IDEAS home Printed from https://ideas.repec.org/p/uts/ecowps/1.html
   My bibliography  Save this paper

Explaining Health Care Expenditure Variation: Large-sample Evidence Using Linked Survey and Health Administrative Data

Author

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 selfreported survey information that helps explain this variation in large samples. In this paper, we exploit the linkage of a cohort-representative survey of 265,468 Australians age 45 and over to several years of hospital, medical and pharmaceutical records. After calculating total health care cost for each survey respondent, we examine health expenditures due to health shocks and those that are intrinsic to an individual. We find that high fixed-effects are positively associated with age, especially older males, poor health, obesity, smoking, cancer, stroke and heart conditions. Hospital admissions are the largest component of fixed effects. High time-varying expenditures are associated with speaking foreign language at home, low income and low education, suggesting greater exposure to adverse health shocks. For these individuals, health expenditure is comprised mainly of out-of-hospital medical services and drugs.

Suggested Citation

  • Randall P. Ellis & Denzil G. Fiebig & Meliyanni Johar & Glenn Jones & Elizabeth Savage, 2012. "Explaining Health Care Expenditure Variation: Large-sample Evidence Using Linked Survey and Health Administrative Data," Working Paper Series 1, Economics Discipline Group, UTS Business School, University of Technology, Sydney.
  • Handle: RePEc:uts:ecowps:1
    as

    Download full text from publisher

    File URL: http://www.uts.edu.au/sites/default/files/edg_wp1.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Liran Einav & Amy Finkelstein & Stephen P. Ryan & Paul Schrimpf & Mark R. Cullen, 2013. "Selection on Moral Hazard in Health Insurance," American Economic Review, American Economic Association, vol. 103(1), pages 178-219, February.
    2. Hausman, Jerry A & Taylor, William E, 1981. "Panel Data and Unobservable Individual Effects," Econometrica, Econometric Society, vol. 49(6), pages 1377-1398, November.
    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.
    4. Manning, Willard G. & Basu, Anirban & Mullahy, John, 2005. "Generalized modeling approaches to risk adjustment of skewed outcomes data," Journal of Health Economics, Elsevier, vol. 24(3), pages 465-488, May.
    5. Buntin, Melinda Beeuwkes & Zaslavsky, Alan M., 2004. "Too much ado about two-part models and transformation?: Comparing methods of modeling Medicare expenditures," Journal of Health Economics, Elsevier, vol. 23(3), pages 525-542, May.
    6. Productivity Commission, 2009. "Public and Private Hospitals," Research Reports, Productivity Commission, Government of Australia, number 37.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dunn, Abe, 2016. "Health insurance and the demand for medical care: Instrumental variable estimates using health insurer claims data," Journal of Health Economics, Elsevier, vol. 48(C), pages 74-88.
    2. Kurt Lavetti & Thomas DeLeire & Nicolas R. Ziebarth, 2023. "How do low‐income enrollees in the Affordable Care Act marketplaces respond to cost‐sharing?," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 90(1), pages 155-183, March.
    3. Amir Marashi & Shima Ghassem Pour & Vincy Li & Chris Rissel & Federico Girosi, 2019. "The association between physical activity and hospital payments for acute admissions in the Australian population aged 45 and over," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-16, June.
    4. 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.
    5. Julie Shi, 2017. "Efficiency in Plan Choice with Risk Adjustment and Risk-Based Pricing in Health Insurance Exchanges," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 42(1), pages 79-113, January.
    6. Jones, A.M, 2010. "Models For Health Care," Health, Econometrics and Data Group (HEDG) Working Papers 10/01, HEDG, c/o Department of Economics, University of York.
    7. Caballer-Tarazona, Vicent & Guadalajara-Olmeda, Natividad & Vivas-Consuelo, David, 2019. "Predicting healthcare expenditure by multimorbidity groups," Health Policy, Elsevier, vol. 123(4), pages 427-434.
    8. Toni Mora & Joan Gil & Antoni Sicras-Mainar, 2012. "The Influence of BMI, Obesity and Overweight on Medical Costs: A Panel Data Approach," Working Papers 2012-08, FEDEA.
    9. Keane, Michael & Stavrunova, Olena, 2016. "Adverse selection, moral hazard and the demand for Medigap insurance," Journal of Econometrics, Elsevier, vol. 190(1), pages 62-78.
    10. Toni Mora & Joan Gil & Antoni Sicras-Mainar, 2015. "The influence of obesity and overweight on medical costs: a panel data perspective," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 16(2), pages 161-173, March.
    11. Kaushik Ghosh & Irina Bondarenko & Kassandra L Messer & Susan T Stewart & Trivellore Raghunathan & Allison B Rosen & David M Cutler, 2020. "Attributing medical spending to conditions: A comparison of methods," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-17, August.
    12. Marcu, Mircea & Knapp, Caprice & Madden, Vanessa & Brown, David & Wang, Hua & Sloyer, Phyllis, 2014. "Effects of an Integrated Care System on Children with Special Health Care Needs' Medicaid Expenditures," Working Papers 2014-8, University of Alberta, Department of Economics.
    13. Mark Dusheiko & Hugh Gravelle & Stephen Martin & Nigel Rice & Peter C Smith, 2011. "Does Better Disease Management in Primary Care Reduce Hospital Costs?," Working Papers 065cherp, Centre for Health Economics, University of York.
    14. Eugene Frimpong & Daniel R Petrolia & Ardian Harri & John H. Cartwright, 2020. "Flood Insurance and Claims: The Impact of the Community Rating System," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 42(2), pages 245-262, June.
    15. Manos Matsaganis & Theodore Mitrakos & Panos Tsakloglou, 2008. "Modelling Household Expenditure on Health Care in Greece," Working Papers 68, Bank of Greece.
    16. Andreas Werblow & Stefan Felder & Peter Zweifel, 2007. "Population ageing and health care expenditure: a school of ‘red herrings’?," Health Economics, John Wiley & Sons, Ltd., vol. 16(10), pages 1109-1126, October.
    17. Xiao‐Hua Zhou & Huazhen Lin & Eric Johnson, 2008. "Non‐parametric heteroscedastic transformation regression models for skewed data with an application to health care costs," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(5), pages 1029-1047, November.
    18. John F. Scoggins & Daniel A. Weinberg, 2017. "Healthcare Coinsurance Elasticity Coefficient Estimation Using Monthly Cross‐sectional, Time‐series Claims Data," Health Economics, John Wiley & Sons, Ltd., vol. 26(6), pages 795-801, June.
    19. Steven C. Hill & G. Edward Miller, 2010. "Health expenditure estimation and functional form: applications of the generalized gamma and extended estimating equations models," Health Economics, John Wiley & Sons, Ltd., vol. 19(5), pages 608-627, May.
    20. Brilleman, Samuel L. & Gravelle, Hugh & Hollinghurst, Sandra & Purdy, Sarah & Salisbury, Chris & Windmeijer, Frank, 2014. "Keep it simple? Predicting primary health care costs with clinical morbidity measures," Journal of Health Economics, Elsevier, vol. 35(C), pages 109-122.

    More about this item

    Keywords

    health expenditure; health insurance; risk adjustment; panel data;
    All these keywords.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:uts:ecowps:1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Duncan Ford (email available below). General contact details of provider: https://edirc.repec.org/data/edutsau.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.