Intimate Partner Homicide Among Women of Childbearing Age: Identifying Multilevel Risk Factors with Machine Learning.

Abstract

Intimate partner homicide (IPH) remains a major yet understudied cause of maternal mortality among U.S. women of childbearing age (WCBA). We leveraged the National Violent Death Reporting System (NVDRS) and county-level Maternal Vulnerability Index (MVI) data from 2018-2022 to train three machine learning models-logistic regression, random forest, and XGBoost-to classify whether homicides were IPH. Among 11,498 homicides involving WCBA, 33% were IPH. XGBoost achieved the best performance (F1-score = 0.83, AUPRC = 0.87), prompting further examination of key predictors via model explainability. Results indicated that acute interpersonal conflicts (e.g., arguments, jealousy), prior IPV victimization, and structural vulnerabilities (e.g., reproductive healthcare access, physical environment) were influential predictors of IPH. By illustrating the interplay of individual, interpersonal, and broader community-level risk factors, our study shows how machine learning can inform multilevel strategies to prevent IPH and improve maternal health.