Original Research
9 May 2023

Population Genomic Screening for Three Common Hereditary Conditions: A Cost-Effectiveness Analysis

Publication: Annals of Internal Medicine
Volume 176, Number 5
Visual Abstract. Population Genomic Screening for Three Common Hereditary Conditions
The cost-effectiveness of screening the U.S. population for Centers for Disease Control and Prevention Tier 1 genomic conditions is unknown. This study estimated the cost-effectiveness of simultaneous genomic screening for Lynch syndrome, hereditary breast and ovarian cancer syndrome, and familial hypercholesterolemia.

Abstract

Background:

The cost-effectiveness of screening the U.S. population for Centers for Disease Control and Prevention (CDC) Tier 1 genomic conditions is unknown.

Objective:

To estimate the cost-effectiveness of simultaneous genomic screening for Lynch syndrome (LS), hereditary breast and ovarian cancer syndrome (HBOC), and familial hypercholesterolemia (FH).

Design:

Decision analytic Markov model.

Data Sources:

Published literature.

Target Population:

Separate age-based cohorts (ages 20 to 60 years at time of screening) of racially and ethnically representative U.S. adults.

Time Horizon:

Lifetime.

Perspective:

U.S. health care payer.

Intervention:

Population genomic screening using clinical sequencing with a restricted panel of high-evidence genes, cascade testing of first-degree relatives, and recommended preventive interventions for identified probands.

Outcome Measures:

Incident breast, ovarian, and colorectal cancer cases; incident cardiovascular events; quality-adjusted survival; and costs.

Results of Base-Case Analysis:

Screening 100 000 unselected 30-year-olds resulted in 101 (95% uncertainty interval [UI], 77 to 127) fewer overall cancer cases and 15 (95% UI, 4 to 28) fewer cardiovascular events and an increase of 495 quality-adjusted life-years (QALYs) (95% UI, 401 to 757) at an incremental cost of $33.9 million (95% UI, $27.0 million to $41.1 million). The incremental cost-effectiveness ratio was $68 600 per QALY gained (95% UI, $41 800 to $88 900).

Results of Sensitivity Analysis:

Screening 30-, 40-, and 50-year-old cohorts was cost-effective in 99%, 88%, and 19% of probabilistic simulations, respectively, at a $100 000-per-QALY threshold. The test costs at which screening 30-, 40-, and 50-year-olds reached the $100 000-per-QALY threshold were $413, $290, and $166, respectively. Variant prevalence and adherence to preventive interventions were also highly influential parameters.

Limitations:

Population averages for model inputs, which were derived predominantly from European populations, vary across ancestries and health care environments.

Conclusion:

Population genomic screening with a restricted panel of high-evidence genes associated with 3 CDC Tier 1 conditions is likely to be cost-effective in U.S. adults younger than 40 years if the testing cost is relatively low and probands have access to preventive interventions.

Primary Funding Source:

National Human Genome Research Institute.

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Supplemental Material

Supplement. Supplemental Material

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Information & Authors

Information

Published In

cover image Annals of Internal Medicine
Annals of Internal Medicine
Volume 176Number 5May 2023
Pages: 585 - 595

History

Published online: 9 May 2023
Published in issue: May 2023

Keywords

Authors

Affiliations

Gregory F. Guzauskas, MSPH, PhD https://orcid.org/0000-0002-9095-1672
The CHOICE Institute, Department of Pharmacy, University of Washington, Seattle, Washington (G.F.G., S.J.)
Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee (S.G., J.S.S.)
Zilu Zhou, MPH
Department of Health Policy, Vanderbilt University Medical Center, Nashville, Tennessee (Z.Z., J.A.G.)
Jonathan S. Schildcrout, PhD
Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee (S.G., J.S.S.)
Department of Health Policy, Vanderbilt University Medical Center, Nashville, Tennessee (Z.Z., J.A.G.)
Department of Genomic Health, Geisinger, Danville, Pennsylvania (M.S.W.)
Jing Hao, PhD, MD, MS, MPH https://orcid.org/0000-0001-9332-3842
Department of Genomic Health and Department of Population Health Sciences, Geisinger, Danville, Pennsylvania (J.H.)
Laney K. Jones, PharmD, MPH https://orcid.org/0000-0002-6182-5634
Department of Population Health Sciences and Heart Institute, Geisinger, Danville, Pennsylvania (L.K.J.)
Scott J. Spencer, MPA, MA, PhD
Institute for Public Health Genetics, University of Washington, Seattle, Washington (S.J.S.)
Shangqing Jiang, MPH
The CHOICE Institute, Department of Pharmacy, University of Washington, Seattle, Washington (G.F.G., S.J.)
David L. Veenstra, PharmD, PhD*
The CHOICE Institute, Department of Pharmacy, and Institute for Public Health Genetics, University of Washington, Seattle, Washington (D.L.V.)
Josh F. Peterson, MD, MPH* https://orcid.org/0000-0002-7553-0749
Department of Biomedical Informatics and Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee (J.F.P.).
Acknowledgment: The authors thank Hadley Stevens Smith, PhD, MPSA, for her valuable contributions to the cascade testing module.
Grant Support: By grant R01 HG009694 from the National Human Genome Research Institute.
Reproducible Research Statement: Study protocol, statistical code, and data set: The model structure and inputs are fully represented in the Supplement and in the referenced submodel publications. The modeling code is not available separately. Readers interested in recreating the model should contact Dr. Guzauskas (e-mail, [email protected]) with questions.
Corresponding Author: Josh F. Peterson, MD, MPH, Director, Center for Precision Medicine, Professor of Biomedical Informatics and Medicine, Vanderbilt University Medical Center, 2525 West End Avenue, Suite 1500, Nashville, TN 37203; e-mail, [email protected].
Author Contributions: Conception and design: G.F. Guzauskas, S. Garbett, J.S. Schildcrout, J.A. Graves, M.S. Williams, J. Hao, S.J. Spencer, D.L. Veenstra, J.F. Peterson.
Analysis and interpretation of the data: G.F. Guzauskas, S. Garbett, J.A. Graves, M.S. Williams, J. Hao, L.K. Jones, S.J. Spencer, S. Jiang, D.L. Veenstra, J.F. Peterson.
Drafting of the article: G.F. Guzauskas, S. Garbett, J.A. Graves, S.J. Spencer, J.F. Peterson.
Critical revision for important intellectual content: G.F. Guzauskas, J.S. Schildcrout, J.A. Graves, M.S. Williams, J. Hao, L.K. Jones, S.J. Spencer, S. Jiang, D.L. Veenstra, J.F. Peterson.
Final approval of the article: G.F. Guzauskas, S. Garbett, Z. Zhou, J.S. Schildcrout, J.A. Graves, M.S. Williams, J. Hao, L.K. Jones, S.J. Spencer, S. Jiang, D.L. Veenstra, J.F. Peterson.
Statistical expertise: G.F. Guzauskas, S. Garbett, J.S. Schildcrout, J.A. Graves, S.J. Spencer, D.L. Veenstra.
Obtaining of funding: J.A. Graves, M.S. Williams, J. Hao, D.L. Veenstra, J.F. Peterson.
Administrative, technical, or logistic support: J. Hao, D.L. Veenstra, J.F. Peterson.
Collection and assembly of data: G.F. Guzauskas, Z. Zhou, M.S. Williams, J. Hao, L.K. Jones, S.J. Spencer, S. Jiang, D.L. Veenstra, J.F. Peterson.
This article was published at Annals.org on 9 May 2023.
*
Drs. Veenstra and Peterson contributed equally to this work.

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Gregory F. Guzauskas, Shawn Garbett, Zilu Zhou, et al. Population Genomic Screening for Three Common Hereditary Conditions: A Cost-Effectiveness Analysis. Ann Intern Med.2023;176:585-595. [Epub 9 May 2023]. doi:10.7326/M22-0846

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