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DyMH_LU: a simple tool for modelling and simulating the health status of the Luxembourgish elderly in the longer run

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  • GENEVOIS Anne-Sophie
  • LIEGEOIS Philippe
  • PI ALPERIN Maria Noel

Abstract

We are facing one of the most important demographic events of the last decades in Europe: the population ageing process. This process will have significant economic effects particularly on health. As most diseases are age-related, this process might imply a proportionally higher share of individuals with declining health. Being able to forecast the health status of the population can help to deal with concerns about the financial and social sustainability of several public policies including health. In this paper, we present the DyMH_LU model, a dynamic microsimulation model focused exclusively on the health status of the Luxembourgish population. One of its major characteristics is that it simulates more than sixty different diseases and limitations in the activities of daily living. All this simulated information can be aggregated in order to compute, for each period, the overall health status of each individual, the marginal distribution of each disease among the total population and the global health status of the entire population. The starting point of the DyMH_LU model is the information collected in 2015 in the Wave 6 of the SHARE database that targets individuals aged 51 or older. The simulation period covers 2017 until 2045.

Suggested Citation

  • GENEVOIS Anne-Sophie & LIEGEOIS Philippe & PI ALPERIN Maria Noel, 2019. "DyMH_LU: a simple tool for modelling and simulating the health status of the Luxembourgish elderly in the longer run," LISER Working Paper Series 2019-06, Luxembourg Institute of Socio-Economic Research (LISER).
    • Handle: RePEc:irs:cepswp:2019-06
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    1. Gijs Dekkers, 2015. "The simulation properties of microsimulation models with static and dynamic ageing a brief guide into choosing one type of model over the other," International Journal of Microsimulation, International Microsimulation Association, vol. 8(1), pages 97-109.
    2. Carmen M. Reinhart & Graciela L. Kaminsky, 1999. "The Twin Crises: The Causes of Banking and Balance-of-Payments Problems," American Economic Review, American Economic Association, vol. 89(3), pages 473-500, June.
    3. MUSSARD Stéphane & PI ALPERIN Maria Noel, 2016. "A Two-parameter Family of Socio-economic Health Inequality Indices: Accounting for Risk and Inequality Aversions," LISER Working Paper Series 2016-15, Luxembourg Institute of Socio-Economic Research (LISER).
    4. María Noel Pi Alperin, 2016. "A multidimensional approach to measure health," Economics Bulletin, AccessEcon, vol. 36(3), pages 1553-1568.
    5. Linc Thurecht & Laurie Brown & Mandy Yap, 2011. "Economic Modelling of the Prevention of Type 2 Diabetes in Australia The Diabetes Model," International Journal of Microsimulation, International Microsimulation Association, vol. 4(3), pages 71-80.
    6. David Boisclair & Aurélie Côté-Sergent & Jean-Yves Duclos & Alexandre Lekina & Steeve Marchand & Pierre-Carl Michaud, 2015. "COMPAS: Un modèle de microsimulation santé pour le Québec," CIRANO Working Papers 2015s-03, CIRANO.
    7. Anil Gupta & Ann Harding, 2007. "Introduction and Overview," International Symposia in Economic Theory and Econometrics, in: Modelling Our Future: Population Ageing, Health and Aged Care, pages 1-40, Emerald Group Publishing Limited.
    8. Jinjing Li & Cathal O'Donoghue, 2013. "A survey of dynamic microsimulation models: uses, model structure and methodology," International Journal of Microsimulation, International Microsimulation Association, vol. 6(2), pages 3-55.
    9. Gijs Dekkers & Eveline van Leeuwen, 2010. "Guest Editorial - Special Issue On Methodological Issues In Microsimulation," International Journal of Microsimulation, International Microsimulation Association, vol. 3(2), pages 1-2.
    10. ., 2007. "Introduction and overview," Chapters, in: International Economic Law and the Digital Divide, chapter 1, Edward Elgar Publishing.
    11. Bertrand Candelon & Elena-Ivona Dumitrescu & Christophe Hurlin, 2012. "How to Evaluate an Early-Warning System: Toward a Unified Statistical Framework for Assessing Financial Crises Forecasting Methods," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 60(1), pages 75-113, April.
    12. 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.
    13. Martin Spielauer, 2007. "Dynamic microsimulation of health care demand, health care finance and the economic impact of health behaviours: survey and review," International Journal of Microsimulation, International Microsimulation Association, vol. 1(1), pages 35-53.
    14. Immervoll, Herwig & Lindstrom, Klaw & Mustonen, Esko & Viitamaki, Heikki, 2005. "Static data ‘ageing’ techniques: accounting for population changes in tax-benefit microsimulation models," EUROMOD Working Papers EM7/05, EUROMOD at the Institute for Social and Economic Research.
    15. Bertrand Candelon & Elena-Ivona Dumitrescu & Christophe Hurlin, 2012. "How to evaluate an Early Warning System ?," Working Papers halshs-00450050, HAL.
    16. 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.
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    More about this item

    Keywords

    Dynamic microsimulation; Health; SHARE; Luxembourg;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • I10 - Health, Education, and Welfare - - Health - - - General

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