APPLICATION OF ORDINAL LOGISTIC REGRESSION ANALYSIS IN DETERMINING RISK FACTORS IN CHILDREN WITH MALNUTRITION
Malnutrition remains a significant public health concern, particularly affecting children in many developing countries. Identifying the risk factors associated with malnutrition is crucial for implementing effective interventions and reducing its prevalence. In this study, we aimed to employ ordinal logistic regression analysis to identify the risk factors contributing to malnutrition in children by using the data of children aged 6 months to 60 months visiting the General Paediatrics department of Amrita Institute of Medical Sciences, Kochi. After the ethical committee approval. Methods: Based on weight-for-age anthropometric index (Z-score) child nutrition status is categorized into three groups-severely undernourished (< -3.0), moderately undernourished (-3.0 to -2.01) and nourished (≥ -2.0). Since nutrition status is ordinal, an OLR model – proportional odds model (POM) is used to find predictors of malnutrition. Methods: Based on weight-for-age anthropometric index ( $Z$-score) child nutrition status is categorized into three groups-severely undernourished ( $<-3.0$ ), moderately undernourished (-3.0 to -2.01 ) and nourished ( $\geq-2.0$ ). Since nutrition status is ordinal, an OLR model proportional odds model (POM) is used to find predictors of malnutrition. Results: The OLR (POM) model showed that four significant risk factors associated with child malnutrition are parity with more than 2 children, Household status with $\leq 6$ members, presence of Infectious or Non-infectious disease, and Socio-economic status with upper class family, Among the four factors, parity with more than 2 children showed most significant predictor of malnutrition.