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Hospital- and patient-related characteristics determining maternity length of stay: A hierarchical linear model approach

Author

Listed:
  • Leung, K.-M.
  • Elashoff, R.M.
  • Rees, K.S.
  • Hasan, M.M.
  • Legorreta, A.P.

Abstract

Objectives. The purpose of this study was to identify factors related to pregnancy and childbirth that might be predictive of a patient's length of stay after delivery and to model variations in length of stay. Methods. California hospital discharge data on maternity patients (n = 499 912) were analyzed. Hierarchical linear modeling was used to adjust for patient case mix and hospital characteristics and to account for the dependence of outcome variables within hospitals. Results. Substantial variation in length of stay among patients was observed. The variation was mainly attributed to delivery type (vaginal or cesarean section), the patient's clinical risk factors, and severity of complications (if any). Furthermore, hospitals differed significantly in maternity lengths of stay even after adjustment for patient case mix. Conclusions. Developing risk-adjusted models for length of stay is a complex process but is essential for understanding variation. The hierarchical linear model approach described here represents a more efficient and appropriate way of studying interhospital variations than the traditional regression approach.

Suggested Citation

  • Leung, K.-M. & Elashoff, R.M. & Rees, K.S. & Hasan, M.M. & Legorreta, A.P., 1998. "Hospital- and patient-related characteristics determining maternity length of stay: A hierarchical linear model approach," American Journal of Public Health, American Public Health Association, vol. 88(3), pages 377-381.
    • Handle: RePEc:aph:ajpbhl:1998:88:3:377-381_6
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    Cited by:

    1. Chungkham Singh & Laishram Ladusingh, 2010. "Inpatient length of stay: a finite mixture modeling analysis," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 11(2), pages 119-126, April.
    2. Xirasagar, Sudha & Lin, Herng-Ching, 2006. "Effects of payment incentives, hospital ownership and competition on hospitalization decisions for ambulatory surgical procedures," Health Policy, Elsevier, vol. 76(1), pages 26-37, March.
    3. Yau, Kelvin K. W. & Lee, Andy H. & Ng, Angus S. K., 2003. "Finite mixture regression model with random effects: application to neonatal hospital length of stay," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 359-366, January.
    4. Sara Dias & Valeska Andreozzi & Rosário Martins, 2013. "Analysis of HIV/AIDS DRG in Portugal: a hierarchical finite mixture model," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 14(5), pages 715-723, October.
    5. Eva Williford & Valerie Haley & Louise-Anne McNutt & Victoria Lazariu, 2020. "Dealing with highly skewed hospital length of stay distributions: The use of Gamma mixture models to study delivery hospitalizations," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-18, April.
    6. Attila Cseh & Brandon Koford, 2010. "The Impact of Maternity Minimum Stay Mandates on Hospitalizations: An Extension," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 16(4), pages 395-409, November.
    7. Tamar Lasky & Jay Greenspan & Frank R Ernst & Liliana Gonzalez, 2012. "Pediatric Vancomycin Use in 421 Hospitals in the United States, 2008," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-7, August.
    8. repec:kap:iaecre:v:16:y:2010:i:4:p:395-409 is not listed on IDEAS

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