Using network analysis to illuminate the intergenerational transmission of adversity

Objective: The effects of maternal exposure to adverse childhood experiences (ACEs) may be transmitted to subsequent generations through various biopsychosocial mechanisms. However, studies tend to focus on exploring one or two focal pathways with less attention paid to links between different pathways. Using a network approach, this paper explores a range of core prenatal risk factors that may link maternal ACEs to infant preterm birth (PTB) and low birthweight (LBW). Methods: We used data from the Avon Longitudinal Study of Parents and Children (ALSPAC) (n = 8379) to estimate two mixed graphical network models: Model 1 was constructed using adverse infant outcomes, biopsychosocial and environmental risk factors, forms of ACEs, and sociodemographic factors. In Model 2, ACEs were combined to represent a threshold ACEs score (>= 4). Network indices (i.e., shortest path and bridge expected influence [1-step & 2-step]) were estimated to determine the shortest pathway from ACEs to infant outcomes, and to identify the risk factors that are vital in activating other risk factors and adverse outcomes. Results: Network analyses estimated a mutually reinforcing web of childhood and prenatal risk factors, with each risk connected to at least two other risks. Bridge influence indices suggested that childhood physical and sexual abuse and multiple ACEs were highly interconnected to others risks. Overall, risky health behaviours during pregnancy (i.e., smoking & illicit drug use) were identified as 'active' risk factors capable of affecting (directly and indirectly) other risk factors and contributing to the persistent activation of the global risk network. These risks may be considered priority candidate targets for interventions to disrupt intergenerational risk transmission. Our study demonstrates the promise of network analysis as an approach for illuminating the intergenerational transmission of adversity in its full complexity.