Bifactor models of psychopathology using multi-informant and multi-instrument dimensional measures in the ABCD study

JCPP advances Pub Date : 2024-02-26 DOI:10.1002/jcv2.12228
Grace R. Jacobs, Stephanie H. Ameis, Peter Szatmari, John D. Haltigan, Aristotle N. Voineskos
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Abstract

Background

Due to limitations of categorical definitions of mental illness, there is a need for quantitative empirical investigations of the dimensional structure of psychopathology. Using exploratory bifactor methods, this study investigated a comprehensive and representative structure of psychopathology in children to better understand how psychotic-like experiences (PLEs), autism spectrum disorder (ASD) symptoms, impulsivity, and sensitivity to reward and punishment, may be integrated into extant general factor models of psychopathology.

Methods

We used seven child-report and three parent-report instruments capturing diverse mental health symptoms in 11,185 children aged 9–10 from the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study. We built on previous modeling frameworks by conducting both split sample and full sample factor analytic approaches that harnessed recent methodological advances in bifactor exploratory structural equation modeling (B-ESEM) to examine a wide range of psychopathology measures not previously integrated into a single analysis. Validity of psychopathology dimensions was examined by investigating associations with sex, age, cognition, imaging measures, and medical service usage.

Results

All four factor analytic models showed excellent fit and similar structure within informant. PLEs loaded most highly onto a general psychopathology factor, suggesting that they may reflect non-specific risk for mental illness. ASD symptoms loaded separately from attention/hyperactivity symptoms. Symptoms of impulsivity and sensitivity to reward and punishment loaded onto specific factors, distinct from externalizing and internalizing factors. All identified factors were associated with clinically relevant risk factors, providing preliminary evidence for their construct validity.

Conclusion

By integrating diverse child-report and parent-report psychopathology measures for children in the ABCD sample, we deliver data on the quantitative structure of psychopathology for an exceptionally large set of measurements and discuss implications for the field.

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在 ABCD 研究中使用多信息量和多工具维度测量的精神病理学双因素模型
由于精神疾病分类定义的局限性,需要对精神病理学的维度结构进行定量实证调查。本研究采用探索性双因素方法,调查了儿童心理病理学的综合代表性结构,以更好地了解如何将精神病样经历(PLE)、自闭症谱系障碍(ASD)症状、冲动性以及对奖惩的敏感性整合到现有的心理病理学一般因素模型中。我们使用了七种儿童报告工具和三种家长报告工具,捕捉了青少年大脑认知发展研究(ABCD)中 11,185 名 9-10 岁儿童的各种心理健康症状。我们在以往建模框架的基础上,采用了分离样本和全样本因子分析方法,利用双因子探索性结构方程建模(B-ESEM)在方法学上的最新进展,对以前未整合到单一分析中的多种心理病理学测量方法进行了研究。通过研究心理病理学维度与性别、年龄、认知能力、影像测量和医疗服务使用情况之间的关联,对心理病理学维度的有效性进行了检验。PLEs在一般精神病理学因子上的负荷最高,这表明它们可能反映了非特异性的精神疾病风险。ASD 症状与注意力/多动症状分别加载。冲动症状和对奖惩的敏感症状被加载到特定因子上,与外化和内化因子不同。通过整合ABCD样本中不同的儿童报告和家长报告精神病理学测量方法,我们提供了有关精神病理学定量结构的数据,这些数据的测量集合非常庞大,我们还讨论了这些数据对该领域的影响。
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