Development and Validation of Algorithms to Estimate Live Birth Gestational Age in Medicaid Analytic eXtract Data. Issue 1 (17th October 2022)

• Record Type: Journal Article
• Authors: Zhu, Yanmin; Thai, Thuy N.; Hernandez-Diaz, Sonia; Bateman, Brian T.; Winterstein, Almut G.; Straub, Loreen; Franklin, Jessica M.; Gray, Kathryn J.; Wyss, Richard; Mogun, Helen; Vine, Seanna; Taylor, Lockwood G.; Ouellet-Hellstrom, Rita; Ma, Yong; Qiang, Yandong; Hua, Wei; Huybrechts, Krista F.
• Is Part Of: Epidemiology. Volume 34:Issue 1(2023)
• Page Start: 69
• Page End: 79
• Publication Date: 2022-10-17
• Subjects: gestational age -- Medicaid -- validation study -- claims-based algorithm -- machine learning; Epidemiology -- Periodicals; Epidemiology -- Environmental aspects -- Periodicals; Epidemiology -- Periodicals; 614.405
• DOI: 10.1097/EDE.0000000000001559
• Languages: English
• ISSNs: 1044-3983

Abstract

Abstract : Background: While healthcare utilization data are useful for postmarketing surveillance of drug safety in pregnancy, the start of pregnancy and gestational age at birth are often incompletely recorded or missing. Our objective was to develop and validate a claims-based live birth gestational age algorithm. Methods: Using the Medicaid Analytic eXtract (MAX) linked to birth certificates in three states, we developed four candidate algorithms based on: preterm codes; preterm or postterm codes; timing of prenatal care; and prediction models – using conventional regression and machine-learning approaches with a broad range of prespecified and empirically selected predictors. We assessed algorithm performance based on mean squared error (MSE) and proportion of pregnancies with estimated gestational age within 1 and 2 weeks of the gold standard, defined as the clinical or obstetric estimate of gestation on the birth certificate. We validated the best-performing algorithms against medical records in a nationwide sample. We quantified misclassification of select drug exposure scenarios due to estimated gestational age as positive predictive value (PPV), sensitivity, and specificity. Results: Among 114, 117 eligible pregnancies, the random forest model with all predictors emerged as the best performing algorithm: MSE 1.5; 84.8% within 1 week and 96.3% within 2 weeks, with similar performance in the nationwide validation cohort. For all exposure scenarios, PPVs were >93.8%, sensitivities >94.3%, and specificities >99.4%. Conclusions: We developed a highly accurate algorithm for estimating gestational age among live births in the nationwide MAX data, further supporting the value of these data for drug safety surveillance in pregnancy. See video abstract at, http://links.lww.com/EDE/B989 .