Evidence map›Paper›PMID 42323503›Full record

ArticleDrug safety2026

Developing a Hierarchical Algorithm to Identify Pregnancies and Determine Gestational Age from Nationwide Linked Health Data in Taiwan.

Miyuki Hsing-Chun Hsieh, Zoe Chi-Jui Chang, Chih-Wan Lin, Brian Meng-Hsun Li, Edward Chia-Cheng Lai, Wan-Ting Huang

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Article in Drug safety, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Miyuki Hsing-Chun HsiehSchool of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan City, 70101, Taiwan.ORCID http://orcid.org/0000-0002-7995-7408
Zoe Chi-Jui ChangSchool of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan City, 70101, Taiwan.ORCID http://orcid.org/0009-0003-9351-603X
Chih-Wan LinDivision of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA.ORCID http://orcid.org/0009-0008-8236-9392
Brian Meng-Hsun LiSchool of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan City, 70101, Taiwan.ORCID http://orcid.org/0009-0007-7445-8806
Edward Chia-Cheng LaiSchool of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan City, 70101, Taiwan.ORCID http://orcid.org/0000-0002-5852-7652
Wan-Ting HuangGlobal Health Program, College of Public Health, National Taiwan University, Taipei City, 10055, Taiwan. huangwt@g.ntu.edu.tw.ORCID http://orcid.org/0000-0002-4344-9567

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAdministrative health data are vital for investigating medication safety during pregnancy. In Taiwan, while the National Health Insurance Research Database (NHIRD) and Birth Certificate Application (BCA) capture pregnancy data, no standardized approach exists for integrating these sources to identify episodes and assign gestational age (GA). This study aimed to develop a hierarchical pregnancy-identification algorithm tailored to Taiwan's linked claims and birth registry data.

methodsWe adapted an ICD-10-CM/PCS-based algorithm to the Taiwanese coding environment, incorporating clinician input, local billing practices, and birth registry information. The algorithm refines pregnancy outcome classification, estimates pregnancy start dates, and assigns GA. As a proof of concept, we applied the final algorithm to 2016-2022 data to summarize outcome distributions.

resultsWe developed a seven-step hierarchical algorithm that uses claims codes to classify pregnancy outcomes, groups records into episodes using spacing rules, assigns pregnancy start dates from ordered prenatal services, validates and refines GA via linkage to the BCA database, determines the number of pregnancies and fetuses, and checks the plausibility of GA and inter-pregnancy intervals. A total of 1,696,229 pregnancies contributed by 1,169,779 women were identified; outcome distributions (69.3% live birth, 16.3% spontaneous abortion, 5.7% elective abortion, 6.0% ectopic pregnancy, 1.4% delivery with unknown outcome, 1.1% stillbirth, 0.2% trophoblastic and other abnormal products of conception) were consistent with national statistics and prior literature.

conclusionsThis hierarchical algorithm provides a transparent, reproducible framework for identifying pregnancies and estimating GA in Taiwan. It establishes a critical foundation for future real-world studies of medication safety during pregnancy.

Indexed as

AlgorithmsGestational AgeAdultBirth CertificatesDatabases, FactualFemaleHumansPregnancyPregnancy OutcomeRegistriesTaiwan

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.