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Published on: October 3, 2018
Risk factors and a prediction model for ASD symptoms in Chinese preschool children
Xiaoqi Zhong1, Qiyun Jin2, Shilin Zhang3
1School of Public Health, Zunyi Medical University, Zunyi, China.
Insights
A new nomogram effectively predicts autism spectrum disorder (ASD) symptoms in Chinese preschoolers using familial and child factors. This tool aids early, community-based screening for ASD.
Area of Science:
- Child Psychology
- Developmental Pediatrics
- Public Health
Background:
- Rising global prevalence of autism spectrum disorder (ASD) necessitates early screening tools.
- Resource-limited community settings require practical screening solutions.
- Focus on Chinese preschool children for targeted intervention.
Purpose of the Study:
- Identify key risk factors for ASD symptoms in preschool children.
- Develop an individualized prediction model for ASD symptoms.
- Create a practical tool for early ASD detection in community settings.
Main Methods:
- Cross-sectional study of 13,641 children aged 3-6 years in Guizhou, China.
- Autism Behavior Checklist used for ASD symptom screening.
- LASSO regression and multivariable logistic regression for model development.
- Nomogram created for individualized risk prediction.
- Model performance evaluated using AUC, calibration curves, and decision curve analysis.
Main Results:
- 2.38% of participants screened positive for ASD symptoms.
- Independent risk factors identified: lower fondness for child, inconsistent parenting, poorer sleep, family history of mental disorders.
- Protective factors: higher parental education, balanced caregiving time.
- Nomogram showed moderate discrimination (AUC=0.757) and good calibration.
- Clinical utility demonstrated for specific probability thresholds.
Conclusions:
- A practical nomogram integrating familial and child factors for ASD symptom prediction was developed and validated.
- The tool demonstrates good performance and clinical applicability.
- Offers a valuable approach for early, community-based screening of preschool children for ASD.
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.
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