Abstract
Objective: Models of psychometric screening to identify individuals with neurodevelopmental disabilities (NDDs) have had limited success. In Aotearoa/New Zealand, routine developmental surveillance of preschool children is undertaken using the Before School Check (B4SC), which includes psychometric and physical health screening instruments. This study aimed to determine whether combining multiple screening measures could improve the prediction of NDDs. Methods: Linked administrative health data were used to identify NDDs, including attention deficit hyperactivity disorder, autism spectrum disorder and intellectual disability, within a multi-year national cohort of children who undertook the B4SC. Cox proportional hazards models, with different combinations of potential predictors, were used to predict onset of a NDD. Harrell’s c-statistic for composite models were compared with a model representing recommended cutoff psychometric scores for referral in New Zealand. Results: Data were examined for 287,754 children, and NDDs were identified in 10,953 (3.8%). The best-performing composite model combining the Strengths and Difficulties Questionnaire, the Parental Evaluation of Developmental Status, vision screening and biological sex had ‘excellent’ predictive power (C-statistic: 0.83) compared with existing referral pathways which had ‘poor’ predictive power (C-statistic: 0.68). In addition, the composite model was able to improve the sensitivity of NDD diagnosis detection by 13% without any reduction in specificity. Conclusions: Combination of B4SC screening measures using composite modelling could lead to significantly improved identification of preschool children with NDDs when compared with surveillance that rely on individual psychometric test results alone. This may optimise access to academic, personal and family support for children with NDDs.