The Youth and Childhood Adversity Scale: a step towards developing a new measure of adversity and its severity.

Abstract:

Background: Early adversity (EA) can contribute to the onset, manifestation, and course of various mental disorders. Measuring EA is still conceptually and psychometrically challenging due to issues such as content coverage, item-wording, scaling methods, and validation procedures. Further, despite research demonstrating the importance of the severity of EA, most EA scales solely focused on the 'presence-versus-absence' indicator of adverse events.Objective: To address these potentially relevant gaps, we have developed a 13-item measure of EA, the Youth and Childhood Adversity Scale (YCAS). Beyond a dichotomous assessment of whether a set of adverse events have been experienced, this scale also assesses the respective severity of these events.Methods: We evaluated the YCAS in a sample of 596 adolescent students (ages 16-19) and a second sample of 451 medical students (ages 18-30+). Exploratory factor analysis was used to determine the underlying structure as proposed by the data, which was then tested with confirmatory factor analysis. We psychometrically assessed both factor scores and sum scores.Results: In both samples, a one-factorial solution was found for both responses to dichotomous items and severity items. Item loadings had a broad range, with minimum loadings of .1-.2 and maximum loadings of .7-.9. Irrespective of the response type, this factor exhibited good reliability (omega total, range: .80 - .89) and was associated with a range of mental-health outcomes, self-esteem, and childhood maltreatment. The fit of the model resembling sum scores was not satisfactory, but the sum score reliability (coefficient alpha, range: .78 - .89) was acceptable and most of the associations with the validation measures held.Conclusions: The YCAS allows an efficient, reliable, and valid assessment of EA and its severity. It covers a reasonable breadth of events, whilst simultaneously being parsimonious. We discuss next steps of how to improve this measure to fully capture the complexity of EA.