Abstract

Background: Early school dropout is an economic, social, and individual problem. In the Netherlands, Youth Health Care (YHC) service is engaged in child’s development and collects data on psychosocial health (Strengths and Difficulties Questionnaire (SDQ)). This study aimed at (1) assessing whether SDQ scores at 10 and 14 years old are predictive of school dropout (2) developing a school dropout prediction model using relevant child and family factors available to YHC professionals at regular consultations. Methods: YHC data from 24,988 children born 1996-2001 were linked to child, family and school characteristics. Early school dropout was defined as having left the school without diploma by the age of 17. Two multilevel logistic regression models were built with predictors measured at the ages of 10 and 14. Models’ performance was assessed using ROC curve. Findings: Child’s SDQ, gender, and parents’ socio-economic status were predictors of early school dropout. A strong interaction was present between SDQ and the three levels of the secondary education track. Prediction model for age 10 and age 14 (lowest level track) showed moderate prediction performance (ROC value 0.70/0.69, respectively). Prediction quality for children from higher level secondary education tracks was poor. Interpretation: School dropout can be to some extent anticipated as early as age of 10. The proposed prediction models can contribute to early risk stratification where window of opportunity exists for interventions aimed to strengthen ties with school. Information on mental health diagnoses and academic performance should be considered in future research to improve prediction accuracy. Funding Statement: The study was funded by Limburg Province subsidy as part of the ‘4Limburg project’ (https://www.4-limburg.nl/over-4limburg). Declaration of Interests: Authors declare no conflict of interest. Ethics Approval Statement: The study was approved by medical ethical committee of Maastricht Academic Hospital and Maastricht University (METC azM/UM 2020-1573).

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