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Updated: Sep 20, 2025

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
Published on: June 21, 2018
Accounting for Intergenerational Cascade Testing in Economic Evaluations of Clinical Genomics: A Scoping Review
Jason Zischke1, Nicole White2, Louisa Gordon3
1Health Economics Group, QIMR Berghofer Medical Research Institute, Brisbane, Australia; School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia.
Objectives:
Clinical genomics is emerging as a diagnostic tool in the identification of blood relatives at risk of developing heritable diseases. Our objective was to identify how genetic cascade screening has been incorporated into health economic evaluations.
Methods:
A scoping review was conducted to identify how multiple generations of a family were included in economic evaluations of clinical genomic sequencing, how many and which relatives were included, and uptake rates. Databases were searched for full economic evaluations of genetic interventions that screened multiple generations of families and were in English language, and no restrictions were made for disease or publication type. Data were synthesized using a narrative approach.
Results:
Twenty-five studies were included covering a range of diseases in various countries. Markov cohort models were mostly used with hypothetical populations and unsupported by clinical evidence. Cascade testing was either the primary intervention or secondary to the index cases. The number and type of relatives were based on assumptions or identified through population or family records, clinical registry data, or clinical literature. Studies included only immediate family members and the uptake of testing ranged between 20% and 100%. All interventions were reported as cost-effective, and a higher number of relatives was a key driver.
Conclusions:
Several economic evaluations have considered the impacts of cascade testing interventions within clinical genomics. Ideally, models supported with high-quality clinical data are needed and, in their absence, transparent and justifiable assumptions of uptake rates and choices about including relatives. Consideration of more appropriate modeling types is required.
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