Analysis of influencing factors and nomogram of attention-deficit hyperactivity disorder in school-age children
TL;DR
The nomogram prediction model developed in this study demonstrates good predictive performance and may serve as a preliminary tool for early identification of at-risk children, though external validation is needed before clinical implementation.
Abstract
Introduction Attention-deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in school-aged children (6–13 years) that can lead to significant functional impairment if not identified early. This study aims to identify factors associated with ADHD among school-age children and evaluate the predictive value of a risk nomogram. Methods We conducted a retrospective study including 155 school-age children diagnosed with ADHD at the Minhang District Mental Health Centre (Shanghai) between June 2022 and May 2025, and 155 age-matched healthy controls who underwent physical examination during the same period. Clinical and demographic data were collected via standardized questionnaires. Logistic regression analyses were performed to identify risk factors, and a nomogram prediction model was constructed. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration plots, and the Hosmer–Lemeshow test. Results Multivariate logistic analysis identified maternal alcohol consumption during pregnancy (OR = 6.975), maternal smoking during pregnancy (OR = 3.785), maternal anxiety and depression during pregnancy (OR = 3.826), family history of ADHD (OR = 9.449), parental violence (OR = 3.369), difficulty falling asleep (OR = 7.317), and daily screen time ≥ 2 hours (OR = 1.755) as independent risk factors for ADHD. Family harmony was identified as a protective factor (OR = 0.455). The nomogram prediction model demonstrated good discriminative ability with an AUC of 0.817 (95% CI: 0.769–0.864) at a cut-off value of logit(P) > 0.471. The model showed satisfactory calibration (Hosmer–Lemeshow test, P = 0.355) with a sensitivity of 75.48% and specificity of 76.77%. Discussion ADHD in school-age children is influenced by multiple factors, including prenatal exposures, family environment, sleep patterns, and daily screen time. The nomogram prediction model developed in this study demonstrates good predictive performance and may serve as a preliminary tool for early identification of at-risk children, though external validation is needed before clinical implementation. These findings highlight the importance of targeted preventive interventions addressing modifiable risk factors, particularly focusing on prenatal care, family harmony, sleep hygiene, and screen time management, to reduce the probability of ADHD in school-age children.