IDEAS home Printed from https://ideas.repec.org/a/wly/hlthec/v28y2019i11p1293-1307.html
   My bibliography  Save this article

The effect of bariatric surgery on health care costs: A synthetic control approach using Bayesian structural time series

Author

Listed:
  • Christoph F. Kurz
  • Martin Rehm
  • Rolf Holle
  • Christina Teuner
  • Michael Laxy
  • Larissa Schwarzkopf

Abstract

Surgical measures to combat obesity are very effective in terms of weight loss, recovery from diabetes, and improvement in cardiovascular risk factors. However, previous studies found both positive and negative results regarding the effect of bariatric surgery on health care utilization. Using claims data from the largest health insurance provider in Germany, we estimated the causal effect of bariatric surgery on health care costs in a time period ranging from 2 years before to 3 years after bariatric intervention. Owing to the absence of a control group, we employed a Bayesian structural forecasting model to construct a synthetic control. We observed a decrease in medication and physician expenditures after bariatric surgery, whereas hospital expenditures increased in the post‐intervention period. Overall, we found a slight increase in total costs after bariatric surgery, but our estimates include a high degree of uncertainty.

Suggested Citation

  • Christoph F. Kurz & Martin Rehm & Rolf Holle & Christina Teuner & Michael Laxy & Larissa Schwarzkopf, 2019. "The effect of bariatric surgery on health care costs: A synthetic control approach using Bayesian structural time series," Health Economics, John Wiley & Sons, Ltd., vol. 28(11), pages 1293-1307, November.
    • Handle: RePEc:wly:hlthec:v:28:y:2019:i:11:p:1293-1307
    DOI: 10.1002/hec.3941
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/hec.3941
    Download Restriction: no
    File URL: https://libkey.io/10.1002/hec.3941?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. Tiago M. Fragoso & Wesley Bertoli & Francisco Louzada, 2018. "Bayesian Model Averaging: A Systematic Review and Conceptual Classification," International Statistical Review, International Statistical Institute, vol. 86(1), pages 1-28, April.
    2. Commandeur, Jacques J. F. & Koopman, Siem Jan & Ooms, Marius, 2011. "Statistical Software for State Space Methods," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 41(i01).
    3. Petros Pechlivanoglou & Jaap E. Wieringa & Tim de Jager & Maarten J. Postma, 2015. "The Effect of Financial and Educational Incentives on Rational Prescribing. A State‐Space Approach," Health Economics, John Wiley & Sons, Ltd., vol. 24(4), pages 439-453, April.
    4. Elleby, Christian & Hansen, Henrik & Yu, Wusheng, 2015. "Domestic Price and Welfare Effects of the 2007-11 Indian Grain Export Restrictions," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205578, Agricultural and Applied Economics Association.
    5. Alberto Abadie & Javier Gardeazabal, 2003. "The Economic Costs of Conflict: A Case Study of the Basque Country," American Economic Review, American Economic Association, vol. 93(1), pages 113-132, March.
    6. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    7. Charles J. Courtemanche & Daniela Zapata, 2014. "Does Universal Coverage Improve Health? The Massachusetts Experience," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 33(1), pages 36-69, January.
    8. Leeflang, Peter S.H. & Bijmolt, Tammo H.A. & van Doorn, Jenny & Hanssens, Dominique M. & van Heerde, Harald J. & Verhoef, Peter C. & Wieringa, Jaap E., 2009. "Creating lift versus building the base: Current trends in marketing dynamics," International Journal of Research in Marketing, Elsevier, vol. 26(1), pages 13-20.
    9. Nikolay Doudchenko & Guido W. Imbens, 2016. "Balancing, Regression, Difference-In-Differences and Synthetic Control Methods: A Synthesis," NBER Working Papers 22791, National Bureau of Economic Research, Inc.
    10. Andreas Billmeier & Tommaso Nannicini, 2013. "Assessing Economic Liberalization Episodes: A Synthetic Control Approach," The Review of Economics and Statistics, MIT Press, vol. 95(3), pages 983-1001, July.
    11. Noémi Kreif & Richard Grieve & Dominik Hangartner & Alex James Turner & Silviya Nikolova & Matt Sutton, 2016. "Examination of the Synthetic Control Method for Evaluating Health Policies with Multiple Treated Units," Health Economics, John Wiley & Sons, Ltd., vol. 25(12), pages 1514-1528, December.
    12. Xu, Yiqing, 2017. "Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Models," Political Analysis, Cambridge University Press, vol. 25(1), pages 57-76, January.
    13. Aurélia Lépine & Mylène Lagarde & Alexis Le Nestour, 2018. "How effective and fair is user fee removal? Evidence from Zambia using a pooled synthetic control," Health Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 493-508, March.
    14. Arceneaux, Kevin & Gerber, Alan S. & Green, Donald P., 2006. "Comparing Experimental and Matching Methods Using a Large-Scale Voter Mobilization Experiment," Political Analysis, Cambridge University Press, vol. 14(1), pages 37-62, January.
    15. Harvey, Andrew C. & Trimbur, Thomas M. & Van Dijk, Herman K., 2007. "Trends and cycles in economic time series: A Bayesian approach," Journal of Econometrics, Elsevier, vol. 140(2), pages 618-649, October.
    16. Susan Athey & Guido W. Imbens, 2017. "The State of Applied Econometrics: Causality and Policy Evaluation," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 3-32, Spring.
    17. Steven L. Scott & Hal R. Varian, 2015. "Bayesian Variable Selection for Nowcasting Economic Time Series," NBER Chapters, in: Economic Analysis of the Digital Economy, pages 119-135, National Bureau of Economic Research, Inc.
    18. Ferman, Bruno & Pinto, Cristine Campos de Xavier, 2016. "Revisiting the synthetic control estimator," Textos para discussão 421, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    19. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    20. Daniel Kinn, 2018. "Synthetic Control Methods and Big Data," Papers 1803.00096, arXiv.org.
    21. Abadie, Alberto & Diamond, Alexis & Hainmueller, Jens, 2010. "Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 493-505.
    22. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881.
    23. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    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. Christoph F. Kurz & Adriana N. König, 2021. "The causal impact of sugar taxes on soft drink sales: evidence from France and Hungary," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 22(6), pages 905-915, August.
    2. Patrick Toman & Nalini Ravishanker & Nathan Lally & Sanguthevar Rajasekaran, 2023. "Latent Autoregressive Student- t Prior Process Models to Assess Impact of Interventions in Time Series," Future Internet, MDPI, vol. 16(1), pages 1-17, December.

    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. Bruno Ferman & Cristine Pinto & Vitor Possebom, 2020. "Cherry Picking with Synthetic Controls," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 39(2), pages 510-532, March.
    2. Irene Botosaru & Bruno Ferman, 2019. "On the role of covariates in the synthetic control method," The Econometrics Journal, Royal Economic Society, vol. 22(2), pages 117-130.
    3. Samuel Verevis & Murat Üngör, 2021. "What has New Zealand gained from The FTA with China?: Two counterfactual analyses†," Scottish Journal of Political Economy, Scottish Economic Society, vol. 68(1), pages 20-50, February.
    4. Muhammad Jehangir Amjad & Devavrat Shah & Dennis Shen, 2017. "Robust Synthetic Control," Papers 1711.06940, arXiv.org.
    5. Camilla Beck Olsen & Hans Olav Melberg, 2018. "Did adolescents in Norway respond to the elimination of copayments for general practitioner services?," Health Economics, John Wiley & Sons, Ltd., vol. 27(7), pages 1120-1130, July.
    6. Giulio Grossi & Marco Mariani & Alessandra Mattei & Patrizia Lattarulo & Ozge Oner, 2020. "Direct and spillover effects of a new tramway line on the commercial vitality of peripheral streets. A synthetic-control approach," Papers 2004.05027, arXiv.org, revised Nov 2023.
    7. Kaul, Ashok & Klößner, Stefan & Pfeifer, Gregor & Schieler, Manuel, 2015. "Synthetic Control Methods: Never Use All Pre-Intervention Outcomes Together With Covariates," MPRA Paper 83790, University Library of Munich, Germany.
    8. Daniel Albalate & Germà Bel & Ferran A. Mazaira-Font, 2021. "Decoupling synthetic control methods to ensure stability, accuracy and meaningfulness," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 12(4), pages 549-584, December.
    9. Daniel Kinn, 2018. "Synthetic Control Methods and Big Data," Papers 1803.00096, arXiv.org.
    10. Viviano, Davide & Bradic, Jelena, 2023. "Synthetic Learner: Model-free inference on treatments over time," Journal of Econometrics, Elsevier, vol. 234(2), pages 691-713.
    11. Michael Funke & Kadri Männasoo & Helery Tasane, 2023. "Regional Economic Impacts of the Øresund Cross-Border Fixed Link: Cui Bono?," CESifo Working Paper Series 10557, CESifo.
    12. Carvalho, Carlos & Masini, Ricardo & Medeiros, Marcelo C., 2018. "ArCo: An artificial counterfactual approach for high-dimensional panel time-series data," Journal of Econometrics, Elsevier, vol. 207(2), pages 352-380.
    13. Wu, Rongxin & Tan, Zhizhou & Lin, Boqiang, 2023. "Does carbon emission trading scheme really improve the CO2 emission efficiency? Evidence from China's iron and steel industry," Energy, Elsevier, vol. 277(C).
    14. Yi‐Ting Chen, 2020. "A distributional synthetic control method for policy evaluation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(5), pages 505-525, August.
    15. Joseph Cummins & Brock Smith & Douglas L. Miller & David Eliot Simon, 2023. "Matching on Noise: Finite Sample Bias in the Synthetic Control Estimator," Working papers 2023-07, University of Connecticut, Department of Economics.
    16. David Gilchrist & Thomas Emery & Nuno Garoupa & Rok Spruk, 2023. "Synthetic Control Method: A tool for comparative case studies in economic history," Journal of Economic Surveys, Wiley Blackwell, vol. 37(2), pages 409-445, April.
    17. Alice Lépissier & Matto Mildenberger, 2021. "Unilateral climate policies can substantially reduce national carbon pollution," Climatic Change, Springer, vol. 166(3), pages 1-21, June.
    18. Susan Athey & Mohsen Bayati & Nikolay Doudchenko & Guido Imbens & Khashayar Khosravi, 2021. "Matrix Completion Methods for Causal Panel Data Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1716-1730, October.
    19. Ferman, Bruno & Pinto, Cristine, 2016. "Revisiting the Synthetic Control Estimator," MPRA Paper 73982, University Library of Munich, Germany.
    20. Daniel Albalate & Germà Bel & Ferran A. Mazaira-Font, 2020. "Ensuring Stability, Accuracy and Meaningfulness in Synthetic Control Methods: The Regularized SHAP-Distance Method," IREA Working Papers 202005, University of Barcelona, Research Institute of Applied Economics, revised Apr 2020.

    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:28:y:2019:i:11:p:1293-1307. 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.