Risk adjusting cesarean delivery rates: a comparison of hospital profiles based on medical record and birth certificate data.
OBJECTIVE
Compare the discrimination of risk-adjustment models for primary cesarean delivery derived from medical record data and birth certificate data and determine if the two types of models yield similar hospital profiles of risk-adjusted cesarean delivery rates.


METHODS
The study involved 29,234 women without prior cesarean delivery admitted for labor and delivery in 1993-95 to 20 hospitals in northeast Ohio for whom data abstracted from patient medical records and data from birth certificates could be linked.


METHODS
Three pairs of multivariate models of the risk of cesarean delivery were developed using (1) the full complement of variables in medical records or birth certificates; (2) variables that were common to the two sources; and (3) variables for which agreement between the two data sources was high. Using each of the six models, predicted rates of cesarean delivery were determined for each hospital. Hospitals were classified as outliers if observed and predicted rates of cesarean delivery differed (p < .05).


RESULTS
Discrimination of the full medical record and birth certificate models was higher (p < .001) than the discrimination of the more limited common and reliable variable models. Based on the full medical record model, six hospitals were classified as statistical (p < .01) outliers (three high and three low). In contrast, the full birth certificate model identified five low and four high outliers, and classifications differed for seven of the 20 hospitals. Even so, the correlation between adjusted hospital rates was substantial (r = .71). Interestingly, correlations between the full medical record model and the more limited common (r = .84) and reliable (r = .88) variable birth certificate models were higher, and differences in classification of hospital outlier status were fewer.


CONCLUSIONS
Birth certificates can be used to develop cesarean delivery risk-adjustment models that have excellent discrimination. However, using the full complement of birth certificate variables may lead to biased hospital comparisons. In contrast, limiting models to data elements with known reliability may yield rankings that are more similar to rankings based on medical record data.
DiGiuseppe
D L
DL
Department of Medicine, Case Western Reserve University, Cleveland, OH, USA.
Aron
D C
DC
Payne
S M
SM
Snow
R J
RJ
Dierker
L
L
Rosenthal
G E
GE
D000328:Adult; D001719:Birth Certificates; D002585:Cesarean Section / Q000706:statistics & numerical data*; D015331:Cohort Studies; D003625:Data Collection; D005260:Female; D006302:Health Services Research; D006801:Humans; D008485:Medical Audit / Q000379:methods*; D008499:Medical Records; D015233:Models, Statistical; D009775:Obstetrics and Gynecology Department, Hospital / Q000145:classification* / Q000706:statistics & numerical data; D009820:Ohio / Q000453:epidemiology; D010818:Practice Patterns, Physicians' / Q000706:statistics & numerical data*; D011247:Pregnancy; D020379:Risk Adjustment; D014600:Utilization Review