IDEAS home Printed from https://ideas.repec.org/a/wly/hlthec/v26y2017is2p106-126.html
   My bibliography  Save this article

Drug therapy adherence and health outcomes in the presence of physician and patient unobserved heterogeneity

Author

Listed:
  • Vincenzo Atella
  • Federico Belotti
  • Domenico Depalo

Abstract

Understanding the role that drug adherence has on health outcomes in everyday clinical practice is central for the policy maker. This is particularly true when patients suffer from asymptomatic chronic conditions (e.g., hypertension, hypercholesterolaemia, and diabetes). By exploiting a unique longitudinal dataset at patient and physician level in Italy, we show that patients and physicians unobserved characteristics play an important role in determining health status, at least as important as drug adherence. Most importantly, we find that both adherence and prescribed treatment regimen effects are highly heterogeneous across physicians, highlighting their crucial role in shaping patients' health status.

Suggested Citation

  • Vincenzo Atella & Federico Belotti & Domenico Depalo, 2017. "Drug therapy adherence and health outcomes in the presence of physician and patient unobserved heterogeneity," Health Economics, John Wiley & Sons, Ltd., vol. 26(S2), pages 106-126, September.
    • Handle: RePEc:wly:hlthec:v:26:y:2017:i:s2:p:106-126
    DOI: 10.1002/hec.3570
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/hec.3570
    Download Restriction: no
    File URL: https://libkey.io/10.1002/hec.3570?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
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Verbeek, Marno & Nijman, Theo, 1992. "Testing for Selectivity Bias in Panel Data Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 33(3), pages 681-703, August.
    2. John Fitzgerald & Peter Gottschalk & Robert Moffitt, 1998. "An Analysis of Sample Attrition in Panel Data: The Michigan Panel Study of Income Dynamics," Journal of Human Resources, University of Wisconsin Press, vol. 33(2), pages 251-299.
    3. Cropper, M L, 1981. "Measuring the Benefits from Reduced Morbidity," American Economic Review, American Economic Association, vol. 71(2), pages 235-240, May.
    4. John M. Abowd & Francis Kramarz & David N. Margolis, 1999. "High Wage Workers and High Wage Firms," Econometrica, Econometric Society, vol. 67(2), pages 251-334, March.
    5. Verbrugge, Lois M. & Jette, Alan M., 1994. "The disablement process," Social Science & Medicine, Elsevier, vol. 38(1), pages 1-14, January.
    6. Wagstaff, Adam, 1986. "The demand for health : Some new empirical evidence," Journal of Health Economics, Elsevier, vol. 5(3), pages 195-233, September.
    7. Galama, T. & Hullegie, P. & Meijer, E. & Outcault, S., 2012. "Empirical evidence for decreasing returns to scale in a health capital model," Health, Econometrics and Data Group (HEDG) Working Papers 12/05, HEDG, c/o Department of Economics, University of York.
    8. Terence C. Cheng & Pravin K. Trivedi, 2015. "Attrition Bias in Panel Data: A Sheep in Wolf's Clothing? A Case Study Based on the Mabel Survey," Health Economics, John Wiley & Sons, Ltd., vol. 24(9), pages 1101-1117, September.
    9. Titus J. Galama & Patrick Hullegie & Erik Meijer & Sarah Outcault, 2012. "Is There Empirical Evidence For Decreasing Returns To Scale In A Health Capital Model?," Health Economics, John Wiley & Sons, Ltd., vol. 21(9), pages 1080-1100, September.
    10. Vincenzo Atella & Joanna Kopinska, 2014. "The impact of cost-sharing schemes on drug compliance in Italy: evidence based on quantile regression," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 59(2), pages 329-339, April.
    11. M. Hashem Pesaran & Qiankun Zhou, 2018. "Estimation of time-invariant effects in static panel data models," Econometric Reviews, Taylor & Francis Journals, vol. 37(10), pages 1137-1171, November.
    12. Galama, T. & Hullegie, P. & Meijer, E. & Outcault, S., 2012. "Empirical evidence for decreasing returns to scale in a health capital model," Health, Econometrics and Data Group (HEDG) Working Papers 12/05, HEDG, c/o Department of Economics, University of York.
    13. Adam Wagstaff, 1993. "The demand for health: An empirical reformulation of the Grossman model," Health Economics, John Wiley & Sons, Ltd., vol. 2(2), pages 189-198, July.
    14. Frank, Richard G. & Zeckhauser, Richard J., 2007. "Custom-made versus ready-to-wear treatments: Behavioral propensities in physicians' choices," Journal of Health Economics, Elsevier, vol. 26(6), pages 1101-1127, December.
    15. Michael Grossman, 1972. "The Demand for Health: A Theoretical and Empirical Investigation," NBER Books, National Bureau of Economic Research, Inc, number gros72-1, March.
    16. Mariana Carrera & Dana Goldman & Geoffrey Joyce, 2013. "Heterogeneity in Cost-Sharing and Cost-Sensitivity, and the Role of the Prescribing Physician," NBER Working Papers 19186, National Bureau of Economic Research, Inc.
    17. Gerdtham, U. -G. & Johannesson, M. & Lundberg, L. & Isacson, D., 1999. "The demand for health: results from new measures of health capital," European Journal of Political Economy, Elsevier, vol. 15(3), pages 501-521, September.
    18. Nijman, T.E. & Verbeek, M.J.C.M., 1992. "Testing for selectivity in panel data models," Other publications TiSEM 7ec34a6c-1d84-4052-971c-d, Tilburg University, School of Economics and Management.
    19. Vincenzo Atella & Franco Peracchi & Domenico Depalo & Claudio Rossetti, 2006. "Drug compliance, co‐payment and health outcomes: evidence from a panel of Italian patients," Health Economics, John Wiley & Sons, Ltd., vol. 15(9), pages 875-892, September.
    20. Sergei Koulayev & Niels Skipper & Emilia Simeonova, 2013. "Who Is in Control? The Determinants of Patient Adherence with Medication Therapy," NBER Working Papers 19496, National Bureau of Economic Research, Inc.
    21. Anna A. Levine Taub & Anton Kolotilin & Robert S. Gibbons & Ernst R. Berndt, 2011. "The Diversity of Concentrated Prescribing Behavior: An Application to Antipsychotics," NBER Working Papers 16823, National Bureau of Economic Research, Inc.
    22. Vincenzo Atella & Francesco D’Amico, 2015. "Who is responsible for your health: is it you, your doctor or the new technologies?," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 16(8), pages 835-846, November.
    23. Dyfrig A. Hughes & Adrian Bagust & Alan Haycox & Tom Walley, 2001. "The impact of non‐compliance on the cost‐effectiveness of pharmaceuticals: a review of the literature," Health Economics, John Wiley & Sons, Ltd., vol. 10(7), pages 601-615, October.
    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. Domenico Depalo, 2020. "Explaining the causal effect of adherence to medication on cholesterol through the marginal patient," Health Economics, John Wiley & Sons, Ltd., vol. 29(S1), pages 110-126, October.
    2. Katharina E. Blankart & Friederike Arndt, 2020. "Physician-Level Cost Control Measures and Regional Variation of Biosimilar Utilization in Germany," IJERPH, MDPI, vol. 17(11), pages 1-14, June.
    3. Katharina E. Blankart & Frank R. Lichtenberg, 2020. "Are patients more adherent to newer drugs?," Health Care Management Science, Springer, vol. 23(4), pages 605-618, December.
    4. Federico Belotti & Joanna Kopinska & Alessandro Palma & Andrea Piano Mortari, 2022. "Health status and the Great Recession. Evidence from electronic health records," Health Economics, John Wiley & Sons, Ltd., vol. 31(8), pages 1770-1799, August.
    5. Joan Costa‐Font & Rosella Levaggi, 2020. "Innovation, aging, and health care: Unraveling “silver” from “red” herrings?," Health Economics, John Wiley & Sons, Ltd., vol. 29(S1), pages 3-7, October.
    6. Domenico Depalo & Jay Bhattacharya & Vincenzo Atella & Federico Belotti, 2019. "When Technological Advance Meets Physician Learning in Drug Prescribing," NBER Working Papers 26202, National Bureau of Economic Research, Inc.
    7. Di Novi, Cinzia & Leporatti, Lucia & Levaggi, Rosella & Montefiori, Marcello, 2022. "Adherence during COVID-19: The role of aging and socio-economics status in shaping drug utilization," Journal of Economic Behavior & Organization, Elsevier, vol. 204(C), pages 1-14.
    8. Cinzia Di Novi & Lucia Leporatti & Marcello Montefiori, 2020. "Older patients and geographic barriers to pharmacy access: When nonadherence translates to an increased use of other components of health care," Health Economics, John Wiley & Sons, Ltd., vol. 29(S1), pages 97-109, October.
    9. Lucia Leporatti & Rosella Levaggi & Marcello Montefiori, 2021. "Beyond price: the effects of non-financial barriers on access to drugs and health outcomes," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 22(4), pages 519-529, June.

    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. Titus J. Galama & Hans van Kippersluis, 2013. "Health Inequalities through the Lens of Health Capital Theory: Issues, Solutions, and Future Directions," Tinbergen Institute Discussion Papers 13-076/V, Tinbergen Institute.
    2. Burggraf, Christine, 2017. "Russian demand for dietary quality: Nutrition transition, diet quality measurement, and health investment theory," Studies on the Agricultural and Food Sector in Transition Economies, Leibniz Institute of Agricultural Development in Transition Economies (IAMO), volume 86, number 86.
    3. Burggraf, Christine, 2017. "Russian demand for dietary quality: Nutrition transition, diet quality measurement, and health investment theory," Studies on the Agricultural and Food Sector in Transition Economies 269539, Institute of Agricultural Development in Transition Economies (IAMO).
    4. repec:zbw:iamost:269539 is not listed on IDEAS
    5. Bolin, Kristian & Liljas, Bengt & Lindgren, Björn, 2014. "Individual technologies for health - the implications of distinguishing between the ability to produce health investments and the capacity to benefit from those investments," Working Papers in Economics 587, University of Gothenburg, Department of Economics.
    6. Glauben, Thomas & Grecksch, Wilfried & Burggraf, Christine, 2015. "Stochastic control of individual's health investments," EconStor Conference Papers 249588, ZBW - Leibniz Information Centre for Economics.
    7. Galama, Titus & Kapteyn, Arie, 2011. "Grossman’s missing health threshold," Journal of Health Economics, Elsevier, vol. 30(5), pages 1044-1056.
    8. Michael Grossman, 2022. "The demand for health turns 50: Reflections," Health Economics, John Wiley & Sons, Ltd., vol. 31(9), pages 1807-1822, September.
    9. Jochen Hartwig & Jan-Egbert Sturm, 2018. "Testing the Grossman model of medical spending determinants with macroeconomic panel data," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 19(8), pages 1067-1086, November.
    10. Galama, Titus & Kapteyn, Arie, 2011. "Grossman’s missing health threshold," Journal of Health Economics, Elsevier, pages 1044-1056.
    11. Manoj K. Pandey, 2013. "Elderly's Health Shocks and Household's Ex-ante Poverty in India," ASARC Working Papers 2013-01, The Australian National University, Australia South Asia Research Centre.
    12. Titus Galama, 2011. "A Contribution to Health Capital Theory," Working Papers WR-831, RAND Corporation.
    13. Galama, T. & Hullegie, P. & Meijer, E. & Outcault, S., 2012. "Empirical evidence for decreasing returns to scale in a health capital model," Health, Econometrics and Data Group (HEDG) Working Papers 12/05, HEDG, c/o Department of Economics, University of York.
    14. Paul Contoyannis & Andrew M. Jones & Nigel Rice, 2004. "The dynamics of health in the British Household Panel Survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 19(4), pages 473-503.
    15. Gerdtham, U. -G. & Johannesson, M. & Lundberg, L. & Isacson, D., 1999. "The demand for health: results from new measures of health capital," European Journal of Political Economy, Elsevier, vol. 15(3), pages 501-521, September.
    16. Juergen Jung & Chung Tran, 2016. "Market Inefficiency, Insurance Mandate and Welfare: U.S. Health Care Reform 2010," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 20, pages 132-159, April.
    17. Jung, Juergen & Tran, Chung, 2022. "Social health insurance: A quantitative exploration," Journal of Economic Dynamics and Control, Elsevier, vol. 139(C).
    18. Xavier Pautrel, 2018. "Environmental Policy and Health in the Presence of Labor Market Imperfections," TEPP Working Paper 2018-09, TEPP.
    19. Juergen Jung & Chung Tran, 2016. "Market Inefficiency, Insurance Mandate and Welfare: U.S. Health Care Reform 2010," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 20, pages 132-159, April.
    20. Gerdtham, Ulf-G. & Johannesson, Magnus, 1999. "New estimates of the demand for health: results based on a categorical health measure and Swedish micro data," Social Science & Medicine, Elsevier, vol. 49(10), pages 1325-1332, November.
    21. Martina Celidoni & Vincenzo Rebba, 2017. "Healthier lifestyles after retirement in Europe? Evidence from SHARE," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 18(7), pages 805-830, September.

    More about this item

    JEL classification:

    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

    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:26:y:2017:i:s2:p:106-126. 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.