Black-white differences in chronic stress exposures to predict preterm birth: interpretable, race/ethnicity-specific machine learning model.

Journal: BMC pregnancy and childbirth
PMID: 38909177

Abstract

BACKGROUND: Differential exposure to chronic stressors by race/ethnicity may help explain Black-White inequalities in rates of preterm birth. However, researchers have not investigated the cumulative, interactive, and population-specific nature of chronic stressor exposures and their possible nonlinear associations with preterm birth. Models capable of computing such high-dimensional associations that could differ by race/ethnicity are needed. We developed machine learning models of chronic stressors to both predict preterm birth more accurately and identify chronic stressors and other risk factors driving preterm birth risk among non-Hispanic Black and non-Hispanic White pregnant women.

Authors

  • Sangmi Kim
    Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA, United States.
  • Patricia A Brennan
    Department of Psychology, Emory University, Atlanta, GA, USA.
  • George M Slavich
    Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA.
  • Vicki Hertzberg
    Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA, USA.
  • Ursula Kelly
    Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA, USA.
  • Anne L Dunlop
    Department of Gynecology and Obstetrics, School of Medicine, Emory University, Atlanta, GA, USA.

Keywords

External Resources

Popular Topics
Recent Journals