Evidence map›Paper›PMID 38909177›Full record

ArticleBMC pregnancy and childbirth2024

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

Sangmi Kim, Patricia A Brennan, George M Slavich, Vicki Hertzberg, Ursula Kelly, Anne L Dunlop

Abstract read
In one paragraph

Article in BMC pregnancy and childbirth, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Sangmi KimNell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA, USA. sangmi.kim@emory.edu.
Patricia A BrennanDepartment of Psychology, Emory University, Atlanta, GA, USA.
George M SlavichDepartment of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA.
Vicki HertzbergNell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA, USA.
Ursula KellyNell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA, USA.
Anne L DunlopDepartment of Gynecology and Obstetrics, School of Medicine, Emory University, Atlanta, GA, USA.

Funding

Race/Ethnicity-Specific Algorithms of Chronic Stress Exposures for Preterm Birth Risk: Machine Learning ApproachK01NR019651 · NINR · EMORY UNIVERSITY · PI KIM, SANGMI · 2022 to 2024
$441k
NINR NIH HHS K01 NR019651NINR NIH HHS K01NR019651
6 · The paper itself

Abstract

backgroundDifferential 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.

methodsMultivariate Adaptive Regression Splines (MARS) models were developed for preterm birth prediction for non-Hispanic Black, non-Hispanic White, and combined study samples derived from the CDC's Pregnancy Risk Assessment Monitoring System data (2012-2017). For each sample population, MARS models were trained and tested using 5-fold cross-validation. For each population, the Area Under the ROC Curve (AUC) was used to evaluate model performance, and variable importance for preterm birth prediction was computed.

resultsAmong 81,892 non-Hispanic Black and 277,963 non-Hispanic White live births (weighted sample), the best-performing MARS models showed high accuracy (AUC: 0.754-0.765) and similar-or-better performance for race/ethnicity-specific models compared to the combined model. The number of prenatal care visits, premature rupture of membrane, and medical conditions were more important than other variables in predicting preterm birth across the populations. Chronic stressors (e.g., low maternal education and intimate partner violence) and their correlates predicted preterm birth only for non-Hispanic Black women.

conclusionsOur study findings reinforce that such mid or upstream determinants of health as chronic stressors should be targeted to reduce excess preterm birth risk among non-Hispanic Black women and ultimately narrow the persistent Black-White gap in preterm birth in the U.S.

Indexed as

Black or African AmericanMachine LearningPremature BirthStress, PsychologicalWhiteAdultFemaleHumansPregnancyRisk AssessmentRisk FactorsUnited StatesWhite PeopleYoung AdultChronic stressMachine learningPRAMSPreterm birthRacial/ethnic disparity

Identifiers

PMID38909177
PMCPMC11193905

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