IDEAS home Printed from https://ideas.repec.org/a/wly/hlthec/v21y2012i8p902-912.html
   My bibliography  Save this article

How Price Responsive Is The Demand For Specialty Care?

Author

Listed:
  • Matthew L. Maciejewski
  • Chuan‐Fen Liu
  • Andrew L. Kavee
  • Maren K. Olsen

Abstract

Objectives Outpatient visit co‐payments have increased in recent years. We estimate the patient response to a price change for specialty care, based on a co‐payment increase from $15 to $50 per visit for veterans with hypertension. Design, Setting, and Patients A retrospective cohort of veterans required to pay co‐payments was compared with veterans exempt from co‐payments whose nonequivalence was reduced via propensity score matching. Specialty care expenditures in 2000–2003 were estimated via a two‐part mixed model to account for the correlation of the use and level outcomes over time, and results from this correlated two‐part model were compared with an uncorrelated two‐part model and a correlated random intercept two‐part mixed model. Results A $35 specialty visit co‐payment increase had no impact on the likelihood of seeking specialty care but induced lower specialty expenditures over time among users who were required to pay co‐payments. The log ratio of price responsiveness (semi‐elasticity) for specialty care increased from −0.25 to −0.31 after the co‐payment increase. Estimates were similar across the three models. Conclusion A significant increase in specialty visit co‐payments reduced specialty expenditures among patients obtaining medications at the Veterans Affairs medical centers. Longitudinal expenditure analysis may be improved using recent advances in two‐part model methods. Published 2011. This article is a US Government work and is in the public domain in the USA.

Suggested Citation

  • Matthew L. Maciejewski & Chuan‐Fen Liu & Andrew L. Kavee & Maren K. Olsen, 2012. "How Price Responsive Is The Demand For Specialty Care?," Health Economics, John Wiley & Sons, Ltd., vol. 21(8), pages 902-912, August.
  • Handle: RePEc:wly:hlthec:v:21:y:2012:i:8:p:902-912
    DOI: 10.1002/hec.1759
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/hec.1759
    Download Restriction: no

    File URL: https://libkey.io/10.1002/hec.1759?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Phelps, Charles E & Newhouse, Joseph P, 1974. "Coinsurance, the Price of Time, and the Demand for Medical Services," The Review of Economics and Statistics, MIT Press, vol. 56(3), pages 334-342, August.
    2. 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.
    3. Wong, M.D. & Andersen, R. & Sherbourne, C.D. & Hays, R.D. & Shapiro, M.F., 2001. "Effects of cost sharing on care seeking and health status: Results from the Medical Outcomes Study," American Journal of Public Health, American Public Health Association, vol. 91(11), pages 1889-1894.
    4. Kanika Kapur & Geoffrey F. Joyce & José J. Escarce & Krista A. Van Vorst, 2000. "Visits to primary care physicians and to specialists under gatekeeper and point-of-service arrangements," Open Access publications 10197/278, School of Economics, University College Dublin.
    5. Liu, Lei & Strawderman, Robert L. & Cowen, Mark E. & Shih, Ya-Chen T., 2010. "A flexible two-part random effects model for correlated medical costs," Journal of Health Economics, Elsevier, vol. 29(1), pages 110-123, January.
    6. Wedig, Gerard J., 1988. "Health status and the demand for health : Results on price elasticities," Journal of Health Economics, Elsevier, vol. 7(2), pages 151-163, June.
    7. Manning, Willard G. & Mullahy, John, 2001. "Estimating log models: to transform or not to transform?," Journal of Health Economics, Elsevier, vol. 20(4), pages 461-494, July.
    Full references (including those not matched with items on IDEAS)

    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. Onur Başer & Joseph C. Gardiner & Cathy J. Bradley & Hüseyin Yüce & Charles Given, 2006. "Longitudinal analysis of censored medical cost data," Health Economics, John Wiley & Sons, Ltd., vol. 15(5), pages 513-525, May.
    2. 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.
    3. Tor Iversen & Eline Aas & Gunnar Rosenqvist & Unto Häkkinen & on behalf of the EuroHOPE study group, 2015. "Comparative Analysis of Treatment Costs in EUROHOPE," Health Economics, John Wiley & Sons, Ltd., vol. 24(S2), pages 5-22, December.
    4. Marcel Bilger & Willard G. Manning, 2015. "Measuring Overfitting In Nonlinear Models: A New Method And An Application To Health Expenditures," Health Economics, John Wiley & Sons, Ltd., vol. 24(1), pages 75-85, January.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. Stefano Calciolari & Aleksandra Torbica & Rosanna Tarricone, 2013. "Explaining the Health Costs Associated with Managing Intracranial Aneurysms in Italy," Applied Health Economics and Health Policy, Springer, vol. 11(4), pages 427-435, August.
    10. Liu, Lei & Conaway, Mark R. & Knaus, William A. & Bergin, James D., 2008. "A random effects four-part model, with application to correlated medical costs," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4458-4473, May.
    11. Bilger M. & Manning W.G, 2011. "Measuring overfitting and mispecification in nonlinear models," Health, Econometrics and Data Group (HEDG) Working Papers 11/25, HEDG, c/o Department of Economics, University of York.
    12. David Powell, 2019. "The Distortionary Effects of the Health Insurance Tax Exclusion," American Journal of Health Economics, University of Chicago Press, vol. 5(4), pages 428-464, Fall.
    13. Manos Matsaganis & Theodore Mitrakos & Panos Tsakloglou, 2008. "Modelling Household Expenditure on Health Care in Greece," Working Papers 68, Bank of Greece.
    14. Westerhout, Ed & Folmer, Kees, 2018. "The Effects of Capping Co-Insurance Payments," Other publications TiSEM 828746fb-4fb0-465b-bdff-3, Tilburg University, School of Economics and Management.
    15. Lanlan Wang & Ping Qin, 2017. "Distance to work in Beijing: Institutional reform and bargaining power," Urban Studies, Urban Studies Journal Limited, vol. 54(6), pages 1385-1406, May.
    16. Kathleen Carey & Theodore Stefos, 2011. "Measuring the cost of hospital adverse patient safety events," Health Economics, John Wiley & Sons, Ltd., vol. 20(12), pages 1417-1430, December.
    17. Westerhout, Ed & Folmer, Kees, 2018. "The Effects of Capping Co-Insurance Payments," Discussion Paper 2018-050, Tilburg University, Center for Economic Research.
    18. Jones, A. & Lomas, J. & Rice, N., 2014. "Going Beyond the Mean in Healthcare Cost Regressions: a Comparison of Methods for Estimating the Full Conditional Distribution," Health, Econometrics and Data Group (HEDG) Working Papers 14/26, HEDG, c/o Department of Economics, University of York.
    19. 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.
    20. S. Veen & R. Kleef & W. Ven & R. Vliet, 2015. "Improving the prediction model used in risk equalization: cost and diagnostic information from multiple prior years," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 16(2), pages 201-218, March.

    More about this item

    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:wly:hlthec:v:21:y:2012:i:8:p:902-912. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/5749 .

    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.