Nursing

Demography

Multilevel model

Public health

Ethnic group

Gender studies

Multivariate analysis

Intersectionality

Health equity

Computer science

Anthropology

Psychology

Health and Retirement Study

Internal medicine

Sociology

Medicine

Race (biology)

Machine learning

Child Abuse & Neglect

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. 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. 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). 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. 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.

Disparities in adverse childhood experiences by race/ethnicity, gender, and economic status: Intersectional analysis of a nationally representative sample