Using network analysis to illuminate the intergenerational transmission of adversity in the ALSPAC cohort

Abstract

AbstractObjectiveThe 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).MethodsWe used data from the Avon Longitudinal Study of Parents and Children (ALSPAC) (n = 8 379) 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 were estimated to determine the shortest pathway from ACEs to infant outcomes, and to identify the risk factors that are most vital in bridging these variables.ResultsIn both models, childhood and prenatal risk factors were highly interrelated. Childhood physical abuse, but not threshold ACEs, was directly linked to LBW. Further, exposure to second-hand smoke, developing gestational hypertension, prenatal smoking, first time pregnancy, not being White, and older age were directly linked to LBW, while developing gestational diabetes, having previous pregnanc(ies), and lower educational attainment were associated with PTB. Only pre-eclampsia was directly linked to both outcomes. Network indices and shortest pathways plots indicate that sexual abuse played a central role in bridging ACEs to other risks and poor infant outcomes. Overall, prenatal smoking was determined as the most influential bridge node.ConclusionsAs child physical abuse was directly linked to low birthweight, and child sexual abuse and prenatal smoking were the most influential bridge nodes, they can be considered priority candidate targets for interventions to disrupt intergenerational risk transmission. Further, our study demonstrates the promise of network analysis as an approach for illuminating the intergenerational transmission of adversity in its full complexity.