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The evaluation of health policies through dynamic microsimulation methods


  • Eugenio Zucchelli

    () (Centre for Health Economics (CHE), University of York, UK; Alcuin A Block, University of York, Heslington, York, YO10 5DD, UK)

  • Andrew M Jones

    () (Department of Economics and Related Studies (DERS), University of York, UK;)

  • Nigel Rice

    () (Centre for Health Economics (CHE), University of York, UK; Alcuin A Block, University of York, Heslington, York, YO10 5DD, UK)


This paper presents an overview of microsimulation as a method to evaluate health and health care policies and interventions. After presenting a brief survey of microsimulation models and applications we describe the main features of the approach and how these are implemented in practice. We pay particular attention to the innovative features of dynamic microsimulation as a method of ex-ante policy evaluation. The final section presents a critical overview of the most recent health-dedicated dynamic microsimulation models including POHEM and FEM, two of the most comprehensive dynamic microsimulation models for health. We describe how these models are used to simulate lifecycle health trajectories and associated health care costs under competing policy scenarios to illustrate the power of microsimulation as a valid and relevant tool for policy evaluation.

Suggested Citation

  • Eugenio Zucchelli & Andrew M Jones & Nigel Rice, 2012. "The evaluation of health policies through dynamic microsimulation methods," International Journal of Microsimulation, International Microsimulation Association, vol. 5(1), pages 2-20.
  • Handle: RePEc:ijm:journl:v:5:y:2012:i:1:p:2-20

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    Cited by:

    1. Charlotte Geay & Grégoire de Lagasnerie & Makram Larguem, 2015. "Intégrer les dépenses de santé dans un modèle de microsimulation dynamique : le cas des dépenses de soins de ville," Économie et Statistique, Programme National Persée, vol. 481(1), pages 211-234.
    2. C. GEAY & M. KOUBI & G. de LAGASNERIE, 2015. "Evolution of outpatient healthcare expenditure, a dynamic micro-simulation using the Destinie model," Documents de Travail de la DESE - Working Papers of the DESE g2015-15, Institut National de la Statistique et des Etudes Economiques, DESE.
    3. Alison Ritter & Nagesh Shukla & Marian Shanahan & Phuong Van Hoang & Vu Lam Cao & Pascal Perez & Michael Farrell, 2016. "Building a Microsimulation Model of Heroin Use Careers in Australia," International Journal of Microsimulation, International Microsimulation Association, vol. 9(3), pages 140-176.
    4. Lay-Yee, Roy & Milne, Barry & Davis, Peter & Pearson, Janet & McLay, Jessica, 2015. "Determinants and disparities: A simulation approach to the case of child health care," Social Science & Medicine, Elsevier, vol. 128(C), pages 202-211.
    5. Charlotte Geay & Grégoire de Lagasnerie & Makram Larguem, 2014. "Evolution of outpatient healthcare expenditure due to ageing in 2030, a dynamic micro-simulation model for France," Sciences Po publications 28, Sciences Po.

    More about this item


    microsimulation methods; policy evaluation; health; public health interventions;

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • I10 - Health, Education, and Welfare - - Health - - - General
    • J11 - Labor and Demographic Economics - - Demographic Economics - - - Demographic Trends, Macroeconomic Effects, and Forecasts


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