Using network analysis to illuminate the intergenerational transmission of adversity

<b>Objective:</b> 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). <b>Methods:</b> We used data from the Avon Longitudinal Study of Parents and Children (ALSPAC) (<i>n</i> = 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 &amp; 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. <b>Results:</b> 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 &amp; 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. We took a network approach to assessing links between ACEs and birth outcomes.ACEs, other prenatal risk factors, and birth outcomes had complex inter-connectionsHealth behaviours in pregnancy were indicated as optimal intervention targets. We took a network approach to assessing links between ACEs and birth outcomes. ACEs, other prenatal risk factors, and birth outcomes had complex inter-connections Health behaviours in pregnancy were indicated as optimal intervention targets.

**研究目的：** 母亲暴露于不良童年经历（Adverse Childhood Experiences, ACEs）所产生的影响，可通过多种生物心理社会机制传递至后代。然而，现有研究多聚焦于探索1至2条核心通路，对不同通路间的关联关注不足。本文采用网络分析方法，探究一系列可将母亲ACEs与婴儿早产（preterm birth, PTB）、低出生体重（low birthweight, LBW）关联起来的核心产前风险因素。

**研究方法：** 本研究使用雅芳父母与儿童纵向队列研究（Avon Longitudinal Study of Parents and Children, ALSPAC）的数据（样本量n=8379），构建了两类混合图形网络模型：模型1纳入不良婴儿结局、生物心理社会与环境风险因素、各类ACEs及社会人口学因素；模型2则将ACEs整合为阈值得分（≥4）。通过计算网络指标（即最短路径、桥梁预期影响力[1步与2步]），确定ACEs至婴儿结局的最短通路，并识别在激活其他风险因素与不良结局中发挥关键作用的风险因素。

**研究结果：** 网络分析结果显示，童年期与产前风险因素间形成了相互强化的网络，每一项风险均至少与另外两项风险存在关联。桥梁影响力指数表明，童年期躯体虐待与性虐待、以及多重ACEs与其他风险因素存在高度互联。总体而言，孕期健康风险行为（即吸烟与违禁药物使用）被界定为“活跃”风险因素，可直接或间接影响其他风险因素，并推动全局风险网络的持续激活。此类风险可作为干预措施的优先候选靶点，以阻断代际风险传递。本研究证实了网络分析方法在全面解析不良经历代际传递复杂性方面的应用价值。

本研究采用网络分析方法评估ACEs与出生结局间的关联。ACEs、其他产前风险因素与出生结局间存在复杂的互联关系。孕期健康风险行为被确定为最优干预靶点。

本研究采用网络分析方法评估ACEs与出生结局间的关联。ACEs、其他产前风险因素与出生结局间存在复杂的互联关系。孕期健康风险行为被确定为最优干预靶点。