Literature DB >> 10916589

Use of a mathematical model to evaluate breast cancer screening policy.

R D Baker1.   

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.

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Year:  1998        PMID: 10916589     DOI: 10.1023/a:1019046619402

Source DB:  PubMed          Journal:  Health Care Manag Sci        ISSN: 1386-9620


  6 in total

1.  A model for breast cancer screening.

Authors:  G J van Oortmarssen; J D Habbema; P J van der Maas; H J de Koning; H J Collette; A L Verbeek; A T Geerts; K T Lubbe
Journal:  Cancer       Date:  1990-10-01       Impact factor: 6.860

2.  Simplified models of screening for chronic disease: estimation procedures from mass screening programmes.

Authors:  N E Day; S D Walter
Journal:  Biometrics       Date:  1984-03       Impact factor: 2.571

3.  An analysis of the benefits of serial screening for breast cancer based upon a mathematical model of the disease.

Authors:  M Shwartz
Journal:  Cancer       Date:  1978-04       Impact factor: 6.860

4.  Interval cancers in the National Health Service Breast Screening Programme.

Authors:  A E Johnson; J Shekhdar
Journal:  Br J Radiol       Date:  1995-08       Impact factor: 3.039

5.  MBS: a model for risk benefit analysis of breast cancer screening.

Authors:  J T Jansen; J Zoetelief
Journal:  Br J Radiol       Date:  1995-02       Impact factor: 3.039

6.  Estimation of mean sojourn time in breast cancer screening using a Markov chain model of both entry to and exit from the preclinical detectable phase.

Authors:  S W Duffy; H H Chen; L Tabar; N E Day
Journal:  Stat Med       Date:  1995-07-30       Impact factor: 2.373

  6 in total
  6 in total

1.  Selected papers from the 3rd Conference on Quantitative Modelling in the Management of Health Care. University of Salford, England, United Kingdom, 5-7 September 2000.

Authors: 
Journal:  Health Care Manag Sci       Date:  2002-11

2.  Sensitivity analysis for healthcare models fitted to data by statistical methods.

Authors:  Rose D Baker
Journal:  Health Care Manag Sci       Date:  2002-11

Review 3.  Calibration methods used in cancer simulation models and suggested reporting guidelines.

Authors:  Natasha K Stout; Amy B Knudsen; Chung Yin Kong; Pamela M McMahon; G Scott Gazelle
Journal:  Pharmacoeconomics       Date:  2009       Impact factor: 4.981

4.  Withdrawing low risk women from cervical screening programmes: mathematical modelling study.

Authors:  C Sherlaw-Johnson; S Gallivan; D Jenkins
Journal:  BMJ       Date:  1999-02-06

5.  Breast cancer screening services: trade-offs in quality, capacity, outreach, and centralization.

Authors:  Evrim D Güneş; Stephen E Chick; O Zeynep Akşin
Journal:  Health Care Manag Sci       Date:  2004-11

6.  Bayesian versus Empirical Calibration of Microsimulation Models: A Comparative Analysis.

Authors:  Stavroula A Chrysanthopoulou; Carolyn M Rutter; Constantine A Gatsonis
Journal:  Med Decis Making       Date:  2021-05-08       Impact factor: 2.749

  6 in total

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