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