Using network analysis to illuminate the intergenerational transmission of adversity

Area of Science:

• Perinatal epidemiology
• Developmental psychology
• Public health

Background:

• Maternal adverse childhood experiences (ACEs) can negatively affect offspring through complex biopsychosocial pathways.
• Previous research often examines isolated pathways, neglecting the interconnectedness of risks linking maternal ACEs to infant outcomes.
• A network approach is needed to understand the multifaceted transmission of adversity across generations.

Purpose of the Study:

• To explore the network of prenatal risk factors connecting maternal ACEs to infant preterm birth (PTB) and low birthweight (LBW).
• To identify key risk factors and pathways involved in the intergenerational transmission of adversity using a network analysis framework.

Main Methods:

• Utilized data from the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort (n=8379).
• Estimated mixed graphical network models to analyze relationships between ACEs, biopsychosocial factors, environmental risks, and infant outcomes.
• Calculated network indices (shortest path, bridge expected influence) to identify critical pathways and influential risk factors.

Main Results:

• Network analysis revealed a complex, mutually reinforcing web of childhood and prenatal risk factors.
• Childhood physical and sexual abuse, and multiple ACEs, showed high interconnectedness with other risks.
• Risky health behaviors during pregnancy (smoking, illicit drug use) emerged as critical 'active' factors influencing the entire risk network.

Conclusions:

• Network analysis effectively illuminates the complexity of intergenerational adversity transmission.
• Identifying interconnected risk factors, particularly maternal health behaviors, offers priority targets for interventions.
• Disrupting these pathways can help mitigate the long-term impact of maternal ACEs on infant health outcomes.