Development and Validation of a Predictive Model for Individual Risk Prediction of Stunting in Ethiopia: A Predictive Modeling Study

ABSTRACT Background and Aims Stunting is a height for age Z score falls bellow ‐2 standard deviation. Untreated stunted cases have lifelong consequences like cognitive development, increased risk of infection and long‐term health and economy burden. Although stunting remains highly prevalent in Ethiopia, there has been no prior attempt to develop an individualized risk prediction model. This study will develop and validates a predictive model to improve targeted intervention in Ethiopia. Methods Data from 2019 Mini Ethiopian Demographic Health Survey comprised of 2079 children's below 2 years. Data analysis was done using STATA version 17 and R version 4.4.1 software. Least absolute shrinkage and selection operator were used to select variables for Multilevel Multivariable Analysis. Nomogram was developed and model's performance was assessed through the area under the receiver operating characteristic curve and calibration plots. Bootstrapping techniques were applied to internally validate the accuracy of the model. Additionally, decision curve analysis was conducted to examine its clinical and public health applicability. Results The prevalence of stunting was 27.8% [95% CI: 24.96, 30.89]. The developed nomogram comprised 8 predictors: Maternal education, residence, sex a child, age of a child, Current feeding status, usage of bottle feeding, twin status and marital status. The area under the receiver operating characteristic curve of the original model was (AUC = 0.722, 95% CI; 0.698, 0.747) whereas the after bootstrap model produced prediction accuracy of an AUC of 0.719 (95% CI; 0.693, 0.744). Internal validation was performed using the bootstrapping method, demonstrating reasonably corrected discriminative ability. Decision curve analysis showed that the model provided a greater net benefit than strategies of treating all or none, particularly for threshold probabilities exceeding 19%. Conclusion This study developed and internally validated a predictive model for stunting in children under 2 years in Ethiopia, with strong discriminatory power (AUC 0.729) and calibration. The model, incorporating eight key predictors, offers a practical tool for clinical decision‐making through a user‐friendly nomogram.