Evidence map›Paper›PMID 41848193›Full record

ArticleAmerican journal of epidemiology2026

A new tool for pregnancy research: a unified definition for major congenital malformation across ICD eras.

Thuy N Thai, Nicole E Smolinski, Sonja A Rasmussen, Junko Nagai, Thorben Kurzbach, Yanning Wang, Almut G Winterstein, Judith C Maro

Abstract read
In one paragraph

Article in American journal of epidemiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
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

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

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

8 authors.

Thuy N ThaiDepartment of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, MA, United States.ORCID 0000-0003-4544-1160
Nicole E SmolinskiDepartment of Pharmaceutical Outcomes and Policy, University of Florida, Gainesville, FL, United States.ORCID 0000-0002-0866-4579
Sonja A RasmussenDepartment of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD, United States.ORCID 0000-0002-0574-4928
Junko NagaiThe Office of Institutional Research, Meiji Pharmaceutical University, Tokyo, Japan.ORCID 0000-0002-9069-5752
Thorben KurzbachDepartment of Pharmaceutical Outcomes and Policy, University of Florida, Gainesville, FL, United States.ORCID 0009-0003-0264-9604
Yanning WangDepartment of Pharmaceutical Outcomes and Policy, University of Florida, Gainesville, FL, United States.ORCID 0000-0002-4263-3944
Almut G WintersteinDepartment of Pharmaceutical Outcomes and Policy, University of Florida, Gainesville, FL, United States.ORCID 0000-0002-6518-5961
Judith C MaroDepartment of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, MA, United States.ORCID 0000-0001-9900-2142

Funding

Big data apprOaches fOr Safe Therapeutics in Healthy Pregnancies (BOOST-HP)R01HD110107 · NICHD · HARVARD PILGRIM HEALTH CARE, INC. · PI MARO, JUDITH, WINTERSTEIN, ALMUT G · 2022 to 2025
$2.5M
National Institute of Child Health and Human Development (NICHD) R01HD110107NICHD NIH HHS R01 HD110107
6 · The paper itself

Abstract

Composite major congenital malformation (MCM) outcomes are commonly used to assess teratogenic effects of prenatal medication exposure, but this approach dilutes effect estimates when the risk is confined to a specific MCM. Tree-based scan statistics address this by screening outcomes using a hierarchical tree, enabling detection of specific risks without predefined hypotheses. To apply this method across ICD-9-CM and ICD-10-CM eras, we developed a unified hierarchical outcomes tree for MCM. We selected ICD-9-CM and ICD-10-CM codes classified as congenital anomalies, removing minor malformations, chromosomal anomalies, and single-gene conditions. A multilevel tree was built based on the Multi-level Clinical Classification Software, General Equivalence Mappings, and expert review. We validated the tree using birth cohorts from MarketScan and Medicaid databases (2011-2013; 2016-2018), assessing the balance of MCM prevalences within 1 year of birth via standardized mean differences (SMDs). The final tree included 1023 codes, organized into 244 clinical MCM groups at the most granular level. We identified 572 107 (2011-2013) and 360 167 infants (2016-2018) in MarketScan and 362 820 and 3 500 589 infants in Medicaid. All SMDs were below 0.1, indicating consistency across coding eras. This hierarchical MCM tree bridges ICD-9-CM and ICD-10-CM, enabling consistent outcome definitions and enhancing the detection of specific teratogenic risks.

Indexed as

Congenital AbnormalitiesInternational Classification of DiseasesAbnormalities, Drug-InducedFemaleHumansInfant, NewbornPregnancyUnited Statescongenital anomalyICD-10-CMICD-9-CMmalformationtree-based scan

Identifiers

PMID41848193
PMCPMC13419262

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.