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Elective primary caesarean delivery: accuracy of administrative data
Lisa M Korst1, Kimberly D Gregory, Jeffrey A Gornbein
1Cedars-Sinai Medical Center Burns Allen Research Institute, and Department of Obstetrics and Gynecology, Division of Women's Health Services Research and Policy, University of California, Los Angeles, CA, USA. LKORST@chla.usc.edu
Paediatric and Perinatal Epidemiology
|March 5, 2004
Summary
This study validated using ICD-9-CM codes to identify elective primary caesarean deliveries. Administrative data showed high accuracy, supporting its use for monitoring obstetric care quality.
Area of Science:
- Obstetrics and Gynecology
- Health Services Research
- Medical Informatics
Background:
- Caesarean delivery rates are a key indicator of obstetric care quality.
- Elective primary caesarean deliveries, performed before labor, constitute a significant portion of all caesarean births.
Purpose of the Study:
- To validate a model using ICD-9-CM codes for identifying and categorizing elective primary caesarean deliveries by indication.
- To assess the accuracy of administrative data in reflecting specific indications for elective primary caesarean births.
Main Methods:
- Compared ICD-9-CM codes with medical record diagnoses for women without prior caesarean deliveries.
- Evaluated 12 potential indications for elective primary caesarean delivery.
- Calculated sensitivity, specificity, and accuracy of administrative data against medical records as the gold standard.
Main Results:
- Elective primary caesarean delivery identification by administrative data showed 73.1% sensitivity, 98.1% specificity, and 96.6% accuracy compared to medical records.
- Administrative coding for all 12 conditions demonstrated high specificity but variable sensitivity, with accuracy ranging from 83.9% to 100%.
Conclusions:
- Administrative data, while requiring further refinement for certain codes, demonstrates significant potential for monitoring elective primary caesarean deliveries.
- Continued efforts to utilize administrative data are supported for quality assessment in obstetric care.
