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Updated: May 1, 2026
Assessment of Child Anthropometry in a Large Epidemiologic Study
Published on: February 2, 2017
Validity of birth certificate-derived maternal weight data
Lisa M Bodnar1, Barbara Abrams, Marnie Bertolet
1Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA; Department of Obstetrics, Gynecology, and Reproductive Sciences, School of Medicine, University of Pittsburgh, Pittsburgh, PA; Magee-Womens Research Institute, Pittsburgh, PA.
Background:
Studies using vital records-based maternal weight data have become more common, but the validity of these data is uncertain.
Methods:
We evaluated the accuracy of prepregnancy body mass index (BMI) and gestational weight gain (GWG) reported on birth certificates using medical record data in 1204 births at a teaching hospital in Pennsylvania from 2003 to 2010. Deliveries at this hospital were representative of births statewide with respect to BMI, GWG, race/ethnicity, and preterm birth. Forty-eight strata were created by simultaneous stratification on prepregnancy BMI (underweight, normal weight/overweight, obese class 1, obese classes 2 and 3), GWG (<20th, 20-80th, >80th percentile), race/ethnicity (non-Hispanic white, non-Hispanic black), and gestational age (term, preterm).
Results:
The agreement of birth certificate-derived prepregnancy BMI category with medical record BMI category was highest in the normal weight/overweight and obese class 2 and 3 groups. Agreement varied from 52% to 100% across racial/ethnic and gestational age strata. GWG category from the birth registry agreed with medical records for 41-83% of deliveries, and agreement tended to be the poorest for very low and very high GWG. The misclassification of GWG was driven by errors in reported prepregnancy weight rather than maternal weight at delivery, and its magnitude depended on prepregnancy BMI category and gestational age at delivery.
Conclusions:
Maternal weight data, particularly at the extremes, are poorly reported on birth certificates. Investigators should devote resources to well-designed validation studies, the results of which can be used to adjust for measurement errors by bias analysis.
