The power of local research to inform adverse childhood experiences in substance use prevention in adolescents and adults

Background: The impact of adverse childhood experiences on substance use has been well reported, however, less well documented is looking at the comparison of youth and adult substance use and their respective adverse childhood experiences. This study leveraged local data sources to support prevention efforts inside a state-level working group and examined research questions that explored the relationship between reported adverse childhood experiences and substance use for youth and adult samples at the state level. Methods: This study conducted a series of logistic regression models (95% CI) between substance use outcomes with different age group populations to investigate the relationship between adverse childhood experiences and substance use for each group. Adverse childhood experiences scores and substance use were examined using two Arizona datasets: 1) Arizona Youth Survey (n = 42,009) and 2) the Behavioral Risk Factor Surveillance System (n = 5328). Results: The results of youth and adult datasets were consistent: users with adverse childhood experiences scores of 4 to 6 had a positive association with more substance use. When the variables were examined, showing the entire sample of youth and adult groups compared to those subgroups with a score of zero, a score of 1 to 3, and a score of 4 to 6, the overall pattern was the same; the more frequent use of substances was directly associated to the group with higher scores. Additionally, findings support increased attention on prevention and intervention efforts with higher reports of adverse childhood experiences as well as substance use. Conclusions: These findings demonstrate how local research can help prioritize prevention resources and increase the value of data-based decision-making. Policy-makers and providers can examine youth and adult data to compare priorities and assess for planning purposes. Specifically, it is possible to replicate known research findings, identify the most impacted subpopulations, and forecast the community’s future needs.