Prediction of Cyanotic Congenital Heart Disease Risk in U.S. Births

Cyanotic congenital heart disease (CCHD) remains a major contributor to infant morbidity and mortality in the United States, yet the influence of maternal social determinants of health on risk is not fully understood. This study examined associations of maternal age, education, and race/ethnicity with the live birth prevalence of CCHD using recent national birth data from the Centers for Disease Control and Prevention (CDC) National Vital Statistics System (2022–2023). CCHD was identified from birth certificate records and analyzed as a binary outcome. Regression analyses were performed to evaluate relationships between maternal characteristics and CCHD occurrence. Overall, CCHD was a rare outcome with a modest decline in prevalence between the two years examined. Increasing maternal age was associated with higher odds of CCHD, while Latina ethnicity was associated with lower odds compared to the reference group. Other racial/ethnic categories and maternal education level were not significantly associated with CCHD risk in adjusted analyses. These findings suggest that certain maternal factors, particularly age and ethnicity, are associated with variation in the live birth prevalence of CCHD and underscore the need for further research into underlying environmental and structural contributors not captured in standard birth records.

Authors

Institutions

Publication Details

Journal
Journal of Cardiovascular Development and Disease
Published
2026-04-25
DOI
https://doi.org/10.3390/jcdd13050178
Primary Topic
Congenital Heart Disease Studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Prediction of Cyanotic Congenital Heart Disease Risk in U.S. Births

Frank W. Sellke, Riya Reddy, Marwan Saad
Journal of Cardiovascular Development and Disease
Congenital Heart Disease Studies
article

Prediction of Cyanotic Congenital Heart Disease Risk in U.S. Births

Frank W. Sellke, Riya Reddy, Marwan Saad
article en

Abstract

Cyanotic congenital heart disease (CCHD) remains a major contributor to infant morbidity and mortality in the United States, yet the influence of maternal social determinants of health on risk is not fully understood. This study examined associations of maternal age, education, and race/ethnicity with the live birth prevalence of CCHD using recent national birth data from the Centers for Disease Control and Prevention (CDC) National Vital Statistics System (2022–2023). CCHD was identified from birth certificate records and analyzed as a binary outcome. Regression analyses were performed to evaluate relationships between maternal characteristics and CCHD occurrence. Overall, CCHD was a rare outcome with a modest decline in prevalence between the two years examined. Increasing maternal age was associated with higher odds of CCHD, while Latina ethnicity was associated with lower odds compared to the reference group. Other racial/ethnic categories and maternal education level were not significantly associated with CCHD risk in adjusted analyses. These findings suggest that certain maternal factors, particularly age and ethnicity, are associated with variation in the live birth prevalence of CCHD and underscore the need for further research into underlying environmental and structural contributors not captured in standard birth records.

Journal of Cardiovascular Development and DiseaseVol. 13(5)
Brown University (US), Rhode Island Hospital (US)
Good health and well-being
Openalex Percentile: Top 6%
Congenital Heart Disease Studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.