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Using Mathematical Models to Inform Public Policy for Cancer Prevention and Screening

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Epidemiologic Studies in Cancer Prevention and Screening

Part of the book series: Statistics for Biology and Health ((SBH,volume 79))

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Abstract

This chapter introduces the reader to the use of mathematical modeling applied to policy questions in cancer screening and prevention. While randomized trials and observational studies are the mainstays for evaluating the effectiveness of screening and prevention interventions, situations may arise where these methods may not be feasible due to time, ethical, or other constraints. Mathematical models can help to fill these gaps by synthesizing available evidence, often from disparate sources, and estimating outcomes across a range of policy alternatives. Predictions from models can aid policymakers in understanding the trade-offs in benefits and risks among policies under design or evaluation. The goal of this chapter is to help orient policymakers to this methodology. An overview of the methods is provided followed by examples to illustrate the range of policy applications where modeling can be used as well as how policymakers and modelers can collaborate. Real-world examples include the use of modeling to aid the design of the national cervical cancer screening program in the Netherlands, modeling to develop a package of tobacco control policies in the USA, and modeling to evaluate the contributions of cancer screening and treatment to observed trends in breast cancer mortality.

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References

  • Berry DA, Cronin KA, Plevritis SK, Fryback DG, Clarke L Zelen M et al for the Cancer Intervention and Surveillance Modeling Network (CISNET) Collaborators. Effect of screening and adjuvant treatment on mortality from breast cancer. N Eng J Med 2005; 353:1784-1792.

    Google Scholar 

  • Boer R, de Koning HJ, van Oortmarssen GJ, van der Maas PJ. In search of the best upper age limit for breast cancer screening. Eur J Cancer 1995a; 31:2040-2043.

    Article  Google Scholar 

  • Boer R, Plevritis S, Clarke L. Diversity of model approaches for breast cancer screening: A review of model assumptions by the Cancer Intervention and Surveillance Modeling Network (CISNET) breast cancer groups. Stat Methods Med Res 2004; 13:525-538.

    Article  MathSciNet  MATH  Google Scholar 

  • Bos AB, van Ballegooijen M, van Oortmarssen GJ, Habbema JDF. Women who participate in spontaneous screening are not at higher risk for cervical cancer than women who attend programme screening. Eur J Cancer 2002; 38:827-831.

    Article  Google Scholar 

  • Clarke LD, Plevritis SK, Boer R, Cronin KA Feuer EJ. A comparative review of CISNET breast models used to analyze U.S. breast cancer incidence and mortality trends. J Natl Cancer Inst Monogr 2006; 36:96-105.

    Google Scholar 

  • Cronin KA, Feuer EJ, Clarke LD, Plevritis SK. Impact of adjuvant therapy and mammography on U.S. mortality from 1975 to 2000: Comparison of mortality results from the CISNET breast cancer base case analysis. J Natl Cancer Inst Monogr 2006; 36:112-121.

    Google Scholar 

  • Dewilde S, Anderson R. The cost-effectiveness of screening programs using single and multiple birth cohort simulations: A comparison using a model of cervical cancer. Med Decis Making 2004; 24:486-492.

    Article  Google Scholar 

  • Eddy DM. Screening for breast cancer. Ann Intern Med 1989;111:389-399.

    Google Scholar 

  • Evaluation Committee on Early Detection of Cervical Cancer. Report about the second round of the mass screening program in the pilot regions Nijmegen, Rotterdam and Utrecht (In Dutch). Ministerie VOMIL, Leidschendam, 1984.

    Google Scholar 

  • Goldie SJ, Gaffikin L, Goldhaber-Fiebert JD, Gordillo-Tobar A, Levin C, Mahe C et al. Cost-effectiveness of cervical-cancer screening in five developing countries. N Engl J Med 2005; 353:2158-2168.

    Article  Google Scholar 

  • Gotzsche PC, Olsen O. Is screening for breast cancer with mammography justifiable? Lancet 2000; 355:129-134.

    Article  Google Scholar 

  • Habbema JDF, van Oortmarssen GJ, Lubbe JTN, van der Maas PJ. The miscan simulation program for the evaluation of screening for disease. Comput Methods Programs Biomed 1984; 20:79-93.

    Article  Google Scholar 

  • Habbema JDF, van Oortmarssen GJ, Lubbe JTN, van der Maas PJ. Model building on the basis of Dutch cervical cancer screening data. Maturitas 1985; 7:11-20.

    Article  Google Scholar 

  • Health Council of the Netherlands. Vaccination for cervical cancer. Health Council, The Hague, The Netherlands, 2008.

    Google Scholar 

  • Health Council of the Netherlands. Screening for cervical cancer. Health Council, The Hague, The Netherlands, 2011.

    Google Scholar 

  • Knudsen AB, McMahon PM, Gazelle GS. Use of modeling to evaluate the cost-effectiveness of cancer screening programs. J Clin Oncol 2007; 25:203-208.

    Article  Google Scholar 

  • Kulasingam SL, Havrilesky L, Ghebre R, Myers ER. Screening for cervical cancer: A decision analysis for the U.S. Preventive Services Task Force. Agency for Healthcare Research and Quality, Rockville, MD, 2011.

    Google Scholar 

  • Kuntz KM, Lansdorp-Vogelaar I, Rutter CM, Knudsen AB, van Ballegooijen M, Savarino JE et al. A systematic comparison of microsimulation models of colorectal cancer. Med Decis Making 2011; 31:530-539.

    Article  Google Scholar 

  • Levy DT, Benjakul S, Ross H, Ritthiphakdee B. The role of tobacco control policies in reducing smoking and deaths in a middle income nation: Results from the Thailand SimSmoke simulation model. Tob Control 2008a;17:53-59.

    Article  Google Scholar 

  • Levy DT, Cho SI, Kim YM, Park S, Suh MK, Kam S. SimSmoke model evaluation of the effect of tobacco control policies in Korea: The unknown success story. Am J Publ Hlth 2010; 100:1267-1273.

    Article  Google Scholar 

  • Levy DT, Hyland A, Higbee C, Remer L, Compton C. The role of public policies in reducing smoking prevalence in California: Results from the California tobacco policy simulation model. Health Policy 2007a;82:167-185.

    Article  Google Scholar 

  • Levy DT, Nikolayev L, Mumford E, Compton C. The Healthy People 2010 smoking prevalence and tobacco control objectives: Results from the SimSmoke tobacco control policy simulation model (United States). Cancer Causes Control 2005;16:359-371.

    Article  Google Scholar 

  • Levy DT, Ross H, Powell L, Bauer JE, Lee HR. The role of public policies in reducing smoking prevalence and deaths caused by smoking in Arizona: Results from the Arizona tobacco policy simulation model. J Public Health Manag Pract 2007b; 13:59-67.

    Google Scholar 

  • Levy DT, Tworek C, Hahn EJ, Davis RE. The Kentucky SimSmoke tobacco policy simulation model: Reaching Healthy People 2010 goals through policy change. South Med J 2008b; 101:503-507.

    Article  Google Scholar 

  • Mandelblatt JS, Cronin KA, Bailey S, Berry DA, de Koning HJ, Draisma G et al. Effects of mammography screening under different screening schedules: Model estimates of potential benefits and harms. Ann Intern Med 2009;151:738-747.

    Google Scholar 

  • Mukherjee S. The emperor of all maladies: A biography of cancer. Scribner, New York, 2010.

    Google Scholar 

  • National Health Insurance Council. Principles for the restructuring of the organised cervical cancer screening programme-final decision (In Dutch). ZiekenfondsRaad, Amstelveen, The Netherlands, 1993.

    Google Scholar 

  • Nelson HD, Tyne K, Naik A, Bougatsos C, Chan BK, Humphrey L. Screening for breast cancer: An update for the U.S. Preventive Services Task Force. Ann Intern Med 2009; 151:727-737.

    Google Scholar 

  • Rebolj M, van Ballegooijen M, Berkers LM, Habbema JDF. Monitoring a national cancer prevention program: Successful changes in cervical cancer screening in the Netherlands. Int J Cancer 2007;120:806-812.

    Article  Google Scholar 

  • Rutter CM, Knudsen AB, Pandharipande PV. Computer disease simulation models: Integrating evidence for health policy. Acad Radiol 2011:18:1077-1086.

    Article  Google Scholar 

  • Stout NK, Knudsen AB, Kong CY, McMahon PM, Gazelle GS. Calibration methods used in cancer simulation models and suggested reporting guidelines. Pharmacoeconomics 2009; 27:533-545.

    Article  Google Scholar 

  • Stout NK, Rosenberg MA, Trentham-Dietz A, Smith MA, Robinson SM, Fryback DG. Retrospective cost-effectiveness analysis of screening mammography. J Natl Cancer Inst 2006; 98:774-782.

    Article  Google Scholar 

  • Tosteson ANA, Stout NK, Fryback DG, Acharyya S, Herman B, Hannah L et al. Cost-effectiveness of digital mammography breast cancer screening: Results from ACRIN DMIST. Ann Intern Med 2008; 148:1-10.

    Google Scholar 

  • US Preventive Services Task Force. Screening for breast cancer: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med 2009; 151:716-726.

    Google Scholar 

  • van Ballegooijen M, Boer R, van Oortmarssen GJ, Koopmanschap MA, Lubbe JTN, Habbema JDF. Cervical cancer screening programme: Age ranges and intervals. An updated cost-effectiveness analysis (In Dutch). . Department of Public Health, Erasmus University, Rotterdam, 1993.

    Google Scholar 

  • van Ballegooijen M, Rutter CM, Knudsen AB, Zauber AG, Savarino JE, Lansdorp-Vogelaar I et al. Clarifying differences in natural history between models of screening: The case of colorectal cancer. Med Decis Making 2011; 31:540-549.

    Article  Google Scholar 

  • van den Akker-van Marle ME, van Ballegooijen M, van Oortmarssen GJ, Boer R, Habbema JDF. Cost-Effectiveness of Cervical Cancer Screening: Comparison of Screening Policies. J Natl Cancer Inst 2002;94:193-204.

    Google Scholar 

  • Vogelaar I, van Ballegooijen M, Schrag D, Boer R, Winawer SJ, Habbema JDF et al. How much can current interventions reduce colorectal cancer mortality in the U.S.? Cancer 2006; 107:1624-1633.

    Google Scholar 

  • Zauber AG, Knudsen AB, Rutter CM, Lansdorp-Vogelaar I, Savarino JE, van Ballegooijen M, Kuntz KM. Cost-effectiveness of CT colonography to screen for colorectal cancer, Technology Assessment Report. Agency for Healthcare Research and Quality, Rockville, MD, 2009. (Available at: http://www.cms.gov/determinationprocess/downloads/id58TA.pdf)

    Google Scholar 

  • Zauber AG, Lansdorp-Vogelaar I, Knudsen AB, Wilschut J, van Ballegooijen M, Kuntz KM. Evaluating test strategies for colorectal cancer screening: A decision analysis for the U.S. Preventive Services Task Force. Ann Intern Med 2008; 149:659-669.

    Google Scholar 

  • Zauber AG, Lansdorp-Vogelaar I, Wilschut J, Knudsen AB, van Ballegooijen M, Kuntz KM. Cost-effectiveness of DNA stool testing to screen for colorectal cancer, Technology Assessment Report. Agency for Healthcare Research and Quality, Rockville, MD, 2007. (Available at: http://www.cms.gov/determinationprocess/downloads/id52TA.pdf)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, 133 Brookline Avenue, 6th Floor, Boston, MA, 02215, USA

    Natasha K. Stout

  2. Surveillance Research Program Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD, USA

    Michelle C. Dunn & Eric J. Feuer

  3. Department of Public Health, Erasmus MC University Medical Center, Rotterdam, The Netherlands

    J. Dik F. Habbema

  4. Pacific Institute for Research and Evaluation, University of Baltimore, Baltimore, MD, USA

    David T. Levy

Authors
  1. Natasha K. Stout
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  2. Michelle C. Dunn
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  3. J. Dik F. Habbema
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  4. David T. Levy
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  5. Eric J. Feuer
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Corresponding author

Correspondence to Natasha K. Stout .

Editor information

Editors and Affiliations

  1. University of Toronto, 392 Lakeshore Road East, Oakville, L6J 1J8, Ontario, Canada

    Anthony B. Miller

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Stout, N.K., Dunn, M.C., Habbema, J.D.F., Levy, D.T., Feuer, E.J. (2013). Using Mathematical Models to Inform Public Policy for Cancer Prevention and Screening. In: Miller, A. (eds) Epidemiologic Studies in Cancer Prevention and Screening. Statistics for Biology and Health, vol 79. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-5586-8_21

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