Predictors of Preterm Delivery in the COVID-19 Era: A Retrospective Cohort Study of a Large, Integrated HealthSystem
Year: 2026
Type: article
Abstract: INTRODUCTION: Preterm delivery is the leading cause of perinatal mortality and long-term neonatal morbidity in the United States, and rates are rising. Research has shown that COVID-19 infection may increase the possibility of preterm delivery. However, these studies disproportionately focus on third trimester and iatrogenic cases, often overlooking risk across the entire pregnancy or without adjusting for prior preterm delivery and sociodemographic factors. To address these gaps, the authors investigated the association between COVID-19 at any point in pregnancy and preterm delivery while exploring other predictors of preterm delivery. METHODS: tests, the authors explored associations between preterm delivery and other sociodemographic and clinical characteristics. Data were abstracted from electronic medical records. RESULTS: Of 14,250 participants, 994 (7%) experienced COVID-19 infection during pregnancy. Overall, 1313 (9%) had preterm delivery. In univariate logistic regression analyses, those with COVID-19 were more likely to be < 35 years old, Hispanic or Black, non-English-speaking, obese, and multiparous. COVID-19 infection during pregnancy was associated with increased odds of preterm delivery in the authors' unadjusted model but not in the multivariable adjusted model. Being ≥ 35 years old, Hispanic, Black, nulliparous, hypertensive, immunocompromised, or having a prior preterm delivery or gestational or chronic diabetes were significantly associated with higher odds of preterm delivery in the authors' multivariable model. CONCLUSION: COVID-19 infection throughout pregnancy was not associated with increased odds of preterm delivery. Racial disparities and other key covariates like history of preterm delivery may confound the reported increase in preterm delivery.
Source: The Permanente Journal
DOI: 10.7812/tpp/26.024
Institution: Kaiser Permanente
Language: English
Subfield: Obstetrics and Gynecology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W7225811014
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