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

Pharmacoeconomics. 2022 Feb;40(2):215-223. doi: 10.1007/s40273-021-01103-9. Epub 2021 Oct 21.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Australia
  • Child
  • Cost-Benefit Analysis
  • Family*
  • Genomics*
  • Humans
  • Surveys and Questionnaires