Cumulative risk, factor analysis, and latent class analysis of childhood adversity data in a nationally representative sample

Background: Childhood adversity is a multifaceted construct that is in need of comprehensive operationalisation. Objective: The aim of this study was to explore the optimal method to operationalise a scale of adverse childhood experiences (ACEs). Participants and setting: Data were from Wave 1 of the Personality and Total Health (PATH) Through Life Project (N = 7485, 51% women). Participants from three age groups (20–25, 40–45, 60–65) retrospectively reported their childhood experiences of domestic adversity on a 17-item scale (e.g., physical abuse, verbal abuse, neglect, poverty). Methods: We compared three approaches to operationalising the 17-item scale: a cumulative risk approach, factor analysis, and latent class analysis (LCA). The cumulative risk and dimensional models were represented by a unidimensional and two-dimensional model respectively using confirmatory factor analysis (CFA). Results: The cumulative risk approach and LCA were viable approaches to operationalising ACE data in PATH. CFA of the dimensional model produced latent factors of threat and deprivation that were highly correlated, potentially leading to problems with multicollinearity when estimating associations. LCA revealed six classes of ACEs: high adversity, low adversity, low affection, authoritarian upbringing, high parental dysfunction, and moderate parental dysfunction. Conclusion: Our study found multiple latent classes within a 17-item questionnaire assessing domestic adversity. Using both the cumulative method and latent class approach may be a more informative approach when examining the relationship between ACEs and later health outcomes. Future ACE studies may benefit by considering multi-dimensional approaches to operationalising adversity.