APA
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MacNeill, L. A., Zhang, Y., Giase, G. M., Wiggins, J. L., Norton, E. S., Smith, J. D., … Wakschlag, L. S. (2025). Developmental Foundations of a Pediatric Mental Health Risk Calculator for Young Children. Academic Pediatrics.
Chicago/Turabian
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MacNeill, Leigha A., Yudong Zhang, Gina M. Giase, J. L. Wiggins, Elizabeth S. Norton, Justin D. Smith, Matthew M. Davis, et al. “Developmental Foundations of a Pediatric Mental Health Risk Calculator for Young Children.” Academic pediatrics (2025).
MLA
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MacNeill, Leigha A., et al. “Developmental Foundations of a Pediatric Mental Health Risk Calculator for Young Children.” Academic Pediatrics, 2025.
BibTeX Click to copy
@article{leigha2025a,
title = {Developmental Foundations of a Pediatric Mental Health Risk Calculator for Young Children.},
year = {2025},
journal = {Academic pediatrics},
author = {MacNeill, Leigha A. and Zhang, Yudong and Giase, Gina M. and Wiggins, J. L. and Norton, Elizabeth S. and Smith, Justin D. and Davis, Matthew M. and Raven, Julia G. and Poleon, Roshaye B and Yu, Qiongru and Smyser, C. and Rogers, Cynthia E. and Luby, Joan L. and Allen, Norrina B. and Wakschlag, Lauren S.}
}
OBJECTIVE To advance clinical utility of an emerging risk calculator for identifying when to worry and when to act when young children show signs of mental health concerns in pediatric care, we: (1) replicate an early childhood mental health risk algorithm (DECIDE); (2) determine preliminary predictive utility of additional child and parenting assets, advancing a strengths-based framework to reduce the likelihood of biased identification.
METHODS Data were from two independent studies: The national Future of Families and Child Wellbeing Study (FFCWS; N=2,763) and the regional Mental Health, Earlier Synthetic Cohort study (MHESC; N=323). Predictors were assessed in toddlerhood/early preschool age. Internalizing/externalizing problems were measured in older preschoolers, serving as outcomes. Epidemiologic risk prediction methods were applied to: (1) replicate the DECIDE risk algorithm domains comprised of demographics, child irritability, and adverse childhood experiences; and (2) examine the added predictive utility of child and parenting assets. Predictive utility was based on area under the curve (AUC) and/or the integrated discrimination improvement (IDI).
RESULTS The DECIDE algorithm was replicated in FFCWS and MHESC (AUC=.70 for both studies; IDI=.07 in FFCWS and.06 in MHESC). IDIs indicated predictive utility for child assets beyond the existing DECIDE algorithm in both studies, and for parenting assets in FFCWS.
CONCLUSIONS Robust validation of predictive algorithms is critical for assessing generalizability. Reducing bias in early mental health risk algorithms via a strengths-based approach is key to equitable decision-making. This work lays the foundation for implementation of early mental health decision tools in routine care of young children.