Health Care Disparities in Children:
A Joint Article Collection from Journal of Pediatric Health Care and The Journal of Pediatrics
The COVID-19 pandemic has highlighted the effects of toxic stresses in our society that stem from systematic and structural racism and inequities. Such stresses include poverty, homelessness, disabilities, unemployment, civil unrest, food insecurity, substance abuse, social isolation and limited access to care. The purpose of this virtual issue compiled by The Journal of Pediatrics and the Journal of Pediatric Health Care is to further name and explicate health care disparities in children and youth associated with toxic stress, and to highlight approaches to reduce such inequities.
- Machine learning holds the possibility of improving racial health inequalities by compensating for human bias and structural racism. However, unanticipated racial biases may enter during model design, training, or implementation and perpetuate or worsen racial inequalities if ignored. Pre-existing racial health inequalities could be codified into medical care by machine learning without clinicians being aware. To illustrate the importance of a commitment to antiracism at all stages of machine learning, we examine machine learning in predicting severe sepsis in Black children, focusing on the impacts of structural racism that may be perpetuated by machine learning and difficult to discover.