• Cesarean section;
  • case mix;
  • risk adjustment;
  • classification and regression trees;
  • recursive partitioning

Objective. To develop a methodology to identify indications and normative rates for elective primary cesarean delivery using administrative data.

Data Sources/Study Setting. All delivery discharges in 1995, as reported to the California Office of Statewide Health Planning and Development (secondary data).

Study Design. Retrospective population based study.

Data Collection/Extraction. 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.

Principal Findings. 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.