title: Using administrative data to identify indications for elective primary cesarean delivery.
abstract: OBJECTIVE
  To develop a methodology to identify indications and normative rates for elective primary cesarean delivery using administrative data.
  
  
  METHODS
  All delivery discharges in 1995, as reported to the California Office of Statewide Health Planning and Development (secondary data).
  
  
  METHODS
  Retrospective population based study.
  
  
  METHODS
  Data were entered into a recursive partitioning algorithm to develop a hierarchy of conditions by which patients with multiple conditions could be sorted with respect to the binary outcome of labor or elective primary cesarean without labor. This hierarchy was examined for its clinical consistency, validated on a second sample, and compared with results obtained from logistic regression.
  
  
  RESULTS
  Four percent (19,664) of delivery discharges in 1995 underwent elective primary cesarean. Twelve clinical conditions contributed to the hierarchy, and accounted for 92.9 percent of all women experiencing elective primary cesarean delivery. The remaining 7.1 percent of the elective primary cesarean cases were classified as "unspecified."
  
  
  CONCLUSIONS
  A standardized methodology (utilizing recursive partitioning algorithms) for assigning indications for elective primary cesarean is presented. This methodology relies on administrative data, classifies women with complex comorbidity patterns into clinically relevant subpopulations, and can be used to establish normative rates for benchmarking specific indications for cesarean delivery.
authors.affiliation: Cedars Sinai Medical Center and Burns Allen Research Institute, Department of Obstetrics and Gynecology, Los Angeles, CA 90048, USA.
authors.forename: Kimberly D
authors.initials: KD
authors.lastname: Gregory
authors.forename: Lisa M
authors.initials: LM
authors.lastname: Korst
authors.forename: Jeffrey A
authors.initials: JA
authors.lastname: Gornbein
authors.forename: Lawrence D
authors.initials: LD
authors.lastname: Platt
mesh_terms: D000465:Algorithms; D002140:California / Q000453:epidemiology; D002585:Cesarean Section / Q000706:statistics & numerical data*; D015897:Comorbidity; D017558:Elective Surgical Procedures / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D038801:International Classification of Diseases; D007744:Obstetric Labor Complications / Q000145:classification / Q000453:epidemiology; D010342:Patient Acceptance of Health Care / Q000706:statistics & numerical data*; D011247:Pregnancy; D012189:Retrospective Studies; D020379:Risk Adjustment; D013220:State Health Planning and Development Agencies; D014481:United States; D014600:Utilization Review / Q000379:methods*