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Realising the Value of Linked Data to Health Economic Analyses of Cancer Care: A Case Study of Cancer 2015

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  • Paula K. Lorgelly

    (15 Innovation Walk, Monash University)

  • Brett Doble

    (15 Innovation Walk, Monash University)

  • Rachel J. Knott

    (15 Innovation Walk, Monash University)

Abstract

There is a growing appetite for large complex databases that integrate a range of personal, socio-demographic, health, genetic and financial information on individuals. It has been argued that ‘Big Data’ will provide the necessary catalyst to advance both biomedical research and health economics and outcomes research. However, it is important that we do not succumb to being data rich but information poor. This paper discusses the benefits and challenges of building Big Data, analysing Big Data and making appropriate inferences in order to advance cancer care, using Cancer 2015 (a prospective, longitudinal, genomic cohort study in Victoria, Australia) as a case study. Cancer 2015 has been linked to State and Commonwealth reimbursement databases that have known limitations. This partly reflects the funding arrangements in Australia, a country with both public and private provision, including public funding of private healthcare, and partly the legislative frameworks that govern data linkage. Additionally, linkage is not without time delays and, as such, achieving a contemporaneous database is challenging. Despite these limitations, there is clear value in using linked data and creating Big Data. This paper describes the linked Cancer 2015 dataset, discusses estimation issues given the nature of the data and presents panel regression results that allow us to make possible inferences regarding which patient, disease, genomic and treatment characteristics explain variation in health expenditure.

Suggested Citation

  • Paula K. Lorgelly & Brett Doble & Rachel J. Knott, 2016. "Realising the Value of Linked Data to Health Economic Analyses of Cancer Care: A Case Study of Cancer 2015," PharmacoEconomics, Springer, vol. 34(2), pages 139-154, February.
    • Handle: RePEc:spr:pharme:v:34:y:2016:i:2:d:10.1007_s40273-015-0343-2
    DOI: 10.1007/s40273-015-0343-2
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    1. Randall P. Ellis & Denzil G. Fiebig & Meliyanni Johar & Glenn Jones & Elizabeth Savage, 2013. "Explaining Health Care Expenditure Variation: Large‐Sample Evidence Using Linked Survey And Health Administrative Data," Health Economics, John Wiley & Sons, Ltd., vol. 22(9), pages 1093-1110, September.
    2. Borislava Mihaylova & Andrew Briggs & Anthony O'Hagan & Simon G. Thompson, 2011. "Review of statistical methods for analysing healthcare resources and costs," Health Economics, John Wiley & Sons, Ltd., vol. 20(8), pages 897-916, August.
    3. A. J. Culyer & J. P. Newhouse (ed.), 2000. "Handbook of Health Economics," Handbook of Health Economics, Elsevier, edition 1, volume 1, number 1.
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    Cited by:

    1. Daisuke Goto & Ya-Chen Tina Shih & Pascal Lecomte & Melvin Olson & Chukwukadibia Udeze & Yujin Park & C. Daniel Mullins, 2017. "Regression-Based Approaches to Patient-Centered Cost-Effectiveness Analysis," PharmacoEconomics, Springer, vol. 35(7), pages 685-695, July.
    2. Eberechukwu Onukwugha, 2016. "Big Data and Its Role in Health Economics and Outcomes Research: A Collection of Perspectives on Data Sources, Measurement, and Analysis," PharmacoEconomics, Springer, vol. 34(2), pages 91-93, February.
    3. Eberechukwu Onukwugha, 2016. "Big Data and Its Role in Health Economics and Outcomes Research: A Collection of Perspectives on Data Sources, Measurement, and Analysis," PharmacoEconomics, Springer, vol. 34(2), pages 91-93, February.

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