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A Cognitive Development Chart for School-age Children and Adolescents

Simioni, A. R.; Pine, D.; Sato, J. R.; Pan, P. M.; Fonseca, R. P.; Schaefer, J.; Miguel, E. C.; Mari, J. d. J.; Gadelha, A.; Bressan, R. A.; Rohde, L. A.; Salum, G. A.

2019-11-27 primary care research
10.1101/19012963
Show abstract

ObjectiveTo evaluate the reliability and predictive utility of a time-efficient cognitive development chart that seeks to identify children and adolescents with high-risk for multiple outcomes such as mental health problems, substance use, and educational difficulties. MethodWe analyzed data from the Brazilian High-Risk Cohort for Psychiatric Disorders (HRC), a longitudinal school-based study conducted from 2010-2011 to 2013-2014. Participants were 2,239 children and adolescents, 6 to 17 years of age, who completed the cognitive assessment at baseline. The task used to track cognitive development was the Two Choice Reaction Time task (<3 minutes of duration, computer-based), which assesses the accuracy and speed of perceptual decision-making. Mental health, substance use, and educational outcomes were assessed by validated standardized methods. Key variables were measured at baseline and 3-year follow-up. The predictive utility was assessed using static (deviations from the age-expected performance at baseline) and dynamic (deviations from the age-expected change in performance over time) indicators. ResultsThe reliability of the task parameter was high (intra-class correlation coefficient = 0.8). Static indicators of cognitive development significantly predicted concurrent mental, intellectual and educational difficulties, as well as incident and persistent educational difficulties and substance use in the 3-year follow-up. Dynamic indicators predicted persistent mental health problems. ConclusionPrimary-care and mental health professionals need a time-efficient tool for tracking deviations from age-expected cognitive development, which predicts multiple unwanted outcomes at the same time. If replicated, future results could support the generation of tools for tracking risk for mental health, substance use, and educational difficulties.

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