Prevention of and early intervention in youth adversity is critical for later life success; however, current tools are limited in their ability to collect population-level data on childhood adversity, as they rely on questionnaires completed in-person. Identification of adverse childhood experiences (ACEs) using data routinely collected for other purposes represents a promising avenue for population-level screening. The goal of this study was to examine the association between ACEs identified using administrative data and diagnosed health conditions to further demonstrate the utility of administrative data for identifying ACEs. This retrospective , cross-sectional study used linked health and education administrative data for school-aged youth in Tennessee during the 2018–2019 academic year. Administrative ACEs (ADM-ACEs) were identified using an algorithm that flags healthcare encounters indicative of child maltreatment, foster care placement, and/or maternal health conditions. We conducted descriptive statistics and multivariate modified Poisson regression models to estimate the relative risk of diagnosed mental health conditions, substance use disorders, headaches, nausea, and sleep disorders associated with ADM-ACEs. Of 678,907 youth in the sample, 105,954 (15.6%) experienced at least 1 ADM-ACE and 22,774 (3.3%) experienced multiple ADM-ACEs in the 2018–2019 academic year. Exposure to ADM-ACEs was associated with elevated risk of all health conditions examined. Presence of multiple ADM-ACEs consistently showed a larger association with adverse health outcomes. ADM-ACEs related to maltreatment/peer violence and foster care/family disruption were associated with the highest risks of all health conditions. ADM-ACEs like cumulative ACE measures are associated with worse health outcomes, further suggesting that the ADM-ACE algorithm is a valuable avenue for population-level screening of childhood adversity when it occurs.
Sell et al. (Mon,) studied this question.