Background

Adverse childhood experiences (ACEs) are disturbingly common and consequential. Priority should be given to identifying populations that bear a disproportionate share of the burden of ACEs, but such disparities have received limited attention to date.

Objective

This study analyzes data from the National Longitudinal Study of Adolescent to Adult Health, a nationally representative sample in the U.S., to explore variation in ACEs by race/ethnicity, economic status, and gender.

Methods

In addition to using conventional statistical methods to generate unadjusted and adjusted estimates, we conduct an intercategorical intersectional analysis of variation in ACEs using multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA).

Results

Descriptively, we find that ACEs are more prevalent overall among the poor than the non-poor, among most racial/ethnic minority groups than non-Hispanic Whites, and among females than males. However, multivariate regression results indicate that gender is not a robust correlate of cumulative adversity and that economic status moderates racial/ethnic differences. MAIHDA models further expose heterogeneity in aggregate ACE scores between intersectional strata representing unique combinations of gender, race/ethnicity, and economic status.

Conclusions

The MAIHDA results confirm that conclusions based on unadjusted group differences may be spurious. While most variance in ACE scores is explained by additive main effects, accounting for intersections among social categories generates a more complex portrait of inequality. We compare our work to prior studies and discuss potential explanations for and implications of these findings for research on disparities.

中文翻译：

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种族/民族、性别和经济状况在不良童年经历方面的差异：对具有全国代表性的样本进行交叉分析

背景

童年不良经历 (ACE) 令人不安地普遍且后果严重。应优先确定承担 ACE 负担不成比例的人群，但迄今为止，这种差异受到的关注有限。

客观的

本研究分析了美国全国代表性样本“青少年到成人健康全国纵向研究”的数据，以探索种族/民族、经济地位和性别对 ACE 的影响。

方法

除了使用传统的统计方法来生成未调整和调整的估计值之外，我们还使用个体异质性和判别准确度 (MAIHDA) 的多级分析对 ACE 的变化进行了跨类别交叉分析。

结果

描述性地，我们发现 ACE 在穷人中比非穷人更普遍，在大多数种族/少数族裔群体中比非西班牙裔白人更普遍，在女性中比男性更普遍。然而，多元回归结果表明，性别不是累积逆境的强相关性，经济状况可以缓和种族/民族差异。MAIHDA 模型进一步揭示了代表性别、种族/民族和经济地位的独特组合的交叉层之间总 ACE 分数的异质性。

结论

MAIHDA 结果证实，基于未经调整的群体差异的结论可能是虚假的。虽然 ACE 分数的大多数差异是由附加的主效应解释的，但考虑社会类别之间的交叉会产生更复杂的不平等现象。我们将我们的工作与先前的研究进行比较，并讨论这些发现对差异研究的潜在解释和影响。