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

Yan Meng, Philip M. Clarke and Ilias Goranitis ()
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Yan Meng: University of Melbourne
Philip M. Clarke: University of Melbourne
Ilias Goranitis: University of Melbourne

PharmacoEconomics, 2022, vol. 40, issue 2, No 6, 215-223

Abstract: 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.

Date: 2022
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DOI: 10.1007/s40273-021-01103-9

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