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Dynamic microsimulation of health care demand, health care finance and the economic impact of health behaviours: survey and review

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  • Martin Spielauer

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    (Socio-Economic Analysis and Modeling Division, Statistics Canada, 100 Tunney's Pasture Driveway, Ottawa K1A 0T6 CANADA)

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    Abstract

    This paper reviews the issues to be faced in attempting to create a microsimulation of health care demand, health care finance and the economic impact of health behaviour. These issues identified via an in-depth review of seven dynamic microsimulation models, selected from an initial set of 27 models in order to highlight the main differences in approaches and modelling options currently adopted. After presenting a brief description of each of the seven selected models, the main modelling approaches are summarized and critically appraised using five main distinguishing criteria. These criteria are the use of alignment techniques, model complexity (as reflected in the range of variables used), theoretical foundations, type of starting population, and the extent and detail of financial issues covered. Building upon this appraisal, the paper goes on to show how the ‘12 SAGE lessons’ apply in the field of health care microsimulation. The trade-off between complexity and predictive power is shown to be key. Finally an appendix summarises the main features of all 27 of the dynamic microsimulation models originally surveyed.

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    File URL: http://ima.natsem.canberra.edu.au/IJM/V1_1/IJM_1_1_5.pdf
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    Bibliographic Info

    Article provided by Interational Microsimulation Association in its journal International Journal of Microsimulation.

    Volume (Year): 1 (2007)
    Issue (Month): 1 ()
    Pages: 35-53

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    Handle: RePEc:ijm:journl:v:1:y:2007:i:1:p:35-53

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    Web page: http://ima.natsem.canberra.edu.au/index.htm

    Related research

    Keywords: health care; microsimulation;

    References

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    1. Rebecca Cassells & Ann Harding & Simon Kelly, 2006. "Problems and Prospects for Dynamic Microsimulation: A review and lessons for APPSIM," NATSEM Working Paper Series 63, University of Canberra, National Centre for Social and Economic Modelling.
    2. Merz, Joachim, 1991. "Microsimulation -- A survey of principles, developments and applications," International Journal of Forecasting, Elsevier, vol. 7(1), pages 77-104, May.
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    Cited by:
    1. Jakub Bijak & Jason Hilton & Eric Silverman & Viet Dung Cao, 2013. "Reforging the Wedding Ring," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 29(27), pages 729-766, October.
    2. Binod Nepal & Laurie Brown & Simon Kelly & Richard Percival & Phil Anderson & Ruth Hancock & Geetha Ranmuthugala, 2011. "Projecting the Need for Formal and Informal Aged Care in Australia: A Dynamic Microsimulation Approach," NATSEM Working Paper Series 11/07, University of Canberra, National Centre for Social and Economic Modelling.
    3. Laurie Brown & Binod Nepal & Heather Booth & Sophie Pennec & Kaarin Anstey & Ann Harding, 2011. "Dynamic Modelling of Ageing and Health: The Dynopta Microsimulation Model," NATSEM Working Paper Series 11/14, University of Canberra, National Centre for Social and Economic Modelling.
    4. van Sonsbeek, Jan-Maarten & Alblas, Ridwan, 2012. "Disability benefit microsimulation models in the Netherlands," Economic Modelling, Elsevier, vol. 29(3), pages 700-715.
    5. Zucchelli, E & Jones, A.M & Rice, N, 2010. "The evaluation of health policies through microsimulation methods," Health, Econometrics and Data Group (HEDG) Working Papers 10/03, HEDG, c/o Department of Economics, University of York.

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