Modelling health care expenditure : a new microsimulation approach to simulating the distributional impact of the Pharmaceutical Benefits Scheme

  • Deborah Schofield

    Student thesis: Doctoral Thesis


    In this thesis, a microsimulation model was developed using methods which were intended to overcome the main criticism of earlier models developed in Australia - that their estimation of the distribution of health benefits1 across income groups was not accurate. To determine whether the new model - called the Person Level Model of Pharmaceutical Benefits (PLM-PB) - was more accurate, two typical means-based models were also built to replicate the most commonly used methods in Australia. A comparison of the results of the three models revealed that while they produced comparable results at the aggregate when compared with administrative data, the PLM-PB was much more accurate in capturing distributional differences by beneficiary and medication type. The PLM-PB also indicated that, as anticipated, PBS benefits were more pro-poor than earlier means-based models had suggested. The PLM-PB had another important advantage in that the method also captured the variation in the use of medication and thus the subsidy received within sub-populations. As the PLM-PB was found to be more accurate than the means-based model, a multivariate analysis of the distribution of PBS subsidy across a number of socio-economic groups was undertaken as an example application of the model. It was found that health status (defined by number of recent illnesses) and concession card type were most important in explaining the amount of PBS subsidy received. This indicates that the distribution of PBS expenditure meets the policy objectives of assisting those most in need, whether need is defined as poor health or low income. 1 Benefits refer to expenditure as transfers from government to individuals rather than the general health benefits of using medication.
    Date of Award1999
    Original languageEnglish
    SupervisorDavid Pederson (Supervisor), Tania Prvan (Supervisor) & Ann Harding (Supervisor)

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