Date of Award

2009

Degree Type

Thesis

Degree Name

Bachelor of Science Honours

Faculty

Faculty of Computing, Health and Science

First Advisor

Ute Mueller

Second Advisor

Dorota Doherty

Abstract

Decision analytic modelling enables decision makers to assess the cost-effectiveness associated with a proposed change in a cunent system without physically implementing the changes. This can be achieved by formulating a mathematical model that represents all the major events occuning in the system through fmmulas and algorithms, and estimating the likely outcomes along with their costs. This type of modelling has been identified by the State Health Research Advisory Council (SHRAC) of the Western Australian Depmiment of Health as an asset for the plmming of health care investments in the future. One such area in which the Western Australian Department of Health has identified the need for future planning is in the improvement of perinatal outcomes of Aboriginal women living in rural and remote areas of Western Australia. Various investigations into policy changes that have given some evidence of improving pregnancy outcomes have recently provided the need for an appropriate decision analytic model to be constructed. This requires the formulation of a mathematical model that can simulate the pregnancy events and outcomes consistent with those observed in practice. This thesis will outline a mathematical model with the objective to simulate a large cohort of individual Aboriginal women going through pregnancy in remote regions of W A that is representative of the populations' current outcomes. The scope of the model is limited to the prediction of clinical outcomes during the antenatal period of pregnancy for individual patients whilst the implementation of costs will not be considered. The validity of the simulation model will be shown to be very accurate by providing comparisons of simulated outcomes to those from the observed data. A discussion on the benefits of the methods used to construct this model will then be identified, concluding with a range of further uses this model could be applied to.

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