Volume 42 | Number 2 | April 2007

Abstract List

Mark C. Hornbrook Ph.D., Evelyn P. Whitlock, Cynthia J. Berg, William M. Callaghan, Donald J. Bachman, Rachel Gold, F. Carol Bruce, Patricia M. Dietz, Selvi B. Williams


To develop and validate a software algorithm to detect pregnancy episodes and maternal morbidities using automated data.

Data Sources/Study Setting

Automated records from a large integrated health care delivery system (IHDS), 1998–2001.

Study Design

Through complex linkages of multiple automated information sources, the algorithm estimated pregnancy histories. We evaluated the algorithm's accuracy by comparing selected elements of the pregnancy history obtained by the algorithm with the same elements manually abstracted from medical records by trained research staff.

Data Collection/Extraction Methods

The algorithm searched for potential pregnancy indicators within diagnosis and procedure codes, as well as laboratory tests, pharmacy dispensings, and imaging procedures associated with pregnancy.

Principal Findings

Among 32,847 women with potential pregnancy indicators, we identified 24,680 pregnancies occuring to 21,001 women. Percent agreement between the algorithm and medical records review on pregnancy outcome, gestational age, and pregnancy outcome date ranged from 91 percent to 98 percent. The validation results were used to refine the algorithm.


This pregnancy episode grouper algorithm takes advantage of databases readily available in IHDS, and has important applications for health system management and clinical care. It can be used in other settings for ongoing surveillance and research on pregnancy outcomes, pregnancy‐related morbidities, costs, and care patterns.