Using Machine Learning to Identify Predictors of Maternal and Infant Hair Cortisol Concentration Before and During the COVID-19 Pandemic.

Hair cortisol concentration (HCC) has been theorized to reflect chronic stress, and maternal and infant HCC may be correlated due to shared genetic, physiological, behavioural, and environmental factors, such as stressful life circumstances. The current study examines HCC as a retrospective indicator of hypothalamic pituitary adrenal (HPA) axis activity in mothers and infants in the context of a major ecological stressor, the COVID-19 pandemic. First, we will compare HCC across two cohorts of mothers and their infants at 6 months postpartum. One cohort was recruited before the COVID-19 pandemic (N = 154; 77 mothers, 77 infants) and another cohort was recruited during the first wave of COVID-19 lockdowns in the United States (N = 120; 60 mothers, 60 infants). Next, we will apply machine learning to identify indicators of psychological stress that best predict maternal and infant HCC across these two cohorts. Our set of predictors will include pre- and postpartum measures of maternal perceived stress, parenting stress, and depressive symptoms. Finally, we will test for within-dyad covariation in mother-infant HCC and investigate whether covariation changes with respect to mothers' psychological stress or their experience of the pandemic. Our findings will inform research on hair cortisol as a measure of psychological stress across the peripartum window, particularly in the context of large-scale stressors.