.1016/j.jval.2013.04.005.

    Article  PubMed  Google Scholar 

  23. Stark Z, Boughtwood T, Phillips P, Christodoulou J, Hansen DP, Braithwaite J, et al. Australian genomics: a federated model for integrating genomics into healthcare. Am J Hum Genet. 2019;105(1):7–14.

    Article  CAS  Google Scholar 

  24. Gaff CL, Winship IM, Forrest SM, Hansen DP, Clark J, Waring PM, et al. Preparing for genomic medicine: a real world demonstration of health system change. NPJ Genom Med. 2017;2(1):1–9.

    Google Scholar 

  25. Herriges JA, Shogren JF. Starting point bias in dichotomous choice valuation with follow-up questioning. J Environ Econ Manag. 1996;30(1):112–31.

    Article  Google Scholar 

  26. Langford IH, Bateman IJ, Langford HD. A multilevel modelling approach to triple-bounded dichotomous choice contingent valuation. Environ Resour Econ. 1996;7(3):197–211.

    Google Scholar 

  27. Bateman IJ, Langford IH, Jones AP, Kerr GN. Bound and path effects in double and triple bounded dichotomous choice contingent valuation. Resour Energy Econ. 2001;23(3):191–213.

    Article  Google Scholar 

  28. Hanemann M, Loomis J, Kanninen B. Statistical efficiency of double-bounded dichotomous choice contingent valuation. Am J Agric Econ. 1991;73(4):1255–63.

    Article  Google Scholar 

  29. Alberini A, Boyle K, Welsh M. Analysis of contingent valuation data with multiple bids and response options allowing respondents to express uncertainty. J Environ Econ Manag. 2003;45(1):40–62.

    Article  Google Scholar 

  30. Brazier JE, Roberts J. The estimation of a preference-based measure of health from the SF-12. Med Care. 2004;42(9):851–9. https://doi.org/10.1097/01.mlr.0000135827.18610.0d.

    Article  PubMed  Google Scholar 

  31. Hagenaars AJM, De Vos K, Asghar Zaidi M, et al. Poverty statistics in the late 1980s: research based on micro-data. Office for Official Publications of the European Communities, Luxembourg, 1994.

  32. Cameron TA, James MD. Efficient estimation methods for “closed-ended” contingent valuation surveys. Rev Econ Stat. 1987;69:269–76.

    Article  Google Scholar 

  33. Heckman JJ. Sample selection bias as a specification error. Econometrica. 1979;47(1):153–61.

    Article  Google Scholar 

  34. Donaldson C, Jones AM, Mapp TJ, Olson JA. Limited dependent variables in willingness to pay studies: applications in health care. Appl Econ. 1998;30(5):667–77.

    Article  Google Scholar 

  35. Donaldson C, Shackley P, Abdalla M, Miedzybrodzka Z. Willingness to pay for antenatal carrier screening for cystic fibrosis. Health Econ. 1995;4(6):439–52. https://doi.org/10.1002/hec.4730040602.

    Article  CAS  PubMed  Google Scholar 

  36. Wright CF, FitzPatrick DR, Firth HV. Paediatric genomics: diagnosing rare disease in children. Nat Rev Genet. 2018;19(5):253.

    Article  CAS  Google Scholar 

  37. Donaldson C, Shackley P, Abdalla M. Using willingness to pay to value close substitutes: carrier screening for cystic fibrosis revisited. Health Econ. 1997;6(2):145–59.

    Article  CAS  Google Scholar 

  38. Boyle KJ, Johnson FR, McCollum DW, Desvousges WH, Dunford RW, Hudson SP. Valuing public goods: discrete versus continuous contingent-valuation responses. Land Econ. 1996;72:381–96.

    Article  Google Scholar 

  39. Frew EJ, Whynes DK, Wolstenholme JL. Eliciting willingness to pay: comparing closed-ended with open-ended and payment scale formats. Med Decis Mak. 2003;23(2):150–9. https://doi.org/10.1177/0272989X03251245.

    Article  Google Scholar 

  40. Mitchell RC, Carson RT. Using surveys to value public goods: the contingent valuation method. 1989. Washington, DC: Resources for the Future.

  41. Ryan M, Scott DA, Donaldson C. Valuing health care using willingness to pay: a comparison of the payment card and dichotomous choice methods. J Health Econ. 2004;23(2):237–58.

    Article  Google Scholar 

  42. Ready RC, Navrud S, Dubourg WR. How do respondents with uncertain willingness to pay answer contingent valuation questions? Land Econ. 2001;77(3):315–26.

    Article  Google Scholar 

  43. van der Pol M, Shiell A, Au F, Johnston D, Tough S. Convergent validity between a discrete choice experiment and a direct, open-ended method: comparison of preferred attribute levels and willingness to pay estimates. Soc Sci Med. 2008;67(12):2043–50.

    Article  Google Scholar 

  44. Yeung RY, Smith RD, Ho LM, Johnston JM, Leung GM. Empirical implications of response acquiescence in discrete-choice contingent valuation. Health Econ. 2006;15(10):1077–89. https://doi.org/10.1002/hec.1107.

    Article  PubMed  Google Scholar 

  45. Leung J, Guria J. Value of statistical life: adults versus children. Accid Anal Prev. 2006;38(6):1208–17. https://doi.org/10.1016/j.aap.2006.05.009.

    Article  PubMed  Google Scholar 

  46. Stark Z, Dolman L, Manolio TA, Ozenberger B, Hill SL, Caulfied MJ, et al. Integrating genomics into healthcare: a global responsibility. Am J Hum Genet. 2019;104(1):13–20.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The authors would like to thank all the participants of Mitochondrial Diseases Flagship, Leukodystrophy Flagship, Epileptic Encephalopathy Flagship, Brain Malformation Flagship, KidGen Renal Genetics Flagship, Complex Neurological and Neurodegenerative Conditions Flagship, and their families. We appreciate the support from the coordination team of Australian Genomics and Melbourne Genomics. The findings and views reported in this paper are those of the authors.

Author information

Authors and Affiliations

  1. Health Economics Unit, Centre for Health Policy, Melbourne School of Population and Global Health, University of Melbourne, 207-221 Bouverie St., Parkville, Melbourne, VIC, 3010, Australia

    Yan Meng, Philip M. Clarke & Ilias Goranitis

  2. Australian Genomics Health Alliance, Melbourne, VIC, Australia

    Yan Meng & Ilias Goranitis

  3. Murdoch Children’s Research Institute, Melbourne, VIC, Australia

    Yan Meng & Ilias Goranitis

  4. Nuffield Department of Population Health, Health Economics Research Centre, University of Oxford, Headington, UK

    Philip M. Clarke

Authors
  1. Yan Meng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Philip M. Clarke
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ilias Goranitis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ilias Goranitis.

Ethics declarations

Funding

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 Melbourne Genomics Health Alliance is funded by the State Government of Victoria and the 10 Alliance members. The research conducted at the Murdoch Children’s Research Institute was supported by the Victorian Government’s Operational Infrastructure Support Program.

Conflict of interest

The authors declare no conflicts of interest.

Ethics approval

Ethics approvals were granted by the Melbourne Health Human Research Ethic Committee (HREC) (Ref no. HREC/16/MH/251), the UnitingCare Health HREC (Ref no. 1717), the Tasmanian Health and Medical HREC (Ref no. H0016443), and the Northern Territory Department of Health and Menzies School of Health Research HREC (Ref no. 2017–2999), as part of the project “Australian Genomic Health Alliance: Preparing Australia for Genomic Medicine”. The Melbourne Genomics Health Alliance study received ethics approval from the Melbourne Health Human Research Ethics Committee (Ref no. HREC/13/MH/326).

Consent to participate

Informed written consent to participate was obtained from study participants or the parents of study participants.

Consent for publication

Informed written consent for publication was obtained from study participants or the parents of study participants.

Availability of data and material

Participants’ consent to the study prohibits sharing of data.

Code availability

Code is available upon request.

Author contributions

Concept and design: PMC, IG; acquisition of data: YM, IG; analysis and interpretation of data: YM, PMC, IG; drafting of the manuscript: YM, PMC, IG; critical revision of the paper for important intellectual content: YM, PMC, IG; statistical analysis: YM, PMC, IG; obtaining funding: PMC; supervision: PMC, IG.

Supplementary Information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Meng, Y., Clarke, P.M. & Goranitis, I. The Value of Genomic Testing: A Contingent Valuation Across Six Child- and Adult-Onset Genetic Conditions. PharmacoEconomics 40, 215–223 (2022). https://doi.org/10.1007/s40273-021-01103-9

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40273-021-01103-9

Search

Navigation