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An actuarial investigation into maternal out-of-hospital cost risk factors

Published online by Cambridge University Press:  05 February 2018

Jananie William ,
Catherine Chojenta ,
Michael A. Martin  and
Deborah Loxton
Jananie William*
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, University Drive, Callaghan NSW 2308, Australia
Michael A. Martin
Affiliation:
Research School of Finance, Actuarial Studies and Statistics, Australian National University, Canberra ACT 0200, Australia
Deborah Loxton
Affiliation:
Research Centre for Generational Health & Ageing, The University of Newcastle, University Drive, Callaghan NSW 2308, 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: jananie.william@anu.edu.au
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Abstract

This paper adopts an actuarial approach to identify the risk factors of government-funded maternal out-of-hospital costs in Australia, with a focus on women who experience adverse birth outcomes. We use a two-phase modelling methodology incorporating both classification and regression trees and generalised linear models on a data set that links administrative and longitudinal survey data from a large sample of women, to address maternal out-of-hospital costs. We find that adverse births are a statistically significant risk factor of out-of-hospital costs in both the delivery and postnatal periods. Furthermore, other significant cost risk factors are in-vitro fertilisation, specialist use, general practitioner use, area of residence and mental health factors (including anxiety, intense anxiety, postnatal depression and stress about own health) and the results vary by perinatal sub-period and the patient’s private health insurance status. We highlight these differences and use the results as an evidence base to inform public policy. Mental health policy is identified as a priority area for further investigation due to the dominance of these factors in many of the fitted models.

Keywords

Generalised linear modelClassification and regression treeMaternal out-of-hospital costsAdverse birthMental health policy

Information

Type
Paper
Information
Annals of Actuarial Science , Volume 13 , Issue 1 , March 2019 , pp. 1 - 35
Copyright
© Institute and Faculty of Actuaries 2018 

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