Subjects

Abstract

Purpose

Great uncertainty exists about the costs associated with whole-genome sequencing (WGS).

Methods

One hundred cardiology patients with cardiomyopathy diagnoses and 100 ostensibly healthy primary care patients were randomized to receive a family-history report alone or with a WGS report. Cardiology patients also reviewed prior genetic test results. WGS costs were estimated by tracking resource use and staff time. Downstream costs were estimated by identifying services in administrative data, medical records, and patient surveys for 6 months.

Results

The incremental cost per patient of WGS testing was $5,098 in cardiology settings and $5,073 in primary care settings compared with family history alone. Mean 6-month downstream costs did not differ statistically between the control and WGS arms in either setting (cardiology: difference = −$1,560, 95% confidence interval −$7,558 to $3,866, p = 0.36; primary care: difference = $681, 95% confidence interval −$884 to $2,171, p = 0.70). Scenario analyses showed the cost reduction of omitting or limiting the types of secondary findings was less than $69 and $182 per patient in cardiology and primary care, respectively.

Conclusion

Short-term costs of WGS were driven by the costs of sequencing and interpretation rather than downstream health care. Disclosing additional types of secondary findings has a limited cost impact following disclosure.

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Acknowledgements

This study was supported by National Institutes of Health grants U01-HG006500, K01-HG009173, KL2-TR001100, and R01-HG007063, and Career Development Award IK2-CX001262 from the US Department of Veterans Affairs Clinical Sciences Research and Development Service. This work was conducted with support from Harvard Catalyst | The Harvard Clinical and Translational Science Center (National Center for Research Resources and National Center for Advancing Translational Sciences, National Institutes of Health grant UL1-TR001102) and financial contributions from Harvard University and its affiliated academic health-care centers. The authors thank 5AM Solutions (Rockville, MD), for their help in customizing the workflow of the “My Family Health Portrait” Web tool for this study. The authors also thank Kenneth Freedberg, Marc Williams, and Ann Wu for their intellectual contributions to the design of these analyses.

Author information

Author notes

    • Robert C Green
    •  & Dmitry Dukhovny

    The last two authors contributed equally to this work.

Affiliations

  1. Division of Genetics, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, USA

    • Kurt D Christensen
    • , Carrie L Blout
    •  & Robert C Green
  2. Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA

    • Kurt D Christensen
    • , Jason L Vassy
    •  & Robert C Green
  3. Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, USA

    • Jason L Vassy
  4. Section of General Internal Medicine, VA Boston Healthcare System, Boston, Massachusetts, USA

    • Jason L Vassy
  5. Department of Clinical Pharmacy, Center for Translational and Policy Research on Personalized Medicine (TRANSPERS), University of California San Francisco, San Francisco, California, USA

    • Kathryn A Phillips
    •  & Michael P Douglas
  6. Philip R. Lee Institute for Health Policy and Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California, USA

    • Kathryn A Phillips
  7. Partners HealthCare Laboratory for Molecular Medicine, Cambridge, Massachusetts, USA

    • Danielle R Azzariti
    • , Kalotina Machini
    •  & Heidi L Rehm
  8. Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA

    • Christine Y Lu
    •  & Natasha K Stout
  9. Department of Population Medicine, Harvard Medical School, Boston, Massachusetts, USA

    • Christine Y Lu
    •  & Natasha K Stout
  10. Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, Texas, USA

    • Jill O Robinson
    • , Kaitlyn Lee
    •  & Amy L McGuire
  11. Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA

    • Jennifer M Yeh
  12. Division of General Pediatrics, Boston Children’s Hospital, Boston, Massachusetts, USA

    • Jennifer M Yeh
  13. Department of Pathology, Harvard Medical School, Boston, Massachusetts, USA

    • Kalotina Machini
    •  & Heidi L Rehm
  14. Department of Pathology, Brigham and Women’s Hospital, Boston, Massachusetts, USA

    • Kalotina Machini
    •  & Heidi L Rehm
  15. Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA

    • Heidi L Rehm
    •  & Robert C Green
  16. Partners HealthCare Personalized Medicine, Boston, Massachusetts, USA

    • Robert C Green
  17. Department of Pediatrics, Oregon Health & Science University, Portland, Oregon, USA

    • Dmitry Dukhovny

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Competing interests

The authors declare the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: R.C.G. reports personal fees from Illumina, Helix, GenePeeks, Veritas, and Ohana and is a cofounder with equity in Genome Medical. D.D. reports consulting for Vermont Oxford Network, Gerson Lehrman Group, and ClearView Healthcare Partners and being faculty for Vermont Oxford Network outside the submitted work. The other authors declare no conflict of interest.

Corresponding author

Correspondence to Kurt D Christensen.

Supplementary information

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DOI

https://doi.org/10.1038/gim.2018.35

Supplementary material is linked to the online version of the paper at http://www.nature.com/gim