Background: Race is commonly used as a proxy for multiple features including socioeconomic status. It is critical to dissociate these factors, to identify mechanisms that affect infant outcomes, such as birth weight, gestational age, and brain development, and to direct appropriate interventions and shape public policy.
Methods: Demographic, socioeconomic, and clinical variables were used to model infant outcomes. There were 351 participants included in the analysis for birth weight and gestational age. For the analysis using brain volumes, 280 participants were included after removing participants with missing magnetic resonance imaging scans and those matching our exclusion criteria. We modeled these three different infant outcomes, including infant brain, birth weight, and gestational age, with both linear and nonlinear models.
Results: Nonlinear models were better predictors of infant birth weight than linear models (R2 = 0.172 vs. R2 = 0.145, p = .005). In contrast to linear models, nonlinear models ranked income, neighborhood disadvantage, and experiences of discrimination higher in importance than race while modeling birth weight. Race was not an important predictor for either gestational age or structural brain volumes.
Conclusions: Consistent with the extant social science literature, the findings related to birth weight suggest that race is a linear proxy for nonlinear factors related to structural racism. Methods that can disentangle factors often correlated with race are important for policy in that they may better identify and rank the modifiable factors that influence outcomes.
Keywords: Birth weight; Brain volumes; Gestational age; Linear regression; Machine learning; Neural networks; Race; Socioeconomic status.
© 2023 The Authors.