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Developing a Hierarchical Algorithm to Identify Pregnancies and Determine Gestational Age from Nationwide Linked
Miyuki Hsing-Chun Hsieh1,2,3, Zoe Chi-Jui Chang1,2, Chih-Wan Lin4
1School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan City, 70101, Taiwan.
Drug Safety
|June 20, 2026
Summary
A new algorithm accurately identifies pregnancies and estimates gestational age using Taiwan health data. This enables crucial research into medication safety during pregnancy.
Area of Science:
- Health Informatics
- Reproductive Epidemiology
- Pharmacoepidemiology
Background:
- Administrative health data are crucial for medication safety research during pregnancy.
- Taiwan's National Health Insurance Research Database (NHIRD) and Birth Certificate Application (BCA) lack a standardized method for integrating pregnancy data.
- Accurate identification of pregnancy episodes and gestational age (GA) is essential for reliable analysis.
Purpose of the Study:
- To develop a hierarchical algorithm for identifying pregnancies and estimating gestational age (GA) using linked Taiwanese health claims and birth registry data.
- To create a standardized, reproducible framework for pregnancy data analysis in Taiwan.
- To facilitate future research on medication safety during pregnancy.
Main Methods:
- Adapted an ICD-10-CM/PCS-based algorithm to the Taiwanese context, incorporating clinical input and local practices.
- Developed a seven-step hierarchical algorithm for classifying pregnancy outcomes, defining episodes, estimating start dates, and validating GA.
- Linked claims data with the Birth Certificate Application (BCA) database for GA refinement and outcome validation.
Main Results:
- Successfully identified 1,696,229 pregnancies from 1,169,779 women between 2016-2022.
- The algorithm accurately classified pregnancy outcomes (e.g., live birth, spontaneous abortion, ectopic pregnancy).
- Outcome distributions aligned with national statistics, validating the algorithm's performance.
Conclusions:
- The developed hierarchical algorithm offers a transparent and reproducible method for pregnancy identification and GA estimation in Taiwan.
- This framework is foundational for future real-world studies on medication safety in pregnant populations.
- The study addresses a critical gap in utilizing administrative health data for reproductive health research.