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Dynamic Policy Modeling for Chronic Diseases: Metaheuristic-Based Identification of Pareto-Optimal Screening Strategies

Author

Listed:
  • Marion S. Rauner

    (Department of Innovation and Technology Management, School of Business and Economics, University of Vienna, Vienna, Austria)

  • Walter J. Gutjahr

    (Department of Statistics and Decision Support Systems, School of Business and Economics, University of Vienna, Vienna, Austria)

  • Kurt Heidenberger

    (Department of Innovation and Technology Management, School of Business and Economics, University of Vienna, Vienna, Austria)

  • Joachim Wagner

    (Department of Accounting, School of Business and Economics, University of Vienna, Vienna, Austria)

  • Joseph Pasia

    (Department of Production and Operations Management, School of Business and Economics, University of Vienna, Vienna, Austria)

Abstract

We present a risk-group oriented chronic disease progression model embedded within a metaheuristic-based optimization of the policy variables. Policy-makers are provided with Pareto-optimal screening schedules for risk groups by considering cost and effectiveness outcomes as well as budget constraints. The quality of the screening technology depends on risk group, disease stage, and time. As the metaheuristic solution technique, we use the Pareto ant colony optimization (P-ACO) algorithm for multiobjective combinatorial optimization problems, which is based on the ant colony optimization paradigm. Our approach is illustrated by a numerical example for breast cancer. For a 10-year time horizon, we provide cost-effective screening schedules for selected annual and total budgets. We then discuss policy implications of 16 mammography screening scenarios varying the screening schedule (annual, biennial, triennial, quadrennial) and the rate of women tested (25%, 50%, 75%, 100%). Due to the model's flexible structure, interventions for multiple chronic diseases can be considered simultaneously.

Suggested Citation

  • Marion S. Rauner & Walter J. Gutjahr & Kurt Heidenberger & Joachim Wagner & Joseph Pasia, 2010. "Dynamic Policy Modeling for Chronic Diseases: Metaheuristic-Based Identification of Pareto-Optimal Screening Strategies," Operations Research, INFORMS, vol. 58(5), pages 1269-1286, October.
    • Handle: RePEc:inm:oropre:v:58:y:2010:i:5:p:1269-1286
    DOI: 10.1287/opre.1100.0838
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    Cited by:

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    2. Ting-Yu Ho & Shan Liu & Zelda B. Zabinsky, 2019. "A Multi-Fidelity Rollout Algorithm for Dynamic Resource Allocation in Population Disease Management," Health Care Management Science, Springer, vol. 22(4), pages 727-755, December.
    3. 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.
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    6. Hessam Bavafa & Sergei Savin & Christian Terwiesch, 2021. "Customizing Primary Care Delivery Using E‐Visits," Production and Operations Management, Production and Operations Management Society, vol. 30(11), pages 4306-4327, November.
    7. Liesiö, Juuso & Salo, Ahti & Keisler, Jeffrey M. & Morton, Alec, 2021. "Portfolio decision analysis: Recent developments and future prospects," European Journal of Operational Research, Elsevier, vol. 293(3), pages 811-825.
    8. Karl F. Doerner & Vittorio Maniezzo, 2018. "Metaheuristic search techniques for multi-objective and stochastic problems: a history of the inventions of Walter J. Gutjahr in the past 22 years," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 26(2), pages 331-356, June.

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