Citing this article

A standard form of citation of this article is:

Abello, Annie, Lymer, Sharyn, Brown, Laurie, Harding, Ann and Phillips, Ben (2008). 'Enhancing the Australian National Health Survey Data for Use in a Microsimulation Model of Pharmaceutical Drug Usage and Cost'. Journal of Artificial Societies and Social Simulation 11(3)2 <http://jasss.soc.surrey.ac.uk/11/3/2.html>.

The following can be copied and pasted into a Bibtex bibliography file, for use with the LaTeX text processor:

@article{abello2008,
title = {Enhancing the Australian National Health Survey Data for Use in a Microsimulation Model of Pharmaceutical Drug Usage and Cost},
author = {Abello, Annie and Lymer, Sharyn and Brown, Laurie and Harding, Ann and Phillips, Ben},
journal = {Journal of Artificial Societies and Social Simulation},
ISSN = {1460-7425},
volume = {11},
number = {3},
pages = {2},
year = {2008},
URL = {http://jasss.soc.surrey.ac.uk/11/3/2.html},
keywords = {Base Data, Drug Usage, Microsimulation, Pharmaceutical Benefits, Scripts, Statistical Matching},
abstract = {While static microsimulation models of the tax-transfer system are now available throughout the developed world, health microsimulation models are much rarer. This is, at least in part, due to the difficulties in creating adequate base micro-datasets upon which the microsimulation models can be constructed. In sharp contrast to tax-transfer modelling, no readily available microdata set typically contains all the health status, health service usage and socio-demographic information required for a sophisticated health microsimulation model. This paper describes three new techniques developed to overcome survey data limitations when constructing 'MediSim', a microsimulation model of the Australian Pharmaceutical Benefits Scheme. Comparable statistical matching and data imputation techniques may be of relevance to other modellers, as they attempt to overcome similar data deficiencies. The 2001 national health survey (NHS) was the main data source for MediSim. However, the NHS has a number of limitations for use in a microsimulation model. To compensate for this, we statistically matched the NHS with another national survey to create synthetic families and get a complete record for every individual within each family. Further, we used complementary datasets to impute short term health conditions and prescribed drug usage for both short- and long-term health conditions. The application of statistical matching methods and use of complementary data sets significantly improved the usefulness of the NHS as a base dataset for MediSim.},
}

The following can be copied and pasted into a text file, which can then be imported into a reference database that supports imports using the RIS format, such as Reference Manager and EndNote.


TY - JOUR
TI - Enhancing the Australian National Health Survey Data for Use in a Microsimulation Model of Pharmaceutical Drug Usage and Cost
AU - Abello, Annie
AU - Lymer, Sharyn
AU - Brown, Laurie
AU - Harding, Ann
AU - Phillips, Ben
Y1 - 2008/06/30
JO - Journal of Artificial Societies and Social Simulation
SN - 1460-7425
VL - 11
IS - 3
SP - 2
UR - http://jasss.soc.surrey.ac.uk/11/3/2.html
KW - Base Data
KW - Drug Usage
KW - Microsimulation
KW - Pharmaceutical Benefits
KW - Scripts
KW - Statistical Matching
N2 - While static microsimulation models of the tax-transfer system are now available throughout the developed world, health microsimulation models are much rarer. This is, at least in part, due to the difficulties in creating adequate base micro-datasets upon which the microsimulation models can be constructed. In sharp contrast to tax-transfer modelling, no readily available microdata set typically contains all the health status, health service usage and socio-demographic information required for a sophisticated health microsimulation model. This paper describes three new techniques developed to overcome survey data limitations when constructing 'MediSim', a microsimulation model of the Australian Pharmaceutical Benefits Scheme. Comparable statistical matching and data imputation techniques may be of relevance to other modellers, as they attempt to overcome similar data deficiencies. The 2001 national health survey (NHS) was the main data source for MediSim. However, the NHS has a number of limitations for use in a microsimulation model. To compensate for this, we statistically matched the NHS with another national survey to create synthetic families and get a complete record for every individual within each family. Further, we used complementary datasets to impute short term health conditions and prescribed drug usage for both short- and long-term health conditions. The application of statistical matching methods and use of complementary data sets significantly improved the usefulness of the NHS as a base dataset for MediSim.
ER -