Adjusting cesarean delivery rates for case mix

Health Serv Res. 1997 Oct;32(4):511-28.


Objectives: (1) To describe the issues in developing a clinical predictor of cesarean delivery that could be used to adjust reported cesarean rates for case mix, and (2) to compare its performance to other, simpler predictors using clinical and statistical criteria.

Data sources: Singleton births greater than 2,500 grams in Washington State in 1989 and 1990 for whom mothers and infant hospital discharge records could be matched to birth certificate data.

Design: Statistical analysis of retrospective merged hospital and birth certificate data, which were used to develop variables and models to predict the probability that any particular delivery would be a cesarean.

Principal findings: Merged data led to better predictor variables than those based on one source. A simple four-category hierarchical classification into births with prior cesarean, breech but no prior cesarean, first birth, and other explains 30 percent of the variance in individual cesarean rates. The full clinical model fit the data well and explained 37 percent of the variance. Multiparas without serious complications comprised 35 percent of the mothers and averaged less than 2 percent cesareans. A hospital's predicted cesarean rate depends strongly on the proportion of its births that are first births.

Conclusion: Government and private agencies have reported cesarean rates as measures of hospital performance. Depending on data and resources available, both simple and complex measures of case mix can be used to adjust reported rates. These adjustments should not include all variables related to the rates. Proper adjustments may not alter hospital rankings greatly, but they will improve the validity and acceptability of the reports.

Publication types

  • Comparative Study
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Birth Certificates
  • Cesarean Section / statistics & numerical data*
  • Diagnosis-Related Groups / classification*
  • Female
  • Humans
  • Infant, Newborn
  • Patient Discharge / statistics & numerical data
  • Pregnancy
  • Probability
  • Regression Analysis
  • Retrospective Studies
  • Risk Assessment
  • Washington