Contrasting cumulative risk and multiple individual risk models of the relationship between Adverse Childhood Experiences (ACEs) and adult health outcomes

Marianna D. Lanoue, Brandon J. George, Deborah L. Helitzer, Scott W. Keith

Research output: Contribution to journalArticlepeer-review

25 Scopus citations


Background: A very large body of research documents relationships between self-reported Adverse Childhood Experiences (srACEs) and adult health outcomes. Despite multiple assessment tools that use the same or similar questions, there is a great deal of inconsistency in the operationalization of self-reported childhood adversity for use as a predictor variable. Alternative conceptual models are rarely used and very limited evidence directly contrasts conceptual models to each other. Also, while a cumulative numeric 'ACE Score' is normative, there are differences in the way it is calculated and used in statistical models. We investigated differences in model fit and performance between the cumulative ACE Score and a 'multiple individual risk' (MIR) model that enters individual ACE events together into prediction models. We also investigated differences that arise from the use of different strategies for coding and calculating the ACE Score. Methods: We merged the 2011-2012 BRFSS data (N = 56,640) and analyzed 3 outcomes. We compared descriptive model fit metrics and used Vuong's test for model selection to arrive at best fit models using the cumulative ACE Score (as both a continuous or categorical variable) and the MIR model, and then statistically compared the best fit models to each other. Results: The multiple individual risk model was a better fit than the categorical ACE Score for the 'lifetime history of depression' outcome. For the outcomes of obesity and cardiac disease, the cumulative risk and multiple individual risks models were of comparable fit, but yield different and complementary inferences. Conclusions: Additional information-rich inferences about ACE-health relationships can be obtained from including a multiple individual risk modeling strategy. Results suggest that investigators working with large srACEs data sources could empirically derive the number of items, as well as the exposure coding strategy, that are a best fit for the outcome under study. A multiple individual risk model could also be considered in addition to the cumulative risk model, potentially in place of estimation of unadjusted ACE-outcome relationships.

Original languageEnglish (US)
Article number239
JournalBMC Medical Research Methodology
Issue number1
StatePublished - Sep 29 2020


  • ACEs
  • Adverse childhood experiences
  • Behavioral risk factor surveillance system
  • Childhood adversity
  • Model comparison
  • Model selection

ASJC Scopus subject areas

  • Epidemiology
  • Health Informatics


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