Primary Care in an Aging Society: Building and Testing a Microsimulation Model for Policy Purposes

  • Authors:

  • Janet Pearson;Roy Lay-Yee;Peter Davis;David O'Sullivan;Martin Von Randow;Ngaire Kerse;Sanat Pradhan

  • Affiliations:

  • Centre of Methods and Policy Application in the SocialSciences (COMPASS), University of Auckland, New Zealand;Centre of Methods and Policy Application in the SocialSciences (COMPASS), University of Auckland, New Zealand;Centre of Methods and Policy Application in the SocialSciences (COMPASS), University of Auckland, New Zealand;Centre of Methods and Policy Application in the SocialSciences (COMPASS), University of Auckland, New Zealand;Centre of Methods and Policy Application in the SocialSciences (COMPASS), University of Auckland, New Zealand;Centre of Methods and Policy Application in the SocialSciences (COMPASS), University of Auckland, New Zealand;Centre of Methods and Policy Application in the SocialSciences (COMPASS), University of Auckland, New Zealand

  • Venue:
  • Social Science Computer Review
  • Year:
  • 2011

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Abstract

The authors describe the development of a microsimulation model of primary medical care in New Zealand for 2002 and demonstrate its ability to test the impact of demographic ageing, community support, and practitioner repertoire. Micro-level data were drawn from four sources: two iterations of the New Zealand Health Survey (NZHS 1996/1997 and 2002/2003); a national survey of ambulatory care in New Zealand (New Zealand National Primary Medical Care Survey [NPMCS] 2001/ 2002); and the Australian National Health Survey (ANHS). Data from the New Zealand surveys were statistically matched to create a representative synthetic base file of over 13,000 individuals. Probabilities of health experiences and general practitioner (GP) use derived from the ANHS, and of GP activity derived from the NPMCS were applied via a Monte Carlo process to create health histories for the individuals in the base file. Final health care outcomes simulatedâ聙聰the number of visits in a year, the distribution of health conditions, and GP activity levelsâ聙聰were validated against external benchmarks. Policy-relevant scenarios were demonstrated by a forward projection to 2021 and by implementing counterfactuals on key attributes of the synthetic population. The results showed little change in model-predicted health care outcomes. There is potential for this approach to address policy purposes.