Development and psychometric properties of the parent version of the Profile of Neuropsychiatric Symptoms (PONS) in children and adolescents

Abstract

Background: The use of neuropsychiatric Patient Centred Outcome Measures (PCOMs) in routine child mental health and paediatric services is very time consuming and often requires multiple scales being completed as no single scale covers all areas of psychopathology. The use of a web-based programme can overcome these problems and contribute to improved use of PCOMs in clinical practice. We aim to develop a web-based scale (using HealthTracker™) to screen and identify young people with significant neuropsychiatric symptoms to enable early intervention. Methods: Qualitative development of the Profile of Neuropsychiatric Symptoms (PONS) and quantitative evaluation of the psychometric properties of the PONS scale (parent version). Parents of 929 from the general population and 147 with neuropsychiatric disorders (5-18 years old) completed the PONS online. In addition, those children with neuropsychiatric disorders were assessed for the presence of current and lifetime psychiatric disorders using the Development and Well-Being Assessment (DAWBA). Results: The PONS scale (parent version) consists of 30 symptom domains rated on a 7-point scale for both frequency and impairment. We found an intra-class correlation coefficient for single measures was 0.44 (0.42-0.46 95 % CI, F = 22.84, p ≤ 0.0001) and for average measures was 0.96 (0.95-0.96 95 % CI, F = 22.84, p ≤ 0.0001). The factor analysis showed a 4-factor model: Neurodevelopmental Disability; Behavioural and Emotional Dysregulation; Psychoses and Personality Dysfunction; and Anxiety and Depression. The receiver operating characteristic area for the 4-factors was 0.96 (SE = 0.006; 0.95-0.97 95 % CI). Conclusions: The PONS scale (parent version) is a web-based PCOM on the HealthTracker™ system that is a rapid, engaging measure that has excellent reliability and validity. The system allows for automated scoring and immediate feedback of statistical cut-off points and assists clinicians with diagnostic decision-making and optimises use of clinician time.