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Heterogeneity in caregiving-related early adversity: Creating stable dimensions and subtypes

Published online by Cambridge University Press:  22 March 2022

Aki Nikolaidis*
Child Mind Institute, New York, NY, USA
Charlotte Heleniak
Department of Psychology, Columbia University, New York, NY, USA
Andrea Fields
Department of Psychology, Columbia University, New York, NY, USA
Paul A. Bloom
Department of Psychology, Columbia University, New York, NY, USA
Michelle VanTieghem
Department of Psychology, Columbia University, New York, NY, USA
Anna Vannucci
Department of Psychology, Columbia University, New York, NY, USA
Nicolas L. Camacho
Department of Psychology, Columbia University, New York, NY, USA
Tricia Choy
Department of Psychology, Columbia University, New York, NY, USA
Lisa Gibson
Department of Psychology, Columbia University, New York, NY, USA
Chelsea Harmon
Department of Psychology, Columbia University, New York, NY, USA
Syntia S. Hadis
Department of Psychology, Columbia University, New York, NY, USA
Ian J. Douglas
Department of Psychology, Columbia University, New York, NY, USA
Michael P. Milham
Child Mind Institute, New York, NY, USA
Nim Tottenham
Department of Psychology, Columbia University, New York, NY, USA
Corresponding author: Aki Nikolaidis, email:


Early psychosocial adversities exist at many levels, including caregiving-related, extrafamilial, and sociodemographic, which despite their high interrelatedness may have unique impacts on development. In this paper, we focus on caregiving-related early adversities (crEAs) and parse the heterogeneity of crEAs via data reduction techniques that identify experiential cooccurrences. Using network science, we characterized crEA cooccurrences to represent the comorbidity of crEA experiences across a sample of school-age children (n = 258; 6–12 years old) with a history of crEAs. crEA dimensions (variable level) and crEA subtypes (subject level) were identified using parallel factor analysis/principal component analysis and graph-based Louvain community detection. Bagging enhancement with cross-validation provided estimates of robustness. These data-driven dimensions/subtypes showed evidence of stability, transcended traditional sociolegally defined groups, were more homogenous than sociolegally defined groups, and reduced statistical correlations with sociodemographic factors. Finally, random forests showed both unique and common predictive importance of the crEA dimensions/subtypes for childhood mental health symptoms and academic skills. These data-driven outcomes provide additional tools and recommendations for crEA data reduction to inform precision medicine efforts in this area.

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© The Author(s), 2022. Published by Cambridge University Press

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