DANVILLE, Pa. – Routinely screening adults age 40 and younger for three genetic conditions that carry a high risk of serious illness is cost-effective based on quality and quantity of life measures, a new study shows.
Published May 9 in Annals of Internal Medicine, the study evaluated the cost effectiveness of a one-time, all-in-one genomic screening of adults ages 20 to 60 for three conditions: hereditary breast and ovarian cancer syndrome; Lynch syndrome, the most common cause of hereditary colorectal cancer; and familial hypercholesterolemia, which increases low-density lipoprotein cholesterol and the likelihood of heart disease and stroke at a younger age. The Centers for Disease Control and Prevention has determined that there is sufficient evidence to consider population-wide genetic testing for these conditions; however, current practice guidelines focus on screening patients with a high-risk family history.
To analyze cost effectiveness, the research team used the quality-adjusted life-year (QALY), a generic measure that assesses the quality and quantity of life gained to determine the value of medical interventions. The study defined interventions costing no more than $100,000 per QALY gained as cost-effective, a long-standing and well-accepted benchmark in the United States.
Assuming a genetic test costs $250—and, for positive results, a confirmation test costing another $250—the study found that one-time screening of adults age 40 and younger was cost-effective. For example, among 30-year-olds, the incremental cost of adopting routine screening came to $68,600 per QALY gained. For this group, screening would be cost-effective at the $100,000 per QALY threshold if the test cost $413 or less. For 40-year-olds, the cost of a single test would need to be $290 or less, and for 50-year-olds, the cost would need to be $166 or less to be cost-effective.
Data from Geisinger's MyCode® Community Health Initiative provided real-world evidence to inform the cost-effectiveness model.
"Our data analysis on the MyCode population, including the prevalence of disease and uptake of risk-reducing interventions after disclosure of genetic results, contributed key parameter estimates for the study model," said Jing Hao, Ph.D., M.D., assistant professor in the Department of Population Health Sciences at Geisinger and one of the study's principal investigators. "The model accounts for a range of factors in sensitivity analyses and indicates that the cost-effectiveness of population genomic screening can be further improved with optimized real-world implementation leading to better disease management."
The study team included researchers from Geisinger, Vanderbilt University Medical Center and the University of Washington.
