Incremental cost-effectiveness of algorithm-driven genetic testing versus no testing for Maturity Onset Diabetes of the Young (MODY) in Singapore.

Published

Conference Paper

BACKGROUND:Offering genetic testing for Maturity Onset Diabetes of the Young (MODY) to all young patients with type 2 diabetes has been shown to be not cost-effective. This study tests whether a novel algorithm-driven genetic testing strategy for MODY is incrementally cost-effective relative to the setting of no testing. METHODS:A decision tree was constructed to estimate the costs and effectiveness of the algorithm-driven MODY testing strategy and a strategy of no genetic testing over a 30-year time horizon from a payer's perspective. The algorithm uses glutamic acid decarboxylase (GAD) antibody testing (negative antibodies), age of onset of diabetes (<45 years) and body mass index (<25 kg/m2 if diagnosed >30 years) to stratify the population of patients with diabetes into three subgroups, and testing for MODY only among the subgroup most likely to have the mutation. Singapore-specific costs and prevalence of MODY obtained from local studies and utility values sourced from the literature are used to populate the model. RESULTS:The algorithm-driven MODY testing strategy has an incremental cost-effectiveness ratio of US$93 663 per quality-adjusted life year relative to the no testing strategy. If the price of genetic testing falls from US$1050 to US$530 (a 50% decrease), it will become cost-effective. CONCLUSION:Our proposed algorithm-driven testing strategy for MODY is not yet cost-effective based on established benchmarks. However, as genetic testing prices continue to fall, this strategy is likely to become cost-effective in the near future.

Full Text

Duke Authors

Cited Authors

  • Nguyen, HV; Finkelstein, EA; Mital, S; Gardner, DS-L

Published Date

  • November 2017

Published In

Volume / Issue

  • 54 / 11

Start / End Page

  • 747 - 753

PubMed ID

  • 28835481

Pubmed Central ID

  • 28835481

Electronic International Standard Serial Number (EISSN)

  • 1468-6244

International Standard Serial Number (ISSN)

  • 0022-2593

Digital Object Identifier (DOI)

  • 10.1136/jmedgenet-2017-104670