Application Of Ordinal Logistic Regression Analysis In Determining Risk Factors In Children With Malnutrition

Background: Malnutrition remains a significant public health concern, particularly among children in developing countries. Identifying the risk factors associated with varying severity levels of malnutrition is essential for designing effective interventions and reducing its burden. Methods: This study employed ordinal logistic regression analysis to identify risk factors contributing to malnutrition among children aged 6 to 60 months attending the General Paediatrics Department of Amrita Institute of Medical Sciences, Kochi, following ethical committee approval. Nutritional status was assessed using the weight-for-age anthropometric index (Z-score) and categorized into three ordered groups: severely undernourished (< −3.0), moderately undernourished (−3.0 to −2.01), and nourished (≥ −2.0). Given the ordinal nature of the outcome variable, an ordinal logistic regression proportional odds model was applied to determine predictors of malnutrition. Results: The proportional odds model identified four significant risk factors associated with increasing severity of malnutrition: parity of more than two children, household size of six or fewer members, presence of infectious or noninfectious disease, and socioeconomic status. Among these factors, parity of more than two children emerged as the most significant predictor of malnutrition severity. Conclusion: Ordinal logistic regression proved to be an effective analytical approach for identifying determinants of malnutrition severity in children. The findings underscore the importance of addressing family size, household conditions, disease burden, and socioeconomic factors in nutrition-focused interventions. These results can inform policymakers and healthcare providers in developing targeted strategies to reduce childhood malnutrition and improve health outcomes