Appropriate definition of childbirth using Japanese administrative database: A cross-sectional cohort validation study

Background Claims data analysis is useful in clinical research. However, no validation studies have been conducted using established algorithms to define childbirth among women. The aim of this study was to establish and validate algorithms to define childbirth from a claims database. Methods The DeSC database, including claims data for approximately 13 million people, as well as parent–child identifiers (IDs) as family information obtained from insurers, was used. Seven algorithms were designed using combinations of diagnosis-related codes with a suspected flag for childbirth (A), diagnosis-related codes without a suspected flag (B), and medical procedure codes (C). The combinations were A, B, C, A and/or C, and B and/or C. Parent–child IDs were used to determine the mother’s month and year of childbirth based on the child’s month and year of birth. The gold standard for the month and year of childbirth was defined as the child’s month and year of birth among women aged 15–49 years linked by parent–child IDs during the observation period. We calculated sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), Kappa Index, and Youden Index for each algorithm. To validate algorithms for estimating second childbirth during the observation period, which would become useful and valuable in defining childbirth, identification of second childbirth was started 2–24 months after the first, given that the average age difference was two years. Results A total of 854,626 women were included in this study, of whom 37,934 were aged 15–49 years at the time of parent–child ID assignment and classified as experiencing childbirth during the observation period. The algorithm with the highest value was “A or C” (Kappa Index: 0.69, sensitivity: 65.8%, specificity: 99.0%, PPV: 74.4%, and NPV: 98.4%). For second childbirth, algorithm “A or C” showed that 11-month difference had the highest Youden Index at 0.551. Conclusion We developed algorithms based on claims data and established an optimal algorithm for estimating childbirth. This validated algorithm can be used for accurate estimation of childbirth to clarify pregnancy- and childbirth-related diseases in future claims database studies.