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Explaining Health Care Expenditure Variation: Large‐Sample Evidence Using Linked Survey And Health Administrative Data

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  • Randall P. Ellis
  • Denzil G. Fiebig
  • Meliyanni Johar
  • Glenn Jones
  • Elizabeth Savage

Abstract

Explaining individual, regional, and provider variation in health care spending is of enormous value to policymakers but is often hampered by the lack of individual level detail in universal public health systems because budgeted spending is often not attributable to specific individuals. Even rarer is self‐reported survey information that helps explain this variation in large samples. In this paper, we link a cross‐sectional survey of 267 188 Australians age 45 and over to a panel dataset of annual healthcare costs calculated from several years of hospital, medical and pharmaceutical records. We use this data to distinguish between cost variations due to health shocks and those that are intrinsic (fixed) to an individual over three years. We find that high fixed expenditures are positively associated with age, especially older males, poor health, obesity, smoking, cancer, stroke and heart conditions. Being foreign born, speaking a foreign language at home and low income are more strongly associated with higher time‐varying expenditures, suggesting greater exposure to adverse health shocks. Copyright © 2013 John Wiley & Sons, Ltd.

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  • 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.
    • Handle: RePEc:wly:hlthec:v:22:y:2013:i:9:p:1093-1110
    DOI: 10.1002/hec.2916
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    3. 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.
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    Cited by:

    1. Randall P. Ellis & Pooja G. Mookim, 2013. "K-Fold Cross-Validation is Superior to Split Sample Validation for Risk Adjustment Models," Boston University - Department of Economics - Working Papers Series wp2013-026, Boston University - Department of Economics.
    2. Buchmueller, Thomas C. & Johar, Meliyanni, 2015. "Obesity and health expenditures: Evidence from Australia," Economics & Human Biology, Elsevier, vol. 17(C), pages 42-58.
    3. Meliyanni Johar & Glenn Jones & Elizabeth Savage, 2012. "Healthcare Expenditure Profile of Older Australians: Evidence from Linked Survey and Health Administrative Data," Economic Papers, The Economic Society of Australia, vol. 31(4), pages 451-463, December.
    4. A. A. Withagen-Koster & R. C. Kleef & F. Eijkenaar, 2018. "Examining unpriced risk heterogeneity in the Dutch health insurance market," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 19(9), pages 1351-1363, December.
    5. Pavitra Paul & Ulrich Nguemdjo & Natalia Kovtun & Bruno Ventelou, 2021. "Does Self-Assessed Health Reflect the True Health State?," IJERPH, MDPI, vol. 18(21), pages 1-16, October.
    6. Feras Kasabji & Alaa Alrajo & Ferenc Vincze & László Kőrösi & Róza Ádány & János Sándor, 2020. "Self-Declared Roma Ethnicity and Health Insurance Expenditures: A Nationwide Cross-Sectional Investigation at the General Medical Practice Level in Hungary," IJERPH, MDPI, vol. 17(23), pages 1-17, December.
    7. Paula Lorgelly & Brett Doble & Rachel 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.
    8. Denzil G. Fiebig, 2017. "Big Data: Will It Improve Patient-Centered Care?," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 10(2), pages 133-139, April.
    9. Jing Chen & Randall P. Ellis & Katherine H. Toro & Arlene S. Ash, 2015. "Mispricing in Medicare Advantage Risk Adjustment," Boston University - Department of Economics - Working Papers Series wp2015-020, Boston University - Department of Economics.
    10. Sungchul Park & Anirban Basu, 2018. "Alternative evaluation metrics for risk adjustment methods," Health Economics, John Wiley & Sons, Ltd., vol. 27(6), pages 984-1010, June.
    11. Camilo Cid & Randall P. Ellis & Verónica Vargas & Juergen Wasem & Lorena Prieto, 2015. "Global Risk-Adjusted Payment Models," Boston University - Department of Economics - Working Papers Series wp2015-021, Boston University - Department of Economics.
    12. 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.
    13. Julie Shi & Yi Yao & Gordon Liu, 2018. "Modeling individual health care expenditures in China: Evidence to assist payment reform in public insurance," Health Economics, John Wiley & Sons, Ltd., vol. 27(12), pages 1945-1962, December.
    14. Donald J. Wright, 2013. "An Equilibrium Model of General Practitioner Payment Schemes," The Economic Record, The Economic Society of Australia, vol. 89(286), pages 287-299, September.
    15. Hayen, A.P. & van den Berg, M.J. & Meijboom, B.R. & Struijs, J.N. & Westert, G.P., 2015. "Incorporating shared savings programs into primary care : From theory to practice," Other publications TiSEM 2e26be96-1dc3-41fe-8dc9-c, Tilburg University, School of Economics and Management.

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    More about this item

    JEL classification:

    • I10 - Health, Education, and Welfare - - Health - - - General
    • I13 - Health, Education, and Welfare - - Health - - - Health Insurance, Public and Private
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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