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Updated: Jun 19, 2025

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Black-White Differences in Chronic Stress Exposures to Predict Preterm Birth: Interpretable, Race/Ethnicity-Specific

Sangmi Kim1, Melinda K Higgins1, Patricia Brennan2

  • 1Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA, USA.

Studies in Health Technology and Informatics
|July 25, 2024
PubMed
Summary

Machine learning models accurately predict preterm birth (PTB) risk. Models identified key factors, including chronic stressors like low education and violence, particularly for non-Hispanic Black women, enabling race-specific interventions.

Keywords:
Chronic stressPRAMSdisparitymachine learningpreterm birth

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Area of Science:

  • Reproductive Health
  • Machine Learning in Healthcare
  • Health Disparities

Background:

  • Preterm birth (PTB) remains a significant public health concern with complex contributing factors.
  • Existing predictive models may not fully capture the nuances of PTB risk across different racial/ethnic groups.
  • Identifying specific chronic stressors is crucial for targeted PTB prevention strategies.

Purpose of the Study:

  • To develop accurate machine learning models for predicting preterm birth (PTB).
  • To identify key predictors of PTB, distinguishing between non-Hispanic (N-H) Black and N-H White women.
  • To pinpoint the influence of chronic stressors on PTB risk within these populations.

Main Methods:

  • Multivariate Adaptive Regression Splines (MARS) machine learning models were developed.
  • Models utilized data from the Pregnancy Risk Assessment Monitoring System (2012-2017).
  • Model performance was assessed using 5-fold cross-validation and Area Under the Curve (AUC).

Main Results:

  • MARS models demonstrated high predictive accuracy for PTB (AUC: 0.754-0.765).
  • Key predictors across populations included prenatal care visits, premature rupture of membranes, and medical conditions.
  • Chronic stressors (e.g., low maternal education, violence) significantly impacted PTB prediction specifically for N-H Black women.

Conclusions:

  • Interpretable, race/ethnicity-specific MARS models accurately predict PTB.
  • The models elucidate the magnitude of effect of life stressors on PTB risk.
  • Findings support the development of tailored interventions addressing social determinants of health for PTB prevention.