Comparing approaches to identify live births using the Transformed Medicaid Statistical Information System

Abstract

Objective: To evaluate the performance of different approaches for identifying live births using Transformed Medicaid Statistical Information System Analytic Files (TAF).

Data sources: The primary data source for this study were TAF inpatient (IP), other services (OT), and demographic and eligibility files. These data contain administrative claims for Medicaid enrollees in all 50 states and the District of Columbia from January 1, 2018 to December 31, 2018.

Study design: We compared five approaches for identifying live birth counts obtained from the TAF IP and OT data with the Centers for Disease Control and Prevention (CDC) Natality data-the gold standard for birth counts at the state level.

Data collection/extraction methods: The five approaches used varying combinations of diagnosis and procedure, revenue, and place of service codes to identify live births. Approaches 1 and 2 follow guidance developed by the Centers for Medicare and Medicaid Services (CMS). Approaches 3 and 4 build on the approaches developed by CMS by including all inpatient hospital claims in the OT file and excluding codes related to delivery services for infants, respectively. Approach 5 applied Approach 4 to only the IP file.

Principal findings: Approach 4, which included all inpatient hospital claims in the OT file and excluded codes related to infants to identify deliveries, achieved the best match of birth counts relative to CDC birth record data, identifying 1,656,794 live births-a national overcount of 3.6%. Approaches 1 and 3 resulted in larger overcounts of births (20.5% and 4.5%), while Approaches 2 and 5 resulted in undercounts of births (-3.4% and -6.8%).

Conclusions: Including claims from both the IP and OT files, and excluding codes unrelated to the delivery episode and those specific to services rendered to infants improves accuracy of live birth identification in the TAF data.

Keywords: Medicaid; administrative data uses; maternal and perinatal care and outcomes; obstetrics/gynecology.