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A Brief, Global History of Microsimulation Models in Health: Past Applications, Lessons Learned and Future Directions

Author

Listed:
  • Deborah J Schofield

    (Centre for Economic Impacts of Genomic Medicine (GenIMPACT), Macquarie University, Australia.)

  • Melanie J B Zeppel

    (Centre for Economic Impacts of Genomic Medicine (GenIMPACT), Macquarie University, Australia.)

  • Owen Tan

    (Centre for Economic Impacts of Genomic Medicine (GenIMPACT), Macquarie University, Australia.)

  • Sharyn Lymer

    (Faculty of Pharmacy, The University of Sydney, Australia.)

  • Michelle M Cunich

    (Faculty of Pharmacy, The University of Sydney, Australia.)

  • Rupendra N Shrestha

    (Centre for Economic Impacts of Genomic Medicine (GenIMPACT), Macquarie University, Australia.)

Abstract

This review discusses the evolution of microsimulation models in health over the past three decades. We focus on three aspects of health microsimulation. First, we describe the origins and applications of health microsimulation, including how early research challenged early methodologies and led to the development of more rigorous models. We describe limitations of early means-based models and how more detailed methods, which are based on health-specific input data, overcame many of the early data shortfalls and assumptions. Second, we discuss the global expansion of applications of microsimulation to health over the last ten years. Many health microsimulation models have focussed on health expenditure, the ageing population, diabetes, mortality modelling, and spatial models. Health models over the past few decades have expanded to many countries including Canada, Africa, the United States, the UK, Sweden, the Netherlands, New Zealand and Australia. Finally, we describe future developments, including emerging research fields for microsimulation and health and how the early development of health microsimulation models provides important lessons for emerging applications. These include the emerging field of genomics and precision medicine, and the diagnosis and treatment of childhood cancers and rare diseases. We suggest research directions, including the need for good data to avoid model errors, and highlight some pitfalls to avoid.

Suggested Citation

  • Deborah J Schofield & Melanie J B Zeppel & Owen Tan & Sharyn Lymer & Michelle M Cunich & Rupendra N Shrestha, 2018. "A Brief, Global History of Microsimulation Models in Health: Past Applications, Lessons Learned and Future Directions," International Journal of Microsimulation, International Microsimulation Association, vol. 11(1), pages 97-142.
  • Handle: RePEc:ijm:journl:v10:y:2018:i:1:p:97-142
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    References listed on IDEAS

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    Cited by:

    1. Tobias Schoch & André Müller, 2020. "Treatment of sample under-representation and skewed heavy-tailed distributions in survey-based microsimulation: An analysis of redistribution effects in compulsory health care insurance in Switzerland," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 14(3), pages 267-304, December.
    2. Richiardi, Matteo & Bronka, Patryk & van de Ven, Justin & Kopasker, Daniel & Vittal Katikireddi, Srinivasa, 2023. "SimPaths: an open-source microsimulation model for life course analysis," Centre for Microsimulation and Policy Analysis Working Paper Series CEMPA6/23, Centre for Microsimulation and Policy Analysis at the Institute for Social and Economic Research.
    3. Richiardi, Matteo & Bronka, Patryk & van de Ven, Justin, 2023. "Back to the future: Agent-based modelling and dynamic microsimulation," Centre for Microsimulation and Policy Analysis Working Paper Series CEMPA8/23, Centre for Microsimulation and Policy Analysis at the Institute for Social and Economic Research.

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    More about this item

    Keywords

    HEALTH ECONOMICS; SPATIAL MODELLING; CANCER MICROSIMULATION; PRECISION MEDICINE;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • I1 - Health, Education, and Welfare - - Health

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