Validation of algorithms to ascertain clinical conditions and medical procedures used during pregnancy.
OBJECTIVE
To evaluate the validity of health plan administrative and claims data to identify pre-gestational and gestational diabetes, obesity, and ultrasounds among pregnant women.


METHODS
A retrospective study was conducted using the administrative and claims data of three health plans participating in the HMO Research Network. Diagnoses, drug dispensings, and procedure codes were used to identify diabetes, obesity, and ultrasounds among women who were pregnant between January 2006 and December 2008. A random sample of medical charts (n = 222) were abstracted. Positive predictive values (PPVs) were calculated. Sensitivity also was calculated for obesity among women for whom body mass index data were available in electronic medical records at two sites.


RESULTS
Overall, 190 of 222 cases of diabetes (86%) were confirmed (82% for gestational diabetes and 74% for pre-gestational diabetes). The PPV for codes to identify ultrasounds was 80%. Whereas the PPV for obesity-related diagnosis codes was high (93%), and the sensitivity was low (33%).


CONCLUSIONS
Health plan administrative and claims data can be used to accurately identify pre-gestational and gestational diabetes and ultrasounds. Obesity is not consistently coded.
Meyers Primary Care Institute and University of Massachusetts Medical School, Worcester, MA, USA. sandrade@meyersprimary.org
Susan E
SE
Andrade
Tiffany A
TA
Moore Simas
Denise
D
Boudreau
Marsha A
MA
Raebel
Sengwee
S
Toh
Beth
B
Syat
Inna
I
Dashevsky
Richard
R
Platt
D000293:Adolescent; D000328:Adult; D000465:Algorithms; D016208:Databases, Factual; D003924:Diabetes Mellitus, Type 2 / Q000175:diagnosis / Q000453:epidemiology; D016640:Diabetes, Gestational / Q000175:diagnosis / Q000453:epidemiology; D004813:Epidemiology / Q000706:statistics & numerical data; D005260:Female; D006176:Gynecology / Q000706:statistics & numerical data; D006801:Humans; D007345:Insurance Claim Review / Q000706:statistics & numerical data; D038801:International Classification of Diseases; D016347:Medical Records Systems, Computerized / Q000706:statistics & numerical data; D008875:Middle Aged; D015233:Models, Statistical; D009765:Obesity / Q000175:diagnosis / Q000453:epidemiology; D009774:Obstetrics / Q000706:statistics & numerical data; D011237:Predictive Value of Tests; D011247:Pregnancy; D011248:Pregnancy Complications / Q000175:diagnosis* / Q000453:epidemiology; D011897:Random Allocation; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D013038:Specialization; D055815:Young Adult