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Estimating Cost Savings from Early Cancer Diagnosis

Author

Listed:
  • Zura Kakushadze

    (Quantigic ® Solutions LLC, 1127 High Ridge Road, #135, Stamford, CT 06905, USA
    Free University of Tbilisi, Business School & School of Physics, 240, David Agmashenebeli Alley, 0159 Tbilisi, Georgia
    Zura Kakushadze, Ph.D., is the President and a Co-Founder of Quantigic ® Solutions LLC and a Full Professor in the Business School and the School of Physics at Free University of Tbilisi.)

  • Rakesh Raghubanshi

    (Two29 Consulting LLC, 46 Sewell Avenue, Piscataway, NJ 08854, USA
    Rakesh Raghubanshi, B.A. is an independent consultant with over 20 years of experience in the field of pharmaceutical research and development and has consulted for such companies as Cordis Corporation (then a Johnson & Johnson company, now a Cardinal Health company), Bayer AG and Ferring Pharmaceuticals.)

  • Willie Yu

    (Centre for Computational Biology, Duke-NUS Medical School, 8 College Road, Singapore 169857, Singapore
    Willie Yu, Ph.D., is a Research Fellow at Duke-NUS Medical School.)

Abstract

We estimate treatment cost-savings from early cancer diagnosis. For breast, lung, prostate and colorectal cancers and melanoma, which account for more than 50% of new incidences projected in 2017, we combine published cancer treatment cost estimates by stage with incidence rates by stage at diagnosis. We extrapolate to other cancer sites by using estimated national expenditures and incidence rates. A rough estimate for the U.S. national annual treatment cost-savings from early cancer diagnosis is in 11 digits. Using this estimate and cost-neutrality, we also estimate a rough upper bound on the cost of a routine early cancer screening test.

Suggested Citation

  • Zura Kakushadze & Rakesh Raghubanshi & Willie Yu, 2017. "Estimating Cost Savings from Early Cancer Diagnosis," Data, MDPI, vol. 2(3), pages 1-16, September.
    • Handle: RePEc:gam:jdataj:v:2:y:2017:i:3:p:30-:d:110841
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    Citations

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    Cited by:

    1. Hari Darshan Arora & Anjali Naithani, 2023. "Some distance measures for triangular fuzzy numbers under technique for order of preference by similarity to ideal solution environment," OPSEARCH, Springer;Operational Research Society of India, vol. 60(2), pages 701-719, June.
    2. Wei Chen & Yixin Lu & Liangfei Qiu & Subodha Kumar, 2021. "Designing Personalized Treatment Plans for Breast Cancer," Information Systems Research, INFORMS, vol. 32(3), pages 932-949, September.

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