背景:种族/民族不同程度地暴露于慢性应激源可能有助于解释早产率的黑白不平等。然而,研究人员没有调查累积,互动式,以及慢性应激源暴露的人群特异性及其与早产可能的非线性关联。需要能够计算可能因种族/族裔而异的这种高维关联的模型。我们开发了慢性压力源的机器学习模型,可以更准确地预测早产,并在非西班牙裔黑人和非西班牙裔白人孕妇中识别慢性压力源和其他驱动早产风险的风险因素。
方法:开发了多变量自适应回归样条(MARS)模型,用于非西班牙裔黑人的早产预测,非西班牙裔白人,以及来自CDC妊娠风险评估监测系统数据(2012-2017年)的组合研究样本。对于每个样本群体,使用5倍交叉验证对MARS模型进行训练和测试。对于每个人口,ROC曲线下面积(AUC)用于评估模型性能,并计算了早产预测的变量重要性。
结果:在81,892名非西班牙裔黑人和277,963名非西班牙裔白人活产(加权样本)中,与组合模型相比,表现最好的MARS模型具有较高的准确性(AUC:0.754~0.765),种族/民族特异性模型的性能相似或更好.产前护理就诊的次数,胎膜早破,在预测人群早产方面,医疗条件比其他变量更为重要。慢性应激源(例如,低母亲教育和亲密伴侣暴力)及其相关因素仅预测非西班牙裔黑人妇女的早产。
结论:我们的研究结果强调,应针对慢性应激源等健康的中期或上游决定因素,以降低非西班牙裔黑人妇女的早产风险,并最终缩小持续的黑白关系在美国早产中的差距
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.
METHODS: Multivariate 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.
RESULTS: Among 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.
CONCLUSIONS: Our 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.