Evidence map›Paper›PMID 38770996›Full record

ArticleAmerican journal of medical genetics. Part A2024

Classification of isolated versus multiple birth defects: An automated process for population-based registries.

Renata H Benjamin, Joanne M Nguyen, Margaret Drummond-Borg, Angela E Scheuerle, Peter H Langlois, Mark A Canfield, Charles J Shumate, Laura E Mitchell, A J Agopian

Abstract read
In one paragraph

Article in American journal of medical genetics. Part A, 2024. 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

9 authors.

Renata H BenjaminDepartment of Epidemiology, Human Genetics, and Environmental Sciences, The University of Texas Health Science Center at Houston (UTHealth) School of Public Health, Houston, Texas, USA.ORCID 0000-0001-6406-4247
Joanne M NguyenBirth Defects Epidemiology and Surveillance Branch, Texas Department of State Health Services, Austin, Texas, USA.
Margaret Drummond-BorgBirth Defects Epidemiology and Surveillance Branch, Texas Department of State Health Services, Austin, Texas, USA.
Angela E ScheuerleDivision of Genetics and Metabolism, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, Texas, USA.ORCID 0000-0002-9025-3276
Peter H LangloisDepartment of Epidemiology, Human Genetics, and Environmental Sciences, UTHealth School of Public Health at Austin, Austin, Texas, USA.
Mark A CanfieldDepartment of Epidemiology, Human Genetics, and Environmental Sciences, UTHealth School of Public Health at Austin, Austin, Texas, USA.
Charles J ShumateBirth Defects Epidemiology and Surveillance Branch, Texas Department of State Health Services, Austin, Texas, USA.
Laura E MitchellDepartment of Epidemiology, Human Genetics, and Environmental Sciences, The University of Texas Health Science Center at Houston (UTHealth) School of Public Health, Houston, Texas, USA.ORCID 0000-0003-1151-5096
A J AgopianDepartment of Epidemiology, Human Genetics, and Environmental Sciences, The University of Texas Health Science Center at Houston (UTHealth) School of Public Health, Houston, Texas, USA.ORCID 0000-0002-5874-4155

Funding

A multidisciplinary approach for identifying and characterizing novel congenital malformation syndromesR01HD093660 · NICHD · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI AGOPIAN, A.J., LUPO, PHILIP · 2018 to 2022
$2.0M
Integrating Epidemiologic and Genomic Data to Elucidate the Genetic Overlap Between Congenital Anomalies and Pediatric CancerR01CA284531 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Chad Daniel Huff, Philip Lupo · 2023 to 2026
$1.9M
Centers for Disease Control and Prevention (CDC) NU50DD000102Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) 5R01HD093660HRSA HHSNCI NIH HHS R01 CA284531NICHD NIH HHS R01 HD093660Texas Department of State Health Services (TX DSHS) HHS000962600001Texas Department of State Health Services (TX DSHS) HHS00117790001
6 · The paper itself

Abstract

Epidemiologic studies of birth defects often conduct separate analyses for cases that have isolated defects (e.g., spina bifida only) and cases that have multiple defects (e.g., spina bifida and a congenital heart defect). However, in some instances, cases with additional defects (e.g., spina bifida and clubfoot) may be more appropriately considered as isolated because the co-occurring defect (clubfoot) is believed to be developmentally related to the defect of interest. Determining which combinations should be considered isolated can be challenging and potentially resource intensive for registries. Thus, we developed automated classification procedures for differentiating between isolated versus multiple defects, while accounting for developmentally related defects, and applied the approach to data from the Texas Birth Defects Registry (1999-2018 deliveries). Among 235,544 nonsyndromic cases in Texas, 89% of cases were classified as having isolated defects, with proportions ranging from 25% to 92% across 43 specific defects analyzed. A large proportion of isolated cases with spina bifida (44%), lower limb reduction defects (44%), and holoprosencephaly (32%) had developmentally related defects. Overall, our findings strongly support the need to account for isolated versus multiple defects in risk factor association analyses and to account for developmentally related defects when doing so, which has implications for interpreting prior studies.

Indexed as

RegistriesSpinal DysraphismAbnormalities, MultipleClubfootCongenital AbnormalitiesFemaleHeart Defects, CongenitalHumansInfant, NewbornMaleTexasalgorithmsbirth defectscase classificationcongenital abnormalitiesregistries

Identifiers

PMID38770996
PMCPMC11502279

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Registered trials

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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.