This study by Kazemi et al. examined the relationship between social determinants of health (SDOH) and the incidence of suspected child abuse (SCA) related to traumatic brain injury (TBI). Pediatric TBI is a leading cause of trauma-related mortality and long-term disability in children and adolescents, with SCA-related cases often requiring more intensive surgical intervention. This retrospective study reviewed medical records from 5977 cases at an urban Mid-Atlantic Pediatric ED and Level 1 Pediatric Trauma Center. TBI cases were identified using the CDC’s National Center for Injury Prevention and Control framework, applying International Classification of Diseases, 10th Revision codes. To evaluate SDOH measures, researchers used PLACES, a CDC initiative that includes nine measures: persons older than 65, lack of broadband internet subscription, crowding, housing cost burden, no high school diploma among adults older than 25, persons living below 150% of the poverty level, person of racial or ethnic minority status, single-parent households, and unemployment among people older than 16. To contextualize the predictive value of PLACES SDOH measures, they incorporated the Social Deprivation Index (SDI). The SDI was developed using factor analysis, which identified 7 indicators of possible SDOH: poverty, education, single-parent households, rental housing, overcrowding, vehicle access, and nonemployment among adults younger than 65 years. They used crude and multivariate regression models to evaluate associations between SDOH measures and SCA. Machine learning techniques, particularly eXtreme Gradient Boosting (XGBoost), were employed to identify the most influential SDOH features associated with SCA. The Synthetic Minority Over-sampling Technique (SMOTE) was used to address class imbalance, and 10-fold stratified cross-validation and Bayesian hyperparameter tuning enhanced model reliability. The study found that the following SDOH measures were significantly associated with higher odds of SCA: no broadband internet subscription among households, housing cost burden among households, and no high school diploma among adults 25 years and older, persons of racial or ethnic minority status, single-parent households, unemployment among people 16 years and older in the labor force, and SDI. Machine learning reinforced these findings, ranking over half of the significant measures as more influential than SDI scores and identified unemployment, crowded housing, and racial or ethnic minority status as the strongest contributors to SCA-related TBI. Overall, the study shows that SDOH measures can be used to identify children at increased risk of SCA and that machine learning can help determine and refine which measures most significantly predict SCA risk.