Predicting Mental Health Risk from Early-Life Adversity: A Population-Based Study of Canadian Adults: Prédiction du risque pour la santé mentale liée à l'adversité en début de vie : Étude fondée sur une population d'adultes canadiens.

ObjectivesBuilding on prior population-level studies, this replication study explored the predictive accuracy of retrospectively-reported early-life adversity (ELA) for individual psychopathology risk in a Canadian population survey, with measurements focused on direct/severe ELA and occurring during the COVID-19 pandemic.MethodsNationally-representative, cross-sectional data from 7,608 Canadians surveyed in 2022 were analysed. Group-level differences were assessed via logistic regression, and predictive accuracy of ELA was tested via area under the curve (AUC) analyses.ResultsGroup-based analyses found that the odds of mental health problems rose with increasing ELA, albeit nonlinearly. Across psychopathology domains, predictive accuracy was poor (AUC = 0.62-0.67). Using a high-risk cut-off of ≥4 ELAs, sensitivity values were low (0.14-0.23), while specificity was high (0.93-0.94). Similarly, positive predictive values were low (0.08-0.22), while negative predictive values were high (0.92-0.97).ConclusionsELA screening performs poorly at the individual level. While high-risk cut-offs may rule out poor mental health for individuals with fewer ELAs, it fails to accurately identify those with psychopathology. Predictive accuracy does not improve under conditions of collective stress or by focusing on direct/severe ELA. Presently, ELA screening is unsuitable for guiding intervention allocation. Further research is needed to determine whether screening can be refined to improve mental health risk prediction.Plain Language Summary TitleEarly-life adversity and adult mental health: Is Canadian screening getting it right?