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Impact of the Red Code Process Using Structural Equation Models

  • Eduardo Pérez CastroEmail author
  • Flaviano Godínez JaimesEmail author
  • Elia Barrera RodríguezEmail author
  • Ramón Reyes Carreto
  • Raúl López Roque
  • Virginia Vera Leyva
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 301)

Abstract

This paper proposes an ad hoc model to explain the relationships between latent and observed variables, which influence the results of the care of pregnant woman with obstetric emergency before and after the implementation of a standardized process called Red Code. It has used information from medical records of pregnant women who were treated in the emergency service of the Hospital de la Madre y el Niño Guerrerense, Guerrero, Mexico. Based on expert judgment, 19 observed variables were grouped into 5 latent variables: first hemodynamic state, second hemodynamic state, obstetric-gynecological history, treatments, and results of EMOC. An ad hoc model was proposed that includes the first four latent variables as independent and the last one as a latent dependent variable. To asses the proposal, goodness-of-fit indexes for the fitted structural equation model were used. It was concluded that the results are mainly affected by obstetric-gynecological history and second hemodynamic status for the before red code period and obstetric-gynecological history and treatment for the red code period.

Keywords

Red code Structural equation models Obstetric emergency 

References

  1. 1.
    Owili, P.O., Muga, M.A., Mendez, B.R., Chen, B.: Quality of maternity care and its determinants along the continuum in Kenya: a structural equation modeling analysis. PloS One 12(5), e0177756 (2017)CrossRefGoogle Scholar
  2. 2.
    Chebbo, A., Tan, S., Kassis, C., Tamura, L., Carlson, R.W.: Maternal sepsis and septic shock. Crit. Care Clin. 32(1), 119–135 (2016)CrossRefGoogle Scholar
  3. 3.
    American College of Obstetricians, Gynecologists, et al.: Hypertension in pregnancy. Report of the American college of obstetricians and gynecologists task force on hypertension in pregnancy. Obstet. Gynecol. 122(5), 1122–1131 (2013)Google Scholar
  4. 4.
    World Health Organization, UNICEF, et al.: Monitoring emergency obstetric care: a handbook (2009)Google Scholar
  5. 5.
    Romero-Ibarguengoitia, M.E., Vadillo-Ortega, F., Caballero, A.E., Ibarra-González, I., Herrera-Rosas, A., Serratos-Canales, M.F., León-Hernández, M., González-Chávez, A., Mummidi, S., Duggirala, R., et al.: Family history and obesity in youth, their effect on acylcarnitine/aminoacids metabolomics and non-alcoholic fatty liver disease (NAFLD). Structural equation modeling approach. PloS One 13(2), e0193138 (2018)CrossRefGoogle Scholar
  6. 6.
    Ae Ri, J., Jang, K.S.: Structural equation modeling on health-related quality of life of patients with ankylosing spondylitis. Iran. J. Public Health 46(10), 1338–1346 (2017)Google Scholar
  7. 7.
    Lee, S.-Y., Song, X.-Y.: Basic and Advanced Bayesian Structural Equation Modeling: With Applications in the Medical and Behavioral Sciences. Wiley, New York (2012)Google Scholar
  8. 8.
    Kline, R.B.: Principles and Practice of Structural Equation Modeling. Guilford Publications, New York (2015)Google Scholar
  9. 9.
    Rosseel, Y.: Lavaan: an R package for structural equation modeling and more. Version 0.5–12 (beta). J. Stat. Softw. 48(2), 1–36 (2012)Google Scholar
  10. 10.
    R Core Team: R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2017)Google Scholar
  11. 11.
    Steiger, J.H.: Understanding the limitations of global fit assessment in structural equation modeling. Personal. Individ. Differ. 42(5), 893–898 (2007)CrossRefGoogle Scholar
  12. 12.
    Hu, L., Bentler, P.M.: Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct. Equ. Model.: Multidiscip. J. 6(1), 1–55 (1999)CrossRefGoogle Scholar
  13. 13.
    Bentler, P.M.: Comparative fit indexes in structural models. Psychol. Bull. 107(2), 238–246 (1990)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Facultad de Matemáticas, Universidad Autónoma de GuerreroChilpancingoMexico
  2. 2.Unidad de Innovación Clínica y Epidemiológica del Estado de GuerreroHospital de la Madre y el Niño GuerrerenseChilpancingoMexico
  3. 3.Hospital de la Madre y el Niño GuerrerenseChilpancingoMexico

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