Harnessing the power of child development records to detect early neurodevelopmental disorders using Bayesian analysis

Abstract

Aim: This study aims to analyse the developmental data from public health nurses (PHNs) to identify early indicators of neurodevelopmental disorders (NDDs) in young children using Bayesian network (BN) analysis to determine factor combinations that improve diagnosis accuracy. Methods: The study cohort was 501 children who underwent health checkups at 18 and 36-month. Data included demographics, pregnancy, delivery, neonatal factors, maternal interviews, and physical and neurological findings. Diagnoses were made by paediatricians and child psychiatrists using standardised tools. Predictive accuracy was assessed by the receiver operating characteristic (ROC) curve analysis. Results: We identified several infant/toddler factors significantly associated with NDD diagnoses. Predictive factors included meconium-stained amniotic fluid, 1 min Apgar score, and early developmental milestones. ROC curve analysis showed varying predictive accuracies based on evaluation timing. The 10-month checkup was valid for screening but less reliable for excluding low-risk cases. The 18-month evaluation accurately identified children at NDD risk. Conclusion: The study demonstrates the potential of using developmental records for early NDD detection, emphasising early monitoring and intervention for at-risk children. These findings could guide future infant mental health initiatives in the community.