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Using micro-simulation to create a synthesised data set and test policy options: The case of health service effects under demographic ageing

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  • Davis, Peter
  • Lay-Yee, Roy
  • Pearson, Janet

Abstract

Objectives To assess micro-simulation for testing policy options under demographic ageing.Methods Individual-level data were drawn from the New Zealand Health Survey (1996/7 and 2002/3), a national survey of ambulatory care in New Zealand (2001/2), and the Australian National Health Survey (1995). Health service effects assessed were visits to the family doctor, and rates of prescribing and referral. We created a representative set of synthetic health histories by imputation and tested the health service effects of different policy scenarios. These were created by varying ageing and morbidity trajectories, degree of social support available, and intensity of practitioner behaviour.Results The set of synthetic health histories created by combining the data sources generated outcomes reasonably close to external benchmarks. Altering the age distribution of 2002 to approximate settings for 2021 produced no change in rates of visiting, prescribing, or referral for the 65-and-over population. Quantifying the health service effects of different scenarios showed no impact on visit rates by varying social support, but substantial differences for visits between high and low morbidity scenarios and for prescribing and referral rates according to practitioner behaviour.Conclusions There is potential for micro-simulation to assist in the synthesis of data and to help quantify scenario options for policy development.

Suggested Citation

  • Davis, Peter & Lay-Yee, Roy & Pearson, Janet, 2010. "Using micro-simulation to create a synthesised data set and test policy options: The case of health service effects under demographic ageing," Health Policy, Elsevier, vol. 97(2-3), pages 267-274, October.
    • Handle: RePEc:eee:hepoli:v:97:y:2010:i:2-3:p:267-274
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    References listed on IDEAS

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    1. 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.
    2. Mielczarek, Bożena & Zabawa, Jacek, 2021. "Modelling demographic changes using simulation: Supportive analyses for socioeconomic studies," Socio-Economic Planning Sciences, Elsevier, vol. 74(C).
    3. Oliver Mannion & Roy Lay-Yee & Wendy Wrapson & Peter Davis & Janet Pearson, 2012. "JAMSIM: a Microsimulation Modelling Policy Tool," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 15(1), pages 1-8.
    4. Aldren Gonzales & Guruprabha Guruswamy & Scott R Smith, 2023. "Synthetic data in health care: A narrative review," PLOS Digital Health, Public Library of Science, vol. 2(1), pages 1-16, January.

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