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An actuarial investigation into maternal hospital cost risk factors for public patients

Published online by Cambridge University Press:  20 July 2017

Jananie William ,
Michael A. Martin ,
Catherine Chojenta  and
Deborah Loxton
Jananie William*
Affiliation:
Research School of Finance, Actuarial Studies and Statistics, Australian National University, Canberra ACT 0200, Australia
Michael A. Martin
Affiliation:
Research School of Finance, Actuarial Studies and Statistics, Australian National University, Canberra ACT 0200, Australia
Catherine Chojenta
Affiliation:
Research Centre for Generational Health & Ageing, The University of Newcastle, Australia
Deborah Loxton
Affiliation:
Research Centre for Generational Health & Ageing, The University of Newcastle, Australia
*
*Correspondence to: Jananie William, Research School of Finance, Actuarial Studies and Statistics, College of Business and Economics, Australian National University, Canberra ACT 0200, Australia. Tel: +61 2 6125 7311; E-mail: [email protected]
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Abstract

We investigate an actuarial approach to identifying the factors impacting government-funded maternal hospital costs in Australia, with a focus on women who experience adverse birth outcomes. We propose a two-phase modelling methodology that adopts actuarial methods from typical insurance claim cost modelling and extends to other statistical techniques to account for the large volume of covariates available for modelling. Specifically, Classification and Regression Trees and generalised linear mixed models are employed to analyse a data set that links longitudinal survey and administrative data from a large sample of women. The results show that adverse births are a statistically significant risk factor affecting maternal hospital costs in the antenatal and delivery periods. Other significant cost risk factors in the delivery period include mode of delivery, private health insurance status, diabetes, smoking status, area of residence and onset of labour. We demonstrate the efficacy of using actuarial techniques in non-traditional areas and highlight how the results can be used to inform public policy.

Keywords

Generalised linear mixed modelClassification and Regression TreeMaternal hospital costsPublic health costsAdverse birth outcome
Type
Papers
Information
Annals of Actuarial Science , Volume 12 , Issue 1 , March 2018 , pp. 106 - 129
Copyright
© Institute and Faculty of Actuaries 2017 

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