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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: [email protected]
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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
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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References

Alder, J., Fink, N., Bitzer, J., Hosli, I. & Holzgreve, W. (2007). Depression and anxiety during pregnancy: a risk factor for obstetric, fetal and neonatal outcome? A critical review of the literature. Journal of Maternal-Fetal & Neonatal Medicine, 20(3), 189–209.Google Scholar
Australian Institute of Health and Welfare (2007). Health expenditure Australia 2005-06. In Australian Institute of Health and Welfare (Ed.), Health and Welfare Expenditure Series No. 30. Australian Institute of Health and Welfare, Canberra.Google Scholar
Australian Institute of Health and Welfare (2016). Health expenditure Australia 2014-15. In Australian Institute of Health and Welfare (Ed.), Health and Welfare Expenditure Series No. 57. Australian Institute of Health and Welfare, Canberra.Google Scholar
Australian Longitudinal Study for Women’s Health (ALSWH) (2014). Technical Report 2014. University of Queensland and The University of Newcastle, Newcastle.Google Scholar
Australian Prudential Regulatory Authority (2016). Private Health Insurance Membership Trends. APRA, Sydney.Google Scholar
Boyce, P. & Hickey, A. (2005). Psychosocial risk factors to major depression after childbirth. Social Psychiatry and Psychiatric Epidemiology, 40(8), 605–612.Google Scholar
Brockman, M. & Wright, T. (1992). Statistical motor rating: Making effective use of your data. Journal of the Institute of Actuaries, 119, 457–543.Google Scholar
Brown, W.J., Dobson, A.J., Bryson, L. & Byles, J.E. (1999). The Australian Longitudinal Study on Women’s Health: on the progress of the main cohort studies. Journal of Women’s Health & Gender-Based Medicine, 8(5), 681–688.Google Scholar
Bryant, R. (2008). Improving Maternity Services in Australia. Department of Health and Ageing, Canberra.Google Scholar
Centre for Health Economics Research and Evaluation (2009). Extended Medicare Safety Net: review report, Centre for Health Economics Research and Evaluation, University of Technology Sydney, Sydney.Google Scholar
Centre for Health Economics Research and Evaluation (2011). Extended Medicare Safety Net: review of capping arrangements report, Centre for Health Economics Research and Evaluation, University of Technology Sydney, Sydney.Google Scholar
Chojenta, C. (2013). Prevalence, antecedents and perceptions of efficacy of treatments of postnatal depression in Australia. (PhD), University of Newcastle, Newcastle.Google Scholar
Chojenta, C., Loxton, D. & Lucke, J. (2012). How do previous mental health, social support, and stressful life events contribute to postnatal depression in a representative sample of Australian women? Journal of Midwifery & Women’s Health, 57(2), 145–150.Google Scholar
Chojenta, C., Lucke, J., Forder, P. & Loxton, D. (2016). Maternal health factors as risks for postnatal depression: a prospective longitudinal study. PLoS One, 11(1), e0147246.Google Scholar
Chollet, D., Newman, J. & Sumner, A. (1996). The cost of poor birth outcomes in employer-sponsored health plans. Medical Care, 34(12), 1219–1234.Google Scholar
de Jong, P. & Heller, G.Z. (2008). Generalized Linear Models for Insurance Data (volume 17). Cambridge University Press, Cambridge, UK.Google Scholar
Ellis, R., Fiebig, D., Johar, M., Jones, G. & Savage, E. (2013). Explaining health care expenditure variation: large-sample evidence using linked survey and health administrative data. Health Economics, 22(9), 1093–1110.Google Scholar
Frees, E., Jin, X. & Lin, X. (2013). Actuarial applications of multivariate two-part regression models. Annals of Actuarial Science, 7(2), 258–287.Google Scholar
Gilbert, W.M., Nesbitt, T.S. & Danielsen, B. (2003). The cost of prematurity: quantification by gestational age and birth weight. The American College of Obstetricians and Gynecologists, 102, 488–492.Google Scholar
Gresham, E., Forder, P., Chojenta, C., Byles, J., Loxton, D. & Hure, A. (2015). Agreement between self-reported perinatal outcomes and administrative data in New South Wales, Australia. BMC Pregnancy & Childbirth, 15, 161.Google Scholar
Haberman, S. & Renshaw, A.E. (1996). Generalized linear models and actuarial science. The Statistician, 45(4), 407–436.Google Scholar
Hedegaard, M. (2002). The effects of antenatal stress and anxiety on pregnancy outcome. Journal of Affective Disorders, 68(1), 95–96.Google Scholar
Howson, C., Kinney, M. & Lawn, J. (2012). Born Too Soon: The Global Action Report on Preterm Birth. World Health Organisation, Geneva.Google Scholar
Johar, M., Jones, G. & Savage, E. (2012). Healthcare expenditure profile of older Australians: evidence from linked survey and health administrative data. Economic Papers, 31(4), 451–463.Google Scholar
Luke, B., Bigger, H., Leurgans, S. & Sietsema, D. (1996). The cost of prematurity: a case-control study of twins vs singletons. American Journal of Public Health, 86(6), 809–814.Google Scholar
Measey, M., Charles, A., d’Espaignet, E., Harrison, C., Deklerk, N. & Douglass, C. (2007). Aetiology of stillbirth: unexplored is not unexplained. Australian and New Zealand Journal of Public Health, 31(5), 444–449.Google Scholar
Medicare Australia (2016). Medicare Benefits Schedule Book. Medicare Australia, Canberra.Google Scholar
Mistry, H., Heazell, A.E.P., Vincent, O. & Roberts, T. (2013). A structured review and exploration of the healthcare costs associated with stillbirth and a subsequent pregnancy in England and Wales. BMC Pregnancy & Childbirth, 13, 236.Google Scholar
O’Leary, C., Bower, C., Knuiman, M. & Stanley, F. (2007). Changing risks of stillbirth and neonatal mortality associated with maternal age in Western Australia 1984-2003. Paediatric and Perinatal Epidemiology, 21(6), 541–549.Google Scholar
Powers, J. & Loxton, D. (2010). The impact of attrition in an 11-year prospective longitudinal study of younger women. Annals of Epidemiology, 20, 318–321.Google Scholar
Powers, J., Loxton, D., O’Mara, A., Chojenta, C. & Ebert, L. (2013). Regardless of where they give birth, women living in non-metropolitan areas are less likely to have an epidural than their metropolitan counterparts. Women and Birth, 26(2), e77–e81.Google Scholar
Ringborg, A., Berg, J., Norman, M., Westgren, M. & Jonsson, B. (2006). Preterm birth in Sweden: What are the average lengths of hospital stay and the associated inpatient costs? Acta Paediatrica, 95(12), 1550–1555.Google Scholar
Schmied, V., Johnson, M., Naidoo, N., Austin, M.P., Matthey, S., Kemp, L., Mills, A., Meade, T. & Yeo, A. (2013). Maternal mental health in Australia and New Zealand: a review of longitudinal studies. Women and Birth: Journal of the Australian College of Midwives, 26(3), 167–178.Google Scholar
William, J., Chojenta, C., Martin, M. & Loxton, D. (2017). An actuarial investigation into maternal hospital cost risk factors for public patients. Annals of Actuarial Science, 1–24.Google Scholar
Wisborg, K., Barklin, A., Hedegaard, M. & Henriksen, T.B. (2008). Psychological stress during pregnancy and stillbirth: prospective study. BJOG: An International Journal of Obstetrics & Gynaecology, 115(7), 882–885.Google Scholar