UC San Diego

Introduction

Adverse childhood experiences (ACEs) are associated with poor psychosocial and health outcomes in adulthood. Veterans and females experience ACEs disproportionately. A greater understanding of this disparity may be achieved by examining the relationship between distinct ACE patterns and these demographic characteristics. Therefore, this study examined distinct ACE patterns and their association with Veteran status, sex, and other demographics in a nationally representative sample of U.S. adults to inform interventions tailored to ACE patterns experienced by specific groups.

Materials and methods

Latent class analysis (LCA) was conducted with data from the National Epidemiologic Survey of Alcohol and Related Conditions-III, a nationally representative structured diagnostic interview conducted from 2012-2013. The target population was the noninstitutionalized adult population living in the USA. The analytic sample was 36,190 (mean age 46.5 years; 48.1% male). Of these participants, 3,111 were Veterans. Data were analyzed between September 2020 and January 2021.

Results

Latent class analysis revealed a four-class solution: (1) "Low adversity" (75.3%); (2) "Primarily household dysfunction" (9.0%); (3) "Primarily maltreatment" (10.7%); and (4) "Multiple adversity types" (5.1%). Compared to "Low adversity," members in the other classes were more likely to be Veterans (odds ratio (OR)C2vC1 = 1.33, ORC3vC1 = 1.55, ORC4vC1 = 1.98) and female (ORC2vC1 = 1.58, ORC3vC1 = 1.22, ORC4vC1 = 1.65). While lower education and income were also related to higher adversity class membership, Veteran status and sex were the strongest predictors, even when controlling for education and income.

Conclusions

Distinct and meaningful patterns of ACEs identified in this study highlight the need for routine ACE screenings in Veterans and females. As in the current study, operationalizing and clustering ACEs can inform screening measures and trauma-informed interventions in line with personalized medicine. Future work can test if classes are differentially associated with health outcomes.