(413) Differential exposure to antenatal steroids by sociodemographic factors among late preterm births

To determine if antenatal steroid exposure among late preterm births varies based on a patient’s sociodemographic characteristics.

Study Design:

This is a cross-sectional analysis using the 2021 US natality data. The primary outcome was receipt of steroids among singleton late preterm births (34-36 weeks of gestation). The rates of steroid exposure were compared among the following sociodemographic factors: age, race, ethnicity, primary payer, and education.  A multivariable logistic regression model was constructed with all the sociodemographic factors and clinical factors that were hypothesized to potentially influence clinical decision-making around steroid use, including gestational age, parity, fetal sex, chronic and pregnancy-related hypertension, and pre-existing and diabetes. Models used cluster robust standard errors based on county of birth.

Results:

In 2021, there were 279,305 singleton late preterm births, of which 17.5% were exposed to antenatal steroids. The rates of steroid exposure varied by all examined sociodemographic factors, with the largest range observed for payer (12.5% for self-pay to 20.1% for private insurance) (Table). In the multivariable model that accounted for clinical factors, there were no differences by maternal age; however, other sociodemographic factors were associated with steroid exposure (adjusted odds ratios shown in Table). Compared with White, Non-Hispanic, and privately individuals, odds of steroid exposure were lower in most other racial groups, in Hispanic individuals, and in non-commercially insurance patients. Higher levels of education were associated with higher odds of steroid exposure.

Conclusion: Despite no recommendations to consider sociodemographic factors when offering the intervention, steroid exposure among late preterm infants varied significantly by these observed factors. Additional work to understand the drivers of these differences, such as physician, hospital, or regional factors, can inform policy solutions to eliminate potential disparities.