Early antenatal risk factors for births before arrival: An unmatched case–control study

• Published in: Acta obstetricia et gynecologica Scandinavica Vol. 103; no. 2; pp. 294 - 303
• Main Authors: Hubble, Talia Rose, Nair, Manisha, Aye, Christina Y. L., Mathewlynn, Sam, Greenwood, Catherine, Impey, Lawrence
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.02.2024; John Wiley and Sons Inc
• Subjects: Birth; births before arrival; Case-Control Studies; Discriminant analysis; Female; Health risks; Humans; Infant, Newborn; maternal health; Original; Pregnancy; Pregnancy complications; Prenatal Care; Risk Factors; social factors; social factors; maternal health; births before arrival
• ISSN: 0001-6349; 1600-0412
• DOI: 10.1111/aogs.14720

Introduction Birth before arrival is associated with maternal morbidity and neonatal morbidity and mortality. Yet, timely risk stratification remains challenging. Our objective was to identify risk factors for birth before arrival which may be determined at the first antenatal appointment. Material and methods This was an unmatched case–control study involving 37 348 persons who gave birth at a minimum of 22+0 weeks' gestation over a 5‐year period from January 2014 to October 2019 (IRAS project ID 222260; REC reference: 17/SC/0374). The setting was a large UK university hospital. Data obtained on maternal characteristics at booking was examined for association with birth before arrival using a stepwise multivariable logistic regression analysis. Data are presented as adjusted odds ratios with 95% confidence intervals. Area under the receiver‐operator characteristic curves (C‐statistic) were employed to enable discriminant analysis assessing the risk prediction of the booking data on the outcome. Results Multivariable analysis identified significant independent predictors of birth before arrival that were detectable at booking: parity, ethnicity, multiple deprivation, employment status, timing of booking, distance from home to the nearest maternity unit, and safeguarding concerns raised at booking by clinical staff. Our model demonstrated good discrimination for birth before arrival; together, the predictors accounted for 77% of the data variance (95% confidence interval 0.74–0.80). Conclusions Information gathered routinely at booking may discriminate individuals at risk for birth before arrival. Better recognition of early factors may enable maternity staff to direct higher‐risk women towards specialized care services at an early point in their pregnancy, enabling time for clinical and social interventions. Birth before arrival is a cause of adverse obstetric outcomes. Its risk may be predicted by information provided by pregnant women in their first antenatal appointment.