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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
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Bruce Schmeiser, 1982. "Batch Size Effects in the Analysis of Simulation Output," Operations Research, INFORMS, vol. 30(3), pages 556-568, June.
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