Impact of the transition from icd–9–cm to icd–10–cm on the identification of pregnancy episodes in us health insurance claims data

Abstract

Background: Before October 2015, pregnancy cohorts assembled from US health insurance claims have relied on medical encounters with International Classification of Diseases-ninth revision-clinical modification (ICD-9-CM) codes. We aimed to extend existing pregnancy identification algorithms into the ICD-10-CM era and evaluate performance. Methods: We used national private insurance claims data (2005–2018) to develop and test a pregnancy identification algorithm. We considered validated ICD-9-CM diagnosis and procedure codes that identify medical encounters for live birth, stillbirth, ectopic pregnancy, abortions, and prenatal screening to identify pregnancies. We then mapped these codes to the ICD-10-CM system using general equivalent mapping tools and reconciled outputs with literature and expert opinion. Both versions were applied to the respective coding period to identify pregnancies. We required 45 weeks of health plan enrollment from estimated conception to ensure the capture of all pregnancy endpoints. Results: We identified 7,060,675 pregnancy episodes, of which 50.1% met insurance enrollment requirements. Live-born deliveries comprised the majority (76.5%) of episodes, followed by abortions (20.3%). The annual prevalence for all pregnancy types was stable across the ICD transition period except for postterm pregnancies, which increased from 0.5% to 3.4%. We observed that ICD codes indicating gestational age were available for 86.8% of live-born deliveries in the ICD-10 era compared to 23.5% in the ICD-9 era. Patterns of prenatal tests remained stable across the transition period. Conclusion: Translation of existing ICD-9-CM pregnancy algorithms into ICD-10-CM codes provided reasonable consistency in identifying pregnancy episodes across the ICD transition period. New codes for gestational age can potentially improve the precision of conception estimates and minimize measurement biases.