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Use of a mathematical model to evaluate breast cancer screening policy

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  • Rose Baker

Abstract

A model of breast cancer screening was developed, in which the processes of tumour origination and growth, detection of tumours at screening, presentation of women with cancers to their GP, and of survival after diagnosis were modelled parametrically. The model was fitted to data from the North‐West of the UK, for 413 women who screened positive, and for 761 women who developed interval cancers. Model validation comprised verification that the final model fitted the data adequately, together with the comparison of model predictions with findings by other workers. The mathematical model was used to assess different screening policies, and to ask “what if” questions. Taking the cost of breast cancer to be the sum of the cost of screening and the cost of PYLL (person years of life lost due to cancer), the optimal screening policy was calculated. The costs of the current policy and of other possible screening policies were found, together with their effects on life lost and on mortality. The tentative conclusion was that if monies can be found to extend the screening programme, for example to carry out one more screen per woman, most benefit would be obtained by reducing the start age of screening by 3 years. Copyright Kluwer Academic Publishers 1998

Suggested Citation

  • Rose Baker, 1998. "Use of a mathematical model to evaluate breast cancer screening policy," Health Care Management Science, Springer, vol. 1(2), pages 103-113, October.
  • Handle: RePEc:kap:hcarem:v:1:y:1998:i:2:p:103-113
    DOI: 10.1023/A:1019046619402
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    Citations

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    Cited by:

    1. Lisa M. Maillart & Julie Simmons Ivy & Scott Ransom & Kathleen Diehl, 2008. "Assessing Dynamic Breast Cancer Screening Policies," Operations Research, INFORMS, vol. 56(6), pages 1411-1427, December.
    2. Jonathan E. Helm & Mariel S. Lavieri & Mark P. Van Oyen & Joshua D. Stein & David C. Musch, 2015. "Dynamic Forecasting and Control Algorithms of Glaucoma Progression for Clinician Decision Support," Operations Research, INFORMS, vol. 63(5), pages 979-999, October.
    3. Turgay Ayer, 2015. "Inverse optimization for assessing emerging technologies in breast cancer screening," Annals of Operations Research, Springer, vol. 230(1), pages 57-85, July.
    4. Natasha Stout & Amy Knudsen & Chung Kong & Pamela McMahon & G. Gazelle, 2009. "Calibration Methods Used in Cancer Simulation Models and Suggested Reporting Guidelines," PharmacoEconomics, Springer, vol. 27(7), pages 533-545, July.
    5. Nikolai Mühlberger & Gaby Sroczynski & Artemisa Gogollari & Beate Jahn & Nora Pashayan & Ewout Steyerberg & Martin Widschwendter & Uwe Siebert, 2021. "Cost effectiveness of breast cancer screening and prevention: a systematic review with a focus on risk-adapted strategies," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 22(8), pages 1311-1344, November.
    6. Christian Wernz & Yongjia Song & Danny R. Hughes, 2021. "How hospitals can improve their public quality metrics: a decision-theoretic model," Health Care Management Science, Springer, vol. 24(4), pages 702-715, December.
    7. Turgay Ayer & Oguzhan Alagoz & Natasha K. Stout & Elizabeth S. Burnside, 2016. "Heterogeneity in Women’s Adherence and Its Role in Optimal Breast Cancer Screening Policies," Management Science, INFORMS, vol. 62(5), pages 1339-1362, May.
    8. James F. O’Mahony & Joost van Rosmalen & Nino A. Mushkudiani & Frans-Willem Goudsmit & Marinus J. C. Eijkemans & Eveline A. M. Heijnsdijk & Ewout W. Steyerberg & J. Dik F. Habbema, 2015. "The Influence of Disease Risk on the Optimal Time Interval between Screens for the Early Detection of Cancer," Medical Decision Making, , vol. 35(2), pages 183-195, February.
    9. Stavroula A. Chrysanthopoulou & Carolyn M. Rutter & Constantine A. Gatsonis, 2021. "Bayesian versus Empirical Calibration of Microsimulation Models: A Comparative Analysis," Medical Decision Making, , vol. 41(6), pages 714-726, August.

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