Background

Different types of childhood maltreatment (CM) are key risk factors for psychopathology. Specifically, there is evidence for a unique role of emotional abuse in affective psychopathology in children and youth; however, its predictive power for depressive symptomatology in adulthood is still unknown. Additionally, emotional abuse encompasses several facets, but the strength of their individual contribution to depressive affect has not been examined.

Method

Here, we used a machine learning (ML) approach based on Random Forests to assess the performance of domain scores and individual items from the Childhood Trauma Questionnaire (CTQ) in predicting self-reported levels of depressive affect in an adult general population sample. Models were generated in a training sample (N = 769) and validated in an independent test sample (N = 466). Using state-of-the-art methods from interpretable ML, we identified the most predictive domains and facets of CM for adult depressive affect.

Results

Models based on individual CM items explained more variance in the independent test sample than models based on CM domain scores (R² = 7.6 % vs. 6.4 %). Emotional abuse, particularly its more subjective components such as reactions to and appraisal of the abuse, emerged as the strongest predictors of adult depressive affect.

Limitations

Assessment of CM was retrospective and lacked information on timing and duration. Moreover, reported rates of CM and depressive affect were comparatively low.

Conclusions

Our findings corroborate the strong role of subjective experience in CM-related psychopathology across the lifespan that necessitates greater attention in research, policy, and clinical practice.

中文翻译：

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解开童年虐待和忽视对成年抑郁情绪的影响：一般人群样本中的机器学习方法

背景

不同类型的儿童虐待 (CM) 是精神病理学的关键风险因素。具体而言，有证据表明情感虐待在儿童和青少年的情感精神病理学中具有独特的作用；然而，它对成年期抑郁症状的预测能力仍然未知。此外，情感虐待包括多个方面，但尚未检查他们个人对抑郁情绪的贡献强度。

方法

在这里，我们使用基于随机森林的机器学习 (ML) 方法来评估儿童创伤问卷 (CTQ) 中的领域分数和单个项目在预测成人一般人群样本中自我报告的抑郁情绪水平方面的表现。模型在训练样本 ( N  = 769) 中生成，并在独立测试样本 ( N  = 466) 中进行验证。使用可解释的 ML 中最先进的方法，我们确定了 CM 对成人抑郁影响最具预测性的领域和方面。

结果

基于单个 CM 项目的模型比基于 CM 域得分的模型解释了独立测试样本中的更多差异（R ²  = 7.6 % vs. 6.4 %）。情感虐待，特别是其更主观的成分，例如对虐待的反应和评估，成为成人抑郁情绪的最强预测因素。

限制

对 CM 的评估是回顾性的，缺乏有关时间和持续时间的信息。此外，报告的 CM 和抑郁情绪的发生率相对较低。

结论

我们的研究结果证实了主观经验在 CM 相关精神病理学整个生命周期中的重要作用，这需要在研究、政策和临床实践中给予更多关注。