Validation of Algorithms to Identify Small or Large for Gestational Age in the Korean Nationwide Healthcare Database

Objective: Small for gestational age (SGA) and large for gestational age (LGA) are critical endpoints in perinatal pharmacoepidemiology, yet gestational age is frequently missing in administrative databases. This study validated algorithms for identifying SGA and LGA in the Korean National Health Information Database (NHID). Methods: We linked the NHID with national vaccination and infant health screening registries (2018-2021) to create a validation cohort of 94,159 pregnancies with reference standard gestational age and birth weight; infants with birth weights below the 10th or above the 90th percentile were considered SGA or LGA, respectively. We evaluated four algorithms: ICD-10 diagnosis codes from infant claims (Method A), maternal claims (Method B), either infant or maternal claims (Method C), and birth weight combined with estimated gestational age (Method D). Results: ICD-10-based algorithms consistently underestimated prevalence, showing high specificity but low sensitivity (<17%). In contrast, Method D demonstrated superior performance: for SGA, sensitivity was 89.3%, specificity 96.8%, and positive predictive value (PPV) 71.6%; for LGA, sensitivity was 77.5%, specificity 98.3%, and PPV 84.7%. Conclusion: SGA and LGA can be identified with reasonable accuracy in the NHID using birth weight and estimated gestational age, whereas diagnosis codes alone can underestimate prevalence.