Mexican School of Public Health, National Institute of Public Health, Cuernavaca, Morelos, Mexico.
Department of Epidemiology, Boston University School of Public Health, Boston, MA, United States.
Child Abuse Negl. 2023 Sep;143:106328. doi: 10.1016/j.chiabu.2023.106328. Epub 2023 Jun 26.
Research on the effect of adverse childhood experiences (ACEs) on adult outcomes has typically relied on retrospective assessment of ACEs and cumulative scores. However, this approach raises methodological challenges that can limit the validity of findings.
The aims of this paper are 1) to present the value of directed acyclic graphs (DAGs) to identify and mitigate potential problems related to confounding and selection bias, and 2) to question the meaning of a cumulative ACE score.
Adjusting for variables that post-date childhood could block mediated pathways that are part of the total causal effect while conditioning on adult variables, which often serve as proxies for childhood variables, can create collider stratification bias. Because exposure to ACEs can affect the likelihood of reaching adulthood or study entry, selection bias could be introduced via restricting selection on a variable affected by ACEs in the presence of unmeasured confounding. In addition to challenges regarding causal structure, using a cumulative score of ACEs assumes that each type of adversity will have the same effect on a given outcome, which is unlikely considering differing risk across adverse experiences.
DAGs provide a transparent approach of the researchers' assumed causal relationships and can be used to overcome issues related to confounding and selection bias. Researchers should be explicit about their operationalization of ACEs and how it is to be interpreted in the context of the research question they are trying to answer.
关于不良童年经历(ACEs)对成年后果影响的研究通常依赖于 ACEs 的回顾性评估和累积分数。然而,这种方法存在方法学上的挑战,可能会限制研究结果的有效性。
本文旨在 1)展示有向无环图(DAG)在识别和减轻与混杂和选择偏差相关的潜在问题方面的价值,2)质疑累积 ACE 分数的意义。
调整发生在童年之后的变量可能会阻断总因果效应的部分中介途径,而在成年变量的条件下进行调整,这些变量通常作为童年变量的替代物,可能会导致混杂分层偏差。由于暴露于 ACEs 会影响成年或进入研究的可能性,因此在存在未测量混杂的情况下,通过限制对受 ACEs 影响的变量的选择,可能会引入选择偏差。除了因果结构方面的挑战外,使用 ACEs 的累积分数假设每种逆境对给定结果的影响相同,考虑到不同的逆境风险,这是不太可能的。
DAG 提供了研究人员假设的因果关系的透明方法,可以用于克服与混杂和选择偏差相关的问题。研究人员应该明确他们对 ACEs 的操作化,以及如何在他们试图回答的研究问题的背景下进行解释。