IDEAS home Printed from https://ideas.repec.org/a/kap/hcarem/v22y2019i4d10.1007_s10729-018-9449-3.html
   My bibliography  Save this article

Simulation model of the relationship between cesarean section rates and labor duration

Author

Listed:
  • Karen T. Hicklin

    (University of North Carolina at Chapel Hill)

  • Julie S. Ivy

    (North Carolina State University)

  • James R. Wilson

    (North Carolina State University)

  • Fay Cobb Payton

    (North Carolina State University)

  • Meera Viswanathan

    (RTI International)

  • Evan R. Myers

    (Duke University School of Medicine)

Abstract

Cesarean delivery is the most common major abdominal surgery in many parts of the world, and it accounts for nearly one-third of births in the United States. For a patient who requires a C-section, allowing prolonged labor is not recommended because of the increased risk of infection. However, for a patient who is capable of a successful vaginal delivery, performing an unnecessary C-section can have a substantial adverse impact on the patient’s future health. We develop two stochastic simulation models of the delivery process for women in labor; and our objectives are (i) to represent the natural progression of labor and thereby gain insights concerning the duration of labor as it depends on the dilation state for induced, augmented, and spontaneous labors; and (ii) to evaluate the Friedman curve and other labor-progression rules, including their impact on the C-section rate and on the rates of maternal and fetal complications. To use a shifted lognormal distribution for modeling the duration of labor in each dilation state and for each type of labor, we formulate a percentile-matching procedure that requires three estimated quantiles of each distribution as reported in the literature. Based on results generated by both simulation models, we concluded that for singleton births by nulliparous women with no prior complications, labor duration longer than two hours (i.e., the time limit for labor arrest based on the Friedman curve) should be allowed in each dilation state; furthermore, the allowed labor duration should be a function of dilation state.

Suggested Citation

  • Karen T. Hicklin & Julie S. Ivy & James R. Wilson & Fay Cobb Payton & Meera Viswanathan & Evan R. Myers, 2019. "Simulation model of the relationship between cesarean section rates and labor duration," Health Care Management Science, Springer, vol. 22(4), pages 635-657, December.
  • Handle: RePEc:kap:hcarem:v:22:y:2019:i:4:d:10.1007_s10729-018-9449-3
    DOI: 10.1007/s10729-018-9449-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10729-018-9449-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10729-018-9449-3?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hideaki Takagi & Yuta Kanai & Kazuo Misue, 2017. "Queueing network model for obstetric patient flow in a hospital," Health Care Management Science, Springer, vol. 20(3), pages 433-451, September.
    2. Weinstein, M.C. & Coxson, P.G. & Williams, L.W. & Pass, T.M. & Stason, W.B. & Goldman, L., 1987. "Forecasting coronary heart disease incidence, mortality, and cost: The coronary heart disease policy model," American Journal of Public Health, American Public Health Association, vol. 77(11), pages 1417-1426.
    3. Oguzhan Alagoz & Cindy L. Bryce & Steven Shechter & Andrew Schaefer & Chung-Chou H. Chang & Derek C. Angus & Mark S. Roberts, 2005. "Incorporating Biological Natural History in Simulation Models: Empirical Estimates of the Progression of End-Stage Liver Disease," Medical Decision Making, , vol. 25(6), pages 620-632, November.
    4. Steven M. Shechter & Cindy L. Bryce & Oguzhan Alagoz & Jennifer E. Kreke & James E. Stahl & Andrew J. Schaefer & Derek C. Angus & Mark S. Roberts, 2005. "A Clinically Based Discrete-Event Simulation of End-Stage Liver Disease and the Organ Allocation Process," Medical Decision Making, , vol. 25(2), pages 199-209, March.
    5. Dugas, Marylène & Shorten, Allison & Dubé, Eric & Wassef, Maggy & Bujold, Emmanuel & Chaillet, Nils, 2012. "Decision aid tools to support women's decision making in pregnancy and birth: A systematic review and meta-analysis," Social Science & Medicine, Elsevier, vol. 74(12), pages 1968-1978.
    6. Bruce Schmeiser, 1982. "Batch Size Effects in the Analysis of Simulation Output," Operations Research, INFORMS, vol. 30(3), pages 556-568, June.
    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. Burhaneddin Sandıkçı & Lisa M. Maillart & Andrew J. Schaefer & Oguzhan Alagoz & Mark S. Roberts, 2008. "Estimating the Patient's Price of Privacy in Liver Transplantation," Operations Research, INFORMS, vol. 56(6), pages 1393-1410, December.
    2. Burhaneddin Sandıkçı & Lisa M. Maillart & Andrew J. Schaefer & Mark S. Roberts, 2013. "Alleviating the Patient's Price of Privacy Through a Partially Observable Waiting List," Management Science, INFORMS, vol. 59(8), pages 1836-1854, August.
    3. Enver Yücesan, 1993. "Randomization tests for initialization bias in simulation output," Naval Research Logistics (NRL), John Wiley & Sons, vol. 40(5), pages 643-663, August.
    4. Oguzhan Alagoz & Lisa M. Maillart & Andrew J. Schaefer & Mark S. Roberts, 2007. "Determining the Acceptance of Cadaveric Livers Using an Implicit Model of the Waiting List," Operations Research, INFORMS, vol. 55(1), pages 24-36, February.
    5. Song, Wheyming Tina, 1996. "On the estimation of optimal batch sizes in the analysis of simulation output," European Journal of Operational Research, Elsevier, vol. 88(2), pages 304-319, January.
    6. Rasstrigin, M. & Kitaev, A. & Pleshackova, E., 2023. "Forecasting spending on orphan diseases to maintain the long-run financial sustainability of healthcare system," Journal of the New Economic Association, New Economic Association, vol. 59(2), pages 120-141.
    7. William S. Weintraub & Samuel S. Gidding, 2016. "PCSK9 Inhibitors: A Technology Worth Paying For?," PharmacoEconomics, Springer, vol. 34(3), pages 217-220, March.
    8. Maria Bruni & Domenico Conforti & Nicola Sicilia & Sandro Trotta, 2006. "A new organ transplantation location–allocation policy: a case study of Italy," Health Care Management Science, Springer, vol. 9(2), pages 125-142, May.
    9. Yuta Kanai & Hideaki Takagi, 2021. "Markov chain analysis for the neonatal inpatient flow in a hospital," Health Care Management Science, Springer, vol. 24(1), pages 92-116, March.
    10. Michele Kohli & Cheryl Attard & Annette Lam & Daniel Huse & John Cook & Chantal Bourgault & Evo Alemao & Donald Yin & Michael Marentette, 2006. "Cost Effectiveness of Adding Ezetimibe to Atorvastatin Therapy in Patients Not at Cholesterol Treatment Goal in Canada," PharmacoEconomics, Springer, vol. 24(8), pages 815-830, August.
    11. Jack P. C. Kleijnen & Susan M. Sanchez & Thomas W. Lucas & Thomas M. Cioppa, 2005. "State-of-the-Art Review: A User’s Guide to the Brave New World of Designing Simulation Experiments," INFORMS Journal on Computing, INFORMS, vol. 17(3), pages 263-289, August.
    12. Duraikannan Sundaramoorthi & Victoria Chen & Jay Rosenberger & Seoung Kim & Deborah Buckley-Behan, 2010. "A data-integrated simulation-based optimization for assigning nurses to patient admissions," Health Care Management Science, Springer, vol. 13(3), pages 210-221, September.
    13. David F. Muñoz & Peter W. Glynn, 2001. "Multivariate Standardized Time Series for Steady-State Simulation Output Analysis," Operations Research, INFORMS, vol. 49(3), pages 413-422, June.
    14. David Goldsman & Keebom Kang & Seong‐Hee Kim & Andrew F. Seila & Gamze Tokol, 2007. "Combining standardized time series area and Cramér–von Mises variance estimators," Naval Research Logistics (NRL), John Wiley & Sons, vol. 54(4), pages 384-396, June.
    15. Anthony Ebert & Ritabrata Dutta & Kerrie Mengersen & Antonietta Mira & Fabrizio Ruggeri & Paul Wu, 2021. "Likelihood‐free parameter estimation for dynamic queueing networks: Case study of passenger flow in an international airport terminal," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(3), pages 770-792, June.
    16. Michael F. Drummond;Adrian Towse, 1998. "From Efficacy to Cost-Effectiveness," Briefing 000438, Office of Health Economics.
    17. Mingchang Chih, 2019. "An Insight into the Data Structure of the Dynamic Batch Means Algorithm with Binary Tree Code," Mathematics, MDPI, vol. 7(9), pages 1-8, August.
    18. Yizhe Xu & Tom H. Greene & Adam P. Bress & Brian C. Sauer & Brandon K. Bellows & Yue Zhang & William S. Weintraub & Andrew E. Moran & Jincheng Shen, 2022. "Estimating the optimal individualized treatment rule from a cost‐effectiveness perspective," Biometrics, The International Biometric Society, vol. 78(1), pages 337-351, March.
    19. Zeynep Erkin & Matthew D. Bailey & Lisa M. Maillart & Andrew J. Schaefer & Mark S. Roberts, 2010. "Eliciting Patients' Revealed Preferences: An Inverse Markov Decision Process Approach," Decision Analysis, INFORMS, vol. 7(4), pages 358-365, December.
    20. Mustafa Akan & Oguzhan Alagoz & Baris Ata & Fatih Safa Erenay & Adnan Said, 2012. "A Broader View of Designing the Liver Allocation System," Operations Research, INFORMS, vol. 60(4), pages 757-770, August.

    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:kap:hcarem:v:22:y:2019:i:4:d:10.1007_s10729-018-9449-3. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.