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

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

doi:10.1186/s12874-020-01120-w

Abstract

BackgroundA 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.MethodsWe 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.ResultsThe 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.ConclusionsAdditional 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.

Highlights

A very large body of research documents relationships between self-reported Adverse Childhood Experiences and adult health outcomes
We investigated differences in model fit and performance based on operationalization of an Adverse Childhood Experiences (ACEs) predictor variable in a cumulative risk model vs a multiple individual risk model when applied to three commonly studied health outcomes
Our primary goal in this research was to evaluate the fit and performance of a ‘multiple individual risk’ model, where all ACE events are separately entered into a single prediction model, in contrast to a ‘cumulative risk model’ approach for predicting adult health outcomes

Summary

Introduction

A very large body of research documents relationships between self-reported Adverse Childhood Experiences (srACEs) and adult health outcomes. We refer to this type of design and data structure as the ‘ACEs Framework’ [1] and to questionnaire responses over a specific set of adversity events contained in these datasets as srACEs (self-reported ACEs) While this tradition arguably began with the landmark 1998 Felitti et al Kaiser ACE’s Study [2], versions of the Kaiser group srACE questions are used in several other largescale health surveys including the CDC’s Behavioral Risk Factor Surveillance System (BRFSS) survey [3]. The recent critical publications are conceptual reviews, not empirical reports Large data sources such as the BRFSS survey represent a significant investment of research resources; the BRFSS effort surveys over 450,000 individuals each year, with a yearly budget over $18 million [3].

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