Evidence map›Paper›PMID 41643636›Full record

ArticleEpidemiology and health2026

Evaluation of gestational age by pregnancy outcomes and distribution of pregnancy-related codes in Korean claims data.

Woo-Jung Kim, Yunha Noh, Yongtai Cho, Eun-Young Choi, HyunJoo Lim, Hyesung Lee, Ju-Young Shin

Abstract read
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Article in Epidemiology and health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Woo-Jung Kim *Department of Pharmaceutical Industry, Sungkyunkwan University School of Pharmacy, Suwon, Korea.
Yunha Noh *College of Pharmacy, Chonnam National University, Gwangju, Korea.
Yongtai ChoDepartment of Pharmacy, Sungkyunkwan University School of Pharmacy, Suwon, Korea.
Eun-Young ChoiDepartment of Pharmacy, Sungkyunkwan University School of Pharmacy, Suwon, Korea.
HyunJoo LimDepartment of Biohealth Regulatory Science, Sungkyunkwan University, Suwon, Korea.
Hyesung LeeDepartment of Medical Informatics, Kangwon National University College of Medicine, Chuncheon, Korea. hs.lee@kangwon.ac.kr.
Ju-Young ShinDepartment of Pharmacy, Sungkyunkwan University School of Pharmacy, Suwon, Korea. shin.jy@skku.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThis study aimed to evaluate a fixed-duration algorithm for gestational age (GA) estimation according to pregnancy outcomes and to describe the GA distribution of pregnancy-related codes in Korea.

methodsWe included 351,055 pregnancy episodes (2019-2022) from linked data between the National Health Insurance Service and the Korea Immunization Registry Information System (KIRIS). GA from claims data was estimated by subtracting fixed durations from the delivery date (algorithm-based GA), and GA derived from KIRIS was defined as the gold standard. Accuracy was evaluated as the proportion of episodes in which the difference between the estimated GA and the reference standard fell within ±2 weeks. We described the distributions of the GA at which each prenatal test, pregnancy complication, and diagnostic code was recorded.

resultsAlgorithm-based GA estimation showed high accuracy for live births (92.2% within ±2 weeks) but markedly lower accuracy for non-live birth outcomes, including stillbirth (3.3%), termination (7.2%), spontaneous abortion (45.2%), and ectopic pregnancy (20.0%). In additional analyses aimed at identifying potential indicators for improving GA estimation, most events occurred within clinically expected timeframes, although some individual codes exhibited poor temporal alignment.

conclusionsAlgorithm-based GA estimation using claims data performed well for live births but demonstrated limited accuracy for non-live birth outcomes. Incorporating information from prenatal tests and pregnancy complications may enhance GA estimation.

Indexed as

AlgorithmsGestational AgePregnancy OutcomeAdultFemaleHumansInsurance Claim ReviewPregnancyRepublic of KoreaAlgorithmsClaims dataGestational ageKoreaPregnancy outcomesPrenatal tests

Identifiers

PMID41643636
PMCPMC13033438

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