Positive predictive value of computerized records for major congenital malformations.
OBJECTIVE
To assess the positive predictive value of computerized records in a linked database of vital records and infant claims, with medical record confirmation to detect congenital malformations in a Medicaid population.


METHODS
Study subjects were selected from cases identified for three studies of congenital malformations in the Tennessee Medicaid (TennCare) population including 173 827 (studies 1 and 2) and 519 465 (study 3) mother/infant pairs. Possible malformations were identified from computerized databases of birth certificates linked with maternal and infant claims. Medical records were reviewed for all possible congenital malformations and positive predictive values were calculated for each data source and for each malformation.


RESULTS
Among 1430 potential congenital malformations identified from either birth certificates or inpatient claims, 67.7% were confirmed by medical record review. The positive predictive value varied considerably depending on the data source and the organ system. For example, cardiac defects had a very low positive predictive value when identified from birth certificates, and somewhat higher positive predictive value when identified from inpatient claims. Orofacial defects had 90.9% positive predictive value from birth certificates and inpatient claims. Requiring evidence of a diagnostic or therapeutic procedure increased the positive predictive value to >90% for specific defects, but substantially reduced the number of included cases.


CONCLUSIONS
Depending on the defect, computerized claims data linked to vital records offer opportunities for identifying birth defects in populations of vulnerable persons. However, for many defects, medical record confirmation is likely to be required to provide valid identification of malformation occurrence.
Cooper
William O
WO
Department of Pediatrics, Vanderbilt University School of Medicine, Nashville, TN 3723-2504, USA. william.cooper@vanderbilt.edu
Hernandez-Diaz
Sonia
S
Gideon
Patricia
P
Dyer
Shannon M
SM
Hall
Kathleen
K
Dudley
Judith
J
Cevasco
Marisa
M
Thompson
Amanda B
AB
Ray
Wayne A
WA
D001719:Birth Certificates; D000013:Congenital Abnormalities / Q000175:diagnosis / Q000453:epidemiology*; D016208:Databases, Factual / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D007231:Infant, Newborn; D007344:Insurance Claim Reporting / Q000706:statistics & numerical data*; D008484:Medicaid / Q000706:statistics & numerical data; D016347:Medical Records Systems, Computerized / Q000706:statistics & numerical data*; D011237:Predictive Value of Tests; D013714:Tennessee / Q000453:epidemiology; D014481:United States / Q000453:epidemiology