The impact of COVID‐19 infection on neonatal birth weight and outcomes

Abstract Background Since its outbreak in 2019, the novel severe acute respiratory syndrome coronavirus‐2 (COVID‐19) outbreak has had a devastating impact worldwide. Pregnancy has been shown to worsen the clinical outcome in those affected with COVID‐19 infection. While data demonstrate poorer maternal outcomes in those infected with COVID‐19, published studies on neonatal outcomes after antenatal maternal infection are conflicting—specifically as it relates to neonatal birth weight. We hypothesize that maternal COVID‐19 infection increases the risk for small‐for‐gestational age (SGA) neonatal birth weight. Objective (1) To estimate the association between antenatal maternal COVID‐19 infection and the risk for SGA and (2) to assess whether gestational timing of infection or severity of COVID‐19 infection differentially impacts the risk of SGA. Study design This was a retrospective cohort study that included all pregnant patients with COVID‐19 infection and that randomly selected uninfected controls from October 2020 to June 2022 at a single tertiary care center. Multivariable logistic regression was performed to estimate the independent effect of maternal COVID‐19 infection on SGA neonatal birth weight. We used similar logistical regression models to estimate the independent effect of COVID‐19 exposure on SGA stratified by trimester and COVID‐19 severity. Results Of 1011 patients in the study, 502 patients had COVID‐19 infection. Overall, COVID‐19 infection was not associated with an increased risk of SGA, adjusting for confounding by maternal age, hypertension, pre‐gravid body mass index (BMI), and race. However, first trimester infection was associated with a twofold increased risk in SGA (adjusted odds ratio [aOR], 2.3; 95% confidence interval [CI], 1.1–5.1), compared to uninfected controls, while infection in the second and third trimesters was not associated with SGA. Moderate/severe disease was associated with a twofold increased risk of SGA (aOR, 2.1; 95% CI, 1.1–3.5) compared to uninfected controls. Conclusion While COVID‐19 infection was not uniformly associated with SGA in the whole cohort, individuals with moderate/severe disease or those exposed in the first trimester had an increased risk of delivering an SGA neonate. Fetal growth surveillance for COVID‐19 infection can be reserved for those with moderate/severe infection or those infected in the first trimester.

Authors

Institutions

Publication Details

Journal
Pregnancy
Published
2025-11-01
DOI
https://doi.org/10.1002/pmf2.70137
Primary Topic
COVID-19 Impact on Reproduction
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The impact of COVID‐19 infection on neonatal birth weight and outcomes

Advaita Punjala‐Patel, Austin French, Rebekah Lassiter, Sophia Alvarado et al.
Pregnancy
COVID-19 Impact on Reproduction
article

The impact of COVID‐19 infection on neonatal birth weight and outcomes

Advaita Punjala‐Patel, Austin French, Rebekah Lassiter, Sophia Alvarado, David M. Stamilio
article en

Abstract

Abstract Background Since its outbreak in 2019, the novel severe acute respiratory syndrome coronavirus‐2 (COVID‐19) outbreak has had a devastating impact worldwide. Pregnancy has been shown to worsen the clinical outcome in those affected with COVID‐19 infection. While data demonstrate poorer maternal outcomes in those infected with COVID‐19, published studies on neonatal outcomes after antenatal maternal infection are conflicting—specifically as it relates to neonatal birth weight. We hypothesize that maternal COVID‐19 infection increases the risk for small‐for‐gestational age (SGA) neonatal birth weight. Objective (1) To estimate the association between antenatal maternal COVID‐19 infection and the risk for SGA and (2) to assess whether gestational timing of infection or severity of COVID‐19 infection differentially impacts the risk of SGA. Study design This was a retrospective cohort study that included all pregnant patients with COVID‐19 infection and that randomly selected uninfected controls from October 2020 to June 2022 at a single tertiary care center. Multivariable logistic regression was performed to estimate the independent effect of maternal COVID‐19 infection on SGA neonatal birth weight. We used similar logistical regression models to estimate the independent effect of COVID‐19 exposure on SGA stratified by trimester and COVID‐19 severity. Results Of 1011 patients in the study, 502 patients had COVID‐19 infection. Overall, COVID‐19 infection was not associated with an increased risk of SGA, adjusting for confounding by maternal age, hypertension, pre‐gravid body mass index (BMI), and race. However, first trimester infection was associated with a twofold increased risk in SGA (adjusted odds ratio [aOR], 2.3; 95% confidence interval [CI], 1.1–5.1), compared to uninfected controls, while infection in the second and third trimesters was not associated with SGA. Moderate/severe disease was associated with a twofold increased risk of SGA (aOR, 2.1; 95% CI, 1.1–3.5) compared to uninfected controls. Conclusion While COVID‐19 infection was not uniformly associated with SGA in the whole cohort, individuals with moderate/severe disease or those exposed in the first trimester had an increased risk of delivering an SGA neonate. Fetal growth surveillance for COVID‐19 infection can be reserved for those with moderate/severe infection or those infected in the first trimester.

PregnancyVol. 1(6)
Atrium Health Wake Forest Baptist (US), Wake Forest University (US)
Openalex Percentile: Top 57%
COVID-19 Impact on Reproduction
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.