ORIGINAL RESEARCH article
Front. Psychiatry
Sec. Autism
This article is part of the Research TopicDecoding environmental influences on autism risk: A multidisciplinary approachView all articles
Risk Factors and a Prediction Model for ASD Symptoms in Chinese Preschool Children
Provisionally accepted
- 1Zunyi Medical University, Zunyi, China
- 2Zunyi Maternal and Child Health Care Hospital, Zunyi, China
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Background: The global prevalence of autism spectrum disorder (ASD) is rising, creating an urgent need for practical early screening tools, especially in community and resource-limited settings. This study aimed to identify key risk factors and develop an individualized prediction model for ASD symptoms in Chinese preschool children. Methods: A cross-sectional study was conducted in 2024, involving 13,641 children aged 3–6 years from 32 kindergartens in Guizhou Province, China. ASD symptoms were screened using the Autism Behavior Checklist. Predictor variables were selected via LASSO regression with 10-fold cross-validation. A multivariable logistic regression model was constructed and presented as a nomogram. Model discrimination was evaluated by the area under the receiver operating characteristic curve (AUC) with bootstrapped 95% confidence intervals (CI). Calibration was assessed using calibration curves and the Hosmer-Lemeshow test, and clinical utility was measured by decision curve analysis. Results: Among the participants, 324 (2.38%) screened positive for ASD symptoms. Multivariable analysis identified several independent risk factors: lower degree of fondness for the child (OR=1.53, 95% CI: 1.29–1.81), inconsistency in parenting beliefs (OR=1.17, 95% CI: 1.06–1.30), poorer sleep quality (OR=1.55, 95% CI: 1.33–1.80), and a family history of mental disorders (OR=2.80, 95% CI: 1.81–4.32). Higher parental education (OR=0.86, 95% CI: 0.78–0.94) and balanced caregiving time (OR=0.82, 95% CI: 0.76–0.88) were protective factors. The nomogram demonstrated moderate discrimination (AUC=0.757, 95% CI: 0.731–0.782), was well-calibrated, and provided a net clinical benefit for threshold probabilities between 0.1% and 19.6%. Conclusion: We successfully developed and validated a practical nomogram that integrates multiple familial and child-level factors for predicting ASD symptoms. This tool exhibits good performance and clinical applicability, offering a valuable approach for early community-based screening of preschool children.
Keywords: ASD symptoms, nomogram, Prediction model, Preschool children, Risk factors
Received: 19 Nov 2025; Accepted: 05 Feb 2026.
Copyright: © 2026 Zhong, Jin and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Zhijun Liu
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