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Population-level transitions in observed difficulties through childhood and adolescence.

Elia Benhamou; Danyal Akarca; Joe Bathelt; Sue Fletcher-Watson; Duncan E Astle

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Title: Population-level transitions in observed difficulties through childhood and adolescence.
Author: Elia Benhamou; Danyal Akarca; Joe Bathelt; Sue Fletcher-Watson; Duncan E Astle
Source: Developmental Psychology · DOI 10.1037/dev0001874 Published 2025-02-27 · Open source record
License: CC BY 4.0 deed

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Abstract In an attempt to better characterize the complexity of difficulties observed within developing populations, numerous data-driven techniques have been applied to large mixed data sets. However, many have failed to incorporate the core role of developmental time in these approaches, that is, the typical course of change in behavioral features that occurs over childhood to adolescence. In this study, we utilized manifold projections alongside a gradient-boosting model on data collected from the Millennium Cohort Study to unpack the central role of developmental time in how behavioral difficulties transition between the ages of 5, 11, and 17. Our analysis highlights numerous observations: (a) Girls develop relatively greater internalized behavioral problems during adolescence; (b) in the case of a chaotic home environment, co-occurring internalizing and externalizing difficulties tend to persist during childhood; (c) peer problems were the most likely to persist over the whole 12-year period (especially in the presence of early maternal depression and poor family relationships); and (d) there were two pathways with distinct risk factors leading to antisocial behaviors in adolescence—an early-childhood onset pathway and later adolescent onset pathway. Our findings provide evidence that investigations of child and adolescent difficulties must be open to the possibility of multiple subgroups and variability in trajectory over time. We further highlight the crucial role of family and social support and school experience-related factors in predicting children’s outcomes. Keywords: development, machine learning, Millennium Cohort study, childhood, adolescence Public Significance Statement Using a large sample of data from the Millennium Cohort Study, followed from the age of 9 months to 17 years, we use data-driven methods to examine developmental change in children’s observed behaviors as they grow up. We identify clusters of children over more than a decade and establish key factors that predict longitudinally particular profiles and transitions from childhood into adolescence.

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Original journal: Developmental Psychology
Published: 2025-02-27
DOI: 10.1037/dev0001874
Open record: PMC12243395

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